Marko Sarstedt

The behavioral sciences underestimate the uncertainty of research findings and thus overestimate replicability. Metrologists in the physical sciences quantify all material components of uncertainty, even if some components must be quantified using non-statistical means. Behavioral science should follow suit.

Parceling—using composites of observed variables as indicators for a common factor—strengthens loadings, but reduces the number of indicators. Factor indeterminacy is reduced when there are many observed variables per factor, and when loadings and factor correlations are strong. It is proven that parceling cannot reduce factor indeterminacy. In special cases where the ratio of loading to residual variance is the same for all items included in each parcel, factor indeterminacy is unaffected by… Mehr anzeigen

Parceling—using composites of observed variables as indicators for a common factor—strengthens loadings, but reduces the number of indicators. Factor indeterminacy is reduced when there are many observed variables per factor, and when loadings and factor correlations are strong. It is proven that parceling cannot reduce factor indeterminacy. In special cases where the ratio of loading to residual variance is the same for all items included in each parcel, factor indeterminacy is unaffected by parceling. Otherwise, parceling worsens factor indeterminacy. While factor indeterminacy does not affect the parameter estimates, standard errors, or fit indices associated with a factor model, it does create uncertainty, which endangers valid inference. Weniger anzeigen

Higher-order constructs, which facilitate modeling a construct on a more abstract higher-level dimension and its more concrete lower-order subdimensions, have become an increasingly visible trend in applications of partial least squares structural equation modeling (PLS-SEM). Unfortunately, researchers frequently confuse the specification, estimation, and validation of higher-order constructs, for example, when it comes to assessing their reliability and validity. Addressing this concern, this… Mehr anzeigen

Higher-order constructs, which facilitate modeling a construct on a more abstract higher-level dimension and its more concrete lower-order subdimensions, have become an increasingly visible trend in applications of partial least squares structural equation modeling (PLS-SEM). Unfortunately, researchers frequently confuse the specification, estimation, and validation of higher-order constructs, for example, when it comes to assessing their reliability and validity. Addressing this concern, this paper explains how to evaluate the results of higher-order constructs in PLS-SEM using the repeated indicators and the two-stage approaches, which feature prominently in applied social sciences research. Focusing on the reflective-reflective and reflective-formative types of higher-order constructs, we use the well-known corporate reputation model example to illustrate their specification, estimation, and validation. Thereby, we provide the guidance that scholars, marketing researchers, and practitioners need when using higher-order constructs in their studies. Weniger anzeigen

Partial least squares (PLS) has been introduced as a “causal-predictive” approach to structural equation modeling (SEM), designed to overcome the apparent dichotomy between explanation and prediction. However, while researchers using PLS-SEM routinely stress the predictive nature of their analyses, model evaluation assessment relies exclusively on metrics designed to assess the path model’s explanatory power. Recent research has proposed PLSpredict, a holdout sample-based procedure that… Mehr anzeigen

Partial least squares (PLS) has been introduced as a “causal-predictive” approach to structural equation modeling (SEM), designed to overcome the apparent dichotomy between explanation and prediction. However, while researchers using PLS-SEM routinely stress the predictive nature of their analyses, model evaluation assessment relies exclusively on metrics designed to assess the path model’s explanatory power. Recent research has proposed PLSpredict, a holdout sample-based procedure that generates case-level predictions on an item or a construct level. This paper offers guidelines for applying PLSpredict and explains the key choices researchers need to make using the procedure. Weniger anzeigen

Partial least squares path modeling (PLSPM) and generalized structural component analysis (GSCA) constitute composite-based structural equation modeling (SEM) methods, which have attracted considerable interest among methodological and applied researchers alike. Methodological extensions of PLSPM and GSCA have appeared at rapid pace, producing different research streams with different foci and understandings of the methods and their merits. Based on a theoretical comparison of PLSPM and GSCA in… Mehr anzeigen

