Leadership and Operational Indexes for Supply Chain Resilience 3031323637, 9783031323638

Table of contents :
Preface
Contents
List of Figures
List of Tables
1 Resilience and Its Key Drivers in the Supply Chain
1.1 Supply Chain (SC)
1.2 Supply Chain Resilience (SCR)
1.3 Supply Chain Flexibility (SCF)
1.4 Supply Chain Agility (SCA)
1.5 Supply Chain Efficiency (SCE)
1.6 Supply Chain Alerts (SCAL)
1.7 Supply Chain Leadership (SCL)
1.7.1 Transactional Leadership (TSL)
1.7.2 Transformational Leadership (TFL)
1.8 Conclusions
References
2 Definition of Variables and Research Problem
2.1 SC Resilience (SCR)
2.2 SC Flexibility (SCF)
2.3 SC Agility (SCA)
2.4 SC Efficiency (SCE)
2.5 SC Alerts (SCAL)
2.6 SC Leadership (SCL)
2.7 Research Problem and Objective
References
3 Methodology
3.1 Literature Review—Rational Validation
3.2 Questionnaire Design—Judge Validation
3.3 Questionnaire Application to Industry
3.4 Data Capture and Debugging
3.5 Statistical Data Validation
3.6 Descriptive Analysis of the Sample and Items
3.7 Structural Equation Model
3.7.1 Direct Effects
3.7.2 Sum of Indirect Effects
3.7.3 Total Effects
3.7.4 Mediation Effects
3.8 Model Interpretation and Practical Implications
3.9 Sensitivity Analysis
References
4 Model 1. Leadership Style and Its Impact on Operational Performance and Supply Chain Resilience
4.1 Model Variables
4.2 Hypotheses in the Model
4.2.1 Relationship Between TSSCL and SCF
4.2.2 Relationship Between TSSCL and SCA
4.2.3 Relationship Between TFSCL and SCF
4.2.4 Relationship Between TFSCL and SCA
4.2.5 Relationship Between SCF and SCR
4.2.6 Relationship Between SCA and SCR
4.3 Latent Variable Validation
4.4 Structural Equation Model Evaluation
4.4.1 Direct Effects and Validation of Hypotheses
4.4.2 Sum of Indirect and Total Effects
4.4.3 Sensitivity Analysis
4.5 Discussion of Results
4.5.1 Structural Equation Modeling
4.5.2 Sensitivity Analysis
4.6 Conclusions and Managerial Implications
References
5 Model 2. Impact of Leadership on Supply Chain Efficiency, Alertness, and Resiliency
5.1 Model Variables
5.2 Hypotheses in the Model
5.2.1 Relationship of TSSCL with SCE
5.2.2 Relationship of TSSCL with SCAL
5.2.3 Relationship of TFSCL with SCE
5.2.4 Relationship of TFSCL with SCAL
5.2.5 Relationship of SCE with SCR
5.2.6 Relationship of SCAL with SCR
5.3 Latent Variable Validation
5.4 Evaluation of the Structural Equation Model
5.4.1 Direct Effects and Validation of Hypotheses
5.4.2 Sum of Indirect and Total Effects
5.4.3 Sensitivity Analysis
5.5 Discussion of Results
5.5.1 Structural Equation Modeling
5.5.2 Sensitivity Analysis
5.6 Conclusions and Management Implications
References
6 Model 3. Impact of Leadership on Operational Variables and Supply Chain Resilience
6.1 Model Variables
6.2 Hypotheses in the Model
6.3 Latent Variable Validation
6.4 Structural Equation Model Evaluation
6.4.1 Direct Effects and Validation of Hypotheses
6.4.2 Sum of Indirect and Total Effects
6.4.3 Sobel Test
6.4.4 Sensitivity Analysis
6.5 Discussion of Results
6.5.1 Structural Equation Modeling
6.5.2 Sensitivity Analysis
6.6 Conclusions and Management Implications
References
7 Model 4. Flexibility, Agility, and  Alertness as Precursors to Supply Chain Efficiency
7.1 Model Variables
7.2 Hypotheses in the Model
7.2.1 Relationship of SCAL with SCF, SCA, and SCE
7.2.2 Relationship Between SCF to SCA and SCE
7.2.3 Relationship Between SCA to SCE
7.3 Latent Variable Validation
7.4 Evaluation of the Structural Equation Model
7.4.1 Direct Effects and Validation of Hypotheses
7.4.2 Sum of Indirect and Total Effects
7.4.3 Sensitivity Analysis
7.5 Discussion of Results
7.5.1 Structural Equation Modeling
7.5.2 Sensitivity Analysis
7.6 Conclusions and Management Implications
References
8 Model 5. Impact of Leadership on Operating Ratios and Resilience
8.1 Model Variables
8.2 Hypotheses in the Model
8.2.1 Relationship of Leadership to Supply Chain Indexes and Resiliency
8.3 Laten Variable Validation
8.4 Structural Equation Model Evaluation
8.4.1 Direct Effects and Validation of Hypotheses
8.4.2 Sum of Indirect and Total Effects
8.4.3 Sensitivity Analysis
8.5 Discussion of Results
8.5.1 Structural Equation Modeling
8.5.2 Sensitivity Analysis
8.6 Conclusions and Management Implications
References

Citation preview

SpringerBriefs in Applied Sciences and Technology Maribel Mendoza Solis · Jorge Luis García Alcaraz · Juan Manuel Madrid Solórzano · Emilio Jiménez Macías

Leadership and Operational Indexes for Supply Chain Resilience

SpringerBriefs in Applied Sciences and Technology

SpringerBriefs present concise summaries of cutting-edge research and practical applications across a wide spectrum of fields. Featuring compact volumes of 50 to 125 pages, the series covers a range of content from professional to academic. Typical publications can be: • A timely report of state-of-the art methods • An introduction to or a manual for the application of mathematical or computer techniques • A bridge between new research results, as published in journal articles • A snapshot of a hot or emerging topic • An in-depth case study • A presentation of core concepts that students must understand in order to make independent contributions SpringerBriefs are characterized by fast, global electronic dissemination, standard publishing contracts, standardized manuscript preparation and formatting guidelines, and expedited production schedules. On the one hand, SpringerBriefs in Applied Sciences and Technology are devoted to the publication of fundamentals and applications within the different classical engineering disciplines as well as in interdisciplinary fields that recently emerged between these areas. On the other hand, as the boundary separating fundamental research and applied technology is more and more dissolving, this series is particularly open to trans-disciplinary topics between fundamental science and engineering. Indexed by EI-Compendex, SCOPUS and Springerlink.

Maribel Mendoza Solis · Jorge Luis García Alcaraz · Juan Manuel Madrid Solórzano · Emilio Jiménez Macías

Leadership and Operational Indexes for Supply Chain Resilience

Maribel Mendoza Solis Department of Electric Engineering and Computer Sciences Universidad Autónoma de Ciudad Juárez Ciudad Juárez, Chihuahua, Mexico

Jorge Luis García Alcaraz Department of Industrial Engineering and Manufacturing Universidad Autónoma de Ciudad Juárez Ciudad Juárez, Chihuahua, Mexico

Juan Manuel Madrid Solórzano Department Industrial Design Universidad Autónoma de Ciudad Juárez Ciudad Juárez, Chihuahua, Mexico

Emilio Jiménez Macías Department of Electrical Engineering University of La Rioja Logroño, Spain

ISSN 2191-530X ISSN 2191-5318 (electronic) SpringerBriefs in Applied Sciences and Technology ISBN 978-3-031-32363-8 ISBN 978-3-031-32364-5 (eBook) https://doi.org/10.1007/978-3-031-32364-5 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

Resilient supply chains (SCR) are becoming increasingly necessary for companies; thus, strengthening them has become a survival priority in the market because they cannot avoid disruptive events. However, companies can forecast and prepare contingency plans to reduce these risks and events. CSR not only supports disruptive events but also helps mitigate risks and address customers’ personalized demands. Companies require collaboration from all members and leadership from the focal company to achieve SCR. In Mexico, several maquiladora industries are owned by foreign corporations and headquartered in other countries. These companies are established in the national territory because of the advantages of highly skilled labor, governmental trade agreements with other countries, and proximity to the USA and Canada as consumer markets. These companies import raw materials and export finished products, focusing only on assembly processes. Ciudad Juarez, in the state of Chihuahua (Mexico), has a strategic position due to its proximity to the USA and, in January 2023, had 322 maquiladora industries of this type, which have global and international supply chains. However, these companies began arriving in the ’60s. These companies mainly originated in the USA, Germany, Japan, South Korea, China, and Italy. These companies share technology, resources, and information and are connected to international supply and distribution networks. In November 2022, Ciudad Juarez imported US$41.822 billion and exported US$41.843 billion, representing an enormous movement of goods. The flow interruption in any partner significantly harms the entire international supply network and hence the need to know the leadership style of the managers in these companies (especially the supply chain) and the efficiency indices used to measure agility, flexibility, and alertness systems to ensure resilience. This book provides definitions regarding these variables and reports some structural equation models for measuring supply chain leadership’s impact on supply chain resilience, using agility, flexibility, efficiency, and alertness systems as mediators. This book has supplementary material in a database to be opened with Excel. The material reports five structural equation models described in the book, which can be

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Preface

consulted at https://doi.org/10.17632/v7mtjhj93h.1 and contains: the survey used, descriptive analysis of the sample, and the rest of the sheets present the outputs of the models run in the WarpPLS software and the other validation indices used. The book consists of eight chapters. The first three are introductory chapters and provides concepts, defines a research problem, and describes the methodology used. The next five chapters are reporting structural equation models in which the variables are related differently. These models are validated with the information provided by 231 responses to a survey applied to managers and engineers working in the Mexican maquiladora industry. These models were statistically evaluated using the partial least squares technique at a 95% confidence level. Chapter 1 is called “Resilience and Its Key Drivers in the Supply Chain”. It illustrates a literature review of the variables, their importance, and the items used to measure them. The variables analyzed were resilience, flexibility, agility, efficiency, alertness, and leadership in the supply chain. Chapter 2 is called “Definition of Variables and Research Problem” and presents the definition for each variable analyzed, some studies that have been carried out and are relevant to this research, the problem in the context of the Mexican maquiladora industry, and the research objective. Chapter 3 is called “Methodology” and describes all the activities carried out in this study. These activities are the literature review, creation of the questionnaire and its application to the industrial sector, data debugging, techniques used for data analysis, conclusions, and industrial and managerial implications. Chapter 4 is called “Model 1. Leadership Style and Its Impact on Operational Performance and Supply Chain Resilience” and analyzes the first structural equation model, which integrates five variables. The independent variables were transactional supply chain leadership (TSSCL) and transformational supply chain leadership (TFSCL); the mediating variables were flexibility (SCF) and agility in the supply chain (SCA); and the dependent variable was supply chain resilience (SCR). Chapter 5, named “Model 2. Impact of Leadership on Supply Chain Efficiency, Alertness, and Resiliency”, presents the second structural equation model, which integrates five variables. The independent variables are TSSCL and TFSCL, and the mediating variables are supply chain efficiency (SCE), supply chain alerting (SCAL), and the dependent variable SCR. The variables are related through six hypotheses. Chapter 6 is called “Model 3. Impact of Leadership on Operational Variables and Supply Chain Resilience” and reports the third structural equation model, which integrates seven variables. The independent variables are TSSCL and TFSCL; the mediating variables are SCE, SCF, SCA, and SCAL; and the dependent variable is SCR. The variables are related to twelve hypotheses and can be said to be integrating the previous models into one. Chapter 7 is called “Model 4. Flexibility, Agility, and Alertness as Precursors to Supply Chain Efficiency” and represents the fourth structural equation model. The model integrates the four performance variables, SCF, SCA, SCAL, and SCE, related to the six hypotheses.

Preface

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Finally, Chap. 8, called “Model 5. Impact of Leadership on Operating Ratios and Resilience”, establishes the fifth structural equation model, which is already a secondorder model. In this model, the independent variable is the leadership integrated by TSSCL and TFSCL, the mediating variable is integrated by the constructs SCE, SCF, SCA, and SCAL, and the response variable is SCR. These variables are related using three hypotheses and represent an integrative approach to all studied variables. Ciudad Juárez, Mexico Ciudad Juárez, Mexico Ciudad Juárez, Mexico Logroño, Spain

Maribel Mendoza Solis Jorge Luis García Alcaraz Juan Manuel Madrid Solórzano Emilio Jiménez Macías

Contents

1 Resilience and Its Key Drivers in the Supply Chain . . . . . . . . . . . . . . . . 1.1 Supply Chain (SC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Supply Chain Resilience (SCR) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Supply Chain Flexibility (SCF) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Supply Chain Agility (SCA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 Supply Chain Efficiency (SCE) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6 Supply Chain Alerts (SCAL) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.7 Supply Chain Leadership (SCL) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.7.1 Transactional Leadership (TSL) . . . . . . . . . . . . . . . . . . . . . . . . 1.7.2 Transformational Leadership (TFL) . . . . . . . . . . . . . . . . . . . . . 1.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 1 3 4 5 6 7 8 9 11 14 14

2 Definition of Variables and Research Problem . . . . . . . . . . . . . . . . . . . . . 2.1 SC Resilience (SCR) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 SC Flexibility (SCF) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 SC Agility (SCA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 SC Efficiency (SCE) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 SC Alerts (SCAL) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 SC Leadership (SCL) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7 Research Problem and Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

19 19 21 22 23 24 25 26 27

3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Literature Review—Rational Validation . . . . . . . . . . . . . . . . . . . . . . . 3.2 Questionnaire Design—Judge Validation . . . . . . . . . . . . . . . . . . . . . . . 3.3 Questionnaire Application to Industry . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Data Capture and Debugging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Statistical Data Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 Descriptive Analysis of the Sample and Items . . . . . . . . . . . . . . . . . . 3.7 Structural Equation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7.1 Direct Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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3.7.2 Sum of Indirect Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7.3 Total Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7.4 Mediation Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.8 Model Interpretation and Practical Implications . . . . . . . . . . . . . . . . . 3.9 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

37 37 38 38 39 39

4 Model 1. Leadership Style and Its Impact on Operational Performance and Supply Chain Resilience . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Model Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Hypotheses in the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1 Relationship Between TSSCL and SCF . . . . . . . . . . . . . . . . . 4.2.2 Relationship Between TSSCL and SCA . . . . . . . . . . . . . . . . . 4.2.3 Relationship Between TFSCL and SCF . . . . . . . . . . . . . . . . . 4.2.4 Relationship Between TFSCL and SCA . . . . . . . . . . . . . . . . . 4.2.5 Relationship Between SCF and SCR . . . . . . . . . . . . . . . . . . . . 4.2.6 Relationship Between SCA and SCR . . . . . . . . . . . . . . . . . . . 4.3 Latent Variable Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Structural Equation Model Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.1 Direct Effects and Validation of Hypotheses . . . . . . . . . . . . . 4.4.2 Sum of Indirect and Total Effects . . . . . . . . . . . . . . . . . . . . . . . 4.4.3 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Discussion of Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.1 Structural Equation Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.2 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6 Conclusions and Managerial Implications . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

43 43 44 44 45 45 46 46 47 47 48 50 50 51 51 51 53 54 56

5 Model 2. Impact of Leadership on Supply Chain Efficiency, Alertness, and Resiliency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Model Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Hypotheses in the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 Relationship of TSSCL with SCE . . . . . . . . . . . . . . . . . . . . . . 5.2.2 Relationship of TSSCL with SCAL . . . . . . . . . . . . . . . . . . . . . 5.2.3 Relationship of TFSCL with SCE . . . . . . . . . . . . . . . . . . . . . . 5.2.4 Relationship of TFSCL with SCAL . . . . . . . . . . . . . . . . . . . . . 5.2.5 Relationship of SCE with SCR . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.6 Relationship of SCAL with SCR . . . . . . . . . . . . . . . . . . . . . . . 5.3 Latent Variable Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Evaluation of the Structural Equation Model . . . . . . . . . . . . . . . . . . . 5.4.1 Direct Effects and Validation of Hypotheses . . . . . . . . . . . . . 5.4.2 Sum of Indirect and Total Effects . . . . . . . . . . . . . . . . . . . . . . . 5.4.3 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Discussion of Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.1 Structural Equation Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.2 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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5.6 Conclusions and Management Implications . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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6 Model 3. Impact of Leadership on Operational Variables and Supply Chain Resilience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Model Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Hypotheses in the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Latent Variable Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 Structural Equation Model Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.1 Direct Effects and Validation of Hypotheses . . . . . . . . . . . . . 6.4.2 Sum of Indirect and Total Effects . . . . . . . . . . . . . . . . . . . . . . . 6.4.3 Sobel Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.4 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5 Discussion of Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5.1 Structural Equation Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5.2 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6 Conclusions and Management Implications . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

75 75 76 76 77 78 80 80 80 82 82 82 86 89

7 Model 4. Flexibility, Agility, and Alertness as Precursors to Supply Chain Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 7.1 Model Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 7.2 Hypotheses in the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 7.2.1 Relationship of SCAL with SCF, SCA, and SCE . . . . . . . . . . 92 7.2.2 Relationship Between SCF to SCA and SCE . . . . . . . . . . . . . 93 7.2.3 Relationship Between SCA to SCE . . . . . . . . . . . . . . . . . . . . . 94 7.3 Latent Variable Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 7.4 Evaluation of the Structural Equation Model . . . . . . . . . . . . . . . . . . . 96 7.4.1 Direct Effects and Validation of Hypotheses . . . . . . . . . . . . . 96 7.4.2 Sum of Indirect and Total Effects . . . . . . . . . . . . . . . . . . . . . . . 97 7.4.3 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 7.5 Discussion of Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 7.5.1 Structural Equation Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . 98 7.5.2 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 7.6 Conclusions and Management Implications . . . . . . . . . . . . . . . . . . . . 100 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 8 Model 5. Impact of Leadership on Operating Ratios and Resilience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Model Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Hypotheses in the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.1 Relationship of Leadership to Supply Chain Indexes and Resiliency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 Laten Variable Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4 Structural Equation Model Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . 8.4.1 Direct Effects and Validation of Hypotheses . . . . . . . . . . . . .

105 105 106 106 108 109 110

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Contents

8.4.2 Sum of Indirect and Total Effects . . . . . . . . . . . . . . . . . . . . . . . 8.4.3 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5 Discussion of Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5.1 Structural Equation Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5.2 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.6 Conclusions and Management Implications . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

110 111 111 111 112 112 114

List of Figures

Fig. 1.1 Fig. 1.2 Fig. 1.3 Fig. 1.4 Fig. 1.5 Fig. 1.6 Fig. 1.7 Fig. 1.8 Fig. 1.9 Fig. 2.1 Fig. 2.2 Fig. 2.3 Fig. 2.4 Fig. 3.1 Fig. 4.1 Fig. 4.2 Fig. 5.1 Fig. 5.2 Fig. 6.1 Fig. 6.2 Fig. 7.1 Fig. 7.2 Fig. 8.1 Fig. 8.2

Traditional supply chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Trend in supply chain research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Trend in supply chain resilience research . . . . . . . . . . . . . . . . . . . . Research trends in the SCF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SCA research trend . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SCE research trend . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Trend in leadership research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Trend of transactional leadership research . . . . . . . . . . . . . . . . . . . Trends in transformational leadership research . . . . . . . . . . . . . . . . Process flexibility of supply chain . . . . . . . . . . . . . . . . . . . . . . . . . . Supply chain agility perspectives (Saeed et al. 2019) . . . . . . . . . . . Criteria in supply chain alertness . . . . . . . . . . . . . . . . . . . . . . . . . . . Literature review of supply chain leadership . . . . . . . . . . . . . . . . . . Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Proposed model with the mediating variables of flexibility and agility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Evaluation of the initial model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Proposed model with the mediating variables of efficiency and alertness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Evaluation of the initial model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Proposed model with the mediating variables of efficiency, flexibility, agility, and alertness . . . . . . . . . . . . . . . . . . . . . . . . . . . . Evaluation of the initial model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Proposed model with supply chain indicators . . . . . . . . . . . . . . . . . Evaluation of the initial model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Proposed model with SC mix leadership . . . . . . . . . . . . . . . . . . . . . Evaluation of the initial model . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2 2 3 5 6 7 9 11 13 21 22 25 26 32 48 49 64 65 79 79 95 97 108 109

xiii

List of Tables

Table 2.1 Table 2.2 Table 2.3 Table 4.1 Table 4.2 Table 4.3 Table 4.4 Table 4.5 Table 4.6 Table 5.1 Table 5.2 Table 5.3 Table 5.4 Table 5.5 Table 5.6 Table 6.1 Table 6.2 Table 6.3 Table 6.4 Table 6.5 Table 6.6 Table 6.7 Table 7.1 Table 7.2 Table 7.3 Table 7.4 Table 7.5 Table 7.6 Table 8.1 Table 8.2 Table 8.3

Resilience in the supply chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . Supply chain agility area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Supply chain performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Latent variable validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Model efficiency indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary of direct effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Indirect effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Total effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sensitivity analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Latent variable validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Model efficiency ratios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary of direct effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Indirect effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Total effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sensitivity analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Latent variable validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Model efficiency ratios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary of direct effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Indirect effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Total effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mediating test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sensitivity analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Latent variable validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Model efficiency indexes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary of direct effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Indirect effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Total effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sensitivity analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Latent variable validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Model efficiency indexes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary of direct effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

20 22 23 48 49 50 50 51 52 64 65 66 66 67 68 77 78 78 80 81 82 83 95 96 97 97 98 99 108 109 110 xv

xvi

Table 8.4 Table 8.5 Table 8.6

List of Tables

Indirect effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Total effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sensitivity analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

110 110 111

Chapter 1

Resilience and Its Key Drivers in the Supply Chain

Abstract This chapter presents a literature review of the variables analyzed in the book using different structural equation models. Initially, a brief review of the supply chain is justified because all the variables to be analyzed are subject to it. Afterward, the variables of resilience, flexibility, agility, efficiency, alerts, and leadership (transformational and transactional) were applied to the supply chain. The definition, importance, literature review, and factors used to measure each variable are presented. Keywords Supply chain · Resilience · Flexibility · Agility · Efficiency · Alerts · Leadership

1.1 Supply Chain (SC) For Goetschalckx (2011), the supply chain (SC) is an integrated network of resources and processes that integrates raw material acquisition, products in process, finished products, and distribution to the final customer. According to Govindan et al. (2022), the SC integrates suppliers, manufacturers, and intermediaries such as transporters, warehousers, retailers, and even the end customer. Therefore, SC can be defined as the entities interacting throughout the supply, production, and distribution network to convert raw materials into semi-finished and finished products available to satisfy the final customer’s demand. The SC comprises two channels: the supply channel, in which the company acquires all material resources to carry out its productive operations, and the distribution channel, in which the company sends its products to the client or final consumer. There are different entities through these two channels, such as suppliers at different levels, wholesalers, retailers, and intermediaries, such as transportation and warehousing companies. Figure 1.1 illustrates supply chain members, where carriers are represented by the letter T and warehouses by a triangle. In recent years, the study of SC has increased from various perspectives. Current studies have focused on supply chain integration and performance (Li et al. 2022), © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. M. Solis et al., Leadership and operational indexes for supply chain resilience, SpringerBriefs in Applied Sciences and Technology, https://doi.org/10.1007/978-3-031-32364-5_1

1

2

1 Resilience and Its Key Drivers in the Supply Chain

Fig. 1.1 Traditional supply chain

supply chain innovation (Malacina and Teplov 2022), supply chain visibility (Kalaiarasan et al. 2022), green supply chain (Ofori Antwi et al. 2022), supply chain resilience (Maharjan and Kato 2022), circular supply chain (Taddei et al. 2022), supply chain 4.0 (Govindan et al. 2022), among others. Figure 1.2 shows the trend in the number of published papers in the Scopus database, using the keyword “supply chain” in the title, abstract, or keywords (blue line). The first document published on this topic was published in 1969; however, it took almost three decades to trigger a growth in the number of documents published and the interest of academics in this research area. In the last 20 years, the number of articles published has increased from hundreds to thousands, which shows the current importance of this topic owing to globalized production systems. This is demonstrated by the fitted second-degree polynomial curve, which shows an adjustment of 96.57% (red and dotted line). It is important to mention that during 2022, 9212 publications associated with the supply chain were identified; however, they were not considered to adjust the trend line to not affect the trend line adjustment. 14000

Documents

12000 10000

y = 7.8184x2 - 31022x + 3E+07 R² = 0.9657

8000 6000 4000 2000 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024

0

Years Fig. 1.2 Trend in supply chain research

1.2 Supply Chain Resilience (SCR)

3

1.2 Supply Chain Resilience (SCR) SCR is the set of skills and capabilities that a firm possesses to withstand disruptive events and be able to continue its productive operations, adapt and recover in a vulnerable environment, and succeed in meeting customer demand (Ivanov and Dolgui 2020; Kahiluoto et al. 2020; Queiroz et al. 2022). SCR shows a great capacity to face challenges under uncertainty and successfully bounce back to the members, who show great commitment and strong relationships that help them respond quickly to variations during the disruptive phenomenon. SCR has become more relevant in recent years owing to market volatility, uncertainty in the environment and supply risks, political and economic crises in countries, and natural and man-made disasters. On the other hand, Basu et al. (2022) indicate that resilience helps companies to preserve their market presence and maintain their brand image by showing efficiency in the supply of goods. SCR is a new term; the first document was reported in 2007, and since then, publications on this topic have increased; however, it was not until 2015 that the trend curve shot up exponentially, which implies its relevance and interest in the academic world. Figure 1.3 shows the results obtained in the Scopus database using the keyword “supply chain resilience” to represent its growth; however, 198 publications associated with SCR in 2022 were excluded not to affect the adjusted trend line and decrease the value of R2 . Measuring SCR is not easy; however, Shin and Park (2021) indicate four items that focus on responding to the company’s ability to anticipate events through the use of forecasts, the ability to respond and adapt to the event, and finally, the ability to recover. The items are:

Documents

200 150

y = 4E-282e0.3233x R² = 0.9435

100

Years Fig. 1.3 Trend in supply chain resilience research

2021

2019

2017

2015

2013

2011

2007

0

2009

50

4

1 Resilience and Its Key Drivers in the Supply Chain

• How well is your firm prepared for good disruptive event recovery (anticipation)? • How quickly can your firm’s material flow be restored after a disruptive event (respond)? • How quickly can a firm deal with a disruptive event (adapt)? • How easily can a firm recover its normal operating performance after a disruptive event (recover)?

