Research Methodology in Management and Industrial Engineering 3030408957, 9783030408954

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Management and Industrial Engineering

Carolina Machado J. Paulo Davim   Editors

Research Methodology in Management and Industrial Engineering

Management and Industrial Engineering Series Editor J. Paulo Davim, Department of Mechanical Engineering, University of Aveiro, Aveiro, Portugal

This series fosters information exchange and discussion on management and industrial engineering and related aspects, namely global management, organizational development and change, strategic management, lean production, performance management, production management, quality engineering, maintenance management, productivity improvement, materials management, human resource management, workforce behavior, innovation and change, technological and organizational flexibility, self-directed work teams, knowledge management, organizational learning, learning organizations, entrepreneurship, sustainable management, etc. The series provides discussion and the exchange of information on principles, strategies, models, techniques, methodologies and applications of management and industrial engineering in the field of the different types of organizational activities. It aims to communicate the latest developments and thinking in what concerns the latest research activity relating to new organizational challenges and changes world-wide. Contributions to this book series are welcome on all subjects related with management and industrial engineering. To submit a proposal or request further information, please contact Professor J. Paulo Davim, Book Series Editor, [email protected]

More information about this series at http://www.springer.com/series/11690

Carolina Machado J. Paulo Davim •

Editors

Research Methodology in Management and Industrial Engineering

123

Editors Carolina Machado Department of Management School of Economics and Management University of Minho, Campus Gualtar Braga, Portugal

J. Paulo Davim Department of Mechanical Engineering University of Aveiro, Campus Santiago Aveiro, Portugal

ISSN 2365-0532 ISSN 2365-0540 (electronic) Management and Industrial Engineering ISBN 978-3-030-40895-4 ISBN 978-3-030-40896-1 (eBook) https://doi.org/10.1007/978-3-030-40896-1 © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

Methodological issues, namely in the field of management and engineering, assume a relevant role when we aim to develop a deep and constructive research able to lead to the empirical and theoretical development of these fields of action. How can we design a research project? What kind of paradigm should we follow? Should we develop a qualitative/phenomenological research or a quantitative/positivistic one? What technics for data collections can we use? Should we use the entire population or a sample? What kind of sampling techniques can we have? These are only some of the multiple issues that all of us, as researchers in these fields of study face every time when we look to develop our research. Conscious of these issues, this new book looks to provide discussion and the exchange of information on principles, strategies, models, techniques, applications, and methodological options possible to develop in research in these areas of study. It aims to communicate the latest developments and thinking on the research methodologies subject in the different areas, with a particular emphasis to the management and industrial engineering fields, worldwide. It seeks cultural and geographic diversity in studies highlighting research methodologies that can be used in these different study areas. This book has a special interest in research on important issues that transcend the boundaries of single academic subjects. The main aim of this new book, entitled Research Methodology in Management and Industrial Engineering, is to provide channel of communication to disseminate knowledge about research methodology between academics and researchers, giving a special glance in the management and industrial engineering fields. This book can serve as a useful reference for academics, researchers, managers, engineers, and other professionals in related matters with research methodologies. Contributors were encouraged to identify the theoretical and practical implications of their methodological options to the development and improvement of their different study and research areas. Organized in six chapters, Research Methodology in Management and Industrial Engineering looks to cover in chapter one “A Reviewer’s Perspective: Which Mistakes Do Authors Often Make in Qualitative International Business Research?”; while the second chapter discusses “Evaluating Corporate Social Responsibility/ v

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Preface

Sustainability Strategic Maturity: Some Methodological Options.” The third chapter deals with “Technology Forecasting: Recent Trends and New Methods”; the fourth chapter presents the “Methodology Used for Determination of Critical Success Factors in Adopting the New General Data Protection Regulation in Higher Education Institutions”; the fifth chapter speaks about “Emotional Intelligence and Leadership: A 360-Degree View in the Electronics Industry in Portugal.” Finally, the sixth chapter focuses “The Role of Institutional Leadership in Employee Motivation, Satisfaction and Personal Development—Design of a Research Proposal.” Understood as a critical tool to more effectively achieve the desired success in the research field, the present book can serve as a useful reference for academics, lecturers, researchers, graduated and postgraduate students, managers, engineers, educational researchers, and other professionals in related matters with research methodology. The Editors acknowledge their gratitude to Springer for this opportunity and for their professional support. Finally, we would like to thank to all chapter authors for their interest and availability to work on this project. Braga, Portugal Aveiro, Portugal

Carolina Machado J. Paulo Davim

Contents

A Reviewer’s Perspective: Which Mistakes Do Authors Often Make in Qualitative International Business Research? . . . . . . . . . . . . . . . . . . . Tiia Vissak

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Evaluating Corporate Social Responsibility/Sustainability Strategic Maturity: Some Methodological Options . . . . . . . . . . . . . . . . . . . . . . . . António Marques-Mendes and Maria João Nicolau dos Santos

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Technology Forecasting: Recent Trends and New Methods . . . . . . . . . . Gema Calleja-Sanz, Jordi Olivella-Nadal and Francesc Solé-Parellada Methodology Used for Determination of Critical Success Factors in Adopting the New General Data Protection Regulation in Higher Education Institutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . José Fernandes, Carolina Feliciana Machado and Luís Amaral

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Emotional Intelligence and Leadership: A 360-Degree View in the Electronics Industry in Portugal . . . . . . . . . . . . . . . . . . . . . . . . . 111 José Rebelo dos Santos, Lurdes Pedro and Sandra Nunes The Role of Institutional Leadership in Employee Motivation, Satisfaction, and Personal Development—Design of a Research Proposal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 Maria Heliodora Matos and Carolina Feliciana Machado Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151

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Editors and Contributors

About the Editors Carolina Machado received her Ph.D. degree in Management Sciences (Organizational and Politics Management area/Human Resources Management) from the University of Minho in 1999, Master’s degree in Management (Strategic Human Resource Management) from Technical University of Lisbon in 1994, and degree in Business Administration from University of Minho in 1989. Teaching in the Human Resources Management subjects since 1989 at University of Minho, she is, since 2004, Associated Professor, with experience and research interest areas in the field of Human Resource Management, International Human Resource Management, Human Resource Management in SMEs, Training and Development, Emotional Intelligence, Management Change, Knowledge Management, and Management/HRM in the Digital Age. She is Head of the Department of Management and Head of the Human Resources Management Work Group at University of Minho, as well as Chief Editor of the International Journal of Applied Management Sciences and Engineering (IJAMSE), Guest Editor of journals, books Editor, and book Series Editor, as well as reviewer in different international prestigious journals. In addition, she has also published both as editor/co-editor and as author/co-author several books, chapters, and articles in journals and conferences. e-mail: [email protected] J. Paulo Davim received his Ph.D. degree in Mechanical Engineering in 1997, M.Sc. degree in Mechanical Engineering (materials and manufacturing processes) in 1991, Mechanical Engineering degree (5 years) in 1986, from the University of Porto (FEUP), the Aggregate title (Full Habilitation) from the University of Coimbra in 2005, and the D.Sc. from London Metropolitan University in 2013. He is Senior Chartered Engineer by the Portuguese Institution of Engineers with an MBA and Specialist titles in Engineering and Industrial Management, as well as, in metrology. He is also Eur Ing by FEANI-Brussels and Fellow (FIET) by IET London. Currently, he is Professor at the Department of Mechanical Engineering of the University of Aveiro, Portugal. He has more than 30 years of teaching and research experience in

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manufacturing, materials, mechanical, and industrial engineering, with special emphasis in machining and tribology. He has also interest in management, engineering education, and higher education for sustainability. He has guided a large number of postdoc, Ph.D., and master’s students as well as has coordinated and participated in several financed research projects. He has received several scientific awards. He has worked as evaluator of projects for European Research Council (ERC) and other international research agencies as well as examiner of Ph.D. thesis for many universities in different countries. He is the Editor-in-Chief of several international journals, Guest Editor of journals, books Editor, book Series Editor, and Scientific Advisory for many international journals and conferences. Presently, he is an Editorial Board member of 30 international journals and acts as reviewer for more than 100 prestigious Web of Science journals. In addition, he has also published as editor (and co-editor) more than 125 books and as author (and co-author) more than 10 books, 80 chapters, and 400 articles in journals and conferences (more than 250 articles in journals indexed in Web of Science core collection/h-index 54+/9500+ citations, SCOPUS/h-index 59+/11500+ citations, and Google Scholar/h-index 76+/19000+). e-mail: [email protected]

Contributors Luís Amaral School of Engineering, University of Minho, Guimarães, Portugal Gema Calleja-Sanz Serra Húnter fellow, Institute of Industrial and Control Engineering, Universitat Politècnica de Catalunya - BarcelonaTech (UPC), Barcelona, Spain José Rebelo dos Santos Polytechnic Institute of Setubal, College of Business Administration, Setúbal, Portugal Maria João Nicolau dos Santos ISEG—Lisbon School of Economics and Management, University of Lisbon, Lisbon, Portugal José Fernandes School of Economics and Management, University of Minho, Braga, Portugal Carolina Feliciana Machado Department of Management, School of Economics and Management, University of Minho, Braga, Portugal António Marques-Mendes ISEG—Lisbon School of Economics and Management, University of Lisbon, Lisbon, Portugal Maria Heliodora Matos Department of Management, School of Economics and Management, University of Minho, Braga, Portugal Sandra Nunes Polytechnic Institute of Setubal, College of Business Administration, Setúbal, Portugal Jordi Olivella-Nadal Institute of Industrial and Control Engineering, Universitat Politècnica de Catalunya - BarcelonaTech (UPC), Barcelona, Spain

Editors and Contributors

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Lurdes Pedro Polytechnic Institute of Setubal, College of Business Administration, Setúbal, Portugal Francesc Solé-Parellada Universitat Politècnica de Catalunya - BarcelonaTech (UPC), Barcelona, Spain Tiia Vissak University of Tartu, Tartu, Estonia

A Reviewer’s Perspective: Which Mistakes Do Authors Often Make in Qualitative International Business Research? Tiia Vissak

Abstract This chapter summarizes 20 main suggestions on which mistakes to avoid in submitting qualitative (mainly case study-based) International Business research to academic journals and books, but also to academic conferences. I developed these suggestions based on my extensive experience from reviewing and publishing articles. Some of my suggestions are directly the case study methodology- and International Business-field-related, while some others are relatively universal. Thus, they could be beneficial for scholars preferring other methodologies and focusing on other research areas as well: especially, for Ph.D. students and other less experienced scholars trying to publish their work without involving more experienced/senior coauthors. Although following all of these suggestions will not automatically guarantee success (my own articles still get rejected, too, from time to time), ignoring them could lead to rejection.

1 Introduction In the world of “publish or perish,” it is very important to understand which mistakes to avoid in publishing academic articles. Prominent journals accept only a small fraction of submitted studies (in some, the acceptance rate is below 3%), but less prestigious journals become more and more selective in which studies to publish, too. Moreover, even book editors and conference organizers do not accept all submitted studies. Publishing qualitative research can be especially challenging as some editors and reviewers seem to object case study based papers—especially, the ones based on a single case—as such. Thus, not knowing the “rules of the game” can result in experiencing disappointment after disappointment. I developed the following 20 guidelines mainly based on my experiences from reviewing and publishing journal articles.1 I went through all reviews I had written in the last ten years, but also through all the reviews that I had received during the period, T. Vissak (B) University of Tartu, Tartu, Estonia e-mail: [email protected] © Springer Nature Switzerland AG 2020 C. Machado and J. P Davim (eds.), Research Methodology in Management and Industrial Engineering, Management and Industrial Engineering, https://doi.org/10.1007/978-3-030-40896-1_1

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and I also checked the notes I had made during “meet the editors” sessions at various conferences. Naturally, someone might argue that several universities offer courses on academic publishing, and numerous books and journal articles focus on this topic (see, for instance, Brennan 2019; Clark et al. 2016; De Lange et al. 2018; Fayolle and Wright 2014; Mariotto et al. 2014; Sheppard 2015). Still, despite the abundance of such information, when reviewing papers, I have had to suggest substantially rewriting or, in some cases, rejecting a large number of qualitative articles due to several—and, unfortunately, very frequent and repeating—problems. Consequently, as there still seems to be need for advice on how to publish qualitative academic articles, in this chapter, I will explain which mistakes I have frequently encountered and provide some advice on how to avoid rejection from academic journals, books and conferences.

2 Mistakes and Suggestions 2.1 Selecting an Unsuitable Journal Before submitting your article, check that this journal is suitable for your study. If your article does not focus on exporting or International Business from any perspective (just studying, e.g., product development practices of Vietnamese food producers or Nigerian farms’ successful advertising strategies is not enough if they do not export anything, do not invest abroad and have no other foreign operations), then rather select another journal, not anything directly International Business-related. Otherwise, the editor will most probably desk-reject your work (will not send it to the reviewers). This does not mean that studies on the above-mentioned topics will have no chance anywhere: an outlet focused on business, management or marketing in a broader perspective, or a journal with a regional focus might find them quite suitable. NB! Check what kinds of papers (e.g., qualitative vs. quantitative) on which topics the potential “target” journal has published before. If you will find at least a couple of articles on your topic, and if this outlet has published qualitative studies before, you might have chosen the right place for your work. NB! It might be reasonable to cite some of these studies in your article, too, as this will convince the editor and the reviewers that your work contributes to what the journal has published before. If this is not the case, check which outlets you mainly cited in your study: Maybe another journal is much more suitable. If necessary, consult the editor(s).

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2.2 Not Explaining Why Your Topic Is Important/Novel Editors and reviewers expect to see a good explanation of why you have decided to study this topic. You should clearly explain where the “research gap” is and why the readers should care about this (for some advice, see, e.g., Grant and Pollock 2011; Lange and Pfarrer 2017; Nicholson et al. 2018). For instance, if you state that according to most of the literature (and cite some studies), a foreign market exit is a sign of failure, but according to the interviews that you made in four case firms, exiting foreign markets is a strategic choice, then this is a relatively good explanation. On the other hand, if you mention that hundreds of articles have focused on born globals but authors have never studied such firms in your hometown, or that no one has studied this particular (not well known) firm before, then this will probably not convince the reviewers that you do something novel and valuable enough. Of course, it is also not a good strategy to state that this topic or context is novel if it actually is not. For instance, if you only claim that small firms have received less attention than large ones and/or that firms from emerging economies have been largely ignored in the literature, then a reviewer might state that numerous authors have used such arguments for decades and such studies are not so rare any more. Thus, explain if something else is important/novel about this issue. You should also explain what is new about your topic if you “forget” which context—for instance, country or firm—you are studying. A hint on how to demonstrate novelty: Read some recent articles on your topic and pay special attention on their introduction and conclusion sections as in the former, the authors usually explain why it is necessary to study this issue, while in the latter they give some useful suggestions on what topics future studies should cover. Cite such arguments (e.g., “According to several authors (A 2018; B et al. 2019…) this topic needs more research attention as…”)! NB! In terms of “recent,” I really mean something published during the last 3–5 years: Several topics that were novel, e.g., in the 1990s (like the emergence of born globals) are not so under-researched any more.

2.3 Claiming “Too Much” in Terms of Your Contribution Being too modest is not a good strategy (you should really emphasize the contribution of your study; otherwise, you might receive a [desk-]rejection letter soon, especially if you submitted your work to one of the “top” journals) but claiming too much can lead to the rejection of your article, too. For example, you might have discovered something interesting about a few case firms but the result is not necessarily a “new theory.” Also, do not claim that you “proved” or “refuted” a previous theory if you only collected data from a few firms from a specific region, country or city (e.g., from Southern Portugal or from Venezuela or from Shanghai). Moreover, you should also not claim that this region, country or city represents a much larger

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region (e.g., that Barcelona represents the whole Western Europe or that Tokyo represents the whole Asia). NB! You should keep the above advice in mind when you select a title for your article, too! For example, if you have “European exporters” in your title but you only studied two firms from Paris or Nice, then the reviewer might not be positive about your study: “Two French exporters” would be a better choice.

2.4 Not Reading the “Right” Literature A strong literature review part is very important for qualitative (and naturally, also quantitative) articles: Just presenting interesting case study data is not enough unless you are trying to publish a “teaching case” (something that professors can use in their classrooms). In the literature review part, it is important to demonstrate that you know both the earlier literature and the newest studies on this topic plus, of course, the work published in the period in between (e.g., you cannot only cite some studies from the 1970s and some published in a few recent years). Reviewers tend to reject articles that ignore the newest work and/or the important studies published before. This can be a sign that the authors do not know the “field” well enough, or, in case of ignoring the newest articles, that several other journals already rejected their work and they have not cared enough to update it before submitting it to another one. It is also not good to focus too much on the much-cited works of a couple of “key” authors. Yes, they might be very important and, thus, you should mention them, but if you will only give an overview of a couple of such studies in the literature review part, this will not convince the reviewers that you really know the literature. You have to be aware of what these authors have done in recent years (in case they are still active), and how other authors have developed their work further. Here are some suggestions regarding how to find useful literature. 1. Use different keywords, not only the one(s) you plan to use in the title of your article: For example, if you write about exports, you might also find some very suitable studies if you use “internationalization” or “Uppsala model” or “born global” as your search terms. To reduce the number of results (e.g., “export” is also common in studies in the fields of Chemistry and Engineering and not necessarily with the same meaning), you can select the right fields if the database allows it—for instance, in Scopus you can select “Business, Management and Accounting” and “Economics, Econometrics and Finance”—or use additional keywords. 2. Some databases allow you to sort the literature by novelty (to find the newest work on your topic) but also by the number of citations (this way you will not miss the important studies that many authors cite). Use this feature. 3. If you write about a specific country or industry, do not forget to cite some previous studies on it: The reviewers will check if your work is really the first one on, e.g., Mexican born globals or Chinese state-owned exporters or Turkish

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family firms or Italian wine producers. NB! You should still cite some “general” export and/or internationalization literature, too: For example, you cannot only search for studies on Chilean food exports, as your study also has to contribute to wider export literature. 4. Sometimes, a “snowball” method can be very useful: read some articles—especially, literature review articles—on your topic and have a look at their list of references: you might discover several studies that you have not cited yet (NB! some of them might not be in your “favorite” databases). Read those studies, and check which journal articles, book chapters and books they have cited; go through those, and you could find some more work to read and cite. In addition, after finding out who are the most important authors in your field (e.g., Johanson and Vahlne in case of the Uppsala model) you could search for their other articles, too. 5. Keep in mind that some databases are a few months “behind” in adding the newest articles, especially those still “in press.” Thus, to find out what the prominent journals (for instance, Journal of International Business Studies, Journal of World Business, International Business Review and Global Strategy Journal), and your potential “target” journals have recently published on your topic, also check their websites. NB! Do not become an “impact factor snob” or a “number of citations snob”! This means that you should not ignore important studies due to the reason that they were published in outlets with low impact factors or without impact factors (e.g., in books), or that you should not cite some studies as they have not collected a certain number of citations every year. The outlet itself does not determine the value of someone’s work (yes, sometimes even “top” journals have published studies containing some statements that are somewhat “suspicious”), and the number of citations is not always a good indicator, either: the quality of the work and its relevance to your topic matter more. Still, despite the above advice, keep in mind that excessive use of articles published in “predatory journals” can harm your article’s publication chances as such outlets do not have a good reputation due to lacking a rigorous peer-review process! Another important issue: Do not cite your own previous work too much. Of course, if you have written several important articles on this topic, it is completely normal that you cite some of them (otherwise, the reviewers might suggest that you do that, and criticize you that you have ignored important work). Still, citing, e.g., 10 conference papers and/or working papers and/or papers published in “predatory journals”—especially if they do not contribute much to this particular topic—will not leave a good impression of your article. Moreover, if you only cite your own work excessively, the reviewers will most probably guess who you are and some might refuse to review your study as in most academic journals, the process is meant to be “double blind” (the reviewers should not know who you are and you should not know who they are) to reduce subjectivity.

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2.5 Not Using the Literature Properly In the literature review part, you should give a good overview of what other authors have done before on your topic and what you can conclude based on this. This means that you should really analyze the literature and compare similarities and differences between different research streams, statements, etc. Composing a figure or a table summarizing previous studies on your topic is usually a good strategy (see Fig. 1 and Table 1 for some ideas), as this helps you to give a better overview of other authors’ work and, if necessary, to explain why you selected specific sub-topics, developed these propositions, etc. NB! You should be careful not to classify some studies into “wrong” categories, call certain “literature” a “theory,” claim that this particular study was the first to use a certain term if it actually was not (e.g., if someone did it in a book chapter years before), etc.: In that case, the reviewers will most probably reject your article. If in doubt, check several earlier studies, as, unfortunately, sometimes, (1)… (Author A, 1993)

X

(2)... (Authors B and C, 2003; …)

(3)… (Author D, 1995, 2005; …)

(4) … (Author E et al., 1999, 2005)

– stronger or direct relationships

Y

– weaker or indirect relationships

Fig. 1 An example of how to summarize the literature: factors affecting X and Y

Table 1 An example of how to present the findings of previous studies Study

Indicators and their measurements

Data and method(s)

Findings

Author A (1999)

Indicator 1 (measured as…) Indicator 2 (calculated as…) …

… cases from …; a case survey

Young firms are more likely to ….

Authors B and C (2018)

Indicator 3 (based on the respondents’ perceptions of the following 5 aspects: …)

… cases from…; a survey

Exporters’ …. is higher than … especially if …; … is also important for some firms

Indicator 1 (measured similarly as in Author A [2010]) Indicator 24 (… = …/….)

1 case from…; a database of …

Larger manufacturing firms are affected more by … if …

… Author Y (1975)

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some authors have misunderstood the literature, while the reviewers and editors have not discovered this and have decided to publish such work. NB! It is not good enough to write all the text in the style “A stated…, B studied…, C concluded…” as you should show that you were able to conclude something important based on it. So, rather write, e.g., “While according to A (1982) and B (2017), … is important for early internationalization, C (2003) found that in case of…, this is not always necessary, while D (2019) added that….” Have a look at Shepherd and Wiklund (2020) to get some ideas. If your article has propositions/preliminary conclusions (e.g., that certain network relationships are useful for internationalization), then do not forget to explain clearly why you developed these particular ones (for some suggestions, read, e.g., Sparrowe and Mayer 2011). The reviewers will not usually appreciate reading a 10-pagelong literature review suddenly followed by several propositions: Instead, devote a few paragraphs to develop each of them separately (e.g., explain that according to studies 1, 2, 3 and 4, such network relationships are useful for internationalization and, consequently, you also assume that they are useful). NB! In case the previous literature is conflicting (e.g., some authors have found network relationships useful, while some have discovered that they can harm firms’ internationalization), do not forget to mention this and explain why you still decided to choose one “side” in your proposition!

2.6 Being Unclear About Definitions Unfortunately, some authors forget to explain clearly what they actually study in their article. For instance, many scholars have studied born globals, but they have not all used the same definition. If you claim to study born globals, too, then you should clearly explain how you defined them and how you checked that your case firms were in that category. Otherwise, the reviewers might doubt that your “born globals” were actually “global” according to the most popular definitions (e.g., maybe you studied firms that only exported to 1–2 neighboring countries, or maybe your case firms did not internationalize early enough), and they would request you to use a more suitable term (e.g., “international new ventures”). NB! If you study a “wide” concept like export performance, networking or knowledge, then you should be very clear about how you define it and why you prefer this particular definition (do not forget to cite some important studies to support your arguments!), as again, there is no consensus in the literature on how they should be defined and measured. Otherwise, the reviewers might get disappointed if they understand this concept in a very different way.

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2.7 Not Explaining Why You Used Specific Data Do not try to “hide” what you actually studied in your journal article, book chapter or conference paper: The reviewers expect to understand based on your title, abstract and introduction already what the work was based on, but, of course, you should provide a more detailed explanation about your data and method(s) in the methodology section. Still, explain shortly already in the introduction why it is especially important to study this particular region (e.g., Europe), country (e.g., Finland or Poland) or a firm that has behaved in a specific way (e.g., the one that has experienced several foreign market re-entries). NB! In case of studying two or more countries or cases, also explain why studying them is especially important: for example, if they are similar or different in a specific way (size, history, “success” or “failure” in something, etc.), and why studying similar or different countries or case firms is extremely necessary. For instance, you might read Patton’s (2015, pp. 266–273) 40 different principles for case selection and thereafter, explain if your multiple cases were “typical,” “confirming” or “disconfirming” (or why your single case was “critical” or whether it represented an “exemplar of a phenomenon of interest”). NB! The literature review part should also take into account the context you studied, and you should convince the reviewers that you used suitable data and method(s) for studying this topic!

2.8 Not Explaining How and Why You Used This Method You will find a large number of books and articles on why and how to use a particular qualitative method for data collection and analysis (see, e.g., Denzin and Lincoln 2017; Dubois and Gadde 2002; Dul and Hak 2008; Eisenhardt 1989; Eisenhardt and Graebner 2007; Flyvbjerg 2006; Gibbert et al. 2008; Jonsen et al. 2018; Mariotto et al. 2014; Miles et al 2014; Piekkari and Welch 2011; Stake 2010; Vissak 2010; Welch et al. 2011; Yin 2018). There is also considerable literature on mixed methods (Cameron and Molina-Azorin 2011; Creswell 2014; Edmonds and Kennedy 2017; Fetters and Molina-Azorin 2019; Hurmerinta-Peltomäki and Nummela 2006). Read and cite some of those works (and additional ones, as the above list is not comprehensive!): preferably some more recent ones and some earlier but important ones, too. Also, have a look at some recent articles (preferably also some published in the last 3–5 years in prominent journals) written on your topic and read what the authors did in their methodology and data analysis sections, and how they explained what and how they did in their research. Learn from those studies, and also have a look at the suggestions of Aguinis et al. (2018), Bansal and Corley (2012), Berends and Deken (2020), Cuervo-Cazurra et al. (2016), and Zhang and Jason (2012). In addition, you could consider depicting your research process in a figure (see Fig. 2) or summarizing your data collection in a table (see Table 2).

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GOALS To understand … via: • … • … THE RESEARCH PROCESS 1. Studying the literature on … 2. Selecting … case(s) that… ... … 10. Finishing the article

Fig. 2 The research process: an example based on Vissak et al. (2020)

Table 2 Data collection: an example based on Vissak et al. (2020) Data sources

Informants

Time

Duration (minutes)

Outcomes

Semi-structured interviews

… and …

October 2019

60

Understanding…

Case survey

…, … and …

December 2019

25 for…, 30 for…, 35 for…

Getting information about…

Follow-up interview

…

January 2020

25

Getting more information about … and …

…

…

…

…

Archival documents

2019–2020

Data triangulation

NB! You have to convince the readers that you have correctly understood what the method is good for, what its limitations are, and what certain terms (e.g., inductive, deductive and abductive, grounded theory, coding, first- and second-order themes, internal and external validity, triangulation, etc.) mean (read the methodological literature to understand their meanings!), or otherwise, you will receive a rejection letter soon. For instance, it is not a good idea to state that you followed the Gioia method (see, e.g., Gioia et al. 2013) if you actually did not. Moreover, if you conducted interviews in several firms, you should also use several quotes in the text: Otherwise, you will reduce the richness of the results that is one of the strengths of case study research.

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Also, do not forget to explain how you collected the data. For instance, if you conducted interviews, then mention when (month, year), with whom, why with these respondents, what kind of questions (e.g., semi-structured, open-ended) you asked about what, in which language you conducted these interviews, how you recorded them, etc. NB! Several “top” journals (but not only those) expect you to make several interviews in your case firm(s) especially if you only studied a few of them (see, e.g., Kindsiko and Poltimäe 2019) and you have to convince them that you achieved “data saturation” (Fusch and Ness 2015)! If you made a case survey, too (see, e.g., Dul and Hak 2008; Yin 2018), then give an overview of how you composed your questionnaire: Based on which previous studies, why these, did you also change or add some questions, why, what exactly your scales mean: For example, what is “1” and what is “7,” etc. In case of only using earlier data, explain why it was not possible to use newer data (e.g., from the last 3 years: Maybe the case firm went bankrupt or perhaps it has a new owner or a manager who has refused to provide any updates?). Otherwise, the reviewers will certainly ask you to explain all these issues in the next version of your article, or, in case it has several other problems, too (or if you submitted your work to a “top” journal), they might recommend rejecting it.

2.9 Distorting Data Of course, you should also never “play” with the data: Add, remove or distort something to achieve “desired” results. This is extremely unethical, and it could end your career. Science is about studying the truth, irrespective of whether it “agrees” or “disagrees” with current theories! So, you must never, e.g., claim that your case firm was a born global if it actually was not, or add a few numbers or “quotes” not based on real firms to “prove” your “theory” or “reject” someone else’s. You should also never give the impression that you interviewed this firm a few months ago if actually you went there 10 years ago when you collected data for your thesis (in that case, try to revisit the firm again and you might get some very interesting updates!). By the way, some editors and reviewers may ask you to reveal the identity of the case firm so that they can check themselves if you told a true story or not (still, even in such cases, in the published paper, you can write about Firm 1, Firm 2, etc. without their real names if the interviewees requested anonymity). This should reduce the occurrence of “fairy tale” cases. NB! You should also not hide or over-emphasize evidence to make your interviewee(s) happy: Your job is to explain what the real situation looked like, not to become the firm’s advertising agent! A teaching case can be based on hypothetical situations, and it can be compiled based on several firms at the same time, as it has a different goal: To make students discuss how they would solve a specific real or potential problem, but research cases are different! Thus, if your current case firm refuses to provide useful information, or if you cannot tell the whole truth due to the interviewees’ wish to display only their best side(s), try to find some other cases.

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2.10 Not Discussing the Results Well Enough The case description can be very interesting (for some advice on how to present your results, look at Reay et al. 2019), but without a strong discussion part, your paper could still get rejected as an academic article should also discuss the results. The discussion part is very important as there, you can demonstrate your skills to synthesize both the literature and your empirical results. In this part, you should discuss if your results were similar or different compared to the findings of previous studies, what could have caused particular differences, etc. NB! It is not enough to state that your results were very similar or very different compared to, e.g., the Uppsala model; you should not forget to cite particular articles compared to which you state this! A figure (e.g., see Fig. 3) or a table (see Table 3) summarizing your results can impress the reviewers, especially if you also explain in detail how you developed it and what it exactly shows (do not only say “we summarized the results in Fig. X”). NB! It is also possible to integrate discussion into your case study results— e.g., to state that “X (Author A, 1995) and Y (Author B et al., 2019)—affected the case firm’s internationalization the most, as Interviewees 1 and 2 emphasized that….” Still, even in the case of choosing this format, you should explain what was similar and different compared to previous studies. The latter is especially important as differences can help to develop the field further (as not many academics will care whether your study will be the 10256th or 23761st to state that internationalization can be beneficial for firms). NB! The discussion part is not the right place to introduce many new articles that you have not mentioned before. If you suddenly discover that another literature stream is more important to explain your results (e.g., if your firms’ behavior is more similar to what the initial Uppsala model stated, or if these firms were …, … and …

… and …

Earlier internationalization is… due to…

Later internationalization is… due to… Time, …

Fig. 3 An example on summarizing the results of the study on the role of … during the internationalization process

Table 3 An example on summarizing the results of a proposition-based study Proposition

Result and explanation

P1: … reduces …

Not supported as … (see Table X)

P2: … affects … positively

Received some support as according to Interviewee 2, … (also see Appendix A)

P3: … increases…

Supported as according to all interviewees… and according to Fig. Y, these firms’… increased in …–… when …

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effectual, not causal in their decision-making but you did not discuss such literature much enough), then update your introduction and literature review accordingly, too.

2.11 Not Concluding the Article Comprehensively The conclusions section is extremely important: The readers need to understand what you found out in the article, why your results are important (which important “research gap(s)” you filled), and which managerial (and/or policy) and research implications you have developed based on your results. NB! You should not copypaste any text from the rest of your article directly to this section, but you should still summarize briefly your main conclusions regarding your findings, their contributions to the literature and the resulting implications (for some advice, look, e.g., at Geletkanycz and Tepper 2012). NB! Managerial, policy and research implications should be useful, and not too “primitive”: For example, do not only state that exporting can be beneficial for firms or that exporters need more support: In that case, the reviewers might say that most authors suggest that, and that your work does not provide anything new. Naturally, you can suggest researchers to study more firms in the future, but you should also try to give more suggestions regarding, e.g., which additional aspects they should cover or what they should study more deeply and why in their studies. NB! Take into account that some readers will initially only have a look at your article’s title, abstract, introduction and conclusions, and if they will not find anything useful, they will not read the rest of your text. Also, keep in mind that the conclusions part is not suitable for suddenly giving an overview of some additional literature or data (e.g., dozens of quotes from managers): You should do this earlier. A final hint: Similarly to the introduction, the conclusions part should not be too long: 1–4 pages are usually fine (depending on the “target” journal’s traditions; thus, check what it has recently published), but writing 10 pages is clearly too much.

2.12 Lack of “Logic” in Terms of the Title, Literature, Data, etc. The reviewers reject some articles because they do not seem “logical”: For instance, the authors discuss some topics in the introduction or the literature review, but ignore them in the rest of the article, or have a certain term in the title but it gets almost no attention in the study or vice versa. Some authors also discuss too much literature in the introduction but forget to explain why the topic is important and what they will do in the rest of the text (for some suggestions on how to write the introduction, read, e.g., Ahlstrom 2015), while some suddenly introduce propositions without explaining why they developed these particular ones. Moreover, in some cases, the

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data, the method and the literature do not form a good match. For instance, the authors call their case firms “born globals,” but they do not directly discuss born globals in the literature review, or they aim to “prove” something based on a few cases (sometimes you can really do that, but not always: For example, if you studied one Italian family firm and found something, this will not guarantee that all others will be similar). NB! The literature review should match your empirical part. So, for instance, do not write too much about foreign direct investment theories if your cases are about locally owned exporters that have never invested abroad. Such problems will most probably result in rejection of your article. NB! Your implications should be logically based on what you discovered in your study: Do not suddenly, e.g., suggest managers or policy-makers to do something if your results do not support this! For some further advice on how to “make sense” and which mistakes to avoid, look at Patriotta (2017), Varadarajan (1996), and Wang (2019). Finally, as many authors tend to become “blind” regarding their study’s mistakes (this is their “baby” and they “love” it; moreover, deleting some paragraphs is sometimes hard as writing them took considerable effort), it might be useful to show it to some colleagues or present it at conferences before submitting it to a journal.