Partial least squares path modeling (PLSPM) and generalized structural component analysis (GSCA) constitute composite-based structural equation modeling (SEM) methods, which have attracted considerable interest among methodological and applied researchers alike. Methodological extensions of PLSPM and GSCA have appeared at rapid pace, producing different research streams with different foci and understandings of the methods and their merits. Based on a theoretical comparison of PLSPM and GSCA in terms of model specification, parameter estimation, and results evaluation, we apply a text analytics technique to identify links between dominant topics in methodological research on both methods. We find that researchers have put effort on clearly distinguishing factor and composite models and their implications for the methods’ performance. We also identify an increasing interest in more complex model specifications such as mediating effects and higher-order models. The evidence of converging and diverging PLSPM and GSCA streams of research points out opportunities for advancing the evolution of composite-based SEM. Weniger anzeigen

Exploring theoretically plausible alternative models for explaining the phenomenon under study is a crucial step in advancing scientific knowledge. This paper advocates model selection in Information Systems (IS) studies that use Partial Least Squares path modeling (PLS) and suggests the use of model selection criteria derived from Information Theory for this purpose. These criteria allow researchers to compare alternative models and select a parsimonious yet well-fitting model. However, as our… Mehr anzeigen

Exploring theoretically plausible alternative models for explaining the phenomenon under study is a crucial step in advancing scientific knowledge. This paper advocates model selection in Information Systems (IS) studies that use Partial Least Squares path modeling (PLS) and suggests the use of model selection criteria derived from Information Theory for this purpose. These criteria allow researchers to compare alternative models and select a parsimonious yet well-fitting model. However, as our review of prior IS research practice shows, their use—while common in the econometrics field and in factor-based SEM—has not found its way into studies using PLS. Using a Monte Carlo study, we compare the performance of several model selection criteria in selecting the best model from a set of competing models under different model set-ups and various conditions of sample size, effect size, and loading patterns. Our results suggest that appropriate model selection cannot be achieved by relying on the PLS criteria (i.e., R2, Adjusted R2, GoF, and Q2), as is the current practice in academic research. Instead, model selection criteria, in particular the Bayesian information criterion (BIC) and the Geweke-Meese criterion (GM), should be used due to their high model selection accuracy and ease of use. To support researchers in the adoption of these criteria, we introduce a five-step procedure that delineates the roles of model selection and statistical inference, and discuss misconceptions that may arise in their use. Weniger anzeigen

Researchers have long been aware of the mathematics of factor indeterminacy. Yet, while occasionally discussed, the phenomenon is mostly ignored. In metrology, the measurement discipline of the physical sciences, uncertainty – distinct from both random error (but encompassing it) and systematic error – is a crucial characteristic of any measurement. This research argues that factor indeterminacy is uncertainty. Factor indeterminacy fundamentally threatens the validity of psychometric… Mehr anzeigen

Researchers have long been aware of the mathematics of factor indeterminacy. Yet, while occasionally discussed, the phenomenon is mostly ignored. In metrology, the measurement discipline of the physical sciences, uncertainty – distinct from both random error (but encompassing it) and systematic error – is a crucial characteristic of any measurement. This research argues that factor indeterminacy is uncertainty. Factor indeterminacy fundamentally threatens the validity of psychometric measurement, because it blurs the linkage between a common factor and the conceptual variable that the factor represents. Acknowledging and quantifying factor indeterminacy is important for progress in reducing this component of uncertainty in measurement, and thus improving psychological measurement over time. Based on our elaborations, we offer a range of recommendations toward achieving this goal. Weniger anzeigen

Purpose The purpose of this paper is to review and extend recent simulation studies on discriminant validity measures, contrasting the use of cutoff values (i.e. heuristics) with inferential tests. Design/methodology/approach Based on a simulation study, which considers different construct correlations, sample sizes, numbers of indicators and loading patterns, the authors assess each criterion’s sensitivity to type I and type II errors. Findings The findings of the simulation study provide… Mehr anzeigen

