Innovation in Sustainable Management and Entrepreneurship: 2019 International Symposium in Management (SIM2019) [1st ed.] 9783030447106, 9783030447113

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Springer Proceedings in Business and Economics

Gabriela Prostean Juan José Lavios Villahoz Laura Brancu Gyula Bakacsi   Editors

Innovation in Sustainable Management and Entrepreneurship 2019 International Symposium in Management (SIM2019)

Springer Proceedings in Business and Economics

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Gabriela Prostean Juan José Lavios Villahoz Laura Brancu Gyula Bakacsi •

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Editors

Innovation in Sustainable Management and Entrepreneurship 2019 International Symposium in Management (SIM2019)

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Editors Gabriela Prostean Polytechnic University of Timişoara Timișoara, Romania

Juan José Lavios Villahoz University of Burgos Burgos, Spain

Laura Brancu West University of Timișoara Timișoara, Romania

Gyula Bakacsi Budapest Business School Budapest, Hungary

ISSN 2198-7246 ISSN 2198-7254 (electronic) Springer Proceedings in Business and Economics ISBN 978-3-030-44710-6 ISBN 978-3-030-44711-3 (eBook) https://doi.org/10.1007/978-3-030-44711-3 © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Introduction

The volume Innovation for Sustainable Management and Entrepreneurship is a reply from researchers in business and economy to corporate exigency, unleashed by the unprecedented expansion of top technologies. The contributors to this volume, fascinated by the new industrial era, tried to harmonize the need of organizations for sustainable development with the needs of the society, thus creating concrete approach models and algorithms within the 15th International Symposium in Management (SIM 2019). The symposium gathered researchers, academics, and practitioners who exchanged experiences and research results in their complex fields of interest. The scientific papers presented at the symposium debated on the challenges and innovations in management and entrepreneurship. The research included in this volume offers practical and complex scenarios applicable in the fields of economics and industrial business such as the new digitalization wave in Industry 4.0; management education; new business ideas developed by students with entrepreneurial interests with regard to start-ups; financial and governance management; and supply chain and operations management. Given their theoretical–applicative approach, the issues tackled in this volume are of upmost importance in each of their respective fields: – Innovative business models which support entrepreneurial approaches for sustainable management (discussed in Part II of the volume: Entrepreneurship; Part III: Sustainable management, Part VII: Education management; Part VIII: Third sector organisations management; Part IX: Management of innovation; Part X: Quality Management and Logistics); – Innovative business models used for coping with change and rapid strategies (presented in Part V: Supply chain and operations management; Part VI: Workplace Management); – Economic analyses representative for today’s challenges in the business environment (Part IV: Financial management and governance; Part XI: The economics of small and medium-sized enterprises);

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– IT approaches to support innovation in business for a sustainable management and adapting new technologies by integrating data in order to assure sustainable management in a competitive environment (discussed in Part I: Industry 4.0). The chapters of this volume are presented clearly and concisely, integrating the pillars (be they political or tactical) of various strategies of social responsibility which generate a unique view of the companies on the market, perceived in a sustainable way by the entire competitive environment. The theoretical background of the volume is based on a wide bibliographic research which encompasses critical analyses or case studies. The value of this volume stands both in the complexity of the issues it approaches and in the consistency of its analyses and the solutions it offers, the final general aim of the volume being to give solutions to problems of certain actuality. December 2019

The Editors

Contents

Part I

Industry 4.0 3

1

Aspects of Cyber-Security in Higher Education Institutions . . . . . . Alin-Ciprian Cojocariu, Ion Verzea, and Rachid Chaib

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Alerts in Emergency Situations Using Mobile Technology, Scientometric Visualization Analysis . . . . . . . . . . . . . . . . . . . . . . . . Raluca Repanovici and Anişor Nedelcu

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How to Approach Ethics in Intelligent Decision Support Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Radu Stefan and George Carutasu

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Security and Privacy Implementation Framework as a Result of the Digitalization Process for Organizations in Different Industries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cristian Vartolomei and Silvia Avasilcăi The Impact of Using Digital Technology in Measuring the Marketing Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sebastian Bîrzu

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Correlation “Sustainability–Functions–Competitiveness” for Products in Society 5.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Liana Rodica Pater and Sanda Ligia Cristea

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Part II 7

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Entrepreneurship

Trust at Work and Entrepreneurial Intentions Among Employed Persons in Organizations in Serbia . . . . . . . . . . . . . . . . . . . . . . . . . Predrag Mali, Bogdan Kuzmanović, Milan Nikolić, Edit Terek Stojanović, and Siniša Mitić

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Self-efficacy and Entrepreneurial Intention Among Business Students in Romania . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bogdan Robert Ioane, Nicolae Bibu, and Laura Brancu

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Equal Opportunities in Entrepreneurship in Romania’s West Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 Mădălina Dumitrița Maticiuc, Diana Claudia Sala, and Valentin Partenie Munteanu

10 Ups and Downs of High-Growth Firms in Russia . . . . . . . . . . . . . . 127 Dmitri Pletnev and Victor Barkhatov Part III

Sustainable Management

11 Case Studies of Indoor Air Quality and Sustainability Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 Marco Ragazzi, Matei Tămășilă, Larisa Ivascu, and Cristina Elena Rada 12 Sustainable Development in Russia—Just a Theory? . . . . . . . . . . . 149 V. Barkhatov and D. Benz 13 Another Approach Regarding the Balance Between Natural and Manufactured Ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 Paul Negirla, Sorin Nanu, Ioan Silea, and Octavian Stefan 14 Carbon Emissions, Energy Consumption, and Managing Investment in Renewable Energy . . . . . . . . . . . . . . . . . . . . . . . . . . 183 Roxana Mihaela Sirbu and Claudiu Tiberiu Albulescu 15 Perception, Knowledge, Attitude and Behavior Toward Climate Change—A Survey Among Citizens in Timisoara, Romania . . . . . . 199 Iudit Bere-Semeredi and Adrian-Amedeo Bere-Semeredi 16 Application of Circular Economy Principles in the Luxury Fashion Industry: The Case of the RealReal . . . . . . . . . . . . . . . . . . 219 Manuela Mihăiliasa and Silvia Avasilcăi Part IV

Financial Management and Governance

17 A Review of the Research on Financial Performance and Its Determinants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229 Mihaela Brindusa Tudose and Silvia Avasilcai 18 Financial Constraints and the Structure of the Firm’s Investment: An Application to the Scientific R&D Industry from the Largest EU Countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245 Claudiu Tiberiu Albulescu, Serban Miclea, Matei Tamasila, and Mihaela Vartolomei

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19 Projecting a Strategic Diagnosis System of Corruption Based on Network Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257 Ioan Petrișor and Dana Nedea 20 The Influence of Stakeholders on the Management and Work of a Community Center in Israel . . . . . . . . . . . . . . . . . . . . . . . . . . 271 Nicolae Bibu and Etti Isler 21 Historical Valuation Bases and Drivers of Large InternetEnabled Companies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283 Adelin Trusculescu, Claudiu Tiberiu Albulescu, and Daniel Paschek Part V

Supply Chain and Operations Management

22 Event Log Extraction for the Purpose of Process Mining: A Systematic Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 Dusanka Dakic, Darko Stefanovic, Teodora Lolic, Dajana Narandzic, and Nenad Simeunovic 23 3D Custom-Made Eyeglasses Frames: An Innovative Approach to Enhance Customer Satisfaction . . . . . . . . . . . . . . . . . . . . . . . . . . 313 Karina E Sarzosa G, Carlos D Vallejo A, Angel G Hidalgo O, Tania K Berrezueta E, Esmeralda Kadena, and Ramiro S Vargas C 24 Methodological Views on Agile Testing . . . . . . . . . . . . . . . . . . . . . . 323 Paul Dragos 25 The Implementation of a New Technology Based on the Monte Carlo Simulation in the Field of Sustainable Dependability in Operation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333 Ionut Herghiligiu, Adrian Vilcu, and Marius Pislaru 26 A Study on a Smart Process Optimization Based on TOC . . . . . . . 345 Gabriela Prostean Part VI

Workplace Management

27 A Proposed Ergonomics Maturity Level Framework and Assessment Tool for Easy Business Application . . . . . . . . . . . . 357 Anca Mocan and Anca Draghici 28 The Modern Workplace in Service Management: Possibilities, Realities, New Ways of Working . . . . . . . . . . . . . . . . . . . . . . . . . . . 371 Ieva Kalve 29 Human Resource Competencies Development for Competitive Advantage. A Case from the Food Industry . . . . . . . . . . . . . . . . . . 387 Diana Robescu, Anca Draghici, and Alina Paraschiva

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30 Design Thinking Workshops: Uncovering Facilitator and Participants’ Experiences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403 Bogdan Rusu 31 Generations at Work for a Better Future . . . . . . . . . . . . . . . . . . . . 417 Sebastian Capotescu, Letiția-Alexandra Mălăieș, and Horațiu-Florin Șoim 32 The Impact of Knowledge Management on Intellectual Capital. A Research Approach Using Skandia Navigators . . . . . . . . . . . . . . 431 Corina Dufour, Anca Draghici, and Alina Paraschiva Part VII

Education Management

33 The Study of the Impact of Erasmus+ Mobility Projects on Participants. Data Analysis at “Lucian Blaga” University of Sibiu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 447 Rebecca-Clara Răulea and Dănuț-Dumitru Dumitrașcu 34 Evaluation of the Cultural Intelligence Profile of Moroccan and Romanian Students . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459 Laura Brancu and Zakia Benhida 35 Talent Management and Organizational Performance in Schools. A Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 473 Alina Angela Manolescu and Doina Danaiata Part VIII

Third Sector Organisations Management

36 Analysis of the Competitiveness of Organizations in the Northwestern Region of Romania Through Social Responsibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 487 Rus Gabriel and Rădulescu Corina Michaela 37 The Participation of Women in the Third Sector and Civil Society Organizations—Biased or Neutral? . . . . . . . . . . . . . . . . . . . . . . . . . 499 Montse Fernández 38 Innovative Aspects in Managing Classic Professional Orchestras, as Multiple Stakeholder Organizations . . . . . . . . . . . . . . . . . . . . . . 507 Georgiana Alina Teohari, Nicolae Aurelian Bibu, and Laura Brancu 39 Testing the Trust of Romanian Consumers in E-Commerce . . . . . . 527 Patricia Simona Lup, Romeo Negrea, and Gabriela Ioana Proștean 40 Data—The Important Prerequisite for AI Decision-Making for Business . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 539 Daniel Paschek, Caius Tudor Luminosu, and Mircea Liviu Negrut

Contents

Part IX

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Management of Innovation

41 How Do SMEs Manage to Gain New Markets? Principles to Enhance the Innovation Management System: The Case of a Romanian Manufacturer . . . . . . . . . . . . . . . . . . . . . 555 Amalia Hulubei (Georgescu) and Silvia Avasilcăi 42 Toward a Creative Dynamic Capabilities Creation Framework: The Evidence from Creative Business Ecosystems . . . . . . . . . . . . . 567 Elena Avram, Carmen Aida Hutu, and Adriana Bujor 43 Clustering by Nanotech: The Tunneling Approach . . . . . . . . . . . . . 575 Mihai V. Putz and Ioan Petrisor 44 Using Design Thinking to Create Social Innovation in Digital Services for International Mobility . . . . . . . . . . . . . . . . . . . . . . . . . 591 Anca-Maria Cazac and Silviu Vert 45 Innovation in Business Organizations with Blockchain Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 601 Luminita Hurbean 46 Change Management Strategy and ITIL Implementation Process in an IT Company—Study Case . . . . . . . . . . . . . . . . . . . . . . . . . . . 611 Mădălina Albulescu and Nicolae Bibu Part X

Quality Management and Logistics

47 Sustainable Logistics Concept—Strategic Development Plan of the Timisoara International Airport . . . . . . . . . . . . . . . . . . . . . . 625 Attila Turi, Larisa Ivascu, Marian Mocan, Lucian-Ionel Cioca, and Alin Artene 48 Warehouse Redesign Challenges—Adapting Layout and Improving Process Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 635 Michael Petri, Attila Turi, and Marian Mocan 49 Applying Six Sigma Methodology in the Automotive Industry . . . . 647 Adrian Pugna, Sabina Potra, Romeo Negrea, and Serban Miclea 50 Improving Workflows Through Digital Collaboration in Software Development Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 661 Andra Diaconescu, Olivia Giuca, and Ioan-Radu Lala 51 Modern Manufacturing Processes for SMEs: Lean, Flexible, Agile, Leagile, Green, and Sustainable . . . . . . . . . . . . . . . . . . . . . . 679 Anca-Ioana Munteanu

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Part XI

Contents

The Economics of Small and Medium-Sized Enterprises

52 Economic Efficiency Estimation of Innovations in Combined Forestry and Wood Processing Units in Bulgaria Through Certification in FSC Chain of Custody . . . . . . . . . . . . . . . . . . . . . . 695 Nikolay Neykov, Emil Kitchoukov, Tsvetelina Simeonova-Zarkin, and Aureliu-Florin Halalisan 53 The Analysis of Clusters Competitiveness in the Nord-West Region of Romania . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 705 Alina Natalia Pop and Izabela Ruz 54 An Approach to Process Standardization in the Wood Industry: A Case Study of an Ecuadorian SME . . . . . . . . . . . . . . . . . . . . . . . 717 Tania K Berrezueta E, Angel G Hidalgo O, Esmeralda Kadena, and Ramiro S Vargas C 55 The EU Subsidy Policy Cannot Prevent the Market Exit of Especially Small and Medium-Sized Enterprises . . . . . . . . . . . . . 725 Michael Glowinkel, Marian Mocan, and Manfred Külkens

Part I

Industry 4.0

Chapter 1

Aspects of Cyber-Security in Higher Education Institutions Alin-Ciprian Cojocariu, Ion Verzea, and Rachid Chaib

Abstract The experience of recent years has brought to the forefront a problem that can affect our everyday life: cybercrime. In higher education institutions, this problem is quite acute due to the increase in the number and types of equipment that can be connected to data networks. This problem is also influenced by the nature of educational and research activities involving a large number of teaching staff, auxiliary or non-teaching staff and students as well as complex computing and research systems in various fields. Members of higher education institutions are may be victims of cyber-attacks of all kinds (social engineering, DDOS attacks, Trojans, viruses and worms), or the need to find win–win variants as easily as possible can bring some of these into cybercrime. Finding a restricted access variant with different security barriers is essential for the proper functioning of the education and research process in this type of institution. In this context, the research objective is to identify and classify the main threats and attacks on the data network, the various information and research currently faced by higher education institutions. These issues need to be treated with utmost seriousness because, in general, the annihilation of cyber-attacks is done after they have been done, thus completely or partially losing certain data, and the creation of security barriers could limit access by attackers to confidential information of the institution. The results serve to open a new research direction that leads to the development of a security management model tailored to academic activity. Keywords Higher education institutions · Cybercrime · Security barriers · Data network A.-C. Cojocariu (B) · I. Verzea “Gheorghe Asachi” Technical University of Iasi, 67, Dimitrie Mangeron Blvd, Iasi, Romania e-mail: [email protected] I. Verzea e-mail: [email protected] R. Chaib “Frères Mentouri” University of Constantine 1, Constantine, Algeria e-mail: [email protected] © Springer Nature Switzerland AG 2020 G. Prostean et al. (eds.), Innovation in Sustainable Management and Entrepreneurship, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-44711-3_1

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1.1 Introduction Since the twentieth century, written information, stored and transmitted on paper, has begun to be replaced, almost entirely in some fields of activity, with electronic ones. Also, the emergence of electronic signature will soon lead to the complete digitization of documents and finally the disappearance of paper-based official documents, which need signature and stamp. The experience of recent years has brought to the forefront a problem that can affect our everyday life: cybercrime. In higher education institutions, this problem is quite acute due to the increase in the number and types of equipment that can be connected to data networks. This problem is also influenced by the nature of educational and research activities involving a large number of teaching staff, auxiliary or non-teaching staff and students as well as complex computing and research systems in various fields [1]. The main aspects of cyber-security in higher education institutions are related to frequency of cyber-attacks, limited IT resources and cyber-security culture.

1.2 Frequency of Cyber-Attacks in Higher Education Institutions Members of higher education institutions are may be victims of cyber-attacks of all kinds (social engineering, DOS attacks, Trojans, viruses and worms), or the need to find win–win variants as easily as possible can bring some of these into cybercrime. Cyber-attacks at higher education institutions are typically data-targeted attacks, and personal information can help attackers to defraud banking systems, obtain information about research in some areas or even disrupt the facility’s ability to function. The frequency of cyber-attacks increases proportionally to the increase in the number of devices that can connect to the servers of an institution [2]. For example, in the “Tudor Vladimirescu” Student Campus of “Gheorghe Asachi” Technical University from Iasi, about 70% of students connect two or more devices to the campus servers, according to the data centre analysis of the university. The most commonly used methods of cyber-attack on systems in higher education institutions are phishing, ransom attacks, denial of service (DOS) and distributed denial of service (DDOS).

1.2.1 Phishing Phishing is a method of identity theft, so for those who launch such cyber-attacks, any information which is precious will resort to any means to get what they want. However, many measures will be taken, and they discover new ways of penetrating security barriers between computers or networks and the Internet. At this time,

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Fig. 1.1 Spear phishing attack model used to launch targeted attacks. Copyright © 2014 by Sood and Richard [3]

phishing has been taken on a huge scale in higher education institutions in Romania, which are very exposed, even more exposed than institutions in other parts of the world. But that does not mean we cannot protect against this method of attacking documents (Fig. 1.1). In the higher education institutions, when a phishing attack occurs, the attacker sends his emails or SMSes to his clients from various companies or important institutions. The subject of the email and its content differs from a phishing attack to another, indicating malfunctions or technical errors that need to be resolved by reentering the personal data of the attacker and going up to messages that promise certain pecuniary benefits. This type of cyber-attack is almost as old as the Internet. Criminals use so-called social engineers to trick users, infecting computers with various types of viruses, stealing their money from cards or their identity, and taking their data and even some digital documents related to studies or research.

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1.2.2 Ransom Attacks Ransom attack is a type of malicious software designed to block access to a computer system until a sum of money is paid. Although ransom attacks are usually aimed at individuals, it is only a matter of time before business is targeted as well. In Romanian higher education institutions, this phenomenon mainly targets the devices of all those who connect to their networks, students, didactic and teaching staff, non-academic staff and visitors. Lock screens are common to both encryptions and screen lockers, and they encourage victims to purchase a crypto-currency, like Bitcoin, to pay the ransom fee. Once the ransom is paid, customers receive the decryption key and may attempt to decrypt files. Decryption is not guaranteed as multiple sources report varying success of decryption after paying ransoms, and sometimes customers never receive the keys. Some attacks install malware in the computer system even after the ransom is paid and data released (Fig. 1.2). In many cases, ransomware victims who pay are “marked” for future attacks, and their information is sold to other ransomware attackers. This form of cyber-attack is mostly used in higher education institutions and beyond. If government agencies report 5.9% and healthcare institutions report 3.5% of all ransom attack victims, the education institutions report about 13% [5].

1.2.3 Denial of Service (DOS) and Distributed Denial of Service (DDOS) Denial-of-service (DOS) and distributed denial-of-service (DDOS) attacks are two of the most dangerous cyber-threats that higher education institutions face. These forms of attacks may have financial implications such as those of a successful DOS attack. Security surveys indicate that the cost of a DDOS attack is calculated on average between $ 20,000 and 40,000 per hour. DOS attack is a cyber-attack on an individual computer or on multiple computer connections that reduces, restricts or prevents legitimate users from accessing resources. In a DOS attack, attackers “flood” the victim’s system with service or traffic demands to overload resources. The DOS attack leads to the inaccessibility of a particular Internet site (usually the targeted person/company) or poor performances within the network (Fig. 1.3). DDOS attack is a multitude of compromised systems (called “farms”) that attack a single victim using a virus or botnets to infect a system by turning it into a zombie. The user is not aware that his/her system is involved in an attack. After infecting tens or hundreds or thousands of systems, it will be very easy to attack any system or network. According to CERT.RO, the attacks of the DDOS are rising approximately 33% in companies and institutions which are affected by this attack in 2017, compared to 17% in 2016 [5].

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Fig. 1.2 Microsoft decrypted alert [4]

1.3 The Information Security Model The information security model consists of software methods of ensuring access control to system resources and services. This model is divided into two important levels: levels of access security and levels of security of services.

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Fig. 1.3 DDOS attack model by Karabulut [6]

• The security level of the access includes: – access to the system that determines the conditions and when the system is accessible, it is directly responsible for managing the access records; – access to the account that verifies the user’s name and password; – the right of access to various files, resources, services, data and information, which determines what privileges a user or group of users has. • The security level of the services controls the access to the services of a system, which can be a computer or a computer network, and is divided into: – the control of the services responsible for the warning and reporting functions of the service states, as well as the activation of various services offered by the respective system; – rights to services that determine exactly how a particular account uses access to file resources, details and information.

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The computer and communication security model presented above must comply with the security levels without being a “top-down” bypass on the networks/departments [7]. This involves securing access to the same security management model and having respective security levels and security barriers. The information must be classified according to the level of risk where they come from, and their transmission between the departments of a security level or between them is being made under strict record [8].

1.4 Limited IT Resources in Higher Education Institutions This is particularly important in higher education institutions because the shortage of staff with experience and skills in cyber-security, along with the scarcity of material resources due to the limited budget for the purchase of high-performance equipment, leads to the impossibility of monitoring all devices and activities from the network. Morally degraded IT infrastructures are an important cause of the vulnerability of IT systems. Those who are part of the IT teams must ensure that older equipment and solutions benefit from at least updates or, if those products are no longer supported by suppliers, are replaced by modern equivalents. However, in complex and highly distributed networks, patch and replacement programs require significant resources [9]. The statistical data reported for the year 2017 on the website www.cert.ro indicates as follows: 33.71% (2.89 million) of the total IPs allocated to the national cyberspace were involved in at least one cyber-security alert processed by CERT.RO in 2017, which decreased compared to 2016 when the percentage of 38.72% (2.92 million) was recorded; 83.63% (115.60 million) of alerts processed concern vulnerable computer systems, meaning that they are out of date, insecure or improperly configured, being thus exposed cyber-attacks targeting the exploitation of their vulnerabilities. 10.32% (14.33 million) of alerts processed refer to computer systems compromised, meaning that they either have been infected with different forms of malware or have been exploited and used by attackers in various types of spam attacks and campaigns, the vast majority being registered in the Real-time Black-hole Lists (RBLs); 5.88% (8.17 million) of alerts processed target information systems infected with malware-type botnet, the latter being characterized by the fact that it has mechanisms that allow attackers to remotely control infected computer systems. Thus, a significant decrease was compared to 2016 when the percentage of 12.81% (14.12 million) was recorded, confirming the downward trend of the botnet phenomenon at international level;

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1,709 Web domains have been reported to Ro as compromised, down approximately 84% compared to 2016 (10,639). The figure represents approximately 0.18% of the total domains registered in Romania in December 2017 (944,145) 37 and about 0.38% of total domains (438,366) [5]. In higher education institutions, most of these problems arise due to the lack of resources, both financial, for the acquisition of state-of-the-art technology and the lack of qualified personnel who can train, supervise and intervene promptly in case of detecting a cyber-security issue. To keep up with the current reality, it is necessary to update cyber-security systems that include network visibility solutions and process automation [9].

1.5 Cyber-Security Culture in Higher Education Institutions In higher education institutions, many students and staff members have no knowledge of cyber-security. For this reason, along with the expansion of technology use, they increase the cyber-attack area on the institution. To be able to use technology and innovation in research in good conditions without compromising security, higher education institutions need to develop a defence strategy against these digital security aspects. Developing a cyber-security culture can cause an IT user to verify the authenticity of the sender of a mail requesting various personal information or selecting a link or opening an attachment [10]. Achieving a cyber-security culture plan is essential. This can be done in five steps as follows: 1. Knowledge of the institution, which has an essential role in the realization of the cyber-security culture plan; 2. Measure the current level of the cyber-security culture of the target audience at the academic level; 3. Developing a cyber-security culture plan in this institution; 4. Implementing an updated cyber-security culture plan at the institution level; 5. Acquiring state-of-the-art, efficient, anti-cyber-attack programs [11].

1.6 Conclusions The three aspects of digital security must be treated with utmost seriousness because it influences the good course of the institution at all levels. Finding a restricted access variant with different security barriers is essential for the proper functioning of the education and research process in this type of institution. In this context, the research objective is to identify and classify the main threats and attacks on the data

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network, the various information and research currently faced by higher education institutions. These issues need to be treated with utmost seriousness because, in general, the annihilation of cyber-attacks is done after they have been done, thus completely or partially losing certain data, and the creation of security barriers could limit access by attackers to confidential information of the institution. The results serve to open a new research direction that leads to the development of a security management model tailored to academic activity [12].

References 1. Oprea, D.: Protection and Security of Information Systems (2017) 2. Tirziu, A.M.: Protection and Security of Information at the Level of National Public Authorities from Romania (2009) 3. Sood, A., Richard. E.: 2014. Targeted Cyber Attacks 4. Koos, R.: Delete “Attention! Your Files are Encrypted (Microsoft Decrypted) Virus (2016) 5. CERT.RO.: Report on the Evolution of Threats in 2017 (2017) 6. Karabulut, K.G., Buyukcorak, S., Cepheli, Ö.: Hybrid Intrusion Detection System for DDOS Attacks (2016) 7. Rochefort, S.: Equipment Security (2018) 8. Cojocariu, A.: Research and Contributions Regarding the Management of the Physical, Computer and Communications Security System in the Higher Education Institutions (2018) 9. Lobban, I.: 10 Steps to Cyber Security (2012) 10. Fisher, B., Sloan, J. (eds.): Campus Crime: Legal, Social and Policy Perspectives. Charles C. Thomas, Springfield, IL (2007) 11. The European Union Agency for Network and Information Security (ENISA).: Cyber Security Culture in Organizations (2017) 12. Wallace, D.: Management of Security Services in Higher Education. Loughborough University, UK

Chapter 2

Alerts in Emergency Situations Using Mobile Technology, Scientometric Visualization Analysis Raluca Repanovici and Ani¸sor Nedelcu

Abstract The purpose of this paper is to identify the most influential documents using mobile technology for creating alerts in emergency situations and the conceptual research structure by analysing the most influential authors, cited papers, and cited journals. In order to accomplish these objectives, the authors used the Dimensions database. Articles from 2010 to 2019 were considered, and a total number of 2500 of articles were analysed. This analysis provides a thorough visualization of the research directions of the field of alerts in emergency situations using mobile applications. Keywords Emergency Management · Emergency Alerts · Mobile Emergency Notification Applications · Scientometric Visualization Analysis · VOS Viewer

2.1 Introduction This paper proposes to identify the most influential and the most documents, the existing collaboration networks, and also the intellectual structure in the field of mobile applications designed for emergency notifications. In order to accomplish these objectives, VOS Viewer software was used. VOS Viewer software was developed by de Van Eck [10]. VOS Viewer is a software tool for constructing and visualizing bibliometric networks and provides advanced visualization tools. These networks may, for instance, include journals, researchers, or individual publications, and they can be constructed based on citation, bibliographic coupling, co-citation, or co-authorship relations, and geographic areas influence upon the researched field. Within the present research, VOS Viewer version 1.6.11 was used [10].

R. Repanovici (B) · A. Nedelcu Transilvania University of Bra¸sov, Bra¸sov, Romania e-mail: [email protected] A. Nedelcu e-mail: [email protected] © Springer Nature Switzerland AG 2020 G. Prostean et al. (eds.), Innovation in Sustainable Management and Entrepreneurship, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-44711-3_2

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In the field of mobile alert application for emergency situations, there are only few scientometric studies using the mapping of these topics. Therefore, we shall briefly extract from the abstracts the main topics of these articles and introduce them in the reference list. None of these articles applies the bibliometric methodology in the field of the mobile application for emergency alerts. The main topics of scientometric studies on mobile applications alerts are listed in Table 2.1. This study will provide, for academic and industrial environments, an overview in the field of mobile application notifications for emergency situations and will identify new approaches and the newest related topics in the field. Furthermore, there will be extensive information about the authors and papers with the highest influence and the development directions of different aspects concerning the mobile emergency applications. This article also proposes to contribute to a better understanding of the alerts in emergency situations created by mobile applications, by providing useful information to researchers working in this area. In the next section, we present in brief the scientometric methods, then we detail upon the research methodology and then the research and collaboration tendencies are presented for the field of mobile emergency applications, and then, the main results are represented. Finally, the conclusions, implications, and limitations of this study are highlighted. Table 2.1 Main topics of scientometric studies on mobile application alerts topics Title of the article

Citation

Abstract

A scientometric analysis of mobile technology publications

Santhakumar and Kaliyaperumal [8]

“This paper focus on the growth and development of mobile technology research in terms of publication output as reflected in Web of Science database. During 2000–2013 a total of 10,638 publications were published in the field. The average number of publications published per year was 759.86 and the highest number of publications 1495 were published in 2013”

Scientific production on mobile information literacy in higher education: a bibliometric analysis (2006–2017)

Pinto et al. [7]

“This paper offers a bibliometric analysis of the scientific production on Mobile Information Literacy in Higher Education published between 2006 and 2017, taking into account papers covered by Web of Science, Scopus, Library and Information Science Abstracts, Library and Information Science and Technology Abstract, and Education Resources Information Center” (continued)

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Table 2.1 (continued) Title of the article

Citation

Abstract

The knowledge network dynamics in a mobile ecosystem: a patent citation analysis

Lee and Kim [5]

“The paradigm of the mobile ecosystem is rapidly changing, especially since the introduction of smart devices. New important players are emerging, and the scope of the mobile ecosystem is expanding and encroaching on the technological boundaries of other IT ecosystems. However, our understanding of the mobile ecosystem has been limited given the few existing studies. Therefore, in this paper, we empirically examine the network structure of a mobile ecosystem by measuring the technology knowledge flows between firms based on a patent citation analysis of the mobile industry”

Citation-based analysis of literature: a case study of technology acceptance research

Hsiao et al. [4]

“This article presents a case that examines the technology acceptance research through the newly developed citation-based approach, in particular main path analysis and edge-betweenness clustering analysis. Based on the citation network constructed from a total of 1555 journal articles from the period 1989 to 2014, the most critical 50 citations were identified and used as the basis to map the major knowledge flow in technology acceptance research”

A new bibliometric approach to measure knowledge transfer of internationally mobile scientist

Aman [1]

“This study introduces a new bibliometric approach to study the effects of international scientific mobility on knowledge transfer. It is based on an analysis of internationally mobile and non-internationally mobile German scientists publishing in journals that are indexed in Scopus. Using bibliometric data such co-authored articles, references and lexical abstract terms from the Scopus database, a method is presented that is based on cosine similarity to measure the similarity of the knowledge base of authors and their co-authors”

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2.2 Scientometric Methods There are several reasons to use scientometric methods in research. First, we can get an overview of the literature in the field. Second, the traditional methods provide a critical analysis but a subjective choice of the selected articles, and third, the studies using the date have more relevance when different subjects are analysed [3]. The realization of the intellectual structure of a research field can be achieved through the analysis of co-citation. Co-citation is defined as the frequency with which two documents are quoted together by other documents. In this analysis, it has been considered that two articles cited together have similar topics and a higher frequency of co-citation involves a higher affinity among them [9]. Another used technique is bibliographic coupling which handles the number of references commonly used by two documents, as a measure for similarity. This technique offers a higher connectivity perspective of two documents analysed [11]. A different technique is based on the analysis of authors coupling, or coauthorship, which studies the social structure of a certain field. The authors coupling provide data about the authors and their affiliations in institutions and countries. The analysis of keywords, or co-words, represents the use of keywords for the conceptual structure study in the research field. It is the only method based upon using the document’s content in order to build a measure of similarity, while other methods are indirectly coupling the documents by co-citation and co-authorship. This technique is one of the most effective methods for developing emerging tendencies and topics in a scientific domain [6]. For the field of mobile applications designed for emergency alerts, we propose to apply the methods described above in order to accomplish the mapping of scientific research by: • • • • •

Creating the research question; Compiling the obtained databases; Result analysis; Data visualization; Data interpretation. This research defines the following questions:

1. Which documents have the highest influence in the field of the mobile application designed for emergency situations? 2. Which is the intellectual structure of the mobile application designed for emergency situations domain and how did it evolve? First, we created the citations analysis in order to discover the most influential documents in the field, and second, the analysis of co-citation was chosen in order to identify the intellectual structure of the domain.

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2.3 Collecting Data Data compiling includes the selection of the databases, filtration of reference results, and their refining. Next, the software must be chosen, and also, the manner of visualizing the information shall be decided. We decided to use the Dimensions database, https://www.dimensions.ai/ [2]. Dimensions database was developed in a partnership with over 100 top research organizations around the world. This database aims at eliminating the barriers from the path of discovery and innovation, allowing the users to find and access faster the most relevant information, to analyse the academic results and wider research and gather information for future activities. In order to select the information and include it in our analysis, we defined the steps for synthesizing the information. We used the research strategy: “alerts” AND “mobile technology”, as shown in Fig. 2.1. As shown in Fig. 2.2, 7591 results were obtained, and we selected the publications from 2010–2019 in the following domains: information systems, public health, artificial intelligence, business and management, and communication techniques. The inclusion principle was the interest of the researchers for creating a mobile alert

Fig. 2.1 Conditions of documents inclusion and exclusion, image from dimensions

Fig. 2.2 Number of publications in the field between 2010 and 2019

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Fig. 2.3 Image export centre for dimensions

application for emergency situations and the interest areas for its realization. By using Export Centre, we obtained the database as exposed in Fig. 2.3.

2.4 Data Analysis by Co-citations Analysis Various types of analysis can be performed based on co-citation analysis, author co-citation, and journal co-citation.

2.4.1 Document Co-citation Analysis The first study is regarding the co-citation network in the field of mobile applications designed for emergency alerts. According to small, the citation frequency of articles represents a key concept, methods, or experiments in the field. So, this analysis allows us to investigate which documents define the intellectual structure in the field of mobile applications designed for emergency alerts. Out of 2500 publications analysed, there were 55,920 cited articles, but only 318 met the condition of a minimum of 10 citations per article, as shown in Figs. 2.4 and 2.5. Nodes of the same colour belong to the same cluster. The association method of the link intensity used by VOS Viewer software identified five clusters in total, as listed in Table 2.2. The total number of links is 12,818, and the total connections between links are 23,375.

2.4.2 Author Co-citation Analysis The second analysis was performed on the co-citation network. This analysis contributes to the knowledge of the intellectual structure of different subjects, considering the author as a set of works published by the author.

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Fig. 2.4 Image of VOS Viewer of cited sources

Fig. 2.5 Visualization VOS Viewer of the co-citation network of the documents

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Table 2.2 Analysis of most cited articles’ clusters Cluster

Colour

No. of cited documents

Most cited article

No. of links

No. of citations

C1

Red

119

Free et al.

221

67

C2

Green

64

Ngabo et al.

114

53

C3

Blue

58

Heron

187

81

C4

Mustard

43

Hrishna

204

64

C5

Mauve

34

Lester

206

68

For 2500 analysed publications, a total number of 200,666 authors are cited at least 10 times. For each of the 5455 authors, 1000 were selected, as displayed in Figs. 2.6 and 2.7. Each node illustrates an author, and their size indicates the number of times the author was mentioned in these 2500 publications. The link between two nodes indicates a co-citation network. The tighter the connection, the higher the influence power is. The nodes were also grouped according to similarity, as listed in Table 2.3.

Fig. 2.6 List of most cited authors generated by VOS Viewer

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Fig. 2.7 Visualization VOS Viewer of the most cited authors

Table 2.3 Analysis of most cited authors’ clusters Cluster

Colour

C1

Red

C2

Green

C3

Blue

C4

Mustard

No. of cited documents

Most cited article

No. of links

450

Majeed

996

79

309

Free

851

234

Sping

934

104

Michii

841

148

Prabhekom

936

64

10

46

238 3

Limmathurotsakul

No. of citations

2.4.3 Journal Co-citation Analysis The third type of analysis is the journal co-citation, and it is used in the thematic study. The citation frequency of two sources highlights similarities between the journal scope and the research subjects. In our sample of 2500 documents analysed, a total number of 8089 sources were identified, out of which 1230 have at least 10 citations. In Figs. 2.8 and 2.9, we have listed the most cited 100 sources. Each node represents a source, and its size represents the number of citations received by a publication. The link between the sources represents a co-citation network. The nodes are grouped according to similarity, as listed in Table 2.4. The sources of the same cluster and those which are nearby are similar than others, as shown in Fig. 2.9.

2.5 Conclusions This article aims at adding value and knowledge in the field of mobile applications designed emergency alerts. A scientometric research was performed using

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Fig. 2.8 List of most cited journals generated by VOS Viewer

Fig. 2.9 Visualization VOS Viewer of the most cited journals

VOS Viewer by applying three methods: citations analysis, co-citation analysis, and journal co-citation. Over 2500 documents from the Dimensions database were analysed. The analysis of authors’ co-citations showed that the most important authors in the research field are: Free Caroline, Nahhfabi Mosen and Shiffman Saul. The most cited articles belong to the authors: Free (2013) in PLOS ONE and Heron (2013) in British Journal of Health Psychology. The most cited journals are: Jawa Internal Medicine,

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Table 2.4 Analysis of most cited journals’ clusters Cluster

Colour

No. of cited journals

Most cited journal

No. of links

No. of citations

C1

Red

29

Jawa internal medicine

99

387

C2

Green

22

Journal of medical internet research

99

1866

C3

Blue

22

American journal of public health

99

256

C4

Mustard

18

Computers in human behaviour

94

224

C5

Mauve

9

PLOS ONE

99

1001

Journal of Medical Internet Research, American Journal of Public Health, Computers in Human Behaviour, PLOS ONE, and British Journal of Health Psychology. We also have to mention the main limitations of this article. The selected database and the interpretation of the visualization maps were biased. The number of the analysed documents was limited to the years 2010–2019.

References 1. Aman, V.: Scientometrics 117, 227–247 (2018). https://doi.org/10.1007/s11192-018-2864-x 2. Dimensions. ai. (2019). Dimensions [online]. Available at: https://www.dimensions.ai/. Accessed 21 June 2019 3. Fabregat-Aibar, L., Barberà-Mariné, M., Terceño, A., Pié, L.: A bibliometric and visualization analysis of socially responsible funds. Sustainability 11(9), 2526 (2019) 4. Hsiao, C.H., Tang, K.Y., Liu, J.S.: Scientometrics 105, 1091 (2015). https://doi.org/10.1007/ s11192-015-1749-5, J. Am. Soc. Inf. Sci. 24, 265–269 (1973) 5. Lee, S., Kim, W.: Scientometrics 111, 717 (2017). https://doi.org/10.1007/s11192-017-2270-9 6. Mora-Valentín, E.M., Ortiz-de-Urbina-Criado, M., Nájera-Sánchez, J.J.: Mapping the conceptual structure of science and technology parks. J. Technol. Transf. 43, 1410–1435 (2018) 7. Pinto, M., Fernández-Pascual, R., Caballero-Mariscal, D., et al.: Scientometrics 120, 57 (2019). https://doi.org/10.1007/s11192-019-03115-x 8. Santhakumar, R., Kaliyaperumal, K.: Scientometrics 105, 921 (2015). https://doi.org/10.1007/ s11192-015-1710-7 9. Small, H.: Co-citation in the scientific literature: a new measure of the relationship between two documents 10. VOS Viewer.: VOS Viewer—visualizing scientific landscapes [online]. Available at: https:// www.vosviewer.com/. Accessed 9 June 2019 (2019) 11. Zupic, I., Cater, T.: Bibliometric methods in management and organization. Organ. Res. Methods 18 (2015)

Chapter 3

How to Approach Ethics in Intelligent Decision Support Systems Radu Stefan and George Carutasu

Abstract Each decision-making, a fundamental human process, has been more and more supported by computer systems in the second part of the last century the socalled decision support systems (DSS). In the twenty-first century, intelligent decision support systems (IDSS) utilize artificial intelligence (AI) techniques to enhance and improve support for decision-makers. Ethics, as the branch of philosophy dealing with concepts of right and wrong conduct, was solely attributed to human intelligence, behaviour or decision. With artificial intelligence-based systems, the notion AI Ethics (or machine ethics) refers to ethical decision-making by machines. Specifically, in the field of IDSS, where AI techniques participate (at least partially) in the decision-making process, it is of utmost importance to design such systems with ethical considerations. In this paper, we present a classification of ethical values as an underlying concept for a framework consisting in a set of rules that need to be part of any IDSS design. Applied to a decision process for candidate pre-selection (e.g. hiring), we illustrate the pitfalls that lead to biased decisions and methods for mitigation of “wrong decisions”, from an ethical point of view. We suggest how to operationalize Ethics by Design and how to approach Ethics Build-In AI-based systems. Keywords Artificial intelligence · Applied ethics · Machine ethics · Decision support systems

R. Stefan (B) · G. Carutasu Politehnica University of Timisoara, UPT, Timisoara, Romania e-mail: [email protected]; [email protected] G. Carutasu e-mail: [email protected] G. Carutasu Romanian-American University-URA, Bucuresti, Romania © Springer Nature Switzerland AG 2020 G. Prostean et al. (eds.), Innovation in Sustainable Management and Entrepreneurship, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-44711-3_3

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3.1 Introduction Decision-making is a fundamental human process, considered to be at the core of our interaction with the world. We know that humans can make “good” and “poor” decisions that are understood at first as a binary output. That holds true for structured decision problems (e.g. decision on the shortest route between two points) where there is a known optimal solution. Such problems can be solved algorithmically without decision support systems. At the other end, humans decide outcomes for so-called unstructured problems, that do not have any agreed-upon criteria, nor solution, and therefore, those are mainly dependent on the preference of the decision-maker. For example, deciding on a life partner could be considered an unstructured decision, as it is mainly based on preference with a rather unique and specific set of criteria. In between those two types of problems (structured and unstructured), we find a wide range of semi-structured problems, that typically have some common set of agreed-upon criteria, yet still require human input and to some degree also preference. For example, a business decision on whether to invest in a specific market at a given time could be a semi-structured problem. These types of problems are suitable for decision support systems (DSS), as the combination of human interaction and analytical methods can be used in conjunction to obtain optimal solutions. When artificial intelligence (AI) techniques are used as part of the DSS, we refer to an intelligent decision support system (IDSS). Our human history evidences many unethical decisions, even for the time they have been taken (e.g. superiority of some nations over others). We know, that ethical standards also evolved with time, and something considered ethical a few centuries ago (e.g. slavery), might not fit the society norms nowadays, thus being categorized as unethical. In the today’s world, IDSS is more and more used for decisions in business or political complex problems that may have substantial impact on the society. While we expect humans to behave ethical, we claim that artificial intelligence (in contrast to human intelligence) might reach unethical conclusions, as there is no formal approach to ethical concerns in artificial intelligence. In the following chapters, we will discuss how AI plays a role in the decision-making when using IDSS, the pitfalls of biased data and decisions as well as a framework to classify and avoid common ethical biases.

3.2 Intelligent Decision Support Systems (IDSS) Intelligent decision support systems (IDSS) are decision support system that utilizes artificial intelligence techniques. Typically, decision support systems referrer to an interactive computer system that assists decision-makers to utilize data, models and

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Fig. 3.1 Structure of a decision support system adjusted from [2]

knowledge to solve structured, semi-structured or unstructured problems [1]. In the literature, the decision-maker (human) is part of the system (Fig. 3.1). There are several AI tools in IDSS to enhance and improve support for the decision-maker. Some use fuzzy logic, case-based reasoning, neural network or genetic algorithms, among others. Surely, with increasing complexity of decision problems, a mixture of AI tools might be used within the processing unit of an IDSS [2]. Independent of the underlying AI tools and/or techniques used, we claim that all IDSS are prone to ethical biases, as those generally arise rather from the data. Nevertheless, in this paper we will focus on the neural network AI technique that is considered to be the most common for general use.

3.3 Artificial Intelligence In this chapter, we want to define artificial intelligence in the wider sense and more specifically in the scope of this paper. One of the first definitions of AI is the one by Professor John McCarthy, who is considered to be the one to coin the term AI itself [3]: every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. –John McCarthy, Professor, Dartmouth College—1956

A strict definition of intelligence would probably go far beyond the scope of this paper, as it has been defined in many ways so far, including capacity for logic, learning, self-awareness, reasoning, emotional knowledge, planning, creativity, understanding and problem-solving. While intelligence is most often studied in humans, it has been observed (at least partially) in non-human animals and even in plants. Further in this paper, we will refer to human or natural intelligence in contrast to artificial intelligence. The term artificial intelligence refers to machines (hardware and software) that aim to emulate and replicate human intelligence in a way that an observer cannot judge

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Fig. 3.2 Schematic AI process

by the outcome of a given task whether the process was carried out by a human or by a machine. This is the so-called Turing test, named after its inventor: Alan Turing. While in the original Turing test the observer (or interrogator) was limited to using written questions for the determination, nowadays this limitation is often removed, as natural language is a common trait and a necessity for advanced AI systems. Today, we refer to the total Turing Test that would require computers to possess the following capabilities: natural language processing, knowledge representation, automated reasoning, machine learning, computer vision and robotics. In today’s world, the concept of artificial intelligence is primarily concerned with rational action, so that intelligent systems would achieve the best possible outcome, measured against an ideal performance [4]. That is that artificial intelligence is evaluated by the “acting” or outcome, and not by the way of “thinking” to achieve the desired outcome (Fig. 3.2). Nowadays, most AI implementations are still carrying out simple tasks that do not pose a risk to human integrity. However, in future, when AI systems will become more complex, more interconnected and with that act on important problems of society, we need to make sure they act ethically. We claim that ethics needs to be an integral part of designing and building intelligent systems.

3.4 Ethics in AI Ethics or moral philosophy is a branch of philosophy that seeks to resolve questions to human morality using antonymic concepts such as right and wrong, good and evil. Philosophers today usually divide ethical theories into three general subject areas [5]: • Metaethics • Normative Ethics and • Applied Ethics Metaethics is concerned with the origin of ethical principles and their meaning. The Normative Ethics takes a more practical approach and deal with moral standards that normalize right and wrong behaviour. Lastly, Applied Ethics involves examining specific controversial issues (e.g. abortion, animal rights, capital punishment, etc.). A

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sub-field of Applied Ethics refers to Ethics of Technology. Already back in 1974, Hans Jonas poses a fundamental thesis on The Imperative of Responsibility—In Search of an Ethics for the Technological Age—translated 10 years later into English [6]. His work gives birth to the field of Ethics of Technology. Finally, for the scope of this paper, we will look only into the part of the Ethics of Technology that deals with artificial intelligence. Thus, we call it Ethics of Artificial Intelligence. Even if today’s achievements do not formally achieve true intelligence by its most formal definitions, we have many examples where complex solutions make use of concepts like learning, evaluating, emotions, decision-making and more. We claim that specifically: the decision-making of an information system based on artificial intelligence is purely rational, without considering ethical evaluation, such as good or bad decision, in the wider context, which is typically unknown to the entity taking the decision.

3.5 Related Work In the last decade, the increased research in the field of AI and Data Science caused a debate in academia and mass media around the potential ethical impact on artificial intelligence build-in various information systems and partially being used for decision-making with potential effect on humans and society. Scientists around the world research methodologies on how ethical challenges could be overcome in building and using AI-related information systems. We would like to review the state-of-the-art in the field in the following section. The initial work was related to privacy by design (especially in software engineering); a field closely related to ethics, however, focused mainly on data privacy and protection. The framework proposed by Guerses et al. [7] to embed foundational principles in the development stage of systems describes the initiation and integration of potential privacy issues at the design phase of the system. It mainly focuses on data protection that is the unintended and unwanted access to private data. From the point of view of ethical consideration, the framework remains limited; nevertheless, the approach can be considered for an Ethics by Design framework. Privacy by design relies on the considerations at the design phase and can be evaluated by the Privacy Impact Assessment [8]. Related to the topic of this paper, is the area of research ethics, mainly present in other academic fields, such social sciences and medicine, relying on established codes and ethical guidelines for conducting research (e.g. as mentioned in The Oxford Textbook of Clinical Research Ethics [9]). Other researches aim to introduce a “framework for think through [ethical] issues” [10] or (in business communication) a practical way of reflection for practitioners to reason their decisions [11]. Beyond the research in academia, the emerging notion of data ethics has been driven over the last decade by several governmental institutions, especially in the European Union, but not only. Among them are, for example, the “Responsible Use of Data” [12], and the “Data Science Ethical Framework” [13]. However, those

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initiatives focus mainly on privacy issues and relate mainly to the EU General Data Protection Regulation (known as GDPR). Considering the implications of ethics in AI-related technology, we can conclude that neither of the various disciplines involved (computer science, law, social sciences) can succeed in isolation, but it rather needs a common approach.

3.6 Ethics by Design Methodology From the review in the previous chapter, some common references can be extracted and adapted for a methodology on Ethics by Design, mainly inspired by the software engineering approach privacy by design. As the name (Ethics by Design) implies, we want to present as a primary stage of the research process a framework where ethical issues are dealt with in a proactive rather than a reactive manner. In typical research projects as well as in commercial projects, the ethical issues are addressed through conversations between social scientists and technologists [14]. Indeed, ethics is not a criterion of performance, neither for research projects nor for most commercial projects; it should be, however, considered a must have process in any domain of AI influenced decisions. An Ethics by Design methodology for research projects in AI is presented by D’Aquin [14] and is depicted in the following Fig. 3.3. The above process outlines the project stages and hints at ethical issues that are to be considered in every state. While the process might be suitable for research projects in AI, we need to extend the scope for design, implementation and operation of intelligent decision support systems. The data scientist and technologists involved at every stage of the project might have limited knowledge on assessing the potential ethical impact. Hence, we developed a more concrete framework for design and assessment of AI-related decision systems, which is based on ethical values. The framework of ethical values will be presented in the next chapter, after which it should be applied to an empirical case study.

3.7 Classification of Ethical Values Most books published on ethical challenges in artificial intelligence have been published in the last decade. It is not only single researchers taking on this field of study, but also many national bodies do have a code of ethics or conduct (e.g. BCS—British Computer Society, ACM—Association for Computing Machinery, GI—Gesellschaft fuer Informatik in Germany, etc.). Most of them are members of the IFIP.org, the International Federation for Information Processing. Even some countries, mainly in the European Union, have adopted legislation on personal data protection. The IFIP federation also compiled a book: Ethics of Computing [15] that retails for a

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Fig. 3.3 Stages of a research process with ethical approval and questions to be asked

few hundred dollars, which made it not very attractive to the wide public. Also published 20 years ago, it does not discuss the newest dilemmas that arose with the rapid advances of computer technology in general and the associated artificial intelligence in particular. Nevertheless, most of the regulation efforts are towards information processing and the protection of information. There is no effort at scale to enforce a standard practice on ethical design, production and use of intelligent systems. Recently, Microsoft as a technology provider and vendor of artificial intelligence technology (compute, frameworks, tools, etc.) has released a book by the name The Future Computed: Artificial Intelligence and its role in society [16]. The book is available for free, which makes it accessible; however, it looks at technology from a perspective that focuses on the proprietary solutions. In the second chapter, the authors discuss the principles, policies and laws required for the responsible use of artificial intelligence. Further, they claim that the values AI needs to respect are: Figure 3.4 Ethical Values, adapted from [16], suggest a comprehensive set of rules that should be induced in every AI system. That is that the above values should govern the design as well as the implementation of any solution. The same should be valid for the data used to training it. Let us look at each of them. Fairness suggests that all humans interacting with the systems or human-specific data that is processed should be treated fairly. The implementation of Fairness is a severe risk when training an AI system, as it might draught the wrong conclusion from a correct set of data.

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Fig. 3.4 Ethical values, adapted from [16]

Reliability and Safety will ultimately result in the necessary trust. Thus, it is important to demonstrate all the time, that systems are operating within a clear set of parameters and there is a way to verify it. Privacy and Security tide back to the privacy and security of personal information. It is an imperative for trust in AI systems, to protect and to safeguard personal information especially in the digital era of our society nowadays. Inclusiveness means that no one should be excluded from the benefits of an AI system (or any technological system in the benefit of the society). At least one in ten people around the world live with disabilities of various kinds. Transparency is one of the underlying principles for all other values. When AI systems make decisions that impact people’s lives, it is imperative that people understand how the decisions were made. The challenge here is that it cannot be achieved with simply publishing the algorithm of the system and/or its architecture, as many people using the system might not be literate in the domain to fully understand the design. Accountability is probably one of the most challenging to achieve. While it is easy to understand accountability in theory, in practice there are many individuals involved in the design and operation of an AI system. Also, the legislation currently in most countries does not suffice to deal with such matters. Another classification of ethics in computers and information is given by the code of ethics called PAPA, an acronym for Privacy, Accuracy, Property, Accessibility. The PAPA code of ethics was published1 more than 30 years ago, and it refers mostly to the information processing, not necessarily to information systems based on artificial intelligence. However, there is a significant overlap between the two models of classification.

1 PAPA

2019.

code of ethics: https://www.gdrc.org/info-design/4-ethics.html, retrieved on 26 February

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3.8 Ethical Bias in AI—Empirical Case Study In order to provide a framework to approach the ethical values when using artificial intelligent technologies are used, we would like to highlight the pitfalls of biased data sets. In the following example, we will assume a company analyses the current workforce in terms of performance and profile in order to determine the best profile to be hired in future. The model will be inputted will all available personnel information, including current performance. The expected outcome should provide the key characterizes that would describe the highest probable performance (Table 3.1). The above data set shall be selected for the top performers (20% of the employee population) and shall be further analysed with artificial intelligence technologies for factors and characteristics that are most likely to have led to a better performance (Fig. 3.5). The above machine learning model uses regression based on the data set in order to determine the top 20% of employees that are most likely to perform at best among the whole population. The model itself is not of core interest to this paper as it should only illustrate any arbitrary artificial intelligence methodology that would compare humans and draw conclusions upon them. The output of the model is captured in the Table 3.2. Analysing the data in the table shows clearly biases some characteristics. Especially the gender ratio drops significantly by 3 percentage points in the top 20% data set versus the full data set. An ethical wrong conclusion might result in the fact that female employees are less performed compared to male employees. While it might be the case, by coincidence, in this specific example it surely does not apply in more general terms. In other words, while the data might correlate, it does not imply a causality. The other data in Table 3.3 does not suggest at first significant discrepancies. The only suspicious characteristic is the place of birth that was equally distributed in the initial data set, and some places are missing in the top 20% data set. Nevertheless, this is statistically normal, as the number of possibilities (42 states) is high compared to the top 20% data set with 99 data points. However, from an ethical point of view, place of birth should not be considered when analysing performance. Table 3.1 Data set of company employees

Key characteristics

Key Data

Number of employees in data set

500

Performance target

Top 20%

Ratio of female employees

14%

Average age (in years)

42

Postgraduates

4%

Average month in the company

36

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Fig. 3.5 Model for data selection

Table 3.2 Comparison of data sets Key characteristics

Full data set

Top 20%

Number of employees in data set

500

99

Ratio of female employees (%)

14

11

Average age (in years)

42

40

Postgraduates

4%

5%

Average month in the company

36

35

Place of birth

Equal distributed across 42 states

3 states missing

The above sample provides examples that can be considered trivial. When dealing with very large data sets (also known as “big data”) in non-trivial environments, data is prone to biases and should be always analysed for ethical challenges. In the following chapter, we show a methodology on how the ethical values presented in the previous chapter can help mitigate ethical issues.

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Table 3.3 Ethical dimensions Ethical dimension

Condition

Test

Fairness

All humans should be treated fairly!

Are the data points contained in a data set differentiating individual in a discriminating way?

Reliability and safety

The system is trustworthy!

Would any user that interacts with the system understand how it operates and could the user verify that the system operates correctly?

Privacy and security

All personal information is protected!

Is the data used by the system protected against unsecure exposure or reverse engineering?

Inclusiveness

The system works for anyone!

Can the system be used and operated by any human? (People with disabilities, people in other cultures?)

Transparency

The system does not harm humans!

Would any (even non-digitally literate) user understand how decision is taken?

Accountability

Define responsibilities!

Who is responsible and liable for outcomes of AI systems, especially if something goes wrong?

3.9 Applied Ethics in AI In this chapter, we focus our attention on how ethics (or moral philosophy) should be an integral part of designing and building or implementing AI-based systems decision-making. Mainly, we look at decision support systems (DSS) in general and into so-called intelligent decision support systems (IDDS) where IDDS refers to decision support systems that utilize artificial intelligence techniques to enhance and improve support for the decision-maker. In doing so, we need to distinguish between two approaches that allow for ethical considerations in AI-based systems. Firstly, during the design phase of those systems it is crucial that humans involved in the process of designing and implementing artificial intelligence-based rational agents are aware of ethical risks to humanity and have a proactive approach towards ethics. Secondly, when the AI-based system handles tasks autonomously (at run-time), it should have at least a component that is able to evaluate decision based on ethical “rules”. Therefore, going further we would like to focus on the following dimensions: • The morality/ethics of how humans design, construct, use and treat artificial intelligent entities and • The machine ethics, which is a moral behaviour of the artificial intelligent entity

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Thus, in the following and throughout this paper we will refer to: • Ethics by Design and • Ethics Build-In respectively. The most common ethical failures of intelligent decision support systems known today are related to so-called biased database (data set) or knowledge base. Rather seldom this occurs in the model base. One of the most recent examples is the Amazon AI recruiting tool that has been used between 2014 and 2017 to help review resumes and make recommendations. The AI-based system was inputted with data from the past decade, where most candidates hired were male. Consequently, the system was more favourable to male applicants. In fact, the software literally downgraded resumes that contained the word “women” or implied that the applicant was female, for example when the applicant attended a women’s college.2 There are many other examples, especially in the recent years when AI-based systems make their way out of the labs into production environments. Designers of AI-based systems need to ensure that data sets used as input or as training (in case of neuronal networks) are free from biases. The rule to be followed: Correlation does not imply Causality! Correlations leading to causality are a pitfall for ethical bias. For example, the well-known example: “As ice cream sales increase, the rate of drowning deaths increases sharply”. The statement might be true and can be proven by correlation. However, the (simple) correlation does not account for the fact that both activities (buying ice cream and engaging in water sports) increase heavily in the summer months, thus they correlate in time. Therefore, we advise any designer of AI-based system to always check for causality not only correlation. As humans are also prone to confirmation bias, in our intention to construct a system that operates with high accuracy, confirming our anticipation, we might even more tend to give more importance to correlation versus causality. In the following, we summarize a checklist for AI designers and developers that allows to avoid most common pitfalls. In addition, there are some other considerations that help to achieve and control ethical standards. A small data set that can be easier controlled for biases and ethical consideration can be in many cases very effective to use. Therefore, another rule to follow: Quality over Quantity—more data does not (always) mean we reach a better decision. 2 Article

published by agency press Reuters on 10 October 2018. Retrieved on 27 January 2019—https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazonscraps-secret-ai-recruiting-tool-that-showed-bias-against-wosmen-idUSKCN1MK08G.

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Especially in Knowledge Systems, but also in general we encounter models constructed based solely on data sets. Typically, those are designed and implemented by developers, as they are familiar with artificial intelligence technologies. The danger here lies in the lack of expertise-related subject matter and with that the whole system is prone to ethical (and other) malfunctions. The rule here: Always consider an (subject matter) expert opinion! After previously discussing Ethics by Design, we turn now our attention to the Machine Ethics or Ethics Build-In. In the last decade, many researchers have studied and published their findings on machine ethics. In the book Machine Ethics [17], the authors gather a series of publications around the topic. Several challenges for a successful implementation of Ethics Build-In arise from the following areas of ethics: • No common and universally valid logical set of ethical standards exists, which could be translated to machines. • Ethical or moral philosophy considerations are not persistent in time. • Ethics may differ significantly among cultures. • Ethical influences may vary in different domains of applications. In essence, the Ethics Build-In should allow machines to differentiate between right and wrong (or good and evil) depending on the dimensions and context the system is used in. Another set of challenges to achieve an intelligent decision support system based on AI is derived from the current architecture of such systems. In fact, all artificial intelligence systems are based on algorithms that are operated on computational machines. Therefore, as all computing devices are universal Turing machines that process strings of 0s and 1s, according to a few simple rules (the algorithm). As a consequence, any ethical consideration would need to be added as a set of rules within the algorithm. Finally, we will end up with the question: Is Ethics computable? When we consider ethics as being a being a sum of the below: • • • • • • • •

Virtue, Right and wrong, Free will, The highest good, Compassion, Moral excellence, Responsibility, Values.

At last, we want to analyse the results presented in the previous chapter, using the ethical values as a methodology to mitigate ethical issues (Table 3.4).

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Table 3.4 Analysis of ethical dimensions and ethical issue mitigation Ethical dimension

Condition

Test

Conclusion

Ethical issue mitigation

Fairness

All humans should be treated fairly!

Are the data points contained in a data set differentiating individual in a discriminating way?

The data set used consisted of an output of all employees including their assessed performance in the last years

The data set needs to undergo an ethical analysis to whether a relevant and a minimal set of data was used

Reliability and safety

The system is trustworthy!

Would any user that interacts with the system understand how it operates and could the user verify that the system operates correctly?

Given the scenario, the system including its output is most likely to be used by human resources and management departments

Most likely, the uses have limited or no knowledge on how the system operates. The system needs an additional layer making its operation transparent to the user

Privacy and security

All personal information is protected!

Is the data used by the system protected against unsecure exposure or reverse engineering?

The data set used consisted of an output of all employees including their assessed performance in the last years

The data set used could have been minimalized, as some information might not be needed. Any systems using personal data should be protected well

Inclusiveness

The system works for anyone!

Can the system be used and operated by any human? (People with disabilities, people in other cultures?)

This aspect is not part of this experiment

In order to fulfil on the value of inclusiveness, the system should have an adequate interface and should consider cultural difference (continued)

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Table 3.4 (continued) Ethical dimension

Condition

Test

Conclusion

Ethical issue mitigation

Transparency

The system does not harm humans!

Would any (even non-digitally literate) user understand how decision is taken?

Probably not as there are nowadays no means available. Some companies such as the start-up Bonsai invests in explainable AI

Understanding on how a decision is taken when AI is used can be a separate research area. We expect here more progress in the upcoming years

Accountability

Define responsibilities!

Who is responsible and liable for outcomes of AI systems, especially if something goes wrong?

This dimension was also not considered in this case study

Governance and ownership should be clearly defined, at the time the project is defined

3.10 Conclusion The two approaches of Ethics by Design and Ethics Build-In were presented in detail. While the topic of Ethics by Design is not new to the scientific community and there are at least 20 national and global bodies, it seems to be still difficult to enforce a standardized framework for that approach. Most guidelines and suggestions, including the introduction of artificial intelligence and ethics in formal as well as specific education, are very practical. Still, a lack of consistency suggests a moderate adoption. The second approach of Ethics Build-In faces in most practical cases major challenges in implementation. Besides the technical challenges, the cultural boundaries and the continuous evolution of ethics increase the difficulty for properly selecting and determining the context of operation. From the literature, we observe that the most common approach to Ethics Build-In lies in the so-called Artificial Moral Agent (AMA). Autonomous AMAs could be designed in a way to act on cultural boundaries and domain-specific matters. All system that operate based on artificial intelligence and consequently take decisions should consult autonomous AMAs for ethical considerations. The two approaches of Ethics by Design and Ethics Build-In used upon the six ethical values and build a solid foundation for a framework that should be applied to any endeavour to create and operate decision support systems based on artificial intelligence, in order to avoid serious ethical issues that can harm humans and societies.

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References 1. Sprague, R.H., et al.: Decision Support Systems: Putting Theory into Practice (1993) 2. Phillips-Wren, G., Mora, M., Forgionne, G.A., Gupta, J.N.D.: An integrative evaluation framework for intelligent decision support systems. Eur. J. Oper. Res. 195(3), 642–652 (2009) 3. Novet, J.: Everyone Keeps Talking About A.I.—Here’s What it Really is and why it’s so hot now. [Online]. Available: https://www.cnbc.com/2017/06/17/what-is-artificial-intelligence. html (2017). Accessed 09 Oct 2019 4. Russell, S., Norvig, P.: Artificial Intelligence A Modern Approach, 3rd edn (2010) 5. Fieser, J.: Internet Encyclopedia of Philosophy (A Peer-Reviewed Academic Resource). [Online]. Available: https://www.iep.utm.edu/ethics/ (2019). Accessed 09 Oct 2019 6. Morgan, M.G., Jonas, H.: The imperative of responsibility: in search of an ethics in the technological age. J. Policy Anal. Manag. (1985) 7. Gürses, S., Troncoso, C., Diaz, C.: Engineering privacy by design. Computers, privacy and data protection. In: Conference on Computers, Privacy & Data Protection (2011) 8. Wright, D., De Hert, P.: Privacy Impact Assessment (2012) 9. Rawbone, R.: The oxford textbook of clinical research ethics. Occup. Med. (Chic. Ill) (2013) 10. Guillemin, M., Gillam, L.: Ethics, reflexivity, and ‘Ethically important moments’ in research. Qual. Inq. (2004) 11. Backus, N., Ferraris, C.: Theory meets practice: using the Potter Box to teach business communication ethics. Assoc. Bus. Commun. Annu. Conv. (2004) 12. H. of Commons, S. and Technology, and Committee: Responsible Use of Data. Published on 28 November 2014 by authority of the House of Commons London: The Stationery Office Limited, 2014 13. Hancock, M.: Data Science Ethical Framework (2016) 14. D’Aquin, M., Troullinou, P., O’Connor, N.E., Cullen, A., Faller, G., Holden, L.: Towards an ‘ethics by design’ methodology for AI research projects. In: AIES 2018—Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society (2018) 15. Berleu, J., Brunnstein, K.: Ethics of Computing. Chapman & Hall (1996) 16. Microsoft, 22. Microsoft—The Future Computed Artificial Intelligence and Its Role in Society - 2018 (2018) 17. Anderson, M., Anderson, S.L.: “Machine ethics. In: IEEE Intelligent Systems (2006)

Chapter 4

Security and Privacy Implementation Framework as a Result of the Digitalization Process for Organizations in Different Industries Cristian Vartolomei and Silvia Avasilc˘ai Abstract Computer security, cyber-security or security of information technologies is the field that has to do with the protection of the computer infrastructure and/or telematics and all the information contained in it. This area includes any type of software such as databases or files, hardware, computer networks and anything that involves confidential information on a computer. Security audit assessments have become key tools for organizations due to the increasing number of cyber-attacks. Increasingly, organizations need to strengthen their defenses against data breaches, cyber-crime and fraud, and to ensure a more robust security posture. Considering the aforementioned aspects, this paper introduces the digitalization process within organizations, the cyber-security types of threats and countermeasures, followed in the end by the current approaches of the security and privacy mechanisms implementation and the development of a security and privacy framework along with the necessary phases for its implementation within organizations in every industry.

4.1 Introduction The technological advances that we have been practically involved in recent years lead us to the practicality of carrying out procedures, transactions or services that previously resulted in a loss of time or accessibility. With these new methods of doing things, there are also some negative elements that would properly address their situation. These problems are directed toward information security. Information security refers to the analysis and application of the methods and means used to protect our system from possible modifications, elimination or disclosure of information. In information security, we refer to the care and correct handling C. Vartolomei (B) · S. Avasilc˘ai Department of Engineering and Management, “Gheorghe Asachi” Technical University, 28 Blvd. Mangeron, TEX1, 700050 Iasi, Romania e-mail: [email protected] S. Avasilc˘ai e-mail: [email protected] © Springer Nature Switzerland AG 2020 G. Prostean et al. (eds.), Innovation in Sustainable Management and Entrepreneurship, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-44711-3_4

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of the data that is shared through the internet. It is now something new to know that these data have a fundamental value for both the company and for the clients, because there are topics aimed at the integrity and pocket of the participants. That is why the computerized system facilitates the processes of sending, handling and accessibility of the files, confidential information and characters that people share on the sites. Likewise, this type of data management and ease of distribution, in turn, has its counterpart, which must be the task of the site to offer a strong and complete defense that guarantees the security of shared information. A large part of information security research is technical in nature with limited consideration of people and organizational issues. The incredible lack of precautions that we commit when we confidently store all our important information in mass storage devices has as a result that our computer is the focus of attention for cyber-criminals. Currently, we are experiencing several episodes of cyber-attacks against companies. Important organizations, both public and private, have suffered attacks on their computer systems. These cyber-attacks not only affect customers or investors of the companies but may also affect the national or regional security of the different states besides the company itself. If a company wants to be competitive in these times, it must have systems, resources and agile information and communications technology (ICT) platforms with a high level of availability, which requires effective management and a broad process of digital transformation. The process of digital transformation, in which most organizations and society in general are immersed, allows attackers to commit attacks against the computer’s security of companies from anywhere in the world using only a computer as a tool. Therefore, organizations have to pay special attention to protect themselves against possible attacks since no one is safe from different types of malware. In this way, during this research we will approach concepts as the digitalization process in different industries, the cyber-security importance with regards to the types of attacks and protection methods as a result of the aforementioned process, the current approaches of the security and privacy mechanisms implementation, and in the end, we will develop a security and privacy framework with its specific phases for implementation, adding value to the current processes as ISO 270001, ISMS and NIST 800-171.

4.2 Digitalization and Cyber-Security at a Glance For some time now, in our everyday talk, the word “digital” has entered in a way described by the digital camera, digital TV, smartphones, laptops and other elements from the IoT concept. When we wonder what digitalization is, this concept should not be referenced only to electronics or computer science, because it can be applied every time someone has to do with representation or with the measurement of quantity or quantities. The physical quantities that characterize all the phenomena around us vary within a continuous range of values; in other words, they can take on any value,

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even values that are so close to each other that they cannot measure the difference with the existing tools. Digitalization can also be defined as a transfer of data from the paper to the computer, or from analog to digital, which allows the data to be managed quickly, safely and by multiple users at the same time. For a digitalized company, the income will certainly be increased, because this process also allows visibility of the online products or services, greatly expanding the landscape of possible customers. Furthermore, this way of proceeding allows to have accurate data and statistics of the business, leading this digital revolution to be the future of all companies in the world. The digital transformation is an organizational decision that implies the definition of plans and programs that integrate all areas of the business. In addition that, in turn, prioritizes specific actions that are strategic in the hand of global trends in technology or opportunities that can be exploited in the local market. This process also allows new possibilities and business strategies to emerge, besides a high business communication based on the appearance of new technologies. All these reconstructs the organizational dynamics having a great relationship with the digitalization, without being taken as a simple online business, since the transformation in the companies must be guided by the technologies and tools for the implementation of the digital strategy, for an adaptation and evolution. From digitalization to IoT is just a matter of understanding that one without the other one cannot exist. As the author says in [1], the ontology of the IoT is therefore essentially structured into three layers, inhabited by three kinds of “things” in a “symbiotic interaction” with each other, through an overarching unified infrastructure: the physical, the digital and the virtual entities. […] Physical “things” have digital counterparts and virtual representations. In this threefold cosmology, we (meaning human beings) relate to our environment just like any other entity through our multiple digital counterparts and virtual representations. The uniform set of characteristics attributed to all three kinds of entities, such as identity, personality and intelligence, converge into a single attribute: being smart. Moreover, “smart things/objects” are also provided with the agency as they are “expected to actively participate in business, information and social processes” (Fig. 4.1). Regarding the economic potential, a range of technologies has been a major source of total productivity growth. In 2018, MGI estimated that an additional $13 trillion could be added to global GDP by 2030 from today through digitalization, automation and AI, as these technologies create major new business opportunities and productivity gains are reinvested in economies [3]. From IoT to cyber-security, which is one of the pillars belonging to the results of the digitalization process in different industries, a simple definition will say its words: “Cyber-security—the ability to protect or defend the use of cyberspace from cyber-attacks” [4]. As the definition presents, its main duty is to fight against cyberattacks, which can come from different sources, along with social engineering (or manipulation), malware (viruses, trojans, computer worms, etc.), intruder codes that operate on databases (SQL injections), violations derived from the loss or incorrect use of devices and access, among others. These cyber-attacks are usually aimed at

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Fig. 4.1 Combinatorial effects of the new technologies accelerating the technological change [2]

accessing, changing or destroying sensitive information, extorting money from users, or interrupting normal business processes [5]. Cyber-security domain wears an important role in the information technology, considering that different entities as government, corporate institutions, medical organizations or regular users are handling different amounts of sensitive information day-by-day on their IoT devices. For a corporate institution, this sensitive information includes data on its production systems, its organization, products, investments, export, import, its production plants, its staff and customers. That is, everything that revolves around an organization, its past, present and future and is likely to be stolen or tampered with for criminal purposes by third parties. This forces these same companies to have the best security systems to control these threats that may even condition the viability of the company. Therefore, cyber-security focuses on the proactive and constant search, as well as on the identification and understanding of possible threats in computer systems capable of evading existing filters. It requires the participation of trained personnel (developers, analysts, data scientists, etc.) who master the relevant technologies to avoid or counteract the damage.

