Christian M. Ringle is a Chaired Professor of Management and the Director of the Institute of Human Resource Management and Organizations (HRMO) in the Department of Management Sciences and Technology at the Hamburg University of Technology (TUHH), Germany. He holds a PhD from the Faculty of Business and Economics at the University of Hamburg. Among other research stays and appointments, Ringle was a visiting researcher at the Georgia State University and the Osaka City University and he was a Conjoint Professor at the University of Technology Sydney and the University of Newcastle in Australia. His research addresses human resource management, organization, marketing, strategic management, and quantitative methods for business and market research. His contributions in these fields have been published in journals such as International Journal of Research in Marketing, Information Systems Research, Journal of Business Research, Journal of Leisure Research, Journal of Service Research, Journal of the Academy of Marketing Science, Long Range Planning, MIS Quarterly, and Tourism Management. Since 2018, Ringle has been included in the Clarivate Analytics' Highly Researchers list. He offers online courses and regularly teaches doctoral seminars on multivariate statistics, especially the PLS-SEM method, and the use of the statistical software such as SmartPLS. Address: Hamburg University of Technology (TUHH), Germany
This study aims to contribute to the existing literature on higher education marketing by proposi... more This study aims to contribute to the existing literature on higher education marketing by proposing and empirically testing a theoretical model linking higher education quality, student satisfaction, and subjective well-being. The bottom-up spill over theory, the stimulus-organism-response theory, and the expectancy-disconfirmation theory, inform the development of the theoretical model of the study. A cross-sectional survey design is adopted, and data are collected from a sample of students from Mauritian Universities. The model is estimated and tested using a variance-based and prediction-oriented approach to structural equation modelling, specifically partial least squares structural equation modelling (PLS-SEM). The results demonstrate that approximately one-fifth of university students’ subjective well-being is explained by the quality of their student life and their satisfaction with higher education services. Based on these empirical results, we discuss and present key implications for higher education marketing.
Purpose
The purpose of this study is to investigate how working from home (WFH) affects the relat... more Purpose The purpose of this study is to investigate how working from home (WFH) affects the relationship between internal corporate social responsibility (ICSR) and employee creativity in times of uncertainty when employees’ occupational stress increases and their identification with their company decreases.
Design/methodology/approach Applying social identity theory, the authors derive and test the hypotheses presented in this study regarding ICSR’s direct effects on employee creativity, given the amount of time they spent on WFH and the role of threat in this relationship. The authors use partial least squares structural equation modeling to analyze the various effects. Via an online questionnaire and using the snowball technique, the authors collected data from 158 participants in different industries in Germany.
Findings The empirical results of this study show that ICSR activities increase employee creativity, partly by reducing one harmful aspect of stress, namely, threat. In addition, the authors find that WFH moderates this effect, such that the higher the degree of WFH, the weaker the ICSR activities’ effects are.
Research limitations/implications This study focused on the respondents’ WFH situation during the global COVID-19 pandemic. As such, this research contributes to understanding the roles that modern work practices, human resource management (HRM) and ICSR actions play in respect of employee creativity. The authors expand the theoretical understanding, which is based on social identity theory, by showing that the greater the amount of time spent on WFH, the more it reduces ICSR’s positive effect on employee creativity. The findings of this study open avenues for future research and longitudinal studies that compare the ICSR effects during and after the pandemic, as well as for those that compare WFH and its effects on organizational creativity.
Practical implications This study shows that managers should encourage appropriate ICSR measures in their organizations and should specifically consider the work setting (i.e. WFH or at the office) as a boundary factor for these measures’ effectiveness. However, ICSR actions, such as anti-discrimination measures, are less effective in respect of building the employee–employer relationship and supporting employees’ identification with and commitment to the company when they work from home. Given the economic benefit of decreased turnover rates and the societal benefit of a company output with higher creativity levels, this study has an impact from both an economic and a societal perspective.
Originality/value This study sheds light on employee creativity and ICSR’s roles in current HRM practice, which is still underexplored. More importantly, to the best of the authors’ knowledge, this study provides the first empirical evidence of a hitherto overlooked mechanism explaining ICSR activities’ effects on, or their perceived threat to, employee creativity.
In line with calls to stimulate methodological diversity and support evidence-based human resourc... more In line with calls to stimulate methodological diversity and support evidence-based human resource development (HRD) through quantitative competencies, we present a methods demonstration leveraging open-source tools and lesser-known quantitative research methods to support the HRD research community and applied HRD in the workplace. In this paper, we provide an informative introduction to partial least squares structural equation modeling (PLS-SEM). We discuss PLS-SEM application trends in the field of HRD, present key characteristics of the method, and demonstrate up-to-date metrics and evaluation guidelines using an illustrative model. Our PLS-SEM demonstration and explanations can serve as a valuable resource for practitioners concerned with substantiating results for organizational stakeholders and support researchers in methodological decision-making while avoiding common pitfalls associated with less familiar methods. Our step-by-step demonstration is conducted in open-source software and accompanied by explicitly coded operations so that readers can easily replicate the illustrative analyses presented.
A review of studies published in Industrial Marketing Management over the past two decades and mo... more A review of studies published in Industrial Marketing Management over the past two decades and more shows that these studies not only used partial least squares structural equation modeling (PLS-SEM) widely to estimate and empirically substantiate theoretically established models with constructs, but did so increasingly. In line with their study goals, researchers provided reasons for using PLS-SEM (e.g., model complexity, limited sample size, and prediction). These reasons are frequently not fully convincing, requiring further clarification. Additionally, our review reveals that researchers' assessment and reporting of their measurement and structural models are insufficient. Certain tests and thresholds that they use are also inappropriate. Finally, researchers seldom apply more advanced PLS-SEM analytic techniques, although these can support the results' robustness and may create new insights. This paper addresses the issues by reviewing business marketing studies to clarify PLS-SEM's appropriate use. Furthermore, the paper provides researchers and practitioners in the business marketing field with a best practice orientation and describes new opportunities for using PLS-SEM. To this end, the paper offers guidelines and checklists to support future PLS-SEM applications.
Corporate reputation is important for all types of banks across the world, despite these countrie... more Corporate reputation is important for all types of banks across the world, despite these countries differing culturally. Building on an extended corporate reputation model, we identify the key drivers of customer-based reputation and sustainable customer satisfaction in two culturally different countries, namely China and Germany. We also consider two reputation dimensions—perceived competence and likeability—and their effects on the target construct. Empirical data from 625 German and 734 Chinese commercial bank customers allow us to estimate the corporate reputation model with the partial least squares structural equation modeling (PLS-SEM) method, and by substantiating the relationships by means of a necessary condition analysis (NCA) and a predictive power analysis. By comparing the two countries’ results, we identify their cultural differences. Overall, we confirm the model’s relevance for the two cultures, finding that banks’ perceived attractiveness is the most important driver of both cultures’ customer-perceived bank reputation. By means of an importance-performance map analysis, we identify a large overlap between the two cultures’ set of important constructs, likeability’s much greater importance in Germany, and the perceived quality construct’s relevance in both countries. We contribute to research and scientific knowledge about corporate reputation models by identifying the similarities in and differences between two countries’ markets with respect to the banking sector, all of which have implications for international banks’ management.
As companies in the manufacturing and construction industries strive to meet the EU circular econ... more As companies in the manufacturing and construction industries strive to meet the EU circular economy (CE) targets, they need to develop new capabilities to implement CE activities that can positively influence their product/service innovations. However, companies in both industries, and beyond, still struggle to develop internal capabilities to innovate products and services that would help them in implementing CE principles and move towards the CE. The objective of this research is to analyze the types of innovation capabilities that are needed to enable CE implementation and achieve product/service innovations in two different industrial sectors. Prior research has focused on innovating and implementing circular business models and elaborated less on the innovation capability types. We collected survey data in December 2021–January 2022 that consists of responses from companies operating in Germany (n = 177), including employees in manufacturing (n = 87) and construction companies (n = 90). The results from the partial least squares structural equation modeling (PLS-SEM) based on measurement models from the literature indicate that employees in both sectors overall perceive higher levels of CE implementation capability as important for the company's product/service innovations. Furthermore, the results reveal differences in the way CE innovation capability and IT resource orchestration capability influence CE implementation and product/service innovations in the two sectors. Our study offers theoretical implications on how dynamic capabilities are associated with CE innovations and how they influence companies' product/service innovations based on empirical evidence from two industrial sectors. Those capabilities that are crucial for circular product/service innovations need to be associated with CE implementation capabilities. The results further advise practitioners in the development of CE innovation and CE implementation capabilities and how they are linked to IT resource orchestration capability and provide evidence on their relevance to creating product/service innovations.
This research offers a novel approach that extends the application of importance-performance map ... more This research offers a novel approach that extends the application of importance-performance map analysis (IPMA) in partial least squares structural equation modeling (PLS-SEM) by incorporating findings from a necessary condition analysis (NCA). The IPMA comprises assessing latent variables and their indicators' importance and performance, while an NCA introduces an additional dimension by identifying factors that are crucial for achieving the desired outcomes. An NCA employs necessity logic to identify the must-have factors required for an outcome, while PLS-SEM follows an additive sufficiency logic to identify the should-have factors that contribute to high performance levels. Integrating these two logics into the performance dimension is particularly valuable for prioritizing actions that could improve the target outcomes, such as customer satisfaction and employee commitment. Although the combined use of PLS-SEM and NCA is a recent suggestion, this study is the first to combine them with an IPMA (i.e., in a combined IPMA; cIPMA). A case study illustrates the combined use of PLS-SEM and an NCA to undertake a cIPMA. This innovative approach enhances researchers' and practitioners' decision making, enabling them to prioritize their efforts effectively.
Purpose – The purpose of this paper is to assess the appropriateness of equal weights estimation(... more Purpose – The purpose of this paper is to assess the appropriateness of equal weights estimation(sumscores) and the application of the composite equivalence index (CEI) vis-a-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM). Design/methodology/approach – The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI. Findings – The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM. Purpose – The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-a-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM). Design/methodology/approach – The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI. Findings – The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.
