Eurasian Business Perspectives: Proceedings of the 28th Eurasia Business and Economics Society Conference [1st ed.] 9783030485047, 9783030485054

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Eurasian Studies in Business and Economics 15/2 Series Editors: Mehmet Huseyin Bilgin · Hakan Danis

Mehmet Huseyin Bilgin Hakan Danis Ender Demir Uchenna Tony-Okeke  Editors

Eurasian Business Perspectives Proceedings of the 28th Eurasia Business and Economics Society Conference

Eurasian Studies in Business and Economics 15/2 Series Editors Mehmet Huseyin Bilgin, Istanbul, Turkey Hakan Danis, San Francisco, CA, USA Representing Eurasia Business and Economics Society

More information about this series at http://www.springer.com/series/13544

Mehmet Huseyin Bilgin • Hakan Danis • Ender Demir • Uchenna Tony-Okeke Editors

Eurasian Business Perspectives Proceedings of the 28th Eurasia Business and Economics Society Conference

Editors Mehmet Huseyin Bilgin Faculty of Political Sciences Istanbul Medeniyet University Istanbul, Turkey

Hakan Danis MUFG Union Bank San Francisco, CA, USA

Ender Demir Faculty of Tourism Istanbul Medeniyet University Istanbul, Turkey

Uchenna Tony-Okeke School of Economics, Finance and Accounting Coventry University Coventry, UK

The authors of individual papers are responsible for technical, content, and linguistic correctness. ISSN 2364-5067 ISSN 2364-5075 (electronic) Eurasian Studies in Business and Economics ISBN 978-3-030-48504-7 ISBN 978-3-030-48505-4 (eBook) https://doi.org/10.1007/978-3-030-48505-4 © The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are solely and exclusively licensed 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

Preface

This is Volume 2—Eurasian Business Perspectives of the 15th issue of the Springer series Eurasian Studies in Business and Economics, which is the official book series of the Eurasia Business and Economics Society (EBES, www.ebesweb.org). This issue includes selected papers presented at the 28th EBES Conference, which was held on May 29, 30, and 31, 2019, in Coventry, UK. The conference is hosted by the Centre for Financial and Corporate Integrity (CFCI), Coventry University, in collaboration with Coventry Business School Trading Floor. EBES Executive Board selected David B. Audretsch from Indiana University, USA, as the EBES Fellow Award 2019 recipient for his outstanding contribution to the areas of innovation and entrepreneurship. During the conference, David B. Audretsch received the EBES Fellow Award and gave a speech entitled “Entrepreneurship: The Role of Culture.” Moreover, Klaus F. Zimmermann (Editor-inChief, Journal of Population Economics (SSCI)), David B. Audretsch (Editor-inChief, Small Business Economics (SSCI)), Marco Vivarelli (Editor-in-Chief, Eurasian Business Review (SSCI)), and Dorothea Schäfer (Editor-in-Chief, Eurasian Economic Review (Scopus and ESCI)) organized “JOURNAL EDITORS SPECIAL SESSION—How to Publish in WOS Journals?” During the conference, participants had many productive discussions and exchanges that contributed to the success of the conference where 167 papers by 303 colleagues from 46 countries were presented. In addition to publication opportunities in EBES journals (Eurasian Business Review and Eurasian Economic Review, which are also published by Springer), conference participants were given the opportunity to submit their full papers for this issue. Theoretical and empirical papers in the series cover diverse areas of business, economics, and finance from many different countries, providing a valuable opportunity for researchers, professionals, and students to catch up with the most recent studies in a diverse set of fields across many countries and regions. The aim of the EBES conferences is to bring together scientists from business, finance, and economics fields, attract original research papers, and provide them with publication opportunities. Each issue of the Eurasian Studies in Business and v

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Economics covers a wide variety of topics from business and economics and provides empirical results from many different countries and regions that are less investigated in the existing literature. All accepted papers for the issue went through a peer review process and benefited from the comments made during the conference as well. The current issue covers fields such as human resources, management and marketing. Although the papers in this issue may provide empirical results for a specific county or regions, we believe that the readers would have an opportunity to catch up with the most recent studies in a diverse set of fields across many countries and regions and empirical support for the existing literature. In addition, the findings from these papers could be valid for similar economies or regions. On behalf of the series editors, volume editors, and EBES officers, I would like to thank all presenters, participants, board members, and the keynote speakers, and we are looking forward to seeing you at the upcoming EBES conferences. Istanbul, Turkey

Ender Demir

Eurasia Business and Economics Society (EBES)

EBES is a scholarly association for scholars involved in the practice and study of economics, finance, and business worldwide. EBES was founded in 2008 with the purpose of not only promoting academic research in the field of business and economics but also encouraging the intellectual development of scholars. In spite of the term “Eurasia,” the scope should be understood in its broadest terms as having a global emphasis. EBES aims to bring worldwide researchers and professionals together through organizing conferences and publishing academic journals and increase economics, finance, and business knowledge through academic discussions. Any scholar or professional interested in economics, finance, and business is welcome to attend EBES conferences. Since our first conference in 2009, around 12,011 colleagues from 99 countries have joined our conferences and 6858 academic papers have been presented. EBES has reached 2257 members from 87 countries. Since 2011, EBES has been publishing two journals. One of those journals, Eurasian Business Review (EABR), is in the fields of industrial organization, innovation, and management science, and the other one, Eurasian Economic Review (EAER), is in the fields of applied macroeconomics and finance. Both journals are published quarterly by Springer and indexed in Scopus. In addition, EAER is indexed in the Emerging Sources Citation Index (Clarivate Analytics), and EABR is indexed in the Social Science Citation Index (SSCI) with an impact factor of 2.143 as of 2018. Furthermore, since 2014 Springer has started to publish a new conference proceedings series (Eurasian Studies in Business and Economics) which includes selected papers from the EBES conferences. The 10th, 11th, 12th, 13th, 14th, 15th, 16th, 17th, 18th, 19th, 20th (Vol. 2), and 24th EBES Conference Proceedings have already been accepted for inclusion in the Conference Proceedings Citation Index—Social Science & Humanities (CPCI-SSH). Subsequent conference proceedings are in progress.

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Eurasia Business and Economics Society (EBES)

We look forward to seeing you at our forthcoming conferences. We very much welcome your comments and suggestions in order to improve our future events. Our success is only possible with your valuable feedback and support! With my very best wishes, Klaus F. Zimmermann President EBES Executive Board Klaus F. Zimmermann, Central European University, Hungary Jonathan Batten, Universiti Utara Malaysia, Malaysia Iftekhar Hasan, Fordham University, USA Euston Quah, Nanyang Technological University, Singapore John Rust, Georgetown University, USA Dorothea Schafer, German Institute for Economic Research DIW Berlin, Germany Marco Vivarelli, Università Cattolica del Sacro Cuore, Italy EBES Advisory Board Ahmet Faruk Aysan, Istanbul Sehir University, Turkey Michael R. Baye, Kelley School of Business, Indiana University, USA Mohamed Hegazy, The American University in Cairo, Egypt Cheng Hsiao, Department of Economics, University of Southern California, USA Noor Azina Ismail, University of Malaya, Malaysia Irina Ivashkovskaya, State University – Higher School of Economics, Russia Christos Kollias, University of Thessaly, Greece Wolfgang Kürsten, Friedrich Schiller University Jena, Germany William D. Lastrapes, Terry College of Business, University of Georgia, USA Sungho Lee, University of Seoul, South Korea Justin Y. Lin, Peking University, China Brian Lucey, The University of Dublin, Ireland Rita Martenson, School of Business, Economics and Law, Goteborg University, Sweden Steven Ongena, University of Zurich, Switzerland Peter Rangazas, Indiana University-Purdue University Indianapolis, USA Peter Szilagyi, Central European University, Hungary Amine Tarazi, University of Limoges, France Russ Vince, University of Bath, UK Adrian Wilkinson, Griffith University, Australia Naoyuki Yoshino, Keio University, Japan

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Organizing Committee Klaus F. Zimmermann, PhD, Central European University, Hungary Mehmet Huseyin Bilgin, PhD, Istanbul Medeniyet University, Turkey Hakan Danis, PhD, Union Bank, USA Alina Klonowska, PhD, Cracow University of Economics, Poland Orhun Guldiken, PhD, University of Arkansas, USA Ender Demir, PhD, Istanbul Medeniyet University, Turkey Sofia Vale, PhD, ISCTE Business School, Portugal Jonathan Tan, PhD, Nanyang Technological University, Singapore Ugur Can, EBES, Turkey Reviewers Sagi Akron, PhD, University of Haifa, Israel Ahmet Faruk Aysan, PhD, Istanbul Sehir University, Turkey Mehmet Huseyin Bilgin, PhD, Istanbul Medeniyet University, Turkey Andrzej Cieślik, PhD, University of Warsaw, Poland Hakan Danis, PhD, Union Bank, USA Ender Demir, PhD, Istanbul Medeniyet University, Turkey Oguz Ersan, PhD, Kadir Has University, Turkey Conrado Diego García-Gómez, PhD, Universidad de Valladolid, Spain Giray Gozgor, PhD, Istanbul Medeniyet University, Turkey Orhun Guldiken, University of Arkansas, USA Peter Harris, PhD, New York Institute of Technology, USA Mohamed Hegazy, The American University in Cairo, Egypt Gokhan Karabulut, PhD, Istanbul University, Turkey Christos Kollias, University of Thessaly, Greece Davor Labaš, PhD, University of Zagreb, Croatia Chi Keung Marco Lau, PhD, University of Huddersfield, UK Gregory Lee, PhD, University of the Witwatersrand, South Africa Nidžara Osmanagić-Bedenik, PhD, University of Zagreb, Croatia Euston Quah, PhD, Nanyang Technological University, Singapore Peter Rangazas, PhD, Indiana University-Purdue University Indianapolis, USA Doojin Ryu, PhD, Chung-Ang University, South Korea Uchenna Tony-Okeke, PhD, Coventry University, UK Sofia Vale, PhD, ISCTE Business School, Portugal Manuela Tvaronavičienė, PhD, Vilnius Gediminas Technical University, Lithuania Marco Vivarelli, PhD, Università Cattolica del Sacro Cuore, Italy

Contents

Part I

Human Resources

Brain Drain: A Threat or an Opportunity . . . . . . . . . . . . . . . . . . . . . . . Alice Reissová, Jana Šimsová, and Hana Suchánková Personal Innovativeness and Employability: How Personal Traits Affect Employer Attractiveness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Amila Pilav-Velic, Jasmina Selimovic, and Hatidza Jahic Part II

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Management

Telecommuting Versus Traditional Work Environment: Determinants of Job Satisfaction as Perceived by Individual Contributors and Supervisors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Melfi Masongsong Caranto, Rommel Pilapil Sergio, and Melchor Zabala Oribiana

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Effect of Values Congruence: Are There Any Reasons to Know Values of Different Generations Better? . . . . . . . . . . . . . . . . . . Jolita Vveinhardt and Povilas Foktas

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Business Advisors and Small Businesses: Cooperation in the Framework of the Advisory Process . . . . . . . . . . . . . . . . . . . . . . . Paweł Głodek

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Industrial Drivers of Co-opetition Among Organizations: A Sector-Based Research Within the Context of Population Ecology Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hasan Boztoprak, Nusret Erhan Mutlu, Murat Süslü, and Yıldız Yılmaz Güzey

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Pivoting Strategic Business Approaches in the Strategic Business Advice Process: Lessons Learned from Case Studies of Small Innovative Firms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Katarzyna Łobacz Part III

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Marketing

Uncovering Social Media Users’ Emotions Towards Companies Using Semantic Web Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Livu-Adrian Cotfas, Camelia Delcea, and Ionut Nica Analysing Customers’ Opinions Towards Product Characteristics Using Social Media . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 Livu-Adrian Cotfas, Camelia Delcea, and Ionut Nica Millennial Travelers’ Perception of Terrorism Risks: Evidence from Poland and Slovakia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 Rafał Nagaj

List of Contributors

Hasan Boztoprak Department of Entrepreneurship, Beykent University, Istanbul, Turkey Melfi Masongsong Caranto College of Arts, Criminology, and Education, Jose Rizal University, Mandaluyong City, Philippines Livu-Adrian Cotfas Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, Bucharest, Romania Camelia Delcea Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, Bucharest, Romania Povilas Foktas Vytautas Magnus University, Kaunas, Lithuania Paweł Głodek Department of Entrepreneurship and Industrial Policy, University of Lodz, Lodz, Poland Yıldız Yılmaz Güzey Department of Business Administration, Beykent University, Istanbul, Turkey Hatidza Jahic Department of Economic Theory and Policy, School of Economics and Business, University of Sarajevo, Sarajevo, Bosnia and Herzegovina Katarzyna Łobacz Department of Business Management, Institute of Management, University of Szczecin, Szczecin, Poland Nusret Erhan Mutlu Beykent University, Istanbul, Turkey Rafał Nagaj Institute of Economics and Finance, University of Szczecin, Szczecin, Poland Ionut Nica Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, Bucharest, Romania Melchor Zabala Oribiana University of Leeds, Leeds, UK

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List of Contributors

Amila Pilav-Velic Department for Management and Information Technologies, School of Economics and Business, University of Sarajevo, Sarajevo, Bosnia and Herzegovina Alice Reissová Department of Economics and Management, Jan Evangelista Purkyne University, Ústí nad Labem, Czech Republic Jasmina Selimovic Department of Finance, School of Economics and Business, University of Sarajevo, Sarajevo, Bosnia and Herzegovina Rommel Pilapil Sergio Faculty of Psychology and Management, Canadian University Dubai, Dubai, United Arab Emirates Jana Šimsová Department of Mathematics and Informatics, Jan Evangelista Purkyne University, Ústí nad Labem, Czech Republic Hana Suchánková Department of Foreign Languages, Jan Evangelista Purkyne University, Ústí nad Labem, Czech Republic Murat Süslü Beykent University, Istanbul, Turkey Jolita Vveinhardt Vytautas Magnus University, Kaunas, Lithuania

Part I

Human Resources

Brain Drain: A Threat or an Opportunity Alice Reissová, Jana Šimsová, and Hana Suchánková

Abstract The willingness to move abroad (either for some time or for good) is influenced by a number of factors. The usual interest of researchers is to find which factors influence young people and make them go abroad. This study, on the contrary, aims to determine which obstacles (barriers) prevent them in their decisions to go abroad. The research sample was made up of students with an economic specialization who study at regional public universities in Germany and in the Czech Republic (n ¼ 503). On the basis of stepwise forward logical regression, two barriers have been found in German students that prevent them from going abroad the most. They are the following: “I do not want to leave Germany” and “I do not want to leave the place where I live”. In Czech students, two barriers have also been identified. The first one is the language barrier, and the second one is the same as the German sample: “I do not want to leave the Czech Republic”. A strong relation to their home country awakens the hope that if they leave the country for the purpose of work, they will have a tendency to return in the future. Keywords Brain drain · University students · Motives · Barriers · Intercultural comparison · Work abroad · Czech Republic · Germany

A. Reissová (*) Department of Economics and Management, Jan Evangelista Purkyne University, Ústí nad Labem, Czech Republic e-mail: [email protected] J. Šimsová Department of Mathematics and Informatics, Jan Evangelista Purkyne University, Ústí nad Labem, Czech Republic e-mail: [email protected] H. Suchánková Department of Foreign Languages, Jan Evangelista Purkyne University, Ústí nad Labem, Czech Republic e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2020 M. H. Bilgin et al. (eds.), Eurasian Business Perspectives, Eurasian Studies in Business and Economics 15/2, https://doi.org/10.1007/978-3-030-48505-4_1

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1 Introduction Migration of the qualified workforce, identified by the term brain drain (BD), is at present in the centre of attention in a number of countries. But it is not a brand new phenomenon. It is possible to trace repeated waves of brain drain in Europe already from the Second World War. These waves usually have a different intensity, and they usually affect certain countries only (Campanella 2015). The process of migration has very strong economic consequences. People are attracted not only by better working conditions but also by better living conditions in the target countries. Within the countries of the European Union (EU), a competition for these employees arises both among member and non-member countries (Mahroum 2001). Besides economic factors, also social, political as well as ethical and moral factors play an important role when deciding on migrating (Grecu and Titan 2016). Although it may look like the economic factors are the main determining factors influencing migration, some studies point out the fact that also non-economic factors may have a strong effect, for example, persistent corruption in the country, a lack of democracy and the perception of organizational mistakes in the public and private sectors (Mihăilă 2019). This article, unlike the others dealing with the same topic, does not look for factors motivating people to go to a foreign country, but it concentrates on the identification of barriers, which prevent young and educated people in migration. This research article deals with the phenomenon of brain drain (BD) and its aspects, and it is organized in the following sections: Section 2 provides theoretical outcomes for studies and introduces a critical review of available literature. Section 3 explains the resources for research data and procedures of data collecting and their elaboration. Section 4 contains empirical results, which are based on statistical analysis. Section 5 presents a discussion and findings required by logical regression related to those already presented by other authors. Section 6 explains the limitations of our research and suggestions for future studies and research. Section 7 presents the conclusions for this study.