4.3 Cyber-Security Protection Methods Analyzing the cyber-security environment and understanding that people are the main objective of cyber-attacks; the following suggestions could be suitable for an increased level of protection: 1. Adopt a security posture based on people: Considering the individual risk of each professional, looking at the data to which the user has access, the devices used to access the information of the organization, social behavior and his level of awareness of the risks to which he is subject as an employee.

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2. Train users: It is important to create effective training and awareness campaigns on cyber-security, simulate attacks from different attack vectors and measure the degree of performance of the training received. 3. Assume that the users will fall into the trap: An organization must be aware that cyber-criminals will continue to try to harm users, so at some point, someone from the organization will fall into the trap. In this way, the company must have a plan for these situations (emergency plan). 4. Protection of the user environment: Adopting advanced security technology solutions for the devices to which the employee has access (mobile, cloud computing, endpoint, etc.). 5. Protection of the reputation of the brand: The company must be continuously monitoring all digital channels and avoid the brand being affected. Many times the attacks are not aimed at the organization level; it may go against the clients (fraudulent pages) and may have a negative impact on the business. 6. Have a cyber-security partner: Understanding that security cannot be controlled 100% without the help of specialists who are responsible for preventing attacks against the digital security of the business. However, given today’s information is available to anyone at any time, very few companies are aware of the level of cyber-security they should adopt, many entrepreneurs considering that having installed a licensed antivirus system on the employee’s computer and is enough for a safe browsing on the internet. In addition, going deeper into the technical field, the following security mechanisms shall be considered within an organization: 1. Adopting a licensed antivirus software, because of [6]: 1.1 Ransomware protection; 1.2 Protection of sensitive data: This type of protection includes the protection of personal information, intellectual property and financial data, as more and more cases of credit cards and bank accounts have been compromised by hackers; 1.3 E-mail protection: This form of exchanging information is a very common and handy mechanism for hackers to infect the victim’s system with different forms and viruses, having different behaviors. The protection method for this form is scanning the e-mail attachment each time; 1.4 An improved online security: If the organization operates in the online environment, transactions and payments are made in the same environment or the information is stored in the cloud, the use of an antivirus system is crucial. 2. Implementing a firewall protection mechanism: Firewalls provide very good network security features. However, classical perimeter firewall deployments suffer from limitations due to complex network topologies and the inability to completely trust insiders of the network. Distributed firewalls are designed for alleviating these limitations [7].

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3. Choosing and using the latest hardware and software resources, which offer an increased availability, along with minimal vulnerability risks. 4. Providing access control, considering the fact that only specific users which have different access levels and authorization rights should access the network. This process involves the assignment of individual credentials for each user in the form of username and password, and in some cases, the use of a second-factor authentication mechanism which definitely enhances the level of authentication security. 5. Monitoring and protection of the data integrity by signing the documents with digital signature. 6. Making use of encryption mechanisms in order to avoid the information to the unauthorized users, ensuring in this way the confidentiality of the data. 7. Implementing the encryption of the authentication process between client and server. In this way, the username and password are exchanged via the communication channel in a secure way. 8. Enchanting reliability by using different software and hardware mechanisms in order to monitor the activity and filter the traffic within a private network which communicates with the public networks. 9. Implementation of recovery mechanisms in order to ensure the network security trust against attacks as denial of service, tampering with data and repudiation.

4.4 Current Approaches of Security and Privacy Process Implementation During this research, we have developed a security and privacy implementation framework for companies in different industries, in order to avoid cyber-attacks or to minimize the risks occurred as a result of them. One of the current approaches that we found in this area leads us to a holistic cybersecurity implementation framework which is described by the following Fig. 4.2: As is mentioned in [8], this figure transforms the cyber-security requirements into strategic moves that are eventually executed under the defined framework controls to achieve the required security objectives. […] Finally, the result containing the security objectives will be compared to the targeted objective in order to decide for proactive or corrective actions that will eventually be taken. In addition, this cybersecurity implementation framework is different than others because it implements the security aspects considering a holistic approach. Along with this, the National Institute of Standards and Technology (NIST) provided to large public the Special Publication 800-53 as being a list of 20 security and privacy control groups. The security and privacy control groups outlined by NIST 800-53 are flexible, can be customized and implemented within organizations as being a part of their overall risk management strategy. The controls cover important areas such as access control for different users with different access rights, security

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Fig. 4.2 Holistic approach of cyber-security implementation [8]

and privacy awareness training, incident response or different recommendations for continuous monitoring in order to support organizational risk management [9]. The NIST framework is the quintessential choice of IT Security Managers who seek to take advantage of the best of each good practice framework that currently exists for the world of information technology. This framework proposes a course of future protection actions because its guidelines define the proper management of the risks associated with cyber-security of organizations. Considering the latest revision of this basic NIST publication, we can conclude that it represents a progress regarding the next-generation security and privacy controls that will be needed in order to achieve specific goals, including [9]: 1. Ensure that the information systems are more resistant to different types of cyberattacks; 2. Protect the confidentiality, integrity and availability of the organizations information system; 3. Limiting their negative impact when cyber-attacks occur; 4. Make these information systems more resilient and resilient in general; Along with this, International Standards Organization (ISO) has developed ISO 27001, which ensures that organizations with different needs and requirements are properly managing and protecting their sensitive data with a so-called information security management system (ISMS) that is a system developed for identification and analysis an organization’s information risks, in this way offering protection against different types of cyber threats, similar in design to quality assurance management systems (series ISO 9000) and environmental protection (ISO 14000 series) [10].

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4.5 Proposal of Security and Privacy Process Implementation During this research, we developed a security and privacy process implementation which can be used by any company in different industries, described by the following Fig. 4.3: The proposed process can be described as it follows: 1. Planification phase 1.1 Identification of the systems in needs of security—Composed from all the devices that connect the employees from a company, along with the clients or other organizations with whom the company collaborates. This aspect leads us to hardware resources as laptops, desktops, switches, routers, firewalls, servers, access points and to software resources as operating systems and other applications which are installed and used by the employees. 1.2 Evaluation of the necessary hardware and software resources—Only the resources which will be implemented, in order to achieve the security within the company, by comparing these with the existing ones, checking the compatibility between them, the level of security which can be achieve and how handleable will be for the employees to use them when the implementation process is done. 1.3 Decisions regarding privacy/security standards—Privacy standards are created with the main scope to protect an individual, a public or a private

Fig. 4.3 Proposed security and privacy implementation framework

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organization in order to determine how the information is processed, collected, used in public environments or disclosed by other parties. In this way, different security standards define a set of administrative and technical actions that should be taken in order to cover the confidentiality, availability and the integrity of the data [11]. 1.4 Defining methods and timing of quality control—These methods can be defined as “part of quality management focused on fulfilling quality requirements which are used to assess the conformance of the process implementation by describing the expectations for quality, as well as the attributes of products that indicate whether the quality factors are satisfied or not. 1.5 Risk assessment, including current and future advantages or disadvantages—This part of the process is used to describe the overall process or methods where are identified the hazards and risk factors which have the potential to cause different types of harm. In addition, the advantages when the risk is minimized and disadvantages as a result of the process implementation are considered. 2. Pre-implementation phase 2.1. Prioritization of security process implementation for the employees—Managers must find different ways in order to adopt the best solutions to implement the security process and to avoid the impact in the production, also the clients or contributors to not be affected any way. 2.2. The collection of metadata—This one plays a significant role in the exchange, compatibility and long-term access of and to security mechanisms. 3. Implementation phase 3.1 Hardware/software security mechanisms implementation—Considering the last two phases in which it was performed the evaluation of the necessary hardware and software resources along with the decisions regarding privacy/security standards and the collection of metadata, at this step, the information gathered are implemented within the company. 3.2. Quality control—The defined methods and timing of quality control from the first phase are now considered and applied in order to assess the conformance of the process implementation with the expectations for quality. 4. Post-implementation phase 4.1. Security awareness training for the employees—When the process implementation is done, it is absolutely mandatory for the company to ensure a training session for the employees in order to make them be aware and understand the security and privacy concepts regarding their personal devices, the characteristics of the passwords they use, how to treat suspicious URL’s, the e-mail attachments and be careful of the social engineering attacks.

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4.2. Assessment and evaluation of security mechanisms—The last step of this process is to assess the security mechanisms, both hardware and software, which have been implemented within the company, in order to check if the expectations have been meet and the company has an increased protection level against cyber-attacks. Considering the implementation levels of the process, we can observe that it points out how accurate the implementation of the information has to be, giving the option to the companies to decide how far to go with the process adoption and to decide the level of complexity of it, having in front the requirements of the different stakeholders. The contribution of this security and privacy implementation framework compared with other frameworks that exist is that this one makes easy to find out where the gaps within a company are, and to choose the right action plans in order to cover these gaps. This is possible by creating a security and privacy company profile which represents the position level where the company is located at the analysis time, and considering these aspects, to decide where it wants to be. In this way, a security and privacy profile could also be used to establish important aspects for some organizations, as suppliers or partners. Overall, the proposed security and privacy implementation framework approach allow the managers, the engineers and the IT departments to clearly understand what and where it is necessary to be implemented in order to find out the gaps. It also focuses on protection for all types of information, from that one processed in the hardware and software systems, up to the privacy area. In the end, considering the transition from analog to digital and the cybersecurity aspects in this process, and also considering that the information written on papers along with tangible documents have less and less importance, it is absolutely mandatory to understand that for some companies this approach could still involve significant risks.

4.6 Conclusions Nowadays, computer security has become one of the main concerns of companies. On the other hand, the use of information and communication technologies is becoming more extensive so that the assets need to be protected because of the vulnerabilities’ growth. On the other hand, cyber-attacks are more frequent and complex, reaching very serious consequences such as the disclosure of information among others, tampering with their data and making their computer services unavailable, so having security professionals who can protect assets in the network is essential in all the companies no matter how small or big they are. During this research, we have started with a short introduction about the digitalization process in different industries, the cyber-security concept presented as a pillar for the aforementioned process, followed by a short literature review with regards

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to the most known cyber-security protection mechanisms, the current approaches of security and privacy processes implementation and in the end a proposal of security and privacy process implementation, developed to be applied to any company no matter the industry it belongs to. We concluded that the proposed process helps the result to contain a security and privacy profile that represents the position in which the organization is located and where it wants to be, along with the identification of the current gaps, followed by the choosing of the action plans against them. Regardless of the compliance framework that companies make use of, it is recommended that a flexible and dedicated compliance program which can be adapted to the company needs should be considered, in order to reduce the risks and improve the security and employee’s privacy, in this way, ensuring continuous improvement and demonstrate to the client’s commitment to quality.

References 1. Vermesan, O., Friess, P., Guillemin, P., Gusmeroli, S., Sundmaeker, H., Bassi, A., Mazura, M., Harrison, M., Eisenhauer, M., Doddy, P.: Internet of things strategic roadmap. In: IERCEuropean Research Cluster on the Internet of Things (2011) 2. Digital Transformation of Industries. Demystifying Digital and Securing $100 Trillion for Society and Industry by 2025. In: World Economic Forum (2016) 3. McKinsey Global Institute, May 2019 4. NIST—Glossary of Key Information Security Terms, Revision 2, 2013 5. https://www.cisco.com/c/en/us/products/security/what-is-cybersecurity.html 6. https://thenextscoop.com/antivirus-for-business/ 7. Thames, L.J., Abler, R., Keeling, D.: A distributed firewall and active response architecture providing pre-emptive protection. In: ACM-SE 46 Proceedings of the 45th Annual Southeast Regional Conference on XX, (28–29 Mar 2008) 8. Atoum, I., Abu Ali, A., Ali Otoom, A.: A holistic cyber security implementation framework. In: Information Management and Computer Security, vol. 22, No. 3, pp. 251–264 (2014) 9. NIST—Security and Privacy Controls for Information Systems and Organization, SP 800–53 Rev.5, Aug 2017 10. https://www.iso.org/isoiec-27001-information-security.html 11. https://www.himss.org/library/interoperability-standards/security-standards

Chapter 5

The Impact of Using Digital Technology in Measuring the Marketing Performance Sebastian Bîrzu

Abstract For a general understanding, digitalization has effects on almost all social and economic sectors. To this mega-trend, new topics become relevant, such as data security, data protection, cyber-crime, management in the digital age, training and further education, social media, and the sharing economy. The focus of this paper is on the impact of using digital technology in the measuring the marketing performance, describing it in terms of digitalization concept, which is a key factor for the Industry 4.0, followed by the classic methods of measuring the marketing performance compared with the digitalization in measuring the marketing performance. Keywords Internet · Digital technology · Online/offline marketing

5.1 Introduction Marketing is the business function that, more than any other, has to do with the customer. We can say that marketing is not anything else than the management of a profitable relationship with the customer. Its dual objective is to attract new customers by offering a high value and, at the same time, maintain and cultivate existing customers, satisfying their needs. Effective marketing is fundamental to the success of any organization and it is also a reality of large profit-oriented companies such as universities, hospitals, museums, orchestras, and even religious institutions. Everyone knows marketing because it is everywhere. Marketing comes in the traditional form: in the variety of products on the shelves of shopping centers, in the advertising broadcast by television or advertising in magazines. Today, companies want to become part of our lives and enrich our experiences with their brands; they want to help us live their brands. At home, at school, at work or leisure, marketing is present in almost all of our activities. Besides, it is much more than what the consumer perceives from the outside; behind all the visible events, there is a dense S. Bîrzu (B) Department of Engineering and Management, “Gheorghe Asachi” Technical University, 28 Blvd. Mangeron, TEX1, 700050 Iasi, Romania e-mail: [email protected] © Springer Nature Switzerland AG 2020 G. Prostean et al. (eds.), Innovation in Sustainable Management and Entrepreneurship, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-44711-3_5

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network of people and activities that compete for the attention of the customer and his purchases. Many think that it is only a matter of promotional and sales activities, which should not surprise us, given the daily bombardment of TV commercials, direct mail, telephone promotions, and through the Internet. In reality, sales and advertising are nothing than the tip of the iceberg. Today, the objective of marketing is no longer limited to the conquest of a single transaction, but in a new and far-sighted perspective, it takes concrete form in satisfying the customer’s needs. If the marketing operator can understand the consumer’s needs, develop a high-value product and define effective pricing, distribution, and promotions, and sales become a logical consequence.

5.2 Digitalization—A Key Factor of the Industry 4.0 For a long time, the concept of digitalization of the company has been compared to that of the dematerialization of business documents. The digital documents created on the PC and transmitted via the Web are the natural substitutes for paper documents. This replacement is already underway and digital documents are proving to be valuable allies in terms of efficiency, integration, and productivity. However, still today, the “paperless” digital era is slow to arrive despite the advent of smartphones, tablets, and the cloud. The conversion of paper documents into digital files is only the first step in digitizing business processes. It is a new and revolutionary organizational model, thanks to which it can manage all its activities in an integrated, effective, and collaborative way, thus, eliminating any slowdown and reducing the margins of error. As Vartolomei suggests the digitalization will influence or change every company in the world, regardless of the industry it belongs to [1]. Adopting an appropriate strategy, along with the development of skills and competencies that will meet adaptation to a new environment will be necessary to follow the use of the digitalized context. For example, in the automotive industry, digitalization of approaches such as production, sales, distribution, and, last but not least, suppliers, will significantly improve the quality of planning and management, along with streamlining processes by making more accurate decisions in a shorter time. Also, as a result of digitalization, the companies will replace in a significant way their interaction with the clients, the management side will be improved and its efficiency will be increased, along with the quality, the speed and the relevance of procurement of the information. The changes that will occur due to these actions will also create new processes, which will be perceived at a different and increased level of understanding, due to the disappearance of technological barriers. Increasing company productivity without incurring exorbitant expenses is possible, in many cases, being possible to increase the efficiency of the subjects already involved through the use of management software that helps them in their tasks. These programs are designed to automate some processes, to reduce the execution time, and the possibility of making mistakes.

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There are several useful software for companies: from simple accounting software to complex management software, which can be implemented based on business needs. If you want to gain market share, today it is unthinkable not to have a Web site on which users can find contacts and more information on the products and services provided by the company. The old “word of mouth,” which has dominated for decades has now become digitalized, and the small circle of knowledge has evolved into a community made up of millions of people who interact directly with companies. As a result, online reputation has become as important as offline, if not more. Investing in the visibility and online promotion of the brand and corporate values is an essential condition for those who start a business or want to increase it. “In the era of digitalization, the Internet offers opportunities for companies, which substantially reduce costs by digitizing their business and by having an easy access to a global market, but also for customers who are able to search/obtain information on the desired product, to compare prices and also to save time by the online purchasing of the product. […] Through the Internet, the company can obtain in a short time important information about its prospects so that it can better adapt the business communication to them. In this context, business communication will be customized on the company’s target, focusing on the customer needs” [2]. Digitalization as a process of technological transformation is part of the Industry 4.0, which was mentioned for the first time in the fair of Hannover of 2011 with the intention of putting into action a project that will carry out the conception and development of the smart factory associated with the Fourth Industrial Revolution, a vision of computerized manufacturing with all its processes interconnected with each other making use of the Internet of things (IoT), nowadays it was called as the industrial Internet of things (IIoT). It is said that two years later the German government launched this initiative with the idea of facing the great advances in industrial matters that were taking place in emerging countries such as China, and that cannot compete in production costs, the idea was to overcome them in industrial technology and in the ability to manufacture products individually. Although as has been seen, these countries have not been slow in wanting to get into the car of this Fourth Industrial Revolution once launched in Europe and the USA, where the concept of Industry 4.0 or industrial intelligence unlike in Europe, it is called smart manufacturing or industrial Internet. The term of Industry 4.0 covers many concepts and purposes, but the first advances in this area imply the incorporation of greater flexibility and individualization of manufacturing processes. The automotive industry is a pioneer in the need to implement these flexible and individualized manufacturing processes, and this is where major advances are already being made in this field because manufacturers have to adapt the vehicles to the individual needs of customers. Regarding the history of the Industry 4.0, it started with the Industry 1.0 that was the starting point for the technological development of humankind, which already seen three major revolutions in manufacturing industries, as [3]:

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Fig. 5.1 Four industrial revolutions [3]

• First was when mechanization and steam power changed the whole concept of manufacturing; • Second revolution happened when mass production assembly lines and electrical energy took place and enabled a giant step in production efficiency; • Third revolution brought automation, computers, and robots to production (Fig. 5.1). The impact of the development and implementation of Industry 4.0 can be felt at several levels, such as large ecosystems at the organizational and individual levels (employees and customers). As far as ecosystems are concerned, the new industry affects all their agents, such as customers, suppliers, investors, and regulatory considerations, facilitating interaction, communication, and information sharing among them. Organizations can become more responsive, dynamic, and predictive to the extent that they can implement, adapt, and learn real-time new technologies. Also, for individuals, this industry can be perceived in a completely different way, such as updating the enterprise’s way of doing business within the organization, while for customers it can mean a new way of personalizing and adapting the products and services purchased in meeting needs [1].

5.3 Classic Methods of Measuring Marketing Performance Marketing is the activity, set of institutions, and processes for creating, communicating, delivering, and exchanging offerings that have value for customers, clients, partners, and society [4]. Clark maintained that marketing performance measurement is growing and classifies marketing measures into four categories [5]. 1. Quantification of financial results, here we can talk about profit, sales, and cashflow; 2. Quantification of non-financial results, we share market share, customer loyalty, and brand recognition;

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3. Quantification of inputs, requiring an internal and external audit both on the Macromedia and the micro-organization, as well as the focus on the market; 4. Multiple quantifications are aiming at switching from a series of singular indicators to multidimensional indicators and aiming to achieve a series of measures regarding efficiency and effectiveness. After more than 10 years (2010), Gao adheres to a new trend, as shown in Fig. 5.2, namely marketing integration with the value of the firm [6]. Also, when we talk about measuring marketing performance, we are talking about measuring marketing productivity, and when researchers, as well as practitioners, are discussing marketing performance measurement, we think about the efficiency and effectiveness of marketing performance [7]. In the specialty literature, it is very good to say that marketing effectiveness is the psychological difference between the results you expect and the results you get [8]. The task is about doing things well and the effectiveness is choosing the things to do [9]. Measurement of a company’s marketing performance is based on performance indicators that over time have had a single common denominator and are multidimensional but never the same.

Fig. 5.2 General trends in measuring marketing [5]

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A definition of performance indicators is given by Kotler and Keller, according to which performance indicators represent a set of measures that help firms quantify, compare, and interpret marketing performance [10]. In 2010, Jeffrey K. Liker says in the specialty literature that performance indicators are divided into two categories, the classic markers of marketing and the indicators of the new era of marketing. Performance indicators in the new era of marketing are conversion rate, cost per click, advertising profitability, bounce rate, and all tools that include social media [11]. The main internal and external factors that contributed to the development of this practice and measuring marketing performance were: • The tendency of the company’s management that every department in the organization knows its contribution to the value of the company; • Dissatisfaction with the old performance indicators, knowing that they focus on the company’s past and not on the future; • Evolution in the field of technology; • With the emergence of the Internet and the emergence of products for customer relationship management, performance indicators have changed [12]. Some authors say that to measure the marketing performance of a company, it needs long-term goals and specific objectives [13].

5.4 Digitalization in Measuring the Marketing Performance When talking about digitalization, we must first consider the appearance of the first computer (1946), the emergence of the first computer network (ARPANET), made by the US Army, for securing information and easier communication between different departments which were working on a project with a common goal [14]. Human society is in a new phase, as Drucker (1993) says, a knowledge-based society [15]. The real-time spread of information has brought about the emergence of new terms, such as “economy,” “cubic space,” “virtual,” “e-commerce,” “e-business,” “digital economy” [16]. The first time digitalization was used by Tapscott-1996 and identified four sectors of this economy [17]: 1. Sector of digitized goods and services—bank transfers, information services, software, music sales, and distance education; 2. The services and mixed goods–books, flowers, ordered through the Internet; 3. The IT goods and services production sector: computer-aided design and automated production machines; 4. The information technology industry. New information technologies improve communication, encourage transparency [18], shorten the duration of certain procedures, increase productivity, help with database mining [19], but to find out the needs of the consumer, to know and to tarnish your customers and of course to do online business [20].

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Due to the development of mobile devices, smartphones, tablets, and social platforms, business people will need new business models and a new perception of marketing. The emergence of mobile devices has led to better communication but also better customer satisfaction for each of them. Advertising has become more interactive. The digital environment allows customers to express their opinions, to interact directly with business properties, and to choose where they appear to be cheaper and more qualitative, in terms of products or services. From the perspective of business owners, they can find the cheapest and most effective ways to reach their customers [21]. We will see a huge increase in direct sales, e-commerce, consumers will make acquisitions by comparison, they will be able to examine the product or service they want, even online looking to buy at the best price and also quality, depending on the consumer’s nature in the shortest time and one click away [22]. Marketing will advance global and local marketing. Some estimates suggest that by 2020 there will be about 50 billion devices connected to the Internet [19]. Internet traffic will triple by 2020, globally the increases being recorded mainly by mobile data traffic. Over one billion new Internet users will join the global community from 3 billion in 2015 to 4.1 billion in 2020, and online games will have the fastest growth between residential applications (SeniorSoftware). The performance of the profit-making, nonprofit organization, and the public administration needs to implement a mobile marketing strategy, and adapted, segmented, and addressed marketing to each market segment. In the future, companies must be responsible in society have databases, communicate with clients on social networks and have their own site, offer eco-friendly products, always be informed of what competition is doing, formerly, and, last but not least, the analysis of data and reports to measure marketing performance in the digital era [23], using performance indicators of the new era of marketing. The measurement of marketing performance has increased with the emergence of many software solutions. Currently, for the quantification of marketing performance, a series of KPI dashboards are used to help control the marketing department, which helps to achieve great results and greatly reduce unnecessary spending. Let us first establish what a KPI indigent is, and it is a measurable tool that leads to a specific or general goal. A marketing dashboard contains many indicators, such as site visitors, potential customers, brand performance, customer loyalty monitoring, budgets spent on different marketing campaigns, the cost of a customer, and the cost of interactions. When talking about e-commerce, we can talk here about well-known Google platforms, Google Ads and Facebook Ads. In the measurement of marketing performance, new performance indicators appear: return of investment, pay per click, insights, time spent on the site, presence in the social media, and visitors who dropped out of shopping cart.

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5.5 Conclusions The digitalization of industrial processes, in other words, it is the key element of Industry 4.0 that allows keeping costs down while offering greater quality, flexibility, and efficiency. In the era of smart manufacturing, response times can become extremely fast, being capable of satisfying the demands of the customers and adapting to the trends of an increasingly volatile market. A key performance indicator is a quantifiable measure that a company uses to determine the extent to which set operational and strategic objectives are achieved. This means that different companies have different KPIs depending on their respective performance or priority criteria. At the same time, the indicators usually follow industry standards. There is a subtle difference between key performance indicators and marketing metrics. An important point to remember is that KPIs are marketing metrics, but not all marketing metrics are KPIs. A good manager (but also a professional or a small business owner) must know how to determine which marketing metrics qualify as their performance indicators. These indicators do not necessarily have to be financial but are important in addressing marketing vehicles for management. Without these indicators and without the indications they provide to companies, it is almost impossible for them to reach their full potential. Digitalization is important because it represents itself as being a novel transition, and additionally, it facilitates the process of adaptation of some performance indicators.

References 1. Vartolomei, C.: Challenges of digitalization process in different industries. Before and After, Annual Session of Scientific Papers “IMT ORADEA” (2019) 2. Patrutiu-Baltes, L.: The impact of digitalization on business communication (Case study) SEA. Pract. Appl. Sci. IV(2) (2016) 3. Spectral Engines: Industry 4.0 and how smart sensors make the difference. Online article, retrieved from: https://www.spectralengines.com/articles/industry-4-0-and-how-smartsensors-make-the-difference (2018) 4. AMA: Definition of Marketing. Online article, retrieved from: https://www.ama.org/thedefinition-of-marketing-what-is-marketing/ (2013) 5. Clark, B.H.: Marketing performance measures: history and interrelationships. J. Mark. Manag. 15(8), 718 (1999) 6. Gao, Y.: Measuring marketing performance: a review and a framework. Mark. Rev. 10(1), 28 (2010) 7. Clark, B.H., Ambler, T.: Managerial perceptions of marketing performance: efficiency, adaptability. Effectiveness and satisfaction. Int. J. Bus. Perform. Manag. 3(2) (2001) 8. Clark, B.H.: Marketing performance measurement: evolution of research and practice. J. Strateg. Mark. 8(1), 7 (2000) 9. Drucker, P.: Management: Task, Responsibilities Practices, p. 45. Harper & Row, New York (1974) 10. Kotler, P., Keller, K.L.: Marketing Management, 12th edn, p. 117. Pearson Prentice Hall, Upper Saddle River, New Jersey (2006)

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11. Bodea, A.M.: Research on marketing performance in organizations (Case Study), p. 61 (2011) 12. Seggie, S.H., Phelan, S.E.: Measurement of return on marketing investment: a conceptual paper and the future of marketing metric. Ind. Mark. Manag. 835–836 (2007) 13. Amber, T., Roberts, J.H.: Assessing marketing performance: don’t settle for a silver metric. J. Mark. Manag. 24(7–8), 743–744 (2008) 14. Pelinescu, B.: The new economy and the information society and the evolution of IT in Romania. Econ. Rev. (2007) 15. Drucker, P.F.: The new society of organization. Harvard Bus. Rev. 70, 95–104 (1992) 16. Rosca, G.H.: The new economy. Econ. Inf. Mag. 1(21) (2002) 17. Chron: Benefits of Technology in Business. Online article, retrieved from: https:// smallbusiness.chron.com/benefits-technology-business-336.html (2019a) 18. Chron: The Impact of Technological Change on Business Activity. Online article, retrieved from: https://smallbusiness.chron.com/impact-technological-change-business-activity-2191. html (2019b) 19. Danciu, V.: Marketing of the future, adequate response to environmental changes, theoretical and applied economics. XX(5[582]), 27–48 (2013) 20. Hollensen, S.: Essentials of Global Marketing, pp. 365–367. Pearson Education Limited, Essex, England (2008) 21. Businessavenue: How Many Devices Connected to the Internet will be in 2020, Online article, retrieved from: https://www.businessavenue.ro/cate-dispozitive-conectate-la-internet-vorexista-in-2020/ (2015) 22. StartupCafe: How the world connected to the internet will look over the next 5 years. The most important trends at the global and local levels. Online article, retrieved from https://www.startupcafe.ro/stiri-hitech-21075371-cum-arata-lumea-conectatainternet-urmatorii-5-ani-cele-mai-importante-tendinte-nivel-global-local.htm (2016) 23. ZocsPromotions: Performance indicators in internet marketing. Online article, retrieved from: https://zocspromotions.ro/internet-marketing/indicatori-de-performanta-internet-% 20marketing-kpi/ (2015)

Chapter 6

Correlation “Sustainability–Functions– Competitiveness” for Products in Society 5.0 Liana Rodica Pater and Sanda Ligia Cristea

Abstract The paper aims to contribute to the characterization of the “sustainability T si –functions F i –competitiveness K is of sustainable products Pis ” correlation, which is required to achieve sustainable progress in all organizations and workplaces/human action places {L M/A }, on unlimited term in “Society 5.0/Sustainable Society”. Morphological analysis is used to characterize the correlation “sustainability T si –functions F i –competitiveness K is of sustainable anthropic products Pis ”. On this basis, in practice, one can construct: (1) the integrated profile (PIS) of sustainability T is /of integrated quality T i of the products Pi and (2) the profile (PSPI) of sustainability T is /of integrated quality T i of the improved products Pui for user/final consumer S u in target markets/segments/niches/crenels and all external environments. The PSPI profile is analysed in detail. Applying research results in practice helps to optimize the design, production, distribution, use/consumption, and circularity of sustainable products. Keywords Society 5.0/sustainable society · Sustainable products · Integrated quality · Sustainability · Standard functions of products · Sustainable competitiveness

L. R. Pater (B) Management Faculty in Production and Transportation, University Politehnica Timisoara, 14 Remus Street, 300191 Timisoara, Romania e-mail: [email protected]; [email protected] S. L. Cristea Economics and Business Administration Faculty, West University of Timisoara, 16 Pestalozzi Blvd, 300115 Timisoara, Romania e-mail: [email protected] © Springer Nature Switzerland AG 2020 G. Prostean et al. (eds.), Innovation in Sustainable Management and Entrepreneurship, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-44711-3_6

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6.1 Introduction Humanoid communities followed by human societies, as extremely complex systems, have developed cyclically over the last millions of years and will progress through successive-parallel generations supported by sustainable evolutionary/revolutionary technologies and products. A recent characterization of societal generations considers the succession of societies in which humanity has lived in the past and still lives today [1–4]: • Hunting and gathering society (Society 1.0) with extension 2,000,000 years … 10,000 years, • Agricultural society (Society 2.0) with extension 10,000 years … years 1800, • Industrial society (Society 3.0) with extension of years 1800 … 1960, • Information society (Society 4.0) with the extension of years 1960 … 2020 and has called a new society to follow, • Society of the twenty-first century (Society 5.0) with extension after 2020. Society 5.0 must be declared the “Sustainable Society” which integrates the multitude of future continuous and radical improvements (in natural environments and in anthropic eco-technological/demo-psycho-linguistic/socio-cultural/political/legaladministrative/economic/security environments, etc.) in order to achieve the sustainable progress of humanity, on unlimited term in universe [5–9]. In anthropic environments, in a very broad sense, progress has been, is, and will be initiated through the innovation of products and processes of all categories, both natural and artificial. All product categories, both natural and artificial, are characterized by a T –F–K correlation in their internal and external environments, “integrated quality T i –functions F i –competitiveness K i ”, and specific to the products of the considered societal generation. For the multitude of products {i} of Society 5.0/Sustainable Society, this correlation becomes “sustainability T is –functions F i –competitiveness K is of sustainable products”. The anthropic, natural or combined (anthropic and natural) products {Pi } have always been basic components of workplaces L M or of human action places L A generated by human operators Ou . By means of workplaces L M or of human action places L A , the products {Pi } are implicitly essential components of the hierarchy of systems of generations of anthropic societies (family, group, organization, community, city, district, country, federation of countries, humanity, etc.). The literature studied [10–17] does not refer to the T –F–K correlation for products Pi , which limits the efficiency of the methods of design, production, distribution, maintenance, reconditioning, recycling, and optimization of sustainable products. The present paper aims to contribute to the characterization of the “sustainability T is –functions F i –competitiveness K is of sustainable products” correlation, which is not much researched in literature. The characterization of this correlation is important for achieving sustainable progress in all organizations and workplaces/human action places {L M/A } in Society 5.0/Sustainable Society.

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6.2 Defining the Concepts Used The orientation of the researches and models in this paper starts from the systemic definitions of concepts, from the segmentation model “7 × 7” of the external environments and the categories of resources [18, 19]: • Progress P(st, f, c, g), as a form of change, is defined by the establishment, operation, behaviour, and evolution of real systems classes S R (st, f, c, g)/products in a very broad sense, characterized by the gradual, cyclical growth [integrated into space-time (st), through functional cycles (f ), behavioural cycles (c) and successive-parallel generations(g)], optimized, temporary/durable (on very long term)/sustainable (unlimited) of the following: – Competitiveness K(st, f, c, g) of categories of conscious real systems in their external environments (availability/accessibility of resources, competitive capacity, flexibility of product offer, value of product offer, efficiency, demand/acceptance of products in proximate external environments), – Structural-functional complexities W (st, f, c, g) of real systems, – Structural-functional diversities Z(st, f, c, g) of real systems, – Structural-functional innovative integrations J(st, f, c, g) of real systems, – Welfare B(st, f, c, g) of entities. • Sustainable progress Ps (st, f, c, g), specific for the Society 5.0/Sustainable Society, can be achieved only through sustainable anthropic and natural products Pis (st, f, c, g), through sustainable integrative innovation I ins (st, f, c, g) and through sustainable integrative competitiveness K is (st, f, c, g) of conscious systems S Rcon , as a result of the preponderance of critical factors (causes) favourable (+) to progress. This implies the existence and action of a higher, proactive, sustainable conscience, intelligence, and wisdom. • Generally, the product Pi (st + t, f, c, g) means the desired result of the cyclic transformation processes of the resources Ri (st, f, c, g) within a Dstrpp (st, f, c, g) domain of reality. The undesirable result of the cyclic transformation processes of Ri (st, f, c, g) is called waste Di (st, f, c, g). The differentiation of the resourceproduct/waste is relative. Any resource Ri is a product/waste of certain processes prior to the time t considered, within value/tropic cycles, specific to the domains space-time-resources-processes-products considered. A distinction must be made between: – The product category Pi (st + t, f, c, g) with durable (very long)/sustainable (unlimited) evolution in generations g successive-parallel (structural classes s, functional types f, brands m, dimensional groups d, assortments i of abiotic products/class c, order o, family m, gen g, species s, race r, form f of biotic products) and – The exemplar of an abiotic or biotic natural product/anthropic product Pi (st, f, c) of a single generation g, with an individual and finite life cycle, more or less long-lived.

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• The global quality N gi of an integral product is the set of properties and characteristics {yic } of Pi assortment, which gives it the ability to meet the needs/necessities expressed or implied from its internal M int and external M ext environments in space and time. • Sustainability T s (st, f, c, g) is an integrated cyclical quality of the whole “… → (1) internal environments → (2) real system S R categories, abiotic, biotic, human, intelligent, and wise → (3) external environments → …” to self-generate/exogenerate, to exist, to resist, and to evolve in self-balancing/exo-balancing for an unlimited duration, within the value chains/tropic chains from the space-timeresources-processes-products domains {Dstrp (st, f, c, g)} in the universe. • In principle, in the order of intervention, the main general determinants of the integrated quality T i /sustainability T is of real systems SR (st, f, c, g) in any spacetime-resources-processes-products domains Dstrpp (st, f, c, g) are centred on products Pi (st + t, f, c, g) and wastes Di (st + t, f, c, g) designed, generated, used/reused, reconditioned, and recycled:

T s(st,f,c,g) = f(st, U, CK , V int, Iin, Y , A i, V ext, K, f, c, g) = = f(st, R i, A ext, CK , V int, Iin, Pi, Di, A i, V ext, K , f, c, g) External Internal External environments environm. environm. Existential cycles Outputs Y & Inputs U Competitive capacity Competitiveness

(6.1)

where st represents space-time in universe, U represents the inputs of the real systems S R (st, f, c, g), Y represents the outputs of the real systems S R (st, f, c, g, Ri represents the available resources from the external environments of the real systems S R , resources inevitably limited and, at the same time, partially controllable by the intelligent real systems S Rint with or without human operators Ou , Aext represents disturbances, shocks/aggressions (outside the limits of periodic stability) from external environments to the boundaries and internal environments of the respective real systems S R , C K represents the competitive capacities of the actual real systems S Rp producing products Pi /waste Di , V int represents the internal resilience of the real systems considered S R , which ensures the limited absorption of disturbances, shocks/aggressions from external systems, I in represent the integrative innovations of the actual considered production systems S Rp , capable to generating and cyclically fortifying internal and external resiliencies V, the competitive capacity C K and the competitiveness K of the real systems, including products Pi , Pi represents the products made by considered real systems S Rp and used/consumed/reused/repaired/reconditioned/recycled,

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Di represents unwanted waste, produced/reused/recycled by the actual real systems S Rp , respectively, by the products Pi considered on their existence streams, Ai represents the perturbations, shocks/aggressions (outside the limits of periodic stability) from the real system categories S R (st, f, c, g), from products Pi (st, f, c, g) and waste Di (st, f, c, g) generated by S R over the considered external environments, V ext represents the external resiliencies, of the external environments of the considered real systems S Rp , which assures the limited absorption of Ai perturbations, shocks/aggressions from the considered real systems S Rp , from products Pi and waste Di produced by them, respectively, from the considered external environments, K represents the competitiveness of the real systems S R /products Pi within a space-time-resource-process-product domains, f, c, g represent the functional cycles {f }, the behavioural cycles {c}, and the generational cycles {g} related to the real systems S R /products Pi and the waste Di within a space-time-resource-process-products domains, • Longevity L i of a real system S R (st, f, c) is the lifetime (existence, operation, and behaviour) of a type i specimen of the considered system S R (st, f, c, g) category, made in co-opetition (cooperation and competition) or comperation (competition and cooperation) in its external environments M ext . Average longevity L med (st, f, c)/life expectancy is the average lifetime of specimens i = 1,2, …, n of considered real systems within a generation g, externally productive or biotical reproducible. Longevity is a sub-factor influencing competitive capacity C K and thus indirectly influences the integrated quality T i /sustainability T is (relation 6.1). • Segmentation “7 × 7” of the external environments of the products and the resource/product categories used to define sustainability T s (st, f, c, g) in space-time-resource-process-products {Dstrpp (st, c, g)} domains considers: • (7M) seven categories of external environments M ext (M nat , natural; M dpl , demo-psycho-linguistic; M scu , socio-cultural; M pja , political-juridicaladministrative; M afa , business/technological-managerial; M sec , security) and • (7R) seven categories of Ri resources/Pi products (space-time st; Rnat natural resources/products type substance, energy, information, field, combinations; Rumn human resources as persons with knowledge, skills, and specific abilities to achieve sustainable/durable competitiveness; Rsoc social resources—people abilities to work together competitively, based on sharing the same norms and moral values, of sustainable progress; Rmar artificial material resources as a whole in continuous development and improvement of all the human civilization has produced/produce/will produce; Rinf information resources of knowledge type, creativity, technological, and management know-how, new specific human creations, etc.; Rfin financial resources as “intermediate artificial products”, which provide income and the capital necessary for the transfer, processing, consumption of natural, material, and human resources). Resources can be processed (converted) or they can provide the processing conditions to realize products and services necessary to satisfy the unlimited needs of people, society, and nature. Within the chains of anthropic values, products,

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and services achieved in a sequence become/may become resources for the next sequences of the value chains. • The global function F g (st, f, c, g) of a real system S R (st, f, c, g)/product expresses − → − → the transformation of some system S R inputs U into certain outputs Y , i.e. expresses what it does (can do), which accomplishes (can realize) the system in its operational and functional cycles. This must be clearly and concentrated reflected in the hierarchical definition of the component functions of the global function F gi (st, f, c, g) of a product:  global function F gi , decomposable in – standard (main) functions F 1 , … F 7 , decomposable in ~ grouped functions, F 11 , …, F 7g , decomposable in → elementary, non-decomposable functions F 111 , … F 7ge • Based on the correlation “integrated quality T i –functions F i –competitiveness K i ” for the integral/global anthropic product is detailed in Fig. 6.1, the following standard functions are valid for any assortment Pi (st, f, c, g) [5, 18]: F 1 —ensuring the availability (reliability and maintainability) of product Pi , F 2 —ensuring the identity of the product Pi in its external environments, F 3 —ensuring the functional connectivity/functional connection of the product Pi with all its external environments, F 4 —ensuring human safety (ergonomics, protection of human health, integrity of persons, confidentiality, security, etc.) by product Pi in its external environments, F 5 —ensure the product’s Pi ecologicity in its external environments, F 6 —ensuring the product Pi aesthetics in its external environments, F 7 —ensuring the favourable social/cultural characteristics/effects (communication, education, promotion of culture, sustainable progress, etc.) of product Pi use in all its external environments. This hierarchy of components of the global product function F gi provides a rigorous conceptual basis that allows a precise definition of Pi (st + t, f, c, g) product functions and facilitates its structural-functional optimization and K i ’s competitiveness. • Durable/sustainable integrative competitiveness K id (st, f, c, g)/K is (st, f, c, g) is the ability and capacity of a system of conscious systems (S Scon ) to optimize its internal environments M int , to win in co-opetition (cooperation and competition) or com-peration (competition and cooperation) in its external environments M ext , without degrading or exhausting them, simultaneously, achieving the welfare of the system of systems S Scon over a very long (“durable”)/unlimited (“sustainable”), in domains {Dstrpp (st, f, c, g)} of the universe.

socialeconomic quality N sei (yic) (c= t+1, ..., z), not incorporated into the product Pis

Groups of integrated quality characteristics {yic} for the sustainable integral product [Pis(st,o,f,c,g)] potential quality charac- characteristics of teristics of Pis(st) having (3) availability A(st,o,f,c,g) (0 ... ts) components j (3.1) reliability R(st,o,f,c,g) & characteristics of materials & interconnections (3.2) maintainability of components j M(st,o,f,c,g) [required by the itself sustainable product Pis(st,o,f,c,g)] own structural-functional characteristics (1) identity external connection characteristics (2) connectivity defining for product Pis (required by user Su) (4) human safety: ergonomics, health protection, etc. [required by interconnection with humans Ou] (5) ecologicity: ecological characteristics [required by the biotic and abiotic natural environment Mnat] (6) aesthetic: aesthetic characteristics [required by environments anthropic Mant and natural Mnat] (7) social-cultural characteristics (communication, education, culture, ethics, ...) (required by Mant) (8) economicity and integrated efficiency: economic characteristics costs Cci/ prices Pvi and integrated efficiency Eint: economic at producer, user, at macroeconomic level (markets) & efficiency in all other external environments, determined by the global function Fgi [required by environments: natural Mnat and anthropic Mant] performance functions {Fpi}

global function {Fgi}

reflection variables Ei of the economic and integrated efficiency characteristics of the global function Fgi of product Pis in markets/ segments/ niches and in all external target environments

collateral functions {Fci}

final functions {Ffi}

intermediate functions {Fii}

Function categories of the sustainable integral product [Pis(st,o,f,c,g)] (criterion: internal and external functionality of product Pis)

”economic quality” and integrated efficiency N ei (yic ) (c = v+1,., z)

global quality N gi (yic ) (c=1..., v)

integrated total quality (internal & external) on unlimited term/ sustainability Tis (yic) (c=1, ..., z)

Quality categories of the sustainable integral product [Pis(st,o,f,c,g)] (criterion: sustainable competitiveness of product Pis)

(u) processing the unique assortment Kiu = N ei / N gi (Pvi / N gi) opt

(ms) processing in the mass or processing serial Kims = N gi / N ei (N gi / Pvi) opt

Cases/ product Pis is accomplished by:

Competitiveness Kis of the integral sustainable product [Pis(st,o,f,c,g)]

Fig. 6.1 Correlation “sustainability T is –functions F i –sustainable competitiveness K is ” for an integral/global anthropic product Pis (st, o, f, c, g) M ant —anthropic environments include: M dpl , demo-psycho-linguistic; M scu , socio-cultural; M pja , political-juridical-administrative; M afa , business/technological-managerial; M sec , security

integrated total quality (internal & external) on unlimited term/ Sustainability Tis(yic) (c=1, ..., z)

technical quality N ti (yic) (c = 1, ...., t), embedded in the product Pis

Quality categories of the sustainable integral product [Pis(st,o,f,c,g)] (criterion: incorporating characteristics in the product Pis)

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6.3 A General Model of the Correlation “Sustainability T is –Functions Fi –Sustainable Competitiveness K is ” of Anthropogenic Products Anthropic products Pi (st, f, c, g) [tangible physical goods—technical systems, biosystems, technological systems, production, trade structures, etc.—and intangible services (productive services—results of scientific research/design and consultancy, results of storage, maintenance, processing of business information, etc.—or nonproductive services—results of medical, educational, cultural, social services, etc.)] are the entities, the basic anthropic subsystems in the workplaces L M or in the human activities places L A generated by human operators Ou . The global quality N gi of an integral product is the set of properties and characteristics yic of Pi assortment, which gives it the ability to meet the needs/necessities expressed or implied from its internal M int and external M ext environments in space and time. In final analysis, sustainability T s (st, o, f, c, g) was, is and will be determined by the quality characteristics {yic }, the functioning and behaviour of all workplaces/anthropic action places {L M/A }, composed of human operators/actors Our (st, o, f, c, g) and related technical/biotechnical systems S t (st, o, f, c, g). After 1800, the technological progress of humanity caused the rapid increase of the weight of the influence of the increasingly complex technical systems S t in the evolution of sustainability T s . But the culture, goals, and behaviour of human operators, actors, families, communities, organizations, territorial administrations, governments, etc., and populations in all countries are decisive in achieving T s sustainability globally. Wars, accidents, and actions with severe damage in various fields/social-economic areas, environmental damage, and catastrophes caused by people in history, etc., confirm this reality. The correlation “sustainability T is —functions F i —sustainable competitiveness K is ” detailed for an integral/global sustainable product Pis (st, o, f, c, g) is presented in Fig. 6.1. The meanings of the notation used are: S u —user system of anthropic and/or natural sustainable product [Pis (st, o, f, c, g)], M nat —the abiotic and biotic natural environment for the product [Pis (st, o, f, c, g)], M ant —the anthropic environments of product [Pis (st, o, f, c, g)], including: M dpl , the demo-psycho-linguistic environment; M scu , the socio-cultural environment; M pja , the political-legal-administrative environment; M ino , the innovation environment; M afa , business/technological-managerial environment; M sec , security environment, st—space-time for the product [Pis (st, o, f, c, g)] and its external environments; o, operational cycles, non-decomposable, useful for conceiving and in-depth knowledge of the product; f, functional cycles; c, behavioural cycles; g, generational cycles of the product [Pis (st, o, f, c, g)]. It should be noted first the general basic correlation between the level of sustainability T is and the levels {N i } of the product Pi quality components.

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Tis (st, f, c, g) = Nti (st, f, c, g) + Nsei (st, f, c, g)   = N gi (st, f, c, g) + Nei (st, f, c, g) points

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(6.2)

where T is (st, f, c, g) is the level of sustainability/total integrated quality of the assortment class i, considered in the functional cycles f, f + 1, …, behavioural c, c +1, … and generational g, g + 1, … [there are different integrated qualities: T it , temporarily, on short term; T id , durability, on long/very long term; T is , sustainability, on unlimited term], N ti (st, f, c, g) is the level of technical quality, incorporated in the product, with the objective measurable characteristics yic (c = 1,2, …, t), N sei (st, f, c, g) is the level of social-economic quality with the characteristics yic (c = t + 1, …, z), of reflection-communication (thus physically unincorporated in the product) and partly subjectively evaluable in the case of aesthetic characteristics, such as quality social characteristics, N gi (st, f, c, g) is the level of global quality corresponding to the global function F gi of the assortment i considered, N ei (st, f, c, g) is the level of “economic quality”, corresponding to costs, prices, and economic efficiency at producer, user, and macroeconomic; in practice, the N ei level is difficult to determine because of the unavailability of information and, by simplification, in the first analysis, the “economic quality” N ei (st, f, c, g) is replaced by the sales price Pvi (st, c, g)/the tariff T vi (st, c, g) negotiated in the market at the sale-buying of the assortment i considered. The correlation “sustainability T is —functions F i —sustainable competitiveness K is ” (Fig. 6.1) also allows defining the categories of “standard” components of the total quality correlated with the standard functions F 1 …, F 7 of product Pi : » N 1 the level of availability of the product Pi determined by the quality characteristics yic of materials/matter (substance, energy, and information) and the connections of the product’s components j [determine the reliability R(st, o, f, c, g)], the accessibility of components j, the existence of spare parts/replacement parts, maintenance, etc. [determine the maintainability M(st, o, f, c, g) of product i] (correspondence N 1 ↔ F 1 ), » N 2 the identification quality level of product Pi (identity) determined by the specific structural-functional characteristics yic required by the user S u of the product (correspondence N 2 ↔ F 2 ), » N 3 the quality level of functional connecting (connectivity) of the product Pi with its external environments determined by the related yic quality characteristics (connection with energy/information networks, connection with infrastructure elements, etc.) (correspondence N 3 ↔ F 3 ), » N 4 the quality level of human safety provided by product Pi , determined by the yic quality characteristics unfavourable/favourable to the ergonomic interconnection of the product with the human operators and protection of the integrity/health of human operators, etc. (correspondence N 4 ↔ F 4 ),

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» N 5 the level of environmental quality of the product Pi determined by quality characteristics yic that could modify abiotic and biotic natural environment unfavourably/favourable to the preservation of natural environment and biodiversity, including the re-use/reconditioning/recycling characteristics of afferent product Pi and waste Di (correspondence N 5 ↔ F 5 ), » N 6 the level of aesthetic quality of the product determined by the yic quality characteristics that define the degree of closeness to the subjective beautiful ideal, the elegance of the shapes, the consistency of the colours, the texture, the odour/emitted fragrance, etc. (correspondence N 6 ↔ F 6 ), » N 7 the level of socio-cultural quality of the product determined by the quality characteristics yic that generate disadvantageous/favourable to the general socialcultural progress through communication, education, ethics, employment, promotion of the culture of progress (values, attitudes, behaviours), etc. (correspondence N 7 ↔ F 7 ), » E 8 the level of economic performances of the product reflected by the yic characteristics of “economic quality”: costs, prices, economic efficiency at the producer, user, national economy, global economy (obviously, reflective “economic quality” N ei that uses monetary value units and is a defining component of product’s competitiveness and cannot have functional correspondence at the product level). Given the multitude and great diversity of product quality characteristics yic , generally incomparable characteristics, the assessment of the level of sustainability or of its component partial qualities can be made more rigorous by aggregation using the weighted arithmetic mean or the weighted geometric mean. The absolute level of the global quality N g of a product is determined based on the weighted arithmetic mean (N ga —recommended for yic /yoc or yoc /yic values that do not have a large dispersion) or based on the weighted geometric mean (N gg —recommended for yic /yoc and yoc /yic ratios that have high dispersion): N ga = K

 m 

gc yyocic

+

c=1

N gg = K

m  y gc ic

yoc c=1

v 

 gc yyocic

c=m+1

·

v gc  y oc

yic



  points   points

(6.3)

(6.4)

c=m+1

where K = 1000 (or K = 100) [points] is the quality level of the sustainable product range 0 (the product i according to: (a) all specifications in the technical design documentation for the sustainable product; (b) the comparable sustainable product produced by the competitor/leader; (c) integrated customer/consumer/and legal requirements for sustainability and security, etc.), c = 1,…, m is the set of quality performances yc whose values increase with the increase of the global quality level,

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c = m + 1, …, v is the set of quality performances yc whose values decrease with the increase in the overall quality level, gc is the weight of the importance of yc quality in determining the overall quality level, obviously, v 

gc = 1

c=1

yic is the quality characteristic of the studied product, yoc is the quality feature of the reference sustainable product, usually the sustainable product of the leader on market/segment/niche/crenel. It is necessary that the confidence level of the knowledge of the yc quality characteristics exceeds a minimum level of approx. 60% of the total number of features (generally the number of quality characteristics v = 10–1000), and the i and 0 assortments belong to the same dimensional group.

6.4 Sustainability Profile of Anthropogenic Products Traded in the Markets All anthropic product Pi and waste Di categories were, are, and will be part of two types of flows/processes made in organizations at work/anthropic action places {L M/A }, composed of human operators/actors Ou (st, o, f, c, g) and technical/biotechnical systems S t (st, o, f, c, g): I. Initial flows/processes of innovative exogeneration of Pi1 products themselves and the Di1 wastes produced/achieved by anthropic systems SRp1 with dedicated work/action places (L M/A1 ) as “initial producers”, II. Operating flows/processes derived from Pi1 products and wastes Di1 at other work/anthropic action places {L M/A2 }, which, in turn, as “derived producers” S Rp2 generate/can innovatively generate other products Pi2 and wastes Di2 . Human culture and civilization have progressed, progress, and will progress sustainably through unlimited virtuous cycles of type (I) and (II) generated in networks and continually/periodically radically improved at all levels of the hierarchy of anthropic systems. In various historical periods, human culture and civilization have stagnated and/or regressed periodically through vicious cycles of type (I) and (II), products Pi and waste Di being used with destructive effects of human and natural values in their external environments. The model in Fig. 6.1 and relations (6.1), (6.2), (6.3), (6.4) are at the base of building the profiles of the sustainability of the products Pis (st, o, f, c, g) as components of the work/anthropic action places {L M/A2 } composed of human operators/actors Ou (st, o, f, c, g) and related technical/biotechnical systems S t (st, o, f, c, g) within various organizations.

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In practice, several categories of anthropic product sustainability profiles can be built (relation 6.1; integrated cycles I and II): – Integrated profile (PIS) of sustainability T is /of integrated quality T i of products Pi (st + t, f, c, g) and related wastes Di (st + t, f, c, g) considered throughout their entire life cycle, from the extraction/harvesting of the natural resources necessary for their production, trade, and operation to the total natural recycling of Pi and Di (from the natural environment M nat → anthropic environments M ant → back to the natural environment M nat → …). The PIS integrates the initial flows/processes (I) of Pi and Di ’s exogenous generation with their derived flows/processes (II) with the potential of other innovative generations of other products and wastes. – Profile (PSPI) of sustainability T is /of integrated quality T i of improved products Pui (st, f, c, g) for end user/consumer S u in markets/segments/niches/crenels and for all external environments. The product Pui (st, f, c, g) encompasses [18]: the effective product Pei (considered at the exit of the producing enterprise); product Pui advertising on the market; crediting of payments to the end user S u ; free services: publicity, delivery services (delivery time, transport, installation, reconditioning/recycling, technical assistance, etc.); the effective guarantee (warranty period, service network, spare parts during service, etc.); post-sale and post-use services (lasting maintenance, spare parts insurance/sale, waste recycling, etc.). Improved products Pui (st, f, c, g) have great importance in achieving sustainability in Society 5.0/Sustainable Society. Building the integrated T i quality profile/sustainability T is profile of improved products Pui (st, f, c, g) traded in markets/segments/niches/crenels is influenced by the complexity of the Pi product and its assembled components {j}, partially reflected by the number of quality characteristics yic (c = 1, …, z). Obviously, the higher the number of components j of the product i, the determination of the sustainability profile Pi by calculation (relations 6.3 and 6.4) is more difficult (e.g., for the product with one component, i = j = 1 piece, is relatively easy to build sustainability profile; for a vehicle with a number of components j = 4000, the determination of the sustainability profile is laborious). Figure 6.2 presents the principle of building the profile (PSPI) of the sustainability T is /integrated quality T i of improved products Pui (st, f, c, g) for the end user/consumer S u from target markets/segments/niches/crenels and for all external environments. The value K = 100 [points] for the scaling coefficient in relations (6.3) and (6.4) was accepted when determining the components of the global quality level N gi . For the hypothetical product Pui (st, f, c, g), the integrated quality profile is represented in Fig. 6.2. The analysis of this profile (N 1 , …, N 7 ) allows to formulate the conclusions: • Product Pui does not ensure the normative performances for the achievement of the sustainability according to the standards/norms in force at time t, cyclically perfectible in future (t + t), • Integrated innovation and redesign with the major improvement of product Pui in future generations g + 1, g + 2 is needed, in order to achieve/approach the normed level of sustainability T isn (t),

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Fig. 6.2 Principle of constructing the nominal profile of integrated quality T i /sustainability T is for product Pui (st, f, c, g)

• The effective profile of the integrated product Pui (st, f, c, g) is fluctuating in the functional cycles f /in its behavioural cycles c, being influenced by the following factors: – The perturbations, shocks/aggressions Aext from external environments of the Pui product, and behaviour of human operators/actors Ou (st, o, f, c, g) in anthropic work/action places {L M/A } (influences all levels of quality N 1 , …, N 7 ), – The internal resilience V int of product Pui (influences quality levels N 1 , N 2 , N 3 , N 4 ), – The perturbations, impacts/aggressions Ai on external environments from products Pui (st, f, c, g) and related wastes Di (st, f, c, g) (influences quality levels N 3 , N 5 , N 6 ), – The external resilience V ext of the external environments of the product Pui and related waste Di (st, f, c, g) (influences quality levels N 5 , N 7 ), • The hypothetical product Pui is temporarily competitive in those markets/segments/niches/crenels that prefer the current overall quality ratio N gi (t)/economic performance E 8i (t) offered, correlated with the needs and financial capacities of customers/consumers in the market, • The cyclical optimization of the correlation “sustainability T is –functions F i –sustainable competitiveness K is ” of the anthropic products Pi (st, f, c, g) is the main task of all stakeholders (persons, families, organizations, customers/consumers, populations, etc.) in integrative innovation to achieve sustainable progress, on unlimited term, in the Sustainable Society.

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6.5 Conclusions Starting with 2020, humanity has been stepping up in the new period called “Society 5.0/Sustainable Society”, geared towards sustainable progress achieved through the integral innovation of products and processes of all categories, both natural and artificial. This development implies, among other things, the advanced optimization of anthropic products, defined in the broadest sense of the concept. The optimization of sustainable products is characterized by the following aspects: – It requires a systemic approach of concepts, models, and methods, – It is a cyclical optimization [integrated into space-time (st), through functional cycles (f ), behavioural cycles (c), and successive-parallel generations (g)] and is based on the correlation “integrated quality T –functions F–competitiveness K” of products and processes specific to the considered generations of societies, – The cyclical optimization of the correlation “sustainability T is –functions F i – sustainable competitiveness K is ” of the anthropic products Pi (st, f, c, g) is the main task of all people at work/action places {L M/A } (persons, families, organizations, producers, distributors, public administrations, governments, customers/consumers, populations, etc.) in integrative innovation (initial integrated cycles I. and derivated cycles II) to achieve sustainable progress in “Society 5.0/Sustainable Society”, – The correlation “sustainability T is –functions F i –sustainable competitiveness K is ” of sustainable anthropic products Pis allows the construction of two product profiles useful in practice: (1) integrated profile (PIS) of sustainability T is /of integrated quality T i of products Pi (st + t, f, c, g) and related wastes Di (st + t, f, c, g) during their entire life cycle; (2) profile (PSPI) of sustainability T is /of integrated quality T i of improved products Pui (st, f, c, g) for end user/consumer S u from markets/segments/niches/crenels and all external environments, – For all anthropogenic products Pi the objective is to achieve the standard levels T isn (t)/ideals levels T is (t) of sustainability, – The application of research results in practice contributes to optimizing the design, production, distribution, use/consumption, and circularity of sustainable products. Future research on sustainable product optimization requires in-depth working procedures and conducting comparative case studies for sustainable products of various categories and complexities.

References 1. Keidanren: Society 5.0. Co-creating the Future (Excerpt). Tokyo. http://www.keidanren.or.jp/ en/policy/2018/095_proposal.pdf; Excerpt from the original report (in Japanese). The complete version in Japanese can be accessible from http://www.keidanren.or.jp/policy/2018/095.html (2018). Last Accessed 21 Mar 2019

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2. Government of Japan: What is Society 5.0?. Cabinet Office, Government of Japan, Tokyo. https://www8.cao.go.jp/cstp/english/society5_0/index.html (2018). Last Accessed 21 Mar 2019 3. Government of Japan: The 5th Science and Technology Basic Plan. Cabinet Office, Government of Japan, Tokyo. Homepage, http://www8.cao.go.jp/cstp/kihonkeikaku/5honbun.pdf (2018). Last Accessed 21 Mar 2019 4. Gladden, M.E.: Who will be the members of society 5.0? Towards an anthropology of technologically posthumanized future societies. Soc. Sci. 8(5), 148–187 (2019). https://doi.org/10. 3390/socsci8050148; https://www.mdpi.com/2076-0760/8/5/148. Last Accessed 13 May 2019 5. Popa, H.L.: Systems Theory and Engineering. Concepts, Methods, Models, Competitiveness. (in Romanian). Editura Politehnica, Timisoara (2003) 6. Popa, H.L., Pater, R.L., Cristea, S.L.: A systemic description of sustainable progress. Procedia Soc. Behav. Sci. 124, 322–330 (2014) 7. Popa, H.L.: Integrative innovation as core determinant for sustainable progress. Procedia Soc. Behav. Sci. 124, 460–467 (2014) 8. Tegmark M.: Life 3.0: Being Human in the Age of Artificial Intelligence. Knopf Doubleday Publishing Group, New York (2017) 9. Kaku, M.: The Future of Humanity: Terraforming Mars, Interstellar Travel, Immortality, and Our Destiny Beyond Earth. Knopf Doubleday Publishing Group, New York (2018) 10. Anastas P.T., Zimmerman J.B.: Through the 12 principles green engineering. Environ. Sci. Technol. 1, 95–101 (2003) 11. Cagno, E., Trucco, P.: Integrated green and quality function deployment. Int. J. Product Lifecycle Manag. 2(1), 64–83 (2007) 12. ISO 14006/2011: Environmental management systems—Guidelines for incorporating ecodesign. International Organization for Standardization, Geneva (2011) 13. ISO 20121/2012: Event sustainability management systems—Requirements with guidance for use. International Organization for Standardization, Geneva (2012) 14. Kauffman, J., Lee, K. (eds.): Handbook of Sustainable Engineering. Springer, Berlin (2013) 15. Roach D.: Designing Sustainable Products with QFD. The 26th Symposium on QFD, 15p, Charleston. https://www.researchgate.net/publication/266912455_Designing_ Sustainable_Products_With_QFD (2014). Last Accessed 04 Apr 2019 16. Goedkoop, M., Subramanian, V., Morin, R.: Product Sustainability Information. State of Play and Way Forward. United Nations Environment Programme UNEP DTIE, Paris (2015) 17. Ko, Y.T., Chen, M.S., Lu, C.C.: A systematic-innovation design approach for green product. Int. J. Constr. Eng. Manag. 5(4), 102–107 (2016) 18. Pater, L.R., Popa, H.L.: Microeconomics and Sustainable Competitiveness (in Romanian). Solness, Timisoara (2013) 19. Pater, R.L., Cristea, S.L.: Systemic definitions of sustainability, durability and longevity. Procedia Soc. Behavioral Sci. 221, 362–371 (2016)

Part II

Entrepreneurship

Chapter 7

Trust at Work and Entrepreneurial Intentions Among Employed Persons in Organizations in Serbia Predrag Mali, Bogdan Kuzmanovi´c, Milan Nikoli´c, Edit Terek Stojanovi´c, and Siniša Miti´c Abstract The paper presents the results of the study of the influence of trust at work dimensions on individual entrepreneurial orientation dimensions, achievement dimension, and the theory of planned behavior dimensions. Respondents were employed persons in organizations in Serbia. The sample consists of 540 respondents, out of 72 organizations. It was found that the dimensions related to trust in colleagues have a stronger impact on the observed dimensions of individual entrepreneurial performances than on the dimensions that relate to trust in management. The strongest influence has the dimension of trust in the intentions of the colleagues. Also, the trust in the organization largely intensifies the dimension Subjective norm, which means that based on trust it is easy to perceive the support of people in the environment. Keywords Trust at work · Individual entrepreneurial orientation · Entrepreneurial intentions · Employed persons · Serbia

7.1 Introduction In most cases, research of entrepreneurial intentions refers to unemployed persons (usually students) or to employed persons, in the form of internal entrepreneurial P. Mali Faculty of Economics, University of Belgrade, Belgrade, Serbia e-mail: [email protected] B. Kuzmanovi´c · S. Miti´c Faculty of Technical Science, University of Novi Sad, Novi Sad, Serbia e-mail: [email protected] S. Miti´c e-mail: [email protected] M. Nikoli´c · E. Terek Stojanovi´c (B) Technical Faculty of Mihajlo Pupin, University of Novi Sad, Zrenjanin, Serbia e-mail: [email protected]; [email protected] M. Nikoli´c e-mail: [email protected] © Springer Nature Switzerland AG 2020 G. Prostean et al. (eds.), Innovation in Sustainable Management and Entrepreneurship, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-44711-3_7

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activities within an organization in which they are already employed (internal or corporate entrepreneurship). Thus, there is a significant number of papers dealing with entrepreneurial intentions among students, for example [1, 14, 26, 39], as well as papers dealing with the importance of internal entrepreneurship, for example [2, 32]. Bearing in mind the previous findings, it can be concluded that, so far, entrepreneurial intentions have not been explored to a large extent at persons employed in some existing organizations. This state is indicated by some references [18, 27]. In these references, the importance of studying entrepreneurial intentions of employed persons is also emphasized: The intentions of employees to leave the job they are currently doing and starting entrepreneurial ventures. According to some authors, for example [31, 38], precisely the employed persons are more likely to succeed in an entrepreneurial venture, primarily because of their experience, knowledge, knowledge of the state of the economy, and others. The dilemma exists with regard to the question of the sense of studying entrepreneurial intentions at those already employed. However, the significance of this issue is clear: The opening of new businesses (regardless of whether the founder was previously employed by another organization) always means self-employment, job creation, as well as the liberation of places where these newly created entrepreneurs have previously worked. Employed persons can also have entrepreneurial ambitions and intentions, regardless of that they are having a job. They can be motivated by personal factors, which include the individual Big Five (broad, general) personality traits, as well as fine, relatively easy-to-change personality traits (inclination to risk, innovation, proactivity, need for achievement, self-efficacy, creativity, etc.), but also some situational factors and support of the environment. Therefore, the effects on the entrepreneurial intentions of the employed persons are similar (and complex) as well as it is at unemployed persons. It can be assumed that the biggest difference is precisely in the part that is characteristic for employees, and these are certain relationships in the organization. These relationships can be explored through various elements of organizational behavior. In this paper, the influence of such an element is being examined, which is mutual trust at work. Some research relates to the influence of trust in the work on the intention to leave the organization [10, 12, 17, 20, 30]. It should be emphasized that these researches do not consider entrepreneurship intentions, but only the intention to leave the organization. The research problem of this paper is the influence of trust at work on the development of entrepreneurial intentions of employed persons. The research was conducted in organizations in Serbia, which are therefore the subject of research. Thus, the research aims to examine the degree and direction of the connection between trust in the work and entrepreneurial intentions of the employed persons. Knowledge of these relationships certainly has a theoretical, but also practical significance: research of the problem that has been neglected so far, as well as the creation of theoretical knowledge and the basis for activating the entrepreneurial potentials of the employed persons.

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7.2 Theory and Hypothesis 7.2.1 Trust at Work Trust is a very important aspect of life, and it has a special importance in the business sense, as well as in the theory of organizational behavior. Therefore, trust is an important construct in the research of organizational behavior. Trust can be defined as a firm belief in the reliability, the truth or the ability of someone [34]. In their famous work, Cook and Wall [11] define trust at work as a degree of readiness of an individual to attribute good intentions to others and to believe in words and actions of other people. This degree of readiness will then affect the way that the individual behaves toward others. Among the researchers, there is a general consensus that trust between individuals and groups in organizations is a very important element in achieving and maintaining the long-term stability of the organization itself and the well-being of employees in the organization [11]. Mayer et al. [29] define trust as a willingness of one side to accept the actions of the other side, based on expectations that the other side will perform the action without monitoring or control. Rousseau et al. [37] see trust as a proportionate faith in the intentions and behavior of others. Practically, this means that trust will cause trust and that mistrust will cause mistrust. Beusch [6] sets the concept of trust as a “package” consisting of several different aspects of trust that exist at work. According to Rotter [36], trust is a process that depends on the history of relationships and is based on relevant, but limited patterns of experience. The importance of trust, in the organizational context, stems from the fact that trust, undoubtedly, has an impact on the organization’s performance. Research [9] has shown that employee trust influences three groups of performance of organizations: financial performance, employee productivity, and product/service quality. According to Kurtulus et al. [23], the level of engagement of employees in the workplace depends on the degree of trust of employees in managers, that is, the degree to which employees believe that managers act fairly. Building trust at work depends largely on leaders and managers. Building trust requires a certain amount of time; however, trust can easily and quickly disappear, and it is difficult to recover it to the level before it was lost. Due to this, building and maintaining trust require the full attention of the management [28]. Trust of employees in the leader is an important aspect of the relationship at work [4, 15]. According to Merriman et al. [30], leaders and managers need to be aware of the fact that business communication and trust at the workplace are very important issues for the good functioning of the organization and that their behavior significantly affects the trust of employees. Furthermore, loss of trust of employees can seriously impair the efficiency, productivity, and morale of employees.

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7.2.2 Trust at Work and Intentions to Leave the Organization Trust in the organization often appears as an influential factor when employees decide to leave the organization where they are currently employed in. According to Merriman et al. [30], the lack of trust in the organization, that is, the organizational culture dominated by mistrust, prevents the optimization of skills and knowledge of employees, affects poor performance, and increases the intentions for leaving the organization. Cunningham and MacGregor [12] conducted a survey in the telecommunications industry in Canada (in the province of British Columbia), which involved 535 respondents. The results showed that trust has a negative impact on absenteeism and on the intention to leave the organization. In a survey carried out among social workers in South Korea [10], the relationship between trust and intention to leave the organization was observed. The analyses have confirmed that trust reduces intentions to leave these institutions. In 22 hotels in Malaysia, a study was conducted about the impact of employees’ trust on the intention to leave the hotel [17]. It has been confirmed that trust has a significant negative impact on the intention to leave the hotel. Also, it has been pointed out that trust in the organization is a mediator in relation to career progression and intention to leave the hotel. A big impact on the intentions of leaving the organization has the trust in leaders. A survey carried out in organizations in India [20] has shown that the trust of the employees in the leader has a negative connection with the intentions to leave the organization. Similarly, in a survey in which respondents were middle managers in Turkey [22], it has been shown that trust in superiors has a significant negative impact on the intentions to leave the organization. Another survey conducted in Turkey among Turkish IT professionals [13] aimed to explore the importance and relationships between HR practices in the organization, LMX, perceived organizational support, employee trusts, and intentions to leave the organization. The results showed that trust in a superior is a moderator of the relationship between LMX and the intentions to leave the organization and that trust in the organization is a moderator of the relationship between perceived organizational support and the intentions to leave the organization. A significant number of surveys deal with the influence of trust of nurses on intentions to leave hospitals. Thus, in a study carried out in two Italian hospitals [7], the influence of leadership and trust of employees on burnout at work and the intentions of nurses for leaving the hospital was examined. The researches came to the conclusion that the trust of nurses in the organization is in a negative correlation with intentions for leaving the hospital. A survey conducted among 265 nursing nurses in hospitals in Turkey [5] showed that the trust of nurses in the organization itself (hospitals) is low, while trust in colleagues is at a high level. In addition, the results of the research indicate the importance of building trust among nurses (systemically, by the hospital), in order to reduce their intentions for leaving the job. In the reference [16], it became clear that the trust of nurses in hospital leaders has a direct impact on job satisfaction. Also, trust in the leader has a negative impact on the intention to leave the hospital, regardless of the satisfaction of the work of the nurses. The authors

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believe that such results show that trust in the leader is a very important component of organizational behavior, more important than it is commonly considered in the literature. According to Laschinger and Finegan [24], the lack of trust and respect by the environment in nurses provokes dissatisfaction and encourages the intention to leave the job. Trust can be shaken by poor communication, misunderstanding, as well as denial of opportunities for participation in decision-making. Trust often arises as a mediator at influences on the intentions of leaving work. Kashyap and Rangnekar [21] conducted another survey in India, in which they found that the level of trust of employees in the leader is a partial mediator of the relationship of service leadership and the intention to leave the organization. According to Mulki et al. [33], trust in the supervisor appears as a mediator in the relationship between the ethical climate and the intention to leave the organization. Similarly, according to research results in Thailand [35], transformational leadership reduces intentions to leave the organization, with the mediation of trust in the leader. Jiang and Shen [19] investigated the impact of trust on the intentions of leaving the organization among PR managers (the survey involved 650 PR managers). Trust has proven to be a strong mediator of the relationship between the incentive working environment and the intention to leave the organization. In this paper, two hypotheses are set. They are based on the possibility of having an influence of certain dimensions of trust at work on individual entrepreneurial orientation dimensions, achievement dimension, and the theory of planned behavior dimensions. Thus, the hypotheses are: H1: There is a statistically significant influence on certain dimensions of trust at work on individual entrepreneurial orientation dimensions, achievement dimension, and theory of planned behavior dimensions. H2: There is statistically significant predictive effect of certain dimensions of trust at work on individual entrepreneurial orientation dimensions, achievement dimension, and theory of planned behavior dimensions.