1.3 Supply Chain Flexibility (SCF) SCF represents the ability of a firm’s SC to change rapidly and adapt to the market, cope with uncertainty, and ensure a continuous flow of products and services throughout the SC (Blome et al. 2014; Liu et al. 2019). Pujawan (2004) states to carefully review whether the company must invest in flexibility because it is costly and may not necessarily improve performance and customer satisfaction. Flexibility is the basis for sourcing (Azevedo et al. 2013) and has been used to measure resilience in SC (Shin and Park 2021). However, most studies have focused on flexibility in manufacturing systems (Merschmann and Thonemann 2011), which presents a great research opportunity. Companies have always competed throughout history to dominate the market and stay in the position. However, this competition has increased due to globalization and agreements between countries that have helped increase international trade. This has led to extended SC across borders and with the challenge of being able to supply a market increasingly demanding in time, innovation, and product customization, among other things. This is why SCF has become a strategy: the mass customization of demand, responsiveness management, cost management, and quality management (Liu et al. 2019). SCF began to be studied in 1997, but the appearance of SCR has had a greater impact since 2007. Previously, flexibility was visualized in production and manufacturing systems; however, it was not considered in material flow along the SC. Figure 1.4 shows the trend of documents published in Scopus with the keyword “supply chain flexibility.” There is still a lack of scientific development, so there is an opportunity to expand knowledge on this topic. However, the adjusted trend line indicated an increase over time. By 2022, 17 documents had been published in Scopus, but they were not considered in the illustration so as not to affect the adjustment of the second-degree polynomial curve or the R2 -value. Measuring SCF is not easy, but Shin and Park (2021) propose the following three items: • Adjust the delivery time of the supplier’s order to mitigate disruptions • Adjust production volume capacity in response to a disruption • Adjust delivery schedules to cope with disruptions.

1.4 Supply Chain Agility (SCA)

5

50 45

Documents

40 35 30

y = 0.012x2 - 46.8x + 45668 R² = 0.8644

25 20 15 10

2021

2019

2017

2015

2013

2011

2009

2007

2005

2003

2001

1999

0

1997

5

Years Fig. 1.4 Research trends in the SCF

1.4 Supply Chain Agility (SCA) SCA is a company’s ability to work collaboratively and effectively with its business partners to respond quickly to market changes (Liu et al. 2013; Riquelme-Medina et al. 2022). For their part, Bengtsson and Raza-Ullah (2016) indicate that agility represents the ability to make rapid changes and develop and establish cooperative and competitive relationships between companies. That is, shared resources can make a difference and obtain valuable benefits for companies when a disruptive event occurs, which can lead to obtaining the expected or superior performance. In the business environment, companies depend on each other in the SC; therefore, if a partner makes an inappropriate decision, it can harm the flow and balance of the SC, and that is where agility becomes important. Agility has become essential in establishing long-term collaboration contracts (Bengtsson and Johansson 2012). The union between members of the SC is strengthened by developing cooperative work and visualizing a common goal, which allows sharing risks and management challenges (Gnyawali and Park 2009). This helps obtain strategic positioning in the market by responding efficiently and effectively to uncertain events. The SCF and SCA followed the same trend. Both began to be studied in 1997 and have had a greater impact since 2007 with the appearance of SCR; however, agility shows greater interest than flexibility, having almost 40 more documents published simultaneously, but both denote opportunity in the breadth of the study. Figure 1.5 illustrates the evolution of the number of papers found in the Scopus database with the keyword “supply chain agility,” where an exponential curve has been fitted to represent its growth. It is worth mentioning that the year 2022 identified 47 documents published as of September, which is still higher than the number of publications in 2021; they are not included in the graph because the year is still in progress.

6

1 Resilience and Its Key Drivers in the Supply Chain

60

Documents

50 40

y = 2E-133e0.1531x R² = 0.8882

30 20

2021

2019

2017

2015

2013

2011

2009

2007

2005

2003

2001

1997

0

1999

10

Years Fig. 1.5 SCA research trend

Shin and Park (2021) indicate four items to assess the SCA in a company, which are listed below: • • • •

Adapt SC processes to decrease lead times Adjust SC processes to increase on-time delivery Streamline SC processes to decrease non-value-added activities Adapt SC processes to decrease new product development cycle time.

1.5 Supply Chain Efficiency (SCE) Agile or flexible companies must benefit from companies in their SC, and SCE is one of them. SCE is the members’ ability to manage their operational and management resources together in such a way that they potentiate sustainability over time (Purvis et al. 2016; Shin and Park 2021). Management in SC is an approach to increase efficiency through different parameters and indexes because it can help obtain competitive advantages in market positioning and permanence. For Shafiee et al. (2014), SCE measures the result of members’ integration, which is why global efficiency measurement is difficult. For international companies, performance is the key to measuring current positioning and defining future goals, which is why efficiency is a key indicator in all types of management. The SC provides an overview focused on members’ management functions. Knowing the SCE will help improve operations practices, improve the codependence relationship, and visualize the benefits of sharing resources.

1.6 Supply Chain Alerts (SCAL)

7

70

y = 0.035x2 - 138.37x + 136771 R² = 0.8996

60

Documents

50 40 30 20

2020

2018

2016

2014

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

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0

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Years Fig. 1.6 SCE research trend

According to Talay et al. (2020), SCE is important for achieving long-term economic sustainability. Figure 1.6 illustrates the timeline regarding the number of documents found in the Scopus database with the keyword “supply chain efficiency.” From 2000 onwards, interest in SC efficiency increased, as the data show a second-degree polynomial trend; for example, by 2022, 66 papers have been identified; however, they are not included. A questionnaire that measures SCE has been identified (Shin and Park 2021). The questionnaire had three items, which are listed below: • Decrease distribution costs (including transportation and handling) • Decrease manufacturing costs (including labor, maintenance, and rework costs) • Decrease inventory costs (including inventory investment and obsolescence, work-in-progress, and finished goods).

1.6 Supply Chain Alerts (SCAL) SCAL is a company’s ability to quickly discover changes in SC operations and management in its business environment. So, it consists of monitoring and timely detection of variations that exist or may exist (Li et al. 2017; Queiroz et al. 2022; Shin and Park 2021). To prevent such variations, SCAL constantly monitors and increases SC visibility. The SCAL allows companies to develop timely detection capabilities in the face of unexpected events. Its importance lies in being a tool capable of detecting and

8

1 Resilience and Its Key Drivers in the Supply Chain

continuously monitoring possible disturbances (Queiroz et al. 2022) and simultaneously contemplating opportunities in the challenges present in internal or external environments (Gligor et al. 2013). The company can establish warning signals in the administrative and operational areas of the SC, which can be beneficial during a disruptive event three times: before, during, and after. A search in the ScienceDirect database with the keyword “supply chain alertness” for the last few years indicates that only seven results were found with this keyword for 2022(1), 2021(1), 2019(2), 2018(1), 2015(1), and 2010(1). From the Scopus database, only three results were found. Therefore, it is not possible to create a trend graph because the first published article was published in 2010; it is understood that the concept of SCAL is in the process of development as a new area of research, so it is of utmost importance to collect data and study and analyze the effects in order to contribute to this gap in the literature. A questionnaire that measures SCAL was identified (Shin and Park 2021), which marks five items, as indicated below. • Track macroeconomic changes (i.e., structural shifts in markets caused by economic progress, political and social change, demographic trends, and technological advances) • Detect threats to supply networks (closely monitor deviations from normal operations, including near misses) • Detect sudden changes in demand (via the demand-forecasting method) • Detect unexpected changes in the physical flow throughout SCs • Detail contingency plans and regularly conduct preparedness exercises and readiness inspections.

1.7 Supply Chain Leadership (SCL) Leadership is an individual’s ability to influence, motivate, and enable others to contribute to the effectiveness and success of companies in which they are members (Carreiro and Oliveira 2019; House et al. 2002).The concept of leadership is very old, and throughout history, different theories can be found, such as that of the great man (Carlyle and Adams 1907), trait theory (Bernard 1926), behavioral theory (Lewin et al. 1939), situational theory (Evans 1970), and relational theory (Bass 1985; Burns 1978; Sarachek 1968). However, other theories have not been widely accepted in the academic world. The evolution of leadership has changed in recent decades by focusing on the traits and characteristics of the leader (Robbins and Judge 2013) and the relationships between the leader and the follower, which has benefited the understanding and impact that the leader can have in a company. Currently, leadership is seen as another asset that an organization has and is studied as such. To Mokhtar et al. (2019), the SCL seeks the collaboration of all members to improve the systems of raw material sourcing, production, and distribution of finished products. On the other hand, Sharif and Irani (2012) mention that SCL is responsible

1.7 Supply Chain Leadership (SCL)

9

20000 18000

y = 2E-67e0.0808x R² = 0.9689

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Years Fig. 1.7 Trend in leadership research

for the management of the latter, and according to Akhtar et al. (2017), SCL is the ability of one or more companies to influence decisions, behaviors, and performances among channel members. SCL entails putting aside the individual interests of the companies that make up the network and focusing on strengthening the SC to reach new levels of competition that companies could not fulfill alone. Additionally, AlNuaimi et al. (2021) mention that leadership should be considered as essential as any other organizational resource, such as the SC. Figure 1.7 shows the trend of the number of published documents found in the Scopus database using the keyword “leadership,” and it can be seen that the first document was published in 1872. However, the exponential growth occurred almost one hundred years later, creating different theories that increased the understanding of this topic. This is undoubtedly one of the variables analyzed that has had the greatest impact on the scientific world. One of the theories on leadership is that of relationships and comprises two types of leadership: transactional and transformational, which are defined below.

1.7.1 Transactional Leadership (TSL) The essence of TSL is the exchange between the leader and follower (Aga 2016), whereby certain rewards or penalties are set depending on the level of performance obtained in a specific function or task (AlNuaimi et al. 2021; Mekpor and DarteyBaah 2017). TSL prioritizes supervision and control of followers; however, it may not be as beneficial because it focuses on quantitative rather than qualitative performance

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1 Resilience and Its Key Drivers in the Supply Chain

(Eliyana et al. 2019). Moreover, it works well when the company is stable and has resources to provide the follower; otherwise, if the company is in a critical situation or renewed, it is not advisable to use it. TSL comprises three dimensions: contingent reward, management by passive exception, and management by active exception (Bass 1985; Martin 2017). • Contingent Reward (CR). In this dimension, the leader grants benefits that can be economical or social to his followers (Aga 2016) in exchange for obtaining the desired levels of performance in the company (Gençer and Samur 2016) and is based on exchange. • Management by Active Exception (MBEA). It is based on supervision and constant monitoring to comply with organizational goals (Birasnav and Bienstock 2019). It is oriented toward reviewing possible system deficiencies and promoting alternatives before serious problems arise to maintain the status quo (Arokiasamy et al. 2015). • Management by Passive Exception (MBEP). This dimension may not benefit the transactional leadership style but weakens it because of its careless behavior (Aga 2016). Some researchers eliminate this dimension from the analyses conducted because of its negative impact, as the leader leaves his followers without guidance and only participates if necessary (Aga 2016). Therefore, this dimension can sometimes be seen as liberal leadership or a lack thereof. Figure 1.8 illustrates the evolution of the number of documents found in the Scopus database using the keyword “transactional leadership” (blue line). The first document was published in 1973 with the appearance of the theory of relationships in leadership; however, three decades had to pass before the study of this style began in earnest. The graph shows the second-degree polynomial trend represented by the red line. By 2022, 73 publications associated with TSL had been identified. TSL in SC (TSSCL) is based on a system of rewards and punishments where the leading company benefits or penalizes other companies depending on the level of performance it obtains at each time. According to Dubey et al. (2015), rewards can be tangible or intangible; in the first, the company can give a prize to the other, and in the intangible form, it could be respect and loyalty in alliances, so the leading company influences the others. Birasnav et al. (2015) argue that TSSCL is applied by companies that supervise or audit another in their operations. It may occur in the quality, manufacturing, and materials departments, among others, where it seeks to determine needs and performance to ensure the satisfaction and expected objectives in the SC (Hult et al. 2000). A questionnaire measuring the TSSCL has been identified in the literature by Mokhtar et al. (2019), which marks 13 items and is shown below: • The leading firm lets us know what is expected of us in the supply chain process (CR). • Leading firms encourage uniform procedures in the supply chain process. (CR).

1.7 Supply Chain Leadership (SCL)

140 120

11

y = 0.1026x2 - 407.65x + 405045 R² = 0.9493

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100 80 60 40 20 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019 2021

0

Years Fig. 1.8 Trend of transactional leadership research

• The leading firm decides what shall be done and how it will be done in the supply chain process (CR). • The leading firm maintains definite standards of performance in the supply chain process (CR). • The leading firm asks that we follow established purchasing rules and procedures (CR). • Leading firm rewards our company for achievement (CR). • Our company is punished for fault and misconduct, such as late delivery (CR). • Leading firm tracks our company mistakes (MBEA). • The leading firm concentrates its full attention on dealing with our mistakes (MBEA). • The leading firm concentrates on our failures (MBEA). • The leading firm believes in “if not broken, do not fix it” (MBEP). • The leading firm does not interfere in our company production problems (MBEP). • Leading firm avoids making decisions (MBEP).

1.7.2 Transformational Leadership (TFL) TFL is a leader’s ability to influence followers through inspiration, motivation, and empathy (AlNuaimi et al. 2021). This leadership positively impacts organizations and also influences followers on a personal level (Chatterjee and Kulakli 2015; Martin 2017), as the leader is seen as a mentor who supports, develops, coaches, and cares for followers, presents a shared vision as well as establishes trust and confidence (Yue et al. 2019).

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1 Resilience and Its Key Drivers in the Supply Chain

TFL facilitates company renewal by promoting change (McKean and Snyderman 2019). It empowers employees and promotes a relationship based on empathy, charisma, and loyalty, so they act on an emotional level (Luo et al. 2019). Several studies have related this type of leadership to emotional intelligence (Chatterjee and Kulakli 2015). In addition, it creates and enhances organizational culture, helps followers transcend their interests in favor of the collective and corporate good (Park and Pierce 2020), focuses on values and principles for which he/she is admired and respected, and is highly professional and ethical (Arokiasamy et al. 2015), and empirical evidence states that it is the most effective leadership style (Den Hartog et al. 1999; Park and Pierce 2020). TFL integrates four dimensions: idealized influence, inspirational motivation, intellectual stimulation, and individualized consideration (Bass 1985; Martin 2017). • Idealized influence (II) establishes a relationship based on values and emotions; the leader shares a strategic vision and mission with the follower (Avolio and Bass 2004; Shao et al. 2017). Here, the leader is admired and respected by his followers and has a charismatic attitude. Power is found in that the follower wants to become like his or her leader (Gellis 2001; Park and Pierce 2020), develops autonomy in the subordinate, and possesses ethical conduct based on principles and values (Sandstrom and Reynolds 2020). • Inspirational motivation (IM) enthuses the follower to reach optimal levels in the performance of their activities, which entails high expectations to maximize efforts (Shao et al. 2017), shares his or her vision, and motivates followers to achieve it (Gellis 2001; Park and Pierce 2020), foster optimism (Avolio and Bass 2004), and encourages individual and team spirit (Sandstrom and Reynolds 2020). • Intellectual stimulation (IE) encourages followers to innovatively solve problems and come up with creative ideas, promote intelligence (Avolio and Bass 2004; Shao et al. 2017), break with long-held beliefs, take risks (Park and Pierce 2020), and question procedures and the current system (Sandstrom and Reynolds 2020). • Individualized consideration (IC) promotes new learning opportunities according to their followers’ capabilities; develops their potential, coaches, and mentors (Sandstrom and Reynolds 2020; Shao et al. 2017); addresses needs and concerns on an individual basis (Park and Pierce 2020); and fosters a supportive climate (Avolio and Bass 2004). Figure 1.9 shows the evolution of the number of documents found in the Scopus database with the keyword “transformational leadership.” Like transactional leadership, this style is representative of relationship theory. However, as can be seen in the graph, this style has been studied almost four times more than TSL due to its great acceptance in the academic and business world and its great results in different areas. The research trend presents a second-degree polynomial curve, and it is important to mention that in 2022, 468 publications associated with TFL have been identified. However, they are not integrated into the analysis to avoid affecting the adjusted trend line and decreasing the value of R2 .

1.7 Supply Chain Leadership (SCL)

13

700 600

y = 0.7497x2 - 2987.6x + 3E+06 R² = 0.9755

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2006

2004

2000

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0

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Years Fig. 1.9 Trends in transformational leadership research

TFL fosters relationships based on empathy, charisma, and loyalty; therefore, it is considered emotional leadership (Luo et al. 2019). When TFL is applied to SC (TFSCL), it determines how one company influences another, shaping its actions and behaviors (Mokhtar et al. 2019). Here, the firm inspires others, driving its business partners to reach new levels of efficiency, such as technological innovation (Wamba and Chatfield 2009) or social responsibility (Müller-Seitz and Sydow 2012). This leadership facilitates renewal while promoting change (McKean and Snyderman 2019), establishes long-term relationships with partners (Birasnav and Bienstock 2019), empowers, develops, and coaches them (Yue et al. 2019), and establishes trusting relationships. Empirical evidence affirms that it is the organization’s most effective leadership style (Park and Pierce 2020). To measure TFSCL, ten items were identified by Mokhtar et al. (2019), which are listed below: • The leading firm goes beyond its self-interest for the good of the supply chain (II). • Leading firm talks enthusiastically about what needs to be accomplished in the supply chain (II). • Leading firm clarifies the central purpose underlying their supply chain actions (II). • The leading firm displays power and confidence (II). • The leading firm seeks different views when solving supply chain issues (IS). • The leading firm suggests new ways of solving supply chain issues (IS). • Our company is encouraged to express ideas (IS).

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1 Resilience and Its Key Drivers in the Supply Chain

• The leading firm spends time teaching and coaching us (IC/IM). • Our company gets individual consideration (IC). • The leading firm encourages us to improve our strengths (IC/IM).

1.8 Conclusions As seen in this chapter, the latent variables in the supply chain have been of great importance in the scientific world; all the variables showed an increase in the research trend over the last decade, which shows interest in this topic. The key factors or variables observed to measure each construct, and their importance was also determined, which will be used to propose and analyze the experimental models in the following chapters.

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Chapter 2

Definition of Variables and Research Problem

Abstract This chapter analyzes six latent variables associated with supply chains (SC). These variables included resilience (SCR), flexibility (SCF), agility (SCA), efficiency (SCE), alertness (SCAL), transactional leadership (TSSCL), and transformational leadership (TFSCL). The primary studies on these topics are discussed, the most influential authors are identified, and possible future research directions for each topic are discussed. Similarly, the research problem and context of the Mexican maquiladora industry are presented, and the general objective of the research and the motivation of this book is stated according to the lack of research on this topic. Keywords Resilient supply chain · Flexibility · Agility · Efficiency · Alerting · Leadership

2.1 SC Resilience (SCR) Businesses need to create and strengthen resilient supply chains due to the increased frequency of risks, such as natural disasters, economic crises, epidemic crises (Fabeil et al. 2023), wars (civil, global, commercial, technological), and geopolitical events, among others. These risks are increasingly recurrent, and their occurrence in any part of the supply chain (SC) affects all members, leading to repercussions throughout the supply chain. This is because they are now globalized, and the failure or vulnerability of one company affects all trading partners. Therefore, a lack of supply availability will have a negative impact on the commercial image of companies and may cause customer dissatisfaction and alter consumer loyalty. This is the main reason why companies seek to have high levels of resilience as they retain their customers, deliver all orders on time, and do not receive administrative penalties. SCR is considered in three distinct phases of a disruptive event: anticipation, response, and recovery, which are illustrated in Table 2.1, as well as the key indicators in each phase (Basu et al. 2022; Singh et al. 2019). SCR must establish agility as a means of speed to reach the market on time and consider robust management as a business strategy (Elali 2021). Sharma et al. (2022)

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. M. Solis et al., Leadership and operational indexes for supply chain resilience, SpringerBriefs in Applied Sciences and Technology, https://doi.org/10.1007/978-3-031-32364-5_2

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2 Definition of Variables and Research Problem

Table 2.1 Resilience in the supply chain SCR phase

Phase description

Key indicators

Anticipation

It consists of anticipating the disruptive event and preparing for the changes it may cause. Here, it is necessary to analyze the potential risks of disruption and take actions in the SC to minimize the probability of occurrence, so it is necessary to know and understand the event’s impact if it happens

This phase has indicators that can help to understand the event, such as: Awareness, visibility, alertness, safety, security, sustainability, and risk management culture in SC

Resistance or disruption This phase appears once the disruptive event is detected, and it is the capacity with which the SC withstands, evades, and eliminates the effect, thus maintaining the continuity of its operations

Here, the indicators are flexibility, redundancy, collaboration, revenue sharing, supply chain network, and robustness

Recovery and response

The indicators here are information sharing, speed, agility, adaptability, market position, information sharing, and public–private partnership

In this phase, the disruptive event has already occurred. It is responsible for reducing the negative effect, responding quickly and recovering the status quo and finding ways to use it as a driver to create competitive advantages

state that SCR can cope with customized demand and maintain inventory strategically and surplus capacity. Gebhardt et al. (2022) indicate that building SCR is more important to manage SC dependencies than internal capabilities effectively. Therefore, maintaining connections between members provides control over structure and function. The COVID-19 pandemic has severely affected the SC in enterprises (Sharma et al. 2021). However, it is not yet certain that companies are implementing resilient strategies as management measures in the face of possible future events and therefore, studies considering this field in research are needed. That said, there is a great area of opportunity in research associated with SCR that generates knowledge since academic reports in this area are still scarce, despite the pandemic has encouraged interest in this study, many SC tend to break when a disruptive event occurs, and the recovery time is long and slow, which is why it is necessary to know the tools and the impact they have on the resilience of the network, as support to recover quickly and benefit in adverse situations.

2.2 SC Flexibility (SCF)

21

2.2 SC Flexibility (SCF) The flexibility of a company in supply channels, manufacturing, and distribution provides support for reducing the level of uncertainty that may be present in the environment (Li et al. 2019). That is, all members in the SC must perform functions aimed at adapting their operations and tactics in favor of strengthening connections in the network, which gives companies the ability to face disruptive events that may occur, maintain performance levels in the SC, and even increase them. SCF has emerged as a strategy for organizations’ competitiveness in global SC (Delic and Eyers 2020). It is a strategy for organizations, as keeping up with unstable environments provides great help in sectors that are constantly changing technology, customizing demand, and shortening product life cycles. However, SCF can entail heavy investments of resources; therefore, it is necessary to perform more detailed analyses and review whether it is in the company’s best interest to invest in flexibility or how much flexibility is in the company’s best interest (Pujawan 2004). SCF shows the ability to reconfigure and coordinate resources. Different types of flexibility can be highlighted in the literature, such as flexibility in transportation, postponement, supply base, order fulfillment (Pettit et al. 2013), change in delivery time, and deadlines with suppliers. In other words, flexible sourcing, systems, and distribution serve as a basis for reducing the impact of disruptive events (Tukamuhabwa et al. 2015). Figure 2.1 shows the SCF process (Bing and Yili 2008), which aims to satisfy customer demand. Different methods used in SCF research have also been highlighted, such as case studies (Fayezi et al. 2015) and conceptual (Yu et al. 2018), Delphi study (Yu et al. 2018), model building (Seebacher and Winkler 2015), simulation (Kemmoe et al. 2014), and surveys (Rojo et al. 2018). Flexibility has been widely studied in manufacturing and extended to SC as a performance variable in managing this condition. However, SCF and its impact on SCR have been little studied; therefore, more research is needed to provide information and new knowledge that can help reduce the literature gap. Some studies have found different results; for example, Shin and Park (2021) showed that SCF does not significantly impact SCR, whereas, in other studies, SCF is considered a dimension of SCR (Singh et al. 2019).