2 Literature Review and Theoretical Background 2.1

Theoretical Background

Looking for the theoretical background for the phenomenon, BD is relatively complicated, because the problematics could be looked at from different points of view (economic, psychological, sociological and a number of others). In some cases,

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the individual views may meet or overlap. In his survey, King (2018) introduced five basic theoretical frameworks, which emerge from primarily economic outcomes: (a) (b) (c) (d) (e)

Single market—free movement of labour as a neoliberal theory Migration flow—movement of labour between core and periphery Intra-European mobility—as characteristics of the twenty-first century Youth transition—life-course approach, more biographical Lifestyle migration framework—young people moving to attractive cities

King also points out that none of these theoretical frameworks explain the basis of migration and that it is always necessary to combine the individual outcomes. The neoclassical theories explain the mechanism of working migration as a result of the process of economic development and differences in salaries. Individuals migrate on the one hand because they try to maximize their incomes and on the other hand because they accept rational decisions concerning their costs and advantages. This estimate of costs and revenues is related to individual intentions and personality characteristics such as age, gender, education and so on. Apart from differences in the level of remuneration and attractive working conditions, migration behaviour also includes financial expenditures, such as travel costs, and costs of the period of unemployment in the host country. Psychological costs (leaving family and friends) are also important. The higher the differences in expectations between the home country and the destination country, the higher the rate of migration flow becomes (Gheasi and Nijkamp 2017). Apart from theoretical frameworks, many other different models have been created, e.g. nationalist, internationalist and globalizations models (Ansah 2002).

2.2

Brain Drain as a Threat (Loss of Human Capital)

Many countries worry about brain drain. They are especially the countries which on the one hand invest a lot in the education of young people and on the other hand know that in their economic surroundings they are not able to offer qualified professionals competitive wages and other working conditions as other economically advanced countries (Avramović 2012; Dumitru 2014). The classical Adam Smith theory says that “every individual endeavours to employ his capital as near home as he can, and consequently as much as he can in the support of domestic industry; provided always that he can thereby obtain the ordinary, or not a great deal less than the ordinary profits of stock” (Smith 1776). The concept of “brain drain” (BD) assumes that in the period of globalization, the circulation and exchange of labour and knowledge are expected (Cenci 2015). The problem is not related to economically less developed countries only but to strong economies as well. For example, in Great Britain already in 1989, 40% of university graduates declared the interest in going to work abroad; they expressed their interest in going to the USA (Schuster 1994).

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Within the framework of the EU, there are no migration limitations for European citizens. A natural circulation of qualified labour therefore appears, the most frequent reasons being better working conditions including higher salaries and, last but not least, the possibility of personal and professional growth. At present, this type of migration could be seen in the countries of the Eastern block (especially Bulgaria, Romania and Poland) where the ability of the country to attract and to maintain talented people is very low (Grecu and Titan 2016). The EU is interested in highly qualified workers from so-called third countries. For a number of the EU countries, ageing and regional decrease in population are typical. To be able to maintain their economies, the countries become dependent on foreign qualified labour. Many qualified migrants come from countries where incomes are very low. Qualified migration often has structural characteristics; at present it concerns, for example, medicine and information technologies (Hopkins and Levy 2012). In general, it is possible to say that the phenomenon of BD is related to all scientific and research areas (Grigolo et al. 2010; Sbalchiero and Tuzzi 2017). Provided the governments would be able to concentrate on remigration instead on immigration, they will reach political and economic effectiveness (Campanella 2015). For the host country, the inflow of qualified workers does not have to be only beneficial. The inflow of the volume of the human capital can have both positive and negative consequences to a certain extent. It can be both in the area of the labour market and in the socio-economic area (Gheasi and Nijkamp 2017; Koshulko 2015). For ensuring positive economic impacts and eliminating negative consequences of migration, the mechanism of a successful assimilation process was identified as most important (Carillo et al. 1999). Migration of qualified workforce has negative consequences not only for the country of origin and for the host country but also for the migrant. In this connection, we speak about so-called over-education, when, in case the migrant accepts less qualified work, the loss of human capital appears. This human capital was created and assessed in the country of origin, and it was consequently lost in the host country. They state that, for example, among the immigrants in Greece, the effect of “over-education” was noticed twice as frequently as in native Greek people (Lianos 2019). The reply to the question of why highly qualified workers do not stay in their home countries is being sought by many authors. They come to very similar conclusions. The determining factors for BD are tertiary education, professional preparation and the fact that the quality of the educational system does not provide good opportunities from the point of view of employment. That is why they leave their country and decide to continue in their development there, where they can develop their education and find a well-paid job (Pernia 1976; Ienciu and Ienciu 2015; Oladeji and Gureje 2016). A number of authors have tried to understand the causes for the departure of young, qualified people from the country; this problematics has been examined for a long time (Sukhatme and Mahadevan 1988; Davenport 2004; Bake et al. 2008; Humphries et al. 2013; Štefančiková 2016).

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Brain Drain as an Opportunity

The process of migration can signify both loss and gain at the same time. The countries of origin will suffer from the loss of highly qualified workers, whereas the accepting countries will benefit from acquiring highly qualified workers without investing in their education (Grecu and Titan 2016). This is why some countries try to create various programmes (both motivational and restrictive), which would limit BD. George warns that limiting migration of highly qualified workers would lead to the loss of productivity and innovation (George et al. 2012). Other authors also consider migration of qualified workers as one of the key elements of economic growth and innovation (Boucher and Cerna 2014; Portes and Celaya 2013; Bailey and Mulder 2017; Regets 2007; Ha et al. 2016). Le (2008) suggests using the term “brain circulation” instead of brain drain, since this characterizes the benefit in the transfer of technologies across borders much better, in both directions, and he also points out the benefit in the area of research and development, because it brings about the possibility to learn from the foreign technological base. Hickey (2016) and Stănică (2013) emphasize the positive role that may be played by international migrants in the development of their home countries and communities. A great economic benefit may be the financial transfers that the migrating employees realize in the benefit of their family who stayed in the country of origin (Faini 2007). Especially in developing countries, such financial transfers have a huge potential in the area of diminishing the poverty and the development of local investments because these transfers are resistant to economic regression. The phenomena that were called brain drain in the 1920s gained the image of a brain gain in the 1990s (Faist 2008; Ailenei et al. 2015; Grigolo et al. 2010). A great benefit for the economy and the further development of the country is especially a temporary migration, i.e. when highly qualified workers return to their home country after a certain time. They bring not only the original investments in their education but also other know-how and experience acquired abroad. The question is how large the rate of migrants returning to their country of origin is (Jensen and Pedersen 2007). Some studies are sceptical in this regard, especially when the economic or political situation in their country is dismal and the discrepancy between the supply and demand for highly qualified workers continues (Vizi 1993; Labrianidis and Vogiatzis 2013). If we speak about young, educated people who leave for a foreign country where they acquire new experience and subsequently return to their home country, this phenomenon is very positive and beneficial. But when a young person leaves their home country for good, his or her departure means a significant economic and social loss. The willingness to move abroad (both for some time and forever) is influenced by a number of factors. The usual interest of researchers is to find out which motives influence young people to go abroad. The aim of this study, on the contrary, is to ascertain which barriers influence the decisions of young people about going abroad, i.e. what are the stabilizers for young and highly qualified workers.

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3 Methodology The research sample (n ¼ 503) was made up of students of economic specializations who study at regional public universities in Germany (n ¼ 248) and in the Czech Republic (n ¼ 255) in 2018. The data were acquired by means of written questionnaire method. The base issue was composed of 12 factors that could be considered possible barriers (i.e. barriers that prevent people from moving abroad). These factors are listed in Table 1. In each of the possible barriers a through i, the respondents evaluated their potential significance on the Likert scale of 7 grades, where 1 means “it definitely is not a barrier” and 7 means “it definitely is a barrier”. The respondents were also asked the question: “Would you ever like to work abroad?” For this question, four possible replies were available (probably not, definitely not, probably yes and definitely yes). The main research question was formed in this manner: Which of these presented barriers influence decisions about going to work abroad the most? This influence is described using the stepwise forward logical regression model. The model of logical regression was created for Czech and German students separately. Thus we were able to respond to the second research question: Are the factors creating the greatest barriers for going abroad similar in these neighbouring countries? For collecting the data, MS Excel and MS SPSS were used, and for evaluating it, the method of stepwise forward logical regression was employed.

Table 1 List of factors (barriers) that could be a barrier for going abroad a b c d e f g h i j k l

Language barrier Fear of the unknown, foreign environment Fear of the lack of information No contacts, nobody providing help at the beginning Fear of the loss of contact with the home environment (family, partner, friends) Do not want to leave the house/flat where I live Do not want to leave the region where I live Do not want to leave the country where I live Fear of cultural differences in a foreign country Fear of not being accepted by foreign colleagues (national discrimination) Fear of necessary administration when entering the job Fear of high costs of living

Source: Own processing

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4 Results To be able to respond to the research question, it was necessary to analyse the results acquired by the respondents from the Czech Republic and the results from Germany and subsequently to carry out the comparison of presented findings.

4.1

Analysis of the Evaluation of Barriers: Czech Republic

Figure 1 shows a box plot providing the evaluation of the size of each barrier by Czech students. It explains that five barriers, a (language barrier), b (fear of the unknown, foreign environment), c (fear of the lack of information, d (no contacts, nobody providing help), e (fear of the loss of contacts) and i (fear of high cost of living), were assessed with at least a 5 by 50% of students. These barriers were evaluated as higher. On the contrary, two barriers, g (do not want to leave the region where I live) and i (fear of cultural differences), were evaluated with 3 at most by 50% of students. In all barriers, the whole scale of 1–7 was used by respondents. Table 2 presents descriptive characteristics of these barriers. The values of arithmetic averages show that Czech students see as the greatest barrier for their

Fig. 1 Box plot—survey of evaluations of individual barriers (Czech students). Source: Own processing

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Table 2 Descriptive characteristics of the evaluation of barriers by Czech students

abarrierCz bbarrierCz cbarrierCz dbarrierCz ebarrierCz fbarrierCz gbarrierCz hbarrierCz ibarrierCz jbarrierCz kbarrierCz lbarrierCz

Arithmetic average 4.435294 4.635294 4.568627 4.894118 4.686275 3.909804 3.372549 3.776471 3.34902 3.615686 4.023529 4.45098

Median 5 5 5 5 5 4 3 4 3 4 4 5

Mode 5 5 5 6 7 3 3 4 4 3 4 5

Standard deviation 1.878895 1.744601 1.555469 1.688475 1.87493 1.797774 1.740676 1.757166 1.722863 1.757254 1.623958 1.608463

Sample variance 3.530245 3.043631 2.419484 2.85095 3.515362 3.23199 3.029952 3.087633 2.968257 3.087942 2.637239 2.587155

Kurtosis 0.86126 0.52152 0.22076 0.22768 0.84689 0.94539 0.92668 0.93323 0.79248 0.99439 0.8031 0.39446

Skew 0.47081 0.57153 0.63499 0.78697 0.47306 0.06466 0.199879 0.02358 0.290781 0.161443 0.16056 0.51441

Source: Own processing Table 3 Comparing declared interest in working migration (relative frequency) Czechs Germans

Definitely not 13 15

Probably not 50 61

Probably yes 31 22

Definitely yes 7 2

Total % 100 100

Source: Own processing

eventual migration barrier d (no contacts, nobody providing help). On the contrary, i (fear of cultural differences in a foreign country) represents the smallest barrier. The greatest variability of responses was found for barriers a (language barrier) and e (fear of the loss of contacts, family, partner, friends), and the smallest variability was for barrier c (fear of the lack of information). The barriers influence their decisions about possible migration were also found. The declared interest in working migration is depicted in Table 3. The responses to the question “Would you ever like to work abroad?” were brought together (e.g. probably not and definitely not, probably yes and definitely yes). In this way, we got a dichotomic variable. To describe the dependence of this variable on the barriers and on the age, stepwise forward logical regression was used. So, the explained variable was the willingness to ever work abroad, and explaining variables were age and barriers stated in Table 1. The results of the stepwise forward logical regression for the set of Czech students are given in Table 4. It is seen from Table 4 that on the basis of logical regression, two explaining variables were identified, barrier a (language barrier) and barrier h (do not want to leave the country where I live). The gender was, in this case, not identified as an explaining variable. The values of Wald statistics in Table 3 suggest that all coefficients in barriers a and h in the model are statistically significant. The model shows that the greatest barrier for deciding about going abroad is, for Czech students, barrier a (language barrier). This influence is indirect; the more consent the students

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Table 4 Variables in the equation—Czech students Step 1a Step 2b

hbarrierCz Constant abarrierCz hbarrierCz Constant

B 0.689 2.848 0.430 0.676 4.784

S.E. 0.098 0.414 0.093 0.105 0.664

Wald 49.330 47.315 21.352 41.110 51.965

Df 1 1 1 1 1

Sig. 0.000c 0.000c 0.000c 0.000c 0.000c

Exp(B) 0.502 17.258 0.650 0.509 119.573

a

Variable(s) entered on step 1: hbarrierCz Variable(s) entered on step 2: abarrierCz c Is significant on 0.05). These findings have a meaning that co-opetition tendency of firms does not reflect the differences in general levels of entry and exiting density in industry. In the case of entry of rivals into industry, age and niche width in customer diversity of firms in real estate industry have lower level significance ( p < 0.1). Aged firms avoid getting co-opetition in this industry. The firms that have large

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Table 4 Factor and reliability analysis Factors Entry of rivals into industry

Items Being motivated to get into co-opetition with a new larger rival in case it enters into industry Being motivated to get into co-opetition with pre-existing competitors in case of entry of larger rivals into industry Co-opetition motive in case of entry of new rivals into industry Being motivated to get into co-opetition with a new smaller rival in case it enters into industry Being motivated to get into co-opetition with pre-existing competitors in case of entry of smaller rivals into industry Exiting of Being motivated to get into rivals out of co-opetition with surviving comthe industry petitors in case of exiting of smaller rivals out of the industry Being motivated to get into co-opetition with surviving competitors in case of against exiting of larger rivals out of the industry Co-opetition motive in case of exiting of rivals out of the industry Total variance explained Kaiser-Meyer-Olkin measure of sampling adequacy Bartlett’s test of sphericity: approx. chi-square df Sig.

Factor loading 0.879

Explained variance 39.689

Cronbach’s alpha 0.834

26.230

0.775

0.828

0.740 0.738

0.674

0.874

0.833

0.709 65.919 0.609 1168.54 28 0.000

Source: Authors’ own calculations Table 5 Perception of entry of rivals into industry as a driver for co-opetition in both industry Categories General Age Size Niche width in product diversity Niche width in customer diversity Source: Authors’ own calculations  p < 0.1, p < 0.05, p < 0.01

Kruskal-Wallis H test chi-square Real estate Construction 0.084 8.469 4.974 0.451 1.677 0.014 15.651 3.521 2.771

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customer diversity tend to get co-opetition more than the firms that have narrow customer diversity. We could not find a significant difference in size and niche width in product diversity of real estate firms ( p > 0.05). The niche width in product diversity of firms in construction industry has high-level significance ( p < 0.01). The firms that have narrow product diversity tend to get co-opetition more than the firms that have large product diversity. Albeit of lower level significance, niche width in customer diversity of firms has significance ( p < 0.1). Firms that have narrow customer diversity tend to get co-opetition more. We could not find significant difference in age and size of construction firms ( p > 0.05). Then we analyzed the co-opetition tendency against exiting of rivals and presented the findings in Table 6. In the case of exiting of rivals out of the industry, we could not find a significant difference in age, size, and niche width in product and customer diversity of real estate firms ( p > 0.05). Nevertheless, age and niche width in customer diversity of construction firms has significance (resp. p < 0.1, p < 0.01). The firms that have large customer diversity tend to get co-opetition more than the firms that have narrow customer diversity. Moreover, aged firms avoid from co-opetition, while newly founded firms tend to get co-opetition in this industry. Briefly, the co-opetition tendency of firms in both industries does not reflect entry and exit movements in general. In depth, size and age of firms have no significance to clarify co-opetition among firms in a case of entry into and exit of rivals out of the industry. We found that niche width is partially important for firms in both industries. Especially niche width in customer diversity has significance in both. However, this does not reflect the industrial difference. In both industries, the firms that have narrow customer diversity tend to get co-opetition more.

Table 6 Perception of exiting of rivals out of the industry as a driver for co-opetition in both industries Categories General Age Size Niche width in product diversity Niche width in customer diversity Source: Authors’ own calculations  p < 0.1, p < 0.05, p < 0.01

Kruskal-Wallis H test chi-square Real estate Construction 0.928 3.663 7.990 1.834 1.062 1.216 2.061 2.529 11.860

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9 Discussion and Concluding Remarks The population ecology approach assumes that organizations face different levels of competition depending on the organizational niches which they target (Baum and Singh 1994). Co-opetition, on the other hand, assumes that organizations collaborate with rivals to offer a standalone product or to develop new market niches together to strengthen their respective positions (Bengtsson and Johansson 2012). From this perspective, two approaches overlap, and the population ecology approach provides a base to explore and explain co-opetitive relationships among organizations. In this manner, we designed this research by comparing the two industries: the real estate industry that has relatively lower entrant and exiting rates and the construction industry that has relatively high entrant and exiting rates. We assumed that in the construction industry, mortality rate is high, while in the real estate industry mortality rate is low. We analyzed the demographic features of firms and co-opetition tendency against entry into and exiting of rivals out of the industry. In the high level of entrant and exiting rates, the firms in the construction industry might cooperate with rivals to expand their small-sized shares by setting scale alliances (Dussauge et al. 2000). On the other hand, age has importance for co-opetition relationship in construction industry. This means that newer firms are much more inclined to get into co-opetition compared to older firms and when they are disquieted by a high-level entrant rate. Therefore, they may use co-opetition as a survival strategy in the increasingly crowded industry. The results support the ideas that age of organizations is an important factor for co-opetition motive (Bengtsson and Johansson 2012). Clearly, product and customer diversities representing niche width (Baum and Singh 1994) is important to get co-opetition for the firms in the construction industry. The firms that have narrow width niche tend to get co-opetition more than others against entry of rivals. This finding support the ideas of Bengtsson and Johansson (2012) that the firms establish co-opetitive relationships with large competitors to overcome the liability of their smallness and newness. In conclusion, we integrated two different approaches and tested them empirically. We present the hypothesis results in Table 7. We propound that density difference between industries integrated with age, size, and niche width of organizations does not present a clear explanation for co-opetitive behavior of organizations. In general, we performed quantitative analysis, and we have obtained a limited number of results that are supportive of the explanatory potential of population ecology approach for co-opetition relationship. Although it can emanate from the constraints of the study, the results indicate that arguments of the population ecology fail to provide a complete picture to purely explain co-opetition. In the future, researchers should address the methodological constraints to study the population ecology approach and co-opetition behaviors together.

