State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM): Methodological Extensions and Applications in the Social Sciences ... Proceedings in Business and Economics) 3031345886, 9783031345883

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Springer Proceedings in Business and Economics

Lăcrămioara Radomir · Raluca Ciornea · Huiwen Wang · Yide Liu · Christian M. Ringle · Marko Sarstedt   Editors

State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM) Methodological Extensions and Applications in the Social Sciences and Beyond

Springer Proceedings in Business and Economics

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Lăcrămioara Radomir • Raluca Ciornea • Huiwen Wang • Yide Liu • Christian M. Ringle • Marko Sarstedt Editors

State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM) Methodological Extensions and Applications in the Social Sciences and Beyond

Editors Lăcrămioara Radomir Faculty of Economics and Business Administration Babeș-Bolyai University Cluj-Napoca, Romania

Raluca Ciornea Faculty of Economics and Business Administration Babeș-Bolyai University Cluj-Napoca, Romania

Huiwen Wang School of Economics and Management Beihang University Beijing, China

Yide Liu School of Business Macau University of Science and Technology Taipa, Macao

Christian M. Ringle Department of Management Sciences and Technology Hamburg University of Technology Hamburg, Germany

Marko Sarstedt Ludwig-Maximilians-University, Munich, Germany Babeș-Bolyai University Cluj-Napoca, Romania

ISSN 2198-7246 ISSN 2198-7254 (electronic) Springer Proceedings in Business and Economics ISBN 978-3-031-34588-3 ISBN 978-3-031-34589-0 (eBook) https://doi.org/10.1007/978-3-031-34589-0 © Springer Nature Switzerland AG 2023 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

The use of partial least squares structural equation modeling (PLS-SEM) has gained enormous momentum in the past decade in various business research fields such as accounting, information systems, marketing, strategic management, tourism but also in other non-business disciplines such as computer sciences, engineering, environmental sciences, medical sciences, political sciences, and psychology. The widespread adoption of the method is attributed to the confluence of various factors. PLS-SEM facilitates the estimation of models with many constructs and complex inter-relationships, while also accommodating advanced modeling such as higher-order constructs, nonlinear relationships, and conditional process models. Other strengths of PLS-SEM include the ability to support model comparisons and to test the predictive power of models. Especially the predictive assessment of the results obtained by PLS-SEM allows researchers to substantiate their findings and managerial recommendations, which are predictive in nature. Continued efforts by researchers to refine and improve the method have yielded rich resources (e.g., textbooks and edited volumes on the method, methodological articles, and review papers on the method’s use), which have further fueled the method’s dissemination by creating an understanding how PLS-SEM can support researchers in accomplishing the study goals. In addition, access to software with user-friendly graphical user interfaces and to freely available PLS-SEM packages in the R Statistical Environment have encouraged the use of the method among non-technical researchers and among the researchers who are mindful of costs. This proceedings book includes a collection of manuscripts presented during the 2022 International Conference on Partial Least Squares Structural Equation Modeling Conference (PLS2022) that was held September 6–9, 2022 at the Faculty of Economics and Business Administration of the Babeș-Bolyai University in ClujNapoca, Romania. The conference has been designed to cater the needs of researchers and practitioners who empirically apply and methodologically advance the PLS-SEM method. It is part of a tradition of great conferences in the context of the PLS-SEM method such as the PLS2005 in Barcelona, Spain, the PLS2015 in Seville, Spain, and the PLS2017 in Macau, China. As with the previous conferences, the PLS2022 served as a vehicle to share and discuss new ideas, help each other, and explore new ideas in a friendly environment among friends. v

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Preface

Photograph by Mumtaz Ali Memon, NUST Business School (NBS), Pakistan

The diverse scientific inquiries addressed in this collection of manuscripts testify both the widespread adoption of the method and researchers’ interest to further address methodological issues in PLS-SEM. This book also supplements the resources available for the PLS-SEM community, which, we hope, will expand knowledge and foster novel research in various fields of inquiry. We would like to seize this opportunity to thank all the authors and the numerous colleagues who have devoted their time to reviewing the submissions, thereby helping further improving the manuscripts. Without your consolidated effort, this edited volume wouldn’t have been possible. Thank you! Cluj-Napoca, Romania Cluj-Napoca, Romania Beijing, China Taipa, Macau Hamburg, Germany Munich, Germany

Lăcrămioara Radomir Raluca Ciornea Huiwen Wang Yide Liu Christian M. Ringle Marko Sarstedt

Contents

Part I

Methodology

Empirical Validation of the 10-Times Rule for SEM . . . . . . . . . . . . . . . . Ralf Wagner and Malek Simon Grimm

3

Missing Values in RGCCA: Algorithms and Comparisons . . . . . . . . . . . Caroline Peltier, Laurent Le Brusquet, François-Xavier Lejeune, Ivan Moszer, and Arthur Tenenhaus

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Comparing Local vs Global Clustering with FIMIX-PLS: Application to Marketing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sophie Dominique, Mohamed Hanafi, Fabien Llobell, Jean-Marc Ferrandi, and Véronique Cariou Partial Least Squares Structural Equation Modeling-Based Discrete Choice Modeling: An Illustration in Modeling Hospital Choice with Latent Class Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Andreas Fischer, Marcel Lichters, and Siegfried P. Gudergan

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The Use of PLS-SEM in Engineering: A Tool to Apply the Design Science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ari Melo-Mariano and Ana Bárbara Plá

31

Discovering Issues in Cross-Cultural Adaptation of Questionnaire Through PLS-SEM Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fariha Reza and Huma Amir

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Use of PLS-SEM Approach in the Construction Management Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sachin Batra

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Part II

Contents

Consumer Behavior and Marketing

Understanding the Role of Consumer Psychological Motives in Smart Connected Objects Appropriation: A Higher-Order PLS-SEM Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zeling Zhong and Christine Balagué

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Advertisements that Follow Users Online and Their Effect on Consumers’ Satisfaction and Expectation Confirmation: Evidence from the Tourism Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 Jordi López-Sintas, Giuseppe Lamberti, and Haitham Alghanayem Similarities in Factors Affecting Online Shopping Intention in Ecuador and Peru: A Multigroup Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vasilica-Maria Margalina and Alberto Magno Cutipa Limache

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The Impact of Subjective Norms, Perceived Behavioral Control, and Purchase Intention on Purchase Behavior of Eco-Friendly Food Packaging Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Julen Castillo-Apraiz, Jesús Manuel Palma-Ruiz, and Mauricio Iván García-Montes

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Sustainability of Food Waste in the Agro-industry of Michoacan, Mexico . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Jesús Sigifredo Gastélum-Valdez, Irma Cristina Espitia-Moreno, and Betzabé Ruiz-Morales Predicting Sustainable Consumption Behavior of Europeans Using the CVPAT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 Lena Frömbling, Svenja Damberg, Ulla A. Saari, and Christian M. Ringle Part III

Hospitality and Tourism

The Relationship Among Perceived Value, Tourist Satisfaction, and Citizenship Behaviors: The Difference Between Overnight Tourists and Non-overnight Tourists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 Yangpeng Lin, Di Zhang, and Hongfeng Zhang Nonlinear Relationship Between Intellectual Capital and Hotel Performance: Quadratic Effect in PLS-SEM . . . . . . . . . . . . . . . . . . . . . 129 Lorena Ruiz-Fernández, Bartolomé Marco-Lajara, Pedro Seva-Larrosa, and Javier Martínez-Falcó The U-Shape Influence of Family Involvement in Hotel Chain: Examining Dynamic Capabilities in PLS-SEM . . . . . . . . . . . . . . . . . . . . 133 Lorena Ruiz-Fernández, Laura Rienda, and Rosario Andreu

Contents

Part IV

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Innovation Management and Entrepreneurship

Two-Way Business Innovation in Central Eastern Europe: Analysing Innovative Enterprises Using the PLS-SEM Method . . . . . . . . . . . . . . . 139 Márton Gosztonyi Institutional Context and Entrepreneurship Typologies: The Moderator Role of Environmental Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 Serap Korkarer, Mahmut Hızıroğlu, and Joseph F. Hair Jr. PLS-Based Evaluation of a Digital Transformation Adoption Model for Biopharma Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 Frederick K. Johnson and Chinazunwa C. Uwaoma Entrepreneurial Culture, Management, and Innovation of Dairy Industries in Greece, in a Bureaucracy Environment . . . . . . . . . . . . . . . 193 Athanasios Falaras and Odysseas Moschidis The Influence of Facebook on Value Cocreation: Evidence from the Moroccan Fast-Food Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 Mohammed Hassouni, Abdellatif Chakor, and Siham Mourad Part V

Human Resource Management and Organizational Design and Behavior

Research on Interaction Pattern of Virtual Team in Crisis Situation . . . 229 Shen Yuanyanhang, Jiale Wu, and Xiaodan Yu Predicting the Performance of New Hires: The Role of Humility, Interpersonal Understanding, Self-Confidence, and Flexibility . . . . . . . . 239 Debolina Dutta, Chaitali Vedak, and Varghees Joseph Mindful Leadership Under Fire: A Validation Study of a Hierarchical Component Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245 Stephanie Dygico Gapud and Joseph F. Hair Jr. When Navigating Uncertainty Lead Mindfully . . . . . . . . . . . . . . . . . . . . 277 Stephanie Dygico Gapud and Joseph F. Hair Jr. The Interplay Between Push Factors and Transformational Leadership in Influencing Interorganizational Labor Mobility in Public Sector . . . . 315 Rosemary Massae, Deusdedit A. Rwehumbiza, and John J. Sanga Integrated Leadership: Assessing an Integrated Principal Leadership Practices Construct . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339 Ahmed Mohamed, Ahmad Zabidi Abdul Razak, and Zuraidah Abdullah Exploring the Interrelationship Among Management Accounting Systems, Decentralization, and Organizational Performance . . . . . . . . . 345 Elsa Pedroso and Carlos F. Gomes

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Contents

The Impact of Ethical Leadership on Employee Intrapreneurship, Work–Life Balance, and Psychological Empowerment: A PLS-SEM Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353 Huma Bashir, Mumtaz Ali Memon, Naukhez Sarwar, Asfia Obaid, and Muhammad Zeeshan Mirza Part VI

Education

PLS and Educational Research: Epistemological and Methodological Interpretations in Philosophy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377 Hongfeng Zhang, Haoqun Yan, and Johnny F. I. Lam Public Higher Education Organizational Climate’s Structural Model . . . 383 Joel Bonales-Valencia An Investigation of Predictive Relationships Between University Students’ Online Learning Power and Learning Outcomes in a Blended Course . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391 Yue Zhu, Ming Hua Li, Lu Li, Rong Wei Huang, and Jia Hua Zhang The Effects of Marketing Orientation on the Performance of Higher Learning Institutions in Tanzania: Staff and Students’ Perceptions . . . . 409 Francis Muya and Hawa Tundui Part VII

Health

Participative Leadership Is the Discriminating Factor for Country’s Performance During the COVID-19 Pandemic . . . . . . . . . . . . . . . . . . . . 437 Stephanie Dygico Gapud and George Faint A PLS-SEM Analysis of Consumer Health Literacy and Intention to Use Complementary and Alternative Medicine in the COVID-19 Pandemic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459 Michael Christian, Henilia Yulita, Guan Nan, Suryo Wibowo, Eko Retno Indriyarti, Sunarno Sunarno, and Rima Melati Patient Satisfaction of Brazilian Military Health-Care System: An Exploratory Study by Multivariate Analysis . . . . . . . . . . . . . . . . . . . 475 Ari Melo-Mariano, Simone Borges Simão Monteiro, Ronaldo Rodrigues Pacheco, and Marcelo Ladeira Part VIII

Other Fields of Business Research and Economics

FinTech Loan Continuance Intention: How Far Can Self-Efficacies Go? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 491 Samuel Danilola, Adewumi Odeniran, and Adewumi Otonne Does Coercive Pressure Matter on the Practices and Performance of the Public Procurement? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515 Gerald Zachary Paga Tinali

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Determinants of Behavioral Intention to Use E-Procurement System in Developing Countries: Suppliers’ Perception from Tanzania . . . . . . . . . 537 Deus N. Shatta Receptivity of Eastern and Southern African English-Speaking Countries’ Executives to Use, Diffuse and Adopt Humanitarian Logistics Digital Business Ecosystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 557 Benjamin Ohene Kwapong Baffoe and Wenping Luo A PLS-SEM Model on Auditing and GDPR: The Mediating Role of Internal Audit in Business Management . . . . . . . . . . . . . . . . . . . . . . . 589 Stratos Moschidis, Evrikleia Chatzipetrou, George Drogalas, and Paraskevi Zisiopoulou

Part I

Methodology

Empirical Validation of the 10-Times Rule for SEM Ralf Wagner and Malek Simon Grimm

1 Introduction Structural equation modeling has become indispensable in scholarly empirical research due to its ability to investigate latent constructs. However, researchers always face the central dilemma of determining a sufficient sample size to ensure a converting model, unbiased estimates, and sufficient statistical power. Various rules of thumb have been established to determine the optimal sample size, such as a sample of at least 200 participants (Boomsma 1983), 5–10 observations per estimated parameter (Bentler and Chou 1987), and 10 observations per variable (Nunnally 1975). Monte Carlo simulation and the inverse square root method are alternative and more complex methods that require a deeper understanding of statistical power analysis (Ranatunga et al. 2020). For decades, researchers have cited the 10-times rule as representing a sufficient sample size in structural equation models (SEMs) (Thompson et al. 1995). The 10-times rule suggests that the minimum sample size should be 10 times the maximum number of arrowheads pointing at a latent variable anywhere in the partial least squares (PLS) path model (Hair et al. 2021). According to Google Scholar (October 2022), Thompson et al.’s (1995) initial paper by has been cited over 7000 times. However, an empirical validation of this rule of thumb is still lacking. This paper takes a simulation approach to validate the 10-times rule. Only a few studies have investigated the sample size requirements for SEMs (cf. Wolf et al. 2013; Stone and Sobel 1990), but no extant study has specifically attempted to validate the prominent 10-times rule.

R. Wagner (✉) · M. S. Grimm University of Kassel, Chair for Sustainable Marketing, Kassel, Germany e-mail: [email protected]; [email protected] © Springer Nature Switzerland AG 2023 L. Radomir et al. (eds.), State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM), Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-031-34589-0_1

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R. Wagner and M. S. Grimm

To pursue the research question, we drew upon the European Customer Satisfaction Index (ESCI) data set provided by SmartPLS 3.0 and the respective model, which contains 24 independent variables. We used a random algorithm to induce randomly missing cases (MCs) into the data set to decrease the sample size randomly. A 60-times trait (N = 240) was tested against the original trait (N = 250) and traits with 50 times (N = 200), 40 times (N = 160), 30 times (N = 120), 20 times (N = 80), and 10 times (N = 40). Each trait was tested by an aggregated simulation of 30 data sets per trait, so that each data set had a unique pattern of MCs. We tested model stability by investigating the model fit indices SRMR, d_ULS, d_G, X2, and NFI. It was found that the 60-times trait was nearly as precise as the original trait, whereas the remaining traits suffered significantly in measurement quality.

2 Methodology The simulation data were evaluated using SmartPLS 4.0 and the associated ESCI data set and model containing 24 independent variables. All the available model fit indices (SRMR, d_ULS, d_G, X2, and NFI) as well as the adjusted R2 values were investigated. To address the research question, we induced MCs into the original data set, which contained 250 cases without any missing values (MVs). The characteristics of the MCs can be described by the missing completely at random (MCAR) condition. The MCAR condition requires that the probability of the MVs (and, in this case, MCs) in a given variable is unrelated to all other measures (Grimm and Wagner 2020; Parwoll and Wagner 2012). This condition was ensured by using a code that generated randomly MCs within the spectrum of N with respect to the desired number of MCs. Seven traits were investigated. To prevent occasionally occurring patterns, 30 data sets with unique MCs patterns were generated for each trait (except the original trait). Accordingly, the results depict the average of 30 data sets for the test traits.

3 Results Table 1 illustrates the results of the model fit indices as well as the respective differences from the original trait; Table 2 illustrates the results of the adjusted R2 values. The diverse values shown in Tables 1 and 2 imply that the 60-times trait is the most stable in comparison to the original trait. Biases arise if the number of MCs increases. Because the adjusted R2 will vary according to the variable and from survey to survey, no general conclusion can be derived with this assessment. Notably, the values are increasingly biased as the number of MCs increases. The

Empirical Validation of the 10-Times Rule for SEM

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Table 1 Estimation results and differences in percentage of the model fit indices Trait Original trait 60× trait 50× trait 40× trait 30× trait 20× trait 10× trait

Estimates Estimate Δ Estimate Δ Estimate Δ Estimate Δ Estimate Δ Estimate Δ Estimate Δ

Model fit indices SRMR D_ULS 0.13 5.21 0% 0% 0.13 5.21 1% 0% 0.13 5.36 3% 3% 0.13 5.44 3% 4% 0.13 5.37 3% 3% 0.14 6.33 10% 21% 0.17 8.67 27% 62%

d_G 0.79 0% 0.81 2% 0.88 12% 0.98 25% 1.17 48% 1.67 108% 3.88 339%

X2 993.22 0% 968.59 - 2% 875.43 -12% 772.31 -22% 672.99 -32% 597.36 -38% 546.42 -38%

NFI 0.67 0% 0.66 -1% 0.65 -4% 0.62 -7% 0.6 -10% 0.52 -11% 0.40 -38%

Table 2 Estimation results and differences in percentage of the adjusted R2 values Trait Original trait 60× trait 50× trait 40× trait 30× trait 20× trait 10× trait

Estimates Estimate Δ Estimate Δ Estimate Δ Estimate Δ Estimate Δ Estimate Δ Estimate Δ

R2 Values Complaints 0.28 0% 0.27 -3% 0.28 -1% 0.28 -2% 0.29 2% 0.28 11% 0.28 0%

Expectation 0.25 0% 0.25 0% 0.26 4% 0.27 7% 0.30 19% 0.28 11% 0.25 0%

Loyalty 0.45 0% 0.68 50% 0.68 51% 0.67 48% 0.69 52% 0.68 0% 0.45 0%

Quality 0.31 0% 0.44 43% 0.44 43% 0.45 44% 0.47 53% 0.47 4% 0.31 0%

Satisfaction 0.68 0% 0.31 -55% 0.31 -54% 0.32 54% 0.35 -48% 0.33 10% 0.68 0%

Value 0.34 0% 0.34 -1% 0.34 1% 0.34 1% 0.35 4% 0.37 -4% 0.34 0%

results in Table 1 also imply that a bias is induced and increases when the number of MCs increases. One-sample t-tests were computed to test the statistical significance with the original trait (Fig. 1), with each single trait being tested against the respective original trait. The t-tests were run with the original values of the respective model fit indices. To provide a better comparison, Fig. 1 includes percentage values that indicate the percentage of difference from the original value.

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R. Wagner and M. S. Grimm

Fig. 1 Direct comparison of the model fit indices with statistical significance test. Notes: *p < 0.05; **p < 0.01; ***p < 0.001

The results suggest that d_G, NFI, and X2 in particular are substantially and significantly biased and that d_ULS and SRMR are biased if the number of MCs increases. Overall, the 60-times trait is the most stable in comparison to the original trait, even though the models are slightly skewed with respect to the indices d_G, NFI, and X2. Because the NFI measure is calculated on the basis of the X2 value, the assessment of biases is related within that measure. The most relevant characteristic is the elbow that becomes apparent at the 30-times trait.

4 Conclusion Challenging the common assumption that the PLS algorithm is robust against MVs and small sample sizes, this simulation study yields important implications for the theory and practice of recommending a minimum sample size. The results indicate that the 10-times rule is a misleading heuristic. With a decrease in sample size, the likelihood of biases increases. The 60-times trait performed best in this study and in comparison to the original trait. An elbow in the bias plot suggests that researchers are well advised to use at least a 30-times trait. Further research should adopt different models as test instances and should address the facets of statistical power (inverse square root of error variance) in relation to the fit statistics

Empirical Validation of the 10-Times Rule for SEM

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(heteroskedasticity), an overfitting assessment (outer sample validation routine), endogeneity tests, and non-MCAR MVs.

References Bentler PM, Chou C-P (1987) Practical issues in structural modeling. Sociol Methods Res 16(1): 78–117 Boomsma A (1983) On the robustness of LISREL (maximum likelihood estimation) against small sample size and non-normality. Sociometric Research Foundation, Amsterdam Google Scholar (2022) https://scholar.google.com/ Accessed Oct 2022 Grimm MS, Wagner R (2020) The impact of missing values on PLS, ML and FIML model fit. Advance online publication. Arch Data Sci Ser A 6(1):1–17 Hair JF, Hult GTM, Ringle CM, Sarstedt M, Danks NP, Ray S (2021) Partial least squares structural equation modeling (PLS-SEM) using R. Springer, Cham Nunnally JC (1975) Psychometric theory, 11th edn. McGraw-Hill, New York Parwoll M, Wagner R (2012) The impact of missing values on PLS model fitting. In: Gaul WA, Geyer-Schulz A, Schmidt-Thieme L, Kunze J (eds) Challenges at the interface of data analysis, computer science, and optimization. Springer, Berlin, pp 537–544 Ranatunga RVSPK, Priyanath HMS, Megama RGN (2020) Methods and rule-of-thumbs in the determination of minimum sample size when applying structural equation modelling: a review. J Social Sci Res 15:102–109 Stone CA, Sobel ME (1990) The robustness of estimates of total indirect effects in covariance structure models estimated by maximum. Psychometrika 55(2):337–352 Thompson R, Barclay D, Higgins CA (1995) The partial least squares approach to causal modeling: personal computer adoption and use as an illustration. Technology Studies: Special Issues on Research Methodology 2:284–324 Wolf EJ, Harrington KM, Clark SL, Miller MW (2013) Sample size requirements for structural equation models: An evaluation of power, bias, and solution propriety. Educ Psychol Meas 76(6):913–934

Missing Values in RGCCA: Algorithms and Comparisons Caroline Peltier, Laurent Le Brusquet, François-Xavier Lejeune, Ivan Moszer, and Arthur Tenenhaus

C. Peltier (✉) iCONICS core facility, Institut du Cerveau et de la Moelle Epinière, Inserm U1127, CNRS UMR 7225, Sorbonne Université, Paris, France Centre des Sciences du Goût et de l’Alimentation, CNRS, INRAE, Institut Agro, University of Bourgogne Franche-Comté, Dijon, France CNRS, INRAE, PROBE Research Infrastructure, ChemoSens Facility, Dijon, France INRAE, Dijon, France e-mail: [email protected] L. Le Brusquet Laboratoire des Signaux et Systèmes (L2S), CentraleSupélec, Université Paris-Saclay, Gif-SurYvette, France CentraleSupélec, Gif-sur-Yvette, France e-mail: [email protected] F.-X. Lejeune · I. Moszer iCONICS core facility, Institut du Cerveau et de la Moelle Epinière, Inserm U1127, CNRS UMR 7225, Sorbonne Université, Paris, France Institut du Cerveau, Hôpital Pitié, Paris, France e-mail: [email protected] A. Tenenhaus iCONICS core facility, Institut du Cerveau et de la Moelle Epinière, Inserm U1127, CNRS UMR 7225, Sorbonne Université, Paris, France Laboratoire des Signaux et Systèmes (L2S), CentraleSupélec, Université Paris-Saclay, Gif-SurYvette, France CentraleSupélec, Gif-sur-Yvette, France e-mail: [email protected] © Springer Nature Switzerland AG 2023 L. Radomir et al. (eds.), State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM), Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-031-34589-0_2

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1 Introduction Multidisciplinary approaches are now common in scientific research and provide multiple and heterogeneous sources of measures of a given phenomenon. These sources or blocks can be viewed as a collection of interconnected datasets, and dedicated algorithms are mandatory for providing relevant information from multiblock data. Regularized generalized canonical correlation analysis (RGCCA) is a general statistical framework for multiblock data analysis which gathers many multiblock methods (PCA, PLS, CCA, consensus PCA, PLS-PM, etc.) through a single and very simple iterative algorithm. The global convergence of the algorithm was demonstrated in Tenenhaus et al. (2017). However, multiblock data often have missing structure, i.e., data in one or more blocks may be completely unobserved for a sample (block-wise structure) or partially unobserved (random structure). The probability to observe missing data increases with the number of blocks. It is therefore mandatory to properly handle these missing structures within the framework of RGCCA. In this work, several solutions were investigated and compared on simulations. An R package, “RGCCA,” implementing all the methods is under development and is available on GitHub (https://github.com/rgcca-factory/RGCCA).

2 Background 2.1

Regularized Generalized Canonical Correlation Analysis

Let X1, . . ., XJ be J blocks of variables, each block representing a set of pj variables observed on n individuals. The number and the nature of the variables usually differ from one block to another, but the individuals must be the same across blocks. RGCCA is based on the following optimization problem (Tenenhaus et al. 2017). Maximize

J

c g j,k = 1 jk

cov Xj
wj , Xk
wk s:t:wtj M j wj = 1, 8j 2 f1, . . . , J g

where g(x) is any continuously differentiable convex function. The connection matrix C = (cjk) is a symmetric J*J matrix of nonnegative elements describing the network of connections between blocks that the user wants to take into account. Usually, cjk = 1 for two connected blocks and 0 otherwise. Mj is a positive definite matrix, such as Mj = τj I þ 1 - τj Xtj Xj with 0 ≤ τj ≤ 1. In the context of RGCCA, τj is called shrinkage constant. τj varies between 0 and 1 and interpolates smoothly between maximizing the covariance and maximizing the correlation.

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In this work, we focused on the situation where τ j = 1 and all blocks are connected or connected to a superblock, defined as the concatenation of the individuals blocks.

2.2

Missing Data Literature

Pigott (2001) reviewed different types of strategies to deal with missing data: complete data technique, technique based on available data, imputation methods (including expectation-maximization algorithms), and multi-imputation methods.

2.3 2.3.1

Methodology Complete Data Technique (Complete)

The complete approach consists in computing RGCCA only on the complete individuals. This is the simplest method and is the only one available until now.

2.3.2

Technique Based on Available Data (Passive)

This approach (the so-called passive) follows the approach presented in PLS-PM (Tenenhaus et al. 2005). RGCCA is closely related to the nonlinear estimation by iterative partial least squares algorithm (NIPALS; Wold 1966) as the components, and the axes are estimated alternatively. As in Tenenhaus et al. (2005), means and standard deviations of the variables are computed on all the available data; covariance matrices are computed using all the pairwise available data. This pairwise deletion procedure shows the drawback of possibly computing covariances of different sample sizes and/or different individuals.

2.3.3

Imputation Method (Iterative)

The imputation method (Fig. 1) is iterative and based on the alternated two steps (i) calculating the RGCCA weight vectors wj and components yj = Xjwj and (ii) imputation of the missing values. This imputation was based on the hypothesis that each column of Xj can be estimated by a vector proportional to the RGCCA block component yj. This estimation is especially relevant when each block is unidimensional. Thus, the used reconstruction formulae for imputation was Xj = yj γTj where γj j is a column vector of size pj containing the regression coefficients of yj in the regression of Xj on yj.

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Fig. 1 Iterative algorithm for RGCCA with missing values

This method is directly inspired by the fixed effect model used in Josse and Husson (2012) for PCA with missing data. However, when the missing structure is blockwise, this approach gives the same results for all missing individuals, even if they have very different (non-missing) values in the other blocks. To overcome this issue, we propose to use a “superblock” strategy. In the multiblock literature, a superblock XJ + 1 = [X1, . . ., XJ] is defined as the concatenation of all blocks. In this framework, each block is connected to the superblock (that is, cj, J + 1 = 1 for j = 1, . . ., J and 0 otherwise), and optimization problem (1) reduces to Maximize

J j=1

g cov Xj wj , XJþ1 wJþ1 s:t:wtj MJ wj = 1, 8j 2 f1, . . . , J þ 1g

This optimization problem is fully described in Tenenhaus et al. (2017). When a superblock XJ + 1 is considered, the imputation is obtained by using the following reconstruction formulae XJ + 1 = yJ + 1 γTJþ1 , where γJ + 1 is a column vector of size p = Σpj containing the regression coefficients of yJ + 1 in the regression of XJ + 1 on yJ + 1.with an algorithm similar to Algorithm 1.

2.3.4

Dataset Presentation

These methods were tested on the Russett dataset, available within the RGCCA package. The Russett dataset (Russett 1964) are studied in Gifi (1990) and aims to study the relationships between agricultural inequality, industrial development, and

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political instability. Three blocks of variables were defined accordingly (3, 2, and 2 variables for agriculture, industry, and political, respectively) for 47 countries.

2.3.5

Comparison Method

From the Russett dataset, 20 datasets with 5, 10, 15, 20, and 25% of random missing datasets were simulated. For each block, the norm of the difference between the first weight vector obtained from the full case wj and the one from the missing case wj is calculated. Consequently, the closer to 0 this norm is, the better the method is. We used this method to compare complete, available, iterative, and superblock approach.

3 Results and Discussion From our simulations, it appears that the iterative algorithm (with and without superblock) converges monotonically. Furthermore, the simulations showed that the implemented methods outperformed the complete approach on Russett data (Fig. 2) especially for superblock or iterative method.

Fig. 2 The norm of the difference (mean and standard error) between the first axis of RGCCA based on the complete dataset and the first axis of the different “missing” methods according to the proportion of missing values in the dataset

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4 Conclusions and Implications for Theory and Practice This work presents different methods for taking missing values into account in RGCCA. The iterative method presented in this chapter gave good results on the Russett dataset. The convergence of this algorithm was observed but is still to be studied. Multiple imputations for visualizing the variability induced by the imputation could be implemented according to the model used. Furthermore, the strong hypothesis of unidimensionality of blocks used in this chapter could be relaxed by considering more than one component per block, including deflation steps in the iterative algorithm. Finally, this work was illustrated on the Russett dataset with a small number of variables, blocks, and samples, but aims to be also tested on more datasets.

References Gifi A (1990) Nonlinear multivariate analysis. Wiley, Chichester Josse J, Husson F (2012) Handling missing values in exploratory multivariate data analysis methods. Journal de la Société Française de Statistique 153(2):79–99 Pigott TD (2001) A review of methods for missing data. Educ Res Eval 7(4):353–383 Russett BM (1964) Inequality and instability: The relation of land tenure to politics. World Polit 16(3):442–454 Tenenhaus M, Esposito Vinzi V, Chatelin YM, Lauro C (2005) PLS path modeling. Comput Stat Data Anal 48(1):159–205 Tenenhaus M, Tenenhaus A, Groenen PJF (2017) Regularized generalized canonical correlation analysis: a framework for sequential multiblock component methods. Psychometrika 82(3): 737–777 Wold H (1966) Estimation of principal components and related models by iterative least squares. In: Krishnajah PR (ed) Multivariate analysis. Academic Press, New York, pp 391–420 https://github.com/rgcca-factory/RGCCA

Comparing Local vs Global Clustering with FIMIX-PLS: Application to Marketing Sophie Dominique, Mohamed Hanafi, Fabien Llobell, Jean-Marc Ferrandi, and Véronique Cariou

1 Introduction The use of Partial Least Squares Structural Equation Modeling (PLS-SEM) to estimate model parameters with interrelationships between observed concepts and their corresponding latent variables has gained popularity in marketing research since past decades (Sarstedt et al. 2022). When applied to a set of observations, the determination of a single set of parameters, encompassing the latent variables scores and their associated path coefficients estimations, implicitly assumes that heterogeneity is negligible. In practice, this assumption is not realistic in the social sciences, and there is generally unobserved heterogeneity that can taint the validity of PLS-SEM results. To uncover this potential heterogeneity in the data, one can apply a clustering strategy that aims to identify homogeneous segments of observations sharing the same pattern relationships (i.e., the same path coefficients within a segment).

S. Dominique (✉) Oniris, INRAE, STATSC, Nantes, France Lumivero, XLSTAT, Paris, France e-mail: [email protected] M. Hanafi · V. Cariou Oniris, INRAE, STATSC, Nantes, France e-mail: mohamed.hanafi@oniris-nantes.fr; [email protected] F. Llobell Lumivero, XLSTAT, Paris, France e-mail: fl[email protected] J.-M. Ferrandi LEMNA, Oniris, Nantes, France e-mail: [email protected] © Springer Nature Switzerland AG 2023 L. Radomir et al. (eds.), State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM), Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-031-34589-0_3

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For the last two decades, several clustering techniques have been developed within the PLS-SEM framework, such as FIMIX-PLS (Hahn et al. 2002; Ringle et al. 2010), REBUS (Esposito Vinzi et al. 2008), PLS-POS (Becker et al. 2013), GAS (Ringle et al. 2014), PLS-IRRS (Schlittgen et al. 2016), or more recently PLS-SEM KMEANS (Fordellone and Vichi 2020). Undoubtedly, the finite mixture PLS method (FIMIX-PLS) remains the most popular of them. In FIMIX-PLS, the measurement model is considered common to all observations, while observations can differ from each other only at the structural level. This approach is well suited to the context of social science because it leads to the rational assumption that the measurement scales are identical from one segment to another and observation to observation. Going further, in some situations, heterogeneity can be concentrated only in a part of the structural model and not affect the entire set of path coefficients parameters. This leads to determining part of the path coefficients as common to all the observations and the other part as parameters reflecting heterogeneity, which depends on the observation segments. A marketing expert’s knowledge is required to identify the constant and variable parts of the model for all observations. Our work aims to adapt FIMIX-PLS procedure so that the segmentation is only performed on a subpart of the structural model. Subsequently, local partitioning is introduced as a moderating variable in the PLS-SEM applied to all the observations. We advocate such a rationale to provide clusters, which aim to be more stable and interpretable. This strategy is illustrated using a case study pertaining to marketing. Local vs global partitioning with FIMIX-PLS are compared both in terms of interpretability and model quality.

2 Methodology This analysis was carried out on a sample of 315 French people who are members of a CSA (Community Supported Agriculture) in Nantes in 2011 (Dufeu and Ferrandi 2013). CSA belongs to the family of short supply chains, with products sold directly from producers to consumers. Their members are generally opposed to other modes of retailing, particularly mass distribution. Mainly based on the principle of mutual commitment of both parts, CSA seeks to create strong links and proximity between producers and consumers. The objective of this study on the Nantes CSAs was to model the link between the proximity perceived by members toward their CSA and their trust, satisfaction, and commitment in this CSA that are the key variables of relational marketing. In marketing the concept of proximity is composed of several dimensions. Beyond the geographical proximity, the direct and repeated exchanges between all the actors (relational proximity), the sharing of values (identity proximity), and the knowledge on the production and distribution process (process proximity) are the dimensions evaluated in our work (Bergadaà and Del Bucchia 2009). They can all constitute antecedents of trust, satisfaction, and loyalty. Figure 1 represents the path

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Fig. 1 Studied model

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diagram associated with the six latent concepts detailed above. Trust, satisfaction, and commitment are the founding concepts of relational marketing and are therefore common to all segments that can be defined. The first subpart of the model was chosen to segment the respondents. Indeed, proximity is considered here as an antecedent of the relationship marketing chain; thus, we assume that heterogeneity lies mainly on the relationships between proximity and trust. A first partitioning algorithm was carried out on this subpart of the model leading to a so-called local partition. In parallel, a global partition was determined on the basis of the complete structural model. To compare the two strategies, parameters associated with the complete structural model were estimated independently for each segment of the local partition. Finally, the two partitions obtained (local vs global) were compared in terms of path coefficients and variance accounted for (R2). Both partitions were obtained with FIMIX-PLS algorithm implemented in the SmartPLS software.

3 Results and Discussion The partition obtained from the sub-model consisted of two segments. In contrast, the segmentation on the complete model led to retain identified three segments of observations. This number of segments was chosen with the BIC and CAIC indices (Sarstedt et al. 2011).

3.1

Global Partition

The global partition consists of one main segment (N1 = 201) and two smaller ones (N2 = 80 and N3 = 34). Table 1 presents the results of the model associated with each cluster. The first segment is characterized by a valuation of process and identity proximities (path coefficient, respectively, equals to 0.630 and 0.252). By feeling close to these dimensions, consumers are confident in their CSA which in turn leads to increased satisfaction and commitment. The R2 values for each latent variable are greater than 0.6, indicating that they are well explained by the variables directly related to them and therefore that the structure model associated with segment 1 performs well. Only the identity dimension of proximity explains the trust of segment 2 consumers (path coefficient = 0.347). Notwithstanding, proximity alone explains the trust dimension poorly (R2 = 0.147). Finally, the last segment is characterized by a strong valuation of identity proximity associated with a lower valuation of relational proximity. Moreover, the R2 values are very close to 1; this indicates that the model is fully explained by the relationships defined between the latent variables, which is difficult to achieve in

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Table 1 Results of the global partition models

Relational prox. → Trust Identity prox. → Trust Process prox. → Trust Trust → Satisfaction Satisfaction → Commitment Trust Satisfaction Commitment

Segment 1 (N = 201) Path coefficient 0.074 0.252*** 0.630*** 0.911*** 0.811*** R2 0.774 0.831 0.658

Segment 2 (N = 80)

Segment 3 (N = 34)

-0.149 0.347* -0.055 0.648*** 0.798***

0.130* 0.914*** -0.012 0.957*** 0.999***

0.147 0.420 0.636

0.919 0.916 0.998

Notes: *p-value < 0.05; **p-value < 0.01; ***p-value < 0.001

practice. Moreover, one emphasizes the small size of the segment (N3 = 34). Focusing on the optimization of the structural model, the FIMIX-PLS strategy has led to a partition with a very specific cluster corresponding to an overfitted model with R2 close to 1.

3.2

Local Partition

The local partitioning strategy, that is, to say on the first part of the structural model as shown in Fig. 1, led to consider a partition with two clusters whose respective size are N1 = 228 and N2 = 87. The results associated with the two models are figured out in Table 2. In the largest segment, all three dimensions of proximity are antecedents to the concept of trust, with the proximity of the process being particularly important to the Table 2 Results of the local partition models

Relational prox. → Trust Identity prox. → Trust Process prox. → Trust Trust → Satisfaction Satisfaction → Commitment Trust Satisfaction Commitment

Segment 1 (N = 228) Path coefficient 0.154*** 0.234*** 0.634*** 0.869*** 0.844*** R2 0.857 0.755 0.712

Notes: *p-value < 0.05; **p-value < 0.01; ***p-value < 0.001

Segment 2 (N = 87) -0.245* 0.425*** -0.204* 0.746*** 0.804*** 0.250 0.556 0.646

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trust that consumers place in their CSA (path coefficient = 0.634). The quality of the structural model related to this segment is satisfactory, with R2 values greater than 0.7. The smallest segment values only identity proximity (path coefficient = 0.425). The negative sign of the path coefficients linking process and relational proximity to trust may reveal that consumers have more trust if they share the company’s identity values, but less if the process and relational proximity dimensions are present.

3.3

Local Partition and Global Partition

By comparing the sizes of both partitions, in Table 3, we notice that segments 1 and 2 of each partition group have the same observations. Moreover, the Adjusted Rand Indices (ARI) and Normalized Mutual Information (NMI) are satisfactory, and thus the two partitions are close (ARI = 0.32, NMI = 0.23). From Table 3, it can also be seen that the third segment obtained in the global partition is included into the first segment of the local partition. The average R2 of each segment of the local partition is greater than that obtained with the global partition (segment 1: R2local1 = 0:775 vs R2global1 = 0:754; segment 2: R2local2 = 0:484 vs R2global2 = 0:401). Furthermore, we achieve a larger value for the local partition by computing the average of the R2 weighted by the number of observations on all clusters of each partition (weighted R2local = 0:694 vs weighted R2global = 0:685 ). Therefore, the local partition that groups segment 3 into segment 1 performs better. Thus, this tends to confirm our hypothesis that this third cluster is probably an artifact. The strategy of performing a partition on a subpart of the structural model improves both the quality of the segmentation and its interpretability. To conclude, local partitioning aims at bringing a prior knowledge on the source of the heterogeneity by imposing constraints on the structure for the determination of the path coefficients. These constraints consist in imposing some of the path coefficients to be common for all segments. The underlying constraints aim at (1) improving the quality of the partition and (2) facilitating the interpretation of the clusters obtained. Future works are needed to directly take into account this a priori knowledge to be integrated as constraints within the FIMIX-PLS clustering criterion.

Table 3 Cross-tabulation of the size of the two partitions Global partition Local partition Segment 1 Segment 2 Total

Segment 1 177 24 201

Segment 2 21 59 80

Segment 3 30 4 34

Total 228 87 315

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References Becker J-M, Rai A, Ringle CM, Völckner F (2013) Discovering unobserved heterogeneity in structural equation models to avert validity threats. MIS Q 37(3):665–694 Bergadaà M, Del Bucchia C (2009) La recherche de proximité par le client dans le secteur de la grande consommation alimentaire. Revue Management et Avenir 21:121–135 Dufeu I, Ferrandi J-M (2013) Les ressorts de l’engagement des consommateurs dans une forme particulière de consommation collaborative: les AMAP. Décis Market 72:157–178 Esposito Vinzi V, Trinchera L, Squillacciotti S, Tenenhaus M (2008) REBUS-PLS: a responsebased procedure for detecting unit segments in PLS path modelling. Appl Stoch Model Bus Ind 24(5):439–458 Fordellone M, Vichi M (2020) Finding groups in structural equation modeling through the partial least squares algorithm. Comput Stat Data Anal 147:106957 Hahn C, Johnson MD, Herrmann A, Huber F (2002) Capturing customer heterogeneity using a finite mixture PLS approach. Schmalenbach Bus Rev 54(3):243–269 Ringle CM, Sarstedt M, Mooi EA (2010) Response-based segmentation using finite mixture partial least squares. Theoretical foundations and an application to American customer satisfaction index data. In: Stahlbock R, Crone S, Lessmann S (eds) Data mining. Annals of information systems, vol 8. Springer, Boston, pp 19–49 Ringle CM, Sarstedt M, Schlittgen R (2014) Genetic algorithm segmentation in partial least squares structural equation modeling. OR Spectr 36(1):251–276 Sarstedt M, Becker J-M, Ringle CM, Schwaiger M (2011) Uncovering and treating unobserved heterogeneity with FIMIX-PLS: which model selection criterion provides an appropriate number of segments? Schmalenbach Bus Rev 63(1):34–62 Sarstedt M, Hair JF, Pick M, Liengaard BD, Radomir L, Ringle CM (2022) Progress in partial least squares structural equation modeling use in marketing research in the last decade. Psychol Mark 39(5):1035–1064 Schlittgen R, Ringle CM, Sarstedt M, Becker J-M (2016) Segmentation of PLS path models by iterative reweighted regressions. J Bus Res 69(10):4583–4592

Partial Least Squares Structural Equation Modeling-Based Discrete Choice Modeling: An Illustration in Modeling Hospital Choice with Latent Class Segmentation Andreas Fischer, Marcel Lichters, and Siegfried P. Gudergan

1 Introduction Understanding and predicting individual choices—such as those between different means of transport, hospitals, or retailers—is extremely important in business research (Louviere et al. 2008). One commonly used method to explain and predict consumer choices is discrete choice modeling (DCM), first introduced by Luce and Tukey (1964), formalized by McFadden (1974), and introduced to marketing as choice-based conjoint (CBC) analysis by Louviere and Woodworth (1983). This method allows researchers to estimate the relative likelihood of choosing one option from a set of alternatives. This allows for estimating the utility values (i.e., preference of an attribute level over others, usually zero-centered within each attribute) and determining every attribute’s relative importance weight (i.e., the importance of every attribute as a whole in the consumer’s decision process) (Train 2009).

A. Fischer (✉) Institute of Human Resource Management and Organizations, Hamburg University of Technology, Hamburg, Germany e-mail: andreas.fi[email protected] M. Lichters Faculty of Economics and Business Administration, Chemnitz University of Technology, Chemnitz, Germany e-mail: [email protected] S. P. Gudergan College of Business, Law & Governance, James Cook University, Douglas, QLD, Australia Department of Global Business and Trade, Vienna University of Economics and Business, Vienna, Austria Aalto University School of Business, Aalto University, Helsinki, Finland e-mail: [email protected] © Springer Nature Switzerland AG 2023 L. Radomir et al. (eds.), State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM), Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-031-34589-0_4

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However, since DCM provides results on an aggregated data level, researchers often use the latent class analysis (LCA) introduced by Greene and Hensher (2003) or hierarchical Bayes analysis (Lenk et al. 1996) to uncover distinct segments of individuals in the dataset. As an advantageous alternative, Hair et al. (2019a) suggest using partial least squares structural equation modeling (PLS-SEM) (Hair et al. 2016a, 2019b) for the estimation of individuals’ preference functions drawing on discrete choice experiment (DCE) data. This method permits estimating the coefficients and the importance of both the attribute levels but also the entire attributes. In this research, we substantiate that LCA using PLS-SEM produces similar results compared to those generated using conventional DCM. An advantage of using PLS-SEM draws on its capability to reveal segments in consideration of the relationships between attributes as a whole (rather than attribute levels) and the choice variable. Thereby, the segmentation approach allows for uncovering segments with segment-specific differences related to the entire attribute rather than certain attribute levels when using conventional DCM. Specifying segments based on whole attributes is beneficial for describing and characterizing segments, interpreting results, and drawing conclusions.

2 Methodology Our illustrative application of PLS-SEM in LCA utilizes DCE data from the healthcare sector. The model focuses on the choice of patience between different hospitals in Germany. Schuldt et al. (2017) estimated patient choices concerning hospitals, using a sample of 590 randomly selected participants in three different German cities of the federal state Saxony-Anhalt, which responded to the DCE study in a “paper-and-pen” questionnaire. Within this study, each participant provided answers to eight choice tasks, comprising two options each. Four attributes described each option (distance to hospital, information, number of treatments, and complication rate). Each of these attributes had two levels (distance 1 km or 20 km to hospital, high or low level of information for treatment, high or low number of treatments per year, high or low rate of complications). The authors used Sawtooth Software Lighthouse Studio Sawtooth Software (2019) to create individualized choice designs according to a balanced overlap strategy (Johnson et al. 2013; Orme and Chrzan 2017).

3 Results For the DCM analysis, we revert to the results presented by Schuldt et al. (2017). These authors used a mixed effect logit model with a random effects intercept to estimate attribute level utilities and the relative importance of each attribute. The PLS-SEM results reported here draw on the use of SmartPLS 3 (Ringle et al. 2015).

Partial Least Squares Structural Equation Modeling-Based Discrete. . .

25

60% 50%

45%

48%

40% 30% 20%

23% 24% 19% 18% 13%

10%

10% 0%

Distance

Information

Number of Treatments

Schuldt et al. (2017)

Complication Rate

PLS

Fig. 1 Comparison of importance weights Table 1 Part worth utilities and segment sizes PLS-SEM Part worth utilities

Attribute Distance Information on treatment Number of treatments

Complication rate Segment size

Attribute level 1 km 20 km Brief Detailed More than 100 times Less than 50 times Low High

1

2

3

0.054 -0.054 0.151 -0.151 0.127

0.182 -0.182 0.161 -0.161 0.254

0.019 -0.019 0.642 -0.642 0.106

4 0.00 0.00 0.00 0.00 0.00

-0.127

-0.254

-0.106

0.00

0.659 -0.659 46.8%

0.227 -0.227 36.4%

0.200 -0.200 14.1%

1.00 -1.00 2.7%

Figure 1 shows both the importance weights of our estimates and those of the estimates of Schuldt et al. (2017). The differences are very small and play no role in the interpretation of results. The complication rate is the most important attribute, followed by information and the number of treatments, and then distance. A LCA allows for uncovering distinct segments of individuals with different preferences. Based on the latent variable scores we run a LCA by using the statistical software R (R Core Team 2019) and the flexmix package (Grün and Leisch 2007). Table 1 shows the results of the four-segment solution, Table 2 presents the results from Schuldt et al. (2017), which rely on LCA model proposed by DeSarbo et al. (1995) and implemented in Sawtooth Software Lighthouse Studio (Sawtooth Software 2019). In contrast to the traditional LCA, our method reveals segments at the level of path coefficients in the PLS-SEM and the level of attributes in the DCM.

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Table 2 Part worth utilities and segment sizes (Schuldt et al. 2017) Part worth utilities

Attribute Distance

Attribute level 1 km 20 km Brief Detailed More than 100 times Less than 50 times Low High

Information on treatment Number of treatments

Complication rate Segment size

1

2 -0.084 0.084 0.913 -0.913 0.466

3

4

0.511 -0.511 1.173 -1.173 0.906

1.103 -1.103 0.882 -0.882 2.505

1.170 -1.170 0.218 -0.218 -0.173

-0.906

-0.466

-2.505

0.173

3.103 -3.103 60.1%

0.616 -0.616 20.3%

1.885 -1.885 11.0%

0.263 -0.263 8.7%

66%

70% 60%

54%

50% 40% 30%

21%

20% 10%

9%

15%

16%

13%

5%

0% Distance

Information Schuldt et al. (2017)

Number of Treatments

Complication rate

PLS

Fig. 2 Importance weights Segment 1

Figure 2 shows that the estimated relative importance scores for the largest segment are almost identical when comparing both methods. The complication rate is the most important attribute, after that information on treatment, then the number of treatments, and subsequently distance. The differences between the two methods play again no role in the interpretation. The results for the other segments are, however, different. While the coefficients have consistent signs, they differ in magnitude. The segment sizes are also different, but with both methods the largest two segments cover more than 80% of the individuals. A closer look at the smallest segment—based on PLS-SEM estimation—offers, however, interesting insights. For each respondent, in this segment, each individual choice set throughout the DCM study presented one option with a high and a low complication rate alternative and the respondents choose systematically the alternative with the lower complication rate. Thus, our PLS segmentation successfully

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Table 3 Fit indices for a one- to four-segment solution Criteria AIC BIC EIC

Number of segments 1 2 10559.642 9932.747 10595.405 10011.427 Na 0.815

3 -7810.799 -7689.203 0.793

4 -7982.517 -7818.005 0.781

identified decision-makers with non-compensatory decision-making behavior. Schuldt et al. (2017) did not reveal this segment. To determine the optimal number of segments, Table 3 shows the Akaike information criterion (AIC; Akaike 1974), Bayesian information criterion (BIC; Schwarz 1978), and entropic information criterion (EIC; Ramaswamy et al. 1993) for a one- to a four-segment solution obtained by PLS-SEM and flexmix. In line with the original study, also our segmentation method would suggest four distinct segments of respondents. For the selection of the number of segments, a deeper understanding of the information criteria is, however, important (Oliveira-Brochado and Vitorino Martins 2014). Information criteria are not always useful to determine the optimal numbers of segments, because they don’t take into account, how well separated the segments are (Hair et al. 2016b). Further research is needed to understand the performance of the information criteria in the use of PLS-SEM in DCM.

4 Discussion With this chapter, we substantiate the suitability of PLS-SEM to estimate DCMs drawing on DCE data. The DCM and PLS-SEM results are comparable to other estimation methods; in this case, a multilevel mixed-effects logit model and a LCA, drawing on Sawtooth Software Lighthouse Studio. Using PLS-SEM is promising, because it reduces computational runtimes as compared to hierarchical Bayes estimation, while at the same time, it reduces complexity by focusing on the attribute and not on the attribute level. Furthermore, the use of PLS-SEM enables the researcher to model latent benefit dimensions, by combining multiple attributes into a higher-order construct, when appropriate. More importantly, we show that PLS-SEM in combination with the LCA permits to uncover meaningful and distinct segments in the DCE data and allows uncovering of unobserved heterogeneity. Unobserved heterogeneity is a validity threat in statistical analysis (Becker et al. 2013). The estimation of DCM with PLS-SEM enables the researcher to conduct respondent-specific analysis (e.g., calculating individual R2 values to uncover individual choice behavior) to develop a deeper understanding of the choices. These findings are important on a practical level, as they enable companies, for example, to target individuals belonging to different segments. On a theoretical level, they enable researchers to understand more about the choice process

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(non-compensatory decision-making) and the advantages and disadvantages of different estimation methods. Nonetheless, some limitations pertain to the results that we are presenting here. First, we need to further analyze the differences between traditional LCA and our method, and we need to more fully understand, which data structures are better uncovered with our or the traditional method if the data structure conditions the suitability of a particular method. For instance, does the decisionmaking approach (compensatory versus non-compensatory) affect the suitability of employing PLS-SEM for the estimation of choices? Acknowledgments We also would like to acknowledge the contributions provided by Professor Christian M. Ringle.

References Akaike H (1974) A new look at the statistical model identification. In: Parzen E, Tanabe EK, Kitagawa G (eds) Selected papers of Hirotugu Akaike. Springer series in statistics. Springer, New York, pp 215–222 Becker J-M, Rai A, Ringle CM, Völckner F (2013) Discovering unobserved heterogeneity in structural equation models to avert validity threats. MIS Q 37(3):665–694 DeSarbo WS, Ramaswamy V, Cohen SH (1995) Market segmentation with choice-based conjoint analysis. Mark Lett 6(2):137–147 Greene WH, Hensher DA (2003) A latent class model for discrete choice analysis: contrasts with mixed logit. Transp Res B Methodol 37(8):681–698 Grün B, Leisch F (2007) Fitting finite mixtures of generalized linear regressions in R. Comput Stat Data Anal 51(11):5247–5252 Hair JF, Hult GTM, Ringle CM, Sarstedt M (2016a) A primer on partial least squares structural equation modeling (PLS-SEM). Sage, Los Angeles Hair JF, Sarstedt M, Matthews LM, Ringle CM (2016b) Identifying and treating unobserved heterogeneity with FIMIX-PLS: part I–method. Eur Bus Rev 28(1):63–76 Hair JF, Ringle CM, Gudergan SP, Fischer A, Nitzl C, Menictas C (2019a) Partial least squares structural equation modeling-based discrete choice modeling: an illustration in modeling retailer choice. Bus Res 12(1):115–142 Hair JF, Risher JJ, Sarstedt M, Ringle CM (2019b) When to use and how to report the results of PLS-SEM. Eur Bus Rev 31(1):2–24 Johnson FR, Lancsar E, Marshall D, Kilambi V, Mühlbacher A, Regier DA et al (2013) Constructing experimental designs for discrete-choice experiments: report of the ISPOR conjoint analysis experimental design good research practices task force. Value Health 16(1):3–13 Lenk PJ, DeSarbo WS, Green PE, Young MR (1996) Hierarchical Bayes conjoint analysis: recovery of partworth heterogeneity from reduced experimental designs. Mark Sci 15(2): 173–191 Louviere JJ, Woodworth G (1983) Design and analysis of simulated consumer choice or allocation experiments: an approach based on aggregate data. J Mark Res 20(4):350–367 Louviere JJ, Street D, Burgess L, Wasi N, Islam T, Marley AA (2008) Modeling the choices of individual decision-makers by combining efficient choice experiment designs with extra preference information. J Choice Model 1(1):128–164 Luce RD, Tukey JW (1964) Simultaneous conjoint measurement: a new type of fundamental measurement. J Math Psychol 1(1):1–27 McFadden D (1974) Conditional logit analysis of qualitative choice behavior. In: Zarembka P (ed) Frontiers in econometrics. Academic Press, New York, pp 105–139

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Oliveira-Brochado A, Vitorino Martins F (2014) Identifying small market segments with mixture regression models. Int J Latest Trends Fin Econ Sci 4(4):812–820 Orme BK, Chrzan K (2017) Becoming an expert in conjoint analysis: choice modelling for Pros: Sawtooth Software R Core Team (2019) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna Ramaswamy V, DeSarbo WS, Reibstein DJ, Robinson WT (1993) An empirical pooling approach for estimating marketing mix elasticities with PIMS data. Mark Sci 12(1):103–124 Ringle CM, Wende S, Becker J-M (2015) SmartPLS 3. SmartPLS GmbH, Boenningstedt Sawtooth Software (2019) Lighthouse Studio 9 (Version 9.6.1). Sequim, WA: Sawtooth Software, Inc. https://www.sawtoothsoftware.com/products/online-surveys Accessed 17 Jan 2022 Schuldt J, Doktor A, Lichters M, Vogt B, Robra B-P (2017) Insurees’ preferences in hospital choice—a population-based study. Health Policy 121(10):1040–1046 Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6(2):461–464 Train KE (2009) Discrete choice methods with simulation. Cambridge University Press, London

The Use of PLS-SEM in Engineering: A Tool to Apply the Design Science Ari Melo-Mariano and Ana Bárbara Plá

1 Introduction The growth in using PLS-SEM in the social sciences is a fact. Hair et al. (2019a) explain that since 2015 there has been a significant advance in the number of published papers in the social sciences area, which evidences the growth of the method’s importance. However, PLS-SEM use has not been limited to the social sciences; it has demonstrated its use’s versatility in distinct areas such as Knowledge Management (Cepeda-Carrion et al. 2019), Hospitality and Tourism (Usakli and Kucukergin 2018), Information Systems Research (e.g., Hair et al. 2017), Psychology (e.g., Willaby et al. 2015), Medicine (e.g., Menni et al. 2018), and Engineering (Aibinu and Al-Lawati 2010; Durdyev et al. 2018). The adaptability of the PLS-SEM method to different contexts demonstrates its flexibility in different knowledge areas, positioning it as a “border” tool once it can consolidate different science fields, becoming applicable in contexts where the limits between one science field and another are not well known, as Industry 4.0, or even in the large data volume, as Big Data. According to Schwab (2019), to think about Industry 4.0 is to comprehend the increasing harmonization and integration in the different areas’ discoveries making the fourth industrial revolution unique, the fruit of innovation that resulted from these technologies collaborations from distinct areas that are already real and are transforming the society.

A. Melo-Mariano (✉) · A. B. Plá University of Brasília, Production Engineering Department, DataLab, University Campus Darcy Ribeiro, Brasília-DF, Brazil e-mail: [email protected] © Springer Nature Switzerland AG 2023 L. Radomir et al. (eds.), State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM), Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-031-34589-0_5

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Industry 4.0 has been a significant engineering concern, mainly as regards the interconnection with other knowledge areas and the need for a greater comprehension of the production context. The same interaction inquiries between the areas occur in the data context currently receiving the title of Big Data. For example, Shmueli (2017) explains that one of the Big Data challenges is in the integration of different data sources (with different volumes, variety, and velocity), many times unknown for the Engineering, that is used to work with inanimate data, which can generate erroneous analyses when not considering behavioral aspects. These Engineering challenges favor the search for models and methodologies that dialogue well with different knowledge areas, maximizing results for the research in the area. Engineering has been using a few decades of distinguished research approaches compared to the natural sciences, the Design Science Research. Dresch et al. (2015) explain that traditional scientific research presents descriptions and explanations about existing phenomena, but Engineering is not limited to existing phenomena and systems. It also includes the project and study of phenomena and objects that do not exist yet but will be created by the engineer for the common good, which shows the need for a distinguished research approach. Thus, Engineering research assumes a position based on an artifact that should be developed, validated, and applied, generating an impact on society. The new challenges faced by Engineering and its research assumptions based on design approximate the knowledge area to the current understanding of PLS-SEM from the composites’ perspective (Henseler 2017; Schuberth et al. 2018), by adopting the view of artificial sciences, advocated by Simon (1978), separating what is natural from what is artificial. This research seeks to answer the question: How can the PLS-SEM be inserted in the scientific research context in Engineering? The existing Engineering context is marked by Industry 4.0 and a significant data background (known as the Big Data era), which sometimes favors exploratory research, sometimes confirmatory research, with new constructs, experimental models, or almost experimental, and mainly the scarcity of consolidated literature. These premises can be well developed in the PLS-SEM context. This way is possible to comprehend how the PLS-SEM can be a useful Engineering tool that can collaborate with a better understanding and management of the borders between the different knowledge areas and kinds, being a connection point and favoring in a certain way the areas in which Engineering will be connected with to overcome the new challenges. Thus, this chapter aims to realize the methodological connections between the PLS-SEM and Engineering through Design Science. This chapter is divided into one chapter of background, followed by methodology, results, and discussions, and finally, the conclusions and implications for theory and practice.

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2 Background 2.1

Design Science

According to Dresch et al. (2015), the concept of Design Science (or project science) was born from observations of the Nobel Prize winner Herbert Simon, from what he interprets as Artificial Science, a book launched in 1969. In this work, he detaches what is natural from what is artificial regarding the research object. In this new context, the artificial is everything that is idealized/designed by man, as machines, organizations, and economy, and in this way, the research engages in the study of the creation and projection of new artifacts, or yet, to support the actual problem solving that is not sustained by the paradigm of the natural sciences (Dresch et al. 2015). Aken (2004) describes the design science concept, which includes engineering, medical sciences, and modern psychotherapy, and has as its research mission: develop valid and reliable knowledge to design solutions to problems. It is emphasized that the goal is not the action itself but the knowledge that will be generated and could be used to design new solutions, and then, it will be made an action from that point. Design Science is a scientific paradigm that emerges to help researchers with a goal, a prescription, and, consequently, the creation of knowledge about how to project (Cauchick et al. 2019). It is an approach alternative that promotes investigations through artifacts that will contribute to the creation of new systems or in the improvement of the existing ones, aiming for better results in the actual problem solving, differentiating from the natural sciences that aim the study of known phenomena and problems, and abandoning the concept of new. This way, artificial science sees the actions involving man as artifacts, a connection between the internal environment (artifact organization) and external environment (operating conditions), projected to answer a particular purpose that should be validated and given a satisfactory solution. This construction character favors Engineering research, mainly artifacts in the solution creation context. This way to think about models created as ingredients of a construct is to think in artifacts and, at the same time, in composite models of free constructions, typical of Engineering use. Design Science as a research approach operationalizes its stages in a context called Design Science Research, which is essential to comprehend the difference in the use of both terminologies (one as a method and the other as steps to the method).

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PLS-SEM (Partial Least Square-Structured Equation Model)

The technology increment and the need to improve the understanding of data to decision-making has caused a search for methods to project and deliver relevant results. In this context emerges the partial least square structural equation modeling—PLS-SEM, a union of two critical areas, the econometrics, utilizing predictive models, and the psychometrics, using behavioral models (Hair et al. 2019a). Structural equation modeling (SEM) is a technique that allows the analysis of multiple relations between factors that are not observable and are challenging to measure through variables that function as indicators. Equations are used to evaluate the relationship between these variables using a statistical toolset. The variables represent constructs, which are the unobservable factors that will be analyzed. The SEM then enables modeling and estimating parameters of the relation between the constructs. Henseler (2017) defines construct as “constructions that are theoretically justified.” In other words, it is a term that describes an event that generates theoretical interest. Usually, the constructs are formed by measures made by observation or indicators that can represent the concepts. The SEM realized through the partial least squares (PLS) method becomes a technique that can estimate the cause-and-effect relationship between the constructs (Hair et al. 2012). The PLS cannot represent visual concepts in complex models. However, according to Shmueli et al. (2016), the PLS can “produce parameter estimates of complex models without many of the distributional and other constraints of traditional parametric methods.” The PLS-SEM is more attractive primarily when the research goal focuses on predicting and explaining the variance of the primary constructs through the different explanation constructs (Hair et al. 2012). Usually, the constructs that study behavior are treated via common factors, but in recent years, composite models have been discovered. Behavioral constructs are, generally, latent variables that can be understood and ontological entities, that is, of the science of being, as people’s attributes. However, the design constructs are created as the fruit of thinking (composite constructions) (Henseler 2017), the same as the artifact in Design Science. Similar to the Design Science concepts, the PLS-SEM has its models anchored in artifacts that should be identified in the literature, developed, validated, and applied, delivering valuable results.