Fig. 2.1 Process flexibility of supply chain

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2 Definition of Variables and Research Problem

2.3 SC Agility (SCA) SCA is directly related to the speed and visibility of the network, which mitigates the risks of disruption and reacts effectively to market challenges (Riquelme-Medina et al. 2022). Different studies have placed agility in the response and recovery phase of resilient supply chains (Kamalahmadi et al. 2021; Lücker and Seifert 2017). Different dimensions have been used for its measurements, such as alerts, accessibility, flexibility, speed, decisiveness (Gligor et al. 2015), demand responsiveness, customer responsiveness, and joint planning (Tse et al. 2016), speed, resilience and adaptability (Ivanov and Dolgui 2020), to name a few. According to Saeed et al. (2019), it is necessary to advance the level of visibility and speed to obtain a higher level of agility. Visibility promotes monitoring and forecasting disruptive events, whereas speed promotes recognizing the event and the speed with which it responds. Figure 2.2 shows the supply chain agility perspective, whereas a company advances in speed and visibility, and it also advances in agility. SCA can be divided into two categories, as listed in Table 2.2. The first is based on a physical category that entails companies’ speed and flexibility in the network. The other category is cognitive, which is related to the process of shared information that helps companies as a guide to make assertive decisions, and these entail alerts and accessibility; that is, cognitive alerts support physical ones (Gligor et al. 2013). In other words, SCA allows companies to control and respond proactively to disruptive events without precedent, so it can be said to work in hostile environments as a response measure. Currently, some crises can change the way of life and Fig. 2.2 Supply chain agility perspectives (Saeed et al. 2019)

Table 2.2 Supply chain agility area

Supply chain agility Cognitive area

Physical area

Alertness Accessibility Decisiveness

Swiftness Flexibility

2.4 SC Efficiency (SCE)

23

companies’ future. This forces the reexamination of strategic solutions that support adaptations and rapid responses, including one in the SCA. Do et al. (2021) conducted an exploratory case study showing how SCA supported food SCs during the COVID19 crisis, in which companies adopted agile responses to withstand the pandemic and developed sustainability capabilities to sustain and reposition themselves after the pandemic. Because these events and others are likely to occur in the future, companies need to strengthen the agility of their supply and distribution networks. It is necessary to know the level of impact that SCA has on companies’ SCR, which justifies its study.

2.4 SC Efficiency (SCE) SCs are designed with value-generating activities that eliminate redundancy in their processes and maximize efficiency (Basu et al. 2022). However, over time, SCs are affected by disruptive events that break the flow and impair established efficiency, leading to increased costs and reduced financial benefits for companies. Thus, SCE focuses on optimizing resources, minimizing redundancy, and maximizing return on investment (Trump et al. 2022). SCE has been studied as a performance variable (Basu et al. 2022), as a mediating variable of SCR (Queiroz et al. 2022), and as a counterresponse variable to SCR (Belhadi et al. 2022). Thus, there are contradictions in their study due to the difference in the metrics used to measure it (Gu et al. 2021; Ruiz-Benítez et al. 2018). Some of the metrics used to measure these latent variables are listed in Table 2.3 (Belhadi et al. 2022). Sujitha et al. (2023) conducted a study where they proposed a model to measure the impact of Industry 4.0 practices (IoT implementation, robotic work environment, data integrity) in manufacturing companies and SCE. The results showed that the three variables analyzed had an impact on SCE; however, IoT had the greatest impact because it was based on the tracking of goods, safety, and supervision, which leads to a decrease in risks in the work environment and favors a constant flow in the SC. Table 2.3 Supply chain performance

Efficiency

Resilience

Product quality Rework reduction Lower costs Maintenance failure reduction High utilization Tool reduction Inventory reduction

Process reliability Better planning Bottleneck reduction Better visibility and collaboration Lead-time reduction Customized items Improved order filling Development cycle reduction Capacity flexibility

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2 Definition of Variables and Research Problem

On the other hand, Shin and Park (2019) positioned SCE in a diagram of SCR capabilities, establishing that it improves visibility, adaptability, risk control, agility, flexibility, and alerts. Shin and Park (2021) evaluated SCE as a mediating variable between leadership and resilience, obtaining a significant impact, which leads to SCE if it supports SCR during a disruptive event. Since it was found in the literature that there are contradictions in the relationship between SCE and SCR, this study aims to provide a clearer view of both variables and provide a broader picture of decision-making before, during, and after a disruptive event.

2.5 SC Alerts (SCAL) For Li et al. (2017), SCR comprises three dimensions: preparation, alerts and agility. This study argues that SCAL can detect changes in a network, which can be considered threats or opportunities. These challenges or opportunities may be internal or external, and maintaining a surveillance system is critical to address them while benefiting agility in the network (Li et al. 2009). According to Blackhurst et al. (2005), a company’s alert system is positively related to market influence. At the same time, provides guidance to adapt the SC according to the required changes and maintain market position and wealth generation (Btandon-Jones et al. 2014). The importance of SCAL was demonstrated in a study conducted by Queiroz et al. (2022), who propose a model to explore the resilience capacity in the chain, considering SCAL as a focal variable between supply chain disruption orientation (SCDO) and SCR, directly and indirectly through the resource reconfiguration (RREC) and supply chain efficiency (SCE) variables. The results showed that SCAL had a significant direct and indirect effect; however, indirectly, SCAL → SCE → SCR was rejected, but it was accepted as a partial mediator in SCAL → RREC → SCRE. In addition, Li et al. (2017) report a study to determine the impact of SCR preparedness, alerts, and agility on firms’ financial performance. The study was conducted on 77 companies, showing that these three variables significantly influenced financial performance. Likewise, in the study developed by Jindal et al. (2021), agility was analyzed in four dimensions: alerts, which are divided into analytical capabilities and human resources, as shown in Fig. 2.3. Given the importance of alerts and the fact that they have been used as a dimension and mediating variable in SC, studies are needed to explore their impact during a disruptive event and help fill the gap in the literature. This study contributes to this knowledge gap.

2.6 SC Leadership (SCL)

25

Analytical Capabilities

Data processing models Availability and reliability

Human Resource

Employees to develop meaningful models

Supply Chain Alertness

Fig. 2.3 Criteria in supply chain alertness

2.6 SC Leadership (SCL) Over the years, various leadership styles have been identified, including autocratic, democratic, liberal, and participative (Dyer 1986; Fries et al. 2021), charismatic, transactional, and transformational (Hughes et al. 2018; Liphadzi et al. 2015); however, currently, the transactional and transformational leadership styles developed in relationship theory are the most accepted ones (Bass 1985; Burns 1978; Sarachek 1968). Transactional (TSL) and transformational leadership styles (TFL) greatly contribute to follower influence, although they are not necessarily complementary or mutually exclusive. They are considered one of the most dominant theories (Braun et al. 2013; Wu et al. 2017). Their impacts have been studied in different areas, such as hospitals (Baysak and Yener 2015), academic (Cetin and Kinik 2015), business (Chiu and Walls 2019) and military (Martínez-Córcoles and Stephanou 2017) and recently in the supply chain (Mokhtar et al. 2019). Supply chain leadership (SCL) focuses on the collaboration of all members by defining responsibilities, expected returns, and influencing decisions and behaviors throughout the entire network (Mokhtar et al. 2019). Figure 2.4 summarizes the study by Mokhtar et al. (2019), who presented a literature review of supply chain leadership using SCOPUS and WOS databases with 51 papers, reporting the methodology, practice, and style of leadership. In addition, a literature review was conducted using the SCOPUS database with the keywords “supply chain leadership” and “supply chain resilience,” and only one document was found. The search was then expanded to “leadership” and “supply chain resilience,” and 16 papers were found, but only two analyzed a leadership style in SCR. The first study was conducted by Taseer and Ahmed (2022), who compared the effectiveness of TSL and TFL in achieving SCR, directly and indirectly, using the variables of supply chain flexibility and agility (in sequence). The study was conducted among manufacturing SMEs in Pakistan, and the results indicated that both leadership styles impact SCR. In addition, the sequence between the variables was also significant, although it is worth mentioning that the difference between

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2 Definition of Variables and Research Problem

Employed research methodologies

Supply Chain Leadership

SCL Styles

Practices from SCL studies

Conceptual Mixed Qualitative Quantitative

10 papers 2 papers 15 papers 24 papers

Transformational and transactional General leadership Behaviorist leadership

24 paper 22 papers 5 papers

Operational performance 19 papers Buyer supplier relationships 19 papers Sustainability 13 papers

Fig. 2.4 Literature review of supply chain leadership

the two leadership styles was minimal; however, the one that presented the greatest influence was the transactional style. The second paper, directed by Shin and Park (2021), analyzed the SC leadermember exchange (LMX) and its impact on SCR, where the mediating variables were flexibility, agility, efficiency, and SC alerts; the results showed the impact of leadership on the four variables; however, the flexibility and agility variables were not significant in SCR. Based on the results, it can be determined that there is a great opportunity to conduct studies to determine the impact of SCL on SCR, owing to the scarcity of documents that address this topic.

2.7 Research Problem and Objective The Mexican manufacturing sector is recognized worldwide for its high-quality products. One of these sectors is the Manufacturing, Maquiladora and Export Service Industry (IMMEX), which integrates companies focused on manufacturing and providing exported services, including the famous maquiladora. A maquiladora is defined as a foreign capital company established in the national territory of Mexico. It is characterized by importing raw materials and exporting finished products, generally to the countries of origin or to the USA and Canada (García-Alcaraz et al. 2016). Thus, it takes advantage of the high quality of human resources and their low cost and the free trade agreements that Mexico has with several countries, which allow preferential tariff rates. Mexico currently has 5160 IMMEX-certified companies, of which 486 are located in Chihuahua state, representing 9.42% of the national total, and 322 are located in Ciudad Juarez, representing 6.43% of the national total and 66.25% of the entire state. Thus, the maquiladora industry was the main industrial sector in Ciudad Juarez.

References

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IMMEX generates 2938,160 jobs nationwide and 502,009 jobs in the state of Chihuahua (17.08% of the national total), of which 331,180 are in the industry established in Ciudad Juarez (11.27% of the national total and 65.97% of the total state) (IMMEX 2023). Of these IMMEX workers, many occupy management positions are leaders who make decisions and are in charge of the production processes and various operational personnel. The study of leadership in the maquiladora industry was limited to only four studies. For example, O’Brien and Torres (1993) analyze leadership and combination as a binomial that can lead to excellence, and Moure-Eraso et al. (1994) analyze the leadership of maquiladoras to ensure the health of their workers. Howell et al. (2003) analyze the effectiveness of leadership and discuss the main challenges and expectations, and finally, Morales et al. (2019) analyze the producers of resilience through a factor analysis, this being the most focused on addressing leadership issues in Ciudad Juarez. Due to the lack of research on leadership in the IMMEX sector and its social and economic importance, this book proposes five structural equation models that relate to the abovementioned variables. So managers and leaders in this sector can understand certain variables’ impact on the supply chain’s efficiency and resilience. The results of this study will allow leaders to optimize their resources to achieve their companies’ goals, in addition to being a precedent for many other works that can be generated in this sector.

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Chapter 3

Methodology

Abstract This chapter presents the methodology for answering the research question and achieving the research objectives in nine stages. First, a literature review was conducted to determine the state of the art and identify possible previous research, which allowed for the design of a questionnaire that was then adapted to the geographical context of the research. This questionnaire was applied in the automotive maquiladora industry to obtain a cleaned database to identify extreme and missing values. Subsequently, statistical validation based on indexes was carried out, which allowed us to determine the reliability of the variables analyzed. In addition, a descriptive analysis of the sample and the items that have been analyzed in each latent variable is reported, which are related using structural equation models that are interpreted and analyzed, reporting a sensitivity analysis of the same. Keywords Descriptive analysis · Structural equation modeling · Data cleaning · Statistical validation · Sensitivity analysis To achieve the objective of relating the types of leadership to the agility and flexibility of the supply chain and its efficiency and alert system, the methodology illustrated in Fig. 3.1. Each of these methodological stages includes a series of activities analyzed in detail in the following sections. The methodology begins with a literature review regarding the concepts of transformational and transactional leadership in the supply chain, including agility, flexibility, resilience, and alertness, allowing the application of a questionnaire in the regional manufacturing industry. The data were entered into specialist statistical software, debugged from outliers and missing values, and then every latent variable was validated. The validated variables were then integrated into different structural equation models, and efficiency indices were obtained iteratively and interpreted according to the cutoff values. Furthermore, one of the book’s significant contributions is that it reports a sensitivity analysis for each model presented. It is based on conditional

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. M. Solis et al., Leadership and operational indexes for supply chain resilience, SpringerBriefs in Applied Sciences and Technology, https://doi.org/10.1007/978-3-031-32364-5_3

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3 Methodology

Fig. 3.1 Methodology

probabilities and allows managers to know possible scenarios and facilitate decisionmaking by identifying risk variables and those that favor the benefits they seek for the company.

3.1 Literature Review—Rational Validation A literature review was conducted to understand the current state of the art regarding leadership styles in the manufacturing industry and agility, flexibility, resilience, and alertness in the supply chain. This was done because this book aims to determine the relationship between all those variables. The review was carried out by searching for information using the terms “supply chain agility,” “supply chain leadership,” “supply chain agility,” “supply chain flexibility,” “supply chain resilience,” and “supply chain alertness” in databases as Springer, Ingenta, and Scopus, ScienceDirect, among others. The identified documents were saved, and a database was created using EndNote X9® software with files in the risk extension. EndNote X9® software allows for the identification of duplicated documents reported from different databases. This review aimed to identify the items used to measure the latent variables reported in research from other countries and industrial sectors. The literature review was aimed at generating a first questionnaire draft.

3.3 Questionnaire Application to Industry

33

3.2 Questionnaire Design—Judge Validation The first draft of the questionnaire was divided into the following four sections. 1. Introduction. It describes the objective research that will be conducted and the use that will be made of the information that will be gathered and asks for permission to analyze and publish the results while maintaining the respondents’ anonymity. 2. Demographic information. Factors such as the respondent’s gender, years of experience in their job position, the industrial sector in which the company operates, the respondent’s current position, and the number of years they have worked were investigated. This section contained multiple-choice and open-ended questions. 3. Latent variables. This section is split into subsections based on the latent variables analyzed. Respondents were asked to rate how strongly they agreed or disagreed with each statement or item using a Likert scale ranging from one to five (1 = never, 2 = infrequently, 3 = occasionally, 4 = frequently, 5 = always) (Vonglao 2017). Several authors have identified some latent variables of interest in the literature review. The latent variables were integrated as follows: • Supply chain resilience (four items), supply chain flexibility (three items), supply chain agility (four items), supply chain efficiency (three items), and supply chain alertness (five items) were obtained from Shin and Park (2021). • Transactional leadership (13 items) and transformational leadership (10 items) were obtained from Mokhtar et al. (2019). 4. Acknowledgment. In this section, we thank all the participants and ask their email addresses to send them the analysis results. However, the items identified came from other research, countries, and industrial sectors, so the questionnaire was adapted to the context of the Mexican maquiladora industry; therefore, validation by judges was performed (Gagnon et al. 2018). Six industrial managers and five academics were used to evaluate the items contained in each latent variable, who should evaluate the consistency and appropriateness of the geographical context so that the respondents understand them.

3.3 Questionnaire Application to Industry The questionnaire was administered to personnel directly involved in the production lines according to the following inclusion criteria: • Responders should play a leadership role in a company.

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• Responders should be laboring in companies recognized by the Manufacturing, Maquiladora, and Export Services Industry (IMMEX), which guarantees that the manufacturing sector is the respondent and has a high flow of information and goods. • Respondents should preferably have at least one year of experience in their positions to guarantee familiarity with leadership and operative indexes such as agility and flexibility. Stratified sampling was used in this study (Singh et al. 2016; Arnab 2017) because it tries to collect data only from industries registered in the IMMEX program. The questionnaire was administered from February 15 to July 15, 2022, during which the COVID-19 pandemic was considered active and restricted access to industries. Therefore, the questionnaire was administered online through Google Forms to avoid direct contact with the respondents. IMMEX provided emails of leaders or managers in the industry. In the first email message, potential respondents were invited to answer the questionnaire, explaining the purpose of the survey and providing a link to access it. If there was no response after two weeks, a reminder was sent again, and if there was no response after three weeks, the case was discarded from the analysis.

3.4 Data Capture and Debugging On July 15, 2022, an Excel file was downloaded from Google Forms. However, to facilitate the calculations and debug the information, the Excel file was read using SPSS v.25® software (IBM 2019), where a database was designed. Each column represents an item in the questionnaire, and each row represents a respondent case or response (IBM 2019; Šebjan and Tominc 2015). An acronym and label were generated to identify each latent variable and its associated items fully. The following procedures were performed before any data analysis to clean the data in the database. The main activities for debugging information are as follows. • Missing values identification. If more than ten percent of the questions in the questionnaire were left unanswered, that case was omitted from the analysis (Crambes and Henchiri 2019). In addition, because the data are organized on an ordinal scale, missing values are substituted with the median when the proportion is smaller than 10% (Dray and Josse 2015). • Identification of non-engaged respondents. The standard deviation of each case is estimated, and given that the rating scale runs from 1 to 5, cases with a standard deviation lower than 0.5 are also removed from the study because they represent non-engaged responders (Gnagnarella et al. 2018). • Outlier identification. The values of all the items are standardized, values higher than 4 represent extreme values, and the median is used instead as a measure of the average central measure (Hoffman 2019; Kaneko 2018).

3.6 Descriptive Analysis of the Sample and Items

35

3.5 Statistical Data Validation After having a cleaned database (without extreme values, missing values, and uncommitted respondents), we proceeded to statistically validate the latent variables that have been identified, which helps to determine if the items are correctly assigned. The following indices were estimated to validate the variables (Kock 2018): • R2 and adjusted R2 were used to measure the parametric predictive validity of the latent variables, and the values were anticipated to be greater than 0.02 (Evermann and Tate 2016). • Q2 measures nonparametric predictive validity, and the values obtained must be similar to R2 and greater than zero. • Composite reliability and Cronbach’s alpha indexes are used to test construct validity, and values larger than 0.7 are desired (Adamson and Prion 2013; Kile et al. 2014). • The average variance extracted (AVE) measures the discriminant validity of latent variables, and its values should be greater than 0.5 (Lee 2019). • The full collinearity variance inflation factor (VIF) assesses the degree to which the latent variables are correlated and must have values lower than 5, and ideally values lower than 3.3 (Kock 2019a). It is essential to note that many of these indices were obtained iteratively. Frequently, eliminating some items can improve Cronbach’s alpha index or reduce the collinearity between latent variables. That is why not all items in a latent variable may be integrated into the analysis because they may have been eliminated to improve these indices.

3.6 Descriptive Analysis of the Sample and Items With the database cleaned, cross-tabulations were used to describe the sample using information obtained from the first section of the questionnaire that focused on demographic data, which helped to identify the number of female and male respondents, their jobs, and the companies’ industrial sector in which they worked. A descriptive analysis of the items was performed, and the following values were obtained. • The median was used to measure central tendency because the data came from ratings or assessments on a five-point Likert scale (Iacobucci et al. 2015). A low median value suggests that the activity does not take place or that the benefit is not achieved. In contrast, a high median value suggests that the activity occurs frequently or that the benefit is obtained frequently. • The interquartile range (IR) is the difference between the third and first quartiles and is used to quantify the dispersion. This statistic was also utilized because of the characteristics of the data (Tominaga et al. 2018; Kang and Lee 2005). A low

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IR value suggests that respondents agree on the mean value of the item; however, a high value indicates that there is much dispersion and little consensus over the item’s true value.

3.7 Structural Equation Model The structural equation modeling (SEM) technique quantifies the supposed relationships between various latent variables integrated by items (Avelar-Sosa et al. 2018). SEM allows variables to have different roles in the same model, such as independent and dependent variables (Nitzl 2016). In SEM, the latent variables are denoted by ellipses, and the arrows that connect them indicate their respective associations. The independent latent variable is represented by the arrow’s origin, whereas the arrow’s destination represents the dependent variable. By contrast, the observed items or variables that integrate the latent variables are represented by rectangles (Aktepe et al. 2015). When normal data distribution cannot be assured, data come from assessments given on a Likert scale, or if there is a small sample, the partial least squares (PLS) technique is recommended to evaluate the structural equation model (Martínez-Loya et al. 2018; Willaby et al. 2015). Therefore, this book uses the PLS-SEM approach and WarpPLS v.8 software proposed by Kock (2021). The following indices of model efficiency were examined before moving on to the interpretation of the model. • An average path coefficient (APC) with a significance level of < 0.05. It evaluates the significance of the relationships between variables as a whole. • The model’s predictive value was measured using the Average R-squared (ARS) and average adjusted R-squared (AARS) with a significance level of 0.05. • Average block VIF (AVIF) and average full collinearity VIF (AFVIF) were used to quantify collinearity, and acceptable values must be lower than or equal to 5. • Tenenhaus GoF (GoF), with acceptable values greater than or equal to 0.25, suggests that the data matches the model. If the efficiency indices discussed above are satisfied in the analyzed SEM, then the model interpretation can continue. In SEM, three different effects were evaluated with a confidence level of 95%. As some of the items in the questionnaire might have been removed during the validation process, it is important to point out that not all items in a latent variable are evaluated in the final SEM, given that some can be eliminated after the validation process. For instance, the questionnaire might have seven latent variables; however, after the validation process, it might have only five questions because two were eliminated.

3.7 Structural Equation Model

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3.7.1 Direct Effects In SEM, direct effects are denoted by an arrow that connects two latent variables and represents the hypotheses that need to be tested. It is stated in standardized units and denoted by a β index that indicates the dependence between the two variables being evaluated (Farooq et al. 2018). A p-value was assigned to each β value to determine the statistical significance of the relationship between variables and to accept or reject the null hypothesis H0: β = 0 versus the alternative hypothesis H1: β /= 0. The graph presented before the evaluation shows the proposed hypotheses, whereas the graph presented after the evaluation shows the values achieved. In addition, an R2 value is presented for each dependent variable, indicating the percentage of the variable’s variance that can be attributed to the independent latent variables that explain it. In addition, the effect sizes (ES) are reported, which are decompositions of the R2 value according to the contribution of each independent variable to a dependent variable. This allowed us to identify which independent variables were the most significant and had the best explanatory power in each proposed relationship or hypothesis (Verdam et al. 2017).

3.7.2 Sum of Indirect Effects Relationships between latent variables can be developed through mediating factors. When this occurs, the effects are called indirect effects and can have two or more parts (Boch et al. 2018; Egerer et al. 2018). Several indirect effects can occur between the two variables. In this research, only the sums of indirect effects are provided, and a standardized value is given for each association between latent variables. The β value has a p-value associated with this. Indirect effects are required because they enable the discovery of previously unknown relationships between latent variables and assess the influence of mediating variables that indirectly affect a relationship (Singha et al. 2023). Similarly, ES values are reported to measure the independent latent variable’s capacity to explain the dependent variable’s behavior.

3.7.3 Total Effects The total effects between the variables were represented by the sum of the direct and indirect effects (Schubring et al. 2016). Similarly, a standardized β value was estimated, and a p-value for the statistical significance tests was also provided. In addition, ES is included in the model as a decomposition of the R2 value of the latent dependent variable.

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3.7.4 Mediation Effects The objective of the Sobel test is to analyze the mediating effect of an independent variable on a dependent variable, which is given through a third mediating variable and works adequately when there are large sample sizes (Preacher and Hayes 2008). It was decided to use this test since, according to Preacher and Hayes (2004), mediation exists under four possible scenarios that are present in the models presented in this book: 1. The independent variable significantly affects the mediating variable through a direct effect. 2. The independent variable significantly affects the dependent variable without a mediating variable. 3. The mediating variable has a unique and statistically significant effect on the dependent variable. 4. The effect of the independent variable on the dependent variable is reduced when the mediating variable is excluded. Although these evaluations are purely empirical, for analyzing the mediating effects, this book uses the Sobel calculator, available online at the following link: https://quantpsy.org/sobel/sobel.htm, which only requires the calculation of the direct effects between the variables and the standard error of these relationships. For readers interested in further technical aspects of the test, see Mackinnon and Dwyer (1993). Specifically, this book uses the method proposed by Baron and Kenny (1986), which is based on Aroian (1947) reports, in which the third term of variance is omitted. Readers interested in more recent methods and techniques, please review the report by Shrout and Bolger (2002).

3.8 Model Interpretation and Practical Implications The efficiency indices and parameters obtained were interpreted based on the statistical results of the relationships between the variables in the SEM. If the direct effects between the two variables are not statistically significant, then the direct effects are analyzed because they may occur, and the differences are discussed. In the same way, if the relationship is not significant, the possibility of eliminating it from the SEM analysis is analyzed, rerunning, and reinterpreting the model, so it can be said that the model is iteratively refined. Similarly, the curves associated with the variables are analyzed, identifying the minimum, maximum, and inflection points where the changes occur. This allows for identifying when the best performance is obtained in a response variable and thus planning the investment that can be made in the independent variable.