Corporate reputation is crucial for maintaining and enhancing a company's competitiveness in the ... more Corporate reputation is crucial for maintaining and enhancing a company's competitiveness in the marketplace. To actively manage this important intangible asset, which significantly contributes to a company's value, managers need to understand the relationship between reputation and its antecedents and consequences. The dataset presented in this article stems from a conceptual replication of a seminal model of corporate reputation, its antecedents and effects on customer satisfaction and loyalty. Potential mediators and moderators in these relationships allow us to extend the original model in order to clarify the mechanism through which corporate reputation impacts satisfaction and loyalty. We document some of the model's main effects using partial least squares structural equation modeling (PLS-SEM).
This perspective article on using partial least squares structural equation modelling (PLS-SEM) i... more This perspective article on using partial least squares structural equation modelling (PLS-SEM) is intended as a guide for authors who wish to publish datasets that can be analysed with this method as stand-alone data articles. Stand-alone data articles are different from supporting data articles in that they are not linked to a full research article published in another journal. Nevertheless, authors of stand-alone data articles will be required to clearly demonstrate and justify the usefulness of their dataset. This perspective article offers actionable recommendations regarding the conceptualisation phase, the types of data suitable for PLS-SEM and quality criteria to report, which are generally applicable to studies using PLS-SEM. We also present adjusted versions of the HTMT metric for discriminant validity testing that broaden its applicability. Further, we highlight the benefit of linking data articles to already published research papers that employ the PLS-SEM method.
Purpose
The purpose of this paper is to present integrated generalized structured component analy... more Purpose The purpose of this paper is to present integrated generalized structured component analysis (IGSCA) as a versatile approach for estimating models that contain both components and factors as statistical proxies for the constructs. The paper sets out to discuss the how-tos of using IGSCA by explaining how to specify, estimate, and evaluate different types of models. The paper’s overarching aim is to make business researchers aware of this promising structural equation modeling (SEM) method.
Design/methodology/approach By merging works of literature from various fields of science, the paper provides an overview of the steps that are required to run IGSCA. Findings from conceptual, analytical and empirical articles are combined to derive concrete guidelines for IGSCA use. Finally, an empirical case study is used to illustrate the analysis steps with the GSCA Pro software.
Findings Many of the principles and metrics known from partial least squares path modeling – the most prominent component-based SEM method – are also relevant in the context of IGSCA. However, there are differences in model specification, estimation and evaluation (e.g. assessment of overall model fit).
Research limitations/implications Methodological developments associated with IGSCA are rapidly emerging. The metrics reported in this paper are useful for current applications, but researchers should follow the latest developments in the field.
Originality/value To the best of the authors’ knowledge, this is the first paper to offer guidelines for IGSCA use and to illustrate the method's application by means of the GSCA Pro software. The recommendations and illustrations guide researchers who are seeking to conduct IGSCA studies in business research and practice.
International Journal of Contemporary Hospitality, 2023
Purpose
Partial least squares structural equation modeling (PLS-SEM) has attracted much attention... more Purpose Partial least squares structural equation modeling (PLS-SEM) has attracted much attention from both methodological and applied researchers in various disciplines – also in hospitality management research. As PLS-SEM is relatively new compared to other multivariate analysis techniques, there are still numerous open questions and uncertainties in its application. This study aims to address this important issue by offering guidance regarding its use in contexts with which researchers struggle.
Design/methodology/approach The authors examine the most prominent questions and answers posed in a well-known PLS-SEM discussion forum. The authors do so by using a text analysis technique to identify the most salient topics.
Findings The data analysis identifies three salient PLS-SEM topics (i.e. bootstrapping and significance testing, higher-order constructs and moderation).
Research limitations/implications The results allow us to address the PLS-SEM community’s main methodological issues. The authors discuss each area separately and provide explanations and guidelines.
Practical implications The guidelines on the most important PLS-SEM topics provide decision-making and application aids. In this way, the authors make a decisive contribution to clarifying ambiguities when applying the PLS-SEM method in hospitality management research and other disciplines.
Originality/value There has as yet been no systematic analysis of this kind in the field of PLS-SEM; the authors, therefore, present the first research results. The findings and recommendations provide guidance for PLS-SEM applications in hospitality research and practice.
Purpose
Researchers often stress the predictive goals of their partial least squares structural e... more Purpose Researchers often stress the predictive goals of their partial least squares structural equation modeling (PLS-SEM) analyses. However, the method has long lacked a statistical test to compare different models in terms of their predictive accuracy and to establish whether a proposed model offers a significantly better out-of-sample predictive accuracy than a naïve benchmark. This paper aims to address this methodological research gap in predictive model assessment and selection in composite-based modeling.
Design/methodology/approach Recent research has proposed the cross-validated predictive ability test (CVPAT) to compare theoretically established models. This paper proposes several extensions that broaden the scope of CVPAT and explains the key choices researchers must make when using them. A popular marketing model is used to illustrate the CVPAT extensions’ use and to make recommendations for the interpretation and benchmarking of the results.
Findings This research asserts that prediction-oriented model assessments and comparisons are essential for theory development and validation. It recommends that researchers routinely consider the application of CVPAT and its extensions when analyzing their theoretical models.
Research limitations/implications The findings offer several avenues for future research to extend and strengthen prediction-oriented model assessment and comparison in PLS-SEM.
Practical implications Guidelines are provided for applying CVPAT extensions and reporting the results to help researchers substantiate their models’ predictive capabilities.
Originality/value This research contributes to strengthening the predictive model validation practice in PLS-SEM, which is essential to derive managerial implications that are typically predictive in nature.
Partial least squares structural equation modeling (PLS-SEM) has become an established social sci... more Partial least squares structural equation modeling (PLS-SEM) has become an established social sciences multivariate analysis technique. Since quality management researchers also increasingly using PLS-SEM, this growing interest calls for guidance. Based on established guidelines for applying PLS-SEM and evaluating the results, this research reviews 107 articles applying the method and published in eight leading quality management journals. The use of PLS-SEM in quality management often only draws on limited information and analysis results. The discipline would benefit from the method's more comprehensive use by following established guidelines. Specifically, the use of predictive model assessment and more advanced PLS-SEM analyses harbors the potential to provide more detailed findings and conclusions when applying the method. This research provides first insights into PLS-SEM's use in quality management. Future research should identify the key areas and the core quality management models that best support the method's capabilities and researchers' goals. The results of this analysis guide researchers who use the PLS-SEM method for their quality management studies. This is the first article to systematically review the use of PLS-SEM in the quality management discipline.
Structural equation modeling (SEM) has remained two mutually exclusive domains, factor-based vs. ... more Structural equation modeling (SEM) has remained two mutually exclusive domains, factor-based vs. component-based, depending on whether a construct is modeled by either a factor or a component (i.e., weighted composite of indicators). Research in international management (IM) and international business (IB), however, needs to accommodate a more general model that considers a wide range of constructs from different disciplines at the same time, representing some constructs as factors (e.g., cultural distance and institutional distance) and others as components (e.g., international experience and export intensity). Integrated generalized structured component analysis (IGSCA) is a recently developed statistical method for estimating such models with both factors and components. IGSCA can provide overall fit indexes for model evaluation, including the goodness-of-fit index (GFI) and the standardized root mean squared residual (SRMR). However, the performance of these indexes in IGSCA is not yet investigated. Addressing this limitation, we (a) highlight the limitations of the dominantly used SEM approaches, (b) review the use of different SEM approaches in IM/IB research in the last decade, (c) conduct a simulation study, confirming that both GFI and SRMR distinguish well between correct and misspecified models with both factors and components, and (d) we illustrate the indexes’ efficacy using a model concerning the role of personality traits and international experience in shaping cultural intelligence. Based on the review and the results of the simulation study and the illustrative example, we also propose rules-of-thumb cutoff criteria for each index in IGSCA.
International Journal of Contemporary Hospitality Management, 2022
Purpose
Partial least squares structural equation modeling (PLS-SEM) has attracted much attention... more Purpose Partial least squares structural equation modeling (PLS-SEM) has attracted much attention from both methodological and applied researchers in various disciplines – also in hospitality management research. As PLS-SEM is relatively new compared to other multivariate analysis techniques, there are still numerous open questions and uncertainties in its application. This study aims to address this important issue by offering guidance regarding its use in contexts with which researchers struggle.
Design/methodology/approach The authors examine the most prominent questions and answers posed in a well-known PLS-SEM discussion forum. The authors do so by using a text analysis technique to identify the most salient topics.
Findings The data analysis identifies three salient PLS-SEM topics (i.e. bootstrapping and significance testing, higher-order constructs and moderation).
Research limitations/implications The results allow us to address the PLS-SEM community’s main methodological issues. The authors discuss each area separately and provide explanations and guidelines.
Practical implications The guidelines on the most important PLS-SEM topics provide decision-making and application aids. In this way, the authors make a decisive contribution to clarifying ambiguities when applying the PLS-SEM method in hospitality management research and other disciplines.
Originality/value There has as yet been no systematic analysis of this kind in the field of PLS-SEM; the authors, therefore, present the first research results. The findings and recommendations provide guidance for PLS-SEM applications in hospitality research and practice.