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Table 7 Results of hypotheses Hypotheses H1: In the case of entry of rivals into industry, co-opetition tendency of firms in construction industry is much more than that of firms in real estate industry H1a: In the case of entry of rivals into industry, co-opetition tendency of new firms in construction industry is much more than that of aged firms H1b: In the case of entry of rivals into industry, co-opetition tendency of small firms in construction industry is much more than that of large firms H1c: In the case of entry of rivals into industry, co-opetition tendency of firms that have narrow niche width in product diversity is much more than that of firms that have large niche width in product diversity in construction industry H1d: In the case of entry of rivals into industry, co-opetition tendency of firms that have narrow niche width in customer diversity is much more than that of firms that have large niche width in customer diversity in construction industry H2: In the case of exiting of rivals out of the industry, co-opetition tendency of firms in construction industry is much more than that of firms in real estate industry H2a: In the case of exiting of rivals out of the industry, co-opetition tendency of new firms in construction industry is much more than that of aged firms H2b: In the case of exiting of rivals out of the industry, co-opetition tendency of small firms in construction industry is much more than that of large firms H2c: In the case of exiting of rivals out of the industry, co-opetition tendency of firms that have narrow niche width in product diversity is much more than that of firms that have large niche width in product diversity in construction industry H2d: In the case of exiting of rivals out of the industry, co-opetition tendency of firms that have narrow niche width in customer diversity is much more than that of firms that have large niche width in customer diversity in construction industry

Result Not significant Rejected Not significant Accepted

Accepted

Not significant Accepted Not significant Not significant Rejected

Source: Authors’ own calculations

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Pivoting Strategic Business Approaches in the Strategic Business Advice Process: Lessons Learned from Case Studies of Small Innovative Firms Katarzyna Łobacz

Abstract Strategic business advice is perceived as the way to meet challenges related to business development and strategic change, with direct impact on business growth and productivity. Small innovative firms can benefit from it, as they are constantly seeking for new solutions in order to provide better market offers and are willing to achieve higher rents than industry average. Thus, they need to transform their strategic business approaches, including implementation of new products and increasing business model efficiency. In the paper, the process of business advice is explored, and critical stages of this process are distinguished and explained. The model of strategic business advice process is built based on case studies analysis, in which a process of pivoting business strategies has been especially explored. Therefore, two case studies illustrating pivoting (A) strategic idea of the product and (B) strategic business model approach are presented. It is concluded that strategic business advice is a powerful tool when pivot in company’s strategic orientation is necessary, but several conditions have to be met for the change to happen. Keywords Business advice · Small firm · Innovative firm · Strategic change · Business advice process · Case studies

1 Introduction The use of strategic business advice by small enterprises is seen as one of the ways to meet challenges related to the comprehensive management of the company and its development (Hurmerinta-Peltomäki and Nummela 2004; Hinton and Hamilton 2013). The role of strategic business advice is to facilitate stepped change related K. Łobacz (*) Department of Business Management, Institute of Management, University of Szczecin, Szczecin, Poland e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2020 M. H. Bilgin et al. (eds.), Eurasian Business Perspectives, Eurasian Studies in Business and Economics 15/2, https://doi.org/10.1007/978-3-030-48505-4_7

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to business growth and productivity. Small innovative firms can benefit from it, as they are constantly seeking for new solutions in order to provide better market offers (Pellegrino and Piva 2020) and are willing to achieve higher rents than industry average and thus need to transform their structures and strategic business approaches (Łobacz 2018a). Due to the size, small firms have, by definition, limited internal resources, and very often they also suffer from less favorable access to managerial knowledge, and use of external expertise seems to be for them especially beneficial (HurmerintaPeltomäki and Nummela 2004; Hinton and Hamilton 2013; Rodríguez-López and Souto 2020). Several authors prove that effective use of external knowledge resources affects positively small firms competitiveness (Caloghirou et al. 2004; Kang and Kang 2009; Łobacz 2015). Despite the research results suggest that use of business advice is growing among small innovative firms (Bennett 2007; Johnson et al. 2007; Stawasz et al. 2018), limited impact and satisfaction from advisory services is reported at the same time (Bennett and Robson 2003; Chrisman et al. 2005; Johnson et al. 2007; Yusuf 2010; Stawasz et al. 2018). But, the results of qualitative studies, when strategic business advice is separated from operational-level advice (in which typical problems related to day-to-day operations are addressed), suggest that strategic advice brings lots of benefits for entrepreneurs because of the ability to change business strategies and thus is highly rated by entrepreneurs (North et al. 2011; Głodek 2017). The literature however is scanty in explanations on how the strategic change really happens and what are necessary conditions of effective collaboration with business advisor from the perspective of an entrepreneur. Therefore the aim of this study is to provide an in-depth insight into the process of strategic business advice with emphasis on how strategic business approaches change as a result of a process. The context and entrepreneur(s)-advisor(s) collaboration scheme is described in detail based on analysis of 50 case studies from several European and Asian countries, two out of which were selected as illustrations of the process. Case studies have been elaborated based on interviews with entrepreneurs, owners of small innovative firms, so the perspective of advice receiver is emphasized. The analysis starts with literature review, in which main thoughts regarding business advice for small business are highlighted. Here the differentiation between strategic and operational type support is made, and the process perspective is especially considered. This section is followed by presentation of author’s own study, where a model of entrepreneur-advisor collaboration in the strategic advice process is proposed and supported by case studies. The analysis is concluded with applicability of presented model and reflections related to business advice process management.

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2 Strategic Business Advice for Small Innovative Firms: A Literature Review 2.1

Business Advice and Strategic Business Advice

Business advisors are perceived as external source of knowledge, which is necessary to the firm in the specific context. There are various reasons for using of business advice, to include lack of internal resources (people, time, knowledge), requirement of very specialized piece of expertise, or need for external look at the internal processes or competitive environment context. Therefore business advice has many different forms and includes direct provision of knowledge, delivery of expertise documentation, or even training, coaching, or mentoring. Thus a broad definition of business advisor seems to be useful when the perspective of small innovative firms is regarded. Here, business advisors are perceived as “individuals, whether self-employed or employed within private or public organization, who use their knowledge to add value to firm’s business activity through the provision of short or long term support on the operational or strategic level” (Łobacz and Głodek 2015, p. 488). Strategic level assistance is what distinguishes strategic business advice from all other kinds of support. Strategic advice covers much more complex processes than advisory services focused on simple information transfer or standard business operations, as it addresses strategic problems of business management or strategic elements of business, i.e., market offer, business model, etc., which determine company’s survival and growth. It encompasses an in-depth view, diagnostic activity, and face-to-face support, which is required to facilitate strategic change and so is specified by some authors as transformational assistance (North et al. 2011).

2.2

Strategic Business Advice for Small Innovative Firms

Small innovative firms can be defined as economic units constantly looking for new solutions in order to improve or innovate existing market offers and thus make them more valuable for their current or potential customers. In respect to this, they are looking for rents higher than typically established for the product line or sectors. Based on those conditions, small innovative firms can be regarded as growing enterprises being in the constant development process (Łobacz 2018a). Small size of these companies limits additionally their managerial and staff resources, with direct consequences in involvement of owners-entrepreneurs in strategic, development-related, and decision-making (Łobacz and Głodek 2015). Although small innovative firms constitute a relatively small group among all companies present in economy, huge differentiation of business advice provided specially for those enterprises was observed. Research studies provide evidences that small firms need different types of advice and thus use various types of advisors to

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solve particular problems they face in the process of business development (Bennett and Robson 1999). The problems addressed relate to both operational and strategic level of company management (North et al. 2011), which partly influence the type of formal behavior in relation with advisor (Strike 2012). The research studies explore mostly formal business advice—the advice which was officially contracted—but it was documented that small firms’ owners frequently use informal knowledge sources, which include friends and family innercircle advisors (Soriano and Castrogiovanni 2012), as well as accountants, banks, suppliers, business partners, and sometimes also customers (North et al. 2011). This relates particularly to young firms (operating for less than 3 years) and firms transforming their processes or strategic orientation (North et al. 2011). At the same time, it was observed that formal advice often follows informal knowledge acquisition (North et al. 2011), although the distinction between formal and informal assistance is not clearly cut and its perception depends on circumstances in which the advice is provided (Robson et al. 2008). Nevertheless it is argued that the use of strategic advice by small businesses is restricted to a relatively limited group of small firms (North et al. 2011). Various reasons are given for that by consecutive authors. From the entrepreneur’s perspective, these include moral hazard threat (Hjalmarsson and Johansson 2003), independence loosing threat (Curran et al. 1993), and potential power imbalances (Dyer and Ross 2007). Business advice for small innovative firms is thus perceived as challenging, as there exist high requirements in terms of knowledge as well as personal and professional experience. Mole et al. (2013) highlight that entrepreneurs managing small firms make lower than optimal use of the advisory services that are available and distrust of small firms’ owners towards the quality of advice is here one of the most important obstacles (Scott and Irwin 2009; North et al. 2011). Trust is seen as a factor enabling the implementation of the advice process and facilitating the control of the direction in which the advice relationship is heading (Bennett and Robson 2004). A high level of trust is critically important for strategic business advice, as it allows the entrepreneur to share sensitive information about the company and enables his/her deep involvement in the process (Rind Christensen and Klyver 2006), when results at the beginning of the process are not easily anticipated and clearly defined. This is related to both competence-based trust and goodwill trust, which are both critical for the process (Klein Woolthuis et al. 2005; Łobacz et al. 2016).

2.3

Strategic Business Advice in the Process Perspective

It is argued that in the strategic advice process, the advisor acts as a change agent, who acquires knowledge which helps him gaining an understanding of the company and the context in which it works and participates and then takes part in creation of distinctive new knowledge, specific for this particular firm, and transformed into its strategic asset (Rind Christensen and Klyver 2006; Łobacz et al. 2015). Thus the

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advisor acts as a change process participant. This position is opposed to an expert role, who is assumed to “know what” knowledge and also to “know how” this knowledge should be transferred to the firm (Rind Christensen and Klyver 2006; Łobacz et al. 2015). In the process perspective, strategic business advice is recognized as a sequence of actions in which an entrepreneur and business advisor jointly participate in order to generate solutions able to solve inner strategic problems of the company. In some literature studies, when the perspective of an entrepreneur is regarded, it is referred to utilization of business advice, as opposed to use, which is associated to operationallevel advice (Łobacz et al. 2015). Thus long-term and trust-based relation between an entrepreneur and business advisor is regarded as trigger of multidimensional knowledge flow and new ideas generation, selection, and adjustment to the context of particular firm (Łobacz et al. 2015, 2016). As strategic level support is different from day-to-day advice provision, different behaviors are expected to appear when strategic business advice is considered as opposed to operational advice. Also actions included in the advice process and their sequence can be assumed as different from typical advice process related to operational problems. Based on the literature studies, it can be assumed that strategic business advice has the ability to design and implement a strategic change and pivot strategic aspects of business; it is not clear however how does it really happen and what are measurable outcomes of the process.

3 Methodology In order to analyze the phenomenon of strategic business advice process for small innovative firms, an explorative empirical research has been applied, based on multiple case study methodology (Yin 1989).The individual in-depth interviews have been conducted with 50 small business owners, who have used business advice within the last 3 years. In order to ensure freedom of expression of the interlocutors, the formula of narrative interview was used in the data gathering process. The interview has been conducted based on interview scenario, in which development context of the company and process of business advisor assistance have been especially regarded. Small business was defined as a company employing up to 50 people in conjunction with the condition that owner-entrepreneur takes strategic decisions by him (her)self and that he(she) has personally participated in the advice process. The study was conducted in the years 2014–2018 on a group of small innovative firms located in several European and Asian countries (Poland, Germany, Italy, the UK, Indonesia, and Vietnam). The firms represented different knowledge-intensive sectors (e.g., IT, health, biotechnology, marketing services) and diversified stages of growth (start-up, early growth, and development). The research process started in Poland and has been extended in the later stages in countries with more mature small

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business sectors (Germany, the UK, and Italy). Then case studies from Asian countries have been used to validate the model.

4 Results 4.1

Description of the Process of Strategic Business Advice

In case studies analysis, the emphasis has been placed on the process through which an entrepreneur goes in line with business advisor, whenever good results and satisfaction from advice provided have been reported. The analysis revealed that every time within the process, there was a deep trust relation and long-standing collaboration between an entrepreneur and business advisor. Also the process itself was very specific, and its subsequent stages could have been visibly distinguished. Therefore the process could have been described as follows: diagnosis of the problem and searching for a solution, “coincidental” finding a proper business advisor, conditions agreement and contracting, re-diagnosis of the problem to solve, joint analysis of the problem and possible results, and review of the result/ process and payment. The process has been presented on the Fig. 1. It has been broken down into four distinctive phases: ignition phase, initial phase, pivoting and solution generation phase, and summary phase, all of which were critical for the process. Ignition Phase For the process to start, awareness of the problem must appear, just as the willingness to look for a solution. It does not mean necessarily that business advisor is identified as the one able to deliver knowledge, which can help to solve the problem. Diagnosis of the problem and process of searching for a solution take place. At this stage the diagnosis of the problem is usually incorrect, as it is based on visible symptoms, rather than problem deepen in the company itself. Difficulties in the correct diagnosis result from lack of knowledge of an entrepreneur or difficulties in getting the wider perspective resulting from vital involvement in day-to-day operations. While searching for a solution, entrepreneurs turn to friends and family circles or business communities, look for experts, or take a specialized training in order to be able to solve the problem by their own. When they fail, sometimes coincidentally they meet an advisor, who is able to deal with deepen problems of the company. Sometimes, after several sessions with business advisors, they give up, keeping in mind a low evaluation of business advice usability. Initial Phase “Coincidental” finding a proper business advisor means that several circumstances must happen together for the right people to be met. These are usually people who have the knowledge related to the problem. But, at the same time, good relationship and trust are variables, which determine that the strategic business advice process will take place. An active involvement of an entrepreneur in searching for a business advisor is not necessary—it usually helps and accelerates the process, but it doesn’t guarantee success. In order to find an advisor,

Entrepreneur

Process stage participants Entrepreneur + Advisor

‘Coincidental’ finding a proper business advisor

Initial phase

Entrepreneur + Advisor

Conditions agreement & contracting

Fig. 1 Strategic business advice process. Source: authors’ own study

Diagnosis of the problem and searching for a solution

Stage of the process

Ignition phase

Entrepreneur + Advisor

Re-Diagnosis of the problem to solve

Entrepreneur + Advisor

Joint analysis of the problem and possible results

Pivoting and solution generation phase

Entrepreneur + Advisor

Review of the result/process & payment

Summary phase

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entrepreneurs have to start an open dialog, in which they articulate problems they observe. An open dialog, in which some ideas appear, is a trigger for deepen collaboration. Once collaboration is perceived as beneficial, both entrepreneur and business advisor have to agree conditions of further cooperation (conditions agreement and contracting). This process is usually informal, and if any formal elements are agreed, they are very general. It happens very often that no contract regulating both sides’ responsibilities and benefits is signed. If the contract is signed, it is not specifically related to the problem. Also the payment is fuzzy defined. Usually the payment is dependent upon future development of the company, which means that both entrepreneur and advisor take risk related to results of advice jointly and severally. Pivoting and Solution Generation Phase An open dialog, which starts between entrepreneur and business advisor, results in redefinition of the problem (re-diagnosis of the problem to solve). Appropriate questions are being asked and problems deepen in a company are gradually revealed. In order to achieve that, several actions has to be done, including internal meetings, interactions with customers, interactions with suppliers, or financial statements exploration. Information has to be revealed and honesty is a necessary element of success. Problems are unknown or fuzzily defined; they are also changing dynamically and appearing as results of decisions made and actions executed with no predefined goal to be achieved. At this stage a trust relationship is strengthen. This relates to both competence trust and goodwill trust. Redefinition of the problem leads to pivoting of strategic focus of an enterprise. This happens through joint analysis of the problem and possible solutions. Problems addressed at this stage relate to core products, business models, interactions with external environment, style of management, and internal process organization and lead to substantial improvement of firm’s competitive advantage. Changes at this level usually require a transformation (pivot) in the strategic orientation of the company. This kind of change takes place gradually within the process, which is long-standing and last for several months. Implementation of change requires strategic, direct, and deep interaction between entrepreneur or team of entrepreneurs and advisors. In order to solve a problem, a combination of knowledge of all owners-entrepreneurs and team of advisors with different backgrounds is necessary. As a result a new company-specific knowledge is created. Necessary elements of the process are openness of the entrepreneurs in admitting knowledge gaps and openness for advisors’ suggestions, their limited resistance to share problems and knowledge, and readiness for change. At the same time, effective collaboration requires from business advisor a flexible approach to implementation of changes and generally to cooperation and work, openness for discussion, willingness to share knowledge and experience while the reward is not assured (perception of different sources of reward than direct payment for the service provided), willingness to do something meaningful and to do the work well, personal interest in successful development of the project, entrepreneurial spirit, and visionary approach. Personal deep relation or business relationship causes that the advisor

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is interested in working harder than agreed, especially when determination and deep involvement of the entrepreneur are visible. Summary Phase At the end of the process, substantial benefits are observable. It is time for review of the result/process and payment. Strategic reorientation of the company impacts higher business rents in a long run. Cost and benefit ratio is for an entrepreneur very advantageous. At this stage business advisors usually receive their rewards. Reward is usually based on long-term future collaboration, when advisors receive company’s shares for their work, or become co-owners of managers of the firm. Value of benefits depends on knowledge employed in the process and collaboration efficiency. The case studies illustrating this process perspective, emphasizing the process of pivoting strategic business approaches, have been summarized below.