7.3 Method 7.3.1 Survey Instruments (Measures) The instrument individual entrepreneurial orientation (IEO) was used to measure individual entrepreneurial orientation [8]. The questionnaire consists of 10 items (3 dimensions: (1) Risk-taking, (2) Innovativeness, and (3) Proactiveness). Respondents value through a seven-point Likert scale. Need for achievement was measured through the achievement dimension from the Attitude Toward Enterprise (ATE) test [3]. The dimension consists of four items. Respondents value through a seven-point Likert scale. For the measuring of dimensions the theory of planned behavior, the Entrepreneurial Intention Questionnaire (EIQ) was used [25]. The questionnaire

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consists of 20 items [4 dimensions: (1) Personal attitude, (2) Subjective norm, (3) Perceived behavioral control, and (4) Entrepreneurial intention]. Respondents value through a seven-point Likert scale. Organizational trust (mutual trust at work) was measured using the interpersonal trust at work instrument [11]. The questionnaire consists of 12 items (4 dimensions: trust in colleagues’ intentions, trust in the intentions of management, trust in colleagues’ actions, and trust in management actions). Respondents value through a seven-point Likert scale.

7.3.2 Participants and Data Collection The research was carried out in companies in Serbia. The sample consists of medium and large companies, and according to the type of activity, the sample consists of production, service, and public companies. Respondents were employed in these companies, regardless of gender, age, and level of education (secondary, higher education). In most companies, a large number of questionnaires were distributed. A total of 680 questionnaires were distributed, and 582 questionnaires were filled. 42 questionnaires were rejected due to incomplete responses, so statistical analyses were made with 540 questionnaires (percentage of successfully completed questionnaires was 79.4%). So, the sample consists of 540 respondents from 72 companies.

7.4 Results 7.4.1 Descriptive Statistics Table 7.1 provides descriptive statistics for the observed dimensions. Mean values, standard deviations, and Cronbach’s alpha (α) for each dimension are calculated. The values of Cronbach’s alpha were range from 0.798 to 0.954.

7.4.2 Correlation Analysis Correlation analysis Table 7.2 shows the effects of trust at working dimensions on individual entrepreneurial orientation dimensions (risk-taking, innovativeness, proactiveness), achievement dimension, and the theory of planned behavior dimensions (personal attitude, subjective norm, perceived behavioral control, entrepreneurial intention). Pearson correlation was used (*p < 0.05, **p < 0.01).

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Table 7.1 Descriptive statistics for the observed dimensions Names of dimensions and items

Abbr.

N

Min.

Max.

Mean

Std. Dev.

α

Risk-taking

RT

540

1.00

7.00

4.596

1.491

0.798

Innovativeness

IN

540

1.00

7.00

4.869

1.309

0.848

Proactiveness

PR

540

1.00

7.00

5.770

1.136

0.820

Achievement

ACH

540

1.00

7.00

5.217

1.191

0.866

Personal attitude

PA

540

1.00

7.00

4.557

1.420

0.906

Subjective norm

SN

540

1.00

7.00

5.074

1.309

0.807

Perceived behavioral control

PBC

540

1.00

7.00

4.234

1.326

0.898

Entrepreneurial intention

EI

540

1.00

7.00

3.323

1.619

0.954

Trust in colleagues’ intentions

TW1

540

1.00

7.00

5.528

1.304

0.877

Trust in the intentions of management

TW2

540

1.00

7.00

4.960

1.457

0.881

Trust in colleagues’ actions

TW3

540

1.00

7.00

5.600

1.260

0.919

Trust in management actions

TW4

540

1.00

7.00

5.078

1.540

0.929

Table 7.2 Coefficients of the correlation between the trust at work dimensions and individual entrepreneurial orientation dimensions, achievement dimension, and the theory of planned behavioral dimensions RT

IN

PR

ACH

PA

SN

PBC

EI

TW1

0.167**

0.139**

0.277**

0.134**

0.098*

0.171**

0.096*

−0.002

TW2

0.116**

0.099*

0.199**

0.117**

0.064

0.084

0.059

−0.034

TW3

0.135**

0.113**

0.276**

0.136**

0.063

0.147**

0.081

−0.012

TW4

0.079

0.073

0.182**

0.117**

0.032

0.102*

0.075

−0.047

*p

< 0.05; **p < 0.01

7.4.3 Regression Analysis Regression analysis Table 7.3 shows the predictive effects of trust at work dimensions (independent variables) on individual entrepreneurial orientation dimensions (risktaking, innovativeness, proactiveness), achievement dimension, and the theory of planned behavior dimensions (personal attitude, subjective norm, perceived behavioral control, entrepreneurial intention). Statistically significant predictive effects are shown in bold font.

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Table 7.3 Regression analysis (independent variables: trust at work dimensions; dependent variables: individual entrepreneurial orientation dimensions, achievement dimension, and the theory of planned behavior dimensions) Indep.

R2

Dependent TW1

TW2

TW3

F

Sig

TW4

β RT

0.161

0.139

0.012

−0.147

0.033

4.565

0.001

IN

0.128

0.103

0.011

−0.102

0.022

2.995

0.018

PR

0.148

0.063

0.150

−0.043

0.087

12.784

0.000

ACH

0.047

0.016

0.071

0.037

0.022

3.032

0.017

PA

0.134

0.122

−0.035

−0.139

0.014

1.966

0.098

SN

0.155

−0.093

0.043

0.066

0.032

4.386

0.002

PBC

0.077

−0.061

0.016

0.073

0.011

1.432

0.222

EI

0.052

0.017

−0.018

−0.084

0.003

0.463

0.763

7.5 Discussion 7.5.1 Discussion of the Results of Correlation Analysis (Checking Hypothesis H1) Table 7.2 shows that the dimensions of trust at work, in more than half of cases, have a statistically significant and positive impact on the observed dimensions of individual entrepreneurial performances. From the dimensions of trust at work, the dimensions related to trust in colleagues (TW1—Trust in colleagues’ intentions and TW3—Trust in colleagues’ actions) have a stronger impact than the dimensions related to trust in management (TW2—Trust in the intentions of management and TW4—Trust in management actions). This is, to a large extent, the consequence of statistically significant correlations that the dimensions of trust in colleagues have with the dimensions PR—Proactivity, RT—Risk-taking, and ACH—Achievement, but also with SN—Subjective norm. Trust in colleagues, partly, certainly comes from the understanding, solidarity, and support that an employee gets from colleagues. As a result, employees easily perceive the potential support of people from the environment for (possibly) starting a business venture. Similarly to the previous, the dimension TW4—Trust in management actions has a statistically significant correlation with the dimension SN—Subjective norm. Trust in the quality of the work of management and faith in the good future of the organization gives employees considerable security and confidence that management is supported by people from the organization. Then the employee can perceive that this support to the management will flow to both individuals, and so on himself, and that will also apply if he decides to open his company. It should also be noted that the dimension TW1—Trust in colleagues’ intentions has statistically significant correlations with the dimensions of PA—Attitude toward

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entrepreneurship and PBC—Perceived behavioral control. If an employee believes that he will receive help from his colleagues, if he needs it, this also provides certain security and develops beliefs that such relationships prevail outside the organization. Such beliefs, further can lead to the fact that an employee can think in the following way: People are good, they want to help, keep their word, and then what could be bad if I open my company…? This creates a positive perception of an entrepreneurial call (positive impact on the PA dimension—Attitude toward entrepreneurship). Additionally, the support of a colleague also creates an employee’s conviction that he deserves that support and deserves it because he is a quality person and because he possesses the appropriate knowledge and skills. This is followed by a good perception of one’s own abilities and thus the ability to engage in entrepreneurship (positive impact on the PBC dimension—Perceived behavioral control). Based on the previous expositions, one can conclude that there is a statistically significant influence of certain dimensions of trust at work on individual entrepreneurial orientation dimensions, achievement dimension, and theory of planned behavior dimensions. In this way, the hypothesis H1 was confirmed.

7.5.2 Discussion of the Results of the Regression Analysis (Checking Hypothesis H2) Table 7.3 shows the results of the regression analysis for examining the predictive effects of the dimensions of confidence at work on the dimensions of the observed individual entrepreneurial performances. Dimensions TW1—Trust in colleagues’ intentions (especially) and TW3—Trust in colleagues’ actions have a more predictive effect than the dimensions that relate to trust in management. These results of regression analysis are consistent with the results of the correlation analysis, as discussed earlier. As a distinction in relation to the results of the correlation analysis, it should be noted that the dimension TW4—Trust in management actions has a negative predictive effect on the dimensions PA—Personal attitude and EI—Entrepreneurial intentions. This predictive effect is not statistically significant, but its strength cannot be completely ignored. This phenomenon shows the tendency that an employee’s perception, that management is doing well and that an organization has a great future can reduce entrepreneurial attitudes and intentions. On the contrary, entrepreneurial attitudes and intentions can be enhanced if an employee estimates that the organization’s perspective is not good. It is interesting that the dimension TW2—Trust in the intentions of management has no such impact. If employees believe (or do not believe) that the management has good intentions, to do everything it can, to behave fairly to employees, this will in no case significantly affect their entrepreneurial attitudes and intentions. So, people are activated only when they see what and how management works and only when assessing the perspective of the organization. In other words, for such important, almost life-related issues, intentions are for people

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not so important, but results. Some existing researches also confirm the importance of trust in leaders [20] and confidence in management [22] to reduce the intention to leave the organization. It should be noted that these references examine the intentions about leaving the organization, without considering whether the entrepreneurial intentions are behind it. According to Table 7.3, the corrected determination indices R2 have low values, ranging from 0.003 to 0.087. However, most of these values are statistically significant. Observed according to individual dependent variables (dimensions of observed individual entrepreneurial performances), under the strongest predictive effect of the dimensions of trust at work, are the dimensions PR—Proactiveness, RT—Risk-taking, and SN—Subjective norm. This result is generally consistent with the results of the correlation analysis. On the other hand, under the weakest predictive effect of the dimension of trust at work is dimensions of EI—Entrepreneurial intentions, PBC—Perceived behavioral control, and PA—Attitude toward entrepreneurship, where there is no statistically significant value of the index of determination R2. Based on the previous expositions, one can conclude that there is a statistically significant predictive effect of certain dimensions of confidence at work on the dimensions of individual entrepreneurial orientation, the dimension of the need for achievement, and the dimension of the theory of planned behavior. This confirmed the hypothesis H2.

7.6 Conclusion Of the dimensions of trust at work, the dimensions related to trust in colleagues have a stronger impact on the dimensions of individual entrepreneurial performance than the dimensions that relate to trust in management. In doing so, the strongest impact has the dimension TW1—Trust in colleagues’ intentions. Among other things, this dimension has statistically significant correlations with the dimensions of PA—Attitude toward entrepreneurship, SN—Subjective norm, and PBC—Perceived behavioral control. If an employee believes in the positive intentions of his colleague, he acquires certain security and belief in people, and thus entrepreneurship acts as an acceptable option, people’s support seems to work, and own abilities seem more certain. In general, confidence in an organization generally enhances the dimension of SN—Subjective norm: From trust, it is easy to perceive the support of people in the environment. The TW4 dimension—Confidence in management actions has a negative predictive effect on the dimensions of PA—Attitude toward entrepreneurship and EI— Entrepreneurial intentions. This predictive effect is not statistically significant, but it shows a tendency: Low trust in the work (actions) of management (perceived by employees) can boost entrepreneurial attitudes and intentions. However, the dimension TW2—Trust in the intentions of management has no such impact. Therefore, in such situations, results (actions) are more important to people, than intentions:

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only if they see that the results are bad and that there is no perspective, then the entrepreneurial intentions will be developed. If intentions are bad, they will still not start, as long as the results are good. The limitation of research stems from the fact that it was realized in companies in Serbia. The obtained results and conclusions, first of all, apply to organizations in Serbia. However, it can be assumed that similar relations exist in some other countries, especially countries in the region and other countries in the transition process. The theoretical significance of this research stems from the fact that there is no significant number of papers dealing with entrepreneurial intentions of employees, as well as the impact of various aspects of organizational behavior on these intentions. The practical significance of this research is that it shows that employed persons should be considered as potential entrepreneurs. Strategies and programs for the development of entrepreneurship at the state level should certainly take into account this population, primarily through the organization of entrepreneurial training and providing adequate financial support for employees with entrepreneurial intentions. Acknowledgements This paper is a result of the research activities conducted under the project “Improving the entrepreneurial climate, analysis of aspects, and possible plans of action of young people in the Region of Central Banat,” which is financed by the Provincial Secretariat for Higher Education and Scientific Research of Autonomous Province of Vojvodina, Republic of Serbia. The authors gain consent from the study participants.

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37. Rousseau, D.M., Sitkin, S.B., Burt, R.S., Camerer, C.: Not so different after all: a crossdiscipline view of trust. Acad. Manag. Rev. 23(3), 393–404 (1998) 38. Saraf, N.: What determines entrepreneurial intention in India? J. Entrepreneurship Innov. Emerg. Econ. 1(1), 39–55 (2015) 39. Tkachev, A., Kolvereid, L.: Self-employment intentions among Russian students. Entrepreneurship Reg. Dev. Int. J. 11(3), 187–215 (1999)

Chapter 8

Self-efficacy and Entrepreneurial Intention Among Business Students in Romania Bogdan Robert Ioane, Nicolae Bibu, and Laura Brancu

Abstract This study focused on the correlation between self-efficacy and entrepreneurial intention for a sample comprised of business students from Romania. In addition to investigating self-efficacy as antecedent of entrepreneurial intention, a number of demographic variables were also tested as determinants for self-efficacy. The results disclosed a significant positive relation between general self-efficacy and entrepreneurial intention on business students in Romania. The analysis further revealed a gender imbalance, male respondents displaying a higher score in selfefficacy than the female respondents. In addition, the analysis displayed a positive correlation between work experience and general self-efficacy and a significant correlation between parent workplace and self-efficacy. This correlation between parent workplace and general self-efficacy of their adult children is particularly valuable as there is no previous literature suggesting this finding, and therefore, this study may open a new avenue for future research to investigate this type of cultural parent–children subliminal transfer and the way this influences the children’s life and entrepreneurial choices well into their adulthood and throughout their lives. Keywords Entrepreneurial intention · Self efficacy · Business students · Romania

8.1 Introduction Defined as a person’s confidence about own potential and aptitudes, self-efficacy is one of the most analyzed concepts in psychology due to its impact on motivation and behavior [38]. Intense research efforts were ignited by Albert Bandura, who was the B. R. Ioane (B) · N. Bibu · L. Brancu West University of Timisoara, Timisoara, Romania e-mail: [email protected] N. Bibu e-mail: [email protected] L. Brancu e-mail: [email protected] © Springer Nature Switzerland AG 2020 G. Prostean et al. (eds.), Innovation in Sustainable Management and Entrepreneurship, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-44711-3_8

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first to find that self-efficacy has a major effect on how individuals set goals, behave, and act during tasks and challenges [2]. Self-efficacy is one of the elements influencing behavior, including entrepreneurial behavior. Self-efficacy can be defined as the individual’s confidence in their potential for success. Studies demonstrated the correlation between self-efficacy and motivation in various settings [1, 2, 38]. Positive expected results depend on each individual’s perception of their own abilities, their judgment regarding what they can or cannot achieve, in the manner of a self-fulfilling prophecy. At first, the initial evaluation or perception of abilities is a false definition of the situation, this perception then triggering behavior that makes the initial false definition come true [28]. Self-efficacy research suggested that self-efficacy is related to heightened expectations and ambitions [3], better work performance [37], more intense and better job search activity [11], greater job satisfaction [6], and superior academic achievement [25]. Considering these, we believe an analysis of the impact of self-efficacy on entrepreneurial intention to be very useful, as self-efficacy seems to be strongly linked with the pursuit of entrepreneurship, greater personal effectiveness, and more determination when it comes to difficult tasks [26]. The role of confidence in entrepreneurship has been acknowledged before as being one of the most important factors in framing “the entrepreneurial spirit” [17]. Self-efficacy might have significant implications for understanding entrepreneurial behavior [40] since someone who believes (s)he has abilities needed for entrepreneurial success that will be prone to initiate entrepreneurial actions. Self-efficacy was suggested as major determinant of the strength of entrepreneurial intentions [45]. Despite the increasing attention in the area of entrepreneurship antecedents, a search on Ebsco, Web of Science, and Google Scholar databases for self-efficacy and entrepreneurial intention in Romania revealed that at this time there is a gap in the literature, very few other studies approaching self-efficacy and entrepreneurial intention in Romania. This study intends to fill this gap and identify the level of self-efficacy and entrepreneurial intention of the Romanian business students. The article is organized as follows: after a short introduction presenting the concept of self-efficacy, we will continue with a theoretical background and then enounce the hypothesis of this study, followed by a description of the research methodology. The results and conclusions, as well as an agenda for future studies will conclude the article.

8.2 Self-efficacy and Entrepreneurial Intention The self-efficacy theory [2] emphasizes the source of judgement of efficacy, the individual. His social cognitive theory [1–3] suggested that individuals are not simply shaped by their context and environment, but they take an active part in creating their future. This agency includes a mental conviction of self-efficacy, individuals

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acknowledging to having the power and abilities necessary to react to constraints [30]. Research suggests that the context or circumstance is influenced by fortuity, life developments beyond our control; there are a unlimited number of potential events that may change lives, and this creates uncertainty and confusion. This does not necessarily mean individuals have no control, they can influence the emergence of positive chances by improving their skills and abilities, by being active and outgoing and overall by trying to provoke chance by increasing the type and number of serendipitous encounters. As Louis Pasteur famously declared in 1854, “Chance favors only the prepared mind”; in this line, self-development can help individuals in shaping their life for the better. A Harvard study suggests that agency is critical to student success in the academic environment [14]. In education, students with self-efficacy are more motivated, work harder, persist longer with tasks, will be more determined to find a solution when faced with a more difficult problem, and will recover faster from setbacks. Self-efficacy affects not only how hard they try, but also their perseverance (how long) and their resilience to recover from failure [35]. In life, an individual’s belief in his/her abilities and capacity to perform various roles and tasks will determine whether that individual will engage in entrepreneurial career. Researchers proposed additional self-efficacy instruments, where general selfefficacy refers to the confidence of the individual in his/her abilities to perform any kind of future tasks, and a subset of this is the entrepreneurial self-efficacy that looks at a person’s belief in his/her capacity to engage in entrepreneurial tasks and behaviors. When seen as preceding entrepreneurial intentions, self-efficacy is called entrepreneurial self-efficacy (ESE) [27]. The literature on ESE is relatively solid and shows that ESE influences motivation, intention, behavior, and performance [7, 29]. However, several independent tests that followed the introduction of the concept by Chen et al. [7] led to contradictory results. Moreover, self-efficacy is a stable construct and there is disagreement on whether an entrepreneurial-focused selfefficacy instrument is needed. One reason is the inconsistency in the way researchers approach ESE, varying attempts capturing different dimensions; a second reason is that ESE research strongly relies on university students [27]. Self-efficacy can describe the individual perception of their own capacity to solve a wide variety of functions across a wide variety of circumstances. This is crucial for entrepreneurs, as they need an extremely diversified set of skills and face myriad of unexpected situations. Considering the difficulty to establish a broad, all-inclusive list of tasks specifically associated with entrepreneurship, and the difficulty of capturing all the subtleties of entrepreneurial activities and roles, this paper uses the validated self-efficiency instrument developed by Schwarzer and Jerusalem [34]. This general self-efficacy scale has been shown to be positively correlated to emotion, optimism, work satisfaction, and negatively correlated with depression, stress, burnout, and anxiety [34]. Significant correlations between self-efficacy and entrepreneurial intention were found in many studies on both developed and emerging economies [4, 12, 33, 42]. Interestingly, Kolvereid and Isaksen [22] study on Norwegian business founders,

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as well as Boukamcha [5] on Tunisian trainees did not find evidence for such a correlation [5, 22] as noted by Newman et al. [31]. In addition, self-efficacy is strongly related to work performance [39] and is correlated with entrepreneurial drive [15], and entrepreneurs score higher in selfefficacy than non-entrepreneurs [26] and are higher in self-efficacy than managers [41].

8.3 Significance of the Study/Theoretical Background One of the aims of this study was to identify the level of self-efficacy of the Romanian students, because in contrast with personality, self-efficacy can be taught and refined through education as it changes over time [16]. Identifying a lower level can be useful and allow policymakers and educators to update policies and curriculum in such a way to encourage an increase in self-efficacy. In some countries, the entrepreneurial environment is highly developed, and this situation is existing in the developed western countries who created the infrastructure necessary to entrepreneurs and their ventures. In these countries with a long tradition in entrepreneurship, the societal perception is also one of high status, an entrepreneurial career being coveted and highly regarded by most [36]. In former socialist countries—such as Romania—there is less experience in entrepreneurial ventures, as these countries’ economies were organized differently; therefore, their education did not include entrepreneurship and their education systems are less prepared than those in developed countries in entrepreneurship curriculum [32]. Furthermore, most of the individuals growing up in communist systems have less knowledge in the mechanisms of a market economy, being taught at that time the organization of a communist, planned economy. In addition, some countries with better entrepreneurship environments have customs that include children participation in family business, habits of discussing in family and involving the kids in financial planning and entrepreneurial activities, playing entrepreneurial games such as monopoly and a way of organizing play around entrepreneurial ideas, such as starting lemonade stands and cookie selling activities for youth. This allows most individuals to be exposed to entrepreneurial ideas and concepts from an early age, providing them with experience in various tasks and activities essential to entrepreneurship, experiencing failure early, and gaining an attitude and the necessary knowledge about how entrepreneurial ventures work. The family organization in some countries is also conducive for entrepreneurial activities, for example by encouraging initiative, personal, and financial independence. For instance, in most western English-speaking countries, young adults are expected to earn money and to be independent, leaving home to go study several kilometers away from home and parents. This encourages individuals to live in reality from early adulthood, to create their own future and interpersonal networks, away from personal connections of their families.

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In contrast, people living in former communist countries have different traditions and the younger generations are raised while being shielded from the real world, parents taking care of most financial and house tasks until their children finish college. Even after that, personal family networks are expected to help the young adult when it comes to finding a job, while an entrepreneurial career seems to be an option for less young adults in these less developed, emerging economy countries [21]. Furthermore, according to several studies, it seems that in developing countries the interest in entrepreneurship is not only lower, but even decreasing [9, 19]. Romania is a post-communist, eastern European country on an evolutionary path from an efficiency to an innovation-driven economy. Historically, the Romanian education system did not promote high self-efficacy nor growth mindset, having a grade-focused system similar to the most former communist countries. Although significant changes happened in the last decade, the Romanian education system is still centralized and strict, encouraging rote memorization of very tight curricula. In this system, teachers are powerful and student voice is almost non-existent. Not long ago, the students were expected to abstain from asking questions or from engaging in any debate, and the education system had a role of indoctrination. The role of education was not intended to enlighten young minds but rather to prepare them for entering a mass workplace while subduing their behavior and molding their attitudes and beliefs toward convenient truths. Even today, years after the communist regime was replaced by a democratic one, teaching is still seeing major changes every year and many things are not very different from the past. Therefore, this study intends to offer a glimpse of the status quo in Romania to allow for better future measures to be taken to improve education and insure the education system that gives enough importance to the concepts of growth mindset and self-efficacy. Moreover, research that looks at self-efficacy as precursors of entrepreneurial intention in the context of Romania is sparse even though many researchers—including Bandura—suggested that self-efficacy is a social construct and that its construction may differ across cultures [1, 2]. Individuals exist in interdependence with society, and their self-image and perception of self is dependent of both individual and group influences; these influences vary with the culture of the group where the individual grows and develops. Culture is ingrained in the way in which self-efficacy beliefs are created and in the way self-efficacy is applied [40]. Based on prior research and aiming to clarify the unanswered questions regarding self-efficacy and entrepreneurial intention on students in Romania, we advance the following hypotheses: H1: There is a significant positive correlation between self-efficacy and entrepreneurial intention in Romanian students. H2: There is a significant effect of demographic variables (gender, age, education, subject, work experience, location, parent education, work nature of father, and family income) on the self-efficacy of Romanian students.

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8.4 Method The study data collection was completed with business students from West University of Timisoara1 by using convenience sampling. Data was collected through self-administered questionnaires, and 230 students out of the 250 administered questionnaires fully answered the surveys and the demographic questions anonymously. The response rate was 92%. The excluded questionnaires were found unusable due to the failure of some participants to fully complete all the questionnaire and demographic questions. From the 230 fully completed questionnaires, one was additionally excluded from the self-efficacy calculations due to incomplete demographic answers. The questionnaires were administered in several classes under lecturer supervision. From the total 230 respondents, 72 were male and 158 were female. 150 respondents have 21 or more years old while 80 have between 18 and 20 years old. 65 respondents reported more than 1 year of previous work experience, while 165 respondents reported less than 1 year of previous work experience. Some of our demographic questions focused on parent education and workplace. Here, 20 respondents reported their parents completed general school, 147 respondents reported their parents completed high school education, while 63 respondents reported their parents completed college or higher education. Furthermore, 114 respondents disclosed their parents work in private organizations, 84 in state-owned organizations, while 25 parents have their own business and 7 are unemployed. Regarding family monthly income, 41 reported having an income less than 528.74 Eur (2500 RON), 109 respondents with between 528.75 and 1057.91 Eur (2501–4999 RON), 72 with between 1057.92 Eur and 2114.63 Eur (5000–9999 RON), and 7 with more than 2114.64 Eur (more than 10000 RON). For measuring general self-efficacy, the adapted four-point Likert scale of general self-efficacy by Schwarzer and Jerusalem was used, consisting of 10 items [34]. Example of statements: “I can always manage to solve difficult problems if I try hard enough”; I am confident that I could deal efficiently with unexpected events; I can usually handle whatever comes my way. The 4-point Likert scale ranged from “not at all true” to “exactly true.” In this study, the general self-efficacy scale had an internal consistency of Cronbach alpha = 0.84. For entrepreneurial intention, the instrument followed the empirical studies of Zampetakis and Moustakis [44] and Wu [42] and used the 10 item scale used by Wu [42] and Fatoki [13]. The five-point Likert scale measured from “strongly disagree” to “strongly agree.” The scales were translated into Romanian language by using a blind-back translation process where a translator was used to translate from original English version into Romanian, then the result was translated back into English by another translator; the original and the version resulted after the blind-back translation were compared

1 Participation

was voluntary and anonymous. All the responses were kept confidential and no personal information was collected. The data was used only in aggregate form and only for statistical processing purposes.

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and then adjusted to ensure comparability of language, similarity of interpretability, and degree of understandability [20].

8.5 Results General self-efficacy was measured using ten items, where respondents were asked to report how true each of ten statements were. Table 8.1 displays the means and standard deviations of each of these items. The averages of the items tend to be more or less close to each other. Table 8.2 shows the descriptive statistics for the General self-efficacy items. Hypothesis 1: To test whether there is any association between self-efficacy and entrepreneurial intention, we used the Pearson correlation test to investigate the association between the two constructs. Table 8.3 shows the correlations and the associated p-values. We note that the correlation between general self-efficacy and entrepreneurial intention, as Table 8.3 shows, is a significant positive correlation (r = 0.319, p-value < 0.05), and thus, hypothesis 1 is accepted. After the Pearson correlation test, we conducted principal factor analysis on general self-efficacy data. The results are displayed in Table 8.4. Only the first factor had an eigenvalue that is greater than 1 (3.48). In addition, the loadings on all factors other than the first factor are small (less than 0.4). As such, we take this as proof that this is a unidimensional construct. Cronbach’s alpha for these items is 0.84, which is higher than the cutoff value of 0.7. The next step was to calculate the factor scores based on the results of the factor analysis. Because there is only one factor a single score was calculated for each observation; this is better than simply finding the average of the responses on all items since it takes into account the fact that different items have different loadings on the factor. Next, we performed a one-way analysis of variance test in order to investigate whether between-group differences exist, test performed for each demographic variable. Out of all of the demographic variables, the only ones in which there were a statistically significant differences are gender and work experience. The results are summarized in Table 8.5. We identified two significant results here, males having a higher score on the general self-efficacy than females, and that the more work experience, the higher the level of general self-efficacy. In the following table, only those variables with significant results were reported: Figures 8.1 and 8.2 show graphical representations of the differences between the means of the scores. These are the differences that were found to be statistically significant. We note that while the mean score for females is negative, that of males is positive. We also note that with regard to work experience, the mean of the score increases as experience increases. Hypothesis 2: To test whether there are any significant positive effects of demographic variables on self-efficacy, the test of between-subject test was performed:

230

229

Entrepreneurial intentions

Valid N (listwise)

Statistic 1.00

1.80

Statistic

229

Min.

N

Self-efficacy

Table 8.1 Descriptive statistics

5.00

4.00

Statistic

Max.

3.7280

3.0162

Statistic

Mean

0.96890

0.46003

Statistic

Std. deviation

Std. error 0.161 0.160

Statistic −0.158 −0.741

Skewness

0.140

−0.302

Statistic

Kurtosis

0.320

0.320

Std. error

102 B. R. Ioane et al.

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Table 8.2 Descriptive statistics for general self-efficacy items Mean

sd

“I can always manage to solve difficult problems if I try hard enough”

3.38

0.70

“If someone opposes me, I can find the means and ways to get what I want”

2.97

0.77

“It is easy for me to stick to my aims and accomplish my goals”

2.89

0.70

“I am confident that I could deal efficiently with unexpected events”

3.17

0.73

“Thanks to my resourcefulness, I know how to handle unforeseen situations”

3.03

0.73

“I can solve most problems if I invest the necessary effort”

3.44

0.64

“I can remain calm when facing difficulties because I can rely on my coping ability”

2.68

0.83

“When I am confronted with a problem, I can usually find several solutions”

2.90

0.72

“If I am in trouble, I can usually think of a solution”

2.79

0.70

“I can usually handle whatever comes my way”

2.93

0.74

Table 8.3 Correlation between general self-efficacy and entrepreneurial intention Self-efficacy

Pearson’s correlation

Self-efficacy

Entrepreneurialintention

1

0.319**

Sig. (2-tailed) Entrepreneurial intention

0.000

N

229

229

Pearson’s correlation

0.319**

1

Sig. (2-tailed)

0.000

N

229

230

**Correlation is significant at the 0.01 level (2−tailed) Table 8.4 Principal factor analysis for general self-efficacy Factor 1

Factor 2

Factor 3

Factor 4

gse1

0.4131989

0.3028294

0.0589316

0.0120851

gse2

0.5021123

0.3746294

0.0119407

0.0054635

gse3

0.578197

0.2426753

−0.0512089

−0.0034652

gse4

0.7332489

−0.0306262

0.1047367

−0.0111113

gse5

0.6685039

0.0250639

−0.1316932

−0.0133312

gse6

0.4790511

0.1795552

0.0155533

−0.001891

gse7

0.4583573

−0.2137919

0.1792458

0.0012743

gse8

0.6051832

−0.1703324

0.0943359

−0.0017434

gse9

0.7009249

−0.25417

−0.0484049

0.0119671

gse10

0.6636182

−0.2330169

−0.1541092

0.006502

104 Table 8.5 One-way analysis of variance test for general self-efficacy

B. R. Ioane et al. Variable

Mean

Standard deviation

Female

−0.91

0.96

Male

0.21

0.81

Under 1 year

−0.11

0.93

Between 1 and 2 years

0.26

0.86

More than 3 years

0.32

0.87

Gender

Prob > F 0.0224

Work experience

0.0138

Fig. 8.1 Comparison of the mean of general self-efficacy; statistically significant between-group differences for general self-efficacy and gender

Fig. 8.2 Comparison of the mean of general self-efficacy; statistically significant between-group differences for general self-efficacy and work experience

Table 8.6 shows that demographic variables subject (F(3) = 2.680, p = 0.048) work experience (F(2) = 3.092, p = 0.048), and work nature of father (F(3) = 3.265, p = 0.022) have significant effects in molding their self-efficacy. To further determine which categories of work experience and work of parent have a more significant effect on self-efficacy, the LSD post hoc test was performed in SPSS. The output of multiple comparisons is shown in Tables 8.7 and 8.8:

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Table 8.6 Tests of between-subject effects Dependent variable: self-efficacy Source

Type III sum of squares

Corrected model

9.915a

df

Mean square

F

Sig

26

0.381

1.993

0.004

Intercept

57.029

1

57.029

298.060

0.000

Gender

0.540

1

0.540

2.821

0.095

Age

0.456

1

0.456

2.382

0.124

Education

0.214

2

0.107

0.559

0.573

Work experience

1.183

2

0.592

3.092

0.048

Location

1.364

7

0.195

1.018

0.419

Parent education

0.532

4

0.133

0.696

0.596

Work nature of father

1.874

3

0.625

3.265

0.022

Income of family

1.238

3

0.413

2.157

0.094

Error

38.267

200

0.191

Total

2111.030

227

Corrected total

48.182

226

aR

Squared = 0.206 (Adjusted R Squared = 0.103)

Table 8.7 LSD post hoc test work experience and self-efficacy (self-efficacy as dependent variable) (I) Work Exp.

(J) Work Exp.

Mean difference (I − J)

Std. error

Under 1 year

Between 1 and 2 years

−0.2024*

0.07966

More than 3 years

−0.2059*

Under 1 year

Between 1 and 2 years

More than 3 years

Sig

95% confidence interval Lower bound

Upper bound

0.012

−0.3595

−0.0453

0.09089

0.025

−0.3851

−0.0267

0.2024*

0.07966

0.012

0.0453

0.3595

More than 3 years

−0.0035

0.11071

0.975

−0.2218

0.2148

Under 1 year

0.2059*

0.09089

0.025

0.0267

0.3851

Between 1 and 2 years

0.0035

0.11071

0.975

−0.2148

0.2218

Based on observed means. The error term is Mean Square (Error) = 0.191 *The mean difference is significant at the 0.05 level

The results in Tables 8.7 and 8.8 suggest the highest correlation between work experience, and self-efficacy happens for individuals with at least 1–2 years of experience. This is in line with previous research such as Bandura’s [1, 2] that showed a positive relationship between experience and self-efficacy.

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Table 8.8 LSD post hoc work experience and self-efficacy Position

Categories

Absolute mean difference (I − J)

P-value

1

More than 3 year–under 1 year

0.2059

0.025

2

Between 1 and 2 year–under 1 year

0.2024

0.012

3

More than 3 year–between 1 and 2 year

0.0035

0.975

Table 8.9 LSD post hoc test parent workplace type and self-efficacy (self-efficacy as dependent variable) (I) Work nature of parent

(J) Work nature of parent

Mean difference (I − J)

Std. error

Unemployed

State-owned

−0.1850

0.17216

0.284

−0.5245

0.1545

Private org

−0.3223

0.17042

0.060

−0.6584

0.0137

Own business

−0.2789

0.18705

0.138

−0.6477

0.0900

Unemployed

0.1850

0.17216

0.284

−0.1545

0.5245

Private org

−0.1373*

0.06335

0.031

−0.2622

−0.0124

Own business

−0.0938

0.09979

0.348

−0.2906

0.1029

Unemployed

0.3223

0.17042

0.060

−0.0137

0.6584

State-owned

0.1373*

0.06335

0.031

0.0124

0.2622

Own business

0.0435

0.09676

0.654

−0.1473

0.2343

Unemployed

0.2789

0.18705

0.138

−0.0900

0.6477

State-owned

0.0938

0.09979

0.348

−0.1029

0.2906

Private org

−0.0435

0.09676

0.654

−0.2343

0.1473

State-owned

Private org

Own business

Sig

95% confidence interval Lower bound

Upper bound

Based on observed means. The error term is Mean Square (Error) = 0.191 *The mean difference is significant at the 0.05 level

Table 8.10 LSD post hoc test categories parent workplace type Absolute mean difference (I − J)

Position

Categories

P-value

1

Private org–unemployed

0.322

0.060

2

Own business–unemployed

0.2789

0.138

3

Own business–state-owned

0.0938

0.348

4

Private org–own business

0.0435

0.031

Regarding the correlation between participant self-efficacy and their parent work type, the results are as follows:

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The results in the Tables 8.9 and 8.10 suggest there exists a significant correlation between parent workplace type and participant self-efficacy for those participants whose parents work in private organization or those who have their own business.

8.6 Conclusions In this study, we analyzed the correlation between self-efficacy and entrepreneurial intention for a sample comprised of business students from Romania. The discussion started from an analysis of the literature that indicated a gap in this subject in Romania. In addition to self-efficacy as antecedent of entrepreneurial intention, a number of demographic variables were tested as determinants for self-efficacy level. We tested two hypotheses. The first hypothesis refers to the association between self-efficacy and entrepreneurial intention. The analysis revealed a good and positive correlation between general self-efficacy and entrepreneurial intention, confirming hypothesis 1. This result was relatively expected since the study was performed on business students and as the literature points out, the education received by students in the business field focuses on the development of entrepreneurial abilities and on stimulating self-efficacy [45]. This is a limitation of the current study that will be eliminated in future studies through investigating the correlation among students from other, non-business specializations, and on active entrepreneurs. Nevertheless, due to the scarcity of literature referring to self-efficacy and entrepreneurial intention in Romania, this valuable finding is the first major contribution of this study, finding that will provide a better understanding of entrepreneurship antecedents in Romania. The second hypothesis sought to test whether there are any significant positive effects of demographic variables on self-efficacy on business students in Romania. This is where the results are very interesting. First, the male respondents have a higher score in self-efficacy than the female respondents. Although we cannot generalize, we can say that, in our investigated population, the female students have less confidence in their potential than the male students. This has been seen before in other studies, for example, in Lahdenperä [23], who found that female students have less confidence in their mathematical abilities in comparison with male students [23]. Second, the results show that work experience is correlated with a higher level of self-efficacy. The higher the experience—from less than a year, to over 3 years— the higher the self-efficacy level. This result indicates that experience leads to higher levels of professional and personal abilities, a higher level of accumulated experience as well as of tacit knowledge, leading to a higher level of confidence in own abilities and potential. Third, the results indicate a significant correlation between parent workplace and their adult student children self-efficacy, the level of self-efficacy being significantly higher for those participants with at least one parent who works in private organization or having their own business. As far as our search at the time of writing, no other author investigated the link between parent workplace and their adult child self-efficacy. This identification of the effect of parent workplace on their children

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self-efficacy is our second main contribution for this study, a valuable result as it emphasizes that self-efficacy is a construct learned at home. It can be argued that parents who work in private organizations or have their own business have higher levels of self-efficacy that might have been transmitted subconsciously, though parental example, to their children. This could be an example of subliminal cultural variable transfer that influences the level of general self-efficacy. This result is even more valuable as it may open a new discussion and area for future research, that can investigate this type of parent– children transfer and the way it influences the children’s life well into their adulthood and throughout their lives. Future research efforts may, in addition, be directed toward these directions: • Expanding the sample to investigate the correlation on students from non-business specializations and active entrepreneurs • Expanding the investigation on multiple countries to investigate the extent to which the correlation between self-efficacy and entrepreneurial intention varies from one country and culture to another • Expanding the investigation to include other psychological variables that are culturally bound, as other studies have shown that variables such as achievement orientation, locus of control, and risk taking propensity are essential characteristics of an entrepreneurial profile [8, 24]. These characteristics are less pregnant in masculine cultures [18], and therefore, it can be expected that a comparison on cultural basis could lead to interesting results.

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11. Eden, D., Aviram, A.: Self-efficacy training to speed reemployment: helping people to help themselves. J. Appl. Psychol. 78(3), 352–360 (1993) 12. Farashah, A.: The effects of demographic, cognitive and institutional factors on development of entrepreneurial intention: toward a socio-cognitive model of entrepreneurial career. J. Int. Entrepreneurship 452–476 (2015) 13. Fatoki, O.: The Entrepreneurial intention of undergraduate students in South Africa: the influences of entrepreneurship education and previous work experience. Mediterr. J. Soc. Sci. 5(7), 294–299 (2014) 14. Ferguson, R., Phillips, S., Rowley, J., Friedlander, J.: The influence of teaching. The achievement gap initiative at Harvard University project funded by Raikes Foundation. Harvard University, Cambridge, Massachusetts (2015) 15. Florin, J., Karri, R., Rossiter, N.: Fostering entrepreneurial drive in business education: an attitudinal approach. J. Manag. Educ. 31(1), 17–42 (2007) 16. Gist, M., Mitchell, T.: Self-efficacy: a theoretical analysis of its determinants & malleability. Acad. Manag. Rev. 17, 183–212 (1992) 17. Goldstein, B., Ick, M., Ratang, W., Hutajulu, H., Blesia, J.U.: Using the action research process to design entrepreneurship education at Cenderawasih University. Procedia Soc. Behav. Sci. 228, 462–469 (2016). https://doi.org/10.1016/j.sbspro.2016.07.071 18. Hofstede, G.: Culture’s Consequences: Comparing Values, Behaviors, Institutions and Organizations and Across Nations, 2nd edn. Sage, Beverly Hills, CA (2001) 19. Hoogendoorn., B., Zwan, P.V., Thurik, R.: Sustainable entrepreneurship: the role of perceived barriers and risk. J. Bus. Ethics 1–22 (2016) 20. Ioane, B.: Cultural adaptation of research instruments—the case of materialism scales culturally adapted for use in China. Asian Bus. Res. 7–22 (2017) 21. Kew, J., Herrington, M., Litovsky, Y., Gale, H.: Generation Entrepreneur? The State of Global Youth Entrepreneurship. Youth Business International (2013) 22. Kolvereid, L., Isaksen, E.: New business start-up and subsequent entry into self-employment. J. Bus. Ventur. 866–885 (2006) 23. Lahdenperä, J.: Comparing Male and Female Students’ Self-efficacy and Self-regulation Skills in Two Undergraduate Mathematics Course Contexts. INDRUM 2018, INDRUM Network, University of Agder, Apr 2018, Kristiansand, Norway. ffhal-01849934f (2018) 24. Liang, K., Dunn, P.: Entrepreneurial characteristics, optimism, pessimism, and realism—correlation or collision? J. Bus. Entrepreneurship 22(1) (2010) 25. Luszczynska, A., Gutiérrez-Doña, B., Schwarzer, R.: General self-efficacy in various domains of human functioning: evidence from five countries. Int. J. Psychol. 40(2), 80–89 (2005) 26. Markman, G., Baron, R., Balkin, D.: Are perseverance and self-efficacy costless? Assessing entrepreneurs’ regretful thinking. J. Organ. Behav. 26(1), 1–19 (2005) 27. McGee, J., Peterson, M., Mueller, S., Sequeira, J.: Entreprenorial self-efficacy: refining the measure. Entrepreneurship Theory Pract. 965–988 (2009). https://doi.org/10.1111/j.1540-6520. 2009.00304.x 28. Merton, R.: The self fulfilling prophecy. Antioch Rev. (1948). https://doi.org/10.2307/4609267 29. Miao, C., Qian, S., Ma, D.: The relationship between entrepreneurial self-efficacy and firm performance: a meta-analysis of main and moderator effects. J. Small Bus. Manag. 55(1), 87–107 (2016). https://doi.org/10.1111/jsbm.12240 30. Nagaoka, J., Farrington, C., Ehrlich, S., Heath, R.: Foundations for Young Adult Success. Consortium on Chicago School Research CCSR, funded by the Wallace Foundation. University of Chicago, Chicago (2015) 31. Newman, A., Obschonka, M., Schwarz, S., Cohen, M., Nielsen, I.: Entrepreneurial self-efficacy: a systematic review of the literature on its theoretical foundations, measurement, antecedents, and outcomes, and an agenda for future research. J. Vocat. Behav. 403–419 (2019) 32. Parsyak, V., Solesvik, M., Parsyak, K.: Economic aspects in formation and development of entrepreneurship education. Actual Prob. Econ. 126–133 (2014) 33. P˘aunescu, C., Popescu, M.C., Duennweber, M.: Factors determining desirability of entrepreneurship in Romania. Sustainability 1–22 (2018)

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Chapter 9

Equal Opportunities in Entrepreneurship in Romania’s West Region M˘ad˘alina Dumitrit, a Maticiuc, Diana Claudia Sala, and Valentin Partenie Munteanu

Abstract In the last years, many researches have been carried out in European Union, but less in Romania about equality of chances between men and women on the labor market and business initiation and about the gender gap in entrepreneurship. In the present paper, we aim to provide more clarity regarding the current situation of gender disparities in entrepreneurship in the Western Region of Romania, confirming the conclusions recognized in the literature of female entrepreneurship that claims that gender disparities still exist. In the first part of the research, an analysis of the labor market is carried out using specific indicators, and then the analysis of the number of women’s businesses compared to those of men. The counties covered by this study are Arad, Timis, Caras-Severin and Hunedoara. Keywords Gender disparities · Entrepreneurship · Labour market · Romania

9.1 Introduction The literature generally deals with problems concerning the reasons of women implications in entrepreneurship, the barriers faced in starting a business and successful policies in entrepreneurship. The purpose of this study is to offer a better understanding of the actual situation of gender disparities in entrepreneurship in the Western Region of Romania. The work adds to the literature, by analyzing the situation of the active population, the unemployed rate, the employment rate by gender, the gender distribution of the associates/shareholders of the legal entities information on female entrepreneurship in a developing country, especially in the counties of its M. D. Maticiuc (B) · D. C. Sala · V. P. Munteanu Faculty of Economics and Business Administration, West University of Timis, oara, Timis, oara, Romania e-mail: [email protected] D. C. Sala e-mail: [email protected] V. P. Munteanu e-mail: [email protected] © Springer Nature Switzerland AG 2020 G. Prostean et al. (eds.), Innovation in Sustainable Management and Entrepreneurship, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-44711-3_9

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West Region. Throughout the survey, we have been looking at the shift from the analysis of labor market specific indicators, such as the active population rate, the unemployment rate, in the context in which many of the women’s businesses are supposed to emerge as a result of a need for a job and not as an opportunity, then focusing on observing the number of business initiated by women compared to men in the four counties of the Western Region of Romania.

9.2 Literature Review 9.2.1 Woman Entrepreneurship Female entrepreneurship has often been addressed by reference to the responsibilities of women in family and household [19], but, on the other hand, women are considered to be attracted to the entrepreneurial side more by the necessity of opportunity, mainly due to the flexible working time necessary for the domestic activities [13], but also as a possible way to escape from unemployment [3]. One of the first economists who formulate the theory about entrepreneurship was Richard Cantillon, defining the term entrepreneur as a speculator, referring to different forms that he can take from landowners, entrepreneurs and paid managers. Marshall [15] considered an entrepreneur as a businessman that brought social earnings far higher than his own revenues. Later, Schumpeter claimed the fact that an entrepreneur is a man of action, with vision, a leader, an innovator, being the engine of the economic system. Jean Baptiste Say was the inventor of the term entrepreneur as the individual who leads resources from a higher productivity area in a less productive area, thus gaining more profits. At the end of twentieth century, Peter Drucker added new elements in entrepreneurial theory, focused on changes, innovation and unexpected success. Since the studies of West and Zimmerman in 1987 that looked at female entrepreneurship efforts and continuing with Ezzedeen and Zikic 2012’s [5] research which analyzed the relationship between subordinate behavior and the entrepreneurial women’s legitimacy, the subject of women’s affiliation to the business world is controversial. The topic of female entrepreneurship was also studied by Lerner and Pines in 2010 and Humbert et al. in 2009, highlighting the fact that in the 1970s, female entrepreneurs were not an element of major concern. With the passage of time, the number of female entrepreneurs began to rise. It is an almost worldwide phenomenon that there are fewer women involved in entrepreneurial activities than men [12]. According to statistics produced by the European Commission in Europe, there are a higher number of women than men, but female entrepreneurs represent only one-third of all European entrepreneurs, and more specifically, Europe’s female population is 52%, but only 34.4% are selfemployed in the European Union and 30% are managing start-ups.

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In some actual economics literature, we found that women entrepreneurs are no longer seen as those who run small businesses [1] as they were perceived around the 1950s. Since the 1970s, the entrepreneur woman has been given increasing attention, more precisely in 1976 when Schwarz published in a business journal a paper dedicated to a woman entrepreneur. Subsequently, the research also involved the organization of conferences dedicated to women entrepreneurs and created an International Journal of Gender and Entrepreneurship with the first issue published in 2009 and with continuity so far. The recording of gender differences in entrepreneurship is no longer a hypothesis, but a certainty verified by relevant studies. According to the 2013 Women Entrepreneurship in the OECD, the probability of having a business is more than 3 times higher for men than for women. According to Eurostat and Policy Brief on Women’s Entrepreneurship Report, in 2015, in the European Union, women outnumbered men by approximately 12 million people. This population gender gap is observed in all EU Member States. At the global level, the attention to gender in entrepreneurship has been analyzed by Global Entrepreneurship Monitor, which published a series of reports dedicated to female entrepreneurs, and in 2015, the Female Entrepreneurship Index was launched to measure the level of development of female entrepreneurship worldwide. In 2016, 163 million women have set up businesses in 74 countries around the world, while 111 million have already established businesses (Global Entrepreneurship Monitor Report 2016/2017). Equality between women and men has become a major objective of the EU, and the importance of gender equality continues to be subject to significant discrimination that prevents equal participation of women and men in the labor market. European studies show that there are similar gender-based differences in terms of entrepreneurship. Female businesses have duration and size smaller than those run by men and focus on the traditional sectors of economic activity. It can be noted significant differences in the development of entrepreneurial activities by women and men, differentiations identified in social roles played predominantly by the two genders, which roles offer different possibilities for development and access to knowledge and therefore different economic visions. Generally, women focus on creating small businesses with a small number of employees and profits, while men are targeting to large, higher-performing businesses. Research on the activity entrepreneurship concludes that the presence of women involved in entrepreneurial activities, either as self-employed or as self-employed is much lower than that of men. Given that women continue to be burdened with the majority of family obligations, have limited time for their employment professional and thus frequently opt for part-time or self-employment at home.

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9.2.2 Woman Entrepreneurship in Romania At European Union level, it was found that the potential of female entrepreneurship as a source of earnings for Romania was not exploited, considering possible strategies or public policies at the local or regional level to support women who want to start their own business. According to the Gender Development Index [6], which refers to gender equality in the world, following the analysis of 189 countries, Romania occupies position 52. It is worth mentioning that this was done by measuring health, education and economic resources, differentiated on women and men. The Human Development Index is 0.804 and the male 0.817, which reflects a GDI for Romania of 0.985, which places our country in Group 1. Human Development Reports also introduced another indicator called Gender Inequality Index that includes data on reproductive health, empowerment and economic activity. For 2017, Romania has a Gender Inequality Index of 0.311, ranking our country 68th out of 189 analyzed countries. All this data shows that our country manifests a high degree of inequality between the two genres. In this context, we want to analyze whether these discrepancies also occur at the entrepreneurship level and whether differences are maintained between women-led and male-led businesses. National statistical data shows that there is still no gender equality in the labor market, with disparities in favor of men being maintained in Romania. According to Mastercard Index of Women Entrepreneurs (MIWE) 2018, about 163 million of women from 74 countries managed a new business, this means that gender differences will be reduced in different fields. Although much progress is being made at least at the EU level to increase entrepreneurship rates among women, there are still disparities, which are higher or lower in almost all markets. From a total of 17 countries analyzed in Europe, Romania is among those with an income level between low and upper middle, and from the stage of development perspective, we seem to be innovative and efficient. According to Benchmark: Women Business Owners (as % of total business owners), Romania ranks immediately after Ghana, Russia, Uganda, New Zealand, Australia, Vietnam, Poland and Spain, with a significant representation of women in managing their own business. Women’s entrepreneurial spirit and implicitly the engagement of women in economic life can be a real solution to the disparity existing in Romania in the labor market. However, they are often perceived as a minority in a men’s world. Although in the central government structures, the share of women in relation to men is lower, we can not speak of fierce competition between genders from the entrepreneurial perspective. The demographic decline has become more and more acute, confronting us with a real process of population aging and depopulation. According to Government Emergency Ordinance No. 61/2008 on the implementation of the principle of equal treatment between men and women as regards access

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to goods and services and the provision of goods and services is established the framework for combating discrimination based on gender (Published in the Romanian Official Gazette, Part I, No. 385 of May 21, 2008, approved with amendments by Law 62/2009). Although it exist gender differences, yet there can be no exaggerated competition between women and men in the entrepreneurial sphere, but the rise of women into business is based mainly for immediate practical reasons: The economic need to maintain a family alone or to contribute to the family budget. The main restrictive factor for the development of entrepreneurship by women is the obligations of a family that limits the possibilities for ongoing research, retraining, training and, in general, activities which can help to develop and improve the business.

9.3 Methodology This paper aims to analyze equal opportunities in entrepreneurship. It involves an empirical study and attempts to answer the following question: Is equality of opportunity between women and men supported by entrepreneurial activity dedicated to women? The answer to this question is addressed by analyzing the secondary data gathered from the statistical records of the National Trade Register Office, the National Institute of Statistics of Romania and Mastercard Index of Women Entrepreneurs to compare and analyze gender inequalities in entrepreneurship. The indicators have been chosen so that they can range from general to particular, from the national situation to that specific situation of the Western Region, namely the four counties: Arad, Caras-Severin, Hunedoara and Timis. Analysis of the current situation in the field of female entrepreneurship in Romania and especially in the Western Region was based on the data of the National Institute of Statistics. The number of indicators characterizing the gender aspect in the development of entrepreneurship is limited to: population by participation in economic activity by gender, activity rate, employment rate and unemployment rate calculated for working-age population, employment rates, by educational attainment and gender, distribution by gender of associates/shareholders of active legal entities and gender distribution of self-employed persons/individual enterprises/family businesses.

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9.4 Results and Discussion 9.4.1 Woman Population Situation Analysis in Romania In order to better characterize the situation of female entrepreneurship in the West Region of Romania, we consider an analysis of the labor force indicators as a whole, both at the national and regional levels. In this respect, in Romania, during the 2012–2017 period, we identified the evolution of the main indicators of the labor force, namely the active and employment population and the unemployed situation according to Table 9.1, the data being processed according to Romania’s Statistical Yearbook 2018. According to the data from Table 9.1, it can be noticed that the number of active population in Romania in 2017 registered a slight decrease from 9232 thousand persons in 2012 to 9120 thousand persons. Oscillations were also registered in the case of the employed population and the number of unemployed. Regarding the evolution by gender, the active male population registered higher values over the female population over the whole period. In 2017 compared to 2012, the active female population registered a decrease, from 4051 thousand persons to 3936 people, and the employed female population also decreased by 3805 thousand persons in 2012 to 3777 thousand persons in 2017. From the perspective of the employment rate, in 2017 compared to 2012, it increased from 60.2 to 63.9, while in the same interval, there was also a drop in the number of unemployed by 1.9%, according to Table 9.2. As can be seen, the employment rate of the male population in 2017 compared to 2012 has increased from 67.6 to 71.8, an increase that is noticeable also for female employed populations, the employment rate of which increases from 52.8 in 2012 to 55.8 in 2017, with an increase of 3%. For 2017, it is noted that men have an employment rate higher than that of women by 16 percentage points. Also, the employment rate in Romania was 63.9% compared to 67.7% in the European Union, and the female employment rate in Romania was 55.8 compared to 62.4 EU average. The employment rate in Romania according to the level of professional training by gender in the period 2016–2017 is presented in Table 9.3. It can be seen that the highest rate of the employed population corresponds to the tertiary studies with a tendency to increase both for male and female, for all levels of vocational training. Although the share of the female population employed is lower than that of the male population, in the analyzed years the increase in the share of the female population with higher education was 2.8%, compared to the male one of 0.6%. Also, the male population rate is predominantly higher than that of the female population for all levels of education analyzed. Both demographic and labor market opportunities are significant elements that should be taken into account not only at the territorial level but also at the country level when deciding on economic planning. Women have a significant share in the total number of people practicing their occupation in their own unit.

9202

9243

9159

8979

9120

2014

2015

2016

2017

5184

5145

5243

5228

5191

5181

Source Romanian Statistical Yearbook 2018

9232

8449 8671

3936

8535

8614

8549

8605

3834

3916

4015

4011

4051

Total

2013

Employment Female

Total

Male

Active population

2012

Year

4894

4806

4848

4844

4791

4800

Male

Table 9.1 Population by participation in economic activity by gender (thousands persons)

3777

3643

3687

3770

3758

3805

Female

449

530

624

629

653

627

Total

Unemployed

290

339

395

384

400

381

Male

159

191

229

245

253

246

Female

9 Equal Opportunities in Entrepreneurship in Romania’s West Region 117

64.9

65.7

66.1

65.6

67.3

2014

2015

2016

2017

76.2

74.8

75.3

74.3

73.4

73.2

Source Romanian Statistical Yearbook 2018

64.8

2013

61.6 63.9

58.2

61.4

61.0

60.1

60.2

56.2

56.7

56.9

56.3

56.4

Total

71.8

69.7

69.5

68.7

67.6

67.6

Male

Employment rate Female

Total

Male

Activity rate

2012

Year

55.8

53.3

53.2

53.3

52.6

52.8

Female

4.9

5.9

6.8

6.8

7.1

6.8

Total

5.6

6.6

7.5

7.3

7.7

7.4

Male

Unemployment rate

Table 9.2 Activity rate, employment rate and unemployment rate calculated for working-age population (15–64 years) (%)

4.0

5.0

5.8

6.1

6.3

6.1

Female

118 M. D. Maticiuc et al.

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Table 9.3 Employment rates, by educational attainment and gender (%) 2016 Employment rate

Total

Total

2017

Educational attainment

Total

Tertiary

Secondary

Primary

61.6

86.2

65.2

41.0

Male

69.7

89.1

72.9

Female

53.3

83.6

56.5

Educational attainment Tertiary

Secondary

Primary

63.9

87.9

67.5

42.5

52.1

71.8

89.7

75.2

53.8

31.1

55.8

86.4

58.9

32.1

Source Romanian Statistical Yearbook 2018

Table 9.4 Employment structure, by status in employment and by gender in 2016 and 2017 (%) Year 2016

2017

Status in employment

Total

Male

Female

Employee

73.4

72.2

75

Employer

1

1.3

0.6

Self-employed and member of an agricultural holding or of a co-operative

17.1

21.7

11.1

Contributing family worker

8.5

4.8

13.3

Employee

73.7

72.3

75.5

Employer

1

1.4

0.6

Self-employed and member of an agricultural holding or of a co-operative

17.1

21.6

11.2

Contributing family worker

8.2

4.7

12.7

Source Romanian Statistical Yearbook 2018

The fact that women participate in the labor market is a contribution not only to increasing family income but also to reducing poverty in a certain area, bringing benefits to women in society. Closely related to entrepreneurship is found the structure of employment by professional status presented in Table 9.4. With regard to employees, a decrease of 0.3% is observed. Growth was recorded in the contributing family worker from 8.2 to 8.5. Significant differences are found between women and men in the case of self-employed, with the difference being 10.4% in 2017 and 10.6 in 2016, just in the most important categories from the perspective of entrepreneurship.

9.4.2 Woman Population Situation Analysis in West Region Starting from the situation presented above for the case of Romania, the analysis of the female entrepreneurial environment is to be done at the level of the Western Region, namely Timis, Caras-Severin, Arad and Hunedoara counties, for a more comprehensive picture of the theme. From the centralization of the data available, we

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have prepared a statement of the distribution by gender of the associates/shareholders of the legal entities active on 30.06.2019, and the distribution by gender of the selfemployed persons/individual enterprises/family businesses active on the same date highlights the gender participation of entrepreneurs in the West Region business environment. We followed the data in the evolution of the last 4 years, to see if there were significant changes regarding the “gender component” of the business environment in the West Region (or if the results were relatively constant). We also watched the differences between the counties of the region on the number of entrepreneurs (which is the hierarchy and who is best placed). In 2019, ranking this distribution according to the number of women, at the level of the 41 counties and the capital, Cara¸s-Severin County occupies position 10, Hunedoara 24, Arad 28 and Timi¸s, position 40. We mention that the lowest number of women in this hierarchy is found in Covasna County, namely 3452 (this county is allocated its position 1), Cluj 41st place with 28,304 and Bucharest position 42 with 116,836 women. At the level of the Western Region, of the number of entrepreneurs surveyed, the share represented by women oscillated around 37%, registering an increase from 36.62 in June 2016 to 37.44 in June 2019. At the Hunedoara County level, it finds higher participation of female entrepreneurs in the business environment compared to the other counties analyzed, reaching 40.91% in the year 2019 (Tables 9.5 and 9.6). Similarly, the gender distribution of associates/shareholders of active legal entities, when analyzing gender distribution of self-employed persons/individual enterprises/family businesses, shows that no significant changes were reported from one year to another at the level of each county. For Arad County, the percentage of female self-employed persons/individual enterprises/family businesses increased from 41.46 in 2016 to 41.83 in 2019. In Cara¸s Severin, self-employed increased from 37.17 to 38 55%. Compared to the other counties in Hunedoara, women’s involvement was 39.26% in 2016, increasing to 39.36% in 2019. In Timis County, as in other counties, female participation as a share in the total number of entrepreneurs analyzed is 36.55% on average, rising from 35.89 to 37.29% in the four years of analysis. Regarding the female participation as a share in the total number of selfemployed persons/individual enterprises/family businesses and the total number of associates/shareholders active in legal entities, for all four counties of the West Region, a balance of data is noticeable, of women in the business environment is relatively similar throughout the analyzed region.

9.5 Conclusions In the last decade, there have been many research studies exploring female entrepreneurship, most of them being focused on concluding the findings to make the

2019

2018

41,183

82,962

895,641

Timi¸s

Total West

Total România

20,039

15,010

Arad

8264

Total România

Hunedoara

79,002

848,128

Total West

Cara¸s-Severin

39,024

Timi¸s

18,505

14,663

Arad

7974

Total România

Hunedoara

74,675

796,576

Total West

Cara¸s-Severin

36,934

Timi¸s

17,341

13,958

Hunedoara

Arad

7604

Cara¸s-Severin

2017

16,179

Arad

2016

No. active legal entities

County

Date

29,768

1,326,005

123,762

61,451

21,700

12,537

28,074

1,273,091

119,724

59,348

21,378

12,268

26,730

1,209,590

114,620

56,946

20,522

11,959

25,193

No. associates/shareholders as individuals

Table 9.5 Distribution by gender of associates/shareholders of active legal entities

10,963

492,840

45,891

22,213

8841

4612

10,225

470,536

44,110

21,246

8659

4527

9678

442,927

41,857

20,167

8244

4415

36.83

37.17

37.08004

36.15

40.74

36.79

36.42

36.96

36.84

35.8

40.5

36.9

36.21

36.62

36.52

35.41

40.17

36.92

18,805

833,165

77,871

39,238

12,859

7925

17,849

802,555

75,614

38,102

12,719

7741

17,052

766,663

72,763

36,779

12,278

7544

16,162

Nr.

% 35.85

Nr. 9031

Men

Women

Associates/shareholders distribution by gender %

(continued)

63.17

62.83

62.91996

63.85

59.26

63.21

63.58

63.04

63.16

64.2

59.5

63.1

63.79

63.38

63.48

64.59

59.83

63.08

64.15

9 Equal Opportunities in Entrepreneurship in Romania’s West Region 121

No. active legal entities

8937

16,229

44,436

89,641

964,747

County

Cara¸s-Severin

Hunedoara

Timi¸s

Total West

Total România

49,138 526,007

1,403,664

23,857

9439

37.47

37.44

36.58

40.91

877,657

82,096

41,361

13,633

8297

Nr.

% 37.03

Nr. 4879

Men

Women

Associates/shareholders distribution by gender

131,234

65,218

23,072

13,176

No. associates/shareholders as individuals

Source https://www.onrc.ro/index.php/ro/statistici?id=246

Date

Table 9.5 (continued)

%

62.53

62.56

63.42

59.09

62.97

122 M. D. Maticiuc et al.

2019

2018

13,132

38,936

393,761

Timi¸s

Total West

Total România

10,595

8787

Arad

5933

Total România

Hunedoara

37,661

389,207

Total West

Cara¸s-Severin

12,517

Timi¸s

11,084

8772

Arad

5589

Total România

Hunedoara

36,580

385,710

Total West

Cara¸s-Severin

12,140

Timi¸s

10,783

8510

Hunedoara

Arad

5448

Cara¸s-Severin

2017

10,482

Arad

2016

No. active self-employed persons/individual enterprises/family businesses

County

Date

11,699

426,224

42,065

14,074

9411

6458

12,122

420,044

40,499

13,463

9305

5948

11,783

416,754

39,324

13,049

9012

5803

11,460

No. holders/members self-employed person/individual enterprise/family business

4894

167,854

16,352

5178

3695

2440

5039

164,855

15,636

4890

3655

2195

4896

164,130

15,129

4683

3538

2157

4751

41.83

39.38

38.87

36.79

39.26

37.78

41.57

39.25

38.61

36.32

39.28

36.9

41.55

39.38

38.47

35.89

39.26

37.17

41.46

6805

58.17

60.62

61.13

63.21

60.74

62.22

58.43

60.75

61.39

63.68

60.72

63.1

58.45

60.62

61.53

64.11

60.74

62.83

58.54

%

(continued)

258,370

25,713

8896

5716

4018

7083

255,189

24,863

8573

5650

3753

6887

252,624

24,195

8366

5474

3646

6709

Nr.

Nr.

%

Men

Women

Holders/members distribution

Table 9.6 Gender distribution of self-employed persons/individual enterprises/family businesses

9 Equal Opportunities in Entrepreneurship in Romania’s West Region 123

No. active self-employed persons/individual enterprises/family businesses

5244

8246

12,959

37,044

374,779

County

Cara¸s-Severin

Hunedoara

Timi¸s

Total West

Total România

Source https://www.onrc.ro/index.php/ro/statistici?id=247

Date

Table 9.6 (continued)

15,821 162,398

409,669

5187

3507

2233

39.64

39.25

37.29

39.36

38.55

247,271

24,491

8723

5403

3560

Nr.

Nr.

%

Men

Women

Holders/members distribution

40,312

13,910

8910

5793

No. holders/members self-employed person/individual enterprise/family business

60.36

60.75

62.71

60.64

61.45

%

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characteristics known its specificities and the factors that affect it. Female and male entrepreneurship varies considerably, in terms of their characteristics (incentives, type and size of businesses, etc.). Self-employment is one of the measures used in economic analysis to proxy entrepreneurial activity. One explanation for the gap in self-employment rates between men and women in Romania and Western Region, as well as differences in business characteristics, has been that women have different motivations for going into self-employment. Women’s incentives to initiate entrepreneurial activity are a factor additional differentiation compared to men. It would be useful to deepen our studies on incentives. We found that for the most part of female entrepreneurs, “need” was the main motivation (“need-to-do” entrepreneurship) because the establishment of an enterprise is the alternative solution, preferred by most women looking for a job. Until recently, in Romania, entrepreneurship has not been explored from the gender perspective. Usually, no attention was paid to how the factor “Gender” is associated with difficulty or as a facilitator in addressing market challenges. Although we have identified gender differences in entrepreneurship in Romania, gender tends to have less and less importance in entrepreneurship in Romania and in the Western Region. In Romania, the entrepreneurial activity of women is slightly increasing, according to the latest Global Entrepreneurship Monitor (GEM) 2016/2017 Women’s Report. This is a positive aspect that needs to be supported by governmental and local programs to stimulate women’s involvement in entrepreneurship. Self-employment is an important way to increase women’s participation in the labor market, and public policies that promote and support women’s entrepreneurship are very important. More attention is needed to influence the environment and context to remove barriers to women entrepreneurship at the source. It is necessary to create a government department responsible for supporting women entrepreneurship. Also, it is necessary to create business centers for women in each county, organizing workshops, meetings with successful women entrepreneurs, creating easy access to relevant information for entrepreneurial women and useful to improve the conditions of business development for them. Because the percentage of women involved in entrepreneurship in Romania and Western Region is much lower than that of men, maybe are necessary impact studies on the role of women in the development of local and national entrepreneurship. In Romania, female businesses have a longer duration and size than men, and women focus on traditional areas of economic activity. In this context, creating an online platform for sharing success stories, learning from the challenges faced by other successful entrepreneurs and initiating mentoring programs with mentors at national and even international level are useful. To stimulate women’s involvement in entrepreneurship, we recommend creating an establishment for women entrepreneurship organizations in each large city from the Western Region to transfer knowledge about entrepreneurship, to provide best practices, useful tools for starting and managing a business, also counseling and

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mentoring. Collaborating with these organizations, building partnerships with similar organizations from Romania and other countries, and joining international networks that support women’s entrepreneurship, is useful for entrepreneurship development.