Purpose The purpose of this paper is to review and extend recent simulation studies on discriminant validity measures, contrasting the use of cutoff values (i.e. heuristics) with inferential tests. Design/methodology/approach Based on a simulation study, which considers different construct correlations, sample sizes, numbers of indicators and loading patterns, the authors assess each criterion’s sensitivity to type I and type II errors. Findings The findings of the simulation study provide further evidence for the robustness of the heterotrait–monotrait (HTMT) ratio of correlations criterion as an estimator of disattenuated (perfectly reliable) correlations between constructs, whose performance parallels that of the standard constrained PHI approach. Furthermore, the authors identify situations in which both methods fail and suggest an alternative criterion. Originality/value Addressing the limitations of prior simulation studies, the authors use both directional comparisons (i.e. heuristics) and inferential tests to facilitate the comparison of the HTMT and PHI methods. Furthermore, the simulation considers criteria that have not been assessed in prior research. Weniger anzeigen

This study explores the knowledge infrastructure of methodological research on partial least squares structural equation modeling (PLS-SEM) from a network point of view. We analyze the structures of author, institution, country, and co-citation networks, and disclose trending schemes in the field. Based on bibliometric data downloaded from the Web of Science, we apply various social network analysis and visualization tools to examine the structure of knowledge networks of the PLS-SEM domain… Mehr anzeigen

This study explores the knowledge infrastructure of methodological research on partial least squares structural equation modeling (PLS-SEM) from a network point of view. We analyze the structures of author, institution, country, and co-citation networks, and disclose trending schemes in the field. Based on bibliometric data downloaded from the Web of Science, we apply various social network analysis and visualization tools to examine the structure of knowledge networks of the PLS-SEM domain. Specifically, we investigate the PLS-SEM knowledge network by analyzing 84 methodological studies published in 39 journals by 145 authors from 106 institutions. We find that specific authors dominate the network, whereas most authors work in isolated groups, loosely connected to the network’s focal authors. Besides presenting the results of a country level analysis, our research also identifies journals that play a key role in disseminating knowledge in the network. Finally, a burst detection analysis indicates that method comparisons and extensions, for example, to estimate common factor model data or to leverage PLS-SEM’s predictive capabilities, feature prominently in recent research. Weniger anzeigen

Purpose: Partial least squares structural equation modeling (PLS-SEM) is an important statistical technique in the toolbox of methods that researchers in marketing and other social sciences disciplines frequently use in their empirical analyses. The purpose of this paper is to shed light on several misconceptions that have emerged as a result of the proposed “new guidelines” for PLS-SEM. The authors discuss various aspects related to current debates on when or when not to use PLS-SEM, and which… Mehr anzeigen

Purpose: Partial least squares structural equation modeling (PLS-SEM) is an important statistical technique in the toolbox of methods that researchers in marketing and other social sciences disciplines frequently use in their empirical analyses. The purpose of this paper is to shed light on several misconceptions that have emerged as a result of the proposed “new guidelines” for PLS-SEM. The authors discuss various aspects related to current debates on when or when not to use PLS-SEM, and which model evaluation metrics to apply. In addition, this paper summarizes several important methodological extensions of PLS-SEM researchers can use to improve the quality of their analyses, results and findings. Design/methodology/approach: The paper merges literature from various disciplines, including marketing, strategic management, information systems, accounting and statistics, to present a state-of-the-art review of PLS-SEM. Based on these findings, the paper offers a point of orientation on how to consider and apply these latest developments when executing or assessing PLS-SEM-based research. Findings: This paper offers guidance regarding situations that favor the use of PLS-SEM and discusses the need to consider certain model evaluation metrics. It also summarizes how to deal with endogeneity in PLS-SEM, and critically comments on the recent proposal to adjust PLS-SEM estimates to mimic common factor models that are the foundation of covariance-based SEM. Finally, this paper opposes characterizing common concepts and practices of PLS-SEM as “out-of-date” without providing well-substantiated alternatives and solutions. Research limitations/implications: The paper paves the way for future discussions and suggests a way forward to reach consensus regarding situations that favor PLS-SEM use and its application. Weniger anzeigen