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3.9 Sensitivity Analysis A sensitivity analysis is reported for low (−) and high (+) scenarios of the latent variables in each relationship because the estimations produced by the partial least squares technique are based on standardized values (Kock 2018, 2019b). Consequently, the following probabilities are calculated: • When considered separately, the probability is that the independent and dependent latent variables occur at their respective minimum and maximum values. • The probability that both the dependent and independent latent variables will occur together (simultaneously) in both their high and low levels for each of the four possible combinations of (+, +), (+, −), (−, +), and (−, −), respectively. This possibility is denoted by the symbol &. • The probability that the independent latent variable will occur at a certain level (high or low), given that the independent variable has already occurred at another level, is expressed as a conditional probability (high or low). The probabilities estimated are the combination outcomes (+, +), (+, −), (−, +), and (−, −). These probabilities help to detect risks and essential components in the log of some benefits being evaluated as response variables.

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Chapter 4

Model 1. Leadership Style and Its Impact on Operational Performance and Supply Chain Resilience

Abstract This chapter reports a structural equation model (SEM) with five latent variables. The independent variables are transactional supply chain leadership (TSSCL) and transformational supply chain leadership (TFSCL), with supply chain flexibility (SCF) and supply chain agility (SCA) as mediating variables associated with operational performance and supply chain resilience (SCR) as the dependent variable. The variables are related to six hypotheses validated with information from 231 responses to a survey applied to the Mexican maquiladora industry. The results indicate that TSSCL has a greater impact on SCF and SCA than TFSCL, whereas SCA has a greater effect on SCR than on SCF. Keywords Supply chain · Leadership · Flexibility · Agility

4.1 Model Variables This model aims to determine the impact level of the two types of supply chain leadership on supply chain resilience (SCR), with supply chain agility (SCA) and supply chain flexibility (SCF) as mediating variables, which are related to six hypotheses and justified in the following paragraphs. The model integrates the following five variables: The independent variables are transactional supply chain leadership (TSSCL), which includes thirteen items in its three dimensions, and transformational supply chain leadership (TFSCL), which includes ten items in its four dimensions. As mediating variables, we report the SCF, which contains three items for its measurement; the SCA, which integrates four items for its measurement; and as a dependent variable, the SCR is integrated with four items. Therefore, thirty-four observed variables or items were analyzed to continue with the validation process described below. For the items’ descriptions of every variable, see the questionnaire as supplementary material.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. M. Solis et al., Leadership and operational indexes for supply chain resilience, SpringerBriefs in Applied Sciences and Technology, https://doi.org/10.1007/978-3-031-32364-5_4

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4 Model 1. Leadership Style and Its Impact on Operational Performance …

4.2 Hypotheses in the Model 4.2.1 Relationship Between TSSCL and SCF It is becoming increasingly difficult for companies to remain in the market. Owing to the globalized competition, they must establish strategies in their internal operations and throughout the entire supply chain (SC), which has taken great importance in recent decades. Various authors claim that companies compete not among their operational processes but their SCs (Gong et al. 2018). In addition to high competition, market instability and disruptive events, such as environmental, social, operational, and financial matters, are added high competition. This leads companies to make strategic decisions regarding adaptability and survival, which the leaders of the companies implement. Therefore, several types of research have emerged from analyzing their impact on the company’s decisions and profitability. For example, the TSSCL style establishes roles and responsibilities among SC members (Arokiasamy et al. 2015; Martin 2017), while promoting change (Arokiasamy et al. 2015) and management strategies. On the other hand, SC must also consider flexible environments, which require analyzing flexibility beyond the manufacturing environment, for which business partners in the network must be contemplated and shared responsibilities must be established (Yu et al. 2015). Flexibility in SC is studied internally and externally because it includes procurement, manufacturing, and distribution channels (Jin et al. 2014), which is why SCF is considered a management strategy among companies (Ali et al. 2017). However, the required flexibility level must be analyzed, as it may influence the costs and investments that the company will have to make. Thus, flexibility should be stipulated in SC members’ internal and external contracts, that is, between departments, suppliers, and customers (Liu and Çetinkaya 2009). Because SCF is a management strategy and must be analyzed by the company’s managers, it can be said that it is a function of the leadership style managed by such personnel, and the TSSCL establishes agreements on roles and performances associated with responsibility, alliances, and expected returns (Arokiasamy et al. 2015; Martin 2017), which are spread among SC members to meet customer demand and obtain customer loyalty and satisfaction. TSSCL, through SCF, establishes commercial contracts and constantly interacts, adapting to unexpected market changes to maintain a strategic position for which the following hypothesis is established: H 1 . TSSCL has a positive and significant impact on SCF.

4.2 Hypotheses in the Model

45

4.2.2 Relationship Between TSSCL and SCA Another important factor in SC performance is agility, and several studies have identified SCA as a key element in meeting new competitive and collaborative challenges (Bengtsson and Johansson 2012). In recent times, SCA has become a primary tool for sustainability, and this is because companies cannot achieve the desired level of agility individually, but rather it is achieved as they collaborate effectively with their business partners in the SC and thereby achieve the necessary synergy to be able to respond quickly in an uncertain and changing marketplace (Riquelme-Medina et al. 2022; Wilhelm and Sydow 2018). Focal company leadership is crucial to influence the achievement of SC performance actively. The TSSCL offers active participation and continuous supervision to its follower members in its management by exception dimension to achieve higher performance levels (Aga 2016; Li et al. 2017), which helps to detect problems before they occur (Arokiasamy et al. 2015). Moreover, once they occur, solve them to maintain the status quo (Birasnav et al. 2015). The SCA and the TSSCL respond to changes and adapt processes in the SC to keep it functioning (Li et al. 2017), which requires participation, strength, timely communication, and connectivity among members (Carvalho et al. 2012). Therefore, it is proposed that the TSSCL can impact the SCA because the focal company’s leadership style in the SC decisions and the level of agility it presents is crucial. H 2 . TSSCL has a positive and significant impact on SCA.

4.2.3 Relationship Between TFSCL and SCF The TFSCL establishes the relationship between firms, where the leading firm influences the other followers by modifying their decisions and actions in the SC (Mokhtar et al. 2019). This leadership promotes renewal and change while developing and empowering its business partners; hence the relationship between members is strong and trusting (Yue et al. 2019). Thus, when a disruptive event occurs, it is easier to modify and address irregularities and reach agreements that benefit both parties because of the focal company’s strong relationship with the follower companies in the SC. On the other hand, flexibility is the ability to modify the possible alternatives in the SC for fast response to market changes (Jüttner and Maklan 2011). Likewise, the importance of trust has been seen as part of the network’s flexibility, and this happens because, in the absence of trust, resources or relevant information are not shared (Chan et al. 2009). However, SS flexibility is expensive (Merschmann and Thonemann 2011). Therefore, in-depth studies are needed to determine the level of flexibility required by the company. According to Liu et al. (2019), various authors have proposed models for SCF and conclude that it is composed of six dimensions, which are: flexibility of systems,

46

4 Model 1. Leadership Style and Its Impact on Operational Performance …

the flexibility of operating systems, flexibility in logistics processes, flexibility in the supply network, the flexibility of organizational design, and finally, flexibility in information systems. All of these should be analyzed and integrated by the focal company, which will set the standards to be followed by the companies in the network. The previous environment did not take the necessary actions and exhibited rigidity in integration. Since the TFSCL has stimulation and motivation skills to obtain the trust and loyalty of its follower, and it has been established that trust is a key factor in achieving SCF, and it also allows working together following the leading company, the following hypothesis is proposed: H 3 . TFSCL has a positive and significant impact on SCF.

4.2.4 Relationship Between TFSCL and SCA TFSCL influences follower companies enthusiastically and collaboratively (Birasnav and Bienstock 2019; Mokhtar et al. 2019), stimulates members’ ability to rethink problems and come up with creative solutions (Mokhtar et al. 2019), and SCA responds to change or disruptive events quickly (Li et al. 2017) and efficiently (Ali et al. 2017). Furthermore, in enterprise environments, agility enables knowledge sharing across different structures in SC (Wulf and Butel 2017). Several studies have shown that TFSCL actively influences knowledge sharing in its two dimensions, collected and donated (Le and Lei 2017; Lei et al. 2019; Son et al. 2020), so if it influences within the firm, it also influences SC. TFSCL contains the dimensions of intellectual stimulation and motivational inspiration, which promote reaching higher levels of efficiency, collaborative work, and shared vision in favor of the leading company, which will surely cause decisions to be made quickly. Thus, the following hypothesis is proposed: H 4 . TFSCL has a positive and significant impact on SCA.

4.2.5 Relationship Between SCF and SCR Flexibility has been extensively studied in production systems; however, in SC, there is an opportunity to learn about its implications (Merschmann and Thonemann 2011). According to Tang and Tomlin (2008), a company should not invest a large part of its resources in flexibility to mitigate supply, production process, and demand risks because profits are also obtained with low levels of flexibility. On the other hand, Azevedo et al. (2013) suggest investing in flexibility early, that is, in infrastructure and resources, because flexibility is the basis of sourcing and contributes to better SCR results. This indicates contradictory results, and the context needs to be analyzed. According to Pujawan (2004), flexibility is costly, and a detailed analysis is needed to determine whether the company needs it. SCF has been used to measure SCR,

4.3 Latent Variable Validation

47

and is an important variable when a disruptive event occurs because of its ability to adapt and make decisions (Ali et al. 2017). Because SCF has been used to measure SCR, and both positive and negative results have been obtained in different studies, the following hypothesis is established: H 5 . SCF has a direct and positive effect on SCR.

4.2.6 Relationship Between SCA and SCR SCA is a variable that has been used to measure resilience in SC (Ali et al. 2017; Kamalahmadi and Parast 2016) and has demonstrated its importance in responding speedily in disruptive events while accelerating reaction time and reducing the impact disruption level (Ivanov et al. 2014; Wieland and Wallenburg 2013). For Singh et al. (2019), the emphasis of SCA is to show responsiveness quickly and cope with sudden changes in supply and demand, which leads to lower resource losses and can decrease the financial impact since it is widely known that any disruption in SC leads to increased costs that can be operational, administrative, contract obligations or loss of contracts, to name a few. In addition, SCA has been a primary variable in different studies and has positioned itself as a key indicator for measuring SCR (Delbufalo 2022; Dubey et al. 2014). Therefore, the following hypothesis is proposed: H 6 . SCA has a direct and positive effect on SCR. Figure 4.1 illustrates the relationships between all variables established as hypotheses in the previous paragraphs.

4.3 Latent Variable Validation Table 4.1 shows the validation indices of the five latent variables analyzed. The first row shows the number of items before and after the validation process, and because some items were eliminated, the initial and final numbers may be different. The second and third rows show the values of the R-squared and adjusted R-squared indices, which are greater than 0.02; thus, it is inferred that there is sufficient parametric predictive validity. Likewise, the value of Q-squared is similar to that of R-squared, and it is concluded that there is nonparametric predictive validity. The fourth and fifth rows show the composite reliability index and Cronbach’s alpha, respectively, and given the values obtained, it is concluded that the variables present sufficient content validity. The seventh row shows the complete collinearity value VIF, and given that the values are less than five, it is concluded that there are no collinearity problems. Finally, the average variance extracted for all latent variables was greater than 0.5, indicating convergent validity. Other validation indices can be found in the supplementary material.

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4 Model 1. Leadership Style and Its Impact on Operational Performance …

Fig. 4.1 Proposed model with the mediating variables of flexibility and agility

Table 4.1 Latent variable validation Index

Latent variables

Index

10

TFSCL

TSSCL 6

13

SCF 6

3

SCA 3

4

SCR 4

4

R-squared

0.243

0.419

0.285

Adj. R-squared

0.236

0.414

0.279

Composite reliability

0.927

0.908

0.902

0.898

0.920

Cronbach’s alpha

0.905

0.878

0.837

0.848

0.884

Average variance extracted

0.678

0.623

0.754

0.687

0.741

Full collinearity VIF

2.158

2.601

1.570

2.129

1.604

0.248

0.421

Q-squared

4

4.4 Structural Equation Model Evaluation In this section, we analyze the items in the remaining variables after the debugging and validation processes to integrate them into the structural equation model (SEM) and evaluate them according to the established methodology.

4.4 Structural Equation Model Evaluation Table 4.2 Model efficiency indices

49

Index

Value

Best if

Average path coefficient (APC)

0.298, P < 0.001

P < 0.05

Average R-squared (ARS)

0.316, P = 0.001

P < 0.05

Average adjusted R-squared (AARS)

0.310, P = 0.002

P < 0.05

Average block VIF (AVIF)

1.821

≤ 3.3

Average full collinearity VIF (AFVIF)

2.012

≤ 3.3

Tenenhaus GoF (GoF), ideally 0.469

≥ 0.36

Table 4.2 illustrates the model’s efficiency indices, where the APC, ARS, and AARS indices indicate acceptable predictive validity because all values have an associated p-value of less than 0.05. The VIF and AFVIF values were less than 3.3, indicating that the model had no collinearity problems. Finally, it is observed that the Tenenhaus GoF is greater than 0.36, which indicates that the analyzed data fit the model. It is concluded that the model is valid and can be interpreted. Figure 4.2 illustrates the model evaluated, showing the standardized β values and the associated p-value for the hypothesis test; for each latent dependent variable, the R-squared value is indicated as a measure of the variance explained by the independent variables. According to the p-value, it was concluded that all relationships between the hypotheses were statistically significant.

Fig. 4.2 Evaluation of the initial model

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4 Model 1. Leadership Style and Its Impact on Operational Performance …

Table 4.3 Summary of direct effects

Hi

β and P- value

ES

Decision

H1 TSSCL → SCF

0.406 (< 0.001)

0.197

Accepted

H2 TSSCL → SCA

0.506 (< 0.001)

0.321

Accepted

H3 TFSCL → SCF

0.115 (0.038)

0.046

Accepted

H4 TFSCL → SCA

0.181 (< 0.002)

0.098

Accepted

H5 SCF → SCR

0.141 (0.014)

0.056

Accepted

H6 SCA → SCR

0.440 (< 0.001)

0.230

Accepted

4.4.1 Direct Effects and Validation of Hypotheses Table 4.3 presents the model evaluation with the direct effects between the variables analyzed, which are represented by the arrows in Figs. 4.1 and 4.2. The value of the dependence between the constructs is represented by β and is associated with the p-value to measure the statistical significance of the relationships. For example, the relationship TSSCL → SCF shows values of β = 0.406 and p < 0.001, indicating that when TSSCL increases its standard deviation by one unit, SCF increases by 0.406 units, demonstrating the relationship between the two variables. In addition, the effect size (ES) is indicated as a measure of the variance explained for each effect.

4.4.2 Sum of Indirect and Total Effects Table 4.4 shows the sum of the indirect effects among the variables, in which the β values, associated p-value, and the effect size (ES) can be observed. The largest direct effect was observed for TSSCL → SCR. It is through SCF and SCA, obtained by multiplying 0.406 * 0.141 + 0.506 * 0.440 (β values for the segments) with an associated p-value of 0.001 and an effect size of 0.155. By contrast, the indirect effect of TFSCL → SCR was not statistically significant, as the associated p-value was greater than 0.05. Table 4.5 illustrates the total effects of the variables, which are obtained by adding the direct and indirect effects, the associated p-value, and the size of the effects. For example, for TSSCL and SCR, there is no direct effect, so the total effect is the indirect effect; in contrast, for TSSCL and SCF, there is only a direct effect, so that is the total effect. Table 4.4 Indirect effect

Indirect effect

P-value

ES

TSSCL → SCR

0.280

< 0.001

0.155

TFSCL → SCR

0.096

= 0.070

0.050

4.5 Discussion of Results

51

Table 4.5 Total effects TO

From TSSCL

TFSCL

SCF

SCA

SCF

0.406 (< 0.001) 0.115 (0.002) ES = 197 ES = 0.046

SCA

0.506 (< 0.001) 0.181 (0.038) ES = 0.321 ES = 0.098

SCR

0.280 (< 0.001) 0.096 (0.070) 0.141 (0.014) 0.440 (< 0.001) ES = 0.155 ES = 0.050 ES = 0.056 ES = 0.230

4.4.3 Sensitivity Analysis Table 4.6 shows the probabilities of each variable in four possible scenarios when presented independently in its high (+) and low (−) scenario, together (&) or conditionally (IF). For example, the probability of a TSSCL + scenario causes the SCF + construct to occur with a probability of 0.425. In addition, if TFSCL is presented alone and independently at its high level, it can occur with a probability of 0.173 and at its low level is 0.177, whereas for SCF at its high level, it is 0.169 and at its low level, 0.156. However, these two variables at their high level have a probability of occurrence of 0.074, which is small. An interpretation of these values is given in the conclusion section, as well as their industrial and administrative implications.

4.5 Discussion of Results 4.5.1 Structural Equation Modeling Several aspects were observed when analyzing the direct effects of the model. First, the strongest relationship is between TSSCL and SCA, with the highest explanatory power, followed by SCA and SCR. On the other hand, the weakest relationships, although statistically significant, are those between TFSCL and SCF, as well as between SCF and SCR. In addition, in Fig. 4.2, it is observed that TSSCL has a greater impact on SCF and SCA compared to TFSCL; for example, in the TSSCL → SCF relationship, the effect is almost four times greater than that of the TFSCL → SCF, but also in the TSSCL → SCA relationship, which is almost three times greater than the TFSCL → SCA relationship. This may be because TSSCL is focused on keeping the system running at the required operational level, so it will constantly work with and audit its trading partners in the SC. The sizes of these effects allow us to conclude that TSSCL yields greater results for the Mexican maquiladora industry. Similarly, when reviewing the magnitudes of the effects of SCF and SCA on SCR, it is observed that the largest effect occurs between SCA → SCR, while SCF → SCF

SCR

SCA

SCF

0.199

0.173

−

0.143

−

+

0.208

0.156

−

+

0.169

Probability

+

Level

Table 4.6 Sensitivity analysis

& = 0.078 if = 0.439

& = 0.004 if = 0.024

& = 0.091 if = 0.525

& = 0.009 if = 0.050

& = 0.082 if = 0.463

& = 0.009 if = 0.049

& = 0.082 if = 0.475

& = 0.009 if = 0.050

& = 0.065 if = 0.366

& = 0.022 if = 0.125

& = 0.013 if = 0.073

& = 0.074 if = 0.425

− 0.177

0.173

+

TFSCL

& = 0.004 if = 0.027

& = 0.078 if = 0.486

& = 0.004 if = 0.027

& = 0.095 if = 0.595

& = 0.013 if = 0.081

& = 0.069 if = 0.432

0.160

+

TSSCL −

& = 0.078 if = 0.462

& = 0.009 if = 0.051

& = 0.084 if = 0.513

& = 0.000 if = 0.000

& = 0.069 if = 0.410

& = 0.004 if = 0.026

0.169

& = 0.017 if = 0.103

& = 0.078 if = 0.462

& = 0.000 if = 0.000

& = 0.078 if = 0.462

0.169

+

SCF −

& = 0.061 if = 0.389

& = 0.013 if = 0.083

& = 0.069 if = 0.444

& = 0.000 if = 0.000

0.156

& = 0.013 if = 0.063

& = 0.091 if = 0.438

0.208

+

SCA -

& = 0.061 if = 0.424

& = 0.009 if = 0.061

0.143

52 4 Model 1. Leadership Style and Its Impact on Operational Performance …

4.5 Discussion of Results

53

is three times smaller. According to the respondents, the magnitude of the effects indicates that SCA is more effective in obtaining SCR. Two indirect effects were also analyzed, one of which was statistically significant (TSSCL → SCR) and one was not (TFSCL → SCR), both of which occurred through SCA and SCF mediating variables. Finally, the highest total effects occur in the TSSCL → SCA, SCA → SCR, and TSSCL → SCF relationships.

4.5.2 Sensitivity Analysis Table 4.6 reports the sensitivity analysis for the relationships between the latent variables in the model, with the “+” signs indicating a high level and the “−” sign indicating a low level. The conditional probabilities allow for an interesting analysis; for example, the successful implementation of TFSCL+ encourages the occurrence of other variables, such as SCF+, SCA+, and SCR+, with conditional probabilities of 0.425, 0.475, and 0.525, respectively. Thus, TFSCL+ implementation is only weakly associated with SCF, SCA, and SCR levels, as the conditional probabilities are 0.125, 0.050, and 0.050. It indicates that managers applying this type of leadership will obtain benefits associated with agility, flexibility, and resilience in the case of disruptive events; however, low levels are also likely to occur. However, TFSCL− is weakly associated with SCF+, SCA+, and SCR+ because the conditional probabilities are 0.073, 0.049, and 0.024, respectively. Those probabilities are very low, indicating that not applying this leadership style affects agility, flexibility, and resilience, as they will not be obtainable. Likewise, TFSCL− represents a risk for managers because SCF−, SCA−, and SCR− may occur with probabilities of 0.366, 0.463, and 0.439, respectively, losing the opportunity to gain operational benefits. In addition, TSSCL+ favors the occurrence of SCF+, SCA+, and SCR+ with probabilities of 0.432, 0.595, and 0.486, respectively, which are very similar to those generated by TFSCL+ and indicate the importance of indirect effects because the magnitudes of the direct effects are different. This is demonstrated when it is observed that the occurrence of TSSCL+ is not strongly associated with SCF+, SCA+, and SCR+ because the conditional probabilities are 0.081, 0.027, and 0.027, respectively, which are very low. However, the occurrence of TSSCL generates risk for managers who wish to gain agility, flexibility, and resilience in their supply chains, as the conditional probabilities for SCF−, SCA−, and SCR− occurrence are 0.410, 0513, and 0.462, respectively. Furthermore, TSSCL− is insignificantly associated with SCF+, SCA+, and SCR+, with probabilities of 0.026, 0.000, and 0.051, respectively. Thus, any leadership applied correctly promotes the supply chain’s agility, flexibility, and resilience. It was also observed that the occurrence of SCF+ was an antecedent of SCA+ and SCR+ because the conditional probabilities were 0.462 and 0.462, respectively. However, SCF+ is not associated with SCA− because the probability is 0.000, but it is slightly associated with SCR− because the probability is 0.103, indicating that a high

54

4 Model 1. Leadership Style and Its Impact on Operational Performance …

level of flexibility does not guarantee resilience in the supply chain. It is also observed that SCF− is an antecedent of SCA− and SCR− because their probabilities are 0.444 and 0.389, respectively. Finally, it is observed that SCF− is not associated with SCA+ because the conditional probability is 0.000; however, there is a probability that SCR+ occurs because the probability is 0.083. This indicates that even if companies do not have high levels of supply chain flexibility, it is possible that they are resilient, which indicates that other factors are not analyzed in this research. Finally, it was observed that SCA+ is an antecedent of SCR+ because the conditional probability is 0.438, but it also promotes the occurrence of SCR− with a probability of 0.063. In addition, SCA− is an antecedent of SCR−, because the conditional probability is 0.424, although it is possible to find SCR+ with a probability of 0.061. This again indicates that high levels of agility do not guarantee high resilience in the supply chain.

4.6 Conclusions and Managerial Implications In the initial model shown in Fig. 4.2, six hypotheses were established and based on the results obtained, and the following can be concluded: H1 . There is enough statistical evidence to declare that TSSCL has a direct and positive effect on SCF in the Mexican maquiladora industry since when the first variable increases the standard deviation by one unit, the second latent variable increases by 0.406 units. This finding has implications for managers in Mexican manufacturing industries since, if they want to increase flexibility in the SC, they have to manage the TSSCL with the business partners in the SC, which leads to establishing liability agreements and contracts on both sides. Furthermore, this study found an opposite result to that carried out in Chile, where the results showed that transactional leadership is not significant for obtaining SCF (Rodríguez-Ponce 2007). It can be due to cultural differences and the size of the companies analyzed because the information in our research comes from manufacturing companies with foreign corporations strategically positioned in Mexico. It should also be noted that the Chilean study only considered the company as such and did not consider SC. H2 . There is sufficient statistical evidence to declare that TSSCL has a direct and positive effect on SCA in the Mexican maquiladora industry, given that when the first variable increases the standard deviation by one unit, the second latent variable increases it by 0.506 units. This result presents an implication for managers in manufacturing companies because if they want to decrease lead times and eliminate non-value activities in the SC, using TSSCL with SC members will help obtain better results and improve SCA. This result may be due to the contingent reward dimension in TSSCL, where the expected return levels are proposed. This result is similar to that of Shin and Park (2021), although they used another leadership style in that study, as this significantly impacted SCA.

4.6 Conclusions and Managerial Implications

55

H3 . There is enough statistical evidence to declare that TFSCL has a direct and positive effect on SCF in the Mexican maquiladora industry, given that when the first variable increases the standard deviation by one unit, the second latent variable increases by 0.115 units. The implications of these results can provide information to managers in Mexican manufacturing firms because if they need to increase the SCF, they can do so using the TFSCL. However, the impact will be smaller than using the TSSCL. This low impact is due to the ambiguity in the commitments acquired among SC members; therefore, they may choose not to manage it because of the low efficiency it has demonstrated. However, this coincides with findings reported by RodríguezPonce (2007), who determined that transformational leadership positively impacts the flexibility of strategic decision-making and the effectiveness of the company, although the study only integrates the company as such and is not specific to the SC. H4 . There is enough statistical evidence to declare that TFSCL has a direct and positive effect on SCA in the Mexican maquiladora industry, given that when the first variable increases its standard deviation by one unit, the second latent variable increases by 0.181 units. Therefore, if managers need to increase on-time deliveries and decrease lead times, they could use TFSCL with trading partners; however, TSSCL shows better benefits. This result is similar to that found by Shin and Park (2021), where the leadership style they managed also had an impact on agility in all SC partners, which leads to the conclusion that leadership has an impact on SCA; however, the impact level is determined by the leadership style. H5 . There is sufficient statistical evidence to declare that SCF directly and positively affects SCR in the Mexican maquiladora industry, given that when the first variable increases the standard deviation by one unit, the second latent variable increases by 0.141 units. The literature review supports this result since it was found that SCF is one of the main variables that most impact SCR (Hohenstein et al. 2015; Shin and Park 2019). This helps managers of Mexican manufacturing companies make strategic decisions to invest resources in SCF to obtain better SCR levels. H6 . There is sufficient statistical evidence to declare that SCA has a direct and positive effect on SCR in the Mexican maquiladora industry, given that when the first variable increases the standard deviation by one unit, the second latent variable increases by 0.440 units. The literature review supports this result since it was found that SCF is one of the variables that most impact SCR (Hohenstein et al. 2015; Shin and Park 2019). However, in a study conducted by Shin and Park (2021), SCF did not affect SCR.