Bibliography 1. Baker, T., Aldrich, H.E., Liou, N.: Invisible entrepreneurs: the neglect of women business owners by mass media and scholarly journals in the USA. Entrepreneurship Reg. Dev. 9, 221–238 (1997) 2. Cantillon, R.: An essay on economic theory [1755]. Ludwig von Mises Institute, Auburn, Alabama. ISBN 978-0-415-07577–0 (2010) 3. De Vita, L., Mari, M., Poggesi, S.: Women entrepreneurs in and from developing countries: evidences from the literature. Eur. Manag. J. 32(3), 451–460 (2014) 4. Drucker, P.: Inova¸tia s¸i sistemul antreprenorial. Editura Enciclopedic˘a, Bucure¸sti (1993) 5. Ezzedeen, S.R., Zikic, J.: Entrepreneurial experiences of women in Canadian high technology. Int. J. Gender Entrepreneurship 4(1), 44–64 (2012) 6. Gender Development Index (2017, 2018, 2019). https://hdr.undp.org/en/composite/GDI 7. Global Entrepreneurship Monitor (2017–2018). https://www.gemconsortium.org/report/gem2017-2018-global-report 8. Government Emergency Ordinance no. 61/2008. https://www.mmuncii.ro/pub/imagemanager/ images/file/Legislatie/ORDONANTE-DE-GUVERN/OUG61-2008.pdf 9. Human Development Report (2018). https://hdr.undp.org/sites/default/files/2018_human_ development_statistical_update.pdf 10. Humbert, A.L., Drew, E., Kelan, E.: Gender identity and ICT entrepreneurship in an Irish context. In: Pines, A.M., Ozbilgin, M.F. (eds.) Handbook of Research on High Technology Entrepreneurs. Edward Elgar, Cheltenham (2009) 11. International Journal of Gender and Entrepreneurship. https://www.emerald.com/insight/ publication/issn/1756-6266 12. Kelley, D.J., Brush, C.G., Greene, P.G., Litovsky Y.: Global Entrepreneurship Monitor: 2012 Women’s Report. Technical Report. Global Entrepreneurship Research Association (2013) 13. Kirkwood, J., Tootell, B.: Is entrepreneurship the answer to achieving work–family balance? J. Manag. Organ. 14(3), 285–302 (2008) 14. Lerner, M., Pines, A.M.: Gender and culture in family business: a ten-nation study. Int. J. Cross Cult. Manag. (in press) (2010) 15. Marshall, A.: Principles of Economics. Macmillan, London (1890) 16. Marshall, A.: Elements of Economics of Industry. Macmillan and Co., London (1903) 17. Mastercard Index of Women Entrepreneurs (MIWE) 2018, Benchmark: Women Business Owners. https://newsroom.mastercard.com/eu/files/2018/03/MIWE-2018Report.compressed. pdf 18. Policy Brief on Women’s Entrepreneurship. https://www.oecd.org/cfe/smes/Policy-Brief-onWomen-s-Entrepreneurship.pdf 19. Powell, G., Eddleston, K.A.: Linking family-to-business enrichment and support to entrepreneurial success: do female and male entrepreneurs experience different outcomes? J. Bus. Ventur. 28(2), 261–280 (2013) 20. Say Jean Baptiste, A Treatise on Political Economy, 4th edn (translated by Prinsep C.R. Grigg & Elliot, Philadelphia), pp. 99–100, 127, 330–332 (1845) 21. Schumpeter, J.: Capitalism, Socialism and Democracy. Routledge, London (1996) 22. Schwartz, E.: Entrepreneurship: a new female frontier. J. Contemp. Bus. 5, 47–76 (1976) 23. West, C., Zimmerman, D.H.: Doing gender. Gend. Soc. 1(2), 125–151 (1987)

Chapter 10

Ups and Downs of High-Growth Firms in Russia Dmitri Pletnev and Victor Barkhatov

Abstract The fate of a high-growth company in Russia often turns out to be dramatic. Yesterday’s leaders slow down, introducing an imbalance in the economy and reducing its potential, sometimes go bankrupt. The study aims to identify economic, institutional, and administrative factors that cause rapid growth or consequent fall of fast-growing firms in Russia. The article based on the results of the analysis of 104 high-growth Russian companies that included in the national rating. This paper is used empirical data from different sources and also regression models. This research is limited in time (2010–2017) and space (Russian economy). Also, limitation of the research is data source—not all Russian private high-growth firms are transparent and accessible disclose information about its success story. The main findings are the importance of industry affiliation of company, entrepreneurial nature of the firm, and lack of influence of administrative power on long-term business success. Keywords high-growth firms · gazelles · business success · Russian economy

10.1 Introduction Studies of entrepreneurial activity as a driver of changes in the economy often include the question of who and why ensures the rapid growth of individual industries and countries. Today, a particular type of firms is being singled out, which initiate technological and organizational changes, carry out “creative destruction” in the economy, and create millions of new workplaces. These are fast-growing firms or gazelles. For any national economy in which the market used as the primary way of coordinating the actions of economic agents, such firms are significant participants in economic relations. The study of their origin, development, and the transition to the next qualitatively new phases are relevant topics for research. Of particular interest is D. Pletnev (B) · V. Barkhatov Chelyabinsk State University, Br. Kashirinykh str., 454001 Chelyabinsk, Russia e-mail: [email protected] V. Barkhatov e-mail: [email protected] © Springer Nature Switzerland AG 2020 G. Prostean et al. (eds.), Innovation in Sustainable Management and Entrepreneurship, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-44711-3_10

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the study of the fate of these companies to understand their full role in the economy: Are such firms mere troublemakers and provocateurs of technological, institutional, and economic changes, and after a few years, their activity is fading away? Or, they are independently ready to lead new trends in the economy and extract maximum benefits from their leading position in new markets? The purpose of the article is to investigate the fate (in 2017) of Russian gazelles, recognized as such by the results of activities in 2010–2015, to identify the key factors that led to the success or failure of gazelles.

10.2 Literature Review Starting from the classical work of Birch [6] and his later works [7, 8], the study of fast-growing firms has often been the focus of many scientists. Weinzimmer et al. [44] and Delmar et al. [22] studied the influence of the size factor of firms on the possibility of their rapid growth. Delmar et al. [22] and Haltiwanger et al. [25] examined in detail the influence of the age of firms. Delmar et al. [22] also identified the organizational form as a factor. Bjuggren et al. [9] studied the phenomenon of fast-growing family firms. Brüderl and Preisendörfer [15] analyzed such characteristics as age, size, industry, geographic location, and legal form. The behavior and characteristics of fast-growing firms, as well as empirical research on their number and impact on workplaces creation, were presented in papers by Brown and Mawson [14], Lawless [28], Anyadike-Danes et al. [3], Bravo-Biosca [12] and Salas et al. [43]. Several interesting works are devoted to industry research of fast-growing companies [1, 10, 19, 31]. In [13, 20, 24, 32] revealed country differences in fast-growing firms. The study of the behavior of gazelles in the Russian economy is presented in the works [4, 38, 45, 46]. Acs et al. [2] believe that fast-growing firms are smaller and “younger” than the rest. They also show that there are fast-growing firms in all industries, occupying a larger or smaller share, and in the high-tech sector, their share is highest. Mason and Brown [30], by contrast, argue that fast-growing firms not concentrated in any one sector. Moreno and Coad [34] summarized the previously described characteristics of gazelle firms, describing them in the following way: Fast-growing firms are younger than other similar firms, they are not necessarily small businesses, but they can be of any size, such firms appear in all sectors of the economy. Daunfeldt and Halvarsson [18] summarized empirical studies on the correlation of growth rates among different firms. Bottazzi et al. [11] and Oliveira and Fortunato [36] found a negative autocorrelation of growth rates, as well as a negative relationship between growth and the size of fast-growing firms. A group of research aimed at studying the process of creating jobs by fast-growing firms. This is mainly about the limited ability of fast-growing firms to create jobs [21, 22, 29]. Henrekson and Johansson [26] found that only 4% of small firms create more than half of the workplaces.

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Nichter and Goldmark [35] recognize that rapid growth is a complex and sophisticated phenomenon that carries not only benefits but also dangers for the company. They believe that such studies should be conducted separately for different countries and different markets. Bibu and Sala [5] also point to the complexity that accompanies the rapid growth of firms and believe that the entrepreneur must be a manager and the manager a leader. Mohr et al. [33] believe that the firm should strive for rapid growth, but this can squeeze all resources out of it. In their opinion, all firms that existed for more than five years had a rapid growth at the beginning. At the same time, young firms that did not have a rapid growth at the beginning of the path could not achieve the effect of scale and eventually failed. Coad et al. [17] show that the phenomenon of fast-growing firms lies in the intensive changes. They note that the existing knowledge about the internal characteristics of such firms is insufficient. Brush et al. [16] note that fast-growing firms grow each in their way, without having the same trajectories, growth rates, and profits. Delmar et al. [22] also believe that the industry in which the firm operates determines the growth model and proves that the growth rate of the firm decreases with increasing its age. Coad et al. [17] indicate that only a small number of fast-growing firms create a considerable number of jobs, while the rest have a limited impact on the economy. The same points out and research [15]. de Kok et al. [27], Delmar et al. [22] relied on research on Gibrat’s law, according to which the growth in a firm is proportional to the size and the proportionality factor is random. The results obtained in this paper continue the study presented in an earlier paper [37].

10.3 Methodology and Data For an empirical study of the ups and downs of Russian gazelles, systematized data sources in the Russian segment of the Internet were used, as well as data from the database of the First Independent Rating Agency [23]. The data for the identification of Russian gazelles chosen by a high-growth company rating compiled annually by the leading Russian multimedia holding RBC [42]—the Russian version of the site, https://rbcholding.com/-English-languagepage), with the emphasis placed on the companies included in the rating in the years 2014 [39], 2015 [40], and 2016 [41] (the 2014 rating takes into account the financial results of companies in 2010–2013). Companies included in the rating based on the fact that some criteria met: The annual growth rate of revenue was at least 20%, the minimum revenue in the monitoring start year was 1 billion rubles. This condition significantly limits the sample, throwing out small and medium-sized companies that have achieved high results. The share of the State and its affiliated structures in company capital should not exceed 50%. The company must be at least than half of the domestic-owned (the share of Russians among the ultimate beneficiaries of the company must be at least 50%); The company continues to work as an independent actor. The data obtained from published consolidated financial statements (or statements of the company’s leading division if the consolidated report is not available) were prepared the following IFRS (in some

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cases, in the absence of such data, reports prepared the following US GAAP and RAS standards were used). Appendix 1 presents all the companies mentioned in the 2014–2016 rating, which included in the rating (the top 10 indicated separately). A total of 30 companies participated in the rating in 2014, 50 each in 2015 and 2016. Some companies included in several ratings, so the initial sample represented by 104 companies. On the basis of this information from open sources, factors that could influence the company’s success identified: the status of the owner (his inclusion into the Russian Forbes’ “list of millionaires”, the presence of key stekeholders from VIPs or major companies (regional or all-Russian). All the “gazelles” were typologized, distributed into five classes (see Table 10.1). Using the FIRA PRO database and company profiles on the RosBusinessConsulting Web site (the leading Russian business analytical agency), the company’s revenue trend from 2011 to 2017 was determined, and based on that concluded, what the gazelle fate is. In total, five types of fate were singled out (see Table 10.2). Of the 104 companies in the original sample, only 98 companies were able to obtain the data needed for analysis. Moreover, these companies became the subject of analysis in the article. To conclude the significant influence of the main factors identified, we used the method of testing the materiality of the mean difference, based on the calculation of t-statistics. The rating method was used to compare the success of gazelles in different groups of industries (see Table 10.3), based on which the success rating of business success rating (BSR). This rating looks quite simple and convenient tool for preliminary analysis, with which is possible to formulate hypotheses for further, more detailed research. Table 10.1 Taxonomy of Russian gazelles Gazelle type

Description

“O”—Orthodox

A classic fast-growing company, the main driver of which is entrepreneurial management skills (all firms rated by default were considered orthodox)

“S”—State-affiliated

State-related and using its resources as the main driver

“C”—Corporation-affiliated

Associated with a large corporation and benefiting from major contracts with it

“P”—Connected with VIP

Associated (explicitly or implicitly) with a famous person

“R”— “Reloaded,” restructured, reconsolidated enterprise from old one or many

An enterprise that exists a long time ago and “suddenly” wakes up after many years of hibernation, or it turns out as a result of the unification of many, operating earlier

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Table 10.2 Fate types of Russian gazelles Fate type

Description

CG—Continued growth

The growth rate of the company’s revenue in 2015–2017 is not less than what is needed for the company to be considered a “gazelle” (20% per year)

SG—Slowing growth

The company maintains a positive growth rate in 2015–2017, but growth is slow

RA—Reduced activity

The company’s revenue in 2015–2017 is declining, but normal operations are underway

MA—Merged or acquired

The company in 2015–2017 is absorbed or merged with another, larger

FF—Fully failed

The company crashed, went bankrupt, or stopped working

Table 10.3 Ratings for fate types of Russian gazelles

Fate type

Rating

CG—Continued growth

5

SG—Slowing growth

3

RA—Reduced activity

2

MA—Merged or acquired

1

FF—Fully failed

0

10.4 Results In general, the sample of gazelles turned out to be highly differentiated, it included eleven companies representing the field of agriculture and food (including poultry farms, companies for the production of oil), seven companies associated with the extraction of natural resources and close to the service companies’ wells, nine companies working in the field of development, six companies from the industries, all with the WE and the Internet (including the well-known Yandex and Mail.ru), only three companies from the field of mechanical engineering (including transport engineering), oil refining represented by four companies, wholesale trade—by five companies, Infrastructure and Construction—six companies, and energy (both production and sales)—by four firms. Companies operating in the retail sector had the most significant representation (twenty-three firms). The remaining fourteen companies assigned to the “miscellaneous” category (including real estate rental, railway transport repair, car tuning, tailoring, non-ferrous metallurgy, jewelry). In general, the distribution of companies according to their “fate” was as follows. About a third of all companies (34) that recognized as gazelles in 2014–2016 (according to data with a shift of 1 year ago) continued to grow in the following (up to 2017). Twentynine companies have slowed growth. Almost a quarter of yesterday’s “champions of growth” have reduced the volume of their activities (twenty-three companies), and

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Table 10.4 Distribution of shares of different types of gazelles by achieved results Fate type

Gazelle type O

C

P

S

R

All

CG (%)

47.6

19.4

41.7

33.3

14.3

34.7

SG (%)

31.0

38.7

16.7

33.3

0.0

29.6

RA (%)

14.3

32.3

33.3

16.7

28.6

23.5

MA (%)

2.4

3.2

8.3

0.0

14.3

4.1

FF (%)

4.8

6.5

0.0

16.7

42.9

BSR

3.62

2.81

3.33

3.00

1.43

8.2 3.13

12 firms ceased to exist in the market (four of them were absorbed, and eight went bankrupt or stopped working). Comparison of the distribution of companies of different types according to the results of their activities has a particular interest. The presence of significant differences in the intragroup distribution and the graph presented above may indicate a significant influence of the type of gazelle (e.g., the presence of a powerful patron) on its success in the future. To obtain comparable data in further analysis, not absolute values of the number of companies are used, but their shares in the total number of firms in the group. The distribution of companies by types and results of their activities is presented in Table 10.4. A success rating also calculated for each type of gazelle. It may note that among the orthodox gazelles, the share of those who continue is to grow by almost 13%. Points are higher than the average for the sample, and the share of relatively unsuccessful ones (RA, MA, and FF) is less by 14.4 percentage points, which may indicate higher efficiency of entrepreneurial type of firms in managing rapid growth. The success of the restructured company (R) is significantly lower than all others. Other factors of success for gazelles were also analyzed: the presence of Forbes magazine among the owners of the Russian list of millionaires, the presence of the company among the top 10 in any of the analyzed lists, the company’s work in the regional or metropolitan/all-Russian markets, and the last year of getting into the list (2014, 2015, or 2016). The distribution of companies corresponding to this distribution of t-statistics values is presented in Tables 10.5 and 10.6, respectively. The check was carried out according to the principle of dividing the entire sample into two parts—in which the corresponding criterion fulfilled, and in which it is not. Values of business success rating are presented in the bottom line of Table 10.6. The presence of a rich daddy in the person of the owner, included in the Russian list of Forbes millionaires (the list of 2018 used, in which there were persons with personal status of 500 million dollars and more), was not for fast-growing companies, the factor that increases their survival and success, and the distribution of the result of 23 “Forbes” companies by groups is almost the same as for the whole set, as well as BSR. Analysis of the results for the last year of inclusion in the rating showed that there were no statistically significant differences in the results of companies that were

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Table 10.5 Distribution and BSR of gazelles in different groups Fate type

Forbes-person owned

The last year of fast growth 2013

2014

2015

Included in top 10

Regional companies

CG

9

5

14

15

8

11

SG

7

1

13

15

8

10

RA

5

5

8

10

5

11

MA

0

0

1

3

1

3

FF

2

3

1

4

2

2

Sum

23

14

37

47

24

37

Share (%)

23.5

14.3

37.8

48.0

24.5

37.8

BSR

3.30

2.71

3.41

3.04

3.13

2.97

Table 10.6 t-Statistics to test the hypothesis about the significance of the difference in shares by the specified criteria Fate type

Forbes-person owned

The last year of fast growth

Included in top 10

Regional companies

2013 Rated in 2014

2014 Rated in 2015

2015 Rated in 2016

CG

0.502

0.086

0.506

0.557

0.162

0.818

SG

0.101

3.048

RA

0.228

1.053

0.922

0.483

0.452

0.438

0.340

0.494

0.361

MA

2.056

1.108

2.049

0.576

1.089

0.024

1.355

FF

0.104

1.374

1.800

0.120

0.035

0.832

seen in the rating for the last time in 2014, 2015, or 2016 (such as 23, 14, and 37, respectively) [except the share of companies that have slowed down growth among the rating participants in 2014 (7.1%)]. However, the BSR value differs significantly from year to year: the smallest—in 2104, a little more—in 2016, and the largest—in 2015. The distribution of companies falling into the top 10 ratings (24 firms numbered for three years) practically coincides with the distribution across the entire sample. Companies operating in the regions (37 out of 98) generally performed worse than participants in capital markets (BSR equal to 2.97 vs. 3.23), but this difference is within the statistical error. Analysis of the results of the distribution of companies in different industries according to the results of their activities showed that industry affiliation is a significant factor. The following results deserve the most attention: the share of relatively successful among agribusiness companies and food products, mining companies, and retail trade is significantly higher (CG + SG, 81.9%, 85.8%, 78.3%, respectively). IT and Internet companies live by the principle: all or nothing: 5 out of 6 continue rapid growth, one closes. Wholesale companies are still growing. Oil refining industries became anti-leaders (only one of four continued growth, which slowed down, the

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Fig. 10.1 Business success rating for firms in different industries

rest either reduced sales or completely closed) and infrastructure construction (two companies closed, two reduced activity, one slowed growth, and only one continued rapid growth). BSR values are presented in Fig. 10.1.

10.5 Conclusion It was revealed that only a third of all high-growth companies are entrepreneurial by its nature, the rest affiliated with either large companies, or with the state, or with VIP, and their ups and downs most often explained by a change in the attitude of the parent structure to the company. At the same time, business gazelles show higher efficiency and the ability for further development. Such success factors, which are often associated with the peculiarities of the Russian economy, as “necessary connections,” on the contrary, do not affect the success of gazelles. Also, no significant differences revealed in the fate of the rating leaders (that is, the companies that showed the maximum growth in each of the years studied) and ordinary rating participants. Differences between regional companies and firms operating throughout Russia or in the capital are not found. The size of the time lag between the time when the company included in the rating and 2017 did not affect the distribution according to the type of the company’s fate. Most often, gazelles observed among companies in the agro-industry and food industry, retail, and development. The most successful industries for gazelles are IT and Internet, wholesale and retail trade, and mining. The least successful industries for gazelles are the oil refining, infrastructure, and industrial construction industries. For further research, of particular interest are the analysis of new factors and results, including relations with government, employees, and customers, financial indicators (including the ability to generate profits and sustainability), the degree of involvement of the owner, innovativeness, legal form, and market value.

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Part III

Sustainable Management

Chapter 11

Case Studies of Indoor Air Quality and Sustainability Management Marco Ragazzi, Matei T˘am˘as, il˘a, Larisa Ivascu, and Cristina Elena Rada

Abstract The European Union strategy on air quality management is based on an articulated legislation. In spite of that, some criticalities can emerge in specific cases. The present article focuses on case studies that point out potential criticalities of indoor air quality, in some cases related to outdoor conditions, in other ones depending only on indoor phenomena. For each case, some considerations useful for improving the indoor air quality management are presented. The analyzed cases refer both to the private and to the public sectors, both to health effects and to discomfort conditions. Two specific groups of criticalities have been focused: one concerns particulate matter (zooming on some problems related to different effects that granulometric classes can have on the human health), the other related to the accumulation of CO2 in indoor environments. Solutions for a sustainable management of indoor air quality are discussed case by case. Potentialities of intervention are different depending on the kind of case: theoretical (still at design level) or applied (if the indoor environment of interest already exists). Keywords Indoor air quality · Sustainability · Management · Greenhouse gas · Pollutant

M. Ragazzi University of Trento, via Mesiano 77, Trento, Italy e-mail: [email protected] M. T˘am˘as, il˘a · L. Ivascu (B) Polithenica University of Timisoara, 14 Remus Street, 300191 Timisoara, Romania e-mail: [email protected] M. T˘am˘as, il˘a e-mail: [email protected] L. Ivascu Academy of Romanian Scientists, Splaiul Independentei, 54, Sector 5, 010071 Bucharest, Romania C. E. Rada Insubria University, via G.B. Vico, 46, 21100 Varese, Italy e-mail: [email protected] © Springer Nature Switzerland AG 2020 G. Prostean et al. (eds.), Innovation in Sustainable Management and Entrepreneurship, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-44711-3_11

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11.1 Introduction The European Union (EU) strategy developed in the last decades to face with the problems of air quality is based on an articulated legislation, the 2008/50/EC Directive. The effort made by EU countries is visible and the trend of the monitoring parameters is often favorable. In spite of that, some criticalities can emerge in specific cases both outdoor and indoor, in terms both of emission and of immission of pollutants. Some short review of specific outdoor underestimated criticalities have been recently published [2, 10, 12, 27, 28] pointing out the need of improving the environmental regulation in specific industrial sectors in order to decrease the human health effects. Other recent articles pointed out some criticalities that can be caused by a non-optimized way of release of pollutants into the atmosphere with consequences also on the worker exposure [18, 20, 31]. The present article is complementary to them as based on the analysis of selected case studies of indoor criticalities generated by indoor activities (or human presence) that deserve more attention at regulation and management level. The analysis is not limited to typical health pollutants as the concept of comfort indoor is also involved in order to guarantee high performances in specific activities. Of course, the number of case studies analyzed in the present article is limited, but the aim of the work is to demonstrate that additional effort must be made for a sustainable management of indoor environments.

11.2 Material and Methods The present article analyzes a selection of case studies sought according to the following criteria: 1. Check published literature (e.g., searching in databases as Scopus® ) and unpublished research (known by one author of the article at least) reporting in the last decade case studies referred to potential under-estimation of indoor problems generated by indoor activities. 2. Select case studies where the person in charge of indoor air management (in a company, etc.) could not fully perceive the related problem and that could be caused by a still limited dissemination of the scientific works on the related criticalities. 3. Find or report solutions that the public or private bodies involved could adopt to face with the emerged criticality and to guarantee a good management of indoor air. Solutions could be different in case of existing designed indoor environments.

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11.3 Results and Discussion As a consequence of the adopted methodology, the focus of this article is put on a few selected case studies, analyzed below, all potentially affecting the performances of users and/or workers in the structures/environments where the indoor phenomena can be detected. These cases were considered sufficient to demonstrate that a sustainable management of some indoor environment has not been guaranteed yet in the present regulatory frame. The attention is focused on two kinds of human exposure: • To particulate matter, zooming on different granulometric classes: the finest ones are the most critical for health [4, 17, 26], • To indoor CO2 , this compound is conventionally regarded as a global problem, due to the effects on the climate of Earth; however, if not correctly managed, the inhalation of air with high CO2 concentrations may cause negative effects on the people exposed [3, 22, 30]. 1. Indoor particulate matter exposure (a) Ultrafine particles (UFPs) generation in pizzerias with wood-burning oven [5], ultrafine particles are characterized by a nanoscale size, having a diameter lower than 0.1 µm. (b) Particulate matter (PM) generation and domestic wood combustion [9, 21, 32] indoor exposure comes from the release of particulate in the indoor environment when the off-gas from wood combustion is not correctly conveyed to a chimney that evacuates it; this is the case of old devices. (c) Dust re-suspension during indoor cleaning activities at industrial level [14, 29]; particulate matter is not the only pollutant that can characterize this activity, but the emerged criticality cannot be neglected. 2. Indoor CO2 accumulation (generated not only by human breathing) in indoor environment with unoptimized management of the indoor air [11, 15, 19, 24, 25]: (a) CO2 accumulation in classrooms (b) CO2 accumulation in taxis (c) CO2 accumulation in bedrooms. In the following sections, the case studies are described and discussed in details.

11.3.1 Ultrafine Particles Generation in Pizzerias with Wood-Burning Oven Wood-fired oven is typically preferred by the user of pizzerias. What is not perceived is the related level of exposure to PM (and not only) that some configurations of oven can give, specifically for workers of pizzerias. This topic was analyzed also at

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nanoparticle scale for the first time some years ago, in order to quantify parameters useful to understand the potential risks [5]. Overall, 15 microenvironments were analyzed in an experimental campaign that lasted more than one year. Results demonstrated that surface area and PM1 particle concentrations in pizzerias can be very high. An important role is played by the ventilation conditions. Under normal ventilation conditions, concentrations were found up to two orders of magnitude higher than background levels for number and surface area. PM1 resulted up to 23 times higher. The shape of the face opening of the oven resulted in a key factor in the level of PM exposure toward the indoor environment of pizzerias. Particular attention deserves the hood efficiency too.

11.3.2 Particulate Matter Generation and Domestic Wood Combustion Wood is extensively used for domestic heating in all the Alps. Public administrations see positively this characteristic for the advantages that the use of biomass gives in terms of greenhouse gas emission balance. The population sees that the use of wood favorably for the reduced cost of this fuel. In the last decade, particular attention has been put on the sector in order to improve it at every step to decrease the local impact; indeed, in some mountain areas the contribution of domestic wood combustion to the inhaled dust can be dominant [13, 21]. The problem is not only related to the PM concentrations that can be reached outdoor in areas where wood combustion is performed: a few researches demonstrated that also the indoor release from woodburning devices can be critical. In Fig. 11.1, the accumulation of PM in an apartment

Fig. 11.1 PM in an apartment during wood burning in an open fireplace

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where on open fireplace is used is reported [23]. Values cannot be considered adequate to preserve the health of the occupants.

11.3.3 Dust Re-suspension During Indoor Cleaning Activities at Industrial Level In Fig. 11.2, the results of a real-time measurement of three granulometric classes of PM in a classroom of a university are reported [23]. Looking at the dynamics of PM10 , it is clear that the arrival and departure of the students create a significant resuspension of coarse particles during the hours when teaching is planned (secondary peaks). However, the main peak comes from the cleaning activities that are scheduled during the evening. Luckily, the night period is sufficient to guarantee (in this case) an adequate settling. The exposure of students to anomalous concentrations is limited but: • This result does not come from the adoption of management criteria, thus, other similar situations could face with high concentrations of PM during the teaching period too; • The personnel devoted to cleaning activity could have to face with an asthma attack considering the values reached when present in the room for cleaning activities.

Fig. 11.2 Dynamics of PM classes in a university room during a teaching day

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11.3.4 CO2 Accumulation in Classrooms The CO2 concentration in outdoor environments is often in the range of 300–400 ppm. These values do not affect the comfort and the health of the population. Indeed, looking at proposals for the management of indoor environments where CO2 can accumulate, the German Indoor Air Hygiene Commission and the working group of the Supreme Health Authorities of the Federal States propose the following ranges for CO2 concentrations [1]: • Values “hygienically insignificant” (concentrations lower than 1000 ppm) • Values “hygienically evident” (range 1000–2000 ppm) • Values “hygienically unacceptable” (concentrations higher 2000 ppm). Starting from this proposal, a recent research [22] developed in a university, analyzed the comfort in a few teaching rooms. The performed monitoring pointed out a bad situation when considering some small classrooms. In a case, the monitored room showed a maximum concentration of 3855 ppm. In that case, the number of people present was 44, the corresponding ratio between volume and number of occupants resulted in 4.7 m3 /person. After this peak, the windows were opened by the occupants. It is clear that this solution could be critical during the winter season. A higher concentration was recorded in another room reaching the instrumental end of scale (5000 ppm). In this case, the ratio between volume and number of occupants was 6.8 m3 /person. This peak was recorded after five hours of continuous lessons without window opening. Again, the occupants decided to act manually: when the occupants opened the windows, the concentration dropped to about 1000 ppm. Looking at a university as a “company,” it is clear that an automatic management of indoor air would guarantee high performances of learning.

11.3.5 CO2 Accumulation in Taxis A recent research [6, 16] pointed out a criticality that can characterize the indoor environment of a taxi. The interior air quality was investigated inside 14 taxis during working days in Barcelona, Spain. During the investigation, most taxi drivers decided to drive with windows open, as favored by the season conditions, this aspect kept levels of CO2 low, but exposed them and the users to high levels of traffic-related pollutants. Carbon dioxide concentrations quickly raised to undesirable levels (specifically, higher than 2500 ppm) under closed ventilation conditions; this unoptimized condition could stay high for most of the working day. Consequences could be not only significant in terms of health effects caused by this working environment. An additional criticality comes from the effect that relatively high concentrations of CO2 can have on the level of attention of the drivers: the risk of an accident can change. This problem should favor the development of solutions based on the adoption of (low-cost) sensors as taxi company strategy in order to automatically manage the

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ventilation of the vehicle if CO2 levels raise. The costs of this approach could be balanced by the gain in image that the taxi company could have (more comfort, less accidents).

11.3.6 CO2 Accumulation in Bedrooms In order to seek potential discomfort conditions in domestic indoor environments, a recent work analyzed the case of small bedroom a CO2 accumulation [7]. The idea at the base of this analysis comes from the evolution of some local regulations on residential building criteria that accept the reduction of the volume of the rooms to potentially critical values. A case of bedroom constructed according to the minimal dimensions and with a modern window frame able to limit the dispersion of heat (and consequently of air) toward the outdoor environment was analyzed. Results showed that values hygienically unacceptable (concentrations higher 2000 ppm) can be reached. In practice, there is a need of interdisciplinary approaches when criteria for indoor energy savings are set: the matter is not only energy optimization.

11.3.7 Discussion and Perspectives What emerged in the analysis of the previous case studies allows making the following considerations: 1. Indoor particulate matter exposure (a) The case of ultrafine particles generation in some pizzerias and the case of dust re-suspension during massive indoor cleaning activities demonstrate that using approaches that are beyond the conventional monitoring schemes set by the regulations allows pointing out criticalities of human exposure (specifically of working place exposure) that would remain unperceived if only the conventional/official approach would be applied. (b) The case of particulate matter generation and domestic wood combustion demonstrates that the positive perception of people on the use of this fuel could delay strategies adopted to modernize/substitute ole devices. (c) The case of dust re-suspension during cleaning demonstrates that the sector of industrial cleaning should evolve to a system where methodologies and monitoring could support the improvement of the activities. 2. Indoor CO2 accumulation (a) Intensive teaching as the one that we can have in universities must be performed in adequate rooms. We are in an age of international rankings. In order to integrate the adopted approaches, the parameter indoor CO2 concentration in classrooms should be added when teaching is involved in a

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ranking in order to avoid that only the perception of the students (or other qualitative parameters) be the parameter for evaluating a university. Solutions to this potential phenomenon are viable both adopting manual criteria (as opening the windows cyclically) and exploiting the evolution of the indoor sensors and devices. What is necessary, at least in perspective, is a guideline or a technical norm that should make compulsory the verification of CO2 accumulation in similar contexts (offices included). • A detailed analysis of costs-benefits could quantify the economic damage that a discomfort related to CO2 accumulation in working environment could generate. In this case, discomfort means diseconomy, thus, strategies of improvement, even if very similar to the ones above mentioned, should be easily adopted when compared to the context of teaching environments. The case of taxis is also related to the performances of the taxi driver, thus, the taxi company should promote solutions to guarantee a low concentration of CO2 in the vehicle also to keep high the attention of the driver. • The phenomenon of CO2 accumulation in bedrooms can be related to the authorization of buildings with a reduced volume per room and a limited ventilation of its indoor air. Some municipal building regulations, in Italy, authorize a minimum volume of 20 m3 per room in a context where energy saving requires a high insulation of the building. If the design of the related apartment is not good (in terms of ventilation), the emerged criticality can be a real problem: people affected by this situation could wake up with a headache.

11.4 Conclusions The present article demonstrates that some concepts of indoor air criticalities, wellknown in scientific environments, show a delay in the transfer to the regulatory frame or to the strategic approaches of public/private companies. Reasons for this delay are variable, even if technical solutions are available: in general, the related risks for health or the related diseconomies in terms of performances are not fully perceived. Costs with initiatives, which are not required by the law, could be balanced with improving the company’s image. Acknowledgements This work was supported in part by research grant GNaC2018-ARUT, no. 1359/01.02.2019, financed by Politehnica University of Timisoara.

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References 1. AIR: Umweltbundesamt Gesundheitliche Bewertung von Kohlendioxid in der Innenraumluft. Bundesgesundheitsblatt - Gesundh – Gesundh (2008), pp. 1358–1369 (2008) 2. Alias, C., Benassi, L., Bertazzi, L., Siorilini, S., Volta, M., Gelatti, U.: Environmental exposure and health effects in a highly polluted area of Northern Italy: a narrative review. Environ. Sci. Pollut. Res. 26(5), 4555–4569 (2019) 3. Azuma, K., Yanagi, U., Kagi, N., Osawa, H.: A review of the effects of exposure to carbon dioxide on human health in indoor environment. In: Healthy Buildings Europe 2017 Conference, Lublin, Poland, 2–5 July 2017 (2017) 4. Bell, M.L., Zanobetti, A., Dominici, F.: Evidence on vulnerability and susceptibility to health risks associated with short-term exposure to particulate matter: a systematic review and metaanalysis. Am. J. Epidemiol. 178(6), 865–876 (2013) 5. Buonanno, G., Morawska, L., Stabile, L., Viola, A.: Exposure to particle number, surface area and PM concentrations in pizzerias. Atmos. Environ. 44(32), 3963–3969 (2010) 6. Cioca, L.I., Ivascu, L., Rada, E.C., Torretta, V., Ionescu, G.: The study of sustainable development and technological impact on CO2 reducing conditions: case study of Romania. Sustain. J. 7, 1637–1650. ISSN 2071-1050 (2015) 7. Dalla Valle, L.: Analisi di criticità ambientali mediante monitoraggio non convenzionale di qualità dell’aria [Analysis of environmental criticalities through non conventional monitoring of air quality]. Master of Science thesis, University of Trento (2015) 8. Directive 2008/50/EC of the European Parliament and of the Council, on ambient air quality and cleaner air for Europe. https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri= CELEX:02008L0050-20150918&from=EN 9. Frasca, D., Marcoccia, M., Tofful, L., Simonetti, G., Perrino, C., Canepari, S.: Influence of advanced wood-fired appliances for residential heating on indoor air quality. Chemosphere 211, 62–71 (2018) 10. Gao, J., Kovats, S., Vardoulakis, S., Wilkinson, P., Woodward, A., Li, J., Gu, S., Liu, X., Wu, H., Wang, J., Song, X., Zhai, Y., Zhao, J., Liu, Q.: Public health co-benefits of greenhouse gas emissions reduction: a systematic review. Sci. Total Environ. 627, 388–402 (2018) 11. Goh, C.C., Kamarudin, L.M., Shukri, S., Abdullah, N.S., Zakaria, A.: Monitoring of carbon dioxide (CO2 ) accumulation in vehicle cabin. In: 3rd International Conference on Electronic Design, 7804682, 427–432 (2017) 12. Gudmundsson, G., Finnbjornsdottir, R.G., Johannsson, T., Rafnsson, V.: Air pollution in Iceland and the effects on human health. Review. Laeknabladid 105(10), 443–452 (2019) 13. Hart, J.F., Ward, T.J., Spear, T.M., Rossi, R.J., Holland, N.N., Loushin, B.G.: Evaluating the effectiveness of a commercial portable air purifier in homes with wood burning stoves: a preliminary study. J. Environ. Public Health 2011, 324809 (2011) 14. Hoenig, S., Daniel, S.W.: Industry/university cooperative research activity: particle contamination in the clean room. J. Environ. Sci. 27(2), 48–52 (1984) 15. Markov, D.: Evaluation of indoor air composition time variation in air-tight occupied spaces during night periods. AIP Conf. Proc. 1497, 61–68 (2012) 16. Moreno, T., Pacitto, A., Fernandéz, A., Amato, F., Marco, E., Grimalt, J., Buonanno, G., Querol, X.: Vehicle interior air quality conditions when travelling by taxi. Environ. Res. 172, 529–542 (2019) 17. Nazaroff, W.W.: Indoor particle dynamics. Indoor Air 14(SUPPL. 7), 175–183 (2004) 18. Oh, H.J., Jeong, N.N., Sohn, J.R., Kim, J.: Personal exposure to indoor aerosols as actual concern: perceived indoor and outdoor air quality, and health performances. Build. Environ. 165, 106403 (2019) 19. Persily, A., de Jonge, L.: Carbon dioxide generation rates for building occupants. Indoor Air 27(5), 868–879 (2017) 20. Rada, E.C.: Some considerations on indoor and outdoor impacts of different ways of PM release and odour emission in industrial sectors. In: 22nd International Symposium “Environment and Industry” SIMI 2019, Bucharest, Romania (2019)

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21. Rada, E.C., Ragazzi, M., Malloci, E.: Role of levoglucosan as a tracer of wood combustion in an alpine region. Environ. Technol. 33(9), 989–994 (2012) 22. Ragazzi, M., Albatici, R., Schiavon, M., Navarro, F., Torretta, V.: CO2 measurements for unconventional management of indoor air quality. WIT Trans. Ecol. Environ. 236, 277–286 (2019) 23. Ragazzi, M., Malloci, E., Antolini, D., Rada, E.C., Venturi, M.: LEED indoor air quality. resultati preliminari della ricerca (Preliminary results of the research). Workshop, 20 March 2009, Trento, Italy (2009) 24. Ragazzi, M., Rada, E.C., Zanoni, S., Andreottola, G.: The role of particulate matter in offices for urban air quality management. WIT Trans. Ecol. Environ. 191, 1403–1412 (2014) 25. Ragazzi, M., Rada, E.C., Zanoni, S., Passamani, G., Dalla Valle, L.: Particulate matter and carbon dioxide monitoring in indoor places. Int. J. Sustain. Dev. Plann. 12(6), 1032–1042 (2017) 26. Rohr, A.C., Wyzga, R.E.: Attributing health effects to individual particulate matter constituents. Atmos. Environ. 62, 130–152 (2012) 27. Schiavon, M., Ragazzi, M., Rada, E.C., Magaril, E., Torretta, V.: Towards the sustainable management of air quality and human exposure: exemplary case studies. WIT Trans. Ecol. Environ. 230, 489–500 (2018) 28. Spencer, J., Van Heyst, B.: A review of particulate matter emissions and impacts on human health: a focus on Canadian agricultural and rural emission sources. Can. Biosyst. Eng. 60(1), 69–621 (2019) 29. Trakumas, S., Willeke, K., Grinshpun, S.A., Reponen, T., Mainelis, G., Friedman, W.: Particle emission characteristics of filter-equipped vacuum cleaners. Am. Ind. Hyg. Assoc. J. 62(4), 482–493 (2001) 30. Vehviläinen, T., Lindholm, H., Rintamäki, H., Pääkkönen, R., Hirvonen, A., Niemi, O., Vinha, J.: High indoor CO2 concentrations in an office environment increases the transcutaneous CO2 level and sleepiness during cognitive work. J. Occup. Environ. Hyg. 13(1), 19–29 (2016) 31. Yang, Y., Sun, C., Sun, M.: The effect of moderately increased CO2 concentration on perception of coherent motion. Aviat. Space Environ. Med. 68(3), 187–191 (1997) 32. Zosima, A.T., Ochsenkiihn-Petropoulou, M.T.: Particulate matter emissions from combustion of different types of wood pellet. Fresen. Environ. Bull. 24(1), 146–156 (2015)

Chapter 12

Sustainable Development in Russia—Just a Theory? V. Barkhatov and D. Benz

Abstract Article is devoted to a problem of sustainable development. The problem of sustainable development is relevant almost for all countries of the world. The majority of the countries have the high level of anthropogenic pressure. Russia is not an exception. In Russia, about a half of regions is highly polluted. Economic and ecological components are in contradiction. The main problem of any national economy today is to harmonize economic and ecological efficiency. Methodological tools of the research include correlation, regression and graphical analysis. The authors offer two groups of indicators: One group includes economic indicators of growth, the second—indicators of environmental efficiency. We analyze the correlation between indicators from these groups and construct the nonlinear multiple regression. The purpose of the article is to look for dependence on the economic growth of the region both economic and ecological indicators. The following results were obtained within a quantitative analysis: A regression analysis demonstrates high dependence of the gross regional product growth on labor factor, while fixed capital investment financed by bank credit cannot be assessed as a key factor of the regional growth; a correlation analysis shows moderate level of dependence between industrial growth and increase of emissions to the environment; a graphical analysis demonstrates relation between industrial expansion and ecological effectiveness. The results show that 1% growth in industrial output is accompanied by 0.4% decrease in ecological effectiveness. Keywords Sustainable development · Economic growth · Ecological efficiency · Industrial production

V. Barkhatov · D. Benz (B) Institute of Economy, Business and Administration, Chelyabinsk State University, Kashirinykh Br. Str., 129, Chelyabinsk, Russia e-mail: [email protected] V. Barkhatov e-mail: [email protected] © Springer Nature Switzerland AG 2020 G. Prostean et al. (eds.), Innovation in Sustainable Management and Entrepreneurship, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-44711-3_12

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12.1 Introduction “Sustainable development” is not a new category. It is already settled concept of the development aimed not only on economic parameters, and it is rather, on a certain balance with the environment. This category became widespread for reasons of environmental problems increase in the 1970s. The problems became result of the unlimited market growth of the western countries proved within the concept “Economics”. However, according to the Russian scientists, in particular N. N. Moiseyev, this term is close to the concept “noospheres” which was studied in the works by the academician V. I. Vernadsky [12]. The concept of sustainable development starts to develop actively in an economic and political discourse after a number of the international conferences of ecological orientation and publication a number of documents: “The Limits to Growth” (1972), “Future We Want” (1987), “Sustainable Development Goals” (2015g.). The emphasis was placed on interrelation of economic processes with poverty level, ecological crises. And sustainable development was understood as development, which allows to satisfy the needs of recent people, without depriving future generations of an opportunity to satisfy the requirements [11]. A number of authors from the western academic circles in the 80th and the 90th considered environmental problems in terms of the resources price and consumers readiness to pay for them [8, 9]. Similar approach describes outer effects from ecological damage in categories of environmental pollution, resource depletion and health of the population. The work, based on econometric and graphical tools, is a preparatory stage for the future researches. Further studies are aimed to determine the optimal level of the regional economies growth which would not take precedence over the ecological issues. The authors’ goal is to define sustainable industrial growth scheme with zero or even negative level of the emissions growth.

12.2 Materials and Methods 12.2.1 Features of National Economy Today, in economic development of Russian regions, priorities of economic growth prevail over environmental issues. In recent decades the enhancement of economic growth has constantly been a priotity for Russia. However, often these objectives were achieved without thinking about social and environmental issues. For this reason in December 2016, the Russian President V. Putin said that Russia has to carry out “phased transition to model of sustainable development, and not just to model of sustainable development, but an ecologically sustainable development”. Today, there is such social and economic situation in the country which just dictates the need to consider questions, not only cost efficiency, but also the questions of “social” and “ecological” efficiency. If we take into account figures about the number of the people (7–8 million people) dying every year because of the polluted

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atmosphere and water and the size of economic damage (6–15% of GDP of the country), then we can see the problem depth [13]. The most important is the following: It becomes clear why issues of cost efficiency cannot already be resolved without taking into consideration environmental efficiency. Today, there are a huge number of achievements in science and technology allowing to obtain the forced economic growth in national economies; at the same time, behavior features of the Russians considerably lag behind scientific and technological growth. Therefore, the problem becomes even more relevant when we can see the gap between rates of world technological development and readiness of Russian subjects to accept and apply world achievements so that to reach not only cost efficiency. In spite of the fact that our country acts as the ecological donor of the world, providing it up to 10% of biospheric stability, nevertheless, in Russia, a huge number of the people living in industrial regions are under extremely negative impact from an ecological situation (ITAR-TASS). Today, Russia undertook obligations for transition to sustainable development, which are postulated by the Decree of the Russian President (April 1, 1996, No. 440). The concept of transition of the Russian Federation to sustainable development is focused on quality of the atmosphere, water, territories. Today, nearly a half of urban Russian population lives in conditions of high air pollution [20]. For this reason, more and more attention is paid to questions of environmental protection. In Fig. 12.1, you can see the dynamics of costs for environmental protection for the last 15 years. We can see a positive trend: The volume of costs of environmental protection, let and in the current prices, but nevertheless, increased by 3.8 times. It is about total financing by all subjects of economy. The industrial sector of the country plays not the last role in atmospheric pollution. Therefore, today, the legislator demands to form such industrial technologies which provide updating of real assets not only with a reference point on technical and economic indicators, but also on environmental efficiency. Dynamics of the investments into real capital directed to environmental protection and rational use of natural resources in the Russian Federation is given in Fig. 12.2. In 18 years, this growth in the current prices was 6.8 times. However, the situation becomes not such positive, if we measure costs of environmental protection not in absolute values and compare them with the GDP. Fig. 12.3 demonstrates the reduction of such expenses in the share of GDP. In 15 years, this share was cut almost in a half, and in the last six years, the percent remains fixed at the level of 0.7%.

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Fig. 12.1 Costs on Environmental Protection (in the current prices; million rubles). Source According to Rosstat. Available at: https://www.gks.ru/wps/wcm/connect/rosstat_main/rosstat/ru/ statistics/publications/catalog/doc_1138623506156

12.2.2 Features of the Regional Economy: Case of the Ural Federal District There are eight federal districts in Russia. In the empirical research, the authors analyze the Ural Federal District. It is the second most polluted district in the rating of the pollution level from stationary sources (see Fig. 12.4). Having conducted many researches about the industrial region, the authors can establish a number of facts. An industrial sector is the main source of pollution. 80% of Russian regions are industrial. The average share of industrial production in GDP of Russia makes about 31–32%. For the Ural Federal District, the industry share is at the level of 53.6%; for Chelyabinsk region, it is near 41.4%. This indicator decreased a little for the last decade (approximately from 35.1 to 31.9% on average in Russian regions, from 56.7 to 53.6% in the Ural Federal District, from 45.6 to 41.4% in Chelyabinsk region). Nevertheless, the contribution of industrial production remains

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Fig. 12.2 Investments into real capital directed to environmental protection and rational use of natural resources in Russia (in the current prices; million rubles). Source According to Rosstat. Available at: https://www.gks.ru/wps/wcm/connect/rosstat_main/rosstat/ru/statistics/publications/ catalog/doc_1138623506156

Fig. 12.3 Costs of environmental protection, percentages to GDP. Source According to Rosstat. Available at: https://www.gks.ru/wps/wcm/connect/rosstat_main/rosstat/ru/statistics/publications/ catalog/doc_1138623506156

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Fig. 12.4 Emissions of the pollutants in atmospheric air departing from stationary sources, thousand tons, 2017. FD—Federal District. Source Calculated using data of Rosstat. Available at: https://www.gks.ru/wps/wcm/connect/rosstat_main/rosstat/ru/statistics/publications/ catalog/doc_1138623506156

the basic. Along with falling of a share of industrial production also, the volume of pollution from industrial enterprises is also reduced. The volume of pollution in the atmosphere for the last ten years was reduced by 15.2% on average of Russia. The similar indicator in the Ural Federal District is 39.06%, in Chelyabinsk region— 32.16%. In Russia, about a half of regions is highly polluted. However, the volume of pollution not quickly, but decreases (Table 12.1). If we compare dynamics of pollution from stationary sources and costs to environmental protection, then we can speak even about relative increase in financing. Unfortunately, Chelyabinsk got in the top 15, the anti-rating of the most polluted cities in Russia. The share of pollution from stationary sources falling to the share of the Ural Federal District (Ural Federal District) makes 22% in a total amount of emissions across all Russia (Fig. 12.4). Higher share is the share only of Siberian Federal District. Therefore, the studied problem is relevant for the authors. Chelyabinsk region is the old industrial region. The problem of industrial wastes utilization becomes a key environmental problem for the region. Industrial wastes contain a set of impurity, and therefore, their processing becomes a serious problem. Besides, limits of emissions for industrial drains constantly become tougher. The closed chains and restoration of a product in various productions become more and more priority among industrial companies. These measures represent an additional contribution to protection of water ecosystems and have high potential of cost saving. Despite requirements to measures for pollution abatement, this waste is usually dumped on the land or is unloaded in

61 1177 4179 880

Kurgan region, thousand tons

Sverdlovsk region, thousand tons

Tyumen region, thousand tons

Chelyabinsk region oblact, thousand tons

749

3132

1169

55

5105

19.1

2010

694

3293

1091

47

5126

19.2

2011

678

3520

1129

41

5368

19.6

2012

667

2751

1097

55

4569

18.4

2013

653

2181

1021

43

3899

17.5

2014

627

2146

984

52

3808

17.3

2015

597

2292

906

42

3837

17.3

2016

533

2336

928

44

3840

17.5

2017

Source According to Rosstat. Available at: https://www.gks.ru/wps/wcm/connect/rosstat_main/rosstat/ru/statistics/publications/catalog/doc_1138623506156

20.4 6296

The Ural Federal District, thousand tons

2005

Period

Russia, million tons

Regions

Table 12.1 Pollution in atmospheric air from stationary sources

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reservoirs without appropriate treatment and, thus, becomes a considerable source of environmental pollution and health hazard. The current problem in Chelyabinsk region is the combined dumps of household and economic waste. Construction of new grounds restrains lack of sufficient financing. The quantity of dumps in only Chelyabinsk region exceeds 600, and its area is over 10,197 ha.

12.2.3 Methodology Methodological tools of the research include correlation, regression and graphical analysis. If we abstract from the idea about environmental efficiency and focus attention on economic growth, then the methodology of our research will be the following. For the quantitative analysis, we will use the econometric tools. We will take Cobb–Douglas production function: Q = A · Lα · K β,

(12.1)

where Q—total production; K—capital input; L—labor input; A, α, β—function parameters. Empirical application of the function (12.1) demands specification what indicators we should use as “total production”, “capital input” and “labor input”. Statistics is available on the indicators estimated in monetary units. Therefore, such model (12.1) can be inadequate because of autocorrelation. In order to avoid the autocorrelation, we suggest to modify function (12.1) and to use not sizes but growth rates (indexes). Then, function will look so: β

i Q = A · i Lα · i K ,

(12.2)

where i Q —growth rate of gross regional product (or growth rate of enterprises revenue if it is not all enterprises in the region but enterprises of Sector B or C according RCEA); i L —growth rate of regional companies labor resources; i K —growth rate of regional companies capital resources; A, α, β—function parameters. Besides, authors suggest adding the third regressor to the function (12.2)— growth rate of banking credits directed to real capital. Then, the function will be

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the following: β

γ

i Q = A · i Lα · i K · i BL ,

(12.3)

where i Q —the same as in the Eq. (12.2); i L —the same as in the Eq. (12.2); i K —the same as in the Eq. (12.2); i BL —growth rate of banking credits directed to real capital; A, α, β, γ —function parameters. The research is devoted to the industrial region. In Russia, there is the federal law number 488 from 31.12.2014 “About industrial policy in Russian Federation”. This law defines industry as a list of economic activities including mining, processing production, production providing electric energy, gas and steam, air conditioning, water supply, water disposal, the organization of collecting and recycling, and also elimination of pollution, defined on the basis of the All-Russian Classifier of Economic Activities (RCEA). According to RCEA, it is four sectors: B—mining; C—the processing productions; D—production providing electric energy, gas and steam, air conditioning; E—water supply, water disposal, organization of collecting, recycling, elimination of pollution. In the Ural Federal District, a share of only two sectors (B and C) in a total amount of regional revenue as of the end of 2016 has made 64.35% [26]. In view of the fact that authors of article are residents of the Ural Federal District, the analysis will be concentrated on this federal district. We construct three types of the regression (12.2)—for all sections (According RCEA), section B and section C. Economic and ecological components are in contradiction. The contradiction consists in the following: A key source of regional economic growth is the industrial sector. At the same time, it is the main factor of pollution. Therefore, the main problem of any national economy today is to harmonize economic and ecological efficiency. The authors offer two groups of indicators: One group includes indicators of economic growth, the second—indicators of environmental efficiency. In Fig. 12.5, communication of the studied indicators is represented. For definition of quantitative communication between the economic growth and environmental efficiency, it is possible to use correlation coefficient. If to speak about functional dependence, then authors offer the following idea. We suggest considering economic growth (E 1 ) as the certain function showing dependence on regressors (factors). As the effect variable showing the economic growth, we suggest to consider growth rate of a gross regional product (GRP). E 1 = f (X 1 , X 2 , X 3 , X 4 , X 5 , etc.), where

(12.4)

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Fig. 12.5 Communication of indicators of economic growth and environmental efficiency. Source Authors

E 1 —economic growth, namely growth rate of GRP, X 1 …X 5 —economic growth factors. We suggest to understand as ecological efficiency (E 2 ) an indicator, the return to growth rates of pollution. E2 =

1 Growth Rate of Pollution in the Atmosphere

(12.5)

In addition, the ecological efficiency (E 2 ) can be considered as the function depending on a number of factors. E 2 = f (X 1 , X 6 , X 7 , etc.),

(12.6)

where E 2 —indicator, the return to growth rates of pollution, X 1 , X 6 , X 7 —ecological efficiency factors. The concept presented in the in Fig. 12.6 is offered by the authors as hypothesis. The authors offer two curves: One (E 1 ) shows dependence of economic growth on growth rate of industrial production, the second (E 2 )—dependence of ecological

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Fig. 12.6 Dependence of the economic growth (E 1 ) and ecological efficiency (E 2 ) on growth rates of industrial production. Source Authors

efficiency on the same indicator. The optimum size of industrial growth rate can be found in the point where two curves are crossed. The curve of total effectiveness (TE) shows the sum of two curves E 1 and E 2 . In this case, the optimum level of the industrial production growth (iopt ) will be at the level of the maximum total efficiency (TEmax ). If the region or the country increases the rates of economic growth over optimum size, it will lead to decrease in ecological efficiency. Moreover, the economic growth over optimum size is possible but only on condition of extra investment into ecotechnologies. It is possible to assume that the elasticity of the curve E 2 depends on investments of into eco-technologies. Level of ecological efficiency will decrease smaller speed in the conditions of effectively operating eco-technologies. In Fig. 12.7, the change of the curve E 2 will lead to growth of the optimum index iopt .

12.3 Concept of Green Economy: Emergence History The idea about sustainable economy is discussed decades. In recent years, the discussion around stability became one of the key global problems. It is proved by results of scientific research and the existing degradation of the environment. According to United Nations Environment Programme (UNEP) created for environmental protection in 1972, achievement of stability almost completely depends on formation of the “correct” economy. Therefore, the concept of “green” economy is a key step to achievement of sustainable development.

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Fig. 12.7 Change of optimum growth rate of industrial production (iopt ) because of change of ecological efficiency elasticity (E 2 ). Source Authors

In the modern world, environmental problems are particularly acute enough. Level impurity of the environment is on a limit of admissible borders. The subject of sustainable development is cross-disciplinary. It is studied by scientists of various spheres—biology, ecology, chemistry. More than one decade, close attention of this area is paid also by economists. One of the international leaders in sustainability science is Michael P. Weinstein. He showed the results of his researches in more than 200 publications. Weinstein calls a problem of sustainable development “transdisciplinary” [16, 17]. Moreover, the studied problem dictates the need of correct consciousness creation. This problem is mental. He speaks about the need to train new generation in thinking corresponding to a paradigm of sustainable development. For creation of sustainable peace, it is necessary to make efforts in the field of technical, economic, social, political and personal changes. The problem of sustainable development became relevant when there was an understanding that acceleration of economic growth rates surely is followed with negative consequences in the form of pollution growth in the environment, impossibility to fill the consumed resources, greenhouse gas emissions [1]. Growth of waste production leads to significant growth of consumed resources. In particular, the relationship between per capita energy use and per capita GDP is described by the following equation [5]: (r 2 =0.76), where y—per capita energy consumption, watts; x—per capita GDP (constant US dollars, 2000).

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The indicators provided by approximately 50 countries were used in the research. The studied period is 1980–2003. According to the equation, every additional 1000 dollars of GDP per capita growth leads to electricity consumption growth. Approximate growth rate is 400–500 W per capita. Recently, more and more attention is given to ecological aspects. Green economy is the direction of economy aimed at decrease in environmental risks and ecological deficiencies. The green economy is aimed at sustainable development without destruction of the environment. This direction in economy has been actively developing for the last two decades. The International Chamber of Commerce (ICC) defines green economy as economy where the economic growth and ecological responsibility work together, supporting progress in social development. The term “green economy” was for the first time mentioned in the innovative report for 1989 for the government of the United Kingdom by group of the leading economists–ecologists “The project of green economy” [2]. In 2008, this term was revived in the context of discussions about economic policy in response to numerous global crises. In the context of financial crisis and problems of global recession, UNEP defended the idea about “packages of green incentives” and defined concrete areas in which large-scale state investments could begin “green economy”. In October 2008, UNEP started implementation of the initiative “green economy” for analysis and support of policy in the field of investments into “green” sectors and for greening of unfriendly sectors. Within this initiative UNEP obliged one of initial authors of “The concept of green economy” to prepare the report under the name “Global Green New Course” (GGND) which was was issued in April, 2009. The report offered a combination of political measures which would stimulate economic recoveries and at the same time improve stability of the world economy. GGND urged the governments to allocate a considerable share of the stimulating financing for green sectors and stated three purposes: economic recovery; poverty eradication; reduction of carbon emissions and reduction of ecosystems degradation. In June 2009, in the run-up to the conference of the UN on climate change in Copenhagen, the UN issued the interdepartmental statement for support of “green economy” as transformation for the solution of numerous crises. It was said in the statement about hope that an economic recovery will become a turning point for ambitious and effective international responses to numerous crises, which the mankind on the basis of global green economy faces. In February 2010, ministers and heads of delegations of the Global Forum on Environment at the level of ministers of UNEP in Nusa Dua recognized in the statement that the concept of “green economy” could solve the current problems and give opportunities of economic development and numerous benefits for all countries. In March 2010, the General Assembly agreed that “green economy” in the context of sustainable development and eradication of poverty will become one of two concrete subjects for “Rio 20” (resolution 64/236). It resulted in great international attention to “green economy” and the related concepts.

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The theory of green economy is based on three axioms: It is impossible to expand infinitely a sphere of influence in limited space. And it is impossible to demand satisfaction of infinitely growing needs in conditions of limitation of resources. Everything on the Earth’s surface is interconnected. It is impossible to expand infinitely a sphere of influence in limited space; it is impossible to demand satisfaction of infinitely growing needs in conditions of limitation of resources; everything on the Earth’s surface is interconnected. The establishment of the sustainable development concept in Russia was initiated by the presidential decree which approved the required stages toward the sustainable development [3]. Today, the number of economic researches, not just postulating the importance of the ecological component, but attempting to quantify the sustainable development, is constantly growing [4, 7]. For instance, a sustainable development index was introduced by S. Bobylev, N. Zubarevich and S. Solovyeva; it aggregates all the indicators of the social, economic and ecological development for an objective and balanced analysis [4]. In the modern economic literature covering regional development, we can meet more often such new terms as “ecologically-economic efficiency” or “ecoefficiency”. The link between the regional development and ecologically-economic efficiency is under constant review by the scientific communities of the global economic leaders, in particular China. The results of the research involving economies of the Chinese lands attract interest. According to the deep quantitative analysis, the growth of the land use effectiveness is directly related to the income growth of the rural population, not urban [15]. Today, eco-efficiency is seen as an indicator of the sustainable development level. Generally, it reflects the cumulative effectiveness index covering usage of biological, ecological and social resources. Having considered the dynamics of the resource, environmental and biological efficiency for the period 1978–2016, the researchers note negative dynamics in environmental effectiveness until 1990 [18]. Environmental efficiency dynamics are presented by the U-shaped curve. It shows that during the 1990–2016 period, the level of eco-efficiency was growing, which stimulated total eco-efficiency growth. Along with “eco-efficiency”, a new term of “eco-sufficiency” was introduced. In order to function within the sustainable development concept, modern business must form new strategies of eco-efficiency and eco-sufficiency. Moreover, eco-sufficiency concept must be supported by the non-profit organizations [10]. Sweden is among the countries, where special attention is paid to urban planning. Taking into account the sustainable development, Swedish scientists went even further—they fitted the concept of the sustainable development along with the knowledge management concept into the process of the modern city modeling [14]. In conclusion, we can note that the number of works on sustainable development is constantly growing. The researchers do not confine themselves to the issues of harmful emissions to the environment. They cover the spheres of the sustainable land management, energy saving, urban development, knowledge economy, financial, biological, social and ecological efficiency. They work within the following terms:

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eco-efficient, eco-efficiency indicator, efficiency measurement, energy efficiency, industrial ecology, sustainable, sustainability, sustainable management, sustainable performance, sustainable indicator [6]. It is not important how we name the problem; the point is that today without ecological component, the sustainable development of any country is impossible.

12.4 Calculation and Results In already conducted researches, authors constructed several regressions like (2) and (3). For regressions like (2), we used the following selections; all enterprises of the region (All sectors), the enterprise of mining industry (Sector B according to RCEA), the enterprise of manufacturing industry (Sector C according to RCEA). Factors and selections for each model are shown in Table 12.2. Selection for models like (3) is shown in Table 12.3. Results of all ten functions will be the following (the period 2005–2016). Let’s see in Table 12.4. All constructed equations belong to the indicators placed in the left part of Fig. 12.5 and reflect possible sources of the economic growth. β

Table 12.2 Models like i Q = A · i Lα · i K Models

Regressors 1 (iL )

2 (iK )

Model 1

Growth rate of the average number of workers

Growth rate of the investments into real capital

All sectors

Model 2

Growth rate of the wage fund

Growth rate of the investments into real capital

All sectors

Model 3

Growth rate of the average number of workers

Growth rate of the investments into real capital

Sector B

Model 4

Growth rate of the wage fund

Growth rate of the investments into real capital

Sector B

Model 5

Growth rate of the average number of workers

Growth rate of the investments into real capital

Sector C

Model 6

Growth rate of the wage fund

Growth rate of the investments into real capital

Sector C

Source Authors

Sectors according to RCEA

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γ

Table 12.3 Models like i Q = A · i Lα · i K · i BL Models

Regressors

Sectors according to RCEA

1 (iL )

2 (iK )

3 (iBL )

Model 7

Growth rate of the average number of workers

Growth rate of the investments into real capital

Growth rate of banking credits directed to real capital

Sector B

Model 8

Growth rate of the wage fund

Growth rate of the investments into real capital

Growth rate of banking credits directed to real capital

Sector B

Model 9

Growth rate of the average number of workers

Growth rate of the investments into real capital

Growth rate of banking credits directed to real capital

Sector C

Model 10

Growth rate of the wage fund

Growth rate of the investments into real capital

Growth rate of banking credits directed to real capital

Sector C

Source Authors

Further, we will address the right part of Fig. 12.5. Let us calculate the correlation between growth rates of industrial production and growth rates of pollutions in the atmosphere. The basic data are given in Table 12.5. The correlation coefficient of the studied indicators is +0.6. The positive value of coefficient says that with growth of industrial production, pollution rates also grow. The correlation size indicates moderate communication of the studied indicators. Let us calculate an indicator of ecological efficiency (E 2 ) by Formula (12.5). In Fig. 12.8, we will show the schedule of communication with growth rates of industrial production (X 1 ). It is possible to assume that the curve constructed in Fig. 12.8 is a piece of curve E 2 that can be constructed, using the bigger volume of selection. At one level, the determination coefficient turned out to be rather low (R2 = 0.3518), but we investigate growth rates, not the sizes. Sizes correlate at the level of 0.8–0.9.

12.5 Discussions and Conclusion Unfortunately, all of constructed equations are not completely adequate. However, most equations are significant according to F-statistics. That is why we can make a number of conclusions. Firstly, correlation between growth of investments into real capital and growth of enterprises revenue unambiguously positive. We can see it from the degrees β of Equations 1, 2, 7, 8, 10 (Table 12.4).

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Table 12.4 All models as a result Models

Regression

The coefficient of determination (R2 )

The importance of the equation at the level of 5%

The importance of parameters of the equation at the level of 5%

Equation 1

i Q = 1.029 · i L0.161 · i K0.537

0.63

Significant

A and α are not significant, β is significant

Equation 2

i Q = 1.035 · i L−0.278 · i K0.69

0.64

Significant

A and α are not significant, β is significant

Equation 3

i Q = 1.027 · i L−0.56 · i K0.48

0.38

Non significant

A and α are not significant, β is significant

Equation 4

i Q = 1.094 · i L−0.9 · i K0.637

0.45

Non significant

A and β are significant, α is not significant

Equation 5

i Q = 1.29 · i L4.053 · i K−0.258

0.49

Significant

A and α are significant, β is not significant

Equation 6

i Q = 0.99 · i L1.533 · i K−0.149

0.58

Significant

A and β are not significant, α is significant

Equation 7

iQ = −0.064 1.035 · i L−0.257 · i K0.582 · i BL

0.83

Significant

A, β and γ are significant, α is not significant

Equation 8

iQ = −0.068 1.022 · i L0.232 · i K0.508 · i BL

0.84

Significant

A and α are not significant, β and γ are significant (continued)

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Table 12.4 (continued) Models

Regression

The coefficient of determination (R2 )

The importance of the equation at the level of 5%

The importance of parameters of the equation at the level of 5%

Equation 9

iQ = −0.4 1.328 · i L4.412 · i K−0.135 · i BL

0.49

Non significant

A, α and γ are significant, β is not significant

0.65

Significant

A and β are not significant, α and γ are significant

Equation 10

−0.482 i Q = 0.99·i L1.718 ·i K0.006 ·i BL

Sources Calculated using (1) Data of Rosstat. Available at: https://www.gks.ru/wps/wcm/connect/ rosstat_main/rosstat/ru/statistics/publications/catalog/doc_1138623506156 (2) Database of the first rating agency FIRA PRO. Available at: https://pro.fira.ru/search/index. html#company Table 12.5 Growth Rate of industrial production and growth rate of pollution in the Atmosphere (1996–2016) Year

Growth rate of industrial production

Growth rate of pollution in the atmosphere

Year

Growth rate of industrial production

Growth rate of pollution in the atmosphere

1996

0.840

0.902

2007

1.032

1.001

1997

0.990

1.001

2008

0.980

0.917

1998

1.020

0.978

2009

0.909

0.907

1999

1.002

1.004

2010

1.067

0.970

2000

1.069

1.116

2011

1.012

1.004

2001

1.072

1.066

2012

1.016

1.047

2002

1.058

1.145

2013

1.011

0.851

2003

1.103

1.017

2014

1.007

0.853

2004

1.077

1.123

2015

0.981

0.977

2005

1.033

0.990

2016

1.018

1.008

2006

1.049

1.004

2017

1.019

1.001

Sources Calculated using (1) Data of Rosstat. Available at: https://www.gks.ru/wps/wcm/connect/ rosstat_main/rosstat/ru/statistics/publications/catalog/doc_1138623506156 (2) Database of the first rating agency FIRA PRO. Available at: https://pro.fira.ru/search/index. html#company

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Fig. 12.8 Change of optimum growth rate of industrial production (iopt ) because of change of ecological efficiency elasticity (E 2 ). Sources Calculated using (1) Data of Rosstat. Available at: https://www.gks.ru/wps/wcm/connect/rosstat_main/rosstat/ru/statistics/publications/ catalog/doc_1138623506156. (2) Database of the first rating agency FIRA PRO. Available at: https://pro.fira.ru/search/index.html#company

Secondly, in the fifth, sixth and tenth equations, degree α is significant and positive. It means that with growth of both the number of workers and the wage fund, enterprises revenue will grow. This dependence is characteristic of the processing productions (section C). For all sectors in general and the section B, in particular, we cannot make such conclusion, as degree α in all other equations is not significant. Thirdly, the impact of growth rates of labor resources to revenue is many times higher than the influence of capital resources (degree α in the equations where it is significant, namely in the fifth, sixth and tenth, is higher than degree β which is significant in the equations 1, 2, 7, 8, 10). Fourthly, if we analyze the equations 7–10, we can see the connection between growth of enterprises revenue and growth of the banking credits directed to real capital. In other words, growth rate of the banking credits directed to real capital is higher than the growth rate of enterprises revenue. Meanwhile, we can see this feedback both in the sector “mining” (Sector B) and in the sector “the processing productions” (Sector C). Possibly, the banking credits are so expensive to the industrial enterprises that even with rather essential growth of bank crediting, the similar growth of revenue at

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the same time is not observed. Therefore, it is impossible to call the banking credit as key source of the industrial region growth in any way. If we take into account an ecological component, then accretion of growth rates of GRP is followed also by growth of the pollution released into the atmosphere. The correlation between these indicators is +0.6. For growth rates, we can call such communication close. In the considered research, the concept offered in Fig. 12.6 is debatable. At one end, empirically constructed curve E 2 repeats a form and a slope of the theoretical curve. At the same time, the correlation coefficient between growth rate of industrial production (X 1 ) and growth rate of GRP (E 1 ) is low (only 0.086). It means that growth rates of GRP are not explained by growth rates of industrial production. Moreover, there is a question “Whether really the industrial sector is so significant in the modern conditions?” The industry’s share in the majority of Russian regions is higher than 35% which is certainly cannot be underappreciated, but it should be understood that growth of GRP is possible not only by industry forces. Historically, in Russia, the industrial sector always gave the main share of jobs, and therefore, today, it is not possible to reduce industrial production. As for sustainable development, there is a deep differentiation between Russian regions. While the majority of regions did not master even the concept of division of household garbage, we observe a set of successful examples of processing industrial wastes. Chelyabinsk has also successful experience in processing of household waste. For example, Research and Production Fund “Energiya” developed ecologically safe, highly profitable method of organic matter processing with the use of lowtemperature pyrolysis—thermal shock. Besides, in Chelyabinsk region, the economic multi-purpose melting unit “MAGMA” using industrial gas is designed. This developed unit will allow to simplify building plant on utilization of unassorted garbage and to make its work profitable. In Chelyabinsk region, several successful enterprises function. For example, “Megapolisresurs Group” is the processor of batteries and electronic scrap. “Plant of Ecological Coverings” is the company with a full production cycle—from reception of the fulfilled car tires on utilization and their processing in a rubber crumb, before receiving ecologically safe coverings. “Urals Waste paper” is the company that buys waste raw materials in the territory of Chelyabinsk and the Ural region. The development of Russia, particularly the Urals and Chelyabinsk region cannot be identified as ecologically sustainable. Nevertheless, the successful practices already allow telling about positive vector. What term we would not call the studied problem—“sustainable development” or “green economy”, the numerous set of the researches confirms relevance of the problem and impossibility of further regional development under outdated economic laws. Whether sustainable development in Russia is possible? We can say with hope—yes, it is possible, but only if new sources of economic growth are found.