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3 Methodology This was exploratory research with a qualitative-quantitative approach. It was conducted in three steps: first, describing the principles of Design Science and PLS-SEM; second, presenting the principles of Design Science and PLS-SEM to 15 professionals and professors from different engineering fields to opine about the principles’ adhesion; and third, conducting a systematic review through an adaptation of the theory of consolidated meta-analytical approach from Mariano and Rocha (2017). In step 1, many principles were searched in article databases, getting to books (that had to be acquired by the researchers) that explained the concepts of Design Science and PLS-SEM in a more didactic way, which was very important to step 2, since the experts and professors evaluations depended of the understanding of both approaches. In step 2, professionals and professors from different Engineering areas were searched in person and sometimes online. The information contained in the Background section of this article and the Design Science stages was presented to them, named Design Science Research. Some of the interviewees requested additional material, such as Henseler’s articles. These results showed opinions about the possible connections between Design Science and PLS-SEM in a text presented in the results section. Finally, step 3 is through a systematic review of Web of Science (WoS) and Scopus databases. The review was made using the strings presented by Khan et al. (2019) (“partial least squares structural equation modeling” or “partial least squares structural equation modeling” or “partial least squares path modeling” or “partial least squares path modeling” or “PLS path modeling” or “PLS path modeling” or “path model with latent variables” or “PLS-SEM” or “PLS path model” or “PLSPM” or “SmartPLS” or “PLSgraph” or “PLS-Graph” or “XLSTAT” or “semPLS” or “matrixpls” or “ADANCO” or “PLSgui” or “PLS-GUI” or “LVPLS”). All the papers in Engineering areas in Scopus and WoS were filled with the search endings referring to PLS-SEM to understand the evolution and use of the method in Engineering after that. They analyzed the authors through the number of quotes to have a cluster of the most influential authors (Fig. 1). The four clusters were analyzed to understand the ecosystem in which all the use of PLS-SEM in Engineering was organized. Finally, the union between Design Science and the terms that refer to PLS-SEM was sought to understand what had already been done from this perspective (although it was presented last, it was one of the first steps in this chapter, which motivated the work due to the insufficient number of indexed articles). To analyze the quote network between authors, it was used the VOS viewer. The VOS viewer is free software available to create and visualize bibliographic maps. According to Van Eck and Waltman (2009), the VOS viewer allows the bibliographic map to be shown in different ways, reinforcing an aspect that would prefer. This allows analysis with more options. In general, it indicates a large amount of data uses, but its use is also beneficial in cases of little data.

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Identification of studies via databases and registers

Included

Screening

Identification

Records removed before screening:

Records identified from*: Databases Wos (n =13,678,951) Databases Scopus (n =8,431,638)

("Partial least squares structural equation modeling" OR "Partial least squares structural equation modeling" OR "Partial least squares path modeling" OR "Partial least squares path modeling" OR "PLS path modeling" OR "PLS path modeling" OR "path model with latent variables" OR "PLS-SEM" OR "PLS path model" OR "PLS-PM" OR "SmartPLS" OR "PLSgraph" OR "PLS-Graph" OR "XLSTAT" OR "semPLS" OR "matrixpls" OR "ADANCO" OR "PLSgui" OR "PLS-GUI" OR "LVPLS" AND Engineering

Records removed WoS(n =8,431304) Records removed Scopus(n =13,678,510)

Reports sought for retrieval Databases Wos (n =334) Databases Scopus (n =441)

Records removed Out of Co-citaton and Coupling

4 clusters WoS (n = 16) 4 Clusters Scopus (n =20)

Fig. 1 Research PRISMA process adapted

4 Results and Discussion 4.1

Similarities Between Design Science and PLS-SEM (Partial Least Square-Structured Equation Model)

A first impression is that Design Science is defined through an organization from the bottom up. This organization begins with artifacts following constructs, models, methods, instances, and design propositions (Cauchick et al. 2019): • Construct: Concepts used to describe problems or specify solutions. • Models: Set of elements and relations that represent the general structure of reality. • Method: Set of logical steps needed to effectuate a specific activity. • Instances: Execution of the artifacts in their natural environment, highlighting the viability and efficiency of the artifacts. • Design proposition: Technological rules or project rules are considered theoretical contributions of Design Science.

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The research with the Design Science approach is oriented to problem-solving, being of practical nature. With the creation of artifacts support, these solutions aim to benefit society increasingly. This organization resembles the modeling phases of the PLS-SEM. A second impression about Design Science is that the solution it develops doesn’t have to be a great solution but a satisfactory one. In other words, it means that the main goal is to solve the problem, not necessarily that it is made in the better way it can, which could have impediments to its application in the real world. In the same way, the PLS-SEM is flexible and can present different solutions in different contexts of the research, such as confirmatory, exploratory, predictive, explanatory, and descriptive (Henseler 2018). According to Dresch et al. (2015), the result is considered satisfactory to Design Science when there is a consensus that there is a progress in the solution achieved with the new artifact, in comparison with previous solutions, when exists solutions exist. Another factor to be seen is related to Design Science Research, the stages of Design Science. From the comprehension of the Design Science concept, which is a paradigm, it is possible to understand Design Science Research, a research methodology that applies Design Science. Therefore, it is a suggested structure when one wishes to prescribe solutions or to develop and/or analyze artifacts (Aken 2004). In Fig. 2, it is possible to verify the steps needed to execute the Design Science Research. The methodology starts with problem identification, which consists in identifying, structuring, and comprehending the situation which it is intended to present a solution. Problem understanding is essential to creating a valuable and proper solution. Then it must be done a systematic review of the literature, to justifying all the research that will be done, in other words, to ground the ideas that will be presented in the paper. The next step is to identify options for artifacts to be developed to solve the problem previously detected. This step is usually creative and subjective. After analyzing the options, a proper artifact must be proposed. Finally, with the artifact defined, the project to develop the artifact must be created. In Design Science Research, the researcher also has the role of an artifact designer. According to the interview, both steps occur accordingly to what occurs in the PLS-SEM because the relations between the model’s variables must be guided by the scientific literature or through specialists that can ensure the relation (Hair et al. 2019a). The next step consists of the execution of the project that will result in the development or construction of the artifact chosen in its functional state. The researcher constructs the internal environment of the artifact through algorithms, graphic models, mockups, and other available tools. In order to validate the artifact, the next step is to evaluate the artifact. After defining the requirements that should be validated and which is the desirable performance of the artifact, the artifact should be evaluated to see if it meets what is expected of it. Finally, the explicit learnings must be executed after the evaluation

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Fig. 2 DSR Methodology. Source: Cauchick et al. (2019)

since it is through it that the improvements needed can be perceived, avoiding problems in the future use of the artifact. It was noticed that this same process of validation occurs inside the PLS-SEM, through the reliability (individual, internal), validity (convergent, discriminating), and multicollinearity, to all the A mode and B mode, with the validity in a construct level and indicator level (Hair et al. 2019a, b). To start the conclusion, all the things that occurred during the research should be reported. After that, the generalization to a problem class should be made. It must be said that the technological rule quoted by Aken (2004) is defined as “a general knowledge that connects an artifact with a result desired in an application field.” He says that a technological rule is a primary product of Design Science, which is the same as the generalization of a problem class. In other words, this stage is about the fact that the solution found is not necessarily just a specific solution to a problem but the knowledge that could be applied to a problem class similar to the problem faced. Lastly, the results obtained must be reported. Worth it to say that the paper must be accessible and must have a description of what has been done and of all the results

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found, including problems and limitations faced, since this information will help new research and new pieces of knowledge. The comparison analysis noticed that besides the creation of the artifact as a result of the Design Science Research to solve problems, there is also the generation of prescriptive knowledge that meets the same goal of the artifact lined up with the PLS-SEM context. This way, it can be perceived that both contexts are connected through its application stages, making the PLS-SEM a method that incorporates the Design Science principles. Once the comparisons were finished, a vision was made of the PLS-SEM use over the years in Engineering and its evolution of publications and quotes between authors in Web of Science and Scopus, finishing with the use of the PLS-SEM in Engineering through Design Science. The results demonstrate that the oldest record is the Web of Science database in the Engineering field, using PLS-SEM in the article “Partial Least-Squares Path Modeling with Latent-Variables” by Gerlach et al. (1979). In this work, the authors explain that the partial least squares model is a solution to evaluate complex models, ensuring an evaluation through many sources. This way, it can be perceived that the PLS-SEM can offer solutions to complex models, being very useful to the current challenges, like Industry 4.0 or Big Data, because in both situations, complex models are treated, making the PLS-SEM become a border tool, consolidating different kinds of knowledge. The oldest paper about PLS-SEM + Engineering in the Scopus database is “Transnational terrorism: Prospectus for a causal modeling approach” by Hopple (1982), where the author proposes a causal model to comprehend the causes of transnational terrorism. The author explains that the difficulty of this type of model needs a softer modeling, ensuring flexibility to its calculus. Furthermore, the creation of models is a human artifact, considered an artifact or artificial object. These concepts agglutinate the proximity between Engineering through Design Science and the PLS-SEM. The most quoted record in the Web of Science database in the Engineering field was the paper “Using PLS path modeling in new technology research: updated guidelines” (490 quotes) by Henseler et al. (2016), where authors ratify the increasing use of the tool in the technology field. In the paper, the authors discuss the recent developments in the structural equations via variance area and the possibility of working with composites and factors, being characterized as a formidable statistical tool. It can be perceived that working with composites shows up again as a preponderant factor in the use of techniques in Engineering. In the Scopus database, the most quoted record is the book Modelling Transport (1524 quotes) by de Dios Ortúzar and Willumsen (2011), where the authors present different kinds of modeling to private and public sectors, providing depth in each topic, pedagogically, with the discussion of the role of theory, data, model specification, estimative, validation, and application. This way, it can be noticed that the models always followed different Engineering areas, translating the recreation of the

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Fig. 3 Publication of articles per year in Scopus and WOS

real in models (artificial design) comprehensible and feasible to be manipulated on a smaller scale, ensuring simulations and observations. The publications with the use of PLS-SEM in both databases present growth in Engineering, getting to 73 in Web of Science in 2019 and 68 in Scopus (Fig. 3). The Web of Science database offers the distinction between the different areas of Engineering. This way was possible to count that from the 334 records, 114 papers belonged to Industrial Engineering, 80 to Electrical Engineering, 51 to Civil Engineering, and 45 to Environmental Engineering, and the rest was divided between the other areas. This way, it can be noticed that the use of PLS, at least in the WoS, is diffuse between the different areas of Engineering. Therefore, it generated two network maps to find the relationship between authors and discover the central quote core (Fig. 4). The first cluster is revealed by the papers of Ooi and followers, with the approach of the effects of new technologies and the way of accepting and using via structural equations. Gunasekaran forms a second cluster, where evaluated questions related to manufacturing, such as Lean and green product development. Fifty percent of the author’s papers are made in Brazil, with the collaboration of Brazilian authors. The Chong nucleus approach papers focus on innovation in the supply chain. Finally, the Henseler kernel, in which composite-type structural equation models adhering to Design Science Theory is shown. The Henseler cluster seen in zoom also reveals the participation of Ringle presents in his paper “Gain more insight from your PLS-SEM results The importance-performance map analysis,” the IPMA tool, inside the SmartPLS software, transforming the results in a business application by revealing the variables or indicators that must be prioritized, ratifying once again the use of

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Fig. 4 Network of WoS quote relation. Source: Extracted from Vosviewer

Fig. 5 Network of Scopus quote relation. Source: Extracted from Vosviewer

PLS-SEM in Engineering, once the Design Science always defends final results of their studies, applicable. In the same way, an analysis of the network of authors about the quote dynamic in the Scopus database was made. It found four different nuclei (Fig. 5). It can be noticed that Wamba and Gunasekaran formed the nucleus one. Wamba approaches analysis via PLS-SEM to the business performance in different strategic contexts, such as Big Data analysis, senior management commitment to

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environmental issues, and quality dynamics. Gunasekaran repeats the work identified in WoS and additionally is a coauthor of the paper about PLS-SEM and Big Data. Lai brings studies related to China and mainly in the operations area. Muller works with the study of the supply chains, mainly studying providers. Bamgbade is shown in a cluster studying external and internal factors linked to sustainability in construction. Moreover, finally, Henseler, shows up with most of the papers found in WoS, but with the addition of one editorial about the use of PLS-SEM in the industrial ambit, published in Industrial Management and Data Systems (see Henseler 2016). Ringle and Sarstedt are shown together in articles related to the use of IPMA, just like in WoS. Both figures were made zoom on the name of Henseler because he is a seminal author when considering the PLS-SEM context since the composite perspective, the approach that is directly associated with the context of the artificial design present in the Design Science is used in Engineering. The same associations raised in this study were made before by Venable and Baskerville (2012), where the authors used Design Science Research to analyze the use of PLS-SEM, associating their stages, but in this occasion, the suggestion is the use of the PLS-SEM faces to operationalize the Design Science Research. In 2018 is already shown a study about a model to predict the use of services in mobile health to bring attendance to regions with few resources (Mburu and Oboko 2018). This model is oriented by Design Science and uses PLS-SEM to operationalize the steps of Design Science Research. This way, it can be perceived that the PLS-SEM is increasingly used in Engineering. The development of PLS-SEM and the new perspectives established by Henseler (Henseler 2017; Schuberth et al. 2018) was significant to establish a more extensive contact between PLS-SEM and its use in Engineering. Some studies have already begun to make associations between PLS-SEM and Design Science Research (Venable and Baskerville 2012) and the use of PLS-SEM to operationalize Design Science (Mburu and Oboko 2018). This way to call the attention to the use of PLS-SEM to the challenges faced by Engineering is to establish new possibilities of research in an interdisciplinary context such as Big Data and Industry 4.0, making the PLS-SEM a border tool.

5 Conclusion and Implications for Theory and Practice It can be noticed that the use of PLS-SEM in Engineering has been growing each year and that the propositions made by Henseler (Henseler 2017; Schuberth et al. 2018) about composite models approximate the PLS-SEM to the Design Science Research approach. This way, the PLS-SEM is a tool that can contribute in a relevant way to the interaction challenges between distinct areas, as in the Big Data context, computer sciences, information systems, software engineering, statistic, and mathematics in a more generalist way, or specific contexts, as philosophy, marketing, psychology and anthropology (in artificial intelligence development), and biology

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and medicine (bioinformatic), among others. In such a rich context of information for research modeling, a soft tool, such as PLS-SEM, will help in results that will converge in the different points of view of the different areas. In the same way, it can be noticed that to the Industry 4.0 context, the unification of considered complex approaches, in an isolated way, in research and application (Internet of things, cyber-physics systems, additive manufacture, among others), potentialized in the integration to create the fourth industrial revolution. This way, the PLS-SEM in Engineering, following the Design Science approach, can help create applied models that will help in this knowledge frontier, helping a better interaction between parts. This way, the research problem, which was how the PLS-SEM could be inserted in the scientific research context in Engineering, was solved. As a suggestion for future research, we advise a research PLS-SEM following the Design Science Research steps.

References Aibinu AA, Al-Lawati AM (2010) Using PLS-SEM technique to model construction organizations’ willingness to participate in e-bidding. Autom Constr 19(6):714–724 Aken JEV (2004) Management research based on the paradigm of the design sciences: the quest for field-tested and grounded technological rules. J Manag Stud 41(2):219–246 Cauchick P, Lacerda D, Abackerli A, Carvalho M, Costa S, Lima E et al (2019) Metodologia científica para engenharia. Elsevier, Brasil Cepeda-Carrion G, Cegarra-Navarro JG, Cillo V (2019) Tips to use partial least squares structural equation modelling (PLS-SEM) in knowledge management. J Knowl Manag 23(1):67–89 de Dios Ortúzar J, Willumsen LG (2011) Modelling transport, 4th edn. Wiley, New York Dresch A, Lacerda DP, Autunes Júnior JAV (2015) Design science research: método de pesquisa para avanço da ciência e tecnologia. Bookman, Sao Paolo Durdyev S, Zavadskas EK, Thurnell D, Banaitis A, Ihtiyar A (2018) Sustainable construction industry in Cambodia: awareness, drivers and barriers. Sustainability 10(2):392 Gerlach RW, Kowalski BR, Wold HO (1979) Partial least-squares path modelling with latent variables. Anal Chim Acta 112(4):417–421 Hair JF, Sarstedt M, Pieper TM, Ringle CM (2012) The use of partial least squares structural equation modeling in strategic management research: a review of past practices and recommendations for future applications. Long Range Plan 45(5-6):320–340 Hair J, Hollingsworth CL, Randolph AB, Chong AYL (2017) An updated and expanded assessment of PLS-SEM in information systems research. Ind Manag Data Syst 117(3):442–458 Hair JF, Hult GTM, Ringle CM, Sarstedt M, Castillo-Apraiz J, Cepeda Carrión G, Roldán JL (2019a) Manual de partial least squares structural equation modeling (PLS-SEM), Spanish edn. Sage, Barcelona Hair JF, Risher JJ, Sarstedt M, Ringle CM (2019b) When to use and how to report the results of PLS-SEM. Eur Bus Rev 31(1):2–24 Henseler J (2016) Guest editorial. Ind Manag Data Syst 116(9):1842–1848 Henseler J (2017) Bridging design and behavioral research with variance-based structural equation modeling. J Advert 46(1):178–192 Henseler J (2018) Partial least squares path modeling: Quo vadis? Qual Quan Int J Methodol 52(1): 1–8

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Henseler J, Hubona G, Ray PA (2016) Using PLS path modeling in new technology research: updated guidelines. Ind Manag Data Syst 116(1):2–20 Hopple GW (1982) Transnational terrorism: prospectus for a causal modeling approach. Stud Conf Terrorism 6(1-2):73–100 Khan GF, Sarstedt M, Shiau WL, Hair JF, Ringle CM, Fritze MP (2019) Methodological research on partial least squares structural equation modeling (PLS-SEM) an analysis based on social network approaches. Internet Res 29(3):407–429 Mariano AM, Rocha MS (2017) Revisão da Literatura: Apresentação de uma Abordagem Integradora. In: XXVI Congreso Internacional de la Academia Europea de Dirección y Economía de la Empresa (AEDEM), Reggio Calabria, Italy, pp 427–443 Mburu S, Oboko R (2018) A model for predicting utilization of mHealth interventions in low-resource settings: case of maternal and newborn care in Kenya. BMC Med Inform Decis Mak 18(1):67 Menni C, Lin C, Cecelja M, Mangino M, Matey-Hernandez ML, Keehn L et al (2018) Gut microbial diversity is associated with lower arterial stiffness in women. Eur Heart J 39(25): 2390–2397 Schuberth F, Henseler J, Dijkstra TK (2018) Confirmatory composite analysis. Front Psychol 9: 2541 Schwab K (2019) A quarta revolução industrial. Edipro, Sao Paolo Shmueli G (2017) Research dilemmas with behavioral big data. Big Data 5(2):98–119 Shmueli G, Ray S, Estrada JMV, Chatla S, B. (2016) The elephant in the room: predictive performance of PLS models. J Bus Res 69(10):4552–4564 Simon HA (1978) Rationality as process and as product of thought. Am Econ Rev 68:1–16 Usakli A, Kucukergin KG (2018) Using partial least squares structural equation modeling in hospitality and tourism: do researchers follow practical guidelines? Int J Contemp Hosp Manag 30(11):3462–3512 Van Eck N, Waltman L (2009) Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84(2):523–538 Venable J, Baskerville R (2012) Eating our own cooking: toward a more rigorous design science of research methods. Electron J Bus Res Methods 10(2):141–153 Willaby HW, Costa DS, Burns BD, MacCann C, Roberts RD (2015) Testing complex models with small sample sizes: a historical overview and empirical demonstration of what partial least squares (PLS) can offer differential psychology. Personal Individ Differ 84:73–78

Discovering Issues in Cross-Cultural Adaptation of Questionnaire Through PLS-SEM Analysis Fariha Reza and Huma Amir

1 Background Customers who belong to the bottom of the pyramid (BoP) are marginalized from the mainstream consumer market because of their financial constraints. Literature suggests they desire to bridge the gap between them and the relatively more affluent people by purchasing products that reflect a better social status (Srivastava et al. 2020; Yurdakul et al. 2017). One contemporary example of such aspirational products is smartphones penetrating the BoP segment (Prahalad 2019). While literature suggests that BoP customers purchase aspirational goods to avoid poverty-related shame, it is not fully understood whether such purchases increase healthy engagement with one’s environment and subjective well-being (Dahana et al. 2018). Furthermore, earlier research suggested that BoP customers sometimes sacrificed their immediate needs to save enough to purchase their aspirational products (Atkin et al. 2021). Patience in their everyday lives facilitated such sacrifices and did not let them despair in challenging circumstances. Not much Western marketing literature is available on patience; therefore, studying patience in conjunction with motivation for purchasing aspirational goods, healthy engagement with one’s circumstances, and the subjective well-being of BoP customers gave novel insights (Haybron 2016). Social identity theory provided the theoretical justification for studying motivation for buying aspirational products because of social comparisons that BoP customers made with those who were better off in status

F. Reza (✉) Institute of Business Management, Karachi, Sindh, Pakistan e-mail: [email protected] H. Amir Institute of Business Administration, Karachi, Sindh, Pakistan e-mail: [email protected] © Springer Nature Switzerland AG 2023 L. Radomir et al. (eds.), State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM), Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-031-34589-0_6

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(Jolliffe and Prydz 2021). On the other hand, self-determination theory (Ryan and Deci 2000) provided the theoretical background for studying the relationship between aspirational purchases and healthy engagement with one’s life and the subjective well-being of BoP customers (Jaikumar et al. 2018). Therefore, this research empirically tested the relationships between motivation to purchase a smartphone, intention to purchase a smartphone, self-determination, patience, subjective well-being, and attitude toward life of BoP customers.

2 Methodology 2.1

Population and Sampling

People who had, at times, made an occasional aspirational purchase, despite earning less than USD 8 per day, were the population of this research (United Nations Development Program [UNDP] 2008). Furthermore, such people were living in rented accommodations and were often unable to engage in socially relevant consumption practices, and consequently, they were facing negative social consequences of poverty (Yurdakul et al. 2017). A sample of 641 respondents was chosen through chain referral (Reza et al. 2021). Data was collected through a personally administered questionnaire.

2.2

Data Collection Tool and Method

Well-established scales were adopted to measure motivation to purchase a smartphone, intention to purchase a smartphone, self-determination, subjective well-being, patience, and attitude toward life of BoP customers (Reza et al. 2021). The scale items were translated from English to Urdu (Pakistan’s national language) to make them understandable to the targeted population, keeping in view its shallow linguistic proficiency (Alyami et al. 2021). During pilot testing, it was realized that the 7-point Likert scale response format contained indistinguishable choices for the respondents; therefore, the response format was reduced to 5-point scale.

2.3

Choice of SmartPLS for Data Analysis

Various software and applications are available to analyze quantitative data; among them, SPSS and SmartPLS are more commonly used in market research (Memon et al. 2021). Compared with SPSS PROCESS macro, PLS-SEM software becomes a better choice because it can test multiple dependency relationships altogether, whereas PROCESS macro tests each dependency relationship individually (Hair

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et al. 2019; Hayes et al. 2017; Sarstedt et al. 2022). Hence, multiple dependency relationships were tested in this research through PLS-SEM analysis through SmartPLS. In PLS-SEM analysis, measurement model fit is assessed through outer loadings, composite reliability, bootstrap confidence interval, convergent reliability, and discriminant validity (Sarstedt et al. 2019). After establishing the goodness of fit for the measurement model, the analysis proceeds to determine structural model fit through the coefficient of determination, effect size, Q-Square, and robustness. However, it should be noted that without determining the goodness of fit for a measurement model, a researcher should not test the dependency relationships between constructs that are shown in the structural model (Henseler et al. 2016).

3 Results and Discussion A reflective measurement model was constructed in SmartPLS that complied with theories of motivation, self-determination, and subjective well-being (Reza et al. 2021). After dropping weak indicators from the model, the composite reliability of the scales that measured motivation, intention to make aspirational purchases, patience, and subjective well-being improved (Henseler et al. 2012) as shown in Table 1. However, the low values of average variance extracted (AVE) depicted the low convergent validity of the scales (Hair et al. 2019), as shown in Table 2. Earlier research suggested that if the composite reliability of all the constructs is greater than 0.6, a researcher may proceed with structural model fit despite AVE being less than 0.5 (Ringle et al. 2015); therefore, researchers proceeded with the structural model fit analysis despite this limitation. The discriminant validity of the constructs was determined with the heterotraitmonotrait ratio (Hair et al. 2021). This ratio was less than 0.9 for the constructs, as shown in Table 3, and hence acceptable (Hair et al. 2021; Sarstedt et al. 2019).

Table 1 Composite reliability before and after dropping weak indicators Scale ATL ITP Motivation Patience SD SWB

Composite reliability Using all indicators 0.867 0.837 0.786 0.680 0.692 0.463

After dropping weak indicators 0.881 0.838 0.821 0.784 0.678 0.753

Notes: ATL attitude toward life; ITP intention to purchase; SD self-determination; SWB subjective well-being

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Table 2 Reliability and validity Scale ATL ITP Motivation Patience SD SWB

Cronbach’s alpha 0.884 0.838 0.833 0.792 0.697 0.743

rho_A 0.888 0.841 0.832 0.813 0.765 0.812

Composite reliability 0.881 0.838 0.821 0.784 0.678 0.753

AVE 0.275 0.464 0.343 0.389 0.291 0.518

Notes: ATL attitude toward life; ITP intention to purchase; SD self-determination; SWB subjective well-being; AVE average variance extracted Table 3 HTMT ratio for discriminant validity Scale ITP Motivation Patience SD SWB

ATL 0.192 0.256 0.613 0.363 0.406

ITP

Motivation

Patience

SD

0.380 0.121 0.441 0.163

0.228 0.410 0.125

0.243 0.286

0.413

Notes: ATL attitude toward life; ITP intention to purchase; SD self-determination; SWB subjective well-being

The researchers revisited the tool translation and adaptation process to find out the reasons behind low divergent validity. Although the methodology conformed to the accepted practices established through literature, the unexpected results could be attributed to the multiple understandings of patience by the targeted population and the shallowness of communication resulting from educational and social deprivations.

4 Conclusion It is reiterated here that multiple definitions of “attitude toward life” and “patience” exist in the literature (Bülbül and Arslan 2017; Kunieda 2019). However, clarity of thought requires the medium of language for expression (Kronrod 2022). Due to social exclusion and living reduced lives, the linguistic proficiency of the BoP segment is shallow (Piller 2016). As a result, the respondents had difficulty distinguishing the more nuanced differences between life attitudes and patience. Another plausible reason for low divergent validity between attitude toward life and patience could be the respondents’ religiosity. In the psychology of religion, patience is life-shaping virtue and, therefore, could have been understood as a life attitude (Bülbül and Arslan 2017).

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The limited linguistic repertoire of the BoP segment and the religiosity were two plausible reasons that lowered the validity of well-established tools when used in a different context. These reasons emphasize the need to develop specific tools in the native language by native researchers. In this case, Pakistani researchers who are familiar with the sociocultural characteristics of the BoP population may come forward to design data collection tools that exhibit greater reliability and validity. Furthermore, using emojis instead of word choices may be explored when developing a tool for the BoP population (Sheth 2021). The pervasive use of cell phones (including smartphones) has raised the level of nonverbal communication through emojis in the BoP population. Therefore, using emojis might be a more sensitive way to measure the response of a BoP respondent. While this paper highlighted the limitations in the translated tool’s compromised convergent and divergent validity, new research may target tool refinement or new tool development to measure the BoP attitudes more accurately and with greater sensitivity. Academic and marketing professionals will appreciate such a development as it will enable them to study a marginalized yet multitrillion-dollar segment more effectively.

References Alyami M, Henning M, Krägeloh CU, Alyami H (2021) Psychometric evaluation of the Arabic version of the fear of COVID-19 scale. Int J Ment Heal Addict 19(6):2219–2232 Atkin D, Colson-Sihra E, Shayo M (2021) How do we choose our identity? A revealed preference approach using food consumption. J Polit Econ 129(4):1193–1251 Bülbül AE, Arslan C (2017) Investigation of patience tendency levels in terms of selfdetermination, self-compassion and personality features. Univ J Educ Res 5(9):1632–1645 Dahana WD, Kobayashi T, Ebisuya A (2018) Empirical study of heterogeneous behavior at the base of the pyramid: the influence of demographic and psychographic factors. J Int Consum Mark 30(3):173–191 Hair JF, Risher JJ, Sarstedt M, Ringle CM (2019) When to use and how to report the results of PLS-SEM. Eur Bus Rev 31(1):2–24 Hair JF, Hult GTM, Ringle CM, Sarstedt M, Danks NP, Ray S (2021) Partial least squares structural equation modeling (PLS-SEM) using R. Springer, Cham Haybron DM (2016) The philosophical basis of eudaimonic psychology. In: Joar V (ed) Handbook of eudaimonic well-being. Springer, Cham, pp 27–53 Hayes AF, Montoya AK, Rockwood NJ (2017) The analysis of mechanisms and their contingencies: PROCESS versus structural equation modeling. Australas Mark J 25(1):76–81 Henseler J, Fassott G, Dijkstra TK, Wilson B (2012) Analysing quadratic effects of formative constructs by means of variance-based structural equation modelling. Eur J Inf Syst 21(1): 99–112 Henseler J, Hubona G, Ray PA (2016) Using PLS path modeling in new technology research: updated guidelines. Ind Manag Data Syst 116(1):2–20 Jaikumar S, Singh R, Sarin A (2018) ‘I show off, so I am well off’: subjective economic well-being and conspicuous consumption in an emerging economy. J Bus Res 86:386–393 Jolliffe D, Prydz EB (2021) Societal poverty: a relative and relevant measure. World Bank Econ Rev 35(1):180–206 Kronrod A (2022) Language research in marketing. Found Trends® Market 16(3):308–421

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Kunieda M (2019) Designing for meaningfulness in life: creating new meaning. https://aaltodoc. aalto.fi/bitstream/handle/123456789/39044/master_Kunieda_Masahiro_2019.pdf;sequence=1 Accessed 8 Nov 2021. Memon MA, Ramayah T, Cheah J-H, Ting H, Chuah F, Cham TH (2021) PLS-SEM statistical programs: a review. J Appl Struct Eq Model 5(1):i–xiv Piller I (2016) Monolingual ways of seeing multilingualism. J Multicult Discour 11(1):25–33 Prahalad D (2019, Jan 2) The new fortune at the bottom of the pyramid. Strategy+Business https:// www.strategy-business.com/article/The-New-Fortune-at-the-Bottom-of-the-Pyramid. Accessed 30 May 2022. Reza F, Amir H, Kazmi HAS (2021) Impact of smartphones, self-determination and patience on subjective well-being of bottom of pyramid customers. Revista Brasileira De Marketing 20(2): 279–308 Ringle CM, Silva DD, Bido D (2015) Structural equation modeling with the SmartPLS. Brazil J Market 13(2):56–73 Ryan RM, Deci EL (2000) Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am Psychol 55(1):68–78 Sarstedt M, Hair JF, Cheah J-H, Becker J-M, Ringle CM (2019) How to specify, estimate, and validate higher-order constructs in PLS-SEM. Australas Mark J 27(3):197–211 Sarstedt M, Hair JF, Pick M, Liengaard BD, Radomir L, Ringle CM (2022) Progress in partial least squares structural equation modeling use in marketing research in the last decade. Psychol Mark 39(5):1035–1064 Sheth J (2021) New areas of research in marketing strategy, consumer behavior, and marketing analytics: the future is bright. J Mark Theory Pract 29(1):3–12 Srivastava A, Mukherjee S, Jebarajakirthy C (2020) Aspirational consumption at the bottom of Pyramid: a review of Literature and Future Research Directions. J Bus Res 110:246–259 United Nations Development Programme UNDP (2008) Creating value for all: strategies for doing business with the poor. https://www.undp.org/rwanda/publications/creating-value-all-strate gies-doing-business-poor Accessed 21 Sept 2022. Yurdakul D, Atik D, Dholakia N (2017) Redefining the bottom of the pyramid from a marketing perspective. Mark Theory 17(3):289–303

Use of PLS-SEM Approach in the Construction Management Research Sachin Batra

1 Introduction Partial least squares structural equation modeling (PLS-SEM) is a contemporary multivariate data analysis method to estimate hypothetically established cause-effect relationship models (Zeng et al. 2021). Many scholars have demonstrated the use of PLS-SEM in different business disciplines, namely, marketing, strategic management, management information systems, international business, human resource management, operations management, supply chain management, accounting, and tourism and hospitality (Lee et al. 2011; Hair et al. 2012; Ringle et al. 2012; Peng and Lai 2012; Kaufmann and Gaeckler 2015; Richter et al. 2016; Sarstedt et al. 2020). But in the construction management domain, scholars are less acquainted with the PLS-SEM approach (Zeng et al. 2021). However, since 2010 when the first article was published in the construction management domain using the PLS-SEM approach, Scopus search results indicate an exponential increase in the number of articles, with 2304 published in 2021. Therefore, in this chapter, the author attempted to provide a snapshot of the scientific activity of the use of the PLS-SEM approach in the construction management domain by answering the following research questions: • What are the most influential studies using PLS-SEM in the construction sector? • What is the co-citation network? • What are the domains and levels of analysis in which PLS-SEM is used in construction management research?

S. Batra (✉) NICMAR University, Pune, Maharashtra, India © Springer Nature Switzerland AG 2023 L. Radomir et al. (eds.), State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM), Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-031-34589-0_7

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2 Methodology The mixed method approach is used in the present work, as suggested by Harden and Thomas (2010). However, several authors have highlighted subjective judgment issues using only the systematic review method (He et al. 2017). The mixed-method approach will overcome the mono-method problems as it combines and applies the methods for synthesizing and analyzing the available literary work in the field of inquiry (Harden and Thomas 2010). Figure 1 presents the preferred reporting items for systematic review and meta-analysis (PRISMA) flow diagram. As shown in Fig. 1, the first analysis stage involves bibliometric analysis followed by full content analysis. Bibliometric analysis refers to exploring and visualizing a large volume of the scientific database in the field of inquiry (van Eck and Waltman 2010). Many tools exist for bibliometric analysis, of which biblioshiny (Aria and Cuccurullo 2017) is used in the present study.

Fig. 1 PRISMA flow diagram

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Different bibliometric sources such as Web of Science, Scopus, EBSCOhost, or ProQuest could be used to obtain data for bibliometric analyses. Compared to other databases, Scopus covers a more excellent selection of journals in the field of construction management than Web of Science and contains more recent publications (Chadegani et al. 2013). This validated the usage of Scopus as a data retrieval source.

3 Results and Discussion 3.1

Bibliometric Analysis

3.1.1

Most Influential Studies

The top ten most influential studies concerning total citation score are identified through biblioshiny, as given in Table 1. The study by Banihashemi et al. (2017), published in the International Journal of Project Management, leads the category with 177 total citations, followed by Aibinu and Al-Lawati (2010), published in Automation in Construction with a 144 total citation score.

3.1.2

Co-citation Network

The co-citation network reveals two clusters (Fig. 2). In the blue-colored cluster, scholars have used the PLS-SEM approach by referring to methodological articles; for example, Hair et al. (2011) where the articles also mentioned the use of SmartPLS software, while in the red-colored cluster, scholars referred to Kock (2017) and relied on WarpPLS software for analysis.

Table 1 Top 10 most influential studies of PLS-SEM in the construction sector Author Banihashemi et al. (2017) Aibinu and Al-Lawati (2010) Cao et al. (2014) Lu et al. (2015) Le et al. (2014) Hosseini et al. (2016) Darko et al. (2018) Hussain et al. (2018) Durdyev et al. (2018) Wang et al. (2016)

DOI 10.1016/j.ijproman.2017.01.014 10.1016/j.autcon.2010.02.016 10.1061/(ASCE)CO.1943-7862.0000903 10.1016/j.ijproman.2014.03.004 10.1061/(ASCE)CO.1943-7862.0000886 10.5130/AJCEB.v16i3.5159 10.1016/j.jclepro.2018.07.318 10.3390/su10051415 10.1016/j.jclepro.2018.08.304 10.1016/j.ijproman.2016.07.004

Total citations 177 144 123 121 104 102 94 91 69 68

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Fig. 2 Co-citation network

3.2

Full Content Analysis

The 24 most influential studies based on citations are selected and thoroughly studied to identify the level of analysis using PLS-SEM presented in Table 2. The intention is to explore the answers to the research questions defined in the introduction section. The following seven broad fields are identified in which PLS-SEM is used in the construction sector: • Sustainability: 9 studies (Avotra et al. 2021; Banihashemi et al. 2017; Darko et al. 2018; Durdyev et al. 2018; Hussain et al. 2018; Jain et al. 2020; Kineber et al. 2021; Yusof et al. 2016, 2017). • Building Information Modeling: 5 studies (Ahuja et al. 2016; Cao et al. 2014; Chen et al. 2019; Hosseini et al. 2016; Zhao et al. 2018).

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Table 2 Analysis of the 24 most influential studies S No 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Authors Ahuja et al. (2016) Aibinu et al. (2011) Aibinu and Al-Lawati (2010) Avotra et al. (2021) Abu Bakar et al. (2016) Banihashemi et al. (2017) Cao et al. (2014) Chen et al. (2019) Darko et al. (2018) Durdyev et al. (2018) Hussain et al. (2018) Hosseini et al. (2016) Jain et al. (2020) Kineber et al. (2021) Le et al. (2014) Lu et al. (2015) Wang et al. (2016) Yusof et al. (2016) Yusof et al. (2017) Zaman (2020)

B •

• •

23

Zhang and Qian (2017) Zhao and Singhaputtangkul (2016) Zhao et al. (2018)

24

Zuo et al. (2018)

•

21 22

A •

• • • • • • • • • • • • • • • • • •

•

Domain Building Information Modeling Project Claim Procurement Sustainability Project Performance Sustainability Building Information Modeling Building Information Modeling Sustainability Sustainability Sustainability Building Information Modeling Sustainability Sustainability Human Resource Management Project Performance Human Resource Management Sustainability Sustainability, Project Performance Project Success, Human Resource Management Risk Management Risk Management Risk Management, Building Information Modeling Human Resource Management, Project Success

Notes: “B” is the basic level of analysis; “A” is the advanced level of analysis

• Project Success/Project Performance: 5 studies (Abu Bakar et al. 2016; Lu et al. 2015; Yusof et al. 2017; Zaman 2020; Zuo et al. 2018). • Human Resource Management: 4 studies (Le et al. 2014; Wang et al. 2016; Zaman 2020; Zuo et al. 2018). • Risk Management: 3 studies (Zhang and Qian 2017; Zhao and Singhaputtangkul 2016; Zhao et al. 2018). • Project Claim: 1 study (Aibinu et al. 2011). • Procurement: 1 study (Aibinu and Al-Lawati 2010).

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4 Conclusions, Implications, and Future Directions Fertile grounds for research using PLS-SEM in construction management came to light as the outcome of this chapter. Seven domains identified where construction management scholars commonly use PLS-SEM are Sustainability, Building Information Modeling, Project Success/Project Performance, Human Resource Management, Risk Management, Project claims, and Procurement. These domains will provide the direction for the scholars to explore further and take advantage of using PLS-SEM. The results presented in Table 2 indicate that almost 78% of the articles applied PLS-SEM at the basic level of analysis. Hence, to get better insights into the research work in the construction management area, there exists broad scope for using PLS-SEM at the advanced level. For example, the use of heterogeneity (Batra 2023), higher-order constructs, measurement invariance, cross-validated predictive ability test (CVPAT), endogeneity (Batra and Rastogi 2023), confirmatory tetrad analysis, finite mixture analysis, and multiple mediation analysis. Further, failure to use advanced techniques may lead to misleading interpretations.

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Part II

Consumer Behavior and Marketing

Understanding the Role of Consumer Psychological Motives in Smart Connected Objects Appropriation: A Higher-Order PLS-SEM Approach Zeling Zhong and Christine Balagué

1 Introduction By 2025, there will be more than 75 billion Internet of Things (IoT) connected devices in use in the world, according to Statista (2016). Despite the success of these devices, extant literature shows the existence of barriers limiting their mass diffusion. One of the major challenges is about how to integrate SCOs in consumers’ daily routines, which corresponds to consumer SCO appropriation. However, literature mainly focused on the acceptance context (Chuah et al. 2016; Huarng et al. 2022; Hubert et al. 2019; Lu et al. 2019); little research has studied SCO users’ appropriation stage. To fill this gap, our research investigates the role of psychological motives in SCO appropriation from a consumer perspective. To test our hierarchical framework, we follow the latest methodological developments about composite-based models (Schamberger et al. 2020) and hierarchical component models (Crocetta et al. 2021; Sarstedt et al. 2019) in partial least square structural equation modelling (PLS-SEM) approach.

Z. Zhong (✉) EDC Paris Business School, OCRE Research Lab, Puteaux, France e-mail: [email protected] C. Balagué Paris-Saclay University, Univ Evry, IMT-BS, LITEM, Evry-Courcouronnes, France Institut Mines-Télécom Business School, Evry, France e-mail: [email protected] © Springer Nature Switzerland AG 2023 L. Radomir et al. (eds.), State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM), Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-031-34589-0_8

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2 Background Understanding consumer experience with SCO is a priority in today’s interactive marketing research (Wang 2021). A great deal of research has analyzed consumer SCO acceptance by applying traditional models of early adoption. In today’s technology-enhanced world, more recent attention focused on a deeper understanding of consumer experience with SCO by going beyond the early adoption stage (Benamar et al. 2020; Hoffman and Novak 2018; Kirk et al. 2015; Zhong 2019). Appropriation is the way in which individuals use, adapt, and integrate a technology into their daily routines by taking possession of the technology (Carroll et al. 2003). Most existing work on technology appropriation analyzes collective appropriation. The individual level of appropriation has recently obtained a growing attention in marketing research (Kirk et al. 2015). Sartre (1943) proposed three appropriation modes: control (mastery of the technology by individuals), knowledge (information regarding the characteristics of technology retained in individual’s memory), and creation (changes to the technology influenced or made by individuals to better fit their needs). Adapting this framework to marketing context, Belk (1988) suggests a fourth appropriation mode—contamination—and confirms the relevance of the mentioned appropriation modes in digital context (Belk 2017). Adopting these modes, Mifsud et al. (2015) add two more appropriation modes in a service context: psychological possession (individuals consider a technology as “it’s my own”) and consciousness (awareness of the effects of a technology). Zhong and Balagué (2019) confirm the relevance of the abovementioned appropriation modes in the IoT context. To achieve theoretical parsimony (Hair et al. 2019a), we conceptualize SCO appropriation as a higher-order construct with its concrete first-order subdimensions. Based on the theoretical foundations of individual appropriation, the conceptualization of the multidimensional construct “SCO appropriation” is more suitable under the composite view (Law and Wong 1999): SCO appropriation is the outcome of its concrete subdimensions. In line with MacKenzie et al. (2005), after checking for five conditions of formative construct development, we formulate the following hypothesis: H1: Consumer SCO appropriation is a higher-order construct composed of six reflectively measured first-order dimensions: psychological possession, selfadaptation, knowledge, control, consciousness, and creation. Self-identity is the personal identity developed by individuals. Three elements are relevant to the need for self-identity (Pierce et al. 2003): coming to know oneself (individuals want to know more about themselves), expressing self-identity (individuals want to express themselves to others), and maintaining self-identity (individuals want to maintain the continuity of self-image with their past). A large number of prior studies (e.g., Belk 2013, 2017; Pierce and Jussila 2011; Reed et al. 2012) describe the link between the motive for self-identity and the feelings of possession toward objects which could be considered as a self-extension.

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Individuals experience the feeling of possession while appropriating the target by making it their own (Kirk et al. 2015). Thus, in the SCO context: H2: Self-identity positively influences consumer SCO appropriation. H2a: Coming to know self-identity positively influences consumer SCO appropriation. H2b: Expressing self-identity positively influences consumer SCO appropriation. H2c: Maintaining the self-identity positively influences consumer SCO appropriation. Individuals have a need for territoriality (Ardrey 1966); they long for owning a space. Pierce et al. (2003) suggest that humans need a physical or virtual place that makes them feel psychically comfortable, familiar, as at home. The motive for owning a certain place is linked to possession feelings (Ardrey 1966). In the SCO context, individuals would experience the feeling of possession while appropriating the SCO by integrating it into their daily routines if they perceive it as their territory, thus: H3: Territoriality positively influences consumer SCO appropriation. Individuals have a need to be effective and to feel competent for effectively interacting with their environment (White 1959). Prior work describes a positive relationship between the need for effectance and efficacy and the feeling of possession toward a target (Dittmar 1992; Van Dyne and Pierce 2004). In the SCO context, individuals would appropriate the SCO to fit their need for effectively interacting with their environment. Thus: H4: Efficacy and effectance positively influences consumer SCO appropriation.

3 Methodology To enhance the external validity of our study, we collected data with a quantitative survey using a large marketing research panel in France from Kantar on 505 French smartwatch users. To test our conceptual model, we applied PLS-SEM in software SmartPLS 3.3.9 (Ringle et al. 2015), since PLS-SEM can efficiently handle formative constructs and complex models (Hair et al. 2019b; Sarstedt et al. 2016). We measured all the constructs with validated scales from previous literature adapted to the SCO context: subdimensions of appropriation (Mifsud 2016), self-identity which is a second-order construct formed by three subdimensions (Karahanna et al. 2015), territoriality (Zou et al. 2017), and efficacy and effectance (Bellini et al. 2016). We measured items with five-point Likert scales ranging from “strongly disagree” to “strongly agree” (see Appendix).

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4 Results and Discussion 4.1

First-Order Measurement Model

The reliability and validity of the first-order reflective measurement model are satisfactory. The assessment results (Table 1) about items-loadings (Carmines and Zeller 1979), Cronbach’s alpha (Nunnally 1978), average variance extracted (AVE) (Fornell and Larcker 1981), composite reliability (Hair et al. 2011), and DijkstraHenseler’s ρ values (Dijkstra and Henseler 2015; Rademaker et al. 2019) confirmed the convergent validity and internal consistency reliability. Discriminant validity is also satisfactory, confirmed by results (Table 2) regarding the heterotrait–monotrait ratio of correlations (HTMT) after using a bootstrapping procedure with 5000 subsamples (Franke and Sarstedt 2019; Henseler et al. 2015).

4.2

Second-Order Measurement Model

We assessed the relationship between lower-order components and higher-order components. Hypothesis 1 is supported by evidence (Table 3) from confirmatory tetrad analysis in PLS (Hair et al. 2017). To validate the formative higher-order SCO appropriation construct and selfidentity construct, we followed the procedure outlined in Hair et al. (2022). The evidence from a bootstrapping procedure (5000 subsamples) supported the convergent validity (Hair et al. 2022) of higher-order components by running a redundancy analysis (Chin 1998). The results of the variance inflation factor (VIF) (Hair et al. 2011) showed no collinearity issues among the first-order constructs. We examined the weights and significance of the first-order components and their higher-order components (Fig. 1) by using a bootstrapping procedure (5000 subsamples). The results provided clear evidence for the validity of the reflective-formative nature of SCO appropriation construct and self-identity construct.

4.3

Structural Model

Following the recommendations of Sarstedt et al. (2021) and Hair et al. (2019a, b), we assessed structural model based on collinearity inspection, significance and relevance of hypothesized relationships (Fig. 2), and explanatory power and outof-sample predictive power (R2, Q2, PLSpredict). To estimate higher-order constructs, we applied the disjoint two-stage approach which permits the application of all structural model evaluation criteria, to minimize the parameter bias in our structural model relationships (Sarstedt et al. 2019). Evidence from bootstrapping procedure regarding all inner VIF (Hair et al. 2011) showed no collinearity issues.

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Table 1 Internal consistency reliability and convergent validity of the first-order constructs Convergent validity First-order construct

Manifest variable

Consciousness (C)

C1 C2 C3 C4 CO1 CO2 CO3 CO4 CO5 CR1 CR2 CR3 CR4 K1 K2 K3 K4 K5 EE1 EE2 EE3 EE4 EE5 EE6 MEE7 CKO1 CKO2 CKO3 ESI1 ESI2 ESI3 T1 T2 T3 T4 T5 T6 MSI1 MSI2 MSI3

Control (CO)

Creation (CR)

Knowledge (K)

Efficacy and effectance (EE)

Come to know oneself (CKO) Express selfidentity (ESI) Territoriality (T)

Maintain selfidentity (MSI)

Loadings > 0.70 0.820 0.803 0.804 0.840 0.773 0.765 0.840 0.793 0.856 0.788 0.865 0.851 0.848 0.834 0.825 0.814 0.783 0.840 0.803 0.789 0.826 0.802 0.829 0.772 0.787 0.878 0.908 0.891 0.889 0.865 0.844 0.847 0.832 0.821 0.850 0.839 0.834 0.850 0.842 0.794

AVE > 0.50 0.667

Internal consistency reliability Composite Cronbach’s rho_A reliability alpha 0.70–0.95 > 0.70 > 0.70 0.889 0.835 0.834

0.650

0.903

0.876

0.866

0.703

0.904

0.861

0.859

0.672

0.911

0.887

0.878

0.642

0.926

0.909

0.907

0.797

0.922

0.873

0.872

0.750

0.900

0.834

0.833

0.701

0.934

0.917

0.915

0.688

0.869

0.775

0.773

(continued)

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Table 1 (continued) Convergent validity First-order construct Psychological possession (PP) Self-adaptation (SA)

Manifest variable PP2 PP3 PP4 SA1 SA2 SA3

Loadings 0.849 0.877 0.870 0.896 0.917 0.917

AVE 0.749

0.828

Internal consistency reliability Cronbach’s Composite alpha rho_A reliability 0.900 0.833 0.833

0.935

0.900

0.897

Evidence from blindfolding and PLSpredict procedures (Hair et al. 2019b; Shmueli et al. 2019) supported a strong explanatory power and a medium predictive power of the structural model.

5 Conclusions and Implications for Theory and Practice Our study provides meaningful implications. First, our research enhances the understanding of the role of consumer psychological motives in SCO appropriation and strengthens the conceptual framework for future research. Second, our study empirically validates consumer SCO appropriation as a higher-order construct based on six underlying sets of practice. The findings support three significant psychological drivers of SCO appropriation. The study provides theoretical and empirical insights to individual technology appropriation. Third, our study provides evidence that the new developments of PLS-SEM are extremely suitable for analyzing complexities and dynamics in consumer behavior, especially when modelling hierarchical composite-based concepts. Our study also provides important insights for practice. The findings could help SCO manufacturers and marketing managers to better understand connected consumers’ motives to integrate SCO in their daily routines as well as their concrete appropriation practices. This deeper understanding would allow SCO practitioners to better adapt their offerings to SCO market. In return, consumers whose needs are better met will engage into more loyal behaviors when “taking possession” of a SCO and the associated services that they perceive as “it’s my own.”

Consciousness Control Creation Knowledge Efficacy and effectance Come to know oneself Express selfidentity Territoriality Maintain selfidentity Psychological possession Selfadaptation

0.676 0.891 0.709

0.276

0.340

0.361 0.325

0.423

0.347

0.443

0.442

0.448 0.389

0.604

0.509

Control

0.829 0.713 0.823 0.618

Consciousness

0.547

0.817

0.658 0.426

0.423

0.436

0.668 0.611

Creation

0.341

0.465

0.372 0.296

0.355

0.353

0.746

Knowledge

Table 2 Discriminant validity of the first-order constructs

0.231

0.437

0.441 0.381

0.446

0.330

Efficacy and effectance

0.415

0.475

0.494 0.527

0.677

Come to know oneself

0.358

0.457

0.568 0.670

Express selfidentity

0.580

0.763

0.563

Territoriality

0.291

0.410

Maintain selfidentity

0.725

Psychological possession

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SCO appropriation 1: Consciousness. Control. Creation. Knowledge 2: Consciousness. Control. Knowledge. Creation 4: Consciousness. Control. Creation. Psychological possession 6: Consciousness. Creation. Psychological possession. Control 7: Consciousness. Control. Creation. Selfadaptation 10: Consciousness. Control. Knowledge. Psychological possession 16: Consciousness. Control. Psychological possession. Self-adaptation 22: Consciousness. Creation. Knowledge. Selfadaptation 26: Consciousness. Creation. Self-adaptation. Psychological possession

Table 3 CTA-PLS results for SCO appropriation Sample mean (M) -0.052 0.000 0.264 -0.071 0.150 0.024 0.283 -0.152 0.071

Original sample (O) -0.053 0.001 0.269 -0.072 0.153 0.026 0.288 -0.154 0.072

0.037

0.037

0.048

0.019

0.038

0.015

Standard deviation (STDEV) 0.032 0.023 0.048

1.942

4.196

6.037

1.376

3.992

4.622

t-statistics (|O/ STDEV|) 1.674 0.026 5.650

0.053

0.000

0.000

0.169

0.000

0.000

pvalues 0.095 0.979 0.000

-0.030

0.160

-0.005

-0.001

-0.025

-0.001

-0.259

0.050

-0.003

0.002

-0.116

CI Low adj. -0.142 -0.062 0.141 0.001

Bias 0.001 -0.001 -0.005

0.177

-0.054

0.425

0.078

0.263

-0.029

CI Up adj. 0.035 0.064 0.406

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Understanding the Role of Consumer Psychological Motives in. . .

Fig. 1 Results of assessment of the second-order formative measurement model

Fig. 2 Structural model test result

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Appendix Questionnaire Higher-order construct SCO appropriation

First-order construct Consciousness

Control

Creation

Knowledge

Psychological possession

Selfadaptation Self-identity

Come to know oneself Express selfidentity Maintain selfidentity

Items I am aware of what I need to do to make my daily life easier. I can see the different tasks I have to do to take advantage of my SCO. I understand what is expected of me in my care (regular use of associated services, respect for the instructions for use). I am aware of the uses that the SCO manufacturers expect of me. I feel in control of what I do. I feel able to take charge of myself. It is easy for me to realize the uses. I have the feeling to master the uses that the SCO manufacturers expect from me. I feel I can manage the uses initially conceived. I feel like I can choose the uses of my SCO. I feel I have adapted this SCO to my everyday life. I feel I have adapted my SCO to my everyday life. I feel I have created a SCO that suits me. I feel like I know the SCO well that makes my daily life easier. I am familiar with the SCO I use. I feel I know my SCO well and how to use it. Compared to most users, I know quite a bit about my SCO. For those around me, I am someone who knows my SCO well. This SCO belongs to me. I talk about my SCO, as MY “SCO.” This SCO is part of me. This SCO is part of my life. This SCO has led me to organize my days differently. This SCO has led me to adapt my daily life. This SCO has led me to change my pace of life. I feel a need to develop a sense of self-identity. I feel a need to discover what kind of person I am. I feel a need to learn about myself. I feel a need to express who I am. I feel a need to express my personality. I feel a need to express my self-identity. I have a need that who I am today also incorporates my past. I have a need that my past be an important part of my selfidentity. I feel a need that who I am today does not ignore my past. (continued)

Understanding the Role of Consumer Psychological Motives in. . . Higher-order First-order construct construct Efficacy and effectance

Territoriality

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Items I have the skills needed to use SCO. I am motivated to use new technologies. I can effectively operate the functionalities of SCO. I am confident about how to use the tools of SCO. I can use the SCO. I know what the features of this technology look. I am curious about exploring new technologies. When using this SCO, I feel like having a place to inhabit. of my own territory. of home. of comfort. of security. of inner peace.

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Advertisements that Follow Users Online and Their Effect on Consumers’ Satisfaction and Expectation Confirmation: Evidence from the Tourism Industry Jordi López-Sintas, Giuseppe Lamberti, and Haitham Alghanayem

1 Introduction The Internet and social media have changed consumer behavior and how tourism companies conduct their business (Dwivedi et al. 2021). The massive potential audience available, spending many hours a day using social media across various platforms, pushed marketers to embrace social media as a marketing channel and develop new marketing strategies (Appel et al. 2020). Retargeting has emerged as an online advertising strategy (Varnali 2019). According to Berman (2018), retargeting advertisements allow e-tourism companies to create highly personalized travel plans and offer competitive deals based on their previous browsing history. However, this kind of strategy is very sensitive as it may affect how consumers view their travel-related purchases and thus their postpurchase satisfaction. For example, better-placed deals than the one consumer purchased can negatively affect their post-purchase satisfaction. Further, its effect can be different on diverse consumer groups defined by sociodemographic variables such as gender, age, education, or the amount of time the consumer spends online daily. Research has already analyzed the retargeting effect on consumer behavior. However, most of the literature on retargeting has been focused on pre-purchase topics such as ethical and regulatory concerns (Aalberts et al. 2016; De Lima and

J. López-Sintas · G. Lamberti Department of Business, School of Economics and Business, Universitat Autònoma de Barcelona, Barcelona, Spain e-mail: [email protected]; [email protected] H. Alghanayem (✉) Department of Business Administration, College of Science and Arts in Sajir, Shaqra University, Shaqra, Saudi Arabia © Springer Nature Switzerland AG 2023 L. Radomir et al. (eds.), State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM), Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-031-34589-0_9

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Legge 2014; Leenes and Kosta 2015), consumer perceptions of retargeting (Smit et al. 2014; Ur et al. 2012), and best practices for campaign effectiveness in terms of timing, delivery, and repetition (Bleier and Eisenbeiss 2015; Lambrecht and Tucker 2013; Li et al. 2021; Sahni et al. 2019). Limited research has been conducted on the impact of post-purchase retargeting on consumer behaviour, and there is a lack of evidence regarding potential variations in this effect across diverse consumer groups. Thus, it remains crucial that the role of retargeting is studied and viewed beyond website visits in response to post-purchase satisfaction levels among e-tourism industry consumers and their expectation confirmation. Using the expectation confirmation theory (Bhattacherjee 2001) as a framework in this chapter, we relate three main constructs: expectation confirmation, satisfaction, and repurchase intention, with retargeting intended as a measurement of how competing the post-purchase retargeted ads consumers experienced compared to their purchase. The retargeting construct was developed considering the model of consumer’s buying decision (Dubrovski 2001) and including four indicators (price, features, value, and company reputation). In this chapter we assume that the effect of more competitive retargeting advertisement acts as a violation of consumers original expectations, thus affecting their levels of expectations confirmation according to violated expectation model (Pinquart et al. 2021). Also, it is assumed to have effects toward satisfaction as suggested in previous retargeting literature (Baek and Morimoto 2012; Lambrecht and Tucker 2013; Li et al. 2021). This chapter contributes to and extends existing research on online advertising and retargeting in the tourism sector by (1) analyzing the effect of retargeting on the causal chain expectations-satisfaction-repurchase intention of online consumers and (2) exploring the existence of different consumer groups identified by gender, age, education, and daily time spent on social media where the effect of retargeting on expectations and satisfaction is different.

2 Methodology E-tourism online buyers in Saudi Arabia with an active account on social media platforms, who purchased tourism services online and experienced post-purchase retargeted ads, were sampled, surveyed, and analyzed using measurements of the four constructs (see Table 1). A total of 402 samples was achieved, and the demographic profile of respondents for this chapter showed that consumers were 45.52% female and 54.58% male. The most frequent age was 25–34 years (37.8%). Furthermore, most respondents hold a bachelor’s degree (51.99%) and used social media between 1 and 5 hours daily (65.67%). Partial least squares structural equation modeling (PLS-SEM) was used to fit our model (Hair et al. 2017). Several scholars indicate that the PLS-SEM method is the most adequate when conceptual variables are estimated as composites (i.e., linear combination of indicators) (Cepeda-Carrion et al. 2019). The hybrid multigroup

Advertisements that Follow Users Online and Their Effect on Consumers’. . .

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Table 1 Definitions and sources of constructs Construct Expectations confirmation Satisfaction Repurchase intentions Retargeting

Definition Degree to which perceptions match (confirmation) or differ from (disconfirmation) expectations A measurement that determines how happy customers are with their purchase Degree of likelihood of making another purchase with the e-tourism website or application A measurement of how competing the post-purchase retargeted adverts consumers experienced

Source Bhattacherjee (2001), Oliver (1980)

Del Bosque and San Martín (2008), Bhattacherjee (2001), Oliver (1980) Bhattacherjee (2001), Hellier et al. (2003) New scale developed and drawn from consumer buying-decision model (Dubrovski 2001)

approach (Lamberti 2021) was used to explore gender, age, education, and daily time spent on social media as possible sources of heterogeneity in consumer behaviors. This approach combines classical multigroup analysis (MGA) with Pathmox analysis (Lamberti et al. 2016). The most significant different groups are first identified using Pathmox. The obtained groups are then compared using the PLS-MGA test (Henseler et al. 2009) after assuring measurement invariance by applying the MICOM procedure (Henseler et al. 2016). The statistical analysis was performed using SmartPLS 3.3 (Ringle et al. 2015) and the R software genpathmox package V.0.5 (Lamberti 2014).