2.13 Plagiarism Plagiarism, including self-plagiarism (incorrectly reusing you previously published work), is unacceptable, and results in getting rejected from journals, books and conferences; moreover, you could lose your job and scientific degree and harm your future academic career due to severe unethical conduct. You must never claim or give an impression that you “developed” something (a figure, a table, an idea, a conclusion, even a sentence or a phrase) if you only copied or translated it (translating something will not make it “yours”!). NB! Most journals use software to discover plagiarism, and many reviewers check it additionally, too (e.g., some use search engines in addition to software), as software cannot find everything automatically. If in doubt, what is allowed and what is not acceptable in citing and paraphrasing, consult, e.g., Indiana University (2019). Moreover, to avoid this issue, be very careful how you work with the literature: Cite everything correctly from the beginning as later, you will forget from where and in which form (e.g., a direct quote or something that you rewrote) you found something.

2.14 Trying to Re-publish Your Previous Work Journals’ policies about publishing parts or full texts of your working papers, conference papers and (Ph.D.) theses differ. For some, a paper developed from such work is acceptable even if you used pages of copy-pasted paragraphs, figures and tables

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from it (you used your own work and the previous material was not “published” as they do not regard working papers, conference papers and (Ph.D.) theses “real publications”). On the other hand, some others regard this unacceptable unless you change at least a half of your text and/or if you remove it from the Internet (otherwise, the reviewers might guess who you are; moreover, this might reduce the number of your final article’s downloads and citations as some readers might download and cite your earlier version). To avoid rejection, read the journal’s or (its) publishing house’s policy about republishing your previous work. In case of questions, contact the editor. If this journal has very strict rules but you do not wish to make substantial changes in your work, submit it to a journal that has different policies about this. NB! Some journals might announce that you will keep the copyright of your work after publishing it there (especially if you pay for this). Still, as such work is considered “published already,” most other journals will refuse to re-publish your work! However, in case of retaining the copyright, you can re-publish your figure or table without the previous publishing house’s permission in another outlet in case you cite it correctly (while if these rights belong to a publishing house, you might have to pay for getting this permission).

2.15 Language and Spelling Problems Sometimes authors submit their work to journals without properly checking the spelling and grammar. This can cause rejection of the article. Reviewers understand that most authors are not native English speakers, but still, they cannot “forgive” numerous mistakes (e.g., in using “its” vs. “it’s” or “affect” vs. “effect” or past vs. present tense or not translating some text from Spanish or other languages—e.g., “descripción de items” and “internacionalization”—into English). Also, unclear text or using non-academic language (e.g., calling someone’s study a “genius masterpiece,” using too many exclamation marks or stating: “We believe that our results show…”) will certainly not improve the reviewers’ opinion of your article. Moreover, you should avoid “mixing” American and British English spelling, and you should follow your “target” journal’s policy regarding the use of active and passive voice, etc. If necessary, find someone—preferably a person who is competent in English language, but also knows your field well enough (without the latter knowledge, the language editor might incorrectly change some terms and cause further confusion)—to correct your text. In addition to language problems, misspelling the names of several authors, but also journal and article titles, is a problem that can even lead to rejection of your article. You have to show that you respect previous work. In case of “difficult” names, you might copy-paste them to your file to be sure that you, e.g., did not accidentally miss a letter. NB! You should not “correct” the names automatically in your text redactor, as this will distort many of them. Finally, in case of using abbreviations (unless they are extremely widely used like, e.g., USA, SME and FDI), you should

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not forget to explain what they mean: The reviewers do not wish to play a “guessing game” of what, e.g., UnCaStd could be.

2.16 Length and Format Problems Excessive article (or even abstract) length can be a serious problem: In general, you should follow the journal’s requirements. In special cases (e.g., if you need more space to display your extremely rich case study results as this is especially important for developing the topic further), you should consult the editor. You might get some more space (5–20% is quite realistic in such cases), but a journal article is not a book, and if it is extremely long, the reviewers will probably propose to reject it. You might initially feel that it is impossible to shorten your work, but, actually, you could still consider some possibilities. For example, you could display some literature or some methodological information (e.g., when you conducted the interviews, with which respondents, etc..) in a table instead of giving a long overview of it in the main text, or shorten some sentences (see, e.g., Cutts 2013 for several useful guidelines) or some parts. For instance, a 7-page-long introduction is too long, anyway: so try to bring out your main points on 2–3 pages, or otherwise your readers might give up reading your work. Also, if you have 200 sources in the list of references, but you do not use many of them actively in your article but only state that A (1996), B (2015) and C (2019) also studied internationalization, then go through them: maybe, e.g., 100 would be enough, too. You should also check that your list of references and the rest of the article follow the journal’s requirements. This is especially important in the final stages of your article’s development, but some editors expect you to follow the journal’s requirements even when you submit the initial version (following a different journal’s format can be a sign that you tried that journal first, and after rejection, started looking for a new “home” for your work). Still, even when you submit your first version and the journal offers you the “your paper your way” (any format is fine) option, be correct in terms of providing enough information in the list of references (author(s), publication year, article title, journal name, volume, number, and page numbers). Moreover, check that it contains all used sources, and that you refer to the “right” tables and figures in your text. The reviewers will not appreciate carelessness (e.g., citing some authors in the text but not showing all sources in the list of references or vice versa, or stating that the article is still “in press” if it was actually published a couple of years ago, or referring to Table 2 if you really meant Table 3). You should show respect toward the readers of your article, and the studies and authors you cite. Another important issue: your figures or tables should be clear (not “foggy”) and they should not display too much information. The readers can use the “zoom” function if they prefer reading the PDF or HTML version of your work, but in printed form, numbers or letters in, e.g., 5pt size are really too small.

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2.17 Not Responding to the Editor’s and the Reviewers’ Comments Properly The editors try to select reviewers who are specialists in your field (in terms of your main topic, methodology or both). Reviewers (and some editors, too) spend considerable time (well, usually at least a few hours) on reading and commenting your work (without being paid for this), and you should respect their contribution. Thus, it is not enough to state: “we made all the necessary changes”: The editor and the reviewers wish to see a report of what you did in detail. You should go through their comments one by one (first, respond to the editor(s) in case you received some comments from them and then, to Reviewer 1, Reviewer 2, etc..) and explain what exactly you changed in response to each particular comment or why you did not do it. For instance, if the reviewer told you that the first sentence on page 3 was unclear, you should respond: “We changed that sentence. This is our new version: ‘….’” or “We removed that sentence as…” or “We did not change this sentence but added another one explaining that….” NB! It is not a good strategy do delete some of the most critical comments and hope that the reviewers have forgotten about them: They can see their previous comments in the journal’s system, and if they discover this, they might suggest the editor to reject your article. NB! Your response should be polite and “academic”: For example, calling someone “ignorant” will not help you! Sometimes making some changes is impossible—for instance, you cannot update the data as the interviewee refuses to give you any additional information about the case firm. In that case, explain very clearly, why you cannot follow this suggestion: If the editor and the reviewers will accept your explanation, your article still has a chance. Still, if the reviewers tell you to make 20 major changes in your study and you can only make a few minor ones, you will most probably not get a positive decision. In this case, try to find a more suitable “home” for your work. In the (hopefully) rare case when you feel that the reviewer(s) did not really understand your field or method(s), and/or did not read your work carefully, contact the editor. Also, do that if they suggested you to cite dozens of (probably their own) articles not directly associated with your topic or method: This is highly unethical. NB! Keep in mind that if a journal rejects your article, you should still take into account all the useful comments you received: You can only ignore the ones that did not “make sense” (NB! Some weeks later, you may comprehend that even such comments can be a sign that you should clarify or change something). Next time you might be more “lucky” with reviewers but still, you have to understand that the same reviewer might have to read your submission to the new journal, too (especially if your exact field is narrow as only a few scholars have specialized in it). Then, in case of discovering that you did not even try to make any important changes, the reviewer will inform the editor and thereafter, most probably you will have to start searching for another journal or book for your work.

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2.18 Trying to Do Everything Alone Sometimes it is good to write articles alone as then you have control over what and when to do: You do not have to postpone anything due to, e.g., your co-authors’ heavy workload or health or family problems, or to argue on how to rewrite a certain paragraph. Moreover, ability to write articles alone, if necessary, can be beneficial in getting some jobs. On the other hand, people have different backgrounds in terms of methods, knowledge about specific literature streams, etc. It is impossible to know everything about everything. So, if you are not very competent or confident in some aspects necessary for writing a good journal article, consider co-operating with someone who is especially good at something that you lack. As a result, you could divide the work by specializing in what you do best and publish more. NB! You could find potential co-authors at your university, at conferences or via contacting your potential “candidates” via e-mail after you have read other studies on your topic and identified scholars whose field is close to yours. Of course, there is no guarantee that such co-operation will always succeed: Some potential “candidates” might never reply to your e-mails, while some might be too busy to contribute much and some others might wish to change the article in the “wrong” way according to your opinion. In such cases, try to find someone else or finish the work alone.

2.19 Not Learning from Your Mistakes and Not Updating Your Skills Getting a paper accepted depends on your skills but to some extent, also on your “luck” with editors and reviewers. Indeed, sometimes it is possible to publish a previously rejected paper in another journal without making considerable changes. Still, this does not mean that you should not learn from your previous “failures.” If your articles always tend to receive similar criticism (e.g., that your methodology part is too weak or that you do not explain the importance of your topic well enough), then this is a sign that you should try to improve these parts in your future articles. Thus, read more articles, book chapters and books on how to write better articles, and attend “meet the editors” sessions at conferences, too. NB! Keep in mind that methodologies (and theories as well) keep developing. Thus, if a “top” journal accepted a certain type of a paper (for instance, one with an extremely short methodology section), e.g., 20 or 40 years ago, this does not mean that they would consider something similar acceptable now. Consequently, you have to keep learning, too, even if you have managed to publish several papers already!

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2.20 Giving Up Too Soon Getting an acceptance decision after submitting the initial version to a “top” journal is extremely rare (“pay and we will accept everything already in a few days” type of journals are different but publishing in “predatory” journals can harm your career). NB! Even very successful professors receive very negative reviews and rejection letters sometimes (even from conferences that usually tend to accept most submissions!), and some have admitted that they tried several outlets (naturally, not simultaneously as this is not allowed) before their work was finally accepted by one of the “top” journals! In case of rejection, you will have to start looking for a more suitable outlet while in case of “revise and resubmit” or “major revisions,” your paper might still have a chance in this journal. Thus, after getting critical reviews, your life is not over, although initially it might be hard to understand why the reviewers did not appreciate your (“absolutely fantastic”) work. If your paper was not rejected but the reviews are still very critical, try to wait for a week or two, if possible (calming down takes time, especially if you are still inexperienced), and then read them again: Then, you will probably discover that at least some of the reviewers’ suggestions were very reasonable. Deal with these, and then proceed with working on others. NB! Even after making all the changes the reviewers suggested, your paper might get another “major revisions” decision (this can happen, and this is not the “end of the road,” either). In that case, try to rewrite it again! If necessary, consult your colleagues and/or involve (additional) co-authors, and/or ask for an extension of the submission deadline. You might not always get it (especially, if you submitted your work to a special issue or book that has very strict deadlines) but sometimes you will.

3 Conclusion In this chapter, I summarized 20 mistakes that can lead to rejection of qualitative articles in the field of International Business. NB! I cannot guarantee that following all of these will always result in getting all your studies accepted, as this is not always a matter of skills: you also need some “luck,” especially if you try to submit your work to “top” journals that have very low acceptance rates. The latter have to reject articles that are not “wrong” in any important way, as they just do not have space for all good work they receive. Thus, to publish in such journals, your work has to seem “special” for them for some reason(s)—the topic, data, results, etc.—and what may seem “special” to one editor may seem too ordinary and uninteresting to another. Moreover, you need some “luck” with reviewers: For example, if they are against (single) case studies or qualitative research as such, it is not easy to convince them otherwise even if you will follow my suggestions and cite lots of literature on case study methodology.

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Still, I can warn you that ignoring these suggestions—especially, the ones regarding plagiarizing and distorting your data—can become detrimental for your further academic career, while not properly responding to the reviewers’ comments can very probably lead to rejection. Thus, read these suggestions again, and have a look at the studies in the list of references, and hopefully, you will be “lucky” enough to get your work accepted and published! Note 1. I have reviewed several articles for International Business Review, Journal of Business Research, Baltic Journal of Management, the Journal of East-West Business, the European Management Journal and the European Journal of International Management, but also a few articles for other journals, e.g., Journal of World Business, Global Strategy Journal, Research Policy, Small Business Economics, International Small Business Journal, Journal of Small Business and Enterprise Development, Journal of International Entrepreneurship, etc. I have also reviewed numerous Ph.D. thesis proposals and conference articles and acted as a Track Chair for various conferences. I have published my work in International Business Review, Baltic Journal of Management, Journal of International Entrepreneurship, Journal of East-West Business, Journal of East European Management Studies and several other journals and in numerous books and presented it actively at international conferences. Acknowledgements This work was supported by the Institutional Research Funding IUT20-49 of the Estonian Ministry of Education and Research and by the Estonian Research Council’s grant PUT 1003. I also wish to thank the anonymous reviewer who read a very preliminary version of this text: I took some of your recommendations into account.

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Miles, M. B., Huberman, A. M., & Saldana, J. (2014). Qualitative data analysis: A methods sourcebook. Thousand Oaks, CA: Sage. Nicholson, J. D., LaPlaca, P., Al-Abdin, A., Breese, R., & Khan, Z. (2018). What do introduction sections tell us about the intent of scholarly work: A contribution on contributions. Industrial Marketing Management, 73, 206–219. Patriotta, G. (2017). Crafting papers for publication: Novelty and convention in academic writing. Journal of Management Studies, 54(5), 747–759. Patton, M. Q. (2015). Qualitative research & evaluation methods: Integrating theory and practice. Thousand Oaks CA: Sage. Piekkari, R., & Welch, C. (Eds.). (2011). Rethinking the case study in international business and management research. Cheltenham: Edward Elgar. Reay, T., Zafar, A., Monteiro, P., & Glaser, V. (2019). Presenting findings from qualitative research: One size does not fit all! In T. Zilber, J. Amis, & J. Mair (Eds.), The production of managerial knowledge and organizational theory: New approaches to writing, producing and consuming theory, Research in the sociology of organizations (Vol. 59, pp. 201–216). Bingley: Emerald. Shepherd, D. A., & Wiklund, J. (2020). Simple rules, templates, and heuristics! An attempt to deconstruct the craft of writing an entrepreneurship paper. Entrepreneurship: Theory and Practice. https://doi.org/10.1177/1042258719845888. Sheppard, J. P. (2015). Getting published: Achieving acceptance from reviewers and editors. Journal of Asia Business Studies, 9(2), 117–132. Sparrowe, R. T., & Mayer, K. J. (2011). Publishing in AMJ—Part 4: Grounding hypotheses. Academy of Management Journal, 54(6), 1098–1102. Stake, R. E. (2010). Qualitative research: Studying how things work. New York: Guilford Press. Varadarajan, P. R. (1996). From the editor: Reflections on research and publishing. Journal of Marketing, 60(4), 3–6. Vissak, T. (2010). Recommendations for using the case study method in international business research. The Qualitative Report, 15(2), 370–388. Vissak, T., Francioni, B., & Freeman, S. (2020). Foreign market entries, exits and re-entries: The role of knowledge, network relationships and decision-making logic. International Business Review, 29(1), 101592. Wang, J. (2019). Becoming a responsible writer. Human Resource Development Review, 18(2), 167–172. Welch, C., Piekkari, R., Plakoyiannaki, E., & Paavilainen-Mäntymäki, E. (2011). Theorising from case studies: Towards a pluralist future for international business research. Journal of International Business Studies, 42(5), 740–762. Yin, R. (2018). Case study research and applications: Design and methods. Los Angeles: Sage. Zhang, Y. (A.), & Shaw, J. D. (2012). Publishing in AMJ—Part 5: Crafting the methods and results. Academy of Management Journal, 55(1), 8–12.

Evaluating Corporate Social Responsibility/Sustainability Strategic Maturity: Some Methodological Options António Marques-Mendes and Maria João Nicolau dos Santos

Abstract In this chapter, we share our methodological perspectives on research into the strategic maturity of corporate responsibility. The original research aimed to explore the concept of strategic maturity in the context of corporate responsibility of the companies in the PSI-20 index of the Lisbon Stock Exchange, as well as the contribution of these companies to the creation of sustainable value in Portugal. The study contributed theoretically to the knowledge about strategic corporate responsibility and its critical success factors. Methodologically, it opened new perspectives thanks to the combination of research techniques. From a practical point of view, it contributed to the development of new diagnostic tools. In this chapter, we clarify the entire research framework (questions, objectives and contributions), introduce the research design and the reasons for choosing a mixed methods research strategy using a sequential explanatory model. We conclude by presenting our approach to data collection and analysis, participant selection, and we reflect on the validity and usefulness of this research model and strategy.

1 Introduction The objective of this chapter involves setting out and detailing the methodology adopted for a research project studying not only the level of social responsibility/sustainability maturity of Portuguese companies listed on the Lisbon PSI-20 stock market index but also their contribution towards generating sustainable value in Portugal. The methodology reflects the logics underlying the resolution of research problems (the discrepancy between what we know and what we do not yet know). This thus explains the set of core decisions relative to the processes and stages in a A. Marques-Mendes · M. J. N. dos Santos (B) ISEG—Lisbon School of Economics and Management, University of Lisbon, Lisbon, Portugal e-mail: [email protected] A. Marques-Mendes e-mail: [email protected] © Springer Nature Switzerland AG 2020 C. Machado and J. P Davim (eds.), Research Methodology in Management and Industrial Engineering, Management and Industrial Engineering, https://doi.org/10.1007/978-3-030-40896-1_2

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research project designed in order to return valid, relevant, significant and credible results. In this chapter, we therefore set out explanations of the factors influencing the research practices developed and clarifying the logics applied. We particularly seek to specify the methodological options deployed in the resolution of research problems. In keeping with this objective, the structure of the chapter is as follows: (1) initially clarifying the starting questions, the objectives and the potential contributions of the research; (2) we then set out the research design; (3) while also detailing the theoretical framework that guided the research, exploring the strategic CSR concept (SCSR), the maturity model (developed for studying SCSR in an analytical matrix format) as well as the notion of sustainable added value; and (4) advancing with the presentation of certain considerations about the method applied in the selection of participants and the gathering and analysis of the data, before, finally, (5) closing with our concluding remarks.

2 Conceptualisation 2.1 Considerations on the Relevance of Research Studies on corporate social responsibility (CSR) and corporate sustainability (CS) are no recent development. However, in recent years, research on related themes has focused more closely on evaluating the strategic character they perform in striving above all to grasp how CSR interrelates with business strategy to enable the creation of various forms of value (economic, social and environmental). One of the questions drawing particular attention approaches the analysis of the different levels of CSR/CS maturity. Within this framework, CS initiatives may be strategic, integrated into the global vision of the company, interconnected with their mission, the principle, assumptions and targets established by the strategy or, on the contrary, they may be more fragmented, disconnected from the company reality, simultaneously without producing any value whether to society and its stakeholders or to the company. Understanding the characteristics displayed by each level of maturity and the conditions associated with the maturity process thus becomes an important step forwards in advancing knowledge in this field. Another dimension requiring research derives from analysis of the impacts of CSR/CS and their potential contributions towards sustainability, especially those facets interrelating with the creation of value. Furthermore, studying the relationship between these two variables, therefore, the relationship between the maturity levels displayed by companies as regards CSR/CS and the impacts generated in terms of their overall sustainability, and emerges as of particular relevance and correspondingly constituting a field that still requires deeper clarification. Despite the advances made, there nevertheless still remains a lack of knowledge about these specific themes. The awareness of these shortcomings in the literature underpins

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the research subsequently undertaken. Hence, this research project spanned strategic CSR, and especially sought to study: (1) the level of CSR/CS maturity of Portuguese companies listed on the Lisbon stock market and (2) its contribution towards the creation (destruction) of sustainable value in Portugal.

2.2 Research Questions The research approached the strategic CSR/CS concept and sought to evaluate the role performed by CSR/CS in this corporate sample. This above all involved analysing the level of CS/CSR strategic maturity alongside the processes deployed for the creation of sustainable value. Thus, there were a series of interrelated questions that motivated this research focus. Taking the context into consideration, the starting questions for this research were: 1. Do listed Portuguese companies create sustainable value and contribute towards national sustainability? 2. What levels of strategic CSR/sustainability maturity have these companies attained? 3. What is the validity of the diagnosis model (analytical matrix) designed for the analysis of strategic CSR/CS maturity? a. This question was structural to responding to the two preceding questions with the first stage of the research involving the construction of a analytical model/matrix (based on the theoretical-conceptual dimensions) in order to diagnose the CSR strategy maturity profile of these companies and the factors leveraging these factors.

2.3 Research Objectives The research objectives reflect in concrete statements that summarise the aims we hope to achieve with this project. The objectives thus present the purposes of the study under development, identifying and describing the central variables to the research and structuring the processes for development. This research focused on the Portuguese context, specifically strove to obtain the following objectives: 1. Objective 1: researching the national context, the CSR/CS maturity of Portuguese companies listed on the Lisbon stock market (strategic orientation and integration) and, within this group, especially those making up the PSI-20 index of leading companies. This also seeks to grasp the motives for getting involved in CSR initiatives, hence, understanding what are the determining factors/motivations for corporate socially responsible behaviours;

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a. Secondary objective 1: evaluating the CSR level of maturity (strategic orientation and integration) of PSI-20 listed firms; b. Secondary objective 2: identify what are the factors that explain the existence of different types of CSR/CS maturity among PSI-20 listed firms; 2. Objective 2: understand whether there is the creation or destruction of value by 20 companies. Clarify this process of creation or destruction of sustainable value by these companies. Explore the factors explaining the aforementioned process in Portugal; 3. Objective 3: make an integrated and consolidated comparison of the processes of creating social and corporate value by different types of PSI-20 companies.

2.4 Research Contributions and the Expected Results The purpose of any research process is to add value to the respective field of knowledge. The contributions may stem from the various phases of the research process and arise from having been able to return certain specific research results. In the present case, we may identify the following contributions: 1. Theoretically, this contributed towards our understanding of and knowledge about the strategic CSR/CS of listed Portuguese companies and as well as about the factors influencing their respective successes/failures; 2. Methodologically, in deploying a combination of research techniques for gathering and analysing data, this also opened additional perspectives on discussing results in the CSR/CS field; 3. In practice, in establishing a CSR strategic maturity diagnosis instrument, this contributed towards understanding the path that companies need to take in order to return appropriate levels of performance. These contributions stem from having been able to: 1. Clarify the creation of sustainable value of PSI-20 listed companies and the factors contributing towards this end; 2. Identify the characteristics shared by the companies with the highest levels of CSR strategic maturity; 3. Understand the driving forces of integration and the explanatory factors for social responsible behaviours and the greater levels of CSR/CS strategic maturity of these companies; 4. Describe the models of socially responsible behaviours of the different corporate segments of companies identified and their respective sustainable value creation processes; 5. Understand the mediation of the different explanatory factors for the socially responsible strategic behaviours within the scope of their strategic maturity.

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3 Theoretical Background We embarked on this research into the CSR/CS maturity of Portuguese firms listed on the Lisbon stock market—members of the PSI-20 index—and into their contributions to the creation of sustainable value in Portugal after having earlier explored the CSR/CS theoretical field. Critical reflection on this conceptual field enabled us to outflank these problems and define the instruments needed to improve knowledge on the questions under analysis. In terms of the theoretical-conceptual framework, this integrated three fundamental theoretical approaches that functioned as pillars to the research analytical framework applied, specifically: a. The concept of strategic CSR/CS, which had formerly been set out by the authors (Marques-Mendes and Santos 2015; Marques-Mendes 2019); b. Analysis on the creation of sustainable value, which took place based on the sustainable added value model (SusVA), proposed by Figge and Hahn (2002); c. The concept of strategic CSR/CS maturity in accordance with designing an analytical model (in a matrix) format capable of identifying the CSR/CS profile, also previously put forward by the authors (Marques-Mendes and Santos 2016; Marques-Mendes 2019). In order to analyse the first theoretical concept, Marques-Mendes and Santos (2016) suggest that strategic CSR/CS spans three dimensions: the integration of environmental and social concerns into the prevailing corporate strategy; an effective alignment between strategic CS policy formulation and the creation of social value. Without the existence of added value to society, in addition to the generation of value to the company, we are not in a position to talk about strategic CSR and hence identifying the structural link between the creation of value and the level of maturity that a company displays in this field. Understanding strategic CSR/CS therefore involves grasping its consequences based upon a tripartite perspective incorporating a strong component of sustainability and considering not only its efficiency but also its effectiveness. Estimating these consequences and the value they convey thus represents an essential step towards evaluating CS/CSR strategic maturity. For the second analytical dimension, focused on the theme of value creation, we adopted the SusVA model proposed by the researchers Figge and Hahn (2002). This was deemed to represent a methodology able to respond to the research demands given that, as a means of evaluating business sustainability, this measures the effective and efficient utilisation of economic, environment and social resources in an integrated approach. Additionally, this expresses the results of this evaluation as a monetary sum and therefore easily understandable to those less aware of sustainability related issues. Its application returned benefits given this avoided the need for the traditional methodology for such evaluations based on the impacts of the damage caused and instead applying environmental and social assets as scarce resources that need deploying in ways that create value (this thereby moves on from the traditional approaches that focused on the impacts caused and the damage inflicted). SusVA tells

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us to what extent an entity contributed to a higher level of sustainability in resource utilisation due to the fact these resources were consumed by the entity under analysis rather than the reference entity. Taking into consideration the different forms of capital, this method might be seen as an integrated measure of profitability. Approaching the creation of sustainable value through this method not only enabled the individual evaluation of companies but also comparisons between their performances. However, this did not provide for analysis of the underlying conditions to this creation of value nor for studying how each level of CSR maturity interrelates with the contributions a company makes towards sector sustainability. To this end, undertaking this research also required understanding the different levels of CSR/CS maturity attained by the companies and to what extent they depend on antecedents and consequences. Hence, we needed not only to gain complete knowledge about the processes of sustainable value creation but also, from the research perspective, this had to include deepening the analysis and identifying the factors that motivate and determine such value creation and how their respective organisation into systems conditions and influences the level of CSR strategic maturity. This third conceptual approach, which enables analysis of the corporate profile produced by the level of CSR/CS strategic maturity, took place following a broad review of the literature and establishing its state-of-the-art. Furthermore, this deployed a model of theoretical maturity in an integrated matrix format (structured according to the drivers of CSR/CS), and then tested in terms of their quality and validity. This proposition of a maturity model is in no way new to management research. Such models first emerged in the 1970s in the literature on information technology systems. As put forward by Ormazabal et al. “maturity staging models deconstruct the operating processes of a firm, with each stage representing a more effective and efficient use of the firm’s resources for achieving the firm’s goals” (2017, p. 28). In turn, Kohlegger et al. propose that a maturity model “represents phases of increasing quantitative or qualitative capability changes of a maturing element to assess its advances with respect to defined focus areas” (2009, p. 59) that span such diverse factors as processes, resources and human resource competences. Greater maturity interlinks with higher levels of performance (Bititci et al. 2014). Our research made recourse to the strategic nature of CSR/CS as a feature of this maturity, susceptible for analysis through the integrating matrix.1 The maturity model proposed by Marques-Mendes and Santos (2015) and Marques-Mendes (2019) provides a methodical examination of the corporate sustainability strategy with the objective of identifying the stage of development obtained by companies and their respective corporate sustainability strategic profiles. The model contains two organisational dimensions: the horizontal dimension that takes corporate sustainability strategic maturity into consideration in accordance with a continuum that ranges from the absence of any alignment between the corporate

1 In

this document, we adopt the notions of maturity, maturity model and maturity assessment proposed by Bititci et al. (2014).

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strategies and sustainability (phase of negation) through to a strong and robust alignment and identification between these two facets (phase of transformation). This dimension incorporates six different stages of maturity that enable analysis of the performance and development of firms as they progress towards the full incorporation of sustainability into their strategic business activities. The vertical dimension, in turn, is a descriptive scale in keeping with its organisation across three dimensions (with nine corresponding attributes) that seek to identify and characterise the type of companies in terms of: their values and culture; the management processes implemented in companies; and the sustainable value created through the internal and external alignment procedures. This last dimension, consequentialist and pragmatic in type, holds crucial importance throughout all the model given that our notion of business sustainability is, by definition, pragmatic and focused on the consequences of actions (Marques-Mendes 2019). In summary, understanding the levels of CSR/CS maturity and orientation requires differentiating across various levels and types of value creation or destruction in Portugal and that this deepens our knowledge about the underlying processes as a means of being able to better grasp the factors that co-occur across the different levels of sustainable added value. These two core questions and concerns (value and maturity) orient and structure the research is carried out.

4 Research Design 4.1 Introduction This project contained an exploratory dimension (Saunders et al. 2009; Bryman 2012; Gerring 2004, 2016) as it sought to validate concepts and develop and test a model of analysis (a tool for undertaking the diagnosis of the prevailing level of CSR strategic maturity) (Eisenhardt 1989; Ketokivi and Choi 2014). This also reflects an instrumental facet (Stake 1995, 2005; George and Bennett 2005) as this intentionally selected a restricted group of companies with specific characteristics over which we held a reasonable degree of control. Given that the creation of sustainable value was a key factor in the proposed definition of strategic CS, we began by questioning the companies as regards this dimension. The firms were segmented in accordance with their value creation performance levels as measured by SusVA (Figge and Hahn 2002). As a method, SusVA is able to clarify the structure of causality associated with the creation of sustainable value and understand which dimensions to the CS performance best explain the process. The application of SusVA enables both the stratification of companies and the respective comparisons between the different groups to identify patterns and regularities. This therefore represents a comparative study of these corporations. However, SusVA does display certain limitations that ensured we added a secondary phase to the research, capable of clarifying the level of company maturity,

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Fig. 1 Research design (Source Authors’ own)

its CSR/CS orientation and its level of integration into its strategic architecture. This positioned us to respond to the third research question as well as to explore the validity of the CSR/CS maturity model. From this perspective, this research applied a sequential program of comparative studies to the corporations making up the PSI-20 index that firstly involved the deployment of a quantitative approach (study A) followed up by a qualitative approach (study B). Figure 1 sets out the research structure design. In study A, we purposely selected corporations listed on the PSI-20 index. The application of the SusVA analytical method returned two types of results: 1. Individuals (by company) and 2. Comparative (among the companies making up the sub-groups segmented in accordance with their creation of sustainable value and their contribution to the sustainability benchmark—in our case, the Portuguese economy). From the individual company level analysis, the results returned by SusVA were themselves important to better understanding the corporate strategic CS profiles and their underlying strengths. From the comparative point of view, SusVA enabled the contrasting of companies according to their levels of sustainable added value. The resulting hierarchy identified at least two corporate segments with the maximum variation in the creation of sustainable value and that therefore added differing levels of sustainable value to the reference framework. Study A also enabled us to select the individual companies for inclusion in the second study (B).

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In the qualitative B study, the data collection method involved extended semistructured interviews in order to explore the differentiated profiles identified in the first study and applying the critical dimensions and attributes from the previously established maturity model as the reference (Marques-Mendes and Santos 2016). This combination of approaches sought to generate: 1. Additional understandings about the processes involved in the creation of sustainable value at the selected companies; 2. A means of evaluating and validating the model; 3. Further understanding of the dynamics associated with each different profile. We thus deployed a mixed research model, partially combined, sequential and governed by one of the methods Quan→QUAL (Leech and Onwuegbuzie 2007) or, alternatively expressed, a sequential explanation with priority attributed to the qualitative component in the participant selection process (Creswell and Plano Clark 2007, 2017). In brief, this research project therefore incorporated two sequential and interdependent studies that sought to answer the research questions. To obtain this goal, we adopted pragmatism as the research paradigm and developed a comparative study of the corporate groups through the application of a research strategy that made recourse to mixed methods in accordance with a sequentially explicative architecture. Researchers should explicitly define the paradigms they apply so that the researcher’s position becomes completely clear (Holliday 2007). Dalsgaard (2014) states that “pragmatism is a well-developed theoretical position, it offers a rich body of work to draw upon” and, in our perspective, was able to delimit the scope of our research project. The essence of pragmatism reflects in the ‘pragmatic maxim’, sometimes also referred to as the primacy of practice (Putnam 1994). The ‘pragmatic maxim’, in Peirce’s conceptualisation, encapsulates a method for making the content of concepts clear: we thereby clarify concepts by identifying their practical consequences, and whenever there are no practical consequences of a philosophical question, it holds no relevance (Hookway 2012). This constitutes a method for determining the meaning of ideas as, according to Peirce, to determine what a concept truly means, we have to test it against the objective world. The pragmatic meaning of a proposition stems from its empirical character, hence, the practical consequences of its usage. The meaning of ideas lies more in the actions they lead to than in their causes and antecedents. For this reason, pragmatism assumes a character to reach beyond the consequentialist to also become verificationist as the concept’s meaning ends up mingling with its verification processes. While other traditions insist on the precedence of models and theories, pragmatism focuses on the clarification of the concepts per se through analysis of their consequences. This philosophical orientation is crucial to understanding the subsequent steps of the project. Our research was based on pragmatism with our strategic CS concept also applying the same philosophical principles: governed by the consequences of actions and the verification of certain practical factors, we are only able to ascertain

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the idea of strategic CS through its consequences, thus, the sustainable value whether undergoing creation or destruction.

4.2 A Mixed Methods Research Strategy Deploying a Sequential Explanatory Model We here adopted a combination of quantitative and qualitative approaches, usually referred to as mixed method research (or mixed research). The decisions we correspondingly took are explained and justified below.