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References 1. Ananda, J., Hampf, B.: Measuring environmentally sensitive productivity growth: An application to the urban water sector. Ecol. Econ. 116, 211–219 (2015) 2. Barbier, E.B., Markandya, A., Pearce, D.W.: Environmental sustainability and cost-benefit analysis. Environ. Plan. 22, 1259–1266 (1990) 3. Bobylev, S.: Sustainable development: a paradigm for the future. World Econ. Int. Relat. 61(3), 107–113 (2017) 4. Bobylev, S., Zubarevich, N., Solovyeva, S.: Crisis calls: how to measure stability of development? Econ. Questions 1, 147–160 (2015) 5. Brown, J., Burnside, W., Davidson, A., DeLong, J., Dunn, W., Hamilton, M., Nekola, J., Okie, J., Mercado-Silva, N., Woodruff, W., Zuo, W.: Energetic limits to economic growth. Bioscience 61(1), 19–26 (2011) 6. Caiado, R., Dias, R., Mattos, L., Quelhas, O., Filho, W.: Towards sustainable development through the perspective of eco-efficiency—a systematic literature review. J. Cleaner Prod. 165(890–904), 2019 (2017). https://doi.org/10.1016/j.jclepro.2017.07.166. Accessed 23 Aug 7. Daly, H., Farley, J.: Ecological Economics: Principles and Applications. Island Press, Washington, DC (2004) 8. Dasgupta, P., Southerton, D., Ulph, A., Ulph, D.: Consumer behaviour with environmental and social externalities: implications for analysis and policy. Environ. Resour. Econ. 65(1), 191–226 (2016). https://link.springer.com/article/10.1007/s10640-015-9911-3. Accessed 23 Aug 2019 9. Dasgupta, A., Dasgupta, P.: Socially embedded preferences, environmental externalities, and reproductive rights. Popul. Dev. Rev. 43(3), 405–441 (2017) 10. Heikkurinen, P., Young, W., Morgan, E.: Business for sustainable change: extending ecoefficiency and eco-sufficiency strategies to consumers. J. Clean. Prod. 218, 656–664 (2019). https://doi.org/10.1016/j.jclepro.2019.02.053. Accessed 23 Aug 2019 11. Meadows, D.H., Randers, J., Meadows, D.L., Behrens, W.W.: Predely Rosta [The Limits to Growth]. MSU Publ., Moscow. https://drive.google.com/file/d/ 0B94jiYiyxxDHckthb1RxOVN2TVE/view (1991). Accessed 23 Aug 2019 12. Moiseev, N.N.: Noosfera Vernadskogo i princip koehvolyucii [Vernadsky’s noosphere and principle of a koevolyution]. Vestnik ehkologicheskogo obrazovaniya v Rossii [Bulletin of ecological education in Russia] 1(67), 4–7 (2013) 13. Putin, V.: 7–8 million people die every year in the world due to air pollution. https://ria.ru/ 20161227/1484712091.html?in=t (2016). Accessed 23 Aug 2019. 14. Shahraki, A.A.: Sustainable regional development through knowledge networks: review of case studies. Front. Architectural Res. (2019). https://doi.org/10.1016/j.foar.2019.04.004. Accessed 23 Aug 15. Wang, Zh., Chen, J., Zheng, W., Deng, X.: Dynamics of land use efficiency with ecological intercorrelation in regional development. Landscape Urban Plan. 177(303–316), 2019 (2018). https://doi.org/10.1016/j.landurbplan.2017.09.022. Accessed 23 Aug 16. Weinstein, M.P., Turner, E.R., Ibáñez, C.: The global sustainability transition: it is more than changing light bulbs. Sustain. Sci. Pract. Policy 9, 4–15 (2013). https://www.researchgate.net/ publication/267633187_The_global_sustainability_transition_it_is_more_than_changing_ light_bulbs. Accessed 23 Aug 2019 17. Weinstein, M.P., Litvin, S.Y., Krebs, J.M.: Restoration ecology: ecological fidelity, restoration metrics, and a systems perspective. Ecol. Eng. 65, 71–87 (2014) 18. Yang, L., Yang, Y.: Evaluation of eco-efficiency in China from 1978 to 2016: based on a modified ecological footprint model. Sci. Total Environ. 662, 581–590 (2019). https://doi.org/ 10.1016/j.scitotenv.2019.01.225. Accessed 23 Aug 2019 19. All-Russian Classifier of Economic Activities. https://regforum.ru/okved/razdel/. Accessed 23 Aug 2019 20. Bases of State Policy in the field of Ecological Development of Russia until 2030 (approved by the Russian President of April 30, 2012). https://base.garant.ru/70169264/. Accessed 23 Aug 2019

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21. Database of the First rating agency FIRA PRO. https://pro.fira.ru/search/index.html#company. Accessed 23 Aug 2019 22. The federal law from 12/31/2014 N 488-FZ (an edition from 12/31/2017) “About industrial policy in the Russian Federation” https://www.consultant.ru/document/cons_doc_LAW_173119/. Accessed 23 Aug 2019 23. The Future we want https://www.un.org/disabilities/documents/rio20_outcome_document_ complete.pdf. Accessed 23 Aug 2019 24. Putin called Russia the ecological donor of the world. ITAR-TASS News Agency (2016) https:// tass.ru/obschestvo/3909819. Accessed 23 Aug 2019 25. Russian regions: social-economic indicators. (2017) https://www.gks.ru/wps/wcm/connect/ rosstat_main/rosstat/ru/statistics/publications/catalog/doc_1138623506156. Accessed 23 Aug 2019 26. Sustainable Development Goals https://sustainabledevelopment.un.org/post2015/ transformingourworld. Accessed 23 Aug 2019 27. United Nations Environment Programme. https://www.unenvironment.org/. Accessed 23 Aug 2019

Chapter 13

Another Approach Regarding the Balance Between Natural and Manufactured Ecosystems Paul Negirla, Sorin Nanu, Ioan Silea, and Octavian Stefan

Abstract One can notice nowadays in almost every field of existence that there is an extra number of products for which companies are looking intensively, through different marketing methods, to find customers. It becomes obvious, therefore, for the companies that manufacturing a product is much easier than marketing it nowadays. After taking into consideration, the costs for all stages of development, production and marketing, it is rather hard to find a market for the products where the companies are also able to gain profit. The proposed paper presents a possible future approach. On the one hand, it will refer to a planned development of things/products required by law through a maximum use time. On the other hand, it will refer to the design of the products’ structural components, which will enable the launch of a new industry, where the used products could be reintroduced in the manufacturing cycle after the use period indicated by the manufacturer is overdue. This paper will also present acting solutions at a macro-level, aspects regarding the interactions between transnational companies and the estimated implications related to natural resources all of them being intelligent systems with enormous contributions to the stabilization of the natural ecosystem stabilization and to natural environment as a whole. Keywords Ecosystems · Raw materials · Design · Planning · Recovery · Organizations

P. Negirla (B) · S. Nanu · I. Silea · O. Stefan Politehnica University Timisoara, Piata Victoriei No. 2, Timisoara, Romania e-mail: [email protected] S. Nanu e-mail: [email protected] I. Silea e-mail: [email protected] O. Stefan e-mail: [email protected] © Springer Nature Switzerland AG 2020 G. Prostean et al. (eds.), Innovation in Sustainable Management and Entrepreneurship, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-44711-3_13

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13.1 Introduction Recent manufacturing concepts analysis over 78 key papers published in the last 24 years identified the major research gaps in sustainable manufacturing processes allowing researchers to clearly identify research opportunities [1]. Several management methods describe a sustainable development process regulation, such as CSA. Corporate sustainability assessment (CSA) in manufacturing is a framing of tools that guide the organization towards sustainable practices and indicate how the same organization contributes to a global sustainable development (SD) [2]. An examination on the results obtained by companies applying different sustainable development strategies has shown [3] the effectiveness of push strategies in manufacturing industries and the lack of a complete development cycle including the life span of products and the customer behaviour. On the other hand, new models emerged and solved some of the sustainable development issues. One of them being the optimal lot sizing [4], a primary tool applied by lean practitioners to reduce inconsistency in the manufacturing system to cut down inventories, which are often considered as a type of waste in the lean culture. This model reduces uncertainty and waste using a weighted fuzzy goal programming approach.

13.1.1 Social Context In the last 40 years, the world underwent major changes, primarily due to the evolution of computers, electronics and automation. The development of these science branches had both a positive and a negative influence on the economic and social changes of mankind, as well as on the natural ecosystem (the environment). Such themes, in various approaches, may be found in [5–7]. The positive changes are to be found in: the exponential increase of the possibilities in terms of communication/information exchange and implicitly in education, the emergence of technologies based on extremely versatile and effective means of production, the merging of the producers and production concentration (through globalization—which has also a major negative impact), high-quality products manufactured in very short production cycles, marketing systems reaching each potential beneficiary, the research/development/design human resource connected in real time, etc. The informatics infrastructure named Internet, where it is available and succeeded to erase physical distances by making real-time communication possible. The negative and surely unwanted changes may be found in: the deterioration of the balance of several natural ecosystems due to the intensive exploitation of the existing resources, the regional conflicts aiming to gain influence over the areas rich in raw materials, the excessive pollution of the natural ecosystem by waste and wornout products, the fierce competition among producers, the impact on the employment

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rate (unemployment), the higher wealth differences between countries/regions, the higher danger for the occurrence of epidemics, particularly in the conflict regions, the population migrating from poor countries (impacted by conflicts) to developed countries and the growing global imbalances as regards the effective operation of health systems (proved by illnesses spreading, such as AIDS, Zika virus, fever, etc.). All of the above-mentioned issues are acknowledged by the international organizations [8–10] and are subject of debates with the purpose of determining the causes and their impact, as well as with the purpose of determining means of combating them. However, we may consider such debates as a partial approach to the problems/scopes [11, 12] and not as a system as a whole.

13.1.2 Modelling Approach The production of goods (as defined by both political systems—capitalism [13, 14] and communism [15]) is considered the central element of our planet’s ecosystem, and its stability and sustainable development may be ensured exclusively by planning and controlling the manufacture of the products needed by the human race. The production of goods is scattered all over the world and the mind-set, the culture level, religion, the political system (political constraints), the climatic conditions, the natural resources, the specificity of a certain product are quasi-objective factors that we should consider; the ecosystem on the whole must be looked at as comprising a plurality of interconnected systems. Their stability and implicitly the stability of the entire ecosystem require a modelling approach, which has to meet a political consensus, to highlight cross-links having quantitative and qualitative influences on the goods production—inputs and outputs of raw materials, semi-finished products, energy, final products—and to meet the worldwide agreed production planning. The way of involving the producers to obtain the products and their responsibilities thereto are crucial and considered as a challenge nowadays. We attempt to detail these aspects herein below within certain limits.

13.2 Basic Structural Elements of Products Manufacturing Figure 13.1 shows that, finally, the product goes out of use. All the other steps, beginning from the demand for the product and ending at the delivery of the product to the end user/beneficiary were carefully studied and improved in time [16, 17]. The same figure shows that the reservoir from which we extract raw materials, energy and even personnel, is the same in which we dispose of what goes out of use (including some kinds of waste) is the ecosystem named the natural environment. Similar structures to the one shown in Fig. 13.1, connected to and independent from each other to a certain extent, each containing a higher or a lower volume of

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Fig. 13.1 Structural elements of the product manufacturing

products, make up the actual production and consumption system—the ecosystem that we dwell in. The deterioration of the balance of this ecosystem, more and more visible, compels us to carefully consider the destination of the products, upon their going out of use. Thus, we will be able to control/optimize raw materials and energy inputs. We arrive at an inverse connection, intended to stabilize the exploitation of raw materials and

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energy consumption. The name of this period must be different from re-use/recycling, as it also requires a new intervention on the product in the framework of a new emerging industry, which we attempt to name it “regeneration of raw materials, materials and energy”.

13.3 Factors and Organizations Influencing the Regeneration/Balance of the Natural Ecosystem Since the beginning, it must be mentioned that it is impossible to achieve the same efficiency, regardless of the activity, so it is admitted that regeneration is higher than 0%, but lower than 100%. The way the left raw materials and energy, which are neither regenerated, nor lost, but are transformed, impact the natural ecosystem is a challenge for the future. It should be noted that the differences in the economic, social and educational/cultural/religious development of various regions/areas/countries need to be considered as having a significant impact in keeping the balance of the natural ecosystem. In this context, an adequate review of the factors contributing to the regeneration of raw materials, materials and energy will point out: • The macro-level leverage means (political, legal, economic, educational), at the level of the interaction among transnational companies (globalization) as well as. • Estimated implications (in principle) on natural resources—all being intelligent systems having significant contributions to (de)stabilizing the natural ecosystems of the natural environment as a whole. In our opinion, it is possible to identify three major groups of factors influencing the stability of the manufacturing system and thereby the natural ecosystem. 1. A first group of factors may be found by simply analysing the manner of using the products and their life cycle. The obvious conclusion is that there are no clear rules on this subject and each end user enjoys a great freedom in this respect. It may be said that this area is governed by chaos. Only the environmental and operational safety regulations enforced by each state can be considered as restrictions. In these circumstances, it is difficult for each producer to develop even a year to year forecast, regarding the (market) demand for its manufactured products. Both overproduction and the lack of products generate an imbalance in the ecosystem. 2. A second group of factors derives from the fact that at this time, the producers do not include the materials that may be recovered from their own products, which went out of use, in the calculations for their production. This aspect has a direct impact both on the natural resource reserve and on the quality of the environment, polluted by the products that went out of use and were not included in a recovery process.

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3. Besides the above-mentioned groups, which we agree that may be controlled one way or another, there are also important random factors. In our opinion, these factors are: political divergence, armed conflicts, natural disasters and epidemics. They are also acknowledged as risk factors in the business strategies of the companies and they may completely alter the social and economic activities in the areas where they occur. There are various kinds of organizations in the society, which are linked to this group of factors: world organizations, non-governmental organizations, producers’ organizations, consumers’ organizations, scientific and educational organizations, political organizations, states, producers/companies. In spite of the fact that all these structures declare that they act to the benefit of humans and environment, depending on the subject matter, any of them may act in their favour or contrary to them. Most of the time, these organizations develop useful estimates regarding the positive or negative influence of certain manufacturing or other kinds of activities, but also regarding the efficiency of the means to influence the evolution of such phenomena, one way or another. Sometimes, they are vested with the power to set standards, regulations and sanctions and their objectivity becomes the object of political dispute. It is not our objective to detail all these aspects (factors and organizations acting punctually with regard to a problem or another in a certain manufacturing system), but to highlight the macro-elements that we believe to intervene on and influence the ecosystem as a whole. The paradigm of producing for the supreme interest of human well-being must be completed by the paradigm of producing “without deteriorating the balance of the natural ecosystems”. Therefore, in this paper, we are proposing that a product should have priority from the point of view of its “destiny”, once it is out of use—as a condition/measure to minimize the imbalance of the natural ecosystem. More particularly, it is suggested that upon completion of a product design, we should consider the manner in which such product may be “destroyed” so that to allow its parts to re-enter in a new manufacturing process, whereby saving raw materials, energy and preventing pollution. Figure 13.2 shows, at a macro-level, the interdependence of the structures that influence a product. Self-control and/or control, or more nicely said “synchronization” of these entities, is a key problem in ensuring the ecosystem balance. We must keep in mind that such structures, particular to each product, are interconnected in the whole global production system. Guiding such a macrostructure, in a manner to avoid the deterioration of the natural balance, requires foremost political commitment and secondly a proper human cultural and educational level (in order to implement the taken decisions).

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Fig. 13.2 Interdependence of the structures that influence a product

13.4 Proposed Manufacturing Approach Aimed to Conserve the Natural Ecosystem Obviously, it is impossible to achieve perfect synchronization and complete conformation with the conditions enforced by the interconnections shown in Fig. 13.2. However, the starting point to restructure the manufacturing process must be determined, in order to achieve a high implementation level (as high as possible) of the

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requirements for conserving the balance of the natural ecosystem. It is certain that implementing the conventions [18–20] set at a global level, for reducing climate deterioration, through the adaptation by each state of their own legal framework—such as to force the companies to undertake proper actions—is the needed legal basis for subsequent changes. In spite of the above conventions, several considerations on the limitations of these and other European regulations have been pointed out [21]. In order to develop predictive reasons to justify certain obligations to be further created through regulations, we believe that certain steps must be undertaken included, for example, in a first 3 years cycle. The first set of conclusions may be drawn based on a pilot project (internationally financed). Hereinbelow, we are referring to these steps based on the assumption that certain products and manufacturing products were defined in the pilot project without also mentioning the needed bodies for their (the steps) implementation. Step 1—each manufacturer should monitor/review, for a 3 years period, the state of its products after going out of use and should make suggestions regarding: (a) The materials and the limits/percentage for their recovery after the product go out of use; (b) A maximum useful period for each product. Step 2—collecting data from the manufacturers and setting maximum useful times for categories/types of products at a worldwide level; Step 3—classifying the products in categories based on materials and the re-use degree of their component parts; Step 4—involving “green” organizations in data validation; Step 5—based on the terms that limit products use, regulations (and sanctions) are needed to compel the goods’ owners, at a worldwide level, to return the product going out of use (or not used any more) for dismantling/re-processing; Step 6—compelling the manufacturers to develop (individually or in cooperation), in parallel to the manufacturing process, industrial units to collect, dismantle, sort and process their own products gone out of use in order to recover materials; Step 7—at a global level, compelling each supplier of raw materials, which are obtained through the exploitation of the environment, to negotiate with its clients the new delivered quantities, diminished by those that had to be or have been recovered through processing their own products that have gone out of use; Step 8—each producer should estimate its manufacturing volume for a new year based on the volume of products sold in the previous 3 years. The first set of real data may be obtained at the end of the first cycle of the pilot project that is proposed to target several products and companies meeting the condition to participate in the experiment. Further, it is possible to proceed to update the data annually, applying the principle of sliding/gliding, under the form “last in last out”. The conclusions may be summarized and the necessary adjustments may be carried out, then the project may be extended.

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As mentioned hereinbefore, it takes courage to reach a decision at the apolitical level, which should compel manufacturers, and end users as well to comply with such a challenge. In the following chapter, we summarize the advantages deriving from adopting such a manufacturing/consumption system.

13.5 Conclusions Whilst the climate and the environment are visibly deteriorating, i.e. the natural ecosystem is on the verge of losing balance, the political factors at connivance with the companies are hesitant to take concrete steps. Presently, their only aim is to obtain profit, preferring its increasing every year. This paper has attempted to analyse the manufacturer-end user system, in order to prepare a proposal/action option that will potentially stabilize the decline, or at least to slow down the destruction of the natural ecosystem. Aiming at finding the balance of the ecosystem that we are living in, the paper proposes the adoption of a planned manufacturing system, which involves a synchronization between the companies and the end users and a general commitment to establish maximum usage terms (imposed to end users by applicable law) for each product. Usually, the end users use the goods beyond their normal life cycle, even beyond obsolescence. This “freedom” determines the producers’ uncertainty in their production and needed human resources planning. Even in case that a product is not used, the end user should be compelled to hand it over to the manufacturer, for re-processing, upon expiration of the lifespan (set by the producer as from the purchasing date). Then again, the goods going out of use must be brought to the attention of the manufacturers. This aspect is very important and is another key point of the paper in the context of keeping the balance of the natural ecosystem. Nonetheless, a few organizations show early worry for their items, which are exhausted to a limited degree, through “buy-back”, yet this is a long way from what it ought to be. In light of the two previously mentioned components, we accept that from a certain perspective, the producer end client framework may work and keep up the natural ecosystem at balance. The paper proposes to collect the first set of real data and genuine information in a pilot venture. The favourable circumstances resulting from the implementation of the actions proposed in the paper (and wished for/monitored at an overall worldwide level) may be: 1. Exploitation of raw materials and energy resources becomes rational; 2. By giving up out-of-use products, the environmental pollution is avoided; 3. Production planning enters to a cyclic rhythm, therefore, it has much fewer products in the inventory; 4. Increased/improved stability of workplaces and a more accurate forecast of the personnel needs, for various specializations;

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5. Improved quality and diminished product costs, but also a more sustainable distribution of manufacturers throughout the world; 6. Approaching products design taking also into consideration the destination perspective thereof when going out of use; 7. Creating new workplaces in newly founded facilities for dismantling and processing/recovering out-of-use products; 8. Including elements in the school curricula, properly preparing the population for the new manufacturer–end user relations.

References 1. Moldavska, A., Torgeir, W.: A Holistic approach to corporate sustainability assessment: incorporating sustainable development goals into sustainable manufacturing performance evaluation. J. Manufact. Syst. 50, 53–68 (2019). ISSN 0278-6125 2. Singla, A., Ahuja, I., Sethi, A.: An examination of effectiveness of technology push strategies for achieving sustainable development in manufacturing industries. J. Sci. Tech. Policy Manag. (2018) 3. Pathak, P., Singh, M.: Sustainable manufacturing concepts: a literature review. 4, 1–13 (2017) 4. Tayyab, M., Sarkar, B., Ullah, M.: Sustainable lot size in a multistage lean-green manufacturing process under uncertainty. Mathematics 7, 20 (2018) 5. Rafique, R., et al.: Global and regional variability and change in terrestrial ecosystems net primary production and NDVI: a model-data comparison. Remote Sens. 8(3), 177 (2016) 6. Uitto, J., Shaw, R.: Sustainable Development and Disaster Risk Reduction: Introduction, pp. 1– 12. Springer, Japan (2016) 7. Terrado, M., et al.: Model development for the assessment of terrestrial and aquatic habitat quality in conservation planning. Sci. Total Environ. 540, 63–70 (2016) 8. Moore, J., Pubantz, J.: The New United Nations: International Organization in the Twenty-First Century. Routledge (2015) 9. Thewissen, S.: Technological change, offshoring, and labour market institutions in international perspective. In: The 23rd International Conference of Europeanists. CES (2016) 10. Van Hoorn, A., Maseland, R.: How institutions matter for international business: institutional distance effects vs institutional profile effects. J. Int. Bus. Stud. 47(3), 374–381 (2016) 11. Halseth, G.R., et al.: Cumulative effects and impacts: the need for a more inclusive, integrative, regional approach. In: The Integration Imperative, pp. 3–20. Springer International Publishing (2016) 12. Helming, K., Wiggering, H.: Sustainable Development of Multifunctional Landscapes. Springer Science & Business Media (2013) 13. Marx, K.: Capital, Vol 1: A Critical Analysis of Capitalist Production. Penguin Classics (1992). ISBN 0140445684 14. Piketty, T.: Capital in the Twenty-First Century (2014). ISBN 9780674430006 15. Lenin, V.: The State and Revolution. Kessinger Publishing (2004). ISBN 1419183478 16. Urban, G.L., Hauser, J.R.: Design and Marketing of New Products, 2nd edn. Prentice-Hall (1993). ISBN-10: 0132015676 17. Spieth, P., Hahn, R.: Hybrid Business Models for Sustainability: A Business Model Design Perspective. In: SMS 34th Annual International Conference “Strategy in a World of Networks” (2014) 18. Cohn, T.: Global Political Economy (2015). ISBN 9780205075836

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19. Rieu-Clarke, A., Moynihan, R.: Transboundary Water Governance and Climate Change Adaptation: International law, Policy Guidelines and Best Practice Application. UNESCO (2015) 20. Haigh, N.: EU Environmental Policy (2015). ISBN 978-1-138-89030-5 21. Korhonen, J., Honkasalo, A., Seppälä, J.: Circular economy: the concept and its limitations. Ecol. Econ. (2018)

Chapter 14

Carbon Emissions, Energy Consumption, and Managing Investment in Renewable Energy Roxana Mihaela Sirbu and Claudiu Tiberiu Albulescu

Abstract This paper tests the long-run role of renewables’ share in electricity production and of total energy consumption in explaining the CO2 emissions resulting from fuel combustion. As documented earlier in the literature, the energy consumption has one of the most important influences on the carbon emissions. Nevertheless, there is no agreement about the role of renewables in environmental protection. Further, the endogeneity effect between CO2 emissions on one side, and renewables and energy consumption on the other side, is practically neglected by previous empirical studies. We shed light on these questions, and we perform a panel data empirical investigation for 44 countries covering the time span of 1990–2017. Our Pooled Mean Group (PMG) estimator reveals that the energy consumption has, indeed, a long-run positive effect on CO2 emissions, whereas the role of renewables is inconclusive. This result is confirmed even in the case of a separate sample composed by 12 European Union (EU) countries. Our findings thus show that the share of renewables is still very small to generate a global downturn in CO2 emissions. Indeed, the effect of renewables is marginal and investments in this area should be encouraged to ensure environmental protection at long term. Keywords CO2 emissions · Renewable energy sources · Energy consumption · Panel data

14.1 Introduction Encouraging investment in renewable energy is essential for preserving the energy security of a country, but also for the reduction of greenhouse gas emissions and, therefore, for the environmental protection. However, at global level, the renewable energy steel represents a small portion in the total energy production compared to R. M. Sirbu (B) · C. T. Albulescu Politehnica University Timisoara, Victoriei Square, No. 2, 300006 Timisoara, Romania e-mail: [email protected] C. T. Albulescu e-mail: [email protected] © Springer Nature Switzerland AG 2020 G. Prostean et al. (eds.), Innovation in Sustainable Management and Entrepreneurship, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-44711-3_14

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the conventional sources (i.e., carbon and fossil fuels) but continued to increase during the last decades. At the same time, the carbon emissions, which represent most of greenhouse gas emissions, slightly decreased at the European Union (EU) level, but not at global level. Against this background, the academic research has not found yet a clear answer to the question about the long-run role of renewables on the CO2 emissions. To shed light on this issue, we test the long-run nexus between CO2 emissions, share of renewables in the total energy production, and the energy consumption. To do so, we perform an investigation for 44 countries (panel approach) for the period of 1990–2017, relying on Enerdata statistics. The determinants of CO2 emissions are largely analyzed by two competing strands of the literature. First, we have the environmental Kuznets curve (EKC) theory developed by Kuznets [19] and refined by Holtz-Eakin and Selden [15], stating that between income and carbon emissions, there is an inverted U-shaped connection. Recent empirical studies in this area validate the EKC hypothesis (e.g., [5, 25, 26]), or, on the contrary, provide little evidence about the connection between the emissions and the GDP per capita (e.g., [6, 29]). Second, the pollution haven hypothesis (PHH) advanced by Copeland and Taylor [12] shows that the foreign investment has a positive role in enhancing carbon emissions in countries with week environmental policies. This theory is recently validated by Sapkota and Bastola [25] for a set of countries from Latin America and by Solarin et al. [28] for Ghana. On the contrary, Zhang and Zhou [30] reject the PHH hypothesis in the case of China. Recent works on this topic use a plethora of control variables to validate or invalidate these two hypotheses. Nevertheless, the role of the renewable energy production for the reduction of carbon emission is not enough investigated. Moreover, as reported by the empirical literature, the effect of renewables on CO2 emissions seems to be inconclusive. Likewise, Cherni and Jouini [10] and Menyah and Wolde-Rufael [22], respectively, report the absence of the Granger causality between renewable energy consumption and carbon emissions, whereas Aliprandi et al. [4] mention that the influence of the renewables’ share in the total energy production on CO2 emissions is lower than expected. On the contrary, Zoundi [31] and Inglesi-Lotz and Dogan [18] for a selected set of countries from Africa, Sinha and Shahbaz [27] in the case of India, and Chen et al. [9] for China, discover that the role of renewable energy production on CO2 emissions is significant in the long run. With a focus on a large panel of 128 countries, covering the period of 1990–2014, Dong et al. [13] also report that energy intensity of renewables leads to a decline in CO2 emissions. With a focus on EU countries, over the period of 1990–2017, Albulescu et al. [2] report similar results. We add to this narrow strand of literature, and we make several contributions to the existing studies. First, we consider the endogeneity issue between CO2 emissions and the production of renewable energy. Following the adoption of recent international agreements, especially at the EU level, regulators imposed strict norms of CO2 emissions. Therefore, if the level of carbon emissions is too high, countries might be forced to invest in renewable energy sources.

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Second, we inquire the role of the energy consumption in influencing CO2 emissions in the long run.1 In line with other studies, we posit that the energy consumption has one of the largest influences on the carbon emissions. However, different from previous studies, we state that the carbon emission also influences the energy consumption, given that countries with good environmental culture encourage the reduction of energy consumption. In addition, it is well known that the total energy consumption is largely influenced by the business cycle. Therefore, to correct the influence of the business cycle, we also consider in our analysis the energy intensity. Third, our panel includes both I(1) and I(0) variables. Therefore, we chose a consistent estimator developed by Pesaran et al. [23], namely the Pooled Mean Group (PMG). This approach is particularly appealing for macro-panels and allows the distinction between the long- and the short-time effects of renewables and energy consumption on carbon emissions. The PMG relies on the maximum likelihood methodology and represents an autoregressive distributed lag (ARDL) specification. However, PMG requires homogenous panels. Therefore, to verify if our findings prove to be robust, we also resort to a mean group (MG) approach. This technique developed by Pesaran and Smith [24] is designed for heterogenous panels. The remaining of our research is the following one. Section 14.2 presents in brief the research hypotheses. Data sources, general statistics, and methodology are described in the next section (Sect. 14.3). Section 14.4 is dedicated to the main findings, whereas Sect. 14.5 describes the robustness results. The last section presents the conclusions.

14.2 Research Hypotheses Finding solutions for the reduction of CO2 emissions represent a hot topic for both practitioners and policymakers, and also for academics. The environmental degradation perpetuates, and the international agreements and national regulations seem ineffective for the moment (see the estimates by Albulescu et al. [2]). As shown in Fig. 14.1, the level of CO2 emissions continues to climb at global level after the signature of the Kyoto Protocol in 1997. To this negative phenomenon contribute in particular the emerging economies, whereas the EU countries recorded a slight reduction in carbon emissions. The question is what should be done to enregister a downturn of emissions at global level? One solution is to invest in renewable energy sources. As shown in Fig. 14.2, the percentage of renewables in the total electricity production (a proxy for the energy production) increased since the 2000s and gained remarkable moments at the EU level, rising from 14.78% in 2000 to 30.18% in 2017. This is a great progress for the

1 Total energy consumption increased at global level with more then 60% in 2017 compared to 1990,

according to Enerdata statistics [14].

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Fig. 14.1 CO2 emissions from fuel combustion (MtCO2 ). Source Enerdata Global Energy Statistical Yearbook (2018)

Fig. 14.2 Share of renewables in total electricity production (%). Source Enerdata Global Energy Statistical Yearbook (2018)

use of green energy sources. However, is this effort enough to sustain the negative trend in carbon emissions recorded by the EU countries during the last decade? Further, the total energy consumption continued to increase at global level (Fig. 14.3). This might be the principal cause of the positive trend in carbon emissions, in particular in the short run. Nevertheless, starting with 2006, the EU countries considerably reduced the total energy consumption. Therefore, the investment

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Fig. 14.3 Total energy consumption (Mtoe). Source Enerdata Global Energy Statistical Yearbook (2018)

in renewables and the control of energy consumption can be considered essential factors in the reduction of CO2 emissions in the EU, which acts as a world leader for the environmental protection during the last two decades. Starting for these stylized facts, we empirically test the following hypotheses, relying on the assumption that the endogeneity effect between our variables is very high: Hypothesis 1: Renewables have a negative impact on short- and long-run CO2 emissions; Hypothesis 2: Energy consumption/energy intensity has a positive and strong effect on carbon emissions in both short- and long-time horizons; Hypothesis 3: The influence of renewables’ production on emissions is higher in the EU sample, compared to the World sample. Intuitively, the simple model we test is the following (Fig. 14.4):

Fig. 14.4 The relationship between CO2 emissions, renewables and energy consumption

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As we notice in Fig. 14.4, between renewables and energy consumption we may have a bidirectional influence. On the one hand, an increase in renewables determines a decrease in energy prices, which favors the consumption of energy. On the other hand, if the total energy consumption increase, given the limited characteristic of conventional sources, investments in renewables became necessary.

14.3 Data, Preliminary Analysis, and Methodology Description 14.3.1 Data Sources and Stationarity Analysis We use statistics with an annual frequency covering a period from 1990 to 2017, using Enerdata database. This database contains statistics for 44 countries; out of each 12 are EU members.2 All G7 countries and most of the Organisation for Economic Co-operation and Development (OECD) countries are included in our sample, which is very representative for carbon emissions at global level. Our series are represented by CO2 emissions related to fuel combustion in MtCO2 , expressed in natural log (CO2 emissions), by the renewables as percentage in the electricity production (renewables), by the natural log of total energy consumption (Mtoe) (energy consumption), and by the economic output energy intensity, expressed in constant PPP (koe/$2015p) (energy intensity). Table 14.1 is dedicated to the presentation of summary statistics of our data, comparing the entire sample (44 World countries) and EU sample (12 countries). We notice that the share of renewables has the largest standard deviation and varies between 0% in countries as Saudi Arabia and 99% in Norway. The CO2 emissions are, on average, higher in the World sample compared with the EU sample, so is the energy intensity. For the choice of the empirical approach that allows us to correctly assess the connection between carbon emissions, renewables and energy consumption, we perform a preliminary analysis and we apply panel unit root (stationarity) tests from the first generation, which are recommended if the cross-sectional independence hypothesis is accepted. If all series are stationary, that is I(0), we can use either the classic generalized method of moments (GMM) approach, or a panel vector autoregression (VAR), to consider the endogeneity issue between our variables. If all the series are I(1), we need to apply a series of panel cointegration tests to validate the case of a long-run connection. However, if some series are I(0) whereas other series are I(1), 2 The

countries are: Romania, Spain, UK, Sweden, Portugal, Poland, Netherlands, Italy, Germany, France, Czech Republic, Belgium, (EU countries), Turkey, Norway (EU frontiers), Uzbekistan, Ukraine, Russia, Kazakhstan (CIS countries), USA, Canada (North America), Colombia, Venezuela, Brazil, Mexico, Chile, Argentina (Latin America), Thailand, Malaysia, Taiwan, India, South Korea, Japan, Indonesia, China (Asia), New Zeeland, Australia (Pacific), South Africa, Nigeria, Egypt, Algeria (Africa), United Arab Emirates, Kuwait, Saudi Arabia, Iran (from Middle East).

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Table 14.1 Summary, general statistics Mean

Std. Dev

Min

Max

44 countries (World) CO2 emissions Renewables

5.401 23.71

1.192 25.61

2.056 0.000

9.137 99.82

Energy consumption

4.622

1.110

0.990

8.040

Energy intensity

0.147

0.103

0.044

0.849

5.263

0.877

3.644

6.862

12 countries (EU) CO2 emissions Renewables

18.93

15.76

1.120

63.29

Energy consumption

4.490

0.806

2.846

5.871

Energy intensity

0.113

0.038

0.062

0.262

we need to use the variables in the first difference and to focus on the relationship at short term. Alternatively, we might use the pool mean estimator to consider both the short- and the long-run dimensions. We start with the Levin et al.’s [20] test (LLC) which does not assume heterogeneity in the autoregressive coefficient, and we continue with the Im et al.’s [17] test (IPS), which is less restrictive and assumes units’ heterogeneity in a dynamic panel specification [7]. While the LLC test supposes a common unit root type of process, the IPS test presumes the existence of individual unit root processes. For each variable, we have: yi,t = αi + βyi,t−1 +

p 

φi j yi,t− j + εi,t ; i = 1, 2, . . . , N , t = 1, 2, . . . , T,

j=1

(14.1) where yi,t represents the variables included in our model, αi is the individual fixed effect, εi,t are independent errors. For all i, the null hypothesis is β = 0, whereas the alternative (opposite) hypothesis is β = βi < 0, for all i = 1, 2, . . . , N . The LLC test is based on the adjusted t-statistic: tβ∗

   σˆ βˆ μ∗T tβ , = ∗ − NTS N 2 σT σT∗ σˆ ε˜ 

(14.2)

where μ∗T is the mean adjustment, whereas σT∗ is the standard deviation adjustment. The IPS test relies on an augmented Dickey–Fuller (ADF) statistic that is averaged across countries [16]. If ti T ( pi , φi ), where φi = (φi,1 , . . . , φi, pi ), is the t-statistic for checking the presence of unit root in the series for the i group, the IPS test becomes:

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t_barNT =

N 1  ti T ( pi , φi ). N i=1

(14.3)

If the standardized t_bar (Wt_bar) converges toward a standard Gaussian distribution under the null of unit roots presence, we have:

Wt_bar

√  N N t_bar N T − N −1 i=1 E[ti T ( pi , 0)|φi = 0  = N N −1 i=1 Var[ti T ( pi , 0)|φi = 0 ]

d

→

T,N →∞

N (0, 1) (14.4)

A different way to perform tests for detecting panel unit roots’ presence supposes the use of Fisher’s approach and combines the p-values from individual, crosssectional unit root tests. This method applied by Maddala and Wu [21] and afterward by Choi [11] relies on specifications designed for time series. If we assume continuous statistics, then the associated p-values ( pi ) are uniform variables (0; 1). The Maddala and Wu’s [21] test (Fisher-ADF) implies the specification of the lags considered in each cross-sectional ADF regression and the statistic (PMW ) with a chi-square distribution is: PMW = −2

N 

log( pi ).

(14.5)

i=1

Choi [11] proposes a similar test (Fisher-PP) designed for large N sample (Z MW ): Z MW

√ −1

 N N N PMW − E −2 log( pi ) i=1 log( pi ) + N  = = − . √  N Var −2 log( pi )

(14.6)

In our empirical exercise, we apply all these tests. Although the use of tests from the first category, relying on the cross sections, independence assumption can generate an hypothesis over-rejection for the existence of unit roots, if we have cross-sectional dependence [3], these tests are more powerful compared with the second-generation tests if the independence is confirmed. The unit root tests’ findings can be seen in Table 14.2. We notice that, in general, the renewables and energy consumption are stationary series, whereas CO2 emissions and energy intensity are nonstationary. Further, there is no agreement between all tests for each of the analyzed variables. The results remain mitigated even if we consider the presence of a time trend in our series. Consequently, given that our series follows either I(0) or I(1) processes, we apply a consistent methodology based on mean group estimators.

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Table 14.2 Panel unit root tests Level

CO2 emissions

Renewables

Energy consumption

Energy intensity

Levin, Lin and Chu t*

−5.172***

6.303

−4.806***

−1.951**

Im, Pesaran and Shin Wt_bar

−0.439

4.292

−0.621

3.469

ADF–Fisher Chi-square

124.6***

78.19

118.7**

65.89

PP–Fisher Chi-square

145.7***

109.0**

185.0***

81.31

With intercept

With intercept and time trend Levin, Lin and Chu t*

−1.826**

2.854

−4.345***

0.139

Im, Pesaran and Shin Wt_bar

1.067

3.176

1.093

−0.560

ADF–Fisher Chi-square

95.55

77.94

234.5***

99.56

PP–Fisher Chi-square

72.98

113.2**

87.89

146.7*

Notes (i) the null for all tests is the presence of unit roots (the t* test considers common unit root process, whereas all other tests imply individual unit root processes); (ii) *, **, *** are equivalent with a stationary process (in level), significant at 10%, 5%, and 1%

14.3.2 Methodology The so-called MG estimator offers reliable estimates of the parameters’ averages and represents a better alternative for a fixed-effect estimator where time-series data for each group are pooled. Given the fact that for dynamic heterogeneous panels slope coefficient is not compulsory the same, the MG estimator allows for the slope coefficients, intercepts, and error variances to vary between groups and does not consider potential homogeneity. However, Pesaran et al. [23] state that the mean group approach might describe, as the fixed-effect estimator does, an extreme situation. Therefore, they propose a maximum likelihood method, namely the PMG which is consistent with I(0) and I(1) processes, which allows the intercept, the short-run coefficients, and also the variance of errors, to be different across countries, similar to the MG estimator. However, the PMG imposes the equality, between groups, of the long-run coefficients similar to the fixed-effect specification. This way, the PMG estimator allows the computation of the adjustment dynamic that occurs between the short run and the long run [7]. Assuming that our series are I(1) and in the same time cointegrated, and the errors follow an I(0) process in the case of all cross sections and are independent, than the autoregressive distributed lag, ARDL ( p, q1 , . . . , qk ) model of Pesaran et al. [23] is:

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CO2 emissionsi,t =

p 

λi, j CO2 emissionsi,t− j +

j=1

q 



δi, j X i,t− j + μi + εi,t ,

j=0

(14.7) where i represents the groups (countries), t are the periods (years), X i,t is the k×1 vec tor of renewables and energy consumption (energy intensity), δi, j are the coefficients, λi, j are scalars, μi are group effects, and εi,t are the error terms. Equation (14.7) can be transformed or re-parametrized to obtain an error correction specification (see Blackburne and Frank [8]). Therefore, we have a panel vector error correction model (PVECM):   CO2 emissionsi,t =c + φi CO2 emissionsi,t− j − θi X i,t +

p−1 

λi,∗ j CO2 emissionsi,t− j +

j=1

q−1 

δi,∗ j X i,t− j + μi + εi,t ,

j=0

(14.8) where φi represents the error correction adjustment element and θi is the vector explaining the relationships between our selected variables, in the long run.

14.4 Empirical Results To assess the connection of carbon emissions with renewables’ share and energy consumption, we test two different models. The first model (Model 1) investigates the nexus between carbon emissions, renewables, and the energy consumption. However, the energy consumption is largely influenced by the business cycle. Therefore, to correct for the influence of the business cycle, in Model 2 we consider the energy intensity, which represent the ration between total energy consumption and the GDP. This indicator is expressed in constant purchasing power parities, also avoiding the influence of the general price level (inflation). We first test the PMG estimator. However, because our panel might be not homogenous, we also apply the MG estimator for robustness purpose. The main findings of this paper are presented in Table 14.3. For Model 1, we see that the PMG estimator shows that energy consumption only has a positive influence in the long term on carbon emissions. The coefficient of renewables, although negative as expected, is not significant, either in the long run or in the short run. The adjustment coefficient has a negative sign and is significant, stating that the long-run relationship can be tested. In the short run, we have quasisimilar results between the PMG estimator and the MG approach. If we consider the energy intensity in our regression (Model 2), the renewables have a negative, and at the same time, a significant influence on the carbon emissions in the long run, while the energy intensity has a positive and very important influence.

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Table 14.3 PMG and MG main results CO2 emissions

PMG Long-run coefficients

MG Short-run coefficients

Long-run coefficients

Short-run coefficients

Model 1 −0.212*** (0.029)

Adjustment coefficient

−0.376*** (0.036)

Renewables

−0.000 (0.000)

−0.004 (0.003)

−0.007 (0.005)

−0.001 (0.000)

Energy consumption

1.058*** (0.009)

0.702*** (0.078)

0.879 (0.097)

0.547*** (0.070)

c

0.115*** (0.022)

Model 2 −0.012*** (0.004)

Adjustment coefficient

−0.160*** (0.029)

Renewables

−0.068*** (0.019)

−0.008* (0.004)

0.150 (0.102)

−0.004 (0.002)

Energy intensity

16.29*** (1.882)

3.950*** (0.607)

33.58 (33.37)

3.233*** (0.530)

c

0732*** (0.028)

0.903*** (0.173)

Notes (i) in parenthesis, we have the standard errors; (ii) ***, **, * indicate relationship significant at 1%, 5%, 10%; (iii) c is the intercept of the short-run regression; (iv) the adjustment coefficient must have a negative sign and must be different from zero and significant to validate the long-run relationship

These results show that the investment in renewable energy sources may downturn the emissions at global level, but the increased demand for energy is more important and contributes to CO2 emissions. However, these findings are not confirmed by the MG estimator, questioning thus their robustness. Further, in the short run, the energy intensity is the only factor with a significant influence on CO2 emissions. More precisely, 1% increase in energy intensity generates more than 3% increase in carbon emissions. Nevertheless, given then the role of renewables on the CO2 emissions is inconclusive, we perform a robustness analysis, considering in our sample only the EU countries included in Enerdata database. The renewable sources’ share almost doubled in the EU in the last decade. Therefore, we expect that renewables have a stronger effect on CO2 emissions for this sample.

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14.5 Robustness Analysis As in the previous case, in our robustness analysis we test two models (Models 1 and 2) considering the energy consumption and energy intensity, respectively. At the same time, we compare the estimates of the PMG and MG specifications. Model 1 (Table 14.4) confirms our intuition, showing that the renewables have a negative and significant impact on the long-run carbon emissions. However, the effect of renewables is marginal. The influence is not significant in the short run, showing that the production but also the consumption of renewable energy might contribute to a downswing of CO2 emissions at global level in time. The marginal long-run effect of renewables is not documented in Model 2, where the energy intensity has a huge influence on emissions on the long run. In fact, the significant influence of the total consumption of energy and the energy intensity on carbon emissions is documented by most of the recent studies (e.g., [1, 25]), while the effect of renewables is questionable for the moment (please refer to Aliprandi et al. [4]). To sum up, we have discovered that the consumption of energy and energy intensity have both a long- and a short-run positive influence on carbon emissions. This result is consistent comparing a PMG and a MG specification and across samples. Table 14.4 PMG and MG robustness results CO2 emissions

PMG Long-run coefficients

MG Short-run coefficients

Long-run coefficients

Short-run coefficients

Model 1 −0.113* (0.066)

Adjustment coefficient

−0.265*** (0.053)

Renewables

−0.006*** (0.000)

−0.000 (0.001)

−0.011*** (0.003)

0.001 (0.001)

Energy consumption

1.064*** (0.023)

0.858*** (0.083)

0.371 (0.299)

0.786*** (0.069)

c

0.068 (0.043)

0.799*** (0.242)

−0.029** (0.033)

−0.339*** (0.072)

Model 2 Adjustment coefficient Renewables

−0.005 (0.005)

−0.004 (0.002)

0.073 (0.081)

−0.001 (0.001)

Energy intensity

41.25*** (11.55)

4.793*** (0.747)

124.4 (117.1)

3.597*** (0.639)

c

0.050* (0.029)

Notes Please refer to the notes of Table 14.3

1.684*** (0.452)

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However, the renewables have no clear influence on CO2 emissions. In the case of World sample, the effect is not significant whereas in the case of EU sample we have a significant but marginal negative effect only in the long run and only for Model 1.

14.6 Conclusions This work attempted to assess the long-run connection existing between CO2 emissions on one side, and investment in renewable energy and total energy consumption, on the other side. To this end, we have performed a panel data investigation for the period 1990–2017 for 44 countries and we have used a PMG estimator (MG for robustness purpose). Our main results show that the total consumption of energy has both a shortand a long-run positive impact on carbon emissions, while the role of renewables is inconclusive. These findings are consistent across the PMG and MG estimator. Furthermore, the findings remain the same if we correct for the effect of business cycle and we use the energy intensity instead of energy consumption. For the robustness check analysis and for testing the third hypothesis of the study, we perform the same investigations for a smaller sample of 12 EU countries. As expected, the renewables have a significant but marginal effect on CO2 emissions at long term, but not in the short run. However, this result lacks in robustness, as the coefficients become not significant in Model 2. On the contrary, the level of energy consumption is very important for the CO2 emissions. Two policy implications are generated by our findings. First, the investment in renewables is more important for the energy security than for environmental protection, as the renewables have only a marginal effect in the short run on CO2 emissions. However, ensuring an efficient management of investments in renewables might be the key element to bring down carbon emissions in the very long run. Second, emerging economies should be more focused on the reduction of energy consumption by investing in new technologies, if they want to contribute to the global environmental protection. Acknowledgements This work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS—UEFISCDI, project number PN-III-P1-1.1-TE-20160142.

References 1. Acaravci, A., Ozturk, I.: On the relationship between energy consumption, CO2 emissions and economic growth in Europe. Energy 35, 5412–5420 (2010) 2. Albulescu, C.T., Artene, A.E., Luminosu, C.T., Tamasila, M.: CO2 Emissions, Renewable Energy and Environmental Regulations in the EU Countries (2019). Available at SSRN: https:// ssrn.com/abstract=3364909 or https://dx.doi.org/10.2139/ssrn.3364909

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3. Albulescu, C.T., Pépin, D., Tiwari, A.K.: A re-examination of real interest parity in CEECs using old and new generations of panel unit root tests. Bull. Econ. Res. 68(2), 133–150 (2016) 4. Aliprandi, F., Stoppato, A., Mirandola, A.: Estimating CO2 emissions reduction from renewable energy use in Italy. Renew. Energy 96, 220–232 (2016) 5. Apergis, N., Ozturk, I.: Testing environmental Kuznets curve hypothesis in Asian countries. Ecol. Ind. 52, 16–22 (2015) 6. Baek, J.: Environmental Kuznets curve for CO2 emissions: the case of Arctic countries. Energy Econ. 50, 13–17 (2015) 7. Bangake, C., Eggoh, J.C.: Pooled Mean Group estimation on international capital mobility in African countries. Res. Econ. 66, 7–17 (2012) 8. Blackburne, E.F., III., Frank, M.W.: Estimation of nonstationary heterogeneous panels. Stata J. 7, 197–208 (2007) 9. Chen, Y., Wang, Z., Zhong, Z.: CO2 emissions, economic growth, renewable and non-renewable energy production and foreign trade in China. Renew. Energy 131, 208–216 (2019) 10. Cherni, A., Jouini, S.E.: An ARDL approach to the CO2 emissions, renewable energy and economic growth nexus: Tunisian evidence. Int. J. Hydrogen Energy 42, 29056–29066 (2017) 11. Choi, I.: Unit root tests for panel data. J. Int. Money Finance 20, 249–272 (2001) 12. Copeland, B., Taylor, M.S.: North-South trade and the environment. Q. J. Econ. 109, 755–787 (1994) 13. Dong, K., Hochman, G., Zhang, Y., Sun, R., Li, H., Liao, H.: CO2 emissions, economic and population growth, and renewable energy: empirical evidence across regions. Energy Econ. 75, 180–192 (2018) 14. Enerdata: Global Energy Statistical Yearbook (2018) 15. Holtz-Eakin, D., Selden, T.M.: Stoking the fires? CO2 emissions and economic growth. J. Public Econ. 57, 85–101 (1995) 16. Hurlin, C.: What would Nelson and Plosser find had they used panel unit root tests? Appl. Econ. 42, 1515–1531 (2010) 17. Im, K.S., Pesaran, M.H., Shin, Y.C.: Testing for unit roots in heterogeneous panels. J. Econometrics 115, 53–74 (2003) 18. Inglesi-Lotz, R., Dogan, E.: The role of renewable versus non-renewable energy to the level of CO2 emissions a panel analysis of sub- Saharan Africa’s big 10 electricity generators. Renew. Energy 123, 36–43 (2018) 19. Kuznets, S.: Economic growth and income inequality. Am. Econ. Rev. 45, 1–28 (1955) 20. Levin, A., Lin, C., Chu, C.: Unit root test in panel data: asymptotic and finite sample properties. J. Econometrics 108(1), 1–24 (2002) 21. Maddala, G.S., Wu, S.: A comparative study of unit root tests with panel data and a new simple test. Oxford Bull. Econ. Stat. 61(S1), 631–652 (1999) 22. Menyah, K., Wolde-Rufael, Y.: CO2 emissions, nuclear energy, renewable energy and economic growth in the US. Energy Policy 38, 2911–2915 (2010) 23. Pesaran, M.H., Shin, Y., Smith, R.P.: Pooled mean group estimation of dynamic heterogeneous panels. J. Am. Statistical Assoc. 94(446), 621–634 (1999) 24. Pesaran, M.H., Smith, R.P.: Estimating long-run relationships from dynamic heterogeneous panels. J. Econometrics 68(1), 79–113 (1995) 25. Sapkota, P., Bastola, U.: Foreign direct investment, income, and environmental pollution in developing countries: panel data analysis of Latin America. Energy Econ. 64, 206–212 (2017) 26. Shahbaz, M., Shafiullah, M., Papavassiliou, V.G., Hammoudeh, S.: The CO2 –growth nexus revisited: a nonparametric analysis for the G7 economies over nearly two centuries. Energy Econ. 65, 183–193 (2017) 27. Sinha, A., Shahbaz, M.: Estimation of environmental Kuznets curve for CO2 emission: role of renewable energy generation in India. Renew. Energy 119, 703–711 (2018) 28. Solarin, S.A., Al-Mulali, U., Musah, I., Ozturk, I.: Investigating the pollution haven hypothesis in Ghana: an empirical investigation. Energy 124, 706–719 (2017) 29. Yang, H., He, J., Chen, S.: The fragility of the environmental Kuznets curve: revisiting the hypothesis with Chinese data via an “extreme bound analysis.” Ecol. Econ. 109, 41–58 (2015)

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30. Zhang, C., Zhou, X.: Does foreign direct investment lead to lower CO2 emissions? Evidence from a regional analysis in China. Renew. Sustain. Energy Rev. 58, 943–951 (2016) 31. Zoundi, Z.: CO2 emissions, renewable energy and the environmental Kuznets curve, a panel cointegration approach. Renew. Sustain. Energy Rev. 72, 1067–1075 (2017)

Chapter 15

Perception, Knowledge, Attitude and Behavior Toward Climate Change—A Survey Among Citizens in Timisoara, Romania Iudit Bere-Semeredi and Adrian-Amedeo Bere-Semeredi Abstract Climate change has notable effects in the recent decades, as impacting the ecological environment, social–economic development and public health, affecting the citizens’ everyday lives. The extreme meteorological events recorded in recent years in Romania and the threats posed by climate change have been particularly pronounced in cities. Climate change is a significant threat to the well-being of people and affects all of the social–economic sectors. The purpose of the study is to assess the perception of Timisoara’s citizens on climate change. Our key findings reveal data on citizens’ perception, attitude, behavior and general knowledge of climate change and on the importance of climate change in the citizens’ lives. The study results provide valuable insight into the future work of the local government decisionmakers to develop well-defined strategies and mitigation plans for climate change and appropriate adaptation steps. Moreover, understanding the need for knowledge and information on climate change is crucial in reshaping the communication tools and channels for more targeted information dissemination, enabling collaboration in a new and improved way. The citizens’ attitude and support as policy receivers are highly important for the successful implementation of the future Local Climate Action Plan. Keywords Climate change · Citizens · Perception · Knowledge · Behaviour

I. Bere-Semeredi (B) Faculty of Management in Production and Transportation, Politehnica University of Timisoara, 14 Remus street, 300191 Timisoara, Romania e-mail: [email protected] A.-A. Bere-Semeredi The City Hall of Timisoara, 1 C.D. Loga Boulevard, Timisoara, Romania e-mail: [email protected] © Springer Nature Switzerland AG 2020 G. Prostean et al. (eds.), Innovation in Sustainable Management and Entrepreneurship, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-44711-3_15

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15.1 Introduction According to the European Environment Agency Report “Climate Change, Impacts and Vulnerability in Europe 2016” [1], climate change affects the well-being of ecosystems, economic sectors and human health [2], across Europe. At global level, the three decades after 1985 were likely the warmest periods of the last almost one and half century [3]. The sensitivity of both natural and human systems to climate change is evidenced by observable impacts related to climate change, irrespective of the causes. Greenhouse gas (GHG) emissions decreased in the majority of sectors in between 1990 and 2017 [4], but new aspirational targets are still needed. The “2030 Agenda for Sustainable Development” (2015) gave new impetus to global efforts to achieve sustainable development [5]. As far as GHG emission reduction is concerned, the EU has set ambitious targets for 2030, targeting three key areas: a reduction of at least 40% in GHG emissions compared to 1990 levels, a percentage of at least 27% renewable energy in energy generation and an improvement of at least 27% in energy efficiency [6]. Additionally, further emission cut targets have been set by the European Commission for 2050 [7]. In such context, cities are the major contributors to climate change, the urban areas counting for over 70% of the Earth’s CO2 emissions in the global final energy use, with a significant amount of GHG emissions, while urban areas cover just around 2 per cent of the planet surface. At the same time, cities, towns and villages are the communities heavily vulnerable to climate change. In the urban areas across the world, millions of people are affected by the rising sea levels, inland floods, severe storms and powerful cyclones or increased precipitation, alternate with periods of more extreme heat and cold. The future forecast, mentioned in the IPCC Fifth Assessment Report [8] (following the previous report [9]), is not very optimistic: “It is also very likely that heat waves, defined as spells of days with temperature above a threshold determined from historical climatology, will occur with a higher frequency and duration” [10]. Romania is not an exemption in point. Extreme weather events were registered in all of the country’s regions, especially severe windstorms, heavy precipitations, floods, heat wave droughts, alternate with cold waves. And ever since, events that were very rare in the past may become much more frequent in the future [11]. In such context, the future municipal strategic planning for the future must address both improving performance of ongoing programmes and redefining performance to meet new challenges [12]. In the last years, Timisoara was the scene of extremely dangerous weather phenomena, manifested in the form of strong windstorms, torrential rains in short periods of time alternating with periods of high temperatures and relative humidity and drought, but especially with very steep changes from periods warm in cold periods. Long-term and short-term anomalies regarding weather phenomena and especially temperature variations have a strong impact on vulnerable population groups. Such phenomena, of great social concern, have tested the capacity of both local public

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authorities and citizens themselves. It is therefore necessary to analyze the topic of climate change in all its aspects, in order to heighten the preventive and reactive capacities of local administrations. The Paris Agreement [13] recognizes the role of local public authorities at city and regional levels as well as the role of the private sector, of the civil society and of other interested parties in addressing climate change, inviting them to scale up their efforts in order to reduce emissions, heighten resilience and lower vulnerability to the adverse effects of climate change. Moreover, the Paris Agreement highlights the necessity of upholding and promoting regional and international cooperation. Behavioral change and citizens’ attitude are mostly neglected in the analyses undertaken in the field of climate change mitigation and adaptation. For meeting the international and national targets established by the Paris Agreement and by the “National (Romanian) Action Plan to implement the national strategy on climate change and economic growth based on low-carbon economy for the period 2016–2020” and further extended for the year 2030, radical change of behavior and lifestyle with high emissions like those who register today is required. Addressing the latter, the present study sought to provide a synopsis of the current perception, knowledge, attitude and behavior regarding climate change amid residents in different neighborhoods in Timisoara.

15.2 Materials and Methods The citizens’ interactions with the climate change matter occur at all levels, but most existing researches focus on international or national governments, economic trends, research and development as industrial and economical drivers or technological aspects that are considered main “engines” for the climate change. Even though at national, regional and local level there already exist climate change plans in preparation or implementation, the factors that influence decision-makers and behavior at the individual or collective level have received little attention. After implementing the Sustainable Energy Action Plan at local level, it is more than obvious that the individual behavior drives societal change through knowledge, raising awareness and adoption of new available technologies and supports the local climate change policies.

15.2.1 The Study Site The survey was realized in the municipality of Timisoara, one of the first 3 cities of Romania. The city is the capital of Timis County, with a population of 328,168 inhabitants (on January 1, 2019). Timisoara was and remains an important economic, social and cultural center. In terms of geography, Timisoara is located in the intersection of 45th north latitude parallel with the 21st east longitude meridians, in the

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West Plain of Banat, within the Bega–Timis–Caras hydrographic basin. The survey was drawn to cover all historical and larger neighborhoods and smaller-size urban areas of interest called “zone.”

15.2.2 The Research Sample Definition The survey was run in the spring of 2019, by two researchers, experienced in qualitative research as well as in climate change and public administration topics, in close collaboration with the City Hall of Timisoara—Environmental Directorate—the specialized department entrusted with the development of the “Sustainable Energy Action Plan of the Municipality of Timisoara for 2014–2020” [14] and responsible for the future development of the Climate Change Action Plan for the Municipality of Timisoara, as signatory of the “Covenant of Mayors for Climate and Energy” [15] (in November 2018). Three hundred and twelve citizens were involved in the survey, in the segment of population aged 15 years and over (285,963 residents), in Timisoara. The chosen interview method was face to face, which allowed for an accurate screening, capture of nonverbal cues, focus and emotions captured. Twelve respondents were contacted directly in each neighborhood or “zone.” The citizens were asked to take 15 minute to fill a questionnaire on environmental issues and climate change, to help the municipality develop the future local strategy and action plans in such area. The interview was run on the public domain of Timisoara, in each neighborhood, the respondents being asked only for data on gender, age, type of housing, education and profession. The young citizens (age 15–18) were interviewed in the presence of at least one parent, on verbal consent thereof. The survey area covered all types of buildings, from rural- and villa-type houses, to multifamily housing blocks (condominiums) (Table 15.1). Table 15.1 Demographic characteristics of the inhabitants and citizens involved in the survey Timisoara1

Population Total

Males

Females

Total

Man

Woman

Population

328,186

153,228

174,958

312

154

158

15–24

23,597

11,922

11,675

75

41

38

25–39

83,000

40,039

42,961

82

37

45

40–54

78,524

37,295

41,229

76

42

34

+55

100,842 (30.8%)

42,295

58,547

75

34

41

285,963

131,551

154,412

Citizens involved in the survey

Age

Total (15+ ) 1 Romanian

National Institute of Statistics, data available for January 1, 2019

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In terms of demography, the respondents’ target group was formed by citizens classified into four age groups: 15–24, 25–39, 40–54 and +55; the proportion of men and women was equal. In terms of level of respondents’ education level, 56% of them were graduates of high education (bachelor’s degree), 26.6% were graduates of a university education program and only 12.5% of them had graduated secondary school education (gymnasium). From the perspective of respondents’ social–professional status, all main categories were represented: self-employed, managers and assimilated positions, white-collars (mainly public servants), workers, house workers, unemployed persons, pensioners and of course the young generation, represented by students (Table 15.2). Prior to research activity, a review of the literature on perception, attitude, behavior and knowledge regarding climate change was performed. It should be emphasized from the outset that at European level, Eurobarometer surveys on climate change were carried out every two years between 2008 and 2017 in the Member States of the EU [16]. One thousand and thirty-three inhabitants of Romania were involved in Table 15.2 Characteristics of the inhabitants involved in the survey Timisoara

Citizens involved in the survey Total

Man

Woman

106

54

52

50

24

26

156

76

80

Living conditions—type of housing • Rural type of house • Villa type • Multi-flat buildings (condominium) Education levels (completed studies) • Secondary school

39

21

18

175

76

99

• University

83

51

32

• Still studying

15

6

9

• Self-employed persons

28

21

7

• Managers, administrators and assimilated positions

17

10

7

• White-collar workers, associated professionals, clerks

15

9

6

• Manual workers (elementary, trades, service and sales, operators and assemblers, farmers)

68

32

36

24

• High school (bachelor’s degree)

Social–professional category

• House persons

26

2

• Unemployed persons

19

13

6

• Retired/pensioners

31

13

18

108

54

54

• Students (high school and university)

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the study realized in 2017. Eurobarometer survey on climate change was a trustful source of data and inspiration, the same or similar questions being used, in order to perform a comparative analysis of results between EU-28 and the Municipality of Timisoara. The questionnaire developed for Timisoara follows in part the EU survey but aims to express knowledge of the local case. That is why the questionnaire was tailored for the municipal needs, oriented to support the local management informed-decision. The questionnaire used two types of questions: closed-ended responses and questions with multiple answer possibilities.

15.2.3 Aspects of the Research Ethics and Methodology The present study has been developed with the support of the City Hall of Timisoara— Environmental Directorate. All the subjects involved in the research understood the purpose and nature of the present study and partook voluntarily and anonymously. Under such research, no personal data were collected, so it was not necessary to collect informed consent statement from participants prior to their participation in the study. No records (video, audio, photographs) were shot in the interview time. The interview research method used in the research was based on a designed questionnaire. Its structure consists of two parts: the former dedicated to the perception and basic knowledge of citizens on the topic of climate change and climate alongside other current problems of mankind and the latter dedicated to aspects related to the action taken on climate change—responsibility, behavior and attitude through personal involvement or actions already implemented to reduce GHG emissions. An emphasis was placed on determining the gaps between citizen’s understanding, knowledge, attitude and behaviors on climate change and the public authorities’ expectation in terms of implementation of the future actions, including the common steps toward addressing such today’s challenges.

15.3 Research Results and Comments 15.3.1 Perception of the Most Serious and Urgent Single Problem of the World Following the Special Eurobarometer on Climate Change 2017 Report, at the beginning of the study, participants were asked about their opinion on the most serious problem facing humanity today, being presented eight challenges of today’s world. The first question (Q1) addressed to citizens was: “Which of the following listed issues do you think is the most single serious and urgent problem facing humanity at this time?” The potential responses were read each time randomly and repeated two

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or three times, offering the possibility to read responses from the computer screen for a better view of all alternatives of response. This procedure was applied to all questions, allowing the participants to state appropriate responses. Eight main challenging problems of the world were advanced in response, to pick from: (1) hunger, lack of drinking water and poverty; (2) the problem of terrorism; (3) climate change; (4) today’s world economic situation; (5) the threat of armed conflicts; (6) the issue of nuclear weapons; (7) the demographic boom; and (8) the spread of dangerous infectious diseases. The respondents were asked to select one response only, corresponding to the main serious and urgent problem of the world. The respondents were asked to indicate difficulties in paying bills (with three options for answer—most of the time, sometimes or never) and to indicate if they already implemented actions to fight climate change (with two options for response: yes and no). The percentage of people who believe that global climate change is a serious problem of the world was 23.4%, with a significant increase in the proportion of citizens mentioning climate change in the EU-28 (12%) according to Special Eurobarometer 459 Climate Change Report March 2017 or at national level (9%) under the same survey. In further analysis of the responses of citizens in social–demographic perspective, the following results indicate that: • Men (58.9%) are more likely than women (41.1%) to name climate change as a very serious problem of the mankind. • The citizens from condominiums and rural type of houses, the citizens with high education and those who never or almost never have difficulties in paying bills are more likely to indicate climate change as one of the most serious problems facing the world as a whole, followed by citizens from rural type of houses and students. • Respondents aged 55 or over, followed closely by the respondents of 25–39 aged group, are the age groups that tend to indicate climate change as the largest global challenge. • The percentage of “no action to fight climate change” group is more numerous (70%) compared to the citizens who have taken measures in this regard (30%) (Figs. 15.1 and 15.2). Following the analysis of the results of the citizens’ answers on the most serious problem of humanity today, analyzed in the prospect of the social–demographic groups, the following conclusions can be drawn: • Citizens of Timisoara consider that “climate change” represents the most single serious threat on the world level (23%), followed by “hunger, lack of drinking water and poverty” (22%) and “the economic situation” (22%), compared with EU-28 survey performed in 2017. The Eurobarometer on climate change shows low ranking for “climate change” (12%), high for “poverty, hunger and lack of drinking water” (28%) and just 9% for the “the economic situation.” • “International terrorism” has been named as the only single global problem of 8% of citizens compared to the EU-28 survey (24%).

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Fig. 15.1 Results regarding the perception of citizens (broken into groups and number of citizens) on climate change, representing the most serious problem of mankind today

• The share of respondents naming the “the increasing global population” and “spread of dangerous infectious diseases” as one of the most single serious global problems at global level is higher in Timisoara (7 and 6%) than at European level (6 and 3%). • “Armed conflicts” and “proliferation of nuclear weapons” were cited in a lower proportion (8 and 2%) than at EU level (9 and 6%).

15.3.2 Perceived Seriousness of Climate Change The next question (Q2) addressed to the citizens was: “How serious a problem do you think climate change is today at global level?”, having the possibility to choose between “serious problem,” “fairly serious problem” and “not a serious problem” using a scale from 1 to 10 (Table 15.3). Seventy-four percentage of the respondents consider that climate change is a very serious problem of humankind, while almost a quarter (23%) see it is a fairly

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Fig. 15.2 Results on the perception of citizens about the most serious problem of mankind today

serious problem. The final results are very close to EU-28 survey, realized in 2017. An extremely small percentage (3%) appreciates climate change as a not serious problem.

15.3.3 Climate Change and the Main Eight Challenging Problems of the World The third question (Q3) addressed to the citizens was: “Which other of the following topics do you consider to be the most serious problem facing the world as a whole?” a question with maximum four answer possibilities (Table 15.4). After analyzing the results of the questionnaire, the following social–demographic groups have been identified as the most likely to mention climate change as one of the most serious problems facing the world today: • White-collar group (100%), students (92%), respondents aged between 15 and 39 (90 and 91%), secondary school-level group (88%), woman (81%) and citizens who occasionally face difficulties paying bills (79%); • Citizens who answered that they have taken personal action against climate change (99%);

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Table 15.3 Results of citizen’s perception on seriousness of climate change Climate change

Serious (1–4)

Fairly serious (5–6)

Very serious (7–10)

EU-28 (%)

6

18

74

Timisoara, 2019 (%)

3

23

74

Man

3

43

108

Woman

7

28

121

15–24

2

15

62

25–39

0

11

71

40–54

1

16

59

+ 55

7

29

39

Gender (no. of citizens)

Age groups (no. of citizens)

Education—finalized studies (no. of citizens) Secondary school

0

8

31

High school (bachelor’s degree)

9

34

132

University education

1

27

55

Still studying

0

2

13

Social–professional category (no. of citizens) Self-employed persons

0

9

19

Managers, administrators and assimilated positions

0

4

13

White-collars, associated professionals, clerks

0

0

13

Manual workers (elementary, trades, service and sales, operators and assemblers, farm and related workers)

0

12

56

House persons

6

6

15

Unemployed persons

3

12

4

Retired

1

15

16

Students (high school and university)

0

13

95

Actions taken to fight climate change (no. of citizens) Yes

0

6

141

No

10

65

90

• Citizens in the 40–54 age group (79%), especially compared to those over the age of 55 (60%); • Manual workers (77%), particularly when compared with house person (58%), retired person (46%) and the unemployed person groups (27%); • People who do not experience financial difficulties in paying bills (86%);

70

64

EU-28

Timisoara, 2019

Woman

87

71

39

25–39

40–54

+ 55

98

67

62

40

Secondary school

High school (bachelor’s degree) level

University-level education

Still studying

Education (finalized studies)

83

15–24

Age groups

69

70

Man

Gender

Hunger, drinking water, poverty (%)

Timisoara

0

34

31

21

44

33

16

28

26

34

30

62

Terrorism (%)

47

69

81

88

60

79

91

90

81

77

74

43

Climate change (%)

34

90

76

67

86

83

75

71

81

74

77

42

World economic situation (%)

20

47

51

62

63

46

38

66

52

52

50

34

Threat of armed conflicts (%)

Table 15.4 Citizens’ perception results on the other urgent problems facing the world today

0

23

18

11

32

15

13

18

11

27

19

27

Nuclear weapons (%)

7

52

32

11

46

43

26

22

33

34

36

23

Increasing global population (%)

47

26

46

44

24

33

59

44

47

33

39

22

(continued)

Spread of dangerous infectious diseases (%)

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Hunger, drinking water, poverty (%)

75

71

80

37

27

16

39

98

Self-employed persons

Managers and administrators

White–collar, associated profess, clerks

Manual workers (elementary, trades, service and sales, operators and assemblers, farm and related workers)

House persons

Unemployed

Retired

Students (high school and university)

Social–professional category

Timisoara

Table 15.4 (continued)

16

49

53

62

8

27

12

29

Terrorism (%)

92

46

27

58

77

100

89

68

Climate change (%)

66

91

85

89

50

100

77

90

World economic situation (%)

49

68

74

81

21

20

30

50

Threat of armed conflicts (%)

10

17

58

20

8

7

36

29

Nuclear weapons (%)

14

65

69

47

14

40

59

40

Increasing global population (%)

56

20

16

23

82

40

30

25

(continued)

Spread of dangerous infectious diseases (%)

210 I. Bere-Semeredi and A.-A.Bere-Semeredi

Hunger, drinking water, poverty (%)

97

Rarely/never

Don’t know

89

62

Yes

No

Actions taken to fight climate change

59

78

Sometimes

28

Mostly

Payment difficulties (energy bills)

Timisoara

Table 15.4 (continued)

31

28

17

24

33

57

Terrorism (%)

72

99

90

86

79

51

Climate change (%)

82

71

65

80

81

87

World economic situation (%)

57

44

55

47

44

71

Threat of armed conflicts (%)

23

11

11

23

17

28

Nuclear weapons (%)

38

25

14

32

42

53

Increasing global population (%)

39

44

53

34

41

28

Spread of dangerous infectious diseases (%)

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• Managers (89%), especially when compared with self-employed person (68%) or unemployed person groups (27%); • An interesting group is formed by the respondents that answered “Don’t know” for the question related to difficulties in paying bills but mentioned “climate change” with a high percentage (90%). This group is in fact formed by students or younger citizens still living in the parental home, with no significative implication in the bill payments. Timisoara percentage is much higher (74%) than the one registered at EU-28 level (43%). We believe that such high rate is a consequence of the extreme weather events in the latest three years, in which many families suffered material losses.

15.3.4 Responsibility for Tackling Climate Change The second part of the survey was dedicated to the measures taken regarding climate change, the citizens being asked about their opinion on the institution or group of institutions that they consider responsible for addressing and taking measures to combat the effects of climate change, but also about the personal steps taken in this regard. Respondents were offered a set of possible responses expressing actions to mitigate the effects of climate change being asked about their involvement in the implementation of such actions. The proposed list of responsible institutions, organizations and persons included: (1) Romanian government, (2) the EU, (3) business and industry sectors, (4) regional and local authorities, (5) the respondent itself, (6) environmental groups, (7) other, (8) all of them, (9) none, (10) don’t know. The respondents were able to choose as many responses as they wished, from the list of options mentioned. As can be seen in Fig. 15.3, in Timisoara, the EU and the national government are the most commonly indicated responsible institutions, 54%, respectively 52%, compared with the EU-28 survey in 2017 that indicate lower percentage (39, respectively 43%). Four out of ten citizens indicated the business sector and industry, but also the regional and local authorities (42%) as the areas, respectively the institutions responsible for combating the effects of climate change, while in the EU-28 survey were mentioned in a lower percentage, 38%, respectively 22%. A high and unexpected percentage was noticed regarding the answer which indicates the respondent’s personal involvement as responsible for tackling climate change (24%), higher than at EU-28 level (22%). The percentage of respondents who believe that the responsibility for combating the effects of climate change falls on all of the actors of the society (24%) is higher than in the EU-28 survey of 2017 (20%). The environmental protection associations were indicated only to a small extent (11%). The questionnaire proposed a list of actions in different sectors (as depicted in Fig. 15.4): waste management, eco-buying, eco-friendly transport, energy efficiency, transport patterns, renewable energy sources, etc. Respondents could choose one or more options from the list provided, with no limit to the number of answers allowed.

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Fig. 15.3 Results of the questionnaire regarding responsibility for tackling climate change

More than four in ten respondents (42%) have said that they try to reduce the amount of waste generated and that they separate their waste for recycling purposes. Just under four in ten have responded that they try to reduce the number of disposable products they use (plastic bags, packaging, etc.) and buy seasonal food produced locally. At least three in ten choose to replace the old house appliances with new, energy-efficient ones. Eco-friendly means of transport as alternative to the private cars—bicycles, public transport or walking instead using cars—was mentioned just by 21% of respondents. All the results are far below the EU-28 survey realized in 2017. Avoiding short-haul flight, buying a fuel-efficient car and smart metering installation are actions indicated by 6% of respondents, in a lower proportion than in EU-28 survey (10, 9 and 8%). Actions regarding switching to an energy supplier that provides a greater share of energy from renewable sources register at the lower end of the scale (3%). The number of respondents in the “no action” category is surprisingly high, 18%, compared with the levels recorded in EU-28 (9%). However, we noticed that some of those who answered that they had taken no personal action in the fight

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Fig. 15.4 Results regarding citizen’s personal action for tackling climate change

against climate change did chose one or more actions from the list provided for the following question. Citizens who identified climate change as a very serious problem facing the world today are more likely to have taken some sort of action compared to those that did not identify climate change as a real problem of the world. In the final part of the survey, the citizen’s attitude and engagement toward climate change were studied (research results are depicted in Fig. 15.5). Five questions with short-ended answers (yes/no) were used: (1) “Would you like to participate as a volunteer to the environmental activities and works alongside the municipality?”, (2) “Do you consider that sacrificing some individual benefits could solve current climate change problems?”, (3) “Do you intend to participate in the future local organized activities in the topic of climate change?”, (4) “Did you participate in the last year to local environmental activities?” and (5) “Would you like to join to the actual local effort to mitigate climate change?”. Public participation is an

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Fig. 15.5 Results regarding the citizen’s attitude and engagement actions for tackling climate change

extremally important aspect of local planning, especially on the topic of climate change, because the success of implementations of actions and measures resides to an equal extent in the municipality’s efforts and the citizens’ commitment and individual support. A positive reaction was registered among Timisoara’s citizens, manifested by the willingness for future participation to the local environmental activities and for participating as volunteer to the environmental activities and works.