4.2 4.2.1

Strategic Business Advice Process: Case Studies Case A: Pivoting Strategic Idea of the Product

The case concerns a small firm which offers a cloud-based IT software supporting company management targeted at large enterprises (B2B IT service). The firm was founded by three entrepreneurs after receiving of financial support from publicprivate seed capital fund. All entrepreneurs had a technical education background in IT-related field. Additionally 10 years’ work experience in large companies, as software developers and managers, gave to entrepreneurs an insight into unmet market need, as well as an experience to develop, manage, and sell IT products. Before founding a company, they developed an innovative product—a software dedicated to support large companies administration processes. Although the product has been positively validated from the technical perspective and so beta users were satisfied with using it, there was a problem with selling the product—with convincing large organizations to pay for the final product. The primary source of advice for the company was board of supervisors, formation of which was required by a seed fund, stated literally in the investment contract. The level of business knowledge of its members was, however, not very highly rated by the entrepreneurs, so for a long time, they could not receive a good advice on the problem they experienced. For reasons independent from the firm and investment fund representatives, the composition of board member had to change. As a result, the entrepreneurs could propose to replace them with people of their choice. But it was not easy for them to find appropriate advisors. (“We looked for them intensively and we established a range of different contacts. In Poland there are very few cloud start-ups which have succeeded.”) Eventually the entrepreneurs found advisor with appropriate industry-specific business experience which was agreed as the most important criterion. (“At that moment, we had constructed the board of supervisors, in which there were several great mentors experienced

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specifically in cloud-based applications. (. . .) gradually we exchanged them [the members of the board] for people who have experience in sales, in the development of start-ups and the board have become very, very valuable.”) After several months the board of advisors’ members has been replaced, and then effective collaboration has started. During the meetings, at which an intensive interaction was observed, the right questions were asked, and thus the real problems were sequentially revealed, and then, the constructive debate on the real business problems of the firms has started. Business advisors served as coordinators of problem extraction process, focusing the discussion on critical issues. (“The board of advisors helped us not to get distracted by issues that were not critical to the firm’s core business.”) First of all, the advisors concentrated the discussions on the future, when thinking about problems that the entrepreneurs were facing. Naturally marketing problems had to be addressed; in particular advertisement and distribution channels were especially regarded. The entrepreneurs had many ideas on how the sales can be improved, resulting from their own knowledge and experience—all of them directed towards intensification of product and company promotion. (“At that time (. . .) we had a lot of ideas, for example: let’s find a company in the neighboring countries which will help us distribute our product, or we may find people to affiliate marketing (. . .) and maybe let’s go to a few conferences.”) But the advisors proposed to look at the problem differently: to focus strongly on website marketing and check if the product is saleable at all, or change in the product core functionalities if necessary. (“We received a strong message that we needed first to learn to sell our product directly from the website. If it proves that the product is saleable, then it will be worth trying to start sequentially building the network of sales channels. Before doing this, it must be checked whether the product has to be tailored to the specific audience.”) After doing this the entrepreneurs realized that they have to change their business strategy significantly and, most of all, they have to change the product itself. Long-lasting discussions between entrepreneurs and board members lead to identification of actions that should be taken for the problems to be solved. The advisors encouraged the entrepreneurs to contact their customers in person, not online, as they did before. The objective was to hear the voice of the customers directly. When the entrepreneurs met their clients, they realized that their product, to a large extent, was used for different purposes than it was originally designed. They have learnt that the customers valued mostly one specific feature of the software, more than its core function. As results of this investigation were confusing for both entrepreneurs and advisors, the board members decided to test the product themselves. This allowed them to give meaningful feedback on the software functionalities from the external (end users’) perspective but also to actively participate in the process of improved product design at the later stages. (“He [one of the board members] told us: if we make a product for marketing personnel, we cannot listen to engineers and IT people. They are able to generate thousands of ideas of new features that are not really needed. He said: Listen to everybody, not just

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programmers, if you don’t make a product for managing software development. That was a lesson crucially important to us and to the future of the firm.”) As a result, substantial changes in product were implemented. The voices of customers and advisors as external parties were heard and considered. It took several months to redefine a problem and redesign a product. After the marketability of the product has significantly increased, the company could start the market expansion. Increase in sale had a direct impact on financial benefits which followed. A process of strategic business advice of case study A has been summarized in Table 1.

4.2.2

Case B: Pivoting Strategic Business Model Approach

The case study explores collaboration between a group of advisors and a company delivering paramedical and analytical services in the veterinary sector. The entrepreneur was an experienced researcher, but his business practice was very limited. The cooperation between the entrepreneur and the advisors started when the entrepreneur was searching for the funding options to commercialize his invention— specific gene test for animals. He needed an assistance to prepare a grant proposal to apply for public investment with the aim to found a start-up. Unfortunately after meetings with recommended advisors, he realized that his business idea was not appropriate for the public grant he thought of. But he was advised to look for possibility of venture capital investment, as his technology had high business potential. Before the investment and company founding was possible, the long-standing advisory process took place (lasting in its basic phase approx. 6 months), in which a comprehensive and competitive strategic business concept and thus business model had to be developed. The advice was provided by a group of people (partners in a consulting company) collaborating with a public-private seed capital fund. They had the knowledge related directly to development of new innovative ventures. The open and deep discussions have begun. Many meetings, in which the entrepreneur, two to three advisors, and one seed fund representative jointly participated, were organized. The advisors started with building an awareness of the business context. Firstly they encouraged an entrepreneur to look at his business idea from the broad perspective, just as they did themselves. The discussions were focused on understanding of mutual relationships between possible services to be commercialized, which enable to achieve synergy in the short and long run and further on the potential pathways of new firm development. This process resulted in substantial redesign of service portfolio to be implemented into the market and business strategy relating to how will this happen. A combination of non-innovative, based on common knowledge services addressed to wide markets and innovative, new technology-based, shorter range services was considered as targeted business strategy. The idea standing behind this approach was to create a profitable competitive advantage, at the same time ensuring long-term business sustainability.

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110 Table 1 Case A strategic advice process description: Pivoting strategic product idea Stage of the process Diagnosis of the problem and searching for a solution

“Coincidental” finding a proper business advisor

Conditions agreement and contracting

Re-diagnosis of the problem to solve

Joint analysis of the problem and collaborative results generation

Review of the result/process and payment

Source: authors’ own study

Description The entrepreneurs realized that they have to sell their product more effectively. The problem was that their targeted audience liked the product and was interested to use it for free, but the firm encountered a significant problem with the conversion of free beta users to clients willing to pay for the final product The natural source of business advice was board of advisors that the firm had in hand. Therefore this board of advisors was addressed with problems, which entrepreneurs experienced at this stage Once the problem has been presented, no appropriate answer was found on how the sales can be improved Coincidentally there was a need to change members of the board of advisors. It was a chance to look for different people The entrepreneurs were looking for advisors with specific cloud IT business experience to help them market the product. After a while they realized that sale-related knowledge in line with start-up development experience would be also beneficial in their current position. New people were recruited, and eventually a group of advisors has been involved in the collaboration process Specifically the advisors have become members of board of advisors, which was a legal requirement for the firm. The contract was not specific to the problem. A group of 5–7 people were involved in advisory process at all time When the entrepreneurs stared first working with new board of advisors, they wanted to address the problem on how can they better market the product. The focus was in particular on marketing problems, especially advertisement and distribution channels. They had many ideas on strategies to increase sales and were looking for advice on which one would be the most appropriate But the advisors encouraged the entrepreneurs to consider (by communicating with their customers) how the product is used by the customers. After that the entrepreneurs realized that the product itself requires substantial changes, not marketing The advisors engaged themselves in product testing, which resulted in ability to participate actively in product redesign process. They guided the process by stressing the perspective of end users. After product redesign the marketing strategies have been developed in more detail The advisory process lasted for several months Review of the process was very positive. Main benefits were survival on the market, as well as sale increase. Both entrepreneurs and advisors were equal beneficiaries of the process. As a result the effective collaboration has been continued

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The meetings which laid the foundations for new business concepts were long, complex, and highly interactive. New ideas resulted from rich and open knowledge exchange process between the entrepreneur and advisors. During the sessions many ideas were put forward by both sides, commented on, to become at the end of the basis for the subsequent new ideas. (“It’s hard to determine who the author of a particular idea is. We were sitting in this room, a few people. He asked: what else is on the market, what could be done? Someone thought: the German market is serviceoriented (. . .) and the question [was addressed to the originator]: Can you do it? [answer:] Of course, no problem.”) As a result, a new firm’s business model was reconsidered and significantly rebuild. (“The result of our work was a complete change in the design, from an initial concept of a kind of ‘local veterinary super-clinic’ to the idea of “a laboratory with a national scope.”) No payment was agreed at the beginning of advice process, which started as a very informal. The entrepreneur simply had no financial resources to pay possible advisory fee. At the end of the process, a number of shares of the new company were took up by advisors, who provided assistance in business idea transformation and reshaping of business strategic orientation. A process of strategic business advice of case study B has been summarized in Table 2.

5 Conclusions The conducted analysis allowed to observe a presence of a consistent scheme of strategic advice process provided for small innovative firms. Firms considered represented different technology-intensive sectors and diversified stages of growth (start-up, early growth, and development). Six stages of this process have been identified: diagnosis of the problem and searching for a solution, “coincidental” finding a proper business advisor, conditions agreement and contracting, re-diagnosis of the problem to solve, joint analysis of the problem and possible results, and review of the result/process and payment. Two of those stages have been diagnosed as critical for solution generation and pivoting strategic orientation of the company. Thus it was evidenced that strategic business advice is a powerful tool when transformation of product idea or business model is necessary. But pivot in company’s strategic orientation is possible only if an entrepreneur is open and ready for change. Moreover it seems to be unlikely that the change which result from business advice is possible without a vital involvement of advisors, as it seems to be an output of a group creative process. Strategic business advice can be thus perceived as group activity, in which many actors representing differentiated fields of expertise jointly participate. It was also observed that one of the constraints, which make the strategic advice impossible to start, is the problem that occurs at the very initial stage of the process, that is, diagnosis of the problem, which should be solved with the advisor’s support.

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Table 2 Case B strategic advice process description: Pivoting strategic business model approach Stage of the process Diagnosis of the problem and searching for a solution

“Coincidental” finding a proper business advisor

Conditions agreement and contracting

Re-diagnosis of the problem to solve

Joint analysis of the problem and results generation

Review of the result/process and payment

Source: authors’ own study

Description The entrepreneur was looking for assistance in writing a proposal to apply for a public funding for founding a start-up in the veterinary sector. The business idea was to commercialize a new technology he had invented At first meetings with the potential advisors, he learnt that his case was not appropriate for the public funding he considered. But he was suggested to look for venture capital investment as his technology (know-how related to the veterinary) had a high commercial potential The entrepreneur and his idea were introduced to the group of advisors specializing in new technology-based ventures development. They collaborated with public-private seed fund, which was interested to invest in a new company The advice was provided without any upfront payment. The agreement was to receive shares in the future company if the investment process will be successful. The entrepreneur was not able to pay for the advice process at that stage. A group of two to three advisors and the representative of the seed fund were involved in the process at all times. No specific requirements towards business advisors were specified The collaboration with business advisors started with understanding of scope of services an entrepreneur was able to deliver and a new technology he owned. This was based on long-lasting discussions and market screening. It was decided that initial business model has to be redesigned to ensure market survival and long-term success An entrepreneur along with a team of advisors started regular sessions at which development potential of the new firm, as well as the mutual relation of services and capabilities to achieve synergy, was discussed. The meetings were long and complex. At the meetings many ideas were jointly generated. They were put forward by both sides, commented on, and then became the basis for the subsequent new ideas. In this way the market offer and related business model have been developed At the end of the process, a substantial redesign of the business model was observed. A combination of non-innovative, based on common knowledge services addressed to wide markets and innovative, new technologybased, shorter range services was designed as targeted business strategy. The idea standing behind this approach was to create a profitable competitive advantage, at the same time ensuring long-term business sustainability The process of advice in its basic phase lasted approx. 6 months, and then it took few months more. At the end of the process, the advisors received an adequate number of shares in the company. Thus the collaboration has continued. As a result of the process, the company has been established, and the successful market offer allowed it to grow. Both entrepreneur and group of advisors were beneficiaries of those results

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It was observed that diagnosis of the problem which is made by a firm owner himself is usually wrong (totally or partially wrong). It is because it is based on the observable symptoms occurring at the operational level without consideration of broader strategic perspective. In result, the search for external advice is wrongly addressed and focused around narrowly defined problem. If so, the advisor who knows the answers to the real problem cannot be found. The analysis revealed critical characteristics of the process, to include its highly participative nature of both entrepreneur and advisor and thus the need of their close collaboration, knowledge sharing and joint knowledge creation, and strategic solution generation. The research results deliver an evidence that to some extent this process is “coincidental” and to be effective it requires long-term cooperation (more than 6 months) and time involvement of all parties engaged. Moreover result orientation, openness for new options, and devotion to work on change enhance the probability of success and process outputs. The study supports the results of other studies in the field, providing an additional evidence that trust is an enabling factor of strategic business advice (Bennett and Robson 2004; Scott and Irwin 2009; North et al. 2011; Łobacz et al. 2015). However the use of qualitative methods in conjunction with process approach allowed to look deeper into the phenomenon and determine relations between trusts in the small firm owner—business advisor relation and other aspects of advice process and thus conditions which imply the business advice to take place effectively. Moreover the process perspective makes it visible that attitude towards business advisor, problems and change, changes within the process, and trust relationship is established shortly before and during the re-diagnosis of the problem to solve process stage. It was also evidenced that informal advice is a vital element of the process, after which some form of formalization is included to regulate collaboration process (Rind Christensen and Klyver 2006; North et al. 2011; Łobacz 2018b). Fuzzily defined scope of work at the beginning of the process makes the process more flexible and thus more prone to strategic pivot. Case studies demonstrated how framework agreements for advice services were gradually introduced after understanding that a detailed analysis of the company’s operations and changes at the strategic level was necessary.

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Part III

Marketing

Uncovering Social Media Users’ Emotions Towards Companies Using Semantic Web Technologies Livu-Adrian Cotfas, Camelia Delcea, and Ionut Nica

Abstract In the last few years, online social media networks have witnessed an amazing growth in their worldwide usage, with millions of users constantly publishing messages containing opinions on virtually any imaginable topic, including opinions about companies. Accurately understanding these opinions could provide an almost real-time overview of how the company and its actions are perceived by the general public. While existing approaches used for analyzing the opinions expressed in social media messages commonly limit themselves in discovering the polarity of the messages, expressed as a positive, negative, or neutral value, in the present paper, we use semantic web technologies and natural language processing in order to uncover actual feelings, such as happiness, surprise, or disappointment. The emotions are structured in a hierarchy using an ontology, thus offering the possibility to analyze the overall opinion regarding the company at different levels of granularity. The proposed approach is validated by performing an analysis of the public perception towards four well-known technology companies. Keywords Social media analysis · Semantic web · Emotion analysis · Ontology · Natural language processing

1 Introduction Knowing how customers and the public at large perceive a certain company is a key element for its long-term survivor. The importance of analyzing the customer’s opinion has been discussed in varied domains including higher education (Ghobehei et al. 2019) and airplane travel (Moslehpour et al. 2018), and various assessment approaches have been proposed in the scientific literature. While traditional L.-A. Cotfas (*) · C. Delcea · I. Nica Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, Bucharest, Romania e-mail: [email protected]; [email protected]; [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2020 M. H. Bilgin et al. (eds.), Eurasian Business Perspectives, Eurasian Studies in Business and Economics 15/2, https://doi.org/10.1007/978-3-030-48505-4_8

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marketing studies, such as surveys, can be used to investigate even the most specific aspects related to the public’s perception, they also require both time and effort, frequently providing insights from a limited number of persons (Deák and Hajdu 2013). Other approaches use techniques from the artificial intelligence field, such as agent-based modeling (Delcea et al. 2019), in order to better understand how the interactions between the members of the public affect the perception regarding the company. Online social media networks currently include millions of message published daily on the most diverse topics, many of them containing rich emotion indicators concerning companies and brands (Ghiassi et al. 2013). Such indicators can be uncovered with the help of sentiment and emotion analysis, two important areas of the natural language processing—NLP field (Bello-Orgaz et al. 2016). While sentiment analysis is used to determine a negative, positive, or neutral score, also known as polarity (Aloufi and Saddik 2018), emotion analysis can lead to deeper insights by focusing on the actual feelings, such as happiness, appreciation, or disappointment (Cambria et al. 2012). Closely monitoring the perception of social media users is also important since negative comments regarding a company can easily reach a large number of users and thus need to be addressed as soon as possible (Money et al. 2017). A semantic web-based approach for analyzing social media users’ emotions towards the products and services offered by a company has been proposed in Cotfas et al. (2016). Thanks to using an emotion ontology that structures emotions in a hierarchy, starting from general ones to more particular ones, the public’s opinion can be analyzed at different levels of emotions granularity. In this context, the present paper adapts the approach proposed in Cotfas et al. (2016) with the purpose of analyzing the public’s opinion concerning companies from social media messages, with the help of emotion analysis and semantic web technologies. Moreover, the identified perception can be easily analyzed in the context of the business sector to which the company belongs. The paper is organized as follows. The second section focuses on the semantic web ontologies that form the bases of the proposed approach. The third section of the paper includes the steps taken in order to detect the emotions expressed by social media users. The last section summarizes the paper and introduces possible future research directions.