Partial least squares structural equation modeling (PLS-SEM) has become a standard tool for analyzing complex inter-relationships between observed and latent variables in tourism and numerous other fields of scientific inquiry. Along with the recent surge in the method’s use, research has contributed several complementary methods for assessing the robustness of PLS-SEM results. Although these improvements are documented in extant literature, research on tourism has been slow to adopt the… Mehr anzeigen

Partial least squares structural equation modeling (PLS-SEM) has become a standard tool for analyzing complex inter-relationships between observed and latent variables in tourism and numerous other fields of scientific inquiry. Along with the recent surge in the method’s use, research has contributed several complementary methods for assessing the robustness of PLS-SEM results. Although these improvements are documented in extant literature, research on tourism has been slow to adopt the relevant complementary methods. This article illustrates the use of recent advances in PLS-SEM, designed to ensure structural model results’ robustness in terms of nonlinear effects, endogeneity, and unobserved heterogeneity in a PLS-SEM framework. Our overarching aim is to encourage the routine use of these complementary methods to increase methodological rigor in the field. Weniger anzeigen

Purpose This paper provides a comprehensive, yet concise, overview of the considerations and metrics required for PLS-SEM analysis and result reporting. Preliminary considerations are summarized first, including reasons for choosing PLS-SEM, recommended sample size in selected contexts, distributional assumptions, use of secondary data, statistical power, and the need for goodness-of-fit testing. Next, the metrics, as well as the rules of thumb, that should be applied to assess the PLS-SEM… Mehr anzeigen

Purpose This paper provides a comprehensive, yet concise, overview of the considerations and metrics required for PLS-SEM analysis and result reporting. Preliminary considerations are summarized first, including reasons for choosing PLS-SEM, recommended sample size in selected contexts, distributional assumptions, use of secondary data, statistical power, and the need for goodness-of-fit testing. Next, the metrics, as well as the rules of thumb, that should be applied to assess the PLS-SEM results are covered. Besides covering established PLS-SEM evaluation criteria, the overview includes new guidelines for applying (1) PLSpredict, a novel approach for assessing a model’s out-of-sample prediction, (2) metrics for model comparisons, and (3) several complementary methods for checking the results’ robustness. Design/methodology/approach This paper provides an overview of previously and recently proposed metrics, as well as rules of thumb, for evaluating the results of research, based on the application of PLS-SEM. Findings Most of the previously applied metrics for evaluating PLS-SEM results are still relevant, but scholars need to be knowledgeable about recently proposed metrics (e.g., model comparison criteria) and methods (e.g., endogeneity assessment, latent class analyses, PLSpredict) and when and how to apply them. Research limitations/implications Methodological developments associated with PLS-SEM are rapidly emerging. The metrics reported in this paper are useful for current applications, but scholars need to continuously seek the latest developments in the PLS-SEM method. Originality/value In light of more recent research and methodological developments in the PLS-SEM domain, guidelines for the method’s use need to be continuously extended and updated. This paper is the most current and comprehensive summary of the PLS-SEM method and the metrics applied to assess its solutions. Weniger anzeigen

Researchers often use partial least squares structural equation modeling (PLS-SEM) to estimate path models that include formatively specified constructs. Their validation requires running a redundancy analysis, which tests whether the formatively measured construct is highly correlated with an alternative measure of the same construct. Extending prior knowledge in the field, this paper aims to examine the conditions favoring the use of single vs multiple items to measure the criterion construct… Mehr anzeigen

Researchers often use partial least squares structural equation modeling (PLS-SEM) to estimate path models that include formatively specified constructs. Their validation requires running a redundancy analysis, which tests whether the formatively measured construct is highly correlated with an alternative measure of the same construct. Extending prior knowledge in the field, this paper aims to examine the conditions favoring the use of single vs multiple items to measure the criterion construct in redundancy analyses. Weniger anzeigen