15.4 Conclusions The research results achieved from the study have been summarized as the following: • In Timisoara, climate change is considered one of the most important global issues along with hunger, lack of drinking water, poverty and the economic well-being. • A large majority of Timisoara’s citizens see climate change as a real and very serious problem of the world. • From the citizens’ point of view, the European Union and the national government are the primary entities responsible for tackling climate change. • Nearly half of the people questioned have said that they have taken personal action in the fight against climate change. • A large majority of Timisoara’s citizens have a positive attitude and behavior toward energy efficiency and eco-friendly transport means. • Understanding of the notion of climate change is sometimes limited, it often being confused with other urban issues. During the survey, when we mentioned about climate change, we noted causes misunderstood for effects, the latter seemingly better acknowledged than the causes. • The importance ascribed to issues pertaining to climate change and the mitigation of its effects, the seriousness with which the topic is approached and the fact that national and local public authorities are considered to be the main actors responsible for fighting its effects, are all reflected in the views of the citizens.

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The survey allows a better understanding of the local status of climate change. Such conclusions help the local authority to develop the future Climate Action Plan effectively, by engaging the local community in a transparent, public debate about the practical actions that can be taken to mitigate the effects of climate change and adapt to climate change. Finally, the survey allows for future research in the areas of energy poverty, learning, awareness raising and communication of climate change problems. Acknowledgements This paper would not hot have been possible without the voluntary and anonymous participation of citizens. Their participation is sincerely appreciated. Furthermore, the interview assisted us to gain insights and perception of each respondent who participates in the survey, opening new paths into the study of climate change and pro-environmental behavior field.

References 1. European Environment Agency (EEA).: Climate change, impacts and vulnerability in Europe 2016. https://www.eea.europa.eu/publications/climate-change-impacts-andvulnerability-2016 (2016) 2. Kendrovski, V., Schmoll, O.: Priorities for protecting health from climate change in the WHO European Region: recent regional activities. Bundesgesundheitsbl 62, 537 (2019). https://doi. org/10.1007/s00103-019-02943-9 3. Gonzalez-Ortiz, A., Reichel, A., Tejedor Arceredillo A., Unterstaller, A., Meiner, A., Lukewille, A., Bastrup-Birk, A., Marquardt, D., Tryfon, E., Peris Aguilo, E., Dogan Ozturk, E., Antognazza, F., Dejean, F., Martin, J., Biala, K., Adams, M., Tomescu, M., Kazmierczyk, P.N.Z., Kristensen, P., Fernandez, R., Pignatelli, R., Speck, S.U., Schilling, S., Vanneuville, W., Trier, X. and Hoogeveen Y.: European Environment Agency, Publications Office of the European Union. https://www.eea.europa.eu/data-and-maps/indicators/global-and-europeantemperature-9/assessment (2018) 4. Mandl, N., Pinterits, M.: Annual European Union greenhouse gas inventory1990–2017 and inventory report 2019, European Commission, DG Climate Action, European Environment Agency (EEA), European Topic Centre on Climate Change Mitigation and Energy (ETC/CME) supported by the Joint Research Centre (JRC) and Eurosta. https://www.eea. europa.eu/publications/european-union-greenhouse-gas-inventory-2019 (2019) 5. United Nation, Transforming our world: the 2030 Agenda for Sustainable Development. https://sustainabledevelopment.un.org/post2015/transformingourworld; https://ec.europa.eu/ clima/policies/strategies/2030_en 6. COM (2019) 22.: Reflection Paper towards a Sustainable Europe by 2030, European Commission, issued in 30 Jan 2019. https://ec.europa.eu/commission/sites/beta-political/files/rp_ sustainable_europe_30-01_en_web.pdf (2019) 7. COM (2011) 112: A Roadmap for moving to a competitive low carbon economy in 2050, 08 Mar 2011. https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=COM:2011: 0112:FIN:EN:PDF 8. IPCC.: Fifth Assessment Report AR5 Synthesis Report: Climate Change 2014. https://www. ipcc.ch/assessment-report/ar5/ 9. IPCC.: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Geneva: IPCC. Available at https://www.ipcc.ch/site/assets/uploads/2018/05/SYR_AR5_FINAL_full_ wcover.pdf

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10. Collins, M.R.K., Arblaster, J., Dufresne, J.-L., Fichefet, T., Friedlingstein, P., Gao, X., Gutowski, W.J., Johns, T., Krinner, G., Shongwe, M., Tebaldi, C., Weaver, A.J., Wehner, M.: Long-term climate change: projections, commitments and irreversibility. In: Stocker, T.F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V., Midgley, P.M. (eds.) Climate change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK, and New York 11. Dosio, A., Mentaschi, L., Fischer, E.M., Wyser, K.: Extreme heat waves under 1.5 °C and 2 °C global warming. Environ. Res. Lett. 13, 054006 (2018). https://doi.org/10.1088/1748-9326/ aab827 12. Wauters, B.: Strategic management in the public sector: a tool for improving performance of ongoing operations or for refining performance to meet new challenges? Report for the European Commission’s Public Administration and Governance network. https://ec.europa. eu/esf/transnationality/sites/esf/files/pag_network_strategy_paper_full.pdf (2017) 13. United Nations.: Paris Agreement. https://unfccc.int/sites/default/files/english_paris_ agreement.pdf (2015) 14. Sustainable Energy Action Plan of the Municipality of Timisoara for 2014–2020. https://www. primariatm.ro/index.php?meniuId=2&viewCat=3832 (2014) 15. The Covenant of Mayors for Climate and Energy. https://www.covenantofmayors.eu/IMG/pdf/ covenantofmayors_text_en.pdf 16. Special Eurobarometer 459—Climate Change, Survey requested by the European Commission. Directorate-General for Climate Action and co-ordinated by the Directorate General for Communication, Wave EB87.1—TNS opinion & social. https://ec.europa.eu/clima/sites/clima/ files/support/docs/report_2017_en.pdf (2017)

Chapter 16

Application of Circular Economy Principles in the Luxury Fashion Industry: The Case of the RealReal Manuela Mih˘ailiasa and Silvia Avasilc˘ai

Abstract The present linear economic system needs to be redesigned in order to protect the ecosystems and ensure a sustainable integration of resources. One of the industries that have raised concerns in the last years due to its increasing inefficient use of resources and negative impact on the environment is the fashion industry. Sustainability and transparency have become increasingly important as a response to the consumers’ and companies’ growing concerns in mitigating their impact on the environment. The article describes one of such companies that has managed, through its business model, to disrupt the traditional business modus operandi. The research centers around how the business of The RealReal successfully operates through the application of certain principles of the circular economy. Keywords Circular economy · Circular fashion · Network effect · Sustainability

16.1 Introduction The linear economy model generates waste, deteriorates ecosystems and inefficiently uses energy and materials. These drawbacks have determined policy makers into rethinking the economic system into one based on circularity. In the new circular system, resources are kept in use as long as possible, while the maximum value is extracted from them during usage, and waste by-products, at the end of their life cycle, are continuously regenerated [1–3]. The potential benefits of the circular economy appear to be numerous. Not only economic benefits for organizations such as material cost savings, reduced price volatility and improved security of supply can be achieved, but also environmental pressures and impacts can be reduced [4]. The economic system based on the circular economy principles endeavors to eliminate, M. Mih˘ailiasa (B) · S. Avasilc˘ai Faculty of Industrial Design and Business Management, “Gheorghe Asachi” Technical University of Iasi, Ias, i, Romania e-mail: [email protected] S. Avasilc˘ai e-mail: [email protected] © Springer Nature Switzerland AG 2020 G. Prostean et al. (eds.), Innovation in Sustainable Management and Entrepreneurship, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-44711-3_16

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minimize and track the usage of toxic substances, to use renewable energy and to subscribe to the zero-waste principle. Thus, a more sustainable developed and overall balanced society is expected to arise [5]. One of the most polluting industries in the world is the textile industry, due to its use of a resource-intensive supply chain that causes enormous amounts of waste and heavily pollutes the air, water and soil [6, 7]. Textile waste is generated through the entirety of the life cycle of a textile product, and it is, currently, generally discarded in a landfill, with very little recycling and reusing. The textile and clothing industry is a big part of the global economy, with the clothing industry employing 300 million across the value chain, worldwide, and worth USD 1.3 trillion [8]. More than 60% of the textiles used is represented by clothing items and has doubles in size in the last 15 years [8]. The rise is determined, mainly, by the fast fashion phenomenon, that emphasizes a quick turnaround of new styles, lower prices and an increased number of collections that are offered each year. The fashion clothing industry system operates in an almost completely linear manner. To produce clothes, massive amounts of nonrenewable resources are used and lost due to landfill or incineration, after the short amount of time the clothes are worn [9]. More than half of the fast fashion items are disposed of in under a year [10]. The fashion linear system has many negative consequences and undervalued economic opportunities, usage of resources, pollution, natural environment degradation and societal impact at the local, regional and global scales. Though difficult to quantify, if these setbacks would be addressed, the overall benefit to the world economy would reach EUR 160 billion [9]. Worldwide, the average number of times a garment is worn has decreased by 36% in the last 15 years. As a consequence, each year there is a missed opportunity for gaining USD 460 billion in discarded clothing if the consumers would continue to wear their clothes, instead of buying new ones [11]. Sustainable fashion brands with business models that are based on the circular economy principles make up the circular fashion initiative. Circular fashion implies that every aspect of the life cycle of the garment is cyclical and starts with the design phase and continues with sourcing, production, reusing and recycling [12]. Changing customer demands may be the most powerful influence in shifting fashion brands and retailers, and those that do not respond quickly enough risk being left behind. Customers concerned with social and environmental issues are now demanding sustainable and ethical fashion [13]. Young customers are demanding unlimited access to fresh styles, while others are looking for online platforms where luxury and vintage garments can be found. These growing customer segments require the industry to change [14]. The Ellen MacArthur Foundation has suggested a new framework for a new textile economy, in which, a crucial role has the increase of clothing utilization [8]. This can be achieved in two ways, either through continuous utilization of the same product by one consumer or by encouraging the buying and selling of second-hand clothing, also known as pre-loved. The latter allows the extension of the product life cycle of a garment so that it does not end up in a landfill. Companies like The RealReal operate e-commerce sites that allow their consumers to buy and sell pre-loved luxury and

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high-street items on their platforms. As the second-hand luxury market continues to grow into a sector worth USD 25 billion, it surpassed the growth of the primary luxury goods industry [13].

16.2 Case Study: The RealReal The following is a summary of the case study regarding the The RealReal’s business model and the implementation of circular economy principles. The first part contains and overview of the company, while the second one described the business model and how it implements the principles of the circular economy.

16.2.1 Profile of the Company The The RealReal is a brand owned by the The RealReal Incorporated, a company based in San Francisco, United States of America (USA), that was founded in 2011 by Julie Wainwright. The company developed an online marketplace platform (www. therealreal.com) that has become one of the largest online platforms for authenticated, consigned luxury goods [15]. In offline, The RealReal opened three retails shops in New York and Los Angeles, USA. Recently, the company raised USD 300 million in an initial public offering, with a market cap of USD 2.39 billion [16]. Through the online platform, the user benefits from an end-to-end service by using trusted, curated goods. The online e-commerce platform reaches buyers globally and unlocks supply from consignors all over the world. Though initially the platform offered access only to luxury second-hand garments for women, men and children, nowadays, the buyers can have access to luxury jewelry, furniture, décor and fine art as well. The mission of the company is to extend the life cycle of luxury goods by and honoring luxury brands through empowering consigners and buyers, thus promoting the recirculation of luxury goods, rather than creating waste. The company is dedicated in promoting circular fashion and reshaping consumer purchasing behavior [15]. As a challenge, the luxury resale market is difficult to access, outdated and encumbered with counterfeit merchandise, while many luxury goods are unused and stored by their owners. The RealReal addresses these challenges by providing an easy to access service for both buyers and sellers, through a proprietary technology platform and data. The seller ships the merchandise for free to the warehouse, can schedule a free in-home pickup in some areas or brings it into one of the 11 consignment offices across the country owned by the company. The consignors have access to My Sales, a dynamic part of the platform, through which they can track their shipments, receive market data such as which items are trending or sold quicker. The goods are processed by the specialized personnel that authenticates, photographs, inputs prices

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and uploads the items on the online platform. The objective is to sell the item as quickly as possible (within 30 days). Thus, the online marketplace sell-through (the ratio between demand and supply in gross merchandise volume) reached high levels of 93% for 2017 and 96% for 2018. Only items under a certain list of brands are accepted. After the sale, the seller receives a percentage of the selling price (up to 85%) by direct deposit, site credit or check. The percentage rises with frequent merchandise input. For the buyers, the online platform and mobile application offer a vast and curated supply of pre-owned luxury goods that amounted to 9.4 million items in 2018, an increase of 2.6 million over the previous year [15]. The RealReal has also built a community of trained experts that inspect the quality, condition and authenticity of every luxury item that it is sold. Thus, the buying process and the goods trusted by the consumer and drives repeat purchases. The company’s revenue is generated through the orders that are processed by the online marketplace, mobile application and the three offline retail stores from New York and Los Angeles, USA. In 2018, over 1.6 million orders were processed, signifying and increase by 42% from 2017. The average order value is also increased by 2% at USD 446 since 2017. The gross merchandise value has risen by 44% since 2017, to USD 710.8 million, generating a total revenue of USD 207.4 million (up 55% over 2017) and a gross profit of USD 136.9 million (up 56% from 2017) [15]. The RealReal offers also several reward programs such as the Consignor Affiliate Program and the Influencer Program. In the Consignor Affiliate Program, any new referral to a new consignor that consigns new items is rewarded with a USD 5 for each item and a commission of 5% if the sale price of the consigned items is over USD 3000. The Influencer Program rewards the influencers to promote The RealReal on their social media or Web site when items are bought by their followers. Thus, for any purchase from their followers, the influencer receives a 5% commission or a 7% one, if it is a first-time buyer. Generally, top influencers earn, on average, USD 5700 per year, with the top one receiving approximately USD 60.000 per year due to the Influencer Program. Also, for the buyers, The RealReal offers a monthly subscription of USD 10 (First Look) and USD 30 (First Look Platinum), where buyers pay to have a 24 h preferential access to new items, items on sale or other promotions, before everyone else, as well as free express shipment. In the last year, The RealReal has ventured to business clients as well, through two dedicated programs: The Trust and Estates Services and the Business Sellers. The first program is for the trusts and estates that own collection of luxury items. The company offers to appraise the collections, to price the items and to sell them, with in-home pickup. For business sellers, The RealReal offers to sell the stock of merchandise from previous seasons. A new initiative of the company, that was born from partnering with Ellen MacArthur Foundation and Stella McCartney, is undergoing, where each consignor that consigns a Stella McCartney item receives USD 100 for shopping on the platform.

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16.2.2 Business Model and Principles of Circular Economy Application The RealReal Inc. was designated as one of the leading 50 disruptor companies in 2018, that challenges the status quo of the economic system through a disrupting business model [17]. The business model of The RealReal is based on a dropshipping e-commerce platform, that is a retail model that provides an online platform for consumers to sell their goods to other consumers, and where sellers are not responsible for any fulfillment costs. The seller is, generally, also the consumer of other goods from the online platform. In the case of The RealReal, the platform has started as a consumer-to-consumer endeavor, but, nowadays, the business model includes business-to-consumer as well. In the resale luxury business models, there are several challenges that The RealReal is addressing on a continuing basis. One of them is the hardship that the consignor faces when the products need to be shipped, dropped off and self-listed on a peer-to-peer platform. The RealReal has managed to develop a well-thought procedure, where they eliminate this friction from the consignor, through the free in-home pickup, consignment shops where the items can be dropped off, or free shipping. Another challenge is the luxury market itself that is riddled with counterfeiting merchandise. For this, The RealReal employs more than 100 specialized personnel, from brand authenticators to horologists, gemologists and art curators in order to build the trust for buyers in buying second-hand goods. The third challenge is the fragmentation of supply in the luxury market, where the items that are available for resale are, generally, distributed across several retail outlets, with a high price variation, leaving the consignors to have fewer gains, and, thus, fewer incentives to sell. This is tackled by the pricing strategy employed by the company, that ensures the goods are suitable priced, in a reliable range, with hardly any fluctuation (outside the sales period). The real cornerstone of The RealReal business model is derived by their major competitive strength—the scaling and powerful network effect. The RealReal is the largest online marketplace for consigned luxury goods that has built its critical mass through the benefits they offer the buyers and sellers. The network effect replicates itself through the different rewards program and consigners—buyers’ engagement, where consigners become buyers and vice versa. The flywheel further accelerates the momentum [15]. Other competitive strengths of the company include: the end-to-end service they provide the consignors, the efficient scaled up operations with continuing process optimization, proprietary data algorithms that give unique operational insights, a talented and experienced senior management team that has built a culture of innovation in the organization. The RealReal growth was also influenced by the present opportunity provided by the luxury market and the trends in consumer behavior. Due to the awareness campaigns that are sustained by many organizations for the transition toward a circular economic system, the consumers accept more and more the reusing and reselling

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of goods, with a focus on sustainability. Furthermore, the diversity in the offered merchandise resolves the increasing need of the consumers for individuality, newness and uniqueness. As to the luxury market, it is estimated that on a yearly basis, hundreds of billions of dollars’ worth of luxury goods are bought and stored by consumers. Between 2000 and 2014, the average number of garments bought by a consumer has risen by 60%, with many items left underutilized [10]. The RealReal unlocks the access to the stored luxury goods and provides access to the platform for many buyers worldwide [15]. The company’s growth strategies are based on: increasing the number of buyers and sellers and their lifetime value, building furthermore The RealReal brand, increasing the number of offered goods in existing categories, investing in infrastructure and operational innovation and growing internationally and expanding the offline presence in strategic locations [15]. As well as strengths and opportunities, The RealReal business model encounters risks. The major ones that could negatively affect the company are [15]: • Brands set up their own pricing strategy and they strongly influence how the items are valued by The RealReal. If the brands decide to lower their prices, this would generate a decrease in the revenues generated through the platform, as well. • The supply of pre-owned of luxury goods can decrease in amount and attractiveness, thus generating a decrease in sales and a number of buyers and sellers. • Scaling up the business geographically may encounter hardships in finding the suitable placement of warehouses and other logistical facilities and, thus, limiting the access to them. • The company’s revenues depend on the consumer’s discretionary spending that may take a downturn if the economic climate negatively changes, though the luxury market is generally thought to be resilient to economic cycles. • The authentication process can fail and counterfeiting goods can reach buyers, thus generating a decrease in sales. • The company can fail to: sufficiently increase the number of buyers and sellers, develop and optimize the operational strategy accordingly, promoting and sustaining the brand. Though a highly differentiated business model, The RealReal faces competition in both online and offline retail. Traditional technology-based companies, such as eBay, represent a threat to the company’s business model because their business model can be replicated. In terms of the luxury industry, offline consignment stores were and are still present, along with pawn shops and auction houses, like Sotheby’s, and are reselling many luxury goods, just like The RealReal. The RealReal business model is built on the premises of endorsing and embracing sustainability. This aspect is underlined by the platform’s users’ preferences. Fifty-six percentage of the entire consignors that use the platform described has been motivated to use The RealReal for the environmental impact that it has and for extending the life cycle of luxury goods.

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The RealReal also became in April 2019 the first company, in the resale industry, that joined the UN Climate Change’s Fashion Industry Charter for Climate Action. The Charter’s mission is to reduce by 30% the carbon emission of the fashion industry by 2030 and zero emissions by 2050. The company is also a member of the Ellen MacArthur Foundation’s Circular Economy 100 USA initiative that brings together organizations that are interested in developing a self-sustaining circular economic system. The collaboration with the foundation has resulted in the company’s development of their proprietary Sustainability Calculator, that quantifies energy output, greenhouse gases and water usage offset, by the trending items on the platform. On the first Monday of October, The RealReal founded the National Consignment Day to encourage people to consign. Moreover, on this day, the Sustainability Calculator was launched in 2018. Though it is focused only on women’s apparel, the calculator took into consideration 3.2 million garments that were consigned on the platform. The calculator revealed in 2018 that since inception, The RealReal achieved energy savings to the amount of 329 million liters of water and 87 million driving miles in greenhouse gas emissions, or 3494 trips by car around the world.

16.3 Conclusions The year 2018 was described as the reckoning year in the fashion industry, in terms of sustainability, while 2019 was the time for an urgent awakening [14]. The self-disrupt change that companies should embrace is related to satisfying the consumers’ increasing demands for sustainability and ultra-transparency, having an active stance on social issues, being digital oriented and increasing time to market. The RealReal has managed to be one of these organizations that have embraced change and has undertaken a sustained effort in bringing circular fashion into the mainstream.

References 1. D’Amato, D., Droste, N., et al.: Green, circular, bio economy: a comparative analysis of sustainability avenues. J. Clean Prod. 168, 716–734 (2017). https://doi.org/10.1016/j.jclepro.2017. 09.053 2. Ellen MacArthur Foundation.: Towards the Circular Economy, vol. 1. Economic and Business Rationale for an Accelerated Transition, vol. 1. Retrieved 19 Sept 2019, from https://www.ellenmacarthurfoundation.org/assets/downloads/publications/EllenMacArthur-Foundation-Towards-the-Circular-Economy-vol.1.pdf (2013a) 3. Ellen MacArthur Foundation.: Towards the Circular Economy, vol. 2. Opportunities for the Consumer Good Sector, vol. 2. Retrieved 19 Sept 2019, from https://www. ellenmacarthurfoundation.org/assets/downloads/publications/TCE_eport-2013.pdf (2013b)

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4. Kalmykova, Y., Sadagopan, M., Rosado, L.: Circular economy—from review of theories and practices to development of implementation tools. Resour Conserv. Recycl. 135, 190–201 (2018). https://doi.org/10.1016/j.resconrec.2017.10.034 5. Geisendorf, S., Pietrulla, F.: The circular economy and circular economic concepts—a literature analysis and redefinition. Thunderbird Int. Bus. Rev. 60(5), 771–782 (2018). https://doi.org/ 10.1002/tie.21924 6. Gardetti, M.A.: Introduction and the concept of circular economy. In: Muthu, S.S. (ed.) Circular Economy in Textiles and Apparel, pp. 1–11. https://doi.org/10.1016/B978-0-08-102630-4. 00001-7 (2019) 7. Leonas, K.K.: The use of recycle fibres in fashion and home products. In: Textile Science and Clothing Technology. Textiles and Clothing Sustainability: Recycled and Upcycled Textiles and Fashion. Retrieved from https://www.springer.com/gp/book/9789811021459 (2017) 8. Ellen MacArthur Foundation.: A New Textiles Economy: Redesigning Fashion’s Future. Retrieved 19 Sept 2019, from Ellen MacArthur Foundation https://www. ellenmacarthurfoundation.org/publications/a-new-textiles-economy-redesigning-fashionsfuture (2017) 9. Global Fashion Agenda, & The Boston Consulting Group.: Pulse of the fashion industry report 2017. Retrieved 19 Sept, 2019, from https://www.globalfashionagenda.com/wp-content/ uploads/2017/05/Pulse-of-the-Fashion-Industry_2017.pdf (2017) 10. McKinsey & Company.: Style that’s sustainable: a new fast-fashion formula. Retrieved 19 Sept 2019, from https://www.mckinsey.com/business-functions/sustainability/our-insights/ style-thats-sustainable-a-new-fast-fashion-formula (2016) 11. Koszewska, M.: Circular economy in textiles and fashion—the role of a consumer. In: Muthu, S.S. (ed.) Circular Economy in Textiles and Apparel, pp. 183–206. https://doi.org/10.1016/ B978-0-08-102630-4.00009-1 (2019) 12. Rathinamoorthy, R.: Circular fashion. In: Muthu, S.S. (ed.) Circular Economy in Textiles and Apparel, pp. 13–48. https://doi.org/10.1016/B978-0-08-102630-4.00002-9 (2019) 13. Global Fashion Agenda, & The Boston Consulting Group.: Pulse of the fashion industry report 2018. Retrieved 19 Sept 2019, from file:///Users/manuelamihailiasa/Downloads/Pulse_of_the_fashion_industry_report_2018.pdf (2018) 14. McKinsey & Company, Business of Fashion.: The State of Fashion 2019. Retrieved 19 Sept 2019, from https://www.mckinsey.com/~/media/mckinsey/industries/retail/our%20insights/ the%20state%20of%20fashion%202019%20a%20year%20of%20awakening/the-state-offashion-2019-final.ashx (2019) 15. The RealReal Inc.: FORM S-1 Registration Statement under the Securities Act of 1933. Retrieved 19 Sept 2019, from https://www.sec.gov/Archives/edgar/data/1573221/ 000119312519163007/d720814ds1.htm#toc720814_7 (2019) 16. Toma, G.: The RealReal IPO: First Startup from Resale’s New Wave to Go Public Sees Shares Soar. Retrieved 19 Sept 2019, from Forbes https://www.forbes.com/sites/glendatoma/2019/06/ 28/the-realreal-ipo-luxury-reseller-latest-retailer-to-go-public/ (2019) 17. CNBC.: Meet the 2018 CNBC Disruptor 50 companies. Retrieved 19 Sept 2019, from https:// www.cnbc.com/2018/05/22/meet-the-2018-cnbc-disruptor-50-companies.html (2018)

Part IV

Financial Management and Governance

Chapter 17

A Review of the Research on Financial Performance and Its Determinants Mihaela Brindusa Tudose and Silvia Avasilcai

Abstract To carry out the review, the study was designed in such a manner as to enable us to: (a) identify the degree of interest that researchers displayed for scientific grounding of concepts they operate with and (b) identify the degree to which new lines of research have been shaped on determinants of financial performance. Based on a sample of 45 articles which analyzed the corporate financial performance, published during 2014–2019, was established a database which details: the researches’ topic; dependent and independent analyzed variables (and the indicators used for their assessment); samples; sources of data and periods in which they have been collected; results of the research; and authors’ contributions in defining the concept of performance. In terms of study’s first aim, we have shown that authors are concerned with grounding concepts with which they operate, but they mostly focus on the determinants and not on the financial performance. In terms of determinants of the financial performance, the study reveals that the research is more detailed and they extend the analyses with new variables (such as ethics of stakeholders, corporate lobbying, corporate culture, green credit or non-financial reporting) for explaining the dynamics of the financial performance. Keywords Performance · Financial performance · Determinants · Researches · Authors’ contributions

17.1 Introduction The field of debates regarding performance is wide-spreading, covering almost all areas of social and engineering sciences. The milestone that fueled the interest (the validity of which has been confirmed for the entire time frame of debate) was the M. B. Tudose (B) · S. Avasilcai Gheorghe Asachi Technical University of Iasi, Iasi, Romania e-mail: [email protected] S. Avasilcai e-mail: [email protected] © Springer Nature Switzerland AG 2020 G. Prostean et al. (eds.), Innovation in Sustainable Management and Entrepreneurship, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-44711-3_17

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fact that the continuous enhancement of the performance is the primary aim of every company [9]. Being part of the performance management, measurement of corporate performance has been of interest for academia and business fields [58]. Performance and financial performance, respectively, are the key elements for assessing the operations of corporate organizations’ activities. Contrary to traditional approach (which has analyzed only the financial performance from the perspective of its financial determinants), the following research has also included in the debate the non-financial factors. This happened because it was presumed that has to reflect not only its performance regarding financial historical information but also any other purpose which may be important for the company [25]. From our perspective, performance (and implicitly financial performance) is an artifact, by which the success of an organization is measured in the context of a free, competitive and globalized market. To understand it correctly, performance should be approached from, at least, three perspectives: those interested in carrying out and measuring performance, the context determining performance and the time period relevant to measuring performance. The main aim of the paper was to review developments in research on financial performance and its determinants. We have formulated the following hypotheses: (a) researchers for scientific grounding of concepts they operate with; (b) there are new lines of research regarding determinants of financial performance. For achieving the aim, the research is structured into the following sections: The first chapter focuses on the most important aspects identified in the literature in the field of the concept of performance and its determinants; the second offers details regarding the methodology of research; the next chapter presents the results of the study; finally, the last one summarizes conclusions and presents future lines of research.

17.2 Literature Review on Performance, Financial Performance and Its Determinants The first attempts to define the concept of performance (in terms of achieving or not achieving organizational aims) started from a wider term—effectiveness. Later, performance was presented as being the degree to which an organization takes into account its resources and goals as a social system [60]. Therefore, performance was no longer a concept of organization’s efficacy but rather an indicator of the manner, in which it successfully reached its goals [11]. According to Peterson et al. [53], the performance reflects the capacity and ability of an organization to efficiently use its available resources in order to obtain the results matching its established goals, taking into account their relevance for stakeholders. Later on, performance has been accepted as a barometer indicating the current status of a business and trends in its development [52].

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Taken into consideration the relation between financial performance and global performance, some authors pointed out that corporate financial performance is the key to ensure the survival and growth of the company [7]. Even in the field of corporate finance, the concept of performance was seen as a complex construct which has many facets [46] or as a controversial issue due to its multidimensional features [56]. Most studies approach financial performance from the perspective of the degree to which financial goals of an organization are reached; in this context, its results are measured in monetary terms. Analyzed in the past, the financial performance reveals the level of effectiveness and efficiency of an organization in fulfilling its purpose [32]; ability of the company to efficiently manage and use its resources [39, 50], respectively, the efficacy and efficiency of the management in using the company resources [16]; ability of corporations to capitalize its market share [50]; and the measure of what has been accomplished by an organization that reflects proper results in a certain period of time [5]. Analyzed in the future, the financial performance reflects the ability of the company to create economic value [50], maximize the wealth of its owners/founders [4], attract investors, generate earnings for them [1, 32] and contribute to the economy of a country, in general [45]. Therefore, performance is variable and requires judgment using a causal model which reflects the way in which current actions might influence future results [33]. Systems of assessment of financial performance developed in two stages [58]. In the first stage (1880–1980), the financial indicators (viewed as traditional) underlying assessment were [68]: profit, return on investment and productivity. In the second stage, due to changes on the world market, financial performance assessment focused mainly on strategic priorities associated with product/service quality and company flexibility, ensuring the maintenance of a competitive advantage. The limits of traditional measurement systems (based on profit margin and profit growth) started a revolution in measuring the performance of business [28]. Moreover, studies abandoned their interest for profit maximization and focused on value growth for stakeholders. In this context, the concept of corporate sustainability was developed and the number of stakeholders interested in the company’s performance increased [37]. So, systems of performance assessment took into account three dimensions (financial, social and environmental) and provided to companies the needed support for short-term as well as long-term management [62]. Regarding its determinant factors, some authors [8] have observed that most of the research has targeted at the financial performance from the perspective of the impact of the environmental factors (regarding concentration, growth, capital investment, size, advertising, imports and exports, minimum efficient scale and barriers to entry of the industry) and the organizational strategy (growth, market share, research and development, debt, diversification, quality of product/service, vertical integration, capital investment, firm advertising and corporate social responsibility). Because there are few studies which point to the problems of the corporation, the authors have claimed that more work is needed on this general family of financial performance determinants [8].

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Most recent studies on determinants of financial performance employ causality models between a dependent variable (financial performance) and one or more independent (explanatory) variables. The dependent variable (the financial performance) is assessed based on two categories of data: accounting and market. Measures referring to accountancy are determined using the information from a certain period of time, such as return on assets (ROA), return on equity (ROE), return on sales (ROS), return on investment (ROI), earnings per share (EPS), net profit margin (NPM), income, revenue and operating cash flow. Measures referring to market (company’s market value, Tobin’s Q) represent the performance of the market. Contrary to market-based measures, accounting-based measures may highlight the internal decision-making process of the company and the performance of the managers [42]. The most frequently used are ROA (the ratio between gross profit to total assets) and ROE (the ratio between net profits to own equity). ROA reflects the level of efficiency of the company’s management in obtaining an income from exploiting its resources [30]. ROE represents the ratio between the generated income and shareholder’s equity capitalized on the financial statements of the company [36]. An increased level of the two measures of performance indicates the efficiency of a company in utilizing its resources and funds [70]. The literature in the field [18, 34, 41] also stresses out that research on organizational performance faces some risks related to: correct identification of determinants and their measurement instruments, selection of data sources (secondary sources provide historical information; primary data, based on observation, are not relevant to longer periods of time, sample representativeness (lack of homogeneity limits representativeness; focus on specific fields/industries limits generalizations. Two methods of financial performance analysis have been often used: analysis based on financial ratios (making it possible to diagnose financial health of companies) and cash flow analysis (helping managers manage—for operational, financial and investment activities—so that business sustainability be ensured). Carrying out these analyses (on determinants) helps in achieving higher financial performance and increases the company’s positive influences in society but also on the environment [6].

17.3 Research Methodology To build the sample of studies grounding the analysis, we have set the following selection criteria: (a) research topic (content of studies makes reference to financial performance and its determinants); (b) quality of reference sources (articles published in ISI-indexed journals and ISI-abstracted articles, with or without an impact factor); and (c) year of publication. We have accessed platforms of prestigious publishers (Springer, Web of Science, Wiley, Emerald); the search criteria used on these platforms were: keywords (financial performance), type of search (in the content of papers), type of articles (open access), ordering by period of publication (the most recent).

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We have selected 53 articles, published between 2014 and 2019. Out of 53 articles, we have selected only those researching corporate financial performance as a dependent variable. We have eliminated four articles, in which financial performance was among independent variables, three articles which analyzed performance of entities having a financial and banking profile and one article which dealt with performance of not-for-profit organizations. The final sample included 45 articles, of which 9 belonged to Romanian authors. Based on the collected articles, we built a database (a table) containing the following elements: author; researched topic; the sample on which the study was based (including sources of collected data); the period of collected data; indicators used to assess financial performance; instruments for assessment of determinants (treated as independent variables or control variables); results of research; details comprising the contribution of authors to the definition of the concept of performance. Using a simple and robust counting methodology, we have searched for arguments that will validate/invalidate the two assumed hypotheses.

17.4 Results and Discussion A partial summary of collected data is shown in Tables 17.1 and 17.2. The overview of researched topics showed that Romanian and foreign authors converge in their interest in performance determinants. The common aspects of the research were: intellectual capital, innovation, management structure and size, implemented management systems, corporate governance, capital structure and financial leverage, features of companies, sustainability of strategies and industries. However, some research topics (such as the analysis of the financial performance from the perspective of management of client relations, organizational culture, strategic cost management, sustainability performance, corporate lobbying, sustainable leadership practices, impact of macroeconomic factors and market orientation) are not analyzed by Romanian authors. The analysis of the entire sample of articles enabled us to identify the following aspects: 36 articles were written by foreign authors and 9 by Romanian authors; 25 articles presented analyses of samples comprising less than 120 companies, and 15 contain empirical research on samples comprising between 121 and 500 companies; five articles had samples of 1000 and 5025 companies, respectively; 27 articles presented empirical analyses of listed companies, 5 referred to small and medium-sized enterprises (SMEs), and 13 included mixed samples. None of the articles presented state enterprises in the analyzed samples; 40 articles presented empirical analyses of non-homogenous samples in terms of field of activity; only 5 articles focused on a specific industry; 35 studies were conducted on databases collected from secondary sources (usually from annual financial statements and annual reports), 8 used primary data, and two studies used mixed data. In 27 articles, financial performance was assessed by means of return on assets (ROA) and/or return on equity (ROE); other used indicators were: sales growth,

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Table 17.1 Contributions of Romanian authors Authors

Topics of research (and determinants of financial performance)

Anghel et al. [2]

Intellectual capital and financial performance (market-to-book ratio and R&D expenditure efficiency)

Dumitru et al. [14]

Innovative information technologies and performance (in the company there is a unique computer network)

Dutescu et al. [15]

Sustainability and financial key performance indicators (turnover, operational costs, cash amount, bank amount, loans)

Iona¸scu et al. [23]

ISO (9001,14001) and OHSAS 18001 certification and financial performance (certified management system for company)

Ionas, cu et al. [24]

Women on boards and financial performance (proportion of female members on board and women president of the board)

Muller et al. [44]

Board characteristics of best practices and financial performance (foreign directors, women directors, number of board, non-executive directors, independent non-executive directors, etc.)

Nenu et al. [48]

Capital structure, risk and firm performance (short-term debt ratio, long-term debt ratio, company’s size, asset tangibility, growth opportunity, effective tax rate, reinvestment rate, quick liquidity ratio, current liquidity ratio, market capitalization, depreciation, cash ratio)

Siminic˘a et al. [63]

Corporate sustainability strategies and financial performance (substantive action, symbolic action, green marketing)

Vut, a˘ et al. [69]

Corporate social responsibility and financial performance (decent working conditions, human rights, environmental performance)

turnover, net profit margin, return on sales, operating margin, (operating) cash flow, earnings per share, leverage, price-to-earnings ratio, sustainable growth rate, Tobin’s Q, total net income, growth ability, operational ability and solvency ability, the annual variation of the net profit; two studies used indirect measures (such as respondents’ perceptions regarding the long-term historical organizational growth—in sales and net profits). In 28 articles, financial performance was analyzed based on historical (accounting) information; mixed information is used in 10 articles; two articles used only market values; in 5 articles, financial performance was analyzed based on respondents’ perceptions. Only in 9 articles was identified an interest for defining financial performance Table 17.3; in the other articles, financial performance has not been explicitly defined (the only details about performance refer to indicators used for assessment); on the other hand, in all articles, independent variables (associated with determinants of financial performance) are minutely and in detail discussed. Although all 45 articles present debates on determinants of financial performance, phrases such as corporate performance, firm performance or just performance are used in the title of the articles (but also in their content). This fact indicates that some authors substitute the concept of financial performance with that of performance. In terms of what has been discussed above, the first hypotheses have been partially validated. Researchers show interest in grounding scientifically the concepts

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Table 17.2 Contributions of foreign authors Authors

Topics of research (and determinants of financial performance)

Al-Sa’eed [1]

Ownership structure, dividends and firm performance (ownership structure and dividends)

Arda et al. [3]

Quality and environmental management practices and firm performance (supporting the overall objectives, planning process, goals and targets, the reviews of the management, the resource of the management, nonconformities, preventive and corrective actions, data and internal communication)

Batchimeg [5]

Internal factors and financial performance (growth, profitability, capital structure, liquidity)

Chatman et al. [10]

Organizational culture and financial performance (adaptability, integrity, collaborative, results orientation, customer and detail orientation)

Dobija and Kravchenko [12]

Supervisory board composition and firm financial performance (percentage of independent members on supervisory board and percentage of experienced members on supervisory board)

Donkor et al. [13]

Innovative capability (product, process, solution, behavioral, IT capability and training for managers), strategic goals (vision, mission statement, strategies and actions for objectives) and financial performance

Egbunike and Okerekeoti [16]

Macroeconomic factors, firm characteristics and financial performance (official lending rate during a year; annual change in the CPI; official exchange rate during a year; and economic growth)

García-Sánchez and Martínez-Ferrero [17]

Chief executive officer ability, CSR and financial performance (corporate social responsibility performance; managerial ability; and support growth)

Hamdan et al. [19]

IT governance and firm performance (IT governance; board characteristic—IT, size, independence)

Henri et al. [20]

Strategic cost management and performance (the executional and structural cost management, tracking of environmental costs and the implementation of environmental initiatives)

Ho et al. [21]

Market orientation, innovation and financial performance (customer and competitor orientation, inter-functional coordination, innovation) (continued)

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Table 17.2 (continued) Authors

Topics of research (and determinants of financial performance)

Hussain et al. [22]

Sustainability performance and financial performance (environmental, social and governance disclosure score, economic and social sustainability performance, environmental sustainability performance, etc.)

Kassi et al. [27]

Market risk and financial performance (the level of financial leverage, book-to-market ratio, gearing ratio)

Khan and Subhan [29]

Board diversity, audit and firm performance (board diversity and quality audit)

Kiessling et al. [31]

Market orientation, CSR and financial performance (customer orientation, customer interaction, market orientation)

Kurniaty, et al. [32]

Stock return and financial performance (the proportion of the independent commissioner, the institutional, managerial and public ownership, stock return, corporate value)

Lin et al. [35]

Corporate social responsibility and corporate financial performance (CSR, intellectual capital, industry type)

Lin [36]

Corporate lobbying and corporate financial performance (corporate lobbying and organizational slack)

Luo et al. [38]

Green credit on operational efficiency and financial performance (green credit, expectation and supervision channel, capital allocation channel)

Mahrani and Soewarno [39]

Good corporate governance mechanism, CSR and financial performance (number of independent board of commissioners, institutional ownership, audit quality and CSR)

Manrique and Martí-Ballester [40]

Environmental and financial performance during a global financial crisis (corporate environmental performance, emission and resource reduction, product innovation)

Mehmood et al. [43]

Corporate diversification, financial structure and firm performance (product diversification, corporate diversification, financial structure)

Naude et al. [46]

Network effects and SME performance (emotional intelligence, entrepreneurial style, network structure, external networking behaviors) (continued)

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Table 17.2 (continued) Authors

Topics of research (and determinants of financial performance)

Neeveditah et al. [47]

The environmental management systems and financial performance (pollution control, waste reduction, recycling, cutting use of energy, cutting paper consumption, etc.)

Odrizola and Baraibar-Diez [49]

Participation of women in companies and financial performance (work-life balance and female participation)

Orozco et al. [50]

Board size and financial and reputational corporate performance (corporate reputation and board size)

Palaniappan [51]

Board characteristics of corporate governance and financial performance (size, independence, board meetings and CEO duality)

Pizzi [54]

Non-financial reporting, environmental strategies and financial performance (earnings per share, ROE, expertise and environmental risk indicator)

Pletzer et al. [55]

Female representation on corporate boards and firm financial performance (female representation)

Pucheta-Martinez and Garcia-Meca [57]

Institutional directors and firm performance (pressure-sensitive directors, pressure-resistant directors on boards, board independence, levier, foreign investors)

Saliba de Oliveira et al. [61]

Innovation and financial performance (innovation impact, innovation efforts, internal and external innovative activity, governmental support, organizational and marketing activities, human capital)

Sjödin et al. [64]

The strategies concerning relational governance regarding financial performance in servitization and advanced service provision (service innovation, perceived switching costs, explicit contracts, etc.)

Sroufe and Remani [65]

Management, social sustainability, reputation and financial performance (the social sustainability and the sustainability of management and reputation)

Suriyankietkaew and Avery [66]

Sustainable leadership practices and financial performance (labor relations, valuing employees, social responsibility, vision)

Taebi Noghondari, Noghondari [67]

Financial leverage, ownership concentration and financial performance (financial leverage and ownership concentration)

Xu and Wang [71]

Intellectual capital, financial performance and sustainable growth (capital employed efficiency, human capital efficiency, structural capital efficiency)

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they operate with, and they mainly focus on determinants and not on financial performance. Out of 9 articles, in which we have identified concerns for defining financial performance, only 7 articles contain short original definitions; the other 2 articles adopted earlier formulated views Table 17.3. The results of the study validate other hypotheses that was formulated by Richard et al. [59], according to which performance is very important in research of the management but its structure and definition are rarely explained (or conceptualized). Romanian authors included in the sample adopted the same assumption. To assess the validity of the second adopted hypotheses, we started from the outcomes of earlier broader studies [8]. According to this study, most researchers investigated financial performance from the perspective of the impact of environmental factors and organizational strategy (being few studies which are tackling organizational issues). Table 17.3 Evidence on interest for defining financial performance Authors

About financial performance

Al-Sa’eed [1]

The performance represents the ability of the firm to maximize the wealth of its investors and to generate earnings from their investments [4]

Batchimeg [5]

The performance is the measure of what has been accomplished by an organization that reflects proper results in a certain period of time

Egbunike and Okerekeoti [16]

A better performance emphasizes the efficiency and efficacy of the management in using the resources of the company which contribute to the economy of the country [45] A set of definitions regarding the concept of performance has been provided by Lebans and Euske [33]

Kurniaty et al. [32]

Financial performance emphasizes how effective and efficient an organization is in achieving its objectives. Attracting investors requires improving financial performance

Mahrani and Soewarno [39]

For achieving financial performance, the company has to manage and control its resources. Financial performance can be assessed by using financial ratios and capitalizing financial statements

Naude et al. [46]

The concept of performance was seen as a complex construct which has many facets; therefore, the performance may be measured using a large number of indicators

Orozco et al. [50]

Financial performance reflects the ability of the company to create economic value in the context of efficiently using resources for increasing its market share

Saliba de Oliveira et al. [61]

Financial performance is linked with ROA, ROS and operating margin

Suriyankietkaew and Avery [66]

Financial performance is very important to ensure the survival and growth of the organization; for achieving this goal, the organization has to focus on its growth and balance

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A detailed presentation of variables (admitted as determinants) is shown in Tables 17.1 and 17.2, based on which financial performance is analyzed (from a sample of 45 articles of this study). By comparing information from the two tables presenting earlier results, we have identified a development in analysis on determinants of the financial performance Table 17.4. This development occurred from two perspectives: 1. Extensive—the analyzed studies bring evidence identifying new factors affecting performance (such as proactivity of environment, human rights, ethics of stakeholders); 2. Intensive—the analyzed studies bring evidence showing increase in the degree of complexity of analyses; for example, researchers do not limit their studies to analyzing the influence of specific factors (such as innovation, governance, company management, etc.), but also to studying their features (attributes): innovative capability, IT governance, good corporate governance mechanism, diversity and abilities of board and so on Table 17.4. Finally, we believe that the diversity of samples, processed data, used indicators for the assessment of variables (dependent or independent) and models of analysis confirm what the predecessors have reported [8, 34, 41],—research still lacks a tradition of systematic replication, which limits the representativeness and hinders the generalization of results. Table 17.4 Evidence regarding new directions of research of the financial performance Determinants → new attributes for determinants

New determinants

Growth—growth opportunity Environment—environmental sustainability performance, expertise environment, environmental risk Capital—the efficiency of the employed, human and structural capital R&D—R&D expenditure efficiency Debt—official lending rate during a year Diversification—product diversification and corporate diversification Innovation—innovative capabilities and service innovation CSR—CSR performance, good corporate governance mechanism, diversity and abilities of board, IT governance, substantive and symbolic action, decent working conditions Board—board diversity, pressure-sensitive directors, pressure-resistant directors on boards Audit system—certified management system and quality audit

Human rights, ethics of stakeholders, corporate culture, corporate lobbying, proactivity of environment, computer network, green market, green credit, organizational slack, non-financial reporting

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17.5 Conclusions The main aim of this study was to review the research on financial performance and its main determinants. The results show that the researchers’ efforts caused a development in management research and a consolidation of the implementation of increasingly complex methodologies. Theoretical development of research (although a few experiences were commonly shared) enabled a better understanding of performance. Although there is no consensus in concept definition, the researchers’ opinions converge to a dominant line in definitions (formulated earlier): Financial performance reflects the capacity of an organization to use available and limited resources in achieving results (in monetary terms) according to the set goals, taking into account their relevance for the stakeholders. The final aim of performance assessment is getting to know the current state of relevance of organizational goals, and the efficiency and efficacy of their business. Based on our research, we have revealed that authors did not give the same importance in defining the variables they have analyzed; the financial performance is rarely defined; instead, its determinants are given special attention. The same conclusion was reached by Richard et al. [59] who admitted that performance is a very important landmark in research of the management but its structure and definition are rarely explained (or conceptualized). However, we point out to the fact that modern studies have reported lack of understanding and clear clarification of the concept of performance [26]. By examining the determinants of financial performance, we observed an interest for extensive (by identifying new factors having an influence on performance) and intensive development (as researchers do not analyze only the influence of specific factors, but also their features). The triggers of research development were diverse and included: hypothesis underlying studies, validity and viability of earlier formulated ideas, transformations of global society, nature of sample, nature of data, etc. Due to sample, data processing and used indicator diversity for the assessment of (dependent or independent) variables and models of analysis, the results of empirical studies are mixed. To ensure the originality, the study has also focused on the results of the Romanian researchers. These reveal that the Romanian school is concerned with the determinants on financial performance of the Romanian companies. The lines of research of the Romanian authors converge with those of the other researchers, but they are far of covering all the determinants of the financial performance which have been analyzed from the perspective of the economies of other countries. In terms of what has been presented, we consider that the current research brings contributions on three levels: scientific (because it presents in an original way the state of debates regarding financial performance and its determinants); methodological (because it offers support to the researchers who are interested in finding out new directions of research); and practical (because it offers support to the practitioners who are interested in enhancing the financial performance of the company which they lead).

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Research limitations and future lines of research. As the number of studies dealing with performance has increased over time, we should underline that to carry out this work, we have selected the most recent papers so this study does not pretend to be complete. Also, we have to admit that a partial discussion of debates (focused on the interest for defining performance and identifying its determinants) could be seen as an incomplete scientific grounding. Therefore, we consider a follow-up of this research by extending the sample of articles, analyzing methodologies underlying empirical studies and identifying their results.

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Chapter 18

Financial Constraints and the Structure of the Firm’s Investment: An Application to the Scientific R&D Industry from the Largest EU Countries Claudiu Tiberiu Albulescu, Serban Miclea, Matei Tamasila, and Mihaela Vartolomei Abstract This work assesses the influence of financial constraints on firms’ investment structure. Previous works state that financially constrained firms chose to invest more in tangible assets, in the detriment of intangible assets. However, none of these researches investigated the structure of investment of firms from scientific R&D industry. Therefore, we add to the exiting literature, and we test the impact of leverage, liquidity, and profitability on the R&D firms’ investment structure. We use data at firm level from 2007 to 2014, for 269 companies, located in France (163), Germany (67), and the UK (39), drawing a comparison between these countries. Our GMM analysis shows no significant impact of leverage and liquidity on the structure of investment. For Germany and UK, an increased profitability allows firms to invest more in long-term, intangible assets. These findings remain the same under different difference- and system-GMM specifications. Keywords Capital structure · Firm investment · Financial constraints · R&D firms

18.1 Introduction The existing literature shows that financially constrained firms usually invest in shortterm, tangible capital [13, 17, 23]. The preference for more liquid capital as machinery and equipment, production structures or inventory, can be explained in the case of C. T. Albulescu (B) · S. Miclea · M. Tamasila · M. Vartolomei University Politehnica Timisoara, P-ta Victoriei No. 2, 300006 Timisoara, Romania e-mail: [email protected] S. Miclea e-mail: [email protected] M. Tamasila e-mail: [email protected] M. Vartolomei e-mail: [email protected] © Springer Nature Switzerland AG 2020 G. Prostean et al. (eds.), Innovation in Sustainable Management and Entrepreneurship, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-44711-3_18

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financially constrained firms by two mechanisms [23]. On the one hand, lenders grant loans if the probability to increase the firms’ cash flow in the next period is high. That is, firms with credit constraints prefer short-term capital, susceptible to generate pledgeable outcomes. On the other hand, by anticipating future financial constraints, managers consider that the investment in illiquid, intangible capital, including research and development (R&D) and marketing expenditures, including human capital, might generate a high liquidation cost in the long run. Thus, their precautionary behavior influences the decision to invest in short-term capital. The empirical studies confirm, in general, the theoretical framework. In this line, Peyer and Shivdasani [24] investigate firms’ internal allocation of resources and show that financial pressures create a preference for investment projects that generate high levels of cash flow. Similarly, Aghion et al. [2] address the interaction of financial constraints and the structure of investment, showing that for the long-term investment the cyclical returns are smaller, whereas the liquidity risk is higher. Almeida et al. [5] develop a theoretical framework and test it empirically to show that financial constraints determine firms to opt for shorter payback periods, and thus, to invest in more pledgeable assets. In addition, Aghion et al. [3] sustain that the R&D to capital ratio is more procyclical for financially constrained firms. Eisfeldt and Rampini [15] adopt a different approach and state that financially constrained firms purchase used capital because the timing of cash outflows diminishes. Different from the existing bulk of literature, we focus on the scientific R&D industry1 to investigate the interactions between firms’ financial constraints and the structure of investment, using a panel data framework. The scientific R&D industry is particular and deserves special attention because it is highly innovative. Firms activating within this industry generate research and innovation products. Different from other firms, they are not strongly constrained to invest in intangible assets to develop themselves in the future. In addition, the presence of the public sector within this industry is relatively high (public research institutes). Further, the scientific R&D industry generates high added value, and the understanding of the investment structure in this industry is crucial for anticipating the business cycles. The fact that R&D capital contributes to productivity and economic growth is well known (e.g., Liik et al. [20]). Nevertheless, ours is the first paper addressing the nexus between structure of investment and financial constraints for this particular industry in selected European countries. We also contribute to the literature by drawing a comparison between scientific R&D industries from the largest European Union (EU) countries, namely the UK, Germany, and France. We use AMADEUS statistics for the period 2007–2014, which show that 7050 firms activate in this industry at the EU level (data extracted in December 2016). Most of them (80%) are located in the old EU member states, while 20% are companies from the new member states. Most of these firms (44%) are located in France (661), Germany (1580), and UK (916). Therefore, comparing the financial constraint’s influence on investment’s structure in these economies is relevant for the R&D industry at the EU level and helps us to identify discrepancies, 1 Industry

code no. 72, from the AMADEUS (Bureau van Dijk) database.

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if existing, between the conduct of financial management of firms acting in the R&D industry in the aforementioned three countries. However, satisfactory data are obtained for only 269 firms located in these three countries. The third contribution of this paper to the previous literature relies in the empirical approach. In this line, we resort to a panel data approach for each country, and we use a battery of control variables in order to investigate the relationship between firms’ financial constraints (the leverage, namely the ratio between long-term debt and total assets) and the structure of investment (the ratio between the level of tangible assets and total fixed assets). On the one hand, we consider the role of liquidity and profitability in explaining the structure of investment. On the other hand, we assess the role of the business cycle. Finally, we address the endogeneity issues between the structure of investment, firms’ financial constraints, and the business cycle. It is expected that cash, and thus liquidity, allows firms that are financially constrained to ‘hedge’ future investment against output downturns [1]. For example, Bates et al. [8] underline that for the industrial firms located in USA, if the cashto-asset ratio increases, it is associated with R&D-intensive actions. Therefore, the amelioration of the liquidity ratio will negatively impact our interest variable, that is, the structure of investment. Indeed, liquid firms are less financially constrained and may invest in long-term assets. The same impact on the structure of investment should be triggered by an increase in firms’ profitability. Cooper and Haltiwanger [14] highlight the nonlinear relationship between investment on the one hand and profitability on the other. However, profitable firms are less financially constrained and might prefer long-term assets. Finally, it is expected that firms invest in short-term, tangible assets, during economic downturn periods. Crisis periods force firms to obtain pledgeable outcomes. That is why we expect that the economic growth rate will have a negative impact on the structure of investment, as defined in the present paper. Nevertheless, the influence of business cycle on the structure of investment is not straightforward. As López-García et al. [21] show, the long-term investment should increase during downturns, in agreement with the opportunity-cost theory. This happens because a fall of their relative costs occurs, associated with the forgone output. Given the complex interactions between the structure of investment and the explanatory variables, an endogeneity bias can occur in our estimations. For example, the structure of investment influences the liquidity and profitability level of firms acting in the R&D industry. Gatchev et al. [17] show how do changes in investment influence the financing decisions. The structure of investment can also influence the business cycle. Therefore, to overcome this potential bias, the generalized method of moments (GMM) estimator is used. First, we resort to a difference-GMM estimation following Arellano and Bond [6]. Second, given the characteristics of our sample, with a small T, we apply a system-GMM estimation, which has better finite sample properties [9]. Our analysis points to an insignificant impact of firms’ leverage on the structure of investment. The remaining part of our work is divided into sections as follows: Sect. 18.2 is dedicated to the literature review. Data, descriptive statistics, and the methodology

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are described afterward. Finally, we show and discuss the results of the empirical analysis, and we draw afterward the conclusions.

18.2 Review of the Literature The existing literature shows that the financial flexibility of firms determines their capital structure [7]. In this line, Peyer and Shivdasani [24] analyze the resources’ internal allocation in the case of those firms that use external financing sources for recapitalization. They are between the first ones arguing that the existence of important credit constraints for firms determines them to invest in short-term tangible assets. Since then, several studies confirmed this hypothesis. For example, Eisfeldt and Rampini [15] argue that firms with high leverage will prefer to hold short-term used capital, because of the timing of investment. Resorting to a panel data approach for French manufacturing firms, for the period 1996–2004, Musso and Schiavo [22] state that financial constraints positively influence the productivity growth in the short run. In 2010, Aghion et al. [2] build a theoretical model to explain why firms engage in different types of investment. Further, Aghion et al. [3] develop this framework and show that long-term investment became procyclical for those firms that face stronger credit constraints. In addition, Almeida et al. [5] show how future financial constraints affect the present investment decision. More recently, Pérez-Orive [23] focuses on the distinction between investments in short- and long-term assets, building a theoretical model calibrated on the US economy. The novelty of their approach consists in a new explanation of the incentive to invest in liquid projects during economic downturn periods. None of these papers investigates the role of liquidity and profitability in explaining the interactions between financial constraints and the structure of investment. An exception is the paper by Acharya et al. [1] who argue that cash allows creditconstrained firms to find protection against potential shortfalls in the future income. Their theoretical intuition is tested using a sample of US manufacturing firms for the time span 1971–2001. A similar investigation is performed by Bates et al. [8] in the case of US financial firms over the period 1980–2004, stating that an increase in the cash-to-asset ratio determines firms to invest in long-term assets. Conversely, Aghion et al. [4] state that long-term projects involve a liquidity risk, which determines financially constrained firms to invest in short-term assets. Profitability also influences the structure of investment. A high level of profitability allows firm to invest in longterm assets because the financial management is not pressed to obtain immediate gains. However, as Cooper and Haltiwanger [14] underline, the connection between investment and the firms’ profitability level is clearly a nonlinear one. The relationship between the structure of investment on the one side, and the leverage, liquidity, and profitability on the other side, has particular features for the R&D industry. This sector represents an indirect direct channel which connects finance and growth [11]. There are few studies that investigate the determinants

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of investment in R&D industry. In this line, Hall and Mairesse [19] explain the underinvestment in R&D for individual French manufacturing firms, by a drop in the productivity level. Brown et al. [11] investigate for high-tech firms the dynamics of long-term investment and report a significant influence of cash flow. Nevertheless, investment in R&D industry is influenced by the business cycle. Moreover, credit constraints interfere between R&D investment and business cycle. First, Braun and Larrain [10] underline that those sectors which are highly dependent on external finance do not perform well during recessions. Second, Gourio and Kashyap [18], using plant-level data and drawing a comparison between Chile and the USA, state that investment spikes are highly procyclical. Third, Chen [12] demonstrates how the business cycle influences firms’ financing policies. Aghion et al. [3] incorporate all these effects in their 1994–2004 research on 13,000 French firms. Testing the impact of credit constraints on R&D behavior of firms, the authors show that R&D investment is countercyclical if there are limited credit constraints. However, it becomes procyclical and plummets during recessions for leveraged firms. As we have retained in our analysis only leveraged firms, we expect an increase in our structure of investment ratio during economic contraction periods.

18.3 Data Description and Methodology This section presents the methodology and data used in the research: We use firmlevel data from 2007 to 2014, for 269 companies located in France (163), Germany (67), and the UK (39), drawing a comparison between these countries.

18.3.1 Data We use annual statistics from 2007 to 2014, from the AMADEUS database (Bureau van Dijk).2 We focus on the most developed scientific R&D industries from the EU, namely France (661 firms), Germany (1580 firms), and the UK (916 firms). For a large part of firms, we have incomplete, missing data, which represent an issue for the broken panel bias. Therefore, in the first step we have retained in the analysis only those firms with complete data. In the second step, we have analyzed the structure of our sample. We have noticed that for a part of the retained companies, the level of intangible assets is zero, for the entire analyzed period. Furthermore, a part of the retained firms presents zero level of their long-term debt. In this context, in order to obtain reliable empirical results, we have eliminated from our sample the firms for which the intangible assets’ level, or the level of long-term debt ratio is zero, for the entire analyzed period. The final sample covers 163 firms from France (25% out of 2 Data are available in AMADEUS for the last 10 years. The authors can share the employed dataset

upon request.

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Table 18.1 Summary statistics Investment

Leverage

Liquidity

7.959

2.587

ROA

GDP

France Mean

69.79

SD

35.42

Min Max

0.000 100.0

13.75 0.000 116.3

3.185 0.076 61.92

3.300 16.31 −99.3 67.16

0.650 1.581 −2.900 2.400

Germany Mean

81.62

12.16

SD

25.10

18.84

Min Max

5.810 99.99

0.000 92.51

4.470

1.112

1.150

7.130

16.20

2.884

0.173

−92.33

−5.600

71.60

4.100

2.779

−3.858

0.924

4.772

24.02

2.226

0.030

−99.03

−4.300

62.75

3.100

85.37

UK Mean

71.13

SD

38.47

Min Max

0.000 100.0

33.45 114.2 0.000 112.4

44.20

the total number of firms in this country), 67 firms from Germany (4.2% out of the total number), and 39 firms from the UK (4.2% out of the total number). All data are extracted from AMADEUS, with the exception of the economic growth rate, extracted from Eurostat. The summary statistics of our sample can be found in Table 18.1. We notice that, in general, the German companies are more oriented toward shortterm investment than the French firms. In addition, we see that the UK R&D companies present a higher degree of indebtedness, as compared to French and German companies. We further on apply a series of panel unit root tests (LLC, IMPS, Fisher-type), to see if our series are stationary in level (Table 18.2). As it can be seen, for France and Germany, the first-generation unit root tests for panel data indicate that all the series are stationary. A small exception appears for the UK, in the case of the liquidity ratio and GDP growth rate, where only two out of four tests show the stationarity. All in all, we can conclude that our series are I(0).

18.3.2 Methodology To deal with endogeneity issues that appear between the structure of investment and the explanatory variables, we use the GMM approach. We first resort to the Arellano and Bond’s [6] difference-GMM approach:

18 Financial Constraints and the Structure of the Firm’s …

251

Table 18.2 Panel unit root results Investment

Leverage

Liquidity

ROA

GDP

France LLC t*

−1979***

−9.038***

−34.83***

−39.87***

−55.16***

IPS W-stat

−163.7***

−35.74***

−4.575***

−8.753***

−17.17***

Fisher-ADF χ2

656.1***

449.0***

648.5***

740.3***

1221 ***

Fisher-PP χ2

792.7***

596.5***

729.6***

903.8***

1455 ***

−37.17***

−65.30***

−49.90***

−14.73***

−26.48***

Germany LLC t* IPS W-stat

−4.572***

−10.68***

−5.504***

−5.320***

−7.588***

Fisher-ADF χ2

199.6***

233.4***

207.2***

255.0***

319.9***

Fisher-PP χ2

213.7***

235.6***

339.7***

250.5***

490.3***

UK LLC t*

−256.2***

−15.26***

−5.198***

−9.411***

−4.294***

IPS W-stat

−28.68***

−1.499*

0.104

−2.716***

0.393

Fisher-ADF χ2

70.54

77.79**

85.44

128.5***

55.77

Fisher-PP χ2

114.9***

88.11***

146.9***

209.4***

97.50*

Notes (i) The null hypothesis in the case of all tests is the presence of unit roots (the t* test assumes common unit root process, whereas all the other tests assume individual unit root processes); (ii) *, **, ***, mean stationarity (in level) significant at 10%, 5, % and 1%

yi,t =

t−1 

θ j yi, j + βz i,t + μi,t + u i,t

(18.1)

t− p

where y is the structure of investment, z is the set of independent, explanatory variables, θ is the first lag parameter of y; ui,t are the errors of the model that vary over countries and time. This considers the lagged levels of the regressors as instruments but can introduce a bias in the results, especially for small T samples, as ours. Therefore, Blundell and Bond [9] develop the system-GMM technique to overcome this problem. This estimator corresponds to a system of two equations that are simultaneous. The first one is in level, whereas the second one is in first difference. Practically, the instruments are now represented by both lagged first differences and lagged levels of variables, but the methodology might generate a proliferation of the instruments. To check this issue, we apply a Sargan test. Further, we control for the presence of heteroskedasticity, and we resort to the Pagan–Hall test. The heteroskedasticity’s existence requires the use of robust errors.

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18.4 Empirical Results The Pagan–Hall test indicates, in all the cases, the absence of heteroskedasticity. Therefore, GMM errors are used in estimations. We mention that the leverage ratio is considered an exogenous, pre-determined variable, because there is no a priori reason to consider that a specific investment structure determines firms to proceed to external financing. All the other variables are considered as endogenous. Two models are tested for each estimator and for each country. The first model (Model 1) does not take into account the business cycle. The second model (Model 2) includes in the analysis the economic growth rate. The findings are highlighted in Table 18.3 and generate several conclusions. First, there is a large consensus between the difference estimator and system-GMM estimators, for all the three countries. Second, we do not find any significant result regarding the impact of credit constraints on investment’s structure. However, the coefficient sign is positive, showing that financially constrained firms prefer short-term assets, in agreement with the previous empirical literature. The lack of significance can be explained by the fact that, in general, the leverage level of R&D firms is reduced in the analyzed countries. Further, the way we assess the financial constraint might influence these findings. As Farre-Mensa and Ljungqvist [16] mention, financially constrained firms do not necessarily have a behavior which shows that they undergone high leverage ratios. Third, there is no significant influence of liquidity ratio on the investment structure for R&D firms. Fourth, it appears that the profitability level negatively impacts the structure of investment. That is, high profitable firms afford to invest in long-term assets, as expected. Nevertheless, even if these results are obtained under both estimators, there are significant only for Germany and the UK, but not for France. Fifth, the impact of business cycle is largely insignificant. A small exception appears for the UK (Model 2—system-GMM estimation), where the economic growth negatively affects the investment structure. That is, in economic downturn periods, the British R&D companies prefer shortterm investment, which generates rapid outcomes. This result is in contrast to the opportunity-cost theory (see López-García et al. [21]). However, this last result might be affected by the instruments over proliferation, as indicated by the Sargan test.

18.5 Conclusions This study attempted to assess how financial constraints for firms influence their structure of investment, with a focus on the scientific R&D industry from France, Germany, and the UK. It is also intended to see if the conduct of financial management is different in the selected countries. To shed light to these challenges, we perform

18 Financial Constraints and the Structure of the Firm’s …

253

Table 18.3 Results of the difference-GMM and system-GMM analysis Difference-GMM France

Model 1

System-GMM Model 2

Model 1

Model 2

c

13.91**

12.07**

−2.792

−2.628

Lag (1)

0.793***

0.821***

1.035***

1.035***

Leverage

0.043

0.042

0.010

0.011

Liquidity

−0.074

−0.092

−0.115

−0.144

ROA

−0.044

−0.044

−0.002

−0.004

−0.150

GDP Cross sections (observations)

163 (978)

163 (978)

Sargan statistic: over-identification

−0.133 163 (1141)

163 (1141)

χ2 = 64.61 [0.152]

χ2 = 78.88 [0.115]

c

13.91**

12.07**

−2.792

−2.628

Germany

Model 1

Model 2

Model 1

Model 2

c

39.17***

46.83***

2.122

5.979*

Lag (1)

0.514***

0.419***

0.976***

0.927***

Leverage

0.041

0.046

−0.036

−0.028

Liquidity

0.046

0.035

0.084

0.067

ROA

−0.101***

−0.089***

−0.083**

−0.078**

67 (469)

67 (469)

χ2 = 59.98 [0.267]

χ2 = 91.81 [0.012] Model 2

GDP Cross sections (observations)

0.042 67 (402)

67 (402)

Sargan statistic: over-identification

0.091

UK

Model 1

Model 2

Model 1

c

32.93***

14.90*

2.440

0.821

Lag (1)

0.509***

0.761***

0.955***

0.982***

Leverage

0.019

0.015

0.009

0.011

Liquidity

0.220

0.037

−0.023

−0.019

ROA

−0.124**

−0.115*

0.002

0.007

39 (273)

39 (273)

χ2 = 115.6 [0.000]

χ2 = 128.4 [0.000]

−0.480

GDP Cross sections (observations)

39 (234)

39 (234)

Sargan statistic: over-identification

−0.565**

c

39.17***

46.83***

2.122

5.979*

Lag (1)

0.514***

0.419***

0.976***

0.927***

Notes (i) *, **, *** means relationship which is significant at 10%, 5%, and 1% significance level; (ii) Model 1—without GDP and Model 2—with GDP; (iii) Sargan test is designed to underline the over-identifying problems related to the instruments (the p-values for the Sargan statistic are shown in square brackets); (iv) Lag (1) is the first lag of the structure of investment

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a panel data investigation and we use GMM estimators for firm-level data, over the period 2007–2014. Earlier works suggested that financially constrained companies are determined to invest in short-term, tangible assets. However, these studies did not address the particular case of the scientific R&D industry, nor did they consider the role of financial performance and business cycle in influencing the structure of investment— leverage relationship. Hence, this study is complementary to previous analyses and shows that in the case of the R&D industry, financial constraints have no significant influence on the investment structure. In addition, the profitability of firms influences the structure of investment, but this result is reported only for the German and UK firms. Further, the impact of economic cycle on investment’s structure is unclear and inconclusive. These results have implications in corporate finance, as it appears that the companies’ risk-taking behavior does not affect their investment attitude. However, a comfortable level of profitability allows R&D firms to invest in long-term, intangible assets, which are susceptible to generate economic benefits in the long run. Needless to say, the paper has several limitations, as it does not make a distinction between the crisis period and the following one, when the firms’ investment behavior suffers significant changes (we might, however, admit that our investigation which starts in 2007 addresses the post-crisis period). Moreover, the data sample includes both private and public companies, with different investment strategies. In addition, the results characterize a sample of well-established companies on the market, without considering the impact of the new entries. Further developments of this research should address these issues. Acknowledgements This work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS—UEFISCDI, project number PN-III-P1-1.1-TE-20160142.