Environmental, social and governance indicators are often used to assess an organization’s ethica... more Environmental, social and governance indicators are often used to assess an organization’s ethical and long-term sustainability and performance. Organizations with high environmental, social and governance scores tend to perform consistently and exceed market expectations (Rajesh and Rajendran, 2020). In particular, organizations that incorporate environmentalnwelfare into their mission statement or core values not only achieve their organizational goals, but also achieve positive individual outcomes, such as employee engagement, willingness to take on organizational responsibilities and low turnover. Given the numerous environmental issues (e.g. pollution, global warming and waste management) that every industry faces directly or indirectly, there is an increased need and growing pressure for organizations to adopt sustainable strategies. As a result, the topic of green human resource management (GHRM) continues to attract the attention and interest of HRM scholars. A simple Google Scholar search (as of May 31, 2022) reveals 3,550,000 studies on this topic. Hence, GHRM is becoming one of the most important tasks
Research in international business and management (IM) is highly complex, contextual, and spans v... more Research in international business and management (IM) is highly complex, contextual, and spans various subfelds and related theoretical lenses. These characteristics pose research challenges that require utilizing sophisticated research designs and methodologies. This paper and our focused issue on ‘The use of partial least squares structural equation modeling (PLS-SEM) and complementary methods in IM research’ further clarify whether and how researchers using PLS-SEM can address (some of) the challenges in IM research. We explain how researchers can benefit from the (advanced) capabilities that PLS-SEM ofers; either as a stand-alone method or in triangulation eforts that leverage complementary approaches. In addition, we review the IM literature for PLS-SEM applications and evaluate whether and how researchers are already using these approaches. We identify some room for improvement when it comes to the application of both more advanced PLS-SEM capabilities and the triangulation of PLS-SEM with qualitative data analyses, and techniques such as fuzzy set qualitative comparative analyses and necessary condition analyses. For better guidance, we refer to PLS-SEM application examples in which the methodological advances are used.
Purpose
Researchers often stress the predictive goals of their partial least squares structural ... more Purpose Researchers often stress the predictive goals of their partial least squares structural equation modeling (PLS-SEM) analyses. However, the method has long lacked a statistical test to compare different models in terms of their predictive accuracy and to establish whether a proposed model offers a significantly better out-of-sample predictive accuracy than a naïve benchmark. This paper aims to address this methodological research gap in predictive model assessment and selection in composite-based modeling.
Design/methodology/approach Recent research has proposed the cross-validated predictive ability test (CVPAT) to compare theoretically established models. This paper proposes several extensions that broaden the scope of CVPAT and explains the key choices researchers must make when using them. A popular marketing model is used to illustrate the CVPAT extensions’ use and to make recommendations for the interpretation and benchmarking of the results. Findings This research asserts that prediction-oriented model assessments and comparisons are essential for theory development and validation. It recommends that researchers routinely consider the application of CVPAT and its extensions when analyzing their theoretical models.
Research limitations/implications The findings offer several avenues for future research to extend and strengthen prediction-oriented model assessment and comparison in PLS-SEM.
Practical implications Guidelines are provided for applying CVPAT extensions and reporting the results to help researchers substantiate their models’ predictive capabilities.
Originality/value This research contributes to strengthening the predictive model validation practice in PLS-SEM, which is essential to derive managerial implications that are typically predictive in nature
This study aims to contribute to the existing literature on higher education marketing by proposi... more This study aims to contribute to the existing literature on higher education marketing by proposing and empirically testing a theoretical model linking higher education quality, student satisfaction, and subjective well-being. The bottom-up spill over theory, the stimulus-organism-response theory, and the expectancy-disconfirmation theory, inform the development of the theoretical model of the study. A cross-sectional survey design is adopted, and data are collected from a sample of students from Mauritian Universities. The model is estimated and tested using a variance-based and prediction-oriented approach to structural equation modelling, specifically partial least squares structural equation modelling (PLS-SEM). The results demonstrate that approximately one-fifth of university students’ subjective well-being is explained by the quality of their student life and their satisfaction with higher education services. Based on these empirical results, we discuss and present key implications for higher education marketing.
Purpose
The purpose of this study is to investigate how working from home (WFH) affects the relat... more Purpose The purpose of this study is to investigate how working from home (WFH) affects the relationship between internal corporate social responsibility (ICSR) and employee creativity in times of uncertainty when employees’ occupational stress increases and their identification with their company decreases.
Design/methodology/approach Applying social identity theory, the authors derive and test the hypotheses presented in this study regarding ICSR’s direct effects on employee creativity, given the amount of time they spent on WFH and the role of threat in this relationship. The authors use partial least squares structural equation modeling to analyze the various effects. Via an online questionnaire and using the snowball technique, the authors collected data from 158 participants in different industries in Germany.
Findings The empirical results of this study show that ICSR activities increase employee creativity, partly by reducing one harmful aspect of stress, namely, threat. In addition, the authors find that WFH moderates this effect, such that the higher the degree of WFH, the weaker the ICSR activities’ effects are.
Research limitations/implications This study focused on the respondents’ WFH situation during the global COVID-19 pandemic. As such, this research contributes to understanding the roles that modern work practices, human resource management (HRM) and ICSR actions play in respect of employee creativity. The authors expand the theoretical understanding, which is based on social identity theory, by showing that the greater the amount of time spent on WFH, the more it reduces ICSR’s positive effect on employee creativity. The findings of this study open avenues for future research and longitudinal studies that compare the ICSR effects during and after the pandemic, as well as for those that compare WFH and its effects on organizational creativity.
Practical implications This study shows that managers should encourage appropriate ICSR measures in their organizations and should specifically consider the work setting (i.e. WFH or at the office) as a boundary factor for these measures’ effectiveness. However, ICSR actions, such as anti-discrimination measures, are less effective in respect of building the employee–employer relationship and supporting employees’ identification with and commitment to the company when they work from home. Given the economic benefit of decreased turnover rates and the societal benefit of a company output with higher creativity levels, this study has an impact from both an economic and a societal perspective.
Originality/value This study sheds light on employee creativity and ICSR’s roles in current HRM practice, which is still underexplored. More importantly, to the best of the authors’ knowledge, this study provides the first empirical evidence of a hitherto overlooked mechanism explaining ICSR activities’ effects on, or their perceived threat to, employee creativity.
In line with calls to stimulate methodological diversity and support evidence-based human resourc... more In line with calls to stimulate methodological diversity and support evidence-based human resource development (HRD) through quantitative competencies, we present a methods demonstration leveraging open-source tools and lesser-known quantitative research methods to support the HRD research community and applied HRD in the workplace. In this paper, we provide an informative introduction to partial least squares structural equation modeling (PLS-SEM). We discuss PLS-SEM application trends in the field of HRD, present key characteristics of the method, and demonstrate up-to-date metrics and evaluation guidelines using an illustrative model. Our PLS-SEM demonstration and explanations can serve as a valuable resource for practitioners concerned with substantiating results for organizational stakeholders and support researchers in methodological decision-making while avoiding common pitfalls associated with less familiar methods. Our step-by-step demonstration is conducted in open-source software and accompanied by explicitly coded operations so that readers can easily replicate the illustrative analyses presented.
A review of studies published in Industrial Marketing Management over the past two decades and mo... more A review of studies published in Industrial Marketing Management over the past two decades and more shows that these studies not only used partial least squares structural equation modeling (PLS-SEM) widely to estimate and empirically substantiate theoretically established models with constructs, but did so increasingly. In line with their study goals, researchers provided reasons for using PLS-SEM (e.g., model complexity, limited sample size, and prediction). These reasons are frequently not fully convincing, requiring further clarification. Additionally, our review reveals that researchers' assessment and reporting of their measurement and structural models are insufficient. Certain tests and thresholds that they use are also inappropriate. Finally, researchers seldom apply more advanced PLS-SEM analytic techniques, although these can support the results' robustness and may create new insights. This paper addresses the issues by reviewing business marketing studies to clarify PLS-SEM's appropriate use. Furthermore, the paper provides researchers and practitioners in the business marketing field with a best practice orientation and describes new opportunities for using PLS-SEM. To this end, the paper offers guidelines and checklists to support future PLS-SEM applications.
Corporate reputation is important for all types of banks across the world, despite these countrie... more Corporate reputation is important for all types of banks across the world, despite these countries differing culturally. Building on an extended corporate reputation model, we identify the key drivers of customer-based reputation and sustainable customer satisfaction in two culturally different countries, namely China and Germany. We also consider two reputation dimensions—perceived competence and likeability—and their effects on the target construct. Empirical data from 625 German and 734 Chinese commercial bank customers allow us to estimate the corporate reputation model with the partial least squares structural equation modeling (PLS-SEM) method, and by substantiating the relationships by means of a necessary condition analysis (NCA) and a predictive power analysis. By comparing the two countries’ results, we identify their cultural differences. Overall, we confirm the model’s relevance for the two cultures, finding that banks’ perceived attractiveness is the most important driver of both cultures’ customer-perceived bank reputation. By means of an importance-performance map analysis, we identify a large overlap between the two cultures’ set of important constructs, likeability’s much greater importance in Germany, and the perceived quality construct’s relevance in both countries. We contribute to research and scientific knowledge about corporate reputation models by identifying the similarities in and differences between two countries’ markets with respect to the banking sector, all of which have implications for international banks’ management.
As companies in the manufacturing and construction industries strive to meet the EU circular econ... more As companies in the manufacturing and construction industries strive to meet the EU circular economy (CE) targets, they need to develop new capabilities to implement CE activities that can positively influence their product/service innovations. However, companies in both industries, and beyond, still struggle to develop internal capabilities to innovate products and services that would help them in implementing CE principles and move towards the CE. The objective of this research is to analyze the types of innovation capabilities that are needed to enable CE implementation and achieve product/service innovations in two different industrial sectors. Prior research has focused on innovating and implementing circular business models and elaborated less on the innovation capability types. We collected survey data in December 2021–January 2022 that consists of responses from companies operating in Germany (n = 177), including employees in manufacturing (n = 87) and construction companies (n = 90). The results from the partial least squares structural equation modeling (PLS-SEM) based on measurement models from the literature indicate that employees in both sectors overall perceive higher levels of CE implementation capability as important for the company's product/service innovations. Furthermore, the results reveal differences in the way CE innovation capability and IT resource orchestration capability influence CE implementation and product/service innovations in the two sectors. Our study offers theoretical implications on how dynamic capabilities are associated with CE innovations and how they influence companies' product/service innovations based on empirical evidence from two industrial sectors. Those capabilities that are crucial for circular product/service innovations need to be associated with CE implementation capabilities. The results further advise practitioners in the development of CE innovation and CE implementation capabilities and how they are linked to IT resource orchestration capability and provide evidence on their relevance to creating product/service innovations.