3 Results After validating the measurement model following Hair et al. (2019) (see Tables 2, 3, and 4), our results showed (see Fig. 1 and Table 5) a significant relationship between retargeting on expectation (β = -0.324), while surprising, retargeting on satisfaction was low and not significant (β = -0.032). As was expected, expectation positively affects satisfaction (β = 0.655), and satisfaction increases consumers’ intention to repurchase (β = 0.670), corroborating previous findings (Alghamdi et al. 2018; Rajeh et al. 2021; Zhong et al. 2015). In addition, the model’s predictive power (see Table 6) was moderate-high according to the PLSpredict procedure (Shmueli et al. 2019). However, the effect of retargeting was not homogenous. Instead, it varied primarily according to the time spent online and the age of the consumers. The hybrid multigroup analysis revealed the presence of three different groups of consumers labeled, respectively, as heavy social media users (LM1), younger-moderate social users (LM2), and older-moderate social users (LM3) that were characterized by differences in how retargeting affect expectation and satisfaction (see Table 7). After assuring invariance of constructs between groups (see Table 8) following the

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Table 2 Reliability and validity criteria Constructs/items Expectation confirmation (EC) EC1 EC2 EC3 Satisfaction (ST) ST1 ST2 ST3 ST4 Repurchase intentions (RI) RI1 RI2 Retargeting (RE) RE1 RE2 RE3 RE4

Factor loadings

2.50%

97.50%

0.870 0.885 0.865

0.827 0.846 0.817

0.902 0.915 0.902

0.872 0.887 0.889 0.848

0.839 0.847 0.846 0.794

0.901 0.919 0.921 0.894

0.904 0.862

0.874 0.813

0.926 0.898

0.814 0.879 0.867 0.816

0.747 0.844 0.824 0.764

0.864 0.908 0.899 0.860

Cronbach’s alpha 0.845

CR 0.906

AVE 0.763

0.720

0.876

0.78

0.866

0.909

0.713

0.897

0.929

0.765

Table 3 Heterotrait-monotrait (HTMT) Repurchase intentions → expectation confirmation Retargeting → expectation confirmation Retargeting → repurchase intentions Satisfaction → expectation confirmation Satisfaction → repurchase intentions Satisfaction → retargeting

Value 0.719 0.375 0.206 0.763 0.828 0.271

2.50% 0.602 0.223 0.073 0.680 0.741 0.137

97.50% 0.830 0.518 0.369 0.832 0.904 0.403

Table 4 Assessment of common method bias VIF

Expectation confirmation 1.962

Repurchase intentions 1.908

Retargeting 1.108

Satisfaction 2.352

MICOM procedure (Henseler et al. 2016), we compared the three consumer groups using the MGA-PLS test (Henseler et al. 2009). We found that the effect of retargeting on expectations confirmation and satisfaction was significant when comparing the LM1 and LM3. Furthermore, it was significant and higher for the LM1 compared with LM3. This finding can be explained by the fact that heaver social media users who spend longer periods of time are more likely to be retargeted more frequently. We also found that the effect on expectation on satisfaction was significant and higher for LM2 than LM1.

Advertisements that Follow Users Online and Their Effect on Consumers’. . .

Fig. 1 Path diagram results

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Table 5 Path coefficients, bootstrap IC R2, Q2, and SRMR H Path coefficients H1 Expectation confirmation on satisfaction H2 Satisfaction on repurchase intentions H3 Retargeting on expectation confirmation H4 Retargeting on satisfaction Repurchase intentions R2 = 0.449; Q2 = 0.345 Satisfaction R2 = 0.443; Q2 = 0.335 SRMR = 0.054

β 0.655 0.670 -0.324 -0.032

2.50% 0.587 0.594 -0.444 -0.126

97.50% 0.737 0.738 -0.196 0.053

p-value 0.000 0.000 0.000 0.489

Sig. Yes Yes Yes No

Table 6 PLSpredict procedure Constructs RI1 RI2 ST1 ST2 ST3 ST4

PLS RMSE 1.280 1.313 1.133 1.121 1.179 1.222

Q2predict 0.017 0.018 0.044 0.039 0.045 0.027

LM RMSE 1.287 1.324 1.135 1.129 1.183 1.217

Difference -0.007 -0.011 -0.002 -0.007 -0.004 0.005

Table 7 Multigroup comparison results

Paths LM1 -0.568*** Retargeting on expectation confirmation Retargeting on -0.217*** satisfaction 0.440*** Expectation confirmation on satisfaction Satisfaction on 0.697*** repurchase intentions

LM2 LM3 -0.392*** -0.117 NS

PLS-MGA test LM1 vs LM1 vs LM2 vs LM2 LM3 LM3 p-value 0.203 0.001 0.091

-0.750NS

0.090 NS

0.348

0.037

0.096

0.763***

0.609*** 0.018

0.214

0.185

0.662***

0.655*** 0.767

0.588

0.888

Notes: ***p < 0.001, NS non-significant. Italicized means significant 0.025) which traditionally would indicate a moderate effect but in the case of nonlinear relationships implies a large effect (Hair et al. 2021). Although our results are in line with the ones presented in the systematic review of the literature by Pedro et al. (2018), who finds the relationship between the three components of intellectual capital and performance to be nonlinear, they are not in line with the findings of Haris et al. (2019), as they found an inverse U-shaped relationship. Taking into account the trend toward the introduction of moderation in quadratic relations (Haans et al. 2016), we also cheek the moderating effect of manger gender on the abovementioned relationship which is statistically significant (β = 0.132), and the effect size is also large ( f2 = 0.026). That means that intellectual capital in hotels runs by women has a higher effect on hotel performance than intellectual capital managed by men. However, our findings shed light on the role of manager gender in intellectual capital performance considering the gap raised previously in other research (Giuliani and Poli 2019) where it is stated that the effects of manager gender on intellectual capital performance were still unclear and needed further research as the results achieved up to date did not converge. Nowadays, these findings could also be found in emerging economics where female directors drive intellectual capital performance (Smriti and Das 2021).

4 Conclusions This research was aimed to explore the intellectual capital within a currently topical sector, hospitality. Hotels were facing important challenges derived from the knowledge society, but now they are immersed in a turbulent environment due to the COVID-19 crisis. Under this situation, scholars need to develop empirical models to test hypotheses that will help business managers to improve their resilience. The turbulent context in which hotels compete today requires a manager with the ability to combine and integrate intangible resources such as highly skilled employees, partner relationships, and accumulated organizational knowledge. In addition, we have justified the necessity of hotels to enhance the knowledge and intangible resources embedded in their employees, routines, social relationships, and so forth as one of their main targets in the search of strength to cope with the environment.

References Ali F, Rasoolimanesh SM, Sarstedt M, Ringle CM, Ryu K (2018) An assessment of the use of partial least squares structural equation modeling (PLS-SEM) in hospitality research. Int J Contemp Hosp Manag 30(1):514–538 Baima G, Forliano C, Santoro G, Vrontis D (2020) Intellectual capital and business model: a systematic literature review to explore their linkages. J Intellect Cap 22(3):653–679

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Costa V, Silva L, Paula L (2020) Intellectual capital and its impact on business performance: an empirical study of Portuguese hospitality and tourism sector. Intang Cap 16(2):78–89 Giuliani M, Poli S (2019) Which relationship between gender diversity, intellectual capital and financial performance? Int J Bus Manag 14(10):101–115 Haans RF, Pieters C, He ZL (2016) Thinking about U: theorizing and testing U-and inverted U-shaped relationships in strategy research. Strateg Manag J 37(7):1177–1195 Hair JF, Sarstedt M, Pieper TM, Ringle CM (2012) The use of partial least squares structural equation modeling in strategic management research: a review of past practices and recommendations for future applications. Long Range Plan 45(5–6):320–340 Hair JF, Sarstedt M, Ringle CM (2019) Rethinking some of the rethinking of partial least squares. Eur J Mark 53(4):566–584 Hair JF, Sarstedt M, Ringle CM, Gudergan SP (2021) Advanced issues in partial least squares structural equation modeling (PLS-SEM). Sage, Thousand Oaks, CA Haris M, Yao H, Tariq G, Malik A, Javaid HM (2019) Intellectual capital performance and profitability of banks: evidence from Pakistan. J Risk Financ Manag 12(2):56 Isola WA, Adeleye BN, Olohunlana AO (2020) Boardroom female participation, intellectual capital efficiency and firm performance in developing countries: evidence from Nigeria. J Econ Finance Adm Sci 25(50):413–424 Kogut B, Zander U (1992) Knowledge of the firm, combinative capabilities, and the replication of technology. Organ Sci 3(3):383–397 Li YQ, Liu CHS (2018) The role of problem identification and intellectual capital in the management of hotels’ competitive advantage-an integrated framework. Int J Hosp Manag 75:160–170 Martín-de Castro G, Díez-Vial I, Delgado-Verde M (2019) Intellectual capital and the firm: evolution and research trends. J Intellect Cap 20(4):555–580 Martínez-Martínez A, Cegarra-Navarro JG, Garcia-Perez A, Vicentini F (2020) Extending structural capital through pro-environmental behaviour intention capital: an outlook on Spanish hotel industry. J Intellect Cap 22(3):633–652 Pedro E, Leitão J, Alves H (2018) Intellectual capital and performance: taxonomy of components and multi-dimensional analysis axes. J Intellect Cap 19(2):407–452 Rasoolimanesh SM, Ali F, Jaafar M (2018) Modeling residents’ perceptions of tourism development: linear versus non-linear models. J Destin Mark Manag 10:1–9 Serenko A, Bontis N (2013) The intellectual core and impact of the knowledge management academic discipline. J Knowl Manag 17(1):137–155 Smriti N, Das N (2021) Do female directors drive intellectual capital performance? Evidence from Indian listed firms. J Intellect Cap 23(5):1052–1080 Subramaniam M, Youndt MA (2005) The influence of intellectual capital on the types of innovative capabilities. Acad Manag J 48(3):450–463 Wilke EP, Costa BK, Freire OBDL, Ferreira MP (2019) Interorganizational cooperation in tourist destination: building performance in the hotel industry. Tour Manag 72:340–351

The U-Shape Influence of Family Involvement in Hotel Chain: Examining Dynamic Capabilities in PLS-SEM Lorena Ruiz-Fernández, Laura Rienda, and Rosario Andreu

1 Introduction Family businesses are a major part of the global business community, and their development and evolution are fundamental to the success of the global economy. These businesses face an increasingly dynamic and turbulent environment that forces them to develop capabilities to adapt to changes and maintain their competitive advantages. In particular, the analysis of the strategies followed by these companies has attracted the attention of numerous researchers, both from a theoretical and practical point of view, giving rise to the progressive construction of a rich and exhaustive field of study (Sharma 2004; Kraus et al. 2011; Alayo et al. 2021). Even though it is true that the relevance of family businesses extends to all sectors of the economy, representing the largest group of companies in the world (Arregle et al. 2019), the percentage of family businesses present in the Spanish hotel industry is very high (Andreu et al. 2018), which underpins the importance of studying their idiosyncratic characteristics in the development of dynamic capabilities that influence the innovative and economic performance of hotels. In our case, we focus on the dynamic capabilities (DC) framework following the idea of Park et al. (2019), who state the need to use the theory DC as a frame of reference to research how family businesses develop their capabilities and how they affect performance. In addition, despite the fact that DC have been considered the basis of firms surviving in a dynamic environment, there has been little research on understanding the DC of family firms (Wang 2016).

L. Ruiz-Fernández (✉) · L. Rienda · R. Andreu Business Management Department, Faculty of Economic and Business, University of Alicante, Alicante, Spain e-mail: [email protected]; [email protected]; [email protected] © Springer Nature Switzerland AG 2023 L. Radomir et al. (eds.), State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM), Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-031-34589-0_16

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Considering recent research that suggested linear relationships do not always explain the business reality well (Sarstedt et al. 2020; Ahrholdt et al. 2019) and the complexity and uniqueness of the underlying relationships between familiarity and the tourism enterprise, as well as the contradictory results that can be found in the empirical literature (Miller and Le Breton-Miller 2006; García-Castro and Aguilera 2014; De Massis et al. 2015), we propose to assess whether there is a nonlinear relationship between family involvement and the development of dynamic capabilities of sensing, capturing opportunities, and reconfiguring their resources. Given the need to take into account the perceptions, attitudes, and intentions of those involved in order to achieve a deeper understanding in this area, our method has been based on data obtained from a single survey of Spanish 3–5-star hotel chains with and without family influence (Marco-Lajara et al. 2021). To model and test the underlying relationships, we have used the PLS technique based on structural equation modelling (Wilson et al. 2014).

2 Methodology The final sample was composed by 107 hotel chains located in Spain with 3- to 5-star hotels. We addressed a survey to managers to ask them about the constructs under study using previously validated Likert measurement scales, with response ranges from 1 = strongly disagree to 7 = strongly agree. Family management was measured with the percentage of family member in management positions. Dynamic capabilities have been measured according to Teece’s (2007) proposal for empirical data analysis (second-order variable created by sensing, seizing, and reconfiguring). The collected data were subsequently analyzed with SmartPLS v3.3 (Hair et al. 2022), using the technique of variance-based partial least squares structural equation modelling (PLS-SEM). This methodology allows representing, estimating, and testing a theoretical network of linear relationships between variables that may be unobserved, i.e., latent variables such as family involvement or dynamic capabilities, and has been defined as a suitable means to estimate non-linear effects in the field of family business (Basco et al. 2022).

3 Results and Discussion Nonlinear (quadratic) effects involve an interaction of Y1 with itself (Y1*Y1), so they can be understood as a special case of a moderation model in which the relationship between Y1 and Y2 is moderated by Y1 -self-moderation (Hair et al. 2021). Thus, in line with Rigdon et al. (2010), we can model self-moderation using an interaction term, and the same approaches can be used as with moderation effects (Hair et al. 2022), although the 2-stage approach is recommended. After introducing the quadratic effect as an interaction term, we explain the results from bootstrapping

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based on 10,000 subsamples, the percentile approach and the “unsigned changes” option (Hair et al. 2022; Streukens and Leroi-Werelds 2016). Both the linear relationship (β estimated: -0.241) and the quadratic term (β estimated: 0.267) are statistically significant, implying a U-shaped relationship between family involvement and the development of dynamic capabilities. However, a significant coefficient for the quadratic term is necessary, but not sufficient according to Haans et al. (2016). Therefore, following Hair et al. (2021), we test the size of the f 2 effect to assess the impact of the interaction term on the R2 and test the relevance of this curvilinear relationship. Since the f 2 of the quadratic effect was 0.043, as suggested by Ghasemy et al. (2020) or Hair et al. (2021) following Kenny (2018), we concluded that the effect size was large, implying a high relevance of the significant nonlinear relationship between family address and the deployment of dynamic capabilities. These results are in line with recent calls for papers such as Basco et al. (2022) and respond to their call to shed more light on nonlinear relationships in the field of family business.

4 Conclusions In line with the most burning question in the field of family business research, and the subject of study of works such as Daspit et al. (2019), our work allows us to delve into topical questions in the science of family business: How does family involvement in Spanish hotel chains affect results? The above leads us to extend a call to researchers in the field of family business to delve deeper into the relationships analyzed to bring to light more complex relationships that allow us to explain the business reality in a more profound way. It is not enough to detect mediations or moderations; it is also important to know the U-shaped or inverse U-shaped relationships. This consideration may be latent and would allow a better understanding of the particularities of the development and strategic management of family businesses through the view of dynamic capabilities.

References Ahrholdt DC, Gudergan SP, Ringle CM (2019) Enhancing loyalty: when improving consumer satisfaction and delight matters. J Bus Res 94:18–27 Alayo M, Iturralde T, Maseda A, Aparicio G (2021) Mapping family firm internationalization research: bibliometric and literature review. Rev Manag Sci 15(6):1517–1560 Andreu R, Claver-Cortés E, Quer D, Rienda L (2018) Family ownership and Spanish hotel chains: an analysis of their expansion through internationalization. UCJC Bus Soc Rev 59:40–75 Arregle JL, Hitt MA, Mari I (2019) A missing link in family firms’ internationalization research: family structures. J Int Bus Stud 50(5):809–825

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Basco R, Hair JF Jr, Ringle CM, Sarstedt M (2022) Advancing family business research through modeling nonlinear relationships: comparing PLS-SEM and multiple regression. J Fam Bus Strat 13(3):100457 Daspit JJ, Long RG, Pearson AW (2019) How familiness affects innovation outcomes via absorptive capacity: a dynamic capability perspective of the family firm. J Fam Bus Strat 10(2): 133–143 De Massis A, Kotlar J, Campopiano G, Cassia L (2015) The impact of family involvement on SMEs’ performance: theory and evidence. J Small Bus Manag 53(4):924–948 García-Castro R, Aguilera RV (2014) Family involvement in business and financial performance: a set-theoretic cross-national inquiry. J Fam Bus Strat 5(1):85–96 Ghasemy M, Teeroovengadum V, Becker J-M, Ringle CM (2020) This fast car can move faster: a review of PLS-SEM application in higher education research. High Educ 80:1121–1152 Haans RF, Pieters C, He ZL (2016) Thinking about U: theorizing and testing U-and inverted U-shaped relationships in strategy research. Strateg Manag J 37(7):1177–1195 Hair JF, Sarstedt M, Ringle CM, Gudergan SP (2021) Advanced issues in partial least squares structural equation modeling (PLS-SEM). Sage, Thousand Oaks, CA Hair JF, Hult T, Ringle CM, Sarstedt M (2022) A primer on partial least squares structural equation modeling (PLS-SEM), 3rd edn. Sage, Thousand Oaks, CA Kenny DA (2018) Categorical moderator and continuous causal variable. http://davidakenny.net/ cm/moderation.htm Accessed 14 Mar 2021 Kraus S, Harms R, Fink M (2011) Family firm research: sketching a research field. Int J Entrep Innov Manag 13(1):32–47 Marco-Lajara B, Ruiz-Fernández L, Seva-Larrosa P, Sánchez-García E (2021) Hotel strategies in times of COVID-19: a dynamic capabilities approach. Anatolia 33(4):525–536 Miller D, Le Breton-Miller I (2006) Family governance and firm performance: agency, stewardship, and capabilities. Fam Bus Rev 19(1):73–87 Park HY, Misra K, Reddy S, Jaber K (2019) Family firms’ innovation drivers and performance: a dynamic capabilities approach. J Fam Bus Manag 9(1):4–23 Rigdon EE, Ringle CM, Sarstedt M (2010) Structural modeling of heterogeneous data with partial least squares. In: Malhotra NK (ed) Review of marketing research (review of marketing research), vol 7. Emerald Group Publishing Limited, Bingley, pp 255–296 Sarstedt M, Ringle CM, Cheah JH, Ting H, Moisescu OI, Radomir L (2020) Structural model robustness checks in PLS-SEM. Tour Econ 26(4):531–554 Sharma P (2004) An overview of the field of family business studies: current status and directions for the future. Fam Bus Rev 17(1):1–36 Streukens S, Leroi-Werelds S (2016) Bootstrapping and PLS-SEM: a step-by-step guide to get more out of your bootstrap results. Eur Manag J 34(6):618–632 Teece DJ (2007) Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance. Strateg Manag J 28(13):1319–1350 Wang Y (2016) Environmental dynamism, trust and dynamic capabilities of family businesses. Int J Entrep Behav Res 22(5):643–670 Wilson SR, Whitmoyer JG, Pieper TM, Astrachan JH, Hair JF, Sarstedt M (2014) Method trends and method needs: examining methods needed for accelerating the field. J Fam Bus Strat 5(1): 4–14

Part IV

Innovation Management and Entrepreneurship

Two-Way Business Innovation in Central Eastern Europe: Analysing Innovative Enterprises Using the PLS-SEM Method Márton Gosztonyi

1 Introduction Innovation is a complex phenomenon. Innovation recognizes both the first light bulb invented by T.A. Edison in 1879 and new product packaging (Podręcznik 2005). Although the innovation comes from the Latin word innovatis, meaning renewal, creating something new (Tokarski 1980), nowadays it is mostly used as a synonym for change (Janasz and Kozioł 2007). Enterprises are complex drivers of economic growth (Acs and Szerb 2007; Audretsch and Keilbach 2007; Audretsch and PeñaLegazkue 2012; Audretsch et al. 2006; Audretsch et al. 2008; Fritsch 2008; Noseleit 2013; Spencer et al. 2008); thus, enterprises’ ability to innovate is a key factor in stimulating economic growth in Central Eastern Europe (CEE) (Acs and Szerb 2007; González-Pernía and Peña-Legazkue 2015; Wong et al. 2005). Consequently, the study of innovation has always played a key role in business research. Recently, however, an even greater increase in research on innovative or productive enterprises has been noted (Audretsch and Keilbach 2007; Baumol 2010; Davidsson 2006; Samuelsson and Davidsson 2009). However, most studies explore entrepreneurial innovation in developed economies; thus, we know little about the extent to which enterprises in CEE economies implement innovation-driven entrepreneurship. To this end, in our paper, we examine the factors that contribute to the emergence of innovation among enterprises in the context of a CEE economy based on two main theories: the classical Schumpeterian theory and the enterprises’ productive-side effects theory. According to the literature, CEE companies can be characterized by many limitations in relation to innovative activity. Since this kind of activity involves a big risk (Gorzeń-Mitka 2013), a high financial commitment, and requires adequately M. Gosztonyi (✉) Universiti Malaya, Asia-Europe Institute, Kuala Lumpur, Malaysia © Springer Nature Switzerland AG 2023 L. Radomir et al. (eds.), State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM), Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-031-34589-0_17

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educated and creative staff, innovation often goes beyond the capacities of CEE companies (Okręglicka 2014). However, the researchers also draw attention to the fact that a strengthening process seems to be emerging among those enterprises which products or services are based on the innovation-based demand of their customers (Sachpazidu-Wójcicka 2017; Latusek-Jurczak 2014). Another barrier in the CEE market is the creation of interorganizational linkages and different kinds of cooperative relationships in order to create a competitive advantage, such as innovation networks (Latusek-Jurczak 2014). Therefore, CEE companies’ owners, management and employee’s perspective of innovation shows an internal contradiction that, on the one hand, affirms innovation as the primary source of income but, on the other hand, shows significant risk due to the necessity of assigning additional expenses for this purpose (Szopik-Depczyńska 2015; Zygmunt 2017; Lewandowska and Stopa 2018). Semi-peripheral countries are countries with strong entrepreneurial intensity (de Noronha Vaz and Nijkamp 2009), as incomes are relatively low, so an entrepreneurial career is more favourable to earn a decent income. A growing trend suggests that knowledge-based economy facilitate the flow of innovations within and between semi-peripheral, centre and peripheral regions and are key sources of innovation and economic growth in these countries (Huggins and Izushi 2007; Huggins and Johnston 2009; Grossman and Helpman 1994; Harris 2001; Ibert 2007; Zucker et al. 2007; Romer 1986; Glaeser 1999; Ikeda 2008). For this reason, it is essential to analyse semi-peripheral countries in innovation research, as their interaction with centre and peripheral countries outlines the process of innovation in the developed countries as well. Linking innovation and business is, by no means, a new idea. The classical economics theories of enterprise have all given prominence to the concept of innovation. Perhaps one of the most influential ones, Schumpeter’s (1934) and Weber’s (1978 [1921–1922]) entrepreneurial theory has defined innovation as a central concept of entrepreneurs. It creates ‘creative destruction’, which can underpin long-term economic growth. Innovation, in this sense, is a necessary condition for the action of the ‘cultural innovator’, that is, the entrepreneur. The causal chain leading to business innovation in this theory clearly leads from the entrepreneur to the innovation. In these theories, the understanding of the entrepreneur’s personality, innovation attitude and psychological and sociological characteristics are key elements for the creation of business innovation. Innovation is, thus, assumed to be the key function of the sociological archetype of an entrepreneur. Kirton’s (1987) highimpact theory explores the styles, cognitive elements of creativity, decision-making and problem-solving processes and outlines the profile of an innovative entrepreneur. In our research, we use these theories, as we emphasise the entrepreneur’s personality as playing a key role in innovation. Thus, we focus on what visions can be related to entrepreneurs, how they can be placed in a creative-cognitive context, what their incentives are, why they started their business, how they perceive problems and how they respond to them. Contradicting Schumpeter’s theory, Baumol (1968) did not interpret business innovation from the demand side of entrepreneurs but from the supply side of enterprises and from the competition among them. According to this theory,

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productive enterprises are the ones primarily encouraging entrepreneurs to innovate and not the other way around. Moreover, in Kirzner’s (1973) theory, the exploration of opportunity as a source of innovation is a central element of entrepreneurship. This theoretical direction reverses the chain of causality and designates the innovation environment as an entity that leads to the development of the innovative entrepreneur. Thus, business innovation occurs mainly in firms that combine multiple abilities, knowledge, resources and skills (Fagerberg 2006). Higher levels of economic activity create new business opportunities, which means that entrepreneurs may become interested in entering new markets and exploiting new business opportunities by creating more competitive products (Hayton 2005). Therefore, the enterprise’s economic activity positively affects innovation (Zotto and Gustafsson 2008). Although at first glance a contradiction can be discovered between the two theoretical directions, we will show in our paper that the two opposite theories can drive the innovation on the market at the same time. In our paper, we work with a complex definition of entrepreneurial innovation: business innovation in our research is a successful realisation and materialisation of something new; this can refer to gradual, radical or revolutionary changes in techniques, products, processes, ways of thinking or organisations (Mckeown 2008; Taylor and Schroeder 2003). Thus, innovation is about creating positive change, which reduces costs and maximises productivity (Taylor and Schroeder 2003). Therefore, we focus on technological innovation, technology change and technology development. Furthermore, the emergence of entrepreneurial innovation is assumed to be a three-sided effect in our paper. It is assumed, in line with the classical Schumpeterian theory, as an effect from the entrepreneur side, as an effect from the enterprise itself and as a result of socio-economic mechanisms (environment). Since the 2000s, certain segments of innovation research that emphasise the role of enterprises in the innovation process have begun to build their theories on the typology of the sustainable enterprise (Hockerts and Wüstenhagen 2010). Indeed, the market system has seen the emergence of sustainable development companies that have created innovative techniques, products or processes that drive environmental or social goals and succeed in the consumer market. In these enterprises, environmental and social goals are an integral part of these businesses’ economic strategy (Dacin et al. 2002; DiMaggio 1988; Holm 1995; Ostrom 1990). Behind the achievement of social goals and the emergence of entrepreneurial activities for environmental development are specific market failures that firms seek to address through innovation (Cohen and Winn 2007; Cohen et al. 2008). Agreeing with Stevenson and Jarrillo-Mossi (1986), we believe that the emergence of innovative enterprises is not merely an entrepreneurial skill or exclusively an attribute of a particular enterprise, but a process. Hence, the contextual role of culture and the socio-economic-environmental context must be included in the analysis of innovation to have a systemic understanding of innovative enterprises in semi-peripheral markets. However, the inclusion of the entire macro-context in the analysis is not an easy task due to its complexity. In our research, we create a simulation of the macrocontext, in which we examine the effect of the macro-context on innovation only

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through cognitive, contextual beliefs based on Bandura (1986) and Bruton et al. (2010). The triple (entrepreneur–enterprise–environment) theoretical impact mechanism is an appropriate context for exploring business innovation in CEE countries and can highlight the direction of causal chains that primarily determine the development of innovation.

2 Methodology Our research fits into the methodological framework of process tracing, which is primarily an inductive methodological approach useful in theory development (Bennett and Checkel 2015). Our theoretical framework includes hypothetical, interrelated events and processes that require the (re)constitution of agents through social and economic structures, and an analysis of cognitive processes and process tracing was performed to examine these complex mechanisms of action (Blatter and Haverland 2012). Our process tracing analysis was performed using partial least squares-structural equation modelling (PLS-SEM). PLS-SEM is a method of analysing complex systems; it provides a system-wide interpretation (Hair et al. 2019; Hair et al. 2022). On the one hand, from a structural analysis point of view, it approaches the data points with substantive hypothesis testing to explore the relationships between exogenous and endogenous latent variables, and on the other hand, it builds on and analyses system theory at the system level (Hair et al. 2016; Sarstedt et al. 2021; Basco et al. 2022). When examining complex systems such as business innovations (Byrne and Callaghan 2013), examining the explanatory variables separately is insufficient; analysing them at the system level would be more worthwhile. The effects in the model of business innovations form a nonlinear (Nicolis 2012), third-order system (Deacon 2007), which is far from the equilibrium (Reed and Harvey 1996) and has multiphase correlations (DeLanda 2005) that can be described by autopoiesis (Maturana and Varela 1980), structure, hierarchy (Cilliers 2001) and control parameters, respectively. Therefore, being a multivariate technique that can capture latent dimensions and examine their combined effects at a systemic level, SEM is an appropriate method for researching business innovations in developing economies (Dijkstra and Henseler 2015). It allows the simultaneous examination of the whole set of equations and, where appropriate (e.g., when evaluating interactions), the correction of errors in the equations, as it simultaneously estimates the model parameters and the fit of the model (Johnson and Sohi 2014). PLS-SEM seeks to maximise the full explained variance of endogenous constructs/indicators when estimating model parameters (Hair et al. 2017; Reinartz et al. 2009). It treats the constructs as a complex system and uses the full variance to estimate the parameters of the model and not to explain it. PLS-SEM is a two-layer modelling process. The first layer (measurement model) consists of the latent generated from the measured variables and the variables to be explained defined in

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the hypothesis. Thus, the first layer captures the relationship between the manifest and the latent variables. The creation of this model can be reflective or formative (Sarstedt et al. 2022). 1 The second layer (structural model) identifies the causal relationships between the latent variables. The evaluation of the PLS-SEM measurement models is based on the value of multicollinearity (variance inflation factor (VIF)) developed for determining the reliability of internal consistency, the reliability of indicators (average variance extracted (AVE)), the convergence validity and the heterotrait-monotrait ratio (HTMT) (Rasoolimanesh 2022). In the case of reliable and valid measurement models, it is possible to move on to the evaluation of the structural model, for which several metrics were used as well. In the models, we measured the direct, indirect and total effects, and their strength is expressed as the standardised path coefficients (βs) and their significance (Hair et al. 2017). Path coefficients range from -1 to +1, where higher absolute values indicate stronger (predictive) relationships between constructs. The commonly used p-value and the explanatory force f 2 were also used to evaluate the model. Perhaps the best-known statistic to quantify the magnitude of the prediction error for the whole model is the root mean square error of predictions (RMSE), but this measure is rarely used for PLS-SEM. Rather, the PLS-SEM methodology uses the standardised root mean square residual (SRMR) and the Bentler and Bonett index, also known as the normed fit index (NFI). SRMR converts both the sample covariance matrix and the theoretical covariance matrix into correlation matrices. The SRMR allows the measurement of the differences between the average magnitude of observed and expected correlations as an absolute measurement for the model’s fit criterion.

3 Sample and Variables Our research sample is based on the 2021 Hungarian and Polish Global Entrepreneurship Monitor (GEM) data. Each sample was developed by random multistage stratified sampling, as a result of which the samples are representative of the regional location of Hungarian and Polish small- and medium-sized enterprises (SMEs) and the age and gender of Hungarian and Polish entrepreneurs. The Hungarian sample constituted 2016 and the Polish 8000 responses. However, for the present analysis, we further narrowed our sample to those respondents who had businesses or planned to start a business. Therefore, we worked with a sample of 366 responses in the Hungarian dataset and 1037 in the Polish one, after data cleansing. In our analysis, we sought a causal explanation of the enterprises implementing innovation, for which we used the space stretched by 31 measured variables (Table 1). The 31 measured variables plotted eight latent variables, which were grouped according to the theoretical categories (entrepreneur–enterprise–

1 The reflective approach seeks to maximize overlap between indicators, while the formative model seeks to minimize it.

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Table 1 Measured and latent variables included in the analysis Variable name sumotiv1 sumotiv2 sumotiv3 sumotiv4 creativ oppism

proact

vision

susdg_env

susdg_pri

susdg_soc

omcstnat

sucpgovres

Question To make a difference in the world To build great wealth or have a very high income To continue a family tradition To earn a living because jobs are scarce Other people think you are highly innovative You rarely see business opportunities, even if you are very knowledgeable in the area Even when you spot a profitable opportunity, you rarely act on it Every decision you make is part of your long-term career plan When making decisions about the future of your business, you always consider environmental implications such as preservation of green areas, reduction of the emission of pollutants and toxic gases, selective garbage collection and conscious consumption You prioritize the social and/or environmental impact of your business above profitability or growth When making decisions about the future of your business, you always consider social implications such as access to education, health, safety, inclusive work, housing, transportation, quality of life at work, etc. Do you have any customers in the following locations? Elsewhere in your country? In your country, the government has so far effectively responded to the economic consequences of the coronavirus pandemic

Latent variables Reason of business

Abbreviation ROB

Creative visionary

CV

Environmental and social impact of business decisions

BDES

Crisis as an opportunity

CO

Theorybased categories Individual/ innovator context

Context of business innovations

(continued)

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Table 1 (continued) Variable name sucpnewopp

sucptech1

sucptech2

omnewprod

suacts

suown suowners

suwage

omcrgrow

omcstexp

omown omrstart

Question The coronavirus pandemic has provided new opportunities that you want to pursue with this business In response to the coronavirus pandemic, has your business made any changes in its use of digital technologies for selling your product or service? Do you expect your business will use more digital technologies to sell your product or service in the next 6 months? Are any of your products or services new to people in the area where you live, people in your country or the world? Over the past 12 months, have you done anything to help start this new business? Will you personally own all, part or none of this business? How many people, including yourself, will both own and manage this new business? Has the new business paid any salaries, wages or payments in kind, including your own, for more than 3 months? And compared to 1 year ago, are your expectations for business growth much lower, somewhat lower, about the same as a year ago, somewhat higher or much higher? Do you have any customers in the following locations? Outside your country? Do you personally own all, part or none of this business? Did you start this business?

Latent variables

Abbreviation

An enterprise that offers an innovative product or service

EOIPS

Owned international turbulent business

OITB

Theorybased categories

(continued)

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Table 1 (continued) Variable name equalinc

nbgoodc

nbmedia

nbsocent

nbstatus

easystart

omnewproc

Question In my country, most people would prefer that everyone had a similar standard of living In my country, most people consider starting a new business a desirable career choice In my country, you will often see stories in the public media and/or the Internet about successful new businesses In my country, you will often see businesses that primarily aim to solve social problems In my country, those successful at starting a new business have a high level of status and respect Subjective perception of the social context Are any of the technologies or procedures used for this product or service new to people in the area where you live, the people in your country or the world?

Latent variables Observation of the business context

Abbreviation OB

Subjective perception of the social context Innovative technology

SC

IT

Theorybased categories Socio-economic context

Innovation

environment). Based on this, we measured the effect of individual entrepreneurial innovation with three latent variables, the effect of entrepreneurship with three latent variables and the effect of the cognitive macroenvironment on the development of innovation with one latent variable. We measured the effect of the entrepreneur on innovation, as well as the extent to which the goal of the entrepreneur was to change the world and achieve high income and wealth or continue the family tradition (ROB), the extent to which the entrepreneur can be considered creative and visionary (CV) and the extent to which the entrepreneur makes decisions based on sustainability goals (BDES). We measured the effects of the company on the innovation, as well as the extent to which the company sees a crisis as an opportunity (CO), if the company has a new product or service or if it has renewed itself in some other way in the past year (EOIPS), and whether the company is on a turbulent, growing trajectory economically and has international connections (OITB). We measured the effect of the macro-context from the cognitive side, and we considered in the analysis whether the entrepreneur is convinced that the media give a prominent role to entrepreneurs

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in the socio-economic context and whether the entrepreneur is convinced that the businesses in the socio-economic environment address social problems (OB). As an outcome variable, we used the emergence of innovative technology (IT), which embodies business innovation, as it includes whether a particular enterprise has a new technology, process, product or service. The measured variables were characterised as having a small, random amount of missing data in each sample (3–5%). Therefore, the missing data were replaced with the artificial intelligence-based bagging methodology (Friedman and Popescu 2003). Of the sampled enterprises, in Hungary 18.7% have implemented some form of technological innovation (omnewproc), while this was only 7.3% in Poland, and only 15.1% of enterprises have developed a new product or service (omnewprod) in Hungary and only 6.3% in Poland. This means that only about one-fifth of businesses are innovative businesses in Hungary and only one-eleven in Poland. These innovative businesses are young businesses in both country (HU: 38.3%, PL: 37.4%). Surprisingly, most of them operate in the low-tech sector, in the retail or in the professional service sector. Innovative enterprises operate mostly in the capital and county capitals, as well as in the developed regions of the countries. There is a particularly high proportion of men and those who have a high level of education among their CEOs.

4 Hypotheses In our research, we aim to determine the factor effects that distinguish innovative companies in the emerging market of tow CEE countries (Hungary and Poland) from the needs-based companies. Based on the literature, we assume a triple-side framework for our innovation model in which the impact of the individual, that is, the entrepreneur, the impact of business performance and the macro-context (i.e. the cognitive socio-economic environment) are identified as factors that affect the emergence of innovation. Thus, our hypothesis system explores the relationship between entrepreneurship and innovation in a semi-peripheral country. Furthermore, this system of hypotheses also allows us to explore the cause-effect relationships between the three factors, so we can answer which theoretical trend explains why businesses create innovations. We formulated 17 hypotheses, which are presented in Table 2. Each hypothesis assumes the presence of a particular mechanism of effect among the latent variables. The system of these mechanisms of effects outlines the business innovation model operating in the developing country, which is shown in Fig. 1. In the PLS-SEM-based model 2 of innovative enterprises, we used first-order latent variables both counties’ model, which are indicated by circles in Fig. 1. The models were created from a total of eight first-order latent variables, each of which is 2

The analysis was performed with the SmartPLS 3v software (Ringle et al. 2015).

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Table 2 Description of hypotheses No. H1 H2 H3 H4 H5 H6 H7 H8 H9 H10 H11 H12 H13 H14 H15 H16 H17 H18 H19 H20 H21 H22 H23 H24

Description Crisis as an opportunity has a positive impact on the development of innovative technology Crisis as an opportunity has a positive impact on business decisions Crisis as an opportunity has a positive impact on reason for business Observation of the business context has a positive impact on the reason for business Observation of the business context has a positive impact on creative visionary Observation of the business context has a positive impact on the development of innovative technology Observation of the business context has a positive impact on crisis as an opportunity Observation of the business context has a positive impact on business decisions Reason for business has a positive impact on the development of innovative technology Reason for business has a positive impact on an enterprise that offers an innovative product or service Creative visionary has a positive impact on business decisions Creative visionary has a positive impact on an enterprise that offers an innovative product or service Innovative technology has a positive impact on creative visionary Business decisions has a positive impact on the enterprise that offers an innovative product or service Business decisions has a positive impact on the development of innovative technology Business decisions has a positive impact on an owned international turbulent business An enterprise that offers an innovative product or service has a positive impact on an owned international turbulent business An enterprise that offers an innovative product or service has a positive impact on the development of innovative technology An enterprise that offers an innovative product or service has a positive impact on the crisis as an opportunity An owned international turbulent business has a positive impact on the development of innovative technology Subjective perception of the social context has a positive impact on the development of innovative technology Subjective perception of the social context has a positive impact on business decisions Subjective perception of the social context has a positive impact on reason for business Subjective perception of the social context has a positive impact on the observation of the business context

built into the base models as a reflective measurement model. Although it is worth to mention that the composite reliability of the measured variables of the reflective models was above the 0.095 threshold in three cases in the Polish model, these variables may not avoid redundancy (see Table 6 in Appendix). The AVE values for the constructs are also higher than the cut-off value (0.50); thus, the criterion for the convergence validity of the models is met. As conceptually similar indicators have been included in the measurement models, the HTMT values must be less than 0.90. All cases fulfil this requirement, which implies that the reflective models also meet the discriminant criterion. Table 7 in the Appendix summarises the indicator

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Fig. 1 The system of the research hypotheses, the measured variables and the latent variables of the PLS-SEM model of innovation

loadings and collinear statistics (VIF) of the measured variables in each country. In our models, the VIF values for the measured variables are adequate in all cases (above 1.0), which indicates that collinearity is absent. Furthermore, reflective indicator loadings above 0 are also suitable. 3 3

In our study, to compare Hungarian and Polish data, it would be obvious to carry out a multigroup analysis (Matthews 2017). Although, we chose another method for the comparison, because we think that the construction of the latent variables depends to a large extent on the given sociocultural context, and they may show differences between the two countries. As a result of all this, we used the same model that was described at the hypotheses part in our paper as a starting point for both countries; however, the final latent variables were then developed taking into account the specifics of the given country. As a result of this, the latent variables in the two models do not contain the same measured variables in all cases, but were formed along the lines of what kind of latent variable can be formed from the basic set of variables in the given country. Consequently, it was not possible to carry out a multigroup analysis during the comparison, because the models of the two countries do not match perfectly. Consequently, to facilitate easier understanding, we did not include the SC latent variable in the Hungarian figures, because a latent variable matching the model could not be formed based on the measured variables.

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A 5000-sample bootstrap method 4 was used to test the significance of the path coefficients. The SRMR of the models is around 0.10 (HU: 0.09, PL: 0.10), which is a good fit (Hu and Bentler 1998), and the NFI is around 0.900 (HU: 0.912, PL: 0.900), which indicates an acceptable fit (Lohmöller 1989) in the models. Our results, therefore, show that the models fit the statistical criteria. The t-values for the structural model are shown in Fig. 2 and Table 3. The table and the figure show only the significant path coefficients. Based on our significant models (Fig. 3), business innovation is shaped by all three dimensions (entrepreneur, enterprise and environment) in Hungary and in Poland as well, but in a different casual system (Table 4). In Hungary as a direct effect, technological innovation is affected by the latent variables of environmental and social impact of business decisions (β = 0.11), an innovative product or service (β = 0.46) and the possession of an owned international turbulent business (β = 0.21). However, to realise these direct effects, a number of other effects must be also present. Environmental and social impact of business decisions should be affected by crisis as an opportunity (β = 0.13), creative visionary (β = 0.21) and observation of the business context (β = 0.16). For the effect of an enterprise that offers an innovative product or service to the innovative technology, the effect of reason for business (β = 0.28) is needed, as well as the effect of environmental and social business decisions (β = 0.34) and creative visionary (β = 0.12). The impact of an owned international turbulent business requires the environmental and social impact of business decisions (β = 0.16) and the impact of an enterprise that offers an innovative product or service (β = 0.45). Finally, it is important to highlight the impact of the observation of business context on the model as it applies to crisis as an opportunity (β = 0.27), creative visionary (β = 0.19) and reason for business (β = 0.21). In the case of Hungary, the role of subjective perception of the social context in innovation cannot be measured. On the contrary, in the case of Poland, it is one of the most important areas of technological innovation (β = 0.15). Compared to the Hungarian model, the Polish model is built on the basis of an opposite cause-andeffect network. Technological innovation and the enterprise innovation determine the innovative entrepreneur, and technological innovation depends directly on subjective perception of environment. Technical innovation has a strong influence on why someone intends to create a business (β = -0.19) and how creative they feel about themselves (β = 0.12). Although no significant correlation can be observed in the model between crisis as an opportunity and technical innovation, the factor plays an important role in business decisions (β = 0.228) and reason of business (β = 0.482). In the model, the social environment appears as a strong influencing factor in the development of innovation, and the innovations coming from the side of the business, and it shapes the innovative entrepreneur.

4

The bootstrap method is a nonparametric procedure that allows testing the statistical significance of different PLS-SEM results, including the significance testing of the path coefficient, R2 values, VIF and HTMT values.

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Fig. 2 The PLS-SEM and bootstrap model of business innovation in Hungary (N = 366) and Poland (N = 1037)

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Table 3 Results of the bootstrap procedure (Hungary)

BDES → EOIPS BDES → IT BDES → OITB CO → BDES CV → BDES CV → EOIPS EOIPS → IT EOIPS → OITB OB → BDES OB → CO OB → CV OB → ROB OITB → IT ROB → EOIPS

Path coefficient (β) Original sample Bootstrap 0.34 0.34 0.11 0.11 0.16 0.16 0.13 0.13 0.21 0.21 0.12 0.12 0.46 0.46 0.45 0.45 0.16 0.16 0.27 0.28 0.19 0.19 0.21 0.21 0.21 0.21 0.28 0.28

STDEV 0.04 0.04 0.05 0.05 0.05 0.05 0.06 0.05 0.05 0.05 0.05 0.05 0.05 0.05

Bootstrap confidence intervals (2.5%; 97.5%) (0.117; 0.536) (-0.053; 0.260) (0.057; 0.246) (-0.065; 0.324) (0.144; 0.272) (0.101; 0.135) (0.398; 0.516) (0.371; 0.525) (0.085; 0.244) (0.195; 0.346) (0.128; 0.246) (0.107; 0.296) (0.144; 0.272) (0.117; 0.430)

In summary, the business innovation in Hungary and in Poland takes place in a complex system; however, in the two countries, innovation develops based on a completely different causal chain. While in Hungary the innovative entrepreneur is the one who creates innovation in a non-innovative environment, in Poland we can observe the exact opposite; innovative entrepreneurs are formed through the innovative environment and business. Based on these, the innovation development of Hungary can be described with the classic Schumpeterian theory, while the innovation in Poland can be characterized with enterprises’ productive-side effects theory. As a summary of our research results, Table 5 presents the verification of our hypotheses. We could not verify H1, H3, H6, H9, H13, H19 and H21–H24 among our hypotheses on the Hungarian dataset, but all the other hypotheses were verified. In the Polish model, we cannot verify H1, H4, H6–H8, H10, H12, H14–H18, H20 and H22. Based on which the two countries operate on the same market, the business innovation is created with two completely different logics; therefore, business innovation can be explained with two different theories.

5 Discussion In our paper, we examined, in the case of semi-peripheral countries (Hungary and Poland), based on representative data, the casual effects for business innovation, based on the two most important theoretical trends. Our data show that the proportion of innovative enterprises is low in both countries, accounting for about one-fifth in Hungary or one-eleven in Poland of all

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Fig. 3 The path coefficients and factor weights of the Hungarian (N = 366) and Polish (N = 1037) models

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Table 4 Results of the bootstrap procedure (Poland)

CO → BDES CO → ROB CV → BDES EOIPS → CO IT → CV IT → ROB OB → CV SC → IT SC → OITB SC → ROB

Path coefficient (β) Original sample 0.228 0.482 0.130 -0.232 0.120 -0.019 -0.279 -0.155 -0.102 -0.114

Bootstrap 0.237 0.481 0.126 -0.235 0.122 -0.019 -0.282 -0.159 -0.103 -0.113

STDEV 0.107 0.079 0.048 0.104 0.033 0.009 0.031 0.040 0.040 0.038

Bootstrap confidence intervals (2.5%; 97.5%) (-0.223; 0.196) (-0.163; 0.150) (-0.103; 0.086) (-0.195; 0.194) (-0.066; 0.062) (-0.019; 0.015) (-0.062; 0.056) (-0.079; 0.075) (-0.075; 0.084) (-0.075; 0.076)

enterprises. These businesses are typically SMEs, located in the more economically developed parts of the country, in large cities. To explore the impact mechanisms that lead to innovations, we analysed our data using the PLS-SEM methodology. Our results show that business innovation in semi-peripheral countries results from the combined effect of a complex tripartite system (entrepreneur-enterprise-environment). However, the causality of these factors differs from country to country. In countries where the environment is less innovative, such as Hungary, the entrepreneur has a direct impact on the creation of innovation, and the influence of the environment almost completely disappears. In these systems a creative and visionary entrepreneur must create an enterprise with which they ‘[want] to change the world’ while gaining a satisfactory amount of wealth. Contrary to this, in the economy where the environment is more supportive of innovation, like Poland, the enterprise itself have a direct impact on the creation of innovation, and this has an impact to the creation of innovative entrepreneur. In these systems the enterprise itself must achieve a level of economic development embedded in an international network. In addition, the enterprise needs to handle crisis issues and obstacles as opportunities. All this can create the appearance of a new product and service in the company, which has a direct positive impact on the appearance of entrepreneur innovation. Thus, while in one case the development of innovation can be explained mainly by the Schumpeterian theory, in the other case, the enterprises’ productiveside effects theory can be fruitful in the explanation. An additional but important result of our study is that innovation in semiperipheral countries can be closely linked to sustainable goals, as both the entrepreneur and the business must prioritise goals and decisions in keeping with social and environmental sustainability to crate innovation. Our study is not without limitations. For our model, we used two sample of two CEE countries; therefore, the study can be made more accurate if other CEE

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Table 5 Verification of the research hypotheses

CO → IT CO → BDES

Hypothesis H1 H2

CO → ROB

H3

OB → ROB

H4

OB → CV

H5

OB → IT OB → CO

H6 H7

OB → BDES

H8

ROB → IT

H9

ROB → EOIPS CV → BDES

H10

CV → EOIPS

H12

IT → CV

H13

BDES → EOIPS BDES → IT

H14

BDES → OITB EOIPS → OITB EOIPS → IT

H16

EOIPS → CO

H19

OITB → IT

H20

SC → IT

H11

H15

H17 H18

Hungary Relationship Not significant Significant, positive Not significant Significant, positive Significant, positive Not significant Significant, positive Significant, positive Not significant

Result Not verified Verified Not verified Verified Verified

Poland Relationship Not significant Significant, positive Significant, positive Not significant

Result Not verified Verified Verified Not verified Verified

Not verified Verified

Significant, negative Not significant Not significant

Verified

Not significant

Not verified

Not verified

Significant, negative Not significant

Verified

Significant, positive Not significant

Verified

Verified

Not verified Not verified

Significant, positive Significant, positive Significant, positive Not significant

Verified

Significant, positive Significant, positive Significant, positive Significant, positive Significant, positive Not significant

Verified

Significant, positive Not significant

Verified

Not significant

Not verified

Verified

Not significant

Not verified

Verified

Not significant

Not verified

Verified

Not significant

Not verified

Not verified

Significant, negative Not significant

Verified

Significant, negative Not significant Significant, negative Significant, negative

Verified

Verified Verified Not verified

Verified

H21

Significant, positive Not significant

SC → BDES SC → ROB

H22 H23

Not significant Not significant

Not verified Not verified

SC → OITB

H24

Not significant

Not verified

Not verified

Not verified

Not verified

Not verified

Not verified

Not verified Verified Verified

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European Union countries are analysed. Our research can be further refined by including additional macro-contextual variables as well. In our research, we included only cognitive variables to measure the macro-context, but this could be extended to include either performance requirements (McClelland 1961) or risk-taking variables (Gartner and Carter 2003), as well as measures of context regulators, values and institutions. In addition, we think that it would be important to supplement the analysis with measures of state corruption and freedom of economic competition. By highlighting these metrics, it would be possible to further deepen the knowledge about the countries following Schumpeterian logic. We could understand that the innovative entrepreneurs in these countries, like Hungary, may follow an innovation structure which is independent of their environment because the market itself does not operate along the lines of a free and open economy. In summary, the materialisation of business innovation is the result of a complex three-dimensional mechanism of effects that requires not a single factor but a combination of factors. In all of this, there is a need for entrepreneurs who can ‘dream’ and stimulate innovation along the lines of sustainability. However, having a macro-environment is equally important, as it would allow them to implement these innovations and develop their entrepreneurial spirit and can create the innovative entrepreneur. Furthermore, there is a need for an economically prosperous business with international connections, which can provide an appropriate framework for creating new products and services, and the tendency to see challenges as opportunities and not as problems. The combined effect of all this is needed for the creation of business innovation in the CEE market, which can lead later to economic growth.

Appendix

Table 6 Reliability and validity of the latent variables (Hungary and Poland) Hungary

BDES CO CV EOIPS IT OB OITB ROB

Poland Composite reliability 0.83 0.73 0.79 0.79 1.00 0.76 0.78 0.81

Average variance extracted (AVE) 0.71 0.59 0.65 0.65 1.00 0.62 0.54 0.68

BDES CO CV EOIPS IT OB OITB ROB SC

Composite reliability 0.92 1.00 0.74 0.94 1.00 0.85 1.00 1.00 0.76

Average variance extracted (AVE) 0.80 1.00 0.59 0.89 1.00 0.66 1.00 1.00 0.52

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Table 7 Indicator loadings of measured variables and their VIF (collinear statistics) values (Hungary and Poland) Hungary creativ nbmedia nbsocent omcrgrow omcstexp omcstnat omnewproc omnewprod omown suacts sucpnewopp sumotiv1 sumotiv2 susdg_env susdg_soc vision

Poland Indicator loadings 0.91 0.89 0.67 0.73 0.73 0.52 1.00 0.85 0.74 0.76 0.95 0.89 0.75 0.87 0.82 0.69

VIF 1.12 1.07 1.07 1.12 1.19 1.06 1.00 1.10 1.24 1.10 1.06 1.16 1.16 1.22 1.22 1.12

creativ easystartL nbgoodcL nbmediaL nbstatusL omcrgrow omnewproc opportL sucpnewopp sumotiv1 suown suowners susdg_env susdg_pri susdg_soc suskillL vision

Indicator loadings 0.86 0.76 0.81 0.79 0.85 1.00 1.00 0.54 1.00 1.00 0.94 0.94 0.94 0.85 0.89 0.84 0.67

VIF 1.04 1.41 1.62 1.41 1.52 1.00 1.00 1.27 1.00 1.00 1.49 1.49 1.28 1.99 1.19 1.13 1.04

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Institutional Context and Entrepreneurship Typologies: The Moderator Role of Environmental Uncertainty Serap Korkarer, Mahmut Hızıroğlu, and Joseph F. Hair Jr.

1 Introduction In the past two decades, the field of entrepreneurship has expanded rapidly as an independent discipline. However, the theoretical foundations of the “opportunity recognition” phenomenon, one of the most important concepts in the discipline, are still being questioned (Alvarez et al. 2016; Shane and Venkataraman 2000). The debate centers around the epistemological foundations of “opportunity recognition” and focuses on the question of whether there are opportunities waiting to be discovered regardless of entrepreneurs’ perceptions, in contrast to opportunities created by the actions of entrepreneurs (Alvarez and Barney 2007). “Opportunity creation,” rooted in Schumpeter’s notion of “creative destruction,” views the opportunities as a subjective phenomenon within which entrepreneurs emerge. Moreover, opportunity definition is linked to creativity in a fragmented and ambiguous context in which the entrepreneur creates reality rather than choosing it (Weick 1979). On the contrary, “opportunity discovery” from a Kirznerian perspective assumes that opportunities exist objectively in the market, independent of the entrepreneur. The job of the entrepreneur, therefore, is to identify these opportunities and take advantage of them. Despite their epistemological differences, and that the type of entrepreneurial actions (EA) is context dependent (Sarasvathy et al. 2003), these two different definitions of opportunity complement each other. Nevertheless, due to the claim

S. Korkarer (✉) · M. Hızıroğlu Department of Management, Faculty of Economics, Istanbul University, İstanbul, Turkey e-mail: [email protected] J. F. Hair Jr. Mitchell College of Business, University of South Alabama, Mobile, AL, USA e-mail: [email protected] © Springer Nature Switzerland AG 2023 L. Radomir et al. (eds.), State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM), Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-031-34589-0_18

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that the phenomenon of “opportunity discovery” (Kirznerian entrepreneurship) is more common in practice (Filser et al. 2020), only one aspect of opportunity identification is the subject of empirical research. Criticisms of the understanding that a single comprehensive definition of opportunity is necessary to make progress in the field of entrepreneurship (i.e., Alvarez and Barney 2020; Foss and Klein 2020) indicates there is a need for empirical studies that explore both aspects of this phenomenon. Few empirical studies incorporate both aspects of opportunity definition in the research design. For instance, de Jong and Marsili (2015) investigated the extent to which opportunities, realized in the form of new products introduced in the market, could be considered of Schumpeterian versus Kirznerian nature. Similarly, Smith et al. (2009) examined how relative differences in the degree of opportunity tacitness relate to the process of opportunity identification. Another study by Vaghely and Julien (2010) described a model which can provide a framework for understanding the entrepreneur’s use of information to identify opportunities. Finally, Welter (2012) described the boundary conditions between opportunity types and used these boundary conditions to offer empirical guidance for testing the different types of opportunities. Although the abovementioned studies provide new and interesting insights into the nature and antecedents of different types of EA, only Welter’s work (2012) has linked the opportunity type to context. Studies that associate context with entrepreneurship conceptualize entrepreneurship as a single construct and examine the impact of contextual features on entrepreneurship (Alvarez and Urbano 2011). To date, empirical consideration on how institutional context (IC) influences the different type of EA is a relatively neglected issue. Moreover, the literature on EA which is generally combined with institutional theory has frequently been studied in developed countries (Su et al. 2017). It is implicitly assumed that countries have their own national entrepreneurship system (Busenitz et al. 2000) and that an IC in a country is perceived in the same way by everyone. But according to the Etzioni’s assessment of the legitimacy of entrepreneurship, due to the different subcultures, ideologies, and religious understandings in a society, the institutional environment can be perceived differently within a country (Etzioni 1987). The objective of the present study, therefore, is to empirically examine the opportunity identification debate by investigating the role of the normative, regulatory, and cognitive perceptions of IC on creative and discovery EA. Based on a contingency approach, the behavior of actors is further conditioned by the environmental uncertainty (EU). Welter (2012) claims the existence of EU can define the line between creation and discovery and therefore outline the possible actions of entrepreneurs in the face of uncertainty (Knight 1964; Sarasvathy 2001). In this study we further posit that EU moderates the relationship between the perception of IC and the type of EA. The data for this research is collected in Turkey, a rich source for institutional theory studies (Taş and Hızıroğlu 2016). Turkey has a market with free competition, which generally exhibits an oligopoly structure. While the role of the state is as an actor supporting the private sector, it also shapes the market (Bugra 2007; Taş and Hızıroğlu 2016). Moreover, the bourgeoisie is not immune to the ideological and

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Environmental Uncertainty Perceived Institutional Context

+H2a +H1a

Regulative Context

Discovery EA

+H1 Normative Context Creative EA Cognitive Context

+H2

Fig. 1 Theoretical model

political divisions in Turkey, which in turn may affect the perceptions of entrepreneurs. Thus, the ideologies of interest groups and ruling elites in Turkey shape both their own and followers’ thoughts and actions and assume the position of an important source of power for them. Given these observations, the main contributions of this study are (1) to explore whether the ideological, political, and ethnic differences in a developing country can lead to differentiation in the perceptions of the institutional environment and (2) to test whether this difference has an impact on the types of EA, and thereby provide empirical evidence of the ongoing phenomenon of opportunity recognition in the field of entrepreneurship.

2 Theoretical Model and Hypotheses Figure 1 shows the model we investigate. The theoretical model suggests firms’ IC is directly related to their strategic EA and that EU plays a moderating role in this relationship. The proposed variables are drawn from different disciplines and have been applied in previous theoretical explanations and empirical studies. In this context, each variable in the model is explained below.

2.1

Entrepreneurial Actions

EA, as a set of ideas, beliefs, and actions that enable the creation of future goods and services, are not present in the current market (Sarasvathy et al. 2003; Venkataraman 1997), thereby facilitating the potential in the environment and taking advantage of

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the opportunities (Shane and Venkataraman 2000). The EA literature focuses on processes (Cohen and Winn 2007; Dean and McMullen 2007), opportunities, and threats at multiple levels. However, the connections between the processes are not well understood (Shepherd and Patzelt 2011). In this study we propose that the relationships between multiple levels can be explained by studying causal-predictive mechanisms, situational factors, action formation, and transformation mechanisms (Coleman 1986; Hedström and Swedberg 1998). Different assumptions of teleological theories (such as motivation theory, Maslow (1943); institutional theory, DiMaggio and Powel (1983)) about the nature of the actor, nature of the entrepreneur’s objective, and nature of the decision-making context on entrepreneur’s same actions lead to different types of EA (Alvarez and Barney 2007). Based on those assumptions, Sarasvathy et al. (2003) proposed three different, context-dependent perspectives: allocative, discovery, and creative. The allocative perspective is related to allocative efficiency which is valid only when a competitive market works perfectly (Sarasvathy et al. 2003). Therefore, only the discovery and creative perspectives are examined in this research.

2.1.1

Discovery EA

Opportunities created by external causes such as changes in technology, consumer preferences, and the context of the firm or industry (Dew et al. 2004; Kirzner 1973) are independent of individual opportunities, waiting to be discovered outside (Alvarez and Barney 2007; Knight 1964). Entrepreneurial firms must be alerted to proactively take advantage of information asymmetry despite the errors in the market. However, since profit opportunities will attract the attention of competitors and imitators, companies are expected to pursue these opportunities quickly with their competitive aggressive behavior style (Sundqvist et al. 2012). Therefore, firms must explore and pursue existing and latent markets (Sarasvathy et al. 2003). In this view, the focus is on asymmetric information between economic actors. The acquisition of knowledge creates opportunities for entrepreneurs to discover based on their knowledge advantage and other complementary skills (Venkataraman 1997). More specifically, Kirzner (1973) noted that alert entrepreneurs discover opportunities by shifting the “invisible” hand toward pareto optimal solutions.

2.1.2

Creative EA

The “opportunity-creating entrepreneurship” approach is based on the Schumpeterian and evolutionary economics tradition (Alvarez and Barney 2007). The increase in the level of dynamism causes supply and demand to be unknown, and economic innovations in areas such as finance and marketing must be identified to create opportunities (Grove et al. 2018; Schumpeter 1934). The most important distinguishing feature is that the entrepreneur has a causal role in the emergence of opportunities (Alvarez and Barney 2007). Thus, there is no “end” until the creative

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process takes place. That is, opportunities cannot be understood until they exist and only after engaging in an iterative process of action and reaction (Buchanan and Vanberg 1991; Weick 1979). Therefore, firms need autonomy to be innovative, take more risk (Lumpkin and Dess 1996), and “research” has almost no meaning. In creativity, the entrepreneur acts and observes how the market and customers react to their actions (Alvarez and Barney 2007; Gartner 1985; Weick 1979).

2.2

Institutional Context

Firms need to meet the demands of the technical environment to produce/serve effectively and efficiently (Scott 1995). However, the behavior of firms is a response to social pressures such as conformity and legitimacy, which they are generally exposed to due to the symbolic environment created by other organizations (Suddaby 2013), while also a product of the IC (Greenwood and Hinings 1996; Meyer and Rowan 1977; North 1991; Tolbert et al. 2011). Institutions have three types of mechanisms that affect the behavior and actions of institutions, individuals, and organizations: regulative, normative, and cognitive (Scott 1995). These institutions send signals and support firms by providing opportunities such as stability, social support, access to resources, and legitimacy (Baum and Oliver 1991). Thus, companies can identify strategic opportunities such as identifying gaps in the market and understanding customer wants and needs (Adner and Helfat 2003). Different actors in the same context choose appropriate and legitimate options for the time being under bounded rationality (Oliver 1991), and take new, creative, and different actions through different perspectives (Daft and Weick 1984; Etzioni 1987; Knight 1964; Milliken 1987). In discovering opportunities, entrepreneurial firms have to be alert to take advantage of information asymmetry despite the errors in the market (Kirzner 1973). Research shows that alert entrepreneurs regularly scan their environment for potential ideas (Busenitz 1996). Entrepreneurs rely on different types of information and clues (Lindsay and Craig 2002) and discover more opportunities by choosing effective options among research tactics, information processing ability, and determined opportunities (Murphy 2011). When firms assume their context to be measurable and clear, they discover opportunities by collecting information and making rational analyzes and measurements of opportunities (Daft and Weick 1984). Thus, firms are expected to take advantage of the opportunities in the market (Khandwalla 1987). For example, Gaur and Lu state that multinational firms have built their R&D centers in the USA due to regulatory factors such as being well advanced in copyright protection (Gaur and Lu 2007). Therefore, we propose the following hypothesis: H1: There is a positive relationship between firms’ perceptions of institutional context and their discovery entrepreneurial actions.