4.2.1

Conceptual Justifications for Using Mixed Methods Research

Johnson and Onwuegbuzie (2004) propose that mixed methods research represents a research paradigm in its own right that emerged more recently and stands out as a third research culture bridging the difference between the until recently incompatible ‘Quanti-Quali’ dichotomy. Interactions between the quantitative and qualitative dimensions may occur across three levels (Greene 2015): the data collection and analysis, the methodology and the research philosophy. Its origins interrelate with the introduction of the triangulation concept into the methodological lexicon (Webb et al. 1966; Denzin 1978; Jick 1979), which bestowed a legitimacy effect on combining methods from different traditions in the theories and practices of scientific research. Mixed methods research takes on different names in the literature and we therefore need to clarify the definition (Creswell and Plano Clark 2017). Creswell et al. suggest that: a mixed methods research design at its simplest level involves mixing both qualitative and quantitative methods of data collection and analysis in a single study. A more elaborate definition would specify the nature of data collection (e.g., whether data are gathered concurrently or sequentially), the priority each form of data receives in the research report (e.g., equal or unequal), and the place in the research process in which the “mixing” of the data occurs such as in the data collection, analysis, or interpretation phases of inquiry. Combining all of these features into a single format suggests the following definition: A mixed methods study involves the collection or analysis of both quantitative and/or qualitative data in a single study in which the data are collected concurrently or sequentially, are attributed priority, and incorporates data integration at one or more stage in the research process. This definition, although a reasonable beginning, pointing to the consideration of mixed methods research designs, masks several additional questions. (2003, p. 165)

4.2.2

Designing the Mixed Methods Approach

Tashakkori and Teddlie (2003) list more than 30 different mixed method models. Not surprisingly, given such a diversity of formats, attempts have been made to produce

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Table 1 Type of mixed method research design as a sequence of decisions

Level of

Priority

ImplementaƟon

interaction

awarded

sequence

CombinaƟon Research

Independent

QualitaƟve

MulƟphase

design Data collecƟon

InteracƟve

QuanƟtaƟve

Concurrent

Data analysis

Same

SequenƟal

InterpretaƟon

Source Authors’ own

Table 2 Project research model according to different classificatory systems Authors

Type of mixed method research model

Leech and Onwuegbuzie (2007)

Mixed method, partially combined, sequential, one method dominates over the other (Quan→QUAL)

Teddlie and Tashakkori (2009)

Multiphasic sequential, partially combined (QUAN→QUAL)

Creswell and Plano Clark (2017)

Explanatory sequential with priority awarded to the qualitative component and applying the participant selection variant

Source Authors’ own

classifications capable of identifying the critical choices researchers face and simplify research design processes. The objective of various authors has always been to uncover the dimensions researchers may apply in conceptualising their research model. The usefulness of these typologies is undeniable with the most relevant those proposed by Leech and Onwuegbuzie (2007), Teddlie and Tashakkori (1998, 2003, 2006) and Creswell et al. (2003), updated by Creswell and Plano Clark (2007, 2017). Creswell and Plano Clark (2007, 2017) state that key decisions have to deal with the interrelationships among the quantitative and qualitative components of a study. They list four of such decisions in the modelling process: (i) the level of interaction between the quantitative and qualitative components; (ii) the priority attributed to the respective quantitative and qualitative components; (iii) the sequence of implementation; (iv) the different components subject to combination? In this research project, our decisions were as follows (Table 1; grey highlight). In summary, and considering the classifications systems mentioned above, we may describe this research project as follows (Table 2).

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5 Empirical Research 5.1 Data Collection Considerations Given the characteristics of mixed research, the process of data collection may be lengthier, although it is not more complex. As Johnson and Turner (2003) point out, using a mixed method approach requires some clarification as regards how the data collection methods are combined. These authors suggest that there are two alternative combinations: the intra-method combination and the inter-method combination (2003, pp. 298–299). The first sets out the application of a single method throughout the different phases of the study, which includes or may include different types of components. The inter-method combination, in turn, requires a single study applies different (multiple) methods regardless of whether or not they adopt more quantitative or qualitative approaches. In the case of this study, we deployed an inter-method combination. There are multiple typologies of data collection methods (Teddlie and Tashakkori 2009). Teddlie and Tashakkori (2009, p. 206) refer, however, to the excellence of that put forward by Johnson and Turner (2003, p. 298). Although there are certain many more data collection instruments and methods, Johnson and Turner (2003, p. 298) consider only six in their typology, deeming them as the most relevant: questionnaires, interviews, focus groups, tests, observation and secondary data. This typology (presented as a matrix organised according to two dimensions: methods of data collection versus the extent of the qualitative dimension to the research approach) sets out the different modalities in order to reflect the scope for applying each data collection method/technique. Considering our research design and our participant selection strategy, there were two different phases to data collection (regarding each of the two interrelated studies): 1. In the first study, the data collection method is purely quantitative and including only secondary (business and national economy) data, corresponding to type 18 in the Johnson and Turner typology; 2. In the sequential second study, the data collection is based on qualitative interviews, with the introduction of a selection of closed questions—typology type 5—with the purpose of validating the content of some of the open questions as well as clarifying some of the pragmatic research facets. The justification for this data collection strategy stems from its approach as a comparative inter-company study and the interest in exploring each company subgroup (as thoroughly as possible) in order to understand their performance in study A, (that shaped the selection of participants for study B), concerning their national economic sustainable added value profiles (see Fig. 2).

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Fig. 2 Data collection process adopted, highlighting segmentation throughout the process as well as the existing interrelationships and influences (Source Authors’ own)

5.2 Data Analysis Considerations Yin (2014, p. 136) suggests, when discussing case study research that data analysis processes should adopt one of the following four general strategies: (a) start from preestablished theoretical propositions; (b) work the data from scratch; (c) develop a case from a descriptive and well-organised framework; (d) test the plausible alternative explanations. The orientation followed in this study most closely resembles the first one: starting out from pre-established theoretical propositions. We departed from a well-defined theoretical framework and a set of propositions that pervade data analysis as had indeed already happened in data collection. Analysis involves making decisions about the interpretation for application. Ours were slightly more complex as the findings originated from sequencing studies. When we study a dataset, we choose from different possibilities. Onwuegbuzie and Combs (2011, p. 3) identify thirteen criteria present for decisions taken before, during and after the analytical process in mixed research approaches: justification for usage, the underlying philosophy, types of data for analysis, types of data analysis for undertaking, temporal sequences of mixed analysis, levels of interaction between quantitative and qualitative analyses, priority attributed to different types of analysis, the number of analytical phases, connections to other components in the research design,

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the process phase in which analysis decisions are taken, the type of generalisation, analytical orientation and the transversal nature of the analysis. Our data analysis process involved: 1. In the first study (A), the data analysis was purely quantitative, applying secondary data collected from companies and the national economy to generate critical information about the sustainability of companies. Such results allowed us, on the one hand, to characterise the value creation profile of each firm, on the other hand, and based on this information, we established two corporate sub-groups for in depth analysis in the second phase of data analysis; 2. In the second study (B, sequential), the data analysis focused on these selected participants in keeping with the analysis performed in study A. This was qualitative in nature, based on qualitative data collected using semi-structured interviews. This analysis was deepened by incorporating the quantitative analysis results from study A. The analysis took two directions: the companies were analysed not only individually but also comparatively in order to gain an integral perspective on their levels of performance able to answer this study’s research questions. We analysed this qualitative data through recourse to content analysis, more specifically thematic content analysis; 3. There was furthermore the research scope for the integrated analysis of the results of both studies. Our research interrelated the analysis carried out by each of the two studies. According to Teddlie and Tashakkori (2009, p. 274), our approach was thus mixed sequential analysis as the analysis took place at two different times according to a chronological order in which analysis of one moment either emerges independently or is dependent on the other. Teddlie and Tashakkori (2009) propose a typology of analysis strategies that divides them into two main types: those in which the first moment enables the selecting of different groups of individuals based on the previously developed quantitative analysis; and those in which the first moment leads us to select groups of attributes/themes for later qualitative exploration. In our case, we chose the first of these types. This data analysis strategy was justified, within the scope of our comparative strategy, as we were interested in analysing each company sub-group in terms of their sustainability performances and the factors standing out whether as relevant or even determinant to the strategic maturity profile of companies. This analysis lets us draw conclusions not only about the companies, instrumental in this study, but also regarding the proposed maturity model and its viability and validity as an integrated strategic CS analytical framework.

5.3 Participants Selection We need to clarify an aspect of the logic binding together the research questions to the conclusions: the issue of participant selection. Several authors agree that the

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participant selection study phase is a fundamental component of the overall process (Eisenhardt 1989; Stake 2005; George and Bennett 2005; Seawright and Gerring 2008; Yin 2009) as individual behaviours may contribute to clarifying some of the circumstances and characteristics of a larger population. The participant selection process in our research project, in summary, contained the following features: it was a multi-company comparative two-tailed (Yin 2009, 2014; Ghauri and Gronhaug 2002) or polar (Gerring 2016) process; seeking to identify participants with the greatest degrees of variation (Seawright and Gerring 2008) while aiming at theoretical replication (Yin 2009), which deployed an exploratory causal selection strategy (Gerring 2016), applying several levels or multilevels (Onwuegbuzie and Collins 2007), in an integrated sequential approach (Onwuegbuzie and Collins 2007; Teddlie and Tashakori 2009; Johnson and Christensen 2016), with a non-random/intentional choice of participants throughout the entire process (Onwuegbuzie and Leech 2005; Onwuegbuzie and Collins 2007).

5.3.1

Rationale to the Participant Selection Model

When a study contains more than one participant, we are dealing with a comparative, multi-case or multiple-case design as was the case with this present research project. Yin (2014) states that one of the differences between individual and multiple cases is that the logic of comparative cases is replication rather than sampling. Participants, Yin therefore says, should be selected either regarding a prediction of similar results—literal replication—or the prediction of contrasting results but also for predefined grounds—theoretical replication. In our project, participants were chosen on the grounds of theoretical replication. The reason for adopting such a model lies in the development of a well-framed theoretical model (Yin 2009). We departed from a conceptual framework comprising a three-dimensional definition of strategic CSR/CS, and a CS strategic maturity model to act as an analytical integrative framework for our analysis. By defining certain key variables and distinct phases of development on the path to full maturity, we established the context guiding both research design and participant selection. In accordance with Yin’s nomenclature, the design our comparative study, thereby adopted was bi-caudal or two-tailed. Generalisation should also constitute a key concern (Bryman 2004; Onwuegbuzie and Leech 2007; Saunders et al. 2009), regardless of whether more intentional or more random. Generalisation may be simply statistical, or analytical—conceptual power—(Miles and Huberman 1994; Onwuegbuzie and Collins 2007). In our case, the research sought to be susceptible for analytical generalisation. There are several typologies in the literature systematising the various ways of selecting research project participants. The most relevant are those put forward by Onwuegbuzie and Collins (2007), Teddlie and Yu (2007), Seawright and Gerring (2008) and Johnson and Christensen (2016). These typologies categorise our research project as follows (Table 3).

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Table 3 Authors’ options according to different typologies Participant selections

Typologies

Intentional, integrated, sequential in nature

Onwuegbuzie and Collins (2007)

Sequential, integrated and multilevel in nature, non-probabilistic with maximum variation, focusing on cases of confirmation and disconfirmation

Teddlie and Yu (2007)

Intentional, aiming at analysing differences between two groups originating from applying SusVA

Seawright and Gerring (2008)

Causal exploratory in a polar comparative study

Gerring (2016)

Integrated, sequential and intentional

Johnson and Christensen (2016)

Source Authors’ own

As our study carried out integrated and sequential participant selection, and in accordance with the recommendations of Sandelowski (1995, 2000), Creswell (2002), Bryman (2004), Stake (2005), Onwuegbuzie and Collins (2007), Teddlie and Tashakkori (2009), Yin (2009, 2014) and Creswell and Plano Clark (2017), we chose two specific individuals from each sub-group (after applying SusVA) in order to compare cases demonstrating maximum variation levels (according to the two-tailed or polar perspective).

5.4 Specific Criteria Applied in Participant Selection Our sample population was made up of all the companies listed on the Lisbon stock exchange (Euronext Lisboa). We focused on quoted companies as our units of analysis as these companies have dispersed their share capital, reflecting the importance attributed to the business environment to company activities and the implicit acceptance of adaptation as fundamental to their external actions (Figge and Hahn 2002, 2004a, b). Due to the market and societal circumstances they face, the information reporting duties of these companies are regulated by the Portuguese Securities Market Commission (CMVM), which establishes the scope and nature of the information content for disclosure, which ensures they are particularly exposed to public scrutiny. Moreover, our pragmatic philosophy and the CS definition we propose imply that corporate sustainability must create value not only for the company but also for society. We applied SusVA as a method for assessing sustainable added value in order to explore this dimension with one method’s assumptions stipulating that the information on the units of analysis must be public, published, available and easily accessible (Figge et al. 2006).

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From the listed companies, we intentionally first selected the constituents of the PSI-20 index, which aggregates the companies with the highest levels of market capitalisation and liquidity. PSI-20 companies have greater visibility among their respective stakeholders (Abreu and Mendes 2011), because of their particular national and international exposures as well as the requirements necessarily met in order to list on the index. These reasons make them potentially more sensitive to the material issues (not only financial but also social and environmental) that bind them to their environments and make them stand out among other listed companies (in terms of information management and their responsiveness to markets and society). These features rendered them the appropriate objects of study in keeping with the respective research aims. Given the limitations existing in terms of the availability of information on economic-financial, social and environmental indicators at the benchmark level, we necessarily had to focus our attention on studying companies listed on the PSI-20 over the 2010–2015 period. Our purpose was to analyse their behaviours over time and their contribution to the sustainability of the benchmark, the types of contribution made while also exploring the most relevant determinants. The 2010–2015 period was a particularly turbulent period in Portugal, especially in terms of the capital markets, with the listed companies experiencing several formal incidents that resulted in the PSI-20 index containing fewer than the 20 companies stipulated for a substantial proportion of this period. Of the PSI-20 companies over the 2010–2015 period, only ten companies made publicly available the consistent, sufficient and necessary information on a number of social and environmental indicators (other than financial), capable of application in the referenced method and in our sustainability analysis, and therefore eligible for inclusion our research sample: 1. 2. 3. 4. 5.

Pulp and paper—Altri, Portucel (now renamed The Navigator Company), Communications—CTT Energy—EDP, EDP Renováveis, Galp, REN Retail—Jerónimo Martins, Sonae Construction—Mota-Engil.

The application of SusVA allowed us to generate information that individually and comparatively characterised the companies according to their contributions to the benchmark’s sustainability. In our case, SusVA made it possible to identify the companies listed in the PSI-20 making more and/or less positive contributions to national sustainability (sustainable value). The results obtained therefore allowed for the establishing of two sub-sets of companies (5 + 5). In order to understand the reasons underlying this sustainable value creation, we were interested in selecting exemplary cases displaying maximum levels of variation, capable of assisting in the analysis of the causal dynamics and returning conclusions as regards the validity of the maturity model (and any potential changes requiring introduction). From the two sub-sets, we extracted and analysed the relevant individuals (2 + 2) within the

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context of the key model dimensions in order to clarify the reasons underlying the contributions made to sustainability and the sustainable profile of enterprises. We therefore applied this as a test for selecting the companies.

6 Final Comments on the Results In practice, the application of this method brought about relevant and significant results providing a deeper understanding of corporate sustainability in Portugal. As a consequence of this application, we were able to understand that, among the PSI20 index listed firms, there are different sustainable value creation profiles. We were specifically able to identify two sub-groups of companies with the maximum variation in sustainable added value that we designate as high and low sustainable performance companies. Those returning the greatest level of value creation were also those displaying the greatest level of CSR/CS strategic maturity with the different profiles demonstrably influenced by concrete factors and diverse determinants. With this method, we can report results in terms of the differentiated levels of strategic maturity attained by these companies as well as how they are going about integrating CSR/CS into corporate life and just which attributes and dimensions impact on the CS/CSR strategic maturity of these companies. As stated at the beginning of this chapter, we were furthermore able, through the application of this methodology, to clarify the extent of sustainable value creation by PSI-20 listed companies and the factors, thereby contributing and to better understand the driving forces for integration and the explanatory factors behind the socially responsible behaviours and the greater levels of CSR/CS strategic maturity of these companies while describing the models of social responsible behaviours prevailing in the different corporate segments identified and the processes for the creation of sustainable value; and, finally, to grasp the mediation ongoing among the different explanatory factors for the strategically responsible social behaviours in terms of the prevailing level of strategic maturity.

7 Concluding Remarks This chapter sought to set out the most significant methodological features of research carried out on the CSR/CS strategic maturity of PSI-20 listed companies (Portugal). We correspondingly strove to detail the fundamental factors of our research, organised in keeping with the key research options taken: the research philosophy and strategy as well as the methods for data collection and analysis. We therefore aim to connect the research conceptualisation phase to that of research design without ever overlooking the fundamental questions as regards the original theoretical framework. As already stated, we believe this research made theoretical and practical contributions that were only possible in large part due to the design of the research project

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then implemented, itself a source of differentiation given the deployment of a combination of research techniques that enabled the opening up of additional perspectives that deepened the discussion of the results and the conclusions reached for the field of strategic CSR/CS. We correspondingly adopted a pragmatic philosophical position (consequentialist, inter-subjectivist-based, realistically transactional, fallible) for a mixed methodology strategy that followed a sequentially explanatory architecture, and thereby designing a research structure with an exploratory orientation having intentionally selected a group of companies able to ascertain the validity of the maturity model proposed for analysing strategic CSR/CS. The existence of various research objectives led to the undertaking of two studies (A and B) that took place sequentially and contributing mutually to the objectives. The studies carried out to provide both for individual analysis but also for collective analytical approaches. We believe that this presentation of the methodology clarifies the logic applied in resolving the research problems identified while emphasising the most relevant issues encountered and the contexts to the methodological decisions taken.

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Technology Forecasting: Recent Trends and New Methods Gema Calleja-Sanz, Jordi Olivella-Nadal and Francesc Solé-Parellada

Abstract Because of the big and increasing importance of technology, the analysis and forecasting of technology trends and futures are more and more important. Although precise predictions are not possible, technology forecasting provides useful insights that are badly needed. The basic principles and methods of technology forecasting basically remain the same. However, the current capacities of obtaining information, communicating and processing data have modified fundamentally the application and possibilities of the methods. Additionally, new methods derived from the existing ones have appeared. The technology forecasting methods have been divided into five blocks: environmental scanning, expert opinion, trend analysis and statistical methods, modelling and simulation, scenarios and roadmapping. For each block, main concepts, recent evolution and new methods are presented.

1 Introduction It seems not necessary to emphasise how important is to have information on the present and future evolution of the technology. New technologies have the potential to have a considerable impact on business processes, organisations, cultures and interactions among them. Therefore, a big number of decisions of public administration, research and education institutions, and companies, take into account, at least implicitly, certain assumptions on the future of the technology. For this reason, the analyses of technologies evolution and future are numerous both in the technical and G. Calleja-Sanz Serra Húnter fellow, Institute of Industrial and Control Engineering, Universitat Politècnica de Catalunya - BarcelonaTech (UPC), Barcelona, Spain J. Olivella-Nadal (B) Institute of Industrial and Control Engineering, Universitat Politècnica de Catalunya - BarcelonaTech (UPC), Barcelona, Spain e-mail: [email protected] F. Solé-Parellada Universitat Politècnica de Catalunya - BarcelonaTech (UPC), Barcelona, Spain © Springer Nature Switzerland AG 2020 C. Machado and J. P Davim (eds.), Research Methodology in Management and Industrial Engineering, Management and Industrial Engineering, https://doi.org/10.1007/978-3-030-40896-1_3

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research fields. These works form the technology forecasting (TF) field, which is a consolidated area of knowledge and provides useful insights on the future of technology. However, it seems that precise or fully reliable predictions are not possible most of the times. The uncertainty of the future of technology is due to the nature itself of technology development. Sometimes, experiments and tests give unexpected results that make useless the previous plans and predictions. Naturally, big surprises are not so common. In technology development, the principles behind what is being developed are known. So that, results of development processes usually are in line with what was expected. In spite of this, unforeseeable result cannot be discarded, particularly at middle and long term. Another source of uncertainty comes from the lack of knowledge on the resources that will be mobilised to develop a certain technology. Technology evolution does not happen by itself but is the result of human activity and effort. Usually, to reach a remarkable progress, a huge amount of resources has to be mobilised. Therefore, technology progress depends on the decisions of the organisation involved in its development, notably research institutions, companies and public administrations. The process of adoption of these decisions is probably too complex to be forecasted with certainty. In effect, the efforts and resources expended in developing a technology depend on a complex set of factors. The context in which technological development takes place is represented in Fig. 1. Scientific knowledge is the basis to the development of enabling technologies, that is to say, technologies that are going to make possible technological innovation. The development of basic technologies and technology innovation itself give place to technological change. The basic actors of these activities are research institutions, start-ups and established companies, and are founded mostly by public administrations, capital risk founds and, in general, investors. Besides, technological innovation depends on market demand and on the social impact of the innovations, which in turn depend on a long list of factors. Therefore, the decisions on what technologies will be developed are extremely difficult to predict. In fact, these decisions are not always rational but influenced by a passing fad. The logic enthusiasm for the possibilities of some new technologies gives place to the so called ‘hypes’, that is to say, phases in which the expectations are exaggerated. Social and market dynamics behind technology development and innovation are very complex and unpredictable. Additionally, the complexity of the process of technological development is growing. Technological development is a self-reinforcing system. The scientific and technological developments use intensively other previously developed technologies. The availability of better methods and tools to measure any kind of phenomena and higher computational capacity, for example, increases research capacity. On the other side, richer societies tend to spend more resources on science and technology. Since technological development fosters technological growth, the development process is self-sustaining also from this point of view. Therefore, the speed and intensity of technological development can be expected to be increasingly high.

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Fig. 1 Context of technological change

For all these reasons, precise and fully reliable predictions of the future of the technology seem not possible and any kind of prediction ever more difficult. However, at the same time, humanity future is increasingly dependent on technology and, therefore, the analysis of technology prospects is more and more necessary. In spite of its limitations, TF methods approximate some short- and middle-term technology development processes and are able to identify possibilities and potential scenarios, for example. In general, TF methods provide useful insights on the future of technology and are receiving an increasing amount of attention by both the research and professional communities. Most of the TF methods were already existent in the past century. However, they are evolving. Nowadays, the availably of information and the computational capacity is significantly higher than in the past. This clearly affects how TF is done. The rest of the chapter is devoted to present the evolution of TF methods, notably when applied to research works. In particular, in Sect. 2, some concepts are presented, Sect. 3 highlights recent trends and new developments of different kinds of TF methods and, finally, Sect. 4 includes closing remarks.

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2 Concepts 2.1 Technology Forecasting Technology forecasting can be defined as a systematic means to ‘analyse and evaluate performance parameters, timing of advancements, new concepts, products, processes, market penetration and sales in a given time frame with probability statements, on a relatively high confidence level, by capturing technology opportunities/threats from technological changes’ (Cho and Daim 2013). A technological forecasting includes, then, four main elements (Martino 1993): (1) the technology being forecasted, specifying whether it is for a single technical approach or for a more general technology; (2) the time of the forecast, stating clearly whether it is a single point in time or a time span; (3) the characteristics of the technology, given in terms of functional capability, i.e., a quantitative measure of its ability to perform a function; and (4) the probability associated with the forecast, which may be stated in several ways, such as the probability of achieving a given level of functional capability, the probability of achieving a given level by a certain time and the probability distribution over the levels that might be achieved by a specific time.

2.2 Objectives The value of TF lies in its usefulness to give guidance for the direction of promising technology developments in order for managers to make better decisions (Martino 1993). To do this, several specific objectives can be addressed: • Provide valuable information in order to assist managers in making well-informed decisions and setting priorities for R&D (Haleem et al. 2019). In particular, identify research needs to fill the gap between future requirements and current technological capabilities. • Search probable ranges of future environments to more systematically find the right path to a successful R&D policy and to assist in R&D planning (Gerdari et al. 2011). • Exploit the understanding, as well as manage the risk of emerging technologies and innovations (Haleem et al. 2019).

2.3 How to Make Predictions A large number of methods have been evolved for TF. One of the most important factors to adequately predict the right technological change in a certain future

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is to select the appropriate TF method for a given situation, since TF results are greatly influenced by the methods used (Börjeson et al. 2006; Levary and Han 1995). A proper application of TF methods demands that the technique used needs to be time-, context- and technology-specific (Firat et al. 2008). Many publications state that it is often advantageous to combine the results of several different TF methods, because of the complexity of TF and the fact that TF methods can only deal with limited aspects of a given forecasting case (Cho and Daim 2013; Haleem et al. 2019; Gerdari et al. 2011; Firat et al. 2008). A TF method is designed to extract information and produce conclusions from data sets. What differences one TF method from another is the way data is collected and analysed. Typically, the methods used for TF are determined by the availability of data and experts, the context in which the forecast will be applied and the needs of the expected users (National Research Council, Division on Engineering and Physical Sciences, Committee on Forecasting Future Disruptive Technologies 2017). Most of the early TF methods were developed using qualitative judgement-based techniques, based on the opinions of technology experts. The most representative example is the Delphi method, which offers a structured way to collect expert’s opinions and is widely used even today. In contrast, extrapolation methods use quantitative statistical data about past events to figure out a pattern for the future. Other TF methods are based on scenario building and analysis in order to understand future situations based on interactions of a variety of factors.

2.4 Kinds of Methods One common classification for TF methods is to differentiate between “exploratory’ or ‘normative’ (Roberts 1969; Gordon 1992). TF methods can also be partitioned into exploratory, normative and a blend of the two categories, according to the classification given by the Technology Futures Analysis Methods Working Group (Technology Futures Analysis Methods Working Group 2004) (Table 1). Exploratory technological forecasting is the means to predict futures based on the past and present state of the art (Roberts 1969). They include Delphi method, crossimpact analysis and trend extrapolation. Normative forecasting methods, instead, assess future goals, missions and needs to trace backwards and determine the necessary actions to achieve these points. They include relevance trees, morphological analysis and technological roadmapping (Cho and Daim 2013). At the same time, according to the characteristics of the results, the methods can be divided into qualitative, quantitative and hybrid ones (Kim and Ju 2019). Qualitative methods utilise the intuition, insight, expertise and decision of experts, which include scenario development, expert opinion and qualitative trend analysis. Quantitative methods use statistical data and models that include trend extrapolation, modelling, simulation and bibliometrics (Georghiou 2008). Hybrid ones, finally, combine both approaches.

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Table 1 A classification of technology forecasting methods (Roper et al. 2011; Martino 1993; Roberts 1969) Term

Definition

Characteristics

Exploratory

The efforts to predict the technological state of the art that might be possible in the future

Start from today’s basis of knowledge and are oriented towards the future Rely on characteristic curves or patterns such as S-shaped Project future possibilities based on past and present data Suggest options for resource allocation

Normative

The statement what technologies ought to be or need to be achieved in the future

First assess future goals, needs and desires and work backwards to the present Too complex and mathematically intricate Meaningful when objectives are critical and specific Acknowledgement of constraints (natural assets, company resources, etc.) Awareness of responsibility towards nation or society

Exploratory/normative

Can be used in both approaches

They merge exploratory and normative characteristics listed above

3 Evolution of Methodologies 3.1 Environmental Scanning Methods 3.1.1

Overall Perspective

Environmental scanning is a technique for detecting early signs of potentially important developments, through the systematic identification, monitoring and analysis of possible threats and opportunities in an organisation’s external environment. It mainly focuses on new technologies and the changes that could affect their penetration or acceptance in the marketplace (Phillips et al. 2007). Most studies look at scanning in various environmental categories: social, technological, economic, environmental, political, legal, and ethics, among others. The objective of scanning the environment is to develop an overview of what trends need to be watched, where important developments are taking place, and who the key actors are and might be. The method calls for distinguishing what is constant, what changes and what is constantly changing. It explores new and surprising matters

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Table 2 Environmental scanning methods (Cho and Daim 2013; Martino 1993; Haleem et al. 2019; Roper et al. 2011) Description

Scan the environment for gathering information about the subject of a forecast

Some methods

Bibliometrics (commonly used interchangeably with Scientometrics), patent analysis, text mining, technology mining, Webometrics, social network analysis (SNA)

Conditions for use

Information useful for a forecast must be available and accessible

Strengths

It can provide useful information from a wide variety of sources

Weaknesses

Information overload can occur in absence of selectivity, filtering and structure

as well as persistent problems and trends, including issues that go beyond the limits of current thinking and that challenge past assumptions. A wide variety of methods are utilised for environmental scanning. They include systematic analysis of communications media sources (and the Internet), content analysis tools (mostly text and data mining tools to point out emerging attitudes and social or political movements), review of reports from specialised consultancies (to suggest new markets or business models) and examination of databases (usually patent and bibliometric databases, to give early warning of scientific and technological developments). The main characteristics and elements of the environmental scanning methods are presented in Table 2, while some of the most important are defined next: • Bibliometrics (or scientometrics) refer to the application of quantitative analysis to measure publications and scientific output and generate indicators for policy and management. • Patent analysis is a technique for transforming the statistical information attached to patents into useful information for specific purpose. • Text mining is a systematic analysis of content in natural language text to extract useful intelligence from electronic text sources. • Technology mining refers to text mining of science, technology and innovation information. It makes exploitation of text databases for producing knowledge about emerging technologies or current activities (e.g. profiling who is doing what in a target area). While many organisations engage in environmental scanning, most often it is not carried out routinely, but in a ‘one-off’ fashion when a new activity is planned. This may save costs, but hinders learning opportunities (Miles et al. 2016). Additionally, if the organisation becomes too tied to specific scanning methods and data sources, it is possible that alternatives unveiling important warnings are neglected. Environmental scanning can be thought of the central input to technology forecasting research, since all technologists do it in some way or another. A solid scan of the environment can provide the foundations to define strategies for anticipating future developments and therefore gain as much time as possible. It can also help in assessing trends to be fed into a scenario creation process (Madnick and Woon 2008).

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Recent Evolution

Regarding the recent evolution of environmental scanning methods, the increasing communication and data processing capacities have generated fundamental changes. New knowledge, technologies and innovations are included in an ever-increasing number of scientific and non-scientific media sources. Since most of this information is digitalised, it can be obtained online and systematically analysed by using appropriate software solutions. In recent years, the rapid expansion of big data and information technologies has enabled various methods of handling large amounts of information in order to reduce the effort required for obtaining useful information. Based on this evolution, the environmental scanning methods have dramatically increased their capacity of analysis and possibilities. For example, patent analysis has been applied using bibliometric and text mining techniques to analyse the future trend of development of hydrogen storage materials (Chanchetti et al. 2016). Finally, software resources related to technology mining are evolving rapidly. For instance, there is an increasing number of software tools and visualisation tools for the uncovering of relevant information about innovative technologies based on text mining (Picanço-Castro et al. 2018). Among them, it can be mentioned the software VantagePoint, which extracts technical intelligence from massive data repositories to support patent analysis, or Aureka, a software tool offered by Thomson Reuters which provides attractive visualisations of relationships among patents.

3.1.3

New Methods

Among the environmental scanning methods that have been recently powered by the explosion of information technologies, Webometrics and social network analysis (SNA) are two relevant methods that are becoming widely applied in practice to gather information for technology forecasting purposes (Ciarli et al. 2016). Webometrics is a research field concerned with the quantitative study of the web, including websites, web pages, words in web pages, and search engine results (Björneborn and Ingwersen 2001). The significance of the web as a communication channel and for hosting an increasingly wide array of documents spans a wealth of possibilities for data mining. Although the extent to which these methods have been used to forecast future technologies is unclear, there is a body of evidence showing that tools based on web content analysis are applied to analyse and predict technological developments. For instance, an architecture of intelligent information system has been proposed to support decision-making in forecasting the energy infrastructure development (Kopaygorodsky 2018). Specifically, the open linked data from government sector and commercial systems are employed as additional sources of information and the use of text mining techniques are found to improve quality of forecast results.

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On the other side, social network analysis (SNA) is a method to study and analyse the patterns and impacts of interactions between social actors (Jun 2012). The defining feature of SNA is the concept that social structure and the relatively position of the diverse actors (i.e. individuals, groups or organisations) in the network are important for understanding the outcomes of the collective (Park and Kwahk 2013). Despite SNA has been widely used in social sciences and has been implicitly used to map scientific developments [e.g. through the network analysis of bibliometric data (Van der Besselaar and Leydesdorff 1996)], the use of SNA for technology forecasting is relatively new (Nugroho and Saritas 2009). One common application of SNA in technology forecasting is the analysis of inter-organisational collaboration networks. Here, tech mining tools are used to help identify key R&D players and to study their relations (research networks). The insights gained can be useful to help characterise the new and emerging science and technology at its early stage. The resulting insights gained can then serve to understand groupings of collaborators with potentially different interests or research agendas and to inform strategic technology planning and management (Zhang and Tang 2018; Huang et al. 2009). Fast-growing companies such as Quid and Trensition are providing tools to map the world’s emerging technologies by searching and analysing text-based data, allowing other organisations to gain more insight about specific sectors (Zülch and Börkircher 2012; Quid 2019; Trensition 2019). The sources of web data are quite diverse and include information available from social media and social networks, trade publications, academic databases, registered patents, search engine results on society, economy and politics, and websites of government, business and start-ups. As a result, complex (and often time-consuming) algorithms are required to scan these data sources in order to identify trends and relations. One of the advantages of these engine analyses is that results are given in an interactive network map, which greatly facilitates visual understanding of trends and central themes. In addition, these methods encourage the participation of a very diverse body of stakeholders, experts and non-experts, and allows for a significantly higher amount of opinions and information. However, the results are still biased towards those stakeholders who intensively use Internet for social communication. Another major drawback of these studies is that the reference or base is unclear due to the open-ended nature of the web, and therefore the results obtained can be unreliable (Ciarli et al. 2016). Recent applications of SNA include forecasting of development of autonomous vehicles for selecting analogous technologies (Li et al. 2019), mapping of solar energy technologies through open innovation (de Paulo and Porto 2017) and identification of experts and possible project investments in wireless power transmission (Owaishiz et al. 2019).