Conflicting relationships between the economic, social and environmental elements of business sustainability, which are also referred to as the Triple Bottom Line (TBL), characterise today's business environment. While the TBL has recently attracted considerable research, not a single study has empirically tested the direct and indirect effects between the TBL elements. By addressing this gap in research, this study sheds light on the structural properties focusing on the direct effects and the… Mehr anzeigen

Conflicting relationships between the economic, social and environmental elements of business sustainability, which are also referred to as the Triple Bottom Line (TBL), characterise today's business environment. While the TBL has recently attracted considerable research, not a single study has empirically tested the direct and indirect effects between the TBL elements. By addressing this gap in research, this study sheds light on the structural properties focusing on the direct effects and the indirect effect (i.e., mediation) between the TBL elements. The results of two cross-industrial studies in Norway and Spain indicate that the TBL's economic element has a direct effect on the environmental element, with the social element mediating this effect. The results reported here offer long-needed empirical insights into the interplay between the three TBL elements, which have important implications for research and practice. Weniger anzeigen

Partial least squares structural equation modeling (PLS-SEM) has become a key method in international marketing research. Users of PLS-SEM have, however, largely overlooked the issue of endogeneity, which has become an integral component of regression analysis applications. This lack of attention is surprising because the PLS-SEM method is grounded in regression analysis, for which numerous approaches for handling endogeneity have been proposed. To identify and treat endogeneity, and create… Mehr anzeigen

Partial least squares structural equation modeling (PLS-SEM) has become a key method in international marketing research. Users of PLS-SEM have, however, largely overlooked the issue of endogeneity, which has become an integral component of regression analysis applications. This lack of attention is surprising because the PLS-SEM method is grounded in regression analysis, for which numerous approaches for handling endogeneity have been proposed. To identify and treat endogeneity, and create awareness of how to deal with this issue, this study introduces a systematic procedure that translates control variables, instrumental variables, and Gaussian copulas into a PLS-SEM framework. We illustrate the procedure's efficacy by means of empirical data and offer recommendations to guide international marketing researchers on how to effectively address endogeneity concerns in their PLS-SEM analyses. Weniger anzeigen

IBM SPSS Statistics gehört zu den populärsten Statistikprogrammen im Studium, in der Forschung und in der Praxis. Leider führen viele Anwender ihre Analysen ausschließlich mit Hilfe der grafischen Benutzeroberfläche von SPSS durch. Dabei können über die Steuersprache SPSS Syntax viele Prozeduren schneller und eleganter realisiert werden. Der souveräne Umgang mit der SPSS Syntax bietet einen unschätzbaren Vorteil für die tägliche Arbeit von Anwendern, die mit der Analyse von Daten zu tun… Mehr anzeigen

IBM SPSS Statistics gehört zu den populärsten Statistikprogrammen im Studium, in der Forschung und in der Praxis. Leider führen viele Anwender ihre Analysen ausschließlich mit Hilfe der grafischen Benutzeroberfläche von SPSS durch. Dabei können über die Steuersprache SPSS Syntax viele Prozeduren schneller und eleganter realisiert werden. Der souveräne Umgang mit der SPSS Syntax bietet einen unschätzbaren Vorteil für die tägliche Arbeit von Anwendern, die mit der Analyse von Daten zu tun haben.
Das Buch ist eine integrierte Einführung in die Steuersprache von IBM SPSS Statistics. Neben den notwendigen Syntax-Grundlagen behandelt es die Themengebiete Datenaufbereitung, Datentransformation und -modifikation sowie die Makro- und Matrixsprache, die in der 3. Auflage grundlegend überarbeitet wurden. Die Neuauflage wurde den Entwicklungen von SPSS angepasst, sprachlich verbessert und um weitere Anwendungsbeispiele ergänzt, die anhand realer Daten u. a. des J. D. Power and Associates Customer Satisfaction Index veranschaulicht werden. Das Buch legt besonderen Wert auf die gute Nachvollziehbarkeit der Beispiele durch begleitende Übungen. Die verwendeten Datensätze sind als kostenfreies Zusatzmaterial erhältlich. Das Buch bietet eine prägnante und umfassende Anleitung zur effizienteren Arbeit mit IBM SPSS Statistics und ist sowohl als Einstiegsliteratur für Programmieranfänger, als auch als Nachschlagewerk für fortgeschrittene Anwender geeignet.
Das Buch wurde auf Grundlage der Version 25.0 von IBM SPSS Statistics erstellt, kann aber auch für andere Versionen verwendet werden. Weniger anzeigen