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Chapter 5

Model 2. Impact of Leadership on Supply Chain Efficiency, Alertness, and Resiliency

Abstract This chapter presents a structural equation model (SEM) with five latent variables. The independent variables were transactional supply chain leadership (TSSCL) and transformational supply chain leadership (TFSCL), which included SC efficiency (SCE) and SC alerts (SCAL), and the dependent variable was SC resilience (SCR). These variables are related, forming six hypotheses validated with information collected from 231 responses to a survey applied to the Mexican maquiladora industry. The model was evaluated using the partial least squares technique integrated into WarpPLS 8 software. Findings indicate that TSSCL has a greater impact on SCAL, while in SCE, both types of leadership showed similar impacts; however, SCAL surprisingly shows almost four times greater impact on SCR than SCE. Keywords Supply chain · Leadership · Efficiency · Alerts · Resilience

5.1 Model Variables This chapter shows five variables that are analyzed to measure the impact level of the independent variables on the mediating variables and, in turn, on the dependent variable, generating a structural equation model with six hypotheses to be analyzed, which are justified in the second part of this chapter. The first independent variable is transactional supply chain leadership (TSSCL), measured through 13 observed variables that integrate the three dimensions. The second independent variable is transformational supply chain leadership (TFSCL), which requires ten observed variables to integrate four dimensions for its analysis. The mediating variables are supply chain efficiency (SCE), which is measured through three observed variables; the second variable is supply chain alerts (SCAL), measured through five observed variables; and the dependent variable is supply chain resilience (SCR), measured through four observed variables. As described above, a total of thirty-five observed variables were integrated into five latent variables. The variables observed in each construct are defined below, establishing the relationships between them as hypotheses.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. M. Solis et al., Leadership and operational indexes for supply chain resilience, SpringerBriefs in Applied Sciences and Technology, https://doi.org/10.1007/978-3-031-32364-5_5

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5.2 Hypotheses in the Model 5.2.1 Relationship of TSSCL with SCE Leadership is seen as a catalyst in companies because it can accelerate and manage the resources available to achieve organizational goals, thus fulfilling the company’s vision. Thus, it is considered an essential resource with equal importance to any other available physical or monetary resource (AlNuaimi et al. 2021). The leadership managed by the personnel in charge of an organization can lead it to position itself in the market or disappear, even more so when it comes to SC. The most relevant leadership theory has recently focused on the transactional and transformational leadership (Viana Feranita et al. 2020). Both consider the influence on the follower; however, they differ in how they influence the achievement of the established goals. Leadership in the supply chain has grown over the last decade, starting with the study by Defee et al. (2010), where this application was first proposed. In SC, leadership is crucial for the proper functioning of the organization, which leads an organization to position itself as the leader and adequately influence other members. In this case, TSSCL provides valuable tools for managing these members because it is based on the constant supervision of the activities performed (Mekpor and Dartey-Baah 2017; Aga 2016; AlNuaimi et al. 2021). It indicates that a leading company will continuously work with audit business partners to achieve the required efficiency. TSSCL establishes ways for lead and follower members to interact through performances that will be evaluated and rewarded or punished according to expected outcomes (Howladar et al. 2018) and facilitate the exchange of information in the SC, which can lead to substantial improvements in SC performance and efficiency (Brown 2016). Operational performance is important in leadership because it leads to higher service and product quality levels, thus increasing operational efficiency in the SC (Ul-Hameed et al. 2019). Such efficiency brings benefits to meeting consumer needs, which may be due to speed and timeliness of delivery (Harun et al. 2019). Similarly, SCE emphasizes process yields in favor of achieving the expected economic profitability of the activities carried out in the SC (Talay et al. 2020), which leads the company to be competent over time and remain active. The creation of strategies and continuity plans in favor of the focal company (Urciuoli 2015) can be generated through efficiency in the SC. The TSSCL entails managing active and passive management strategies and plans among the members of the SC to decrease the costs associated with distribution, creating a more efficient flow; therefore, the following hypothesis is established. H 1 . TSSCL has a positive and significant impact on SCE.

5.2 Hypotheses in the Model

61

5.2.2 Relationship of TSSCL with SCAL Supply chain alertness (SCAL) is a new variable used to discuss resilience and supply chains. However, more studies are needed in this research area because of increasing uncertainty in the market. SCAL supports the timely detection and continuity in disruptive events (Li et al. 2017). Different authors claim that it is an indispensable variable that benefits operational and administrative resilience in SC (Queiroz et al. 2022; Li et al. 2017). It also improves SC visibility, enhances oversight capacity (Mandal 2019), and is responsible for examining and detecting possible disturbances or changes in the SC network. On the other hand, TSSCL also bases one dimension on the constant supervision of followers (Fletcher et al. 2019). While providing insight and leadership in decisionmaking before, during, and after a disruptive event in the SC, it seeks to detect anomalies in the systems to fix and correct failures before they occur (Arokiasamy et al. 2015, 2016; Aga 2016). Both variables are related to the importance of actively monitoring SC to decrease risk and strengthen relationships to solidify it. Thus, we propose the following hypothesis: H 2 . TSSCL has a positive and significant impact on SCAL.

5.2.3 Relationship of TFSCL with SCE On the other hand, the TFSCL establishes the capabilities to influence in favor of exceeding established expectations and transcending individual interests into collective interests in SC member organizations seeking a common good (Park and Pierce 2020). The inspirational motivation dimension is used to achieve this, where the focal company motivates others to perform under optimal conditions and achieve new performance levels in their operations, thus maximizing effort (Shao et al. 2017). Thus, TFSCL seeks to improve network efficiency by decreasing internal and external costs (manufacturing, inventories, transportation), promoting problem-solving innovation, and presenting a shared vision (Yue et al. 2019). In addition, when the lead firm collaborates with SC members, it obtains increased SC visibility, which avoids inefficiencies in actions and decisions (Scholten and Schilder 2015; Shin and Park 2021). Previous studies have shown that SCE increases when leadership exists in innovation (Yoon et al. 2016) or generates greater agility and cost reduction (Jamjumrus and Sritragool 2019; Pretorius et al. 2022). In the same manner, the efficiency in the SC can be included in the design, making more direct flows that can be achieved by evaluating all activities, eliminating those that are repetitive, and only establishing activities that generate value in the SC (Basu et al. 2022). The leading company establishing the SC must ensure that all integrated members have the same vision in their chains to have an efficient and robust network. According to Martin (2017), this shared vision is achieved through the idealized influence dimension of TFSCL, where followers are influenced to achieve better

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performance and shared collaboration. Park and Pierce (2020) state that this is the most effective leadership style. Given that TFSCL promotes followers’ attitudes and skills to achieve new solutions to problems and stimulate and inspire innovation and the creation of new systems to replace the traditional and make the company and SC more efficient, the following hypothesis is proposed: H 3 . TFSCL has a positive and significant impact on SCE.

5.2.4 Relationship of TFSCL with SCAL TFSCL has shown the ability to break business paradigms as it seeks new ways of doing things, questioning established systems, and analyzing routine procedures that are traditionally held (Sandstrom and Reynolds 2020; Prabhu and Srivastava 2023). Furthermore, in this type of leadership, deficiencies in the system seek to strengthen it (Park and Pierce 2020). In this type of leadership, risks are taken to lead the market, which allows SCAL to be a key tool because it is responsible for detecting changes in the network, possible threats to supply, and deviations in operations (Eltantawy 2016; Mandal 2019). However, TFSCL also seeks SC security for raw materials (Shin and Park 2019, 2021), achieved through collaborative strength, trust building, and education of the leading company toward its collaborators. This relates to the dimensions of idealized influence and individualized consideration of this type of leadership. Thus, it seeks to create trust and develop members’ potential, training, and empowerment to search for better SC alternatives, which allows the leading company to strengthen its network operations, detect and prevent disruptive events, and manage operational risks (Park and Singh 2022). In addition, generating alerts allows us to control risks and plan for possible contingencies better before they occur (Yue and Zhang 2008; Sharma et al. 2022). SCAL is a prevention tool in which a company develops tactics to eliminate or prevent SC interruptions (Li et al. 2015). This strategy involves a high level of leadership and focuses on continuous improvement. Therefore, we intend to determine whether the TFSCL contributes to improving the alert system through its dimensions, and the following hypothesis is established: H 4 . TFSCL has a positive and significant impact on SCAL.

5.2.5 Relationship of SCE with SCR SCE is considered a key element in disruptive events because it can measure or reduce the costs incurred in activities associated with transportation or inventories (Queiroz et al. 2022). In their report, Shin and Park (2021) determined that SCE exerts a

5.3 Latent Variable Validation

63

positive influence on improving the resilience of companies when facing problems and unforeseen events, and for their part, Ramezankhani et al. (2018) mention that fluctuations in SC affect the flow of resources in a company (information, products, money). Efficiency is always measured at the operational and administrative levels, generating an index. However, the study of efficiency cannot be internally limited to the company; when measuring whether the company is efficient in the market, it is necessary to consider the efficiency of the supply network of raw materials and the distribution of finished products. At present, many factors can generate SCR, such as the use of common and nonspecific goods and services, and it is not easy to obtain them (Chopra et al. 2021; Trump et al. 2022). However, the best way to achieve resilience is to achieve efficient activities throughout the SC (Belhadi et al. 2022), which implies the proper use of available resources (Novotny and Folta 2013) and sustainability (Wang and Zhao 2022). Therefore, we propose the following hypothesis: H 5 . SCE has a positive and significant impact on SCR.

5.2.6 Relationship of SCAL with SCR Companies plan and model scenarios in their SCs to anticipate disruptive events and to set warning parameters (Ghadge et al. 2013). For example, Mubarik et al. (2021) used SCAL as a subconstruct or dimension of SCR, in which threats, unexpected changes in supply networks, demand, and unexpected disruptions in SC were detected. On the other hand, Saeed et al. (2019) used SCAL as a dimension of SC agility, which in turn has been used to measure SCR, and Shin and Park (2021) used it as a mediating variable between leadership and SCR. This study assumes that SCAL is a necessary antecedent to achieving SCR because it allows the identification of indices that do not perform well; thus, the following hypothesis is established. H 6 . SCAL has a positive and significant impact on SCR. Figure 5.1 illustrates the relationships between the variables, which have been proposed as research hypotheses.

5.3 Latent Variable Validation Seven indices were used to validate the five latent variables, as shown in Table 5.1, where each construct contained the number of initial and final items in the first row. The following rows show the R-squared and adjusted R-squared values, with results greater than 0.02, indicating sufficient parametric predictive validity. Then, the composite reliability index and Cronbach’s alpha are shown. Given the results, it is concluded that they present sufficient content validity, and the average variance

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5 Model 2. Impact of Leadership on Supply Chain Efficiency, Alertness, …

Fig. 5.1 Proposed model with the mediating variables of efficiency and alertness

extracted is presented with values greater than 0.5 for all variables. The penultimate row shows the complete collinearity value of the VIF. Given that the results are less than 5, it is concluded that there is sufficient convergent validity. Finally, the Q-squared value shows nonparametric predictive validity. Other validation indices can be found in the supplementary material. Table 5.1 Latent variable validation Index

TFSCL 10

TSSCL 6

13

SCE 6

3

SCAL 3

5

SCR 4

4

R-squared

0.147

0.378

0.373

Adj. R-squared

0.14

0.373

0.367

Composite reliability

0.927

0.908

0.922

0.929

0.92

Cronbach’s alpha

0.905

0.878

0.872

0.898

0.884

Average variance extracted

0.678

0.623

0.797

0.766

0.741

Full collinearity VIF

2.158

2.434

1.223

1.84

1.767

0.147

0.381

0.374

Q-squared

4

5.4 Evaluation of the Structural Equation Model

65

5.4 Evaluation of the Structural Equation Model The variables that have continued after the debugging and validation process are analyzed in this section and used to form the constructs, which are incorporated into the structural equation model and evaluated according to the methodology. The evaluated model presents different efficiency indices, which are presented in Table 5.2, where the APC, ARS, and AARS values denote acceptable predictive validity because the associated p-values are less than 0.05. It is also observed that the VIF and AFVIF values are less than 3.3, indicating that the model does not have collinearity problems. Finally, it was observed that the Tenenhaus GoF was greater than 0.36; therefore, it can be concluded that the model is valid and can be interpreted. Figure 5.2 illustrates the model evaluated, in which the standardized β value and the p-value of each of the hypotheses are presented. In each latent dependent variable, the R-squared value is indicated as a measure of the variance explained by the independent variables. Table 5.2 Model efficiency ratios

Index

Value

Average path coefficient (APC)

0.291, P < 0.001

Average R-squared (ARS)

0.299, P = 0.001

Average adjusted R-squared (AARS)

0.293, P = 0.001

Average block VIF (AVIF), ideally ≤ 3.3 1.720 Average full collinearity VIF (AFVIF), ideally ≤ 3.3

1.884

Tenenhaus GoF (GoF), ideally ≥ 0.36

0.465

Fig. 5.2 Evaluation of the initial model

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Table 5.3 Summary of direct effects

Hypothesis

β & P-value

ES

Decision

H1 TSSCL → SCE

0.212 (< 0.001)

0.076

Accepted

H2 TSSCL → SCAL

0.485 (< 0.001)

0.292

Accepted

H3 TFSCL → SCE

0.202 (< 0.001)

0.071

Accepted

H4 TFSCL → SCAL

0.171 (= 0.004)

0.086

Accepted

H5 SCE → SCR

0.138 (= 0.016)

0.050

Accepted

H6 SCAL → SCR

0.539 (< 0.001)

0.322

Accepted

5.4.1 Direct Effects and Validation of Hypotheses Table 5.3 shows the structural equation model findings, showing the corresponding hypothesis between constructs in the first column, followed by the direct effects, effect size, and decision taken. The dependence value between the constructs is represented by β, which is associated with a p-value that measures the statistical significance of the relationships. For example, the relationship between TFSCL and SCAL shows values of β = 0.485 and p < 0.001, indicating that when TFSCL increases its standard deviation by one unit, SCAL increases by 0.485 units, demonstrating that the relationship between both variables is statistically significant.

5.4.2 Sum of Indirect and Total Effects Table 5.4 presents the β values, associated p-values, and the effect size (ES) for the sum of indirect effects. The largest indirect effect is TSSCL through SCE and SCAL to SCR, which is obtained by multiplying 0.212 * 0.138 + 0.485 * 0.539 with an associated p-value of 0.001 and effect size of 0.161, indicating that it is statistically significant. Table 5.5 presents the total effects between constructs obtained by adding direct and indirect effects. The magnitude of these effects is also shown. For example, between TSSCL and SCR, there is no direct effect, so the total effect is indirect, whereas, for TSSCL and SCE, there is only a direct effect, so this is the total effect. Table 5.4 Indirect effect

β value

P-value

Effect size

TSSCL → SCR

0.291

(< 0.001)

0.161

TFSCL → SCR

0.120

(= 0.032)

0.063

5.5 Discussion of Results

67

Table 5.5 Total effects To

From TSSCL

TFSCL

SCE

0.212 (< 0.001) ES = 0.076

0.202 (< 0.001) EN = 0.071

SCAL

0.485 (< 0.001) ES = 0.292

0.171 (0.004) ES = 0.086

SCR

0.291 (< 0.001) ES = 0.161

0.120 (0.032) 0.063

SCE

SCAL

0.138 (0.016) ES = 0.050

0.539 (< 0.001) EN = 0.322

5.4.3 Sensitivity Analysis Table 5.6 shows the probabilities of each variable in the possible scenarios when presented independently in its high (+) and low (−) scenarios, together (&) or conditionally (IF). For example, the probability of a TSSCL+ scenario caused the SCE+ construct to occur with a probability of 0.270. In addition, if TSSCL is presented alone and independently at its high level, it is 0.160 and at its low level, 0.169, while for SCE at its high level, it is 0.156 and at its low level, 0.143. Together, these two variables at their high level have a probability of occurrence of 0.043, which is low.

5.5 Discussion of Results 5.5.1 Structural Equation Modeling Research conducted in the manufacturing industry of Ciudad Juarez has shown that TSSCL offers better results for both SCE and SCAL. For example, in the SCE variable, although the impact of TSSCL was greater, the difference was small compared to the impact of TFSCL; however, in the SCAL variable, the impact of TSSCL was almost three times greater than that of TFSCL. Another important result was regarding the SCAL, where the weight was almost four times greater in the SCR than in SCE. This result is striking because it would have been expected that the results would be different and that efficiency would have a better impact than alerts in the SCR. However, these results may be because the variables observed in the SCAL construct seek the timely detection of threats that may arise in the supply, while SCE is measured in the form of cost reduction in the SC; at the time of a disruptive event, costs do not decrease, but rather increase, as well as in the recovery phase. It is important to mention that these results contribute to the knowledge of leadership applied in the maquiladora industry since the styles have been divided in this study, differentiating which is better. For example, it is an advance to the study of Howell et al. (2003), since, in this study, the styles are not divided and are analyzed

SCR

SCAL

SCE

0.199

0.173

−

0.195

−

+

0.212

0.143

−

+

0.156

Probability

+

Level

Table 5.6 Sensitivity analysis

& = 0.078 if = 0.462

& = 0.009 if = 0.051

& = 0.078 if = 0.486

& = 0.004 if = 0.027

& = 0.100 if = 0.590

& = 0.009 if = 0.051

& = 0.087 if = 0.541

& = 0.009 if = 0.054

& = 0.043 if = 0.256

& = 0.022 if = 0.135

& = 0.004 if = 0.026

& = 0.043 if = 0.270

− 0.169

0.160

+

TSSCL

& = 0.009 if = 0.050

& = 0.091 if = 0.525

& = 0.013 if = 0.075

& = 0.091 if = 0.525

& = 0.026 if = 0.150

& = 0.048 if = 0.275

0.173

+

TFSCL −

& = 0.078 if = 0.439

& = 0.004 if = 0.024

& = 0.108 if = 0.610

& = 0.009 if = 0.049

& = 0.048 if = 0.268

& = 0.009 if = 0.049

0.177

& = 0.022 if = 0.139

& = 0.061 if = 0.389

& = 0.009 if = 0.056

& = 0.069 if = 0.444

0.156

+

SCE −

& = 0.043 if = 0.303

& = 0.026 if = 0.182

& = 0.035 if = 0.242

& = 0.022 if = 0.152

0.143

& = 0.004 if = 0.020

& = 0.113 if = 0.531

0.212

+

SCAL −

& = 0.078 if = 0.400

& = 0.004 if = 0.022

0.195

68 5 Model 2. Impact of Leadership on Supply Chain Efficiency, Alertness, …

5.5 Discussion of Results

69

generically. Also, it is an advance to the report by Morales et al. (2019), who point to leadership as a precursor of resilience in the supply chains of the maquiladora industry through factor analysis technique. However, in this study, the relationships between the variables are found, even though they are quantified, and now managers know which style best favors them. In the same way, the results of this study help managers in the maquiladora industry to know the importance of having metrics that allow them to identify risks, which will serve as alerts that will allow them to take immediate corrective actions. This is the first document that analyzes this variable in this productive sector.

5.5.2 Sensitivity Analysis The sensitivity analysis is reported in Table 5.6 for the relationships between the constructs of the model, with the “+” signs indicating a high level and the “−” sign indicating a low level. These results indicate that managers and administrative personnel in the maquiladora industry should consider investing in developing leaders with both styles in the supply chain. The results show that TSSCL+ is an antecedent of SCE+, SCAL+, and SCR+, because the conditional probabilities are 0.270, 0.541, and 0.487, respectively. This finding is interesting because the relationship with SCE is low, indicating that leadership is a necessary attribute, but not the only one, since the capabilities of human resources, machinery, and equipment must also be analyzed. TSSCL+ was poorly associated with SCE−, SCAL−, and SCR−, with conditional probabilities of 0.135, 0.054, and 0.027, respectively. Again, even when TSSCL+ is present, SCE− is likely to occur, and good leadership of this type does not guarantee SCE. However, TSSCL− is a high risk for SCAL− and SCR− because the conditional probabilities are 0.590 and 0.462, respectively, but not for SCE−. Thus, the probability was 0.256. This indicates that even with TSSCL−, it is possible not to have SCE− since this factor depends on many other SC partners. Similarly, TSSL− is not an antecedent of high SCE+, SCAL+, and SCE+ because the conditional probabilities are as low as 0.026, 0.051, and 0.051, respectively. It is also observed that TFSSL+ is a strong antecedent of SCAL+ and SCR+ because the conditional probabilities of occurrence are 0.525 and 0.525, respectively, but not SCE+ because, in this case, the probability is only 0.275, again indicating that this leadership style does not guarantee SCE. In addition, TFSCL+ does not have a strong association with SCAL− and SCR− because the conditional probabilities are 0.075 and 0.050, respectively. The same cannot be said for the relationship with SCE− because the probability is higher (0.150). Similarly, TFSCL− is a strong antecedent of SCAL− and SCR-, as the conditional probabilities are 0.610 and 0.439, respectively, but not as high as SCE−, as the probability of obtaining SCE− is 0.268−, which is considered high. It was again concluded that TFSCL− is not fully conducive to SCE−. However, managers should

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5 Model 2. Impact of Leadership on Supply Chain Efficiency, Alertness, …

be aware that TFSCL− is not associated with SCE+, SCAL+, or SCR+, as the conditional probabilities are 0.049, 0.049, and 0.024, respectively. It has also been observed that SCE+ is a precursor of SCAL+ and SCR+ since the conditional probabilities between both variables are 0.444 and 0.389, respectively. However, it is observed that SCE+ is only weakly associated with SCAL− since because the conditional probability is only 0.056, but it does have a significant relationship with SCR− since because this probability, in this case, is 0.139, which indicates that SCE+ does not guarantee that fast responses are yielded through SCR. Similarly, SCE− can be a precursor of SCAL− and SCR− because the conditional probabilities are 0.242 and 0.303, respectively, which are lower than those of the other variables, where these values are higher than 0.5. Finally, SCE− is not a determinant of SCAL− and SCR− because the probabilities, in this case, are 0.152 and 0.182, respectively, indicating a moderate relationship. Finally, it is observed that SCAL+ is a clear antecedent of SCR+ because the conditional probability is 0.531 and is only weakly associated with SCR− because the probability is only 0.020 with the latter. The above is ratified by observing that SCAL− leads to SCR− because the probability is 0.400 and is validated by observing that the conditional probability with SCR− is only 0.022. This indicates that a system for monitoring performance indices and risks in a supply chain always favors rapid response to disruptive events.

5.6 Conclusions and Management Implications In the initial model shown in Fig. 5.2, six hypotheses were established and based on the results obtained, and the following can be concluded: H1 . There is sufficient statistical evidence to indicate that TSSCL has a direct and positive effect on SCE in the Mexican maquiladora industry, given that when the first variable increases the standard deviation by one unit, the second latent variable increases by 0.212 units. This finding implies that leaders who apply the TSSCL leadership style significantly influence SCE, which can help company managers decrease inventory, handling, manufacturing, and transportation costs. TSSCL can develop this because the contingent reward dimension establishes the objectives and goals of the SC functions. These results are similar to those found by Shin and Park (2021), in which, although another leadership style was used, it also affected SCE. H2 . There is sufficient statistical evidence to affirm that TSSCL has a direct and positive effect on SCAL in the Mexican maquiladora industry, given that when the first variable increases the standard deviation by one unit, the second latent variable increases by 0.485 units. This finding is of utmost importance for industry managers because it was shown to have a strong impact on SCAL, which obtained strong results on SCR. Thus, it opens a scenario in which if a manager wants to improve resilience, he/she must focus on risk detection. The TSSCL in the management by active exception dimension emphasizes constant monitoring. Our findings are similar to those of Shin and Park (2021), in which SCAL showed better results.