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3.2 Expert Opinion 3.2.1

Overall Perspective

In forecasting or decision-making contexts, subjective expert opinions are extremely useful when quantitative data is insufficient or too costly to obtain and modelling is difficult or impossible. Typically, the expertise or knowledge of a single individual is not enough to undertake a proper forecast or make a sound decision, thus all along organisations have sought to gather the opinions of groups of experts with the aim to combine their skills and generate a forecast. Main elements of expert opinion are presented in Table 3. Forecasts produced by groups have important limitations. First, the outcome of the process may be adversely influenced by the highest ranking of a hierarchy or a dominant individual, who would cause other group members go along with the opinions of the dominant member. Second, group discussions may deal with a large amount of information that is not relevant for the forecast but that still affects the result. Lastly, groupthink can occur when experts interact openly. Groupthink refers to a situation in which individuals renounce to their ideas and opinions in order to search consensus. The drawbacks of group forecasts led to the development of structured methods. Among these is the Delphi method, a popular method proposed in the 1940s by RAND Corporation. Since its invention, numerous applications have been published, and many other methods related to it have been developed. The Delphi method is one useful forecasting method for systematically extracting expert judgements, with a focus on obtaining an informed consensus view of the most likely future. Delphi is a structured approach that uses a questionnaire to elicit responses (typically opinions) from a group of experts (panel) or social actors, in an Table 3 Expert opinion (Cho and Daim 2013; Martino 1993; Haleem et al. 2019; Roper et al. 2011) Description

The opinions of experts in a particular subject are obtained and analysed in order to forecast or understand technological development

Some methods

Focus groups (workshops, panels), interviews, surveys, Delphi, participatory techniques

Conditions for use

It is used when there is insufficient or no data available or in the case when the organisation desires to maintain the secrecy of its product, process or technology before its market introduction

Strengths

Expert forecasts can compare to high-quality models internalised by experts who cannot or will not disclose them explicitly Fairly simple and inexpensive to implement Can provide a feasible long rage forecast (20–30 years) if trend analysis based on data is not possible

Weaknesses

Experts are difficult to find and their forecasts are often wrong Questions posed are often ambiguous or unclear If experts are allowed to interact, the forecast may be biased by extraneous social and psychological factors

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attempt to solve a problem, typically in a decision-making or forecasting context. The Delphi Method has three attributes—anonymity, iterative controlled feedback and statistical group response (i.e. the individual responses to the questionnaire are combined into a median response)—that are designed to minimise the distortive effects of interacting groups. In practice, Delphi studies begin with a questionnaire soliciting opinions about a topic. Open or semi-open questions are gathered and, as data are collected, the questions become more structured in subsequent rounds in order to verify previous consensus, test assessments and finalise decision-making models. Participants are asked for providing their responses and offer a supporting argument for their opinions. Once the questionnaires have been collected, an anonymous statistical summary of the experts’ forecasts are fed back to the participants, who are then invited but not obligated to revise their initial responses based on those of the other experts. It is expected that, by proceeding iteratively, the range of the answers for each question will narrow and the group will converge towards a satisfactory view of the most probable future. The stop criteria of this process can be the number of rounds, the achievement of consensus or the stability of results (Rowe and Wright 1999).

3.2.2

Recent Evolution

In general, expert opinion methods have not changed considerably during the last few years. Experts have nowadays a wider access to information that they had in the past, and this can influence their capacity to predict. Moreover, present online communication and management of information tools can make the process of obtaining and processing information easier. Finally, in some cases, stakeholders’ representatives participate in the process together with other experts (Puig-Pey et al. 2019). In spite of these advances, basic principles and functioning of these methods remain the same.

3.2.3

New Methods

In recent years, Spatial Delphi has emerged as a new line of research (Di Zio 2018) based on the introduction of the Geographical Information Systems (GIS) technology to treat forecast and decision problems concerning the choice of a geographical location. The main innovation stands in the new concept of ‘geo-consensus’, which differs from the ‘consensus’ of the classical Delphi in that the experts of the panel pursue a convergence of opinions on a limited geographical area. Here, the use of GIS technology is essential to support the convergence of opinions in a digital map. The fundamental idea of Spatial Delphi is to narrow the opinions of panellists on the territory, in order to spot the point (or at least a narrow region) on which there is consensus among the experts. As stated by the inventors of the method (Di Zio and Pacinelli 2011), Spatial Delphi can be applied in the present, to select the best geographical location to place products, services or buildings; in the future, to predict

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where an event with an associated probability will be most likely to occur; and in the underground, to find elements that are not visible (e.g. archaeology, geology and oceanography). One interesting development is Real-Time Delphi that derives from Delphi method and is intended to eliminate the previous shortcomings of the method (Gordon and Pease 2006). Real-time Delphi is a roundless, computerised Delphi, which allows for a greater efficiency of execution time and the participation of a large number of experts. Recently, a new method called Real-Time Spatial Delphi has been developed, which combines the logic of Real-Time Delphi, which is roundless and interactive, and the geographical focus of Spatial Delphi. This method runs on a WebGIS platform which makes use of a variety of tools and functionalities that make it flexible and applicable in a wide range of fields, such in landscape gardening, pinpointing points of origin and potential courses of epidemics, location of future crimes, or planetary exploration (Di Zio 2018). Further developments could be achieved with the combination of these methods with other methodologies, such as scenarios or technology list.

3.3 Trend Analysis and Statistical Methods 3.3.1

Overall Perspective

Trend analysis and statistical methods essentially encompass all numerical- and mathematical-based methods for technology forecasting. The analysis of general trends can also be included in this group (Martino 2003). It has to be mentioned that trend analysis is often applied to information obtained by using other methods, for example, Bibliometrics analysis. For instance, the term frequency and change over time of technological terms in patents and academic publications have been used to identify which prior technologies lead to a new one and to detect technologies with the highest impact (Segev et al. 2014). Some central aspects of this group of methods are presented in Table 4. Most well-known methods belonging to the group of trend analysis and statistical methods are presented next: • Trend analysis, often also referred to as trend extrapolation, is based on the assumption that the future will be a reasonable projection of some type of time series data, and existing trends will continue over time, not producing a different pattern. First, forecasters need to derive an equation that best fits a given set of historical data points, identify trends or cycles in past data, and finally extrapolate the line or curve obtained into the future to produce a forecast. Therefore, choosing the appropriate fitting curve for extrapolation is crucial to forecasting success. • Trend impact analysis is a method aimed at enhancing the accuracy and usability of trend extrapolation. In this sense, trend impact analysis combines statistical

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Table 4 Trend analysis and statistical methods (Haleem et al. 2019; Firat et al. 2008; Roper et al. 2011; Ciarli et al. 2016) Description

Mathematical and statistical techniques used for prediction via the continuation of quantitative historical data into the future It is useful to evaluate the utmost level of technology growth and forecasting when innovation is expected to achieve a specific life stage cycle

Some methods

Trend extrapolation (curve fitting), trend impact analysis, cross-impact analysis, S-curves, megatrend analysis, web searches analysis

Conditions for use

It requires sufficient past data for analysis

Strengths

It offers substantial, data-based forecasts of measurable and verifiable parameters

Weaknesses

It requires a fairly amount of data to be effective It works only for quantifiable parameters It is vulnerable to outlier data points Projections can be misleading for long-term frames Trend analysis methods do not explicitly address causal mechanisms

extrapolation with expert judgements to identify a set of future situations. Here, the critical part is to estimate from the experts’ judgements the magnitude of impact at each significant event on the trend. One weakness of this method is that it just renders an independent forecast of each event, without evaluation of possible combination of each event or calculation of probability of impact of coupled events. • Cross-impact analysis attempts to predict the probability of specific events and determine how related events influence one another. In other words, it helps determine how interactions between events would affect resulting developments and reduce uncertainty in the future. • S-curves (also called growth curves and S-shaped curves) represent the regularity of systems’ growth, as an initial slow change, followed by a fast change and then finishing in a slow change again. Various scientists and researchers have discovered, reinvented and adapted these S-curves of nonlinear growth many times for different fields of knowledge, and have been found to accurately model the rate at which a new technology penetrates the market as well as the rate at which it substitutes for an older technology (Roper et al. 2011). • Megatrend analysis broadens both the scope of analysis in time (several decades) and topic (broad issues, including general social issues). Megatrend analysis usually involves broad tendencies such as technological progress in information science and computing, or the growth in the creation and dissemination of information. Generally, trend analysis methods are mostly used by industries [namely the textile, automobile and electronics industry (Roper et al. 2011)], and governments in order to analyse the disruptive potential of new technologies (e.g. Ryu and Byeon 2011). The analysis of Internet search data is limited mainly to academia, and to a certain extent to government agencies (e.g. to predict a disease outbreak) and companies (e.g. to predict values of stock markets). These uses commonly focus on short or medium time forecasts and have mainly informational purposes (Ciarli et al. 2016).

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Recent Evolution

Also, in the case of trend analysis and statistical methods, we can identify an evolution of the methods based on new management of information possibilities and computational capacities. In particular, but not limited to, newer versions of trend impact analysis and cross-impact analysis that claim to be better at practicality and accuracy than the originals have been proposed. In effect, new means of accounting for future impacts in trend impact analysis have been explored (Agami et al. 2008). A new more complex algorithm for trend impact analysis has been defined. The algorithm takes into account not only the impact of occurrences of unprecedented future events on the future trend, but also the different severity degrees associated with each event. This idea of severity degrees is novel. A natural disadvantage of this sophisticated algorithm is that it increments the amount of expert judgements needed for each event compared to the original method, though the authors claim that ‘it is still relatively simple and easy to use’ (Agami et al. 2008). Additionally, other recent works have explored how trend impact analysis can be used in scenario construction (Ray et al. 2017), and how the method can be married with fuzzy logic to create more justifiable estimates than those given by experts (Lee et al. 2015a). On the other side, while traditional cross-impact analysis originated as a standalone method, recent applications have involved the use of cross-impact in combination with other techniques. The combination of cross-impact and simulation modelling is one of the most promising combinations that have been developed (Gordon 2009a; Walters and Javernick-Will 2015). In addition, although cross-impact studies focus on interactions between couples of events, in the real-world interactions may involve triples or even higher-order combinations of events. However, if these interactions were to be analysed, the complexity of expert judgement collection would increase enormously. Consequently, new promising methods of collecting judgments have been developed to improve the efficiency of the method.

3.3.3

New Methods

As in the case of environmental scanning methods, new techniques have emerged from the analysis of the vast amounts of data available online. In particular, web searches analysis has emerged as a useful tool. Web searches analysis is performed mostly by using Google Trends and Google Correlate tools. Google Trends and Google Correlate tools are examples of nowcasting, i.e. the prediction of the very near future (Popescu 2017). These two tools exploit how particular terms are trending in Internet searches, i.e. what people searches over time and in different places. Google Insights is a refined version of Google Trends for tracking recorded search activity, breaking down the data by location, time range and category. It provides users with a search volume index computed as the volume of queries users enter into Google over a defined period in a given geographic area,

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relative to the total number of searches during that time and location (Duwe et al. 2018). These data are used to improve the forecasting performance by adding information-seeking behaviour of individuals (Ciarli et al. 2016). For example, Google Trends has been suggested as a useful tool to predict future trajectories of performance with regard to competition between rival technologies [e.g. HD DVD and Blu-Ray (Duwe et al. 2018)]. The series of Google searches for a technology product (for instance, Apple laptops) can also be compared to visually correlate inflection points in the searches for Apple laptops with actual Apple laptops sales (Wu and Brynjolfsson 2015). These correlations between searches and specific events can be checked using another Google service, Google Correlate. With Google Correlate, input is a data series and output is a set of queries whose frequency follows a similar pattern (Ciarli et al. 2016). In this way, it allows study of correlation between queries or, more interestingly, between real (weekly or monthly) data and queries [such as the sales of products in the example above or between disease outbreaks and Google queries (Verna et al. 2018)]. A number of publications have investigated the robustness of query data and used them in various applications. For instance, positive relationship between Internet search index and tourist arrivals of Beijing has been detected (Sun et al. 2019); a high correlation between searches of the term ‘unemployment’ and unemployment rates in Spain has been found, confirming the robustness of Google search data (González-Fernández and González-Velasco 2018); and it has also been observed that increased Google searches predict increased volatility and trading volume in the largest companies listed in the Oslo Stock Exchange, showing that Google searches are more related to future than current trading activity (Kim et al. 2019). Despite the diffusion and application of Internet, queries and data in different disciplines, to our knowledge similar methods and data, have only very recently started to emerge in the technology forecasting literature. However, some works following this approach can be mentioned. A network made up of 30 metrics obtained from information that is available on the Internet for free that can be used to track emerging technologies has been proposed (Carbonell et al. 2018). Additionally, an approach aimed at structuring the information of big data and predict the characteristics and evolution of emerging technologies has been presented (Bildosola et al. 2018). The proposed approach combines the application of several methods, namely text mining and natural language processing, visualisation techniques and trend analysis.

3.4 Modelling and Simulation 3.4.1

Overall Perspective

Modelling consists of identifying the fundamental features of a system and translating them mathematically through variables and functions. Modelling helps in reducing

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the complexity and cost of most real life systems, by generating a simplified or reduced-scaled version of reality and depicting only the elements of the reality relevant for the purposes of the model. For instance, a road map is a model of a highway system. Likewise, simulation considers a system in terms of its key components and the main relations between these. Compared to modelling, the main difference of simulation is that it projects how the system will operate over time and/or in a different place, or how it may respond to specific interventions, usually by extrapolating from the real world to a hypothetical situation. Any question beginning with ‘What if…’ creates a simulation, because the answer requires some activity that changes the anticipated possible future. Simulations are used to explore the impact of changes in variables affecting all types of systems, in a wide range of disciplines. Some central aspects of modelling and simulation methods in the technology forecasting field are presented in Table 5. Some modelling and simulation methods are the following: • Input–output models are mostly utilised in economic and market analysis. Here, the model represents the flows of production from one industry to another and ultimately to the demand components and shows which components of economy must supply what types of production. Input–output models can be helpful in technology forecasting to identify bottlenecks or price challenges in implementing a new technology, and, at the same time, to spot opportunities for mitigating such shortages with innovative solutions (Roper et al. 2011). • System Dynamics is a modelling method involved with the structure of a system and the feedback among its variables. Here, a system refers to a group of interdependent or autonomous entities working together for a common cause (Reddi Table 5 Modelling and simulation (Cho and Daim 2013; Haleem et al. 2019; Firat et al. 2008; Roper et al. 2011; Hesselink and Chappin 2019) Description

A simplified representation of real-world phenomena It may be used to predict the behaviour of various variables and display the future conduct of complex systems

Some methods

Input–output analysis, agent modelling, system dynamics, conditional forecasting

Conditions for use

The data used must allow the model to capture the basic structure and main aspects of the real environment

Strengths

Models and simulation can predict the behaviour of complex systems simply by secluding vital system aspects from unessential detail Help provide insight into the structure and behaviour of complex systems Easy to perform ‘what-if’ analysis

Weaknesses

Models usually rely on quantitative parameters, thereby neglecting potentially important qualitative factors Models that are not heavily data based may be misleading and obscure faulty assumptions Simulation models might be expensive to build and difficult to interpret

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and Moon 2011). A system dynamics model is mostly a map of the causal relations among its variables, including feedbacks among them. These feedbacks are a source of complexity. System dynamics models can represent multiple mechanisms. For instance, (Raux 2003) uses system dynamics to simulate the medium and long-term effects of urban transport policies with reference to sustainable travel. • Agent modelling helps the forecaster understand the behaviour and relationships of social organisations as systems. Here, the source of complexity originates from interaction of agents with their environment and with other agents (Axtell and Andrews 2002). In addition, agents can also make autonomous decisions that generally lead to complex and unpredictable relations. This enables more realistic assumptions about the behaviour of agents and simulations permit analysts to understand the implications of those assumptions (Ciarli et al. 2016). The main advantages of the different forms of system dynamics and agent modelling are the ability to deal with interaction and change in open complex systems where results emerge from underlying behaviour and which allow for ambiguity and unpredictability (Salze et al. 2014), realistic and ease combination of real data, reasonable assumptions on agent behaviour and computational flexibility (Ciarli et al. 2016). On the disadvantages side, system dynamics are mostly used in operations research rather than in complex problems of forecasting, their predictive capacity is limited, and their usefulness for forecasting is not yet sufficiently proven (Forrester 2007). The methods in this category are mainly context driven, although numerous applications in technology and innovation are usually found. Due to the technicalities involved, these models are mostly developed in national organisations generally for research purposes and not strictly for technology forecasting. The forecasting periods range from no prediction used in explanatory models, to models that predict distant future, such in the case of system dynamic models. These models are used mostly for informational purposes and, in the case of agent modelling, as a foundation for scenario discovery, as described in Sect. 3.5.

3.4.2

Recent Evolution

As for most of other technology forecasting methods, the development of larger faster computers means that models of agent modelling or system dynamics methods can incorporate many different kinds of agents interacting with each other and their environment, and simulate their performance according to more complex system rules. Computational advances have also made possible a growing number of agentbased models across a variety of application domains. Straightforward examples include modelling agent behaviour in the stock market, supply chains and consumer markets, to predict the outbreak of epidemics, the threat of bio-warfare and the factors responsible for the collapse of ancient civilisations (Macal and North 2008). In technology forecasting, one might envision a technology delivery system (TDS)

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as a system that could lend itself to computation. By examining the various solutions generated and their sensitivity to different assumptions, the technology forecaster could identify useful changes in the variables of the TDS (Roper et al. 2011).

3.4.3

New Methods

The recent evolution of modelling and simulation methods has been characterised more by the evolution of existent methods than for the appearance of new methods. As an exception, the use of conditional forecasting models can be mentioned. Conditional forecasting models consider how agents will likely to respond to the goal of a controlled agent (e.g. an autonomous vehicle) (Rhinehart et al. 2019).

3.5 Scenarios and Roadmapping 3.5.1

Overall Perspective

Long-term strategic planning is a challenge that all managers face in these times of change and highly unpredictable future. A systematic way of thinking about how the future may unfold is crucial for making informed, sound decisions. One method for thinking ahead in a structured manner is scenarios, which help prepare the mind to recognise the early signals of change before they truly happen. On the other side, roadmaps involve an intended or forecasted sequence of events. The central elements of scenarios and roadmapping when applied to technology forecasting are showed in Table 6. A scenario is a story about plausible paths to a future world, a story sufficiently rich and detailed to illustrate key decisions, events and cause-and-effect links along the way to the future. In this sense, scenarios are not predictions about the future but rather similar to simulations of some possible future states. In technology forecasting, scenarios have been used to envision the development paths of future technologies as well as how they might unfold. The first case, using scenarios to anticipate technological paths, involves the study of the underlying science or engineering and its evolution, the tools needed to develop the technology, and the applications that will drive its commercial use. In the second case, using scenarios to forecast how technologies will roll out into the world requires understanding the potential performance of the new technology and how it will be applied by users and other stakeholders (National Research Council 2010). Each scenario represents an alternative future which is based on a well-defined set of assumptions and conditions (Bouwman and Van der Duin 2003). Usually, the assumptions used in scenarios rely on input from other methods of research, such as, for example, trends analysis. In this sense, key trends and drivers of change are used to define the primary axes along which the scenarios are built. Then, the validity of the assumptions is evaluated by the forecaster, and the outcomes of this evaluation

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Table 6 Scenarios and roadmaping (Cho and Daim 2013; Haleem et al. 2019; Roper et al. 2011; Gordon 2009; Mietzner and Reger 2005) Description

Scenario construction proposes different conceptions of future technology Roadmapping shows a plausible path to achieve future technology

Some methods

Scenarios (scenarios with consistency checks, scenarios management), Scenario-simulation (interactive scenarios, gaming), Technology roadmapping

Conditions for use

The richness of future possibilities is based on specific suppositions and conditions Usable forecasts must be constructed from little data or structural base

Strengths

Scenarios are very flexible since they can exhibit rich portraits of possible futures and incorporate quantitative and qualitative data from other techniques Scenarios often can be used when data are too week to allow the use of other techniques Roadmaps sharpen clarity of strategic vision and provide direction to project teams

Weaknesses

Scenarios tend to be broad and conceptual rather than specific, and are relatively expensive and time-consuming Roadmaps rely on the availability of experts and the subject of study is often too complex to obtain a detailed analysis

are ultimately used to determine the scenario most likely to occur (Haleem et al. 2019). Like scenarios, technology roadmapping is a useful tool used to envision plausible futures. More specifically, a technology roadmap can be seen as a highway roadmap that describes how one could proceed from a starting point to a final destination. As a roadmap, it shows a layout of relationships that can be expected to happen between science, technology and products in the future, in the process of a technology reaching the market or achieving practical application. Thus, it is both an exploratory and a normative forecasting method that helps to plan and coordinate R&D developments as well as identify the future of technological progress. In planning, technology roadmaps can be used to define strategies by identifying the critical steps that should be completed before a desired goal can be achieved. Roadmaps can also be used to help R&D investment decisions by identifying the most cost or time efficient paths (Gordon 2009). In technology forecasting, roadmaps can be used in scenario-based approaches to provide a map of complex developments and their cause-effect interactions, and serve as a sort of scenario outline. By assigning probabilities to the intersections between paths, roadmaps can be used to forecast which steps will be accomplished, the course of the path towards the goal, as well as the timing between steps and ultimately, the estimated timing of the system (Lee et al. 2015b). Roadmaps have important strengths. They link technologies to markets, products capabilities and supplier intent, enable discovery of technology reuse and synergies opportunities, and reveal gaps, challenges and uncertainties in product, technology and capability plans. The weaknesses of roadmaps rely mostly on the need for deep

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expertise and complexity. As in the Delphi method, the outcome reflects the knowledge of the people who participated in it; hence, it is essential to involve the most expert individuals available. Regarding complexity, most topics of roadmapping turn out to be fractal, i.e. more details in analysis lead to more details left to be found, and a balance between depth and superficiality although crucial is often difficult to achieve (Gordon 2009b).

3.5.2

Recent Evolution

Scenarios and roadmapping recent evolution are mostly focused in an increasing complexity of how they are generated. In particular, scenarios are evolving from being qualitative and prepared by single authors, to interactive scenarios that result from creative processes that involve not only a variety of people (experts, users and broader audiences), but also models that can provide quantitative context and guide the scenarios links of causality (Gordon and Glenn 2018). As far as roadmapping goes, the spread of interoperability among online computer software and media is enabling that complex science and technology networks that are located on a website will be accessible for multiple remote users and experts in different disciplines. Thanks to this, they are able to use the networks as a database and also make modifications and provide inputs to reflect their opinions and judgements, like a kind of Real-Time Delphi. Furthermore, with this type of access available, the network can become a dynamic picture of the expectations, discoveries and perceptions of the system. It would be interesting to exploit these networks to collaboratively develop roadmaps for existing challenges, in order to define science and technology initiatives and track them over time. Similarly, applications of science and technology that are potentially threatening could be outlined and tracked. If these activities are supervised by a regulatory institution, roadmaps could be important for monitoring purposes (Gordon 2009b).

3.5.3

New Methods

The advent of more advanced computer technologies facilitating big data processing has fostered the development of quantitative scenarios that use large amounts of data and generate inference. Two of the methods that have been recently developed are robust decision modelling (or robust decision-making (RDM) (Lempert 2002) and scenario discovery (Bryant and Lempert 2010). RDM uses agent modelling to explain the relationships among a number of input variables and outcome indicators (Ciarli et al. 2016). The combinatorial space of all the parameters involved generates deep uncertainties over a large number of dimensions (Lempert 2002). Next, an experimental design can be used to generate thousands of scenarios, each corresponding to a different combination of parameter values. The scenarios are clustered in groups which perform similarly with relation to a number of criteria stablised by the policy maker. These are called robust criteria, as

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opposed to a single optimal criterion, and identify minimum conditions, such as, for example, the maximum probability that a crisis will occur, or the minimum level of pollution abatement. In this sense, MDM allows decision makers to characterise the most important vulnerabilities of a series of candidate strategies, i.e. those scenarios that not meet the minimum criteria, and then helps these decision makers choose among different alternatives to ameliorate these vulnerabilities. In a very recent example, RDM is applied to identify a robust mix of policy instruments—carbon taxes and technology subsidies—for reducing greenhouse emissions (Lempert 2019). On the other side, scenario discovery is an innovative method for developing scenarios under deep uncertainty, which involves a large number of actors with diverging worldviews and values. In scenario discovery a number of analytical tools are used to identify different groups of input combinations and to measure how well each group of input can predict satisfactory scenarios. In brief, scenario discovery facilitates the first step of RDM approach, by concisely summarising a wide range of future states in a way that allows decision makers to understand the strengths and weaknesses of alternative strategies. Recent research studies applying scenario discovery include the discovery of plausible energy and economic futures (Lempert 2019), or the discovery of strategic pathways to manage future coastal flood risk (Ramm et al. 2018).

4 Closing Remarks Technology forecasting involves a long list of different methods that are applied in both the research and professional fields. The present fast evolution of technology and its deep impact on economy and society make predictions on the future of the technology more and more necessary. In this context, technology forecasting methods confront increasing demand and, at the same time, take advantage of the possibilities that new technologies themselves provide. Technology forecasting is not considered able to provide precise and fully reliable predictions, since the elements involved in technology development are too numerous and complex. However, technology forecasting has proved to be able to give useful insight that covers partially the needs of public authorities, the industry and the research institutions when defining their policies and plans. The technology forecasting methods have not experienced recently any kind of revolution. The rationale behind the different methods remains the same that has been predominant for years. Methods such as environmental scanning, expert opinion and trend analysis continue to be widely known and are used in a similar way as in the past. However, the present possibilities to obtain and process information have changed profoundly how the methods are used. Although the logic and reasoning behind the methods remain, the huge amount of information available and the capacity we have to process this information have increased dramatically the capacity of analysis. This is particularly in the case of environmental scanning methods, on one side, and trend analysis and statistical methods, on the other, that are particularly dependent

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on obtaining of information and processing of data capacities. Particularities of the evolution of these and the others groups of methods have been reported. Additionally, to this general evolution of technology forecasting methods, a list of new methods, mostly based on the existing ones, has been developed. These methods include Webometrics, social network analysis, Spatial Delphi, Real-Time Delphi, Real-Time Spatial Delphi, web searches analysis, conditional forecasting, robust decision modelling and scenario discovery. The characteristics of these new methods have also been presented.

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Methodology Used for Determination of Critical Success Factors in Adopting the New General Data Protection Regulation in Higher Education Institutions José Fernandes, Carolina Feliciana Machado and Luís Amaral Abstract With the publication of the General Data Protection Regulation (GDPR), the European Union gives a clear signal that Data Protection is a key issue that needs proper regulation, and it is therefore important to identify a set of Critical Success Factors (CSFs) associated with the implementation of the GDPR in Portuguese Public Higher Education Institutions (HEIs). This article based on a working in progress investigation makes an in-depth discussion and argumentation regarding the adopted methodology to identify a set of CSFs associated with the implementation of the GDPR in Portuguese Public HEIs.

1 Introduction Information technologies are used today at anytime, anywhere, and for almost anything by companies and individuals, bringing advantages in facilitating access to a huge diversity of electronic services provided by the state and private organizations. In the comfort of our homes or at work, everything is just a click away from a computer. This massive use of information technology enhances the emergence of a set of threats and risks commonly associated with improper access by third parties to information deemed confidential. On the other hand, it is known that the processing of personal data has become a business for many companies and is often illegal because it is carried out without the consent of the data holders and without any kind of control. In May 2016, the European Parliament decided to publish the General Data Protection Regulation (GDPR) for various purposes, namely to standardize the national legislation of member states on the issue of Data Protection, to regulate the data market, and not least, to replace the already obsolete Directive 95/46/EC—relating J. Fernandes · C. F. Machado (B) School of Economics and Management, University of Minho, Braga, Portugal e-mail: [email protected] L. Amaral School of Engineering, University of Minho, Guimarães, Portugal © Springer Nature Switzerland AG 2020 C. Machado and J. P Davim (eds.), Research Methodology in Management and Industrial Engineering, Management and Industrial Engineering, https://doi.org/10.1007/978-3-030-40896-1_4

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to the protection of individuals with regard to the processing of personal data and on the free movement of such data. This Directive no longer addressed the Data Protection needs of European citizens in view of rapid technological developments, with a particular focus on systems that collect, process, store, and transmit huge amounts of personal data without any control, such as the Internet of things, big data, social networks, and search engine. The Internet is present everywhere, having helped define what it means today to be a digital citizen (Bartolini and Siry 2016). For Tikkinen-Piri et al. (2018), the entry into force of GDPR will bring a number of advantages for users of information technology as the guarantee of privacy for data holders is strengthened and organizations will finally have a legal framework that indicates very clearly the rules and requirements with which they must work. Of course there will be a cost to the adequacy that organizations will have to make to the GDPR, and thus, according to Tikkinen-Piri et al. (2018) in view of the organizations own characteristics, the challenges will be different and complex, with the impact to be felt in many different dimensions of the activity consequently affecting its resources in different ways. Maybe because the implementation of the GDPR is only mandatory since May 2018, there are no studies in the literature that already allow to accurately assess the prerequisites necessary to successfully implement the GDPR. In this sense, we can say that the regulation has been implemented without prior knowledge of the Critical Success Factors (CSFs) for the implementation of the GDPR. Moreover, these CSFs may be different for different types of organizations or companies. Daniel (1961) was the author who first defined the concept of CSFs as a way of classifying information considered critical for managers, and noted that “In most industries there are usually three to six factors that determine success; these key jobs must be done exceedingly well for a company to be successful.” However, it is Rockart (1979, 1982) and Bullen and Rockart (1981), who have done work related to the identification of CSFs in organizations. Rockart (1979) defines the concept of CSFs as follows: “Critical Success Factors thus are, for any business, the limited number of areas in which results, if they are satisfactory, will ensure successful competitive performance for the organization. They are the few key areas where “things must go right” for the business to flourish. The critical success factors are areas of activity that should receive constant and careful attention from management” (p. 85). According to RJIES—Legal Regulation of Higher Education Institutions, Higher Education in Portugal main objective is the high-level qualification of the Portuguese people, the production and dissemination of knowledge, as well as the cultural, artistic, technological, and scientific formation of their students at international level. In this sense, HEIs have a vast set of personal data concerning students, teachers, researchers, and non-teaching staff. With the entry into force of the GDPR, HEIs will be confronted with many challenges, namely they will need to demonstrate to the control bodies that they have in place the adequate internal structures and procedures to meet the requirements of the new regulation. Thus, for a successful implementation of GDPR in HEIs, it is essential to be able to ensure the existence of a set of CSFs. In this sense, this work

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contributes to the still very small field of theoretical and practical research related to the determination of CSFs related to the application of GDPR to HEIs. In this sense, the main objective of this paper is to determine the CSFs that are associated with the implementation of the GDPR in HEIs. Achieving this objective will result in the achievement of specific objectives, embodied in the following departing questions: Q1—What are the main implications of applying the GDPR? Q2—What are the CSFs that are associated with the implementation of GDPR in Portuguese Public HEIs? Q3—How are the previously CSFs identified mapped in the mission areas of the HEIs? Q4—How are the previously CSFs identified ordered in terms of their importance? Q5—How can a readiness model determine, a priori, the condition of particular HEIs to successfully implement the GDPR?

2 Research Methodology Figure 1 shows the different elements that will be analyzed in the following sessions to describe the research methodology adopted.

2.1 Research Philosophy—The Ontological and Epistemological Paradigm of the Research According to Saunders et al. (2009), the strategy and research methods selected by the researcher are associated with the research philosophy adopted and which is closely related to the way it conceptualizes reality. In this sense, according to Ragab and Arisha (2017), the determination by the researcher of his philosophical position can be a starting point in the investigation process, through the use of a research paradigm that for Guba and Lincoln (1994) is nothing more than a set of beliefs or of basic convictions that represent to an investigator the way he sees, positions himself and interacts with the world around him.

Fig. 1 Different elements from the research methodology. Source From the author

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For Kivunja and Kuyini (2017), this analysis of the way the researcher looks at reality is important because in addition to conditioning what he is going to study, it also has implications on how he will do it, supporting the different options from the methodological point of view as well as the methods and the techniques adopted for collecting and analyzing data. According to Guba and Lincoln (1994), the research paradigms can be framed in three distinct dimensions: the ontological dimension, the epistemological dimension, and the methodological dimension. The beliefs that define each dimension can be summarized, according to Guba and Lincoln (1994), by answering three interrelated, mutually conditioning and limiting questions, which, according to Kivunja and Kuyini (2017), incorporate the core values that each paradigm has. Thus, for Guba and Lincoln (1994), the three questions that are important for an researcher to answer in order to define his research paradigm are the following: Q1: The Ontological Question—“What is the form and the nature of reality and, therefore, what is there that can be known about it?” (p. 108). Q2: The Epistemological Question—“What is the nature of the relationship between the knower or would-be knower and what can be known?” (p. 108). Q3: The Methodological Question—“How can the inquirer (would-be knower) go about finding out whatever he or she believes can be known?” (p. 108). Next, the research work will be positioned from an ontological and epistemological point of view through the answers to questions Q1 and Q2 by Guba and Lincoln (1994). The answer to Q3: methodological question will be answered later in Sect. 2.6 of this document. Q1: The Ontological Question To address the ontological question, it is essential that the researcher defines the way he sees and situates himself within the world that surrounds him. In this sense, it is important to begin by evaluating the values of the investigator, its beliefs and its principles. These will be determinant to assess the meaning that the researcher gives to the reality with direct implications in the way he approaches the research problem (Kivunja and Kuyini 2017). Thus, the knowledge of the researcher’s beliefs and values regarding the world around him will dictate the ontological paradigm that will guide the investigation process. According to Ragab and Arisha (2017), the ontological paradigm can be objectivist or subjectivist. The researchers, who adopt the objectivist ontological paradigm, consider that the reality that will be the target of study is external, independent, and has no relation with the social actors that interact with it (Saunders et al. 2009; Greener 2008; Bryman 2012). The objectivist perspective is used normally in studies within the field of natural sciences (Ragab and Arisha 2017), and the subjectivist perspective is typically used in the area of social sciences (Greener 2008). Researchers, who adopt the subjectivism ontological paradigm, argue that, due to social interaction performed by social actors, the reality is dynamic, continuous and characterizes the social phenomena that are the subject of the study (Saunders et al. 2009; Bryman 2012).

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In this sense, while objectivist researchers advocate the existence of a single social reality, subjectivist researchers believe that there are multiple social realities, as many as the number of social actors involved in the investigation process, representing different ways of seeing the world (Ragab and Arisha 2017). The organizational culture can also be analyzed from the objectivist and subjectivist ontological perspectives (Saunders et al. 2009; Bryman 2012). Researchers who study the functioning of organizations using the objectivist ontological paradigm look at the organizations as rigid hierarchical structures, using a top-down management approach, where all managers and subordinates have their operational objectives and functions very well defined and standardized, acting the organization always in the same way independently of the people who are occupying the different positions in the hierarchy of the organization (Saunders et al. 2009). The objectivist perspective considers that organizations have an organizational culture and an identity that is always the same and that exists beyond the social actors that interact with it, as if the organization possessed a life of its own (Saunders et al. 2009). Researchers who adopt the subjectivism ontological paradigm believe that the identity of the organizations is something dynamic, in constant evolution. The organization’s culture is influenced in a daily basis by the interaction of the social actors. The subjectivist perspective considers that the organization is the result of the interaction of its social actors; the organizational culture is what the social actors want it to be. The organizational culture is not something innate, well on the contrary, is built step by step and it cannot be considered something isolated or manipulated without taking into account the people who are part of it (Saunders et al. 2009; Bryman 2012). Taking into account the need to define the ontological paradigm to be adopted by this research work, there are a set of arguments that apparently would lead to the adoption of an objectivist paradigm. These arguments include the fact that the public HEIs in Portugal are framed in the same set of laws which have the same mission (teaching, research, interaction with society), the same services (Financial services, academic services, human resource services, etc.), and even similar human resources structure (students, researchers, teaching, and non-teaching staff). In view of all these similarities, it can be assumed that the implementation of the GDPR will be a similar process in all national public HEIs, as it is understood that the issue of top management orders based on rules and regulations is sufficient to ensure the implementation and consequent full application of the different aspects of the GDPR in HEIs. However, this research work has a different understanding and will adopt a subjectivist ontological paradigm for the following reasons: (a) The administrative autonomy that faculties and schools possess in the HEIs varies from institution to institution and, in this sense, challenges are also different from the point of view of the implementation of the GDPR, as there may be a greater or less centralization of the implementation process. (b) The resources of the HEIs placed at the service of the implementation of the GDPR are not sufficient nor as relevant as the different wills and meanings attributed by each worker in the context of the performance of the functions assigned to them.