This research offers a novel approach that extends the application of importance-performance map ... more This research offers a novel approach that extends the application of importance-performance map analysis (IPMA) in partial least squares structural equation modeling (PLS-SEM) by incorporating findings from a necessary condition analysis (NCA). The IPMA comprises assessing latent variables and their indicators' importance and performance, while an NCA introduces an additional dimension by identifying factors that are crucial for achieving the desired outcomes. An NCA employs necessity logic to identify the must-have factors required for an outcome, while PLS-SEM follows an additive sufficiency logic to identify the should-have factors that contribute to high performance levels. Integrating these two logics into the performance dimension is particularly valuable for prioritizing actions that could improve the target outcomes, such as customer satisfaction and employee commitment. Although the combined use of PLS-SEM and NCA is a recent suggestion, this study is the first to combine them with an IPMA (i.e., in a combined IPMA; cIPMA). A case study illustrates the combined use of PLS-SEM and an NCA to undertake a cIPMA. This innovative approach enhances researchers' and practitioners' decision making, enabling them to prioritize their efforts effectively.
Purpose – The purpose of this paper is to assess the appropriateness of equal weights estimation(... more Purpose – The purpose of this paper is to assess the appropriateness of equal weights estimation(sumscores) and the application of the composite equivalence index (CEI) vis-a-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM). Design/methodology/approach – The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI. Findings – The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM. Purpose – The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-a-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM). Design/methodology/approach – The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI. Findings – The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.
Corporate reputation is crucial for maintaining and enhancing a company's competitiveness in the ... more Corporate reputation is crucial for maintaining and enhancing a company's competitiveness in the marketplace. To actively manage this important intangible asset, which significantly contributes to a company's value, managers need to understand the relationship between reputation and its antecedents and consequences. The dataset presented in this article stems from a conceptual replication of a seminal model of corporate reputation, its antecedents and effects on customer satisfaction and loyalty. Potential mediators and moderators in these relationships allow us to extend the original model in order to clarify the mechanism through which corporate reputation impacts satisfaction and loyalty. We document some of the model's main effects using partial least squares structural equation modeling (PLS-SEM).
This perspective article on using partial least squares structural equation modelling (PLS-SEM) i... more This perspective article on using partial least squares structural equation modelling (PLS-SEM) is intended as a guide for authors who wish to publish datasets that can be analysed with this method as stand-alone data articles. Stand-alone data articles are different from supporting data articles in that they are not linked to a full research article published in another journal. Nevertheless, authors of stand-alone data articles will be required to clearly demonstrate and justify the usefulness of their dataset. This perspective article offers actionable recommendations regarding the conceptualisation phase, the types of data suitable for PLS-SEM and quality criteria to report, which are generally applicable to studies using PLS-SEM. We also present adjusted versions of the HTMT metric for discriminant validity testing that broaden its applicability. Further, we highlight the benefit of linking data articles to already published research papers that employ the PLS-SEM method.
Purpose
The purpose of this paper is to present integrated generalized structured component analy... more Purpose The purpose of this paper is to present integrated generalized structured component analysis (IGSCA) as a versatile approach for estimating models that contain both components and factors as statistical proxies for the constructs. The paper sets out to discuss the how-tos of using IGSCA by explaining how to specify, estimate, and evaluate different types of models. The paper’s overarching aim is to make business researchers aware of this promising structural equation modeling (SEM) method.
Design/methodology/approach By merging works of literature from various fields of science, the paper provides an overview of the steps that are required to run IGSCA. Findings from conceptual, analytical and empirical articles are combined to derive concrete guidelines for IGSCA use. Finally, an empirical case study is used to illustrate the analysis steps with the GSCA Pro software.
Findings Many of the principles and metrics known from partial least squares path modeling – the most prominent component-based SEM method – are also relevant in the context of IGSCA. However, there are differences in model specification, estimation and evaluation (e.g. assessment of overall model fit).
Research limitations/implications Methodological developments associated with IGSCA are rapidly emerging. The metrics reported in this paper are useful for current applications, but researchers should follow the latest developments in the field.
Originality/value To the best of the authors’ knowledge, this is the first paper to offer guidelines for IGSCA use and to illustrate the method's application by means of the GSCA Pro software. The recommendations and illustrations guide researchers who are seeking to conduct IGSCA studies in business research and practice.
International Journal of Contemporary Hospitality, 2023
Purpose
Partial least squares structural equation modeling (PLS-SEM) has attracted much attention... more Purpose Partial least squares structural equation modeling (PLS-SEM) has attracted much attention from both methodological and applied researchers in various disciplines – also in hospitality management research. As PLS-SEM is relatively new compared to other multivariate analysis techniques, there are still numerous open questions and uncertainties in its application. This study aims to address this important issue by offering guidance regarding its use in contexts with which researchers struggle.
Design/methodology/approach The authors examine the most prominent questions and answers posed in a well-known PLS-SEM discussion forum. The authors do so by using a text analysis technique to identify the most salient topics.
Findings The data analysis identifies three salient PLS-SEM topics (i.e. bootstrapping and significance testing, higher-order constructs and moderation).
Research limitations/implications The results allow us to address the PLS-SEM community’s main methodological issues. The authors discuss each area separately and provide explanations and guidelines.
Practical implications The guidelines on the most important PLS-SEM topics provide decision-making and application aids. In this way, the authors make a decisive contribution to clarifying ambiguities when applying the PLS-SEM method in hospitality management research and other disciplines.
Originality/value There has as yet been no systematic analysis of this kind in the field of PLS-SEM; the authors, therefore, present the first research results. The findings and recommendations provide guidance for PLS-SEM applications in hospitality research and practice.
Purpose
Researchers often stress the predictive goals of their partial least squares structural e... more Purpose Researchers often stress the predictive goals of their partial least squares structural equation modeling (PLS-SEM) analyses. However, the method has long lacked a statistical test to compare different models in terms of their predictive accuracy and to establish whether a proposed model offers a significantly better out-of-sample predictive accuracy than a naïve benchmark. This paper aims to address this methodological research gap in predictive model assessment and selection in composite-based modeling.
Design/methodology/approach Recent research has proposed the cross-validated predictive ability test (CVPAT) to compare theoretically established models. This paper proposes several extensions that broaden the scope of CVPAT and explains the key choices researchers must make when using them. A popular marketing model is used to illustrate the CVPAT extensions’ use and to make recommendations for the interpretation and benchmarking of the results.
Findings This research asserts that prediction-oriented model assessments and comparisons are essential for theory development and validation. It recommends that researchers routinely consider the application of CVPAT and its extensions when analyzing their theoretical models.
Research limitations/implications The findings offer several avenues for future research to extend and strengthen prediction-oriented model assessment and comparison in PLS-SEM.
Practical implications Guidelines are provided for applying CVPAT extensions and reporting the results to help researchers substantiate their models’ predictive capabilities.
Originality/value This research contributes to strengthening the predictive model validation practice in PLS-SEM, which is essential to derive managerial implications that are typically predictive in nature.
Partial least squares structural equation modeling (PLS-SEM) has become an established social sci... more Partial least squares structural equation modeling (PLS-SEM) has become an established social sciences multivariate analysis technique. Since quality management researchers also increasingly using PLS-SEM, this growing interest calls for guidance. Based on established guidelines for applying PLS-SEM and evaluating the results, this research reviews 107 articles applying the method and published in eight leading quality management journals. The use of PLS-SEM in quality management often only draws on limited information and analysis results. The discipline would benefit from the method's more comprehensive use by following established guidelines. Specifically, the use of predictive model assessment and more advanced PLS-SEM analyses harbors the potential to provide more detailed findings and conclusions when applying the method. This research provides first insights into PLS-SEM's use in quality management. Future research should identify the key areas and the core quality management models that best support the method's capabilities and researchers' goals. The results of this analysis guide researchers who use the PLS-SEM method for their quality management studies. This is the first article to systematically review the use of PLS-SEM in the quality management discipline.
Structural equation modeling (SEM) has remained two mutually exclusive domains, factor-based vs. ... more Structural equation modeling (SEM) has remained two mutually exclusive domains, factor-based vs. component-based, depending on whether a construct is modeled by either a factor or a component (i.e., weighted composite of indicators). Research in international management (IM) and international business (IB), however, needs to accommodate a more general model that considers a wide range of constructs from different disciplines at the same time, representing some constructs as factors (e.g., cultural distance and institutional distance) and others as components (e.g., international experience and export intensity). Integrated generalized structured component analysis (IGSCA) is a recently developed statistical method for estimating such models with both factors and components. IGSCA can provide overall fit indexes for model evaluation, including the goodness-of-fit index (GFI) and the standardized root mean squared residual (SRMR). However, the performance of these indexes in IGSCA is not yet investigated. Addressing this limitation, we (a) highlight the limitations of the dominantly used SEM approaches, (b) review the use of different SEM approaches in IM/IB research in the last decade, (c) conduct a simulation study, confirming that both GFI and SRMR distinguish well between correct and misspecified models with both factors and components, and (d) we illustrate the indexes’ efficacy using a model concerning the role of personality traits and international experience in shaping cultural intelligence. Based on the review and the results of the simulation study and the illustrative example, we also propose rules-of-thumb cutoff criteria for each index in IGSCA.
International Journal of Contemporary Hospitality Management, 2022
Purpose
Partial least squares structural equation modeling (PLS-SEM) has attracted much attention... more Purpose Partial least squares structural equation modeling (PLS-SEM) has attracted much attention from both methodological and applied researchers in various disciplines – also in hospitality management research. As PLS-SEM is relatively new compared to other multivariate analysis techniques, there are still numerous open questions and uncertainties in its application. This study aims to address this important issue by offering guidance regarding its use in contexts with which researchers struggle.
Design/methodology/approach The authors examine the most prominent questions and answers posed in a well-known PLS-SEM discussion forum. The authors do so by using a text analysis technique to identify the most salient topics.