2 Semantic Web Ontologies The semantic web is an extension of the present World Wide Web that promotes standard formats and exchange protocols, which allow data to be easily shared between organizations using machine-readable formats (Berners-Lee et al. 2001). Ontologies, defined by Borst (1997) as a “formal specification of a shared conceptualization,” are the primary mean of representing knowledge within the semantic web. They provide a common understanding of the concepts, which they

Uncovering Social Media Users’ Emotions Towards Companies Using Semantic Web. . .

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Fig. 1 Main concepts in the company ontology. Source: authors’ own study

structure in hierarchies, described using specialized languages, such as Resource Description Framework (RDF), RDF Schema (RDFS), and Web Ontology Language (OWL). The information stored in the ontology can be retrieved using a specialized language, known as SPARQL (SPARQL Protocol and RDF query language). Ontologies have already been successfully applied in many social media analysis tasks, including detecting trending news and topics (Ejaz et al. 2018), modeling of extreme financial events (Qu et al. 2016), understanding people behavior in an earthquake evacuation scenario (Iwanaga et al. 2011), analyzing how social media users perceive the products and services offered by companies (Cotfas et al. 2019), as well as their opinions regarding the characteristics of the products and services (Cotfas et al. 2016; Kontopoulos et al. 2013). The semantic web classes and properties needed in order to uncover the public’s opinions concerning companies can be classified into several main groups: • Classes and properties used to describe the companies and the company categories • Classes and properties used to represent the social media messages • Classes and properties used for identifying the expressed emotions • Classes and properties that connect the classes in the previous categories in order to enable advanced data analysis Instead of combining all these classes and properties in a single monolithic ontology, a modular approach has been chosen, which facilitates both the integration of existing ontology and, at the same time, the potential reuse of the proposed ontologies for other types of analysis. In order to describe the company and the company categories, a company ontology (prefix com), shown in Fig. 1, has been defined, structured around the classes com:CompanyCategory and com:Company. The class com:Company is used in order to store details regarding the companies that will be analyzed. Thus, the skos:prefLabel property is used in order to define the official company name, the skos:altLabel is used to define alternative names for the company, such as abbreviations, while skos:hiddenLabel is used for unofficial names and abbreviations, hashtags, and mentions that could be used by social media users when expressing their opinions online.

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Fig. 2 Classes and properties used to represent social media messages. Source: based on Cotfas et al. (2016)

While knowing the public’s opinion towards a certain company is important, a much deeper understanding can be achieved by analyzing that opinion in the broader context of all the companies activating a certain business sector. Analyzing the opinion in the context of the business sector is highly important, since certain sectors, such as the oil industry, are generally less well perceived by the general public, compared to others, such as renewable energy sources. In this regard, the com:CompanyCategory class has been defined, for which the skos:prefLabel property is used in order to define the name of the category. For representing the social media messages, the ontology (prefix tw) described in Cotfas et al. (2016) has been chosen, given the fact that it adheres to the recommended modeling best practices, as they are highlighted in Allemang and Hendler (2011). As shown in Fig. 2, the classes tw:Tweet and tw:TwitterAccount, used for representing the social media messages and the social media accounts, form the bases of the ontology. Both of them are linked with classes from well-known vocabularies such as Basic Geo WGS84 (prefix geo), FOAF (prefix foaf), Dublin Core (prefix dcterms), and SIOC (prefix sioc), thus promoting both interoperability and also tapping into the huge volume of structured data available in the Linking Open Data Cloud (Linked Data Community 2018). For example, the geo:location property defined on the tw:Tweet class is used to represent the geographic location where the tweet has been published and is populated using the “coordinates” property returned by the Twitter API. Using dcterms:created, the time at which the tweet has been published is stored, corresponding to the “created_at” property in the Twitter API. In order to identify the emotions expressed in the social media messages, we have chosen to use the ontology of emotional categories proposed in Francisco et al. (2012), given the fact that it structures the various human emotions in a taxonomy

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em:Emotion rdfs:subClassOf

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Fig. 3 Main concepts in the emotion ontology. Source: based on Francisco et al. (2012)

em:Emoon

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Fig. 4 Storing the emotion analysis results. Source: authors’ own study

and has been developed starting from recognized psychological models. Several emotion ontologies have proposed in the scientific literature, such as the ones described in Roberts et al. (2012) and Hastings et al. (2011). The ontology is structured on several levels, going from more general emotions to more specific ones. The most important top-level emotions are included in Fig. 3. For each class, the ontology declares several instances, corresponding to the words that can be used to convey a particular type of emotion. While the original ontology only includes words in Spanish and English, it can easily be extended to include words from other languages. In Baldoni et al. (2011), the authors enrich the ontology with Italian words. The em prefix is used in the rest of the paper to denote classes or properties belonging to this ontology. Additional properties have been defined in order to connect the classes in the previous categories, with the purpose of enabling advanced data analysis, as shown in Fig 4. Thus, the sma:emotion property has been used to associate the detected emotion to a social media message. The social media message is also connected using the sma:company property with the company that is mentioned in the tweet.

3 Emotion Analysis Process The steps required for uncovering the social media users’ opinion regarding a company are presented in Fig. 5 and further described in the following subsections.

Fig. 5 Emotion analysis steps. Source: authors’ own study

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Retrieve Tweets

The relevant tweets can be collected using the APIs provided by Twitter.1 Thus, the Twitter Public Stream API offers access to the vast number of public tweets published every second. In order to retrieve only the tweets relevant for the analyzed companies, filters have been defined using the company name, hashtags, mentions, and all the known acronyms and abbreviations that are used by social media network users, associated in the ontology using the skos:prefLabel, skos:altLabel, and skos: hiddenLabel properties to the instances of the ci:Company class. The language of the filtered tweets is also restricted to English.

3.2

Preprocessing

A key step in almost any social media analysis endeavor consists in carefully preprocessing the messages, since many of them are written in a casual language, abounding in abbreviations, colloquialisms, and emoticons. An in-depth presentation of how preprocessing should be performed in the context of social media analysis is included in Bao et al. (2014). Therefore, during this step, normalization is performed by removing duplicated letters, converting uppercase letters to lowercase, and removing URLs. The resulting text is tokenized, and the individual tokens are afterwards stemmed.

3.3

Emotion Analysis

Since the novelty of the proposed approach consists in using an end-to-end semantic web-based approach for uncovering and analyzing the attitude of the social media users towards companies, we have used the baseline emotion analysis approach described in Cotfas et al. (2016), which only focuses on discovering the emotions that are explicitly mentioned in the text. It should be noted that using a state-of-theart machine-learning or deep-learning emotion analysis algorithm, such as the ones described in Sailunaz and Alhajj (2019) and Dragoni et al. (2018), would not imply any changes to the rest of the proposed approach. As shown in Cotfas et al. (2016), explicit emotions can be uncovered by comparing the stemmed tokens in the preprocessed tweet with the stemmed versions of the words associated to the emotion classes in the emotion ontology. Thus, a tweet can be associated with one or several emotions. While the employed baseline emotion analysis approach is simple, it was nevertheless found to return fairly

1

https://developer.twitter.com/en/docs.html

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Table 1 Identified emotion classes em:Like em:Love em:Fury em:Hope em:Annoyance

Twitter 9 6 1 0 0 16

Microsoft 7 6 2 1 0 16

Google 5 6 1 0 0 12

Apple 13 7 6 3 1 31

Total 35 25 10 4 1 75

Source: based on Cotfas et al. (2016)

good results when tested on the Sanders Analytics Twitter Corpus.2 The corpus has been created by collecting 5513 tweets mentioning the companies “Twitter,” “Microsoft,” “Google,” and “Apple.” Table 1 includes the list of emotions that have been identified in the analyzed tweets, together with the corresponding number of tweets for each company. It should be mentioned that em:Like and em:Love are both subclasses of the class em:Care_for, which is in turn a subclass of em:Affection. The classes em:Annoyance and em:Fury are subclasses of em:Anger, while em:Hope is a subclass of em: Optimism, which in turn is a subclass of em:Happiness. Thus, the most frequent top-level emotions detected from the emotion ontology are em:Affection, which appears 60 times; em:Anger, which appears 11 times; and em:Happiness, which appears 4 times. The discovered emotions are associated to the respective social media messages with the help of the sma:Emotion property.

3.4

Data Analysis

Given the fact that all the information regarding the social media messages, the companies, and the uncovered emotions is stored in the semantic database, data analysis can be performed with the help of the dedicated SPARQL query language. Figure 6 includes a query that will return the text of the tweets and the uncovered emotions for all the tweets associated with company “Google.” Given the fact that the emotions are organized in a hierarchy with the help of the ontology, the analysis could also be performed at various levels of emotion granularity, from very specific emotions to the top-level ones. Figure 7 includes a query that retrieves all the companies and tweets that express either the emotion em:Anger, or one of its more granular emotions such as em:Annoyance or em:Fury. Moreover, starting from the geo:location property attached to the instances of the tw:Tweet class, advanced geographic analysis can be performed in order to uncover potential differences between the opinions of the public at different levels of geographic granularity: city level, country level, and continent level. On the other 2

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SELECT ?tweetContent ?emotion WHERE { ?tweet sma:emotion ?emotion . ?tweet sioc:content ?tweetContent ?tweet sma:company ?company. FILTER (?company = com:Google) } ORDER BY ASC(?emotion)

Fig. 6 SPARQL query for a specific company. Source: authors’ own study SELECT ?company ?tweetContent WHERE { ?tweet sma:emotion ?emotion . ?tweet sioc:content ?tweetContent ?tweet sma:company ?company . ?emotion rdf:type em:Anger. } ORDER BY ASC(?company)

Fig. 7 SPARQL query for a certain emotion. Source: authors’ own study

side, the dcterms:created property can be used to analyze how the public’s opinion has evolved over time.

4 Conclusions The present paper proposes a novel semantic web-based approach for analyzing the emotions towards companies expressed by social media users, in the millions of tweets published every day. By structuring the emotions in a hierarchy, it is possible to perform analysis at various levels of granularity, starting from very specific emotion, all the way to the general, top-level emotions. Storing the results using semantic web technologies creates the opportunity to use semantic inference and to access the vast amount of knowledge available in Linked Open Data. While the proposed approach has only considered English, it can be extended to other languages. Given the fact that according to the studies in the scientific literature it has been observed that the way in which customers and the society at large perceive the corporate social responsibility practices of a company can have an important impact on its long-term success, as a possible further research direction, we consider analyzing the public’s opinion on social media in relation with the corporate social responsibility practices of the companies. The approach proposed in this paper could also be adapted for other social media networks, besides Twitter.

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Acknowledgments This work was supported by a grant of the Romanian Ministry of Research and Innovation, UEFISCDI, project number PN-III-P1-1.2-PCCDI-2017-0800/86PCCDI/2018— FutureWeb, within PNCDI III.

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Analysing Customers’ Opinions Towards Product Characteristics Using Social Media Livu-Adrian Cotfas, Camelia Delcea, and Ionut Nica

Abstract With the ever-increasing number of social media messages posted daily, millions of users express opinions on various subjects, including opinions concerning the characteristics of products and services that they have already bought or they intend to buy in the near future. Accurately knowing the opinions of such a large number of users in near real time would be invaluable for the companies marketing those products. Thus, in the present paper, we propose an approach based on Semantic Web technologies, natural language processing and machine learning for accurately analysing the social media messages posted on Twitter. Compared to existing approaches, which mainly focus on determining the opinion of the user concerning the entire product, the approach proposed in the present paper offers deeper insights, by taking into consideration the fact that a user might have different and sometimes even contradictory opinions concerning the various characteristics of a single product. We start by creating an ontology for representing the relationships between the products and their characteristics, ontology that is also used for performing named entity recognition, given the fact that various users can employ different terms for referring to the same concept. The ontology is afterwards used in order to filter from the huge number of tweets published every minute only the ones that can prove relevant for the analysis. In the next step, aspect-based sentiment analysis is employed in order to determine the sentiment expressed by the social media user regarding one or several characteristics of the analysed product. The results of the analysis are stored as semantically structured data, thus making it possible to fully exploit the possibilities offered by Semantic Web technologies, such as inference and accessing the vast knowledge in Linked Open Data, for further analysis. Keywords Named entity recognition · Social media analysis · Semantic web · Sentiment analysis · Social media L.-A. Cotfas (*) · C. Delcea · I. Nica Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, Bucharest, Romania e-mail: [email protected]; [email protected]; [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2020 M. H. Bilgin et al. (eds.), Eurasian Business Perspectives, Eurasian Studies in Business and Economics 15/2, https://doi.org/10.1007/978-3-030-48505-4_9

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1 Introduction Social media has become in the last few years one of the most relevant sources for understanding the public’s opinion on various subjects (Bello-Orgaz et al. 2016). In this context, it has been shown that many users post their opinions concerning the characteristics of the products and services that they have already bought or they intend to buy in the near future on social media networks, such as Twitter (Kontopoulos et al. 2013). The importance of accurately knowing the opinions of customers has already been proven in a wide variety of domains, ranging from airplane travel (Moslehpour et al. 2018) to higher education (Ghobehei et al. 2019). Thus, the ability to understand in near real time the huge number of opinions expressed on social media networks would be invaluable for almost any company. Enriching unstructured text, by discovering named entities, such as products or their characteristics, is known as named entity recognition—NER, a technique belonging to the natural language processing—NLP field (Derczynski et al. 2015). Uncovering the opinion of the users towards those characteristics can be achieved by combining NER with another technique from the NLP field, known as sentiment analysis—SA, which determines whether a text expresses a positive, negative or neutral polarity. Determining the opinion of the user towards the product characteristics and not only towards the entire product has the potential to provide deeper insights, by taking into consideration the fact that a user might have different and sometimes even contradictory opinions concerning the various characteristics of a single product. Thus, in the present paper, we propose an approach based on Semantic Web technologies, natural language processing and machine learning for accurately analysing social media messages posted on Twitter. While using ontologies for aspect-based sentiment analysis has been proposed in Kontopoulos et al. (2013), the paper does not consider how the results of the analysis could be stored using Semantic Web technologies. In the present paper, we also improve upon the approach proposed in Cotfas et al. (2019, 2016b), by both taking product characteristics into account and using a machine learning sentiment analysis algorithm. We start by creating an ontology for representing the relationships between the products and their characteristics, ontology that is also used for performing named entity recognition, given the fact that various users can employ different terms for referring to the same concept. By also storing the results of the aspect-based sentiment analysis process using Semantic Web technologies, it becomes possible to easily analyse the public’s opinion at various levels of granularity: concerning a characteristic of a product, concerning the product, concerning a certain characteristic for all the products and concerning all the products of the company. It also becomes possible to use Semantic Web inference to discover new relations inside the data and also to access the vast amount of knowledge in Linked Open Data, for further analysis. The rest of the paper is structured as follows: Sect. 2 describes the proposed aspect-based analysis ontology that is used for representing the relationships

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between the products, their characteristics and the analysis results; Sect. 3 includes the steps taken in order to filter from the huge number of tweets published every minute only the ones that can prove relevant for the analysis and to perform aspectbased sentiment analysis in order to determine the sentiments expressed by the social media users regarding the characteristics of the analysed products; Sect. 4 presents how the analysis results can be further explored to get deeper insights; and the last section summarizes the paper and introduces possible future research directions.