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(c) It is considered that the personal nature that each worker puts in the way he implements the treatment of different processes on a daily basis and how he carries out personal data processing operations when in contact with the different users of services, it has a very relevant, subjective and distinct weight from HEI to HEI. It depends on several factors such as the basic training of workers, their professional experience, how they relate to users and colleagues, and sociocultural aspects associated with the functioning of the organization that will clearly influence positively and negatively the implementation of the GDPR. (d) The organizational culture that will be relevant to the implementation of the GDPR is something that has its own dynamic and is typical of the academic environment, being oriented by a complex set of phenomena that include: a very diverse set of interactions between different social actors (teachers, researchers, students, staff, and users), physical factors associated with facilities and equipment supporting the activity of the institution, rituals associated with the work performance. In this sense, according to Saunders et al. (2009) “It is the meanings that are attached to these phenomena by social actors within the organization that need to be understood in order for the culture to be understood” (p. 111) and the GDPR can be successfully implemented. (e) Thus, this study will not be able to isolate or manipulate the organization ignoring the different social actors, as these plays a very relevant role in the organization and its academic culture. In this sense, the academic culture, that is very particular in this type of organizations, will have to adjust, so that the GDPR can be successfully implemented. (f) The DPOs in charge in the HEIs have very different academic and professional background and develop their work according to their background. It is essential, through appropriate methods and techniques, to collect information on how DPOs conceptualize the implementation of the GDPR in their HEIs taking into account the complexity and heterogeneity of the academic environment. Q2: The Epistemological Question The previous chapter defined how the researcher perceives the world around him from an ontological perspective. It is now important to understand how the researcher will achieve, obtain, and relate to knowledge (Saunders et al. 2009). In this sense, it is important to know the epistemological paradigm adopted to answer the questions of departure. There are according to Saunders et al. (2009) two distinct types of epistemological paradigms: the positivist epistemological paradigm and the interpretativist epistemological paradigm. Researchers who adopt the positivist epistemological paradigm believe that the knowledge of reality is obtained by using objective methods that eliminate hypotheses through testing and experimentation (Greener 2008). The reality has not been created or manipulated through social interaction phenomena associated with the different existing individuals, and therefore should not be inferred or intuited (Easterby-Smith et al. 2018). Thus, the researcher’s goal will be to provide explanations and make

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predictions based on mathematical calculations, so that they can be measured objectively (Kivunja and Kuyini 2017). Through the use of existing theories, the research creates hypotheses that will be tested, using the scientific method (Saunders et al. 2009; Greener 2008) and, in this sense, the research based on this paradigm uses deductive logic (Kivunja and Kuyini 2017; Greener 2008). According to Easterby-Smith et al. (2018), the main features of the positivist epistemological paradigm are as follows: (a) The researcher is totally independent from the research; (b) The objectivity is the only factor that decides what will be investigated; (c) The human behavior is regular, and therefore, fundamental laws must be found to justify it; (d) The deduction as a way of testing hypotheses; (e) The use of quantitative methods to measure facts; (f) The division of problems into smaller parts to be better understood; (g) The choice of a large number of samples that allow for generalization; (h) Cross-sectional analysis of the event under analysis at various times makes it easier to identify the regularity sought. On the other hand, the interpretativist epistemological paradigm argues that reality is not objective and it is difficult to be analyzed using methods typical of the natural sciences. Reality is socially constructed by social actors, and therefore, the researcher needs to take into consideration the facts generated by the multiple interactions of the different social actors that are a very important part of the problem under study (Saunders et al. 2009; Easterby-Smith et al. 2018). The use of the interpretativist epistemological paradigm is appropriate in the social sciences field, as a scientific area that involves people who interact socially in complex ways in multiple levels (Saunders et al. 2009; Greener 2008). According to EasterbySmith et al. (2018), the main characteristics of the interpretativist epistemological paradigm are as follows: (a) The researcher is part of the research process; (b) The human interaction is the main motivation for the beginning of any investigation process; (c) The aim is to increase existing knowledge in a specific area; (d) The facts are induced from the collected data; (e) The views of all parties involved should be taken into consideration; (f) The topic of analysis can be the problem as a whole with all its complexity; (g) The generalization is achieved through theoretical abstraction; (h) For specific reasons, the sample involves a very small number of cases. Taking the above into consideration, this research work will adopt the interpretativist epistemological paradigm for the following reasons:

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(a) The choice of the type of ontological paradigm conditions the choice of the epistemological paradigm (Guba and Lincoln 1994), and in this sense, considering that the ontological paradigm chosen by the researcher was the subjectivist paradigm, the epistemological paradigm chosen will be the interpretativist paradigm; (b) The study is focused on HEIs, in particular on national public HEIs, and the sample involves only eight HEIs; (c) These HEIs have very different realities and different procedures on the way their teachers, researchers, students, non-teaching staff manage data processing operations; (d) These different, complex, and unique realities are closely associated with how teachers, researchers, students, non-teaching staff understand their role within the implementation of the necessary procedures for the operationalization of the GDPR; (e) On the other hand, the DPO nominated by each HEIs has its own academic and professional background, distinct from other DPOs, and therefore has a very unique way of conceiving the implementation of GDPR in its HEIs; (f) Thus, it is important through the use of appropriate methods and techniques to understand all these realities, which are unique and distinct; (g) In this sense, the use of data collection techniques such as semi-structured interviews will inevitably make the researcher interact with the study participants (DPO), making interpretations of what he sees, hears, and understands. These interpretations will also be influenced by the fact that the researcher as a professional background is a worker of a HEIs.

2.2 Research Approach According to Ragab and Arisha (2017), there are two possible research approaches: inductive and deductive. Within the inductive approach, the research process begins with the definition and collection of data, so that one can then, through data analysis, find patterns that support the formulation of a particular theory (Saunders et al. 2009; Yin 1994; Babbie 2011). The researcher using the inductive approach usually works with small samples, unlike the deductive approach that aims for generalization and as such needs large samples. The researcher using the inductive approach will certainly have as one of his main goals to understand the role that different social actors play in the ongoing research, and as such, it makes perfect sense to work with small samples (Saunders et al. 2009). In this sense, the inductive approach has characteristics that make it suitable for management investigation where the study of human behavior is an essential part of the research project (Lancaster 2005). Within the deductive approach, the researcher starts from a pattern, a theory for the definition of data that will be collected, observed, and analyzed and that will

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Table 1 Main differences between deductive and inductive approaches Deductive approach

Inductive approach

Based on scientific principles

Understanding the different meanings that individuals attribute to the events

Departs from theory to data

Understanding closer to the research context

Explains the causal relationship between variables

Collects qualitative data

Collects quantitative data

The research structure is not rigid allowing change in focus

Validity of data by applying controls

The researcher is part of the investigation process

Operationalization of the concepts so that the facts are measured quantitatively

Generalization is not a goal

Very structured approach The researcher is independent of the phenomenon that is being studied Generalization through the use of large samples Source Adapted from Saunders et al. (2009, p. 127)

or will not prove the truth of the occurrence of the existing pattern or theory (Yin 1994; Babbie 2011). According to Saunders et al. (2009), the deductive approach is associated with the natural sciences field and is criticized by defenders of the inductive approach for not taking into account the nature of the different social actors, the whole process being summarized by manipulating a set of variables that make the deductive approach a rigid and poorly suited approach for social science research problems (Table 1). This research adopts the inductive approach based in the following set of arguments: (a) The application of the GDPR in HEIs is a topic that needs further development. (b) This research work emerges as a contribution to the theoretical construction of the theme. (c) As a new area that needs study, the investigation process is part of the identification of data sources, its collection and analysis in order to identify patterns associated with the CSFs related to the application of the GDPR in national public HEIs. (d) The focus of the research are the national public HEIs. Due to his professional background, the researcher has a deep knowledge of the university context, and for that reason, the researcher cannot be an independent part of the process. (e) Generalization is not an objective. The application of the GDPR will have to be carried out in all public and private organizations, profit or non-profit, and it is not possible to infer that the CSFs determined for the national public HEIs may be associated with other organizations, namely private national HEIs. This will certainly be a future study area.

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(f) Finally, the DPOs have very different professional and academic background, and in this sense, they have very different understandings regarding the challenges related to the implementation of the GDPR. For this reason, it is important to know the different opinions that the different DPOs have in relation to the subject of study, through the adoption of an interpretativist epistemological paradigm and the adoption of methods and techniques of data collection mainly qualitative.

2.3 Research Nature According to Saunders et al. (2009) and Neuman (2014), there are three possible ways to classify the nature or purpose of research—exploratory, explanatory, and descriptive. Babbie (2011) states that it is normal for simultaneous use of more than one of these classifications. The exploratory research aims to discover new theories (Saunders et al. 2009), being a way for the researcher to understand something that is happening in the social sciences, to better comprehend a recent or a new topic, by posing the relevant questions, with the aim of increasing the existing knowledge (Babbie 2011; Neuman 2014; Gray 2004). One of the advantages of the exploratory research is the flexibility and adaptability, allowing the change or adjustment of the research process according to the findings resulting from the collection and analyzes of new data or due to the fact that the steps of the investigation process are not yet fully established (Saunders et al. 2009; Neuman 2014). As mentioned previously, these are also characteristics of the inductive research approach (Saunders et al. 2009). According to Babbie (2011), exploratory research main objectives are: (a) To increase existing knowledge on a specific subject; (b) To assess whether further study is relevant for a particular subject; (c) To develop the necessary study methodologies to apply at later stages of the research. According to Saunders et al. (2009), exploratory studies can be developed in three possible ways: (a) Through literature review; (b) Through interviews with experts; (c) Through focus groups. When the nature or purpose of the study is to have a clear understanding of a phenomenon or event, through a detailed observation by the researcher and a subsequent accurate and precise description of the observed situation, then the study in question is situated within the descriptive field (Saunders et al. 2009; Babbie 2011; Gray 2004).

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Often exploratory and descriptive studies combine (Neuman 2014) seeking to answer questions such as—Who? How? When? and Where? (Babbie 2011; Neuman 2014) using data collection techniques such as surveys, fieldwork, document analysis, and comparison of historical facts (Neuman 2014). However, descriptive studies have a limitation because they are not able to answer questions such as—Why? This fact leads to the need to apply an explanatory approach aimed at this purpose (Babbie 2011; Neuman 2014). The purpose of explanatory studies is to establish causal relationships between variables (Saunders et al. 2009) explaining what are the causes and reasons for situations to occur within the study of a particular problem or event, and these studies are often supported by exploratory and descriptive activities previously developed (Neuman 2014). According to Neuman (2014), the objectives of exploratory, descriptive, and explanatory studies are: (a) Exploratory • • • • • •

To know essential facts related to a problem or event; To create a reliable image of the problem or event; To develop questions for future research; To develop new ideas and hypotheses; To evaluate the interest in conducting a study; To create techniques that allow us to assess data from future studies.

(b) Descriptive • To provide the researcher with a very detailed and accurate picture of a problem or event; • To get new data that contradicts data collected in the past; • To create a set of categories; • To categorize the data; • To describe a sequence of steps or stages; • To describe a process or mechanism; • To describe the context of a situation or event. (c) Explanatory • • • • • •

To test a theory or principle; To develop or improve the explanation of a theory; To expand the scope of a theory; To confirm or refute a hypothesis; To relate subjects with theories or general principles; To select the best explanation of a situation or event.

Given the different options outlined above, the research nature of this study is clearly exploratory and descriptive. This option is based on the following set of arguments:

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(a) The theme related to the application of GDPR in HEIs is a very recent topic, very relevant, and with social interest because it relates to the protection of personal data. (b) It is a very recent topic, there is little scientific research on the subject and there is very limited knowledge about the state of implementation of the new regulation in HEIs. (c) Therefore, there is an identified need and opportunity to explore the issue by increasing existing knowledge through a study that identifies and describes the CSFs associated with the process of implementing the GDPR in national public HEIs. (d) The identified CSFs will be categorized within the different areas behind the mission of the HEIs. (e) In the future, it will be important to develop research work to assess whether the CSFs determined for national public HEIs equally apply to private HEIs.

2.4 Research Strategy According to Saunders and Tosey (2013) to answer the research question, the researcher has a set of strategies that he can incorporate in the design of his research process. For Saunders et al. (2009), these strategies can use either the inductive or the deductive approach, and the choice should be guided solely to meet the researcher’s goals and to be able to answer the research question. Saunders et al. (2009) present us with the following research strategies: (a) The Experimental Research This research strategy has the same logic and the same principles that are used in the natural sciences (Neuman 2014), but also has application in the social sciences, particularly in the field of psychology (Saunders et al. 2009). Within experimental research, the researcher manipulates an independent variable by observing the effect on the dependent variable (Gray 2004; Bhattacherjee 2012), and the experiments can take place in the laboratory or in the real world (Neuman 2014). The existence of manipulation of variables makes experimental research more suitable for explanatory studies rather than exploratory or descriptive studies, because the objective is to explain causal relationships by measuring the effect of independent variable manipulation on the dependent variable (Lancaster 2005). Within this research strategy, experiments performed in a laboratory environment have a high internal validity and a low external validity, associated with the fact that the experiment is performed in a controlled laboratory environment (high internal validity) and therefore artificial (low external validity) (Bhattacherjee 2012). For the study to have a high external validity, the research must be performed in a non-laboratory context (Gray 2004). However, in the real world it is difficult to achieve pure experimental research because it is difficult to find groups of individuals (samples) with similar conditions to be

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used randomly (Gray 2004). In these cases, according to Gray (2004), we enter a type of quasi-experimental investigation, where the researcher in a real way context, directly chooses groups of individuals who (in real life) already have the pattern he wishes to test, comparing them directly with other groups where the pattern under study does not meet (Gray 2004; Bhattacherjee 2012). The observed conclusions, according to the deductive approach, may confirm or deny the initially advanced hypotheses (Gray 2004). Thus, according to Lancaster (2005), experimental research can be divided into two types—pure experimental research, where there the selection of individuals is random and quasi-experimental research with non-random sampling. According to Gray (2004) generally, experimental research has the following characteristics: • • • •

Reproduces experimental laboratory techniques with very structured methods; Generates hypotheses; Controls variables that can confirm or deny the hypotheses previously formulated; It is accurate in measuring results with the application of quantitative data analysis techniques; • Generalizes from sample to similar population. (b) Surveys Surveys are a research strategy that is commonly used in the social sciences (Saunders et al. 2009; Babbie 2011) and is a typically deductive approach (Saunders et al. 2009) that can be used in exploratory, descriptive, and explanatory studies (Babbie 2011; Bhattacherjee 2012). Surveys are a predominantly individual-focused research strategy (Babbie 2011) and therefore, are used to collect data about their opinions, preferences, thoughts, and attitudes (Neuman 2014; Bhattacherjee 2012). According to Neuman (2014), this type of research strategy differs from the strategy based on experimental research where there is manipulation of variables, and the generalization can be obtained through random selection of participants. As data collection techniques or tools, the survey-based approach includes the use of questionnaires with open or closed questions (Babbie 2011) that are completed autonomously by survey recipients by using email, web forms, or a specific group of individuals previously selected (Bhattacherjee 2012). In addition to the questionnaires, we can also use interviews, with open or closed questions (Babbie 2011) conducted directly by an interviewer in the presence of the interviewee. This can be done face-to-face, by telephone or in-group, using the focus groups technique (Bhattacherjee 2012). (c) Case study Yin (1994, p. 13) defines a case study as “…an empirical inquiry that investigates a contemporary phenomenon within its real-life context, especially when the boundaries between phenomenon and context are not clearly evident”. From this definition of Yin (1994), we can conclude that a case study is: • Knowledge gained through experiment and observation of facts; • A contemporary phenomenon;

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• A phenomenon that happens in real life; • The study of the phenomenon in the context where it occurs. Case studies can be applied with exploratory, descriptive, or explanatory research approaches (Yin 1994), in a single or multiple time-period, usually being a significant part of the collected qualitative data (Neuman 2014). The case study-based research strategy can be used to provide a detailed description of a contemporary phenomenon, exploring new areas of research or add knowledge to areas under development (Eisenhardt 1989). The researchers use qualitative data collection methods such as observation, structured interviews (Bryman 2012; Eisenhardt 1989; Rowley 2002), questionnaires, and information archives (Eisenhardt 1989). However, according to Locke (2000), case studies can also be viewed as being the limited objects or systems where the researcher intends to study the phenomena of interest, which may be according to Neuman (2014) one or more people, one or more organizations, social phenomena, or events. According to Yin (1994), case studies can be classified into four distinct categories resulting from the combination of singlecase or multiple-case strategies, directly linked with the number of units studied in each case, making it possible to have single-case strategy studies or multiple-case strategy studies, of holistic and embedded type. In this sense, the researcher must select, from among the existing possibilities, the one that best suits his study according to the characteristics that distinguish the single-case strategy from the multiple-case strategy as well as the holistic and the embedded type. Yin (1994) considers the following description: • Single case: This type of strategy is characterized by being a single experiment, or, in extreme situations, a case with characteristics that are considered revealing for research. • Multiple case: This type of strategy is characterized by the realization of multiple experiments each performed in one of the cases selected for study. The aim of this strategy is the replication and in this sense, the investigation procedure should be applied equally in all cases in order to confirm or refute the theory. According to Yin (1994), this type of strategy is currently more widely used than the single-case strategy. As previously mentioned, single- or multiple-case studies can be, according to the type of study units, holistic or embedded type. Yin (1994) defines these two typologies as follows: • Holistic—When the case study focuses on the system or organization under analysis as a whole. • Embedded—When the case study focuses on parts of the subunits of the organization being analyzed. According to Rowley (2002), case studies with an exploratory focus do not begin with theories and hypotheses, so it is necessary to develop a framework that organizes the relevant information that is gathered from multiple sources of information.

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According to Yin (1994), as a significant part of the case studies is related with individual’s behavior or people-related events, interviews are one of the most important sources of information for this research strategy. In this case, according to Yin (1994), interviews are more like an informal conversation between the interviewer and the interviewees, in each case and, in this sense, there may be interest in comparing the opinions of each other in order to verify the ones that prevail. (d) Action Research Lancaster (2005, p. 124) defines action research as involving “… practical hands-on field research in an organization where the researcher has the objective of solving practical, real-world problems in the organization”. In this sense, in this type of research strategy, the researcher and the workers of the organization are key actors and, as such, with active intervention in the life of the organization, interested in the problem or event that is being studied, being directly involved in the results (Greener 2008; Bryman 2012; Gray 2004). Thus, in this research strategy the researcher has the objective of solving practical and real problems in the organization, getting directly involved in their resolution, and may even work as an external consultant in the implementation (Bryman 2012; Lancaster 2005; Bhattacherjee 2012) of actions or measures based on a theory. This theory will be validated or refuted by assessing the success that the practical actions had in solving the problem that the organization was confronted with (Bhattacherjee 2012). According to Bhattacherjee (2012) and Lancaster (2005), this type of strategy compensates for its alleged lack of scientific methodology with a practical approach to problem solving rather than the creation of theories. Theory development is mainly associated with experimental research methodology where research is more fundamental. The collected data are qualitative or quantitative (Gray 2004). According to Lancaster (2005), the researcher can use a wide variety of methods to collect data, such as direct observation, surveys, interviews, and experimental research. Thus, the action research strategy has, according to Lancaster (2005), the following characteristics; • Research is focused on solving practical problems of the organization; • The researcher, as a consultant, is one of the stakeholders in the organization who is interested in solving the identified problems. He is closely involved in the problem resolution and evaluation of the results of implemented measures; • The implemented process is a continuous improvement cycle. This cycle is defined by Lancaster (2005) as research, implementation, evaluation, new research, by Saunders et al. (2009) as diagnostic, planning, action, evaluation, or by Berg (2004) as research questions identification, response to questions research, information analysis, share information with participants. • The implementation of practical measures in the organization in order to solve real problems promotes the professional development of workers.

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(e) Grounded theory Strauss and Corbin (1994, p. 273) define grounded theory as “a general methodology for developing theory that is grounded in data systematically gathered and analyzed”. In turn, Charmaz (2005, p. 507) defines grounded theory as “a set of flexible analytic guidelines that enable researchers to focus their data collection and to build inductive theories through successive levels of data analysis and conceptual development”. In other words, we can conclude that grounded theory is a methodology that aims to inductively develop theory from a set of methods and procedures of extraction and analysis of data performed in a systematic way. According to Bryman (2012), the grounded theory is the framework most commonly used by researchers adopting a qualitative research approach focusing on social phenomena. To Bhattacherjee (2012), grounded theory is used where there is a need to analyze large amounts of data obtained through interviews, direct observations, discussion groups, document analysis, looking for the identification and interpretation of ideas, categories, themes, and patterns (Greener 2008; Lancaster 2005). Grounded theory is inductive in nature (Lancaster 2005; Bhattacherjee 2012) and interpretativist (Greener 2008) insofar as theory is generated through the interpretation of collected raw data (Lancaster 2005). In this sense, data collection should be initiated by the researcher without preconceived ideas about the phenomenon under study (Lancaster 2005), and therefore, there should be no departure theories that condition the process of raw data collection and analysis (Saunders et al. 2009) and may bias the process (Bhattacherjee 2012). This does not mean that researchers should not adequately review the literature, but this should not condition them or have them start the process of collecting and analyzing data with preconceived ideas (Locke 2000). Thus, this research strategy is not suitable for confirming and demonstrating theories deductively (Lancaster 2005), but rather for explaining a particular social phenomenon from which actors construct experiences through subjective social interaction mechanisms (Suddaby 2006) and in particular, it is very useful when it is not clear to the researcher the real nature of the phenomena under study (Lancaster 2005). According to Suddaby (2006), the grounded theory is not: • A justification for the researcher not to perform or ignore the literature review; • The presentation of raw data. The data collected should be analyzed so that conclusions can be drawn to support the formulation of new theories; • Theory testing, content analysis, or simple word counting; • The mechanical application of data analysis procedures; • It is not a perfect theory. There is a constant debate between those who apply the grounded theory and those who write about it. Researchers should avoid the most fundamentalist positions; • It is not easy to apply, and researchers need to be sensible in order to be able to detect patterns, meanings, and connotations, something that improves with experience and training.

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According to Haig (1995), a good grounded theory is the one that: • It is inductively derived from the data; • It was subject to theoretical elaboration; • It is considered adequate to its study field in relation to various evaluation criteria. For Bryman (2012), coding is one of the most important processes of grounded theory, being related to the analysis of raw data and subsequent assignment of codes (names) to parts that may have some theoretical meaning or value for the phenomenon that is being studied. According to Corbin and Strauss (1990), there are three data coding techniques: • Open coding—Interpretive process where raw data related to events, ideas, actions, interactions, perceptions are separated and labeled, giving them a name and comparing them with each other in order to form categories and subcategories. • Axial coding—Process where causal relationships are created between categories and subcategories through the association of codes assigned to conditions, contexts, actions, interactions, and consequences. • Selective coding—Final phase where all categories are unified around a central category that represents the phenomenon under study. Categories that are not possible to unify around a central category need to be further described. (f) Ethnography According to Neuman (2014, p. 435), the Ethnography “… is the description of people and/or their culture”. In this sense, according to Saunders et al. (2009) and Bhattacherjee (2012), ethnography is a research strategy often used in anthropology to study a group of people and its culture, describing in detail their customs and traditions. The researcher uses observation to collect data (Easterby-Smith et al. 2018; Lancaster 2005; Bhattacherjee 2012) and must be completely immersed in the study to be able to describe what people are, by seeing what they see, by feeling what they feel, by perceiving with their own eyes (Lancaster 2005) what is the true meaning of the different social interactions that happen in the group (Saunders et al. 2009; Easterby-Smith et al. 2018; Neuman 2014). According to Bhattacherjee (2012) in order to reach this level of detail in gathering information, the researcher needs to effectively be part of the group and the culture to be studied. To Lancaster (2005), the researcher can take on two distinct roles, the observer spectator that does not mix with the study group, or the observer and participant in the different social interactions of the group being studied. In this case, the researcher shares the daily experiences and feelings of the group and has access to information that, as a mere observer, would not have access to (Lancaster 2005; Bhattacherjee 2012). For Greener (2008), Saunders et al. (2009), and Lancaster (2005), this may pose ethical questions to the research as it will force the researcher to clearly define if he will participate in the social interactions of the group under study with potential impact not only on the phenomenon under study, but also in the life of the studied

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group. To Saunders et al. (2009), this type of strategy is associated with some difficulties, namely because it requires a lot of time on the part of the researcher to be able to get the information that answers the research questions, and because it requires that the group being studied trusts the researchers enough to let them enter their culture. (g) Archival research This type of research strategy uses historical archives, its documents, and databases as a source of information (Easterby-Smith et al. 2018) to answer questions of an exploratory, descriptive, or explanatory nature with impact over time (Saunders et al. 2009). According to Schwartz and Panacek (1996), archival research has the following advantages and disadvantages: Advantages: • Requires fewer resources than more prospective research; • Allows for a quicker production of associations that can evolve with prospective studies; • May be performed at the convenience of the investigator. Disadvantages: • • • •

Has numerous sources of bias; Might raise doubts about internal and external validity of the studies performed; Problems with the absence of data; Difficulty in establishing cause–effect relationships.

Given the different alternatives of existing research strategies, the argument that will be used to select one of them is to ensure that the one chosen is the one that best responds to the previously stated starting questions, thus fulfilling the objectives of the research work. This research work has as main objective—to determine the CSFs that are associated with the implementation of the GDPR in Portuguese Public HEIs. This objective has several dimensions by which it can be analyzed which give valuable insight into the best strategy to adopt. We will start by analyzing the component that refers to the need to determine the CSFs that are associated with the implementation of the GDPR. The GDPR has been released on April 26, 2016, and the European Union has given member states two years to adapt to this new reality. Thus, the GDPR became a mandatory regulation in the member states of the European Union from May 25, 2018. Although it replaces a Directive that is over 20 years old, the new GDPR regulation will have a substantial impact on the activities of organizations, mainly because they have to demonstrate to the national Data Protection authority that they are complying with the provisions of the regulation. Therefore, this is a contemporary subject, which concerns all, natural or legal entities, public or private entities, profit or non-profit entities. In addition, it concerns everyone because today it is not possible to conceive a society without data and without data processing operations, either at personal and/or professional level.

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On the other hand, the institutions will have to create, in accordance with article 37 of the GDPR, a new role—the Data Protection Officer (DPO)—who among other responsibilities listed in article 39 will have to control whether the processing operations data held at the institutions they work for is in accordance with the GDPR. In this sense, the DPOs are the ones in the institutions who know better the state of implementation of the GDPR, as well as the main difficulties, constraints, and implications arising from the implementation. DPOs are clearly the people we want to talk to and convince to participate in this study in order to share their knowledge and their experience related to the implementation process of the GDPR. Thus, in order to access the knowledge of DPO about this subject, interviews will be used as the primary data collection method. The methodological options adopted for data collection, analysis, and processing will be discussed at a later stage in this paper. For the moment, we will concentrate on the arguments to justify the adopted research approach. Following the conduction and transcription of the interviews, the researcher will systematically work the information obtained, according to the procedures defined in the method of CSFs developed by Caralli et al. (2004) based on the initial work performed by Rockart (1979), to determine the CFSs (this method of data analysis will be described later in this paper). In summary, the CFSs that will be listed at the end of the research will be based on the data collected through the DPO. We will now analyze the second component of the main objective that focuses the study on Portuguese Public HEIs. The determination of CSFs can cover many different topics/subjects and can be performed in different public or private organizations regardless of their sector of activity. The existing literature shows that there is a wide range of studies related to the determination of CSFs associated with the most diverse areas of activity. Some specific examples are: the requirements analysis (Rockart 1979), planning and development of information systems (Bullen and Rockart 1981; Edwita et al. 2017), CSFs associated with total quality management (TQM) implementation projects (Hietschold et al. 2014), sustainable urban design (Dias et al. 2018), implementation of document management systems in public entities (Alshibly et al. 2016), implementation of the Lean Six Sigma methodology associated with leadership (Laureani and Antony 2018). However, it was not possible to find studies related to the determination of CSF associated with the implementation of GDPR in national public HEIs. This is important as it limits and contextualizes the topic where the researcher intends to conduct the study (Locke 2000). This study will cover institutions of a specific area of activity that is national public higher education institutions. It is assumed that the implementation of the GDPR in private higher education institutions, or in other different areas of activity such as banking, services, commerce, or health, may have other CSFs associated, as the conditions supporting the implementation of the GDPR will certainly be different. On the other hand, the determination of the CSFs associated with the implementation of the GDPR will take into account the HEI as a whole and not a specific unit, subunit, or service. This approach is based on the idea that in each HEI, there is

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only one data controller who determines the implementation of GDPR and a single DPO responsible for ensuring compliance of data processing operations with GDPR. National public HEIs may be in very different stages of implementation of the new regulation, and in this sense, it is important to obtain information from DPOs from as many national public HEIs as possible, which through their experience will convey information that will allow CSFs to be derived at different stages of implementation. On the other hand, comparing the opinions of the different DPOs will yield more robust and broader CSFs. Below, we present a list of the arguments that justify the choice of the research strategy adopted: • The phenomenon under study is concrete and objective as it has its boundaries well defined. The aim is to determine the CSFs associated with the implementation of the GDPR and not to determine the CSFs associated with any other event or issue related to the activity of organizations in a general or specific way; • The study of CSFs related to the implementation of the GDPR is a new phenomenon. There is still very little research work about this topic and for this reason needs to be further explored. As previously stated, there are a large number of studies related to the determination of CSFs associated with the most diverse areas of activity. However, it was not possible to find studies related to the determination of CSFs associated with the implementation of GDPR in HEIs; • The phenomenon under study is clearly a relevant and contemporary topic; • The phenomenon under study is very present in the real life of organizations and their workers; • The phenomenon under study is clearly relevant to organizations and their employees; • The phenomenon under study will have to be studied and described in the actual context in which it occurs (in the daily life of organizations); • The participation of DPOs in the study will be of very high importance as they are the main actors on what concerns the knowledge and experience in applying the GDPR in the institutions; • To obtain the necessary information to derive the CSF, it is proposed to conduct interviews with the DPOs; • To derive the CSFs will be used the method of CSFs developed by Caralli et al. (2004) through the extraction and systematic analysis of raw data obtained through the interview transcriptions; • The system selected for the study and identification of CSFs is focused in a very specific area which is the national public HEIs; • The study will take into account the HEIs as a whole, and therefore the CSFs that will be identified will cover the institution as a whole, including all units, subunits, and services; • The study will involve the highest possible number of national public HEIs in order to obtain a set of CSFs that can cover the different stages of the implementation of the RGDP in the different institutions. On the other hand, involving as many

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different DPOs as possible from different institutions will allow for a more robust and comprehensive identification of CSFs. Taking into account the above arguments, the selected research strategy is the case study in the sense given by Locke (2000), that is, the determination of the CSFs associated with the implementation of the GDPR in a very specific case, which is the national public HEIs as the case or system defined for the study. The case or system selected is holistic because the study will be applied to the HEIs as a whole and not to one or more of their units, subunits, or services. On the other hand, the case study will be multiple because it will be carried out in more than one national public HEIs and the study methodology will be replicated among the various cases. None of the other research strategies’ options available and described in this paper was chosen because: • Experimental research is not appropriate because this study does not start with the development of starting questions in the form of hypotheses that can be either confirmed or refuted in a deductive process through the data collected during the study. On the contrary, this study intends to inductively generate theory from the data collected in the interviews to be carried out with the DPO of national public HEIs. • Surveys traditionally are deductive approach applied in research where data are mainly handled quantitatively and one of its objectives is to apply to a large number of individuals. In this sense, this is not a research strategy that is considered appropriate for this study that is intended to inductively generate theory through data collected from a small sample of individuals (the DPO) using semi-structured interviews as the main data source. • Action research does not seem appropriate because the researcher will not be part of the team of the different DPO who will implement GDPR in their HEIs nor will he act as an external consultant who advises and guides the DPO in the implementation of the GDPR. This means that the researcher will not intervene from the practical point of view in the implementation of the GDPR. On the other hand, the implementation of the GDPR in HEIs is not considered a problem that can be solved in a practical way, something typical of this research strategy. The implementation of the GDPR is transversal to the HEIs, and it has many implications in all the established procedures and in the culture of the institutions. • The research strategy based on grounded theory could be used to extract data from structured interviews in order to generate the theory inductively. However, in this study, to generate the CSFs associated with the implementation of GDPR in HEIs, a set of procedures defined in the method of CSFs developed by Caralli et al. (2004) from the initial work by Rockart (1979, 1982) will be used to determine CSFs. This method guides the extraction and analysis of data systematically from structured interviews until the desired CSFs are identified. This method is inductive and interpretativist and meets the guidelines previously assumed by the researcher.

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• Ethnography is not appropriate because the researcher will not use observation to study and describe the customs and traditions of a specific group belonging to the HEIs in order to obtain answers to the research questions. • Archival research is not adequate to answer the starting questions because they are not based on past documentary sources, but rather on information that will have to be collected through the DPO of the national public HEIs and prospectively analyzed.