Findings The data analysis identifies three salient PLS-SEM topics (i.e. bootstrapping and significance testing, higher-order constructs and moderation).
Research limitations/implications The results allow us to address the PLS-SEM community’s main methodological issues. The authors discuss each area separately and provide explanations and guidelines.
Practical implications The guidelines on the most important PLS-SEM topics provide decision-making and application aids. In this way, the authors make a decisive contribution to clarifying ambiguities when applying the PLS-SEM method in hospitality management research and other disciplines.
Originality/value There has as yet been no systematic analysis of this kind in the field of PLS-SEM; the authors, therefore, present the first research results. The findings and recommendations provide guidance for PLS-SEM applications in hospitality research and practice.
Environmental, social and governance indicators are often used to assess an organization’s ethica... more Environmental, social and governance indicators are often used to assess an organization’s ethical and long-term sustainability and performance. Organizations with high environmental, social and governance scores tend to perform consistently and exceed market expectations (Rajesh and Rajendran, 2020). In particular, organizations that incorporate environmentalnwelfare into their mission statement or core values not only achieve their organizational goals, but also achieve positive individual outcomes, such as employee engagement, willingness to take on organizational responsibilities and low turnover. Given the numerous environmental issues (e.g. pollution, global warming and waste management) that every industry faces directly or indirectly, there is an increased need and growing pressure for organizations to adopt sustainable strategies. As a result, the topic of green human resource management (GHRM) continues to attract the attention and interest of HRM scholars. A simple Google Scholar search (as of May 31, 2022) reveals 3,550,000 studies on this topic. Hence, GHRM is becoming one of the most important tasks
Research in international business and management (IM) is highly complex, contextual, and spans v... more Research in international business and management (IM) is highly complex, contextual, and spans various subfelds and related theoretical lenses. These characteristics pose research challenges that require utilizing sophisticated research designs and methodologies. This paper and our focused issue on ‘The use of partial least squares structural equation modeling (PLS-SEM) and complementary methods in IM research’ further clarify whether and how researchers using PLS-SEM can address (some of) the challenges in IM research. We explain how researchers can benefit from the (advanced) capabilities that PLS-SEM ofers; either as a stand-alone method or in triangulation eforts that leverage complementary approaches. In addition, we review the IM literature for PLS-SEM applications and evaluate whether and how researchers are already using these approaches. We identify some room for improvement when it comes to the application of both more advanced PLS-SEM capabilities and the triangulation of PLS-SEM with qualitative data analyses, and techniques such as fuzzy set qualitative comparative analyses and necessary condition analyses. For better guidance, we refer to PLS-SEM application examples in which the methodological advances are used.
Purpose
Researchers often stress the predictive goals of their partial least squares structural ... more Purpose Researchers often stress the predictive goals of their partial least squares structural equation modeling (PLS-SEM) analyses. However, the method has long lacked a statistical test to compare different models in terms of their predictive accuracy and to establish whether a proposed model offers a significantly better out-of-sample predictive accuracy than a naïve benchmark. This paper aims to address this methodological research gap in predictive model assessment and selection in composite-based modeling.
Design/methodology/approach Recent research has proposed the cross-validated predictive ability test (CVPAT) to compare theoretically established models. This paper proposes several extensions that broaden the scope of CVPAT and explains the key choices researchers must make when using them. A popular marketing model is used to illustrate the CVPAT extensions’ use and to make recommendations for the interpretation and benchmarking of the results. Findings This research asserts that prediction-oriented model assessments and comparisons are essential for theory development and validation. It recommends that researchers routinely consider the application of CVPAT and its extensions when analyzing their theoretical models.
Research limitations/implications The findings offer several avenues for future research to extend and strengthen prediction-oriented model assessment and comparison in PLS-SEM.
Practical implications Guidelines are provided for applying CVPAT extensions and reporting the results to help researchers substantiate their models’ predictive capabilities.
Originality/value This research contributes to strengthening the predictive model validation practice in PLS-SEM, which is essential to derive managerial implications that are typically predictive in nature
The third edition of A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) gui... more The third edition of A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) guides readers through learning and mastering the techniques of this approach in clear language. Authors Joseph H. Hair, Jr., G. Tomas M. Hult, Christian Ringle, and Marko Sarstedt use their years of conducting and teaching research to communicate the fundamentals of PLS-SEM in straightforward language to explain the details of this method, with limited emphasis on equations and symbols. A running case study on corporate reputation follows the different steps in this technique so readers can better understand the research applications. Learning objectives, review and critical thinking questions, and key terms help readers cement their knowledge. This edition has been thoroughly updated, featuring the latest version of the popular software package SmartPLS 3. New topics have been added throughout the text, including a thoroughly revised and extended chapter on mediation, recent research on the foundations of PLS-SEM, detailed descriptions of research summarizing the advantages as well as limitations of PLS-SEM, and extended coverage of advanced concepts and methods, such as out-of-sample versus in-sample prediction metrics, higher-order constructs, multigroup analysis, necessary condition analysis, and endogeneity.
Concebido como una ampliación del Manual de Partial Least Squares Structural Equation Modeling (P... more Concebido como una ampliación del Manual de Partial Least Squares Structural Equation Modeling (PLS-SEM) (segunda edición)*, esta guía práctica de fácil manejo abarca contenido avanzado de PLS-SEM para ayudar a los estudiantes e investigadores a aplicar técnicas sobre problemas de investigación y a interpretar oportunamente los resultados. El libro aporta un resumen de conceptos básicos antes de centrarse en aspectos más avanzados. Además, ofrece amplios ejemplos del software SmartPLS 3 (www.smartpls.com) y viene acompañado de bases de datos de descarga gratuita. En el libro se subraya la necesidad de aplicar cuidadosamente cualquier enfoque de PLS-SEM para asegurarnos de que éste encaje con el contexto de investigación y las características de los datos.
Partial least squares structural equation modeling (PLS-SEM) has become a popular method for esti... more Partial least squares structural equation modeling (PLS-SEM) has become a popular method for estimating path models with latent variables and their relationships. A common goal of PLS-SEM analyses is to identify key success factors and sources of competitive advantage for important target constructs such as customer satisfaction, customer loyalty, behavioral intentions, and user behavior. Building on an introduction of the fundamentals of measurement and structural theory, this chapter explains how to specify and estimate path models using PLS-SEM. Complementing the introduction of the PLS-SEM method and the description of how to evaluate analysis results, the chapter also offers an overview of complementary analytical techniques. A PLS-SEM application of the widely recognized corporate reputation model illustrates the method.
Proceedings of the Hamburg International Conference of Logistics (HICL), 2020
This volume contains research contributions by an international group of authors addressing innov... more This volume contains research contributions by an international group of authors addressing innovative and technology-based approaches for supply chain management. They present business models and investment options for enhanced strategic decision making as well as recent approaches for supply chain analytics and risk management. This volume, edited by Wolfgang Kersten, Thorsten Blecker and Christian Ringle, provides valuable insights into artificial intelligence and digital transformation in Supply Chain Management with regard to: - Innovation and Technology Management - Advanced Manufacturing, Industry 4.0 and Blockchain - Supply Chain Analytics and Artificial Intelligence - Risk and Security Management - Platform Economy and innovative Business Models
Proceedings of the Hamburg International Conference of Logistics (HICL), 2020
This volume contains research contributions by an international group of authors addressing innov... more This volume contains research contributions by an international group of authors addressing innovative and technology-based approaches for logistics and sustainability. They present simulation studies, systems and models for optimizations and digitalized solutions with a focus on maritime as well port and city logistics. This volume, edited by Carlos Jahn, Wolfgang Kersten, and Christian Ringle, provides valuable insights into digital transformation in logistics with regard to: - Maritime Logistics - Port Logistics - City Logistics - Sustainability - Business Analytics
Proceedings of the Hamburg International Conference of Logistics (HICL), 2019
This volume contains research contributions by an international group of authors addressing innov... more This volume contains research contributions by an international group of authors addressing innovative and technology-based approaches for supply chain management. They present business models and investment options for enhanced strategic decision making as well as recent approaches for supply chain analytics and risk management. This volume, edited by Wolfgang Kersten, Thorsten Blecker and Christian Ringle, provides valuable insights into artificial intelligence and digital transformation in Supply Chain Management with regard to: - Innovation and Technology Management - Advanced Manufacturing and Industry 4.0 - Supply Chain Analytics and Blockchain - Risk and Security Management
Proceedings of the Hamburg International Conference of Logistics (HICL), 2019
This volume contains research contributions by an international group of authors addressing innov... more This volume contains research contributions by an international group of authors addressing innovative and technology-based approaches for logistics and sustainability. They present simulation studies, systems, and models for optimizations and digitalized solutions with a focus on maritime as well port and city logistics. This volume, edited by Carlos Jahn, Wolfgang Kersten, and Christian Ringle, provides valuable insights into digital transformation in logistics with regard to: - Maritime Logistics - Port Logistics - City Logistics - Sustainability - Business Analytics
Logistik im Wandel der Zeit – Von der Produktionssteuerung zu vernetzten Supply Chains, 2019
Die effektive Nutzung von Erfolgspotenzialen bestimmt die Wettbewerbsfähigkeit von Supply Chains ... more Die effektive Nutzung von Erfolgspotenzialen bestimmt die Wettbewerbsfähigkeit von Supply Chains und Unternehmen in der Logistikindustrie. Neben klassischen Erfolgsbereichen wie beispielsweise Kosten und Flexibilität (Kersten, von See, & Wichmann, 2015; Kersten & Singer, 2011), müssen Unternehmen – insbesondere mit Blick auf die Herausforderungen der Digitalisierung (Kersten, von See, & Indorf, 2018) – Risiken kontrollieren (Kersten, Schröder, Feser, & Klotzbach, 2013; Kersten, Schröder, Singer, & Feser, 2012), kontinuierliche Verbesserungen realisieren (Kersten & Ehni, 2015), ihre (Geschäfts-)Prozesse optimieren (Kersten, Klotzbach, & Petersen, 2014) und Innovationen generieren (Kersten, Seidel, & Wagenstetter, 2012). All diesen Herausforderungen stehen auch die Führungsteams in der Luftfahrtindustrie gegenüber.