The transition from consuming goods to consuming services is a topic of great interest for service researchers and has been examined from various perspectives. We provide an overview of how this field of research has been approached by systematically analyzing the current state of the academic literature. We report the results of a social network analysis of the sharing economy and servitization literature, which reveals the structure of the knowledge networks that have been formed as a result… Mehr anzeigen

The transition from consuming goods to consuming services is a topic of great interest for service researchers and has been examined from various perspectives. We provide an overview of how this field of research has been approached by systematically analyzing the current state of the academic literature. We report the results of a social network analysis of the sharing economy and servitization literature, which reveals the structure of the knowledge networks that have been formed as a result of the collaborative works of researchers, institutions, and journals that shape, generate, distribute, and preserve the domains’ intellectual knowledge. We shed light on the cohesion and fragmentation of knowledge and highlight the emerging and fading topics within the field. The results present a detailed analysis of the research field and suggest a research agenda on the transition of goods to services consumption. Weniger anzeigen

When estimating moderating effects in partial least squares structural equation modeling (PLS-SEM), researchers can choose from a variety of approaches to model the influence of a moderator on a relationship between two constructs by generating different interaction terms. While prior research has evaluated the efficacy of these approaches in the context of PLS-SEM, the impact of different data treatment options on their performance in the context of standard PLS-SEM and consistent PLS-SEM… Mehr anzeigen

When estimating moderating effects in partial least squares structural equation modeling (PLS-SEM), researchers can choose from a variety of approaches to model the influence of a moderator on a relationship between two constructs by generating different interaction terms. While prior research has evaluated the efficacy of these approaches in the context of PLS-SEM, the impact of different data treatment options on their performance in the context of standard PLS-SEM and consistent PLS-SEM (PLSc-SEM) is as yet unexplored. Our simulation study addresses these limitations and explores if the choice of approach and data treatment option has a pronounced impact on the methods’ parameter recovery. An empirical application substantiates these findings. Based on our results, we offer recommendations for researchers wishing to estimate moderating effects by means of PLS-SEM and PLSc-SEM. Weniger anzeigen

Partial least squares path modeling (PLS-PM) has become popular in various disciplines to model structural relationships among latent variables measured by manifest variables. To fully benefit from the predictive capabilities of PLS-PM, researchers must understand the efficacy of predictive metrics used. In this research, we compare the performance of standard PLS-PM criteria and model selection criteria derived from Information Theory, in terms of selecting the best predictive model among a… Mehr anzeigen

Partial least squares path modeling (PLS-PM) has become popular in various disciplines to model structural relationships among latent variables measured by manifest variables. To fully benefit from the predictive capabilities of PLS-PM, researchers must understand the efficacy of predictive metrics used. In this research, we compare the performance of standard PLS-PM criteria and model selection criteria derived from Information Theory, in terms of selecting the best predictive model among a cohort of competing models. We use Monte Carlo simulation to study this question under various sample sizes, effect sizes, item loadings, and model setups. Specifically, we explore whether, and when, the in-sample measures such as the model selection criteria can substitute for out-of-sample criteria that require a holdout sample. Such a substitution is advantageous when creating a holdout causes considerable loss of statistical and predictive power due to an overall small sample. We find that when the researcher does not have the luxury of a holdout sample, and the goal is selecting correctly specified models with low prediction error, the in-sample model selection criteria, in particular the Bayesian Information Criterion (BIC) and Geweke-Meese Criterion (GM), are useful substitutes for out-of-sample criteria. When a holdout sample is available, the best performing out-of-sample criteria include the root mean squared error (RMSE) and mean absolute deviation (MAD). Finally, we recommend against using standard the PLS-PM criteria (R2, Adjusted R2, and Q2), and specifically the out-of-sample mean absolute percentage error (MAPE) for prediction-oriented model selection purposes. Finally, we illustrate the model selection criteria’s practical utility using a well-known corporate reputation model. Weniger anzeigen