2.5 Time Horizons According to Saunders et al. (2009), a study in terms of time horizon can be defined as being cross-sectional or longitudinal. Neuman (2014, p. 44) defines cross-sectional studies as “Any research that examines information on many cases at one point in time” and longitudinal studies as “Any research that examines information from many units or cases across more than one point in time”. Thus, when the data are collected in the timeline only once the researcher gets a fixed image of the phenomenon under study at that time, in which case the time horizon of the investigation is defined as cross-sectional type. When the data are collected more than once in the time line, possibly to measure and compare the phenomenon under study over the time horizon of the investigation is defined as longitudinal (Saunders et al. 2009; Easterby-Smith et al. 2018; Neuman 2014). According to Neuman (2014), the cross-sectional or longitudinal types can be applied to exploratory, descriptive, or explanatory studies. The characterization of a study according to its time horizon depends on the design of the research process (Neuman 2014). It is necessary to understand whether it incorporates the need for more than one reading of data, at more than one moment in time, in order to be able to analyze any changes in the phenomenon under study. According to Neuman (2014), the answer to this question determines the type of time horizon research. Due to its duration in time, longitudinal studies need more resources and are also more complex to perform (Bryman 2012; Neuman 2014). According to Neuman (2014), these can have the following configurations: • Time-series research—In order to allow the researcher to analyze stability or change, data are collected more than once in the timeline, for the same or different individuals or cases associated with the phenomenon in study; • Panel study—In order to allow the researcher to analyze the impact of an event, data are collected more than once in the timeline, exactly for the same individuals or cases associated with the phenomenon under study; • Cohort study—In order to enable the researcher to analyze the impact of an event, data are collected more than once in the timeline for a category of individuals or cases associated with the phenomenon under study.

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Given the characteristics of the two possibilities of time horizons previously described, this study is characterized as being cross-sectional because it has no need to collect data more than once in time. As mentioned earlier, the GDPR has been in force since May 25, 2018. After one year, it is important, at this moment, to understand which CSFs are associated with the implementation of the new regulation. In this sense, this study will collect information from the DPO from the different case studies at a specific point in time.

2.6 Methodological Options for Data Collection and Analysis The methodological options for data collection, processing, and analysis adopted by the researcher are closely related to his philosophical position as well as to the type of research approach he intends to undertake. Given these guiding principles, the researcher adopts qualitative or quantitative methods (Ragab and Arisha 2017; Amaratunga et al. 2002). According to Saunders et al. (2009), the qualitative and quantitative terms designate the different techniques that can be used for data collection and processing. Thus, the researcher uses quantitative techniques when collecting and analyzing numerical data and uses qualitative techniques when collecting and analyzing nonnumerical data such as words, photographs, videos (Saunders et al. 2009; Lancaster 2005; Neuman 2014; Amaratunga et al. 2002). According to Bhattacherjee (2012), we can characterize the type of research performed as qualitative or quantitative taking into account the way the researcher collects data and analyzes the data. To Neuman (2014), this choice reflects the way the researcher wants to perform the investigation and how he is involved within the world around him. Quantitative research is characterized by having an objectivist ontological paradigm and a positivist epistemological paradigm, having an deductive approach to research by testing theories, formulating hypotheses and measuring variables, moving from theory to conclusions, seeking generalization and replication (Bryman 2012; Neuman 2014; Creswell 2009). According to Lancaster (2005), quantitative research can only be applied to natural sciences phenomena that can be quantified and measured. On the other hand, qualitative research is characterized by having a subjectivist ontological paradigm and an interpretativist epistemological paradigm, having an inductive approach to research, moving from the data to the theory generation trying to understand and represent in detail the different meanings of the different complex social phenomena that occur in the real world and that are the result of the interaction between the different social actors (Bryman 2012; Neuman 2014; Haig 1995). According to Lancaster (2005), this type of research can only be applied to typical social science phenomena that cannot be quantified. For Bryman (2012), the different characteristics of qualitative and quantitative research can be summarized as follows:

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Quantitative: • • • • • • • • • • • • • •

Objectivist ontological paradigm; Positivist epistemological paradigm; Deductive approach; Based on numerical data; Researcher’s point of view; Researcher does not engage in the investigation process; Test of theories; Static image of social reality; Structured; Seeks generalization; Reliable data; Focuses macro aspects of reality—large samples; Behavior of people; Carried out in a controlled artificial environment. Qualitative:

• • • • • • • • • • • • • •

Subjectivist ontological paradigm; Interpretativist epistemological paradigm; Inductive approach; Based on non-numerical data; Participants’ point of view; Researcher gets involved in the research process; Theory emerges from the data; Dynamic image of social reality; Unstructured; Understanding of contexts; Very meaningful data; Focuses on micro-aspects of reality—small samples; Meaning of people’s actions; Carried out in the natural environment of the study subjects.

Qualitative research is performed using qualitative methods and techniques, and quantitative research is performed using quantitative methods and techniques. According to Saunders et al. (2009), an investigation can be a mono-method type when using a single data collection and analysis technique or can be a multimethod type when using more than one data collection and analysis technique in its study. Thus, according to Saunders et al. (2009), we will have a qualitative mono-method study when data collection and analysis techniques are qualitative or quantitative mono-method when data collection and analysis techniques are quantitative. We can also have, according to Saunders et al. (2009), a multimethod research when the researcher uses more than one research method. Specifically, we have a qualitative multimethod research when using more than one qualitative data collection and analysis technique and a quantitative multimethod when using more than one technique and method of quantitative analysis.

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Saunders et al. (2009) state that it may be advantageous to use more than one method when it is necessary to include different approaches to collect data on different aspects of a particular event or, where it is necessary to complement data already collected, thereby being possible to include all relevant aspects of the research. According to Creswell (2009), mixed methods are used in studies that combine qualitative and quantitative data collection and analysis methods and techniques. Quantitative collected data are analyzed using quantitative procedures, and qualitatively collected data are analyzed qualitatively. Thus, when different data collection and analysis methods are combined in one study, integration is performed at a superficial level since the paradigms that support these methods from an ontological and epistemological point of view are incompatible in their assumptions (Bryman 2012) thus predominating one of the methods (Saunders et al. 2009). Haig (1995) states that there are researchers who adopt a type of mixed method research because they believe it to be a more complete approach to the research problem, making according to Bryman (2012) data collection and analysis methods and techniques autonomous in relation to the philosophical approach that supports the researcher’s convictions. Thus, according to Bryman (2012) and Haig (1995), it is considered that these methods and techniques are autonomous from the ontological and epistemological approach that supports the study and can be used when combining qualitative and quantitative approaches. Lastly, there is according to Saunders et al. (2009), the mixed research model where there is truly a mixture of different methods and techniques in the same study. In this approach, the researcher may have collected data with qualitative techniques and analyzed the data quantitatively, or may have collected the data quantitatively and performed the analysis qualitatively (Saunders et al. 2009). The information the researcher wants to gather to support his study can come from two main sources. A primary source is the collection of data from individuals who are part of the study and therefore represent a source of new information or, alternatively, the researcher can use secondary sources, information that already exists in the public or private domain by other individuals or organizations (Saunders et al. 2009; Kumar 2011). According to Creswell (2009) and Kumar (2011), the most common primary data collection methods in quantitative research are structured interviews, questionnaires, and structured observations, and in qualitative research, the most common methods are unstructured or semi-structured interviews and unstructured or semi-structured observations. Taking into account that the researcher assumes a subjectivist ontological paradigm, an interpretativist epistemological paradigm, an inductive approach to the research process and, given the characteristics of the methodological options previously described, this study will be a mixed multimethod type with a clear predominance of qualitative techniques over quantitative ones.

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Data Collection and Analysis Methods

This study main objective is to determine the CSFs associated with the implementation of GDPR in national public HEIs. Within this main objective, this research aims to achieve the following specific objectives, translated in the following starting questions: Q1—What are the main implications of applying the GDPR? Q2—What are the CSFs that are associated with the implementation of the GDPR in Portuguese Public HEIs? Q3—How are the previously identified CSFs mapped in the mission areas of the HEIs? Q4—How are the previously identified CSFs ordered in terms of their importance? Q5—How can a readiness model determine a priori the condition of a particular HEI to successfully implement the GDPR? To achieve the initial objective and answer the starting questions, from the methodological point of view, the researcher took the following options: • Research philosophy: Subjectivist ontological paradigm and interpretativist epistemological paradigm; • Approach to research: inductive; • Strategy: Holistic multiple case study; • Research methods: Multimethod—mixed methods; • Time horizon: Cross-sectional. It is now necessary to define the choices made regarding the data collection and analysis techniques and procedures, which according to the adopted methodology, will permit to achieve the main objective of the study and provide answers to the starting questions. This study has different moments of data collection and analysis, which are represented in Fig. 2. In the first phase, we will have the first moment of data collection through secondary sources, namely through selected research articles, books, scientific journals,

Fig. 2 Design of data collection and analysis process with mapping in response to research questions. Source From the author

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and other public or private open access or conditioned documents. As a result of this first phase of the data collection process associated with the literature review, we will have the answer to research question Q1 of the study. On the other hand, we will also have a set of topics of interest that will need to be further studied and thus will act as input to the second phase of the data collection and analysis process. In the second phase, we will apply the CSF method to get the CSF list and answer the research question Q2. In this sense, this study proposes to apply the method of CSF developed by Caralli et al. (2004) as a qualitative subjectivist and interpretativist method for data collection and analysis. Caralli et al. (2004) depart from the work developed by Rockart (Rockart 1979) in the area of CSFs determination and information systems planning, presenting; however, in this method, a more structured process to collect, analyze, and derive in a systematic way the CSFs. This slightly altered method can be applied to any organizational initiative (Caralli et al. 2004). The method can be seen in Fig. 3. The method of CSF described by Caralli et al. (2004, pp. 46–89) consists of five activities that will be presented next in a summarized way: Scope Definition One of the first steps of the method is to determine whether CSFs will be derived for the entire organization or for one or more of its operating units. In this first

Fig. 3 Method to determine CSFs. Source Adapted from Caralli et al. (2004, p. 80)

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step, it is also necessary to define the participants in the interviews as the main procedure for data collection. This decision should consider factors such as the type of CSFs to be determined (organizational or operational unit), the structure of the organization (hierarchical or flat), the operating conditions of the organization (if it has an international presence or activity in different industry sectors), and the purpose and objective for the development of the CSFs. Data Collection At this stage, data are collected, and the method of Caralli et al. (2004) is based on the review of critical documents of the organization and the conduction of interviews. When there are no public documents with relevant information to the determination of the CSFs, the process of conducting the interviews should be immediately initiated. The interviews should be properly planned and conducted according to a set of procedures defined in the Caralli et al. (2004) method. At this stage, it is also necessary to organize the information collected. The method of CSF defined by Caralli et al. (2004) is clearly a qualitative interpretativist method because it is based on the collection and analysis of data, on reading relevant documents to the organization as well as on conducting interviews. In qualitative interpretativist research, the interviews are probably the most widely used data collection method (Bryman 2012; Bhattacherjee 2012). Interviews can be classified as being of the following type (Saunders et al. 2009; Greener 2008; Bryman 2012; Gray 2004; Berg 2004): • Structured or standardized interviews; • Semi-structured interviews; • Unstructured or in-depth interviews. Structured or standardized interviews are organized in the form of an interview script or checklist (Bryman 2012) with a set of standardized and pre-prepared questions (Saunders et al. 2009; Gray 2004) with a set of pre-coded answers (Saunders et al. 2009; Bryman 2012). This data collection method is indicated for quantitative analysis (Saunders et al. 2009; Bryman 2012; Gray 2004) and assumes that there is no interaction between the interviewer and the interviewee (Gray 2004). According to Bryman (2012) and Berg (2004), the researcher seeks to attribute the same conditions of response to all interviewees by sending the same questions, in the same sequence, with as much information as possible for response, thus avoiding the need for additional clarification and making it possible to aggregate results.. Semi-structured interviews are based on a script (Greener 2008) in the form of a checklist of questions with open and closed questions that the interviewer intends to ask the interviewee (Gray 2004). The order of questions may be changed, and the researcher can add new questions during the interviews that were not initially included in the script (Saunders et al. 2009; Bryman 2012; Gray 2004). This data collection method is used for qualitative analyses (Saunders et al. 2009; Bryman 2012). Saunders et al. (2009) report that semi-structured interviews are used in exploratory studies and can be used whenever there is a need to know the reasons, attitudes, and opinions of the study participants.

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Finally, in unstructured interviews, the interviewer does not know in advance what questions he should ask (Berg 2004) and, therefore, there is no predetermined list of questions, but rather a list of topics, with no specific order, that the interviewee can speak freely about (Saunders et al. 2009; Bryman 2012; Berg 2004). According to Gray (2004) in unstructured interviews, the interviewer has very little interaction with the interviewee intervening only when he needs a certain theme to be better clarified. This data collection method is used for qualitative analyses (Bryman 2012; Gray 2004). The interviews defined in the method of Caralli et al. (2004) fall into the typology of semi-structured interviews, namely because according to the authors, there is a need for a strong interaction between the interviewer and the interviewee in order to be possible to clarify the interviewees regarding the questions posed that may be open or closed questions. In this sense, Caralli et al. (2004) taking into account various recommendations by Bullen and Rockart (1981) suggest the following: • • • • • •

At the beginning of the interview, its purpose must be clarified; Get the interviewee’s view of his mission in the organization; Discuss the interviewee’s objectives and goals in the short and medium term; Ask a series of questions in order to collect raw data needed to obtain the CSFs; Summarize the interview with the most relevant points; Request the interviewees to prioritize relevant details in relation to identified objectives or the CSFs; • Request the indication of implementation measures to assess whether respondents are achieving their goals, their objectives, and the CSFs. In view of the arguments presented and which characterize the different types of interviews, the method of CSF developed by Caralli et al. (2004) with semistructured interviews is considered appropriate to this study, as a primary method of data collection, for the following reasons: • Taking into account that the researcher positions himself as a subjectivist, from the ontological point of view and as an interpretativist, from the epistemological point of view, this study is characterized as being a multimethod type taking into consideration how it collects, analyzes, and processes the data. In this sense, it adopts as a method of data collection the semi-structured or unstructured interviews, since structured interviews are suitable for quantitative studies; • The option to use semi-structured interviews and not unstructured interviews is justified by the need to have a pre-established list of questions based on certain points of view or themes of different authors collected in the literature review phase and that it is important to bring to the discussion in order to measure its application to the case under study; • In this sense, the method uses a script of questions that is nothing more than a checklist of open and closed questions that the researcher considers important to be raised and that will allow a strong interaction between the researcher interviewer and the interviewee. The researcher thus has an important role in conducting the interview and interpreting the information transmitted by the interviewees;

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• HEIs may be at different stages of the GDPR implementation and therefore DPOs can have very different views about the CSFs that influence the implementation of the GDPR, being important that the interviewer has the possibility to adjust the questions, adapting them to the stage of development of each HEIs. On the other hand, it may also be necessary to ask new questions that are identified as relevant throughout the conduction of the interviews. These requirements fit the characteristics of semi-structured interviews, thus validating the method of Caralli et al. (2004) as being adequate for data collection that complies with the philosophical position of the researcher when using the subjectivist ontological paradigmand the interpretativist epistemological paradigm, as well as the fact that it takes an inductive approach to the research process and the collection procedures and techniques are clearly qualitative, framed in multiple methods of mixed nature. Data Analysis At this stage of application of the method, it is important to work on the raw data collected at the interview stage on the basic components of CSFs using a series of repeatable and consistent processes. For this, Caralli et al. (2004) created the concepts of activity statements and supporting themes. Activity statements reflect actions or activities and should begin with action verbs. Supporting themes reveal the underlying content of CSFs, providing its description or definition, and are drawn from affinity groups of activity statements (Caralli et al. 2004). Thus, it is considered that the data analysis techniques defined in the method of Caralli et al. (2004) are interpretative and qualitative namely because: • The objective of the analysis is the categorization of raw data carried out through a systematic process. • Raw data are interpreted using an affinity analysis technique as a structured way for working on activity statements and supporting themes. This technique provides a consistent way of deriving CSFs with a self-correcting mechanism that allows the investigator to re-examine analysis decisions without introducing additional bias (Caralli et al. 2004). • To create the activity statements from the interview transcripts, it is necessary to obtain the intention of the interviewees in the answers they gave to the questions posed and which will be transformed into activity statements (Caralli et al. 2004). In this sense, it is clear that the analysis process is supported by the interpretation of the raw information provided by the interviewees in order to categorize it by deriving the CSFs. Thus, we can conclude that the analysis techniques recommended by the method of Caralli et al. (2004) are clearly qualitative, meeting the qualitative data collection techniques, both supported by the inductive approach adopted by the researcher, generating the theory through the data collected and systematically analyzed.

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Derive CSFs CSFs are obtained from raw data collected throughout the process, consisting of activity statements, affinity groups, and finally supporting themes (Caralli et al. 2004). Having developed the supporting themes, they should be similarly grouped into groups that will result in CSFs. Analyze CSFs At this final stage, we proceed, through a process of affinity analysis, to the association of CSFs by the mission areas of the organization allowing to identify which areas have the greatest impact on previously determined CSFs (Caralli et al. 2004). This analysis will answer the research question Q3.

2.6.2

The Delphi Method for Prioritizing CSFs

Having identified the CSFs, it is now important, according to the design of the research process, to prioritize the CSFs and thus to answer the research question Q4. For this purpose, the Delphi method was used to obtain the necessary consensus regarding the prioritization of the CSFs obtained in the previous phase. The Delphi method was developed by Norman Dalkey at Rand Corporation in the 1950s (Skulmoski et al. 2007; Avella 2016; Hsu and Sandford 2007) with the view that the opinion of a group of experts is more valid than the individual opinion of a specialist (Keeney et al. 2011). Item forecasting, identification, and prioritization are examples of areas where the method is applied (Okoli and Pawlowski 2004) through consensus building (Avella 2016; Hsu and Sandford 2007) using questionnaires applied to an expert panel, in multiple rounds (Hsu and Sandford 2007). According to Rowe and Wright (1999) and Lee (2001), the Delphi method has the following characteristics: • The anonymity achieved through the use of questionnaires allows the ideas and private opinions to be considered by the group taking into consideration their merit and not the social or professional status of those who propose them. In this way, anonymity frees panelists from any communication barriers, from internal or external pressure, from individuals with a dominant profile, even allowing any personal issues between group members to be overcome. • Communicating between rounds of responses previously given, in a summarized and controlled manner, allows panelists to focus on solving the problem posed, modify, and/or justify their opinions without fear of being censured by other members. • Between rounds or at the end, the researcher communicates the results through a statistical summary or other information deemed appropriate, allowing all panelists to know their peer’s opinion, provided in an anonymous way. According to Hanafin et al. (2007) and Habibi et al. (2014), the Delphi method is highly recommended when:

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• There is a complex and multidisciplinary problem that can be solved through the necessarily subjective analysis and opinions of an expert panel to the detriment of the application of analytical and more objective techniques; • There is lack of consensus on the solution of the problem; • There is lack of knowledge about the problem in question; • Need of anonymity as a condition of participation; • When experts do not know each other, are physically dispersed, and come from different professional areas and positions; • There is no time limitation; • Need to simplify logistics, reducing the time and costs required to hold an in-person event with a large number of experts; • When there are no historical data or there is a possibility that the analysis of technical and economic issues may be influenced by ethical and social dilemmas (Gupta and Clarke 1996); • No other cost-effective method. There may be situations where the application of the Delphi method is not adequate. According to Hallowell and Gambatese (2009), there are alternatives, namely: • Staticized groups: identical to the Delphi method without interaction between participants and communication of results between rounds. The result of applying this method is the aggregate response of the participants from the first question. • Focus groups: In this method, there is a real-time discussion between the panel members. There is no anonymity, and therefore, there may be domain of one element or group over the others. • Nominal group technique: similar to the Delphi method with the difference that there is face-to-face interaction and is therefore not a method that guarantees anonymity. Typically, there are two major types of the Delphi method entitled conventional or classical Delphi and modified Delphi. The difference between both types is in the first round and is substantiated in the following (Avella 2016; Hsu and Sandford 2007; Keeney et al. 2011; Custer et al. 1999): • Conventional Delphi: There is a first round with open (unstructured) questions for the panel to comment freely on the topic raised for discussion. In this case, the goal is to generate ideas for discussion in the following rounds. This Delphi method clearly has an inductive and qualitative approach to the way it collects and analyzes data in this first round (Keeney et al. 2011). The second and subsequent rounds are already conducted in the form of structured questionnaires seeking quantification through classification techniques (Powell 2003). • Modified Delphi: The first round of conventional Delphi usually does not exist, starting the modified Delphi already in the form of a structured questionnaire and the panelists express their opinion about a set of topics already summarized, previously obtained through literature review process, focus groups, or face-to-face interviews. As with conventional Delphi, the second and subsequent rounds are

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already conducted in the form of structured questionnaires seeking quantification through classification techniques (Powell 2003). According to Van Zolingen and Klaassen (2003), the consensus using the Delphi method is reached when there is stability in the responses obtained for an item in two successive rounds, or according to Hasson et al. (2000) when the percentage of consensus among experts, on the items under analysis reaches levels ranging from 50 to 80%. Mitchell (1991) states that in order to reduce panel member fatigue and thus avoid creating conditions that promote the withdrawal of expert participation, the number of rounds used in the Delphi method should be as reduced as possible. There are studies that have reached consensus in one or two rounds (Skulmoski et al. 2007); other studies report that they have reached consensus in three rounds (Hanafin et al. 2007; Powell 2003; Brady 2015; Green 2014). However, Skulmoski et al. (2007) say that typically three rounds are required to reach consensus. Gordon and Helmer (1964) argue that the interval between rounds should be kept to a minimum so that the experts can be motivated to participate and consequently lower dropout rates. Regarding the number of experts per panel, there is no indication in the literature about the ideal number; however, they should be chosen according to the areas of knowledge covered by the study (Hsu and Sandford 2007). According to Skulmoski et al. (2007), the researcher can take into account the following criteria: • Must have knowledge, competence and professional experience in relation to the subject being researched (Avella 2016; Hsu and Sandford 2007; Habibi et al. 2014; Powell 2003); • Must have the ability and willingness to participate in the Delphi study; • Must have time to do so; • Must have adequate communication skills; • Must somehow be affected or have some interest in the study’s conclusions (Hasson et al. 2000). Regardless of the option used, the Delphi method can be viewed as a structured process that is focused on obtaining opinions and ideas (Stewart 2001) about a real problem which according to Skulmoski et al. (2007), Hasson (2000), Hsu and Sandford (2007), and Keeney et al. (2011) can be solved through the use of qualitative and quantitative methods. According to Powell (2003), unstructured data from the first round of conventional Delphi or pre-first round in modified Delphi are analyzed using qualitative content analysis techniques and then transformed into structured data, as the basis of the questionnaires applied in the following rounds, until consensus is reached. In this case, according to Stewart (2001), there is clearly a reductionist and quantitative view as unstructured data are subject to an interpretativist analysis being reduced to a scale whose items will be objectively chosen by the experts and measured by the researcher using statistical techniques until consensus is reached (Keeney et al. 2011). The researcher interacts in successive rounds with the experts in a standardized way being clearly objectivist, positivist, and quantitativist research (Keeney et al. 2011; Stewart 2001). According to Hasson et al. (2000), statistical calculations take place

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in order to determine central trends by calculating mean, median, and mode as well as dispersion levels by calculating standard deviation and interquartile range. According to Keeney et al. (2011) and Stewart (2001), it is also possible to have a subjectivist and interpretativist perspective if we believe that the findings resulting from the application of the Delphi method are shared by the expert group, through a dynamic and interactive process, grounded on a set of opinions that are the result of how experts subjectively perceive the reality where the problem under discussion is included. Besides, according to Hanafin (2007) and Hsu and Sandford (2007), the fact that experts may in successive rounds change their opinion based on feedback from previous rounds, is typically a characteristic of a subjectivist and interpretativist approach rather than objectivist and positivist approach. In this sense, according to Stewart (2001) when defining the philosophical approach associated with the use of the Delphi method in a scientific study, one must take into account the methodology that supports the study as a whole and not so much the methods, techniques, and procedures used. Taking into account the above mentioned, the Delphi method was selected to obtain the consensus needed for CSFs ranking for the following reasons: • This study aims to identify the CSFs associated with the implementation of the GDPR in national public HEIs. In this sense, the method that will allow the identification of the CSFs has already been identified, and it is now necessary to consult a group of experts to carry out their ranking or prioritization. • The Delphi method answers this need because: – It is a suitable method for prioritizing items (Okoli and Pawlowski 2004) by reaching consensus from panel experts. – Allows anonymity (Rowe and Wright 1999), and this is an essential feature and requirement to select the Delphi method. Anonymity is an essential condition for the participation of the experts in the study, being anonymous among each other, but not for the researcher, allowing him to get more qualitative data (Okoli and Pawlowski 2004). – It is a low-cost, efficient method that allows information to be obtained by a group of geographically dispersed experts (Almenara and Moro 2014). This is also an essential condition for the selection of the Delphi method as the HEIs under study are geographically dispersed as well as the potential experts to be consulted. – When compared to alternative consensus-building methods like staticized groups, focus groups, or nominal group technique (Hallowell and Gambatese 2009), the Delphi method allows anonymity, which is an essential condition, something that focus groups and nominal group technique do not allow. On the other hand, it is understood that there should be feedback and interaction controlled by the researcher, between all panel elements, something not allowed by the staticized groups method. – In this study, the philosophical position of the researcher translates into the use of the subjectivist ontological paradigm and the interpretativist epistemological

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paradigm. Additionally, an inductive approach to the research process was performed, and a mixed multimethod model for data collection and analysis was defined. In order to determine the CSFs, an interpretativist method with qualitative techniques has already been defined (the CSF method defined by Caralli et al. 2004) and the Delphi method has now been selected to prioritize the CSFs, which is a method used in qualitative and quantitative research (Skulmoski et al. 2007; Hsu and Sandford 2007; Keeney et al. 2011; Hasson et al. 2000). The Delphi method will only be used to prioritize the CSFs obtained through an interpretativist and subjectivist interactive process of consensus building by the expert panel. To measure the consensus obtained by the experts and thus determine whether the required number of rounds has already been reached, statistical calculations will be performed with the production of numerical values for analysis and this is a feature of quantitative methods. It is expected that as defined by Skulmoski et al. (2007), consensus be reached in three rounds. The Delphi method and associated techniques are considered within the scope of this study to be of mixed type regarding data collection, production, and analysis. – The modified Delphi method (Avella 2016; Hsu and Sandford 2007; Custer et al. 1999) was selected over the conventional Delphi method because the first one starts the process, in the first round, with the questions to be placed at the consideration of previously and carefully selected, among other possible ways, through previously conducted semi-structured interviews. In this sense, this type of the Delphi method is indicated because it allows the previously determined CSFs, obtained through the application of the CSF method defined by Caralli et al. (2004), to be used as data source for Delphi method initialization. – Finally, this study fulfills all the requirements listed by Hanafin et al. (2007) and Habibi et al. (2014) for the use of the Delphi method, namely the need for experts to prioritize items, obtaining consensus as an essential condition for prioritization, the need to guarantee the anonymity of the answers, the problem being complex and the fact that experts are geographically dispersed. The use of the Delphi method as a consensus method has been justified and it is now important, according to the characteristics previously indicated by Hsu and Sandford (2007), Skulmoski et al. (2007), Powell (2003), Avella (2016), and Hasson et al. (2000), to define the composition and size of the expert panel as an essential condition for the application of the Delphi method (Habibi et al. 2014). • The objective of the study is to determine the CSFs associated with the implementation of the GDPR in national public HEIs. This is a new problem and needs further investigation as there is very little research within this area; • HEIs are large organizations with high complexity, distinct realities, and a very individual academic culture, so it is important for experts to know deeply how the different mission vectors—teaching, research, and interaction with society—are operationalized as all these vectors will necessarily be affected by the implementation of the GDPR; • The Article 39 of the GDPR describes the duties of the DPO. According to this Article, the DPO informs and advises the controller or the processor, as well as

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the workers handling the data, about their obligations. The DPO monitors the compliance with the GDPR, with other EU or Member State Data Protection provisions and the policies of the controller or processor concerning personal Data Protection, including the sharing of responsibilities, awareness raising and training of personnel involved in data processing operations, and related audits; For this reason, DPO in HEIs are the people, at this stage, that have the knowledge about the process of implementation of the GDPR, namely regarding the implications arising from its implementation and the existing constraints for national public HEIs; DPO working at HEIs have very different academic and professional profiles, some of them being part of the human resources of the HEIs and others are associated with external entities; DPOs are geographically distant; Generically, the DPO who participated in the semi-structured interviews that originated the list of CSF that will be used in the Delphi method to obtain consensus on their prioritization, have the desire to continue participating in the study, having expressed interest in knowing the results of study; On the other hand, the fact that they participated in the semi-structured interviews and verified that their contributions were essential in defining the list of CSFs that will now need to be prioritized will encourage and motivate DPO to participate as experts in the study.

These arguments meet the indicated criteria by Hsu and Sandford (2007), Skulmoski et al. (2007), Powell (2003), Habibi et al. (2014), Avella (2016), and Hasson et al. (2000) in referring to the need for experts to have knowledge of the study topic, ability, time, and willingness to participate, to feel involved in the process, and to want to have access to the study results. The use of the Delphi method allows the identification of a list of CSFs associated with the implementation of the GDPR in the national public HEIs, prioritized by their degree of importance, and creates the conditions to answer the last question Q5—How can a readiness model GDPR a priori the condition of a particular HEI to successfully implement the GDPR? To answer this question, it is important to define what a readiness model is. Mufti et al. (2018, p. 28613) defines a readiness model as “… a technique to assess an organization or team based on the specified criteria to represent their level of readiness”. In this sense, in the final phase of the study will be proposed a model that allows the HEIs to understand, from the defined criteria and that relates to the mapping of the CSFs previously determined in the mission vectors, the level of readiness of the institutions in order to successfully implement the GDPR.

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Table 2 Summary of the methodological options adopted Research philosophy

Ontological paradigm—Subjectivist epistemological paradigm—interpretativist

Approach

Inductive

Nature

Exploratory and descriptive

Strategy

Case study

Research methods

Multimethod—mixed methods (qualitative and quantitative—predominantly qualitative)

Time horizon

Cross-sectional/Current moment

Data collection and analysis

Literature review Semi-structured interviews The CSF method The Delphi method for prioritizing CSFs

Source From the author

2.7 Conclusion—Brief Summary of the Methodological Options Adopted The methodological options that support this study were previously justified and are summarized in Table 2.

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Emotional Intelligence and Leadership: A 360-Degree View in the Electronics Industry in Portugal José Rebelo dos Santos, Lurdes Pedro and Sandra Nunes

Abstract Good leaders are an essential factor of the business world. Leadership and emotional intelligence are two inseparable concepts given that good leaders must have a high level of emotional intelligence. As a result, it has been common to study emotional intelligence and to look toward relating it with transformational leadership. This is the objective of this research study. The following research involved having 50 participants from an electronics sector company in Portugal respond to the MLQ-S6 Multifactor Leadership Questionnaire from Bass and Avolio (J Eur Indus Training 14: 21–27,1992) in order to identify transformational and transactional leadership dimensions; and also respond to the EIV360—Emotional Intelligence View 360 from Nowack (Facilitator’s guide—Emotional intelligence view 360º, Consulting Tools, Santa Mónica, 1997) to identify the dimensions of emotional leadership competencies. The results of this exploratory study show that the association of emotional intelligence between leadership, as key competencies to potentiate leader performance and determine satisfaction in employee behavior in their professional relationship (Goleman et al. in Os Novos Líderes - A inteligência Emocional nas Organizações, Gradiva, Lisboa, 2002). They also confirm that leadership with transformational traits (visionary for Goleman) lays over a set of emotional competencies with high scores. This study also point to the confirmation that laissez-faire presents an inverse relationship with emotional competencies; however, due to the lack of leaders with clear and strong laissez-faire traits, we cannot here empirically support the relationship between a laissez-faire leadership style and the lack of emotional competencies.

J. R. dos Santos (B) · L. Pedro · S. Nunes Polytechnic Institute of Setubal, College of Business Administration, Setúbal, Portugal e-mail: [email protected] © Springer Nature Switzerland AG 2020 C. Machado and J. P Davim (eds.), Research Methodology in Management and Industrial Engineering, Management and Industrial Engineering, https://doi.org/10.1007/978-3-030-40896-1_5

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1 Introduction Good leaders are an essential factor of the business world. Leadership and emotional intelligence are two inseparable concepts given that good leaders must have a high level of emotional intelligence. As a result, it has been common to study emotional intelligence and to look toward relating it with transformational leadership. This is the objective of this research study. The following research involved having 50 participants from an electronics sector company in Portugal respond to the MLQ-S6 Multifactor Leadership Questionnaire from Bass and Avolio (1992) in order to identify transformational and transactional leadership dimensions; and also respond to the EIV360—Emotional Intelligence View 360 from Nowack (1997) to identify the dimensions of emotional leadership competencies.