Logistics 4.0 and Sustainable Supply Chain Management, 2018
This volume contains research contributions by an international group of authors addressing innov... more This volume contains research contributions by an international group of authors addressing innovative and technology-based approaches for logistics and sustainability. They present simulation studies, systems and models for optimizations and digitalized solutions with a focus on maritime as well port and city logistics. This volume, edited by Carlos Jahn, Wolfgang Kersten, and Christian Ringle, provides valuable insights into Logistics 4.0 and Sustainable Supply Chain Management with regard to: - Maritime and Port Logistics - City Logistics - Sustainability
N. K. Avkiran & C. M. Ringle (Eds.), Partial Least Squares Structural Equation Modeling: Recent Advances in Banking and Finance, 2018
Building and maintaining successful, long-term relationships is one of the crucial tasks in today... more Building and maintaining successful, long-term relationships is one of the crucial tasks in today’s financial sector, the idea being that loyal customers purchase more, demonstrate a higher willingness to spend, and act as advocates for the company. However, there is also some controversy on whether profitability indeed increases with customer loyalty. We will analyze the process of loyalty development and evaluate if and how customer loyalty (positively) affects profitability. We will do so with reference to a four stage sequential loyalty model that grounds on a chain of effects from cognitive loyalty, affective loyalty, conative loyalty to action loyalty. We will make use of PLS structural equation modeling and analyze data of almost 7000 customers of a German bank surveyed by telephone. These analyses will support practitioners in the banking and financial sector in setting-up and steering their customer retention strategies and will provide a theoretical contribution to validating one of the most prominent customer loyalty models.
Advanced Issues in Partial Least Squares Structural Equation Modeling, 2018
Written as an extension of A Primer on Partial Least Squares Structural Equation Modeling (PLS-SE... more Written as an extension of A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) Second Edition, this easy-to-understand, practical guide covers advanced content on PLS-SEM to help students and researchers apply techniques to research problems and accurately interpret results. The book provides a brief overview of basic concepts before moving to the more advanced material. Offering extensive examples on SmartPLS 3 software (www.smartpls.com) and accompanied by free downloadable data sets, the book emphasizes that any advanced PLS-SEM approach should be carefully applied to ensure that it fits the appropriate research context and the data characteristics that underpin the research.
Partial Least Squares Structural Equation Modeling Recent Advances in Banking and Finance, 2018
This book pulls together robust practices in Partial Least Squares Structural Equation Modeling (... more This book pulls together robust practices in Partial Least Squares Structural Equation Modeling (PLS-SEM) from other disciplines and shows how they can be used in the area of Banking and Finance. In terms of empirical analysis techniques, Banking and Finance is a conservative discipline. As such, this book will raise awareness of the potential of PLS-SEM for application in various contexts. PLS-SEM is a non-parametric approach designed to maximize explained variance in latent constructs. Latent constructs are directly unobservable phenomena such as customer service quality and managerial competence. Explained variance refers to the extent we can predict, say, customer service quality, by examining other theoretically related latent constructs such as conduct of staff and communication skills.
Examples of latent constructs at the microeconomic level include customer service quality, managerial effectiveness, perception of market leadership, etc.; macroeconomic-level latent constructs would be found in contagion of systemic risk from one financial sector to another, herd behavior among fund managers, risk tolerance in financial markets, etc. Behavioral Finance is bound to provide a wealth of opportunities for applying PLS-SEM. The book is designed to expose robust processes in application of PLS-SEM, including use of various software packages and codes, including R.
PLS-SEM is already a popular tool in marketing and management information systems used to explain latent constructs. Until now, PLS-SEM has not enjoyed a wide acceptance in Banking and Finance. Based on recent research developments, this book represents the first collection of PLS-SEM applications in Banking and Finance. This book will serve as a reference book for those researchers keen on adopting PLS-SEM to explain latent constructs in Banking and Finance.
Babin, B. J., Sarstedt, M. (Eds.), The Great Facilitator - Reflections on the Contributions of Josep F. Hair, Jr. to Marketing and Business Research, 131-150, Cham: Springer. , 2019
What does it take to obtain 200,000 citations or more in the wider field of business administrati... more What does it take to obtain 200,000 citations or more in the wider field of business administration? A Nobel Prize (or two) wouldn’t be amiss. Lacking such kudos, you need to be Joseph F. Hair, the author of the book on Multivariate Data Analysis, which has garnered 100,000+ citations since its publication in 1979 (Google Scholar, July 2018). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) is, despite its unpromising title, Joe’s second best-cited book, focusing on a specific multivariate analysis method. I have the pleasure and honor of being a coauthor of this book, and, given its success, a many potential authors of best-selling textbooks are no doubt keen on knowing what insights I gained while working with Joe. I can’t promise them an instant bestseller, but I can summarize the key lessons learned from Joe about what makes a great textbook.
Structural equation modeling (SEM) is a statistical analytic framework that allows researchers to... more Structural equation modeling (SEM) is a statistical analytic framework that allows researchers to specify and test models with observed and latent (or unobservable) variables and their generally linear relationships. In the past decades, SEM has become a standard statistical analysis technique in behavioral, educational, psychological, and social science researchers’ repertoire. From a technical perspective, SEM was developed as a mixture of two statistical fields—path analysis and data reduction. Path analysis is used to specify and examine directional relationships between observed variables, whereas data reduction is applied to uncover (unobserved) low-dimensional representations of observed variables, which are referred to as latent variables. Since two different data reduction techniques (i.e., factor analysis and principal component analysis) were available to the statistical community, SEM also evolved into two domains—factor-based and component-based (e.g., Jöreskog and Wold 1982). In factor-based SEM, in which the psychometric or psychological measurement tradition has strongly influenced, a (common) factor represents a latent variable under the assumption that each latent variable exists as an entity independent of observed variables, but also serves as the sole source of the associations between the observed variables. Conversely, in component-based SEM, which is more in line with traditional multivariate statistics, a weighted composite or a component of observed variables represents a latent variable under the assumption that the latter is an aggregation (or a direct consequence) of observed variables.
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Papers by Christian M . Ringle
The purpose of this study is to investigate how working from home (WFH) affects the relationship between internal corporate social responsibility (ICSR) and employee creativity in times of uncertainty when employees’ occupational stress increases and their identification with their company decreases.
Design/methodology/approach
Applying social identity theory, the authors derive and test the hypotheses presented in this study regarding ICSR’s direct effects on employee creativity, given the amount of time they spent on WFH and the role of threat in this relationship. The authors use partial least squares structural equation modeling to analyze the various effects. Via an online questionnaire and using the snowball technique, the authors collected data from 158 participants in different industries in Germany.
Findings
The empirical results of this study show that ICSR activities increase employee creativity, partly by reducing one harmful aspect of stress, namely, threat. In addition, the authors find that WFH moderates this effect, such that the higher the degree of WFH, the weaker the ICSR activities’ effects are.
Research limitations/implications
This study focused on the respondents’ WFH situation during the global COVID-19 pandemic. As such, this research contributes to understanding the roles that modern work practices, human resource management (HRM) and ICSR actions play in respect of employee creativity. The authors expand the theoretical understanding, which is based on social identity theory, by showing that the greater the amount of time spent on WFH, the more it reduces ICSR’s positive effect on employee creativity. The findings of this study open avenues for future research and longitudinal studies that compare the ICSR effects during and after the pandemic, as well as for those that compare WFH and its effects on organizational creativity.
Practical implications
This study shows that managers should encourage appropriate ICSR measures in their organizations and should specifically consider the work setting (i.e. WFH or at the office) as a boundary factor for these measures’ effectiveness. However, ICSR actions, such as anti-discrimination measures, are less effective in respect of building the employee–employer relationship and supporting employees’ identification with and commitment to the company when they work from home. Given the economic benefit of decreased turnover rates and the societal benefit of a company output with higher creativity levels, this study has an impact from both an economic and a societal perspective.
Originality/value
This study sheds light on employee creativity and ICSR’s roles in current HRM practice, which is still underexplored. More importantly, to the best of the authors’ knowledge, this study provides the first empirical evidence of a hitherto overlooked mechanism explaining ICSR activities’ effects on, or their perceived threat to, employee creativity.
Design/methodology/approach – The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI.
Findings – The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to
inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.
Purpose – The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-a-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM).
Design/methodology/approach – The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI.
Findings – The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.
The purpose of this paper is to present integrated generalized structured component analysis (IGSCA) as a versatile approach for estimating models that contain both components and factors as statistical proxies for the constructs. The paper sets out to discuss the how-tos of using IGSCA by explaining how to specify, estimate, and evaluate different types of models. The paper’s overarching aim is to make business researchers aware of this promising structural equation modeling (SEM) method.
Design/methodology/approach
By merging works of literature from various fields of science, the paper provides an overview of the steps that are required to run IGSCA. Findings from conceptual, analytical and empirical articles are combined to derive concrete guidelines for IGSCA use. Finally, an empirical case study is used to illustrate the analysis steps with the GSCA Pro software.
Findings
Many of the principles and metrics known from partial least squares path modeling – the most prominent component-based SEM method – are also relevant in the context of IGSCA. However, there are differences in model specification, estimation and evaluation (e.g. assessment of overall model fit).
Research limitations/implications
Methodological developments associated with IGSCA are rapidly emerging. The metrics reported in this paper are useful for current applications, but researchers should follow the latest developments in the field.
Originality/value
To the best of the authors’ knowledge, this is the first paper to offer guidelines for IGSCA use and to illustrate the method's application by means of the GSCA Pro software. The recommendations and illustrations guide researchers who are seeking to conduct IGSCA studies in business research and practice.