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H3 . There is sufficient statistical evidence to affirm that TFSCL has a direct and positive effect on SCE in the Mexican maquiladora industry, given that when the first variable increases its standard deviation by one unit, the second latent variable increases by 0.202 units. This finding can be considered in the same proportion as that obtained using the TSSCL. However, the dimension in the TFSCL used here is intellectual stimulation, where SC members are developed to solve problems and thereby reduce supply and distribution costs, given that the two leadership styles worked. Considering the result of Shin and Park (2021), who used another leadership style, also obtained similar results to those found in this study. Thus, it can be concluded that leadership is fundamental to obtaining better SC efficiency. H4 . There is sufficient statistical evidence to affirm that TFSCL has a direct and positive effect on SCAL in the Mexican maquiladora industry, given that when the first variable increases its standard deviation by one unit, the second latent variable increases by 0.171 units. Although positive, this finding sets the tone for serious consideration of investing in developing transformational leaders because of its low impact on SCAL. H5 . There is sufficient statistical evidence to affirm that SCE has a direct and positive effect on SCR in the Mexican maquiladora industry, given that when the first variable increases the standard deviation by one unit, the second latent variable increases by 0.138 units. This finding is significant because it could be expected that SCE would have an important effect on SCR; however, this can be interpreted that reducing costs in SC will not guarantee resilience, and at the time of a disruptive event, it may have no or very low impact. This study was low level; however, surprisingly, Queiroz et al. (2022) did not obtain a positive and significant result; therefore, a difference was observed. H6 . There is sufficient statistical evidence to affirm that SCAL has a direct and positive effect on SCR in the Mexican maquiladora industry, given that when the first variable increases the standard deviation by one unit, the second latent variable increases by 0.539 units. This finding was one of the most important in the study because the SCAL variable is new. The literature review found very few studies on its relationships with other variables. However, in a study by Queiroz et al. (2022), where the impact of SCAL on SCR was measured, it was found to be significant and positive, as in this study.

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Chapter 6

Model 3. Impact of Leadership on Operational Variables and Supply Chain Resilience

Abstract This chapter integrates seven variables into a structural equation model (SEM) to determine their relationships. The independent variables were transactional supply chain leadership (TSSCL) and transformational supply chain leadership (TFSCL). In contrast, the mediating variables are supply chain flexibility (SCF), agility (SCA), efficiency (SCE), and alerts (SCAL). The dependent variable is supply chain resilience (SCR). These variables are related using 13 hypotheses validated with information from 231 surveys conducted in the Mexican maquiladora industry using the partial least squares method. The findings indicate that TSSCL has a more significant impact on each mediating variable. Surprisingly, the variable that mostly affects SCR is SCAL, whereas SCF and SCE are not statistically significant. Keywords Supply chain · Leadership · Flexibility · Agility · Efficiency · Alerts

6.1 Model Variables This chapter presents a structural equation model that integrates these seven constructs. The first independent construct is transactional supply chain leadership (TSSCL), which is analyzed through 13 items comprising three dimensions (contingent reward, management by active exception, and management by passive exception). The second independent construct is transformational supply chain leadership (TFSCL), measured through ten items of the four dimensions (idealized influence, inspirational motivation, intellectual stimulation, and individualized consideration). The following four constructs are mediators: supply chain efficiency (SCE), which is measured using three items; supply chain flexibility (SCF), which contains three items for its measurement; supply chain agility (SCA), which integrates four items for its measurement; and supply chain alertness measured through five items. Finally, resilience in the supply chain was included as a dependent construct that uses four items for measurement. Therefore, forty-two observed variables or items were analyzed to continue the validation process described below. This model integrates the two previous models, where the first model analyzes SCF and SCA, whereas the second integrates SCE and SCAL with SCR as the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. M. Solis et al., Leadership and operational indexes for supply chain resilience, SpringerBriefs in Applied Sciences and Technology, https://doi.org/10.1007/978-3-031-32364-5_6

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response. These four mediating variables are analyzed simultaneously to determine how the presence of one can detract from the effects of the other.

6.2 Hypotheses in the Model As this chapter shows, an integral model of the two models presented in Chaps. 4 and 5, the hypotheses are the same; however, the impacts contemplating all the variables are different because of some of them. This chapter refers to what was established in previous chapters. Since this model integrates all variables from the models in Chaps. 4 and 5, the hypotheses are not justified. The hypotheses in Sect. 6.4, Model 1, are as follows. H 2 . TSSCL has a positive and significant impact on SCF. H 6 . TFSCL has a positive and significant impact on SCF. H 3 . TSSCL has a positive and significant impact on SCA. H 7 . TFSCL has a positive and significant impact on SCA. H 10 . SCF has a direct and positive effect on SCR. H 11 . SCA has a direct and positive effect on CRS. The hypotheses justified in Chaps. 5—Model 2 are as follows: H 1 . TSSCL has a positive and significant impact on SCE. H 5 . TFSCL has a positive and significant impact on SCE. H 4 . TSSCL has a positive and significant impact on SCAL. H 8 . TFSCL has a positive and significant impact on SCAL. H 9 . SCE has a positive and significant impact on SCR. H 12 . SCAL has a positive and significant impact on SCR. However, this model also attempts to determine the mediating role of the variables, for which the following hypothesis is established: H 13 . SCE, SCF, SCA, and SCAL mediate the relationship between leadership style (TSSCL and TFSCL) and SCR.

6.3 Latent Variable Validation The validation indices for the seven latent variables are presented in Table 6.1. The first row shows the number of items before and after the validation process, and these

6.4 Structural Equation Model Evaluation

77

Table 6.1 Latent variable validation Índex

TFSCL

TSSCL

SCE

SCF

SCA

SCAL

SCR

10–6

13–6

3–3

3–3

4–4

5–4

4–4

R-squared

0.147

0.243

0.419

0.378

0.404

Adj. R-squared

0.140

0.236

0.414

0.373

0.393

0.922

0.902

0.898

0.929

0.920

Composite reliability

0.927

0.908

Cronbach’s alpha

0.905

0.878

0.872

0.837

0.848

0.898

0.884

Average variance extracted

0.678

0.623

0.797

0.754

0.687

0.766

0.741

Full collinearity VIF

2.169

2.684

1.390

1.681

2.324

1.981

1.794

0.147

0.248

0.421

0.381

0.408

Q-squared

numbers may differ because some observed variables were eliminated during validation. The second and third rows show the R-squared and adjusted R-squared values, respectively, which are greater than 0.02, indicating sufficient parametric predictive validity. In addition, the Q-squared value indicates nonparametric predictive validity. The fourth and fifth rows show the composite reliability index and Cronbach’s alpha, respectively, given the obtained values, indicating sufficient content validity. The seventh row shows the variance inflation index VIF; given that the results are less than five, we conclude there are no collinearity problems. Finally, the average variance extracted for all the latent variables was greater than 0.5, indicating sufficient convergent validity. Other validation indices are shown in the supplementary material.

6.4 Structural Equation Model Evaluation After validation, the structural equation model was integrated with the latent and remaining variables. Table 6.2 presents the model’s efficiency indices, where APC, ARS, and AARS indicate sufficient predictive validity because all values have an associated p-value lower than 0.05. The VIF and AFVIF values were less than 3.3 and indicate that the model did not have collinearity problems. Finally, the Tenenhaus GoF is greater than 0.36; therefore, it can be concluded that the data obtained from the industry fits the model and can be interpreted. Figure 6.2 shows the model evaluated, where the standardized β values and the associated p-value for the hypothesis test can be observed. The R-squared value is indicated as a measure of variance explained by the independent variables for each dependent variable.

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6 Model 3. Impact of Leadership on Operational Variables and Supply …

Table 6.2 Model efficiency ratios

Index

Value

Average path coefficient (APC)

0.251, P < 0.001

Average R-squared (ARS)

0.318, P = 0.001

Average adjusted R-squared (AARS)

0.311, P = 0.002

Average block VIF (AVIF), ideally ≤ 3.3 1.875 Average full collinearity VIF (AFVIF), ideally ≤ 3.3

2.003

Tenenhaus GoF (GoF), ideally ≥ 0.36

0.479

6.4.1 Direct Effects and Validation of Hypotheses Table 6.3 presents the model with direct effects between the constructs, represented by arrows in Figs. 6.1 and 6.2. The dependence value between variables is represented by β, and its p-value is associated with the measurement of statistical significance. For example, the relationship between TSSCL → SCF shows values of β = 0.406 and P < 0.001, indicating that when TSSCL increases its standard deviation by one unit, SCF increases by 0.406 units, demonstrating the relationship between both variables. However, this model, which considers the integration of all the variables, was not significant. Table 6.3 Summary of direct effects

Hypotheses

Direct effect

Decision

β & P-value H1 TSSCL → SCE

0.212 (< 0.001)

Accepted

H2 TSSCL → SCF

0.406 (< 0.001)

Accepted

H3 TSSCL → SCA

0.506 (< 0.001)

Accepted

H4 TSSCL → SCAL

0.485 (< 0.001)

Accepted

H5 TFSCL → SCE

0.202 (< 0.001)

Accepted

H6 TFSCL → SCF

0.115 (= 0.038)

Accepted

H7 TFSCL → SCA

0.181 (=0.002)

Accepted

H8 TFSCL → SCAL

0.171 (= 0.004)

Accepted

H9 SCE → SCR

0.071 (= 0.139)

Rejected

H10 SCF → SCR

0.012 (= 0.428)

Rejected

H11 SCA → SCR

0.227 (< 0.001)

Accepted

H12 SCAL → SCR

0.427 (< 0.001)

Accepted

6.4 Structural Equation Model Evaluation

79

Fig. 6.1 Proposed model with the mediating variables of efficiency, flexibility, agility, and alertness

Fig. 6.2 Evaluation of the initial model

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6 Model 3. Impact of Leadership on Operational Variables and Supply …

Table 6.4 Indirect effect

Indirect effect

P-value

Effect size

TSSCL → SCR

0.342

(< 0.001)

0.189

TFSCL → SCR

0.130

(= 0.022)

0.068

6.4.2 Sum of Indirect and Total Effects Table 6.4 shows the indirect effects among the variables, in which the β values, associated p-values, and the effect sizes (ES) can be observed. The largest indirect effect is TSSCL → SCR, passing through SCF, SCA, SCAL, and SCE until SCR is reached, obtained by multiplying 0.212 * 0.071 + 0.406 * 0.012 + 0.406 * 0.227 + 0.506 * 0.427 with an associated p-value of 0.001 and an effect size of 0.189. Table 6.5 illustrates the total effects of the variables, which were obtained by adding the direct and indirect effects and the size of the effects. For example, for TSSCL and SCR, there is no direct effect, so the total effect is the indirect effect; on the other hand, for TSSCL and SCF, there is only a direct effect, so that is the total effect, and ES represents the effect size.

6.4.3 Sobel Test The Sobel method tests the mediating role of intermediate variables such as SCE, SCA, SCF, and SCAL between leadership (TSSCL, TFSCL) and SCR. According to Abu-Bader and Jones (2021), a fully mediated model occurs when the independent variable ceases to have a statistically significant effect on the independent variable after controlling for the mediating variable; that is, the correlation between the independent variable and the dependent variable is reduced and ceases to be significant. Conversely, if the effect of the independent variable on the dependent variable remains statistically significant (albeit small), the partial mediation model is supported. Table 6.6 summarizes the direct and indirect effects, Z-value, and mediating role determined by the Sobel test.

6.4.4 Sensitivity Analysis Table 6.7 shows the probabilities of each variable in the possible scenarios when they occur independently in their high (+) and low (−) scenarios, together (and) or conditionally (IF). For example, the probability of having a TFSCL+ scenario causes the SCF+ construct to occur with a probability of 0.425. In addition, if TFSCL is presented alone at a high level, the probability is 0.173 and at its low level 0.177. In contrast, for SCF at its high level, it is 0.169 and at its low level 0.156, these two variables at their high level have a probability of occurrence of 0.074.

0.212 (< 0.001) ES = 0.076

0.406 (< 0.001) ES = 0.197

0.506 (< 0.001) ES = 0.321

0.485 (< 0.001) ES = 0.292

0.342 (< 0.001) ES = 0.189

SCF

SCA

SCAL

SCR

TSSCL

From

SCE

To

Table 6.5 Total effects

0.130 (= 0.022) ES = 0.068

0.171 (= 0.004) ES = 0.086

0.181 (= 0.002) ES = 0.098

0.115 (= 0.038) ES = 0.046

0.202 (< 0.001) ES = 0.071

TFSCL

0.071 (0.016) ES = 0.026

SCE

0.012 (< 0.001) ES = 0.005

SCF

0.227 (< 0.001) ES = 0.118

SCA

0.427 (< 0.001) ES = 0.255

SCAL

6.4 Structural Equation Model Evaluation 81

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6 Model 3. Impact of Leadership on Operational Variables and Supply …

Table 6.6 Mediating test Hypothesis 13

Indirect effect

Direct effect

Z-value * P-value

TSSCL → SCE → SCR

0.0150

0.397

1.03 (* 0.29)

Full mediator

TSSCL → SCF → SCR

0.0048

0.1817 (* 0.85)

Full mediator

TSSCL → SCA → SCR

0.0921

3.31 (* 0.0009)

Partial mediator

(* 1.2e−7)

TSSCL → SCAL → SCR

0.2160

TFSCL → SCE → SCR

0.0143

TFSCL → SCF → SCR

0.0013

5.29 0.251

Mediating role

Partial mediator

1.03 (* 0.30)

Full mediator

0.1808 (* 0.85)

Full mediator

TFSCL → SCA → SCR

0.0410

2.22

TFSCL → SCAL → SCR

0.0730

2.49 (* 0.012)

(* 0.02)

Partial mediator Partial mediator

6.5 Discussion of Results 6.5.1 Structural Equation Modeling This research was conducted in the manufacturing industry in Ciudad Juarez (Mexico) and showed that TSSCL has a better impact on the operational variables. For example, the TSSCL → SCF ratio had an almost fourfold greater impact than TFSCL, while the TSSCL → SCA ratio was almost threefold greater. In addition, when analyzing the value of direct effects, it is observed that TSSCL has almost three times greater impact on SCAL than TFSCL, and the same is true for SCE. These results indicate that, in the Mexican maquiladora industry, TSSCL generates better operating results in the production system and supply chain than TFSCL. Similarly, certain operational variables (SCE, SCF, SCA, and SCAL) detract from their direct relationships with SCR. The above is concluded, given that all were statistically significant in Chaps. 4 (Model 1) and 5 (Model 2), where direct relationships were measured. However, in this model, the relationship between SCE and SCF was not statistically significant, indicating that SCA and SCAL were the most critical variables for achieving adequate SCR levels.

6.5.2 Sensitivity Analysis The sensitivity analysis appears in Table 6.7 for the relationships between the model constructs; the “+” signs indicate a high level in the variable, and the “−” sign is at a low level. In this case, because we analyzed a combination of the two models seen in the previous chapters, we also report the probabilities of the occurrence of one operational variable with another. However, these findings were not related to the hypotheses. Therefore, the temporality of events is assumed to identify independent and dependent variables.

SCA

SCF

0.143

−

0.156

−

0.208

0.169

+

+

0.143

&= 0.087 if = 0.513

&= 0.000 if = 0.000

&= 0.095 if = 0.595

&= 0.004 if = 0.027

&= 0.069 if = 0.410

&= 0.004 if = 0.026

&= 0.069 if = 0.432

&= 0.013 if = 0.081

&= 0.043 if = 0.256

&= 0.022 if = 0.135

&= 0.004 if = 0.026

&= 0.043 if = 0.270

−

0.169

0.160

0.156

SCE

+

Probability

−

+

TSSCl

Level

Table 6.7 Sensitivity analysis

&= 0.009 if = 0.050

&= 0.082 if = 0.475

&= 0.022 if = 0.125

&= 0.074 if = 0.425

&= 0.026 if = 0.150

&= 0.048 if = 0.275

0.173

+

TFSCL

&= 0.082 if = 0.463

&= 0.009 if = 0.049

&= 0.065 if = 0.366

&= 0.013 if = 0.073

&= 0.048 if = 0.268

&= 0.009 if = 0.049

0.177

− 0.156

+

SCE 0.143

−

&= 0.000 if = 0.000

&= 0.078 if = 0.462

&= 0.017 if = 0.103

&= 0.065 if = 0.385

0.169

+

SCF

&= 0.069 if = 0.444

&= 0.000 if = 0.000

&= 0.056 if = 0.361

&= 0.000 if = 0.000

0.156

−

&= 0.017 if = 0.083

&= 0.074 if = 0.354

0.208

+

SCF

&= 0.056 if = 0.394

&= 0.004 if = 0.030

0.143

−

&= 0.009 if = 0.041

&= 0.117 if = 0.551

&= 0.009 if = 0.041

&= 0.074 if = 0.347

&= 0.022 if = 0.102

&= 0.069 if = 0.327

0.212

+

SCAL

(continued)

&= 0.078 if = 0.400

&= 0.004 if = 0.022

&= 0.071 if = 0.400

&= 0.09 if = 0.044

&= 0.035 if = 0.178

&= 0.009 if = 0.044

0.195

−

6.5 Discussion of Results 83

SCR

SCAL

0.212

0.195

0.199

0.173

+

−

+

−

Table 6.7 (continued)

&= 0.078 if = 0.462

&= 0.009 if = 0.051

&= 0.078 if = 0.486

&= 0.004 if = 0.027

&= 0.100 if = 0.590

&= 0.009 if = 0.050

&= 0.091 if = 0.525

&= 0.013 if = 0.075

&= 0.091 if = 0.525

&= 0.009 if = 0.054

TFSCL

&= 0.087 if = 0.541

&= 0.009 if = 0.051

TSSCl

&= 0.078 if = 0.439

&= 0.004 if = 0.024

&= 0.108 if = 0.610

&= 0.009 if = 0.049

&= 0.022 if = 0.139

&= 0.061 if = 0.389

SCE

&= 0.043 if = 0.303

&= 0.026 if = 0.182 &= 0.017 if = 0.103

&= 0.078 if = 0.462

SCF

&= 0.061 if = 0.389

&= 0.013 if = 0.083 &= 0.013 if = 0.063

&= 0.091 if = 0.438

SCF

&= 0.061 if = 0.424

&= 0.009 if = 0.061

&= 0.004 if = 0.020

&= 0.113 if = 0.531

SCAL

&= 0.078 if = 0.400

&= 0.004 if = 0.022

84 6 Model 3. Impact of Leadership on Operational Variables and Supply …

6.5 Discussion of Results

85

TSSCL+ is an ancestor of SCF+, SCA+, SCAL+, and SCR+, as the conditional probabilities are high, such as 0.432, 0.595, 0.541, and 0.486, respectively. However, there is not much SCE+ because the probability is only 0.270. Similarly, TSSCL+ is relatively weakly associated with SCE−, SCF−, SCA−, SCAL−, and SCR− because the conditional probabilities are relatively low. Similarly, TSSCL− is an antecedent of SCE, SCF, SCA, SCAL, and SCR because the conditional probabilities are 0.256, 0.410, 0.513, 0.590, and 0.439, respectively, indicating that low levels of this type of leadership pose a risk to a company’s operational goals. This is demonstrated by observing that TSSCL− is not associated with SCE+, SCF+, SCA+, SCAL+, or SCR+, as the conditional probabilities are relatively low. TFSCL+ is a strong antecedent of SCF+, SCA+, SCAL+, and SCR+, because the conditional probabilities are 0.425, 0.475, 0.525, and 0.525, respectively. However, this type of leadership was not a robust antecedent of SCE+, as the probability was only 0.275. This result allows us to conclude that efficiency rates are not guaranteed, regardless of the leadership style. This may be because this factor is associated with employees’ knowledge levels and information flow. Similarly, TFSCL+ is not associated with SCE−, SCF−, SCA−, SCAL−, and SCR− because the conditional probabilities are relatively low; however, the relationship with SCE− is 0.150, which is moderate. In addition, SCAL+ is a strong antecedent of SCE+, SCF+, SCA+, and SCR+ because the conditional probabilities are high (0.327, 0.347, 0.551, and 0.531, respectively), indicating that monitoring the supply chain generates efficiency, flexibility, agility, and resilience. In addition, there is little association with SCE−, SCF−, SCA−, and SCR− because the probabilities are low, guaranteeing that managers justify investments in alert systems. Furthermore, SCAL− represents a risk for SC because the conditional probabilities of obtaining SCE−, SCF−, SCA−, and SCR− are high, at 0.178, 0.400, 0.400, and 0.400, respectively. This is confirmed by observing that SCAL− will not allow SCE+, SCF+, SCA+, and SCR+ to be obtained because the probabilities are relatively low, 0.009, 0.090, 0.004, and 0.040, respectively. Thus, a low-efficiency alert system does not allow for efficiency, flexibility, agility, and resilience in the supply chain. SCF+ is an antecedent of SCE+, SCA+, and SCR+ because it favors its occurrence with conditional probabilities of 0.385, 0.462, and 0.462, respectively. Similarly, SCF+ is only weakly associated with SCE−, SCA− and SCR− because these probabilities are low (0.103, 0.000, and 0.103, respectively). The above indicates that flexibility allows managers to obtain greater agility but does not fully guarantee efficiency and resilience. In this model, the direct effect is not statistically significant. In addition, SCF− is an antecedent of SCE−, SCA−, and SCR−, as the conditional probabilities between the two variables are 0.361, 0.444, and 0.389, respectively, representing a high risk for managers who wish to obtain operating profits. Furthermore, SCF− is not associated with SCE+, SCA+, or SCR+, as the probabilities are relatively zero or low (0.000, 0.000, and 0.083, respectively). Managers

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6 Model 3. Impact of Leadership on Operational Variables and Supply …

should generate flexibility in their production processes to avoid low or zero efficiency, agility, and resilience levels. Another opportunity for managers is to achieve SCA+ because it is a vital precursor of SCE+ and SCR+ with conditional probabilities of 0.354 and 0.438, respectively. This was verified by observing that SCA+ was very weakly associated with SCE− and SCR−, with probabilities of only 0.083 and 0.063, respectively. Similarly, SCA− can generate SCE− and SCR− with probabilities of 0.394 and 0.424, respectively. SCA− was weakly associated with SCE+ and SCR+, as the probabilities were almost zero at 0.030 and 0.061, respectively. Managers should invest in supply chain agility to ensure efficiency and resilience. Finally, SCE+ is an antecedent of SCR+ because it favors it with a probability of 0.389. This does not guarantee that SCR− can occur because the probability is 0.139. Similarly, SCE− is a moderate antecedent of SCR− since it favors it with a probability of 0.303, and at that same occurrence level, it only favors SCE+ with 0.182. In conclusion, efficiency supports resilience, but not in a determinant manner, as do the other variables.