2 Aspect-Based Analysis Ontology Ontologies, defined according to Borst (1997, p. 12) as a “formal specification of a shared conceptualization”, represent the primary means of structuring data within the Semantic Web. They organize concepts in hierarchies, using a shared vocabulary that provides a common understanding of the concepts, of their properties and of the relations between them. Ontologies are represented using standard languages, such as the Resource Description Framework—RDF (RDF Working Group 2014) and the Ontology Web Language—OWL (W3C OWL Working Group 2012). The data stored with the help of ontologies in semantic databases can be retrieved using SPARQL, a specialized query language. New relationships inside the data can be discovered using semantic reasoning engines, through a technique known as inference. Several categories of semantic web classes and properties are needed in order to facilitate aspect-based sentiment analysis: classes and properties designed to represent the social media messages, classes and properties that represent the analysed products and their characteristics as well as the results of the analysis process. Each category has been treated as a separate modular ontology, with the purpose of facilitating their usage, understanding and also their future extension (Ben Abbès et al. 2012). The main classes and properties used in the proposed approach are highlighted in Fig. 1. Thus, for representing the social media messages, we have chosen to use the tw:Tweet class from the ontology described in Cotfas et al. (2016b), which follows the recommended ontology modelling best practices (Allemang and Hendler 2011), such as extending existing ontologies whenever possible. The ontology uses the tw prefix and extends the recognized SIOC (prefix sioc) and FOAF (prefix foaf) ontologies with Twitter specific concepts. The classes and properties used for representing the analysed products and their characteristics, as well as the analysis results, form the aspect-based social media analysis ontology. The asma prefix is used in the rest of the paper to denote classes or properties belonging to this ontology. The analysed products are represented with the help of the asma:Product class, which also serves as an optional base class for more specific categories of products, as shown in Fig. 2, where the asma: SmartPhone class has also been declared. The product characteristics are modelled using the asma:Aspect class. Associating aspects to the instances of the asma:

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Fig. 1 Classes and properties used for aspect-based social media analysis. Source: Developed by the authors

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asma:iPhone4S Fig. 2 Analysed entities and aspects. Source: Developed by the authors

Product is possible using the asma:hasAspect property, as shown in Fig. 2, where the asma:battery, an instance of the amsa:Aspect class, is associated to asma: iPhone4s instance, belonging to the asma:SmartPhone class. For each instance of the amsa:Aspect and asma:Product classes, the property skos:prefLabel is used to associate the official or most frequently used name. Given the fact that social media users can employ different terms when referring to a certain product, one or several alternative spellings, abbreviations or hashtags can be defined with the help of the skos:altLabel property. Less frequent monikers or misspelled names can be associated using the skos:hiddenLabel property. The skos prefix is used to denote the Simple Knowledge Organization System (Miles and Bechhofer 2009). The labels will be used in the following section of the paper to discover the products and their characteristics in social media messages. Once, a product or a characteristic is discovered, it is associated to the social media message, as an instance of the asma:TextAnnotation class, using the asma:

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hasTextAnnotation property. The instance stores the text that has been matched using the asma:textMatch property, the starting character index of the matched text using the asma:textMatchStart property and the index at which the match ends, using the asma:textMatchEnd property. The actual product or product characteristic is associated using a dc:relation property, where the dc prefix is used for the Dublin Core vocabulary (Dublin Core Metadata Initiative 2018). The asma:hasSentiment property associates to the instances of the tw:Tweet class the overall sentiment score, represented as a negative, positive or neutral value, in the interval [ 1,1].

3 Aspect-Based Analysis Process The aspect-based sentiment analysis process of the tweets is organized in several steps, shown in Fig. 3, beginning with the tweet retrieval and finishing with storing the analysis results in the semantic database. The steps are described in further details in the following subsections.

1. Retrieve tweet

2. Pre-process tweet

3. Discover Enes

Yes

Enes discovered?

4. Discover aspects

5. Senment Analysis

6. Store results

Fig. 3 Social media analysis steps. Source: Developed by the authors

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Step 1. Retrieving Tweets Twitter offers several APIs (Twitter 2019) that can be used to access the vast number of social media messages published every day. Out of these APIs, the statuses/filter API provides free access to the real-time stream of tweets, filtered using up to 400 track keywords. Thus, the tweets that could prove relevant for understanding the customers’ opinions towards product characteristics can be filtered using specific keywords, hashtags and mentions such as “@apple” or “#iphone”. Step 2. Pre-processing Social media messages, unlike carefully authored texts, pose significant challenges for natural language processing tasks due to their short and noisy nature (Bao et al. 2014). Users frequently post messages that contain abbreviations, hashtags, mentions, emoticons, words with duplicated letters and URLs. Thus, pre-processing is a key step in order to ensure the accuracy of the sentiment analysis algorithm, as well as to facilitate the identification of the mentioned products and of their characteristics. The text of the retrieved tweets is first split into separate tokens. Afterwards, a normalization procedure is performed, consisting in removing duplicated letters, removing one letter words, removing stop words, removing the “RT” word, standing for retweet, converting all the letters to lowercase and removing all the URLs. Step 3. Discovering Entities Enriching unstructured text, by discovering named entities, such as products or aspects, is known as named entity recognition, a technique belonging to the natural language processing field (Derczynski et al. 2015). While most approaches focus on identifying explicitly mentioned entities, advanced approaches also consider inferring from the given text whether the user is referring to a certain product, even if the product itself is not explicitly mentioned, by relying on contextual information (Cambria et al. 2018). While also inferring the product from the context of the text could potentially increase the coverage of the analysis, by taking into account more tweets and thus opinions from more users, it would most likely also have a negative impact on the accuracy, due to its probabilistic/statistical nature. Thus, in the present study, we focus on discovering explicitly mentioned products, by comparing the stemmed versions of the tokens associated using the properties skos:prefLabel, skos:altLabel and skos:hiddenLabel with the instances of asma:Product class, with the stemmed tokens of the text obtained in the pre-processing step. Stemming has been performed with the help of the SnowballStemmer. Step 4. Discovering Aspects All the tweets in which a product reference has been discovered will be further analysed in order to establish whether the user is also mentioning any aspect of the product. Similar to the approach taken in the previous step, the stemmed tokens in the pre-processed tweet will be compared to the stemmed versions of the tokens associated using the properties skos:prefLabel, skos:altLabel and skos:hiddenLabel

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with the instances of asma:Aspect class that are associated with the identified product. Step 5. Sentiment Analysis Sentiment analysis algorithms can be broadly categorized into bag-of-words (Cotfas et al. 2016a), bag-of-concepts (Dragoni et al. 2018) and machine learning approaches (Aloufi and Saddik 2018). While the use of bag-of-words approaches has been initially highly popular, in recent years, machine learning approaches, especially supervised ones, have become increasingly popular. Among the supervised machine learning algorithms, support vector machines have been shown to provide better results for sentiment classification tasks, compared to algorithms such as Multinomial Naive Bayes—MNB and random forest—RF (Aloufi and Saddik 2018). Starting from the well-known Sanders Analytics Twitter Corpus that contains 5396 tweets collected for the terms “Google”, “Apple”, “Twitter” and “Microsoft”, we have eliminated the tweets marked as irrelevant, from the sentiment point of view, keeping only the positive, negative and neutral tweets, with a count of 3430. The resulting dataset includes 523 positive, 574 negative and 2333 neutral tweets. Using the SVM implementation in the scikit-learn library (Pedregosa et al. 2011) and the representation using Term Frequency—Inverse Document Frequency (TF-IDF) for n-grams in the interval [1,3], we have achieved a 0.74 accuracy and a 0.68 FScore. Step 6. Storing the Results The analysed tweet, together with the results of the analysis process, is represented using the proposed Semantic Web approach and is afterwards stored in a semantic database. For the present study, it has been chosen to use Apache Fuseki, which is able to execute SPARQL queries over the standard HTTP protocol, thus facilitating the integration with applications written in various programming languages. Figure 4 includes an actual tweet from the Sanders Analytics Twitter Corpus, together with the discovered product and aspect annotations.

4 Semantic Web Analysis Given the fact that the results of the analysis process are stored using Semantic Web technologies, they can be further analysed with the help of the SPARQL query language. Figure 5 includes a query that will return the tweets and associated sentiment score for all the tweets that mention the product characteristic “battery”. Performing the same analysis for all the products in the smartphone category can easily be achieved using the query included in Fig. 6, which exploits the fact that all the smartphones are represented as instances of the asma:SmartPhone class. By replacing asma:SmartPhone with asma:Product, the query will return the average sentiment for all the products.

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13 asma:textMatchStart

20

baery

asma:textMatch asma:textMatchEnd

asma:TextAnnotaon

rdf:type

dc:relaon

asma:Aspect

rdf:type

asma:baery asma:hasTextAnnotaon

^^xsd:double My iPhone 4S baery lasted longer than a day. That hasn't happened since my edge iPhone. Nice job, @apple.

asma:hasSenment

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asma:iPhone4S asma:TextAnnotaon

rdf:type

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asma:Product 12

iPhone 4S asma:textMatchStart

3

Fig. 4 Annotations for an actual tweet—https://twitter.com/i/web/status/125550135911518209. Source: Developed by the authors SELECT ?tweet ?sentiment WHERE { ?tweet asma:hasSentiment ?sentiment . ?tweet asma:hasTextAnnotation ?textAnnotation . ?textAnnotation dc:relation ?relation . FILTER (?relation = asma:battery) }

Fig. 5 Determining the overall sentiment associated with a certain product characteristic. Source: Developed by the authors SELECT ?tweet ?sentiment WHERE { ?tweet asma:hasSentiment ?sentiment . ?tweet asma:hasTextAnnotation ?textAnnotation . ?textAnnotation dc:relation ?relation . ?relation rdf:type asma:SmartPhone . }

Fig. 6 Determining the overall sentiment associated with all the smartphones. Source: Developed by the authors

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5 Conclusions In this work, we have presented an approach based on Semantic Web technologies, natural language processing and Machine Learning for accurately analysing social media messages posted on Twitter. Compared to many existing approaches, which mainly focus on determining the opinion of the user concerning the entire product, the approach proposed in the present paper offers deeper insights, by taking into consideration the fact that a user might have different and sometimes contradictory opinions concerning the various characteristics of a single product. The results of the analysis are stored as semantically structured data, thus making it possible to fully exploit the possibilities offered by Semantic Web technologies, such as inference and accessing the vast knowledge in Linked Open Data, for further analysis. Among the possible further research directions, we consider investigating more advanced deep learning algorithms such as Long Short-Term Memory (LSTM) for detecting the products and their characteristics, as well as for sentiment analysis. Acknowledgements This work was supported by a grant by UEFISCDI (“Unitatea Executivă pentru Finanţarea Învăţământului Superior, a Cercetării, Dezvoltării şi Inovării”), project FutureWeb (“Modelarea empirică şi dezvoltarea experimentală a instrumentelor asociate tehnologiilor emergente din domeniul reţelelor sociale online”), project number: PN-III-P1-1.2PCCDI-2017-0800, 86PCCDI/2018.

References Allemang, D., & Hendler, J. (2011). Good and bad modeling practices. In D. Allemang & J. Hendler (Eds.), Semantic web for the working ontologist (2nd ed., pp. 307–324). Boston: Morgan Kaufmann. Aloufi, S., & Saddik, A. E. (2018). Sentiment identification in football-specific tweets. IEEE Access, 6, 78609–78621. Bao, Y., Quan, C., Wang, L., & Ren, F. (2014). The role of pre-processing in twitter sentiment analysis. In Intelligent computing methodologies (pp. 615–624). New York: Springer. Bello-Orgaz, G., Jung, J. J., & Camacho, D. (2016). Social big data: Recent achievements and new challenges. Information Fusion, 28, 45–59. Ben Abbès, S., Scheuermann, A., Meilender, T., & D’Aquin, M. (2012). Characterizing modular ontologies. In 7th International Conference on Formal Ontologies in Information Systems – FOIS 2012 (pp. 13–25). [Online] Graz. Accessed April 14, 2019, from https://hal.archivesouvertes.fr/hal-00710035 Borst, W. N. (1997). Construction of engineering ontologies for knowledge sharing and reuse. Universiteit Twente. Cambria, E., Poria, S., Hazarika, D., & Kwok, K. (2018). SenticNet 5: Discovering conceptual primitives for sentiment analysis by means of context embeddings. The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18) (pp. 1795–1802). Cotfas, L.-A., Delcea, C., & Roxin, I. (2016a). Grey sentiment analysis using multiple lexicons. In Proceedings of the 15th International Conference on Conference on Informatics in Economy (IE 2016) (pp. 428–433). Cluj-Napoca: Bucharest University of Economic Studies Press.

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Cotfas, L.-A., Delcea, C., Segault, A., & Roxin, I. (2016b). Semantic web-based social media analysis. In N. T. Nguyen & R. Kowalczyk (Eds.), Transactions on computational collective intelligence XXII (pp. 147–166). Berlin: Springer. Cotfas, L.-A., Roxin, I., & Delcea, C. (2019). Semantic search in social media analysis. In Proceedings of the 18th International Conference on Conference on Informatics in Economy (IE 2019). [Online]. Education, Research and Business Technologies (pp. 37–42). Accessed September 4, 2019, from http://www.conferenceie.ase.ro/wp-content/uploads/2019/06/ ProceedingsIE2019/semantic_search_in_social_media_analysis.pdf Derczynski, L., Maynard, D., Rizzo, G., van Erp, M., Gorrell, G., Troncy, R., Petrak, J., & Bontcheva, K. (2015). Analysis of named entity recognition and linking for tweets. Information Processing & Management, 51(2), 32–49. Dragoni, M., Poria, S., & Cambria, E. (2018). OntoSenticNet: A commonsense ontology for sentiment analysis. IEEE Intelligent Systems, 33(3), 77–85. Dublin Core Metadata Initiative. (2018). DCMI: DCMI metadata terms. [Online] Accessed November 16, 2018, from http://dublincore.org/documents/dcmi-terms/ Ghobehei, M., Sadeghvaziri, F., Ebrahimi, E., & Afshar Bakeshloo, K. (2019). The effects of perceived brand orientation and perceived service quality in the higher education sector. Eurasian Business Review, 9(3), 347–365. Kontopoulos, E., Berberidis, C., Dergiades, T., & Bassiliades, N. (2013). Ontology-based sentiment analysis of twitter posts. Expert Systems with Applications, 40(10), 4065–4074. Miles, A., & Bechhofer, S. (2009). SKOS Simple Knowledge Organization System. [online] Accessed November 16, 2018, from https://www.w3.org/TR/skos-reference/ Moslehpour, M., Wong, W.-K., Lin, Y. H., & Le Huyen Nguyen, T. (2018). Top purchase intention priorities of Vietnamese low cost carrier passengers: Expectations and satisfaction. Eurasian Business Review, 8(4), 371–389. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine learning in python. Journal of Machine Learning Research, 2825–2830. Accessed September 10, 2019, from http://www.jmlr. org/papers/v12/pedregosa11a RDF Working Group. (2014). RDF – Semantic Web Standards. [Online] Accessed November 18, 2018, from https://www.w3.org/RDF/ Twitter. (2019). Docs – Twitter developers. [Online] Twitter Developers. Accessed August 3, 2019, from https://developer.twitter.com/en/docs W3C OWL Working Group. (2012). OWL 2 web ontology language document overview (2nd ed.). [Online] Accessed November 18, 2018, from https://www.w3.org/TR/owl2-overview/

Millennial Travelers’ Perception of Terrorism Risks: Evidence from Poland and Slovakia Rafał Nagaj

Abstract The subject of the study paper is the risk perception and the decisions of Gen Y tourists regarding tourist destinations in the context of increased terrorist risk in the world. It is examined how Millennial travelers from Poland and Slovakia perceive the travel risk choosing tourist destinations in the context of terrorist risks. Both studied countries are recognized to belong to a region that has a low terrorism risk index. It is examined whether they are willing to forgo safety and security in exchange for economic advantage deciding to outbound travel. In addition it is checked whether sex, frequency of traveling, and travel expenses differentiate risk perception among Millennials. The research results showed that a large proportion of Millennials in this region of Europe are willing to accept tourist risks in the face of terrorism and agree to higher-value travel costs than safety. Gender and the level of travel expenses are factors that determine the perception of travel risk among Millennials, but the frequency of traveling does not. Keywords Millennial travelers · Travel cost · Safety level · Perception of risk · Poland · Slovakia

1 Introduction Tourists’ motivations to travel vary. As Doran et al. (2015) suggest, tourists often try to dissociate themselves from typical tourists in terms of travel motivation. However, even then, they do it in a similar fashion, which means that despite the fact that each tourist has their own reasons to travel, there are some tendencies that are common for all individual tourists and their groups. Hence, the results presented in this article are representative of certain groups of tourists, rather than all travelers. According to George (2010), people’s willingness to travel to a certain destination is the result of a R. Nagaj (*) Institute of Economics and Finance, University of Szczecin, Szczecin, Poland e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2020 M. H. Bilgin et al. (eds.), Eurasian Business Perspectives, Eurasian Studies in Business and Economics 15/2, https://doi.org/10.1007/978-3-030-48505-4_10