Partial least squares structural equation modeling (PLS-SEM) has attracted much attention from both methodological and applied researchers in various disciplines – also in hospitality management research. As PLS-SEM is relatively new compared to other multivariate analysis techniques, there are still numerous open questions and uncertainties in its application. This study aims to address this important issue by offering guidance regarding its use in contexts with which researchers struggle.
Design/methodology/approach
The authors examine the most prominent questions and answers posed in a well-known PLS-SEM discussion forum. The authors do so by using a text analysis technique to identify the most salient topics.
Findings
The data analysis identifies three salient PLS-SEM topics (i.e. bootstrapping and significance testing, higher-order constructs and moderation).
Research limitations/implications
The results allow us to address the PLS-SEM community’s main methodological issues. The authors discuss each area separately and provide explanations and guidelines.
Practical implications
The guidelines on the most important PLS-SEM topics provide decision-making and application aids. In this way, the authors make a decisive contribution to clarifying ambiguities when applying the PLS-SEM method in hospitality management research and other disciplines.
Originality/value
There has as yet been no systematic analysis of this kind in the field of PLS-SEM; the authors, therefore, present the first research results. The findings and recommendations provide guidance for PLS-SEM applications in hospitality research and practice.
Researchers often stress the predictive goals of their partial least squares structural equation modeling (PLS-SEM) analyses. However, the method has long lacked a statistical test to compare different models in terms of their predictive accuracy and to establish whether a proposed model offers a significantly better out-of-sample predictive accuracy than a naïve benchmark. This paper aims to address this methodological research gap in predictive model assessment and selection in composite-based modeling.
Design/methodology/approach
Recent research has proposed the cross-validated predictive ability test (CVPAT) to compare theoretically established models. This paper proposes several extensions that broaden the scope of CVPAT and explains the key choices researchers must make when using them. A popular marketing model is used to illustrate the CVPAT extensions’ use and to make recommendations for the interpretation and benchmarking of the results.
Findings
This research asserts that prediction-oriented model assessments and comparisons are essential for theory development and validation. It recommends that researchers routinely consider the application of CVPAT and its extensions when analyzing their theoretical models.
Research limitations/implications
The findings offer several avenues for future research to extend and strengthen prediction-oriented model assessment and comparison in PLS-SEM.
Practical implications
Guidelines are provided for applying CVPAT extensions and reporting the results to help researchers substantiate their models’ predictive capabilities.
Originality/value
This research contributes to strengthening the predictive model validation practice in PLS-SEM, which is essential to derive managerial implications that are typically predictive in nature.
Based on established guidelines for applying PLS-SEM and evaluating the results, this research reviews 107 articles applying the method and published in eight leading quality management journals.
The use of PLS-SEM in quality management often only draws on limited information and analysis results. The discipline would benefit from the method's more comprehensive use by following established guidelines. Specifically, the use of predictive model assessment and more advanced PLS-SEM analyses harbors the potential to provide more detailed findings and conclusions when applying the method.
This research provides first insights into PLS-SEM's use in quality management. Future research should identify the key areas and the core quality management models that best support the method's capabilities and researchers' goals.
The results of this analysis guide researchers who use the PLS-SEM method for their quality management studies.
This is the first article to systematically review the use of PLS-SEM in the quality management discipline.
Partial least squares structural equation modeling (PLS-SEM) has attracted much attention from both methodological and applied researchers in various disciplines – also in hospitality management research. As PLS-SEM is relatively new compared to other multivariate analysis techniques, there are still numerous open questions and uncertainties in its application. This study aims to address this important issue by offering guidance regarding its use in contexts with which researchers struggle.
Design/methodology/approach
The authors examine the most prominent questions and answers posed in a well-known PLS-SEM discussion forum. The authors do so by using a text analysis technique to identify the most salient topics.
Findings
The data analysis identifies three salient PLS-SEM topics (i.e. bootstrapping and significance testing, higher-order constructs and moderation).
Research limitations/implications
The results allow us to address the PLS-SEM community’s main methodological issues. The authors discuss each area separately and provide explanations and guidelines.
Practical implications
The guidelines on the most important PLS-SEM topics provide decision-making and application aids. In this way, the authors make a decisive contribution to clarifying ambiguities when applying the PLS-SEM method in hospitality management research and other disciplines.
Originality/value
There has as yet been no systematic analysis of this kind in the field of PLS-SEM; the authors, therefore, present the first research results. The findings and recommendations provide guidance for PLS-SEM applications in hospitality research and practice.
directly or indirectly, there is an increased need and growing pressure for organizations to adopt sustainable strategies. As a result, the topic of green human resource management (GHRM) continues to attract the attention and interest of HRM scholars. A simple Google Scholar search (as of May 31, 2022) reveals 3,550,000 studies on this topic. Hence, GHRM is becoming one of the most important tasks
(some of) the challenges in IM research. We explain how researchers can benefit from the (advanced) capabilities that PLS-SEM ofers; either as a stand-alone method or in triangulation eforts that leverage complementary approaches. In addition, we review the IM literature for PLS-SEM applications and evaluate whether and how researchers are already using these approaches. We identify some room for
improvement when it comes to the application of both more advanced PLS-SEM capabilities and the triangulation of PLS-SEM with qualitative data analyses, and techniques such as fuzzy set qualitative comparative analyses and necessary condition analyses. For better guidance, we refer to PLS-SEM application examples in which the methodological advances are used.
Researchers often stress the predictive goals of their partial least squares structural equation modeling (PLS-SEM) analyses. However, the method has long lacked a statistical test to compare different models in terms of their predictive accuracy and to establish whether a proposed model offers a significantly better out-of-sample predictive accuracy than a naïve benchmark. This paper aims to address this methodological research gap in predictive model assessment and selection in composite-based modeling.
Design/methodology/approach
Recent research has proposed the cross-validated predictive ability test (CVPAT) to compare theoretically established models. This paper proposes several extensions that broaden the scope of CVPAT and explains the key choices researchers must make when using them. A popular marketing model is used to illustrate the CVPAT extensions’ use and to make recommendations for the interpretation and benchmarking of the results.
Findings
This research asserts that prediction-oriented model assessments and comparisons are essential for theory development and validation. It recommends that researchers routinely consider the application of CVPAT and its extensions when analyzing their theoretical models.
Research limitations/implications
The findings offer several avenues for future research to extend and strengthen prediction-oriented model assessment and comparison in PLS-SEM.
Practical implications
Guidelines are provided for applying CVPAT extensions and reporting the results to help researchers substantiate their models’ predictive capabilities.
Originality/value
This research contributes to strengthening the predictive model validation practice in PLS-SEM, which is essential to derive managerial implications that are typically predictive in nature
The purpose of this study is to investigate how working from home (WFH) affects the relationship between internal corporate social responsibility (ICSR) and employee creativity in times of uncertainty when employees’ occupational stress increases and their identification with their company decreases.
Design/methodology/approach
Applying social identity theory, the authors derive and test the hypotheses presented in this study regarding ICSR’s direct effects on employee creativity, given the amount of time they spent on WFH and the role of threat in this relationship. The authors use partial least squares structural equation modeling to analyze the various effects. Via an online questionnaire and using the snowball technique, the authors collected data from 158 participants in different industries in Germany.
Findings
The empirical results of this study show that ICSR activities increase employee creativity, partly by reducing one harmful aspect of stress, namely, threat. In addition, the authors find that WFH moderates this effect, such that the higher the degree of WFH, the weaker the ICSR activities’ effects are.
Research limitations/implications
This study focused on the respondents’ WFH situation during the global COVID-19 pandemic. As such, this research contributes to understanding the roles that modern work practices, human resource management (HRM) and ICSR actions play in respect of employee creativity. The authors expand the theoretical understanding, which is based on social identity theory, by showing that the greater the amount of time spent on WFH, the more it reduces ICSR’s positive effect on employee creativity. The findings of this study open avenues for future research and longitudinal studies that compare the ICSR effects during and after the pandemic, as well as for those that compare WFH and its effects on organizational creativity.
Practical implications
This study shows that managers should encourage appropriate ICSR measures in their organizations and should specifically consider the work setting (i.e. WFH or at the office) as a boundary factor for these measures’ effectiveness. However, ICSR actions, such as anti-discrimination measures, are less effective in respect of building the employee–employer relationship and supporting employees’ identification with and commitment to the company when they work from home. Given the economic benefit of decreased turnover rates and the societal benefit of a company output with higher creativity levels, this study has an impact from both an economic and a societal perspective.
Originality/value
This study sheds light on employee creativity and ICSR’s roles in current HRM practice, which is still underexplored. More importantly, to the best of the authors’ knowledge, this study provides the first empirical evidence of a hitherto overlooked mechanism explaining ICSR activities’ effects on, or their perceived threat to, employee creativity.
Design/methodology/approach – The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI.
Findings – The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to
inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.
Purpose – The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-a-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM).
Design/methodology/approach – The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI.
Findings – The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.
The purpose of this paper is to present integrated generalized structured component analysis (IGSCA) as a versatile approach for estimating models that contain both components and factors as statistical proxies for the constructs. The paper sets out to discuss the how-tos of using IGSCA by explaining how to specify, estimate, and evaluate different types of models. The paper’s overarching aim is to make business researchers aware of this promising structural equation modeling (SEM) method.
Design/methodology/approach
By merging works of literature from various fields of science, the paper provides an overview of the steps that are required to run IGSCA. Findings from conceptual, analytical and empirical articles are combined to derive concrete guidelines for IGSCA use. Finally, an empirical case study is used to illustrate the analysis steps with the GSCA Pro software.
Findings
Many of the principles and metrics known from partial least squares path modeling – the most prominent component-based SEM method – are also relevant in the context of IGSCA. However, there are differences in model specification, estimation and evaluation (e.g. assessment of overall model fit).
Research limitations/implications
Methodological developments associated with IGSCA are rapidly emerging. The metrics reported in this paper are useful for current applications, but researchers should follow the latest developments in the field.