Structural equation modeling (SEM) depicts one of the most salient research methods across a variety of disciplines, including hospitality management. Although for many researchers, SEM is equivalent to carrying out covariance-based SEM, recent research advocates the use of partial least squares structural equation modeling (PLS-SEM) as an attractive alternative. The purpose of this paper is to systematically examine how PLS-SEM has been applied in major hospitality research journals with the… Mehr anzeigen

Structural equation modeling (SEM) depicts one of the most salient research methods across a variety of disciplines, including hospitality management. Although for many researchers, SEM is equivalent to carrying out covariance-based SEM, recent research advocates the use of partial least squares structural equation modeling (PLS-SEM) as an attractive alternative. The purpose of this paper is to systematically examine how PLS-SEM has been applied in major hospitality research journals with the aim of providing important guidance and, if necessary, opportunities for realignment in future applications. Because PLS-SEM in hospitality research is still in an early stage of development, critically examining its use holds considerable promise to counteract misapplications which otherwise might reinforce over time. Weniger anzeigen

Partial least squares structural equation modeling (PLS-SEM) has become a key multivariate analysis technique that human resource management (HRM) researchers frequently use. While most disciplines undertake regular critical reflections on the use of important methods to ensure rigorous research and publication practices, the use of PLS-SEM in HRM has not been analyzed so far. To address this gap in HRM literature, this paper presents a critical review of PLS-SEM use in 77 HRM studies published… Mehr anzeigen

Partial least squares structural equation modeling (PLS-SEM) has become a key multivariate analysis technique that human resource management (HRM) researchers frequently use. While most disciplines undertake regular critical reflections on the use of important methods to ensure rigorous research and publication practices, the use of PLS-SEM in HRM has not been analyzed so far. To address this gap in HRM literature, this paper presents a critical review of PLS-SEM use in 77 HRM studies published over a 30-year period in leading journals. By contrasting the review results with state-of-the-art guidelines for use of the method, we identify several areas that offer room of improvement when applying PLS-SEM in HRM studies. Our findings offer important guidance for future use of the PLS-SEM method in HRM and related fields. Weniger anzeigen

This book is an easily accessible and comprehensive guide which helps make sound statistical decisions, perform analyses, and interpret the results quickly using Stata. It includes advanced coverage of ANOVA, factor, and cluster analyses in Stata, as well as essential regression and descriptive statistics. It is aimed at those wishing to know more about the process, data management, and most commonly used methods in market research using Stata.

The book offers readers an overview of the… Mehr anzeigen

This book is an easily accessible and comprehensive guide which helps make sound statistical decisions, perform analyses, and interpret the results quickly using Stata. It includes advanced coverage of ANOVA, factor, and cluster analyses in Stata, as well as essential regression and descriptive statistics. It is aimed at those wishing to know more about the process, data management, and most commonly used methods in market research using Stata.