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Entrepreneurs create meaning about their environment based on the situations they encounter, because “means affect ends” (Weick 1979, p. 64). Signals from the institutions cause entrepreneurs to dream about potential opportunities in the market, which will lead entrepreneurs to create innovations such as products, services, processes, and ultimately markets (Khandwalla 1976). If the legal regulations made by the state increase the cooperation and trust environment among the companies, the level of using external resources to improve the innovation processes of the companies will increase (Iturrioz et al. 2015). For example, Panasonic has established a department called Eco Solutions North America, which focuses on renewable energy and energy efficiency projects in the USA and Canada, based on supportive legal regulations (Zhang et al. 2022). Moreover, national institutional patterns, such as access to research and education institutions, finance, and trained employees, enable managers to make informed EA decisions (Zablah et al. 2012), thus leading individuals to be more likely to produce creative results (Fleming et al. 2007). Societies that exhibit positive value toward creativity and change create a normative environment that leads to an increase in creative EA (Gómez-Haro et al. 2011). In addition, if the context is changing and quite demanding, managers will find the context difficult to analyze. Thus, they will undertake creative initiatives within the uncertain contexts of their environment that cannot be analyzed (Daft and Weick 1984). Therefore, we propose the following hypothesis: H2: There is a positive relationship between firms’ perceptions of institutional context and their creative entrepreneurial actions.

2.3

The Moderating Role of Environmental Uncertainty

In the strategic management and entrepreneurship literature, the firm’s business environment plays a regulating role as it affects the effectiveness of the firm’s strategy (Lumpkin and Dess 2001). EU includes market dynamism, technological dynamism, competitive intensity, and economic changes (Elbanna and Child 2007a). Since firms’ perceptions of uncertainty (ambiguity) are different from each other, they will make their decisions by considering the possibilities they create according to their own perceptions (Duncan 1972). Environmental dynamism has mixed effects on the firm. For example, it may enable firms to pursue more new opportunities in a more dynamic environment (Shane and Venkataraman 2000). On the other hand, to take advantage of these opportunities, firms must be flexible (Teece et al. 1997), take risks (Kets de Vries 1977), apply their initiative and desire to succeed (Weick and Roberts 1993), and take timely actions (Wiklund and Shepherd 2003). Since the demands and needs will not change much in static markets compared to dynamic ones, it is easier to predict compared to dynamic markets. In this case, discovery EA are expected to take advantage of the opportunities in the market (Khandwalla 1987). However, in dynamic markets where competition is high, customers have many alternatives and search for options that are satisfying. For

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this reason, firms in such markets carry out more proactive and aggressive strategies (Sarasvathy et al. 2003; Teece et al. 1997). Therefore, our hypothesis is the following: H1a: Environmental uncertainty plays a moderating role in the relationship between the firm’s perception of institutional context and its discovery EA. In an environment where competition does not occur, opportunities arise from the individuals themselves to initiate change in the economy with technological change and innovation (Buchanan and Vanberg 1991; Sarasvathy et al. 2003). In markets where such dynamism is high, new markets such as Netscape, Airbnb, Über, Amazon, and Tesla are being created (Grove et al. 2018); therefore, creative EA are expected (Teece 2007). Because there is uncertainty in these markets, customer demands are unknown, and companies need to be more innovative (García-Morales et al. 2012). In this case, companies are expected to invest more in research and development activities and take risks (Lumpkin and Dess 2001; Sundqvist et al. 2012). So, we propose as follows: H2a: Environmental uncertainty plays a moderating role in the relationship between the firm’s perception of institutional context and its creative EA.

3 Methodology Due to inconsistent findings of previous studies and the fact that no research has previously been conducted in the context of Turkey, the proposed research model is considered a causal-predictive model for exploratory, explanatory, and predictive purposes (Hair et al. 2022). Existing literature was explored for the connections of the perspectives with each other. Then, proposed relationships are explored and explained using quantitative analysis.

3.1

Research Procedure and Data Collection

An online survey method is used. First, participants were contacted by phone, their permission was obtained, and information was given about the research, and then questionnaires were sent to them in the digital environment (email, social media, WhatsApp) (Creswell and Creswell 2018, p. 90). Convenience, purposive, and snowball non-probabilistic sampling techniques were used (Coşkun et al. 2019, p. 169). The scales were adapted from established and validated scales used in previous studies in accordance with the Turkish context. Permission to use the scales was obtained from the respective authors via email. Table 1 shows the studies from which the scales were adapted.

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Table 1 Studies the scales adapted Scale Discovery EA

Creative EA

Institutional context Environmental uncertainty

Reference Jambulingam et al. (2005) Jaworski and Kohli (1993) Jambulingam et al. (2005) Busenitz et al. (2000) Elbanna and Child (2007a)

# of items 14

12

13 14

Title The Influence of Decision, Environmental and Firm Characteristics on The Rationality of Strategic Decision-Making Market orientation: antecedents and consequences The Influence of Decision, Environmental and Firm Characteristics on the Rationality of Strategic Decision-Making Country institutional profiles: Unlocking entrepreneurial phenomena The Influence of Decision, Environmental and Firm Characteristics on The Rationality of Strategic Decision-Making

The questionnaire statements were translated separately from English to Turkish and from Turkish to English by two bilingual speakers (Brislin 1980). The translated statements were evaluated by four academics in Turkey who are experts in strategic management, institutional theory, and entrepreneurship. The expressions that were not understood enough or had grammatical errors were re-evaluated considering the theoretical framework and were finalized in accordance with the approach and relevant theories. Afterward, the questionnaires were sent to the editors, academicians at different universities, and senior management of the company, the research owner works, for evaluation. After confirmation of smooth understanding of expressions, the questionnaire was finalized, and the face validity of the survey was ensured (Hair et al. 2014). Since entrepreneurial action typologies of firms are being examined, the firm level is the unit of analysis. Considering the most appropriate participant selection criteria of Huber and Power (1985), key influencers (top management, company owner, board member, etc.) are chosen as the participants (Huber and Power 1985). Therefore, the level of measurement can be described as individual. Since the individual data is then subject to a firm-level measurement while analyzing the data, the analysis level is again determined as firm. The variables consist of higher-order constructs with lower-order constructs, designed as reflective-reflective based on theoretical considerations and other similar studies.

3.2

Analysis

The measurement and structural model analyses was executed using partial least squares structural equation modeling (PLS-SEM) and the SmartPLS 4 software. For the analysis, the studies of Hair and colleagues (Hair et al. 2014; Hair et al. 2022)

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were considered. Before starting the analysis, multivariate outliers were checked with the SPSS 25 program using the Mahalanobis distance test (Tabachnick et al. 2013, pp. 91–93); a total of 17 outliers were removed from the data set. In this context, analyses were carried out with 788 survey responses. The sample size complies with the criteria specified in the literature. The scales consist of a total of 53 statements with a sample size of 788. The final sample size was sufficient, therefore, based on the Kock and Hadaya inverse square root method (Hair et al. 2014, p. 100). PLS-SEM was preferred as the analysis technique due to the various advantages mentioned in the literature (Ringle et al. 2012; Sarstedt et al. 2017). However, the main reasons for choosing PLS are its statistical power is higher than ML-based CBSEM (covariance based) (Chin et al. 2003) and the emphasis on prediction of the endogenous constructs (Hair et al. 2022). A post hoc analysis was conducted of the moderator effect. The extended repeated indicators approach was used to assess the higher-order constructs.1

3.2.1

Common Method Variance (CMB)

To minimize the likelihood of CMB, the questionnaire was designed based on guidelines by Podsakoff and colleagues (Podsakoff et al. 2003). The Harman’s single factor method was applied on a post hoc basis to examine CMB, and the common variance extracted was only 24.035% (< 50; Podsakoff et al. 2003). The results are far lower than would indicate a problem with CMB (Babin et al. 2016).

3.2.2

Descriptive Statistics and Correlation Analysis

Table 2 presents the means, standard deviations, and correlation coefficients between the variables (N = 788). There is a moderate relationship between most of the variables. The patterns of the simple correlations support the findings of the multivariate analysis that examines the theoretical relationships between the constructs measured with multi-item scales. The IC and EU are significantly correlated with creative EA and discovery EA. This appears to support the EA process model. The level of agreement with the discovery EA statements is 3.60, while 3.10 with creative one, which could be evaluated as entrepreneurs, is more prone to discovery EA. The average level of agreement value with IC is 2.81, while 2.89 with EU. Based on the average level of agreement values with IC (2.81) and EU (2.89), it may be misleading to interpret the participants as having a very positive perception

1

Details of how the variables were operationalized and original sources for the measures employed for the ones the table of which has not been put on this document can be obtained from the corresponding author.

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Table 2 Descriptive statistics and correlations among variables

Creative Discovery Ins_Cont Environmental Uncertainty

Mean 3.10 3.60 2.81 2.89

S.D. 0.55 0.64 0.61 0.57

Creative 1.000 0.572** 0.142** 0.153**

Discovery

Ins_Cont.

Environmental uncertainty

1.000 0.079* 0.165**

1.000 0.286**

1.000

Notes: *significant at 0.05 level; **significant at 0.01 level, p > 0.05 no statistically significant relation. Pearson correlation coefficient power levels: 0 < r < 0.299 weak; 0.300 < r < 0.599 medium; 0.600 < r < 0.799 strong; 0.800 < r < 0.999 very strong

of their IC and external environment in terms of doing business and entrepreneurship in Turkey. A total of 68.7% of the respondents are male, 37.2% are between the ages of 40 and 49, and 45.9% are undergraduates. In addition, 84.5% are senior management and company owner; 34.6% have been working in their current position for 21 years and above, while 28.3% have been working in a similar position 11–15 years. Therefore, it was concluded that key respondents were familiar with the survey statements. The firms respondents work for are predominantly medium and large enterprises (40% of them are international, 30.1% are in operation for 35 years or more, and 57% are medium and large enterprises). Approximately 50% of the firms are in the manufacturing sector, and 73% are at medium technological level.

3.2.3

Measurement Model Assessment

PLS-SEM theoretical models are evaluated following a two-step process, as outlined in the CCA procedure (Hair et al. 2020). The first step is measurement model assessment which examines and confirms reliability and validity. The second step is structural model evaluation focusing on prediction (Hair et al. 2022). The information presented in Table 3 confirms the validity and reliability of the measurement model constructs.

3.2.4

Structural Model Assessment

Main Model Structural model assessment again follows the CCA procedure (Hair et al. 2020). The VIF values were examined to assess multicollinearity. No problems were identified (< 3.0). Next, the significance of the structural path coefficients (betas) was examined. The path coefficients ranged from rather small to quite high, but all were significant = 0.708 (0.40 could be accepted); i1: square of external loads >= 0.5; i2: 0.60 < reliability 0.7 > 0.7 0.7–0.9 0.927 0.902 0.892

0.897

0.865

0.823

0.844

0.814

0.738

0.884

0.808

0.804

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Table 5 HTMT BUR EC INN MAN

BUR

EC

INN

0.184 0.287 0.080

0.342 0.228

0.300

MAN

all HTMT values significantly differ from 1, completing the discriminant validity check.

4.2

Structural Model Assessment

Next, the model is checked for collinearity. So, the variance inflation factor (VIF) is checked. An ideal VIF value is below 3.0. This was the case for all of the extracted VIFs, so collinearity was not an issue. Then, the bootstrapping procedure is applied to check if the effects on the model are statistically significant. All of the studied direct and indirect effects were statistically significant. Also, f 2 is checked on the PLS algorithm results to extract the effect size among the studied variables. All the effects are higher than 0.02 but lower than 0.15 which means that the effects are small. Then, the model is checked to assess its predictive accuracy. So, PLS predict and blindfolding are executed (Hair et al. 2019; Hair et al. 2022). On PLS predict the root mean squared error (RMSE) and mean absolute error (MAE) are checked for each indicator on the PLS and on the linear model (Table 6). The minority of PLS-SEM indicators extract higher prediction errors than the linear model. This indicates a medium predictive relevance of endogenous constructs (Shmueli et al. 2019). Then blindfolding is executed. If Q2 values are higher than 0, this means that the model has predictive relevance. Specifically, BUR, MAN, and INN Q2 values are 0.016, 0.019, and 0.109, respectively. However, they are lower than 0.25, which means than the model’s predictive accuracy is small. In summary, the results demonstrate that the model has low predictive power and effect size among the studied variables, but the effects are statistically significant. Finally, while the direct effect of entrepreneurial culture to innovation is statistically significant, the same goes for the indirect effects through bureaucracy and management. This makes bureaucracy competitive partial mediator and management complementary partial mediator (Nitzl et al. 2016). The research was conducted with the aim to study the model presented in the case of Greece. The studied effects may be of low impact, but they are still statistically significant (Table 7). The results confirmed the significance of entrepreneurial culture for innovation, in both its direct and its indirect effect. Apart from its contribution to innovation, entrepreneurial culture is fundamental for the encouragement of business management in Greece and therefore the importance of business

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Table 6 Comparison of RMSE and MAE on PLS and linear model

BUR3 BUR1 BUR2 INN2 INN3 INN1 MAN1 MAN2 MAN3

PLS model RMSE 1.286 1.269 0.994 1.098 1.259 1.287 1.001 0.992 0.942

MAE 0.959 1.041 0.775 0.947 1.080 1.090 0.857 0.842 0.711

Linear model RMSE 1.304 1.284 1.008 1.106 1.265 1.292 1.011 0.989 0.940

MAE 0.970 1.053 0.786 0.960 1.086 1.090 0.861 0.843 0.731

Table 7 Path coefficients of the structural model and significance testing results

EC → INN EC → BUR EC → MAN BUR → INN MAN → INN EC → BUR → INN EC → MAN → INN

Path coefficient 0.216 -0.160 0.198 -0.191 0.222 0.035 0.049

95% Bca confidence interval [0.090, 0.327] [-0.289, -0.016] [0.050, 0.313] [-0.303, -0.063] [0.096, 0.335] Ν/Α Ν/Α

Significant ( p < 0.05)? Yes Yes Yes Yes Yes Yes Yes

f 2 effect size 0.053 0.026 0.041 0.043 0.057 N/A N/A

management to innovation. Furthermore, it is apparent that the negative effect of entrepreneurial culture on bureaucracy reduces the negative effect of management on innovation. The situation of bureaucracy, despite having been slightly improved in recent years, still acts as a brake on the effort of businesses to innovate. Furthermore, it is apparent that the negative effect of entrepreneurial culture on bureaucracy reduces the negative effect of bureaucracy on innovation. An additional finding is the fact that, despite the negative effect of bureaucracy on innovation, the indirect effect of entrepreneurial culture on innovation remains statistically significant. This result is the most important finding of this study, as it comes as an answer to the general public opinion that entrepreneurship in Greece cannot succeed. Thus management is a complementary partial mediator, while bureaucracy is a competitive partial mediator (Nitzl et al. 2016).

5 Conclusions and Implications for Theory and Practice Entrepreneurship in Greece can succeed, but an important requirement is the cultivation of the right entrepreneurial culture and therefore effective management. It is up to entrepreneurs to deal with external business environment obstacles by

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improving their entrepreneurial culture and business management and surpass them. It is up to society to change opinion about entrepreneurship and think that real wealth comes from possession of businesses and not employment. Greece needs to be a country where its population will shift from seeking employment to create business opportunities. The chosen sector of dairy industry in this study and the successful appliance of the proposed model highlight this statement. These results create useful output for scholars and policymakers. This model can become the basis for further research in academia. Its application on another field of the economy of Greece or another country would be of interest, so that the necessary comparisons of results will be made. Also, the model could be increased in complexity by adding more variables that entrepreneurial culture utilize. The same goes for the barriers of the external business environment. Actually, they operate as a business filter, where only the fittest survive in every unique business environment. As far as the state is concerned, the results of the survey about bureaucracy show that additional actions need to be taken for the improvement of the external business environment in Greece. Greece’s rank in the Ease of Doing Business Index is 79th, a fact that reflects that there still margin for improvement (World Bank 2019). The state has to secure the best possible conditions for entrepreneurship and operate as an assistant to entrepreneurship in Greece.

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The Influence of Facebook on Value Cocreation: Evidence from the Moroccan Fast-Food Industry Mohammed Hassouni, Abdellatif Chakor, and Siham Mourad

1 Introduction The twenty-first century has been characterized by a change in economic paradigm. The economy has switched from a good dominant logic to a service dominant logic (Vargo and Lusch 2004, 2016). The service dominant logic considers that Goods are no longer consumed for what they are (their resources), but for what they represent to the consumer (Vargo and Lusch 2004, 2006, 2016). Service is viewed as “the application of specialized competences (knowledge and skills) through deeds, processes, and performances for the benefit of another entity or the entity itself” (Vargo and Lusch 2004). This change in orientation implied that value is always jointly produced by customers and businesses. As a result, it is crucial for businesses to consider client feedback while developing an offer (Vargo and Lusch 2004, 2016). Consumers are therefore always Value cocreators. Moreover, the development of social media has altered how businesses previously conducted their marketing. Consumers have become more knowledgeable and demanding than ever before. Thus, companies are likely to listen to them. This increased closeness between companies and consumers shows no signs of abating. New ideas have emerged as a result of this change in marketing techniques, M. Hassouni (✉) University Ibn Tofail, Institut des Métiers du Sport, Kenitra, Morocco e-mail: [email protected] A. Chakor University Mohammed V, Faculté des Sciences Juridiques Economiques et Sociales, Rabat, Morocco e-mail: [email protected] S. Mourad Groupe ISCAE, Casablanca, Morocco e-mail: [email protected] © Springer Nature Switzerland AG 2023 L. Radomir et al. (eds.), State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM), Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-031-34589-0_21

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including buzz, electronic word of mouth, and E-reputation. We could define social media as “a group of internet-based applications that build on the ideological and technological foundations of Web 2.0 and that allow the creation and exchange of user generated content” (Kaplan and Haenlein 2010). Facebook, with almost 3 billion users, is most likely the most widely used social media platform (We are social and Kepios 2022). Facebook can be characterized as a social networking website (Kaplan and Haenlein 2010) where advertising can mostly take the forms of sponsored advertising and Facebook pages (Gaber and Wright 2014). Advertisers can readily tailor their adverts to appeal to their target audience. Gender, location, age, and interest are used as the targeting criteria for sponsored advertising (Gaber and Wright 2014). Facebook pages are free online locations where brands or businesses may exchange material with their clients. The latter pages are run by community managers, whose job it is to engage customers in conversation and keep fans informed about the company’s goods and services. In Morocco, Facebook is the most widely used social networking platform (We are social and Kepios 2022). 31 million Moroccans have access to Internet which represents 84% of the nation’s population. 23 million Moroccans use social media (We are social and Kepios 2022). 18.9 million people have Facebook accounts in Morocco (We are social and Kepios 2022). This amount indicates one of the highest Facebook penetration rates in Africa and is predicted to increase over the next several years (We are social and Kepios 2022). As a result, Moroccan consumers are becoming more connected and engaged with brands on social media. The Moroccan fast-food industry has shown significant growth during the last two decades (Wahabi 2016). It represents a $1.6 billion industry (Wahabi 2016) and doesn’t show any sign of slowdown. The most popular brands are McDonald’s and Pizza hut with 50 points-of-sale across the country (Challenge 2022) in 30 years of presence. The most followed brands on Facebook are Pizza Hut and Domino’s Pizza with 32 million likes and 21 million likes (We are social and Kepios 2022). Thus, the fast-food industry Facebook pages represent active communities where the interaction between consumers and brands can lead to value cocreation. Therefore, the purpose of this chapter is to identify the factors that influence customers to cocreate value on the fast-food industry Facebook pages. Explaining value cocreation on Facebook is crucial for both academics and managers working in the fast-food industry. Value cocreation on social networks has only been the subject of a very small number of empirical investigations (Dhaka 2015; Fan and Luo 2020; Nájera-Sánchez et al. 2020). As a result, studying value cocreation on Facebook in the fast-food industry would contribute to fill a gap in the value cocreation literature. Furthermore, the purpose of this research is to give guidance to community managers working in the fast-food industry.

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2 Background Vargo and Lusch (2004) proposed ten principles to summarize the service dominant logic (see Table 1). The latter ideas were condensed into five central axioms after extensive discussion with the scientific community (Vargo and Lusch 2016). According to the first axiom, service is the application of operant resources (skills and knowledge) to one entity’s benefit (Park and Vargo 2012; Vargo and Lusch 2008, 2016). The second axiom holds that value is always jointly created by two or more parties, with a single beneficiary. The situations considered in this axiom are either the one involving a firm and a beneficiary or the one involving many actors (Vargo and Lusch 2004, 2006, 2016). All actors are considered resource integrators under the third axiom, and the beneficiary’s value is considered under the fourth axiom (Vargo and Lusch 2008). The fifth postulate emphasizes the importance of institutions in coordinating value cocreation (Vargo and Lusch 2016). As a result, the service dominant logic has become the foundation of various cocreation approaches. The shift in emphasis from customers receiving offers to customers influencing offers is underway and shows no signs of abating. Social networking platforms such as Facebook have emphasized this tendency even more by enabling deeper conversations between brands and companies. The subsequent intimate discussions weren’t feasible earlier. Marketers have been able to collect data they couldn’t get before. Table 1 The 10 premises vs. the 5 axioms FP1 service is the fundamental basis of exchange. FP2 indirect exchange masks the fundamental basis of exchange. FP3 goods are a distribution mechanism for service provision. FP4 operant resources are the fundamental source of competitive advantage. FP5 all economies are service economies.

FP6 the customer is always a value cocreator. FP7 the enterprise cannot deliver value, but only offer value propositions. FP8 A service-centered view is inherently customer-oriented and relational. FP9 all social and economic actors are resource integrators. FP10 value is always uniquely and phenomenologically determined by the beneficiary.

Axiom 1/FP1 service is the fundamental basis of exchange. Axiom 2/FP6 value is cocreated by multiple actors, always including the beneficiary. Axiom 3/FP9 all social and economic actors are resource integrators. Axiom4/FP10 value is always uniquely and phenomenologically determined by the beneficiary. Axiom 5/FP11 value cocreation is coordinated through actor-generated institutions and institutional arrangements.

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Prahalad and Ramaswamy (2000) observed a change in consumer role from passive to active. Customers that interact with the business actively cocreate value (Galvagno and Dalli 2014; Prahalad and Ramaswamy 2000). Previous studies have shown that value cocreation is a complex concept with several definitions. For some researchers, value cocreation is “. . . the joint, collaborative, concurrent, peer-like process of producing a new value, both materially and symbolically” (Galvagno and Dalli 2014). Others like Frow et al. (2011) define value cocreation as “An interactive process, involving at least two willing resource integrating actors, which are engaged in a particular form(s) of mutually beneficial collaboration, resulting in value creation for those actors” (Frow et al. 2011). For Mohr and Sarin (2009) cocreation of value involves “collaborative activities that actively engage customers in the design and development of new innovations.” Cocreation is becoming more popular as a practice (Hatch and Schultz 2010; Nájera-Sánchez et al. 2020). Value cocreation has numerous advantages, including increased consumption, positive user experiences, product stimulation, and service innovation (Gentile et al. 2007; Payne et al. 2008; Sawhney et al. 2005; Bitner et al. 2008; Morgan et al. 2018). Customer collaboration determines market offering and requested benefits (Vargo and Lusch 2004; Anning-Dorson 2018). The service encounter determines the quality, quantity, and qualities of the offering (Solomon et al. 1985; Bitner et al. 2000; Santos-Vijande et al. 2016). Another benefit would be that value cocreation enables services recovery (Dong et al. 2008; Alves et al. 2016). This situation happens when a service failure happens and that the company solves the problem through dialogue with the frustrated customer. The idea of cocreation has been treated in various ways, such as in the service dominant logic of Vargo and Lusch (2008, 2016), where value is always cocreated by the consumer, or in the service science logic (where current resources integrate with multiple service systems that result in a system’s well-being (Vargo et al. 2008)); and in the service logic (customers mixing resources supplied by the company with other resources in everyday activities in value creating processes (Grönroos 2008; Grönroos and Ravald 2011; Grönroos and Gummerus 2014; Grönroos et al. 2015)) or other approaches like product development, many-tomany marketing (Galvagno and Dalli 2014; Mingione and Leoni 2020). The concept value cocreation used in this chapter is the one where customers interact with businesses in order to cocreate value (Vargo and Lusch 2008; Galvagno and Dalli 2014; Koskela-Huotari et al. 2016); resulting in customers giving meaning to a company’s offering (Grönroos and Ravald 2011; Ind and Coates 2013; Morgan et al. 2018). The Internet has enabled a shift in the way people think about cocreation. Cocreation in real-world settings is different from that in virtual ones (Sawhney et al. 2005; Füller and Bilgram 2017). Internet affected cocreation of value by fostering active, reciprocal relationships, experiential learning, and mediated interactions amongst customers. Consumer tribes on the Internet (Cova and Cova 2009; Ramaswamy and Ozcan 2016) can be sources of innovation. They enable the development of new products as well as the commercialization of cocreated products.

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Social networking sites facilitate value cocreation and innovation (Sawhney et al. 2005; Törmälä and Saraniemi 2018). Companies can interact with the public quickly and affordably (Jespersen 2010; Piller et al. 2012; Tjandra et al. 2019). Cocreation in social media is influenced by Situated creativity (localized creativity in contingent systems of social interaction) (Potts et al. 2008; Füller and Bilgram 2017) and Crowd-surfing (the practice of outsourcing activities by a firm to an online community or crowd in the form of open call—Whitla 2009). Millions of people follow the Facebook pages of well-known companies like Coke, Nutella, and Pringles, but there are also fewer mainstream companies like Marshmallow Peeps and Bacon Salt that are using the platform to increase customer engagement and spark interest in their goods (Dunay and Krueger 2011). Coke has a number of Facebook communities, some of which have millions of followers. Through Facebook, businesses are actively listening to their customers. For instance, 93 Facebook groups and 14,000 messages helped Cadbury relaunch a chocolate bar named WISPA (Poynter 2007). In 2007, the Bar came back to the shelves. This illustration demonstrates how Facebook may help with value cocreation.

3 Theoretical Framework Several models have been suggested to measure value cocreation (Maguire et al. 2007; Füller et al. 2009; Yi and Gong 2013), but in terms of empirical testing, the DART model continues to be the most suitable (Albinsson et al. 2016; Mukhtar et al. 2012; Payne et al. 2008; Skaržauskaitė 2013; Tanev et al. 2011). Few scholars have empirically tested this model. This methodology was applied in shop experiences in Italy by Spena et al. (2012). Mazur and Zaborek (2014) used quantitative methodologies to apply the DART model in the Polish manufacturing and service industries. This model was used in the context of service loyalty by Albinsson et al. (2016). Taghizadeh et al. (2016) applied this model in the context of innovation strategy and market performance. The DART model was developed by Prahalad and Ramaswamy (2004) to quantify cocreation of value. According to this theoretical framework, the cocreation of value is determined by four constructs: discussion, access, risk management, and transparency. The authors define dialogue as “implies interaction, deep engagement and the ability and willingness to act on both sides” (Prahalad and Ramaswamy 2004). The ability to access information about goods and services is referred to as access. Transparency is the frequent dissemination of accurate information. Understanding the advantages of a company’s offering (items or services) through dialogue, access, and transparency is risk assessment. The key tenet of this model is that customers exchange knowledge about goods and services in the market, which serves as a setting for cocreation experiences. The ability to personalize client experiences is enabled by time, space, and events. Experiences get personalized through cocreation. Value is based on experiences.

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Furthermore, because we are working in a technological context (Facebook brand pages), we coupled the DART model with Davis (1989)’ Technology Acceptance model—TAM. The reason for this selection is that the technology acceptance model is one of the most commonly used models for technological adoption (at least 424 citations according to Lee et al. 2003). The latter model claims that perceived usefulness and ease of use influence technology adoption. The ease-of-use factor was eliminated since it had a similar meaning than the access factor. As a result, we ended up with the DARTU model.

3.1

Dialogue and Value Cocreation

Dialogue is the first main construct in the DART model. Dialogue is thought to be the most important means of engagement and knowledge sharing between a firm and its customers (Ballantyne 2004; Ballantyne and Varey 2006; Grönroos 2004; Prahalad and Ramaswamy 2004; Burgdorff 2018). Several studies have shown that dialogue results in experience cocreation (Prebensen et al. 2013). Other research demonstrated the necessity of understanding and listening to clients (Prahalad and Ramaswamy 2004; Dong and Sivakumar 2017). Discussions can also be used to resolve issues (Hoyer et al. 2010; Alves et al. 2016). As a result, the better the dialogue, the better the experience cocreation. This is why fast-food companies that want to provide their customers with a one-of-a-kind experience must use all of their available resources. As a result, we suggest: H1: Dialogue influences value cocreation on the fast-food Facebook pages.

3.2

Access and Value Cocreation

Because it makes exchanges more efficient, access is thought to improve customer experience (Albinsson et al. 2016). Companies must make tools and information available to customers so they may cocreate valuable experiences (Prahalad and Ramaswamy 2004; Ramaswamy and Ozcan 2016). Tools like social media accounts can improve consumer experiences (Füller and Bilgram 2017). A more interactive relationship between the client and the business is made possible by improved information access. As a result, we come up with the following assertion: H2: Access influences value cocreation on the fast-food Facebook pages.

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215

Risk Management and Value Cocreation

Research has shown that participants in the cocreation process must perform risk assessment (Albinsson et al. 2016; Prahalad and Ramaswamy 2004). Businesses must explain the advantages and disadvantages of their offers (Prahalad and Ramaswamy 2004; Hsieh and Chang 2016). Understanding the risk outcomes enhances decision-making (Kashif et al. 2015). Improved customer relationships are made possible through risk assessment (Garbarino and Strahilevitz 2004; Ramaswamy and Ozcan 2016). As a result, we come up with the following assertion: H3: Risk assessment influences value cocreation on the fast-food Facebook pages.

3.4

Transparency and Value Cocreation

The final component of Prahalad and Ramaswamy (2004)’s paradigm is transparency. Transparency is still essential for productive communication between a business and its clients. Trust, equality, and a better customer experience are all influenced by transparency (Spena et al. 2012; Anning-Dorson 2018). According to Garbarino and Strahilevitz (2004), the interactive environment benefits the organization. Additionally, information symmetry is necessary for transparency (Vargo and Lusch 2008, 2016). As a result, we support the proposition that: H4: Transparency influences value cocreation on the fast-food Facebook pages.

3.5

Perceived Usefulness and Value Cocreation

The definition of perceived usefulness is “a person’s subjective probability that using a new technology will enhance his or her job performance” (Davis 1989). A technology’s likelihood of adoption increases with its level of usefulness (Davis 1989; Morosan 2011; Morosan and DeFranco 2016). Various studies have demonstrated the importance of a technology’s usefulness in influencing a technological adoption (Szajna 1996; Aboelmaged 2010; Morosan 2011; Morosan and DeFranco 2016). Facebook’s usefulness plays a part in providing clients with information that they could require. As a result, we can assume that: H5: The perceived usefulness influences value cocreation on the fast-food Facebook pages. The model is presented in Fig. 1.

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Dialogue H1 Access

H2

Risk Assessment

H3

Transparency

H5

H4

Value Cocreation

Perceived Usefulness Fig. 1 The DARTU model

4 Methodology The research design was quantitative. The questionnaire method was employed. The population of this study was one of the Facebook fast-food brands’ community members in Morocco. Recent studies show that the majority of Moroccans who use social networking sites are young (below 25), educated, urban residents, and belong to the Moroccan Middle class (Oukarfi 2013). As a result, the sample used was convenient. The study took place in a university. The basis for this choice was the convenience of data collecting as well as the fact that in our current setting, university students tend to be fast-food consumers, and follow fast-food brands online. An online questionnaire was distributed to students in April 2019. 384 responses were collected. The majority of the indicators were derived from the literature. The questionnaire is based on the work of Solakis et al. (2017) for the dialogue, access, risk management, and transparency variables. The items related to usefulness were derived from Davis (1989) works. The value cocreation items were inspired from Grisseman and Stokburger-Sauer’s (2012) works. A 1 to 5 agreement Likert Scale was used where 1 means totally disagree and 5 totally agree. Confirmed bilingual researchers translated the survey from English to French. The questionnaire started with a knockout question, indicating the Facebook page followed by the respondents. The respondents had to choose between McDonald’s, Burger King, KFC, Pizza hut, Dominos, Tacos de Lyon, and other brands. If the respondents didn’t follow any brands, they were simply discarded. Afterwards, the respondents could answer to each of the questionnaire variables. For the dialogue, access, risk management, transparency, usefulness, and cocreation of value variables, 4 items were used. The last section of the questionnaire concerned demographic information.

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The PLS-SEM technique was used to test the model, which provides a “robust framework for estimating causal models with latent variables and systems of simultaneous equations with measurement errors” (Henseler et al. 2009). This method is appropriate for both exploratory and confirmatory research (Lowry and Gaskin 2014). The SmartPLS V.3 software was used.

5 Results Table 2 shows the main demographic results: 56% of the respondents were female and 44% male; 60% of the respondents were aged between 18 and 26; 55.2% of the respondents had a bachelor level. The most followed brands were McDonald’s, Pizza Hut, and Burger King. Because of its ability to handle higher order latent constructs and violations of multivariate normality, a PLS approach was chosen. Furthermore, we used non-parametric bootstrapping with 250 replications to obtain the estimates’ standard errors (Hair et al. 2012, 2014; Henseler et al. 2014). First, we evaluated the model’s quality. Following the advice of Henseler and Sarstedt (2013), we used a consistent PLS approach and the SRMR criteria rather than the goodness of fit (GoF). In our chapter, the SRMR (Standardized Root Mean

Table 2 The demographic results Gender Men Women Age group 18–26 27–35 36–45 >46 Education level Bachelor’s level Master’s level PhD level Brands followed McDonald’s Pizza Hut Burger King Dominos Tacos de Lyon KFC Others

44% 56% 60% 31% 6% 3% 55.2% 28.6% 16.1% 22.4% 19.5% 15.6% 14.1% 12.2% 11.7% 4.4%

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D1 0.749

D2

0.817 0.761 0.725

D3

dialogue

D4

0.116 A1

C1 0.710

A2

0.818

0.761 0.761 0.736

A3

0.301

0.680

0.787

access

A4

0.227 GR1 GR2 GR3 GR4

0.730 0.753 0.754 0.662

0.851 0.783

cocreation of value

C2 C3 C4

0.196 risk management 0.167

T1 T2 T3

0.785 0.814 0.804 0.573 transparency

T4 U1 U2 U3

0.831 0.850 0.822 0.659

U4

usefulness

Fig. 2 The overall model

Square Residual) value is 0.075 (< 0.08) indicating a good adjustment (Henseler et al. 2014; Hair et al. 2017). In other words, the difference between observed correlation and expected correlation is acceptable (Henseler et al. 2014). The coefficient of determination (R2) was used to assess the strength of the structural model. The model showed a value of 0.680, which is a moderate explanatory power (see Fig. 2). Second, each concept’s reliability and convergent validity was estimated. The convergent validity was measured by the Average Variance Extracted (AVE). The internal reliability construct was measured by the Dillon Goldstein rho. All the convergent validity and construct’s reliability conditions are satisfied, as shown in Table 3 (reliability greater than 0.7 and convergent validity greater than 0.5). The AVE (average variance extracted) ranged from 0.551 to 0.668. The construct reliability was measured by the Dillon Goldstein Rho which ranged between 0.694 and 0.829 (see Table 3).

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Table 3 Convergent validity and reliability indices Latent variable Access Value cocreation Dialogue Risk management Transparency Usefulness

Convergent validity (AVE) 0.551 0.657 0.583 0.617 0.668 0.631

Reliability (Dillon-Goldstein’s Rho) 0.729 0.829 0.770 0.694 0.775 0.825

Table 4 The hypotheses testing

H1 Dialogue→Value cocreation H2 Access→Value cocreation H3 Risk management→Value cocreation H4 Transparency→Value cocreation H5 Usefulness→Value cocreation

Original sample (O) 0.116

Sample mean (M) 0.120

Standard deviation (STDEV) 0.041

t statistics (|O/ STDEV|) 2.810

p values 0.005

Confirmed

0.301

0.295

0.049

6.172

0.000

Confirmed

0.227

0.228

0.050

4.545

0.000

Confirmed

0.196

0.200

0.066

2.956

0.003

Confirmed

0.167

0.165

0.055

3.035

0.003

Confirmed

To assess discriminant validity, we used a heterotrait–monotrait (HTMT) correlation ratio that was inferior to 0.9. The discriminant validity conditions were satisfied (Henseler et al. 2015). Once the accuracy of the measurements has been confirmed, we can evaluate the model’s structural relationships (see Fig. 2). The hypotheses results showed that the path coefficient and the t value are all significant at t > 1.96 and p < 0.05 (see Table 4). The relationship between Dialogue and value cocreation on the fast-food Facebook pages was confirmed with a t test of 2.810 and a p value of 0.005. Therefore, H1 is accepted. Access shows a significant relationship with the dependent variable, the t test is = 6.172 and the p value = 0.000. Consequently, H2 is accepted. Risk assessment influences the value cocreation on the fast-food Facebook pages, t = 4.545 and p = 0.000. Therefore, H3 is accepted. The relationship between transparency and cocreation is significant with a p-value of 0.003 and a t test of 2.956. Thus, H4 is accepted. The perceived usefulness shows a direct relationship with the value cocreation on the fast-food Facebook pages, t = 3.035 and p = 0.003. As a result, H5 is accepted.

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6 Discussion Prahalad and Ramaswamy (2004) proposed the DART model to explain the value cocreation that occurs between businesses and their customers. In this chapter, we included the perceived usefulness variable as another explanatory variable of the value cocreation process occurring on the Facebook brand pages of fast-food restaurants. This choice was made because the literature supported this orientation (Davis 1989; Schiavone et al. 2014). The chapter findings completely supported the hypothesis tested. Moroccan consumers frequently interact with the fast-food companies they like on Facebook. The latter dialogue can be positive or negative, but it is still a source of information for improving service and core products. This result is coherent with previous studies (Mainardes et al. 2017; Maduka 2016; Schiavone et al. 2014). We could explain this result by the benefits sought by the customer (Kuo and Feng 2013; Solomon et al. 1985; Solomon 2014). Customers dialogue with fast food brands in order to obtain information, make suggestion, or to solve problems. The access result proved to be coherent with previous studious regarding value cocreation (Borges et al. 2016; Ojiaku et al. 2019; Solakis et al. 2021). This could be explained by the fact that most of the fast-food brands convey the information needed by Facebook users in order to cocreate value. The content produced on Facebook satisfies the need for information that Facebook users have. The risk management result was similar than the one of several studies regarding the DART Model (Basceanu 2014; Olejniczak and Pliszka 2019). This result could mean that most of the content available on the Facebook pages of the fast-food industry enables to manage risk for users. The latter can measure the advantages and inconvenience of the content they receive. The transparency result is coherent with other studies about the DART model (Mainardes et al. 2017; Solakis et al. 2021; Nagarethenam et al. 2018). This could be explained by the fact the Facebook users can clearly see positive and negative comment regarding the different Facebook pages which shows that the content is authentic and not manipulated. The perceived usefulness result was similar to the one shown in previous studies (Kim 2018; Hajli 2014; Soltani et al. 2017). This result means that the Facebook users find it useful to interact with the fast-food brands they follow. This result proves that Facebook users can benefit from the interaction with their favorite fastfood brand. This result could also be explained by the potential conversational marketing Facebook users might get from the brand they follow on Facebook.

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7 Conclusion Value cocreation on Facebook is still a new marketing strategy for companies in the fast-food industry today. Facebook’s continuous improvement is expected to facilitate value cocreation between customers and businesses even more in the future (Fagerstrøm and Ghinea 2011; Jalonen 2017). As a result, fast-food companies should enable value cocreation with their customers. This chapter sought to fill a gap in the literature on both social media marketing and cocreation. Several conclusions can be drawn from this chapter. At the theoretical level, the DART and TAM models were associated and evaluated, which is a rare occurrence in the literature. This chapter is one of the few empirical papers on the topic of value cocreation (Dhaka 2015). The Model variables allow Community managers to assess the effectiveness of their Facebook presence at the managerial level. From a managerial standpoint, implementing the DARTU model could provide competitive advantages for companies in the fast-food industry. Companies could develop new products and promotional offers as a result of customer feedback. They can also solve customer-related problems using this model, which supports the service recovery paradox theory (Hart et al. 1990) that states that customers are more satisfied with companies that recover from service failures and admit their mistakes than with those that do not fail to deliver their services. Understanding the model would also allow fast-food companies to better target their offerings to customers because they would know which types of customers love their products and which don’t, thanks to social media options. Overall, having a strong presence on a social media site like Facebook is a great way for businesses to get feedback from their customer base and make operational improvements. One of the chapter limitations is that it was designed for Moroccan university students. Other universities or customer types could be considered for further research. The non-probabilistic nature of the sample makes generalization of the results difficult. Another study could be conducted at the corporate level to determine the understanding of value cocreation that community Managers working in the fast-food Sector can have through Facebook. The model proposed in this chapter could also be applied to other sectors. For example, it would be interesting to test the DARTU model in the context of banking, healthcare, or another industry. Value cocreation is still a very context-specific research topic (Wünderlich et al. 2013). Further research could include other context-related variables related to culture. Further studies could include antecedents of variables such as dialogue and relationship moderators to gain a more comprehensive understanding of the value cocreation phenomenon. The practical outcome would be to recommend strategies that would result in a better customer experience. This chapter’s measurements were largely intentional. As a result, it may be interesting to repeat the study in the same environment with the same participants several months or years later to see if there is any change in behavior.

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Part V

Human Resource Management and Organizational Design and Behavior

Research on Interaction Pattern of Virtual Team in Crisis Situation Shen Yuanyanhang, Jiale Wu, and Xiaodan Yu

1 Introduction With the rapid development of economy and technology, people are facing more and more frequent and serious disasters and crises. Crisis is a low-probability, highimpact event with huge pressure and fuzzy uncertainty (Yu et al. 2008). The negative impact of its continuous development process may spread to other organizations in the same industry, posing a significant threat to organizations and society (Bies 2013). In the face of the crisis, how to respond to crises quickly and efficiently is an urgent problem. More and more scholars are committed to studying the causes of the crisis and how to minimize the damage caused by the crisis (Camillo 2015). Organizational research has found that teams are increasingly used in crisis because teams can respond quickly to dynamic, complex crisis situations (Burke et al. 2006). With the rapid development of information technology, the traditional teams are no longer dominant. The virtual team gained wide attention and recognition in research (Han and Beyerlein 2016). Zellmer-Bruhn et al. (2004) have pointed out that the effectiveness of a team is not determined by individual actions and discourses, but interaction patterns of team members during the task. Interaction patterns have gradually become one of the perspectives of the team effectiveness. Coping with the crisis in the unfamiliar situation in virtual teams is difficult. The establishment of the interaction pattern

S. Yuanyanhang School of Information, Renmin University of China, Beijing, China e-mail: [email protected] J. Wu · X. Yu (✉) School of Information Technology & Management, University of International Business and Economics, Beijing, China e-mail: [email protected] © Springer Nature Switzerland AG 2023 L. Radomir et al. (eds.), State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM), Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-031-34589-0_22

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enables the virtual team members to communicate and communicate more fully and ensure that the task can be finished successfully (Espinosa et al. 2015). However, there is little research in China to explore how the interaction pattern of virtual teams in a crisis environment affects team effectiveness. In addition, the crisis will bring emotional fluctuations. Related studies have shown that emotional responses to existing situations have an impact on the extent of crisis decision-making (Kaplan et al. 2013). Emotions greatly affect team life, and it is feasible to use emotion management to regulate team behavior and improve team effectiveness. However, research on emotions in China is relatively rare. The previous research pays more attention to the individual’s emotional changes, and the team-level emotional research is relatively scarce. Also, there are few researches exploring the mediating role of team emotions between team interaction and team effectiveness. Therefore, this paper introduces the mediation of team emotion, explores the relationship between interaction patterns and team effectiveness in crisis situations, and the intermediary mechanism of team emotion between the two. According to this, we could give some suggestions about virtual teams in crises. Our research questions are: 1. What is the relationship between virtual team members’ interaction patterns, team emotions, and team effectiveness in a crisis situation? 2. Can team emotions mediate the relationship between interaction patterns and team effectiveness? 3. How to improve the team performance through management of emotions and behavioral patterns?

2 Research Model and Hypotheses 2.1

Interaction Pattern and Team Emotion

Interaction pattern is the conventional verbal expression and non-verbal behavior used for collective action and coordination. Through these verbal or non-verbal behaviors, teams can share their own emotional states and gradually reach an emotional integration state by combining the external environment and the shaping of team norms (Kelly and Barsade 2001), which indicates that the interaction pattern has an important influence on the formation of team emotional atmosphere. Spector and Fox (2002) found that the more open the communication, the better the atmosphere of the team. This is because the stable interaction pattern is conducive to the establishment of a fair and just team atmosphere, from which team members can gain recognition, feel a sense of accomplishment, and greatly increase positive emotions, thus creating a good team atmosphere. Levine (2010) found that during emergencies, sufficient information exchange among team members would affect the emotional factors within the team. A team that establishes a stable interaction pattern has stronger flexibility and adaptability can better deal with

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unconventional or non-programmed risks, and maintain a consistent emotional atmosphere. Based on the above analysis, we propose the following hypothesis: H1: In a crisis situation, the stable interaction pattern of virtual team has a positive effect on the formation of positive team emotions.

2.2

Interaction Pattern and Team Effectiveness

The interaction patterns are quite different, and different behavioral activity patterns indeed influence team outcomes. Some scholars have conducted empirical studies on behavioral patterns and linked such differences with team effectiveness (ZellmerBruhn et al. 2004). The interaction pattern of the team is particularly important in the crisis environment. The team changes the interaction pattern according to the feedback of the environment and form a unique interaction pattern, which has stronger adaptability, leading the team to solve problems more efficiently (Jehn and Mannix 2001). Studies have found that teams that have established a stable interaction pattern during a crisis have better team performance, because the smaller the change frequency of communication mode, the faster the team can make consistent decisions through stable interaction (Daniel et al. 2013). Veil et al. (2011) found that the establishment of interaction pattern could reduce the frequency of roundtrip communication and save time. Such communication was effective in irregular or emergency situations, and the mode characterized by simple but uncomplicated interaction predicted higher team efficiency. There are also researchers (Waller et al. 2004; Stachowski et al. 2009) who observed team effectiveness and member behavior through simulated crisis experiment. They found that during the simulated crisis, the nuclear team members with better performance constructed a set of interaction patterns belonging to their own team, helped the team promote knowledge sharing and interaction, and coordinated actions to achieve the ultimate goal more efficiently. Grawitch et al. (2003) verified that in a virtual team, the stable interaction pattern of team members led them to interpret the situation in a timely, clear, and reasonable way, improved the team’s response, understanding, information-sharing and decision-making ability, and demonstrated better creativity and efficiency in the face of crisis. Based on the above, we make the following assumptions: H2: In a crisis situation, the stable interaction pattern of virtual team has a positive effect on team effectiveness. H2a: In a crisis situation, the stable interaction pattern of virtual team has a positive effect on team performance. H2b: In a crisis situation, the stable interaction pattern of virtual team has a positive effect on team satisfaction. H2c: In a crisis situation, the stable interaction pattern of virtual team has a positive effect on team commitment.

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Team Emotion and Team Effectiveness

In the complex and changeable crisis situation, emotion plays a crucial role in searching for information and dealing with abnormal situations. As one of people’s basic psychological processes, emotion affects people’s decision-making capability and behavior. Huy (2011) and Elfenbein (2007) have proved that positive emotions have a positive impact on team performance. Positive emotions are manifested as a state of full energy and devotion, leading to positive behaviors. Through experiments, Barsade et al. (2000) found that the level of emotional difference between virtual team members was associated with their satisfaction. The crisis situation is different from the general situation. The virtual team members have a stronger sense of crisis and sense of urgency due to time constraints, dangerous and urgent tasks, and the emotional fluctuations generated from it will have a more prominent impact on the team’s behavior and decision-making. Based on relevant research results, we propose the following hypotheses: H3: In a crisis situation, the positive team emotion of virtual team has a positive effect on team effectiveness. H3a: In a crisis situation, the positive team emotion of the virtual team has a positive effect on the team’s performance. H3b: In a crisis situation, positive team emotions of virtual teams have a positive effect on team satisfaction. H3c: In a crisis situation, the positive team emotions of the virtual team have a positive effect on the team’s commitment. Combined with relevant literature, existing research results, and emotional meaning construction theory, this paper sorted out the research model of interaction pattern, team emotion, and team effectiveness, as shown in the Fig. 1.

3 Methodology 3.1

Research Design

This study employed experiments to explore the interaction pattern of virtual teams in crisis situations, the mechanism between team emotions and team effectiveness, and further explore the mediating role of positive team emotions. In order to determine the best time to create a sense of crisis, a pre-experiment was conducted before the formal experiment. In the pre-experiment, set the experiment time to 25 min. In the remaining 15 min, the participants were informed that the

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Team Effectiveness

Interaction Pattern

H2a(+)

Team Performance

H2b(+) H2c(+) H1(+) Team Satisfaction H3a(+) H3b(+) Team Emotion

H3c(+)

Team Commitment

Fig. 1 Theoretical model Table 1 Scales Construct Crisis Team emotion Interaction pattern Team effectiveness

Scale Gifford et al. (1979) Qu (2007), “Team Atmosphere Scale” Stachowski et al. (2009) Jehn and Chatman (2000)

experiment time was only 10 min due to uncontrollable situation, thus creating pressure and tension to simulate the crisis situation.

3.2

Measurement

This study adopts the mature scale to ensure a certain degree of reliability and validity. A 5-level scale was adopted in the design of the questionnaire. The scale selection is shown in the following Table 1. Cronbach alpha was used to test whether the project had a high consistency, which is summarized in Table 2. The value of Cronbach alpha for each variable is above 0.7, indicating a high internal consistency among all factors. The scale used by previous scholars is selected in this study, and the content scope is well defined. In addition, the selected scale in this study has undergone a large number of tests and is representative in the use of relevant constructs. Therefore, the questionnaire used in this study has certain content validity.

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Table 2 Cronbach’s alpha coefficients of each factor Dimension Interactive pattern Team emotion Team performance Team satisfaction Team commitment Crisis

Cronbach’s alpha 0.89 0.91 0.79 0.82 0.79 0.71

Table 3 Descriptive analysis Crisis Team emotion Interactive pattern Team performance Team satisfaction Team commitment

Min 2.60 1.29 2.60 3.33 2.75 3.00

Max 4.20 4.86 4.60 5.00 5.00 4.67

Average 3.51 4.13 3.65 4.28 4.06 3.93

Standard deviation 0.414 0.810 0.422 0.393 0.385 0.390

Skewness 0.356 0.118 0.688 0.800 0.561 0.376

Kurtosis 0.169 0.899 0.391 0.626 1.085 0.053

4 Results 4.1

Statistical Description

The purpose of this study is to explore the influence mechanism between the interaction pattern, team emotion and team performance of virtual teams in crisis situations. The mean, standard deviation, and kurtosis of each research variable are shown in Table 3. Likert five-level scale is adopted to measure the data, and the values of the scale can illustrate the different states of the subjects on the scale. The results in the table show that the research subjects did have a sense of crisis during the experiment (crisis mean value 3.51). The experimental data are placed under the 95% confidence interval, and it can be seen that the skewness and kurtosis of each variable are within ±1.96, which conforms to the normal distribution, and subsequent structural equation model analysis can be carried out.

4.2

Preliminary Analysis

Structural equation model (SEM) is a statistical method used to express the relationship between potential variables and observed variables as well as the relationship between potential variables. The method of PLS-SEM selected in this study has a better model predictability than other structural models (Hair et al. 2019; Hair et al. 2022). Composite reliability (CR) was used to measure the observed values to describe reliability and consistency of the corresponding latent variables. CR value above 0.7

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Table 4 Composite reliability (CR) Variable Interaction pattern Team emotion Team performance Team satisfaction Team commitment

Composite reliability (CR) 0.92 0.93 0.88 0.88 0.87

Table 5 Convergent validity and discriminant validity Construct 1. Interaction pattern 2. Team emotion 3. Team performance 4. Team satisfaction 5. Team commitment

AVE 0.610 0.643 0.701 0.654 0.704

1

2

3

4

0.582 0.740 0.405 0.835

0.198 0.652 0.891

0.605 0.484

0.676

5

Table 6 Path coefficients Path Interaction pattern → Team emotion Interaction pattern → Team performance Interaction pattern → Team satisfaction Interaction pattern → Team commitment Team emotion → Team performance Team emotion → Team satisfaction Team emotion → Team commitment

Sample mean 0.897 0.439

Standard deviation 0.046 0.142

tstatistic 19.826 3.093

pvalues 0.000 0.002

0.451

0.114

4.070

0.000

0.405

0.123

3.327

0.001

0.431 0.427 0.485

0.146 0.104 0.130

3.028 4.083 3.762

0.002 0.000 0.000

indicates good internal consistency. According to Table 4, the CR values of the latent variables are all greater than 0.7, indicating good internal consistency. Average extraction variance (AVE) and heterotrait–monotrait ratio (HTMT) are used to judge convergence validity and discriminant validity, respectively. The establishment of convergence validity requires the average extraction variance (AVE) to be greater than 0.5. HTMT is used for discriminant validity, which requires that the HTMT value is less than 0.90. The convergent validity and discriminant validity of the model measured in this study are shown in Table 5, indicating enough convergent validity and discriminant validity. In this study, bootstrapping was used on the path coefficient of the structural model to test whether the causal relationship assumed by the model was established. The results of the path coefficient significance test are shown in Table 6.

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As can be seen from Table 6, interaction pattern affects team emotion and the three dimensions of team effectiveness. Team emotion affects team performance, team satisfaction, and team commitment. The research model is valid.

5 Conclusion Based on the team perspective, this study explores the interaction pattern, team emotion, and team effectiveness of virtual teams in crisis situations, and further examines the mediating role of team emotion between them. The results indicate that the interaction pattern has a positive impact on the formation of team positive emotions and team effectiveness. The positive emotions of the team have a direct impact on team effectiveness. The interaction pattern is mediated by team emotions and has an indirect positive effect on team effectiveness. In the future, we would use mixed methods to obtain more diverse data.

References Barsade SG, Ward AJ, Turner JDF, Sonnenfeld JA (2000) To your Heart’s content: a model of affective diversity in top management teams. Adm Sci Q 45(4):802–836 Bies RJ (2013) The delivery of bad news in organizations: a framework for analysis. J Manag 39(1): 136–162 Burke CS, Stagl KC, Salas E, Pierce L, Kendall D (2006) Understanding team adaptation: a conceptual analysis and model. J Appl Psychol 91(6):1189–1207 Camillo AA (2015) Strategic management and crisis communication interdependence in the global context: a preliminary investigation. Emerg Economy Stud 1(1):37–49 Daniel S, Agarwal R, Stewart KJ (2013) The effects of diversity in global, distributed collectives: a study of open source project success. Inf Syst Res 24(2):312–333 Elfenbein HA (2007) Emotion in organizations: a review and theoretical integration. Acad Manag Ann 1(1):315–386 Espinosa AJ, Nan N, Carmel E (2015) Temporal distance, communication patterns, and task performance in teams. J Manag Inf Syst 32(1):151–191 Gifford WE, Bobbitt HR, Slocum JW (1979) Message characteristics and perceptions of uncertainty by organizational decision makers. Acad Manag J 22(3):458–481 Grawitch MJ, Munz DC, Kramer TJ (2003) Effects of member mood states on creative performance in temporary workgroups. Group Dyn Theory Res Pract 7(1):41–54 Hair JF, Risher JJ, Sarstedt M, Ringle CM (2019) When to use and how to report the results of PLS-SEM. Eur Bus Rev 31(1):2–24 Hair JF, Hult TM, Ringle CM, Sarstedt M (2022) A primer on partial least squares structural equation modeling (PLS-SEM), 3rd edn. SAGE, Los Angeles Han SJ, Beyerlein M (2016) Framing the effects of multinational cultural diversity on virtual team processes. Small Group Res 47(4):351–383 Huy QN (2011) How middle managers’ group-focus emotions and social identities influence strategy implementation. Strateg Manag J 32(13):1387–1410 Jehn KA, Chatman JA (2000) The influence of proportional and perceptual conflict composition on team performance. Int J Conflict Manag 11(1):56–73

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Jehn KA, Mannix EA (2001) The dynamic nature of conflict: a longitudinal study of intragroup conflict and group performance. Acad Manag J 44(2):238–251 Kaplan S, Laport K, Waller MJ (2013) The role of positive affectivity in team effectiveness during crises. J Organ Behav 34(4):473–491 Kelly JR, Barsade SG (2001) Mood and emotions in small groups and work teams. Organ Behav Hum Decis Process 86(1):99–130 Levine EL (2010) Emotion and power (as social influence): their impact on organizational citizenship and counterproductive individual and organizational behavior. Hum Resour Manag Rev 20(1):4–17 Qu R (2007) Theory and demonstration of team emotional atmosphere. Economic Science Press, Beijing Spector PE, Fox S (2002) An emotion-centered model of voluntary work behaviors: some parallels between counterproductive work behaviors and organizational citizenship behaviors. Hum Resour Manag Rev 12(2):269–292 Stachowski AA, Kaplan SA, Waller MJ (2009) The benefits of flexible team interaction during crises. J Appl Psychol 94(6):1536–1543 Veil SR, Buehner T, Palenchar MJ (2011) A work-in-process literature review: incorporating social media in risk and crisis communication. J Contingencies Crisis Manage 19(2):110–122 Waller MJ, Gupta N, Giambatista RC (2004) Effects of adaptive behaviors and shared mental models on control crew performance. Manag Sci 50(11):1534–1544 Yu T, Sengul M, Lester RH (2008) Misery loves company: the spread of negative impacts resulting from an organizational crisis. Acad Manag Rev 33(2):452–472 Zellmer-Bruhn M, Waller MJ, Ancona D (2004) The effect of temporal entrainment on the ability of teams to change their routines, research on managing groups and teams. Elsevier Science Press, Oxford, UK

Predicting the Performance of New Hires: The Role of Humility, Interpersonal Understanding, Self-Confidence, and Flexibility Debolina Dutta, Chaitali Vedak, and Varghees Joseph

1 Introduction External recruitment has become a dominant talent management strategy for many organizations (Gërxhani and Koster 2015). It is increasingly becoming more critical to study the effect of the alignment of human capital to business objectives and its return on investments (Zula and Chermack 2007; Becker and Huselid 2006). The focus on determining a new hire’s performance expands on the human capital theory (HCT) by demonstrating the value created by human resource (HR) policies and practices (Zula and Chermack 2007). Due to the imbalance between the rapid rate of employee recruitment and the shortage of talent, substantial organizational investments in recruiting and training go unrealized (Selden et al. 2013; Harris et al. 2011). Therefore, it becomes an essential requirement for new recruits to meet the organization’s expectations (Cappelli 2019) and deliver on-the-job performance (Breaugh 2014; Gërxhani and Koster 2015; Murphy 2016). This chapter focuses on this critical yet neglected gap in existing research on recruitment and recruitment outcomes and contributes in three ways. First, by employing a longitudinal survey spanning over 16 months, we examine the traits and virtues of applicants recruited by an organization and the subsequent post-hire individual performance. Second, previous studies have based their findings on experimental design, mainly using student respondents, to determine perceptions of performance outcomes, which suffer from serious problems of common method variance. To mitigate this bias, we used real-life applicants and organizational performance reviews instead of self-reported reviews. Third, we did not limit the assessment of new hire performance to a few roles, but looked at a spectrum of job

D. Dutta (✉) · C. Vedak · V. Joseph Indian Institute of Management, Bangalore, India e-mail: [email protected]; [email protected]; [email protected] © Springer Nature Switzerland AG 2023 L. Radomir et al. (eds.), State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM), Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-031-34589-0_23

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roles and seniorities to determine common traits and virtues enabling new hire performance. This, therefore, increases the generalizability of our findings.

2 Theoretical Grounding and Hypotheses We draw on the Human Capital Theory (HCT) to argue that it is the responsibility of Human Resource Development (HRD) practitioners to assess the impact on performance and sustain competitive advantage (Zula and Chermack 2007). HCT suggests that human capabilities acquired by human resources that are durable traits yield positive effects on performance in socially valued activities (David 2001; Becker and Huselid 2006; Subramony et al. 2008). Therefore, human capital frameworks emerge from the idea that labor inputs are not merely quantitative (Zula and Chermack 2007). Both in eastern and western cultures, humility is a multifaceted strength and is primarily regarded as a universal virtue (Argandona 2015; Li et al. 2021; Morris et al. 2005; Owens et al. 2011) and a significant body of work substantiates the positive influence of humble personnel in the workplace at various levels (Owens and Hekman 2016; Owens et al. 2013; Rego et al. 2019; Sousa and van Dierendonck 2017; Zhang et al. 2017; Vera and Rodriguez-Lopez 2004). H1: Humility significantly influences the performance of new hire. Interpersonal understanding (IPU) refers to the individual’s capability to show empathy and look at things from others’ points of view (Knott and Kayes 2012). With this capability, employees develop multiple social ties with different colleagues to improve their performance, and the strength of these ties depends on employees’ level of societal understanding of their workplace (Borgatti et al. 2009). H2a: Interpersonal understanding significantly influences the performance of new hire. H2b: Humility mediates the relationship between Interpersonal understanding and the performance of new hire. Self-confidence is an individual’s perception of control over themselves and their environments based on their past experiences (Mayo et al. 2012), which increases the individuals’ probability of performing well (Compte and Postlewaite 2004). H3a: Self-confidence significantly influences the performance of new hire. H3b: Humility mediates the relationship between Self-confidence and the performance of new hire. Flexibility, on the other hand, is a key requirement for numerous organizations facing uncertain environments and changing customer demands (Molleman and van den Beukel 2007). Research demonstrates the importance of this behavior, arguing that employees must have a wide range of responses to effectively react to different situations, while preserving their integrity and trustworthiness (Mayo et al. 2012).