Originality/value
To the best of the authors’ knowledge, this is the first paper to offer guidelines for IGSCA use and to illustrate the method's application by means of the GSCA Pro software. The recommendations and illustrations guide researchers who are seeking to conduct IGSCA studies in business research and practice.
Partial least squares structural equation modeling (PLS-SEM) has attracted much attention from both methodological and applied researchers in various disciplines – also in hospitality management research. As PLS-SEM is relatively new compared to other multivariate analysis techniques, there are still numerous open questions and uncertainties in its application. This study aims to address this important issue by offering guidance regarding its use in contexts with which researchers struggle.
Design/methodology/approach
The authors examine the most prominent questions and answers posed in a well-known PLS-SEM discussion forum. The authors do so by using a text analysis technique to identify the most salient topics.
Findings
The data analysis identifies three salient PLS-SEM topics (i.e. bootstrapping and significance testing, higher-order constructs and moderation).
Research limitations/implications
The results allow us to address the PLS-SEM community’s main methodological issues. The authors discuss each area separately and provide explanations and guidelines.
Practical implications
The guidelines on the most important PLS-SEM topics provide decision-making and application aids. In this way, the authors make a decisive contribution to clarifying ambiguities when applying the PLS-SEM method in hospitality management research and other disciplines.
Originality/value
There has as yet been no systematic analysis of this kind in the field of PLS-SEM; the authors, therefore, present the first research results. The findings and recommendations provide guidance for PLS-SEM applications in hospitality research and practice.
Researchers often stress the predictive goals of their partial least squares structural equation modeling (PLS-SEM) analyses. However, the method has long lacked a statistical test to compare different models in terms of their predictive accuracy and to establish whether a proposed model offers a significantly better out-of-sample predictive accuracy than a naïve benchmark. This paper aims to address this methodological research gap in predictive model assessment and selection in composite-based modeling.
Design/methodology/approach
Recent research has proposed the cross-validated predictive ability test (CVPAT) to compare theoretically established models. This paper proposes several extensions that broaden the scope of CVPAT and explains the key choices researchers must make when using them. A popular marketing model is used to illustrate the CVPAT extensions’ use and to make recommendations for the interpretation and benchmarking of the results.
Findings
This research asserts that prediction-oriented model assessments and comparisons are essential for theory development and validation. It recommends that researchers routinely consider the application of CVPAT and its extensions when analyzing their theoretical models.
Research limitations/implications
The findings offer several avenues for future research to extend and strengthen prediction-oriented model assessment and comparison in PLS-SEM.
Practical implications
Guidelines are provided for applying CVPAT extensions and reporting the results to help researchers substantiate their models’ predictive capabilities.
Originality/value
This research contributes to strengthening the predictive model validation practice in PLS-SEM, which is essential to derive managerial implications that are typically predictive in nature.
Based on established guidelines for applying PLS-SEM and evaluating the results, this research reviews 107 articles applying the method and published in eight leading quality management journals.
The use of PLS-SEM in quality management often only draws on limited information and analysis results. The discipline would benefit from the method's more comprehensive use by following established guidelines. Specifically, the use of predictive model assessment and more advanced PLS-SEM analyses harbors the potential to provide more detailed findings and conclusions when applying the method.
This research provides first insights into PLS-SEM's use in quality management. Future research should identify the key areas and the core quality management models that best support the method's capabilities and researchers' goals.
The results of this analysis guide researchers who use the PLS-SEM method for their quality management studies.
This is the first article to systematically review the use of PLS-SEM in the quality management discipline.
Partial least squares structural equation modeling (PLS-SEM) has attracted much attention from both methodological and applied researchers in various disciplines – also in hospitality management research. As PLS-SEM is relatively new compared to other multivariate analysis techniques, there are still numerous open questions and uncertainties in its application. This study aims to address this important issue by offering guidance regarding its use in contexts with which researchers struggle.
Design/methodology/approach
The authors examine the most prominent questions and answers posed in a well-known PLS-SEM discussion forum. The authors do so by using a text analysis technique to identify the most salient topics.
Findings
The data analysis identifies three salient PLS-SEM topics (i.e. bootstrapping and significance testing, higher-order constructs and moderation).
Research limitations/implications
The results allow us to address the PLS-SEM community’s main methodological issues. The authors discuss each area separately and provide explanations and guidelines.
Practical implications
The guidelines on the most important PLS-SEM topics provide decision-making and application aids. In this way, the authors make a decisive contribution to clarifying ambiguities when applying the PLS-SEM method in hospitality management research and other disciplines.
Originality/value
There has as yet been no systematic analysis of this kind in the field of PLS-SEM; the authors, therefore, present the first research results. The findings and recommendations provide guidance for PLS-SEM applications in hospitality research and practice.
directly or indirectly, there is an increased need and growing pressure for organizations to adopt sustainable strategies. As a result, the topic of green human resource management (GHRM) continues to attract the attention and interest of HRM scholars. A simple Google Scholar search (as of May 31, 2022) reveals 3,550,000 studies on this topic. Hence, GHRM is becoming one of the most important tasks
(some of) the challenges in IM research. We explain how researchers can benefit from the (advanced) capabilities that PLS-SEM ofers; either as a stand-alone method or in triangulation eforts that leverage complementary approaches. In addition, we review the IM literature for PLS-SEM applications and evaluate whether and how researchers are already using these approaches. We identify some room for
improvement when it comes to the application of both more advanced PLS-SEM capabilities and the triangulation of PLS-SEM with qualitative data analyses, and techniques such as fuzzy set qualitative comparative analyses and necessary condition analyses. For better guidance, we refer to PLS-SEM application examples in which the methodological advances are used.
Researchers often stress the predictive goals of their partial least squares structural equation modeling (PLS-SEM) analyses. However, the method has long lacked a statistical test to compare different models in terms of their predictive accuracy and to establish whether a proposed model offers a significantly better out-of-sample predictive accuracy than a naïve benchmark. This paper aims to address this methodological research gap in predictive model assessment and selection in composite-based modeling.
Design/methodology/approach
Recent research has proposed the cross-validated predictive ability test (CVPAT) to compare theoretically established models. This paper proposes several extensions that broaden the scope of CVPAT and explains the key choices researchers must make when using them. A popular marketing model is used to illustrate the CVPAT extensions’ use and to make recommendations for the interpretation and benchmarking of the results.
Findings
This research asserts that prediction-oriented model assessments and comparisons are essential for theory development and validation. It recommends that researchers routinely consider the application of CVPAT and its extensions when analyzing their theoretical models.
Research limitations/implications
The findings offer several avenues for future research to extend and strengthen prediction-oriented model assessment and comparison in PLS-SEM.
Practical implications
Guidelines are provided for applying CVPAT extensions and reporting the results to help researchers substantiate their models’ predictive capabilities.
Originality/value
This research contributes to strengthening the predictive model validation practice in PLS-SEM, which is essential to derive managerial implications that are typically predictive in nature
This volume, edited by Wolfgang Kersten, Thorsten Blecker and Christian Ringle, provides valuable insights into artificial intelligence and digital transformation in Supply Chain Management with regard to:
- Innovation and Technology Management
- Advanced Manufacturing, Industry 4.0 and Blockchain
- Supply Chain Analytics and Artificial Intelligence
- Risk and Security Management
- Platform Economy and innovative Business Models
This volume, edited by Carlos Jahn, Wolfgang Kersten, and Christian Ringle, provides valuable insights into digital transformation in logistics with regard to:
- Maritime Logistics
- Port Logistics
- City Logistics
- Sustainability
- Business Analytics
This volume, edited by Wolfgang Kersten, Thorsten Blecker and Christian Ringle, provides valuable insights into artificial intelligence and digital transformation in Supply Chain Management with regard to:
- Innovation and Technology Management
- Advanced Manufacturing and Industry 4.0
- Supply Chain Analytics and Blockchain
- Risk and Security Management
This volume, edited by Carlos Jahn, Wolfgang Kersten, and Christian Ringle, provides valuable insights into digital transformation in logistics with regard to:
- Maritime Logistics
- Port Logistics
- City Logistics
- Sustainability
- Business Analytics
This volume, edited by Carlos Jahn, Wolfgang Kersten, and Christian Ringle, provides valuable insights into Logistics 4.0 and Sustainable Supply Chain Management with regard to:
- Maritime and Port Logistics
- City Logistics
- Sustainability
Examples of latent constructs at the microeconomic level include customer service quality, managerial effectiveness, perception of market leadership, etc.; macroeconomic-level latent constructs would be found in contagion of systemic risk from one financial sector to another, herd behavior among fund managers, risk tolerance in financial markets, etc. Behavioral Finance is bound to provide a wealth of opportunities for applying PLS-SEM. The book is designed to expose robust processes in application of PLS-SEM, including use of various software packages and codes, including R.
PLS-SEM is already a popular tool in marketing and management information systems used to explain latent constructs. Until now, PLS-SEM has not enjoyed a wide acceptance in Banking and Finance. Based on recent research developments, this book represents the first collection of PLS-SEM applications in Banking and Finance. This book will serve as a reference book for those researchers keen on adopting PLS-SEM to explain latent constructs in Banking and Finance.
From a technical perspective, SEM was developed as a mixture of two statistical fields—path analysis and data reduction. Path analysis is used to specify and examine directional relationships between observed variables, whereas data reduction is applied to uncover (unobserved) low-dimensional representations of observed variables, which are referred to as latent variables. Since two different data reduction techniques (i.e., factor analysis and principal component analysis) were available to the statistical community, SEM also evolved into two domains—factor-based and component-based (e.g., Jöreskog and Wold 1982). In factor-based SEM, in which the psychometric or psychological measurement tradition has strongly influenced, a (common) factor represents a latent variable under the assumption that each latent variable exists as an entity independent of observed variables, but also serves as the sole source of the associations between the observed variables. Conversely, in component-based SEM, which is more in line with traditional multivariate statistics, a weighted composite or a component of observed variables represents a latent variable under the assumption that the latter is an aggregation (or a direct consequence) of observed variables.