6.6 Conclusions and Management Implications In the initial model shown in Fig. 6.2, 13 hypotheses were established based on the results obtained, and the following conclusions were drawn. H1 . There is sufficient statistical evidence to state that TSSCL has a direct and positive effect on SCE in the Mexican maquiladora industry, given that when the first variable increases the standard deviation by one unit, the second latent variable increases by 0.212 units. This finding implies that the leading company when using TSSCL. This can be achieved by signing stipulated contracts in which both parties responsibilities, obligations, and benefits are clear. On the other hand, companies using TSSCL can benefit from decreasing operating costs through the network. The findings in this study are similar to those of Shin and Park (2021), who demonstrate that the leadership of the leading company had a significant impact on the SCE obtained. It should be noted that the leadership they used was not specifically TSSCL but leadership in general terms. H2 . There is sufficient statistical evidence to state that TSSCL directly and positively affects SCF in the Mexican maquiladora industry, given that the first variable increases its standard deviation by one unit, and the second latent variable does so by 0.406 units. This finding is essential for companies that wish to increase their SCF level, and they can do so by increasing their TSSCL level. However, it is of utmost importance to analyze which processes and operations SCF is needed because it is costly (Merschmann and Thonemann 2011) and may not necessarily impact SC performance. This finding is similar to that of Shin and Park (2021), where leadership significantly impacts SCF; however, in the study conducted by Rodríguez-Ponce (2007) on Chilean SMEs, the results showed no significant impact. It should be

6.6 Conclusions and Management Implications

87

noted that transactional leadership was not considered in the first study, and in the second study, it was only the company and not the entire SC. H3 . There is enough statistical evidence to state that TSSCL has a direct and positive effect on SCA in the Mexican maquiladora industry, given that when the first variable increases its standard deviation by one unit, the second latent variable does so by 0.406 units. This finding implies that a leading firm using TSSCL will increase its SCA, which may be due to the moment of decision-making when the focal firm guides its members to achieve the expected returns. This finding is similar to that of Shin and Park (2021), although they used another leadership style, which significantly impacted the chain’s agility. H4 . There is sufficient statistical evidence to state that TSSCL has a direct and positive effect on SCAL in the Mexican maquiladora industry, given that when the first variable increases its standard deviation by one unit, the second latent variable increases by 0.506 units. This finding opens a whole panorama for research because it is a new variable that needs to be analyzed in more fields and settings. However, the study conducted by Shin and Park (2021) showed similar results to those determined in this study, where it was significant and the variable with the greatest impact. H5 . There is sufficient statistical evidence to state that TFSCL has a direct and positive effect on SCE in the Mexican maquiladora industry, given that when the first variable increases its standard deviation by one unit, the second latent variable increases by 0.202 units. These results show no significant difference in the impact of the two leadership styles on SCE, so the firm can choose to manage either of these to achieve efficiency; however, there is a big difference in other variables. This is also related to the results of Shin and Park (2021), in which other leadership styles were used and were also statistically significant. Therefore, it can be assumed that leadership is vital to obtain higher efficiency. H6 . There is sufficient statistical evidence to state that TFSCL has a direct and positive effect on SCF in the Mexican maquiladora industry, given that when the first variable increases its standard deviation by one unit, the second latent variable increases by 0.115 units. There is a clear difference in this variable when measuring the impact of leadership style on flexibility. The information presented here can greatly help industry managers because it implies that if they want better benefits in SCF, it is advisable to use TSSCL instead of TFSCL. This study coincides with the results obtained by Rodríguez-Ponce (2007), who found that transformational leadership positively impacts the flexibility of strategic decision-making and the effectiveness of the company. However, the study only integrates the company as such and is not specific to SC. H7 . There is sufficient statistical evidence to state that TFSCL has a direct and positive effect on SCA in the Mexican maquiladora industry, given that when the first variable increases its standard deviation by one unit, the second latent variable does so by 0.181 units. It can be concluded that TSSCL obtains better results for this variable. This result is similar to that found by Shin and Park (2021), where the leadership style they managed also had an impact on agility in the chain, which leads to the

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6 Model 3. Impact of Leadership on Operational Variables and Supply …

conclusion that leadership has an impact on the agility of the chain; however, the level of impact is determined by the leadership style. H8 . There is sufficient statistical evidence to state that TFSCL has a direct and positive effect on SCAL in the Mexican maquiladora industry, given that when the first variable increases its standard deviation by one unit, the second latent variable increases by 0.171 units. This finding is positive; however, further studies on this variable are recommended. Both leadership styles showed statistical significance, but TSSCL had a greater impact than TFSCL. Shin and Park (2021) also obtained positive results, although their leadership styles differed. H9 . There is insufficient statistical evidence to state that SCE directly and positively affects SCR in the Mexican maquiladora industry. This finding was not expected because efficiency can be considered a primary tool measured in all types of companies. However, efficiency in this study is measured in the form of cost reduction in the SC, and when an interruption in the SC occurs, costs do not decrease but increase; therefore, the results obtained are not significant in the SCR. This is consistent with the analysis of Queiroz et al. (2022), where a positive and significant result was not obtained; in Shin and Park (2021), the impact was low. It should be noted that in Chap. 4 of this book, the result, although low, was significant; however, two mediating variables were analyzed instead of the four. H10 . There is insufficient statistical evidence to state that SCF directly and positively affects SCR in the Mexican maquiladora industry. The literature review supports this result according to Tang and Tomlin (2008). According to Tang and Tomlin (2008), companies can lose substantial resources when investing in flexibility because they do not help mitigate risks in the market. This is consistent with the results of Shin and Park (2021), where, as in this study, SCF was not significant for SCR. H11 . There is sufficient statistical evidence to state that SCA has a direct and positive effect on SCR in the Mexican maquiladora industry, given that when the first variable increases its standard deviation by one unit, the second latent variable increases by 0.227 units. The literature review supports this result since it was found that SCA is one of the variables that most impact SCR (Hohenstein et al. 2015; Shin and Park 2019). However, in the study conducted by Shin and Park (2021), SCA did not affect SCR. H12 . There is sufficient statistical evidence to state that SCAL has a direct and positive effect on SCR in the Mexican maquiladora industry, given that when the first variable increases its standard deviation by one unit, the second latent variable increases by 0.427 units. This new variable is the one that has had the greatest impact on the SCR in this study, which was not expected; however, it is in agreement with several studies where it was found to have a positive statistical significance (Queiroz et al. 2022; Shin and Park 2021). H13 . This hypothesis was tested using the Sobel method, and it can be concluded that the SCAL and SCA variables mediate between leadership styles (TSSCL and TFSCL) and SCR. However, the SCE and SCF variables were not significant. This may be because low-impact variables may become null when more variables are integrated into a structural equation model.

References

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References S. Abu-Bader, T.V. Jones, Statistical Mediation Analysis Using the Sobel Test and Hayes SPSS Process Macro. Int. J. Quant. Qualit. Res. Methods (2021) N.-O. Hohenstein, E. Feisel, E. Hartmann, L. Giunipero, Research on the phenomenon of supply chain resilience. Int. J. Phys. Distrib. Logist. Manag. 45(1/2), 90–117 (2015). https://doi.org/10. 1108/IJPDLM-05-2013-0128 U. Merschmann, U.W. Thonemann, Supply chain flexibility, uncertainty and firm performance: an empirical analysis of German manufacturing firms. Int. J. Prod. Econ. 130(1), 43–53 (2011). https://doi.org/10.1016/j.ijpe.2010.10.013 M.M. Queiroz, F. Wamba, C.J. Samuel, C. Jose, M.C. Machado, Supply chain resilience in the UK during the coronavirus pandemic: a resource orchestration perspective. Int. J. Prod. Econ. 245, 108405 (2022). https://doi.org/10.1016/j.ijpe.2021.108405 E. Rodríguez-Ponce, Leadership styles, strategic decision making and performance: an empirical study in small and medium-size firms. Interciencia 32(8), 522–528 (2007) N. Shin, S. Park, Evidence-Based Resilience management for supply chain sustainability: an interpretive structural modelling approach. Sustainability 11(2) (2019). https://doi.org/10.3390/su1 1020484 N. Shin, S. Park, Supply chain leadership driven strategic resilience capabilities management: a leader-member exchange perspective. J. Bus. Res. 122, 1–13 (2021). https://doi.org/10.1016/j. jbusres.2020.08.056 C. Tang, B. Tomlin, The power of flexibility for mitigating supply chain risks. Int. J. Prod. Econ. 116(1), 12–27 (2008). https://doi.org/10.1016/j.ijpe.2008.07.008

Chapter 7

Model 4. Flexibility, Agility, and Alertness as Precursors to Supply Chain Efficiency

Abstract This chapter proposes a structural equation model (SEM) that integrates four latent variables. The independent variable is supply chain flexibility (SCF), the mediating variables are supply alertness (SCAL) and supply chain agility (SCA), and the dependent variable is supply chain efficiency (SCE). These variables are related using six hypotheses validated with information from 231 responses to a survey applied to the Mexican maquiladora industry. SEM was evaluated using the partial least squares technique integrated into WarpPLS v.8 software. The results indicate that having a SCAL system helps to obtain better levels of SCF and SCA; however, to obtain better results in SCE, the variable with the greatest impact is SCA. This study makes a significant contribution because no similar model has been found in the literature analyzing these variables’ relationships. Keywords Alerts · Flexibility · Agility · Efficiency · Supply chain

7.1 Model Variables The main objective of this chapter is to determine the interaction between certain variables of the supply chain, such as alerts (SCAL), efficiency (SCE), flexibility (SCF), and agility (SCA). The latent variables or constructs were related using six hypotheses to measure whether there was any effect between them. Constructs integrated into the models were constructed using validated observed variables. SCAL integrates five observed variables, SCF presents three observed variables, SCA integrates four, and SCE contains three observed variables. Therefore, 15 observed variables were presented in this model, which were analyzed to continue with the validation process described below. For the names of the dimensions and items, see the questionnaire as complementary material.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. M. Solis et al., Leadership and operational indexes for supply chain resilience, SpringerBriefs in Applied Sciences and Technology, https://doi.org/10.1007/978-3-031-32364-5_7

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7.2 Hypotheses in the Model 7.2.1 Relationship of SCAL with SCF, SCA, and SCE Detecting macro- and microeconomic changes is currently an issue of vital importance for companies since their survival in the markets depends on it, which are affected by technological, political, social, and environmental issues, to mention a few. Companies must establish a warning system to detect rapid changes in demand and threats in the flow of SC and establish contingency plans that allow them to respond efficiently in adverse situations. Therefore, SCALs are of great relevance in risk awareness management. Rice and Sheffi (2005) affirm that alerts and flexibility are related variables that can help manage risk. Rajesh and Ravi (2015) assert that all supply chain members, both in the supply and distribution channels, must work together to obtain a high level of SCAL. An early warning system provides proactive responses to unforeseen situations and disruptive events, which, under operational conditions, can contribute to and impact flexibility (Hugh 2015). In addition, such a system provides awareness and understanding of the supply chain in which the company is immersed, which provides knowledge about vulnerabilities and the adoption of measures to manage them (Mandal 2019; Soni et al. 2014). A company’s early warnings and SCF can be considered crucial elements before, during, and after a disruptive event (Ahmadi et al. 2022; Ali et al. 2017). Thus, we propose the following hypothesis: H 1 . SCAL has a positive and significant impact on SCF. SCAL is the ability to identify changes before or over time (Li et al. 2008) that can be either external or external to the SC. Analyzing changes in the market can lead to threats and opportunities in the face of competitors (Christopher and Holweg 2011). In other words, a change in market position can be lost, or a better position can be gained, which entails the need to be able to predict the changes that will be generated in search of better risk management and market strategy (Li et al. 2017; Zobel et al. 2021). Gligor et al. (2015) stated that agility requires quick adaptation of tactics and operations in SC and uses alerts as a dimension of agility. Thus, SCALs are established as the primary variable that will impact the supply response, allowing SCAL to be a precursor to SCA (Neiger et al. 2009), where SCA is also a key variable in SCR. Li et al. (2017) showed that SCAL and SCA significantly influence firms’ financial performance; however, they analyzed both variables independently. Therefore, the following hypothesis is proposed: H 2 . SCAL has a positive and significant impact on SCA. SCE aims to reduce costs throughout the SC, including transportation, production, inventory, and distribution. Each link in the chain adds time and cost, which is why efficiency seeks to achieve optimal performance for all SC members. According to

7.2 Hypotheses in the Model

93

Adobor and McMullen (2018) and Galbusera et al. (2016), SCR theory encompasses the SC’s efficiency, adaptive, and evolutionary capacity. The efficiency variable considers the observed variables related to contingency planning and has a controloriented approach. Contingency planning requires an alert system that provides information about market changes (Li et al. 2008; Piprani et al. 2022). According to Ponomarov and Holcomb (2009), SCE can focus on the timing and recovery of SC to face of disruptive event, as it contemplates an emergency plan that entails the creation and implementation of strategies to prevent disruptions in the flow of SC (Golgeci and Ponomarov 2014; Gu et al. 2015). The above indicates that to obtain high-efficiency levels, SCAL mechanisms are required to detect threats to the SC and make contingency plans. Thus, the following hypothesis is proposed: H 3 . SCAL has a positive and significant impact on SCE.

7.2.2 Relationship Between SCF to SCA and SCE SCF presents a significant opportunity for analysis because most research on this topic is focused on manufacturing systems. The SCF must contemplate the internal and external components of the company (Jin et al. 2014). It is not enough to have flexibility in the production system (Pujawan 2004) but must be extended to the entire supply and distribution network (Yu et al. 2015). Similarly, to consider the SCF, the dynamic environment of the SC must be considered. The importance of SCF derives from changes in the market, such as mass customization, unstable industries, and innovation. These changes lead to increased costs and uncertainty in demand (Bing and Yili 2008), and then the SCF can function as a strategy to moderate disruptions in SC (Azadegan et al. 2013). Several authors consider SCF to be an SCA dimension (Eckstein et al. 2015; Gligor et al. 2015), where SCA is defined as a rapid adjustment of tactics in SC operations (Gligor et al. 2015). Therefore, flexibility in the SC brings excellent benefits when a disruptive event occurs because there are options to cope with it and maintain the desired level of performance, providing speed to the SC in response times, for which the following hypothesis is established: H 4 . SCF has a positive and significant impact on SCA. SCF can maintain the efficiency contemplated in SC, and it is claimed that companies running flexible manufacturing systems and supply chains recover faster than their non-flexible counterparts (Adobor and McMullen 2018). This may be because companies with already in place flexible processes and systems can adjust their production volumes depending on demand, which benefits delivery times. In the event of a disruptive event, both time and responsiveness have direct impacts on business efficiency and supply chains. SCF can help to maintain the status quo within a disruptive event and recover faster (Juan and Lin 2020; Martínez Sánchez and Pérez Pérez 2005). However, it is necessary to perform a detailed analysis of the flexibility required by the processes

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7 Model 4. Flexibility, Agility, and Alertness as Precursors to Supply …

and CS since any implementation entails an investment that can directly harm a company’s financial goals and impair efficiency (Pujawan 2004). Because SCF can help improve SCE but perform a cost–benefit analysis, the following hypothesis is proposed. H 5 . SCF has a positive and significant impact on SCE.

7.2.3 Relationship Between SCA to SCE According to Sambamurthy et al. (2003), agility is a firm’s ability to gain a market advantage quickly. However, SCA is the ability of the firm and its business partners in the supply and distribution channels to adapt and respond quickly to changes in the marketplace (Chiang et al. 2012). This speed can generate costs and cause a loss of efficiency (Saeed et al. 2019). Therefore, it is necessary to establish adequate levels of both variables (SCA and SCE) to achieve a balance between them (Saeed et al. 2019), and agility and efficiency can be considered to be related (Adobor and McMullen 2018). Therefore, it can be established that agility in a disruptive moment can generate costs, which will harm the established efficiency. However, it should be noted that speed and adaptability, which are dimensions of agility, will help recover efficiency in a company and can even gain greater performance in the market by responding to other companies of its type in adverse transitions. In addition, the visibility in the network is part of the agility, exposing all those activities and processes that do not add value and for which a network can lose efficiency, which leads to risks of an interruption in the SC. According to Riquelme-Medina et al. (2022), visibility decreases the risks of an interruption in the SC. Thus, the following hypothesis is proposed: H 6 . SCA has a positive and significant impact on SCE. Figure 7.1 shows the relationships between the variables established as hypotheses in the previous paragraphs.

7.3 Latent Variable Validation Table 7.1 shows the validation indices of the five latent variables analyzed. The first row indicates the number of items before and after the validation process. Because some items were eliminated, the initial and final numbers may have differed. The second and third rows show the values of the R-squared and adjusted R-squared indices, respectively, which are greater than 0.02; thus, it is inferred that there is sufficient parametric predictive validity. Likewise, the value of Q-squared is similar to that of R-squared, and it is concluded that nonparametric predictive validity exists.

7.3 Latent Variable Validation

95

Fig. 7.1 Proposed model with supply chain indicators

Table 7.1 Latent variable validation Index

SCAL

Items

5

SCF 4

3 0.267

R-squared Adj. R-squared

SCA 3

4 0.455

SCE 4

3

0.264

0.45

0.278

Composite reliability

0.929

0.902

0.898

0.922

Cronbach’s alpha

0.898

0.837

0.848

0.872

Average variance extracted

0.766

0.754

0.687

0.797

Full collinearity VIF

1.591

1.667

1.936

1.382

0.259

0.455

0.289

Q-squared

3

0.287

The fourth and fifth rows show the composite reliability index and Cronbach’s alpha, respectively. Given the values obtained, it was concluded that the variables had sufficient content validity. The seventh row shows the complete collinearity value of the VIF. Given that the values were less than five, it was concluded that there were no collinearity problems. Finally, the average variance extracted for all the latent variables was greater than 0.5, indicating convergent validity. Other validation indices can be found in the supplementary material.

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7.4 Evaluation of the Structural Equation Model This section analyses the validation process for the variables integrated into the structural equation model (SEM) and is evaluated according to the established methodology. Table 7.2 illustrates the efficiency indices of the model, where the APC, ARS, and AARS indices indicate sufficient predictive validity because all values have an associated p-value of less than 0.05. The VIF and AFVIF values were less than 3.3, indicating that the model had no collinearity problems. Finally, it is observed that the Tenenhaus GoF is greater than 0.36, which indicates that the analyzed data fit the model, and it is concluded that the model is valid and can be interpreted. Figure 7.2 illustrates the model evaluated, showing the standardized β values and the associated p-value for the hypothesis test. The R-squared value measures the variance explained by independent variables for each dependent construct. According to the p-values, it was concluded that all relationships between the hypotheses were statistically significant.

7.4.1 Direct Effects and Validation of Hypotheses Table 7.3 presents the model evaluation with the direct effects between the variables analyzed, which appear as arrows in Figs. 7.1 and 7.2. The dependence value between the variables is represented by β, and a p-value is used to measure the statistical significance of the relationships. For example, the relationship SCAL → SCF shows values of β = 0.517 and p < 0.001, indicating that when SCAL increases its standard deviation by one unit, SCF increases by 0.517 units, demonstrating the relationship between the two variables. In addition, the effect size (ES) is indicated as a measure of the variance explained for each effect. For the relationship SCAL → SCE, ES = 0.163, indicating that SCAL can explain 16.3% of the variability in SCE. Table 7.2 Model efficiency indexes

Index

Value

Best if

Average path coefficient (APC)

0.319, P < 0.001

P < 0.05

Average R-squared (ARS)

0.336, P = 0.001

P < 0.05

Average adjusted R-squared (AARS)

0.331, P = 0.001

P < 0.05

Average block VIF (AVIF)

1.558

≤ 3.3

Average full collinearity VIF (AFVIF)

1.644

≤ 3.3

Tenenhaus GoF (GoF), ideally 0.503

≥ 0.36

7.4 Evaluation of the Structural Equation Model

97

Fig. 7.2 Evaluation of the initial model

Table 7.3 Summary of direct effects

Hypotheses

β & P-value

ES

Decision

H1 SCAL → SCF

0.517 (< 0.001)

0.267

Accepted

H2 SCAL → SCA

0.388 (< 0.001)

0.346

Accepted

H3 SCAL → SCE

0.119 (0.033)

0.163

Accepted

H4 SCF → SCA

0.386 (< 0.001)

0.226

Accepted

H5 SCF → SCE

0.180 (0.003)

0.131

Accepted

H6 SCA → SCE

0.325 (< 0.001)

0.162

Accepted

7.4.2 Sum of Indirect and Total Effects Table 7.4 shows the sum of the indirect effects among the variables in which the β values, associated p-value, and effect size (ES). The largest direct effect is found in the SCF → SCE relationship, and it is given through SCA, which is obtained by multiplying 0.386 × 0.325 (β values for the segments) with an associated p-value of 0.003 and an effect size of 0.054, indicating that SCF can explain 5.4% of the variability in SCE through indirect effects. Table 7.5 illustrates the total effects obtained by adding the direct and indirect effects, the associated p-values, and the effect sizes. For example, for SCF → SCE, Table 7.4 Indirect effect

Indirect effect

P-value

Effect size

SCAL → SCE

0.284

(< 0.001)

0.115

SCAL → SCA

0.200

(< 0.001)

0.117

SCF → SCE

0.126

(0.003)

0.054

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7 Model 4. Flexibility, Agility, and Alertness as Precursors to Supply …

Table 7.5 Total effects SCAL

SCF

SCF

0.517 (< 0.001) ES = 0.267

SCA

0.588 (< 0.001) ES = 0.345

0.386 (< 0.001) ES = 0.226

SCE

0.403 (< 0.001) ES = 0.163

0.305 (< 0.001) EN = 0.131

SCA

0.325 (< 0.001) ES = 0.162

we have an indirect effect passing through SCA plus a direct effect; therefore, the total effect is obtained by summing 0.386 × 0.325 + 0.180 = 0.305 with an associated p-value of < 0.001 and a total effect of 0.131, indicating that SCA accounts for 13.1% of the total variance contained in SCE.

7.4.3 Sensitivity Analysis Table 7.6 shows the probabilities of each variable in four possible scenarios when presented independently in its high (+) and low (−) scenarios, together (&) or conditionally (IF), for all combinations of the variables. For example, the probability of having a SCAL+ scenario causes the SCF+ construct to occur, with a probability of 0.347. In addition, the probability of SCAL occurring independently at its high level was 0.212 and that at its low level was 0.195, whereas for SCF at its high level, it was 0.169, and that at its low level was 0.156. However, the probability of SCAL+ and SCF+ occurring together is only 0.074, which is small. An interpretation of these values is provided in the conclusion section, as well as their industrial and administrative implications.

7.5 Discussion of Results 7.5.1 Structural Equation Modeling Several aspects were observed when analyzing the direct effects of the model. First, the strongest relationship occurs between SCAL and SCF, which has the highest explanatory power, followed by SCA. This indicates that SCAL is a precursor of SCF and SCA, which is why managers should invest in warning systems in the supply chain. Having an early warning system in place will enable managers to make the right decisions with others in the supply chain, facilitating consensus and preventing one member’s actions from diminishing the benefits of others.

7.5 Discussion of Results

99

Table 7.6 Sensitivity analysis Level

SCF

SCA

SCE

SCAL

SCF

SCA

+

−

+

−

+

−

0.169

0.156

0.208

0.143

Probability

0.212

0.195

+

0.169

&= 0.074 if = 0.347

&= 0.009 if = 0.044

−

0.156

&= 0.009 if = 0.041

&= 0.078 if = 0.400

+

0.208

&= 0.117 if = 0.551

&= 0.004 if = 0.022

&= 0.078 if = 0.462

&= 0.000 if = 0.000

−

0.143

&= 0.009 if = 0.041

&= 0.078 if = 0.400

&= 0.000 if = 0.000

&= 0.069 if = 0.444

+

0.156

&= 0.069 if = 0.327

&= 0.009 if = 0.044

&= 0.065 if = 0.385

&= 0.000 if = 0.000

&= 0.074 if = 0.354

&= 0.004 if = 0.030

−

0.143

&= 0.022 if = 0.102

&= 0.035 if = 0.178

&= 0.017 if = 0.103

&= 0.056 if = 0.361

&= 0.017 if = 0.083

&= 0.056 if = 0.394

On the other hand, although statistically significant, the weakest relationships are those between SCAL and SCE and between SCF and SCE, which have been demonstrated in the hypotheses established in previous models. However, SCA had the greatest impact on SCE. This indicates that alerts entail actions that require investments, which often diminish the possible benefits that could be achieved. However, Kazancoglu et al. (2022) and Siagian et al. (2021) state that it is worse not to have them since disruptive events occur without having action plans to resolve them. So, investment in an alertness system is economically justified for managers. Two indirect effects were also analyzed; it should be noted that all of them were significant and that the effect that had the greatest impact was between SCAL → SCE, which is between SCF and SCA, followed by SCAL → SCA. In total effects, the strongest impact was between SCAL and SCA; that is, alert systems in the supply chain allow companies to be more agile, given that decisions are made in time and in advance.

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7 Model 4. Flexibility, Agility, and Alertness as Precursors to Supply …

7.5.2 Sensitivity Analysis The sensitivity analysis is reported in Table 7.6 for the relationships between the constructs in the model, with “+” signs indicating a high level and “−” signs indicating a low level in the constructs. SCAL+ is a strong predictor of SCA+ but moderate for SCF+ and SCE, given that the conditional probabilities of occurrence are 0.551, 0.347, and 0.327, respectively, indicating that an alert system will allow firms to be more agile and will promote flexibility and efficiency rates. This is demonstrated by observing that SCAL+ is only weakly associated with SCF− and SCA−, with probabilities of 0.041 in both cases. However, high-efficiency indices cannot be guaranteed because the conditional probability with SCE− is 0.102. It is also observed that SCAL− is a predecessor of SCF− and SCA− because the conditional probabilities are 0.400 in both cases. However, it is slightly a predecessor of SCE−, as in this case, the probability is only 0.178, which is a low value compared to the other variables. Similarly, SCAL− was only weakly associated with high SCF+, SCA+, and SCE+, with probabilities of 0.044, 0.022, and 0.044, respectively. This indicates that managers who invest in an alert system in their supply chains can gain agility and flexibility but cannot guarantee supply chain efficiency. It was also observed that SCF+ is a strong predictor of SCA+ and a moderate predictor of SCE+ because it promotes the occurrence of SCA+ with conditional probabilities of 0.462 and 0.385, respectively. This is demonstrated by observing that SCF+ is not associated with SCA− and moderately associated with SCE−, with probabilities of 0.000 and 0.103, respectively. This indicates that managers who invest in supply chain flexibility can obtain greater agility but do not correctly guarantee the efficiency indices associated with costs. Similarly, it was observed that SCF− is a strong precursor of SCA but a moderate precursor of SCE−, as the conditional probabilities of occurrence are 0.444 and 0.361, respectively. However, SCF− is not associated with SCA− and SCE− because the probabilities are zero in both cases. This indicates that managers with low flexibility will never achieve high agility and cost efficiency. Finally, the results indicate that SCA+ is a moderate precursor of SCE+ because the conditional probability is 0.354 and is only weakly associated with SCE− because the probability is only 0.083. However, SCA− promoted SCE− because the conditional probability was 0.394. This scenario is not associated with SCE because the probability is only 0.030. This indicates that managers who invest in agility in their supply chains obtain the benefits associated with cost efficiency.