References 1. Acharya, V.V., Almeida, H., Campello, M.: Is cash negative debt? A hedging perspective on corporate financial policies. J. Finan. Intermediation 16(4), 515–554 (2007) 2. Aghion, P., Angeletos, G.-M., Banerjee, A., Manova, K.: Volatility and growth: credit constraints and productivity-enhancing investment. J. Monetary Econ. 57(3), 246–265 (2010) 3. Aghion, P., Askenazy, P., Berman, N., Cette, G., Eymard, L.: Credit constraints and the cyclicality of R&D investment: evidence from France. J. Eur. Econ. Assoc. 10(5), 1001–1024 (2012) 4. Aghion, P., Hémous, D., Kharroubi, E.: Cyclical fiscal policy, credit constraints, and industry growth. J. Monetary Econ. 62, 41–58 (2014) 5. Almeida, H., Campello, M., Weisbach, M.S.: Corporate financial and investment policies when future financing is not frictionless. J. Corp. Finan. 17(3), 675–693 (2011) 6. Arellano, M., Bond, S.R.: Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Rev. Econ. Stud. 58(2), 277–297 (1991) 7. Bancel, F., Mittoo, U.R.: Why do European firms issue convertible debt? Eur. Finan. Manage. 10(2), 339–373 (2004)

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8. Bates, T.W., Kahle, K.M., Stulz, R.M.: Why do U.S. firms hold so much more cash than they used to? J. Finan. 64(5), 1985–2021 (2009) 9. Blundell, R.W., Bond, S.R.: Initial conditions and moment restrictions in dynamic panel data models. J. Econ. 87(1), 115–143 (1998) 10. Braun, M., Larrain, B.: Finance and the business cycle: international, inter-industry evidence. J. Finan. 60(3), 1097–1128 (2005) 11. Brown, J.R., Fazzari, S.M., Petersen, B.C.: Financing innovation and growth: cash flow, external equity, and the 1990s R&D boom. J. Finan. 64(1), 151–185 (2009) 12. Chen, H.: Macroeconomic conditions and the puzzles of credit spreads and capital structure. J. Finan. 65(6), 2171–2212 (2010) 13. Chevalier, J.A., Scharfstein, D.S.: Capital-market imperfections and counter cyclical markups: theory and evidence. Am. Econ. Rev. 86(4), 703–725 (1996) 14. Cooper, R.W., Haltiwanger, J.C.: On the nature of capital adjustment costs. Rev. Econ. Stud. 73(3), 611–633 (2006) 15. Eisfeldt, A.L., Rampini, A.A.: New or used? Investment with credit constraints. J. Monetary Econ. 54(8), 2656–2681 (2007) 16. Farre-Mensa, J., Ljungqvist, A.: Do measures of financial constraints measure financial constraints? Rev. Finan. Stud. 29(2), 271–308 (2016) 17. Gatchev, V.A., Spindt, P.A., Tarhan, V.: How do firms finance their investments? The relative importance of equity issuance and debt contracting costs. J. Corp. Finan. 15(2), 179–195 (2009) 18. Gourio, F., Kashyap, A.K.: Investment spikes: new facts and a general equilibrium exploration. J. Monetary Econ. 54(S1), 1–22 (2007) 19. Hall, B.H., Mairesse, J.: Exploring the relationship between R&D and productivity in French manufacturing firms. J. Econ. 65(1), 263–293 (1995) 20. Liik, M., Masso, J., Ukrainski, K.: The contribution of R&D to production efficiency in OECD countries: econometric analysis of industry-level panel data. Baltic J. Econ. 14(1–2), 78–100 (2014) 21. López-García, P., Montero, J.M., Moral-Benito, E.: Business cycles and investment in intangibles: evidence from Spanish firms. Central Bank of Spain, WP 1219 (2012) 22. Musso, P., Schiavo, S.: The impact of financial constraints on firm survival and growth. J. Evol. Econ. 18(2), 135–149 (2008) 23. Pérez-Orive, A.: Credit constraints, firms’ precautionary investment, and the business cycle. J. Monetary Econ. 78, 112–131 (2016) 24. Peyer, U.C., Shivdasani, A.: Leverage and internal capital markets: evidence from leveraged recapitalizations. J. Finan. Econ. 59(3), 477–515 (2001)

Chapter 19

Projecting a Strategic Diagnosis System of Corruption Based on Network Analysis Ioan Petris, or and Dana Nedea

Abstract Throughout the recent years, there have been witnessed a series of corruption organizational behaviour cases all over the world. Romania, among many other countries, is facing high-profile corruption cases that have been having a major impact on the business environment. The strategic diagnosis of these conditions becomes highly important for businesses, leading to a need for a Strategic Diagnosis Based on Network (SDBN) in order to fight the corruptive behaviour of different stakeholders. This paper will introduce a SDBN of corruption-centred behaviours. The methodology is based on network analysis. A large quantity of event data about corruption, and business corruption, is extracted from the Global Database of Events, Language, and Tone (GDELT). This paper applies SDBN at Global, European and separately EU, and Romanian level. The distinctiveness of this research consists of involving the theory of network analysis, as well as of inspiring strategic directions for inhibiting the corruption behaviours. The results show the need for a SDBN as well as the national conditions for strategic business decisions. The research limits are given by the usage of big data and by the magnitude of corruption behaviours. Future research will apply the SDBN to a larger sample of data. Keywords Network analysis · GDELT · Corruption · Business corruption · Strategic diagnosis system of corruption

I. Petris, or · D. Nedea (B) Management Department, Faculty of Economics and Business Administrations, West University of Timis, oara, Str. J. H. Pestalozzi, nr 16, 300551 Timis, oara, Romania e-mail: [email protected] I. Petris, or e-mail: [email protected] © Springer Nature Switzerland AG 2020 G. Prostean et al. (eds.), Innovation in Sustainable Management and Entrepreneurship, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-44711-3_19

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19.1 Introduction Corruption, defined as being the use of public power for a personal gain, implies a view of supply and demand dimensions of corruption [8]. Most of the studies focus on the demand side, as being the public official, but the supply side, as being the individual or business organization, is also worth the study, as stated by Dixit [4]. Many studies of corruption are focused on the perception of corruption by the population and not as many focuses on the perception of corruption among mass media and press, as a tool for strategic diagnosis of corruption. This paper uses network analysis in order to conduct a diagnosis of corruption and business corruption at Global, European and EU, and national Romanian level, using data extracted from Global Knowledge Graph (GKG) Network Visualizer included in the GDELT Project. The paper structure follows a literature review introducing the main topics discussed, the methodology and data, which introduces the strategic diagnosis model, and the way of collecting and analysing the data, followed by the conclusion of the study along with future work. One of the difficulties in studying the phenomenon of corruption lies within defining it. At the first view, it might seem as being a semantic issue, but the way corruption is defined ends up determining the way it gets measured and modelled [5]. Among literature review, there is a consensus on defining corruption as the use of public power for a personal gain, considering the occurrence of corruption at the overlapping of public power and private wealth [8]. A common transaction is the one when a private individual or a business organization pays a public official in return for a benefit, the bribe increasing the private wealth of the public official [8]. This transaction has both supply and demand dimensions. Dixit [4] states that the focus is mainly in the demand side, as studies tend to focus on government corruption, while the supply side should also be studied, since the business organizations are a major part of corruption transactions. Although the common press constantly exposes the bribery of government and private authorities, the public’s interest rarely extends beyond the details of specific scandals. However, as the episodes accumulate, it becomes clear that more is at stake than is implied by the set of disjointed stories [7]. With this, the study of corruption requires an attention given to mass media and press in order to diagnose not only the perception over corruption, but also the status quo of corruption at a given time. Network analysis uses graph theory in order to construct networks connected by nodes and edges, or specific interactions between those nodes [2]. One important advantage of the network analysis is given by the fact that the topological properties are universal, and they have the power to describe almost any entity [2]. Some of the most important measures used in this paper are average path length, average clustering coefficient, average degree and modularity. Average path length is defined as the average number of steps needed for all the nodes in the network to reach each other [1].

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259

The average clustering coefficient measures how complete the neighbourhood of a node is, the clustering coefficient measuring the degree to which nodes in a graph tend to cluster together. The average clustering coefficient is the mean of all clustering coefficients [9]. The degree of a node calculates the number of edges connected to that node. The average degree calculates the mean of all the degrees, the result being useful in observing the nodes with higher connections [1]. Modularity is used in order to extract the community structure of large networks [3], being a measure of the strength of division of a specific network into communities or clusters.

19.2 Methodology and Data The present paper introduces a theoretical Strategic Diagnosis Based on Network (SDBN) system, in order to determine the non-deliberate corruption strategies among specific groups, at Global, European and EU, and Romanian level, using network analysis methods and measurements. In this work, we have used Global Knowledge Graph (GKG) Network Visualizer included in the GDELT Project. The Global Data on Events, Location, and Tone (GDELT) project, supported by Google, encompasses hundreds of millions of event records, which are extracted from broadcast, print and online news sources from all over the world [6] in real time, being updated at every 15 min. In our research, the month of May 2019 is the reference time interval. The month of May 2019 concludes with the European Parliament Elections in the EU, being considered by our research a month in which corruption could appear more frequently in the mass media. Global Knowledge Graph (GKG) Network Visualizer is scanned for the research parameters, and a list of all people, organizations and locations is compiled as the nodes of the network and the number of times that any two searches occur together. The resulted network diagram measures the degree to which the global news media refers to two terms together over a specific time interval. We conducted several searches through GKG Network Visualizer at the Global, European and European Union scale, as well as at national Romanian scale. At the national Romanian scale, we searched the list of organizations, locations and names, while for the other scales, we only searched the location and organizations list. Also, for Romanian scale, we used a larger interval covering the entire month of May 2019. This is motivated by the purpose of this paper as being the strategic diagnosis projected also at the national Romanian level and the need to construct communities and clustering groups at national level, in order to observe the most influent groups in regard to corruption and business corruption. The scales, parameters, interrogations and the time intervals used are described in Table 19.1.

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Table 19.1 Parameters and time intervals’ search Scale

First parameter

Second parameter

Time interval

Global

Corruption

–

1 May–15 May 2019

Global

Corruption

Business

1 May–15 May 2019

European

Europe

Corruption

1 May–15 May 2019

European

Europe

Business corruption

1 May–15 May 2019

EU

European Union

Corruption

1 May–15 May 2019

EU

European Union

Business corruption

1 May –15 May 2019

Romania

Romania

Corruption

1 May–31 May 2019

Romania

Romania

Business corruption

1 May–31 May 2019

The downloaded data for the specified time intervals and scales present a large number of nodes and edges which allow for a relevant network analysis, as shown in Table 19.2. Downloaded data were analysed using Gephi software. Gephi is an open-source social network analysis and visualization tool, allowing the calculation of various measures of centrality, modularity and clustering or ranking and network partition. For each parameter, the modularity coefficient is calculated, in order to determine the communities in the network; the average degree, in order to determine the degree to which some of the nodes are more influent than others; the average path length, in order to determine the average shortest path in the network; and the average clustering coefficient, to measure the degree to which the nodes tend to cluster together. A high modularity class with more communities indicates dense connections between the nodes within communities and sparse connections between nodes in different communities, while a low modularity class means sparse connections between the nodes within communities and dense connections between nodes in different modules. With other words, a high modularity class indicates more communities but with stronger relations inside communities, and low modularity class indicates less communities with week relations inside communities. In order to construct the Strategic Diagnosis Based on Network (SDBN), we determined the communities in each network following the community detection model introduced by Blondel et al. [3] and based on modularity (number of communities) and clustering coefficient we constructed a diagnosis model (Fig. 19.1). The theoretical SDBN generated is then applied on each scale and parameter in order to determine the possible non-deliberated corruption strategies followed at Global, European and EU, and national Romanian level by different stakeholders (countries, organizations, institutions and different groups of private or political people). The model helps in the specific diagnosis of public and business corruption context. In this model, we constructed the diagnosis model based on low and high network measurements. Still, it has to be taken into consideration the medium measurements of both modularity class and clustering coefficient as determinants for new

19 Projecting a Strategic Diagnosis System of Corruption … Table 19.2 Number of nodes and edges

Network

261 Nodes

Edges

Countries network

181

2488

Organizations and institutions network

552

2025

Countries network

194

3857

Organizations and institutions network

822

3243

Countries network

172

4014

Organizations and institutions network

360

1528

Countries network

193

5991

Organizations and institutions network

839

4940

Countries network

177

4627

Organizations and institutions network

620

3457

Countries network

177

4500

Organizations and institutions network

606

3383

109

2011

Organizations and institutions network

50

219

Names network

72

382

Global corruption

Global business corruption

European corruption

European business corruption

EU corruption

EU business corruption

Romania corruption Countries network

Romania business corruption Countries network

109

2009

Organizations and institutions network

50

219

Names network

72

382

strategies which are more complex and at the interface of the ones presented in this theoretical model. These complex strategies would show a more detailed diagnosis of corruption strategies at the communities’ level. These complex strategies are not discussed and analysed in here, and they will need a separate article in order to construct a more detailed methodology for the detection of these strategies. The main characteristic for the concentration corruption strategies is the low number of communities. This means that the strategies conducted in this area are focused on constructing few communities, with sparse connections inside each community and dense connections between the different communities in the network.

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CLUSTERING COEFFICIENT HIGH LOW

Fig. 19.1 Strategic diagnosis based on network theoretical model Concentration strategies

Diversification strategies

(highly connected)

(highly connected)

Concentration strategies

Diversification strategies

(weekly connected)

(weekly connected)

LOW HIGH MODULARITY CLASS (NUMBER OF COMMUNITIES)

Having sparse connections between the nodes inside communities indicates large communities which are concentrated in order to maintain the relations, but the main focus is on the relations between different communities. This shows that concentration corruption strategies are focused on external relations more than on internal relations, an aspect which is being encountered in networks which have a need for maintaining the external relations with their neighbours, as in the case of countries networks. For the diversification corruption strategies, the main characteristic is the larger number of communities which also have dense connections inside communities. These dense connections allow the formation of small communities which are strongly connected and are focused on corruption strategies inside the community, and not as much outside communities. This shows that corruption strategies are focused and on inside members of the communities, the neighbourhood communities being not as related between them. The clustering coefficient is introduced in this model with the purpose of detecting how complete the neighbourhood of a node is, the clustering coefficient measuring the degree to which nodes in a graph tend to cluster together. A high cluster coefficient means a strong power of gathering together as nodes in communities, while a low cluster coefficient means that the nodes in a network tend to not gather together in communities.

19.3 Results and Discussion The network measurements, as shown in Table 19.3, reveal a difference between countries networks, and organizations and institutions networks for both corruption (public and private corruption) and business corruption. The countries network has

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Table 19.3 Network measurements Network

Modularity/Community

Clustering coefficient

Average degree

Average shortest path

Countries network

0.078/2

0.822

27.492

1.872

Organizations and institutions network

0.431/10

0.852

7.337

2.506

Global corruption

Global business corruption Countries network

0.082/2

0.784

39.763

1.808

Organizations and institutions network

0.453/43

0.809

7.891

2.775

Countries network

0.141/3

0.804

46.674

1.736

Organizations and institutions network

0.263/20

0.857

8.489

2.333

European corruption

European business corruption Countries network

0.14/3

0.816

62.083

1.683

Organizations and institutions network

0.427/14

0.876

11.776

2.428

Countries network

0.113/3

0.81

52.282

1.71

Organizations and institutions network

0.429/9

0.9

11.152

2.007

EU corruption

EU business corruption Countries network

0.18/3

0.812

50.847

1.721

Organizations and institutions network

0.432/18

0.874

11.776

2.331

Countries network

0.202/2

0.805

36.899

1.658

Organizations and institutions network

0.419/4

0.823

8.76

2.097

Names network

0.554/7

0.884

10.611

1.874

Romania corruption

Romania business corruption Countries network

0.201/2

0.804

36. 881

1.659

Organizations and institutions network

0.423/4

0.823

8.76

2.097

Names network

0.554/7

0.884

10.611

1.874

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a lower modularity class, meaning less communities, while organizations and institutions network present higher modularity class and more communities. Both have a higher clustering coefficient, meaning that both networks have a higher degree to which the nodes tend to cluster together. This situation at the national Romanian level for the countries, and organizations and institutions networks follows the measure for concentration strategies, while for the networks of names, for both corruption and business corruption scale, the modularity class is still relatively lower than for Global and European scales, and the clustering coefficient is still high. Taking into consideration the amount of data for Romania being relatively reduced in comparison with the other scales, and the geographic and political area being also smaller, the number of communities, which are 7, will be considered as a high level in this paper in order to normalize the model. For the EU corruption (public and business) scale, organizations and institutions network has a modularity class of nine communities, being considered in this research as high modularity, given the fact that the modularity classes measurements for the other networks are very low (between 2 and 4 communities).

19.3.1 Global, European and EU Scale Diagnosis The diagnosis on these scales shows a difference between countries’ network and organizations and institutions’ network in terms of the number of communities. The higher modularity class and higher clustering coefficient at the organizations and institutions’ scale reveal corruption strategies based on diversification in a large number of highly connected communities (e.g. see Figs. 19.2 and 19.3), while the lower modularity class and higher clustering coefficient at the countries’ scale reveal Fig. 19.2 Global business corruption. Organizations and institutions network

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Fig. 19.3 European business corruption. Organizations and institutions network

corruption strategies based on concentration in a few highly connected communities (e.g. see Figs. 19.4 and 19.5). These results are the same in the case of corruption (public and business) and business corruption at global scale. The interpretation based on SDBN (Fig. 19.1) shows that at the countries’ level, the non-deliberated corruption strategies are concentrated in a few, but large country groups, and every group is highly connected with the other groups. In the case of organizations and institutions’ analysis, SDBN model shows Fig. 19.4 Global business corruption. Countries network

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Fig. 19.5 EU corruption. Countries network

that these groups are sparse and tend to adopt non-deliberated corruption strategies based on diversification, having stronger connections inside the groups than among the groups in the network.

19.3.2 Romanian Scale Diagnosis At the Romanian scale, the diagnosis results indicate non-deliberate concentration corruption strategies in a few highly connected communities at the countries’ and organizations and institutions’ level for both corruption and business corruption, and corruption diversification strategies with a large number of highly connected communities at the names’ level networks. For the networks of names (Figs. 19.6 and 19.7), the diagnosed strategies are based on diversification with a high number of communities which are highly connected. The high modularity class indicates strong connection among the nodes of the communities, but sparse connections between communities in the network. This means that the people involved in corruption cases, for both corruption and business corruption networks, tend to be part of groups; however, these groups are not always connected. In this specific network, we encounter the communities of political groups as being completely separated from the community of private people detected as being involved in corruption cases. A connection among local political people and European and Global political people exists, but it is not very dense. Figure 19.8 shows the results in terms of the strategic diagnosis of corruption, identifying for each network the area in the model. This step allows us to identify the specific strategies followed by stakeholders in the network and could be used in order to detect the necessary actions for following anti-corruption strategies.

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Fig. 19.6 Romania corruption. Names network

Fig. 19.7 Romania business corruption. Names network

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CLUSTERING COEFFICIENT

Global scale: -corruption & business corruption (countries) European scale: -corruption & business corruption (countries) EU scale: -corruption & business corruption (countries) RO scale: -corruption & business corruption (countries) -corruption & business corruption (organizations and institutions)

Global scale: -corruption & business corruption (organizations and institutions) European scale: -corruption & business corruption (organizations and institutions) EU scale: -corruption & business corruption (organizations and institutions) RO scale: -corruption & business corruption (names)

0,5

1

268

-

0

-

10

>10

MODULARITY CLASS (NUMBER OF COMMUNITIES)

Fig. 19.8 Strategic diagnosis based on network model

From the results diagnosis, an important aspect is the missing of detected networks in the area of low clustering coefficient. A possible explanation for this phenomenon is the illegal aspect of corruption itself. Being not legally, socially and ethical accepted, corruption actors and stakeholders need to gather in highly connected communities in order to maintain their activities as hidden as possible. The advantage of highly connected communities for the illegal activities is also explained by the need of keeping the secrets of the community inside community and having a specific code of conduct meant to maintain the status quo of the community.

19.4 Conclusions and Future Work The application of SDBN using network analysis proposed in this article indicates specific non-deliberated corruption strategies followed by different stakeholder groups at Global, European and EU, and Romanian level, using network analysis and mass—media and online news coverage regarding the subject of corruption (public and business corruption) and business corruption as single parameter. The analysis in this article indicates a difference between countries’ networks and organizations and institutions’ network for both corruption (public and private corruption) and business corruption. The difference consists of the presence of few communities strongly connected in the case of countries’ networks and a large number of communities weakly connected in the case of organizations and institutions’ networks.

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The results, in terms of analysis through our proposed diagnosis model, reveal concentration corruption strategies at the countries’ level and diversification corruption strategies at the organizations and institutions’ level, both with a high degree to which the nodes tend to cluster together. Regarding the Romanian level, the diagnosis for the countries and organizations and institutions networks indicates concentration corruption strategies, while for the networks of names, for both corruption and business corruption scales, the diagnosis reveals diversification strategies. These results conclude with the diagnosis of corruption strategies followed by stakeholders and actors involved at Global, European and EU, and Romanian level, with the specific perceptions from mass media and online news, and it offers a diagnosis image of how stakeholder groups tend to cluster together in a corrupt environment. The distinctiveness of this research consists of involving the theory of network analysis, as well as in inspiring strategic directions for inhibiting corruption behaviours. This model can be used by business organizations in order to detect specific corruption strategies followed in different geographic areas and business sectors, and it can also be used by international and national institutions involved in the fight against corruption (either political or non-political). This diagnosis model needs future research in terms of detecting the complex strategies followed in cases of medium network measurements, as well as in involving a larger sample of data and constructing time dynamic networks. This would allow the application of the SDBN model on a more specific context and would reveal the corruption and business corruption strategies at the Global, European and specific national or business sector levels. Future research will also involve the application of this diagnosis model at the level of multiple countries and to extend its application to specific business sectors.

References 1. Albert, R., Barabasi, A.-L.: Statistical mechanics of complex networks. Rev. Mod. Phys. 74, 47–97 (2002). https://doi.org/10.1103/RevModPhys.74.47 2. Barabási, A.-L., Bonabeau, E.: Scale-free networks. Sci. Am. 288, 60–69 (2003) 3. Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. 2008, P10008 (2008). https://doi.org/10.1088/1742-5468/2008/ 10/P10008 4. Dixit, A.K.: Corruption: supply-side and demand-side solutions. In: Dev, S.M., Babu, P.G. (eds.) Development in India: Micro and Macro Perspectives. India Studies in Business and Economics, pp. 57–68. Springer India, New Delhi (2016). https://doi.org/10.1007/978-81-3222541-6_4 5. Jain, A.K.: Corruption: a review. J. Econ. Surv. 15, 71–121 (2001). https://doi.org/10.1111/ 1467-6419.00133

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6. Leetaru, K., Schrodt, P.A.: GDELT: global data on events, location and tone, 1979–2012. International Studies Association, Graduate School of Library and Information Science, University of Illinois at Urbana-Champaign, Champaign, USA (2013). https://data.gdeltproject. org/documentation/ISA.2013.GDELT.pdf 7. Rose-Ackerman, S.: Corruption: A Study in Political Economy. Academic Press, New York (1978) 8. Rose-Ackerman, S. (ed.): International Handbook on the Economics of Corruption. Elgar Original Reference. Edward Elgar, Cheltenham, UK; Northampton, MA (2006) 9. Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393, 440 (1998). https://doi.org/10.1038/30918 10. https://www.gdeltproject.org 11. https://gephi.org

Chapter 20

The Influence of Stakeholders on the Management and Work of a Community Center in Israel Nicolae Bibu and Etti Isler

Abstract This paper relates to the attempt to identify the many stakeholders in the community center in Israel. The central assumption is that successfully identifying the stakeholders and their interests will allow managers to ensure the better functioning of the community center in accordance with its vision and its goals. After characterizing the internal and external stakeholders and after surveying their interests, a theoretical examination of the changes needed in the management of the community center is undertaken in order to reflect their influence. This examination identifies 12 central areas in which change is necessary. They are based on the multiple stakeholder management approaches. An attempt is made to identify which stakeholders are best capable of initiating these changes. The discussion suggests that the development of social initiatives, the creation of round table initiatives, and the institution of regular meetings should involve all the stakeholders. Addressing these may help to achieve the goals of the community center and to promote the interests of the stakeholders. The general conclusion that arises from this paper is the key importance of cooperation between an organization and its stakeholders as a feature of multiple stakeholder management. The stakeholders consider cooperation to be of major importance, while other factors are considered to be less significant. Keywords Community center · Stakeholders · Community · Community center’s director · Stakeholders management

20.1 Introduction This paper attempts to identify the many stakeholders involved in the community center. After defining both the internal and external stakeholders considered being the most significant and after a survey of their interests, an attempt will be made to N. Bibu · E. Isler (B) West University of Timisoara, Timisoara, Romania e-mail: [email protected] N. Bibu e-mail: [email protected] © Springer Nature Switzerland AG 2020 G. Prostean et al. (eds.), Innovation in Sustainable Management and Entrepreneurship, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-44711-3_20

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examine the changes required in the management of the community center, in order to take their influence into account. This paper draws on theoretical and empirical research and surveys. In addition, materials and surveys carried out by professionals working in the field of community centers were examined. This paper is based on an examination of community centers in Israel.

20.2 Community Centers in Israel The community center is a unique framework that was developed in response to the needs of a community, and which was consolidated into an organizational structure. It draws together a wide variety of social, municipal, cultural, and state public services. It meets the diverse and changing needs of neighborhood residents, as well as those of state and local authorities and of other interested parties in the vicinity, such as commercial interests, institutions of formal education, and other competing institutions. In 1969, the Government of Israel in response to the initiative of Zalman Aran, then Minister of Education and Culture in Israel, decided to establish a government association for community centers. Until the establishment of the Israel Association of Community Centers, the various services offered to the community (education, welfare, sports, culture, etc.) were provided by different authorities, without a holistic view of the needs of the community, the family, and the individual. In the community centers, these services were now housed under one authority and could be designed to meet diverse community needs. Since there are no laws or regulations that guarantee the continued existence of the community centers, they must run efficiently and effectively. A center that cannot sense the needs of the community and meet them by running successful programs is likely to end its functioning. This has both advantages and disadvantages. On the one hand, the center must constantly renew itself and adapt to the changing needs of the community in order to maintain its relevance. On the other hand, more recent reasons of government agencies may intrude into the management of the community center and may reduce its role to an organization that offers services to the disadvantaged people community alone [1]. An unresolved tension arises between the public agenda of the community center and the institutional demands from it, on the one hand, and local needs on the other, and between the empowerment of the community and the increase of the center’s influence on its surroundings and on its policies. This tension is clearly evident in relation to politics [2]. The characteristics of the community center are discussed further. A community center serves the interests of both internal and external bodies, and its existence is dependent on the external surroundings and on the need for internal and external

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legitimating in order to ensure legitimacy and resources. It is also susceptible to pressures from competing organizations, local community inhabitants, political bodies, from national, municipal, and neighborhood that threaten its organizational balance. Its target population includes the people from all sectors: Jews, Arabs, secular, religious, and ultra-orthodox. It serves all ages from infancy to old age. It offers all the services that the community center and its stakeholders decide upon. It covers issues such as care for the environment, care for special groups, the development of communities organized around a topic and more. The roles of the community center are the multiple and diverse, such as facilitation of high quality of life and involvement in activities that serve the neighborhood; identification of neighborhood needs and the development of neighborhood programs; operation of neighborhood services and the improvement of their quality and accessibility to residents; representation of neighborhood interests at the municipal and government level; participation of residents in decision-making processes, as part of the strengthening of neighborhood democracy; development of responsible and dedicated community leadership; recruitment and effective use of resources; training and deployment of volunteers. It deals with issues such as culture, community development, early childhood, youth and youth education, retirees, absorption of new immigrants, integration of mental health patients, security, coordination and physical planning, special needs populations, alongside rich and varied recreational activities. Consequently, the vision of the community center is to be the central axis of civil society in its community having as main purpose to offering a framework within which any resident can express him/herself fully. It plays a central role in the initiation of processes intended to improve quality of life in the neighborhood, alongside its other general activities. Community centers consider themselves as a central part of the system of organizations that constitute the Israeli civil society. They are non-governmental organizations and thus must attract resources independently. Nevertheless, they are dependent on state financing for their budgets, either directly or indirectly. Decision-makers in the community center compete with other organizations and must contend with limited budgets. While dealing with the financial issues which determine the dependence or independence of the community center, they must maintain continuity of activities and of quality of service among its teams. We consider that an organization manages its relationship with any given stakeholder group through specific processes and structures. An important feature of “stakeholders management” is the extent to which multiple stakeholders have some degree of representation when decisions that affect multiple groups are being made [3]. We agree that multiple stakeholder management is an important solution for more effective non-profit organizations (NPOs). Our conclusion is that the “complexity of and the need for stakeholder management in (NPOs), are important if they want to be perceived as (more) effective by their numerous stakeholders, and related to that gain insight in how to improve their governance practices” [4].

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20.3 Categories of Stakeholders of a Community Center We have analyzed and identified nine categories of stakeholders of a community center in Israel, such as the director of the community center, the employees, the customers/beneficiaries of the services, the local authority, the executive committee, national authorities and government offices, volunteers, third-sector organizations, and philanthropic stakeholders. The first group, are the directors of the community centers. The role of the director of a community center is extremely complex. He/she is positioned at a central intersection linking the three main decision-making bodies of the community center’s operating system: the local authority, the operating association, and the community center itself. The director is situated at the eye of the storm, expected to navigate between politicians who are not concerned with managerial issues. He is often aware of impending crises and of the absence of resources to deal with these. He simply wants to run the community center professionally and to avoid complications [5]. Ostensibly, the management of very different community centers makes different skill sets, different management styles, and managerial personalities necessary [6]. Clearly, a small community center in a peripheral area is very different from a large urban network of community centers. The second group, the employees, is large. Over 4000 men and women are currently employed by the community centers in Israel. Some are salaried employees; others are contract workers or independent workers who provide services to the community center on an ad hoc basis. Most of the workers hold partial positions. The employees are divided into three main groups: administrative workers, coordinators of the various sections, and counselors of the different courses and activities, where there is a high turnover of workers. Alongside administrative staff, coordinators work with all the relevant bodies as well as with the counselors in order to operate the area of content for which they are responsible. The coordinators who execute the decisions of the executive committee are engaged together with the director in initiating, motivating, and accompanying special projects and processes in the area, based on an analysis of the needs of the community, in cooperation with external professional in the fields of community services, recreational activities, culture, welfare, education, youth, etc. The third group, the beneficiaries, is the citizens from the local community. The nature of the services offered to its beneficiaries by the community center differs according to the specific needs of each community, for example, kosher food, educational services, legal services, welfare, employment, sport, and recreational equipment. The provision of services is facilitated by formal and informal cooperative. The fourth group is the local authority. It is pointed out that the personal elections in Israeli local authorities granted increased power to the head of the local authority [7]. At the same time, the community center became more significant in the life of the community because of the nature of the services it offered. It was perceived as a political asset, so that members of the local council wanted to serve on its

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directorate. The directorship of the community center became a means of political payback, and the members of the directorate were not always involved in its operation. In addition, over the years, government budgets played a smaller role in the budget of the community center, while the role of local budgets increased. Many of the inhabitants who enjoyed the services of the community center preferred to be involved in determining community policies and in its management. In order to meet these changes, the legal structure of the community center was defined and regulations were formulated that expressed the world view of the Association of Community Centers from Israel and that formalized the status of the director. The regulations provide a solution to crises that may arise around the proper management of the community center. Optimally, residents constitute 40% of the board of directors, representatives of the head of the local authority another 40%, and representatives of professional bodies such as the Israel Association of Community Center Education, 20%. Aside from this agreement, there are less formal relations between the community center and the authority that are based on the daily activities of the center. The local authority considers the director as an executor of the demands of the local authority, acting in the interests of the community. He/she is subordinated to the board of directors of the community center, because he/she is a policy executor and not as a policy-maker. The local authority is the center of influence, money, and power [8]. In other words, the director of the center is not perceived as a leader but as a subordinate who carries out instructions. Goldberger [7] suggests that these relations are characterized by their variety and that they range from full cooperation to power struggles between different bodies (branches and departments). Even though the community center operates in a political space, it is bound to act professionally and effectively. At times this obligation creates tension between the head of the local authority and the director of the community center [9]. The activity of the public administration contributes to the involvement of the residents in the nature of social and cultural activities and strengthens the connections between residents [10]. The fifth group, the executive committee, is constituted according to the following algorithm: Up to 40% of the members are appointed by the local authority. In this manner are appointed the members of the council, municipal workers, and public representatives. Where the state is involved, through the Association of Community Centers, it appoints a further 40%. Another 20% is appointed from representatives of the neighborhood in which the center is located. They must have no affiliation to either the local authority or the state [11]. Sometimes they are chosen in local elections, at times by a general meeting of the community center [12]. The sixth is the state authorities and government agencies. Some of the community centers are state run, some by local authorities, by city companies and others. Thus, the relation to the state varies. However, because state budgets and national projects are operated by the community center, there is government affiliation. The largest

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body responsible for the operation of the community center is the Ministry of Education. The Association of Community Centers, established in 1969, is a government company under the auspices of the Ministry of Education. The policies of the Association of Community Centers are determined by a directorate composed of individuals with public stature, who are elected from the team of directors. Regarding the seventh group, volunteers, Snyder and Omoto define volunteering as a deliberate activity undertaken voluntarily, extended over time and executed without expectation of reward or repayment [13]. Often volunteer work takes place under the auspices of formal organizations and serves individuals in need of help. According to [14], volunteering plays an important role in democratic societies and constitutes a springboard to personal and social development and an important tool in times of crisis. It contributes to pluralism, the acceptance of the other, and leads to social strength and cultural sensitivity and to the ensuring of civil rights and socio-ethical values, values integral to the community center. Indeed, volunteers and volunteering are an important element in the work of the community center. The eighth group of stakeholders is composed of third-sector organizations that represent an important part of the structure of civil society. Academic research relates to the third sector as a corporate activity that is not operated or controlled by the state or by non-governmental organizations. It does not operate for purposes of profit, and membership is voluntary [15]. The European Commission notes that unlike informal organizations that operate on a social or familial level, civil society organizations have a formal or institutional existence, operate in a disinterested manner, and are active in the public sphere in order to improve general welfare. The British government sees these organizations as value-driven, and as investing budget surpluses in social, cultural and environmental aims [16]. The ninth group consists of philanthropic stakeholders. Globalization as well as technological, environmental, and financial changes has led to profound changes in forms of giving charity and donations. Philanthropic models have been developed in order to increase the effectiveness of donations, and the boundaries between philanthropy in the private sector and institutional philanthropy or philanthropy as a form of investment have become blurred [15, 17].

20.4 Suggested Changes in the Management of a Community Center Based on the Stakeholder Approach In the previous discussion, the community center was defined, its areas of concern discussed, and its stakeholders surveyed. In light of this mapping, an attempt will be made to examine which changes should be introduced in the community center in order to take the positions and interests of the various stakeholders into consideration. Of the nine different stakeholders identified and surveyed above, the focus has been on

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two internal stakeholders, the director and the workers, and two external stakeholders, the local council, and the executive committee. First, we suggest to building mechanisms of cooperation between the dominant stakeholders in the community center. This can be achieved through professional forums of members of the executive committee and educational forums. In both instances, they should be constructed as learning groups (learning together brings individuals together and in addition participants may have shared interests, e.g. management) or as a support group (in order to dispel professional isolation and to learn from mutual experience) or as a working group that develops regional communal projects. That is the responsibility of the director and the workers. Secondly, we suggest that the director and workers should develop a work plan whose aim is to advance the community center. Thirdly, we suggest that the local authority and the director should develop social initiatives and include the work plan social mechanisms in all activities of the local authority—services for the elderly, youth councils, volunteering, youth, community development, community circles, culture—and any area that the community center deals with. Our fourth proposal for improvement refers to the use of all public facilities for enrichment activities based on emphasizing the mutual responsibility and the development of local community resilience. We propose the introduction of a working model that integrates or delegates authority, based on the transfer of authority to the local committees (establishing a technical and substantive framework for the work, empowering local teams by training and professional learning) and turning the coordinators’ forum into a professional pedagogic authority. This allows for the creation of mutual responsibility and the development of strong bonds between the members of professional support groups, dispels professional isolation, and creates a work model that influences the community. Another proposal refers to the publication of a monthly or bimonthly report issued to all interested parties on the events of the month. The reports should detail the number of participants, new events, and new initiatives and include both general and specific reports of successes, innovations, etc. Another proposal refers to the establishment of neighborhood committees and forums for cooperation. The organization of periodical round tables is based on working with round tables concepts to create continuity. Next proposal is to establish and operate Internet forums for updating and consultation on various activities, events, or any other idea. We propose also to hold regular meetings, three times a year, in order to update the different divisions on events in local councils concerning education and community. Another proposal is to establish clear and measurable criteria and mechanism for allocation of resources between locations and different needs. The final proposal refers to the creation of a community system of recreational activities in order to increase community resilience.

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20.5 Case Study Research We have used a qualitative approach based on the case study method. The community center that was researched according to the way it is presented by its stakeholders. The examination is based on questions regarding their opinions about the characteristics, influence sources, balance of powers as described in this article. Our research methodology consisted of interviews, regular observations, and participatory observations. The researched entity is an interdisciplinary community center, located in the heart of a small city, in the center of the country, near Tel-Aviv. The socioeconomic section of the city is level 8. The city is in a renewing process. It was established in 1948 with the state of Israel, and absorbed many immigrants during its first period. The city gained popularity and became very attractive, due to its proximity to major traffic arteries, to universities, shopping malls, and health centers. Alongside singlestory cottages, towers and high-rise buildings were developed, and the population has tripled its number. The city is currently inhabited by 40,000 people and was recognized in terms of municipal status as a local council in 1954, and as a city in 1992 [18]. According to the Israel Association of Community Centers’ Web site [12], the community center subject of the “Case Study” is the central institution in the field of community and informal education in the city. We will use a changed name for it that is “XYZ community center.” The XYZ community center is operated by the Israel Association of Community Centers, in cooperation with the local municipal authority. It is considered as an independent economic and legal entity. The director of XYZ community center is an employee of the National Association of Community Centers, which is owned by the Israeli State and operates on a national scale. The research was adjusted and conducted in accordance with this article, that is dealing with the stakeholders of the community center, suggesting ways to improve interaction among themselves, with the aim of making the center more efficient, creating such a system in which mutual interest will overcome any conflicts, From the nine categories of stakeholders identified by us, the researcher has focused on interviewing three main stakeholders: the director of the XYZ community center, one of the members of the Center’s Board, and a group of 7 active parents of children in the nursery operated in the center who are living in the city and are part of the local community. The tools of the research consisted of interviews, regular exploring observations, and study of documents and protocols of relevance. In order to conduct the study, the researcher has met with each of the mentioned stakeholders and made exploratory observations on the director’s work and on the Board’s meetings. In essence, the role of the director is to stand in the center of the network with the rest of the stakeholders of the community center, particularly with the local authority, the National Association’s district director, the members of the Board, and the community center clients (beneficiaries).

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The director’s main goal is to bring about an effective and efficient operation of the center, avoiding conflicts with the stakeholders, keeping at the same time, high standards of professionalism and integrity. The main obstacle that was detected was an inherent tension between the local authority representatives and the National Association district director. That is generated by the existing tension between national interests and local interests. For example, issues that were mentioned related to which projects to execute, how to use budgets, what kind of people to hire in a small city in which the pressures to employ relatives is very strong. In my opinion, the role of community center’s director consists of three functions: administrative, financial and political functions. The director does not have a pedagogical function. There are professionals for this function. The board of directors, and the government ministries are important, but the parents, are stakeholders of great importance, and I conduct with them dialogue on a regular basis. Nevertheless, the most significant stakeholder is the local authority and its head. The community center cannot exist without coordination with the local authority. The local authority makes decisions regarding who will sit in the board of directors of the community center, and through the board, who will be employed, in what way will the center function, what size of budget will it have, what are going to be the projects that will be granted to it, and how the coordination with state and governmental entities will be conducted. There is no possibility for the community center to exist, without a prompt coordination with the local authority, that its head has the power to take a decision of closing the center’s activity within one hour. This results from the fact that the non-formal education is not subject to state regulation or legalization. (excerpts from the interview with the director of XYZ community center)

The board member we interviewed is the representative of the state. Her main goal is to fulfill the policy of the Community Centers Association which is the state’s policy. Tension exists especially toward the local authority and also toward other members of the Board who represent interested parties such as commercial entities, and public entities like volunteers. The state’s representative is very much concerned in maintaining the budget framework, and this causes tension toward the community center. We have also interviewed stakeholder belonging to a group of active parents that consists of seven parents, three fathers, and four mothers, aged 30–40. They came about to become active in the community center, as their children participate in the nursery operated in the center. The parents consider themselves as part of the foundations of the community and hold constant communication with the director as well as the Board. The parents feel sometimes frustration and anger, although their special linkage with the center, and the great interest they have toward the institution. The reason is, among other reasons, their feeling that they failed to have the influence on the centers’ management they considered they should have. The local authority, the community center board, and the director appeared to be uncoordinated in many occasions; while on the other hand, the parents and the other users of the community center manage to create a unified group, thanks to the social networks and modern media through groups and forums. In conclusion, we have identified that the community center consists of a large group of stakeholders. Each one of them has his/her own agenda and is responsible

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to promote their group interests. On the other hand, we conclude from the interviews that all of the stakeholders of the community center are committed to the main mutual goal which is the welfare of their community. As for the community center, they consider it as part of the informal education establishment, in which coordination mechanisms are essential to promote services to the community. It should be noted that our findings at the XYZ community center show great similarity to the finding of the research that was carried in this article regarding the nine actors which were identified as stakeholders in general, in community centers.

20.6 Discussion and Conclusions This paper has surveyed the community center, its vision, its components, its aims, and the stakeholders. Through the mapping of the stakeholders, an attempt has been made to examine their role in the delicate fabric of the community center. To this end, research conducted by academic institutions, by the bodies operating the community center, the National Association of Community Centers, state bodies, local authorities and other organizations, has been reviewed. The literature review identified nine categories of important stakeholders. The most significant fields of activity were identified and divided into twelve categories. Next, an attempt was made to examine and recommend changes in the work of the community center in order to take the positions and interests of the stakeholders into account. Table 20.1 Stakeholders versus tasks No.

Proposed change

Community

Local authority

Director

Employees

5.1

Cooperative mechanisms

✓

✓

✓

5.2

Work plans

✓

5.3

Social initiatives

✓

✓

✓

5.4

Public facilities

✓

✓

5.5

Integrative model

✓

✓

✓

5.6

Reports

✓

5.7

Forums for cooperation

✓

✓

✓

5.8

Round tables

✓

✓

✓

✓

5.9

Internet forums

✓

5.10

Ongoing meetings

✓

✓

✓

✓

5.11

Resource allocation

✓

✓

5.12

Recreational activities

✓

✓

✓ ✓

✓

20 The Influence of Stakeholders on the Management and Work of a Community …

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The overall picture, presented in Table 20.1, suggests that in the development of social initiatives, round tables, and ongoing meetings, all the stakeholders should be involved. Focusing on these is likely to contribute to the better achievement of the goals of the community center and to provide a response to the interests of stakeholders. In the establishment of cooperative mechanisms, the establishment of neighborhood committees and forums for cooperation, we found that three of the four stakeholders surveyed should be involved: community, director of the center, and employees. The general conclusion that emerges from this paper is the importance of cooperation in the management of community centers. Cooperation is considered by all stakeholders to be of vital importance as opposed to other factors. Behind the vision, aims, and activities of the community center, central dilemmas exist: who provides and who receives the services of the community center; independence versus dependence; ethical versus financial considerations; a competitive services market; limited financial resources. We suggest that these dilemmas can be better solved through the identification of stakeholders, of their interests and through cooperation into identifying the satisfactory solutions for all.

References 1. Nitzav, 2.

3.

4. 5. 6. 7. 8. 9.

10. 11. 12. 13.

A.:

The

community

center in search of meaning. (2015) Yanai, U.: The Development of Community Centers in Israel. School of Social Work, Hebrew University of Jerusalem, Jerusalem (1985). https://www.reutinstitute.org/he/Publication.aspx? PublicationId=3282. Accessed 20 Jan 19 Johnson-Cramer, M.E., Berman, S.L., Pos, J.E.: Re-examining the concept of ‘stakeholder management’. In: Andriof, J., Waddock, S., Husted, B., Sutherland Rahman, S. (eds.) Unfolding Stakeholder Thinking 2. Relationships, Communication, Reporting and Performance. Routledge, London (2017) Wellens, L., Jegers, M.: Effective governance in nonprofit organizations: a literature based multiple stakeholder approach. Eur. Manage. J. 32(2), 223–243 (2014) Zippori, H.: The Column of the CEO. The Israeli Association of Community Centers, with the assistance of JDC-Israel, Jerusalem (1985) Miles, R., Snow, C.: Organizational Strategy, Structure and Process. McGraw Hill, London (1978) Goldberger, D.: Community Education, Community Centers and Youth and Sports Culture Centers. Hebrew University of Jerusalem, Jerusalem (2007) Ari, M.: Wanted: Directors of a New Breed: Implications for the Management of Community Centers, pp. 4–5. Community Centers, Jerusalem (1991) Ben-Noach, C., Aflalo, M., Levian, G.: The Impact of the Managers of the Community Centers on Their Perception of Self-Efficacy During the Second Lebanon War. The Israeli Association of Community Centers, Jerusalem (2008) Migvanim Internet site Ramat Hasharon. https://www.migvanim.com/. Accessed 31 Jan 19 Union of Local Authorities in Israel. https://www.ulai.org.il/ Israel Association of Community Centers. https://www.matnasim.org.il. Accessed July 2019 Snyder, M., Omoto, A.: Volunteerism: social issues, perspectives and social policy implications. Soc. Issues Policy Rev. 2(1), 1–36 (2008)

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14. Leigh, R.: State of the World’s Volunteerism Report: Universal Values for Global Well-Being. United Nations Volunteers (2011) 15. Salamon, L.M.: Leverage for Good: An Introduction to the New Frontiers of Philanthropy and Social Investment, 1st edn. Oxford University Press, New York & Oxford, UK (2014) 16. Gadron, B., Alon, Y.: Data Base Report, 2007: Patterns and Changes in the Third Sector in the Last 20 Years. University of Ben Gurion, Beer Sheva (2007) 17. Salamon, L.M. (ed.): New Frontiers of Philanthropy: A Guide to the New Tools and New Actors that are Reshaping Global Philanthropy and Social Investing, 1st edn. Oxford University Press, New York & Oxford (2014) 18. Federation of Local Authorities in Israel. https://www.masham.org.il. Accessed July 2019 19. Local Councils. https://www.justice.gov.il/Units/CommitteeToPreventConflictsOfInterest/ Openions/Mosdi/2-1300.pdf. Accessed July 2019

Chapter 21

Historical Valuation Bases and Drivers of Large Internet-Enabled Companies Adelin Trusculescu, Claudiu Tiberiu Albulescu, and Daniel Paschek

Abstract As industries develop, the factors driving their valuations also change. During the initial development phase, perceived business potential is the main factor, while during the growth and maturity phases, other factors such as size, market position, and lastly profitability and cash flow become the main drivers. The study looks at the evolution of the valuation bases and drivers expressed as enterprise value multiples and growth and profitability margins over a period of ten years for large Internet-enabled companies. The study includes all companies in the Internet economy with an enterprise value of over EUR 10 billion. The study aims to test if “winner-takes-it-all” economies experience different trends and consequently ignore vertical segmentation. The study concludes that large Internet-enabled companies switched from revenue to profitability valuation bases about eight years ago (compared to vertical focused studies showing three years) and from growth to profitability drivers about six years ago (compared to vertical focused studies showing last year). The findings are interesting for researchers in the field as it demonstrates that large “winner-takes-it-all” players face different investor expectations compared to other players.

21.1 Introduction Before diving into the bases and drivers of large Internet-enabled businesses, it is essential to understand why Internet companies are subject to different trends and consequently different valuation methodologies. A. Trusculescu (B) · C. T. Albulescu · D. Paschek Politehnica University of Timisoara, P-ta. Victoriei 14, 300006 Timisoara, Romania e-mail: [email protected] C. T. Albulescu e-mail: [email protected] D. Paschek e-mail: [email protected] © Springer Nature Switzerland AG 2020 G. Prostean et al. (eds.), Innovation in Sustainable Management and Entrepreneurship, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-44711-3_21

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21.1.1 The History of Internet-Enabled Businesses In his book “The 4th Revolution”, Floridi argues that the developments in the information and communication technologies, particularly the Internet, lead to a fourth scientific revolution. Floridi argues that the boundaries between online and offline life have broken down with most individuals carrying “smart, responsive objects” which integrate us into an “infosphere.” He also argues that the life online defines more and more of our daily activity, taking over the working, learning, shopping, entertaining, and the way relationships are conducted aspects of life [1, 2]. In addition to being recognized as a disruptive technology [3], it enabled an entire new ecosystem of businesses to emerge which provide the services that individuals seek online. A recent study from the World Bank shows that individuals in some countries spend in excess of eight hours per day online [4]. While the Internet has its roots in the memos written by J. C. R. Licklider in 1962 on the concept of a “Galactic Network” [2], a lengthy development until 1990s was necessary in order for companies to start using it. In June 1993, there were only 130 Web sites, while by the end of 1993, there were 623 [5]. Amazon for instance was incorporated in 1994, launched its Web site in 1995 [6] and went public in 1997 [7]. In this time, the company developed from a simple online book shop to the world’s largest retailer which also offers its own streaming services, an entire own ecosystem for digital goods delivery and hosting services for companies [8]. Nowadays, the number of online businesses is well into the millions. Rachamim concluded in 2014 that there are 12–24 million online stores alone from which 650 thousand with annual sales of over USD 1000 [9].

21.1.2 Particularities of Valuation of Internet-Enabled Businesses Valuing companies that have developed together with the Internet is a new challenge for professionals in the industry. Arguably, the challenge arises primary from the historically very fast technology adoption rates. While the Internet needed a little more than ten years to reach an adoption rate in the United States of 50% of households, electricity (at the beginning of the twentieth century) which arguably provides a significantly higher value to the user needed over 20 years and the telephone nearly 50 years [10–12]. A more dramatic view which further emphasizes the decrease in adoption time for various new technologies is to look at the time a certain service needed to reach 100 million users: the telephone needed 75 years, while the mobile phone a little over 15, the Internet about eight years, the popular messaging app WhatsApp about three years and the popular mobile game Candy Crush 15 months [13]. Working with such high adoption rates is difficult as it is very difficult to assess

21 Historical Valuation Bases and Drivers of Large …

285

additional services that can be provided to such a high user base as well as the competitive pressure. With high adoption rates, competition can grow just as fast the original service. Due to the high growth, valuation professionals had to come up with various metrics that can help valuing internet-based business models. Pablo Fernandez approaches the valuation of Internet companies as a completely different animal compared to the traditional valuation methodologies. He suggests using price per subscriber, pages visited and inhabitant together with more traditional methods such as price per sales [14]. Arguably, the innovative valuation methods were part of the reasons why the “internet bubble” between 1998 and 2000 happened [15]. In addition to the high growth, high adoption characteristics of the various Internet-based models, the worldwide access, and the transparency the Internet provides, lead to a “winner-takes-it-all” business models [16]. In “winner-takes-it-all” economies, the largest player usually sees premium valuations compared to smaller companies. The concept of “winner-takes-it-all” and related economies of scale with high relatively high returns on R&D and marketing investments was already demonstrated by Hand in 2001 [17].

21.1.3 Research Objective Considering the relative “particular” nature of valuations in the Internet-based segment, the study looks at the development of relevant valuation bases and drivers over the last years in publicly listed large Internet-enabled businesses. Generally, as industries mature, the valuation bases and drivers also switch from being more “innovative” or technology/potential focused toward being more revenue/scale focused and ultimately being based on profitability and cash flows. A similar study focusing on inventory-based online retail has been published by Trus, culescu, Dr˘aghici, and Pascheka in November 2017 [18]. The current study tries to see if large Internetenabled companies show a similar trend in the importance of revenues compared to EBITDA-driven valuations and assess if the companies representing “the-winnertakes-it-all” industries experience different trends and infliction points compared to the focused industries such as online retail. We expect the study to be of importance as most studies, except for the one published by Trus, culescu et al. in November 2017, focus on the analysis performed at a certain point in time. For example, Roth addressed in 2014 the differences between revenue-driven valuations and earning-driven valuation; however, his study does not focus on any empirical data and is rather based on his long industry experience. A more comprehensive and also empirical study was performed by Harbott in 2012 included a total of 71 companies from various sub-segments of Internet-based companies which concluded that “Revenue is the dominant driver of Internet market value” [19]. This study, despite including all sub-industries in the same analysis and performing the analysis at the time when the study was performed, introduces many KPIs including some of the more “innovative” one described above. This study

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A. Trusculescu et al.

provides a valuable conclusion as the current study researches the historical development of that conclusion. While the current study will include less companies, the peer group should be comparable, and the conclusions should also be comparable. Last but not least, a study published by Trus, culescu et al. in 2015 studies in detail the valuation bases and drivers of public online e-commerce companies with inventory risk at that particular moment in time, representing another conclusion which is relevant for this study [20].

21.2 Study Methodology The study focuses on large publicly listed Internet-enabled businesses. It sources its data required for the empirical analysis from FactSet, a financial information database offered by FactSet Research Systems Inc. based in Norwalk, the USA. The database offers financial information and analytical software for investment professionals and is one the four comprehensive databases available worldwide [21].

21.2.1 Companies Included in the Study To identify all relevant companies, the screening function of the FactSet database has been used. From the 43,191 publicly listed companies, 965 were classified in “Internet” related industries. In order to account for the “winner-takes-it-all” analysis approach, only companies with an enterprise value above EUR 10 billion have been included [21]. This resulted in 28 companies. In addition to the screening function of FactSet, various industry sources have been consulted to ensure a comprehensive approach. A relatively comprehensive study which was consulted is published by GCA Altium, a German M&A Advisory firm, on a quarterly basis [22]. An additional comprehensive study that was consulted is the one published by William Blair in 2015 [23]. Apple has been considered as it was the only company missing from the “big 5” of the Internet [24]; however, Apple should be excluded because iPhones make up over 61% of Apple’s sales [25]. While one can argue that the growth in iPhone sales is in large driven also by the importance of the Internet, at the end of the day, it still is a device and related software, as opposed to a pure Internet-enabled product and will consequently be removed from the peer group. In order to allow for an analysis over the last ten years, the companies need to be publicly listed for at least this period. Out of the 27 companies identified, 16 meet these criteria. Ctrip, however, shows “patchy” profitability which will render any profitability-based valuation and metric unusable and has therefore also been excluded from the analysis. The final peer group includes 14 companies (Table 21.1).

21 Historical Valuation Bases and Drivers of Large …

287

Table 21.1 Overview of companies included in the study [21] Company

Co.

Description

IPO Date

IPO

Inclusion/exclusion

Amazon

US

General merchandise online retailer and sales of compute, storage, and AWS services

15/05/97

21

Yes

Alphabet

US

Holding company of Google: online search, streaming, maps, Chrome, Android, and other

19/08/04

14

Yes

Alibaba

CN

Online marketplaces for retail and wholesale, also offers cloud computing and other services

19/09/14

3

3 years since IPO

Apple

US

Designs and manufactures iPhones, PCs, other electronics and provides related ecosystem

05/11/84

33

Business model

Baidu

CN

Internet search, online marketing, e-commerce platform with payment and other services

05/08/05

13

Yes

Facebook

US

Social media, WhatsApp, and Oculu

18/05/12

6

6 years since IPO

Microsoft

US

Developing and marketing software and hardware: OSs, computing, business cloud, and others

13/03/86

32

Yes

Booking

US

Online accommodation booking, airline ticket comparison, rental car reservation

30/03/99

19

Yes

Tencent

CN

Online and mobile games, value-added services, online advertising, and other related services

16/06/04

14

Yes

(continued)

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A. Trusculescu et al.

Table 21.1 (continued) Company

Co.

Description

IPO Date

IPO

Inclusion/exclusion

JD.com

CN

Online retailer of electronics and general merchandise incl. audio, video, and books

22/05/14

4

4 years since IPO

Zalando

DE

Online retailer of shoes and apparel

01/10/14

3

3 years since IPO

eBay

US

Online marketplace and classifieds

24/09/98

19

Yes

Mercado-Libre

AR

Online marketplace focused on Latin America

10/08/07

11

Yes

Rakuten

JP

Online B2B2C marketplace and FinTech: online banking, credit cards, insurance, and other

19/04/00

18

Yes

Expedia

US

Online full range travel company for consumers and managed travel for businesses

20/07/05

13

Yes

Ctrip

CN

Online travel company with strong corporate travel management services

09/12/03

14

“Patchy” profitability

Snap

US

Operation of camera platform SnapChat and related services

02/03/17

1

1 year since IPO

Twitter

US

Operation of platform for public self-expression and conversation in real time

07/11/13

4

4 years since IPO

Weibo

CN

Social network focusing on Chinese microblogging Web site Sina Weibo and related services

17/04/14

4

4 years since IPO

58.com

CN

Online classifieds and listing platform focused on China

31/10/13

4

4 years since IPO

(continued)

21 Historical Valuation Bases and Drivers of Large …

289

Table 21.1 (continued) Company

Co.

Description

IPO Date

IPO

Inclusion/exclusion

Netflix

US

Online subscription streaming offering movies, episodes, and own content

23/05/02

16

Yes

PayPal

US

Digital and mobile payments company

06/07/15

3

3 years since IPO

Wirecard

DE

Software and IT for payment processing, outsourcing, and white label

25/10/00

17

Yes

GoDaddy

US

Provider of domain name registration and Web hosting

01/04/15

3

3 years since IPO

VeriSign

US

Provider of domain name registry services and Internet security

30/01/98

20

Yes

Yahoo JP

JP

Japan (JV arm of ex. Yahoo) provides Internet advertising, e-commerce, and other services

04/11/97

20

Yes

Leshi

CN

Chinese online video streaming services

12/08/10

8

8 years since IPO

21.2.2 Data Sources, Valuation Bases, and Drivers Used All data was downloaded from FactSet using their proprietary Excel Plug-in and analyzed using Microsoft Excel. The data was downloaded from 29/02/2008 monthly for all 14 companies comprising over the ten-year analysis period a total of 120 timely data points. For all data points and for all companies, the enterprise value (EV) as well as the last twelve months (LTM) and next twelve months (NTM) revenues and earnings before interest, taxes, depreciation, and amortization (EBITDA) was downloaded resulting in nearly 10,000 observations. While it would be very useful to include more time frames in addition to the LTM and NTM perspectives, it is very difficult and often very inconsistent to download data for such a long time frame from financial databases. Using both NTM and LTM data has the benefit of covering the two most important time frames for valuation: the performance over the last year and the expected performance for the next year. While LTM figures are based on the actual reported quarterly financials of a certain company, the NTM figures are based on averages of what brokers expect for the next twelve months. The valuation of the company is based on its EV which in addition to the value to shareholders also accounts for value for other stakeholders such as banks. By using the LTM and NTM revenues and EBITDA, four different multiples are calculated for

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at each point in time and for all companies. The multiples are calculated by dividing the EV by the four financial metrics to arrive at EV/LTM revenue, EV/NTM revenue, EV/LTM EBITDA, and EV/NTM EBITDA. These multiples will represent the bases of valuation used in the study. The base defines the way on which investors value a certain company. Based on the same financial information, the valuation drivers were calculated. Firstly, the revenue growth was calculated as the percentage change between the NTM and the LTM revenues, while the LTM and the NTM EBITDA margins were calculated by dividing the LTM EBITDA and the NTM EBITDAs by the LTM Revenue and the NTM Revenue. A total of three drivers were considered.

21.2.3 Methodology The goal of the study is to identify which valuation base (valuation multiple or dependent variable) and which drivers (independent variable) best explain the valuation of large Internet-enabled businesses at certain points in time. The study also tries to understand if there is a clear trend or switch over time from one base to another or from a certain driver to another. Considering that the study ultimately looks at correlations between drivers and bases at certain points in time, the simple, linear, cross-sectional regression is sufficient to understand the trends. The study regresses all bases and drivers against each other at 120 different points in time monthly starting March 2008. Each company included in the study represents an observation at a certain point in time.

21.2.4 Relevance of Empirical Results Provided that the analysis regresses four different bases (multiples) against three different drivers over a period of ten years monthly, it is difficult to understand all results; however, a “Min, 25% quartile, Median, 75% quartile, and Max” analysis helps getting an overview of the results and identifies the results which are counterintuitive. The only regressions which seem implausible are the ones regressing LTM and NTM multiples against LTM and NTM margins. These regressions yield on average negative slops which imply that a higher EBITDA margin leads to a lower valuation multiple. Similarly, the intercepts seem unrealistically high implying high valuations for companies that are just breakeven. Consequently, these regressions should be excluded from the conclusions (Table 21.2).

14

31

56

67

25Qua

Median

75Qua

Max

57

3s6

31

24

9

57

36

31

25

8

1.8

3.5

5.6

Median

75% Qua

Max

4.8

2.7

1.4 4.9

2.7

1.4

−1.2

−0.8

14.7

8.1

5.7

3.3

−6.6

−2.3

−2

0.5

−1.3

25% Qua

Min

55.6

39.8

27.4

18

6.3

LTM Margin

LTM EV/EBITDA Growth

NTM Margin

LTM Margin

41

28

22

2

0

Growth

92

64

35

20

5

LTM EV/Rev

Intercept (monthly covering 29/02/2008–01/02/2018)

0

Min

LTM Margin (%)

LTM EV/EBITDA Growth (%)

NTM Margin (%)

Growth (%)

LTM Margin (%)

LTM EV/Rev

R-Square (monthly covering 29/02/2008–01/02/2018)

54.4

39.7

27.5

17.6

6.2

NTM Margin

42

26

20

2

0

NTM Margin (%)

65

45

40

35

24

LTM Margin (%)

5.5

3.8

2.6

1.4

0.3

Growth

3.1

2

1.1

−0.4

−1.4

LTM Margin

NTM EV/Rev

58

43

17

5

0

Growth (%)

NTM EV/Rev

Table 21.2 Overview of overall results of study covering monthly observations 02/2008–02/2018

3.2

1.9

1

−0.8

−1.8

NTM Margin

65

47

41

36

23

NTM Margin (%)

40

24

20

4

0

13.6

8.3

6.5

5

−2.8

Growth

44.3

27.5

21.7

14.1

5.7

45.3

27.6

21.8

14

5.6

NTM Margin

41

24

19

4

0

NTM Margin (%)

(continued)

LTM Margin

NTM EV/EBITDA

86

57

29

19

4

LTM Margin (%)

NTM EV/EBITDA Growth (%)

21 Historical Valuation Bases and Drivers of Large … 291

−1.8

11.2

16.2

25.2

37.3

Min

25% Qua

Median

75% Qua

Max

30.2

18.8

10.6

8.6

6.5

29.9

18.9

10.5

8.6

6.1

182.7

79.6

64.7

54.1

18.3 −28.2

−28 22.6

22.3

1.7

−56.9

0.5

−83.8

−57.4

NTM Margin

−85.8

LTM Margin

LTM EV/EBITDA Growth

NTM Margin

Growth

LTM Margin

LTM EV/Rev

Slope (monthly covering 29/02/2008–01/02/2018)

Table 21.2 (continued)

22.7

14.9

9

5.1

−5.2

21.3

14.2

9.4

8.4

6.9

LTM Margin

NTM EV/Rev Growth

22.1

14.3

9.6

8.5

6.7

NTM Margin

131.8

52.3

41.2

30.9

10.5

15.7

−0.8 15.7

−21.3 −1.6

−32.7

−73.4

NTM Margin

−21.3

−32.7

−69.7

LTM Margin

NTM EV/EBITDA Growth

292 A. Trusculescu et al.

21 Historical Valuation Bases and Drivers of Large …

293

21.3 Empirical Results To understand the results of the study, one should look firstly at bases and secondly at drivers of EV/Revenue bases considering that half of the base versus margin regressions are counterintuitive.

21.3.1 Results—Development of Basis The results for the development of bases are presented in Figs. 21.1 and 21.2. When looking at the development of the R-square values of revenue and EBITDA, LTM and NTM multiples against growth, one can see that LTM and NTM regressions generally show the same picture. When comparing, however, revenue and EBITDA regressions, one can see that EBITDA regressions have gained importance for some time. Compared to the results in the online retail industry presented by Trusculescu et al. [18], which showed that revenue-based multiples played an important role up until mid-2016, the results are surprising suggesting that larger companies might indeed face different valuation expectations from investors or at least face the profitability expectation much earlier compared to smaller ones. Profitability multiples became the base for valuing large online-enabled businesses about six years before the sub-segment online retailers. Fig. 21.1 Development of R-square of LTM EV/revenue and LTM EV/EBITDA versus growth

Fig. 21.2 Development of R-square of NTM EV/revenue and NTM EV/EBITDA versus growth

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Fig. 21.3 Development of R-square of LTM EV/revenue versus Growth, LTM and NTM EBITDA

Fig. 21.4 Development of R-square of NTM EV/revenue versus Growth, LTM and NTM EBITDA

21.3.2 Results—Development of Drivers To understand the development of drivers of valuation bases, one needs to compare the R-squared values of a certain multiple regressed against the analyzed drivers. In this case, since most regressions using the EV/EBITDA multiple yield counterintuitive results, we can only look at NTM and LTM EV/Revenue multiples regressed against the three drivers: growth, LTM and NTM EBITDA margin. The results, seen in Figs. 21.3 and 21.4, show three distinctive conclusions. The first conclusion, which is surprising, is that NTM multiples seem to show relatively higher R-squared values compared to the LTM multiples starting mid-2012. This finding is surprising since NTM is forward looking and is based on market’s expectations rather than observed financial performance. This finding could imply that large Internet-enabled businesses have become more predictable and investors can rely on forecasts to take decisions. The second interesting conclusion is that EBITDA margin is since mid-2012 significantly more important than growth in driving valuations. Compared to the conclusion on the online retail segment derived by Trusculescu et al. [18], the switch from growth to margin in the large Internet-enabled business segment took place about five years earlier compared to the sub-segment online retail. Lastly, the third conclusion is that LTM and NTM EBITDA margins as drivers generally show the same picture. This implies that the markets expectations regarding future margin in the industry are strongly related to the recent past performance.

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21.4 Conclusions The overall conclusion is that large online-enabled companies seem to face different time dynamics and inflection points regarding valuation drivers and bases compared to sub-segments of Internet-enabled companies in general. While the study performed by Trusculescu et al. in 2017 focusing online retail concluded that the bases of valuation in that particular sub-segment changed from being revenue-based to being EBITDA-based in the last three years, while the drivers changed from being growth based to being EBITDA based in the last year [18], this study shows that when looking at large Internet-enabled companies, the switch from revenue to EBITDA bases happened as long as eight years ago, while the change from growth to EBITDA drivers took place about six years ago. The difference suggests that the inflection points for large players took place about five to six years before the rest. While the finding is particularly interesting as it demonstrates that large players face different investor expectations compared to sub-sectors and implicitly smaller players, it also raises more questions regarding the valuation bases and drivers of Internet-enabled companies. Based on the described comparison, it is recommended that researchers not only account for segments and implicitly different business models when analyzing companies in the Internet-based sector, such as focusing only on online retail, but also account for significant size differences and potentially even adjust peer group to exclude the “winners.” Lastly, further research should be done into understanding the best way to measure the relative time of various inflictions. Maybe instead of looking at overall industry development expressed in years and growth rates, it would be useful to look at years since the company is profitable or years since the company went public.

References 1. Floridi, L., Holsopple, B.: The 4th Revolution: How the Infosphere is Reshaping Human Reality, Unabridged edn. Audible Studios on Brilliance Audio (2016) 2. Rocci, L.: Moral, Ethical, and Social Dilemmas in the Age of Technology: Theories and Practice: Theories and Practice. IGI Global (2013) 3. Lyytinen, K., Rose, G.M.: The Disruptive Nature of Information Technology Innovations: The Case of Internet Computing in Systems Development Organizations. MIS Quarterly 27(4), 557–596 (2003) 4. Bauer, R.: #6 from 2016: Media (R)evolutions: time spent online continues to rise. People, Spaces, Deliberation, 03 Jan 2017 [Online]. Available: https://blogs.worldbank.org/ publicsphere/6-2016-media-revolutions-time-spent-online-continues-rise (2017). Accessed 11 Mar 2018 5. Gray, M.: Web Growth Summary. Internet Statistics—Growth and Usage of the Web and the Internet (1996) 6. Amazon, Amazon—Press Room—History & Timeline. Amazon, 2017 [Online]. Available: https://phx.corporate-ir.net/phoenix.zhtml?c=176060&p=irol-corporateTimeline. Accessed 11 Mar 2018 7. Wilhelm, A.: A look back in IPO: Amazon’s 1997 move. TechCrunch, 28 June 2017

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8. Quinn, J.: Amazon Timeline: From Internet Bookshop to the World’s Biggest Online Retailer, 15 Aug 2015 9. Rachamim, O.: How many online stores are there in the world? Digital Commerce 360, 04 Dec 2014 10. Cox, W.M., Alm, R.: Opinion|You Are What You Spend. The New York Times, 10 Feb 2008 11. Felton, N.: Opinion|Consumption Spreads Faster Today. The New York Times (2008) 12. McGrath, R.G.: The pace of technology adoption is speeding up. Harvard Bus Rev, 25 Nov 2013 [Online]. Available: https://hbr.org/2013/11/the-pace-of-technology-adoption-is-speeding-up. Accessed 11 Mar 2018 13. Gould, S.: It took 75 years for the telephone to reach 100 million users … and it took Candy Crush Saga 15 months. Business Insider, 28 July 2015 [Online]. Available: https://www.businessinsider.com/it-took-75-years-for-the-telephone-to-reach-100million-users-and-it-took-candy-crush-15-months. Accessed 11 Mar 2018 14. Fernández, P.: Company valuation methods. The most common errors in valuations. SSRN Electron. J. (2001) 15. Hege, U., Michenaud, S.: The Internet Boom in a Corporate Finance Retrospective (2004) 16. Malik, O.: In silicon valley now, it’s almost always winner takes all. The New Yorker, 30 Dec 2015 17. Hand, J.R.M.: Evidence on the winner-takes-all business model: the profitability returns-toscale of expenditures on intangibles made by U.S. internet firms, 1995–2001. SSRN Electron. J. (2001) 18. Tru¸sculescu, A., Dr˘aghici, A., Paschek, D.: The development over time of valuation bases and drivers in the online retail industry. Procedia—Social and Behavioral Sciences (2017) 19. Harbott, A.: What drives internet company valuation? A business model approach to internet value drivers. MDA Thesis (2012) 20. Tru¸sculescu, A., Dr˘aghici, A., Albulescu, C.-T.: Operational Drivers of Business Valuations in the E-Commerce Sector: Focus on Public Companies That Assume Inventory Risk, pp. 787–794 (2015) 21. FactSet Research Systems. FactSet Software and Database (2017) 22. GCA Altium: Digital, Media & Internet Monitor Q4 2017. GCA Altium (2018) 23. Blair, W.: Internet & Digital Media Insights—Overview, Analysis, and Trends in the Internet & Digital Media Industry. William Blair (2015) 24. Manjoo, F.: Tech’s ‘Frightful 5’ Will Dominate Digital Life for Foreseeable Future. The New York Times, 20 Jan 2016 25. Apple: Apple—FORM 10-K—2017, Oct 2017

Part V

Supply Chain and Operations Management

Chapter 22

Event Log Extraction for the Purpose of Process Mining: A Systematic Literature Review Dusanka Dakic, Darko Stefanovic, Teodora Lolic, Dajana Narandzic, and Nenad Simeunovic Abstract Process mining bridges the gap between process model analysis and dataoriented analysis, by enabling automated discovery of process models, comparison of existing process models with an event log of the same process and improvement of existing process models. Process mining prerequisite is an information system that supports and controls real-life business processes and consequently stores event data, such as messages, transactions, and logs, as event logs in some type of a database. Event data is then extracted, filtered, and loaded into process mining software, where a certain type of process mining can be conducted. Process-aware information systems (PAIS), which assume an explicit notion of a case to correlate events of a process, provide such logs directly. However, many information systems that support execution of business processes are not explicitly process-aware and due to the variability of the event data sources, this phase of process mining is challenging and the most time-consuming. Consequently, various event log extraction techniques, approaches, and tools are being developed, both specific and generic. To make a contribution to the issue, this paper presents a systematic literature review conducted with the aim to answer the questions about genericity of the approaches, applicability by non-experts, and developed feasible tools. Keywords Process mining · Event log · Data extraction D. Dakic (B) · D. Stefanovic · T. Lolic · D. Narandzic · N. Simeunovic Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovica 6, Novi Sad, Serbia e-mail: [email protected] D. Stefanovic e-mail: [email protected] T. Lolic e-mail: [email protected] D. Narandzic e-mail: [email protected] N. Simeunovic e-mail: [email protected] © Springer Nature Switzerland AG 2020 G. Prostean et al. (eds.), Innovation in Sustainable Management and Entrepreneurship, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-44711-3_22

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22.1 Introduction Since the process-oriented view on organizations, some enterprise information systems have adopted explicit process concepts and offer “generic modeling and enactment capabilities for structured business processes” [1]. These information systems store data about process execution in some form. Process mining assumes that it is possible to generate an event log from this data, such that “each event in the event log refers to an activity (i.e., a well-defined step in a process) and is related to a particular case (i.e., a process instance)” [2]. However, not all PAIS record event data in such way. Besides this traditional workflow management systems (WMS) and business process management (BPM) systems, other information systems such as enterprise resource planning (ERP) and customer relationship management (CRM) systems are rather data-centric and object-centric, meaning that “their processes operate on multiple interrelated business objects, each having their own case identifier, their own behavior, and interaction with each other” [3]. Information about business objects (e.g., documents in ERP) is scattered through various data tables [4, 5]. Therefore, extracting event logs from various data sources is the most challenging phase of process mining and can present an obstacle, especially for non-experts, i.e., business analysts that do not have programming knowledge. In the last decade, approaches and corresponding tools were developed to support and automate event log extraction. However, there is yet no generic approach that researchers and practitioners agree on and each approach still has limitations. In current literature, there is no consolidation of these approaches, their characteristics, and challenges, making it difficult for practitioners to conduct this initial phase of process mining. This paper presents the results of a systematic literature review on the event log extraction approaches in the last ten years, conducted based on the guidelines for performing systematic literature reviews in software engineering by Kitchenham [2], with the focus on: event log extraction approaches that non-experts can apply; a possibility of a generic event log extraction approach, applicable to extracting data from every information system and feasible tools that support event log extraction. The remainder of the paper is organized as follows. Section 22.2 presents the basic concepts of process mining and event logs. Section 22.3 describes the systematic literature review methodology. Section 22.4 presents conducted planning the review phase of the systematic literature review. Section 22.5 presents conducting the review phase of the systematic literature review, where primary studies were selected and the review results presented. Section 22.6 discusses the systematic literature review results in detail, and Sect. 22.7 concludes the paper and suggests future research.