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cost-benefit calculation. Potential economic benefits are a motivation to visit a particular tourist destination. The cost of travel and the potential risks associated with a given tourist destination/region must be taken into account. Thus, travel risks and their intensity affect tourists’ decisions (Morakabati and Kapuściński 2016; Garg and Kumar 2017) and their perceptions of regions, particularly in terms of international tourism (Sönmez and Graefe 1998). Moreover, the tourists’ perceptions of risk may vary, depending on their gender, age, financial situation, or the gravity of the threat they are facing. In addition, as pointed out by Kozak et al. (2007), travelers representing different cultures may also perceive risk differently. As shown in the literature on the subject, personality traits are another important factor influencing the level of tourists’ sensitivity to risk (Pizam et al. 2004; Fuchs and Reichel 2011). A social generation and at the same time a group of tourists who are different from other generations as regards their general approach to risk (Doran et al. 2015), tourist motivations (Tomić et al. 2014; Buffa 2015; Ting et al. 2015), and the frequency of traveling (Sofronov 2018) are Millennials (Generation Y). The subject of the study is the Millennial travelers from two Central European countries—Poland and Slovakia, where the number of terrorism-related incidents and, consequently, the level of tourist risk resulting from this type of threats are low (see: Roser et al. 2018). The object of study is Generation Y tourists’ choices regarding tourist destinations, with respect to an increased terrorist threat. In the author’s opinion, it is interesting to investigate the reactions of Millennial tourists from this part of Europe to this type of threat, as well as their perception of terrorist risk in the context of the travel choices they make. Some researchers investigating the role of Generation Y in tourism (UNWTO 2008; Benckendorff et al. 2010) indicate that young people (Millennials included) willingly use new technologies and impersonal information sources when making decisions about going on a tourist trip (Xiang and Gretzel 2009; Žuromskaitė et al. 2018). They often travel guided by an impulse (Chhabra 2012). What is more, traveling is becoming an increasingly important part of their lives (Collins 2013), which is why they form a population group who travel more than other social groups (Sofronov 2018). Bearing the topic of this article in mind, it must be admitted that the problems of risk and its perception, also in tourism, have been discussed in the literature (Kozak et al. 2007; Li et al. 2015; Pavesi et al. 2016; Demir et al. 2019), similar to the issues of security in tourism (Korstanje and Tarlow 2012; Saha and Yap 2014; Chingarande and Saayman 2018) and the impact of terrorist threat on tourism (Enders et al. 1992; Sönmez and Graefe 1998; Pizam and Fleischer 2002; Araña and León 2008; Feridun 2011; Estrada et al. 2015; Samitas et al. 2018). The author’s aim is to evaluate how the Millennial travelers from Poland and Slovakia perceive risk when choosing a tourist destination, in the context of the currently increased terrorist threat around the world. It will be investigated which factor—safety or an economic benefit—is most important for them when making decisions about outbound travel and whether these tourists would be willing to accept higher risk if they could travel to a destination in danger of a terrorist attack for a satisfactorily reduced price. Moreover, the author will investigate whether the

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risk propensity among Millennials from a region with a low level of terrorist threat depends on their sex, frequency of traveling, and travel expenses. The article has several contributions to the literature. First, travel risks perceived by Generation Y have not been thoroughly investigated yet. The majority of studies on this particular social group have concerned demography issues and described the group members’ behaviors on the labor market and their personality traits, including the way they travel. There are a few publications concerning risk propensity, but they have been mostly published in reports or press articles, not in journals. Secondly, research on tourist propensity for and perception of travel risks concerned regions where the level of terrorist risk is high. However, there are no studies concerning tourists from regions where it is low—hence, the study presented in this article concerns two such countries from Central Europe. Thirdly, there is a lack of research on how Millennial travelers from Poland and Slovakia assess the risk of a terrorist attack when they decide to go on a tourist trip. Moreover, the analysis will demonstrate whether in the studied countries, factors like gender, the frequency of travel, and the amount of travel expenses are statistically significant with respect to tourists’ preferences, as it is the case in other parts of the world. The remaining part of the article contains the following: the second part presents a review of literature about risk, the impact of terrorism on tourism, and Millennial travelers. The third part explains the methodology of research conducted by the author, and the next one contains the results of this research and the conclusions. The last, fifth part of the article presents the author’s conclusions, discussion, and the limitations to the work.

2 Literature Review 2.1

Risk and Its Perception in Tourism

The problem of decision-making in a risk situation is widely discussed in the literature ever since Kahneman and Tversky’s theory of perspective (1979), which indicated that people who make a decision under risk conditions do not do it according to the expected utility theory but evaluate the consequences of their decisions in a subjective way. This rule also pertains to tourists and the consumption of tourist services. Various authors analyze different aspects of risk in tourism, such as consumption behavior, tourist retail consumption within the limited risk environment of a holiday (McIntyre 2007), tourists’ motivations (Doran et al. 2015; Li et al. 2015; Chingarande and Saayman 2018), attempts to improve the quality of life (Lee et al. 2018), or the demand for tourism in different kinds of risky situations (Kozak et al. 2007; Fielding and Shortland 2009; Benzion et al. 2009). The perception of risk is not the same as the level of risk, so it is worth remembering that the former does not have to correspond to the actual risk or to the image of a given destination (Chew and Jahari 2014). If someone is not ready to accept a high level of risk, then even if they travel in a safe country but there have

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been some political threats in their own or the neighboring region, they will not be willing to travel to such destinations in this country. According to the cumulative prospect theory (Tversky and Kahneman 1992), the outcome of a prospect depends on how it is perceived. The more dangerous a tourist destination is, the more negatively it will be perceived. As indicated by Pavesi et al. (2016, p. 425) “the perception of risk is assimilated and included into the individual’s set of destination selection criteria, thereby affecting all future destination selections.” Several studies point out that the approach to risk and tourist inclinations vary within the society. These may differ due to age, gender, ethnicity/country of origin and financial status. Staats et al. (2006) believe that gender matters when terrorist threat appears, and women are less willing to travel to such destinations. Similar conclusions were presented by Aschauer (2010), who created a model of crisisstable and crisis-resistant indicators, showing that the awareness of risks in tourism, developed due to terrorism, varies depending on the culture or gender. A slightly different view is presented by McKercher et al. (2011), who are of the opinion that gender modifies the attitude towards tourism only to a small extent, while the significant factor is first of all the nationality, which may play a substantially moderating role (though more among women than men). An opposite opinion has been expressed by Sellick (2004) and Simpson and Siguaw (2008), who claim that gender does not influence the approach to travel risks. At the same time, Simpson and Siguaw (2008) indicate that tourists perceive many types of travel risks, but the seriousness of each of them may depend on the tourists’ ethnicity/country of origin. The region which they come from may influence their demand for foreign tourism, and their nationality may be a factor affecting their attitude to tourism (McKercher et al. 2011; Garg and Kumar 2017). Moreover, we may expect different reactions from people who come from regions where the safety level is very low and who have experienced intense emotions. Benzion et al. (2009), having investigated the inhabitants of Israel—those threatened by war and those living outside the war zone, indicate that people experienced by intense emotions report greater perceived risk. They conclude that the distance from regions in danger, including terrorist threat, reduces feelings that may partially shape risk perceptions (Benzion et al. 2009, p. 35). Literature attaches much less importance to age (Gibson and Yiannakis 2002; Floyd and Pennington-Gray 2004; Simpson and Siguaw 2008; Basarić et al. 2016), though the findings of Sönmez and Graefe (1998) did not confirm that, similar to the relationships between the risk and the risk perception. Literature shows that people’s travel mobility and travel risk perception decline with age (Gibson and Yiannakis 2002; Basarić et al. 2016). What is often indicated as having an influence on travel risk perception is the economic factor, mainly the tourist’s financial situation (Sönmez and Graefe 1998; Sellick 2004; Garg and Kumar 2017). Basarić et al. (2016) include here the fact of being unemployed. He claims that because of a high unemployment rate, many people resign from traveling or travel less often. In the context of risk perception, it is worth paying attention to the observations made by Chien and Jeh (2009). They found that the respondents’ behavior at the decision and demand stages was significantly different, i.e., people declaring strong interest in certain services were not necessarily willing to use them when the price

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increased. Moreover, the preference of risk has an influence on the subjects’ behaviors. It is also important that safety risk information has an impact on travelers, which can be seen when potential tourists declare that they are willing to take a high risk or when they do not nominate safety as their first choice criterion (Koo et al. 2018). When analyzing the impact of safety information on making flight choices, the researchers concluded that all travelers always use it to make the decision. This is so because the concern about safety discourages them take it into account when traveling to specific destinations (Crotts 2003). Travelers, especially the international ones, appear to be sensitive toward any type of risk, predominantly the risk of a terrorist attack (Kozak et al. 2007). The importance of safety is particularly high in tourism because travelers deciding to go on a trip often consider multiple alternatives (Huan and Beaman 2004). As a result, negative information about a given destination may have an immediate negative impact on it (Faulkner 2001), regardless of the provision of high-quality tourism services (Lepp and Gibson 2003; Kozak et al. 2007). According to Morakabati and Kapuściński (2016, p. 506), “terrorism creates an atmosphere of uncertainty that leaves the door open for fear, and the lack of ability to control the risk stops even the most confident traveler.” However, in the opinion of Teitler-Regev et al. (2015), young tourists (students), who have already traveled to high-risk countries are familiar with such a situation, and have not experienced any negative consequences, are more willing to take risks and approach them in a more relaxed manner. It is quite the opposite in the case of people who have had bad experiences.

2.2

Security in Tourism and the Tourist Attitudes of Millennial Travelers

The significance of security and political stability for the development of the tourism sector has been discussed by Chen and Gursoy (2001), Neumayer (2004), Saha and Yap (2014), who stressed that the safety and security of tourists is one of the critical success factors for tourism-led growth (Chingarande and Saayman 2018, p. 800) and the competitiveness of enterprises (Nagaj and Žuromskaitė 2020). Tasci and Boylu (2010) add that a high level of security and safety has an influence on the satisfaction with the journey, which rises when the journey lasts longer. This, in turn, has an impact on positive word-of-mouth and customer loyalty (Kandampully and Suhartanto 2000). Therefore, tourism security must be managed, as it is essential for sector survival and achieving success (Tarlow 2014). Enhancing the quality of life is one of the primary motives for tourists, similar to the emotional value (Lee et al. 2018). A low level of safety, caused by terrorist threat, has such adverse economic consequences that the state should actively prevent them by promoting tourism and increasing the level of security (Samitas et al. 2018). This is because terrorism negatively affects public well-being (Schmid and Muldoon 2015) and tourism sector in general (Enders et al. 1992; Araña and León 2008; Feridun 2011; Samitas et al.

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2018). As remarked by Araña and León (2008) investigating Germans’ reactions, acts of terrorism may evoke a shock in tourists, who, as a result, may change their tourist destinations (the image of destinations affected by terrorism deteriorates, and those which are untouched by terrorism improve). What is more, we can even speak of the tourism sector’s vulnerability to attack, on the basis of which Estrada et al. (2015) created the TAVE-Model. It allows researchers to evaluate the relationship between terrorism and the economic growth of the tourism sector and the national economy in a given country. Naturally, the influence of a growing threat on the tourism sector depends on many factors, including the intensity of threats (Sönmez and Graefe 1998; Seddighi et al. 2001; Pizam and Fleischer 2002; Araña and León 2008; Bassil 2014). As pointed out by Fielding and Shortland (2009), who analyzed the effects of US television news on the demand for tourism in Israel, the decreasing intensity of the Israeli–Palestinian conflict increased the demand for tourist services in Israel among US tourists, which boosted local incomes. Also the psychological aspect, which creates the atmosphere around tourism, is very important (Korstanje and Tarlow 2012), because the picture of a tourist as a victim certainly has a negative influence on the propensity to travel. It should also be remembered that the negative psychological impact of terrorism on individuals disappears in the long run (Rubin and Wessely 2013). Research also proves that not everywhere is this impact necessarily negative (Enders et al. 1992). What is more, some countries may benefit from the fact that in another, neighboring country, a terrorist attack has taken place (Drakos and Kutan 2003). Generally, however, tourists value peace and quiet and usually prefer to avoid places where their lives can be threatened. If the threat at a given tourist destination makes the balance of benefits and losses only slightly positive or the risk is very high, tourists decide not to travel there (Mansfeld and Pizam 2006). As indicated by Seabra et al. (2014, p. 874), “tourists are motivated to acquire information about terrorism in the media, revealing attention to and interest in news regarding this topic, which in turn influences directly their risk perception.” The information provided about tourism and the tourist destination, its positive or negative character, may have a substantial influence on the tourists’ natural preferences. The importance of this factor is particularly high in Millennial travelers (Žuromskaitė et al. 2018), who often seek information themselves and pay more attention to impersonal (media, the number of casualties among tourists) than to personal information when searching for details about travel safety. Young tourists often refuse to read long, full reports but prefer a short, audiovisual message (Fong et al. 2017). In the context of this group, it should also be remembered that tourists are guided by their travel experience when making individual travel decisions. According to Pavesi et al. (2016, p. 423), “the effects of a single travel experience are tangible and affect the tourist’s future” decisions by setting his/her own criteria of choosing the destination. Such experiences have an influence on the evaluation of future tourist trips (Li 2000; Falk et al. 2012) and are the basis for selecting and ranking tourist destinations (McCartney et al. 2009). According to the negative bias theory, people tend to perceive negative events rather than positive (Rozin and Royzman 2001). Younger social generations, including Millennial travelers, often do not have substantial tourist experience of traveling to

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destinations with a high level of terrorist risk. They are also slightly different from the older generations as regards the approach to traveling, as they often visit destinations which can potentially be easy targets of terrorist attacks (Hughes et al. 2008). They live in their own world, the world of technology and the Internet; they often travel in order to visit the maximum number of places (Bencsik et al. 2016). Thus, they are more susceptible to different types of risk, including terrorist attacks. They, naturally, appreciate security at a tourist destination, but they also attach some weight to the economic factor, i.e., the travel costs.

3 Data and Methodology The literature review showed that while traveling, tourists are exposed to different types of risk, and it is an inseparable element of tourism. It was also demonstrated that tourists’ motivations to travel vary, similar to the intensity of traveling, which in turn has an influence on tourist decisions, especially as regards international tourism. One of the risks which occur during international travel is the political threat, which has been strongly connected with terrorism since 2001. The literature shows that tourists’ perception of risk may vary depending on age—the risk propensity is usually higher among younger tourists (there is not, however, a common agreement on that). The subject of the study is Millennial travelers from Poland and Slovakia in Central Europe. Millennials form a social generation which differs from other generations in their general approach to risk and traveling. Hence, in the context of terrorist threat in tourism, the author wants to examine the tourist decisions taken by Millennials from Poland and Slovakia, which are countries included in regions that have a low terrorism risk index. The risk propensity among Millennial travelers has not been thoroughly researched in the literature, and the topics discussed so far do not include this particular social group living in a region with a low terrorism risk index. The object of study is Gen Y tourists’ choices regarding a tourist destination, with respect to an increased terrorist threat. It will be examined whether young tourists from Poland and Slovakia are willing to accept the increased risk by deciding to travel when terrorist threat spreads across the world. In order to achieve the aim set in the article, the author used the results of a survey conducted among people aged 18–36, which is an age group defined by Pendergast (2010) as Generation Y, also referred to as Millennial travelers or Millennial generation. The study was carried out in 2018, in two Central European countries—Poland and Slovakia, which have a low terrorism risk index (Red24 2017; Roser et al. 2018). In Poland, the study was conducted among 849 respondents (students at different stages of university studies at the Faculty of Economics and Management, University of Szczecin) and in Slovakia—among 528 respondents (students of the Faculty of Economics at Matej Bel University in Banská Bystrica). The respondents representing a higher perception of risk in tourism were willing to sacrifice the level of safety in exchange of the economic stimulus in the form of a

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reduced travel cost. The survey questionnaire included two questions used for the purposes of this article: 1. How important are the following factors for you, level of safety and travel costs, when you decide to travel in a given tourist direction? The variables were measured using a 5-level Likert scale (1, not important/totally unnecessary; 2, rather unnecessary; 3, neutral; 4, rather important/or necessary; 5, very important). 2. At what price reduction for a tourist trip would you be willing to travel to a country with a low safety level, using the following scale: 1, 30% price reduction or less; 2, 40% price reduction; 3, 50% price reduction; 4, 70% price reduction; 5, despite the low price, I wouldn’t travel to a destination with a low level of safety, i.e., 100% price reduction. The article presents a two-stage analysis. First, the author investigated the factors determining the decision to travel to a given tourist destination, the percentage of respondents rating the economic factor higher than the level of safety, as well as the percentage of people who are willing to forgo safety and security in exchange for an economic advantage. The author used descriptive analysis (the frequency expressed as a percentage and mean rank), as well as a one-sample proportion test and a twosample proportion z-test (at significance level α ¼ 0.05). He verified the hypothesis that the percentage of persons rating the economic factor higher than the level of safety was at least 10% of all respondents. It was also investigated whether that percentage was the same in both countries. Secondly, it was investigated whether there is a statistically significant difference between the study samples of Millennial travelers depending on the frequency of travel and the traveling expenses per person. To investigate this problem, the author used Mann–Whitney U test, nonparametric Kruskal–Wallis test, and Dunn’s test criteria, assuming the statistical level of significance α ¼ 0.05 and p-value (Asymp. Sig.)  0.05. The study of the statistically significant difference between the study samples of Millennial travelers was conducted for three grouping variables: gender, the frequency of traveling in the last 24 months (1 night or more; the people who did not travel were excluded from the evaluation; the rating scale ranged from 1 ¼ less than once a year to 5 ¼ 8 times or more), and travel expenses, i.e., the total budget for traveling per person (the respondents were divided into groups ranging from 1 up to 200 € to 5 over 1200 €). In order to investigate whether the propensity for a tourist risk in Millennial travelers in Poland and Slovakia shows statistically significant dependence on the abovementioned variables, the author also used the Chi-square test of independence and Kendall’s Tau correlation.