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H4a: Flexibility significantly influences the performance of new hire. H4b: Humility mediates the relationship between flexibility and the performance of new hire. Extending this knowledge, we argue that humble employees having sound interpersonal understanding at the workplace, who maintain self-confidence and retain flexibility in daily tasks, are critical in improving employee performance. Therefore, in this chapter, we framed seven hypotheses supporting our claim.

3 Data and Methods 3.1

Research Design

We used a field design method with real job applicants spanning over two years to a large subsidiary of a multinational organization operating in India to understand applicant trait and their subsequent impact on job performance. The organization specializes in energy storage and management solutions, including solar energy, has operated for over 30 years and has sold its products in more than 36 countries. We captured the new hires’ age, gender, and education from the company records and the new hires went through a yearly company-wide performance review cycle in the subsequent year. The HR department initiated capturing employee performance.

3.2

Measures and Control Variables

Well-validated and standardized questionnaires were used to collect data on all items. Humility of the new hire was captured by a 9-item scale containing items (Owens et al. 2013). While self-confidence was measured by the three items scale (Mayo et al. 2012), flexibility was measured by using a 3-item scale (Mayo et al. 2012). IPU was measured using a 3-item scale (Mayo et al. 2012). Consistent with past research on new hire performance, we controlled for age (Selden et al. 2013; Ng and Feldman 2008), gender (Liu et al. 2008), education (Ng and Feldman 2009), and tenure (Kuipers and Giurge 2017) of the new hires.

4 Analysis and Findings In order to control for common method variance (CMV), we used Harman’s single factor score, in which all items (measuring latent variables) are loaded into one common factor where the variance extracted was 40.41% (less than the threshold of 50%), indicating CMV is not a major concern in the analysis (Podsakoff et al. 2012).

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In determining discriminant validity, we assessed the heterotrait–monotrait (HTMT) ratio of correlations among the constructs (Henseler et al. 2015) and the constructs’ composite reliability and discriminant validity were assessed and within limits. Humility influences new hire performance (β = 0.328, p < 0.01), hence hypothesis 1 was supported. While IPU was significant in predicting new hire performance (β = -0.258, p < 0.01), supporting Hypothesis 2a, the negative correlation with performance indicates a suppressor effect of the mediating variable indicating full mediation (Hair et al. 2014, p. 225). Therefore, hypothesis 2b was also supported. However, self-confidence (β = 0.029, p > 0.05) and flexibility (β = -0.070, p > 0.05) did not have a significant effect on new-hire performance. Hence, hypotheses 3a and 4a were not supported. Other than that, the control variables such as age, gender, and qualification were not found to have any significant relationship with a new-hire performance at the workplace: Since the sample population is collected from a specific organization, the control variables of the participants may not vary widely enough to have a significant impact on the results. The bootstrapping procedure with 5000 resampling runs (Hair et al. 2021) was performed to assess the three mediation hypotheses. The relationship between IPU and humility was significant (β = 0.392, p < 0.01). Due to the suppressor effect, humility wholly mediates the effect of IPU on the new-hire performance. Hence, hypothesis 2b was supported. Flexibility (β = 0.162, p < 0.01) and SC (β = 0.329, p < 0.01) were significantly influencing humility. However, there was no direct influence of flexibility and SC on employee performance. We find that humility wholly mediates the relationship between flexibility and newly hired employees’ performance as well as the relationship between self-confidence and newly hired employees’ performance. Thus, Hypotheses 3b and 4b were supported.

5 Discussion This chapter’s primary contribution is to build a model that emphasizes the critical traits of new hires and their on-job performance, an essential aspect of human capital that has largely been unexplored. The chapter provides a comprehensive systematic understanding of the dynamics and impact of the new hire traits and the interplay with humility and contributes to the body of literature on recruitment by identifying relevant traits of new hires that link to employee performance. As the findings are based on field experiment data involving real job applicants across multiple job families and roles, using real on-job performance data of new hires, it increases the validity of the findings. The findings indicate that humility wholly mediates the influence of self-confidence and flexibility on performance. These results are extremely relevant for practitioners, as it provides guidance on essential traits to be screened for in new hires.

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6 Implications and Limitations Despite the important contributions of the chapter, as with most research, it is not without limitations that should be acknowledged. While this chapter provides direction toward critical individual traits within applicants that could lead to superior on-the-job performance, the survey was based on a sample from an Indian MNC, and the findings may be specific to this particular context. In the interest of parsimony, we were able to focus on a limited set of traits that could impact the on-the-job performance of new hires. Research indicates multiple other traits and competencies, apart from personality traits, which could provide rich direction to practitioners and help recruitment research. Future studies may explore the influence of additional constructs on new hire performance. The implications of our research are significant and multifaceted. By acquiring a holistic understanding of the traits involved in high-performance organizations can develop focused training and development programs, improve the onboarding process, provide managers with tools and strategies for effectively leading and mentoring new hires, and improve retention by providing new hires with development opportunities that are aligned with their strengths. Acknowledgments We thank the anonymous reviewers for their valuable feedback and for helping us to improve our paper.

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Mindful Leadership Under Fire: A Validation Study of a Hierarchical Component Model Stephanie Dygico Gapud and Joseph F. Hair Jr.

1 Introduction The COVID-19 pandemic created various challenges for organizations, including supply chain issues, implementation of work arrangements, the safety of employees, managing financial impact, adapting to changes in consumer behavior, and navigating government regulations. Some organizations were surprised when things did not return to normal quickly, and delays in recognizing the paradigm shift led to decreased efficiency and potential harm (Eastwood 2020; Cecere 2021; Branswell 2022; Raghavan et al. 2021; Sneader and Sternfels 2020; Tucker 2020). Highreliability organizations, such as those in healthcare and air traffic control, are able to consistently avoid catastrophic events through a culture of safety, continuous learning, and mindful leadership. Mindful leadership emerges when team members participate in a dynamic influencing process during times of uncertainty. To improve team performance when dealing with uncertainty, organizations should learn from wildland firefighting teams (Weick and Sutcliffe 2007), which share authority and responsibility and have a less hierarchical managerial approach (Gapud 2019). Understanding differences in shared leadership environment and behavior can help improve leadership strategies and achieve a shared purpose of safely completing missions. Research has shown that sharing authority with those who have knowledge, skills, and abilities improves team performance, and a less hierarchical managerial approach leads to better performance in a hierarchical

S. D. Gapud (✉) Division of Business, Spring Hill College, Mobile, AL, USA e-mail: [email protected] J. F. Hair Jr. Mitchell College of Business, University of South Alabama, Mobile, AL, USA e-mail: [email protected] © Springer Nature Switzerland AG 2023 L. Radomir et al. (eds.), State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM), Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-031-34589-0_24

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organization (Bernstein 2015; Edmondson 2012; Gapud 2019; Fraher et al. 2016; Langer 2010; Weick and Sutcliffe 2007). Gapud and Hair prepared a chapter in this book entitled “When Navigating Uncertainty Lead Mindfully.” Their results provided relevant components for effectively implementing Auftragstaktik.

1.1

Auftsragstaktik

The concept of decentralizing command in the military by fully trusting in the capabilities of, and giving leeway to, those on the frontlines to make strategic decisions based on the command intent is called Auftragstaktik [AT] (Nelsen 1987; Shamir 2010; Wittmann 2017). It is also called mission command. Many wildland firefighting organizations have adopted the concept of Auftragstaktik and called it “leader’s intent” (DeGrosky 2005; Ziegler and DeGrosky 2008). Therefore, the theoretical SEM proposed in this chapter is identified as an Auftragstaktik [AT] model. The theoretical AT model adopted in the study of wildland firefighting teams (Gapud 2019) is applied as the benchmark in exploring the use of mindful leadership within the Gulf Coast City Fire and Rescue organization. The findings will provide an overview of the similarities and differences in Mindful Leadership between the wildland and city firefighting teams.

1.2

The Simplified Version of the Model and Hypotheses

HCM facilitates the creation of a parsimonious model of the hypothesized relationships, as shown in Fig. 1. The model is parsimonious, considering both Mindful Leadership (Fig. 2) and Organizational Mindfulness are third-order higher-order constructs (HOCs). Figures 3 and 4 show the full measurement model illustrating the two third-order higher-order constructs (Mindful Leadership and Organizational Mindfulness), second-order constructs (Shared Leadership and Leader Behavior), and their corresponding lower-order constructs (Voice, Social Support, Shared Purpose, Directive, Transactional, Transformational, Empowering).

1.3

The Hierarchical Component Model of AT

Figure 2 illustrates the collective mindset of wildland firefighting teams where members are trusting and loyal to each other [Trust] (Podsakoff et al. 1990), which operates mindfully [Organizational Mindfulness] (Weick and Sutcliffe 2007). Trust and Organizational mindfulness are the antecedent constructs of Mindful Leadership, enabling Team Potency (Guzzo et al. 1993) and ultimately leading to performance (Gapud 2019). The AT third order HOC Organizational Mindfulness is

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Fig. 1 Simplified version of the theoretical model with hypothesized relationships. Note: Hypothesized relationships of the constructs are illustrated. H1: Organizational mindfulness + ! Mindful leadership. H2: Trust + ! Mindful leadership. H3: Mindful leadership + ! Team potency. H4: Mindful leadership + !Team performance. H5: Team potency + !Team performance

Fig. 2 Hierarchical component model of Mindful leadership (Gapud 2019)

based on the theorizing of Weick and colleagues (Weick et al. 1999; Weick and Sutcliffe 2007, 2008). Weick and Sutcliffe (2007) described an organization as mindful when the teams are always prepared to deal with the unexpected [Anticipation] and can contain and bounce back from problem situations [Containment]. Resilient performance in the age of uncertainty (Weick and Sutcliffe 2007) relies on the practice of anticipation as a strategy that enables an organization to be mindful, which includes being preoccupied with failures [PreOcFail], reluctance to simplify details [RelucSim], and sensitivity to operations [SensOper]. When preventing the

248 Fig. 3 Measurement model of Auftragstaktik (Wildland Firefighters)

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Mindful Leadership Under Fire: A Validation Study of a. . .

Fig. 4 The measurement model of Auftragstaktik (City Firefighters)

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disruptive “unexpected events” which continue to develop despite the anticipation strategies that the organization has implemented, mindful organizational strategies rely on two principles—the commitment to resilience [CommitResil] and deference to expertise [DeferExpert]. The other third-order higher-order construct is Mindful Leadership, which occurs when there is dynamic influencing within the team, enabling any member to rise and take the leadership role when the situation calls for their expertise. The dimensions of Mindful Leadership (Dunoon and Langer 2011; Gapud 2019; Langer and Moldoveanu 2000) are based on the theories of Shared Leadership (Carson et al. 2007) and Leader Behavior (Manz and Pearce 2017; Pearce et al. 2003; Pearce 2010; Pearce and Conger 2003; Pearce et al. 2014). Shared leadership is a team property whereby leadership is distributed among team members rather than focused on one designated leader (Carson et al. 2007). An organization can be infested with leadership disease when the power is concentrated in a charismatic singular authority who can easily turn into a corrupt powerful authoritarian (Manz and Pearce 2017). One cure for leadership disease is shared leadership (Manz and Pearce 2017). A reflective–reflective, repeated indicators approach was used to develop and estimate the model’s HCMs (Organizational Mindfulness and Mindful Leadership) (Becker et al. 2012; Hair et al. 2018; Sarstedt et al. 2019). The hierarchical component model was analyzed using PLS-SEM (Wetzels et al. 2009) following the paradigm set by Sarstedt et al. (2021) and applying the SmartPLS 4.0.6.8 software (Ringle et al. 2022).

1.4

Mindful Leadership

Mindful Leadership is a shared leadership approach in which there are dynamic influences in all directions within the chain of command. For example, mindful Leadership was observed in high-reliability organizations (HRO) that safely and successfully navigate uncertainty as part of their regular routine (Bigley and Roberts 2001; Gapud 2019; Langer 2010; Klein et al. 2006; Pearce et al. 2014; Weick and Sutcliffe 2007). This approach is similar to Gapud and Hair (2021), who applied PLS-SEM to assess the predictive characteristics of the Mindful Leadership HCM. The Mindful Leadership HCM (Fig. 1) was developed using the repeated indicators approach (Becker et al. 2012; Hair et al. 2018; Sarstedt et al. 2019) to estimate hierarchical component model. Mindful Leadership is modeled as a third-order construct with a higher level of abstraction than the second-order subcomponents (Shared Leadership, and Leader Behavior), while simultaneously including several lower-order constructs (LOCs) (Voice, Shared purpose, Social Support, Directive, Transactional, Transformational & Empowering), which cover more concrete traits of this construct. The Mindful Leadership HOC represents an individual’s perceptions of shared leadership in teams operating in a vertical/hierarchical organization (Gapud 2019) as the logical theoretical cause explaining the correlations between the

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leadership behaviors that are being shared (Pearce et al. 2003) and the shared leadership environment (Carson et al. 2007).

2 Methodology The model was evaluated using survey results from Alabama Forestry Commission employees. The same measures were used to gather data from the Gulf Shores Fire and Rescue Department, Gulf Shores, Alabama. The survey link was distributed to Gulf Shores Fire and Rescue Department employees (60 employees), and 40 usable responses were received. The survey involving the Alabama Forestry Commission was sent to 230 employees, and the number of usable responses was 136. Data from both organizations were collected using the Qualtrics platform. Attention question items were used to provide a quality check.

3 Measurement Model Confirmation Results of the validation of the model of Auftragstaktik in wildland firefighting teams are compared with that of the city firefighting teams. Kline (2015) noted a difference between having a model that matches reality and trying to fit a model to the data. If one organization is meant to be the expert in dealing with the unexpected (Weick and Sutcliffe 2007), then organizational scholars will have a tool to use in measuring the organizational mindfulness and the mindful leadership present in any organization. The measurement model procedure of confirmatory composite analysis (CCA) is followed to evaluate the constructs (Hair et al. 2020).

3.1

Indicator Loadings

The outer model was assessed by examining the relationship between the constructs and their indicators. The final measurement models in Figs. 3 and 4 show that all the indicators (in yellow boxes) have loadings at or above 0.708 and loaded significantly in their respective constructs, with all p-values 0.85), convergent validity, and discriminant validity (Hair et al. 2022; Henseler et al. 2015; Franke and Sarstedt 2019). Convergent validity was assessed initially through indicator reliability. The rule of thumb is that standardized indicator outer loadings must be 0.708 or higher (Hair et al. 2014). Table 1 lists the definition of the abbreviations of the constructs in the theoretical model.

3.1.1

Loadings

Figure 4 shows the final measurement model. Most of the retained items have outer loadings above the threshold of 0.708 except for PWF1 (0.67) in the higher-order Organizational Mindfulness construct. These items were retained to support content validity (Hair et al. 2019; Hair et al. 2022), and all were very close to the minimum threshold.

290 Fig. 4 Measurement models showing the path coefficients and R2 values

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Table 2 Validity and reliability of construct scores

Organizational _Mindfulness Anticipation PreOcFail SensOper RelucSim Containment ComtResil DefExper Mindful_Leadership Leader_Behavior Direct Empower Transform Transact Shared_Leadership Voice ShPurp SocSup Team_Potency Team performance Trust

Cronbach’s α 0.961

Composite reliability ρc 0.964

Average variance extracted (AVE) 0.589

0.952 0.753 0.891 0.929 0.901 0.787 0.880 0.960 0.929 0.749 0.853 0.915 0.877 0.957 0.930 0.912 0.910 0.873 0.946 0.928

0.958 0.858 0.915 0.942 0.922 0.862 0.909 0.964 0.943 0.856 0.911 0.940 0.942 0.963 0.950 0.945 0.944 0.908 0.961 0.949

0.618 0.669 0.606 0.672 0.628 0.610 0.625 0.627 0.703 0.667 0.773 0.797 0.891 0.722 0.826 0.850 0.848 0.664 0.861 0.824

Notes: Higher-order construct values are in bold and italics. LOCs are listed below the second-order HOC

3.1.2

Convergent Validity

Convergent validity was also assessed by examining whether average variance extracted (AVE) was above the lower limit of 0.50 (Fornell and Larcker 1981; Hair et al. 2022). Table 2 shows all items of all constructs in the model achieved above average convergent validity. The lowest AVE was exhibited by Organizational Mindfulness (0.59), but was still well above the minimum 0.50 AVE threshold (Hair et al. 2022). This indicates the set of variables for each construct is consistent in what they are intended to measure (Hair et al. 2022). Composite reliability [CR] was also examined. All the reflective constructs in the model exceeded the minimum requirement of 0.70 for composite reliability.

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Discriminant Validity

The HTMT criterion analysis results (Table 3) indicate discriminant validity among Organizational Mindfulness, Mindful Leadership, Team Performance, Team Potency, and Trust. Discriminant validity exists between the second order HOCs Anticipation and Containment and other reflectively measured constructs, including Mindful Leadership, Team Performance, Team Potency, and Trust; and the LOCs Voice, ShPurp (shared purpose), SocSup (social support), Direct (directive), Transact (transactional), Transform, (Transformational), and Empower (Empowering). Discriminant validity was also found between all the second order HOCs, including Leader Behavior, Shared Leadership, Commitment to Resilience, Preoccupation with Failure, Sensitivity to Operation, and Reluctance to Simplify; and other reflectively measured constructs, including Team Performance, Team Potency, and Trust. At the same time, however, discriminant validity is not present between Leader Behavior, Shared Leadership, and their higher order construct (HOC) Mindful Leadership. Similarly, discriminant validity is not present between the third order HOC Organizational Mindfulness, the higher second order constructs Anticipation and Containment, and all the LOCs in the hierarchical component model, including Commitment to Resilience, Preoccupation to Failure, Sensitivity to Operation, and Reluctance to Simplify. These results were expected, however, as the measurement model of the LOCs associated with HOCs are theoretically related to each other (Hair et al. 2022). Since the results provide support for the measure’s reliability and validity, the structural model can now be evaluated.

3.2

Structural Model Assessment

The assessment of the structural model examines the relationship between constructs as well as the model’s explanatory capabilities (Hair et al. 2022). The first step is to establish that the model does not have any collinearity issues. To measure the increase in regression coefficient due to collinearity, a variance inflation factor [VIF] analysis was performed for each set of predictor constructs in the model. Results show that all VIFs were above 0.20 and well below 5.0, which indicates that the model does not exhibit collinearity problems (Hair et al. 2014). The presence of common method variance [CMV] bias can also be determined by evaluating the variance inflation factors [VIF] in the construct level. In a study using a Monte Carlo simulation Kock (2015) illustrated how VIF is a valid measure of common method bias. Results indicate that at the construct level, VIFs in the model ranged from 1.0 to 1.53. Since VIFs are below the 3.3 threshold, the model can be considered free from method bias (Kock 2015; Hair et al. 2020) (Fig. 5 and Table 4). All hypothesized relationships (see Table 5 and Fig. 4) were accepted except for the Mindful Leadership to Team Performance path. The antecedents Organizational

Anticipation CommitResil Containment DeferExpert Direct Empower Leader behavior Mindful leadership Organizational mindfulness PreOcFail RelucSim SensOper Shared leadership Shared_Purpose Social_Support Team performance Team potency Transact Transform Trust Voice

0.84 0.87 0.88 0.51

0.85 0.83 0.89 0.51

0.43 0.54 0.26

0.48 0.45 0.67 0.58 0.47

0.98 1.04 0.98 0.67

0.57 0.64 0.22

0.45 0.55 0.72 0.64 0.66

0.35 0.44 0.63 0.52 0.52

0.41 0.50 0.18

0.96

0.91

1.03

1.08 0.45 0.59 0.64 0.59

Containment

1.04 0.97 0.54 0.70 0.71 0.61

CommitResil

0.86 0.89 0.85 0.55 0.70 0.74 0.73

Anticipation

0.33 0.39 0.58 0.47 0.46

0.35 0.43 0.16

0.78 0.83 0.84 0.44

0.92

0.39 0.53 0.58 0.52

DeferExpert

0.40 0.53 0.77 0.53 0.43

0.60 0.51 0.09 0.54 0.55 0.88 0.80 0.66

0.75 0.71 0.25

0.80 0.68 0.72 0.74

0.68

0.53 0.60 0.51 0.57 0.54

1.04 0.92

Empower

0.70 0.76 0.65

Direct

0.57 0.60 1.04 0.79 0.67

0.74 0.73 0.30

0.81 0.72 0.74 0.75

0.72

0.93

Leader behavior

Table 3 Discriminant validity assessment using the heterotrait–monotrait (HTMT) criterion

0.58 0.60 0.88 0.73 0.93

0.95 0.97 0.38

0.79 0.72 0.76 1.00

0.72

Mindful leadership

0.43 0.54 0.71 0.62 0.65

0.55 0.63 0.21

0.97 1.02 0.98 0.65

Organizational mindfulness

0.39 0.64 0.77 0.71 0.68

0.63 0.69 0.17

0.96 0.93 0.71

PreOcFail

0.47 0.55 0.71 0.62 0.65

0.56 0.64 0.23

0.90 0.65

RelucSim

(continued)

0.42 0.54 0.72 0.65 0.69

0.62 0.68 0.22

0.70

SensOper

When Navigating Uncertainty Lead Mindfully 293

0.98 1.01 0.40 0.54 0.54 0.71 0.62 0.98

Shared leadership

0.93 0.38 0.54 0.53 0.69 0.57 0.79

Shared _Purpose

0.43 0.57 0.54 0.71 0.58 0.87

Social_Support

Notes: *Numbers in bold are HTMT correlation coefficients greater than the 0.90 threshold

Anticipation CommitResil Containment DeferExpert Direct Empower Leader behavior Mindful leadership Organizational mindfulness PreOcFail RelucSim SensOper Shared leadership Shared_Purpose Social_Support Team performance Team potency Transact Transform Trust Voice

Table 3 (continued)

0.76 0.16 0.32 0.28 0.34

Team performance

0.24 0.56 0.41 0.44

Team potency

0.59 0.51 0.47

Transact

0.73 0.64

Transform

0.61

Trust

Voice

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Fig. 5 Path coefficients and p-values for the structural model. Notes: The figure shows the results of the bootstrapping analysis. Numbers in parentheses are the corresponding p-values of the path coefficients. All relationships between constructs are statistically significant except for the path between Mindful leadership and Team performance which has a p-value above 0.05

Mindfulness and Trust were positively related to Mindful Leadership. The relationship with Mindful Leadership and Team Potency is highly significant (1% level). The relationship between Mindful Leadership and Team Performance in the model was not positive and not statistically significant, which led us to conclude that Team Potency fully mediates the relationship between Mindful Leadership and the perceived performance. Mediation occurs when a third variable intervenes or governs the relationship between two other related constructs (Hair et al. 2017). This is consistent with results of previous studies which have shown that Team Potency is the explanatory variable between leadership and performance, or between trust and performance (Bligh et al. 2006; Monteiro and Vieira 2016; Schaubroeck et al. 2011; Sivasubramaniam et al. 2002). To test the bivariate relationship between Mindful Leadership and Team Performance we conducted mediation post hoc analysis. Post hoc analysis consists of examining the data after the experiment has concluded, looking specifically for patterns that have not been specified a priori. Keeping in mind that Mindful Leadership is the higher order construct composed of Shared Leadership and Leader Behavior as its dimensions, it is important to determine the bivariate correlation between Mindful Leadership and Team Performance. Doing so will determine whether the path correlation coefficient result is due mainly to the multivariate relationship in the model. The latent variable scores from the PLS-SEM analysis were used in the computation of the correlation between the three dependent variables. Bivariate correlation analysis will isolate the impact of other variables in the model on the correlation

Voice

Trust

Transform

Transact

Team potency

Team performance

Social_Support

Shared_Purpose

SensOper

Shared leadership

RelucSim

PreOcFail

Organizational mindfulness

Mindful leadership

Leader behavior

Empower

Directive

DeferExpert

Containment

CommitResil

Anticipation

1

Anticipation

1

CommitResil

1

Containment

1

DeferExpert

1

Directive

1

Empower

Table 4 Collinearity statistics—inner variance inflation factors (VIF) values

1

Leader behavior

1.5

1.5

Mindful leadership

Organizational mindfulness 1

PreOcFail 1

RelucSim 1

Shared leadership

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Voice

Trust

Transform

Transact

Team potency

Team performance

Social_Support

Shared_Purpose

SensOper

Shared leadership

RelucSim

PreOcFail

Organizational mindfulness

Mindful leadership

Leader behavior

Empower

Directive

DeferExpert

Containment

CommitResil

Anticipation

1

SensOper

1

Shared _Purpose

1

Social _Support

1.4

1.4

Team performance

1

Team potency

1

Transact

1

Transform

Trust

1

Voice

When Navigating Uncertainty Lead Mindfully 297

0.44 0.44 0.53 -0.01 0.70 0 0 0.17 0.38 0.16

0.43 0.44 0.53 0 0.70 0 0 0.16 0.37 0.16

Sample mean (M)

0.04

0.07

0.04 0.05

0.04

0.11 0.07 0.09 0.07

0.10

Standard deviation (STDEV)

3.66***

0.084

0.264

0.507

0.088 0.285

-0.086 0.079 0.05NS 3.07***

0.242

0.074

-0.085

0.05NS

5.53***

0.624 0.647 0.181 0.845

0.628

97.50%

0.202 0.384 -0.180 0.556

0.253

2.50%

4.08 8.07 0.05 9.64

4.41

t-statistics (|O/STDEV|)

***

***

NS ***

NS

H2*** H3*** H4 NS H5***

H1***

Hypothesis decision/ Statistical significance

Notes: Critical t-values for a two-tailed test are 1.65 (significance level = 10%*), 1.96 (significance level = 5%**), 2.58 (significance level = 1%***). The one-tailed significance of a directional hypothesis at the 0.05 level is 0.98

Direct effects Organizational mindfulness→Mindful leadership Trust→Mindful leadership Mindful leadership→Team potency Mindful leadership→Team performance Team potency→Team performance Indirect effects Organizational mindfulness→Mindful leadership→Team performance Trust→Mindful leadership→Team performance Organizational mindfulness→Mindful leadership→Team potency→Team performance Mindful leadership→Team potency→Team performance Trust→Mindful leadership→Team potency→Team performance

Original sample (O)

Table 5 Path coefficients and results of hypotheses

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between Mindful Leadership and Team Performance. Results of the bivariate correlation show that Mindful Leadership is significantly and positively correlated with Team Performance. This suggests that in the multivariate analysis, the relationship between Mindful Leadership and Team Performance is the effect of other variables in the model—in this situation, the relationship is fully mediated by Team Potency.

3.3

In-Sample Prediction Results

To assess the model’s in-sample fit, we first consider the R2 (Table 6). The coefficient of determination R2, is a measure of in-sample prediction of all endogenous constructs (Shmueli et al. 2019). We find that all the endogenous constructs have high R2 values way above the large (0.2592) predictive accuracy threshold for the field of psychology (Cohen 1992). R2 results confirm the ability of the model to explain the endogenous construct using in-sample data (the Alabama Forestry Commission fire-fighting teams) which should not be used to infer to the population (results for firefighting teams from other organizations) (Rigdon 2012; Sarstedt et al. 2014; Shmueli et al. 2019). The effect size ( f2) is the second measure of the predictive validity. It is an estimate of the predictive ability of each independent construct in the model. The effect size is also considered an in-sample predictive metric. For more detailed information on predictive validity and its derivations and application, please see “Assessing measurement model quality in PLS-SEM using confirmatory composite analysis” (Hair et al. 2020). As seen in Table 7, in-sample effect sizes of the constructs are close to large (e.g., the effect of Organizational Mindfulness, 0.31 Table 6 R2 values of endogenous latent variables in the path model Mindful leadership Team performance Team potency

R2 adjusted 0.60

Psychology R2 rules of thumb High

Marketing R2 rules of thumb Moderately high

0.47 0.28

High High

Moderate Above the weak threshold

Notes: Cohen (1992) Classified the strength of the predictive accuracy (R2 values) as either small (0.0196), medium (0.1304) and large (0.2592) Table 7 Within-sample effect size: f 2 values of constructs Mindful leadership Mindful leadership Organizational mindfulness Team potency Trust

Team performance 0.00

Team potency 0.40

0.31 0.67 0.32

Notes: Rules of thumb for assessing f 2 values follows Cohen (1992)— small (0.02), medium (0.15), and large (0.35)

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and Trust, 0.32 on Mindful Leadership) and above the large threshold (e.g., the effect of Mindful leadership, 0.40 on Team Potency, and the effect of Team Potency, 0.67 on Team Performance).

3.4

Out-of-Sample Predictive Power

To analyze the out-of-sample predictive power of the model, we use the PLS-predict function of the SmartPLS 4.0 software (Ringle et al. 2022) developed by Shmueli et al. (2016). The method uses training and holdout samples to generate and evaluate predictions from PLS path model estimations. Moreover, Hair et al. (2022) provides an overview of how the SmartPLS implementation of the PLS predict algorithm enables researchers to obtain cross-validated prediction errors and prediction error summary statistics (Shmueli et al. 2019). The statistics include the root mean squared error [RMSE], the mean absolute error [MAE], and the mean absolute percentage error [MAPE] (Shmueli et al. 2019). The results assess the predictive performance of the PLS path model for the manifest variables [MV or indicators] and the latent variables [LV or constructs]. Following the rules of thumb for running PLSpredict (Shmueli et al. 2019) we used 10 folds (i.e., k = 10), and 10 repetitions (r = 10). Results show that all the manifest variables in the model have positive Q2predict. To assess the results of a PLS path model, its predictive performance is compared against two benchmarks: the Q2predict value, and the LM [linear model] approach. The Q2predict value compares the prediction errors of the PLS path model to the errors when using mean values. If the Q2predict value is positive, the PLS-SEM model has better predictive performance than the mean values. The LM approach regresses all exogenous indicator variables on each endogenous indicator variable to generate predictions. The PLS-SEM results should produce lower prediction errors for RMSE and MAE than LM. Note that the LM prediction error results as reported by the PLS-SEM software is only available for the indicator variables, and not for the latent variables. The PLS-Predict analysis specified Mindful leadership as the key target construct. PLS-LV residual analysis results shows a near-symmetrical, bimodal graph. Therefore, we use RMSE as the criterion to compare the predictive performance of alternative PLS path models (Shmueli et al. 2019). It is interesting to note that in the PLS Model (see Table 8), the manifest variables belonging to Team Potency and Performance has positive Q2predict, while in the LM, all of the mentioned MVs have negative Q2predict values. This indicates Team Potency and Team Performance are also predicted by the model. Following the rule of thumb in analyzing PLS-Predict (Shmueli et al. 2019), since the RMSE of PLS-SEM < LM in a majority of the manifest variables, we can say that the model has moderate outof-sample predictive capability (Manley et al. 2021). Furthermore, Table 8 shows the Q2predict values of the antecedent and outcome constructs of Mindful Leadership are all positive. Therefore, the model can be used to predict similar relationships of constructs to exist in other hierarchical organizations where leadership is mindfully shared (Fig. 6).

When Navigating Uncertainty Lead Mindfully Table 8 Prediction summary of the lower order antecedent constructs and the dimension constructs of Mindful Leadership

Anticipation ComtResil Containment DefExper Direct Empower Leader behavior Mindful leadership PreOcFail RelucSim SensOper ShPurp Shared leadership SocSup Transact Transform Voice

301 RMSE 0.359 0.531 0.488 0.525 0.451 0.486 0.484 0.593 0.533 0.393 0.478 0.674 0.651 0.640 0.359 0.489 0.592

MAE 0.279 0.398 0.374 0.401 0.322 0.358 0.362 0.437 0.407 0.309 0.363 0.504 0.479 0.470 0.274 0.367 0.436

Q2predict 0.902 0.646 0.770 0.710 0.185 0.543 0.619 0.577 0.683 0.874 0.786 0.299 0.436 0.366 0.205 0.567 0.416

Fig. 6 PLS LV prediction residuals of Mindful Leadership

Figure 7 shows that the PLS is underpredicting the MV LBEmp4 (Our leader engages us in participative goal setting) as evident by the skewedness of the

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Fig. 7 Side-by-side comparison of the manifest variable LBEmp4 prediction residuals generated in PLS and LM Analysis

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residuals to the right (Shmueli et al. 2019). Furthermore, the RMSE of LBEmp4 is smaller in the PLS model (1.17) as compared to the LM (1.30). Three metrics (R2 and f 2 effect size of endogenous variables [in-sample prediction], and PLSpredict [out-of-sample prediction]) were applied to assess the predictive validity of the model. Clearly, the results are a concrete step toward understanding the leadership behavior of teams mindfully sharing the lead in the context of HRO when they are teaming.

3.5

Importance Performance Map Analysis

The Importance-Performance Map Analysis [IPMA] at the construct and at the indicator levels was conducted to extend the PLS-SEM results reporting. At the construct level, the IPMA allows the identification of the predecessor construct that has a relatively high importance (strong total effect), but also low performance (low average latent score), so that improvements can be implemented. For example, in our model, we propose that Team Potency explains the relationship between Mindful Leadership and performance (Hair et al. 2018). Conducting the IPMA by targeting Team Potency, we can determine which among the predecessor constructs (Organizational Mindfulness, Trust, and Mindful Leadership) in the model will have to be focused on in training so we can improve the Team Potency (Fig. 8).

Fig. 8 The Importance-performance map analysis of the constructs

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Fig. 9 The Importance-performance map analysis of the indicators

Figure 9 shows that at the indicator level, improving Trust2 and Trust3 will also have impact on improving Team potency. Moreover, as seen in Fig. 8, Mindful Leadership has the strongest impact (highest total effect, 0.30–0.78) on Team Potency. Moreover, it has low performance (25–42 latent variable score). This connotes that any managerial action to improve Mindful Leadership will have high impact on improving the Team Potency.

4 Discussion 4.1

Summary of Findings

This study began with the assumption that organizations navigating ambiguity and uncertainty and want to be resilient have much to learn from wildland firefighters. Our findings demonstrated how resiliently the teams functioned, maneuvered, and delivered, even as their margins were narrowing when the system was overwhelmed with fire. Our approach assessed a cross-section of the teams’ internal dynamics to develop a model and test the construct relationships. We believe decision-makers reading this manuscript will have a meaningful example of how hierarchy (Leavitt 2005) and shared leadership in teams go hand-in-glove (Pearce et al. 2014; Pearce

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2010). Moreover, our research has exemplified and clarified that the self-leadership of the vertical leader is critical in enabling a team of knowledge workers to focus on achieving their mission (Pearce and Manz 2005). Moreover, Mindful Leadership, as an emerging higher-order construct proposed here, further explains leadership behavior typically emanating from team members. Furthermore, we proposed an expansion of a theory on teaming, suggesting that teaming can occur when a nonhigh-reliability organization (nonHRO) or regular teams within organizations face a challenge or unexpected situation. All hypothesized relationships were accepted except for the path between Mindful Leadership and Team Performance. In this study, as antecedents, Organizational Mindfulness and Trust are positively related to Mindful Leadership. The relationship between Mindful Leadership and Team Performance in the model is not positive and not statistically significant, which led us to conclude that Team Potency fully mediates the relationship between Mindful Leadership and the perceived performance. The qualitative portion of the study provided the examples of team dynamics exemplifying the relationship of the constructs related to HROs. Previous study on HRO focused on naval air traffic control teams exercising mission command, or Auftragstaktik [AT]. Rochlin et al. (1987) illustrated how the characteristics of HRO and AT overlap and intersect. The model in this study encapsulates the structure of constructs related to AT and HRO at the same time. The hierarchical component model, Mindful Leadership, proposed in this study is flexible and adaptive because it is able to capture differences in the leadership behavior exhibited by three clusters of people, based on how they perceived their team performance. This study of Mindful Leadership is a concrete step toward the understanding of the leadership behavior in teams mindfully sharing the lead in the context of HRO. As Bennis (2012, p. 544) stated in his crucibles of authentic leadership, understanding leaders is important because “leaders wield power.” Only when we are mindful that leadership emanates from all members of the team do we begin to realize how to empower them and make the team potent, adaptive, and agile at any time. To claim that the final retained SEM model in this study has attained “statistical beauty, it has to (1) make sense; (2) differentiate between known and unknown; and (3) set conditions for posing new questions” (Kline 2015, p. 22).

4.2

The Model Makes Sense

The model derived in this study is based on previous studies on prominent leadership (Pearce and colleagues) and organization scholars (Weick and colleagues), combined with the firsthand data and personal experience of this author in working with the organization in focus (Alabama Forestry Commission). Kline (2015, p. 68) quoted Bollen’s (1989) argument that “if a model is consistent with reality, then the set of data is consistent with the model. But if the set of data is consistent with the model, this does not imply that the model corresponds to reality.” Therefore, the

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model and clustering procedure should be tested in other HROs to determine if similar clusters of people coexist in the population, and if the model has similar third-order abstraction results, given other sets of data. Moreover, it is prudent to suggest that the model and the series of analysis (clustering and IPMA) be tested using other types of organizations, possibly other high reliability organizations, or any branch of the military. Mindful Leadership is a process leadership style, therefore it is important to note that just like the mindset of organizational mindfulness is not a static state. Like individual mindfulness, constant updates of situational awareness is necessary. Moreover, environmental scanning results can differ at the individual and organizational level. We mentioned that leaders rise in a team based on their knowledge, skills, and abilities. The strategy of small wins in the development of sensitivity to operation encourage team members to speak up (Weick and Sutcliffe 2007). As the authors further explain, just because you see it, don’t assume that someone else sees it too.

5 Conclusions Mindful leadership is an emerging construct on leadership which views leadership not as a position of authority or a hierarchical role, but as an unfolding social process. It is an individual’s perspective of shared leadership of team members working in a high-risk and a rapidly changing context environment. Mindful leadership, when present in an organization, could pave the way to the unique strategy that the company will derive from being mindful of the present and the organization’s context in history. It is the process leadership that occurs when teaming. Moreover, when leadership is shared and authority is allowed to migrate to those organizational members in the frontlines, the vertical leaders could be freed of the minutiae of operations to have more time and energy to pay attention to the bigger picture and make sound strategic decisions (Klein et al. 2006; Klein 1998). Therefore, adopting “mindful leadership” that incorporates a higher level of collective mindfulness and shared leadership constructs can positively influence a firm’s performance and reliability to manage the unexpected. In a sense, the organization would be smarter and better able to adapt to their environment and manage challenges.

6 Implications for Theory and Practice 6.1

Implications to Other Hierarchical Organizations

Other hierarchical organizations wanting to be agile do not need to dissolve their organizational structure. As illustrated in this study, the hierarchy provides stability to the organization while the adapting, sensing, and growth occurred in the areas of

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the organization actively engaged where the rubber meets the road. Providing leeway to those on the frontlines, in terms of making decisions in deploying resources to where they are needed to “put out the fire,” is a strategy that a company can implement. The company can take on the strategy of anticipation when it has more accurate situational awareness. When faced with shifting variables affecting the organization, a strategy of containment is in order. The latter requires reliance on history and organizational learning (Weick and Sutcliffe 2008; Bennis and Nanus 1985). This ability of the teams to deploy resources at a moment’s notice as a competence-based-view strategy for business organizations operating in volatile and uncertain environments is what military science calls Auftragstaktik (Wittmann 2017). The constructs studied in this chapter are similar to the constructs (i.e., trust, anticipation, containment, leadership, mindfulness, situational awareness, common vision, social support, deference to expertise, empowerment of subordinates, etc.) discussed by military science authors (Nelsen 1987; Shamir 2010; Wittmann 2017) when explicating Auftragstaktik. The model in this chapter encompasses the complexity of the concepts relevant to decentralization of command. Future application of the model in military science studies should be explored.

6.2

Finding Evidence of Mindful Leadership in Practice

Be mindful, stupid! To exemplify the strategy of small wins (Weick and Sutcliffe 2007), we will use the famous quote that is a result of environmental scanning and complexity analysis that has defined the rise to power of Bill Clinton—the economy, stupid. Note that it was not Clinton who coined the phrase, it was actually the campaign strategist James Carville (a member of his campaign team) who has keenly summarized the messaging that succinctly communicates the accuracy of the nation’s economic situational awareness and sensitivity to operation. The note was meant to remind his campaign team to be mindful and stay focused on the messaging. However, this simple note was akin to a smoldering material that turned into a wildfire that most political analysts credit Clinton’s rise to the highest office in the country (Bennett 2013; Christiansen 2018). The act of putting out the sign to remind the campaign staffers is sensitivity to operation. As a strategy in managing a highstake uncertain event like a presidential campaign, one ought to develop sensitivity to operation—this means spending time on the front end of operation and writing the three-point messages for the team is a way of letting people know what they are doing and why they are doing it so that the message STICC (situation, task, intent, concern, and calibrate) (Klein 1998). The phrase “It’s the economy, stupid” became the mantra that had brought Clinton a decisive victory in the electoral college of thirty-two states to Bush’s eighteen (Christiansen 2018). It was the accuracy in messaging that tapped into the preoccupation of the population with the ensuing economic failure; the voters needed somebody who could simplify and understand the situation they were in.

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In the Mindful leadership of Brian Chesky, everyone belongs. As mentioned in the introduction of this chapter, when an unexpected event impacts an organization, teaming happens: The pace of work quickens, communication with the team becomes more frequent like when a fast-moving wildfire is nipping at your boots. When the COVID-19 pandemic hit, Brian Chesky was leading a huge global firm that was barely breaking even. How he turned his company to a profitable company that successfully offered its initial public offering in the midst of the pandemic is proof that a high degree of uncertainty sets a mindful leader apart. In his interview with James Manyika (2021), a senior partner emeritus at McKinsey & Company, he said, One of the first lessons I learned is that in a crisis, you have to move much more quickly. People see crises as defining moments, so they try to slow down their decision making, but instead, you have to speed up. You also have to increase the amount of communication you do. The faster things change, the more you need to communicate the change.

Consistent with Bennis (2015), to be a mindful leader, one should be comfortable with operating in times of ambiguity and uncertainty. Similarly, Brian Chesky is mindfully aware of his influence as a leader as he navigated uncertain times during the pandemic. The hardest thing to manage in a crisis isn’t your company—it’s your own thoughts. As a leader, you could think all is doomed. You can ask, “Why me?” You can get paralyzed. Or you can tell yourself, “This is my defining moment, and it will leave indelible marks.” Then you can be optimistic—not with blind optimism but optimism rooted in facts that give you hope of getting out of the situation. If you can project that confidence, abide by your principles, and act quickly, then you can navigate out of the crisis like a captain on a ship that is starting to take on water.

We could continue on how successful Airbnb is since the pandemic served like a fire that forged a new look for the company. Their organizational mindfulness during the crisis brought forth the best in them, as Brian Chesky showed his heart in supporting his team by providing the most generous severance package. Company supported emphasizing respect and recognition of the talents and abilities of the team members that he had to painfully let go. Reading the letter should give any person studying leadership as a process a clear understanding of how mindful leadership works. In this letter, Brian successfully made known the business theory on how his shared leadership environment works, how he made time to listen to the voice of each member, and how he considered all employees as continuing co-owners even though he had to let them go—which, in turn, encourages their continued support in the purpose of the corporation. He exemplified leadership behavior to his team by providing a clear direction in the care of the social, emotional, mental, financial needs of people he had to let go. He empowered them by providing for all the tools they would require—from resume writing, coaching, and creating an outward facing marketing site to highlight the capabilities of the people that would be let go. They were also allowed to keep their company-issued laptops. The letter, he sent to the whole company—can be read at https://news.airbnb.com/a-message-from-cofounder-and-ceo-brian-chesky/.

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Moreover, we mentioned that teaming happens when an unexpected occurs. In the words of Brian Chesky, “A crisis is a spotlight. All hands are on deck. It’s a moment to demonstrate your values and lead by example.” Lastly, the founder letter that Chesky wrote in the IPO is a testament of his sensitivity to operation and preoccupation with failure. He started writing honestly about the recent 20% reduction of the workforce and a lengthy take on the impact of the uncertainty that everyone is experiencing brought about by the pandemic—but then he appealed to the prospective shareholders by showing his commitment to resilience through his mindful crafting of his business theory. “The best thing for shareholders is for society to want us to exist. And society will want us to exist if they think that as Airbnb benefits, they benefit.” He explained that “The rules of business have changed. This idea that the job of a corporation is solely to serve shareholders may have been correct once but talk to anyone under the age of 30—they care about the quality of the products, but they also care about what the company stands for.” As Bennis (2012, p. 544) stated in his crucibles of authentic leadership, understanding leaders is important because “leaders wield power.” Therefore, only when we are mindful that leadership emanates from all members of the team do we begin to realize how to empower them and make the team potent, adaptive, and agile at any time. But then who is in your team? Airbnb’s founders believe that all stakeholders are part of their team. And so, we want to end this discussion on mindful leadership with a forward-facing note from Brian Chesky to exemplify how a leader with the mindset of organizational mindfulness views the future: “I think capitalism needs a little bit of an evolution. We need to adapt to meet the needs and challenges in society, and the next generation of entrepreneurs needs role models. The company is 13 years old. You don’t raise your 13-year-old to be a good 14-year-old. You raise your 13-year-old to be a great adult with a long career over many decades. That is how we are thinking about Airbnb.”

7 Future Studies In this chapter, we explicated that just as the teams of wildland firefighters illustrate, hierarchy and sharing the lead go hand-in-glove. However, what is the behavioral mechanism that actually explains the phenomenon? Auftragstaktik or mission command is a way of flattening bureaucracy by sharing the lead to those on the frontlines. Our results indicate that through mindful leadership organizations can be adaptive and flexible in times of uncertainty when teaming. Why would providing decisionmaking authority that is usually above one’s paygrade make Auftragstaktik work and therefore make the organization adapt to a developing scenario in the workplace? We suggest that the very positive act of the legitimate hierarchical authority in sharing some of the decision-making responsibility to the lower-rank member of the organization provides the unlocking mechanism that broadens the repertoire of action available to the lower-rank team members, much like Fredrickson’s broaden and build theory (Fredrickson 2001).

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However, mindfulness suggests questioning the absolutes; therefore, this unlocking mechanism within the leader/follower relationship has to be researched, measured, and analyzed to contribute knowledge toward greater understanding of the shared leadership theory.

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The Interplay Between Push Factors and Transformational Leadership in Influencing Interorganizational Labor Mobility in Public Sector Rosemary Massae, Deusdedit A. Rwehumbiza, and John J. Sanga

1 Introduction Labor mobility facilitates productive employer–employee matches (Akgündüz et al. 2019). However, in the current world, labor mobility is considered one of the problems that managers in all organizations have to take into consideration, especially when talented employees are to leave (Linhartová and Urbancová 2012). Organizations spend massive resources to make their employees pleased to work effectively and efficiently. Nevertheless, the workers still feel dissatisfied and leave their current organization and join a new one (Chowdhury 2016). Job mobility measures the number of jobs that change throughout an employee’s career, including intra- and inter-organizational changes (Hall 1996). On the other hand, according to Agarwal et al. (2020), labor mobility is the process of an employee(s) moving across different jobs until she/he finds the one with the right fit. Studies show that different types of labor mobility exist. Studies show that labor turnover is widespread, labor mobility and interorganizational labor mobility (ILM), in particular, remains relatively underexplored in management research (Agarwal et al. 2020; Steenackers and Guerry 2016). ILM is also known as turnover intention (TOI) (de Luis Carnicer et al. 2004; Wynen et al. 2013). However, from the human resources management position, ILM is theoretically expensive and may have negative consequences for an organization, such as that of labor turnover. The costs of employee ILM often exceed 100% of the yearly remuneration for the

R. Massae (✉) Institute of Finance Management, Dar es Salaam, Tanzania e-mail: [email protected] D. A. Rwehumbiza · J. J. Sanga University of Dar es Salaam Business School, Dar es Salaam, Tanzania © Springer Nature Switzerland AG 2023 L. Radomir et al. (eds.), State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM), Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-031-34589-0_26

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vacated position due to the recruitment, selection, and training of new employees (Bryant and Allen 2013). The push-pull model invented from the human migration philosophy is a basis for understanding ILM (Haldorai et al. 2019). Push factors have been considered because they consist of psychological machineries for leaving the organization such as job satisfaction and organizational commitment that outweigh the economic factors for leaving (Griffeth et al. 2000). Hence, this chapter’s first and second objectives are to test the direct effect of push factors on ILM in the public sector. The increasing ILM in the public sector can be facilitated by the provisions of the Public Service Management and Employment Policy 1999 as amended in 2008, that there shall be free movement of labor both within the public service and between the public service and the private sector (URT 1999). The provisions of this policy are contrary to the labor retention theories which consider a high rate of labor mobility to be an unhealthy condition for organizations. Few studies have considered the role leaders can play on voluntary turnover dynamics, and maximum of these have been carried out on developed countries such as the United States of America and Europe (Rubenstein et al. 2018; Akgündüz et al. 2019). Further research into the impact of transformational leadership (TL) on employees ILM is worth to be undertaken, to enable generalizability (Sun and Wang 2017; Tse et al. 2013). Consequently, this chapter’s third objective is to test the interaction effect of TL and push factors on ILM, which has never existed, hence bridging up the gap. Tse et al. (2013) show that Social Exchange Theory (SET) can help to explain how and why TL should be considered a necessary “pull-to-stay” factor, deterring employees from forming an intention to leave. Existing studies disclose that, even though determinant factors for leaving or staying are applicable in a broader setting, cultural, customs, traditional, and institutional differences will exist. Hence, the results from developed countries cannot be automatically generalized to non-developed ones (Agarwal et al. 2020; Sousa-Poza and Henneberger 2004). Hence, this chapter has contributed to the existing labor mobility model by examining the moderating effect of TL on the relationship between coworkers’ relationship and work–family conflict and ILM in the public sector.

2 Theoretical Framework and Hypotheses 2.1

Social Exchange Theory (SET)

The theoretical foundation of this study is grounded in Social Exchange Theory (SET) under the “basic rules and norms of exchange” within the social relationship, which is known as reciprocity (Blau 1964). Cropanzano and Mitchell (2005) assert that the core principle of SET is that the association between two social beings depends on how each of these beings respects social rules and customs of exchange.

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Scholars such as Tse et al. (2013) assert that interpersonal trust, mutual loyalty, emotional identification, and continuing reciprocal actions from social exchange relationships become a strong “pull-to-stay” factor, discouraging employees from undertaking ILM. Despite its usefulness in explaining the research variables, SET has been criticized for lacking sufficient theoretical precision (Cropanzano et al. 2017). Hence, Herzberg’s Two-factor Theory complemented it.

2.2

Herzberg’s Two-Factor Theory

Herzberg’s Two-Factor Theory is one of the most significant theories in studying turnover intention and labor mobility in general (Chiat and Panatik 2019). This theory recognizes that employees have two categories of needs affecting job satisfaction: hygiene and motivation. Motivation factors lead to job satisfaction and hence are called motivators; they include achievement, recognition, the work itself, responsibility, advancement, and the possibility of growth (Alshmemri et al. 2017). The hygiene factors are conditions surrounding the job. They include company policy and administration practices, quality supervision, interpersonal relationship, physical working conditions, salary, status and job security, whose negative aspects lead to dissatisfaction (Alshmemri et al. 2017). This chapter assumes that quality leadership behavior and good coworkers’ relationship will reduce ILM for hygiene factors.

2.3

Research Model and Hypotheses

The conceptual model shows the relationship between independent variables (coworkers’ relationship, work–family conflict) and TL, a moderating variable, and ILM, the dependent variable.

2.3.1

Interorganizational Labor Mobility and Push Factors

ILM is a type of employee turnover based on the mobility of employees to a new organization called external turnover (Mbah and Ikemefuna 2012). On the other hand, the TOI is addressed as an indicator of ILM and a substitute for actual turnover behavior (Liljegren and Ekberg 2009). Hence, in this chapter, ILM was used interchangeably with TOI and was considered to be employees’ TOI, which refers to a conscious and deliberate willfulness of Government public employees to seek other alternatives within public service organizations (Tett and Meyer 1993). Scholars such as Chowdhury (2016) and Haldorai et al. (2019) found that the most significant factors for employees to quit a job are the push factors. These include small salary, less fringe benefits, social status, working environment, lack of

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recognition, career development, work–family conflict, and interpersonal tension. The studies above have considered the direct relationship between push factors and ILM. However, this chapter has considered the moderating effect of TL on the relationship between push factors and ILM, which is considered as an extension of the existing ILM model.

Coworkers’ Relationship and Interorganizational Labor Mobility Kim et al. (2013) reveal that coworkers’ relationship negatively correlated with turnover intention in China and Korea. Wong et al. (2017) assert that Gen Y employees emphasize the necessity of friendly interpersonal relationship among coworkers as an indispensable factor for enduring in the hospitality business in China. Consistent with Herzberg et al. (1959), who posit that the work conditions, such as poor relationships with coworkers, cause employees’ dissatisfaction resulting in turnover intention. Based on this discussion, the following hypothesis is formulated: H1: Coworkers’ relationship has a negative influence on interorganizational labor mobility.

Work-Family Conflict and Interorganizational Labor Mobility Work–family conflict occurs when there is interference between work and family life experiences, for example, rigid work hours, work overload, job stress, interpersonal conflict, prevalent travel, career changes, uncooperative supervisor or lack of organizational support (Greenhaus et al. 1989). Yildiz et al. (2021) indicate a positive correlation between work–family conflict and intention to leave. Herzberg et al. (1959) posit that if the organization provides its employees with good work–family policies and sound human resource practices, it will enhance their job satisfaction and intention to stay. The opposite is true when there is an imbalance between work and family activities. With the above in mind, the following hypothesis is expressed: H2: Work–family conflict has a positive influence on interorganizational labor mobility.

The Moderating Role of Transformational Leadership A number of leadership models have been developed forecasting the relationships between leadership and work-related consequences in the public sector context (Moon and Park 2019; Trottier et al. 2008). However, TL is considered more important than transactional leadership (Moon and Park 2019). This is due to the fact that TL motivates and encourages workers to outdo expectations and surpass their own interests for the sake of an organization (Bass

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1985). Studies have considered TL as a pull-to-stay factor as a result of social network within an organization from SET (Tse et al. 2013). Previous studies have shown that TL as a moderator can lessen the impact of both pull and push to leave factors. However, in contrast to the current chapter, those pull and push factors were not explicitly outlined (Waldman et al. 2015). Moreover, TL was found to relate negatively to employees exit behavior (Moon and Park 2019). From the above arguments, the following hypotheses are postulated: H3: Transformational leadership moderates the influence of push factors on interorganizational labor mobility. H3a: The higher (lower) the transformational leadership, the weaker (stronger) the influence of coworkers’ relationships on ILM. H3b: The higher (lower) the transformational leadership, the weaker (stronger) the influence of work–family conflict on ILM.

3 Research Methodology A positivist research philosophy was adopted because the researcher observed and measured social realities to arrive at causal explanation and prediction as a contribution (Saunders et al. 2016). The commonly used approach to theory testing in positivism is a deductive approach associated with highly structured quantitative data analysis methods (Creswell 2012). Thus, a survey strategy was used to collect quantitative data (Saunders et al. 2016).

3.1

Area of Study and Population

The survey was done in the Tanzania mainland. Given the data from the office of the Treasury Registrar, there were 27 government agencies with 8856 employees during the research time (www.estabs.go.tz). However, most of these executive agencies’ human resource management practices are currently centralized. However, this was contrary to the rationale of their establishment, leading to a decreased sense of autonomy, which is expected to decrease morale of not only leaders but also employees. Furthermore, the justification for selecting the public sector, was also based on the provision of Public Service Management and Employment Policy of 1999 as amended 2008. This policy is conflicting the labor retention theories, which posit the necessity of employee retention, to avoid wastage of both human resources and drop in productivity. Geographically, 78% of these executive agencies’ head offices are located in Dar es Salaam, with the rest located in the Coastal region (11%), Morogoro (7%) and Dodoma (4%).

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The Sample Size

The sample size of 383 employees was derived with the help of Yamane (1973)’‘s formula, n = N / [1 + N (e) 2]. Hence, eight agencies with more than 200 employees situated in Dar es Salaam city were used to draw the sample. Specifically, large organizations were preferred for investigation because they have a formal human resource (HR) department that influences HR practices (Michael 2009). This chapter adopts probability simple random sampling technique (Saunders et al. 2016). The list was obtained from the HR department as follows: Tanzania Public Service College (TPSC) = 227, Tanzania Institute of Accountancy (TIA) = 222, Tanzania Airport Authority (TAA) = 768, Tanzania Building Agency (TBA) = 358, Weight and Measure Agency (WMA) = 236, Tanzania Medicine & Medical Device Authority (TMDA) = 266, Tanzania National Roads Agency (TANROAD) = 686, and Government Procurement Service Agency (GPSA) = 266. Thereafter, the sample was selected proportionally as follows: the sample size assumed was n = 460, the total population was N = 3029 divided into 8 agencies, such that N1 = 227 for (TPSC), N2 = 222 for (TIA), N3 = 768 for (TAA), N4 = 358 for (TBA), N5 = 236 for (WMA), N6 = 266 for (TMDA), N7 = 686 for (TANROADS) and N8 = 266 for (GPSA). Then, the sample size was obtained as follows: for N1 = 227, we had P1 = 227/3029 and hence n1 = n*P1 = 460 (227/3029) = 35; n2 = n*P2 = 460 (222/3029) = 34; n3 = n*P3 = (768/3029) = 117; n4 = n*P4 = 460 (358/3029) = 54; n5 = n*P5 = 460 (236/3029) = 36; n6 = n*P6 = 460 (266/3029) = 40; n7 = n*P7 = 460 (686/3029) = 104; n8 = n*P8 = 460 (266/3029) = 40.

3.3

Data Collection

This chapter mainly focused on the primary data collected using a survey tool (Creswell 2012). The researchers conducted the pilot study from March to April 2021. The data were thoroughly analyzed, and the researchers were able to measure the quality of the instrument through data validity and reliability (Saunders et al. 2016). During the large-scale survey, a drop-and-pick method was used to administer the questionnaires due to its efficiency in comparison to others such as phone and the Internet (Jackson-Smith et al. 2016). The participating agencies were contacted first through physical addresses and telephone numbers. After that, the questionnaire was dropped off to each respondent in their respective department. The filled-up questionnaires were later on gathered. Out of 460 surveys incorporated a buffer for the risk of non-response, which is estimated to be 20% for management studies in Tanzania (Goodluck 2009). Therefore, 389 were collected, making a response rate of 84.6%. All received responses were examined for missing values. Responses with a more significant number of

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missing values (more than 15%) were dropped out as per recommendation from Hair et al. (2010). Specifically, 56 questionnaires were dropped out due to their having a lot of missing values. Hence, 333 were retained for data analysis, which had three missing values accounting for only 0.9% of the total data set. Hence the few missing values were retained and given a code of (- 99) (Hair et al. 2017).

3.4

Measurement of Variables

The coworkers’ relationship was measured by six items that were adapted from Tang (1998) and Hain and Francis (2004). The work–family conflict was measured by six items that were adapted from Carlson et al. (2000). Six adapted items measured TL adapted from Jensen et al. (2019). Furthermore, six items were used to measure TOI by adapting Walsh et al. (1985) and Jung and Yoon (2013). The two independent and moderator variables were operationalized based on existing validated scales using seven-point Likert scales ranging from 1 = (strongly disagree) to 7 = (strongly agree). However, the TOI was operationalized based on validated five-point Likert scale ranging from 1 = (strongly disagree) to 5 = (strongly agree). The use of a five-point Likert scale for the dependent variable and a sevenpoint Likert scale for the independent variable, was among the routine therapy taken to avoid Common Method Variance (CMV) (Podsakoff et al. 2003). All variables were captured using multi-item indicators to avoid estimation bias (Hair et al. 2017), (see Table 1).

Table 1 The operationalization and measurement of research variables Variable Interorganizational labor mobility/ turnover intention

Coworkers’ relationships

Work–family conflict

Transformational leadership

Definition The government employees’ turnover intention which is mindful and deliberate willfulness to seek for another alternative within public service Involves integration, workgroup cohesion, social capital, and the primary group that assists with job-related problems The form of interfunctions conflict whereby the responsibilities burdens from the work and family spheres are mutually mismatched in some respect The type of a leader who looks for potential motives in followers seeks to satisfy higher demands, and engages the full person of the follower

Indicators 06

Measure Ordinal

Reference Walsh et al. (1985), Jung and Yoon (2013)

06

Ordinal

06

Ordinal

Tang (1998), Hain and Francis (2004) Carlson et al. (2000)

06

Ordinal

Jensen et al. (2019)

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Data Analysis Method

Survey questionnaire was used to gather the data; however, it introduces the risk of CMV as severe methodological challenge in public management studies (Jakobsen and Jensen 2015). CMV is the systematic variance share among the variables introduced to the measures by the measurement method rather than the measure’s theoretical constructs (Podsakoff et al. 2012). Tehseen et al. (2017) recommend using procedural and statistical remedies to test and control CMV. For example, the researchers adopted procedural remedies, including avoiding common response scale properties for different items, protecting the anonymity of the respondents and avoiding item ambiguity. Furthermore, researchers tested variance inflation factor (VIF) using a full collinearity test as a statistical remedy. CMV can be successfully recognized by using full collinearity test, with a model that passes standard convergent and discriminant validity test (Kock 2015). Using SmartPLS 3.2.7, all VIF values were lower than 3.3, demonstrating that the model was free from CMV, as recommended by (Kock 2015). Table 2 below indicates that all VIF are below 3.3. Partial least squares structural equation modeling (PLS-SEM) was the preferred method because the primary purpose of the research objective is the prediction and explanation of the target endogenous latent variable. PLS-SEM, unlike other methods, depends on composites variables; additionally, it can analyze both reflective and formative measurement models without identification problems (Hair et al. 2011). SmartPLS 3.2.7 was used to calculate two PLS path models in this chapter (Ringle et al. 2015). Evaluation of PLS-SEM results encompasses two stages: The first one is examining the measurement model. If the evaluation supports the measurement model quality, the researchers proceed with the structural model evaluation (Sarstedt et al. 2017). Hence, the structural models were primarily determined based on the following predictive capabilities: the significance of the path coefficient (β) which represents the strength of the relationship; the coefficient of determination (R2); effect size f 2 which represents the magnitude of the hypothesized relationship; predictive relevance (Q2); and PLSpredict (Q2predict) (Hair et al. 2017; Cohen 1988). The R2 denotes the overall structural model’s in-sample predictive power, and PLSpredict represents the structural model’s out-of-sample predictive power (Shmueli et al. 2016).