7.6 Conclusions and Management Implications In the initial model shown in Fig. 7.2, six hypotheses were established, based on which the following conclusions can be drawn:

7.6 Conclusions and Management Implications

101

H1 . There is sufficient statistical evidence to affirm that SCAL has a direct and positive effect on SCF in the Mexican maquiladora industry, given that when the first variable increases the standard deviation by one unit, the second latent variable increases by 0.517 units. This finding has implications for managers in Mexican manufacturing companies because if they wish to increase SCF, they must establish a SCAL system, contemplating their business partners and not in isolation. SCAL must detect changes in markets and supply threats. Based on this, commercial partners can adjust delivery times, making the SC more flexible, offering better solutions to problems, and not delaying delivery time. This is consistent with Rice and Sheffi (2005) findings, who stated that alerts and flexibility show a relationship and support to withstand company risks. H2 . There is sufficient statistical evidence to affirm that SCAL has a direct and positive effect on SCA in the Mexican maquiladora industry, given that when the first variable increases the standard deviation by one unit, the second latent variable increases it by 0.388 units. This result has implications for managers in manufacturing companies because it affects the agility of the entire network. Therefore, implementing an alert system to detect risks in an SC will help adapt processes and reduce activities that do not generate value, resulting in greater speed and shorter delivery times. These results are consistent with the study conducted by Gligor et al. (2015), in which alerts were used as a dimension of agility; that is, alerts impact agility, which is a precursor of agility and favors the adaptation of operations. H3 . There is sufficient statistical evidence to affirm that SCAL has a direct and positive effect on SCE in the Mexican maquiladora industry, given that when the first variable increases the standard deviation by one unit, the second latent variable increases by 0.119 units. The implications of these results can provide information to managers of Mexican manufacturing companies that need to increase their SCE. They can do so using SCAL; however, owing to the impact obtained above, it is recommended that SCA should be developed first. The results obtained are in agreement with those of Golgeci and Ponomarov (2014), which state that it is necessary to have an emergency plan, that is, alerts, in order to be efficient in SC, and the alerts should be based on detecting and foreseeing interruptions in SC. H4 . There is sufficient statistical evidence to affirm that SCF directly and positively affects SCA in the Mexican maquiladora industry, given that when the first variable increases its standard deviation by one unit, the second latent variable increases by 0.386 units. This implies that if managers need to increase and adapt processes to reduce cycle time, they can invest in making operations more flexible in manufacturing and the entire supply network. These results agree with Gligor et al. (2015), who showed that to have SCA, flexibility is needed in the SC to make rapid changes in the processes. H5 . There is sufficient statistical evidence to affirm that SCF has a direct and positive effect on SCE in the Mexican maquiladora industry, given that when the first variable increases its standard deviation by one unit, the second latent variable increases by 0.180 units. Although statistically significant, this result is minimal because the variables observed to measure SCE are based on reducing manufacturing, distribution, and inventory costs. In most cases, SCF requires investment by the

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7 Model 4. Flexibility, Agility, and Alertness as Precursors to Supply …

company; therefore, profits may be harmed in the short or medium terms. This coincides with some research where it is established that SCF substantially improves efficiency in a company (Juan and Lin 2020; Martínez Sánchez and Pérez Pérez 2005). H6 . There is sufficient statistical evidence to affirm that SCA has a direct and positive effect on SCE in the Mexican maquiladora industry, given that when the first variable increases the standard deviation by one unit, the second latent variable increases by 0.325 units. This result is vital because adjusting processes quickly and reducing lead times results in greater SCE efficiency. Thus, managers of Mexican manufacturing companies should focus on higher levels of agility in their production processes and the entire SCE, including their partners. These results are similar to those of other studies, where he states that agility and efficiency are related; however, care must be taken, and a balance between the two must be achieved (Adobor and McMullen 2018).

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I. Golgeci, S. Ponomarov, How does firm innovativeness enable supply chain resilience? The moderating role of supply uncertainty and interdependence. Technol. Anal. Strateg. Manag. 27, 267 (2014). https://doi.org/10.1080/09537325.2014.971003 X. Gu, X. Jin, J. Ni, Y. Koren, Manufacturing system design for resilience. Paper presented at the 25th design conference innovative product creation, CIRP (2015) B. Hugh, Cybersecurity and cyber-resilient supply chains. Technol. Innov. Manag. Rev. 5(4) (2015) Y. Jin, M. Vonderembse, T.S. Ragu-Nathan, J.T. Smith, Exploring relationships among IT-enabled sharing capability, supply chain flexibility, and competitive performance. Int. J. Prod. Econ. 153, 24–34 (2014). https://doi.org/10.1016/j.ijpe.2014.03.016 S.-J. Juan, W.-T. Lin, Effect of the black box of supply chain resilience on supply chain performance with disruption considerations (2020) I. Kazancoglu, M. Ozbiltekin-Pala, S. Kumar Mangla, Y. Kazancoglu, F. Jabeen, Role of flexibility, agility and responsiveness for sustainable supply chain resilience during COVID-19. J. Clean. Prod. 362 (2022). https://doi.org/10.1016/j.jclepro.2022.132431 X. Li, C. Chung, T.J. Goldsby, C.W. Holsapple, A unified model of supply chain agility: the workdesign perspective. Int. J. Logistics Manag. 19(3), 408–435 (2008). https://doi.org/10.1108/095 74090810919224 X. Li, Q. Wu, C.W. Holsapple, T. Goldsby, An empirical examination of firm financial performance along dimensions of supply chain resilience. Manag. Res. Rev. 40(3), 254–269 (2017). https:// doi.org/10.1108/MRR-02-2016-0030 S. Mandal, The influence of big data analytics management capabilities on supply chain preparedness, alertness and agility: an empirical investigation. Inf. Technol. People 32(2), 297–318 (2019). https://doi.org/10.1108/ITP-11-2017-0386 M. Sánchez, Angel, & Pérez Pérez, Manuela., Supply chain flexibility and firm performance. Int. J. Oper. Prod. Manag. 25(7), 681–700 (2005). https://doi.org/10.1108/01443570510605090 D. Neiger, K. Rotaru, L. Churilov, Supply chain risk identification with value-focused process engineering. J. Oper. Manag. 27(2), 154–168 (2009). https://doi.org/10.1016/j.jom.2007.11.003 A.Z. Piprani, N.I. Jaafar, S.M. Ali, M.S. Mubarik, M. Shahbaz, Multi-dimensional supply chain flexibility and supply chain resilience: the role of supply chain risks exposure. Oper. Manag. Res. 15(1–2), 307–325 (2022). https://doi.org/10.1007/s12063-021-00232-w S.Y. Ponomarov, M.C. Holcomb, Understanding the concept of supply chain resilience. Int. J. Logistics Manag. 20(1), 124–143 (2009). https://doi.org/10.1108/09574090910954873 N. Pujawan, Assessing supply chain flexibility: a conceptual framework and case study. Int. J. Integr. Supply Manag. 1, 79–97 (2004). https://doi.org/10.1504/IJISM.2004.004599 R. Rajesh, V. Ravi, Supplier selection in resilient supply chains: a grey relational analysis approach. J. Clean. Prod. 86, 343–359 (2015). https://doi.org/10.1016/j.jclepro.2014.08.054 J.B. Rice, Y. Sheffi, A supply chain view of the resilient enterprise. MIT Sloan Manag. Rev. 47, 41–48 (2005) M. Riquelme-Medina, M. Stevenson, V. Barrales-Molina, F.J. Llorens-Montes, Coopetition in business ecosystems: the key role of absorptive capacity and supply chain agility. J. Bus. Res. 146, 464–476 (2022). https://doi.org/10.1016/j.jbusres.2022.03.071 K.A. Saeed, M.K. Malhotra, S. Abdinnour, How supply chain architecture and product architecture impact firm performance: an empirical examination. J. Purch. Supply Manag. 25(1), 40–52 (2019). https://doi.org/10.1016/j.pursup.2018.02.003 V. Sambamurthy, A. Bharadwaj, V. Grover, Shaping agility through digital options: reconceptualizing the role of information technology in contemporary firms. MIS q. 27(2), 237–263 (2003). https://doi.org/10.2307/30036530 H. Siagian, Z.J.H. Tarigan, F. Jie, Supply chain integration enables resilience, flexibility, and innovation to improve business performance in covid-19 era. Sustainability (Switzerland) 13(9) (2021). https://doi.org/10.3390/su13094669 U. Soni, V. Jain, S. Kumar, Measuring supply chain resilience using a deterministic modeling approach. Comput. Ind. Eng. 74, 11 (2014). https://doi.org/10.1016/j.cie.2014.04.019

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K. Yu, J. Cadeaux, B.N. Luo, Operational flexibility: review and meta-analysis. Int. J. Prod. Econ. 169, 190–202 (2015) C.W. Zobel, C.A. MacKenzie, M. Baghersad, Y. Li, Establishing a frame of reference for measuring disaster resilience. Decis. Support Syst. 140, 113406 (2021). https://doi.org/10.1016/j.dss.2020. 113406

Chapter 8

Model 5. Impact of Leadership on Operating Ratios and Resilience

Abstract This chapter presents a second-order integrative structural equation model (SEM) that integrates three constructs. The independent variable is supply chain mix leadership (SCML), the mediating variable is supply chain indexes (SCI), and the dependent variable is supply chain resilience. These variables are related to three hypotheses validated with information from 231 responses to a survey applied to the Mexican maquiladora industry. SEM was evaluated using the least squares technique integrated into WarpPLS v.8® software. The findings indicate that SCML strongly affects SCI and directly and indirectly affects SCR. The analyses conclude that the leadership of the leading company in the supply chain (SC) and its efficiency support it in reacting adequately to disruptive events. Keywords Leadership · Resilience · Supply chain · Agility · Flexibility · Efficiency · Alerts

8.1 Model Variables In the previous models, the constructs were analyzed independently. In the model in this chapter, several variables were integrated to form a new latent variable, thus generating a second-order structural equation model. The proposed model integrates three variables; the independent variable is supply chain mix leadership (SCML), integrating two constructs, transactional (TSSCL) and transformational (TFSCL) leadership in the supply chain (each with its observed variables, as shown in previous chapters). This leadership integration is justified because a company can adopt either of the two leadership constructs, depending on the circumstances, given that one style is not always used alone. The second or mediating variable is the supply chain index (SCI), integrated by agility (SCA), alerts (SCAL), flexibility (SCF), and efficiency (SCE) in the supply chain. This integration is justified when a company can choose to implement and use each of these indices as indicators because one does not exclude the other and could also use more. Finally, the dependent variable is supply chain resilience (SCR). For all items of the constructs, see the questionnaire as an annex. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. M. Solis et al., Leadership and operational indexes for supply chain resilience, SpringerBriefs in Applied Sciences and Technology, https://doi.org/10.1007/978-3-031-32364-5_8

105

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8 Model 5. Impact of Leadership on Operating Ratios and Resilience

8.2 Hypotheses in the Model 8.2.1 Relationship of Leadership to Supply Chain Indexes and Resiliency Today, leadership can be considered a management strategy that can lead a company to position itself and win the market in stable situations and disruptive events. For AlNuaimi et al. (2021), leadership is an essential company resource. Managers, directors, and administrative personnel are decision-makers who influence a company’s corporate strategies and policies (Quigley and Hambrick 2015). Supply chain leadership can function as a supply chain management responsibility, where the lead firm can influence the actions, decisions, and performance of business partners across the network (Akhtar et al. 2017; Ojha et al. 2018). Leadership in SC is a relatively new topic, with the first studies appearing in the last decade. Less than 50 studies have analyzed this topic in different areas, where different leadership variables of performance styles are studied; however, a comparative study that shows the impact of each leadership style on a company has not yet been conducted (Chen et al. 2021). Using a mix of the two supply chain leaderships (TFSCL and TSSCL), one should take advantage of, for example, the quantitative capabilities that TSSCL offers by setting performance levels (Martin 2017). TSSCL is based on the supervision and control of followers (Aga 2016), rewarding or sanctioning their actions as planned in the performance of the objectives (Aga 2016; AlNuaimi et al. 2021; Mekpor and Dartey-Baah 2017). In the same way, TFSCL offers, through qualitative capabilities, more significant influence on followers, using inspiration, motivation and empathy (AlNuaimi et al. 2021), strengthening trust and confidence (Yue et al. 2019), and projecting a better image for the collective good of the SC constituents (Park and Pierce 2020). Shin and Park (2021) studied SCML leadership based on the leader–follower relationship. They evaluated its impact on resilience by considering intermediate variables regarding flexibility, agility, efficiency, and supply chain alerts. The results indicate that leadership significantly impacted all four independent variables. On the other hand, Sadeghi et al. (2022) conducted a study where they found that the agile–lean strategy based on leadership in the supply chain improves supply chain performance indicators. However, several authors have determined that leadership in SC impacts SC performance (Gosling et al. 2016; Sharif and Irani 2012), and efficiency variables increase operational performance (Ul-Hameed et al. 2019). Thus, alerts provide support in operational and administrative recovery into the SC (Li et al. 2017; Queiroz et al. 2022); flexibility as a management strategy among firms foster the creation of diverse alternatives to face disruptive events (Ali et al. 2017), which enables constant and effective collaboration to respond to market changes (Riquelme-Medina et al. 2022; Wilhelm and Sydow 2018).

8.2 Hypotheses in the Model

107

Considering the above, this research assumes that the leadership established in a company can improve operational indexes in the supply chain. Thus, we propose the following hypothesis: H 1 . SCML has a positive and significant impact on SCI. On the other hand, companies should focus on increasing the level of SCR because of the impact on the loss of orders when a disruptive event arises, in addition to the costs generated in a disrupted environment increasing among the SC members (Chen et al. 2020). However, SCR can also be viewed as a strategy to return to the position held before the disruption or to obtain a better position (Echefaj et al. 2022). This is obtained through the leadership of the leading company and its decisions. One of the strengths of the TFSCL is problem-solving and risk-taking in times of renewal of the company looking for innovative solutions (Avolio and Bass 2004; Park and Pierce 2020; Shao et al. 2017). On the other hand, the TSSCL strives to maintain the status quo in a company and is focused on achieving the established goals (Arokiasamy et al. 2016; Birasnav and Bienstock 2019). Due to this, it can be established that the combination of both leaderships (MSCL) will be beneficial during a disruptive event; the TSSCL can provide significant help in the anticipation and resistance phase and the TFSCL in the recovery and response phase in the SCR. Additionally, Shin and Park (2021) demonstrated that supply chain leadership indirectly affects supply chain resilience. Because the MSCL encompasses qualitative and quantitative attributes, this leadership would include the application in all phases of SCR (anticipation, resistance, recovery, and response). In addition, some studies showed significant results of the impact of leadership on SCR, and the following hypothesis can be established. H 2 . SCML has a positive and significant impact on SCR. Finally, Shin and Park (2019) analyzed the relationship between capabilities in resilience, marking the main elements to measure resilience performance, such as flexibility, collaboration, agility, alerts, information sharing, robustness, risk management culture, security, SC continuity, speed, and trust. According to Stone and Rahimifard (2018), the key indicators are agility, flexibility, visibility, efficiency, and security. According to Pettit et al. (2010), resilience capabilities are flexibility, capacity, adaptability, efficiency, and agility, among others. Since it was found that the variables of flexibility, agility, efficiency, and alerts are essential for measuring SCR independently, the following hypothesis was established. H 3 . SCI has a positive and significant impact on SCR. Figure 8.1 graphically illustrates the relationships between the variables established as hypotheses in the previous paragraphs.

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8 Model 5. Impact of Leadership on Operating Ratios and Resilience

Fig. 8.1 Proposed model with SC mix leadership

8.3 Laten Variable Validation Table 8.1 shows the validation indices of the three latent variables analyzed. The first row shows the number of constructs in the second-order variable and the total number of variables observed. The second and third rows show the values of the R-squared and adjusted R-squared indices, which are greater than 0.02; thus, it is inferred that there is sufficient parametric predictive validity. Likewise, the value of Q-squared is similar to that of R-squared, and it is concluded that there is nonparametric predictive validity. The fourth and fifth rows show the composite reliability index and Cronbach’s alpha, respectively, and given the values obtained, it is concluded that the variables present sufficient content validity. The seventh row shows the complete collinearity values VIF, and given that they are lower than five, it is concluded that there are no collinearity problems between the analyzed variables. Finally, the average variance extracted (AVE) is greater than 0.5 for all latent variables, indicating convergent validity. Other validation indices can be found in the supplementary material. Table 8.1 Latent variable validation Index

SCML

Items

2

SCI 12

4

SCR 14

1

R-squared

0.435

0.416

Adj. R-squared

0.432

0.411

0.867

0.92

Composite reliability

0.923

Cronbach’s alpha

0.832

0.794

0.884

Average variance extracted

0.856

0.62

0.741

Full collinearity VIF

1.95

1.934

1.678

0.437

0.421

Q-squared

4

8.4 Structural Equation Model Evaluation

109

8.4 Structural Equation Model Evaluation Table 8.2 illustrates the model’s efficiency indices, where the APC, ARS, and AARS indices indicate acceptable predictive validity because all the values are associated with a p-value of less than 0.05. The VIF and AFVIF values were less than 3.3, indicating that the model had no collinearity problem. Finally, it is observed that the Tenenhaus GoF is greater than 0.36, which indicates that the analyzed data fit well with the proposed model. It is concluded that the model is valid and can be interpreted. Figure 8.2 illustrates the model evaluated, showing the standardized β values and the associated p-value for hypothesis testing in each relationship or hypothesis. For the dependent construct, the R-squared value measures the variance explained by the independent variables. According to the p-values, it was concluded that all relationships between variables were statistically significant. Table 8.2 Model efficiency indexes

Index

Value

Best if

Average path coefficient (APC)

0.457, P < 0.001

P < 0.05

Average R-squared (ARS)

0.425, P = 0.001

P < 0.05

Average adjusted R-squared (AARS)

0.421, P = 0.002

P < 0.05

Average block VIF (AVIF)

1.710

≤ 3.3

Average full collinearity VIF (AFVIF)

1.854

≤ 3.3

Tenenhaus GoF (GoF), ideally 0.561

Fig. 8.2 Evaluation of the initial model

≥ 0.36

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8 Model 5. Impact of Leadership on Operating Ratios and Resilience

Table 8.3 Summary of direct effects

β & P-value

Hypothesis

ES

Decision

H1 SCML → SCI

0.659 (< 0.001)

0.435

Accepted

H2 SCML → SCR

0.357 (< 0.001)

0.345

Accepted

H3 SCI → SCR

0.354 (< 0.001)

0.207

Accepted

8.4.1 Direct Effects and Validation of Hypotheses Table 8.3 illustrates the model evaluated with the direct effects between the variables, represented by the arrows in Figs. 8.1 and 8.2. The dependence between variables is represented by β and is associated with the p-value to measure the statistical significance of the relationships. For example, the relationship SCML → SCR shows values of β = 0.354 and p < 0.001, indicating that when SCML increases its standard deviation by one unit, SCR increases by 0.354 units, thus demonstrating the relationship between the two variables. In addition, the effect size (ES) indicates the variance explained for each effect.

8.4.2 Sum of Indirect and Total Effects Table 8.4 shows the sum of the indirect effects between variables, where the β values, the associated p-value, and the effect size (ES) appear. Because the model only presents three constructs, there is only one indirect effect, SCML → SCR, obtained by multiplying (0.659 × 0.354) with an associated p-value of 0.001 and an effect size of 0.137, which is statistically significant. Table 8.5 illustrates the total effects of the variables, which are obtained by adding the direct and indirect effects, the associated p-value, and the size of the effects. For example, for SCML → SCR, there is a direct effect and an indirect effect (SCML → SCI → SCR), so the total effect is the sum of the two effects (0.357 + 0.233). For the other variables, the direct effect was the same as the total effect. Table 8.4 Indirect effect

Table 8.5 Total effects

Relationship

Indirect effect

P-value

Effect size

SCML → SCR

0.233

(< 0.001)

0.137

To

From SCML

SCI SCR

SCI

0.659 (< 0.001) EN = 435 0.590 (< 0.001) ES = 0.345

0.354 (< 0.001) ES = 0.207

8.5 Discussion of Results

111

Table 8.6 Sensitivity analysis SCML +

Level SCI

SCR

SCI −

+

−

0.177

0.160

Probability

0.165

0.173

+

0.177

& = 0.078 if = 0.474

& = 0.000 if = 0.000

−

0.160

& = 0.009 if = 0.053

& = 0.104 if = 0.600

+

0.199

& = 0.095 if = 0.579

& = 0.009 if = 0.050

& = 0.091 if = 0.512

& = 0.004 if = 0.027

−

0.173

& = 0.004 if = 0.026

& = 0.078 if = 0.450

& = 0.004 if = 0.024

& = 0.065 if = 0.405

8.4.3 Sensitivity Analysis Table 8.6 shows the probabilities of each variable in four possible scenarios when they occur independently in their high (+) and low (−) scenarios, together (&) or conditionally (IF). For example, the probability of an SCML+ scenario co-occurring with SCI+ is 0.078. In addition, if SCML occurs alone and independently at a high level, the probability is 0.165, and at a low level, it is 0.173. However, the occurrence of SCML+ favored SCI+ , with a probability of 0.474. An interpretation of these values is given in the conclusion section, as well as their industrial and administrative implications.

8.5 Discussion of Results 8.5.1 Structural Equation Modeling Several essential aspects were emphasized when analyzing the direct effects of the model. First, the most robust relationship is between SCML and SCI, which indicates that there is high explanatory power and that leadership strongly impacts the indices in the supply chain. When reviewing the values in Fig. 8.2, it was observed that SCML had a direct and indirect effect on SCR; however, it was shown that the effect level was almost double using SCI. Therefore, it is recommended that company managers use these performance indicators. On the other hand, even without considering SCML, the SCI variable strongly impacts resilience, so it is also recommended to work on it as a whole.

112

8 Model 5. Impact of Leadership on Operating Ratios and Resilience

8.5.2 Sensitivity Analysis The sensitivity analysis is reported in Table 8.6 for the analyzed relationships between the variables. The “ + ” sign indicates a high level for the variable, and the “−” sign indicates a low level. The results indicate that the managers and administrative personnel of the maquiladora industry find it convenient to manage the mixture of transformational and transactional leadership (SCML) to obtain greater benefits in SCI to obtain the best benefits. It is observed that SCML+ is a strong precedent for SCI+ and SCR+ because it promotes the occurrence of these with conditional probabilities of 0.474 and 0.579, respectively, indicating that the implementation of leadership in the supply chain is of vital importance to generate adequate efficiency indexes and face situations before the occurrence of disruptive events. Similarly, it is observed that SCI+ is not significantly associated with SCI− and SCR− because the conditional probabilities are 0.053 and 0.026, respectively, which are low. However, the occurrence of SCML− is a strong antecedent of SCI− and SCR−, since the conditional probabilities are 0.600 and 0.450, respectively, which represents a risk for managers and indicates that a lack of leadership can lead to low efficiency and resilience in the supply chain. In addition, SCML− is not associated with SCI + because the probability is zero but is associated with SCR + because the probability is 0.050, which is low. In conclusion, low levels of SCML can generate low levels of SCI and SCR, affecting the operational and response capacity. Finally, SCI+ is a strong predecessor of SCR+ because it favors it with a conditional probability of 0.512, and it is associated with SCR− because, in that case, it favors it only with a probability of 0.024, which indicates that high-efficiency indices in the supply chain make it easier to face disruptive events. However, SCI− can generate SCR− because the conditional probability is 0.405, representing a risk for managers. Moreover, SCI− is not associated with SCR+, indicating that managers who invest in generating high-efficiency indices are more likely to cope with disruptive events.

8.6 Conclusions and Management Implications In the initial model shown in Fig. 8.2, three hypotheses were established and based on the results obtained, and the following can be concluded: H1 . There is sufficient statistical evidence to affirm that SCML has a direct and positive effect on SCI in the Mexican maquiladora industry, given that when the first variable increases the standard deviation by one unit, the second latent variable increases by 0.659 units. This finding has implications for managers in Mexican manufacturing companies because it is convenient to use mixed leadership according to the situation if they desire to increase the SC indicators.

8.6 Conclusions and Management Implications

113

This result is similar to that of Shin and Park (2021); however, they used different variables to measure leadership. Thus, our findings show that SC members should use quantitative and qualitative strategies because early warning systems that notice changes in demand and situations are suitable for everyone. Therefore, production and supply chain changes can be made more quickly, communication will help reduce cycle times, and transportation and stock costs will decrease. H2 . There is sufficient statistical evidence to declare that SCML has a direct and positive effect on SCR in the Mexican maquiladora industry, given that when the first variable increases the standard deviation by one unit, the second latent variable increases by 0.357 units. This result presents an implication for managers in manufacturing companies because when a disruptive event occurs, it is of utmost importance to manage SCML leadership because of its favorable characteristics. Working together will help increase the follower company’s collaboration in the SC, which is indispensable in this type of event. This result is similar to and different from what Shin and Park (2021) found because their leadership is the supply chain leader-member exchange (SCLMX), which indirectly measures the impact on SCR. However, the results showed that only two of the four variables examined were significant. H3 . There is sufficient statistical evidence to affirm that SCI has a direct and positive effect on SCR in the Mexican maquiladora industry, given that when the first variable increases the standard deviation by one unit, the second latent variable increases by 0.354 units. The implications of these results can provide information to managers in Mexican manufacturing companies because if they need to increase SCR, they can use a mix of key indicators in the supply chain, facilitating their decision-making. These results are similar to and differ from Shin and Park (2021) because, in that study, the indicators of flexibility, agility, efficiency, and supply chain alerts were analyzed separately, and their impact on SCR was measured. The results showed that efficiency and alerts in the chain had a positive and significant impact, while flexibility and agility did not significantly impact SCR. Our study analyzed them jointly, and the impact obtained was positive and significant. That is, adjusting and adapting processes in the supply chain (sourcing, production, and distribution), reducing non-value-generating activities and cycle time, reducing distribution and inventory costs, and having early warning systems to detect macroeconomic changes will help the SC before and during, and after the disruptive event. This can lead it to recover the status quo faster and emerge stronger, gaining a market position by demonstrating the efficiency of its resilience strategies in the market.

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8 Model 5. Impact of Leadership on Operating Ratios and Resilience

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