2 Theoretical Framework Since the late twentieth century, the concept of emotional intelligence has been increasingly accepted within both the scientific community and the business world. Emotional intelligence can be defined as the ability to recognize emotions in oneself and in others, and the capacity to self-motivate and manage emotions both with ourselves and in relationship to others (Goleman 2012). It matches an important part of what makes people successful (Goleman 2004). The importance of emotional intelligence is given to the notion that what matters for success is not only the intelligence quotient but also the capacity to react to challenges in a creative and intelligent way. It is considered that emotional intelligence is inseparable from leadership and there are a numerous studies that support this understanding (Judge and Piccolo 2004; Harms and Crede 2010). Social skills are crucial for effective leadership. This is not a new idea—almost one hundred years ago, the psychologist Edward Thorndike was already saying that a good factory mechanic could not be a good foreman if he did not have social intelligence (Goleman and Boyatzis 2008). Although several studies have referred to different types of intelligence, the concept of emotional intelligence is connected to Howard Gardner’s studies (who does not use this term). In 1983, he identified multiple types of intelligence, distinguishing eight (Gardner 2005): • Linguistic–verbal intelligence is the effective use of words and language to define concepts and express meanings with varying levels of complexity; • Logical–mathematical intelligence allows operationalizing, calculating, and quantifying. It involves the capacity to rationalize using inductive and deductive thinking; • Visual–spatial intelligence allows the creation, interpretation, and handling of images;

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• Musical intelligence corresponds to the capacity of distinguishing rhythms and tones and is also connected to emotions; • Bodily–kinesthetic intelligence can be understood as the capacity to use one’s body to perform physically; • Intrapersonal intelligence is that it is connected to self-reflection and introspective intelligence; • Interpersonal intelligence is the capacity to interact with others; • Naturalistic intelligence is the sensibility toward the natural world. Posteriorly, he proposed a ninth type—existential intelligence—which corresponds to the sensibility toward questions connected to the human existence from birth to meaning to death (Gardner 2005). The genesis of emotional intelligence as a concept comes from Howard Gardner’s studies and integrates two types of intelligence: intrapersonal and interpersonal. However, the development of the concept of emotional intelligence comes firstly from Peter Salovey and John Mayer that in 1993 defined it as “type of social intelligence that involves the ability to monitor one’s own and others’ emotions, to discriminate among them, and to use the information to guide one’s thinking and actions” (Mayer and Salovey 1993, p. 433). Goleman (2019) considers that emotional intelligence integrates five elements: self-awareness, self-discipline, motivation, empathy, and social skills (Goleman 2019): • Self-awareness has to do with being able to identify our own strong and weak points, motivations, values, and their impact on others; • Self-discipline is the capacity to control or redirect one’s own impulses, guiding them toward desired directions; • Motivation is the capacity to value the reaching and the overcoming of objectives; • Empathy is associated with the understanding of others’ emotional characteristics and the ability to act accordingly; • Social skills are the capacity to establish harmonious relationships with others’ in such a way that they can be lead toward specific actions. As a result, emotional intelligence can be understood as a psychological construct that includes emotional, personal, and social capacities and that influences an individual’s capacity of thinking and acting socially efficiently, within a specific context, and that presents a range and variety of differences between authors looking at it and at what capacities measure it (Goleman 2019; Mayer and Salovey 1993). Popularization and strong dissemination of emotional intelligence concept came to set back barriers to scientific knowledge. It has since been understood there is a capacity to integrate emotions and reason, given that emotional intelligence seems to have a significant biological and neurological basis strongly supported by the theories of Damásio (1998) and the studies of LeDoux (2007). Another thing emotional intelligence came to demonstrate, as a reaction to The Bell Curve publication in 1994 by Hernsteian and Murray, that sustained the theory that intelligence would be distributed, is that emotional intelligence can actually be

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learnt (Goleman 1997). Goleman (2019) emphasizes the sustainability of leadership development, as a continuous lifelong experiential learning process; as opposed to intelligence quotient, which according to research does not show variability.

2.1 Leadership in the Organizational Context Leadership can be considered, within organizational contexts, as the capacity to conduct and orient the activities of a group, making it possible for the objectives of the group to be accomplished (Sarnin 2016). Given the complexity of this concept and of putting it into practice, over time there have emerged several theoretical approaches to this (Sarnin 2016). A part of these approaches seeks to explain leadership using as a base the characteristics of leadership itself. As a result, among other concepts, we got the ideas of transformational and transactional leadership. Although the emergence of these ideas is associated with Burns (1978) it was a while later with Bass (1985) that these concepts were operationalized through the creation of a Multifactor Leadership Questionnaire (MLQ) with the aim of identifying the inherent dimensions of transformational and transactional leadership styles (Banks et al. 2016). Goleman (2005) advocates a model of emotional leadership that combines different leadership styles combined with emotional intelligence traits. Leaders with a higher efficiency potential and that show the highest results are those who are flexible enough to adapt to different styles whenever necessary, using the most adequate approach to each moment (Goleman et al. 2002). Leaders with the highest potential are pressuring and directing leaders, although a series of research shows the efficiency of every style, according to its use in different situations. This approach is conceptually close to the principles of Hersey and Blanchard’s model of situational leadership which advocates that in different situations, different styles should be used, as opposed to the more universal theories that defend the existence of a dominant style regardless of the situation (Rego and Cunha 2003). For Goleman (2005), there are six styles of leadership used by executives in organizational settings, according to their emotional intelligence. From these styles, only four turn out as positive: visionary, affiliative, democratic, and coaching, while commanding and pacesetting are styles that have a more negative potential. Best leaders are those that always use the most adequate approach to each moment and that shift from one style to the other whenever necessary. According to the authors, the leaders that master the four positive styles are the ones who present the best results (Goleman, et al. 2002). According to Goleman et al. (2002), the capacity for leadership develops through the development of emotional competence. “Emotional Intelligence capacities are not innate skills, they are acquired skills” (Goleman et al. 2002, p. 58). Understanding this, we can see why there has been a frequent interest in the study of emotional intelligence looking to relate it to transformational leadership as is this case of the researches by Vasilagos et al. (2017), Barling et al. (2000), and Alston et al. (2016).

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Transformational leadership is associated with the influence that leaders have over their followers. This influence is achieved through paying attention to the concerns and needs of their followers and acting accordingly. Trust, admiration, and respect toward the leader are thus generated which contributes to achieving good results. The research by Bass and Avolio (1994) allows us to identify four different dimensions in transformational leadership: idealized influence in which leaders are models for their followers; inspirational motivation; intellectual stimulation; and individualized consideration (Banks et al. 2016). Transactional leadership, on the other hand, has more to do with a cause– effect relationship in which the leaders promote transactions, offering something in exchange to all that follow them. There is no inspirational leader aspect here for the elements of the group, neither is there the charisma and the intellectual stimulation—reward management is the essential leadership element here (Banks et al. 2016). The makeup of this leadership style can be grouped into three categories: contingent reward, management-by-exception, and laissez-faire leadership (Bass and Avolio 1994 referred by Jensen et al. 2019). There is evidence that a good leader has a high level of emotional intelligence and, on the other hand, plenty of registers of executives with a high intelligence quotient and technical preparation that fail when they take on a leadership position (Goleman 2004). With the different styles of leadership, we have associated different components of emotional intelligence (Goleman 2005). Goleman contributed one of the most significant developments to the concept of emotional intelligence and promoted its diffusion in business settings and the study and research of leadership and emotional intelligence in the workplace.

2.2 Perceptual Congruence Perceptual congruence between managers and employees has to do with the level of alignment of internal perceptions and favorable organizational consequences. On the other hand, perceptual incongruence has to do with perceptions that are not correspondent to the real-life situation or problem (Newton and Frahm 2009). The recognition of different perspectives between managers and employees is a central element in this research. The study of perception alignment has been named in literature referring to perceptual congruence (Birkinshaw et al. 2000). It is calculated recurrently, through the differences in scores given by managers and by employees (Birkinshaw et al. 2000). Several studies have compared perceptions between managers and employees regarding a set of variables that have to do with behavior in the workplace, for example, work satisfaction, supervision, and overall satisfaction (Hatfield and Huseman 1982), and the satisfaction with the employees’ work (Wexley et al. 1980), organisational commitment, intent to leave and performance (Hui et al. 2009). Few have

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been the studies about perceptual congruence regarding leadership type and emotional intelligence using measures of multiple perspective feedback systems (Aarons et al. 2017). The use of multiple perspective feedback systems has increased significantly over the past several years (Aarons et al. 2017). 360” programs are often used for developmental feedback and involve generating performance evaluations on target rates from multiple sources such as supervisors, peers, subordinates, as well as from the targets themselves. Proponents suggest numerous advantages of multisource ratings over singlesource ratings, which leads to improved individual performance, contributing to desired organizational outcomes. There are several potential benefits associated with multi-rater feedback mainly based upon the tenet that congruence between self and others is associated with managerial success and effectiveness (Tornow 1993). With the 360 degree analysis, we seek to understand the rating done by each target group and identify similarities or differences between the ratings from the targeted subject and other groups. The bigger those differences are (in particular if the evaluation done by the targeted subject is superior), the more problematic is the situation in what comes to transformational leadership. In opposition, very close values correspond to good transformational leadership.

3 Research Methodology 3.1 Objectives of the Study The objectives of this study are concerned with the following questions (i) to what extent are there gaps in perception in each respondents group? (ii) Does transformational leadership behaviors present scores close to transactional leadership ones? (iii) What are the hierarchies present between transformational leadership and emotional intelligence? The other purpose of this study was to investigate the relationship between the different types of leadership and emotional intelligence. We sought to design some hypotheses of research, in particular: (H1) the existence of positive relationship between transformational leadership and emotional competencies; (H2) the existence of negative relationship between laissez-faire leadership and emotional competencies.

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3.2 Participants A total of six high senior-level managers (three from first-level line and three from second-level line),1 (five males, one female) were recruited from one of the major industrial electronics multinational company, in Portugal. They represented all available senior managers from the top two levels in the organization and included HRM Manager, Operations Manager, Accounting Manager, ICT Manager, Maintenance Manager, and a Final Assembly Manager. The average age was 42 years (range 35–52 years). 83% having a postgraduate degree. The peers, as direct reports and supervisors of these six high senior-level managers, were invited to participate, in a total of 54 individuals (12 peers, 30 direct reports, and 6 supervisors), but four were unable to participate due to overseas postings. A total of 54 employees were surveyed resulting in N = 50 employees responding for an overall response rate of 92.5%. These participants correspond to 14% of the company’s total. The gender composition of the sample was strongly biased toward males (72.1% male/24.7% female). The average age of participants was 39.7 years (SD = 9.01) which showed a trend toward senior people, characteristic of experiment leadership levels in organizations. The inclusion of the categories of leaders at secondlevel line was based on the premise that transformational leadership is not limited to the occupants of the highest or most prominent positions in terms of influencing others—such leaders can be found at all levels of the organization’s hierarchy (Avolio 1999).

3.3 Measurements The following two measuring instruments were used.

3.3.1

The Multifactor Leadership Questionnaire, Form 6-S

Each participant received the shortened form of Northouse (2001) Multifactor Leadership Questionnaire, Form 6-S (MLQ-6S), as developed by Bass and Avolio (1992). MLQ-6S comprises 21 items and measures the following 7 factors pertaining to transformational, transactional, and laissez-faire leadership. Each question is scored on a five-point Likert scale: (1) Totally disagree to (5) Totally agree. The transformational leadership scales comprise the idealized influence, individualized consideration, intellectual stimulation, and inspirational motivation. The transactional scales consist of contingent reward and management-byexception. Meanwhile, the last scale deals with laissez-faire leadership. 1 First-line

level (Directors) corresponds to the management functions immediately below to the CEO; the second-line management functions are dependent on first-line managers.

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This instrument is known as the most frequent and well-researched leadership measurement instrument, in a wide range of organization settings as well with leaders in different cultures (Jensen et al. 2019). The use of inverted scale in the processing of data appeared to be a fundamental condition for overcoming the unfavorable nature of these items.

3.3.2

Emotional Intelligence View 360” (EIV360)

The Emotional Intelligence View 360 (EIV360) survey questionnaire, developed by Nowack (1997), is an instrument that allows the measurement of a set of behavioral indicators of emotional intelligence competencies by oneself, the supervisors, direct reports, and peers based on the dimensions of Goleman’s model (Goleman 1997). Based on the results of the pilot testing and statistical analysis, some revision in item content and wording was done resulting in the copyrighted 2003 74-item version (Nowack 2004). Each question is scored on a seven-point Likert scale: (1) To an Extremely Small Extent to (7) To an Extremely Large Extent. Seventeen scales were developed, each measured by 3–5 questions. A set of interpersonal, social, and communication critical competencies were derived in three dimensions: selfmanagement, relationship management, and communication. Permission from the EIV360 developers to use their scale for the purpose of this research was obtained. The translation was done item-by-item, from English to Portuguese, followed by a retroversion from Portuguese to English, by an independent translator of the first phase, to analyze and minimize the differences between the original version and the adapted version. The version of Multifactor Leadership Questionnaire, Form 6-S (MLQ-6S), translated into Portuguese and validated for the Portuguese context, obtained the following reliability indices: transformational leadership (α = 0.86), transactional leadership (α = 0.79), laissez-faire leadership (α = 0.57) shows a reliable scale that needs attention in analysis. Emotional intelligence View 360” (EIV360) version translated into Portuguese and validated for the Portuguese context, obtained the following reliability indices: Self-management (α = 0.91), relationship management (α = 0.93), and communication (α = 0.93). These scales for the emotional intelligence construct (α = 0.97), and transformational leadership (α = 0.88) shows a reasonable internal validity.

3.4 Design and Data Collection Procedure Participation in research was voluntary, happening through individual contact with top-tier leaders. The nature of the assessment was clarified, and confidentiality and anonymity were both guaranteed. All the procedures that correspond to the measurement tools and their ethics were put in practice: Each leader, with a basis of representation and multiplicity of perceptions, had control over the choice of two

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peers and 3–8 subordinates to receive feedback from; the questionnaires were delivered simultaneously to make the measuring tools easier to apply, directly from the leader and received with a one week deadline in an enclosed envelope which mitigated concerns about confidentiality and anonymity; results from the 360 degree feedback were communicated with complete confidentiality and anonymity, with the exception of the CEO’s results which were not able to be omitted, given that there was only one respondent with these characteristics.

4 Results This study uses mean values of the multiple responses (ratings by self, peer, supervisor, and subordinate) for analyzing the association between emotional intelligence construct and transformational, transactional, and laissez-faire leadership as it had been developed in previous research (e.g., Shipper and Davy 2002). An analysis of the data collection for this study was developed in two ways: firstly, through standard deviations, minimum and maximum for transformational, transactional, laissez-faire leadership variables, and emotional Intelligence competencies: selfmanagement, relationship management, and communication, and secondly using Pearson’s correlations tests among the study variables including model regression.

4.1 Analysis of Perceptual Differences Between Groups The results of the ratings regarding emotional intelligence competencies and different types of leaderships completed by the different groups are presented in Table 1. We can notice that self-evaluation on any competency was always higher, being particularly worth noticing that difference in the management of relationships. However, even in a more detached analysis the differences are not strongly accentuated (Table 2). It is in self-evaluation that transformational leadership stands out more. However, the differences are not strong. Tables 3 and 4 show averages, standard deviation, minimum, and maximum of the constructs under analysis. They show that even though first-line managers show higher averages than second-line managers, second-line managers display dominance as transformational leaders and present high levels of emotional intelligence (Table 5). According to the results of the Pearson correlation test there is a positive and acceptable correlation between transformational leadership and transactional leadership, as well laissez-faire leadership, as shown in Table 5. The Pearson correlation coefficient was equal to 0.527 and 0.445, respectively, that was significant at 0.01 level. There is a positive and significant correlation between transactional leadership

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Table 1 Emotional competencies by group IE dimensions

Statistics

Subordinate

Self

Peer

Supervisor

Global

Self-management

Average

5.3300

5.4319

5.0993

5.3972

5.2949

Relationship management

Communication

SD

0.6618

0.2921

0.6152

0.5380

0.6002

Median

5.4250

5.4125

5.1375

5.5000

5.3458

Mode

5.3300

5.0200

4.800

4.5900

5.3300

Average

5.2802

5.6333

5.2111

5.1778

5.2937

SD

0.6385

0.2899

0.5658

0.3305

0.5632

Median

5.3500

5.5278

5.2694

5.2556

5.3750

Mode

4.5900

5.3800

3.9200

4.7000

4.5900

Average

5.2417

5.4528

5.2583

5.2417

5.2710

SD

0.8791

0.3172

0.6416

0.4727

5.2200

Median

5.3917

5.5750

5.3833

5.4833

5.4167

Mode

5.2200

5.0300

5.8800

4.5000

0.7242

Table 2 Leadership style by group evaluation IE dimensions

Statistics

Subordinate

Self

Peer

Supervisor

Global

Laissez-faire leadership

Average

2.5128

2.6667

2.8889

2.3889

2.6067

Transactional leadership

Transformational leadership

SD

0.5596

0.4714

0.6250

0.6469

0.5859

Median

2.6667

2.6667

3.0000

2.5000

2.6667

Mode

2.6700

2.6667

2.0000

3.0000

2.6700

Average

3.0192

3.2083

3.0764

2.9167

3.0433

SD

0.7816

0.4172

0.3925

0.4037

0.6522

Median

2.6667

3.0000

3.2500

3.1667

3.0000

Mode

3.1700

3.0000

3.0000

3.1700

3.1700

Average

3.0192

3.2083

3.0764

2.9167

3.0433

SD

0.5809

0.3680

0.5567

0.3118

0.5209

Median

3.2083

3.1250

3.0000

2.9583

3.0833

Mode

3.2500

3.0800

2.9200

2.4200

3.0000

Table 3 Average, standard deviation, minimum, and maximum for emotional intelligence and laissez-faire, transactional, and transformational leadership—first-level managers Constructs

N

Min.

Máx.

Average

SD

Variance

Laissez-faire leadership

25

1.33

3.67

2.6933

0.56042

0.314

Transactional leadership

25

1.83

5.17

3.0733

0.68225

0.465

Transformational leadership

25

2.17

4.00

3.1800

0.41085

0.169

Emotional intelligence

25

4.36

6.09

5.4177

0.43967

0.193

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Table 4 Average, standard deviation, minimum, and maximum for emotional intelligence and laissez-faire, transactional, and transformational leadership—second-level managers Constructs

N

Min.

Máx.

Average

SD

Variance

Laissez-faire leadership

25

1.33

3.67

Transactional leadership

25

1.50

3.83

2.5200

0.60919

0.371

2.8633

0.61655

Transformational leadership

25

1.67

0.380

3.92

2.9067

0.58843

0.346

Emotional intelligence

25

3.93

6.65

5.1554

0.70307

0.494

and transformational leadership (R = 0. 679, p < 0.01), and the correlation although positive is less significant compared to emotional intelligence and its dimensions. Examining a correlation between emotional intelligence and transformational leadership, the value of Pearson’s correlation is 0.734 which was significant at the 0.01 level. Therefore, it can be stated that there is a direct and significant relationship between transformational leadership and emotional intelligence and these findings are supported by other studies, namely Barling et al. (2000), Vasilagos et al. (2017), and Alston et al. (2016). In addition, all emotional intelligence dimensions are positively related to one another as expected, since they are all dimensions of the construct emotional intelligence.

4.2 Regression Analysis Simultaneous linear regression was used to analyze the relationship between a dependent variable and more than one independent variables and at the same time to validate the hypothesized model. It concerns how the combination of selected independent variables affects emotional Intelligence. We constructed a multiple linear regression model to understand what impact the different types of leadership (transactional, transformational, and laissez-faire) could have on emotional intelligence. The initial model was constructed considering the three variables independent even though the same was globally significant (F = 25.134; p = 0) and the quality of the adjustment fairly reasonable (R2 = 0.621); the variable “transactional leadership” was not considered statistically significant (p = 0.965). Taking this into account, the variable was not taken into account, and a new model was built with the two independent variables (transformational and laissezfaire leadership). Table 6 presents the summary of this model. In this model, both independent variables are shown to be statistically significant (p = 0), and the quality of the adjustment was still considerably reasonable (R2 = 0.621), obtaining a model that can explain approximately 62% of the variation in emotional Intelligence.

0.077

50

N

50

N

Pearson’s correlation

0.259

50

N

Pearson’s correlation

0.090

50

N

Pearson’s correlation

0.143

50

N

Pearson’s correlation

0.527**

Pearson’s correlation

50

N

**The correlation is significant at the 0.01 level

Communication

Relationship management

Self-management

Emotional intelligence

Transformational leadership

0.455**

50

N

Pearson’s correlation

1

Pearson’s correlation

Laissez-faire leadership

Transactional leadership

Laissez-faire leadership

Constructs/dimensions

Table 5 Correlation matrix among variables

50

0.388**

50

0.495**

50

0.445**

50

0.463**

50

0.679**

50

1

Transactional leadership

50

0.648**

50

0.59**

50

0.690**

50

0.734**

50

1

Transformational leadership

50

0.947**

50

0.930**

50

0.959**

50

1

Emotional intelligence

50

0.863**

50

0.866**

50

1

Self-management

50

0.799**

50

1

Relationship management

50

1

Communication

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Table 6 Regression table for emotional intelligence Model Constant Transformational leadership Laissez-faire leadership

Coefficient 3.010*** 1.042*** −0.343***

Dependent: Emotional intelligence ***Significant at level 0.01 R2 = 0.621; F = 38.518 (p = 0)

The hypotheses tested statistically are discussed as follows: There is a significant relationship between transformational leadership and emotional Intelligence (H1). The relationship between transformational leadership and emotional intelligence is positive (1.042), and the variable transformational leadership was found to be significant (p = 0). The regression analysis is also significant with R2 = 0.621 and F-statistic = 38.518, p < 0.01 as a result proving the hypothesis H1. Therefore, it can be concluded from the study that emotional intelligence can have a positive influence on leadership competencies, at the level of transformational leadership. There is a negative association between laissez-faire leadership and emotional intelligence (−0.343), and the variable laissez-faire was also found to be significant (p = 0) proving the hypothesis H2.

5 Discussion The results of this exploratory research come to reinforce the purpose of the objective that had been laid out which is to say, the expectation and the confirmation that leaders with transformational characteristics (charisma, motivational inspiration, intellectual stimulation, and consideration for others) simultaneously manifest high emotional competencies in their different dimensions (H1)—as shown by some studies (e.g., Lowe et al. 1996; Nowack 1997; Hooper and Potter 2003; Goleman et al. 2002; Caruso and Salovey 2004). The fact that we did not find in our sample leaders with low values of emotional competencies does not allow us to infer the counterproof as empirical evidence. However, the confirmation of the H2 brings some elements to this discussion, not constituting a counterproof, but reinforcing the idea that emotional intelligence has a negative relationship with leadership styles whose characteristics deviate or even oppose those of the transformational leader. Transactional leadership clearly comes very close to transformational leadership, regardless of the high levels of emotional intelligence presented by the target subjects. Yukl (1998) mentions the binomial of transformational and transactional leadership

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to explain how limited the complex phenomena of leadership is: Leadership must always be seen as a process of dyadic influence with the group and the organization, being that there is an influence from within the work context itself. Bass (1985) recognizes that transformational and transactional leadership are distinct but not mutually exclusive: A leader can use both types of leadership in different situations in different moments. Goleman et al. (2002), on same line of thought, underline that the best leaders are those who use the best emotional intelligence approach for each moment and that change leadership style whenever necessary. This approach reinforces the alternative hypothesis, but also meets the results of a number of investigations in which several types of leadership with high levels of emotional intelligence do not require high levels of emotional competencies in all indicators but only in some (Goleman et al. 2002). It is worth noting that although the data may seem to reveal that components of transactional and transformational leadership are present within this organization, in the regression analysis we did not find a significant relationship between transformational leadership and emotional intelligence. Data also points to the confirmation that emotional intelligence and transformational leadership are present in the different hierarchical tiers, not being confined to the top, as defined by Bass and Avolio (1994) and Avolio (1999). There are no significant differences shown between each group’s averages, both in what comes to the most important emotional intelligence competencies and in what comes to transformational leadership.

6 Conclusion The reflections, theories, and approaches that have been laid out in this research clarify the relevance of associating transformational leadership to emotional intelligence and aim to provide both a theoretical and a practical contribution for future research. The results of this exploratory study come to reinforce the line of research that has recently been performed—that of association of emotional intelligence with leadership, as key competencies to potentiate leader performance and determine satisfaction in employee behavior in their professional relationship (Goleman et al. 2002). They also confirm that leadership with transformational traits (visionary for Goleman) lays over a set of emotional competencies with high scores. This study also points to the confirmation that laissez-faire presents an inverse relationship with emotional competencies; however, due to the lack of leaders with clear and strong laissez-faire traits, we cannot here empirically support the relationship between a laissez-faire leadership style and the lack of emotional competencies. The need to continue developing research on this topic in different organizations with distinct types of organizational models seems to be relevant to unravel to what extent the transformational and transactional leadership styles can explain differences in the presence and influence of emotional intelligence levels.

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The convergence of these approaches and the idea that these competencies can be learnt lead, in practical terms, to the need to promote these competencies within the organizational context. Designing methodologies and interventions within training programs and emotional development as well as individual development plans are means of promoting the development of emotional and leadership skills. Top-tier leaders can improve their leadership through the increase of emotional skills and through that increase their efficiency and performance as leaders in their relationships with their teams. This experimental study is bound by some limitations. The statistical analysis of data related to 360o feedback, based on the averages of respondents per group under analysis, can miss objective discrepancies that exist among the different elements of these groups (e.g., peers, subordinates). The chosen methodology to apply the questionnaire could have influenced the results, and the leniency is expected in particular from top-tier subjects that show a tendency to respond with higher and more stereotypical scores, apart from when it comes to the maintenance of the relationship with their subordinates. Some research has recently shown that a subject tends to respond higher in their self-evaluation than when evaluating others (Nowack 1997). Another aspect to note as limitation and that requires specific analyzing is the fact that we cannot infer the causal relationships between emotional intelligence variables and laissez-faire, as the laissez-faire scale presents low validity. What is more, the data usage raises problems in the common method variance. Although Sheskin (2011) refers that this mistake is infrequent and that in this study it could have been minimized by the usage of different answering scales and the usage of inverted items (in the laissez-faire variable) and in the usage of different dimensions in each scale, it is fundamental that in future studies, different measures are developed. Instrument content points to the expectation that they have been reasonably valid, having been subject to internal item consistency with good results. However, the factorial analysis shows results that can be related to the contingency of sample size and not with the value of the data, from a scientific point of view. Yukl (1998) also refers the fact that most research uses MLQ as the main, if not the only, measure of leadership, which may induce skewed answers and results; and on the other hand, the four transformational behaviors are so intensely correlated that it has not been possible to analyze their results separately.

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The Role of Institutional Leadership in Employee Motivation, Satisfaction, and Personal Development—Design of a Research Proposal Maria Heliodora Matos and Carolina Feliciana Machado

Abstract In order to study the institutional leadership in employee motivation, satisfaction, and personal development the present chapter looks to present the design of a research proposal that can serve as a guide for the development of a thesis on this subject. In this sense, and in order to pursue the proposed objectives, in a first moment, we begin by presenting the theme, the research question, and the objectives to be achieved in research. After this Introduction section, the lecturer can find a Literature Revision focusing and discussing in a critical perspective the main theories/models concerning the theme under study, followed by the research methodology underlying the research development. Here, the researcher seeks to present and justify the methodological options adopted. A brief outline of the work is presented, followed by the references used in this research proposal, plus references understood as pertinent to the future development of the research work.

1 Introduction In order to understand the subject in discussion and the main aims of this research, the present Introduction section gives a particular focus to the research Theme, explaining the relevance of its study and the main aims to achieve. Theme The role of institutional leadership in employee motivation, satisfaction, and personal development—Design of a research proposal. Theme Relevance and Objectives to Achieve In the management of modern organizations, there is an increasing emphasis and importance given to the people who work in them, reflecting on the new practices M. H. Matos · C. F. Machado (B) Department of Management, School of Economics and Management, University of Minho, Braga, Portugal e-mail: [email protected] © Springer Nature Switzerland AG 2020 C. Machado and J. P Davim (eds.), Research Methodology in Management and Industrial Engineering, Management and Industrial Engineering, https://doi.org/10.1007/978-3-030-40896-1_6

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of human resource management. In an increasingly competitive and global world that is, at the same time, closer and more instantaneous, we are seeing a growing awareness of the need to provide people working in organizations with the skills and opportunities for growth and development, both personal and professionals. The human being is, by nature, someone who likes challenges, to experience evolution in his way of being and to learn continuously. Through learning, people internalize new concepts, processes, practices, and procedures, with consequences on behavioral changes, which will be reflected throughout their lives and in the places where they operate. At the level of organizational practices and processes, issues related to behavioral management of human resources, such as employee motivation and satisfaction are being perceived by the organizations’ CEOs as important determinants of their success and as key items for the creation of global value and positioning. For centuries, great leaders, especially political and military leaders, have realized that with an available workforce that exhibits high levels of motivation and satisfaction, it has been possible to overcome previously seemingly insurmountable obstacles, greater adherence, and commitment to objectives, thus enhancing the success of the targeted projects. Today, we understand that more than ever. Modern organizations, mobilized by the need to strategically positioning themselves in markets with advantage, seek to provide their human resources with a set of conditions that will enable them to be ready to use their full potential. At this level, we assist to a symbiosis between the emphasis on opportunities for personal and professional development and the search for higher levels of motivation and satisfaction. Cabral-Cardoso (1998) points out that staff can contribute to strengthening a company’s competitive position or constitute a barrier to such competitiveness. According to Beaumont, cited by the same author (1998), people are now seen as an asset, a resource that can and should be managed to contribute to the organization’ results. As a reinforcement, Drucker (2002, p. 445) argues that “Organizations are legal fictions. They do not do, they do not decide, they do not plan anything on their own. Are the individuals who decide and plan. Above all, organizations only ‘act’ to the extent that people act.” In order to understand the paradigmatic change in human resource management, it is essential to look at the role that leaders play in organizations. Leadership is a sine qua non condition in any organization if it is to be successful in any initiative it conducts as well as in conducting day-to-day organizational processes. Organizational leadership has multiple actors, which are not only concentrated at the top, including all hierarchical levels, both vertical and horizontal. Leaders are fundamentally people drivers. Key elements that can, through a plurality of attributes, make other players act according to what was previously established. At human resource management level, when we seek to implement measures focused to change a particular organizational status, it is important to consider the role that managers will play in its implementation. Being a necessary condition to get their involvement, if the various leaders do not identify themselves with the organizational policies, it is a matter of time until those same policies fail.

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In the present research, we seek to study the nature of the actors’ role who play formal leadership roles within CCAM.1 This is of particular interest because CCAMs are more decentralized in structure, when compared with other lending institutions, with ways of implementing their policies and organizational practices distinct from the generality of banking. Due to the extremely relevant role these figures play in the organizational activity, and the growing importance that issues related to the behavioral area of human resource management are having, namely the motivation, satisfaction, and development of employees, it is essential to understand how institutional leaders can act as facilitating or hindering agents of these same issues. We need to highlight CCAM organizational structure is, as mentioned above, quite decentralized, with a central entity—“CC”—and a set of branches (634 branches) spread throughout mainland Portugal and the Autonomous Region of the Azores, which enjoys considerable autonomy. In addition to these units, that are part of SICAM, 45 branches belonging to five non-SICAM 2 units that function as completely independent credit institutions need to be considered. It is interesting to note that each CCAM client can also be associated and that there is a formal leadership position—CCAM Management—which is achieved by election by these members. It is not an essential requirement that all elements occupying such positions have specific management competencies, which on several occasions lead to various ambiguities and disconnections, as these actors occupy higher hierarchical positions than managers (professionals who are supposed to possess knowledge and management skills). According to Rego and Pina and Cunha (2002), leaders have a high potential for intervention, either as role models (agents who constantly learn) or as fosters of an organizational climate prone to synergistic knowledge sharing and learning. Thus, it is pertinent to study how the various types of institutional leadership participate in the CCAM processes, regarding the management of motivation, satisfaction, and development of employees and, equally important, how the followers view them. General objectives, such as: – human resource management (HRM) trends assessment of CCAM regarding motivation, satisfaction, and personal development of employees; – assessment of how the organizational structure of CCAM is adequate for the implementation of HRM policies; – assessment of the real importance that HRM has in CCAM; – and the projection of trends and guidelines for HRM in the banking sector, … imply the establishment of different partial objectives, oriented to the various dimensions addressed by the thesis. The first one involves studying and comparing the social representations of the different institutional leaders and those led. The second sub-objective focuses on the study of the nature of the relationship between the 1 In

order to guarantee the institutions anonymity, from now on we are going to use abbreviations every time we refer to them. 2 The supervision of these institutions is carried out directly by the country Central Bank.

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CCAM and CC, as well as the nature of the relationship between the various CCAM and their articulation with regard to the implementation of CC determination. The third sub-objective aims to study the importance attributed by CCAM’s institutional leaders to managing employee motivation, satisfaction, and development, to evaluate the incentive system and training programs provided within the Financial Institution and to compare the perception of the HR Director of the Central Unit and CCAM Directors on HRM practices. The fourth sub-objective aims to assess how the results can serve to broaden research in HRM in banking and to provide a scenario on the behavioral practice of HRM in the institutions of the Financial System.

2 Literature Review The concept of leadership is one of the least consensual in Organizational Psychology. Leadership is central to the functioning of organizations and is one of the most important determinants of their performance and survival. However, there is still a great deal of controversy about what leadership is or what characteristics organizational actors must possess to be leaders. According to Stogdill (1974), there are almost as many definitions of leadership as there are people who have tried to define the concept. A brief definition of leadership can be given to us by Buelens et al. (2002, p. 450), according to which leadership is no more than “A process of social influence, where the leader seeks the voluntary participation of subordinates in an effort to achieve organizational goals.” Another definition of organizational leadership can be found in the 62 Societies GLOBE Study, where its organizers define this concept as “… an individual’s ability to influence, motivate and allow others to contribute to the effectiveness and success of the organization of which they are members” (Dorfman and House 2004, p. 56). Preston (2001, p. 62) defines leadership as “… the process of influencing others to act to achieve the goals of the leader and the organization. A successful leader sets a goal, achieves it, and in the process satisfies those whose opinion has to be respected.” For Machado (1994, p. 37), “Leadership is presented as the ability to read, interpret and make the organizational culture operative, leading the corporate collective to know and incorporate this force.” In Castro’s opinion (1994, p. 50), leadership “… presupposes certain requirements, including physical and psychological, and to be true requires that it should be freely accepted by others ….” Leadership always involves a process of control and influence over others. Different definitions of leadership differ in type or style: the use of authority, with the distribution of rewards and punishments as a way of exerting influence on the followers, contrasts with non-coercivity. Leadership is expressed or exhibited through interaction between people and necessarily implies the need

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to be followed. For one person to influence, others must be influenced. Moreover, the leader and followers must be minimally organized about some common purpose or agreement, and their scope depends, at least in part, on the relationship between the leader and the leader’s followers. Being just one of a manager’s variety of tasks, the way in which it is exercised has major repercussions not only on the evolution of the company but also on the way the company as an organization is seen by its employees and other stakeholders (Teixeira 1998). Formal leadership differs from informal leadership in the organizational legitimacy of the individual who takes on the role. The formal leader uses a type of authority-based leadership. The informal leader uses a kind of non-coercive or power leadership. For Caldeira and Correia (1996), informal leadership is exercised by people who can influence group members through factors that go beyond the role the organization has assigned to the formal leader. The formal leader often influences one dimension of the task, and informal leaders will naturally emerge to fill in the gaps left by the leadership. While the formal leader often does not coincide with the informal leader, there are advantages when they coincide, particularly because the leader achieves a higher degree of enthusiasm and adherence to organizational tasks and goals from other group members and/or organization. Given the importance of this concept, several theories on organizational leadership will be presented, as well as the links between leadership and organizational change.