Table 2 Multicollinearity assessment results Latent Variable Turnover intention Coworkers relationship Transformational leadership Work–family conflict

VIF

Multicollinearity problem (VIF > 3)

1.030 1.000 1.030

No No No

Notes: VIF means Variance Inflation Factor

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4 Results 4.1

Demographic Characteristics

The results indicated that most respondents were males, 209 (62.8%), whereas females were 124 (37.2%). This reflects the working nature of public service organizations. It could be attributed to the fact that the nature of work in most of these agencies is masculine because it consists of engineers and technicians of various professional backgrounds. In terms of age, the study consists of employees of various age groups as follows: 20–30 years were 79 (23.7%); 31–40 were 152 (45.6%); 41–50 were 69 (20.7%); whereas those above 50 years of age were 33 (9.9%). It implies that youth made a great composition of the working staff. The respondents’ educational backgrounds, including certificate, were 13 (3.6%); Diploma were 33 (9.9%); First degree was 136 (40.9%); Master’s degree were 146 (43.8%), and Doctoral were 5 (1.5%). The results imply that the employers of these agencies educate their employees or employ highly qualified people.

4.2

Assessment Results of a Reflective Measurement Model

This chapter used reflective indicators for its measurement model. Consequently, reliability and validity evaluating criteria were used, to ascertain the quality of the measurement model. Indicator reliability is attained when standardized outer loading is 0.708 or higher (Hair et al. 2017). But outer loadings of indicators ranging from 0.4 to 0.7 were regarded for deletion from the scale only when eliminating them results in the rise of composite reliability or average variance extracted (AVE) above the recommended threshold value, 0.5 (Hair et al. 2017). Hair et al. (2019) propose that indicators exhibiting low loading of 0.4 and below should permanently be removed from the reflective scales. The model was originally composed with 24 indicators; however, after analysis, only 22 indicators passed the required minimum threshold values. Consequently, two indicators were eradicated due to an absence of significant input to AVE and composite reliability. Hence, the revised measurement model results indicated that all variables have achieved the quality indicator reliability. The chapter assessed internal consistency reliability by using composite reliability (ρc). The ρc values ranging from 0.6 to 0.7 are relevant in exploratory research. However, in more advanced research stage, the ρc values between 0.7 and 0.9 are regarded as satisfactory, whereas values above 0.95 are redundant (Hair et al. 2017). Entirely, the latent variables exceeded the minimum required threshold of ρc, proof that there was internal consistency reliability on all measures (see Table 3 below). Convergent validity was measured by using an average variance extracted (AVE), an AVE of 0.5 or higher indicates that, on average, the construct explains more than half of the variance of its indicators (Hair et al. 2017). The AVE values for all

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Table 3 Measurement model evaluation results Latent variable Turnover intention

Code

Loading

TOI1 TOI2 TOI3 TOI5 TOI6

0.891 0.897 0.818 0.913 0.872

CR1 CR2 CR3 CR4 CR6

0.768 0.855 0.725 0.826 0.643

Coworkers’ relationship

Work–family conflict WFC1 WFC2 WFC3 WFC4 WFC5 WFC6 Transformational leadership TL1 TL2 TL3 TL4 TL5 TL6

Composite reliability 0.944

AVE 0.772

0.876

0.588

0.897

0.595

0.894

0.585

0.765 0.819 0.841 0.831 0.667 0.692 0.796 0.883 0.716 0.731 0.731 0.721

reflective latent variables were 0.5 or higher, confirming latent variables convergent validity. The traditional Fornel-Lacker criterion and cross-loading calculation have an undesirable low sensitivity, as such they are unable to sense an absence of discriminant validity. Therefore, Henseler et al. (2015) have suggested a novel measure for evaluating constructs’ discriminant validity using heterotrait–monotrait ratio (HTMT). This discriminant validity assessment overcomes the weaknesses. HTMT 0.85 and HTMT 0.90 are the criteria of assessing discriminant validity; of the two criteria, HTMT 0.85 is the most conservative one. On the other hand, HTMT inference is the statistical test of assessing discriminant validity. HTMT inference is the most liberal approach of all due to its much higher specific values. Hence, with HTMT inference, it is possible to compute bootstrap confidence interval (C.I), whereby the C.I containing Fig. 1 indicates lack of discriminant validity. However, if the value of 1 is not within the C.I range, this recommends that constructs are empirically distinct (Henseler et al. 2015).

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Push Factors Co-workers’ Relationship

H1 Inter-organizational Labor Mobility (Turnover Intention)

Work-family Conflict

H2

H3

H3

Transformational Leadership Fig. 1 Conceptual model Table 4 Discriminant validity assessment using HTMT criterion

Construct Coworkers’ relationship Transformational leadership Turnover intention Work–family conflict

HTMT criterion Coworkers’ relationship

Transformational leadership

0.083 [0.087; 0.154] 0.154 [0.092; 0.250] 0.183 [0.136; 0.263]

0.095 [0.072; 0.187] 0.069 [0.079; 0.163]

Turnover intention

Work–family conflict

0.495 [0.411; 0.572]

Notes: HTMT represents the heterotrait–monotrait ratio; Whereas the 95% bias corrected and accelerated C.I. are specified by the numbers inside the brackets (Henseler et al. 2015) the whole numbers imply the HTMT values

The maximum HTMT value of 0.495 from Table 4 below, is below the most conservative critical HTMT, i.e., 0.85. Additionally, the derived bootstrapping C.I results show that Fig. 1 falls outside the C.I range. As such, the model is considered of high quality due to meeting the threshold criteria for measurement model assessment of reliability, convergent and discriminant validity.

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4.3

Assessment of the Structural Model Relationships

Before assessing structural model relationships, collinearity was examined to make sure that it does not bias the regression results. VIF is used to assess the severity of multicollinearity. Multicollinearity is the correlation of two or more variables (Saunders et al. 2016). The existence of multicollinearity can cause severe problems in estimating the PLS-SEM path coefficient of the individual predictor variable and interpretation of results (Joshi et al. 2012). VIF values above 5 indicate collinearity problems among predictor variables (Becker et al. 2015); nonetheless, it can also happen at a minor value of 3–5. The accepted VIF values should be close to 3 or below, of which the analysis results from Table 2 indicate that multicollinearity is not a problem. Then coefficient of determination (R2) which representing model explanatory power as presented in Figs. 2, 3, and 4. The rule of thumb for R2 is as follows: 0.19 implies weak; 0.33 implies moderate; and 0.67 implies substantial (Chin 1998). However, according to Hair et al. (2017), R2 of 0.10 can be considered satisfactory. Figure 2 above indicates that the R2 for the direct effect model has attained a value of 0.210, which is considered satisfactory, confirming the direct model explanatory power. Precisely, this implies that coworkers’ relationship and work–family conflict explain 21.0% of the variation in ILM, whereas the outstanding 79% will be accounted for by other factors. Additionally, Fig. 3 above indicates that the R2 for the main effect model was 0.219, which is also considered satisfactory. Explicitly, this implies that coworkers’ relationship, work–family conflict, and TL describe

CR2 CR3

0.855 0.725

CR4 CR6

0.826 0.644

TOI1

0.768 Co-workers relationship

CR1

0.891

–0.082

TOI2

0.897 WFC1

0.210

0.822

TOI3

0.910 WFC2

0.765

WFC4

0.819 0.841 0.831 0.662

WFC5

0.692

WFC3

0.437

Tumover intention

0.871

TOI5

TOI6

Work-family conflict

WFC6

Fig. 2 Measurement model for the direct relationship

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Fig. 3 Measurement model for the moderated relationship/direct effect PLS path model

Fig. 4 The measurement model for the interaction effect pls path model

21.9% of the variation in ILM, whereas other factors account the remaining 78.1%. Moreover, Fig. 4 indicates that R2 for the interaction effect model was 0.233. According to Ramayah et al. (2018), R2 change becomes an essential issue in moderation analysis. R2 for the main effect model was 0.219, and R2 for the

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Table 5 PLSpredict results

TOI1 TOI2 TOI3 TOI5 TOI6

PLS-SEM RMSE 1.095 1.165 1.057 1.167 1.090

Q2predict 0.171 0.157 0.131 0.176 0.111

LM RMSE 1.125 1.190 1.089 1.199 1.116

PLS-SEM-LM RMSE -0.030 -0.025 -0.032 -0.032 -0.026

Notes: RMSE represents Root Mean Square Error; LM represents Linear Model; Q2predict represents Out-of-sample prediction power; whereas TOI represents Turnover intention

interaction effect model was 0.233. The R2 change of 0.014 indicates that R2 has changed about 1.4% with the addition of two interaction terms. The next step was to calculate the effect size of the interaction term using the formula f 2 = R2 included - R2 excluded/1 - R2 included. R2 included representing the interaction effect model R2 and R2 excluded representing the main effect model R2. The f 2 obtained was medium which is 0.02. This interpretation was based on the following suggested guidelines 0.005 small, 0.01 medium, and 0.025 large (Kenny 2016). The blindfolding procedure indicates that the influence of push factors on ILM has attained a small Q2 value of 0.15 (15%), confirming the direct effect model predictive relevance. This is according to the suggested Q2 threshold value of 0, 0.25, and 0.50, indicating the PLS path model’s small, medium, and large predictive relevance (Hair et al. 2017). Additionally, Q2 for the main effect model value was 0.155 (15.5%), consequently, confirms the main effect model predictive relevance. Then, the model’s predictive power was calculated by running the PLSpredict procedures with 10 folds and ten reiterations. Out-of-sample predictions are helpful for evaluation when the focus is on the model’s ability to predict the outcome of heretofore unseen cases (Shmueli et al. 2016). The focus was on the model’s key target variable, TOI, and its five scale items. Table 4 shows that all five indicators achieve Q2predict greater than zero, demonstrating that the model outdoes the naïve standard. Furthermore, prediction errors analysis indicates non highly unsymmetric distribution. Henceforth, the appropriate examination concentrates on the root mean square error (RMSE) statistics. The analyses show that the RMSE values produced by the PLS path model were reliably lower than those of the linear model (LM) standard, as indicated in Table 5. According to the rule of thumb for running PLSpredict, PLS-SEM < LM for all TOI scale items: Hence if all indicators in the PLS-SEM analysis have lower RMSE values than the naïve LM standard, the model has high predictive power, consequently the results confirming the model’s large out-of-sample predictive power (Hair et al. 2019).

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Significance Testing Results of the Structural Model

Table 6 shows that coworkers’ relationship exerted a significant influence on ILM, similar to the influence that work–family conflict (WFC) exerted on ILM. Then, bootstrapping examination confirms the significant, negative influence of coworkers’ relationship on ILM [β = -0.087, p < 0.05] (Fig. 5). Further, the bootstrapping analysis indicated that the C.I [-0.146; 0.018] does not contain zero, leading to the acceptance of H1: coworkers’ relationship negatively influences ILM. Equally, the positive relationship between WFC and ILM is significant [β = 0.426, p < 0.001] (Fig. 5). Further, the bootstrapping analysis indicated that the C.I [0.350; 0.493] does not contain zero, which leads to the acceptance of H2: WFC has a positive influence on ILM.

4.4.1

Hypotheses for a Moderation Effect

Table 6 above the bootstrapping analysis shows that only one hypothesis out of two was significant [β = 0.118, p < 0.05] (Fig. 6). Further, the bootstrapping analysis indicated that the C.I [0.036; 0.187] does not contain zero, justifying the moderating effect. The result implies that TL has a moderating role on the relationship between WFC and ILM (WFC*TL→ILM); therefore, compliance with H3b: Hence, the finding implies that the higher the TL, the weaker the influence of WFC on ILM and vice versa for the lower. Table 6 Significance testing results Relationships CR→TOI/ILM WFC→TOI/ILM Moderating effect 1→TOI/ILM Moderating effect 2→TOI/ILM R2 direct effect model R2 main effect model R2 simple effect model Q2 direct effect model Q2 main effect model f 2 of interaction effect

Path coefficients -0.082 0.437 0.019 0.118

t-values 1.832* 10.466*** 0.326 NS

pvalues 0.034 0.000 0.372

95% Confidence interval [-0.135; 0.017] [0.364; 0.501] [-0.077; 0.115]

f2 0.008 0.238 N/A

2.331**

0.009

[0.036; 0.187]

N/A

0.210 0.219 0.233 0.150 0.155 0.020

Notes: NS Not significant, N/A Not applicable COMP Compensation, TD Training and development, TOI/ILM Turnover intention/Interorganizational labor mobility ***p < 0.001, **p < 0.01, *p < 0.05

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0.855 (0.000) 0.725 (0.000)

CR4

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TOI1

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CR6

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Co-workers relationship

CR1

0.891 (0.000)

–0.082 (0.037)

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TOI3

0.910 (0.000)

WFC2

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0.437 (0.000)

0.871 (0.000)

TOI5

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0.819 (0.000)

WFC3 WFC4 WFC5

TOI6

0.841 (0.000) 0.831 (0.000) 0.662 (0.000) 0.692 (0.000)

Work-family conflict

WFC6

Fig. 5 Structural model results for the direct effect model

Fig. 6 Structural model results for the indirect effect model

Furthermore, the interpretation of the moderating effect can be explained more by looking at the interaction plot. Standardized β coefficients are 0.437 from WFC to TOI, 0.120 from TL to TOI, and the interaction effect of 0.118, with R2 of 0.233, as shown in Fig. 4. Hence, by looking at the interaction plot from Fig. 7, we can

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5 4.5

Low TL

TOI/ILM

4

High TL

3.5 3 2.5 2 1.5 1

Low WFC

High WFC

Fig. 7 Simple slope plot for moderating effect of transformational leadership

interpret it as follows; the relationship between WFC and TOI is 0.426 for an average level of TL. For a higher level of TL, the relationship between WFC and TOI increases by the size of the interaction term from 0.437 to 0.555 (i.e., 0.437 + 0.118 = 0.555) because the slope is steep. Similarly, for the lower level of TL, the relationship between WFC and TOI decreases by the size of the interaction term from 0.437 to 0.319 (i.e., 0.437–0.118 = 0.319) because the slope is not steeper. Conversely, the results indicated that TL fails to moderate the association between coworkers’ relationship and ILM [β = 0.019, p = 0.371, C. I (-0.077; 0.115)] (Fig. 6); thus, Hypothesis 3a was insignificant.

4.5

Importance-Performance Map Analysis (IPMA) of the Path Modeling Results

IPMA is generally helpful in generating further findings and conclusions in PLS-SEM results. It identifies predecessor variables with relatively high importance to the target variable TOI/ILM, but which achieve a relatively low performance for explaining ILM and, consequently, for maintaining employee retention. Since all independent variables of this study were measured reflectively, IPMA was limited to the structural model. Figure 8 shows each latent variable’s performance and impact on ILM. The IPMA is divided into four areas, with importance and performance values below and above average (Ringle and Sarstedt 2016). In each IPMA, concentration is on the lower right corner to enhance improvement because items plotted in this area have high importance with low performance. Figure 8 shows that WFC is highly relevant to ILM due to its significant influence. Therefore, government agencies need to maintain the good performance of WFC in organizations by ensuring that effective and efficient WFC programs and policies are implemented. Moreover, government agencies ought to take initiatives to possible overkill or restrain coworkers’ relationship because though it is

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Fig. 8 Importance-performance map analysis

negatively significant, it is not important in explaining employee ILM and their ultimate retention in working organizations. The overall results indicate that both CR and WFC are main predecessors of employees’ ILM, and TL is a significant pull-to-stay factor, government agencies must have appropriate training and development programs to empower their leaders at all levels to acquire TL behavior, which is very important in employee retention. This is so because previous studies reveal that organizational issues such as traumatic work environments and insufficient human resource management practices are the key determinants of WFC and employee TOI (Boamah and Laschinger 2016; Batt and Valcour 2003). Consequently, if the impact of WFC is being minimized by the presence of TL as a moderator variable, other push factors will be indirectly moderated.

5 Discussion The primary goal of this chapter was twofold, to examine the direct impact of push factors, coworkers’ relationship (CR), and WFC on ILM and to analyze the extent to which TL moderates the influence of push factors on ILM in the public sector. The chapter was guided by SET and Herzberg’s Two-Factor Theory. Concerning the first objective (CR→TOI/ILM), the statistical results revealed that “CR has a significant negative influence on ILM.” The results were similar to other studies which confirm that employees who experience healthy interpersonal relations in the form of cooperative work groups, task interdependence, good

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employee relationship, and supportive leaders have conveyed lower TOI (Azam et al. 2017; Jabutay and Rungruang 2020). The results are consistent with the previous studies (e.g., Haldorai et al. 2019) which opine that the CR is significantly negatively related to TOI. The findings draw attention to the management of CR to reduce employees’ ILM. Regarding indicators, empirical findings conform to Emerson (1976) that, when an individual receives favorable treatment from another, he/she feels indebted to repay in kind. Statistical results relating to H2 (WFC→TOI/ILM) indicated that “WFC is significant and positively related to ILM.” The findings are consistent with previous studies by Haldorai et al. (2019) and Yildiz et al. (2021), who showed that WFC significantly positively related to ILM. The findings are in accord with Adisa et al. (2016) and Putra and Suwandana (2020), who assert that employees experiencing work intrusion into their family lives experience broken marriage/family, unhappy workforce, and young misbehaviors, stimulating to seek employment elsewhere. The statistical results for H3a (Moderating Effect 1→TOI/ILM) showed that TL does not significantly moderate the relationship between CR and ILM. The results are consistent with Waldman et al. (2015) who asserted that, despite the direct loyalty supporting effect of TL, followers are still exposed to some push forces for leaving, in this case, CR. However, H3b (Moderating Effect 2→TOI/ILM) was found to be significant: empirical findings established that “the higher the TL, the weaker the influence of WFC on ILM.” The results are in agreement with Tse et al. (2013), indicating that SET helps to explain how and why TL should be considered an essential “pull-to-stay” factor. Thus, integration of WFC with supportive, caring, and empathetic leadership such as TL is likely to enhance employee organizational commitment and lessen withdrawal behaviors (Bass et al. 2003; Avolio et al. 1991).

6 Conclusion This chapter sought to shed light on how leadership affects followers’ responses to push factors, impacting ILM in the public sector. Specifically, the researchers developed and tested hypotheses about how TL shapes followers’ reactions to push factors and how these responses relate to ILM behaviors. This chapter represents empirical efforts validating methodical investigation of the causal relationship between push factors (CR and WFC) and ILM, with moderating effect of TL (Fig. 9). After testing the research model using survey data, it was found that CR is significantly negatively related to ILM, and WFC is significantly positively related to ILM consistently with previous studies. However, the study did not find significant results on the moderating effect of TL, CR, and ILM. The study findings are consistent with (Waldman et al. 2015). Moreover, the study findings reveal that TL*WFC’s interactive effect will reduce ILM in the public sector.

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Push Factors Co-workers’ Relationship

Work-family Conflict

H Inter-organizational Labor Mobility (Turnover Intention) H2

H3

H3

Transformational Leadership Fig. 9 Modified push forces for interorganizational labor mobility in Tanzania public sector

7 Implications for Research This chapter will advance knowledge in the following ways: Firstly, this chapter is most appropriate due to limited research in TL (Tse et al. 2013; Sun and Wang 2017). Scholars have developed a number of theoretical frameworks to forecast employee ILM in the working context (de Luis Carnicer et al. 2004; Haldorai et al. 2019). However, none of the existing studies have integrated TL, push-to-leave factors, and ILM. Hence, the chapter findings reveal that TL can moderate the association between WFC and ILM in the Tanzania public sector. Hence, it is imperative for management to employ TL as an efficient tool to address the consequence of WFC. Secondly, the researchers tested the research model in the context of public agency employees in Tanzania to expand the applicability of TL as a moderator variable. The results were similar to previous scholars who confirmed that TL can act as an essential pull-to-stay factor, lessening the impact of both push and pull to leave factor, increase the generalizability (Oh and Chhinzer 2021; Waldman et al. 2015). Consequently, the researchers gain deeper insight into the interaction effect of TL and push factors on ILM in public sector, hence extending the existing labor mobility model.

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8 Research Limitations and Future Direction The findings of this chapter indicated that the CR, WFC, and TL interactively explain 23.3% of ILM variation. This means that other factors (76.7%) influence ILM; however, they have been not accounted for in this study. Hence, the findings call for further studies to investigate the influence of both push and pull factors on leaving. Furthermore, future studies may consider doing qualitative research design to explore other factors influencing ILM in the public sector. Lastly, it is also worth for future study to do a comparative study between the public and private sectors to ascertain similarities and differences.

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Integrated Leadership: Assessing an Integrated Principal Leadership Practices Construct Ahmed Mohamed, Ahmad Zabidi Abdul Razak, and Zuraidah Abdullah

1 Introduction Principals practice various leadership roles based on situations and policy enactment. Thus, principal leadership continues to evolve promptly in response to new demands of the school environment (Hallinger and Heck 1998). The terms “principal leadership” or “principal leadership practice” have become prevalent terms in the context of educational leadership. Eventually, principal leadership (Hallinger and Heck 1998; Sebastian and Allensworth 2012) and principal leadership practices (Perera 2015) were conceptualized as constructs. Hence, recent literature on leadership in educational context move toward integrated leadership since single leadership style is insufficient to address complexities of present-day school settings. In many instances, integrated leadership comprises transformational leadership and instructional leadership in an educational context. Integration of leadership styles establishes higher-order models that are complex in nature (Thien et al. 2019). However, developing a hierarchical construct could reduce the model complexity and issues of multicollinearity (Hair et al. 2017). Hence, there is a need to develop an accurate measurement model of “integrated principal leadership practices” to minimize the issue of measurement model misspecification in leadership research. Thus, the purpose of the chapter is to validate a reflective–formative model of integrated principal leadership practices

A. Mohamed (✉) Faculty of Education, The Maldives National University, Malé, Maldives e-mail: [email protected] A. Z. A. Razak · Z. Abdullah Faculty of Education, University of Malaya, Kuala Lumpur, Malaysia e-mail: [email protected]; [email protected] © Springer Nature Switzerland AG 2023 L. Radomir et al. (eds.), State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM), Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-031-34589-0_27

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(IPLP) to assist educational leadership researchers who employ PLS-SEM in their research.

2 Background At the beginning of the new century, researchers have called for an integrated model of leadership that combines transformational leadership and shared instructional leadership (Hallinger 2003; Marks and Printy 2003). Over the years, transformational leadership and instructional leadership are the two predominant leadership approaches in educational context (Hallinger 2003). Thus, principals use both the leadership styles collectively for capacity development and school improvement. Principal leadership practices can be defined as “actions taken by principals to influence people, processes, and organizational structures” (Camburn et al. 2010, p. 714). However, conceptualization of principal leadership practices varies with leadership practices taken into consideration (Lai 2015; Sebastian and Allensworth 2012). Transformational leadership and instructional leadership are two distinct firstorder constructs based on the conceptualization and measures of the constructs. Subsequently, it could be proposed that two first-order constructs of transformational leadership and instructional leadership generate the second-order reflective–formative IPLP. Proposed conceptual model of second order IPLP is shown in Fig. 1.

3 Methodology This chapter employed a cross-sectional survey method. The targeted population of the study was teachers working in government schools of Maldives. After data cleaning, a sample of 375 respondents were used. Responses were received from

Nine Indicators

Transformational Leadership Integrated Principal Leadership Practices

Five Indicators

Instructional Leadership

Fig. 1 The proposed second order integrated principal leadership practices construct

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32 government schools. Both the teachers and schools were selected using random sampling. The instrument used for the study was transformational school leadership survey based on core leadership practices (Leithwood 2012). A survey questionnaire was sent to the respondents with the approval from all the concerned institutions. Participation in the survey was voluntary and anonymous. Prior to the actual data collection, a pilot test was conducted to do the exploratory factor analysis. Partial least square-structural equation modelling (PLS-SEM) was employed to assess and validate the IPLP. PLS-SEM is suitable for assessing complex models, including reflective and formative constructs (Hair et al. 2022; Hair et al. 2019; Sarstedt et al. 2019). The study used SmartPLS 3 software to perform the data analysis. The analysis involved assessment of first-order reflective measurement model and assessment of second-order formative measurement model. The assessment of reflective measurement model involves internal consistency, convergent validity, and discriminant validity. All the three were examined. In the next step, the measurement model was evaluated after generating second-order construct, Integrated Principal Leadership Practices (IPLP). To create a second order construct, a two-stage approach recommended by Becker et al. (2012) was used, as the construct is a higher order formative construct. In the assessment of formative measurement model, these three steps were tested: (1) assessing convergent validity of formative measurement model; (2) addressing collinearity issues; and (3) assessing the (statistical) significance and relevance of the formative indicators. A global item was used as a reflective measure in the redundancy analysis.

4 Results The first-order reflective constructs, namely, (1) transformational leadership and (2) instructional leadership were involved to establish integrated principal leadership practices (IPLP). The two first-order reflective constructs were evaluated by considering the criteria for the assessment of the reflective measurement model. Results showed that all the item loadings for the first-order reflective constructs, Instructional Leadership and Transformational Leadership, have factor loadings above 0.708, as shown in Table 1. As a rule of thumb, item loadings should be 0.708 and above (Hair et al. 2017). Results also indicated that the composite reliability for the reflective constructs was within acceptable range after deleting certain items from the model (i.e., 0.948 and 0.950 for instructional leadership and transformational leadership, respectively). The deleted items were ISLIL4, ISLTL4, ISLTL6, and ISLTL12. Thus, composite reliability for the constructs achieved acceptable internal consistency reliability. In addition, Table 1 shows that AVE for both of these constructs is higher than the threshold/cut-off value of 0.5. Hence, the results revealed that convergent validity was ensured for the first-order constructs. Once internal consistency and convergent validity were established, discriminant validity was assessed. Discriminant validity is the extent to which each construct is

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Table 1 Internal consistency reliability and convergent validity of reflective constructs First order construct Instructional leadership (IPLPIL)

Transformational leadership (IPLPTL)

Items IPLPIL1 IPLPIL2 IPLPIL3 IPLPIL5 IPLPIL6 IPLPTL1 IPLPTL2 IPLPTL3 IPLPTL5 IPLPTL7 IPLPTL8 IPLPTL9 IPLPTL10 IPLPTL11

Loadings 0.885 0.884 0.902 0.878 0.879 0.786 0.830 0.794 0.858 0.865 0.802 0.860 0.819 0.801

AVE 0.784

CR 0.948

0.680

0.950

Table 2 HTMT criterion of first order constructs IPLPIL IPLPIL IPLPTL

IPLPTL

0.882 C.I.95 (0.840, 0.916)

distinct from other constructs in a model (Chin 2010). To evaluate discriminant validity, the heterotrait–monotrait (HTMT) criterion can be used (Henseler et al. 2015; Ramayah et al. 2018). In terms of HTMT, Table 2 shows the HTMT ratio (0.882) fulfills the criterion of HTMT 0.90 (Gold et al. 2001). Thus, the result revealed that discriminant validity is established. In addition to HTMT ratio, the result of HTMT inference in Table 2 also shows that the confidence interval does not show a value of 1 on any of the constructs which confirms discriminant validity. As evident in Table 3, the formative construct of integrated principal leadership practices (IPLP) yielded path coefficient of 0.73, more than threshold value of 0.70 (Hair et al. 2017), thus the formative construct (IPLP) has achieved sufficient degree of convergent validity. According to Table 3, VIF values of all the indicators (IPLPIL and IPLPTL) for the formative construct (IPLP) were below the threshold value of 5 (Hair et al. 2017), thus integrated principal leadership construct satisfies the VIF values. This concludes that collinearity is not an issue for the estimation of the PLS path model as collinearity does not reach a critical level in any of the formative indicators. In the third step, the significance and relevance of the outer weights of the formative constructs were assessed. The results in Table 3 showed that weights of both formative indicators (IPLPIL and IPLPTL) were significant. Therefore, the formative measurement model of integrated principal leadership practices with two formative indicators was achieved.

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Table 3 Measurement properties for formative constructs Higher order construct IPLP

Lower order construct IPLPIL IPLPTL

Convergent validity 0.73

Weights 0.366 0.676

VIF 3.155 3.155

t-value weights 3.085** 5.951*

Sig. 0.002 0.000

Notes: > 1.96**

5 Conclusion and Implications In relation to this chapter, a higher order model of integrated principal leadership practices was conceptualized and validated in school context using measurement model assessments. Results presented in the chapter have learned that integrated leadership must be studied as a reflective–formative hierarchical component model in future leadership research. For theoretical implications, results supported the conceptualization of IPLP that was established by transformational leadership and instructional leadership. This chapter contributes to the body of knowledge on school leadership. In addition, the findings informed on added knowledge to integrated leadership that principals’ leadership practices can be perceived as a hierarchical model. One of the important practical implications of the chapter is to use integrated principal leadership practices construct in empirical studies to examine the effect of this construct on other factors in educational settings. The current chapter is limited to a single instrument that captures both instructional and transformational leadership. This limitation could be addressed in future research using a separate set of measures, including the most widely used instructional leadership questionnaires. Additionally, various other dimensions of both transformational leadership and instructional leadership could be included in future research to generate a third-order integrated principal leadership practices construct. Acknowledgments I acknowledge the financial support provided by the Maldives National University (MNU) to present this paper at the 2022 International Conference on Partial Least Squares Structural Equation Modeling (PLS-SEM).

References Becker JM, Klein K, Wetzels M (2012) Hierarchical latent variable models in PLS-SEM: guidelines for using reflective-formative type models. Long Range Plan 45(5–6):359–394 Camburn EM, Spillane JP, Sebastian J (2010) Assessing the utility of a daily log for measuring principal leadership practice. Educ Adm Q 46(5):707–737 Chin WW (2010) Bootstrap cross-validation indices for PLS path model assessment. In: Esposito Vinzi V, Chin W, Henseler J, Wang H (eds) Handbook of partial least squares. Springer handbooks of computational statistics. Springer, Berlin, Heidelberg, pp 83–97 Gold AH, Malhotra A, Segars AH (2001) Knowledge management: an organizational capabilities perspective. J Manag Inf Syst 18(1):185–214

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Hair JF, Hult GTM, Ringle C, Sarstedt M (2017) A primer on partial least squares structural equation modeling (PLS-SEM), 2nd edn. Sage, Thousand Oaks, CA Hair JF, Risher JJ, Sarstedt M, Ringle CM (2019) When to use and how to report the results of PLS-SEM. Eur Bus Rev 31(1):2–24 Hair JF, Hult GTM, Ringle C, Sarstedt M (2022) A primer on partial least squares structural equation modeling (PLS-SEM), 3rd edn. Sage, Thousand Oaks, CA Hallinger P (2003) Leading educational change: reflections on the practice of instructional and transformational leadership. Camb J Educ 33(3):329–352 Hallinger P, Heck RH (1998) Exploring the Principal’s contribution to school effectiveness: 1980-1995. Sch Eff Sch Improv 9(2):157–191 Henseler J, Ringle CM, Sarstedt M (2015) A new criterion for assessing discriminant validity in variance-based structural equation modeling. J Acad Mark Sci 43(1):115–135 Lai E (2015) Enacting principal leadership: exploiting situated possibilities to build school capacity for change. Res Pap Educ 30(1):70–94 Leithwood K (2012) Core practices: the four essential components of the leader’s repertoire. In: Leithwood K, Louis KS (eds) Linking leadership to student learning. Jossey-Bass, San Francisco, CA Marks HM, Printy SM (2003) Principal leadership and school performance: an integration of transformational and instructional leadership. Educ Adm Q 39(3):370–397 Perera CJ (2015) Principal leadership practices and teacher collegiality in Malaysian high performing schools. PhD Thesis, University of Malaya, Kuala Lampur, Malaysia. http:// studentsrepo.um.edu.my/5993/. Accessed 10 Oct 2021 Ramayah T, Cheah J, Chuah F, Ting H, Memon MA (2018) Partial least squares structural equation modeling (PLS-SEM) using SmartPLS 3.0 - an updated and practical guide to statistical analysis, 2nd edn. Pearson, Kuala Lumpur Sarstedt M, Hair JF, Cheah J-H, Becker J-M, Ringle CM (2019) How to specify, estimate, and validate higher-order constructs in PLS-SEM. Australas Mark J AMJ 27(3):197–211 Sebastian J, Allensworth E (2012) The influence of principal leadership on classroom instruction and student learning: a study of mediated pathways to learning. Educ Adm Q 48(4):626–663 Thien LM, Rasoolimanesh SM, Ng AYM, Ramayah T (2019) Conceptualizing and assessing an integrated hierarchical leadership construct in education context. J Appl Struct Equ Model 3(1): 15–30

Exploring the Interrelationship Among Management Accounting Systems, Decentralization, and Organizational Performance Elsa Pedroso and Carlos F. Gomes

1 Introduction Organizations competing in the global market face major challenges. These challenges relate to several factors, including the efficient use of resources and an uncertain external environment. This uncertainty becomes much more challenging for small and medium enterprises (SMEs). Companies sometimes decentralize decision-making power to address this uncertainty and seek more market information. These approaches aim to bring relevant information to managers at all organizational levels so they can respond quickly and effectively to changes in the external environment. They can thus increase their departments’ performance and consequently improve organizational performance. In this process, management accounting systems (MAS) play a key role in managing and sharing this information throughout the organization. As such, decentralization of decision-making may influence the relationship between the use of information provided by MAS and the organizational performance of SMEs.

2 Background The results presented by literature relating the relationship between MAS and organizational performance are not consistent (Cadez and Guilding 2008; Krumwiede et al. 2008; Harrison 2009; Tontiset and Ussahawanitchakit 2010; Hoque 2011; Tuanmat and Smith 2011; Ismail et al. 2018; Jariya and Velnampy

E. Pedroso (✉) · C. F. Gomes University of Coimbra, CeBER, Faculty of Economics, Coimbra, Portugal e-mail: [email protected]; [email protected] © Springer Nature Switzerland AG 2023 L. Radomir et al. (eds.), State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM), Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-031-34589-0_28

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Decentralization of decisions

Integration Aggregation Management accounting system*

Organizational performance

Scope

Timeliness

*Second-order reflective construct

Fig. 1 Conceptual model

2022). These results could suggest the introduction of a variable that moderates this relationship (Baron and Kenny 1986). In a highly competitive market, it is necessary to respond quickly to changes that are taking place dynamically. These responses are mainly operational. Therefore, information from the market is required for decisions made at all hierarchical levels. Thus, increasing decentralization will allow a faster response to the market. Decentralization of the decision process can influence the relationship between MAS and organizational performance, improving access to information for managers at intermediate levels that will lead to a more effective response from these managers and thus increase organizational performance. In this context, this chapter aims to study the relationship between the use of MAS information and the organizational performance of SMEs. In addition, we also analyze the influence of the decentralization of decision-making on this relationship. The conceptual model is presented in Fig. 1.

3 Methodology The data was collected through an online survey of Portuguese SMEs for this research. The names and addresses of 1500 SMEs were obtained from Informa DB, which belongs to the Dun and Bradstreet Worldwide Network. All these enterprises were contacted by phone to explain the objective of the research study. Twelve enterprises, despite several attempts, never answered our calls. Ninety-three enterprises declined to collaborate in this research study for several reasons, such as no longer operating and a lack of autonomy to respond to the questionnaire.

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Table 1 Operationalization of the constructs Constructs Management accounting systems Organizational performance

Decentralization of decisions

Scale The extent of use of MAS information on a 7-point scale could range from “never” (1) to “always” (7). Evaluation of organizational performance for each of the performance measures included in the construct on a 7-point scale, which could range from “unacceptable” (1) to “excellent” (7). Rate the extent of the company’s decentralization on a 7-point scale that could range from “very low” (1) to “very high”(7).

Sources Pedroso and Gomes (2020)

Govindarajan (1984), Cadez and Guilding (2008), Hoque and James (2000), Harrison (2009), Hoque (2011)

Gordon and Narayanan (1984), Abdel-Kader and Luther (2008), Soobaroyen and Poorundersing (2008)

Therefore, an email explaining the purpose of the research and containing the link to the online survey was sent to 1407 Portuguese SMEs. A total of 255 usable responses were obtained from Chief Financial Officers (CFOs), representing a response rate of 18.12%. This response rate cannot be considered high, but is in line with other studies in the same research area. The research instrument used in this chapter was designed based on an extensive literature review. During the first phase of the questionnaire design, it was translated and adapted to the Portuguese business environment. In the second phase, the instrument was submitted to a panel of experts from several organizations. During this phase, particular attention was given to the use of terminology consistent with the background of the survey participants. The final version of the constructs used in this research included 31 items representing the observed variables. All these observed variables were measured on a Likert scale with a range of 1 to 7 (Table 1). Partial least squares-structural equation modeling (PLS-SEM) was used to analyze the data. All the analyses were performed using the IBM-SPSS Statistics version 25 and SmartPLS 3.3.9 (Ringle et al. 2015) and followed procedures suggested in the literature (Hair et al. 2013; Hair et al. 2019).

4 Results and Discussion 4.1

Measurement Model

The first step was assessing the constructs’ reliability. Almost all outer loadings were above 0.708, the threshold recommended by the literature (Hair et al. 2019). Only two measurement items were slightly lower than this threshold (Table 2).

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Table 2 Validity and reliability constructs Construct items Scope SCO01 SCO02 SCO03 SCO04 SCO05 Timeliness TIM01 TIM02 TIM03 TIM04 Aggregation AGG01 AGG02 AGG03 AGG04 AGG05 AGG06 AGG07 Integration INT01 INT02 INT03 Organizational performance ORP01 ORP02 ORP03 ORP04 ORP05 ORP06 ORP07 Decentralization of decisions DEC01 DEC02 DEC03 DEC04 DEC05

Loading

CR 0.905

AVE 0.657

Cronbach’s alpha 0.870

0.914

0.727

0.875

0.916

0.608

0.892

0.936

0.829

0.897

0.894

0.548

0.862

0.912

0.674

0.880

0.838 0.816 0.805 0.796 0.797 0.833 0.866 0.859 0.853 0.794 0.772 0.770 0.822 0.787 0.744 0.767 0.891 0.929 0.911 0.789 0.746 0.766 0.793 0.742 0.656 0.679 0.781 0.835 0.859 0.843 0.783

Additionally, the Cronbach’s alpha values and the Composite reliability (CR) values obtained for each construct exceeding 0.7 indicated sufficient construct reliability. All the average variance extracted (AVE) values were higher than the recommended threshold of 0.5. Based on these results, the items with outer loadings

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Table 3 Discriminant validity Aggregation Decentralization Integration Organizational performance Scope Timeliness

Agg – 0.436 0.879 0.372 0.771 0.727

Dec

Int

OP

Sco

Tim

0.444 0.325 0.413 0.243

0.286 0.628 0.677

0.302 0.312

0.637

–

Notes: None of the correspondent bootstrap confidence intervals includes the value 1 Table 4 Structural model results Path relationship Decentralization - > Organizational performance MAS - > Organizational performance Moderating effect of decentralization Organizational performance

Path coefficient 0.180** 0.256*** 0.121** R2 0.160

95% confidence interval [0.061; 0.325] [0.144; 0.385] [0.008; 0.226]

f2 0.032 0.065 0.019 Q2 0.079

Notes: ***p < 0.001, **p < 0.05, *p < 0.10

slightly below 0.708 were maintained, as their exclusion did not improve the AVE and CR values (Hair et al. 2013). Regarding discriminant validity, the heterotrait–monotrait (HTMT) ratio of correlations was used (Hair et al. 2017; Usakli and Kucukergin 2018). All values for HTMT are less than 0.90 (Table 3), which means that the discriminant validity of the constructs is assured. Regarding the MAS construct, all the loadings of the first-order constructs on the second-order constructs are significant ( p < 0.001) and above 0.765. They indicate that MAS can be measured as the second-order construct proposed by Pedroso and Gomes (2020), reflecting the four dimensions of information characteristics: scope, timeless, aggregation, and integration. The common method variance was verified with Harman’s single-factor test by conducting an exploratory factor analysis (Podsakoff et al. 2003). The results of this test show that the first factor only accounts for 26.28% of the total variance, which means that the data has no common method variance issues.

4.2

Structural Model

The results of the structural model using a bootstrapping procedure with a resampling of 5000 are presented in Table 4. All the paths of relationships are positive and significant. The values of f 2 are also positive and follow a similar rank order of the path coefficients, which means that large significant path values

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Table 5 PLSpredict results Item ORP01 ORP02 ORP03 ORP04 ORP05 ORP06 ORP07

PLS-SEM RMSE 0.947 0.979 0.912 0.823 0.816 1.088 1.030

Q2predict 0.103 0.071 0.081 0.083 0.064 0.081 0.031

LM RMSE 0.994 1.021 0.952 0.857 0.854 1.139 1.089

PLS-SEM - LM RMSE -0.047 -0.043 -0.040 -0.034 -0.037 -0.051 -0.060

correspond to large effect sizes. In addition, Q2 is positive, which means that the model has predictive relevance (Hair et al. 2013). After analyzing the graphs regarding PLS-SEM prediction errors, we found that their distribution does not deviate much from symmetry. For this reason, we based our assessment of the predictive power of our model on the RMSE. The results of PLSpredict, based on ten samples and ten repetitions, are presented in Table 5. Comparing the RMSE values from the PLS-SEM with the linear regression (LM) benchmark, we found that the PLS-SEM produces lower prediction errors for all the indicators, which means that our model has high predictive power for organizational performance (Shmueli et al. 2019). The results of this chapter validate a multidimensional approach to measuring the effectiveness of MAS. They also confirm the four dimensions of MAS and show that these dimensions are distinct and interrelated. According to the results, using information with the characteristics represented by the multidimensional approach to MAS positively influences the organizational performance of SMEs. In addition, the moderation effect of decentralizing decision-making on this relationship was significant. This means that increasing the level of decentralization will increase the influence of the MAS on organizational performance.

5 Conclusions and Implications This chapter analyzes the moderating effect of decentralization on the relationship between management accounting systems and organizational performance. Based on the results, it seems that decentralizing decision processes can enhance the impact of MAS effectiveness on organizational performance. We highlight the innovative nature of the multidimensional MAS and the synergies resulting from its four dimensions of information, which can be created through their balanced development. This multidimensional approach should enrich management accounting knowledge and provide researchers with a valuable tool for measuring the effectiveness of MAS and its influence on organizational performance. In addition, it should

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facilitate the comparison between different studies in the field of management accounting. Overall, it appears that executives of Portuguese SMEs are effectively using accounting management information to anticipate market threats. As such, regardless of company size and the industry to which they belong, executives use management accounting information to predict threats and maintain the competitiveness of their companies in the global market. This chapter contributes to a better knowledge of the factors that can enhance MAS effectiveness and its influence on the performance of SMEs. It also contributes to executive decision support by providing an instrument for assessing the quality of information they use to improve their company’s competitiveness. Acknowledgments: This research has been funded by national funds through FCT – Fundação para a Ciência e a Tecnologia, I.P., Project UIDB/05037/2020.

References Abdel-Kader M, Luther R (2008) The impact of firm characteristics on management accounting practices: a UK-based empirical analysis. Br Account Rev 40(1):2–27 Baron RM, Kenny DA (1986) The moderator–mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol 51(6): 1173–1182 Cadez S, Guilding C (2008) An exploratory investigation of an integrated contingency model of strategic management accounting. Acc Organ Soc 33(7–8):836–863 Gordon LA, Narayanan VK (1984) Management accounting systems, perceived environmental uncertainty and organization structure: an empirical investigation. Acc Organ Soc 9(1):33–47 Govindarajan V (1984) Appropriateness of accounting data in performance evaluation: an empirical examination of environmental uncertainty as an intervening variable. Acc Organ Soc 9(2): 125–135 Hair JF, Hult GTM, Ringle CM, Sarstedt M (2013) A primer on partial least squares structural equation modeling (PLS-SEM). SAGE Publications, Los Angeles Hair JF, Hollingsworth CL, Randolph AB, Chong AYL (2017) An updated and expanded assessment of PLS-SEM in information systems research. Ind Manag Data Syst 117(3):442–458 Hair JF, Risher JJ, Sarstedt M, Ringle CM (2019) When to use and how to report the results of PLS-SEM. Eur Bus Rev 31(1):2–24 Harrison JL (2009) Untangling the value of information scope: an investigation in retail pharmacies. J Manag Organ 15(4):470–485 Hoque Z (2011) The relations among competition, delegation, management accounting systems change and performance: a path model. Adv Account 27(2):266–277 Hoque Z, James W (2000) Linking balanced scorecard measures to size and market factors: impact on organizational performance. J Manag Account Res 12:1–17 Ismail K, Isa CR, Mia L (2018) Evidence on the usefulness of management accounting systems in integrated manufacturing environment. Pac Account Rev 30(1):2–19 Jariya AMI, Velnampy T (2022) The effect of management accounting practices on organizational performance of listed manufacturing sectors in Sri Lanka, with the moderating effect of complexity of production process. SMART J Bus Manag Stud 18(1):30–37

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Krumwiede KR, Suessmair A, MacDonald J (2008) An exploratory study of the factors affecting the implementation success of German cost accounting methods. AAA 2008 MAS Meeting Paper Pedroso E, Gomes CF (2020) The effectiveness of management accounting systems in SMEs: a multidimensional measurement approach. J Appl Acc Res 21(3):497–515 Podsakoff PM, MacKenzie SB, Lee J-Y, Podsakoff NP (2003) Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol 88(5):879–903 Ringle CM, Wende S, Becker J-M (2015) SmartPLS 3. SmartPLS computer program Shmueli G, Sarstedt M, Hair JF, Cheah JH, Ting H, Vaithilingam S, Ringle CM (2019) Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. Eur J Mark 53(11):2322–2347 Soobaroyen T, Poorundersing B (2008) The effectiveness of management accounting systems: evidence from functional managers in a developing country. Manag Audit J 23(2):187–219 Tontiset N, Ussahawanitchakit P (2010) Building successful cost accounting implementation of electronics manufacturing businesses in Thailand: how do its antecedents and consequences play a significant role? J Acad Bus Econ 10(3):1–24 Tuanmat TZ, Smith M (2011) Changes in management accounting practices in Malaysia. Asian Rev Account 19(3):221–242 Usakli A, Kucukergin KG (2018) Using partial least squares structural equation modeling in hospitality and tourism: do researchers follow practical guidelines? Int J Contemp Hosp Manag 30(11):3462–3512

The Impact of Ethical Leadership on Employee Intrapreneurship, Work–Life Balance, and Psychological Empowerment: A PLS-SEM Analysis Huma Bashir, Mumtaz Ali Memon, Naukhez Sarwar, Asfia Obaid, and Muhammad Zeeshan Mirza

1 Introduction Ethical principles influence people’s behaviors and allow them to restrict themselves in accordance with socially desirable behaviors. As a result, the ethical leadership style is gaining widespread recognition from researchers and practitioners alike, who are interested in the emergence of moral crises in various organizational contexts (Freire and Bettencourt 2020). Ethical leadership style not only incorporates the leader’s moral personality traits like trustworthiness, fairness, honesty, and integrity but also alternative ways through which they manage their employees (Joplin et al. 2021). An ethical leader develops and communicates ethical standards that are acceptable in the workplace and holds their employees accountable if they exhibit any unethical behavior (Li and Bao 2020; Treviño et al. 2000). A recent survey indicates that 94% of service professionals in the USA work more than 50 hours per week, with many of them also working on weekends (Apollo Technical 2022). This shows that American workers are struggling to maintain a work–life balance. This trend is also prevalent in Asia as professionals were less optimistic about their work–life balance in 2021 than they were in 2020, with 46% saying it was either good or very good, down from 50% (Hays 2021). According to a Gallup survey, only 24% of employees believe their employers care about their wellbeing (Harter 2022). According to a survey conducted by the World Economic Forum (WEF 2021), 86% of respondents call for moral leadership in businesses, and only about 8% of top executives consistently show moral leadership behavior at

H. Bashir · M. A. Memon (✉) · N. Sarwar · A. Obaid · M. Z. Mirza NUST Business School, National University of Sciences and Technology, Islamabad, Pakistan e-mail: [email protected]; [email protected]; asfi[email protected]; [email protected] © Springer Nature Switzerland AG 2023 L. Radomir et al. (eds.), State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM), Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-031-34589-0_29

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a high level. Considering these statistics, moral/ethical leadership is a need of time in organizations. An insensitive leader gives the impression that the entire organization is insensitive, which makes employees tense, stressed, and burned out (Cowart et al. 2014). On the other hand, a leader’s qualities of support, trust, understanding, and communication are vital for employees to solve problems of work–life balance (Cowart et al. 2014). These qualities are part of ethical leadership, which possesses its own unique form of leadership, albeit it can be practiced with other leadership styles (Schwepker and Dimitriou 2021). According to previous research, ethical leaders are viewed as trustworthy, disciplined, and caring in the decision-making process (Li and Bao 2020). Moreover, ethical leadership can be a source of employees’ positive behaviors in the workplace, such as employee engagement (Joplin et al. 2021), organizational citizenship behavior, and better organizational performance (Sharif and Scandura 2014). Employees typically consider ethical leaders as trustworthy and credible role models so that they start to adopt their leader’s characteristics and also reciprocate such quality treatment by showing more desirable behavior and ethical conduct in the workplace (Li and Bao 2020). One of those behaviors represents intrapreneurial behavior, which is exhibited at a personal level (Farrukh et al. 2021; Revuelto-Taboada et al. 2020). Intrapreneurial behaviors refer to “all actions taken by firm members that relate to the discovery and exploitation of entrepreneurial ideas and opportunities” (Sieger et al. 2013, p. 362). Intrapreneurial behavior is not a part of an employee’s job description, and such behavior does not emerge as a result of an organization’s policies and strategies (Valsania et al. 2016; Rigtering and Weitzel 2013). Therefore, it is crucial to investigate the factors that encourage intrapreneurial behavior in employees within an organization. Previous research recommended probing the impact of ethical leadership on positive work-related outcomes (Suifan et al. 2020). It was suggested that ethical leadership produces an immense impact on work–life balance along with job satisfaction (Freire and Bettencourt 2020). However, the relation between ethical leadership and work–life balance needs to be addressed with mediating effects of different variables (Haar and Brougham 2022). This study has addressed this gap and contributes to the literature by studying employee intrapreneurial behavior and work–life balance. It was intriguing to figure out the effect of ethical leadership on positive outcomes of an individual’s behavior. Past research has explained the impact of ethical leadership on employees as ethical leaders empower them with increased decision-making authority regarding their work and increase their work autonomy (McKenna and Jeske 2021). Psychological empowerment comprises a motivational factor that enables employees to exhibit a sense of intrinsic task motivation. In addition, previous studies suggested psychological empowerment as a variable to consider for future research (Ilyas et al. 2020; Wang et al. 2016). The mediating effect of psychological empowerment has rarely been studied with ethical leadership, intrapreneurial behavior, and work–life balance to date. The causal relationship between moral leadership, entrepreneurial activity, and work–life balance has been examined in this study, with

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the mediating role of psychological empowerment. This study made several contributions to the literature. First, by examining the novel relationship between ethical leadership, intrapreneurial behavior, and work–life balance with the mediating effect of psychological empowerment. Second, the research was conducted in the IT and telecom sector of Pakistan, where it intends to broaden the theoretical foundation on leadership literature in the South Asian context.

2 Theoretical Foundation and Hypothesis Development 2.1

Social Exchange Theory

Social Exchange Theory (SET, Blau 1964) has been extensively utilized in previous literature (Memon et al. 2016; Walumbwa et al. 2020). The theory (Blau 1964) states that actions of an individual depend upon the reactions of another individual. The action of one is a reward for the other and vice versa. Blau (1964) defines social exchanges as actions taken voluntarily by an organization with the intention that employees will reciprocate with their actions in return (Memon et al. 2016). To put this theory concisely, exchange relation happens when two parties enter into a relationship with the understanding that performing for the benefit of the other will be rewarding for them. SET provides a theoretical base for this chapter. An exchange relationship in the workplace is established when an employer contributes to the empowerment and well-being of its employee. In return, employees begin to reciprocate by acting positively and performing well at the job since they feel valued by the organization (Memon et al. 2016). Previous research has found that when leaders encourage their employees in managing work–life domains, acknowledge their effort in improving their job performance, it will help leaders achieve their stated goals (Freire and Bettencourt 2020). Since employees are treated ethically and have the leader’s trust, the relationships are reciprocated by them, and as a result, employees give their best to pay back to the organization. Furthermore, prior research indicates that individuals would engage in intrapreneurial actions when they feel confidence in their abilities/skills and believe that their efforts will be appreciated by their leaders or organizations (Chouchane et al. 2021; Eisenberger et al. 1986). Ethical leaders also play a huge role in empowering employees as they communicate the significance and value of employee’s work, which leads to the sense of fulfillment among employees (Suifan et al. 2020; Brown et al. 2005). Therefore, SET provides a firm theoretical foundation for this chapter.

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Ethical Leadership and Work–Life Balance

Ethical leadership is defined as “the demonstration of normatively appropriate conduct through personal actions and interpersonal relationships, and the promotion of such conduct to followers through two-way communication, reinforcement and decision-making” (Brown et al. 2005, p. 120). Brown et al. (2005) figured out two major aspects of ethical leadership: moral person and moral manager. The former depicts the personality related attributes and characteristics whereas the latter depicts the transformation of follower’s actions in ethical context under his/her supervision. Additionally, ethical leaders’ actions have the power to lessen employee’s workrelated issues since they are viewed as more dependable and thoughtful, which helps employees feel less stressed about their jobs (Demirtas 2013). According to a prior study, leaders are considered as ethical leaders if they build a trust-based relationship with the employee and respect employee’s work–life balance than those who do not (Cowart et al. 2014). Work–life balance refers to “the support by organization in an employee’s personal life aspects, such as flexibility in work hours, personal leaves, leisure time etc.” (Balven et al. 2018, p. 29). According to SET, an ethical leader’s behavior demonstrates fairness, integrity, and trust to their employees, and it triggers employees to reciprocate such behavior (Garba et al. 2018). Such leaders are also respectful toward work–life balance of employees. Previous research indicated that when leaders regard an employee’s work–life balance, employees will reciprocate with extra-role behavior such as Organizational Citizenship Behavior (Poohongthong et al. 2014). With solid foundation of this theory, it is hypothesized: H1: Ethical leadership is positively related to work–life balance.

2.3

Ethical Leadership and Intrapreneurial Behavior

Intrapreneurial behavior in an organizational context is viewed as higher order construct defined as “voluntary employee behavior aimed at generation of ideas, perception of opportunities, creation of new products or the development of new business lines” (Valsania et al. 2016, p. 133). Specifically, Innovative, proactive and risk-taking behaviors of employees in an organization are considered as intrapreneurial behavior (Farrukh et al. 2019). Innovativeness is the ability of an individual to develop new ideas in their work (Farrukh et al. 2019). Proactiveness is the anticipation of an individual to take actions on future needs and challenges instead of waiting to respond to demand (Farrukh et al. 2019). Risk-taking is defined as the ability of an individual to take risks with the intention of providing benefit to the organization, along with the accountability in case of a failure (Farrukh et al. 2019). Such behaviors are mostly exhibited on an individual level by the employees and the managers in an organization with the bottom-top approach in form of extra-role behavior (Mahmoud et al. 2020). In previous studies, it was also concluded that

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leadership stimulates employee extra-role work behaviors (Qiu et al. 2019; Lee and Kim 2017). According to SET, when leaders reduce fear among employees and have trust on them, employee will be engaged in intrapreneurial behavior in return (Farrukh et al. 2021). Hence, it is hypothesized: H2: Ethical leadership is positively related to intrapreneurial behavior.

2.4

Ethical Leadership and Psychological Empowerment

Empowerment generally includes the concept of delegating responsibility and decision-making authority to employees, allowing them to act on their own along with making sure that they have the resources to take decisions (Javed et al. 2017). Psychological empowerment is defined as a “sense of control that employees perceive in the organization, and that sense of control is reflected in four different dimensions: impact, meaning, competence, and self-determination” (Zhang et al. 2021; Mishra and Spreitzer 1998, p. 557). Impact refers to the extent an individual believes that they can influence an outcome as opposed to the difference their work makes in achieving that outcome (Zhang et al. 2021). Meaning refers to an individual’s perception about value of their work according to own thoughts and standards (Zhang et al. 2021). Competence refers to feelings of an individual’s self-efficacy that they can perform a particular task successfully (Zhang et al. 2021). Selfdetermination refers to an individual’s feelings of autonomy and responsibilities in making decisions about work and their work-related actions (Zhang et al. 2021). The performance of the employees usually affected by those in the organization who regularly experience work–life imbalance. People want to work for an organization which supports employee’s well-being, show confidence in their abilities, and intend to be a part of that organization for a longer period (Tirta and Enrika 2020). Employee’s potential success widely depends on their active involvement in the work facilitated by psychological empowerment (Dust et al. 2018). Psychologically empowered employees feel confident as they can shape their workplace roles and duties. In such confidence building and supporting employee well-being, ethical leaders can play a huge role due to their trust on employees and supporting nature. By allowing employees to participate in decision-making and work design, ethical leaders are more likely to help them understand the impact that they have on the organization (Zhu et al. 2004). Such kind of supportive behavior of an ethical leader creates a feeling of empowerment among employees. So, it is hypothesized that: H3: Ethical leadership is positively related to psychological empowerment.

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Psychological Empowerment and Intrapreneurial Behaviors

Individual level intrapreneurial behavior is considered as an important factor for the success of an organization (Mahmoud et al. 2018). Employees who feel empowered are more likely to be creative because they believe their work has meaning and autonomy. Additionally, empowered workers are more likely to use creativity to complete their tasks successfully because of the sense of self-efficacy that is associated with them (Mahmoud et al. 2018; Redmond et al. 1993). Employees who feel empowered tend to be more innovative, proactive, and risk-takers, which ultimately improves performance (Afsar et al. 2017). It has been tested empirically and proven that enhancing employee empowerment and autonomy is a pertinent way of encouraging extra-role behavior (Farrukh et al. 2019). Employees who are more empowered in their jobs have more opportunities to go above and beyond their roles and find more efficient ways to complete their tasks to benefit the organization (Asgari et al. 2012). As a result of being empowered, employees have an innate motivation to improve organizational performance. So, with the firm support from existing literature it is hypothesized that: H4: Psychological empowerment is positively related to intrapreneurial behavior.

2.6

Psychological Empowerment and Work–Life Balance

As per past study, psychological empowerment may have a favorable impact on the employee’s psychological attitude toward their job, behavior at work, and how they perceive their roles in the organization (Panda and Sahoo 2021). Work–life balance and psychological empowerment share the same parameters that characterize individual’s level of satisfaction with their lives, and level of involvement in the workplace (Panda and Sahoo 2021; Ickes et al. 2018; Grealish et al. 2017). When the basic assumptions about an employee’s workplace and job are suitably met, only then employees experience pleasure from professional lives. Unfulfilled perceptions of control limit the productivity and commitment of professionals who experience stress from work–life conflict and both personal and professional demand (Panda and Sahoo 2021). Psychological empowerment increases employee’s sense of personal control which motivates them to work, resulting in favorable organizational and individual level outcomes (Quinn and Spreitzer 1997). When employees have control over issues like intense work pressure and personal issues; psychological empowerment may increase the degree of work–life balance among employees. On the basis of above literature, it is hypothesized that: H5: Psychological empowerment is positively related to work–life balance.

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Mediating Role of Psychological Empowerment

Past research has shown that leadership has a huge impact on the development of intrapreneurial behavior by empowering employees (Valsania et al. 2016). The ethical leader exhibits genuine concern and care for employees and behaves ethically both in their work–life dealings as well as in their personal lives (Chughtai et al. 2015). Employee’s potential gets channelized, and competencies are enhanced when an ethical leader enables them to accomplish their tasks and provides them with developmental opportunities (Ahmad and Gao 2018). Such reciprocated behaviors of both employees and leaders are supported by SET and following hypotheses have been proposed: H6: Psychological empowerment mediates the relationship between ethical leadership and intrapreneurial behavior. H7: Psychological empowerment mediates the relationship between ethical leadership and work–life balance (Fig. 1).

3 Methodology 3.1

Context

The investigation was conducted in the telecommunication and IT sectors in Pakistan. Despite the Covid-19 pandemic, Pakistan’s telecom sector contributed 129% more to the national treasury in 2020 than it did in 2019, indicating its prominence as the main driver of the economy (Samaa web desk 2021). Previous research suggested that the new working modes affected work–life balance of employees in Latvia, which included employees of both genders in the survey and

Fig. 1 Conceptual framework

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concluded that both men and women felt that their ability to focus on the work responsibilities and task is affected by family life, especially the respondents aged 25–44 and those having children under the age of 18 in their homes (Lonska et al. 2021). Hofstede (2001) shed light on clusters of cultures existing across nations like Asian Countries which are characterized with high power distance and high collectivism (Ilyas et al. 2020). There was a need to test how the employees of telecommunication and the IT sector manage their work–life balance under ethical leadership in Pakistan, as the employees in these sectors mostly work in teams and groups under the supervision of their leaders. Also, whether they are empowered enough to perform certain tasks which might result in their intrapreneurial behavior as it is perceived that innovations and creativity is a job demand of employees working in these sectors. It was noteworthy that this chapter has helped in figuring out the ways employees manage their work–life balance and exhibit intrapreneurial behavior under ethical leadership in telecom and the IT sector.

3.2

Instruments

Ethical leadership was measured using a “six-item scale” adapted from Bormann (2017), originally developed by Brown et al. (2005). Ethical leadership is defined as “the demonstration of normatively appropriate conduct through personal actions and interpersonal relationships, and the promotion of such conduct to followers through two-way communication, reinforcement and decision-making” (Brown et al. 2005, p. 120). This chapter adapted the scale and made small amendments such as the beginning of questions replaced “today” with “my supervisor” because the present study didn’t measure ethical leadership on a daily basis. A sample item includes “My Supervisor/Boss listens to what employees have to say.” A nine-item scale was used to measure intrapreneurial behavior which had three dimensions of innovativeness, risk-taking, and pro-activeness adapted from Farrukh et al. (2021), originally developed by Stull and Singh (2005) with reliability of 0.913. Intrapreneurial behavior is defined as “voluntary employee behavior aimed at the generation of ideas, perception of opportunities, creation of new products or the development of new business lines” (Valsania et al. 2016, p. 133). The statements were amended to better fit the context of the chapter, for example, “this employee” in the adapted scale was changed to “I” because data was gathered from employees. A sample item includes “I contribute to the implementation of new ideas at work.” A three-item scale was used to measure work–life balance adapted from (Rashmi and Kataria 2021) originally developed by (Haar 2013) with the reliability of 0.80. In the current study, work–life balance is defined as “support by organization in employee’s personal life aspects, such as flexibility in work hours, personal leaves, leisure time, etc.” (Balven et al. 2018, p. 29). A sample item includes “Nowadays, I seem to enjoy every part of my life equally well”. Psychological empowerment was measured by a “five-item scale” adapted from (Rafique et al. 2021) with the reliability of 0.801 originally developed by (Spreitzer

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1995). Psychological empowerment is defined as a “sense of control that employees perceive in the organization, and that sense of control is reflected in four different dimensions: impact, meaning, competence, and self-determination” (Mishra and Spreitzer 1998, p. 557). The adapted scale has six items, but this chapter eliminated an item due to similarity with another item. The eliminated item is “I can decide on my own how to go about doing my own work” which is similar to the item “I have significant autonomy in determining how I do my job”. A sample item includes “I am confident about my ability to do my job”. All 23 items were measured using a 5-point, Likert-type scale (1 = strongly disagree to 5 = strongly agree). Additionally, the questionnaire was pretested before moving to data collection in order to identify any ambiguity related to the questionnaire statements or demographic information that respondents would not feel comfortable to answer (Memon et al. 2020a). There was no serious issue found in pretesting. All items of survey questionnaire are given in the appendix of the chapter.