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22.2 Process Mining and Event Log Concepts Process mining is a new research field, introduced and defined by the IEEE Task Force on Process Mining in their Process Mining Manifesto [6]. They defined process mining as follows: “The idea of process mining is to discover, monitor, and improve real processes (i.e., not assumed processes) by extracting knowledge from event logs readily available in today’s (information) systems. Process mining includes (automated) process discovery (i.e., extracting process models from an event log), conformance checking (i.e., monitoring deviations by comparing model and log), social network/organizational mining, automated construction of simulation models, model extension, model repair, case prediction, and history-based recommendations.” Figure 22.1 presents prerequisites of process mining and a basic process mining procedure [6]. Software systems support and control real-life business processes, machines, components, organizations, and people. Software systems are assumed to be configured and specified by process models. Consequently, these software systems record events, e.g., messages, transactions, etc., that are later extracted from various data sources and constructed as event logs. Event logs are a starting point for performing three basic types of process mining: discovery, conformance, and enhancement. According to the IEEE Task Force on Process Mining, in a constructed event log, it is assumed that [6]: • “An event refers to a process activity or a task, which is a well-defined step in the process and is related to a particular case, i.e., process instance; • Events are ordered; • The case or process instance is a specific occurrence of a business process, while activity is an operation, part of a case, that is being executed;

Fig. 22.1 Process mining procedure [6]

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• Each case has a unique identifier; • An event log stores information about cases and activities, but also information about resources (person or device used for the execution of the activity), event timestamps (the moment when the event started and/or ended) or other data elements recorded with the event.” In 2010, the IEEE Task Force on Process Mining adopted Extendable Event Stream (XES) as a standard for process mining event logs [6]. XES is an XMLbased format where every event log entry has an event type, a timestamp, and other additional attributes. The Process Mining Manifesto [6] outlined the guiding principles and main challenges of process mining. Guiding principles and challenges that relate to event logs and the scope of this literature review will be discussed. Guiding principles related to event logs are: • GP1: Event data should be treated as first-class citizens; • GP2: Log extraction should be driven by questions; • GP6: Process mining should be a continuous process. GP1 states that the quality of process mining is directly related to the quality of the event log, i.e., the input for process mining. Therefore, the IEEE Task Force on Process Mining suggests quality criteria that an event log should satisfy: “Events should be trustworthy, i.e., it should be safe to assume that the recorded events actually happened and that the attributes of events are correct. Event logs should be complete, i.e., given a particular scope, no events may be missing. Any recorded event should have well-defined semantics. Moreover, the event data should be safe in the sense that privacy and security concerns are addressed when recording the events” [1]. GP2 addresses the challenge of defining a scope of the process mining project, as data sources from which event data will be extracted contain various information, and it is not trivial to decide what information to extract. Therefore, event log extraction should be driven by questions. GP6 suggests that both historical event data and current data can be used for process mining in order to generate “living process models.” Thus, an approach that enables continuous event log extraction should also be a prerequisite. The following challenges are related to event logs: • • • •

C1: Finding, merging, and cleaning event data; C2: Dealing with complex event logs having diverse characteristics; C10: Improving usability for non-experts; C11: Improving understandability for non-experts.

The main C1 challenges regarding finding, merging, and cleaning event data are: “Data may be distributed over a variety of sources; Event data are often “objectcentric” rather than “process-centric”; event data may be incomplete; An event log may contain outliers, i.e., exceptional behavior also referred to as noise; logs may contain events at different levels of granularity; events occur in a particular context (weather, workload, day of the week, etc.)” [6]. Challenges C10 and C11 both

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accentuate that current process mining techniques may be difficult to perform and understand for non-experts. Performance of process mining techniques includes an event log extraction phase, and hence, this challenge applies to the event log extraction approaches as well.

22.3 Methodology For the purpose of conducting this systematic literature review, a procedure for systematic reviews developed by Kitchenham [2] was followed. According to Kitchenham, a systematic literature review can be summarized into three main phases: planning the review, conducting the review, and reporting the review.

22.3.1 Planning the Review Planning the review phase has a goal to determine and elaborate the need for a systematic literature review and to develop a review protocol. The need for a systematic literature review can be established by reviewing existing literature reviews in that particular research area, following the guidelines proposed by Kitchenham [2]. Once the need for a review is established, a review protocol can be developed. The review protocol should shortly present background and previously determined rationale for the survey. The second component of the review protocol is to formulate the research questions. A research question should, according to Kitchenham [2]: “be meaningful and important to practitioners and researchers, lead either to changes in current software engineering practice or increase confidence in the value of the current practice and identify discrepancies between commonly held beliefs and reality.” Furthermore, question structure should take into consideration three different viewpoints: “population, i.e., group of people affected by the review, interventions, i.e. software technologies that address specific issues and outcomes, i.e. factors of importance to practitioners.” After the research questions are formulated, a search strategy for primary studies based on initial scoping determines which search terms and databases will be used. Study selection criteria need to be established, in order to be used as inclusion and exclusion criteria for initial analysis of primary studies and consequently, excluding studies from a systematic review. The next step of the review protocol determines the quality assessment checklist that will be applied to the primary studies, included in the literature review. As Kitchenham states, there is no unique definition of “quality”. However, other authors such as the CRD Guidelines [7] and the Cochrane Reviewers’ Handbook [8] suggest that “quality relates to the extent to which the study minimizes bias and maximizes internal and external validity.” By bias, a tendency to produce results that depart systematically from the “true” results are considered. Internal validity is the extent to which the design and conduct of the study are likely to prevent systematic error.

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External validity, i.e., generalizability and applicability, is the extent to which the effects observed in the study are applicable outside of the study. The last steps are the definition of strategy for the data extraction, synthesis of the extracted data, and a project timetable.

22.3.2 Conducting and Reporting the Review Phase Once the protocol has been developed, the review can start. This involves the following activities [2]: 1. 2. 3. 4. 5.

Identification of research; Selection of studies; Study quality assessment; Data extraction and monitoring progress; Data synthesis.

Reporting the review is the final phase of a systematic literature review, where it is important to communicate the results of a systematic review effectively. Usually, systematic reviews are reported in at least two formats [2]: • In a technical report or in a section of a Ph.D. thesis; • In a journal or conference paper.

22.4 Planning the Review The first activity of planning the review phase should be elaboration of the need for a systematic literature review, by reviewing existing literature review of the subject [2]. However, there are no explicit systematic literature reviews on the subject of event log extraction, rather reviews of process mining field in general, its applicability, state of the art, and challenges [9–12]. Other sources of information about event log extraction approaches could be the related work chapters of studies that are presenting a novelty on the subject. However, these studies only mention previous event log extraction approaches relevant to their particular topic of interest. This systematic literature review presents summarized information about current event log extraction approaches, their usability by non-experts, genericity and developed tools, by answering the research questions. Based on the guidelines from Kitchenham [2], the following research questions are formulated: Q1 Are there any event log extraction approaches that non-experts can apply? Q2 Is there a generic event log extraction approach, applicable for extracting event data from every type of information system?

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Q3 Are there any feasible tools that support event log extraction? For the purpose of this literature review, the following databases where searched: • SCOPUS, • Web of Science and • Google scholar. Elsevier’s Scopus is the largest abstract and citation database of peer-reviewed literature. Web of Science provides access to reliable, integrated, and multidisciplinary research connected through linked content citation metrics from multiple sources within a single interface. Search term defined for search in these databases is presented below: “Process mining” AND “event log” AND extraction AND PUBYEAR > 2008 The inclusion criteria defined for this review are: 1. Paper has to present an approach for event log extraction for the purpose of process mining. 2. The approach has to include detailed information about the developed procedure. 3. The study has to present a feasible approach, applicable in real-life scenarios. Exclusion criteria defined for the review are: 4. Duplicate papers found in different databases should be removed. 5. If one author had more than one paper regarding the same approach, only one paper should be included in the review. For the purpose of this literature review, the data extraction strategy was developed. For each primary study, the following features will be extracted, in order to answer research questions: 1. 2. 3. 4. 5.

Publication year and source type, Developed tool, Feasibility and applicability of the approach, Genericity of the approach and information systems, and Applicability for non-experts.

22.5 Conducting the Review 22.5.1 Identification and Selection of Primary Studies The first activity of conducting the review phase is the identification of the research, i.e., primary studies that will be included in the systematic literature review. Primary studies are identified in accordance with inclusion and exclusion criteria, defined in the review protocol. Figure 22.2 summarizes quantitative evidence of the inclusion/exclusion process in an appropriate flow diagram.

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D. Dakic et al. Initial search result → 220 primary studies → 38 duplicate papers excluded based on criteria 4 → 182 primary studies remaining Review based on criteria 1 → 76 out of 182 primary studies are included in the review

Review based on criteria 2 → 41 out of 76 primary studies are included in the review

Review based on criteria 3 → 28 primary studies are included in the review

13 primary studies excluded based on criteria 5 → 15 out of 28 primary studies remaining for the review

Fig. 22.2 Flow diagram of the exclusion and inclusion process

Search through data sources with previously defined search term resulted in the identification of 220 primary studies. Based on exclusion criteria 4 (duplicate papers found in different databases should be removed), 38 duplicate papers were found and excluded from further research. Review based on criteria 1 (paper has to present an approach for event log extraction for the purpose of process mining) was conducted on the title and abstract of the papers and resulted in the inclusion of 76 primary studies. Review based on criteria 2 (the paper has to include detailed information about the developed approach) included 41 primary studies in the literature review. Out of 41 primary studies, 28 satisfied the criteria 3 (the study has to present a feasible approach, applicable in real-life scenarios). The last step of the inclusion/exclusion process excluded 13 papers based on criteria 5 (if one author had more than one paper regarding the same approach, only one paper should be included in the review). Finally, 15 primary studies are included in the literature review.

22.5.2 Data Extraction and Summarization In this chapter, the main characteristics of primary studies are summarized and presented, following with the tables that present extracted data based on the data extraction strategy. Figure 22.3 presents event log extraction approaches by years in which primary studies that presented the approaches where published. The line diagram shows that two peaks in the number of published approaches occurred, one in 2012, two years after Process Mining Manifesto [6] was published and one in 2017.

22 Event Log Extraction for the Purpose of Process … Fig. 22.3 Summary of event log extraction approaches by years

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Event Log Extraction Approaches by Years

5 4 3 2 1 0

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Table 22.1 presents source types of primary studies, showing that 40% of the approaches were published as journal articles, following with 27% of conference papers and master’s thesis and one published procedure for event log extraction. Three out of four published master’s thesis were conducted at the Eindhoven University of Technology. In the review protocol, three research questions where formulated. Table 22.2 presents the extracted data that answers the first question: Q1: Are there any event log extraction approaches that non-experts can apply? Table 22.2 shows that 20% of the reviewed approaches could be applied by a non-expert, while 80% of the approaches require extensive programming knowledge. Table 22.3 presents the extracted data that answers the second question: Q2: Is there a generic event log extraction approach, applicable for extracting data from every type of information system? During the data extraction phase, it was concluded that the reviewed event log extraction approaches can be grouped by different information system types. ERP systems, which are object and data-centric systems, were used the most. Three approaches [4, 13, 14] showed the potential to be generic, i.e., applicable for every data source and information system type. Furthermore, two approaches dealt with the extraction of event logs from cloud systems [15, 16], one approach presented Table 22.1 Source types of primary studies Source type

Primary studies

%

Journal article

10, 15, 16, 17, 19, 20

40

Conference paper

12, 18, 22, 23

27

Master’s thesis

11, 13, 14, 24

27

Procedure

21

7

Table 22.2 Primary studies and applicability of their approaches by non-experts Applicable for non-experts?

Primary studies

%

Yes

14, 16, 21

20

No

10, 11, 12, 13, 15, 17, 18, 19, 20, 22, 23, 24

80

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Table 22.3 Primary studies and information system types Information system types

Primary studies

%

ERP

10, 12, 13, 19, 21, 22, 23, 24

53

Potentially generic

14, 16, 17

20

Cloud systems

11, 20

13

Non-process-aware information systems

15

7

Unstructured business processes

18

7

a solution for event log extraction from completely non-process-aware information systems [17], and lastly, [18] developed an approach for event log extraction from information systems that support unstructured business processes. Table 22.4 presents information about tools and plug-ins that were developed in order to automate the approaches of event log extraction, sorted ascending by the year in which they were developed and with additional information about the event log format that they generate as a result of event log extraction process. The third question (Q3: Are there any feasible tools that support event log extraction?) addresses the issue of the feasibility of these tools. For each tool or plug-in developed, in the corresponding primary study, at least one case study was presented in order to demonstrate the applicability of the approach. Therefore, although all tools and approaches have certain limitations, they are considered to be feasible. Table 22.4 Developed tools and plug-ins with event log format Tools/plug-ins

Primary studies

Year

Event log format

XES Mapper

14

2010

XES and MXML

Prototype for event log extraction from SAP ECC 6.0

13

2011

CSV

Xtract

12

2012

XES

Event-traces Injector (ETI)

15

2012

MXML

Eventifier

17

2012

XES

MANA

18

2013

XES

Xtract V2

10

2015

XES

Xtract V3

11

2017

XES

Ontology-driven extraction plug-in for ProM

23

2017

XES

Process mining tool and ERP connector

24

2017

XES

OpenSlex and PADAS

16

2018

New format, convertible to XES

XOC Log Generator Plug-in in ProM 6

22

2018

eXtensible Object-Centric event logs (XOC)

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22.6 Discussion This section discusses the results of the conducted systematic literature review, presented in Sect. 22.5. A highest number of the reviewed approaches were published in the years 2012 and 2017. The previously mentioned Process Mining Manifesto was published in 2012 by the IEEE Task Force on Process Mining, hence the high number of approaches published that year. By the year 2017, these first event log extraction approaches were utilized in the industry and showed limitations and challenges, as well as reasons for the improvement. The approaches developed in 2017 and after tend to solve these challenges. Most of the approaches were published in journal articles. However, a significant number of approaches were developed through master’s thesis at the Eindhoven University of Technology, from where the idea of process mining originated. For the purpose of answering the research question Q1, approaches that tend to improve the usability of event log extraction by non-experts were identified. J. C. A. M. Buijs states in his master’s thesis [4] that there is no tool that guides a business analyst toward converting data from a data source to an event log format suitable for process mining without the need to program and therefore creates a tool XES Mapper (later called XESame). In [13], the authors recognized the need for a metamodel that will integrate process and data perspectives and enable multi-perspective event log building and analysis without the knowledge of SQL. In [19], the authors developed a procedure that leads a process analysts with limited knowledge of process mining though event log extraction process, increases the analyst’s understanding of the decisions and their consequences, related to the choice of process instance, activities, and attributes, and provides the process analyst with a practical example or sufficient background information in order to conduct the event log extraction approach. The question Q2 is answered positively, as there are three approaches [4, 13, 14] that can potentially be applied for every type of information system. The primary studies that reported the approaches presented case studies in which event log extraction is conducted on data sources of different information systems or at least provided a guideline for a generic application of their event log extraction approaches and tools. Two of these studies [4, 13] were also found to be applicable for non-experts. On the other hand, most approaches were developed for event log extraction from ERP systems [3, 5, 19–24]. ERP system is object-oriented, i.e., along business documents, and not along business processes. To find the process data behind the static data, the business documents and related activities need to be matched into cases, i.e., the same execution of a process. Therefore, a suitable way of case identification has to be found which is also able to handle complex cases. Two main problems were identified while performing event log extraction from ERP systems: convergence and divergence [3, 4, 15, 20–22, 24]. In [4], the author defines them as follows: Convergence occurs when the same activity is executed on multiple process instances at once; divergence occurs when for one process instance the same activity

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is performed multiple times. Divergence and convergence remain unsolved problems, although several primary studies that developed artifact-centric approaches suggested how to avoid these problems in particular situations [3, 4, 15, 24]. Finally, only two approaches answered positively to all three research questions [4, 13], meaning that these approaches could be used by non-experts (e.g., business analysts with no programming skills), potentially could be applied to any type of information system and offer a tool that supports the developed methodology.

22.7 Conclusion With today’s highly automated business environment, the amount of recorded data is rapidly growing. Correspondingly, new research disciplines that exploit this data, such as process mining, are emerging. Being a relatively new research discipline, process mining encounters challenges. This paper presented a systematic literature review of the most challenging phase of process mining—event log extraction. Existing approaches are identified and the following research questions are answered: Q1: Are there any event log extraction approaches that non-experts can apply? Q2: Is there a generic event log extraction approach, applicable for extracting event data from every type of information system? Q3: Are there any feasible tools that support event log extraction? It is concluded that several approaches do provide higher understandability and usability by non-experts, and however, this is still a small number of approaches. Furthermore, some approaches do have the potential to be generic, i.e., applicable to every information system type. As feasible tools are considered, there are many developed prototypes and plug-ins that researchers can use to automate the event log extraction phase of process mining. Future work should focus on detected challenges, such as convergence and divergence, continuous event log extraction, improving usability for non-experts and present possible solutions. Acknowledgments This article has been produced as part of a research project: No. 47028 “Advancing Serbia’s competitiveness in the EU accession process” supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia for the period 2011th–2019th year.

References 1. Vander Aalst, W.M., Weijters, A.J.M.M.: Process mining: a research agenda. Comput. Ind. (2004). https://doi.org/10.1016/j.compind.2003.10.001 2. Kitchenham, B.: Procedures for undertaking systematic reviews. In: Joint Technical Report, Computer Science Department, Keele University (TR/SE-0401) and National ICT Australia Ltd. (0400011T.1) (2004)

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3. Lu, X., Nagelkerke, M., van de Wiel, D., Fahland, D.: Discovering interacting artifacts from ERP systems. IEEE Trans. Serv. Comput. (2015). https://doi.org/10.1109/TSC.2015.2474358 4. Buijs, J.C.A.M.: Mapping Data Sources to XES in a Generic Way. Master’s thesis, Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, Netherland (2010) 5. Fliegner, W.: Extracting process-related information from ERP systems for process discovery. Res. Logist. Prod. 4(4), 315–329 (2014) 6. Van der Aalst, W.M. et al.: Process mining manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM 2011 Business Process Management Workshops. Lecture Notes in Business Information Processing, vol. 99, pp. 169–194 (2012). https://doi.org/10.1007/978-3-642-281082_19 7. Khan K.S., Ter Riet, G., Glanville, J., Sowden A.J., Kleijnen, J. (eds.): Undertaking Systematic Review of Research on Effectiveness, CRD’s Guidance for those Carrying Out or Commissioning Reviews, CRD Report Number 4, 2nd edn. NHS Centre for Reviews and Dissemination, University of York, IBSN 1 900640 20 1 (2001) 8. Alderson, P., Green, S., Higgins, J.P.T. (eds.): Cochrane reviewers’ handbook 4.2.2 [updated March 2004]. In: The Cochrane Library, Issue 1. Wiley, Chichester, UK (2004) 9. R’bigui, H., Cho, C.: The state-of-the-art of business process mining challenges. Int. J. Bus. Process. Integr. Man. (2017). https://doi.org/10.1504/IJBPIM.2017.088819 10. Dakic, D., Stefanovic, D., Cosic, I., Lolic, T., Medojevic, M.: Business process mining application: a literature review. In: Katalinic, B. (ed.) Proceedings of the 29th DAAAM International Symposium, pp. 0866–0875. Published by DAAAM International, ISBN 978-3-902734-20-4, ISSN 1726-9679, Vienna, Austria (2018). https://doi.org/10.2507/29th.daaam.proceedings.125 11. Tiwari, A., Turner, C.J., Majeed, B.: A review of business process mining: state-of-the-art and future trends. Bus. Process. Man. J. (2008). https://doi.org/10.1108/14637150810849373 12. Van Der Aalst, W.M.: Process mining: overview and opportunities. ACM Trans. Man. Inf. Syst. (2012). https://doi.org/10.1145/2229156.2229157 13. González López de Murillas, E., Reijers, H.A., van der Aalst, W.M.P.: Connecting databases with process mining: a meta model and toolset. Soft. Syst. Model (2019). https://doi.org/10. 1007/s10270-018-0664-7 14. Rodríguez, C., Engel, R., Kostoska, G., Daniel, F., Casati, F., Aimar, M.: Eventifier: extracting process execution logs from operational databases. In: Proceedings of the Demonstration Track of BPM 2012, vol. 940, pp. 17–22 (2012) 15. Santana Calvo, H.A.: Artifact-centric log extraction for cloud systems. Master’s thesis, Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, Netherland (2017) 16. Bernardi, M.L., Cimitile, M., Mercaldo, F.: Cross-organisational process mining in cloud environments. J. Inf. Knowl. Manag. (2018). https://doi.org/10.1142/s0219649218500144 17. Pérez-Castillo, R., Weber, B., Pinggera, J., Zugal, S., de Guzmán, I.G.R, Piattini, M.: Generating event logs from non-process-aware systems enabling business process mining. Enterpr. Inf. Syst. (2011). https://doi.org/10.1080/17517575.2011.587545 18. Esposito, P.M., Vaz, M.A.A., Rodrigues, S.A., De Souza, J.M.: MANA: Identifying and mining unstructured business processes. In: Lecture Notes in Business Information Processing (2013). https://doi.org/10.1007/978-3-642-36285-9-20 19. Jans, M.: From Relational Database to Valuable Event Logs for Process Mining Purposes: A Procedure (2017). https://doi.org/10.13140/RG.2.2.11343.69289 20. Nooijen, E.H.J., van Dongen, B.F., Fahland, D.: Automatic discovery of data-centric and artifact-centric processes. In: Lecture Notes in Business Information Processing (2012). https:// doi.org/10.1007/978-3-642-36285-9_36 21. Piessens, D.A.M.: Event log extraction from SAP ECC 6.0. Master’s thesis, Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, Netherland (2011) 22. Li, G., de Murillas, E.G.L., de Carvalho, R.M., Van der Aalst, W.M.P.: Extracting object-centric event logs to support process mining on databases. In: Information Systems in the Big Data Era (2018). https://doi.org/10.1007/978-3-319-92901-9_16

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Chapter 23

3D Custom-Made Eyeglasses Frames: An Innovative Approach to Enhance Customer Satisfaction Karina E Sarzosa G, Carlos D Vallejo A, Angel G Hidalgo O, Tania K Berrezueta E, Esmeralda Kadena, and Ramiro S Vargas C Abstract Over the years, glasses have become very popular, and since they are a permanent used product, many issues concerning frames have been present. Because of size and weight under which they are manufactured, pain and pressure marks over face areas and the head (nasal bone, temporal bone, temple, and nape) are shown. In this paper, it is proposed an improvement in frames by taking advantage of the technological advances. The study has in focus the students from the Faculty of Applied Sciences and Engineering at the Technical University of Cotopaxi-Ecuador that require the use of glasses due to different types of vision problems. Our work aims: to identify the need for improvement on glasses frames by exploring most common problems; to address these issues by giving a specific solution—new frame model based on 3D scanning; and to compare the existing data with the new data generated through informatics tools. Our findings appear to support well the general problem, and we believe that we have designed an innovative solution to the commercial frames. Keywords Eyeglasses frames · Customization · 3D Printing

K. E. Sarzosa G · C. D. Vallejo A · A. G. Hidalgo O · T. K. Berrezueta E Universidad Técnica de Cotopaxi, Latacunga, Ecuador e-mail: [email protected] C. D. Vallejo A e-mail: [email protected] A. G. Hidalgo O e-mail: [email protected] T. K. Berrezueta E e-mail: [email protected] E. Kadena · R. S. Vargas C (B) Obuda University, Budapest, Hungary e-mail: [email protected] E. Kadena e-mail: [email protected] © Springer Nature Switzerland AG 2020 G. Prostean et al. (eds.), Innovation in Sustainable Management and Entrepreneurship, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-44711-3_23

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23.1 Introduction The innovative idea of eyeglasses dates circa 1266 when Roger Bacon outlined the scientific fundamentals behind the use of eyeglasses [2]. It is thought that the first wearable eyeglasses were invented by the Italian Salvino D’Armate around 1284 but there has been no reliable evidence that proves it [18]. Eyeglasses are commonly used to correct poor vision and for many people, they are a “must-have” product in their daily lives. According to the Vision Council of America, approximately 75% of adults use some sort of vision correction and almost two-thirds (64%) of them wear eyeglasses; 42% of which are men and over 50% women [9]. Conducted studies in Ecuador have shown that 62% of people between 18 and 65 years old are users of them [12]. Moreover, fashion and aesthetic properties of glasses have become important as well. Wearing eyeglasses should be comfortable enough in order to perform daily activities [14]. Because of the increasing number of people wearing eyeglasses and their different requirements and preferences, designing has become an important challenge. To tackle this issue, many researches have been carried out. During the last years, research has been oriented to the innovative concept of smart glasses. For instance, in 2015, Amft et al. [1] presented an architecture for integrating technology into traditional eyeglasses designs. Moreover, several recommendations for further smart eyeglasses developments were given. Smart glasses could replace regular eyeglasses and be very beneficial for tracking the daily activities of elderly users, for assistive functions such as health monitoring or for helping dieticians to make better food choices by using applications already incorporated [21]. An important factor for the eyeglasses’ selection is the ophthalmic frame, which is part of a pair of glasses designed to hold the lenses in a proper position. Over the years, eyeglasses frames have changed by considering style, size, material, shape, and color. However, commercial frames are manufactured based on standard and average face anthropometric head measurements and there is a huge range of head shapes and size variations. Therefore, wearers adapt to frames instead of frames adapt to users. Very often, wearers feel pain where their frames rest on their ears or nose, as well as, pain around the ears because of the clamping force of the frames [14]. It was also confirmed through a survey that was performed to a group of people (students and lecturers) at the Technical University of Cotopaxi—Ecuador. Therefore, we strongly believe that frame customization is required. In some cases, classic eyeglasses frames can be personalized after they have been manufactured; opticians usually heat the plastic to tweak the frames to fit the wearer [21]. This is not the best option, on the contrary, it can be seen as an advantage in terms of time and cost. On the other hand, customer satisfaction and service quality can be linked to some managerially meaningful measures [19]. Customized products could lead to positive customer attitudes.

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In this paper, a new methodology is introduced based on wearers’ head and face anthropometric measurements by using 3D scanning technology and ergonomic considerations. The methodology was tested and evaluated with one individual. Then comparisons between commercial and customized eyeglasses frames are shown and analyzed.

23.2 Criteria Used for Frame Design The following part explains the criteria that were more relevant to our work.

23.2.1 Ergonomics The Greek roots of the word ergonomics: “ergos” meaning work and “nomos” meaning natural law clearly define its principle [16]. However, work implies several aspects, such as environment, tools, and social interaction. Specifically, ergonomics aims to fulfill five requirements: functional efficiency, ease of use, comfort, health, safety, and finally, the quality of working life [17]. The main target of the industry is to properly design a product that ergonomically satisfies the needs of human beings. According to Broberg, Andersen, and Seim, the best design allows future experiments to enhance the object referring to the phrase: “what if…” from the typical user [4]. Recent studies prove that the consumer’s decision criteria evolved from product price to product design [6]. Hence, the importance of an Innovative Product Design (IPD) is continuously increasing [15]. However, what the new market considers innovative or not, has not been clearly defined yet.

23.2.2 Anthropometrics Anthropometry is a branch of science that describes humans’ measurements [16]. This vital subject can be applied to several fields, including nutrition and engineering. For decades, physical measurement methods have effectively been used in the industry. However, during the last two decades, the 3D (three-dimensional) scanning method is replacing traditional methods [7]. The International Textile and Apparel Association (ITAA) argues that although consolidate industries use 3D scanning technology, the rapid enhancement in 3D scanners may be confusing [10]. Thus, constant training regarding the use of new technologies plays an essential role in anthropometrics.

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23.2.3 Human Variation The biological evolution of human beings leads to variability of several aspects of the body. Heredity, sex, and age determine the physical characteristics of a human being [13]. A research conducted by Hanson et al. on the Swedish workforce revealed a variation of nearly 30% in 37 years (e.g., the average male height increased 10%) [11]. Recent methods have replaced traditional manikins with digital human models (DHM) [3]. This novel method can be compelling enough for several designing tasks. However, the variability of body size limits its applicability [8]. Human variation is a crucial factor in ergonomic design as the product should be comfortable for the user.

23.3 Materials and Methods After identifying the problems and defining the consumer’s criteria, we applied suitable methods to make an appropriate judgment.

23.3.1 Questionnaire The following study was conducted using 327 adult subjects (lecturers and students) both female and male belonging to the Technical University of Cotopaxi-Ecuador. Survey data were collected from a questionnaire generated by Google forms and sent out via the Internet and through an application that allowed us to validate the data entry of students and teachers through their institutional emails. The used language was local (Spanish) for easy understanding and simplicity. The questionnaire aimed to assess the customer criteria and the common physical problems caused by the glasses frame. Participants were asked about specific questions related to: frequency of glasses used; types of discomfort that presents; and factors that influence the selection of product and type of product they use.

23.3.2 3D Technology After the analysis of the initial result, one of the respondents became the test subject. Firstly, the test subject bust was scanned for obtaining accurate anthropometric evaluation (see Fig. 23.1).

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Fig. 23.1 3D Scanning of the text subject

23.3.3 Anthropometrics Measurements According to Caum, the eyeglasses frame should have three main contact points [5]. The frame should be in contact with the upper part of the nose and both ears. Based on the parameters suggested by Salvadó, six of the basic anthropometrics were considered for this study case [20] (see Figs. 23.2 and 23.3). Furthermore, to optimize the weight of the frame, nylon was selected as the 3D printing material. Once the eyeglasses frame was printed, the test subject wore the customized frame for one month. The commercial and customized frame was compared to assess the variation in design. Finally, after an interview, the test subject ergonomically evaluated the personalized frame.

Fig. 23.2 Angle of let back [5]

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Fig. 23.3 Anthropometrics: (a) horizontal temple + tip length, (b) pantoscopic tilt, (c) Bizygomatic facial width—FWB, and (d) nostril width

23.4 Results This section demonstrates the findings and contributions made. Based on the methodology, we describe and compare the results of facial anthropometrics and eyeglasses frames regarding commercial and the new customized frames.

23.4.1 Facial Anthropometrics Results New eyeglasses frames were designed and printed using acrylonitrile butadiene styrene (ABS) due to its favorable mechanical properties. Certainly, several anthropometrics could have been used for the new design. However, the new design focused on three main points: the nose and the two ears, as they are the contact points. Table 23.1 summarizes the main frontal measurements. Additionally, it includes the interaxial angle which can be seen from the bottom view of the nose. Primarily, the commercial frames were slightly tighter than the customized ones. Table 23.1 Facial anthropometrics comparison

Description

Commercial

Customized

Bizygomatic facial width (FWB)

13.70 cm

15.70 cm

Nostril width

1.10 cm

0.96 cm

Inter-axial angle

35°

24°

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Table 23.2 Eyeglasses frame comparison Description

Commercial

Customized

Temple + tip

13.30 cm

13.50 cm

Pantoscopic tilt

10°

12°

The angle of let back

90

95°

Weight

364.30 g

277.3 g

Fig. 23.4 Test subject before (commercial frames) and after (customized frames)

23.4.2 Commercial Versus Customized Frames On the other hand, Table 23.2 mainly displays the temple and tip features. Although changes are not noticeable as regarding the front view, the final weight was decreased by nearly 100 g. Finally, the most outstanding results can be observed in Fig. 23.4. The red lines highlight the fact that from a frontal view, the test subject’s ears were not at the same level. In fact, there was an unevenness of 3.92 mm producing an incongruity of the pupillary distance axis.

23.5 Conclusions and Future Work Our research highlighted the importance of glasses frames in health and well-being. The conducted study showed that people that use commercial frames feel discomfort, headache, and pressure marks over face areas and head. The evidence from the answers of the respondents indicates that a new solution is needed. After scanning

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the test subject and based on facial anthropometrics, we realized the asymmetry and how the human adapts to commercial products such as eyeglasses frames. To solve this problem, we have found an innovative solution: 3D-based customization. Our work has led us to conclude that the respective anthropometrics measurements and comparisons between the two models clearly support the idea of new custommade eyeglasses’ frames. One of the questions aimed to identify the personal criteria for choosing the eyeglasses frames from a financial perspective. The respondents preferred well-known brands rather than comfort. However, most of the respondents clearly suffer discomfort during the first three weeks. Further studies should focus on the medical effect of non-ergonomic eyeglasses frames to raise the awareness of the consumers. We strongly believe that our work might motivate commercial firms and field-related researchers to propose/develop innovative solutions. To create satisfied customers, these companies should focus on “customer-centered” strategy. In order to confirm these initial findings, future research should continue to explore the eyewear market and innovations in this sector.

References 1. Amft, O., Wahl, F., Kunze, K.: Making regular eyeglasses smart, 32–43 (2015) 2. Bacon, R.: Opus Majus. University of Oxford Press, London (1900) 3. Berlin, C., Adams C.: Production Ergonomics: Designing Work Systems to Support Optimal Human Performance. pp. 161–174. London: Ubiquity Press (2017) 4. Broberg, O., Andersen, V., Seim, R.: Participatory ergonomics in design processes: the role of boundary objects. Appl. Ergon. 42(3), 464–472 (2011). https://doi.org/10.1016/j.apergo.2010. 09.006 5. Caum Aregay, J.: Tecnología óptica: lentes oftálmicas, diseño y adaptación. Edicions UPC (2001) 6. Churchill, G.A., Surprenant, C.: An Investigation into the determinants of customer satisfaction. J. Mark. Res. 19(4), 491–504 (1982). https://doi.org/10.1177/002224378201900410 7. Dianat, I., Molenbroek, J., Castellucci, H.I.: A review of the methodology and applications of anthropometry in ergonomics and product design. Ergonomics 61(12), 1696–1720 (2018). https://doi.org/10.1080/00140139.2018.1502817 8. Garneau, C.J., Parkinson, M.B.: A comparison of methodologies for designing for human variability. J. Eng. Des. 22(7), 505–521 (2011). https://doi.org/10.1080/09544820903535404 9. Glassescrafter: https://www.glassescrafter.com. Available at: https://www.glassescrafter.com/ information/percentage-population-wears-glasses.html (2019) 10. Griffin, L.A., et al.: Future practices and technologies in anthropometrics and body scanning (2017) 11. Hanson, L., et al.: Swedish anthropometrics for product and workplace design. Appl. Ergon. 40(4), 797–806 (2009). https://doi.org/10.1016/j.apergo.2008.08.007 12. Hernández, L.: Maximización de Untilidades a través de Proyecciones Financieras para el Sector Oftalmológico en el Ecuador. Available at: https://repositorio.puce.edu.ec/bitstream/ handle/22000/5393/T-PUCE-5620.pdf?sequence=1&isAllowed=y (2012) 13. Lescay, R.N., Becerra, A.A., González, A.H.: Antropometría. Análisis comparativo de las tecnologías para la captación de las dimensiones antropométricas. Revista EIA 13(26), 47–59 (2017) 14. Mashima, M., Yoshida, H., Kamijo, M.: Investigation of wearing comfort of eyeglasses with emphasis on pain around the ears, 2100, pp. 4–7 (2011). https://doi.org/10.1109/ICBAKE. 2011.21

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15. Moon, H., Park, J., Kim, S.: The importance of an innovative product design on customer behavior: development and validation of a scale. J Prod Innov Manag 32(2), 224–232 (2015). https://doi.org/10.1111/jpim.12172 16. Pheasant, S.: Bodyspace: Anthropometry, Ergonomics and the Design of Work, 2nd edn. Taylor & Francis, London (2003) 17. Pheasant, S., Haslegrave, C.M.: Bodyspace: Anthropometry, Ergonomics and the Design of Work. CRC Press (2018) 18. Rosen, E.: The invention of eyeglasses. J. Hist. Med. Allied Sci., XI(1), 13–46 (1956) 19. Rust, R.T., Zahorik, A.J.: Customer satisfaction, customer retention, and market share. J. Retail. 69(2), 193–215 (1993). https://doi.org/10.1016/0022-4359(93)90003-2 20. Salvadó Arqués, J.: Tecnología óptica, Lentes oftálmicos, diseño y adaptación (2000) 21. Wahl, F., et al.: Personalizing 3D-printed smart eyeglasses to augment daily life (2017). https:// doi.org/10.1109/BSN.2016.7516224.5

Chapter 24

Methodological Views on Agile Testing Paul Dragos

Abstract The article provides a literature review on agile testing, which encompasses the most important factors in conducting software testing in all aspects of its practice. This is a growing area of academic research due to the increasing role of conducting professional software testing in very short amounts of time. This article maps the main strands of research on agile software testing by showing the different types of models developed and the key takeaways for further research using five articles derived from western literature. The review indicates that much of the literature is framed in terms of comparing the existing agile models or comparing the agile model to other older software development models used such as the waterfall model. Researchers investigating agile testing draw predominantly on multivariate analysis using surveys, questionnaires, documentary analysis and interviews. The theme has gained more and more importance through the last years as more and more companies are adopting the agile software development and testing methodology. A stronger focus is needed in developing a professional testing concept and in determining the key dimension on which the productivity of such approaches is based on. Keywords AGILE · Software · Testing · Methodology

24.1 Introduction There is a growing interest in issues connected with agile testing. This is partly attributable to the increasing importance this topic has gained in the software development industry throughout the last years. Software development organizations have a very important standing throughout industries ranging from banking to automotive. The emergence of industry-specific standard software means that positive and negative perceptions of software development and testing have a significant impact on developers’ reputations. As a result of increased regulatory requirements throughout industries, the need for conducting agile software development has increased. Thus, P. Dragos (B) FEAA, Timisoara, Romania e-mail: [email protected] © Springer Nature Switzerland AG 2020 G. Prostean et al. (eds.), Innovation in Sustainable Management and Entrepreneurship, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-44711-3_24

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more and more software developers use agile methods for their development and testing process. The complex regulatory environments serve as a high entry barrier for software developers. Thus, many software developers operate in oligopolistic environments where they compete with each other in attracting clients. One key trait for competitiveness besides quality is time. If an unlimited amount of time was given, every software developer would produce flawless software. The only difference would be the logic behind the underlying processes and the way of using it correctly. However, in reality, time makes the difference between software-producing companies.

24.2 Historical Background of Agile Testing Research This work focuses on the testing part of the software development methodology. The term model has been understood as a logical description of the testing process, which can be iterative or sequential [1]. A testing methodology has a direct relationship with managing project complexity, thus impacting software adaptability, maintainability, portability and reliability. Communication plays a vital role in test management, especially in agile projects. It has been demonstrated in a study [2] that a significant relationship exists between satisfaction and the chosen methodology. The testing process and the corresponding software quality have been affected by fast-paced evolution of software testing methodologies [3]. Having a look at modern history, in the late 1990s, agile methodologies were mostly focused on addressing documentation, productivity, reliability and simplicity [4]. Compared to processdriven testing methodologies, agile testing focuses on communication [5]. Agile methodologies have been also shown as suitable for changing market conditions without demanding large upfront investments [6]. Despite being widely used throughout the last 20 years, agile methodologies continue showing limitations. These include the presence of tacit (not documented) knowledge, lack of testing guidelines, lack of automation for large-scale projects and high degree of testing freedom [4]. Project results using agile methodologies depended mainly on organizational factors such as commitment, location, time, corporate culture and people factors such as self-motivation or competence [7]. As a result, several methodologies emerged, out of which ten were identified as being agile [8]. The IT industry has considerable interest to adopt new agile methodologies. For this, proper guidelines have to be set up and followed. Existing studies [2, 9, 10] focus on key determinants for choosing agile methodologies and their practical applicability. Frameworks [11] identifying dimensions for agility analysis have been developed. Software development projects have been scrutinized and information was gathered to help managers to choose an appropriate agile testing methodology. Throughout agile software development, testing has been a cornerstone, since the emergence of this field of research, being present in the existing literature. Unfortunately, the terminology used in defining this activity is inconsistent throughout

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literature. Academics and practitioners focus on different types of publications when researching this topic, and unfortunately, it appears the number of practitioner papers has strongly decreased since 2010 (Source: Hellmann et al. Agile Testing: Past, Present, and Future) [12]. Testing is one of the cornerstones of the agile software development process. The testing effectiveness is in addition to bug-fixing and code quality of the most important pillars in agile software development. A systematic field mapping shows that the evidence relating to agile testing is conflicting (R. Jeffries, 2007, The art of fearless programming, pp. 24–30). For years academics have contributed the largest portion of publications. Practitioners have not published validation or evaluation papers. Instead of publishing formal studies, practitioners have published experience papers based on industry know-how. Thus, the opportunity for conducting more research in agile testing is present and this article will try to capitalize on it by establishing a starting point for further research. The term agile refers to the system’s capability to reactively and proactively cover the changes occurring in modern business in terms of income or productivity. Agile Project Management (APM) focuses on project development and delivery. APM goes in the direction of similar disciplines such as PRINCE2, CMMI and ISO90001in terms of measuring quality and identifying potential threats and risks. Agile methodology is based on iterative and incremental development research which dates back to the middle of the twentieth century. Initially, researchers looked at ways to separate design, implementation and testing [13]. The testing phase for the final product went through a validation process. Since the twentieth century, project management became a key in incremental and iterative testing practice. Complex systems were approached with a reductionist mentality. Systems were defined by small units. Every unit had a well-defined goal. Smaller goals fulfilled a larger goal [14]. Cross-disciplinary developments (e.g., in architecture, technical systems methodology and transitional organization) had also an impact on agile testing methodologies [15]. The theoretical background for agile testing relies on key characteristics such as adaptive understanding, decision-making discretion and greater autonomy. These are based on problem-solving approaches in strategic management and architecture [15].

24.3 Core Themes Publications on agile testing focus mainly on agile methodologies. Comparisons among these themes are presented. The present literature emphasizes a list of agile methodologies: • Extreme programming (XP) • Scrum • Rational unified process (RUP)

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• Dynamic systems development method (DSDM) • Crystal methods (crystal clear) • Lean development (LD) • Adaptive software development (ASD) • Kanban • Agile modelling • Rapid product development (PRD) • Feature-driven development (FDD). Few agile methods are commonly used. The main focus of agile methods is represented by business problems and their solving in short time frames. Due to the dynamics of priority changes, many releases are frequently going live in production systems. While using smaller highly motivated teams, the focus relies within consistent and clear communication [16]. Customer needs are addressed differently by different agile methodologies. A difference is made between a practical and a managerial type of approach. In addition, different approaches are used for different phases of the software development cycle. Recommended team compositions vary in order to increase efficiency. Studies have been conducted in order to contrast different agile methodologies using several dimensional aspects. One such study is presented by Abrahamsson et al. [8] which by using six dimensions attempt to compare nine different agile methodologies. The key points are highlighted in Table 24.1: The agile methodology is focused on the so-called iterative approach which consists of short software release cycles. Thus, changes can be made while the software is still being developed. A philosophy in which large requirements are broken down into smaller more simple requirements is widely used for agile projects. Thus, time as well as potential hurdles for the development and testing process can be better estimated. In addition, it helps in assessing the percentage of software which has been implemented and tested in a given moment in time. This procedure allows for a real-time tracking mechanism to be set in place and allows for potential risk recognition in the early stages of development and testing. Generally, software development and testing perform badly when asked to predict the amount of time required for implementing or testing requirements [17]. Planning is based on developers’ estimation on software implementation times. The test plan and test phases are established based on these estimations. Unfortunately, estimations are usually based on best-case-scenario estimates, which are in most cases unrealistic. The possibility of occurring mistakes should be considered and planned. Once the software has been split up into several small requirements, a so-called sprint cycle should be planned for a time frame of one month. A decision will be placed on the estimated budged and required time and man hours required for the implementation and testing process. So-called sprint planning meeting will be organized at the beginning of each sprint where the fixed designed for implementation are discussed. Priorities will be set. It will be assessed how much effort is required for developing and testing every task. A scale can be defined for this purpose. The estimation of a task is done better when in a team discussion as the possibility of

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Table 24.1 Different agile methodologies Agile methodology

Features

Recommended for software testing life cycle phase

Drawbacks

Agile system design (ASD)

Radical software testing

Requirements check, concept design check, unit test, integration and system test

Theoretical

Agile modelling (AM)

Rapid development testing

No recommendation

Used as an extension for other methods

Crystal

Flexible Processes testing

Concept design check, unit test, integration and system test

Scalability

Dynamic system development method (DSDM)

Rapid development testing

Requirements check, design check, unit test, integration and system test

Availability

Extreme programing (XP)

Short release

Requirements check, design check, unit test, integration and system test

Not management oriented

Feature-driven development (FDD)

Very short interactions

Requirements check, design check, unit test, integration and system check

Focused on software implementation and testing

Agile software process (ASP)

Flexibility

Requirements check, design check, unit test, integration and system test

Unpredictable due to constantly changing requirements

Pragmatic programming (PP)

Best practices

No recommendation

Theoretical

Scrum

Adaptability

Requirements check, integration test

Uncompleted testing process

error will be minimized this way. This kind of methodology helps developers and testers better estimate the time they need to complete a task.

24.4 Use of Methodology This study considers the guidelines provided by Petersen et al. [18]. Thus, details on each step of the analysis are provided. Research questions were defined. These

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enabled to define the focus and restrict the scope of the research. This analysis was based on questions such as: 1. What kinds of articles have been published on agile testing? 2. What is agile testing used for? 3. Which models have been used for agile testing? The second part was based on revising the existing literature on agile testing in order to answer the presented research questions. Search strings were selected. A search has been conducted in library catalogues based on key words such as: agile, agile testing, agile software development. It was important to gather as many relevant papers on the selected topic as possible in order to select the most appropriate sources of secondary data. Further studies can search for subsets of testing or on specific agile methodologies. Databases such as SciVerse Scopus od IEEE Xplore were used, as many conferences on topics such as computer science or software engineering is placed there. Articles were excluded if they were in the form of newspaper articles without the rigour of academic papers which include citations and a form of empirical or conceptual theory. The so-called grey literature has also been omitted. Thus, working papers and reports produced outside conventional publication channels that were omitted. Articles related only to software development were also excluded from this study. The main focus was placed on testing and more in detailed agile testing. Thus, the selected articles have this trait in common. The third part of the research was screening the publications for those which contained relevant content for the purpose of this article. Thus, papers were excluded based on their title, reading or abstracts. A large number of papers were excluded until the final five articles were selected for the research. The role of the customer in agile projects is a very important one. The customer can be invited to participate in test activities throughout the development process [19]. Customers may fall short of dealing with test requirements in dynamic development projects. Thus, different test roles can be created to appropriately simulate and test customer activities. Customer activity can be involved in the testing process throughout more practices. These consist of direct involvement of all stakeholders and all customer skills in an effective and productive way. For this purpose, picture upfront and re-calibration, customer boot camps, customer pairings, road shows, programmer holidays, contextual enquiries, tester on site or having a customer as an apprentice tester [20, 21]. Different agile approaches create different organizational cultures [22]. These can be divided in hierarchical, hierarchical, disciplined, democratic or clan-type cultures. Organizations adopting a democratic culture are more suited for adopting agile methodologies. Culture can be analysed on the basis of two contrasting aspects change (spontaneity and flexibility) and stability (continuity and order, control) and focus (competition within an organizational environment). Four main types of culture have evolved throughout time. These are development culture (future-oriented), rational culture (achievement-oriented), hierarchical culture (order- and routine-oriented)

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and group culture (relationship-oriented). It has been demonstrated that agile methods and hierarchical culture are incompatible, as hierarchy structures lead to loss of agility [23]. Different factors can be used to identify agile methodologies for organizations with specific characteristics. Chow and Cao [24] identified six key dimensions which guide the selection process. These comprise including the customer (the customer is perceived as an authority), creating an appropriate team environment (team location and autarky play an important role in this dimension) for capable teams (with a strong training mechanism to form highly competent teams with adaptive management styles), and an appropriate management process (with well-defined processes for concept test, software change and communication of the achieved results). Additional features may be the adoption of appropriate software development and testing techniques (from simple designs to strong testing strategies and refactoring techniques) to allow an appropriate delivery method in small interactions (short delivery cycles). Other authors suggest learning, initiative, innovation and rapid response to change as key determinants for selecting agile development and testing methodologies [25]. Similarly, project dimension, personnel competence, project criticality and safety standards have been considered to be a key in determining whether a project is appropriate for an agile methodology [26]. Literature shows that agile methods have been studied together with organizational culture and project management. Frameworks have been developed to compare agile methodologies [8, 11, 27]. However, studies which map a connection between agile methodologies characteristics and project characteristics can still be improved. Thus, an analytical framework for decision-making regarding the appropriate agile methodology can be created from existing literature.

24.5 Discussion Practical experience at major banks in Germany shows that agile elements can be interpreted very differently. From the requirements stage to creating the functional and technical concepts to implementation followed by technical, functional and acceptance testing, the software development process has never been so dynamic as in present times. Banks have a large list of requirements which need to be implemented in planned releases. Every release usually contains a lot of software changes which will go live at a specific date. Agile development and testing come in where the bank has an urgent requirement which needs to go live before all other requirements planned for a specific release. In such cases, the named requirements will go live before all other requirements. For the development and testing of such requirement, an agile development and testing model are adopted by the bank. Daily meetings are held. The development, processing and testing phases are closely tracked by test management until the desired software quality has been achieved. Given the urgency of such requirements very short interactions are planned and concepts are updated

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in parallel to the implementation and testing phases. Tests are regularly conducted, and towards the end of the implementation process, a regression test over the whole affected software is run in order to ensure that the fix did not disrupt any other vital software components. Finally, at the end of the agile software development and testing process, the requirement goes live and the software development process resumes its release-based development and testing process.

24.6 Conclusion The methodology for developing software in an agile manner has been recognized by software developers. It has many advantages over previously used models such as waterfall. Software planning and testing are simplified. Large requirements are split into smaller tasks. These tasks can be better assessed, implemented and tested. Progress is better tracked. Developers and testers learn better how to estimate and complete a task. In present, the agile methodology is used by most software developers due to the delivery of quality software in a short amount of time. These satisfy stakeholder’s needs by implementing requirements and thus meeting objectives. It appears extreme programming, scrum and rational unified process are most commonly used in the market. This allows research to explore the other existing methods or to elaborate newer methods for agile project management and testing. Perhaps a new hybrid model can be developed which harnesses the strengths of the existing models. Tests can be conducted in real project environment and improvements can be made. Perhaps such new models can take into account the views from practicians as well. This way the newly developed models would have immediate practical use.

References 1. Matkovi´c, P., Tumbas, P.: A comparative overview of the evolution of software development models. Int. J. Ind. Eng. Manag. (IJIEM) 1, 163–172 (2010) 2. Wright, G.P.: Success rates by software development methodology in information technology project management: a quantitative analysis, UMI Number: 3590342, UMI Dissertation Publishing, ProQuest LLC, Michigan (2013) 3. Huo, M., Verner, J., Zhu, L., Babar, M.A.: Software quality and agile methods. In: Proceedings of the 28th Annual International Computer Software and Applications Conference, 2004. COMPSAC 2004, pp. 520–525. IEEE (2004) 4. Cho, J.: A hybrid software development method for large-scale projects: rational unified process with scrum. Issues Inf Syst 10(2), 340–348 (2009) 5. Beck, K., Beedle, M., Cockburn, A., Cunningham, W., Fowler, M., Grenning, J., Highsmith, J., Hunt, A., Jeffries, R., Kern, J., Marick, B., Martin, R.C., Mellor, S., Schwaber, K., Sutherland, J., Thomas, D., van Bennekum, A.: The Agile Manifesto (2001). Available from: https:// agilemanifesto.org/ (12, 2014) 6. Mohammad, A.H., Alwada’n, T., Ababneh, J.M.A.: Agile software methodologies: strength and weakness. Int. J. Eng. Sci. Technol. (IJEST) 5(3), 455–459 (2013)

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7. Vinekar, V., Slinkman, C.W., Nerur, S.: Can agile and traditional systems development approaches coexist? An ambidextrous view. Inf. Syst. Manag. 23(3), 31–42 (2006) 8. Abrahamsson, P., Oza, N., Siponen, M.T.: Agile software development methods: a comparative review1. In: Agile Software Development, pp. 31–59. Springer, Berlin (2010) 9. Coram, M., Bohner, S.: The impact of agile methods on software project management. In: 12th IEEE International Conference and Workshops on the Engineering of Computer-Based Systems, ECBS’05, pp. 363–370. IEEE (2005) 10. Rajagopalan, S., Mathew, S.: Choice of agile methodologies in software development: a vendor perspective. J. Int. Technol. Inf. Manag. 25(1) (2016). Available at: https://scholarworks.lib. csusb.edu/jitim/vol25/iss1/3 11. Qumer, A., Henderson-Sellers, B.: An evaluation of the degree of agility in six agile methods and its applicability for method engineering. Inf. Softw. Technol. 50(4), 280–295 (2008) 12. Hellmann, T.D., Hosseini-Khayat, A., Maurer, F.: (2010) Supporting test-driven development of graphical user interfaces using agile interaction design. In: 2010 Third International Conference on Software Testing, Verification, and Validation Workshops (ICSTW), pp. 444–447 (2010) 13. Larman, C., Basili, V.R.: Iterative and incremental development: a brief history. Computer 36(6), 47–56 (2003) 14. Dybå, T., Dingsøyr, T.: Empirical studies of agile software development: a systematic review. Inf. Softw. Technol. 50(9), 833–859 (2008) 15. Nerur, S., Balijepally, V.: Theoretical reflections on agile development methodologies. Commun. ACM 50(3), 79–83 (2007) 16. Strode, D.E.: Agile methods: a comparative analysis. In: Proceedings of the 19th Annual Conference of the National Advisory Committee on Computing Qualifications, NACCQ, vol. 6, pp. 257–264 (2006) 17. Ghule, S.: Risk analysis and mitigation plan in software development. Int. J. Eng. Sci. Res. Technol. 3(8), 546–548 (2014) 18. Petersen, K., Feldt, R., Mujtaba, S., Mattsson, M.: Systematic mapping studies in software engineering. In: 12th International Conference on Evaluation and Assessment in Software Engineering, pp.71–80. Bari, Italy (2008) 19. Martin, A., Biddle, R., Noble, J.: XP customer practices: a grounded theory. In: Agile Conference, 2009. AGILE’09, pp. 33–40. IEEE (2009) 20. Beyer, H., Holtzblatt, K., Baker, L.: An agile customer-centered method: rapid contextual design. In: Extreme Programming and Agile Methods-XP/Agile Universe 2004, pp. 50–59. Springer, Berlin (2004) 21. Takats, A., Brewer, N.: Improving communication between customers and developers. In: Agile Conference, 2005. Proceedings, pp. 243–252. IEEE (2005) 22. Siakas, K.V., Siakas, E.: The agile professional culture: a source of agile quality. Softw. Process Improv. Prac. 12(6), 597–610 (2007) 23. Iivari, J., Huisman, M.: The relationship between organizational culture and the deployment of systems development methodologies. MIS Q. 31(1), 35–58 (2007) 24. Chow, T., Cao, D.B.: A survey study of critical success factors in agile software projects. J. Syst. Softw. 81(6), 961–971 (2008) 25. Wan, J., Wang, R.: Empirical research on critical success factors of agile software process improvement. J. Softw. Eng. Appl. 3(12), 1131–1140 (2010) 26. Lindvall, M., Basili, V., Boehm, B., Costa, P., Dangle, K., Shull, F., Tesoriero, R., Williams, L., Zelkowitz, M.: Empirical findings in agile methods. In: Extreme Programming and Agile Methods—XP/Agile Universe 2002, pp. 197–207. Springer, Berlin (2002) 27. Geamba¸su, C.V., Jianu, I., Jianu, I., Gavril˘a, A.: Influence factors for the choice of a software development methodology. Account. Manag. Inf. Syst. 10(4), 479–494 (2011)

Chapter 25

The Implementation of a New Technology Based on the Monte Carlo Simulation in the Field of Sustainable Dependability in Operation Ionut Herghiligiu, Adrian Vilcu, and Marius Pislaru Abstract The work approaches a problem from the systemic analysis of the operation dependability (OD) by implementing a statistic modeling based on the Monte Carlo (MC) method for the determination of dependability and maintainability values when the production and quality values are given. The novelty of this approach derives from the methodology of statistic techniques application based on MC method to a technical system, and the comparison of the model results with the results supplied by mathematical and evolutive modeling algorithms, from the standpoint of both solution quality and model applicability character. The systemic analysis of the OD belongs to the methodology of design, research and evaluation of systems integrity engineering, and it consists in: establishing a system of quantifiable indicators, with a simple expression for OD parameters (reliability, maintainability, availability and safety in operation), measuring them on analysis system, system validation according to indices systems, developing an adaptable adjustment and revision system determined by the obtained results and creating a flexible methodology as a tool in the operation dependability design engineering.

I. Herghiligiu · A. Vilcu (B) · M. Pislaru Department of Engineering and Management, Gheorghe Asachi Technical University of Iasi-Romania, D. Mangeron 63, Iasi, Romania e-mail: [email protected] I. Herghiligiu e-mail: [email protected] M. Pislaru e-mail: [email protected] © Springer Nature Switzerland AG 2020 G. Prostean et al. (eds.), Innovation in Sustainable Management and Entrepreneurship, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-44711-3_25

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25.1 Introduction From the functional point of view, OD assumes meeting a target function and implies its accurate definition. In the case of a simple element, the function is defined within an assembly that includes the corresponding element, while in the case of some complex equipment, one can generate multivariable complex functions whose variables depend on various states and operation regimes. From the standpoint on engineering design, the OD concept is an integral part of the design methodology of the systems integrity engineering. Still another collocation that requires attention is “the design methodology” that represents the entirety of research methods used to establish the technical and economical calculations necessary to estimate systems integrity. Designing the integrity and OD implicitly includes the design criteria for reliability, availability, maintainability and security of systems and equipment [3, 8, 16]. The combination of these four concepts leads to the necessity of a methodology that should ensure a good systems design, with desired integrity values. This methodology offers the means to adequately analyze and revise complex engineering projects or models, thus developing RAMS (reliability–availability–maintainability– security) analyze techniques [14, 16]. The RAMS analyze concept is not new, and it was developed progressively, mainly in systems safety field. Many technologies have reached a top development during the last two decades. In fact, most of the production systems generated nowadays will be “technologically obsolete” in a not too far away future. Therefore, the development ideas, as well as the knowledge and techniques utilized for an adequate management of application and maintenance of newly developed systems must be compatible and adaptable, otherwise they will become obsolete. This is also true for the concept of integrity engineering and especially for engineering design integrity [13, 15]. Knowledge of engineering and techniques of complex systems design and development either pertain to a new informational system, compatible in many cases with algorithms that belong to intelligent methodology of automated calculation, or will join the archive of outdated practice. Despite the large number of the already conducted studies in reliability analysis field, many of these techniques seem to be misunderstood and/or misused. Accordingly, in super-projects, the high and unforeseen costs reach in the end up to the final phase (of construction), given the lack of a rigorous and accurate estimation of projects integrity and their models from OD point of view. The present research attempts to define, through a systemic approach, a simple, efficient, flexible statistic method, easy to use for OD management, with high degree of applicability in different technical fields; it takes into account unitarily two of the components of operation safety (maintainability, reliability) in the context of sustainable production–quality policies [10, 11].

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25.2 Materials and Method 25.2.1 Qualitative Modeling–Optimization Data The experimental researches aim, first, to determine the degree of influence of each technical or economical parameter on the level of the resultative characteristic (production), and second, to determine a mathematical correlation between parameters, so as to create the real possibility of controlling production when one of the parameters is known or must be maintained at a predetermined value. The optimization problem in operational management combines elements of safety in operation (maintainability, reliability) and technological flow elements (production and quality). Compared with other field research paper, we will optimize the production function in terms of reliability, maintenance and quality parameter [1, 10–12]. Thus, the transfer function to be optimized is P = f (R, M, Q). The chosen model is a nonlinear of the first order, in the form in which it is desired besides the linear influence to analyze the nonlinear xi ∗ X j form of the independent variables on the output variables (P). Considering of the optimization function parameters, the use of empirical modeling methods is not opportune because this type of method involves a statistical analysis of the experimental results obtained by varying the factors at different levels established intuitively based on the previous experience, the precision of the model increasing by the number the determinations made and the conclusions are valid only within the limits of the values considered for the respective variables. Thus, we used the programmed experiment method, which uses statistical methods in all stages of modeling and optimization (the two steps will not be separated): the determination of the number of experiences and the conditions for their realization (including the moments of time to be applied), the statistical analysis of the value series for the identification of the evolution distributions and finally the generation of conclusions on the model system. The Monte Carlo simulation uses aleatory repeated sampling to simulate the data of a mathematical model attached to a given technical or economical system and to evaluate statistically the results. This method was initially applied in the 1940s, being used nowadays in optimization problems in which the resources are limited or real data collection would be too expensive or impracticable [4, 9]. Hereby, the Monte Carlo method replaces a real phenomenon with a statistic experiment that will be studied later by means of statistic mathematics techniques. Using adequate procedures on a known statistic distribution, computer will generate the aleatory variables that intervene in the model. At the same time, in order to obtain a correct image of the studied process evolution, the aleatory variables must estimate the best the parameters of the real and experimental systems, to be repeated by convenient big times, in order to signalize the main features of the modeled phenomenon.

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In essence, the method associates a statistic model to the real problem; by generating aleatory variables functionally related to the solution, one executes a large number of experiments on the statistic model, and the results are extrapolated to the solution of the deterministic problem [2, 5]. The utilization of this method does not necessarily imply the knowledge of the exact relationship between the parameters to be estimated (unknown mathematical I/O transfer functions); it is enough to signalize the system of conditions in whose presence the corresponding event occurred. Depending on the number of the involved factors, the simulations can be very complex. Yet, at a base level, all the Monte Carlo simulations have four steps [4, 9]: (a) Identify the transfer equation (mathematical expression) to make a Monte Carlo simulation, thus determining the quantitative model of the explored activity. This can be a known mathematical, engineering or business formula, or it can be based on a model created by a programmed experiment (DOE) or a regression analysis. (b) Define the input parameters for each factor of the input/output transfer equation, and determine the statistic distribution of factors. (c) Create aleatory data for the realization of a valid simulation, by creating a very large set of aleatory data for each input—of the order of 100,000 instances. These aleatory data simulate values that might be seen for a long period for each input. Minitab program can easily create aleatory data that follow almost any distribution that might come across. (d) Simulate and analyze the process outputs with generated data by using the transfer equation, to compute the simulated results. Executing a large enough amount of input data simulated on the model will offer a quality modeling of the studied process.

25.2.2 Quantitative Modeling–Optimization Data The numerical data were extracted from the computer of an Op 150C honing machine, with a working week following the following parameters: MTBF [min/week] (Mean Time Between Failure)—numerical parameter for reliability, MTTR [min/week] (Mean Time Ro Repair)—numerical parameter for maintenance, Q [microM] (quality, reported as average per number of units per week), P [pieces/week]. These values were searched for by the patterns generated by the experimental model. Measured values of technological flow parameters reported at one week are shown in Table 25.1.

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Table 25.1 Measured values of technological flow parameters No.

MTBF [min/week]

MTTR [min/week]

P [pieces/week]

Q [microM]

1

2001

215

10,838

0.011

2

3226

133.7

12,099

0.123

3

9955

125

17,421

0.2

4

5022

18.5

14,730

0.055

5

4963

77.5

14,060

0.163

6

7035

45

18,063

0.024

7

2000

15.6

11,169

0.023

8

10,045

35

17,579

0.131

9

7080

0

19,152

0.1

10

4728

312.5

17,019

0.212

11

1642

38.5

10,834

0.052

12

3333

154

12,833

0.131

13

1922

165

12,974

0.15

min

1642

0

10,834

0.011

max

10,045

312.5

19,152

0.212

25.3 Practical Application of Modeling–Optimization Techniques 25.3.1 Statistical Modeling Based on the Programmed Experiment Method The research method adopted—the method of the programmed experiment—allows the considerable reduction of a number of experiences and the establishment of mathematical correlations (regression equations) between the influences manifested by different interdependent parameters in the analyzed process. Thus, we will use the regression method in active experiment, the programming of the experiments being done based on the complete factorial experiment of type 2k [12, 14]. Because the operational safety parameters (M, R) are correlated with the production process parameters (P, Q) [14–16], we will analyze them for the purpose of determining and generalizing these relationships, on the data segment measured with minimum, maximum and center values. Value classes and codes in the programmed experimental model are shown in Table 25.2. The quality parameter has inverted values because a small value corresponds to a superior product quality.

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Table 25.2 Values classes and codes Code

MTBF

MTTR

P

Q

−1.41

1642

0

10,834

0.23

−1

3752

78

12,914

0.18

0

5861

156

14,993

0.12

1

7971

234

17,073

0.07

1.41

10,080

313

19,152

0.01

In Table 25.3, the points in the solution space for the M, P variables are generated, and the last column contains the values of the P-variable measured on the technical system according to the independent variables. Table 25.3 Programmed and experimentally measured values Programmed values for input variables

Measured values

MTBF

MTTR

Q

P

10,080

0

0.12

16,765.00

3752

234

0.07

10,987.00

3752

78

0.18

10,834.00

7971

78

0.07

11,234.00

3752

234

0.18

11,432.00

5861

234

0.12

10,984.00

3752

234

0.18

11,432.00

5861

234

0.12

10,984.00

5861

0

0.12

12,432.00

5861

156

0.12

11,934.00

5861

156

0.12

12,321.00

7971

234

0.18

13,234.00

1642

156

0.12

10,834.00

5861

156

0.12

11,345.00

5861

156

0.23

13,001.00

7971

78

0.18

14,213.00

5861

156

0.12

12,654.00

5861

156

0.12

12,999.00

5861

156

0.12

11,987.00

7971

234

0.07

11,043.00

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25.3.2 Variance and Regression Analysis It analyzes the adequacy of the model in its totality as well as its components [6, 7, 17]: linear and nonlinear (interaction factors) (Table 25.4). The model is adequate for both the linearity and the nonlinearity being present in the regression equation (p-value < 0.05) [17]. It is noted that the MTTR and Q parameters do not statistically influence the production parameter (F and p characteristics), and the nonlinear (interaction) side is present with two terms that contain the MTBF factor. Thus, the weight of the reliability parameter in the production parameter is superior to the other factors. The calculation of the coefficients is done by the least squares method, and their significance is determined by the T-test (Table 25.5). Regression equation in uncoded units is P = 9373 + 0.260 MTBF − 0.003269 ∗ MTBF ∗ MTTR + 4.95 ∗ MTBF ∗ Q (25.1)

Table 25.4 Adequacy of the model Source

DF

Adj SS

Adj MS

P-Value

Model

6

34,599,915

5,766,653

0.000

Linear

3

10,568,413

3,522,804

0.002

MTBF

1

2,697,239

2,697,239

0.023

MTTR

1

1372

1372

0.955

Q

1

286,096

286,096

0.417

2-Way interaction

3

7,824,360

2,608,120

0.007

MTBF*MTTR

1

4,797,373

4,797,373

0.004

MTBF*Q

1

2,302,211

2,302,211

0.033

MTTR*Q

1

11,657

11,657

0.868

Table 25.5 Values and coded coefficients Term

Coef

SE Coef

T-Value

P-Value

Constant

9373

42,299

3.2

MTBF

0.26

7.55

2.58

0.023

MTTR

16.3

94.1

-0.06

0.417

0.0514

Q

−15,606

19,902

-0.84

0.417

MTBF*MTTR

−0.00369

0.001

3.44

0.004

MTBF*Q

4.95

3.54

2.38

0.033

MTTR*Q

−9.9

44.5

-0.17

0.868

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Contour Plot of P vs MTTR, MTBF 200

0 20000 40000 60000

MTTR

150

100

P < – – – – >

0 20000 40000 60000 80000 80000

Hold Values Q 2.42

50

0

2000 3000 4000 5000 6000 7000 8000 9000 10000

MTBF

Fig. 25.1 Contour plot of P in function of MTTR and MTBF parameters (hold Q value at 2.42)

From the equation are eliminated the coefficients that are not significant (p-value > 0.05). Figure 25.1 shows the level curves for the P parameter according to the MTBF and MTTR parameters, maintaining the Q parameter constant. An almost linear (functional) dependence between P and reliability and a relaxed dependence between P and maintenance is observed.

25.3.3 Verifying the Normality of Value Series To verify the distribution of the series, we used the Kolmogorov–Smirnov test for the normal variation. This test compares the empirical function of cumulative distribution of sample data with normal theoretical distribution. The statistical assumptions for verifying the normality of a series are H0 —the series is normally distributed; H1 — the series is not normally distributed. If the calculated p-value of these tests is higher than the alpha chosen level (alpha = 0.1), H1 is rejected (H0 is admitted) as the population is normal [6]. Figure 25.2 shows how closely the data points follow the normal distribution line (the straight line) and p-value. It is noticed that p-value is greater than the threshold value 0.1 and therefore validates the hypothesis that the value series follows normal distributions.

25 The Implementation of a New Technology Based on the Monte Carlo … Probability Plot of MTBF [min/week] Normal

99

80 70 60 50 40 30 20

Mean 4842 StDev 2928 N 13 KS 0.168 P-Value >0.150

95 90

Percent

Percent

95 90

99

80 70 60 50 40 30 20

Probability Plot of MTTR [min/week] Normal Mean 102.7 StDev 92.42 N 13 KS 0.195 P-Value >0.150

10 5

10 5

1

1 0

3000

6000

9000

12000

-100

0

100

a)

Mean 14521 StDev 2997 N 13 KS 0.182 P-Value >0.150

Probability Plot of Q [microM] Normal

99 95 90

Percent

Percent

80 70 60 50 40 30 20

300

b)

Probability Plot of P [pieces/week] Normal

99

200

MTTR [min/week]

MTBF [min/week]

95 90

341

10 5

80 70 60 50 40 30 20

Mean 0.1058 StDev 0.06769 N 13 KS 0.158 P-Value >0.150

10 5

1

1 6000

8000

10000 12000 14000 16000 18000 20000 22000

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30

Q [microM]

P [pieces/week]

c)

d)

Fig. 25.2 Probability plot of MTBF (a) MTTR (b), P (c), Q (d)

25.3.4 Applying the MC Method Several series of 100,000 values were generated for each of the three variables (MTBF, MTTR and Q), and then, the values for P were calculated using the regression equation (25.1). The series of independent variables follow normal distributions fully defined by previously calculated averages and standard deviation (Table 25.6). Table 25.6 Descriptive statistics for MTBF, MTTR, Q and P parameters

Variable

Mean

SE Mean

St. Dev

MTBF [min/week]

4842

812

2928

MTTR [min/week]

102.7

25.6

92

P [pieces/week

14521

831

2997

Q [microM]

0.1058

0.0188

0.0677

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25.3.5 Descriptive Statistics for Production Variable, MC Modeled The average value of a series of values calculated for the P parameter is 11,545 pieces/week (Fig. 25.3) lower than the initially calculated value of 14,520 pieces/week. This is normal due to the fact that there is a difference between the number of experiments (measurements) 20 and the statistic related to a series of 100,000 values calculated according to the linear and nonlinear influence of independent variables. The confidence interval for the average production is [11,541, 11,550], and min-max interval is [8907, 15,273] values that covering all types of production policies. We applied the Anderson–Darling normality test because the generated data are obtained on a known distribution. P-value is 0.395, which validates the hypothesis that the distribution is normal. The confidence interval for the average production is “tight” defined in the interval [11,541, 11,550], with a probability of 95%, which is reflected in a confidence in obtaining a desired output (production) when the reliability, maintenance and quality policies are defined on the production flow. Another indicator of the central tendency is the median, which can be used as a statistical indicator for calculating asymmetry or as an indicator for assessing the significance of the mean. Thus, the value of the median is 11,503 with a 95% confidence interval [11,498, 11,509], values close to the values obtained for the mean,

Summary Report for P [pieces/week] Anderson-Darling Normality Test A-Squared P-Value Mean StDev Variance Skewness Kurtosis N

9000

9900

10800

11700

12600

13500

14400

15300

Minimum 1st Quartile Median 3rd Quartile Maximum

150.32