2.1 Leadership Theories Northcraft and Neale, referred to by Caldeira and Correia (1996), express the view that traditional leadership theories can be categorized into two dimensions. The first dimension focuses on the leader’s personality traits or behavior. The second dimension focuses on the kind of influence the leader has, namely universal or contingent on the situation or context. Universal theories assume that leadership behaviors or leader traits are always the same regardless of the followers or situation. These theories assume that the leader responds consistently or universally to many different situations. Contingency theories assume the opposite assumption, that is, the leader’s leadership and behavior are dependent on his followers and the situations that occur. Thus, the leader can adjust the expression of personality traits or behaviors to respond to the demands of the situation. Management-focused research on leadership dates back to the 1950s, being its inception primarily due to Robert Bales and Douglas McGregor. Bales emphasizes the importance of groups and leadership, which he classifies into two types: task leader and social leader. The former developing his activity by focusing on the group’s objectives in terms of productivity (tasks), the latter seeking to achieve the objectives by acting on the cohesion and encouraging collaboration among group members (Teixeira 1998).

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McGregor (1960) argues that there are, basically, two ways of looking at human nature. One negative—Theory X—arguing fundamentally that people view work as a sacrifice to avoid and as such need and prefer to be directed and controlled. A positive one—Theory Y—which, in general terms, argues that workers can face work naturally, such as rest or leisure, enjoy taking responsibility and prefer selfcontrol. According to the author, managers will behave differently in relation to their subordinates, namely in terms of direction and control styles, as the assumptions of Theory X or Theory Y admit. Theory X is influenced by the classic view of human nature that had as its prime exponents the theories of Taylor, Fayol, and Weber. Theory Y, on the other hand, harmonizes with the tradition of Human Relations where there is an almost total rejection of the type of mechanistic and rational organization defended by the Classical School (Rocha 1997). Although McGregor seeks to make a neutral presentation of both theories, he clearly opts for Theory Y by stressing that organizations are moving toward this theory. Leadership Trait Theories postulate that there are innate characteristics or personality traits, social, physical or intellectual that differentiate leaders from non-leaders or followers (Caldeira and Correia 1996). According to Teixeira (1998), this approach is based on the acceptance of the idea that leaders are already born leaders. Edwin Ghiselli, referred to by Teixeira (1998), identified thirteen components of the leader’s characteristic traits, of which the six most relevant would be: supervisory capacity, need for professional achievement, intelligence, ability and taste for decision making, self-confidence, and ability to the initiative. House (1971) proposed the Charismatic Leader Theory by suggesting that great leaders make fundamental use of four personal characteristics: dominance, self-confidence, the need to influence, and the conviction of moral rectitude. Leaders with these traits are more charismatic than others who do not have them or have in a lesser degree. According to Cattel and Stice (1954), personality traits determine the emergence of leaders and may vary the way they are elected—by peers or by the identification of external observers. These authors pointed out the following personality traits as the most important to the exercise of leadership (Cattel and Stice 1954): emotional stability, dominance, conscientiousness, boldness, wit, absence of anxiety, deliberate control of will, and absence of nervous tension. Silva (1994, p. 41) states that “… reading and studying the latest leadership theories help to contextualize and sediment some of the experience gained. However, I do not think it is possible to ‘manufacture’ leaders, because I consider leadership is an innate ability that can be developed and improved.” Since trait or profile theories were not effective in explaining the leadership phenomenon, researches began to analyze the specific behaviors that differentiate leaders from followers. Thus emerged within the Behavioral Theories of Leadership, we can find the studies from University of Ohio, the University of Michigan, and the management grid of Blake and Mouton. Beginning in 1945, a group of researchers at the University of Ohio identified two dimensions in the behavior of leaders that they called:

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– the initiation framework (to what extent does the leader set goals and structure his or her subordinates’ tasks to best achieve objectives) ; – and consideration (to what extent relationships between leader and subordinates are characterized by mutual trust and respect for the ideas and feelings of subordinates). They classified the various forms of leadership into four types, according to the degree of consideration and structure corresponding to the behavior of a particular leader (Teixeira 1998). Studies were also conducted at the University of Michigan, focused on investigating the behavioral characteristics of leaders related to performance effectiveness. In these studies, two dimensions of leadership behavior emerged which were called task orientation and employee orientation (Teixeira 1998; Caldeira and Correia 1996). Lickert (1967) understands that effective management is strongly oriented toward subordinates, relying on communication so that there is a greater concentration of ideas and objectives. He proposes four leadership styles: autocratic-coercive, autocratic-benevolent, consultative, and participatory. To the author, the sooner organizations abandon the first two styles and adopt the last two styles, the more effective they will become, increasing their productivity as well as the satisfaction of their human resources. In an evolution of previous studies, namely the University of Ohio studies, Blake and Mouton presented a two-dimensional matrix, called the Managerial Grid, in an attempt to demonstrate the existence of a better universal leadership style (Blake and Mouton 1964; Caldeira and Correia 1996; Teixeira 1998). Concerning the Blake and Mouton’s Managerial Grid, it is possible to observe the intersection of two dimensions of leader behavior: on the horizontal axis is concern with production and on the vertical axis, concern with people (Blake and Mouton 1964; Caldeira and Correia 1996; Teixeira 1998). The manager using style 1.1 has minimal concern for both people and production; Manager 5.5 employs a style where he gives a fair balance between concern for people and concern for production, while those who use styles 1.9 and 9.1 take extreme positions: either a total concern for people, neglecting production (style 1.9), or an extreme preoccupation with production (style 9.1) don’t put any weight on their team. To Blake and Mouton (1964), the degree of effectiveness varies from style to style, with the best performing managers employing style 9.9, where they provide excellent organizational (or group) climate, while concern about production is clearly important in its performance. It is becoming increasingly clear that analyzing the phenomenon of leadership and predicting its success is much more complex than merely isolating some personality traits or identifying preferential behaviors. The inability of previous models and theories to achieve consistent results has led to approaches focused on contextual influences. As Caldeira and Correia (1996, pp. 6–7) point out, “… the relationship between style and leadership effectiveness suggested that under condition ‘a’ the style ‘x’ is the most appropriate, while styles ‘y’ and ‘z’ are the most appropriate under condition ‘b’ and ‘c’, respectively.” The effectiveness of leadership then depends on the context.

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The “Path-Objectives” theory developed by House (already referred to in the profile approach) and Mitchell is, according to Teixeira (1998), closely related to Vroom’s theory of expectations about people’s motivation. According to this theory, subordinate performance is most effective if the leader clearly defines the task, provides training, helps them work effectively, and establishes rewards that are directly related to their level of performance. Leadership is seen in terms of the impact on motivation and the subordinates’ needs satisfaction. According to this model, a leader’s behavior is acceptable when employees perceive them as a source of present or future satisfaction (Buelens et al. 2002). The leader’s task is to help subordinates achieve their goals and objectives and to ensure that they are compatible with the mission of the group or organization. Within this framework, there are four types of leadership styles: directive, supportive, participatory, and result-oriented (Luthans 2002; Buelens et al. 2002). House identified four types of leadership behavior: directive, supportive, participatory, and mentor. The nature of the situation the leader faces depends on two sets of contingency factors: environment (task) characteristics and subordinate characteristics. “By adopting the most appropriate leadership style according to his interpretation of these two sets of contingency factors, the leader can increase motivation and job satisfaction by clarifying goals and the path to achieving them” (Teixeira 1998, p. 146). The approach proposed by Tannenbaum and Schmidt “… is the view of leadership as involving a variety of styles, ranging from highly centralized around the boss to fully decentralized where subordinates have maximum freedom within the limits set by the leader. They support the idea of a continuum of leadership behavior based on the assumption that choosing an effective style depends fundamentally on three groups of factors: leader characteristics, subordinate characteristics, and situation requirements” (Teixeira 1998). Indeed, leaders must choose the leadership style that maximizes their chances of effectiveness. Fiedler’s contingency leadership theory proposes that effective group performance depends on a good combination of leadership style in interacting with subordinates and the degree to which the situation is controlled by the leader and gives influence to him (Caldeira and Correia 1996). This author defends that there is no leadership style considered the most effective in any situation, whatever it is. In other words, a person becomes leader not only because of his personality attributes but also because of the coexistence of various factors and the interaction between the leader and subordinates (Teixeira 1998). Fiedler’s model helps to destroy the notions that leadership ability is innate and that a better leadership style may exist regardless of the circumstances. Another contingent model of leadership is the “Leader-Participation” model developed by Vroom, Yetton, and Jago, which has undergone some evolution over time and links leadership behavior with participation in decision making. “Vroom and his collaborators sought to determine the most desirable form of employee participation in decision making by taking into account the different types of task structures, namely routine and non-routine” (Caldeira and Correia 1996, pp. 6–12). The model is based on a complex decision tree, with the most appropriate leadership style

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being determined at the end of the course. Vroom’s model confirms the tendency for leadership research to focus more on the situation and less on the person. Hersey and Blanchard’s contingency leadership approach (also called the “ThreeDimensional Model of Leader Effectiveness”—1993, p. 128) assumes that managers must use different leadership styles depending on the situation at hand. In other words, the leadership style to use depends on the degree of maturity/readiness of the subordinates. It is important that leaders, to be effective, “not only have the ability to diagnose which leadership style is most appropriate to the situation, but also have the ability to correctly apply that style” (Hersey and Blanchard 1993, p. 112). To the original two dimensions of situational leadership—task orientation and relationship orientation—was added a third one, the effectiveness dimension, which the authors refer to as the Environment. This last dimension is pertinent to the authors because of their acknowledgment that the effectiveness of leaders depends on how their leadership style fits into the specific situation (Hersey and Blanchard 1993). The leadership style varies depending on the maturity/readiness of the led. Similar to leadership styles, subordinates’ development levels are situational: A person or a group may be experts, be confident and motivated to perform a given task, but may be less competent, motivated, or committed to the accomplishment of another task. For Hersey and Blanchard (1993), it is the subordinate who determines the leader’s behavior. It is the leader who has to adapt to the subordinate’s level of development. This last premise is a counterpoint to Fiedler’s Situational Engineering and is called Behavioral Flexibility (Pina e Cunha et al. 2005). Almost all of the leadership theories presented above refer to transactional leadership, that is, “… leaders who guide or motivate their followers toward the goals set by rewards for their behavior” (Caldeira and Correia 1996, 6.15). New leadership theories present different types of leaders who, through their personal vision and energy, inspire followers and have a significant impact on their organizations. We talk here about transformational or change leadership, servant leadership, teaching organizations as opposed to learning organizations, and replaced leadership. As Caldeira and Correia (1996) acknowledge, transformational leaders motivate their followers to do more than they intended in transforming group expectations. To Rojot (2000, p. 19), “Globally, the transformational leader is one who has the ability to transcend or profoundly modify situations. It is the process through which major changes in attitudes are introduced.” Blanchard (2000), quoted above in the contingency approach, presents a new form of leadership: servant leadership. He believes that there are two types of leaders: those who are first leaders and those who are first devoted. The first type of leader tries to control, make decisions, and give orders. Instead, devoted leaders only assume leadership if they feel this is the best way to serve. They have no sense of ownership over their position. They see it as an act of service rather than ownership. They easily delegate or share power, and their purpose is to help employees become freer, more autonomous, more capable, and more effective. According to Blanchard (2000), such leaders help produce good results by modeling and encouraging behaviors and values aligned with a shared vision. With them, win both the employees and the organization.

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Tichy and Cohen (1998) express the view that organizations can no longer be learning organizations and that they need to become teaching/educational organizations if they want to survive in today’s highly competitive global marketplace. Teaching/educational organizations are better able to succeed and maintain this success because of their constant focus on leaders’ development. These organizations’ actual leaders are creating the next generation of leaders by teaching them the business critical points and how to deal with them. Consequently, these organizations present an intellectual capital to move on. Replaced leadership theory attempts to identify situations where the leader’s behavior influence is null or insignificant to the subordinates’ performance. Zenger (1991, p. 50) considers the existence of what he calls the leader’s “two faces of power”: “The first face of power is based on the submission of other people. The second is based on the concern with the group’s goals, finding goals that move the group, helping the group formulate them, providing ways to achieve them, and giving to the group members the sense of competence they need to work.” It is this second face of power that belongs to the leader who learns how to lead in a team environment. Zenger (1991) considers that, depending on the type of organization, teams can be permanent or temporary, functional or cross-linked, conventionally supervised or, to varying degrees, autonomous. These organizations want a new kind of leader— more strategic and more responsive to customers, employees, and organizational imperatives. This new kind of leader reacts to the changing nature of the workforce and the pressures of competition. This type of leader arises because the employees’ teams are gaining the experience of taking over the duties that managers have been up to so far beyond the fact that managing organizational change is one of the key concerns of managers/leaders. The need for managers to have political skills to implement decisions was defended by Pfeffer (1992). In his view, both the hierarchical approach and the strategy in developing a strong culture or a strongly shared vision are not sufficient for such implementation, reason why a political action is needed. Organizations need to anticipate change and development through leadership with a strategic vision that goes beyond the context in which they operate. Correia (nd) has the opinion that this strategic vision can become the basis of technological revolutions, segmentation of customers and competitors, triggering new levels of internal competence and motivation, and also an influential source of governments and supranational organizations’ orientations. Herein lies the biggest challenge that leadership cannot escape, aware of its responsibility to customers, employees, investors, and, ultimately, the society. Leadership represents the company in all its relations with the outside world, being at the same time the true link of the social system that is any organization. Increased responsibilities as a change and transformation agent of companies; as an educating agent, in the sense that with its direction and orientation can change behaviors and attitudes, as well as the organizational culture itself. A successful organization requires leadership to be able to identify, at the right time, opportunities to respond promptly and appropriately. It also requires improved diagnostic capabilities to simultaneously detect threats and difficulties in order to

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neutralize or mitigate them. According to Pina and Cunha (2002), organizational change should not only focus on planned change, that is, built according to a preestablished process, but also on emerging change—which is unexpectedly designed in response to problems and local specificities. Pina and Cunha et al. (2005) argue that driving change can, to a considerable extent, involve creating/changing organizational contexts, which is a role that leaders can take on. To leverage the renewal and rejuvenation capabilities of organizations, it is up to them to create organizational environments that foster environmentally adjusted responses. By creating new roles, new responsibilities, new reward systems, new technologies and new interactions, leadership forces new behaviors on its employees, which in turn can lead to a corresponding change in attitudes. In addition, in an iterative logic, the new attitudes feed the desirable behaviors into the change process and remove undesirable behaviors.

3 Research Methodology The work to be done requires the use of different methodological options in order to provide a truly holistic perspective. Thus, different approaches to research methods and techniques will be used, namely methods of quantitative and qualitative origin where, in the case of the former, priority will be given to the survey by questionnaire and, in the latter, the semi-directive interview and documentary survey will be used. With regard to the use of the questionnaire survey, prior to its application, it is essential to define what the universe under study will be, so that the sample size calculation can then be made and the type of sample to be used decided in accordance with the method of application of the questionnaires. The first sample will be calculated based on the SICAM Social Report, with data for 2006. We observed that the CCAMs integrated in SICAM had a total of 3690 effective at December 31, 2006 (excluding human resources allocated to instrumental companies), most of them are men (2088), while 1602 members are women. It is observed that the most representative professional category is highly qualified and qualified professionals (2462), while the least significant is the medium staff (56) (Table 1). Table 1 Functional distribution of CCAMs employees

Men Women Total

Superior staff

Medium staff

Intermediate level staff

Highly qualified professionals

Semi qualified professionals

217

41

439

1.314

54

23

43

15

124

1.148

28

244

1.602

260

56

563

2.462

82

267

3.690

Source CCCAM (2007: 10)

Non-qualified professionals

Total

2.088

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Considering the universe in the study (3690 < 100,000), it appears that it is finite in nature. The confidence level established was three standard deviations (99.7%), where the maximum allowed estimation error was 5%. Therefore, taking these factors into account, the following formula was used for the sample calculation: n=

3σ 2 pq N e2 (N − 1) + 3σ 2 pq

where: n N σ2 p q e

sample size population size chosen confidence level expressed as a number of standard deviations percentage with which the phenomenon occurs complementary percentage (100-p) maximum allowed error

n=

9 × 50% × 50% × 3690 ⇔ n = 724 5%2 × (3690 − 1) + 9 × 50% × 50%

To the universe of 3690 people, it was verified, through the application of the formula, that a sample of 724 elements will be necessary to perform the research. However, an important variable in this research relates to the professional category of employees. The researcher wants to ensure that there is an identical representation of the different functional categories of the population in the sample, reason why the proportions for each professional group have been calculated, resulting in the following distribution (Table 2). It is important to point out that considering that the total value of the proportions’ calculation resulted in a number different from the sample size, 510 questionnaires will be applied to the employees of the different professional groups. This will ensure proportionality in the sample of the different functional categories. Table 2 Sampling distribution of CCAM employees, by professional categories

N Superior staff

36

Medium staff

8

Intermediate level staff Highly qualified professionals Semi qualified professionals Non-qualified professionals Total Source CCCAM (2007: 10)

78 340 11 37 510

The Role of Institutional Leadership in Employee Motivation … Table 3 Sampling distribution of employees from CCAM outside SICAM

141 N

Heads

22

Non-heads

83

Total

105

Source CCCAM (2007: 10)

There are five CCAMs outside SICAM: Bombarral, Chamusca, Leiria, Mafra, and Torres Vedras, which have a total of 268 employees, of which 45, with leadership positions (managers), were extracted. Applying the formula for calculating the sample for finite population gives a result of 207. n=

9 × 50% × 50% × 268 ⇔ n = 207 5%2 × (268 − 1) + 9 × 50% × 50%

Since it is intended to ensure that there is an identical representativeness of the different functional categories of the population in the sample, the proportions were calculated for two professional groups—heads and non-heads, resulting in the following distribution (Table 3). Taking into account the result, 105 surveys will be applied to employees of these two professional groups. In addition to the above, it is also intended to inquire members of the CCAM Directorates, as they also occupy superior hierarchies, despite the fact that they are elected for three years. There are 109 CCAMs which means there are a minimum of 327 Directorate elements. n=

5%2

9 × 50% × 50% × 327 ⇔ n = 240 × (327 − 1) + 9 × 50% × 50%

By applying the formula for calculating the sample size for finite population, we get a result of 240. After calculating the proportions for this group, we found that 136 more surveys were needed. In light of the above, 510 questionnaires will be applied to employees covered by the Mutual Agricultural Credit Enterprise Agreement (with and without leadership functions), 105 to CCAMs outside of SICAM (with and without leadership functions) and 136 questionnaires to different elements of the Directorates, which, given the current number of CCAMs, will cover more than one element of the CCAM Directorates in some cases. Regarding the sampling techniques used, they were non-probabilistic—quota sampling and intentional sampling. For Ghiglione and Matalon (1997), quota sampling seeks to reproduce in the sample how certain variables are distributed in the population to be studied. In the particular case of this study, the concern was to obtain for the sample the same proportion of professional categories with more than 30 elements in the population. As for intentional sampling, for Richardson et al.

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(1985), the sample units (collaborators) are chosen because they have certain characteristics set out in the research objectives. In this case, they were based on the fact that they were employees directly linked to the institutions. In addition, the chosen option of delivering questionnaires—sent by post and electronic mail—requires the use of non-probabilistic techniques due to the resources available for research. The researcher will be the only person actively conducting the research, the person who will send the questionnaires to each CCAM by mail or e-mail, being the same locally distributed. That is, the researcher cannot directly control how the questionnaires will be distributed by different employees, particularly those who do not have leadership roles. Two separate surveys will be applied—one directed at institutional leadership and the other at led. The main reason for this is that it is intended to compare perspectives around human resource policies in Agricultural Credit, contrasting the positions of leaders and followers in managing motivation, satisfaction, and personal development in these organizations. At the same time, with the institutional leaders, the researcher intends to make a comparison between two types of leadership—one having a more technical nature (senior, middle, and intermediate Staff and nonSICAM CCAM Managers) and another, the Directorates Agricultural Credit Banks, which have been elected for three years by members of the Agricultural Credit and who do not necessarily have all the technical training required to perform their duties and are hierarchically above the former. Regarding qualitative methodologies, starting with the semi-directive interview, the researcher will interview some CCAM Managers and Directors, as well as the Human Resource Department Director and the President of CC General Council, once that the themes under analysis correspond to areas that are directly under the responsibility of the former in particular and the latter in general. Through these interviews, in addition to enriching the data obtained to the work, the researcher will also be able to establish a greater relationship between the positions expressed in the questionnaires, to further discern which factors underlie the human resource policies underlying the topics under study that can potentiate or inhibit them. The use of the interview allows a very useful complement to the results of the questionnaire, resulting in a holistic approach, capable of tracing the main organizational dynamics around the human resource practices in question. Regarding the documentary research, this consists in the collection of institutional information of the Agricultural Credit, namely the Consolidated Social Balance of SICAM 2006, in addition to the use of the website of this organization. Thus, it will be possible for the researcher to obtain data related to the area of human resources of the Agricultural Credit, to establish a possible link between the various numbers recorded and the human resource practices related to the motivation, satisfaction, and development of the employees of this credit institution. In short, the researcher will use a varied set of social research methods and techniques, as they all compliment each other and their correct use provides a comprehensive and at the same time deep perspective on the study proposed here.

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4 Final Remarks The competitive situation that organizations are facing has created the need to find new management models that can lead them to success. Modern management trends increasingly imply the need for leaders, not mere employees, as well as different hierarchical structures. In modern management, the way leadership is understood differs greatly from the institutional leader of the orchestral management model. The new models of business organization move toward versatility and lengthening of responsibilities, fostering the development of motivations, and competencies within an organization. The classic view of management had specialization and organization as its top values. The company should function as a juxtaposition of differentiated and specialized skills. Heavy hierarchical structures, with numerous levels, slowed decisions and caused frequent blockages in communication processes. The trends of modern management are to reduce the importance and interest of supervision for the establishment of autonomous teams with a good ability to react quickly to all situations that may compromise the processes’ success. To these teams are set demanding and realistic objectives and are given the possibility to perform self-control in advance of institutional control, that is, employees are given real power (empowerment). At the same time, the long hierarchical pyramids are crushed and the reduction to two or three levels means the end of vertical careers. Promotions become more horizontal, talking more about “practicing management” without hierarchical relationship and manager-coach. This is a move toward polyvalence, extending responsibilities to more important processes or conducting change projects that underpin careers in modern enterprises (Ceitil 1994). This change leads to a decrease in the number of chiefs and an increase in leaders in an organization. It is Ceitil’s (1994) opinion that the fact that there are more leaders in an organization is inseparable from the need for better leaders who can be constituted as leaders of leaders, stressing the fact that the role of a leader in a company is increasingly foster the development of motivations and skills of employees, that is, promote their growth. Human resources are increasingly considered to be the essential resource of an organization, and the current and future manager/leader must focus on valuing that resource in its most delicate and complex dimension: individual. The manager/leader should be able to address each individual in the organization, mobilize their intelligence and energy through intrinsic motivations and encourage free adherence to the company’s project. Organizational leaders should, more than ever, be managers and strategists attentive to the human capital of the organization and devote more time and effort to that function. In particular, they should be concerned with training employees, evaluating managers, identifying talent and designing new forms of organizations. From this perspective, they should demonstrate excellent capabilities in the field of strategic human resource management. The proposed research aims to be a useful contribution to the literature related to human resource management, in a specific sector and still relatively little investigated

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in our country—banking—using, for this purpose, credit institutions with a different structure and dynamics of the remaining ones, that is, all the Agricultural Credit. Firstly, the main objective is to determine the main trends in human resource management in Agricultural Credit, regarding the motivation, satisfaction, and development management of the employees of this institution. Secondly, it is intended that this research provides a comparative view between two main groups—leaders and led, on how they view these trends and dynamics of human resource management. At the same time, this study will bring an insight obtained from the different institutional leaders, trying to show that there is no uniform line of thought about the motivation, satisfaction, and development of professionals. Thirdly, as a national study, it will provide a set of information and data that will have strong empirical support, given the proposed delineation of data collection, and subsequently outline measures to be applied to optimize human resource practices. Fourthly, because it is such a holistic study, it may foster the emergence of other work within the banking sector, leading to increased research in the area of human resource management in our country and, at the same time, allowing comparing various studies related to different credit institutions. Consequently, banking can be perceived as an extremely rich field for the study of organizational dynamics in such sensitive areas as employee motivation, satisfaction, and development. This is of paramount importance, especially at a time when the banking sector is becoming more and more relevant in the determination of national policies and in the daily life of the Portuguese, where it presents itself as the sector with the highest profitability manifested in such a complex context we currently face.

5 Work Plan The following programming is proposed for the research: • First phase: literature review and writing of the first version of the theoretical revision; • Second phase: construction of data collection tools for interviews and questionnaires; • Third phase: data collection—interviews and questionnaires; • Fourth phase: analysis of the results of data collection and preparation of analysis graphs and writing of the empirical section of the work; • Fifth phase: final work review; • Sixth phase: work delivery.

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Index

A Abductive, 9 Academic articles, 1, 2, 11 Action research, 85, 91 Analysis, 8, 24, 25, 27–32, 35–39, 41 Analysis data, 23, 24, 26, 32, 35, 36, 40 Analysis methods, 95, 96 Analytical dimension, 27 Analytical matrix format, 24 Approaches, 23, 24, 27, 28, 30–32, 34–37, 41 Archival research, 88, 92

B Bibliometrics, 49, 51–53, 56

C Case studies, 1, 4, 9, 11, 15, 18, 83–85, 91, 93, 96, 107 Coding, 9 Communicating data, 45 Concepts, 45, 47, 48, 53, 55 Concept’s meaning, 31 Conceptual framework, 37 Conceptual justifications, 32 Concluding remarks, 24, 40 Conclusion, 3, 7, 12, 13, 18 Confidence level, 140 Contributions, 3, 12, 16 Corporate Social Responsibility (CSR), 24– 30, 37, 40, 41 Correlation matrix, 122 Credible results, 24 Critical dimensions, 31

Critical Success Factors (CSF), 71–73, 79, 82, 88–91, 93, 96–101, 104–107 Cross-Impact Analysis, 49, 57, 58

D Data, 3, 4, 6, 8–10, 12, 13, 16, 18, 19, 139, 142, 144 analysis, 35, 36, 78, 83, 86, 89, 100 collection, 8, 9, 23, 32, 34, 35, 78, 80, 81, 83, 84, 86, 89, 93–100, 105, 107 method, 31, 32, 34, 40 procedure, 118 Deductive, 9 approach, 78, 79, 82, 83, 91, 93, 94 Delphi method, 49, 54–56, 64, 101–107 Delphi studies, 55 Departing questions, 73 Design, 116, 118, 129 Design of a research proposal, 129 Determination, 73, 89–91, 97, 98 Diagnostic tools, 23 Distorting data, 10 Documentary survey, 139 Double blind, 5

E Editors, 1–3, 7, 10, 14–18 EIV 360, 118 Electronic industry, 111 Embedded, 84 Emotional intelligence, 111–116, 118–125 Empirical research, 34 Employee motivation, 129, 130, 132, 144 Environmental scanning, 45, 50–52, 58, 65

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152 methods, 50 Epistemological paradigm, 73, 76–78, 80, 93–96, 100, 105, 107 Epistemological question, 74, 76 Ethnography, 87, 92 Evaluating, 24, 26, 27, 31 Expected results, 26 Experimental research, The, 82 Expert opinion, 45, 49, 54, 55, 65 Exploratory, 49, 50, 63 technological forecasting, 49, 63 External validity, 9

F Findings, 5, 6, 11, 12 First-order themes, 9 Foggy, 15 Format problems, 15 Form 6-S, 117, 118

G Gathering data, 24, 26 General Data Protection Regulation (GDPR), 71–73, 75, 76, 78–80, 82, 88–91, 93, 96, 100, 104–106 General objectives, 131 Google Correlate tools, 58 Google trends, 58, 59 Grounded theory, 9, 86, 87, 91

H Higher Education Institutions (HEIs), 71– 73, 75, 76, 78, 79, 82, 88–92, 96, 100, 104–106 Holistic, 84, 91, 96 Holistic perspective, 139 Hypotheses of research, 116

I “Impact factor snob”, 5 Inductive, 9 approach, 78, 79, 93–95, 100, 105 “In press”, 5, 15 Institutional leadership, 129, 131, 142 Intentional sampling, 141 Inter-method combination, 34 Internal validity, 9, 118 International Business research, 1 Interpretativist epistemological paradigm, 76, 77, 80, 93–96, 100, 104

Index Interviews, 3, 9–11, 15, 16 Introduction section, 129 Item-by-item, 118

J Journal requirements, 15

K Kinds of methods, 49

L Lack of “logic”, 12 Language problems, 14 Leadership, 111, 112, 114–125 Length, 15 Literature, 3–9, 11–13, 15, 17, 18 Literature review, 4–8, 12, 13, 132, 144

M Make sense, 13, 16 Maturity model, 24, 28, 30, 31, 36, 37, 39, 41 Maximum allowed estimated error, 140 Measurements, 117, 118 tools, 118 Method, 6, 8, 9, 13, 16, 17, 45–66 Methodological literature, 9 Methodological options, 24, 89, 93, 95, 107, 129, 139 Methodology, 23, 27, 32, 40, 41, 50, 56, 71, 73, 80, 85, 86, 89, 91, 96, 104 Methods of technology forecasting, 45 Mistakes, 1, 2, 13, 14, 17, 18 Mixed-methods, 8, 23, 33, 34 approach, 32 research, 32 research strategy, 32 MLQ-6S, 117 Modelling, 45, 49, 54, 58–62, 64, 66 Modelling and simulation, 45, 59, 60, 62 Models, 25–29, 31, 32, 37, 40 Multifactor Leadership Questionnaire, 117 Multiple case, 8, 84, 96

N New methods, 45, 52, 55, 56, 58, 62, 64, 66 Normative, 49, 50, 63 “Number of citations snob”, 4, 5

Index O Objectives, 48, 50, 129–133, 135, 136, 142– 144 of the study, 116 to achieve, 129 Obtaining information, 45 Ontological paradigm, 74, 75, 78, 93–96, 100, 104, 107 Ontological question, 74 Outline of the work, 129

P Paradigms, 31 Participants, 111, 112, 117 selection, 23, 31, 33, 34, 37 selection model, 37 Participation, 118 Patent analysis, 51, 52 Performance parameters, 48 Personal development, 129, 131, 142 Plagiarism, 13 Portugal, 111, 112, 117 “Predatory journals”, 5 Predictions, 45–48, 57, 58, 61, 62, 65 Processing data, 45 Proposition, 28, 31, 35 Publishing, 1, 2, 13, 14, 18 Publishing house’s policy, 14

Q Qualitative, 139, 142 articles, 2, 18 method, 8, 49 methodologies, 139, 142 research, 1, 18 study, 30, 31 Quantitative, 139, 142 articles, 2, 4 methods, 49 Questionnaires, 139–142, 144 survey, 139, 141, 142 Quota sampling, 141

R Real Time Delphi, 56, 64, 66 Real Time Spatial Delphi, 56, 66 Recent trends, 45, 47 References, 5, 15, 19, 28, 30, 31, 129 Regression analysis, 121, 123, 124 Regression table, 123 Rejection, 1–3, 9, 13–15, 18, 19

153 Relevance of research, 24 Relevant results, 24, 40 Re-publish, 13, 14 Research, 23–32, 34–37, 39, 40, 73–75, 77– 98, 100, 101, 103, 105, 107, 111, 112, 114–116, 118, 119, 123–125, 129, 131–133, 137, 139, 140, 142–144 approach, 78 contributions, 26 design, 23, 24, 29, 30, 33–35, 37, 40 development, 129, 131, 132, 142–144 fields, 46, 52 framework, 23 implications, 12 methodology, 73, 85, 139 methods, 139 model, 23, 31, 33 nature, 80 network, 53 objectives, 25, 41 paradigm, 31, 32 philosophy, 73, 96, 107 practices, 24 problems, 23, 24, 41 project, 23–25, 31, 33, 37, 40 proposal, 129, 40 questions, 25, 30, 31, 36, 129 strategy, 23, 31, 32, 82 techniques, 23, 26, 41 work, 129, 139, 142, 144 Researcher, 12, 129, 134, 140, 142 Results, 111–115, 118, 119, 123–125 Reviewers, 1–8, 10–19 Reviewer’s perspective, 2 Review process, 5 “Right” literature, 4 Roadmapping, 45, 62–64

S Sample size, 139–141 Sampling techniquesnon-probabilistic, 141 Satisfaction, 129–132, 135, 136, 142, 144 Scenarios, 45, 47, 49, 51, 56, 58, 61–66 discovery, 61, 64–66 Second-order themes, 9 Selection of participants, 24, 34 Self-plagiarism, 13 Semi-directive interview, 139, 142 Semi-structured interviews, 31, 36 Sequential explanatory model, 23, 32 Significant results, 40 Simulation, 45, 49, 58–63

154 Single case, 84 Snowball method, 5 Social Network Analysis (SNA), 51–53, 66 Social research methods, 142 Social research techniques, 142 Social sciences field, 77 Spatial Delphi, 55, 56, 66 Specific data, 8 Spelling problems, 14 Statistical methods, 45, 56–58, 65 Strategic corporate social responsibility, 23 Strategic maturity, 23, 25–29, 36, 37, 40 Strategy, 23–25, 27, 28, 34, 36, 37, 40, 41 Surveys, 81, 83, 85, 91, 118, 139, 141, 142 Sustainability, 23–25, 27–30, 36, 38–40 Sustainable value, 23, 25–32, 39, 40

T Teaching case, 4, 10 Technical fields, 45 Technology forecasting, 45, 46, 48, 50–53, 56, 59–63, 65, 66 Technology mining, 51, 52 Technology roadmapping, 63 Text mining, 51, 52, 59 Theme, 129, 142 Theme relevance, 129 Theoretical background, 27 Theoretical concept, 27 Theoretical-conceptual dimensions, 25

Index Theoretical conceptual framework, 27 Theories, 31, 32 360-degree view, 111 Time horizons, 92, 93, 96, 107 Title, 4, 8, 12, 14, 15 Trend analysis, 45, 49, 54, 56–59, 65 Trends impact analysis, 58 Triangulation, 9 Type of sample, 139

U Unethical conduct, 13 Universe / Population, 139, 140 Universe under study, 139 Unsuitable journal, 2 Useful literature, 4 Usefulness, 23, 33

V Validity, 23, 25, 28, 30, 36, 39, 41 Valid results, 24 Variables, 115, 119, 121–123, 125, 140, 141

W WebGIS platform, 56 “Wide” concept, 7 Working in progress investigation, 71 Work plan, 144