3.3

Sampling and Data Collection

For data collection, a quantitative approach with snowball sampling technique was used. The snowball sampling technique, also known as sampling by reference, is used when respondents are difficult to locate and are approached through references or contacts (Memon et al. 2020a; Cooper and Schindler 2011). The investigation was conducted in Covid-19 times, where physical data collection was not permitted in software houses and telecom companies in Pakistan due to the prevailing policies of reduced face-to-face interaction. For the smooth process of data collection, referrals in different software houses and telecom companies were used. Data collection period comprised ten days in which a total of 219 samples were collected. Initially, almost 150 samples were collected without reminders, while the remaining 69 samples were collected by sending reminders. It was challenging to collect data due to the time difference among people working in both sectors. Employees working in software houses mostly worked in evening shifts and had busy schedules so they requested to send them reminders so they can spare time as they volunteered to participate. Out of 219, a few samples were eliminated at initial screening process as they did not meet the criteria of the research work. These included the respondents who filled out the survey inappropriately like straight line patterns or suspicious demographic information (n = 23), and a few didn’t belong to sectors being investigated (i.e., IT and Telecom) (n = 12), leaving the final sample size of 184 for data analysis. Most participants were men (66%), reflecting the predominance of men in Pakistan’s telecom and software sectors. As there were two sectors under investigation, respondents from the telecom industry made up 44% of the sample, while those in the IT sector made up 55%. The majority of the respondents have completed graduation, i.e., 96%. Most of the respondents were Engineers (18%), followed by Assistant Developers (14%),

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Table 1 Demographic information Variable Gender Age

Qualification

Position

Category Male Female 25–30 31–35 36–40 41–45 46–50 51–55 56–60 Bachelors MBA MSc PhD Other Engineers Assistants Field specialist Admin staff Executives Developers Operational staff Others

Frequency 120 64 14 70 52 33 9 4 2 81 62 34 4 3 33 23 13 11 14 26 24 40

Percentage 65.2 34.8 7.6 38 28.3 17.9 4.9 2.2 1.1 43.8 33.5 18.9 2.2 1.6 17.9 12.5 7.1 6.0 7.6 14.1 13 21.7

Operational staff (13%), Assistants (12%), Field Specialists/Executive Employees (7%), and Admin staff (6%). The experience level of the employees ranges from 1–4 years to 14–19 years. Most of the respondents work in Islamabad (42%), followed by Lahore (23%), Rawalpindi (8%), Karachi (6%), and Faisalabad/Multan (4%). The majority of the respondents were married (54%). Final sample size of 184 samples was supported by sample size requirement for non-complex and medium size quantitative study, i.e., 100 to 200 samples (Memon et al. 2020b; Kline 2005). The demographic information of respondents is also summarized in Table 1.

4 Data Analysis and Results The research hypotheses were tested using partial least squares structural equation modelling (PLS-SEM). A two-stage analytical procedure was used in this study, as proposed by Anderson and Gerbing (1988). Internal consistency reliability, convergent and discriminant validity of the measurement model were tested in the first stage, and the structural model was examined in the second stage, a stage for hypotheses testing.

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Measurement Model Assessment

The measurement model underwent testing to determine the discriminant validity (DV), convergent validity (CV), and internal consistency reliability of the constructs used in this chapter. Internal consistency reliability evaluates how well the items reflect the latent constructs (Hair et al. 2014; Ramayah et al. 2018). Internal consistency was tested using composite reliability (Hair et al. 2017). For each construct, a measurement model is deemed satisfactory if its composite reliability is greater than the threshold value of 0.7 (Nunnally 1978; Nunnally and Bernstein 1994; Richter et al. 2016). The findings revealed that the composite reliability for each construct – Ethical Leadership (0.881), Intrapreneurial Work Behavior (0.891), Psychological Empowerment (0.874), and Work–Life Balance (0.956) exceeded the cut-off value (0.7), demonstrating the high internal consistency of the measures. Another measure to consider is CV, which evaluates “the extent to which a measure correlates positively with alternative measures of the same construct” (Hair et al. 2017, p. 112). CV was estimated by examining the item’s outer loading and the average variance extracted (AVE). As a general rule, outer loadings should be 0.708 or greater, while an AVE score of 0.5 is acceptable (Avkiran 2017). Additionally, items with a value of 0.6 outer loading are also considered as accepted (Chin et al. 1997). According to Hair et al. (2017), if factors with higher loadings can account for at least 50% of the variance (AVE = 0.50), indicators with lower factor loadings should be retained. The results show that, apart from IWB6, all of the items had satisfactory outer loadings. As a result, IWB6 “I would be willing to give up some salary in exchange for the chance to try out my business idea if the rewards for success were adequate” was omitted due to a weak factor loading value. All the constructs, including ethical leadership (0.554), intrapreneurial work behavior (0.508), psychological empowerment (0.582), and work–life balance (0.878), attained acceptable AVE after the removal of IWB6. This validated the CV of the constructs. Results of internal consistency reliability and CV tests are summarized in Table 2. This chapter also confirmed the DV by using another method of the heterotrait– monotrait ratio (HTMT, Henseler et al. 2015). An HTMT value greater than 0.90 depicts the lack of DV (Hair et al. 2017). For HTMT, 0.85 is a more conservative cut-off value (Henseler et al. 2015). The HTMT criterion results showed that the present study did not deviate from the assumptions of discriminate validity, as shown in Table 3, with a value of 0.85 HTMT. The overall measurement model’s results show acceptable internal consistency reliability, CV, and DV.

4.2

Structural Model Assessment

To calculate the statistical significance, the bootstrapping method (5000 subsamples was used). The results of this chapter indicated that ethical leadership was

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Table 2 Convergent validity and internal consistency reliability Constructs Ethical leadership

Intrapreneurial work behavior

Psychological empowerment

Work–life balance

Items EL1 EL2 EL3 EL4 EL5 EL6 IWB1 IWB2 IWB3 IWB4 IWB5 IWB7 IWB8 IWB9 PE1 PE2 PE3 PE4 PE5 WLB1 WLB2 WLB3

Loadings 0.789 0.767 0.790 0.687 0.769 0.652 0.662 0.785 0.754 0.701 0.796 0.707 0.600 0.675 0.747 0.782 0.663 0.831 0.779 0.919 0.948 0.944

AVE 0.554

CR 0.881

0.508

0.891

0.582

0.874

0.878

0.956

Notes: IWB6 was deleted due to low loading Table 3 Discriminant validity (HTMT criterion) Ethical leadership IWB PS WLB

Ethical leadership

IWB

PE

0.332 0.440 0.394

0.648 0.329

0.330

WLB

Notes: PE Psychological empowerment, IWB Intrapreneurial work behavior, WLB Work–life balance

significantly positively related to work-life balance (H1: β = 0.285, p =0.000, LL= 0.144, UL=0.400). In contradiction to the initial assumption, the findings of second hypothesis showed that ethical leadership does not have a direct effect on employees’ intrapreneurial behavior (H2: β = 0.085, p =0.101, LL= -0.027, UL=0.195). Further, ethical leadership positively influence psychological empowerment (H3: β = 0.374, p =0.000, LL= 0.262, UL=0.469 and psychological empowerment (H4: β = 0.530, p =0.000, LL= 0.395, UL=0.631) was found to have a positive and significant effect on employees’ intrapreneurial behavior. Furthermore, psychological empowerment was also found to positively and

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Table 4 Hypotheses testing (Direct effect) Hypotheses Ethical leadership→IWB Ethical leadership→PE Ethical leadership→WLB PE→IWB PE→WLB

Beta 0.085 0.374 0.285 0.530 0.183

STDEV 0.067 0.062 0.078 0.070 0.076

p 0.101 0.000 0.000 0.000 0.008

CI [LL] -0.027 0.262 0.144 0.395 0.057

CI [UL] 0.195 0.469 0.400 0.631 0.310

Notes: PE Psychological empowerment, IWB Intrapreneurial work behavior, WLB Work–life balance Table 5 Hypotheses testing (Indirect effect) Hypotheses Ethical_Leadership→Psy_Emp→Intrap_Beh Ethical_Leadership→Psy_Emp→WLB

Beta 0.198 0.069

STDEV 0.043 0.033

CI [LL] 0.117 0.010

CI [UL] 0.283 0.140

Notes: PE Psychological empowerment, IWB Intrapreneurial work behavior, WLB Work–life balance

significantly influence work-life balance of employees (H5: β = 0.183, p =0.008, LL= 0.057, UL=0.310). Sixth (H6) and seventh (H7) hypotheses (H3) tested the mediating effect of psychological empowerment between the relationship of ethical leadership and outcomes (intrapreneurial behavior and work-life balance). This chapter followed Preacher et al.’s (2007) definition of mediation effect according to which “an indirect effect or mediation, is said to occur when the causal effect of an independent variable (X) on a dependent variable (Y) is transmitted by a mediator (M)” (Wu 2017). If the indirect effect of independent variable on dependent variable is significant and the confidence intervals do not contain zero, it means mediation is established (Qian et al. 2017). The results for indirect effect showed that psychological empowerment (H6: β =0.198, LL: 0.117, UL: 0.283) mediates the relationship between ethical leadership and intrapreneurial behavior. The fourth hypothesis H4 tested the mediating nature of psychological empowerment between the relationship of ethical leadership and work–life balance. The results indicated that psychological empowerment (H7: β = 0.069, LL: 0.010, UL: 0.140) mediates the relationship between ethical leadership and work–life balance. All four hypotheses were supported, and the structural model analysis results are reported in Tables 4 and 5. According to Hair et al. (2017), researchers should also report the coefficient of determination (R2), effect size ( f2), and predictive relevance (Q2) in addition to describing the significance of the relationships. R2 measures how well the independent variable or variables explain the corresponding dependent variable or variables. The R2 value indicates that ethical leadership explains 14.5% of employee’s psychological empowerment (R2 = 0.145), 31.7% of employee’s intrapreneurial behavior (R2 = 0.317), and 8.5% of their work–life balance (R2 = 0.085). Following that, f 2 denotes effect size, or how much an independent variable contributes to the R2 of the dependent variable. f 2 could be calculated from the following equation: f 2 = (R2

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included - R2 excluded) ÷ (1 - R2 included). Cohen (1988) suggests f 2 values of 0.02 as small, 0.15 as medium, and 0.35 as large size effect. The results of f 2 showed that ethical leadership has no direct effect on intrapreneurial behavior of employees ( f2 = 0.009), a medium size effect on psychological empowerment ( f2 = 0.163), a small to medium size effect on work–life balance. Psychological empowerment has a large direct effect on intrapreneurial behavior ( f2 = 0.356) and a small size effect on work–life balance ( f2 = 0.034). The Q2 measures the research model’s predictive relevance or out-of-sample predictive power for the specific construct (Chin et al. 2008; Hair et al. 2014). The cross-validated redundancy values of the constructs were calculated via a blindfolding procedure which is a measure of Q2. “Q2 values greater than zero for a certain reflective endogenous latent variable indicate the path model’s predictive relevance for the particular construct” (Hair et al. 2014, p. 178). The results showed a satisfactory level of predictive ability for ethical leadership on psychological empowerment (Q2 = 0.079), intrapreneurial behavior (Q2 = 0.147), and work–life balance (Q2 = 0.068). Values of (R2) and (Q2) were calculated.

4.3

Common Method Bias

As the data were collected by self-reporting survey, so there might be a chance for common method bias (Memon et al. 2020a; Podsakoff et al. 2003). To minimize the impact of common method bias, both procedural remedies and statistical methods were used. At procedural level, i.e., during data collection, participants were assured with their anonymity and their responses were kept confidential and only used for current study purpose (Memon et al. 2016). Additionally, pretesting was conducted in order to make sure that respondents understand statements easily by avoiding difficult terminologies (Memon et al. 2020a). Additionally, a Harman one-factor test (Harman 1976) was used to identify common method bias after data collection. The results showed the maximum variance could be 30%, which indicated there was no issue of common method bias in the current study (Memon et al. 2020a; Babin et al. 2016).

5 Discussion The chapter investigated the relationship between ethical leadership, intrapreneurial behavior, and work–life balance with the mediating effect of psychological empowerment. The first hypothesis (H1) which states that ethical leadership is positively related to work–life balance is supported with a significant direct relationship between ethical leadership and work–life balance. It shows that ethical leadership is effective in improving work–life balance of employees. It implies that ethical leaders have the ability to create ethical environment in the organization which

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nurtures work–life balance of employees. By having a work–life balance, employees will be happier and healthier both physically and psychologically and they will feel less stressed, which is a common problem in the organizations (Bhende et al. 2020; Chiang et al. 2010). The second hypothesis (H2) hypothesized that ethical leadership is positively related to intrapreneurial behavior. Surprisingly, the results of H2 indicates that ethical leadership does not influence employee’s intrapreneurial behavior. It means only ethical aspects of leader behavior are not sufficient to motivate employees to exhibit intrapreneurial behavior at workplace. The results of H2 are consistent with existing studies which state that leaders may use different mediating strategies to encourage employee intrapreneurial behavior, such as an entrepreneurial mindset, job autonomy, organizational identification, and empowerment along with organizational support (Chang et al. 2017; Amankwaa et al. 2019; Lei et al. 2020). The H3 of research work is accepted which states that ethical leadership positively affects psychological empowerment of employees. The results of H3 also indicates that ethical leadership is a source of psychological empowerment among employees. This result is not unexpected because ethical leaders are better at imparting the work values that give the employees a sense of purpose. Additionally, ethical leaders highlight skills and capabilities required for performing the task and offer constructive criticism to instill a sense of competence in their followers. These results also provide a support for a previous study (Suifan et al. 2020) which states that ethical leaders positively affect psychological empowerment and reduce turnover intention among employees working in the banking sector. The findings of H4 indicate that psychological empowerment results in intrapreneurial behavior among employees. Employees are more likely to engage in more creative work to improve job performance when they feel they have control on making job-related decisions, work flexibility and freedom, the power to influence others, and a sense of the work’s meaning. So, the findings also provide support to a previous study by Mahmoud et al. (2018) which states that psychological empowerment is an important factor for nurturing intrapreneurial behavior among middle managers in medium enterprises. The findings of H5 indicate that psychological empowerment is an important aspect in establishing work–life balance. The idea of work–life balance is extremely individualized and subjective, making it specific to each employee. In light of this, an employee’s level of satisfaction depends on maintaining a work–life balance for their overall well-being, which is a psychological state everyone strives to achieve. Unless employees have confidence in their capabilities, they will not feel empowered (Spreitzer 1995). Therefore, results are also aligned with past study which states that the feeling of having capabilities to deal with job requirement results in work–life balance (Ambad and Bahron 2012). The H6 is also accepted which states that mediating effect of psychological empowerment strengthens the relationship between ethical leadership and intrapreneurial behavior of employees. Ethical leaders consider the growth needs of employees and psychologically empower them in the decision-making of their work design. Also, the target audience of investigation were employees from the

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IT and telecom sectors who need great support from their leaders for bringing innovation in their products, ideas, and services. Ethical leader’s sincere and supportive relationship with employees helps them in getting involved in intrapreneurial behavior in the organization. Such intrapreneurial behaviors of employees make them motivated and committed to their jobs and become a source of organizational prosperity. Findings are also consistent, to some extent, with previous studies which state that ethical leadership positively affects employee creativity (Javed et al. 2017). With such a supportive behavior of ethical leaders, employees perceive control over their task which makes them self-confident and enables them to think out of the box and bring novelty in job tasks through innovations. Findings also support previous research which concludes that leaders can stimulate intrapreneurial behavior among the employees by empowering them (Valsania et al. 2016). The H7 of this chapter is also accepted, which states that psychological empowerment mediates the relationship between ethical leadership and low work–life balance. This implies that due to ethical leadership and psychological empowerment in job task, employees will be satisfied with their work and personal life. Employees will have more positive attitude toward work-related tasks under ethical leaders who provide them autonomy in integrating work and non-work life. Employees will be more motivated and engaged in their job tasks and try to maintain balance between professional and personal life. In a previous study, it is concluded that psychological empowerment positively affects work–life balance of employees which also strengthens current study findings (Panda and Sahoo 2021). In line with SET, the results of this chapter revealed that when ethical leaders psychologically empower employees, it results in reciprocated behavior of intrapreneurial behavior and creates a work–life balance along with improved work performance. This shows that employees consider ethical leaders more credible, trustworthy, and supportive and exhibit intrapreneurial behavior and maintain work–life balance well by perceiving psychological empowerment. Employees show positive behavior (i.e., more intrapreneurial behavior) and commitment under ethical leaders who provide opportunities to employees in decision-making related to job tasks.

6 Implications The findings of this study show that ethical leadership is not only imperative in resolving issues of low work–life balance among employees but also carries a key role in shaping positive work behavior among employees, such as intrapreneurial behavior. This fact is valuable not only for scholars but for practitioners as well. Results established the importance of ethical leadership for practitioners of the IT and Telecom sectors in Pakistan. Findings of the chapter demonstrate that ethical leadership influences employee intrapreneurial behavior and work–life balance. Managers in both sectors should demonstrate ethical leadership by creating an ethical atmosphere, safeguarding employees’ rights and dignity, encouraging and

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empowering employees to produce ideas that can be implemented in the organization, without intruding in employees’ personal life. Behaviors modeled by ethical leaders, such as honesty, integrity, and respect, create an empowering environment in a company which leads to the growth of innovative and talented employees, an asset to an organization’s management. Previous research has also recommended that managers should focus on building strategies and policies that adhere to moral and ethical standards for psychological empowerment of employees (Javed et al. 2017). For example, managers can make employees aware of the importance of their work in the organization, provide them autonomy and control over their job, and motivate them intrinsically for work performance rather than through external rewards such as bonuses, raises, and so on. These practices can assist managers in encouraging intrapreneurial behavior among staff members. Organizations should take the initiative to enhance their employees’ quality of life because it has a direct impact on employee well-being, but also has some indirect effects on organizational performance. According to the results, ethical leadership has been discovered to play a role in increasing employees’ ability to balance their personal and professional lives through task empowerment. IT and telecom companies should design policies to incorporate employee needs into their processes, which can only be accomplished by instilling ethical leadership qualities in managers. Managers should create trust with their employees by giving them some autonomy in doing their job responsibilities rather than micromanaging them, as well as some flexibility in working hours to complete their job obligations. With this practice, employees will be more punctual by arriving and leaving the office on time, allowing them to maintain a balance between work and non-work life. Previous research has shown that when firms emphasize work–life balance policies and provide people job control, employees experience fewer negative behaviors at work, such as stress (Bhende et al. 2020; Chiang et al. 2010). By examining a novel relationship between ethical leadership and intrapreneurial behavior, this chapter theoretically makes a significant contribution to the literature on leadership and human resource management. The chapter expands the knowledge on the importance of ethical leadership in promoting intrapreneurial behavior and helps employees in maintaining work–life balance through psychological empowerment. Although the direct influence of ethical leadership on producing intrapreneurial conduct in employees is not significant, the psychological empowerment strengthens the relationship in both sectors considerably. The concept of behavior reciprocity in the SET is strengthened by the fact that employees who work for ethical leaders have a high level of association with the firm and perform better in roles and tasks unrelated to their normal tasks.

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7 Conclusion The primary goal of this chapter was to investigate the impact of ethical leadership on intrapreneurial behavior and work–life balance. Additionally, this chapter investigated the mediating role of psychological empowerment between the relationship of intrapreneurial behavior and work–life balance with ethical leadership. The findings revealed that ethical leadership has a significant direct effect on work–life balance of employees. The direct effect of ethical leadership did not have a significant effect on intrapreneurial behavior of employees, but through psychological empowerment, the effect was more significant. This chapter includes a few limitations. Firstly, as the data is only collected from software houses and the telecom sector of Pakistan, it may not be generalizable to other sectors in Pakistan. Future researchers can replicate this study in other sectors and geographical regions which will further validate the current study’s findings. Moreover, data was only collected from employees in the present study; future studies should incorporate manager’s perspective as well for holistic understanding of subject matter. Additionally, research design was cross-sectional in nature due to time constraints. In future, longitudinal or qualitative research design is also suggested for better clarity and in-depth understanding of subject matter. Lastly, it was detected in findings of investigation that an ethical leader has no direct impact on intrapreneurial behavior, future studies can examine intrapreneurial behavior with other leadership styles.

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Part VI

Education

PLS and Educational Research: Epistemological and Methodological Interpretations in Philosophy Hongfeng Zhang, Haoqun Yan, and Johnny F. I. Lam

1 Introduction In the sphere of social sciences and educational research, empirical research has been in the mainstream and has resulted in a complete set of paradigms and theories. Throughout the development of the methodological system, the epistemology of modern philosophy has a different epistemological orientation. This chapter aims to identify the intellectual implications in social science research, clarify the philosophical foundations of the two research paradigms, and the role played by partial least squares structural equation modeling (PLS-SEM) as an important analytical tool in empirical research in educational research and the future direction of development.

2 The Epistemological Implications of Social Science Research Methods Knowledge in the social sciences is acquired and accumulated mainly with “issue awareness” as a precursor. Questions are sometimes manifested as beliefs, or as determination, and require the guidance of non-rationalistic ways of thinking on the way to facts and truth, namely, “we recognize matters inaccessible to reason, the sacred, and recognize non-rationalistic means to apprehend them” (Grafstein and Dobbs 1988). However, the problem is often rationally presented to us. Empiricists tend to approach questions based on sensory experience, and some “correct” results H. Zhang · H. Yan (✉) · J. F. I. Lam Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao, China e-mail: [email protected]; [email protected]; fi[email protected] © Springer Nature Switzerland AG 2023 L. Radomir et al. (eds.), State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM), Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-031-34589-0_30

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can be obtained under the inductive synthesis of a certain research paradigm. But rationalists believe that human beings have the ability to grasp self-evident rules, axioms, and logic, and that truth can be reduced to the universal necessary propositions and knowledge grasped by this ability, as well as to the system of knowledge and logic rationally deduced from these knowledge propositions. In essence, empiricism and rationalism have very different emphases in constituting a system of knowledge, with the former emphasizing sense experience and the latter valuing rational thought. However, an insistent emphasis on the irreplaceability of sense experience inevitably fragmented facts and ideas and prevented the formation of a universally necessary system of knowledge. Likewise, self-evident truths and principles exist only in a priori concepts and categories, and everything logically deduced from them is isolated from the empirical world. Therefore, “Congenital Comprehensive Judgment” by Kant organically unifies the two epistemologies, that is, in the process of knowing, the a priori categories of universal necessity and the real-world sense experience can be unified by the a priori unity. While freeing the rules of induction and deduction from their limitations, thus enabling the research methodology of the social sciences to gain new insights.

3 The Divide between Empirical and Normative Research In terms of general methodological cognition, research methods in social sciences can be broadly divided into two categories: one category belongs to empirical research. It collects data and information through observation, interviews, questionnaires, experiments, physical objects, and so forth to obtain experience; and then generalizes or describes the experience in-depth, to verify, predict, develop theories, and interpret the meaning. The other category belongs to normative research, which starts from certain identified principles, rationales, or value judgments, and forms universal value judgments or new theoretical discoveries through logical deduction. These two types of studies take empiricism and rationalism as their epistemological foundations, respectively. Scholars who represent these different domains are said to be guided by different theories, assumptions, and norms that often result in misunderstanding or a lack of appreciation for each other’s endeavors (Rosenthal and Buchholz 2000). Research in the social sciences has been developed to date that there is no longer a clear division between the two types of research methods. Many normative studies also require data collection and the use of empirical observation, while the rules of empirical studies themselves contain theoretical presuppositions and value tendencies, and the components of rational analysis have long been nurtured in them. Further, empirical research can be divided into quantitative research and qualitative research, each with different philosophical foundations and ways of data collection and analysis. Quantitative research is undoubtedly more characteristic of natural science, which believes that the use of ever-changing statistical analysis tools and procedural empirical means can make research results reach a level of

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refinement and accuracy. While qualitative research takes phenomenology and hermeneutics as its philosophical foundation, emphasizing in-depth and holistic inquiry into social phenomena in a natural context, forming conclusions and theories from primary data, gaining an interpretive understanding of their behavior and meaning construction through interaction with the research subjects. In addition to empirical research and normative research, there exists a category of conceptual research, which cannot be placed in the same category as the above two types of research at the philosophical level. On the one hand, conceptual research seeks to explain the concept of the research object, the theory formed based on deductive reasoning, so conceptual research can be classified under normative research. On the other hand, it also draws on empirical content, including quantitative and qualitative research, as Kant said, “ pure intuition without pure category is blind,” and these empirical data are combined with the researcher’s observation and reflection based on concepts to obtain normative interpretations. With the rapid development of scientific research and the emergence of a large number of statistical analysis tools, “we may therefore be more certain of the empirical character of any given bit of knowledge than we are of the principle from which that knowledge could be deduced” (Benjamin 1941). It is conceivable that empirical research is gradually occupying an essential place in social science research.

4 Conclusion: Toward a PLS-SEM Empirical Approach to Educational Research The purpose of this chapter is to summarize the application and development of PLS-SEM research methods, an analytical tool for empirical research in educational research. PLS-SEM has been widely used in social science disciplines, and a review of previous literature on PLS-SEM suggests that it should be the preferred approach in the following situations. First, when the research objective is to better understand increasing complexity by exploring extensions of established theories, and the other case is when the path model includes one or more formatively measured constructs (Hair et al. 2019a). Secondly, compared with covariance-based structural equation modeling (CB-SEM), PLS-SEM is a causal-predictive approach that focuses on the explanation of variance in the dependent variables (Hair et al. 2019a). Moreover, PLS-SEM is not only suitable for exploratory research but also confirmatory research (Hair et al. 2022). Strictly speaking, educational research does not belong to the field of social science research. The basic task of education is to cultivate human beings, and the cultivation mainly involves the cognitive, behavioral, and moral development of students, which is closely related to the epistemological, methodological, and ethical aspects at the philosophical level. Therefore, the field of education should not only explore the questions of “what” and “how,” more importantly, the question of “why,” and a deeper understanding of essential restoration is inevitable. The deeper understanding or essence restoration must be closer

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to normative research and qualitative interpretation in empirical research. From another perspective, education is inseparable from social science, because many questions in the educational field are economic, political, social, legal, and so forth. Consequently, educational research can be cross-fertilized with other social science fields and even with natural sciences, and the quantitative research methods advocated by empiricism are similarly applied. As a result, researchers can use more scientific and sophisticated technical means to deal with various connections between educational phenomena as objective existences. From the development of the last decade, PLS-SEM, as a new multivariate statistical data analysis tool, realizes the simultaneous completion of regression modeling, data structure simplification, and correlation analysis between two sets of variables under one algorithm. The research of Hair et al. (2019b) noted that the small sample size capability of PLS-SEM was confirmed to be real, models with a formal specified structure should be analyzed using PLS-SEM and it was superior to regression analysis when evaluating mediation. A study by Boubker et al. (2021) analyzed the impact of entrepreneurship education on the entrepreneurial intentions of Moroccan university students by using PLS-SEM to test the proposed model and found that the entrepreneurial intentions of 98 management students depend mainly on two variables, entrepreneurship education, and entrepreneurial attitudes. Gora et al. (2019) analyzed the data by surveying students from two Romanian public universities using PLS-SEM, thus proposing a model that links the quality management characteristics of higher education institutions to students’ competencies and labor market employment opportunities and emphasizing the direct and indirect relationships between them. In the face of complex and changing educational phenomena, the scientific empirical model of PLS-SEM undoubtedly has advantages over other mathematical and statistical methods and has led to the rapid development of theories, methods, and applications in the field of educational research. Acknowledgments This work was supported by Macao Polytechnic University (RP/FCHS-02/ 2022).

References Benjamin AC (1941) Is empiricism self-refuting? J Philos 38(21):568–573 Boubker O, Arroud M, Ouajdouni A (2021) Entrepreneurship education versus management students’ entrepreneurial intentions. A PLS-SEM approach. Int J Manag Educ 19(1):100450 Gora AA, Ștefan SC, Popa ȘC, Albu CF (2019) Students’ perspective on quality assurance in higher education in the context of sustainability: a PLS-SEM approach. Sustainability 11(17):4793 Grafstein R, Dobbs D (1988) Rationalism or revelation? Am Polit Sci Rev 82(2):579–587

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Hair JF, Risher JJ, Sarstedt M, Ringle CM (2019a) When to use and how to report the results of PLS-SEM. Eur Bus Rev 31(1):2–24 Hair JF, Sarstedt M, Ringle CM (2019b) Rethinking some of the rethinking of partial least squares. Eur J Mark 53(4):556–584 Hair JF, Hult GTM, Ringle CM, Sarstedt M (2022) A primer on partial least squares structural equation modeling (PLS-SEM), 3rd edn. Sage, Thousand Oaks, CA Rosenthal SB, Buchholz RA (2000) The empirical-normative split in business ethics: a pragmatic alternative. Bus Ethics Q 10(2):399–408

Public Higher Education Organizational Climate’s Structural Model Joel Bonales-Valencia

1 Introduction The investigation considered two scopes, a descriptive one because it sought to specify the characteristics of the variables under study, resulting from the review of the facts and data compiled in the Institution, which allowed interpreting its reality in relation to the latent variables, and the second was the structural model in order to measure the degree of relationship between the endogenous variables and the exogenous variables, as well as the degree of association of the variables that determine the organizational climate. To carry out the study, six latent variables were selected: salary, promotions, leadership, motivation, and gender equity and the organizational climate. Based on these variables, dimensions, and indicators (Table 1), a survey was developed with the purpose of being applied to teachers who work in an HEPI in Mexico. The objective of this research is to create a Structural Model with the latent variables of salary, promotions, leadership, motivation, and gender equity of an HEPI, using the PLS-SEM technique. The hypothesis is, the organizational climate of HEPI depends on salary, the granting of promotions to teachers, decisions based on gender equity, labor motivation, and leadership. The HEPI that served as a case study develops teaching activities at a higher level since 1965. The courses it offers are: ten Engineering, two bachelor’s degrees, five master’s degrees and one Doctorate. The object of study is composed of 335 teachers of the Institution, of which 65 have Doctorate, 101 masters, 19 Specialization, and 150 bachelor. For the

J. Bonales-Valencia (✉) Michoacan University of Saint Nicolas of Hidalgo, Morelia, Michoacán, México e-mail: [email protected] © Springer Nature Switzerland AG 2023 L. Radomir et al. (eds.), State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM), Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-031-34589-0_31

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Table 1 Operationalization of latent variable Latent variable Salary

Indicator Complexity Performance Economic characteristics Labor market Government provisions Satisfactory

Promotions

Employee’s merit Antiquity Time Knowledge Experience Skills Promotions’ information

Gender equality

Inequality Discrimination

Motivation

Leadership

Power Achievement Membership Compensations Promotions Incentives Task execution Team activities Worker’s participation Functions’ delegation Decision making Employees’ orientation Tasks’ orientation Goals

Key SC1 SP2 SP5 SE7 SL3 SL4 SG9 SS6 SS8 PM11 PM13 PA10 PT14 PT15 PK17 PE18 PS19 PP12 PP16 GI30 GI34 GD31 GD33 GD32 MP35 MA36 MA37 MM38 MC39 MT40 MI41 LT42 LT43 LA47 LW44 LF45 LD46 LE48 LO49 LO50 LG51 (continued)

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Table 1 (continued) Latent variable Organizational climate

Indicator Rules Common interest Goals Physical proximity Role Integration Cultural similarity Harmony

Key CR21 CC25 CG24 CP27 CL22 CL23 CI19 C120 CS28 CH26 CH29

diagnosis, a survey was constructed with 51 items under the Likert type format. According to the sample size of 335 teachers, with a maximum error of 5% and a reliability of 95%, the consultation (Mitofsky 2009) was used, giving a result of 93 teachers to survey.

2 Methodology 2.1

Organizational Climate

The organizational climate can be described as “the subjective effects received from the system that form the informal style of the administrators and other important environmental factors about the activities, beliefs, values and motivation of the people who work in a given organization.” Based on the above, the organizational climate is composed of several components and this multidimensional nature is important to create a questionnaire and proceed to the evaluation in an HEPI (Table 1).

2.2

Partial Least Square

PLS-SEM is a method that bears some relation to principal components regression; instead of finding hyperplanes of maximum variance between the response and independent variables, it finds a linear regression model by projecting the predicted variables and the observable variables to a new space. Because both the X and Y data are projected to new spaces, the PLS-SEM family of methods are known as bilinear factor models.

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PLS-SEM is used to find the fundamental relations between two matrices (X and Y), a latent variable approach to modeling the covariance structures in these two spaces. The basic PLS-SEM algorithm follows a two-stage approach. In the first stage, the latent constructs’ scores are iteratively estimated via a four-step process (Roldán and Sánchez-Franco 2012). The second stage calculates the final estimates of coefficients (outer weights, loadings, and path coefficients) using the ordinary least squares method for each partial regression in the model. Applying both processes, the PLS-SEM algorithm aims to minimize the residual variances of dependent variables. Accordingly, PLS-SEM rests on a main assumption, that is, the predictor specification, which forms the basis for PLS-SEM modeling (Hair et al. 2011). Hence, the cause-and-effect directions between all the variables need to be specifically defined. Besides, PLS-SEM follows a segmentation process, so, its estimates for a particular construct are limited to the immediate blocks to which it is structurally tied.

2.2.1

Model of Structural Equations

The model of structural equations with PLS-SEM is a statistical technique that allows the calculation of simultaneous estimation of a set of equations in which the concepts (measurement model) and the relationships between them are measured (structural model) and has the capacity to deal with concepts that are not directly observable (Cepeda-Carrión et al. 2022). Based on Barclay et al. (1995), PLS-SEM is an iterative combination of major component analysis linking measurements to constructs and path analysis that allows the development of a construction system. The relationships between indicators and constructs are guided by the theory, and the parameter estimation of relationships between constructs is performed with Ordinary Least Squares (OLS). So PLS-SEM seeks the prediction of dependent variables, both latent and manifest, so it seeks to maximize the explained variance R2 of dependent variables. Unlike traditional methods based on covariances (MBC), PLS-SEM is better suited in predictive applications and theory development or exploratory analysis; however, it can also be used for theory confirmation (Lamberti et al. 2017). Chin et al. (2003) mention that PLS-SEM is a powerful method of analysis because its requirements for the measurement of variables, sample size, and distributions are minimal. PLS-SEM avoids two serious problems: improper or inadmissible solutions and indeterminate factors. PLS-SEM offers a significant advantage over covariance-based (MBC) models, PLS-SEM models allow operating with both reflective (e.g., personality traits, attitudes) and formative indicators (measures that give rise to a latent theoretical construct, for example, the social status construct that includes the indicators occupation, income, place of residence), while the MBC model only operates with reflective indicators (Cepeda-Carrión and Roldán 2004).

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As for the relationships presented in the variables, they can be recursive, when they only go in a single direction, or not recursive, when they are bidirectional. MBCs accept both relationships, while PLS-SEM only accepts one-way relationships. Finally, Falk and Miller (1992) mention that MBC seeks to find structural or functional parameters that explain how the world works, they seek a causality statement, a description of causal mechanisms (rigid modeling). The main problem with this technique is restrictive assumptions, data distribution, measurement levels and large sample size.

3 Results and Discussion The Likert scale was used for processing data; this scale is a very useful tool as it is designed to measure attitudes. From the arithmetical point of view, it is a summation scale and the score or measure of each person in the attitude in question is given by the sum of their responses to questions that were implemented. For this study, we work with 29 items to apply on 93 HEPI professors, which were reviewed in detail and were developed according to each study variable. It can obtain in different kind of score for each variable between the maximum and minimum values as shown in the Table 1. After the estimation of the model, SmartPLS opens the results report per default. The results presentations in the Modeling window give us a first overview of the outcomes. As shown in Fig. 1, we see the standardized outer weights for the formative measurement models. We see the path coefficients as well as the R2 values of the endogenous constructs (shown in the circles) and the discriminant validity. Then each of the measurements that are presented in the construction of this model will be explained. Table 2 shows the Coefficient of Determination (R2). The most used to evaluate the structural model. This coefficient is a measure of the model’s predictive power and is calculated as the squared correlations between a specific endogenous construct’s actual and predicted values. The coefficient represents the exogenous latent variables’ combined effects on the endogenous latent variable. The Leadership (0.387) and Motivation (0.228) can be considered moderate, whereas the R2 value of Climate (0.554) is rather strong. In addition, we can observe the same manner with the R2Adj. Table 3 shows the results report for the path coefficients in matrix format. The table reads from the row to the column. The value of 0.622 is among the latent variables Motivation and Leadership. Climate column is the standardized path coefficient of the relationship from the Genders. Moreover, the three constructs show the internal consistency reliability of each construct (Table 4). Table 5 shows that the highest correlations are those between organisational climate and a) Gender (0.703), b) Promotions (0.569), and c) Leadership (0.554) (see Henseler et al. 2015).

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Fig. 1 Structural model of the latent variables PLS-SEM algorithm Table 2 Coefficient of determination (R2) Variable Climate Leadership Motivation

R2 0.554 0.387 0.228

R2adj 0.544 0.381 0.210

Table 3 Path coefficients Variable I. Climate II. Gender III. Leadership IV. Motivation V. Promotions VI. Salary

I

III

0.569 0.277 0.622

4 Conclusions and Implications for Theory and Practice Finally, some important conclusions can be drawn on the quality variables of the exporting companies using the Partial Least Square model.

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Table 4 Construct reliability and validity Variable Climate Gender Leadership Motivation Promotions Salary

Cronbach’s alpha 0.920 0.541 0.831 0.607 0.705 0.772

rho_A 0.923 0.618 0.858 0.792 0.860 0.795

Composite reliability 0.936 0.739 0.878 0.748 0.812 0.813

Average variance extracted (AVE) 0.677 0.497 0.552 0.439 0.457 0.360

Table 5 Latent variable correlations Variables I. Climate II. Gender III. Leadership IV. Motivation V. Promotions VI. Salary

I

II

III

IV

V

0.703 0.554 0.419 0.569 0.370

0.486 0.454 0.497 0.316

0.622 0.547 0.335

0.441 0.386

0.523

The teachers of the HEPI are interested in the fact that this type of organizational climate measurements is carried out mainly with the purpose of having some situations corrected by the managers of the same. We studied what related to the Theory of Organizational Climate, Human Relations and Systems Theory, as well as the theoretical part of each of the six latent variables, from which their dimensions and indicators were extracted to carry out the field study. Therefore, the result obtained from the general hypothesis is validated in its entirety. The Leadership (0.387) and Motivation (0.228) can be considered moderate, whereas the R2 value of Climate (0.554) is rather strong. The field work responded to the general objective that was proposed in the sense of providing concrete results endorsed with opinions and points of view of the teachers who collaborated in this investigation. Although the salary does not have a strong impact on the organizational climate, there are disagreements among teachers due to the delay in the payment of benefits. After the performance evaluation, teachers would be motivated if the institution grants them financial compensation for their work, to reward those who achieve good results. The correlation of latently variable variables, which had the most impact was Gender with 0.703, Promotions with 0.569 and Leadership with 0.554. Integrating a work team that is responsible for detecting the problem of Organizational Climate existing in the HEPI.

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References Barclay D, Higgins C, Thompson R (1995) The partial least squares (PLS-SEM) approach to casual modeling: personal computer adoption and use as an Illustration. Technol Stud 2:285–309 Cepeda-Carrión G, Roldán JL (2004) Aplicando en la práctica la técnica PLS-SEM en la administración de empresas. In: Conocimiento y Competitividad, XIV Congreso Nacional ACEDE, Murcia. https://idus.us.es/handle/11441/76333. Accessed 13 Mar 2022 Cepeda-Carrión G, Hair JF, Ringle CM, Roldán JL, García-Fernández J (2022) Guest editorial: Sports management research using partial least squares structural equation modeling (PLS-SEM). Int J Sports Market Sponsorship 23(2):229–240 Chin WW, Marcolin BL, Newsted PR (2003) A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Inf Syst Res 14(2):189–217 Falk RF, Miller NB (1992) A primer for soft modeling. University of Akron Press, Akron Hair JF, Ringle CM, Sarstedt M (2011) PLS-SEM: Indeed, a silver bullet. J Market Theory Pract 19(2):139–152 Henseler J, Ringle CM, Sarstedt M (2015) A new criterion for assessing discriminant validity in variance-based structural equation modeling. J Acad Market Sci 43(1):115–135 Lamberti G, Banet Aluja T, Sanchez G (2017) The Pathmox approach for PLS-SEM path modeling: Discovering which constructs differentiate segments. Appl Stochastic Models Bus Ind 33(6): 674–689 Mitofsky WJ (2009) http://72.52.156.225/Tamaño-muestra.aspx. Accessed 23 May 2010 Roldán JL, Sánchez-Franco MJ (2012) Variance-based structural equation modeling: Guidelines for using partial least squares in information systems research. In: Mora M, Gelman O, Steenkamp A, Raisinghan M (eds) Research methodologies, innovations and philosophies in software systems engineering and information systems. IGI Global, Hershey, PA, pp 193–221

An Investigation of Predictive Relationships Between University Students’ Online Learning Power and Learning Outcomes in a Blended Course Yue Zhu, Ming Hua Li, Lu Li, Rong Wei Huang, and Jia Hua Zhang

1 Introduction With the advance of the Internet technologies, people are provided with a flexible access to education through an online or blended learning platform. However, they are facing challenges to successfully accomplish their learning goals, as learning in such an environment requires them to be well-scheduled and remain active, such as making the first move to learning, determining their needs, expressing their learning goals clearly, managing resources and time for learning, applying proper learning strategies, and achieving satisfying learning outcomes (Gedik et al. 2012). In this sense, to be an effective learner, knowing how to learn would be critical for people’s success in a complex and challenging online or blended learning environment. The concept of learning power was developed in the late 1990s and the assessment tool (i.e., the Effective Lifelong Learning Inventory ELLI) has tested the constructs in relation to motivated and effective learners’ dispositions, attitudes, and values (Deakin Crick et al. 2015). Recently, the ELLI has been applied to assess university students’ learning capability in China (Li et al. 2018; Ren 2012; Ren and

Y. Zhu (✉) Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, Zhejiang, China M. H. Li · J. H. Zhang Zhejiang Normal University, Jinhua, Zhejiang, China e-mail: [email protected] L. Li Yantai Zhifu Wanhua Primary School, Zhifu District, Yantai, Shandong, China R. W. Huang Linyi Hedong District Tangtou Neighborhood Gegou Central Primary School, Hedong District, Linyi, Shandong Province, China © Springer Nature Switzerland AG 2023 L. Radomir et al. (eds.), State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM), Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-031-34589-0_32

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Small 2013). Ren and Small (2013) interpreted the ELLI and provided suggestions for teachers’ pedagogical design in online courses. Based on the ELLI, Li et al. (2018) developed a set of instruments measuring Chinese university students’ online learning power and approved the validity and reliability of these instruments. However, few studies have been conducted to test how the factors of online learning power are related to each other and how these factors will affect university students’ course outcomes in a blended learning environment. Therefore, we further tested the validity and reliability of the questionnaire instruments developed by Li et al. (2018) among the population of Chinese university students. Moreover, we examined the inner relationships between the factors of online learning power and the predictive relationships between online learning power and university students’ course outcomes.

2 Background In the era of information and the Internet technologies, we have witnessed a dramatic shift in the way that people learn. The collection, storage, and manipulation of data or information can be digitized and virtually automated by emerging technologies and finally be instantly and widely available through networked access for all learners (Deakin Crick 2007). People can receive education from various online learning platforms, such as Moodle, edX, and Sakai, because the Internet technologies have broken down physical and geographical barriers set by traditional face-toface learning mode. Educational sectors at different levels (e.g., universities, vocational educational organizations, and private training sectors) are offering courses in a blended or online learning environment. As a result, people can undertake education anywhere and at any time to suit their needs, which provides a chance for their lifelong learning for the purpose of self-improvement or professional development. In the context of lifelong learning supported by a flexible access to online or blended courses, it is critical for learners to “know-how” and “know-why,” rather than “know-what” (Deakin Crick 2007). Therefore, a person’s capability of selfawareness and taking responsibility as a learner is a key to success in such a challenging learning environment. Learning power is regarded as peoples’ need to want to learn, awareness of themselves as learners, capacity of being responsible for their own learning trajectories, and maintaining their cognitive and affective selfregulation of learning action, whether in or out of school and over a lifespan (Deakin Crick 2007; Hautamäki et al. 2002). Claxton proposed the ELLI and considered learning as a consequence of the interaction between learning willingness (attitude, value, emotion, intention, and motivation) and learning outcomes (acquired knowledge, skills, and understanding) (Deakin Crick et al. 2004). Learning power is explained as “a form of consciousness characterized by particular dispositions, values and attitudes, expressed through the story of our lives and through the relationships and connections we make with other people and our world” (Deakin Crick 2006, p. 5). In the ELLI project, seven

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dimensions of learning power were identified as changing and learning, critical curiosity, meaning making, creativity, learning relationships, and strategic awareness (Deakin Crick 2006). Alternatively, Kirby (2005) found that learning power contained learning motivation, learning attitude, learning ability, learning efficiency, creative thinking, and creative ability. Based on the concept of learning power theorized by Deakin Crick (2007), Claxton (2007), and Kirby (2005), Li et al. (2018) developed 56 items after two-round questionnaire surveys among 1080 and 1986 university students undertaking online courses in China. The result of factor analysis showed that there were five dimensions of online learning power—resilience, reflection, resourcefulness, relationships, and learning motivation. Li et al. (2018)’s research approved the validity and reliability of their instruments to investigate Chinese students’ learning power in an online learning environment. Some existing research explored how to assist students to build learning power. Stoten (2013) introduced Building Learning Power (BLP) as a new philosophy and framework for learning and teaching in a college. Stoten concluded that BLP challenged students and teachers’ beliefs about learning and there were mixed responses from teachers that BLP is yet to be fully embedded. Thus, changing entrenched learning behaviors seemed to take longer than initially expected. Based on the concept of the ELLI, Ren and Small (2013) provided suggestions for students to enhance their learning capacity and described a framework for curriculum and pedagogy reformation in schools of China. Deakin Crick (2007) emphasized a few pedagogical themes to promote the development of students’ learning power, including (a) teacher commitment to learner-centered values and willingness to make professional judgments, (b) positive interpersonal relationships, (c) developing a language of learning, (d) modeling and imitation, (e) learning dialogue, (f) reflection, (g) learner self-awareness and ownership, (h) students’ responsibility for making choices, (i) sequencing of learning materials, and (j) a toolkit of skills and strategies for learning how to learn. Although the concept and constructs of learning power in both face-to-face and online learning environments have been well interpreted in the reviewed research, it still remains unclear about how the factors of students’ online learning power will relate to each other. Moreover, most of the existing studies focused on the implementation of learning intervention or training sessions to build students’ learning power. Few studies have been done on students’ learning power in a blended learning environment and the relationships between their learning power and course outcomes in such a learning context. Thus, we explored the inner relationships between the factors of Chinese students’ online learning power and the effect of online learning power on their learning outcomes in a blended course.

3 Methodology 3.1

Research Design

We administered a questionnaire survey among 62 Chinese university students who were enrolled in a blended course. The questionnaire contained self-referring

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statements assessing the participants’ online learning power. Meanwhile, the information of the participants’ completion of online learning tasks every week was recorded as their online course engagement. The participants’ course results and marks for all learning tasks were obtained at the end of the course as their learning outcomes. We tested the construct validity of online learning power and explore the relationships between online learning power, online course engagement, and learning outcomes in a blended learning environment.

3.2

Participants

Sixty-two junior teacher education students, who were enrolled in an 18-week blended course offered by a Chinese university, participated in our questionnaire survey. The course aimed to provide the students with theoretical foundation and practice of using educational technologies in teaching. The students attended a two-hour face-to-face tutorial, including practice on Interactive White Board (IWB) every week in a computer lab. Additionally, all course materials and resources were provided online and the students were able to keep communication with their teachers and other students through email, online forum, and mobile at their convenience. In regard to course assessment tasks, the full mark for the course is 100. The students were required to complete (a) weekly online learning tasks from Week 1 to Week 17 (i.e., watching lecture video, participating in online discussion, taking online notes, and completing online questions), (b) PowerPoint and micro-lecture designing task, and (c) a test on IWB operation held in the computer lab. The results of these tasks were summed up as the students’ overall result in the course (Table 1).

Table 1 Detailed information of the course assessments Course assessments Overall online learning tasks

Online lecture video Online discussion Online notes Online questions Designing tasks PPT designing task Micro-lecture designing task Test on IWB operation in a computer lab

Requirement Duration of 15.71 h Ten posts in total Ten notes in total Multiple-choice items From 10 to 15 slides Duration of 6–8 min Completing a required task on IWB

Full mark 25 5 5 15 15 15 20

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Instruments

The scales measuring the participants’ online learning power in our study were based on the tools developed by Li et al. (2018). In their research, the scales were approved to be suitable for the population of university students in China with tested validity and reliability (Cronbach’s alpha =0.99). These scales were self-referring statements, which assessed the participants’ individual differences in resilience, reflection, resourcefulness, relationships, and learning motivation, such as “I can follow my schedule or plan in an orderly way when I learn online,” “I will ask for help and learn from others regarding learning strategies or methods,” and “I can do my best in a team work.” For each item, the participants were asked to respond to the statement on a five-point Likert scale from “1 = not like me at all” to “5 = very much like me.” Concerning the participants’ online course engagement, the data of their completion of online learning tasks were downloaded at the end of each week. Students’ participation in online discussion and learning activities are commonly accepted as indicators of their online course engagement (Cheng et al. 2011; Hara et al. 2000; Picciano 2002). As explained in Table 1, the participants were required to complete four online learning tasks. Except the task of watching online lecture video that was due on the Sunday of Week 16, the due dates for all other online learning tasks were set on the Sunday of the 17th week. Thus, the participants needed to arrange their time to complete these tasks. To achieve a full score for each online learning task, the participants should watch all lecture videos, post at least ten messages to the online forum, submit ten online notes, and answer all online questions correctly. The learning management system (LMS) of the course automatically recorded the information about online learning tasks accomplished by the participants every week, including the number of hours of online video being watched, number of online forum messages and online notes submitted, and scores of online questions. Meanwhile, the LMS calculated and transformed the data of the completed online learning tasks into a total mark for every participant each week, which represented how much work the participant had completed online and how well he or she did the online learning tasks per week. The researchers downloaded these data by the end of each week from Week 1 to Week 17, except Week 9, Week 11, and Week 12, as there were public holidays in these 3 weeks. Furthermore, the researchers ranked all participants in order of the marks they received for their weekly online learning tasks. Therefore, the participants’ online course engagement per week was indexed as the following variables: • • • • •

Number of hours spent watching online lecture video per week Number of posts sent to the online forum per week Number of online notes submitted per week Marks received for all online learning tasks accomplished per week Ranking for the participants according to the marks they achieved per week

As indicated in Table 1, the participants’ overall course results at the end of the course were calculated based on the marks they achieved for all online learning

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tasks, PPT and micro-lecture designing tasks, and test on IWB operation. Therefore, the participants’ overall course results and marks for each course assessment task were indexed as their learning outcomes.

3.4

Procedure

After obtaining the approval from the course coordinator and the Ethics Committee of the university, the participants were informed of the aims of the study, the information to be gauged in the questionnaire survey, and how the data would be collected, stored, and accessed. Finally, the participants were assured that their personal information would not be identifiable in any form of the report. The participants were asked to complete a questionnaire survey about their capability of online learning at the beginning of the course. During the course, the researchers accessed the online learning platform and downloaded the data of completed online learning tasks every Sunday from Week 1 to Week 17. The participants’ marks and rankings of weekly online learning tasks from Week 1 to Week 17 were regarded as the variables of their online course engagement. By the end of the course, the participants’ overall course results and marks for each online task were obtained and indexed as their learning outcomes in the blended course.

3.5

Data Analysis

The analysis on the data from the questionnaire survey, weekly online learning tasks that were completed, and learning outcomes were performed on the Statistical Package for Social Science (SPSS) and SmartPLS. First of all, in order to ensure the construct validity and reliability, factor analysis was conducted on the 62 participants’ responses to all items of online learning power scales. Pearson correlation was used to analyze the significance of correlations between online learning power, completion of weekly online learning tasks, and learning outcomes. The predictive relationships between the above-mentioned factors were investigated through using the partial least squares approach (PLS) for path modeling (Ringle et al. 2005). Structural Equation Modeling through Partial Least Square (PLS-SEM) enabled the researchers to trace variance through a series of regression analyses which balanced an outer model (the items which defined the constructs) with an inner model (relationships articulated between the constructs). The outer model employed traditional factor analysis procedures and variance extraction methods to ensure integrity of the constructs and also computed factor scores that were used to calculate the inner model expressed in terms of regression effects. PLS is capable of making it more appropriate than other techniques such as multiple regression and LISREL, in the case that the sample size is small (e.g., N = 40) (Goodhue et al. 2006).

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4 Results 4.1

Factor Analysis

The participants’ responses to the 59 items measuring their online learning power were subjected to factor analysis. Generally, the items with strong factor loadings higher than 0.5 were used for analysis purposes. As shown in Table 2, a four-factor solution was identified and these factors were labeled as: (a) goal orientation, (b) resilience, (c) problem solving, and (d) metacognitive awareness (KMO = 0.76).

4.2

Descriptive Results

Among the 62 participants, there were 14 male and 48 female students. Regarding online learning power, the percentage of participants, who were above the natural mid-point of goal orientation, problem solving, resilience, and metacognitive awareness, was, respectively, 95%, 76%, 92%, 57%, and 94%. At the end of the course, 58 participants have gained the marks between 90 and 97 for their overall course results and the results for the other participants were ranged from 73 to 86. Table 3 presents the descriptive results for the factors of online learning power and learning outcomes. Regarding the participants’ weekly online course engagement from Week 1 to Week 17, Fig. 1 reveals that the participants remained active in video watching most of the time during the semester, except in the eighth week and at the beginning and end of the course. In regards to the task of online questions, most participants started to work on the questions from Week 13 and answered most questions in Week 15. Similarly, the participants submitted online notes mainly in Week 14 and Week 15. Concerning online forum participation, it appeared that participants were more involved in responding to others’ posts, rather than sending original posts to the online forum. Generally, except the task of watching online lecture video, the participants did not show strong initiation at the beginning of the course, most of the tasks of online forum, online notes, online questions were completed later in the semester.

4.3

Correlations Between Online Learning Power Factors, Online Course Engagement, and Learning Outcomes

It was found that the participants’ overall course results were significantly correlated with most of the marks and rankings of weekly online learning tasks from Week 5 to Week 17. The marks and rankings of weekly online learning tasks completed by the participants later in the semester (e.g., from Week 10 to Week 16) were significantly

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Table 2 Factor analysis: loadings for the items measuring online learning power Loading Factor 1: Goal orientation (AVE = 0.57, Cronbach’s alpha = 0.74) I believe that undertaking online learning would improve my professional skills and benefit my professional development I believe I can expand my horizon and further develop my ability I am more interested in development of my social ability, especially getting to know more colleagues and friends I need to obtain a higher educational qualification to find a better job Factor 2: Resilience (AVE = 0.51, Cronbach’s alpha = 0.88) I am willing to continue extending my learning while learning online. I feel really good when I am absorbed in online learning While learning online, I am able to concentrate on my learning tasks with no distractions I deeply understand learning online is challenging and demands continuous effort I will make a plan when I learn online I will schedule my online learning in a good order I will predict my achievement in online learning I am flexible to make necessary adjustment during online learning As an online learner, I know my strengths and weaknesses Factor 3: Problem solving (AVE = 0.66, Cronbach’s alpha = 0.83) I am determined to find solutions for any problems emerging in online learning, no matter how difficult it would be I will predict what will happen and try to figure out a way to solve any problems which might happen during online learning I will consider whether there is any concept departing from my learning goals in an online course When encountering any problem, I will be aware of details and find out solutions Factor 4: Metacognitive awareness (AVE = 0.54, Cronbach’s alpha = 0.88) I am aware of how the course is structured and presented and what the course consists of I am happy to raise questions in online forum when there is anything I do not understand I like to ask: what and why it would be, if. . .. . . I always see the essence through the surface and draw my own conclusion I am able to build up a linkage between the known and unknown and acquired knowledge and practice I like to figure out how knowledge and course system is structured I like to think about in which way I can learn better online I am able to reflect on my online learning process and figure out how I can learn better I can use imagination and multiple senses to facilitate my learning online Except online course materials, I can use other online resources

0.76 0.78 0.44 0.39 0.73 0.76 0.68 0.66 0.63 0.79 0.83 0.71 0.49 0.49 0.42 0.55 0.53 0.79 0.43 0.50 0.67 0.42 0.92 0.42 0.57 0.59 0.44

Notes: (a) N = 62, (b) extraction method: principal component analysis, oblimin rotation

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Table 3 Descriptive information: online learning power factors and learning outcomes Goal orientation Metacognitive awareness Resilience Problem solving Total score of online learning tasks Marks of PPT designing task Marks of micro-lecture designing task Marks of IWB operation Overall course result

Mean 3.94 3.56 3.63 3.47 97.72

Median 4.00 3.70 3.83 3.50 99.10

SD 0.55 0.61 0.59 0.69 4.08

Skewness –0.45 –1.48 –1.02 –1.27 –3.18

Kurtosis 1.80 3.80 2.30 2.50 10.62

Min 2 1.30 1.56 1.00 0

Max 5.00 5.00 4.78 5.00 92

82.00

82.50

11.18

–6.66

49.26

0

92

83.29

83.00

5.32

–3.94

24.97

50

90

97.10 93.13

100.00 94.00

5.77 3.70

–2.62 –3.38

8.08 14.84

70 73

100 97

Notes: N = 62

related to their overall marks of online learning tasks and designing tasks at the end of the course. The factors of problem solving and metacognitive awareness were found to be related to the overall marks of online learning tasks by the end of the course (Table 4). Moreover, a coherent construct was identified based on the following indices: the rankings of weekly online learning tasks in Week 4 and Week 5; and the marks and rankings of weekly online learning tasks in Week 6, Week 7, Week 8, Week 10, Week 13, Week 14, Week 15, and Week 16 (see Appendix). All these variables were added in the construct of the SmartPLS model one by one progressively to ensure the validity of the construct for each stage could be maintained (Ringle et al. 2005; Esposito Vinzi et al. 2010). According to the result of the construct reliability and validity, the variables of rankings of online learning tasks in eight weeks (4, 5, 6, 7, 10, 14,15, and 16) contributed to the construct of online course engagement (AVE = 0.50, Cronbach’s alpha = 0.88). Thus, the participants’ online course engagement could be indexed by these factors. Pearson correlation test shows that the factors of online power, online course engagement, results of online tasks, PPT and micro-lecture design, and overall course results were significantly related to each other (Table 4).

4.4

Path Modeling of the Impact of Online Learning Power on Learning Outcomes

The impact of the participants’ online learning power factors on their learning outcomes at the end of the course was further investigated by the PLS-SEM approach for path modeling (Ringle et al. 2005). The researchers assumed that the participants’ online learning power might not only have an impact on how they were engaged in online learning, but also influence their learning outcomes by the end of

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400 2.50 2.00 1.50 1.00 .50 .00

W1 W2 W3 W4 W5 W6 W7 W8 W10 W13 W14 W15 W16 Mean of number of hours spent watching online lectur video from Week 1 to Week 17, n = 62 3 2 1 0 W1

W2 W3 W4 W5 W6 W7 W8 W10 W13 W14 W15 W16 Mean of number of online quesions completed from Week 1 to Week 17, n = 62

W17

W1

W2

W3 W4 W5 W6 W7 W8 W10 W13 W14 W15 W16 Mean of number of online notes submitted from Week 1 to Week 17, n = 62

W17

W1

W2 W3 W4 W5 W6 W7 W8 W10 W13 W14 W15 W16 W17 Mean of number of posts submitted to online forum from Week 1 to Week 17, n = 62

W1

W2 W3 W4 W5 W6 W7 W8 W10 W13 W14 W15 W16 Mean of number of reponses to peers posts submitted in online forum, n = 62

W17

W1

W8 W10 W13 W14 W15 W16 W2 W3 W4 W5 W6 W7 Mean of weekly marks of online learning tasks from Week 1 to Week 17, n = 62

W17

3 2 1 0

2

1

0

3 2 1 0

15 10 5 0

Fig. 1 Mean of weekly online learning tasks completed from Week 1 to Week 17

Notes: (a) N = 62, (b) *p < 0.05, **p < 0.01

1. Goal orientation 2. Resilience 3. Metacognitive awareness 4. Problem solving 5. Online course engagement 6. Overall marks for online learning tasks 7. Marks for PPT designing task 8. Marks for micro-lecture designing task 9. Marks for IWB application 10. Overall course results

1 –

2 0.49** –

3 0.31* 0.79** –

4 0.49** 0.79** 0.65** –

5 –0.19 –0.12 –0.02 –0.04 –

6 0.28* 0.45** 0.44** 0.69** 0.26* – 0.00 –0.06 –0.08 –0.08 0.26* 0.03 –

7

8 –0.03 –0.12 –0.10 –0.05 0.28* 0.07 0.12 –

Table 4 Correlations between online learning power factors, online course engagement, and marks of course assessment tasks 9 –0.09 0.11 0.06 0.04 0.27* 0.06 0.64** 0.07 –

10 0.01 0.13 0.14 0.19 0.47** 0.53** 0.71** 0.35** 0.71** –

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Fig. 2 The initial model of the impact of online learning power on the participants’ learning outcomes

the course. The initial model tested is shown as Fig. 2, which presents the initially expected relationships between online learning power, online course engagement, and learning outcomes. This form of descriptive modeling operates on an exploratory basis, as it is distinct from other forms of structural equation modeling which were based on confirmatory principles rather than regression. As a result, rather than testing a prior theory, one advantage of the PLS approach is that it allows sequences to be depicted in terms of descriptive relationships, which presents variance analyses in terms of both direct and indirect effects (Zhu et al. 2016). The procedures were reviewed in depth in the handbook by Esposito Vinzi et al. (2010). The researchers employed a formative measurement model to further test (a) any inner-relationships between the factors within the construct of online learning power; and (b) the relationships between the factors of online learning power and the participants’ learning outcomes by the end of the course. Bootstrapping in PLS-SEM is a nonparametric procedure and less subject to violation of normality assumption. This enables the researchers to test the statistical significance of various PLS-SEM results, such as path coefficients (Davison and Hinkley 1997; Efron and Tibshirani 1994; Unal and Uzun 2021). Any non-significant relationships were trimmed off to produce a parsimonious and descriptive model. As shown in Fig. 3, problem-solving and online course engagement directly predicted the participants’ overall marks of online learning tasks, which in turn affected their overall course results. As a driving force, goal orientation predicted the participants’ overall marks of online learning tasks through the mediation by resilience and problem solving. The factors of overall marks of online learning tasks and Micro-lecture designing task could predict 38% of the variance in overall course results. Goal orientation and resilience, respectively, explained 65% and 68% of the variance in metacognitive awareness and problem solving. There was no mediation by online course engagement on the relationships between the online learning power

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Fig. 3 Final model of the impact of online learning power on the participants’ learning outcomes

factors, overall marks of online learning tasks, and marks of Micro-lecture designing task.