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4 Results The first step was evaluating the importance of the factors which determine the journey (Table 1). Data analysis indicated that in both countries, to over 1/4 of the respondents, the level of safety and travel costs are both important or very important factors determining their decision to travel to a given tourist destination. In both countries in question, considerably more respondents evaluate the importance of the economic stimulus positively (in Poland—78.1% and in Slovakia—82.6%) than negatively, i.e., as unimportant or rather unimportant (4.3% and 3.8%, respectively). It is worth noticing, however, that the importance of both factors is evaluated in a similar way in both countries, which is confirmed by the mean rank and the sum of ranks. In Poland, the importance of travel costs is rated slightly higher. This means that a large part of the Millennials included in the study attached great importance to the issue of safety and the economic aspect alike. However, from the point of view of the propensity to take tourist risks, it is important which factor (the travel costs or safety level) is of greater importance to tourists making travel decisions. Data analysis showed that in both countries, it is the safety and security level that is usually rated higher, still, as many as 1/4 of the Slovakian and 29.9% of the Polish respondents rated the economic factor higher than the safety factor. The statistical verification by means of the one-sample proportion test for both countries (for Poland, z ¼ 19.345; for Slovakia. z ¼ 11.489) demonstrated a statistically significant percentage of respondents who showed a high perception of tourist risk, i.e., who rated the significance of travel costs higher that the safety level, when deciding to go on a tourist trip. The two-sample proportion z-test indicated that among the population of respondents who evaluated the economic factor (travel costs) higher than the level of safety, when taking a decision to travel to a given tourist destination, the proportions were not the same in both countries, with more representatives of this approach in Poland than in Slovakia. In order to investigate whether the Millennial travelers were willing to forgo safety and security in exchange for an economic advantage, it was investigated at

Table 1 Rating the importance of travel determining factors

Factors determining traveling Level of safety Travel cost

Country PL SVK PL SVK

PL Poland, SVK Slovakia Source: Survey data

Descriptive statistics Mean Std. rank deviation 4.112 1.029 4.189 0.907 4.114 0.879 4.083 0.845

1

2

3

4

5

Factor 1 rated higher than factor 2

% 3.2 1.5 1.1 1.1

% 4.9 4.2 3.2 2.7

% 14.0 11.7 17.7 17.0

% 33.2 39.0 39.5 45.1

% 44.6 43.6 38.6 34.1

% 32.5 34.8 29.9 25.0

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60%

39.8%

40% 23.9%

23.5% 14.4%

20% 6.0% 5.3%

16.4%

5.5% 7.6%

0% up to 30%

40% 50% Level of price reduction

70%

100%

Fig. 1 Distribution of respondents according to the level of travel cost reduction, at which they would agree to visit a country with a high level of risk. Source: Survey data Table 2 Gender as a factor determining respondents’ perception of travel risks: Mann–Whitney U test statistics Country Test statistics Importance of travel cost Willingness to forgo safety while reducing travel cost

Poland U 62,206.50 55,254.00

Z 2.8271 5.0165

p-value 0.0046 0.0000

Slovakia U 19,688.00 17,290.00

Z 1.9067 3.6144

p-value 0.0565 0.0003

Source: Own calculations  The statistically significant results for the H1 hypothesis are bold

what level of price reduction they would be ready to do that (Fig. 1). A large percentage of the Millennials participating in the study in both countries are ready to take a risk and forgo safety and security in exchange for an economic advantage. Only 57.7% in Poland and 39.8% in Slovakia would not travel to a country with a high level of terrorist risk, even if the overall cost of their trip was lowered. The remaining respondents from this part of Europe are willing to travel despite the existing threat. The results also indicate that the lower the price bonus, the fewer the respondents agree to resign from safety. Although the percentage of Slovaks (60.2%) ready to accept such a “deal” is higher than that among Polish Millennials (42.3%) (which slightly contradicts the contents of Table 1), more people in Poland than in Slovakia are ready to take risks at a low (up to 30%) price reduction (6.0% and 5.3%, respectively). In the next part of the study, the author analyzed the influence of gender, tourist experience (measured by the frequency of traveling), and the average travel expenses on the perception of terrorist threat among Millennials from Poland and Slovakia. First, it was investigated whether gender causes differences in the evaluation of the terrorist risk. The study was conducted with respect to the evaluation of the importance of travel costs as a determining factor, as well as the willingness to forgo safety and security in exchange for an economic advantage (Table 2).

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Table 3 Gender as a factor determining respondents’ perception of travel risks: Descriptive statistics Factors determining traveling Importance of travel cost

Country PL SVK

Willingness to forgo safety while reducing travel cost

PL SVK

Sex Female Male Female Male Female Male Female Male

Descriptive statistics Mean Std. rank deviation 4.18 0.84 3.95 0.97 4.12 0.84 3.94 0.86 4.28 1.13 3.78 1.34 3.95 1.15 3.47 1.23

1

2

3

4

5

% 0.6 2.2 0.9 1.9 4.4 10.4 4.7 7.5

% 2.6 4.8 2.8 1.9 5.0 7.0 6.2 13.2

% 16.2 21.7 15.6 22.6 12.8 18.7 21.8 30.2

% 39.7 38.7 44.5 47.2 14.4 21.7 24.2 22.6

% 40.9 32.6 36.0 26.4 63.5 42.2 43.1 26.4

Source: Own calculations

The results of the study by means of the Mann–Whitney U test indicated that in the case of Poland, there is one statistically significant difference between women and men, both as regards the evaluation of the importance of travel costs as a factor determining the decision to travel to a given tourist destination and the readiness to resign from safety for the benefit of an economic stimulus (travel cost reduction). In the case of the respondents from Slovakia, such statistically significant differences occurred only as regards the readiness to resign from safety for the benefit of the economic stimulus (travel cost reduction). Due to the statistically significant differences between the respondents grouped by gender, the descriptive statistics were investigated (Table 3). The results of the study indicate that among the respondents from both countries, women rate the importance of travel costs higher than men, and a larger percentage of women than men evaluate this factor as important or very important when choosing a tourist destination. From the point of view of risk propensity, it is significant what percentage rate the importance of travel costs higher than safety, so this particular aspect was analyzed. The results of the analysis showed that attaching great importance to price does not directly lead to risk propensity, as this percentage was 25.5% in Poland and 24.4% in Slovakia for women and, respectively, 29.6% and 28.3% for men. This means that men display a stronger risk propensity than women. Similar conclusions are provided by the second variable, which is the willingness to forgo safety in exchange for an economic advantage (travel cost reduction). Only 42.2% of men in Poland and 26.4% in Slovakia, as well as 63.5% and 43.1% of women in respective countries, would not resign from safety in exchange for an economic benefit. This means that men display a stronger risk propensity than women. It concerns both countries included in the study. What is important, as many as 17.4% of men in Poland and 20.7% in Slovakia declare that they would trade safety for a substantial cost reduction, i.e., up to 40% (compared to 9.4% and 11.9% of women, respectively).

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The next factor considered in the analysis was the frequency of traveling. In order to investigate whether there is a statistically significant difference among the respondents with respect to this variable, the Kruskal–Wallis test was used (Table 4). The results of the analysis indicated that Polish respondents can be grouped according to the frequency of traveling, while there are no statistically significant differences here for respondents from Slovakia. The analysis of the percentage of respondents choosing the economic factor over security and safety, conducted by means of descriptive statistics, indicated that there were no differences between the studied groups, with respect to the frequency of travel. Therefore, it was decided to carry out a more in-depth analysis of the studied phenomenon. In order to determine which groups are different, post hoc testing was conducted (Dunn’s test). The study showed that there was no significant difference in values among the groups. This means that in the case of respondents from Poland and Slovakia, the frequency of traveling does not cause statistically significant differences between groups, both as regards the evaluation of the importance of travel costs and the willingness to forgo safety and security in exchange for an economic advantage. Next, it was examined whether there were differences between the respondents grouped according to the level of travel expenses. Kruskal–Wallis test statistics demonstrated that such differences occur for respondents from Poland and Slovakia alike (Table 4). Hence, it was investigated which groups were different. Post hoc testing (Dunn’s test) showed that in Poland, there were statistically significant differences in evaluating the importance of travel costs for expenses ranging from 0–200 € to 201–899 € ( p ¼ 0.0313), from 0–200 € to 801–1200 € ( p ¼ 0.0007), and from 201–400 € to 801–1200 € ( p ¼ 0.126). For Slovakian respondents, the statistically significant differences in the evaluation of the importance of travel costs were found for expenses ranging from 0–200 € to 201–400 € ( p ¼ 0.0014). As regards the willingness to forgo safety in exchange for a reduced travel cost (indicating the satisfactory price), it was found that in Poland, there was a significant difference between the respondents who spent 0–200 € and 401–800 € ( p ¼ 0.0248) on a trip. In Slovakia, similar differences are observed with expenditure ranging from 0–200 € to over 1200 € ( p ¼ 0.0276), from 201–400 € to over 1200 € ( p ¼ 0.0274), from 401–800 € to over 1200 € ( p ¼ 0.0143), as well as from 801–1200 € to over 1200 € ( p ¼ 0.0329). The results of the statistical analysis required a confirmation of the conclusions drawn so far and an examination by means of the Chi-square test of independence, whether there is any correlation (null hypothesis) between travel risks perceived by the Millennials included in the study and their gender, the frequency of traveling, and the level of travel expenses (Table 5). The results confirmed the earlier conclusions from the statistical analysis and indicated that in Poland and Slovakia, there is no correlation between the importance of travel costs and the frequency of traveling or between the willingness to forgo safety in exchange for reduced travel costs and the frequency of traveling. It was also found that among Polish respondents, there was a correlation between their perception of travel risks (importance of travel costs and the willingness to forgo safety in exchange for reduced travel costs) and gender and the level of travel expenses. On

Frequency of traveling Poland H p(4, N ¼ 849) value 9.8406 0.0432 12.8025 0.0123

Source: Own calculations  Grouping variable: frequency of traveling and travel costs  The statistically significant results for the H1 hypothesis are bold

Test statistics Importance of travel cost Willingness to forgo safety while reducing travel cost

Grouping variable Country

Table 4 Evaluation of perceived travel risks: Kruskal–Wallis test statistics Slovakia H (4, N ¼ 528) 4.5121 2.5382 pvalue 0.3411 0.6378

Travel costs Poland H (4, N ¼ 849) 24.5774 15.6798

pvalue 0.0001 0.0035

Slovakia H (4, N ¼ 528) 18.7980 11.6417

pvalue 0.0009 0.0202

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Table 5 Evaluation of perceived travel risks: Chi-square test of independence statistics Country

Poland Chisquare

Test statistics Independence variable: sex Importance of travel cost 12.2091 Willingness to forgo safety while reducing 34.3046 travel costs Independence variable: the frequency of traveling Importance of travel cost 27.5344 Willingness to forgo safety while reducing 23.2347 travel cost Independence variable: the level of travel expenses Importance of travel cost 68.8362 Willingness to forgo safety while reducing 28.4377 travel cost

pvalue

Slovakia Chisquare

df

4 4

0.0159 0.0000

5.8075 15.3828

4 4

0.2140 0.0040

16 16

0.0359 0.1076

19.0654 19.5901

16 16

0.2653 0.2392

16 16

0.0000 0.0280

44.8478 35.9945

16 16

0.0001 0.0029

df

pvalue

Source: Own calculations  The statistically significant results for the H1 hypothesis are bold Table 6 The correlation between the perception of travel risks and gender, the frequency of traveling, and the level of travel expenses: Kendall’s Tau correlation Dependent variable Importance of travel cost Willingness to forgo up safety while reducing travel cost

Country Poland Slovakia Poland Slovakia

Sex 20.0974 20.0841 20.1797 20.1514

Frequency of traveling 20.0831 0.0166 20.0746 0.0112

Level of travel expenses 20.1322 20.1246 20.1157 0.0149

Source: Own calculations The statistically significant results are bold



the other hand, in Slovakia, there was a correlation between the perception of travel risks (the importance of travel costs and the willingness to forgo safety in exchange for reduced travel costs) and the level of travel expenses. As regards gender as an independent variable, there is a correlation between the willingness to forgo safety in exchange for reduced travel costs and gender, and there is no correlation between the importance of travel costs and gender. Finally, it was investigated how strong the correlation was between Polish and Slovakian Millennial travelers’ risk propensity and independent variables. The measuring tool used for that purpose was Kendall’s Tau (Kendall rank correlation coefficient). The results are presented in Table 6. The analysis of the correlation showed that in Poland, there is a statistically significant, though weak, correlation between the studied independent variables and the importance of travel costs and the willingness to forgo safety in exchange for reduced travel costs. In Poland, the strongest correlation was observed with respect to travel expenses, which means that the larger the sums spent on the journey,

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the smaller the importance of travel costs. At the same time, with the decreasing scale of travel price reduction, the respondents would be ready to resign from safety and travel to a country with a higher level of terrorist threat. In Slovakia, the strongest correlation exists in the case of gender—women are more sensitive to terrorist threat in tourism than men. As regards the remaining independent variables, there is no statistically significant correlation.

5 Conclusions The aim of the article was to evaluate the perception of tourist risk related to terrorism by Millennial travelers from Poland and Slovakia, where the level of terrorist threat is low (red24 2017; Roser et al. 2018). It was investigated which factor—safety or economic benefit—plays the most important role for them when they decide to travel and whether these tourists are ready to accept an increased risk and travel to a tourist destination where the terrorist threat is high, provided they are offered a reduced cost of travel. Moreover, the author investigated whether the risk propensity changed among Millennials, depending on their gender, frequency of traveling, and the expenses incurred for the trip. The researcher focused on this particular group of tourists because they represent a social generation, who willingly use impersonal sources of information when they decide to go on a tourist trip, and travel is playing an increasingly important role in their lives. Moreover, they often do not have any experience in traveling to tourist destinations with a higher level of terrorist threat; they are also generating a growing income for the tourism sector. It has often been indicated in the literature on the subject that when people make decisions in risk conditions, they do not follow the theory of expected usability but evaluate the outcomes of their decisions in a subjective way. Moreover, it has been emphasized that the perception of risk does not have to be the same as the actual risk. Moreover, based on the analysis of countries where the level of tourist risk is higher, it has been shown that the approach to risk and tourist inclinations may vary due to different factors, the most common ones being the tourist’s age, nationality, country of origin, and the experience of intense emotions. It has also been stressed that in the context of terrorist threat, the level of security and safety is often important, as terrorism creates an atmosphere of uncertainty and has an impact on tourists’ experience. The results of the analysis conducted in this article lead to several basic conclusions. The first of them is that a substantial percentage of Millennial travelers in the studied countries (about 1/3) typically display an inclination to take tourist risks in the face of terrorist threat and prioritize the cost of travel over travel security. They show this attitude even in a situation when they travel to countries recognized as having a high terrorist threat index. Perhaps, it is related to what was suggested by Rubin and Wessely (2013), who said that the negative psychological effect of terrorism on individuals disappears over time. It should be added, however, that

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despite the fact that the economic factor plays a very important role in the decisionmaking process regarding the direction of travel, often more important than safety, a considerable number of Millennial travelers would never take the risk and travel to a tourist destination with a very high level of terrorist threat (57.7% in Poland and 39.8% in Slovakia). It was also established that the perception of risk among the respondents may change under the influence of certain factors. Gender is definitely a factor that determines the perception of travel risk among Millennials from this region. Unlike in other regions of the world, the frequency of traveling does not have an impact on the perception of tourism risk. Moreover, it was found that the importance of travel costs and the willingness to sacrifice safety in exchange for an economic stimulus depend on the level of travel expenses, although the correlation in the countries in question is different. The results also showed that the perception of tourist risk among Millennials from this region, in a situation of a terrorist threat, is not a part of their tourist experience (the frequency of traveling). The results are then contradictory to those indicated by Li (2000) and Falk et al. (2012) for countries, where the level of threat is heightened. The results of the study also point to the fact that countries from the same region differ in the perception of travel risk, i.e., travelers coming from various countries may differ as regards their approach to the security level at tourist destinations. Poland and Slovakia are different from each other, to a certain extent. In this way, the study results are congruent with what was stated by Garg and Kumar (2017) and Nagaj and Žuromskaitė (2018). The paper makes several contributions in the literature. The author investigated the travel risks as perceived by Generation Y, which had not been thoroughly studied earlier, because research had been focused on other aspects than those discussed in the article. Furthermore, the article filled in certain research gaps by presenting an analysis of tourist inclinations and travel risks as perceived by tourists coming from regions where the terrorist threat is very low (the study was conducted for two such countries from Central Europe: Poland and Slovakia, not studied earlier in this context). In addition, the author examined the influence of factors such as gender, the frequency of traveling, and the travel expenses, in the countries included in the study. So far, similar research had been conducted for other regions of the world and for those where the terrorist risk index is high. The study presented in the article has certain limitations. The results cannot be generalized for the whole population of the studied countries but refer only to the research samples. In the circumstances, the author believes that more research concerning countries from this region of Europe is necessary and future studies should take the abovementioned limitations into account. Acknowledgments The project is financed within the framework of the program of the Minister of Science and Higher Education under the name “Regional Excellence Initiative” in the years 2019–2022, project number 001/RID/2018/19, the amount of financing PLN 10,684,000.00.

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