5 Discussion We employed Li et al.’s (2018) scales to assess university students’ online learning power and a five-factor solution was identified as goal orientation, resilience, problem solving, metacognitive awareness, and resource management. These factors well contribute to the concept of online learning power at a significant level. This finding adds to the construct validity of the scales of online learning power by Li et al. (2018). We also found that, within the construct of online learning power, goal orientation in online learning performed as a driving force to predict the participants’ resilience that impacted their metacognitive awareness and problem solving. It is also worthwhile to note that the predictive influence of online learning power on the participants’ overall course results was mediated through their overall marks of online learning tasks. This finding highlights the critical role played by online learning power in students’ academic achievement in a blended learning environment. The students with higher level of self-regulating their online learning are more likely to receive better learning outcomes (Saba 2012; Sharma et al. 2007). Furthermore, keeping up with online course schedule and completing online learning tasks in a timely manner can be critical for students’ success in a blended course. The results showed that (a) the participants’ overall marks received for online learning tasks and Micro-lecture designing task were found to be determinants for their overall course results; and (b) the participants with more active online course engagement tended to achieve better course results by the end of the course. Thus, it would be essential for online learners to well schedule their online learning activities, stick to their learning plans, and remain motivated and active to ensure their high level of online course input. This finding is consistent with the research result by

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Table 5 Number of the participants working on the task of watching online lecture video Number of the participants watching online video

W1 4

W2 28

W3 43

W4 48

W5 52

W6 59

W7 59

W8 59

W9 60

W10 60

W13 62

Notes: N = 62, W = Week

Davies and Graff (2005) and Chen and Jang (2010), who found that the students’ online course engagement, such as frequent accessing online course content, was very important for their success in online learning. Furthermore, we did not identify the significant mediated relationship between the factors of online learning power, online course engagement, and learning outcomes. Instead, we found a direct prediction from problem solving and online course engagement on the students’ overall marks of their online learning tasks. This finding is different from previous research by Zhu et al. (2016), who reported that the impact of university students’ learning goal orientation, self-management and application of metacognitive strategies on their learning outcomes in a blended course was mediated through online course participation indexed by online forum contribution, weekly learning reports, and hours spent in online learning. The potential reasons for the above non-significant mediated relationships in our study maybe due to the abnormality of the data about the participants’ weekly online learning tasks. The descriptive results indicated that the participants did not remain active in the online forum participation throughout the semester. The majority of them started to work on online forum tasks from Week 13 and most of them responded to others’ opinions instead of sending original posts to the course forum. Regarding the task of watching online lecture video, 34 and 19 participants did not begin to watch the video, respectively, in Week 2 and Week 3 (Table 5). It is assumed that the data collected from the course platform may represent certain aspects of the participants’ online course input. But due to the delayed involvement in online learning, such data may not indicate their online learning process in a comprehensive way. This implies that, in addition to delivery instruction, online course designers and teachers ought to apply different teaching strategies to motivate students to participate in online learning on their initiative and create a supportive and welcoming online learning community through the usage of online forum.

6 Conclusion and Implications Discussion In summary, our research approved the validity and reliability of online learning power scales developed by Li et al. (2018) and ensured the suitability of the scales among the population of university students in China. More importantly, our investigation depicted how the factors of online learning power could affect the participants’ online learning outcomes. Although only problem solving was found to

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significantly predict the participants’ learning outcomes, such a predictive relationship provides implications for future research using more comprehensive data, that can well represent their online learning progress, and exploring effective teaching methods to improve students’ online learning power and leverage their motivation to participate in online learning activities. In terms of improving students’ online learning power, assessing their capacity of online learning is a good beginning. As students answer a range of questions about themselves (e.g., the assessment tools developed by Deakin Crick et al. 2004, and Li et al. 2018), their responses to these questions can provide information about how they perceive themselves in relation to each of the dimensions of online learning power. Then teachers can create students’ individual learning profile and class learning profile to gain a good understanding of students’ online learning capability, respectively, at an individual and group level (Deakin Crick et al. 2004). Furthermore, teachers can create a learner-centered learning context and design online learning activities to promote students to take responsibility of their own learning (Deakin Crick et al. 2004). Daily routines in online learning activities can be used to scaffold students’ ability to regulate their learning (Tarullo et al. 2009). Online course content should be well-structured to make it easy for students to prioritize and schedule their learning. Learning intervention and training on online learning capacity are necessary to improve students’ effective use of self-regulation and metacognitive and cognitive learning strategies (Barnard-Brak et al. 2010; Hu 2007; Rowe and Rafferty 2013; Tsai 2009). Teachers can also provide topics to prompt students to participate in online discussion. Additionally, peer-review or selfreflective questions may encourage students to review their learning process in a web-based note-taking tool. Self-evaluation or self-assessment and peer-review can improve students’ problem-solving skills by using critical reflective thinking skills, identifying issues, and seeking for solutions for the issues emerging during learning process (Bixler 2008; Chang 2007). Finally, teachers can facilitate enquiry-based learning or project-based learning and empower students with freedom and responsibility to personalize their online learning content and tools (McLoughlin and Lee 2010; Saba 2012). With self-control on their learning tasks and pace, students can be taught with various learning strategies, including researching, applying critical thinking, managing information and resources, problem solving, and keeping resilient (Zhu et al. 2016). Continuous and constructive feedbacks will benefit students to keep their online learning on the right track and be aware of the use of cognitive and metacognitive strategies. In summary, students’ online learning power is critical for their success in a blended learning environment. Such ability can be trained and improved, but teachers need to invest a great amount of effort to design and implement learning intervention (Stoten 2013). It would be critical for university educators to foster students’ ability to control their own learning and continue to improve themselves to be effective learners to derive the benefits provided by blended courses, but also have confidence to face and deal with challenges emerging during blended learning.

Appendix

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The Effects of Marketing Orientation on the Performance of Higher Learning Institutions in Tanzania: Staff and Students’ Perceptions Francis Muya and Hawa Tundui

1 Introduction Studies on market orientation in higher learning institutions (HLIs) have received an increased attention in the current decade (Anabila et al. 2019; Domański 2014; Chandler et al. 2021; Khuwaja 2016; Khuwaja et al. 2015; Mokoena 2018; Niculescu et al. 2013; Sefnedi 2017; Rivera-Camino and Molero Ayala 2010; Starck and Zadeh 2013; Tjahjadi et al. 2022). The attention is due to the challenges in the environment where HLIs operate (Akonkwa 2009; Beneke and Human 2010; Mok 2001; Mokoena 2018; Nicolescu 2009), and being marketing-oriented is seen as the best survival mechanism (Hilman and Kaliappen 2014; Moorman and Rust 1999; Rivera-Camino and Molero Ayala 2010; Tabaku and Mersini 2013). Marketing orientation offers a number of benefits such as a better understanding of customers and competitors (Dolnicar and Lazarevski 2009; Newman 2002), the possibility of increasing market share, profit and innovativeness and of becoming competitive both domestically and internationally (Hemsley-Brown and Oplatka 2006; Maringe 2004, 2005, 2006; Stachowski 2011; Tajeddini et al. 2006). Other possibilities include realizing institutional goals, such as surviving as an organization, increasing institutional professional reputation, improving facilities and faculty and increasing enrolment and endowment (Ivy 2001; Nguyen and LeBlanc 2001; Polat and Donmez 2010) and adapting to demands in the society (Caruana et al.

F. Muya (✉) Department of Business and Entrepreneurship Studies, National Institute of Transport, Dar es Salaam, Tanzania e-mail: [email protected] H. Tundui School of Business, Mzumbe University, Morogoro, Tanzania e-mail: [email protected] © Springer Nature Switzerland AG 2023 L. Radomir et al. (eds.), State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM), Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-031-34589-0_33

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1998). Furthermore, marketing orientation can increase accessibility to financial resources (Caruana et al. 1998; Narver and Slater 1990; Svenson and Wood 2007), students’ retention (Ackerman and Schibrowsky 2007; Lichiello 2014; Shepherd 2008) and loyalty among students in the HLIs, hence lowering the costs of attracting new ones (Rowley 2003). Despite all these marketing orientation–related benefits, there is however no well-established, collectively agreed and acknowledged links between marketing orientation and performance in the higher education context (Eikenberry and Kluver 2004; Dolnicar and Lazarevski 2009; Rotfeld 2008). Although studies on marketing orientation are many on industries such as manufacturing, pharmaceutical, retailing and tourism, studies on HLI marketing orientation in Tanzania are still very limited. In addition, the marketing orientation studies carried out on HLI in different parts of the world dwelled on universities (Anabila et al. 2019; Domański 2014; Khuwaja 2016; Khuwaja et al. 2015; Niculescu et al. 2013; Sefnedi 2017; Rivera-Camino and Molero Ayala 2010; Starck and Zadeh 2013), ignoring other HLIs such as technical institutions which also operate under the same environment. Furthermore, many studies concentrated on testing the direct links between marketing orientation and performance (Burgess and Nyajeka 2007; Niculescu et al. 2013; Zebal and Goodwin 2012), ignoring the contribution of indirect effects through mediating constructs in refining and understanding causal relationship (Wu and Zumbo 2008). The effects of marketing orientation as revealed in the past studies cannot be generalized across sectors and countries with different economic, political and cultural structures, hence inspiring studies on country specific and sectoral basis, especially in HLI contexts in Tanzania. This chapter, therefore, intends to fill the identified gaps by assessing the direct and indirect relationship between marketing orientation and performance in Tanzania HLIs, supporting the arguments that continual survival and success of HLI hinge upon being marketing-oriented.

2 Literature Review The foundation of this chapter is built on theories that view marketing as essential for optimal organizational performance, especially marketing mix theory (Al-Fattal 2010; Baker and Saren 2010; Borden 1984; Filip 2012; Ivy 2008; Goi 2009), which includes components of programme, place, promotion, price, process and physical facilities, and market orientation theory (Felcman 2012; Kiessling et al. 2015; Kohli and Jaworski 1990; Narver and Slater 1990; Niculescu et al. 2013), which includes components of administration, mentoring and intelligence generation. Wymer et al. (2006) viewed marketing orientation as an approach to managing an organization through marketing tactics to achieve the organization’s goals and objectives. According to Kurtinaitienė (2005), marketing orientation (sometimes called market orientation) contributes to the understanding and implementation of the marketing concept. These explanations relate to those provided by Perreault and McCarthy (2002, p. 35) who view marketing orientation as “trying to carry out the

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marketing concept instead of just trying to get customers to buy what the firm has produced.” The relationship between marketing orientation elements and performance-related aspects is diverse as hereunder explained.

2.1

Marketing Orientation and Retention

While exploring the application of marketing thinking to students’ retention, Shepherd (2008) maintained that marketing is essential for retaining students. This is possible through persuading students using a variety of means. The findings align with those in a study by Beck (2007) who found that market orientation positively affected retention at two selected public Carnegie High Research Activity Institutions. The findings imply that organizations committed to a market-oriented culture will likely produce better retention results than those who are not. In connection to the price element of marketing orientation, Polat and Donmez (2010) found that price-related activities are essential in a firm–customer relationship. Hanover Report (2014) found redesigning of scholarship and bursary as essential for students’ retention. In the same vein, Hasan et al. (2009) found that tuition fees and flexible tuition approaches are highly important students’ costsrelated aspects in higher education. Carabajal (2012) found that financial aid is a number one factor influencing students’ decision as to whether to enrol to a particular university or not. The findings are in line with the findings in a study by Ackerman and Schibrowsky (2007) who found that financial bonds, such as scholarships, work–study opportunities, transportation, on-campus housing, tuition discounting, financial aid package, subsidized student health insurance programmes and no-interest emergency loans, are essential for students’ retention. In connection to physical facilities as an element of marketing orientation, Mokaya (2013) revealed that well-spaced classrooms, adequate and ample spacing in the libraries and adequate science laboratories are among the key factors in academic achievement and retention. Similar results are reported by Lau (2003) who found that dormitories, study rooms, facilities for the disabled, career centres, social and professional organizations and computer technology are essential physical facilities in students’ retention in any education setting. In relation to the effects of the programme element of marketing orientation, Mokaya (2013) considered adequate participation in cocurricular activities as a very important aspect. These results are also supported by Ivy (2008) who viewed aspects such as the design of the degree, properly developed and adapted to meet students’ needs, curriculums and programme duration as important programme elements in HLI contexts. In addition, Rowley (2003) considered refocusing on the curriculum to make it more vocationally relevant as an important factor in students’ retention. Furthermore, Ackerman and Schibrowsky (2007) considered structural bonds such as policies that force students to finish their years at the colleges and placement services for alumni as important in students’ retention. Another important element of marketing orientation is process. According to Ivy (2008), the procedures at HLIs such as registration for the correct courses, correct

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calculation and entering of students’ marks or grades against their names, awarding of correct qualification, finance system, accommodation, timetabling, library and graduation are issues that must be in place throughout students’ life at the HLIs. All these are important in order to ensure the highest level of student satisfaction. On the effects of intelligence generation on reputation, Shapiro (1988) underscored the importance of cross-functional information dissemination (ID) and inter-functional coordination. The findings are in line with those of Zhang et al. (2017) who revealed that information dissemination (ID) was a very important driver of marketing orientation of Chinese firms. In another perspective, Ivy (2001) identified market analysis as an important factor in strengthening the positions of universities. In the same vein, Ramaseshan et al. (2002) also found that market information collection and dissemination are critical to the overall success of new products that have an implication on competitiveness and hence high reputation. These findings are supported by Caruana et al. (1998) who considered responsiveness as very critical in HLIs’ overall performance, just after information collection. Furthermore, Hamadu et al. (2011) found a significant impact of marketing intelligence dissemination on the performance of a business. Using the components stated above, Muya and Tundui (2020) studied the relationship between marketing orientation and students’ retention in nine Tanzania higher learning institutions. Marketing orientation was measured by 7 elements (programme, people, place, promotion, price, process, and physical evidences/facilities) with a total of 26 indicators measured at a 5-point Likert scale, retention measured using 8 indicators. Data were collected from 373 students and analysed using partial least squares structural equation modelling (PLS-SEM) technique with the assistance of SmartPLS Version 3.0. The study established the ability of marketing orientation to predict retention. A positive and significance relationship between process, programme, price, promotion and place, and students’ retention was established while insignificant relationship was found between people and physical evidence against retention. Reputation was confirmed as an important mediator in the research model. The study, though involved only students, shed important light on the applicability of marketing approach in an education environment.

2.2

Marketing Orientation and Competitiveness

Regarding the effects of marketing orientation on competitiveness, Hemsley-Brown and Oplatka (2006) view marketing as essential for having a competitive edge and gaining a larger share of the international and domestic markets. Their views are supported by Maringe (2005) who agreed that higher education (HE) is experiencing tougher competition in attracting students from rivals; thus, their managers and academics need to consider marketing as a viable philosophy and strategy for developing a HE sector that meets the needs of home-based and international customers. Dwyer and Kim (2003) identified a number of marketing-oriented

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components such as place (accessibility of destination), administration (skills of the employees) and intelligence generation (information and intelligence system) as essential for a firm’s competitiveness. Furthermore, on the place element of marketing orientation, Aydın (2013) concluded that the location of a HLI does influence students’ choice of a university. Location was therefore a source of sustainable competitive advantage for the studied universities. This finding is consistent with the finding in a study by Beneke and Human (2010) who identified geographical location as among the most important factors in a scholar’s decision of a place of further study. Huang (2012) found that the right location is essential for attracting more students and revenues contributing to HLI competitiveness. The findings are in line with the findings reported by Sezgin and Binath (2011) who found that location, the proximity of the campus to the city centre and exchange programmes are important factors in students’ decision of a college of study. In views of Pringle and Huisman (2011), an institution located along well-established public transit routes has a competitive over those located in poor transit links. In another perspective relating to the programme element of marketing orientation, Polat and Donmez (2010) found differentiation of products/ services from the products/services offered by competitors as important competitiveness driver. The findings corroborate to those of Dwyer and Mellor (1992) who found the importance of products developed types in firms’ competitiveness. In the same vein, Sezgin and Binath (2011) found that curricula with novelty and flexibility as well as the language of instruction are among the significant factors in a firm’s competitiveness. Maringe (2005) considered all elements of the marketing mix as important, suggesting that universities should try to blend all these elements into their marketing strategies in order to remain competitive in the marketplace. On the effects of marketing orientation element of intelligence generation on competitiveness, Ross et al. (2013) asserted that free flow of information between stakeholder groups is important so that everyone has knowledge that is consistent and accurate across departmental/institutional boundaries, hence improving their competitive advantage. Zebal and Goodwin (2012) also found that, for the private university sector, gathering information about the current university environment on issues such as changing regulations and an analysis of wider competitor pressures that could be emerging is more important than the analysis of the strengths and weaknesses of defined competitors. Dwyer and Kim (2003) found intelligence system as an important aspect of a firm’s competitiveness.

2.3

Marketing Orientation and Reputation

Being marketing-oriented is essential for firms’ reputation through different channels. In relation to the promotion element of marketing orientation, Walsh and Beatty (2007) established that persuasive communications can enhance a corporate reputation to the extent of customers engaging in more positive word of mouth. This is supported by Almquist (2007) who found that word of mouth is an important tool

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in customers and prospects perceptions of company’s values. In addition, LloydSmith and An (2019) found that advertising own firm’s corporate social responsibility (CSR) activities and industry-level CSR spillover positively contribute to the firm’s reputation. Han et al. (2019) found that social network services (SNS)’ sales promotion results in a significant increase in loyalty. Place is another important element of marketing orientation and can potentially contribute to institutions’ reputation. According to Bennett and Gabriel (2001), clear distribution policies and channel cooperation can lead to corporate reputation for convenient availability and positive channel. On the effects of pricing on institutions’ reputation, Campbell (1999) views objective pricing policies as a means of building corporate reputation for price fairness and value authentic. On the effects of intelligence generation on reputation, Ivy (2001) asserted that it is important for universities to conduct a market analysis to establish their market position and to present an institutional image effectively. In the same vein, Nguyen and LeBlanc (2001), based on theories developed by economists, claimed that a consensus is that reputation is the result of past actions of an organization. On the effects of administration and physical facilities as elements of marketing orientation, Nguyen and LeBlanc (2001) argued that faculty members and facilities are critical factors that helped to determine students’ perceptions of the image or reputation of a higher education institution. These findings are supported by Rajh and Došen (2009) who found a positive and strong effect of company employees on service brand image. Castro et al. (2005) stressed the importance of employee in the market orientation and performance of organizations. Elsewhere, Polat and Donmez (2010) argued that effective marketing promises several benefits including enhanced company image, improved customer loyalty and improved reputation.

2.4

Marketing Orientation and Quality Teaching

Marketing orientation is an important source of quality through a number of routes. Polat and Donmez (2010) argued that effective marketing is essential for improved total quality. Castro et al. (2005) emphasized the importance of coordinating the company’s internal activities with the aim of continuously creating value and quality for the customer. Ivy (2008) views aspects such as the design of the degree, which is properly developed and adapted to meet students’ needs, curriculums and programme duration as important programme elements in the HLI context. In another study, Luk (1997) found a positive relationship between marketing culture and service quality (SQ), a similar stance reached by Jaensson and Uiso (2015). Crosby (1996) views human resource such as tutors in HLI as the prime focus in service quality management and performance in service delivery. In not a quite different perspective, Voon (2008) established that service-driven market orientation (SERVMO) has a significantly strong and positive relationship with service quality, with employee orientation being the most dominant factor. The role of employees is also recognized by Ye and Liang (2010) and Chigozirim and Mazdarani (2008). A

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study by Matear et al. (2002) found that market orientation contributes to performance through a dual mechanism in that it contributes both directly and with innovation mediating the contribution. SeyedJavadin et al. (2012), while investigating the effects of internal marketing orientation (IMO) on service quality (SQ) and the mediating role of organizational citizenship behaviour (OCB), found that internal marketing activities are positively influencing OCB; OCB is having a meaningful relationship with SQ and OCBs and, as being influential, playing a crucial mediation role in the success of SQ and external marketing. The findings are supported by Edo et al. (2015) who verified the positive influence of IMO on employees’ attitudes (job satisfaction and trust) and, through them, on performance (service quality perceived by customers that affect their level of satisfaction). From the foregoing discussion, it can be hypothesized as follows: H1: There is a positive relationship between mentoring (MENT) and students’ retention (RET). H2: There is a positive relationship between physical facilities (PHYIC) and students’ retention (RET). H3: There is a positive relationship between price (PRIC) and students’ retention (RET). H4: There is a positive relationship between programme (PROG) and students’ retention (RET). H5: There is a positive relationship between process (PROC) and students’ retention (RET). H6: There is a positive relationship between place (PLAC) and competitiveness (COMP). H7: There is a positive relationship between programmes (PROG) and competitiveness (COMP). H8: There is a positive relationship between intelligence generation (INTEL) and competitiveness (COMP). H9: There is a positive relationship between administration (ADMIN) and reputation (REP). H10: There is a positive relationship between intelligence generation (INTEL) and reputation (REP). H11: There is a positive relationship between promotion (PROM) and reputation (REP). H12: There is a positive relationship between administration (ADMIN) and quality teaching (TEAQ). H13: There is a positive relationship between process (PROC) and quality teaching (TEAQ). H14: There is a positive relationship between reputation (REP) and competitiveness (COMP). H15: There is a positive relationship between quality teaching (TEAQ) and competitiveness (COMP). H16: There is a positive relationship between retention (RET) and competitiveness (COMP).

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3 Methodology 3.1

Procedure and Participants

The chapter adopted a quantitative cross-sectional survey whereby data were collected from nine HLIs in Tanzania mainland from July 2019 to January 2020. The sample size for the survey was 800 respondents who were randomly and conveniently selected. However, the responses were obtained from 739 respondents, which is equal to 92% responses rate. The chapter data were collected from staff (49.5%) and students (50.5%) unlike previous studies that used only a single group of respondents (Caruana et al. 1998—head of schools only; Rajh and Došen 2009—students only; Zhang et al. 2017—managers only), hence minimizing the danger of biased results. Regarding the gender of the respondents, the majority (53%) were males and the remaining respondents (46%) were females. Regarding their age, the majority (58.1%) were between 20 and 31 years, 19.8% were between 31 and 40 years, 11.9% were between 41 and 50 years, and 5.4% were over 60 years of age. Regarding their level of education, the majority (49.5%) had a bachelor’s degree, followed by 20.2% who had a PhD, 20.2% had a master’s degree, 6% had a postgraduate diploma, and finally a few (1.5%) had a diploma.

3.2

Variables and Measurements

Many previous scholars used the phrase market orientation, but their discussion focus was entirely on the implementation of marketing principles. Such scholars used either MKTOR (Narver and Slater 1990) or MARKOR (Kohli et al. 1993; Kohli and Jaworski 1990) scale to measure market orientation. This chapter adopted the phrase marketing orientation as used by McNeal and Lamb (1980), Gummesson (1990), Kurtinaitienė (2005), Šályová et al. (2015), Sharp (1991) and Domański (2014) to refer to the implementation of marketing principles. Marketing orientation is measured by using nine exogenous constructs: six constructs (programme [PROG], place [PLAC], promotion [PROM], price [PRIC], process [PROC] and physical facilities [PHYIC]) are from the Education Marketing Mix (EMM) (Ivy 2008) and three constructs (administration [ADMIN], mentoring [MENT] and intelligence generation [INTEL]) are from University MARKOR (UM) (Niculescu et al. 2013) measured at a 5-point Likert scale. The combination of the Education Marketing Mix and University MARKOR was important to offset weaknesses available in some measure of marketing orientation. According to Andreasen and Kotler (2014), using all elements of the “marketing mix,” and not just communication is one of the important characteristics in an organization that has fully adopted marketing orientation. The marketing mix framework, however, suffers from the weakness of having too much internal orientation. This weakness is against the proposition provided by Wymer et al. (2006) that an

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organization that has a marketing orientation is able to focus on its various activities and external communications to project a consistent image of itself and influence the way the external world perceives it. The components of the University MARKOR helped to compensate for the weaknesses on the marketing mix framework. It has to be noted that a combination of Education Marketing Mix (EMM) and University MARKOR (UM) gave rise to marketing orientation as the predictor construct. Performance as an outcome variable was measured using four endogenous constructs of retention (RET) (Lichiello 2014; Niculescu et al. 2013), reputation (REP) (Gajić 2012; Chun 2005; Conard and Conard 2000), competitiveness (COMP) (Štimac and Šimić 2012) and quality teaching (TEAQ) (Zebal and Goodwin 2012); all are measured at 5-point Likert scale.

3.3

Data Analysis and Results

The partial least squares structural equation modelling (PLS-SEM) using SmartPLS Version 3.0 was used to test the direct and indirect effects between marketing orientation and performance through two major stages of assessment of the measurement model followed by the assessment of the structural model. PLS-SEM was used in this chapter because the main intention was to assess the prediction power of the exogenous variables on endogenous variables and confirmation of marketing orientation theories (Hair et al. 2017). It was used also because of the complexity of the structural model that has many constructs and many indicators. PLS-SEM is also relevant for determining the direct and indirect effects of predictor variables on the outcome variable(s) (Hair et al. 2016). Hair et al. (2017) indicated that PLS-SEM is among the leading data analysis tools in marketing studies and leading journals. The existence of user-friendly software like SmartPLS Version 3.0 also supported the uses of PLS-SEM (Hair et al. 2011).

3.4

Assessment of Measurement Model

The assessment of the measurement model included examining the indicators loadings, assessing internal consistency reliability, assessing the convergent validity of each construct measure and finally assessing the discriminant validity (Hair et al. 2019). These results are presented in Fig. 1 and Table 1. Table 1 shows internal consistency and convergent validity results as among the key activities in the assessment of the measurement model. Due to its strength over Cronbach’s alpha, composite reliability (CR) was used to measure the internal consistency of items where all constructs of the research model were above 0.7 and less than 0.95 as recommended by Hair et al. (2019). These results, therefore, revealed the presence of internal consistency of the scales and therefore higher levels of reliability. The same results were also reported by Khuwaja (2016) who found a

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Fig. 1 Indicator’s loadings and coefficient of determination

Table 1 Constructs’ reliability and validity Constructs ADMINI COMP INTEL MENT PHYIC PLAC PRIC PROC PROG PROM REP RET TEAQ

Composite reliability (CR) 0.861 0.858 0.833 0.842 0.927 0.783 0.805 0.791 0.866 0.817 0.873 0.767 0.844

Average variance extracted (AVE) 0.556 0.548 0.504 0.517 0.647 0.555 0.581 0.560 0.522 0.529 0.579 0.523 0.579

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Table 2 Discriminant validity results MENT PHYIC PLAC PRIC PROC PROG PROM REP RET TEAQ

1 0.622 0.456 0.293 0.356 0.496 0.393 0.430 0.307 0.397 0.445

2

3

4

5

6

7

8

9

10

0.487 0.453 0.498 0.874 0.495 0.580 0.504 0.590 0.535

0.355 0.305 0.717 0.292 0.448 0.365 0.383 0.404

0.428 0.577 0.406 0.511 0.385 0.404 0.256

0.566 0.389 0.501 0.240 0.355 0.299

0.481 0.598 0.523 0.621 0.501

0.333 0.275 0.325 0.456

0.355 0.403 0.375

0.617 0.670

0.490

high level of internal consistency reliability for University MARKOR in the higher education context. Using average variance extracted (AVE), it was found that all constructs had a value of 0.50 and above which indicated that on average each construct in our model explains more than half of the variance of its indicators revealing the presence of convergent validity. Similar results are reported by Khuwaja (2016) who found a high level of convergent validity for University MARKOR. Figure 1 shows that indicator’s loadings for the majority of indicators were above 0.70 as recommended, which implies that each construct explains more than 50% of the indicator’s variance, thus providing acceptable item reliability (Hair et al. 2019). Discriminant validity that is the extent to which a construct is truly distinct from other constructs by empirical standards (Hair et al. 2019) was measured using the heterotrait–monotrait ratio (HTMT). The results are presented in Table 2. The results in Table 2 revealed the presence of discriminant validity as all values were less than 0.9 as recommended. The presence of discriminant validity was also observed in a study by Khuwaja (2016). Taking into account that the measurement model was satisfactory, the next step was the assessment of the structural model (Hair et al. 2019).

3.5

Assessment of Structural Model

Before assessing the structural relationships, collinearity must be examined to make sure that it is not biased on the regression results (Hair et al. 2019). When collinearity is not a problem, the assessment of the structural model follows, which include considering the coefficient of determination (R2), f 2 effect size, the blindfoldingbased cross-validated redundancy measure (predictive relevance as measured by Stone–Geisser’s Q2) as well as the statistical significance and relevance of the path coefficients (Hair et al. 2019). Collinearity using variance inflation factor (VIF) was examined after running the PLS algorithm, and the results are presented in Table 3.

420 Table 3 Collinearity assessment

Table 4 Coefficient of determination

F. Muya and H. Tundui Constructs ADMINI COMP INTEL MENT PHYIC PLAC PRIC PROC PROG PROM REP RET TEAQ

COMP

REP 1.383

1.267

1.383

RET

TEAQ 1.313

1.722 1.486 1.149 1.202 2.021 1.234

1.244

1.313

1.198 1.602 1.318 1.613

Endogenous constructs COMP REP RET TEAQ

R2 0.320 0.229 0.196 0.255

R2 adjusted 0.314 0.226 0.191 0.253

The VIF values revealed the presence of few indicators with collinearity and no constructs suffered from collinearity problem as their variance inflated factors (VIF) were lower than 3 as can be seen in Table 3. The other important activity in the assessment of the structural model was assessing the coefficient of determination (R2), for each endogenous variable as presented in Table 4. Table 4 shows the coefficient of determination (R2), for each endogenous variable implying the explanatory power of the exogenous variables on the endogenous variables. Regarding f 2 effect size, the results revealed that the majority of f 2 values (except REP on COMP = 0.181) were below 0.02, which implied small effects. On predictive relevance, as measured by Stone–Geisser’s Q2 that postulates that the model must be able to adequately predict each endogenous latent construct’s indicator (Hair et al. 2011), the values of Q2 for each model’s construct and indicators were above zero, thus providing support for the predictive relevance of marketing orientation model for the four endogenous constructs. Furthermore, statistical significance and relevance/size of the path coefficients were examined to assess the extent to which the data reflect the hypothesized relationships, and the results are presented in Table 5. Table 5 shows that 11 relationships ( p < 0.05) were significant, implying that the respective hypotheses are supported, and five relationships ( p > 0.05) were insignificant, resulting in the rejection of those hypotheses. Table 5 also shows the relative importance of the exogenous driver constructs in predicting the dependent constructs. In predicting the dependent construct COMP, place element of the marketing mix (PLAC = 0.115) is the most important among all constructs. In

The Effects of Marketing Orientation on the Performance of Higher. . . Table 5 Size of path coefficients and statistical significance

Paths REP → COMP ADMINI → REP ADMINI → TEAQ MENT → RET PROC → TEAQ PROC → RET PROM → REP PLAC → COMP TEAQ → COMP RET → COMP PHYIC → RET PRIC → RET PROG → RET INTEL → REP INTEL → COMP PROG → COMP

Path coefficients 0.444 0.386 0.364 0.231 0.214 0.161 0.144 0.115 0.090 0.087 0.080 0.054 0.040 0.038 0.007 -0.098

421 p-values 0.000 0.000 0.000 0.000 0.000 0.003 0.001 0.001 0.043 0.018 0.084 0.189 0.320 0.352 0.853 0.012

contrast, intelligence generation (INTEL = 0.007) has very little influence on COMP, and the programme (PROG = -0.098) has negative effects on COMP. On the other hand, mentoring (MENT = 0.231) is the most important among all constructs in predicting the endogenous construct RET, followed by process (PROC = 0.161). However, physical evidence (PHYIC = 0.080), price (PRIC = 0.054) and programme (PROG = 0.040) had very little influence on RET. The relative importance of the exogenous driver constructs in predicting the endogenous constructs reputation (REP) indicates that administration (ADMINI = 0.386) is the most important followed by promotion (PROM = 0.144). However, intelligence generation (INTEL = 0.038) had very little influence on reputation. The results indicate that administration (ADMINI = 0.364) is the most important factor in predicting the endogenous construct quality teaching, followed by process (PROC = 0.214). Finally, the results show that endogenous construct reputation (REP) is the primary driver of HLI competitiveness (REP = 0.444) and quality teaching (TEAQ = 0.090), but retention (RET = 0.087) had very little influence on COMP.

3.6

Mediation Effects

The relationship between marketing orientation and performance is sometimes channelled via a number of variables (Maydeu-Olivares and Lado 2003; Kamboj and Rahman 2017). This type of relationship was also assessed in this chapter especially on the roles of reputation (Arikan et al. 2016; Sridhar and Mehta 2018) and teaching quality (Osarenkhoe et al. 2017) as mediators. The results are presented in Table 6.

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Table 6 Specific indirect effects R1 R2 R3 R4 R5 R6 R7 R8 R9 R10

Relationships ADMINI → REP → COMP INTEL → REP → COMP PROM → REP → COMP MENT → RET → COMP PHYIC → RET → COMP PRIC → RET → COMP PROC → RET → COMP PROG → RET → COMP ADMINI → TEAQ → COMP PROC → TEAQ → COMP

Coefficients 0.171 0.017 0.064 0.020 0.007 0.005 0.014 0.003 0.033 0.019

p-values 0.000 0.351 0.001 0.031 0.223 0.287 0.066 0.385 0.058 0.060

The results of mediation effects in Table 6 revealed the presence of mediation between the constructs (R1, R3 and R4) and the absence of mediation for a number of relationships (R2, R5, R6, R7, R8, R9 and R10). Among the major findings in this analysis is that teaching quality does not mediate the relationship between many constructs.

3.7

Comparison of Staff and Students’ Opinions

As the chapter findings come from both staff and students, a comparison was done through multigroup analysis (MGA) to test whether the differences between group-specific path coefficients are statistically significant. The PLS-MGA findings (Column 2) revealed a significant difference between staff and students on the relationship between (1) intelligence generation and reputation—INTEG → REP (2) and between physical facilities and retention—PHYIC → RET. Similarly, the Welch-Satterthwaite test findings (column 3) show significant differences between staff and students on the relationship between (1) intelligence generation and reputation—INTEG → REP and on the relationship between (2) physical facilities and retention—PHYIC → RET, implying the absence of significant differences between the groups. All these findings are presented in Table 7. The general picture that can be deduced based on Table 7 is that there is absence of significant differences between staff and students on the relationship between marketing orientation and performance.

4 Discussion Among the hypotheses in this chapter was the presence of a positive relationship between mentoring (MENT) and students’ retention (RET). This hypothesis was supported (β = 0.231, p < 0.05), revealing that mentoring is significantly and

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Table 7 PLS-MGA between staff and students Paths (1) ADMINI → COMP ADMINI → REP ADMINI → TEAQ INTEL → COMP INTEL → REP MENT → COMP MENT → RET PHYIC → COMP PHYIC → RET PLAC → COMP PRIC → COMP PRIC → RET PROC → COMP PROC → RET PROC → TEAQ PROG → COMP PROG → RET PROM → COMP PROM → REP REP → COMP RET → COMP TEAQ → COMP

p-values (students vs. staff) (2) 1.000 0.870 0.893 0.273 0.002 0.376 0.565 0.086 0.044 0.563 0.937 0.947 0.378 0.060 0.711 0.223 0.171 0.316 0.270 0.908 0.459 0.956

p-values (students vs. staff) (3) 0.996 0.866 0.895 0.278 0.002 0.384 0.565 0.115 0.037 0.584 0.935 0.978 0.396 0.053 0.702 0.223 0.182 0.323 0.275 0.909 0.461 0.957

Remarks (4) Insignificant Insignificant Insignificant Insignificant Significant Insignificant Insignificant Insignificant Significant Insignificant Insignificant Insignificant Insignificant Insignificant Insignificant Insignificant Insignificant Insignificant Insignificant Insignificant Insignificant Insignificant

positively related to retention at a significance level of 5%. These results portray a very true picture as the presence of mentoring-related activities within a higher learning institution may lead to higher education students’ academic performance and proper decision-making, issues that are essential for their survival. These findings are supported by Rowley (2003) who advocated mentoring-related activities such as the use of personal tutor systems and encouraging electronic interaction between staff and students. The findings were also supported by Ackerman and Schibrowsky (2007) who emphasized that students’ retention is possible through social bonding like all interpersonal interactions that exist such as student-advisor, student-instructors and accessible faculty and support staff, all of which are mentoring-related aspects. The importance of mentoring in students’ retention is also underscored by the Hanover Report (2014) that advocated the presence of academic advising and support (as the top factor), social connectedness, approachability of faculty and staff, availability of mentoring and coaching programmes and the presence of well-trained employees to provide accurate information and friendly responses to enquiries. Fetsco et al. (2016) also found a positive contribution of mentoring and advising on students’ retention. Hameed and Amjad (2011) found a positive relationship between faculty, advisory staff and the classes with the

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student’s college experience. These results support the Tinto Model (Tinto 1993) observations that the more integrated a student is into the social systems of the institution, the more likely a student will be committed to the institution and to the goal of graduation and, as a result, the more likely the student will persist to graduation at that institution. Another hypothesis in this chapter was the presence of a positive relationship between physical facilities (PHYIC) and students’ retention (RET). This hypothesis was rejected (β = 0.080, p > 0.05), which revealed that physical facilities are positively but insignificantly related to retention. These findings are true due to the fact that the presence of enough physical facilities may result in a high level of costs that may discourage students from staying in an education institution due to failure to bear the associated costs. Similar findings are also reported in a study by Anis et al. (2018), Carter and Yeo (2016) and Muya and Tundui (2020). This finding is in contrast to the findings from the previous studies by Mokaya (2013) and Lau (2003) who found a positive role of physical facilities on students’ retention. On whether there is a positive relationship between price and students’ retention, this hypothesis was rejected (β = 0.054, p > 0.05), which implies that price is positively but insignificantly related to students’ retention. These findings call for rethinking on the price charged by HLIs whereby higher rates may jeopardize students’ continuity with their studies. This shows the importance of sponsorship opportunities beyond fees among students for enhancing their access to education and comfortable stay in a HLI. The results on this hypothesis are close to those by Muya and Tundui (2020) but far below those by Ackerman and Schibrowsky (2007), Hanover Report (2014), Hasan et al. (2009), Carabajal (2012) and Polat and Donmez (2010), who found price-related activities such as redesigning of scholarship, bursary, tuition fees amount and flexible tuition approaches as essential for students’ retention. Another hypothesis was whether there is a positive relationship between programme and students’ retention. This hypothesis was rejected (β = 0.040, p > 0.05), which implies that programme is positively but insignificantly related to students’ retention. The findings imply that students enrol at an institution because there is a specific programme to fulfil their specific purposes. In course of proceeding with studies, students may attain low performance if they joined wrong programme, something which may evict the students from the HLI. In addition, if there is a programme at an upper level such as a master’s degree, this may force the student to stay in a particular HLI for continuing with studies. The findings in this hypothesis do not support the previous research by Ivy (2008), Mokaya (2013), Muya and Tundui (2020) and Rowley (2003) who revealed that programme-related activities such as adequate participation in cocurricular activities, design of the degree, properly developed and adapted curriculum to meet students’ needs, curricula and programme duration and curriculum that is more vocationally relevant, as important factors in students’ retention. In this chapter, it was also hypothesized that there is a positive relationship between process and students’ retention. This hypothesis was supported (β = 0.161, p < 0.05), which implies that the process is positively related to students’ retention. These findings are in line with the findings reported in a study by Ivy (2008) and Muya and Tundui (2020) who appreciated the importance of

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processes at HLI such as registration for the correct courses, correct calculation and entering of students marks or grades against their names, awarding of correct qualification, finance system, accommodation, timetabling, library and graduation as important throughout students’ life at the HLI. In addition to the above hypotheses, in this chapter, it was explored whether there is a positive relationship between place and competitiveness. This hypothesis was supported (β = 0.115, p < 0.05), which implies that the place is positively related to competitiveness. The results are supported by other studies such as Aydın (2013), Beneke and Human (2010), Dwyer and Kim (2003), Hemsley-Brown and Oplatka (2006), Maringe (2005), Sezgin and Binath (2011) and Pringle and Huisman (2011), who found the importance of place-related aspects on firms’ competitiveness. On whether there is a positive relationship between programmes and competitiveness, although the path coefficient was statistically significant, this hypothesis was rejected (β = -0.098, p < 0.05) as the sign is reversed thereby failing to support the hypothesis that the programme is positively related to competitiveness. These results are in contrast with the findings from other studies (i.e. Dwyer and Mellor 1992; Polat and Donmez 2010; Sezgin and Binath 2011) that found the importance of programme-related aspects on firms’ competitiveness. Another hypothesis in this chapter was the presence of a positive relationship between intelligence generation and competitiveness. This hypothesis was rejected (β = 0.007, p > 0.05), which implies that intelligence generation has an insignificant relationship with competitiveness. These results differ from the findings from other studies (i.e. Dwyer and Kim 2003; Ross et al. 2013; Zebal and Goodwin 2012) that revealed that intelligence generation–related aspects such as free flow of information between stakeholders, gathering information on issues like changing regulations and an analysis of wider competitor pressure as important on the firms’ competitiveness. The contradiction can be due to the nature of high learning institutions whose vulnerability due to limited information is low in comparison with other for-profit organizations. Furthermore, in this chapter, a positive relationship was hypothesized between administration and HLI reputation. This hypothesis was supported (β = 0.386, p < 0.05), which implies that HLI administration is positively related to reputation. The results are in line with the findings from other studies (Castro et al. 2005; Polat and Donmez 2010; Rajh and Došen 2009), which support the role of administration-related aspects such as the best employee on the firms’ reputation. In another attempt, it was hypothesized that there is a positive relationship between intelligence generation and HLI reputation. This hypothesis was rejected (β = 0.038, p > 0.05), which implies that intelligence generation is insignificantly related to reputation. These results were surprising as many other studies (i.e. Caruana et al. 1998; Castro et al. 2005; Hamadu et al. 2011; Shapiro 1988; Zhang et al. 2017) acknowledged the impact of intelligence generation–related aspects on the firms’ performance. The findings imply that even if higher learning institutions are better on intelligence generation, other efforts are essential to enhance their reputation. On whether there is a positive relationship between promotion and HLI reputation, this hypothesis was supported (β = 0.144, p < 0.05) which implies that HLI promotion is positively related to HLI reputation. The

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findings are in line with those from other studies (Almquist 2007; Lloyd-Smith and An 2019; Han et al. 2019; Walsh and Beatty 2007), which reported the importance of promotion-related aspects such as persuasive communication, word of mouth, advertising and sales promotion on reputation. Another hypothesis was on the presence of a positive relationship between administration and quality teaching. This hypothesis was supported (β = 0.364, p < 0.05), revealing that university administration positively and significantly influences quality teaching. The findings corroborate with those by other researchers (i.e. Crosby 1996; Edo et al. 2015; Jaensson and Uiso 2015; Polat and Donmez 2010; Voon 2008) who revealed the effects of administration aspects on quality teaching. Another hypothesis was on the presence of a positive relationship between process and quality teaching. This hypothesis was supported (β = 0.214, p < 0.05) implying that university processes positively influence quality teaching. The findings are supported by findings in a study by Yorke (1999), who established that improving quality teaching requires a number of processes such as quality assurance mechanisms both internally and externally and attaining certification such as ISO 9000. Among the hypotheses tested included whether reputation has a positive relationship with competitiveness (β = 0.444, p < 0.05). This hypothesis was supported as the p values were less than the significance level of 0.05. The analysis revealed further that reputation mediates the relationship between administration and competitiveness (R1) (β = 0.171, p < 0.05) and between promotion and competitiveness (R3) (β = 0.064, p < 0.05). As both indirect effects and direct effects are significant, the presence of partial mediation (Hair et al. 2017) can be deduced. The effects of reputation were also reported by Nguyen and LeBlanc (2001) and Hussin et al. (2000) who revealed that institutional reputation contributed to improved students’ loyalty. Nuraryo et al. (2018) and Munisamy et al. (2014) found reputation as having positive impacts on students’ retention. The findings on positive and significant effects of reputation on satisfaction and retention are in line with the findings from a study by Keh and Xie (2009) who found a positive relationship between reputation and customer’s belief-consistent feelings of commitment. Similar findings are also reported by Arikan et al. (2016) who revealed a positive and significant direct effect of reputation on customer satisfaction and on customer loyalty. Furthermore, the analysis revealed lack of mediation by reputation on the relationship between intelligence generation and competitiveness (R2) (β = 0.017, p > 0.05). Other hypothesis in this chapter was that quality teaching has a positive relationship with competitiveness (β = 0.090, p < 0.05). The hypothesis was supported due to the presence of a significant relationship between quality teaching and competitiveness. Furthermore, the analysis revealed that there is no mediation by quality teaching on the relationship between administration and competitiveness (R9) (β = 0.033, p > 0.05) and on the relationship between process and competitiveness (R10) (β = 0.019, p > 0.05). The results were contrary to the findings of Sun and Pang (2017) and Whang (2017) who found quality as essential in shaping firm’s competitiveness both nationally and globally but also contrary to Hussin et al. (2000) who found teaching quality as a very important variable in the higher education context.

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The findings are however supported by Sari et al. (2018), who found an insignificant relationship between service quality and students’ retention that matters for HLI competitiveness. Finally, it was hypothesized that retention has a positive relationship with competitiveness (β =0.090, p < 0.05). The hypothesis was supported due to the presence of a significant relationship between retention and competitiveness. Furthermore, the analysis revealed the presence of mediation by retention on the relationship between mentoring and competitiveness (R4) (β = 0.020, p < 0.05). These results corroborate with those by Gengeswari and Padmashantini (2013) and Voss and Voss (2008). However, the results revealed lack of mediation by retention on the relationship between physical facilities and competitiveness (R5) (β = 0.007, p > 0.05), price and competitiveness (R6) (β = 0.005, p > 0.05), process and competitiveness (R7) (β = 0.014, p > 0.05) and programme and competitiveness (R8) (β = 0.003, p > 0.05).

5 Conclusion This chapter has successfully assessed and presented the effects of marketing orientation (MO) on performance in a higher education context using PLS-SEM; the data were analysed using SmartPLS Version 3.0. The consideration of the coefficient of determination (R2) of the endogenous variables revealed predictive accuracy of MO on HLI performance. Such results refute the results of previous studies that considered marketing orientation as not a suitable strategy for institutions such as HLI. The path coefficient revealed the relative importance of exogenous variables on the endogenous construct. The relationships between the exogenous variables and endogenous variables were significant in the most hypothesized relationships, which also implies the influence that marketing orientation has on the performance of HLI. The relationship between MO and performance is mediated by reputation and retention, aspects that strengthened the relationship between the two. The results in this chapter lead to the conclusion that marketing orientation (MO) is a relevant strategy for improving HLI performance through various channels. This chapter recommends for the adoption of marketing orientation among HLIs as a key initiative for attaining optimal and sustainable performance. This chapter has some limitations worth noting. The chapter adopted a crosssectional time horizon, which limited the understanding of the effects of marketing orientation on performance for a long span of years. Given enough time, longitudinal studies can be undertaken to assess marketing orientation–performance relationship for a long span of years. The chapter was written based on the data collected from the education industry; future studies can be undertaken in other industries as similar knowledge on other industries is limited in Tanzania. Due to time and resource concerns, only some HLIs were involved in this chapter. For better generalizability of results, future studies can expand the number of HLIs or involve all of them. As the chapter used data obtained from a single country (intra-countries), for better

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generalization of results, future studies can be of inter-countries to establish marketing orientation–performance relationship across the countries. Acknowledgments I express my heartfelt appreciation to the management of National Institute of Transport (NIT) for financing my PhD Studies, which resulted in coming up with this chapter.

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Part VII

Health

Participative Leadership Is the Discriminating Factor for Country’s Performance During the COVID-19 Pandemic Stephanie Dygico Gapud and George Faint

1 Introduction The COVID-19 pandemic has impacted every nation in this globally interconnected world. It seems that a country achieves superior performance in protecting its citizens from the pandemic based not simply on the availability and access to abundant resources (vaccine, health personnel, hospital beds, personal protective equipment [PPEs], etc.) in a country. For example, the United States should have all the technology and the knowledge at its disposal to combat the virus and should rank very highly as one of the successful countries in combatting the pandemic. By the time of writing this chapter, the United States ranked number 36 in Bloomberg’s COVID-19 Resilience Ranking. At the same time, Chile and Vietnam (ranked 13 and 18, respectively) outranked the United States, while the top five went to South Korea, the United Arab Emirates, Ireland, Norway, and Saudi Arabia (Hong et al. 2022).

S. D. Gapud (✉) Division of Business, Spring Hill College, Mobile, AL, USA e-mail: [email protected] G. Faint Sorrell College of Business, Troy University, Dothan, AL, USA e-mail: [email protected] © Springer Nature Switzerland AG 2023 L. Radomir et al. (eds.), State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM), Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-031-34589-0_34

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COVID-19 as the Unfolding External Jolt for the Natural Experiment on Leadership

The COVID-19 pandemic has thrown the world into uncertainty and complexity (Wernli et al. 2021) where the world’s resilience depends upon the participation of multiple agents in different sectors of the society. We will use the global pandemic caused by COVID-19 as the natural experiment (Dunning 2012; Sieweke and Santoni 2020) to analyze the impact of leadership style, particularly shared or participative leadership on the variation of COVID-19 outcome in different countries. The leadership style during times of crisis is the intangible property of the country that is largely responsible for the COVID-19 outcome. How the leader influences the behavior of the citizens was communicated in the country head’s personal attitude, speeches, and policy during the pandemic (Usman et al. 2021). Some denied the existence and potency of the virus, while others took it seriously (Berdy 2020; Frieden 2021; Vinopal 2021). Women head of states like Prime Minister Jacinda Ardern of New Zealand, Chancellor Angela Merkel of Germany, Tsai Ing-wen of Taiwan, and Danish Prime Minister Mette Frederiksen made headlines in popular media as well as became case studies for academic articles for the effectiveness of their response (Mayer and May 2021; Olagnier and Mogensen 2020; Tsai 2020; Vinopal 2021). Leading during a time of uncertainty requires mindfulness of the complexity of the situation (Langer 2010; Weick and Sutcliffe 2011). Previous literature suggests that many factors influence societal compliance with rules and guidelines implemented to combat the pandemic. Leadership, national culture (Barney and Wright 1998; Gokmen et al. 2021; Huynh 2020; Lagman et al. 2021; Wang 2021), transparency, and trustworthiness of information are often cited as factors that caused infodemic during the pandemic (Luengo and García-Marín 2020; Berdy 2020). Additionally, the likelihood that all COVID-19 funds and other resources allocated actually go to the appropriate organizations and programs may be measured by the Corruption Perception Index (CPI) for the country to estimate the contribution to the strategy that the country takes on (Barney 1986; Corruption Cuts 2020; Cruz-Ozorio 2020; Rubio 2021; Transparency International 2021). Corruption is defined as the abuse of entrusted power for private gain (Pozsgai-Alvarez 2020). Some countries consolidated power using the COVID-19 pandemic as an excuse, yet according to Transparency International (2021), their research shows that the same government left citizens without emergency aid.

1.2

Proposed Theories to Explain Causality Observed in the Leadership Natural Experiment

We followed the theorizing of Lord and Maher (1992) which influenced House et al. (2004), when they embarked on the GLOBE project, and identified factors that

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explain how various societies behaved and responded to their leaders as influenced by their cultural values. We further argue that the corruption perception partly explains the ability of the leader to influence the community mobility during the pandemic. Furthermore, we propose that the number of individuals infected and the number of deaths at the time when there was still no vaccine available in the world are largely impacted by the quarantine behavior in the community, which is impacted by how the community perceive and process the information or messaging by the leader.

1.3

Leadership: An Intangible Resource of an Organization

Borrowing from the resource-based view theory, we posit that leadership is a valuable, invisible, and intangible resource tied to the firm, which is the differentiating factor (Barney 1991; Barney and Arikan 2008; Peteraf 1993; Wernerfelt 1984) that impacts the level of control an organization has during times of uncertainty. Leadership is defined as the ability of an individual to influence, motivate, and enable others to contribute toward the effectiveness and success of the organizations of which they are members (House et al. 2004). When a leader enables others to contribute toward the fulfillment of the organizational goal, the leadership style should be participative or what they call shared leadership. The ability of the leader to encourage experts to participate in the decisionmaking is crucial to the effectiveness of the organization. When experts are allowed to contribute and influence the strategy formulation of an organization, be it a small team, a firm, or a country makes the organization adaptive even during times of uncertainty (Weick and Sutcliffe 2011). Moreover, beyond command and control (Pearce and Manz 2005), when leadership is shared by allowing experts to participate in the dynamic influencing processes during times of global crisis (Avolio and Gardner 2005; Bligh et al. 2006; Pearce and Wassenaar 2014), innovative solutions are created because talents are maximized, corruption is minimized, and the greater good is served (Manz and Pearce 2017; Pearce et al. 2008). The participative global leadership dimension score of several countries obtained from the GLOBE project was used to determine how the impact of sharing the leadership in a country influences the COVID-19 situation. In their 2020 annual report on corruption perceptions around the world, Transparency International noted that “Corruption and emergencies feed off each other, creating a vicious cycle of mismanagement and deeper crisis. The large sums of money required to deal with emergencies and the need for urgency in disbursing these funds form a perfect storm for corruption” (Transparency International 2020). To cope with the urgent need to provide relief during the pandemic, numerous countries relaxed their public procurement regulations, which were in place “to limit the discretion of public officials and promote competition among sellers in order to avoid potential collusion between bureaucrats and contractors” (Gallego et al. 2020, p. 1).

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Fig. 1 The proposed model. Notes: Hypothesized relationships of the constructs are illustrated. H1: Corruption – !Participative leadership, H2: Culture + ! Participative leadership, H3: Participative leadership + ! Community mobility, H4: Community mobility – ! New cases, H5: Community mobility – !Total cases, H6: Community mobility – !Total deaths

Therefore, it is posited that the perception of corruption in a country erodes social contract, and it impacts the leader’s strategy and therefore the ability to govern during the pandemic. Moreover, it is argued that the ability of a leader to formulate a successful strategy during a pandemic provides the resource heterogeneity that determines the success of the nation’s strategy to curtail the impact of the COVID-19. Furthermore, national culture and practices affect what leaders do (House et al. 2002; Huynh 2020). We also propose that community mobility during the initial part of the pandemic illustrates followership as impacted by the leadership in a country is significantly related to the country’s pandemic situation. We used the Google’s Community Mobility Report to determine the effectiveness of the influence of the participative leadership in a country as influenced by the national culture and by the Corruption Perception Index. In this chapter, a structural equation model was conceptualized (Fig. 1) based on the findings of the GLOBE research that “the attributes and practices that distinguish cultures from each other, as well as strategic organizational contingencies, are predictive of the leader’s attributes and behaviors, and organizational practices, that are most frequently enacted and are most effective” (House et al. 2002, p. 9). The Corruption Perception Index was added as the other factor influencing the leadership in a country aside from the dimensions of the national culture. The deaths due to COVID-19 and current cases as the dependent variable are also explored.

2 Methodology The model was evaluated using secondary data. The results of the GLOBE project (https://globeproject.com/) were used for the participative leadership. The GLOBE project identified six global culturally endorsed leadership dimensions (charismatic/ value-based, team-oriented, participative, humane-oriented, autonomous, and self-

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protective). In this chapter, the country data on participative global leadership dimensions (GLDs) are used to test whether allowing experts in the decision-making during the pandemic has an impact on how the pandemic situation varies in each country. Participative leadership is defined as “the degree to which managers involve others in making and implementing decisions” (House et al. 2004, p. 675). Leadership is the ability of an individual to influence, motivate, and enable others to contribute toward the effectiveness and success of the organizations of which they are members (House et al. 2004). “Participative Leadership includes two primary leadership dimensions labeled (a) non participative and (b) autocratic” (Dorfman et al. 2012, p. 506). Culture is defined as the collective programming of the mind that distinguishes the members of one group or category of people from others (Hofstede 1984). There are two sources of national cultural index, the GLOBE project and Hofstede’s country insight. To avoid common method bias, the data for culture were taken from the Hofstede’s country insight (2020). Hofstede and his coauthors’ continued research on culture started in the 1980s and to date has covered 76 countries. All the six Hofstede’s dimensions of culture (uncertainty avoidance, masculinity, power distance, long-term orientation, collectivism, and indulgence) were incorporated in the analysis. Hofstede (2011, p. 8) defined the cultural dimensions as follows: 1. Power distance, related to the different solutions to the basic problem of human inequality 2. Uncertainty avoidance, related to the level of stress in a society in the face of an unknown future 3. Individualism versus collectivism, related to the integration of individuals into primary groups 4. Masculinity versus femininity, related to the division of emotional roles between women and men 5. Long-term versus short-term orientation, related to the choice of focus for people’s efforts: the future or the present and the past 6. Indulgence versus restraint, related to the gratification versus control of basic human desires related to enjoying life Data for the corruption perception in the model were taken from the Corruption Perception Index (2020) published by the research arm of the Transparency International. Transparency International (2020) is a global movement whose mission is to stop corruption and promote transparency, accountability, and integrity at all levels and across all sectors of society. The data for the community movement statistics were taken from Google’s COVID-19 Community Mobility Report (2020). Using similar technology and process to develop their maps, Google aggregated and anonymized data to track the movement of individuals during the pandemic. Google has publicly available global data on movement trends during the pandemic over time by geography (i.e., cities and country). In this chapter, the reported changes in community movement such as retail and recreation, groceries and pharmacies, parks, transit stations, and workplaces were used in the analysis except for the residential. The data for the

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changes in movement from people’s home (residential) were not used as they seem to sum up all the other movements. We can use either the residential data or the five other categories to illustrate and differentiate the COVID-19 situations in various countries on October 15, 2020. We chose to include more variables because doing so will allow us to observe intricate widespread multivariate in the structural equation model. The earliest data date February 15, 2020; however, Google stopped tracking changes in community movement in October 15, 2022. The historical data can still be downloaded as a csv file from the website. The COVID-19 statistics (Worldometers 2020) were obtained on October 15, 2020. It is published by Dadax, a small media company that generates revenue from advertising. During the pandemic, it reported real-time statistics on new cases, total deaths, and total cases. Worldometers recently changed its name to Worldometer, which tracks real-time statistics on various topics such as energy, food, society and media, and world population. Worldometer is listed by the American Library Association as an outstanding free reference website (RUSA 2011). It is important to note that the COVID-19 and community mobility data were taken when there was still no approved vaccine available on the market. Therefore, our model is illustrating a paradigm of a snapshot in time of the relationships of the variables without the effect of any vaccine in the pandemic situation in the world. Furthermore, the country’s COVID-19 situation is largely impacted by the dyadic relationship of the leader–follower dynamics as influenced by culture and perception of corruption in a country. Not all the countries with COVID-19 and CPI in the world are included in the analysis. We are limited by the availability of the national culture and the leadership data. Some countries have not yet been surveyed by Hofstede or by the GLOBE project. We only included 59 countries with complete data from secondary sources. SmartPLS Version 4.0.8.3 (Ringle et al. 2022) was the software used in the partial least squares structural equation modeling (PLS-SEM). SmartPLS was selected because it allowed easy creation, analysis, and validation of models (Sander and Teh 2014).

3 Results 3.1

Measurement Model Confirmation

We followed the series of steps in confirmatory composite analysis (CCA) to confirm our partial least squares structural equation model. The CCA offers several advantages over other approaches for confirming measurement models consisting of linear composites (Hair et al. 2020).

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Estimate of Loadings and Significance

The outer model was assessed by examining the relationship between the constructs and their indicators. The results show that the two reflectively measured constructs (culture and community mobility)’s measures are reliable and valid (Table 1 and Fig. 2). Three community mobility variables out of five were retained in the final model. The community mobility construct in the model is measured by the movement to the workplace (Mob_Work), travel to purchase and go to recreation places (Mob_Ret_Rec), and the number of people used the available mass transportation, that is, trains, airplanes, and bus (Mob_Travel). Moreover, three of the six Hofstede’s cultural dimensions were retained in the model: masculinity (H_Masc), uncertainty avoidance (H_UA), and power distance (H_PD). The original sample (O) shows the original estimate of the outer weight, while the sample mean (M) is the bootstrap-calculated value (Hair et al. 2017). The standardized loadings of the variables should have a value of at least 0.708 and an associated t-statistics above 1.96 to be significant for a two-tailed test at the 5% level (Hair et al. Table 1 Outer loadings and significance of the variables of latent constructs

H_Masc ← National culture H_PD ← National culture H_UA ← National culture Mob_Retail_Rec ← Community mobility Mob_Travel ← Community mobility Mob_Work ← Community mobility Total deaths ← Total deaths

Original sample (O) 0.70

Sample mean (M) 0.53

Standard deviation (SD) 0.35

t-statistics (|O/SD|) 2.02

pvalues 0.04

0.96 0.77 0.96

0.95 0.65 0.97

0.03 0.24 0.01

28.53 3.16 73.68