The book offers readers an overview of the entire market research process from asking market research questions to collecting and analyzing data by means of quantitative methods. It is engaging, hands-on, and includes many practical examples, tips, and suggestions that help readers apply and interpret quantitative methods, such as regression, factor, and cluster analysis. These methods help researchers provide companies with useful insights. Weniger anzeigen

Eines Tages ist es so weit: Der Nachwuchs steht ins Haus und damit eine Reihe von Planungsproblemen. Ein sozialverträglicher Name und ein Kinderwagen mit maximaler sozialer Anerkennung müssen gefunden werden. Nach der Geburt wird es nicht einfacher! Wie viele Windeln muss ich vorrätig halten? Wie bestimme ich die kürzeste Kinderwagentour zwischen Bäckerei, Spielplatz, Apotheke, Supermarkt und Drogeriemarkt? Viele Unwägbarkeiten machen das Elterndasein anstrengend. Aber das muss nicht sein!… Mehr anzeigen

Eines Tages ist es so weit: Der Nachwuchs steht ins Haus und damit eine Reihe von Planungsproblemen. Ein sozialverträglicher Name und ein Kinderwagen mit maximaler sozialer Anerkennung müssen gefunden werden. Nach der Geburt wird es nicht einfacher! Wie viele Windeln muss ich vorrätig halten? Wie bestimme ich die kürzeste Kinderwagentour zwischen Bäckerei, Spielplatz, Apotheke, Supermarkt und Drogeriemarkt? Viele Unwägbarkeiten machen das Elterndasein anstrengend. Aber das muss nicht sein! Dieses Buch schafft Abhilfe, indem es wichtige Planungsprobleme des elterlichen Alltags mit betriebswirtschaftlichen Methoden analysiert und löst. In der 2. Auflage wurden zwei neue Probleme zur Maximierung der elterlichen Zufriedenheit gelöst. Weniger anzeigen

Composite-based structural equation modeling (SEM), and especially partial least squares path modeling (PLS), has gained increasing dissemination in marketing. To fully exploit the potential of these methods, researchers must know about their relative performance and the settings that favor each method’s use. While numerous simulation studies have aimed to evaluate the performance of composite-based SEM methods, practically all of them defined populations using common factor models, thereby… Mehr anzeigen

Composite-based structural equation modeling (SEM), and especially partial least squares path modeling (PLS), has gained increasing dissemination in marketing. To fully exploit the potential of these methods, researchers must know about their relative performance and the settings that favor each method’s use. While numerous simulation studies have aimed to evaluate the performance of composite-based SEM methods, practically all of them defined populations using common factor models, thereby assessing the methods on erroneous grounds. This study is the first to offer a comprehensive assessment of composite-based SEM techniques on the basis of composite model data, considering a broad range of model constellations. Results of a large-scale simulation study substantiate that PLS and generalized structured component analysis are consistent estimators when the underlying population is composite model-based. While both methods outperform sum scores regression in terms of parameter recovery, PLS achieves slightly greater statistical power. Weniger anzeigen

In this research note, we reflect critically on the use of sampling techniques in advertising research. Our review of 1028 studies published between 2008 and 2016 in the four leading advertising journals shows that while current academic literature advocates probability sampling procedures, their actual usage is quite scarce. Most studies either lack information on the sampling method used, or engage in non-probability sampling without making adjustments to compensate for unequal selection… Mehr anzeigen

In this research note, we reflect critically on the use of sampling techniques in advertising research. Our review of 1028 studies published between 2008 and 2016 in the four leading advertising journals shows that while current academic literature advocates probability sampling procedures, their actual usage is quite scarce. Most studies either lack information on the sampling method used, or engage in non-probability sampling without making adjustments to compensate for unequal selection probabilities, non-coverage, and sampling fluctuations. Based on our results, we call on researchers to revisit the fundamental aspects of sampling to increase their research results’ rigour and relevance. Weniger anzeigen

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… Mehr anzeigen

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. Weniger anzeigen

Researchers have recently strongly questioned the robustness of the attraction effect, according to which adding a decoy option to an existing choice set affects consumers’ choice behavior. Tying in with this debate, we identify the persistent use of hypothetical choices in the domain to be a major shortcoming in attraction effect research. In an experiment on the attraction effect with a realistic choice setting that fosters external validity, we manipulate the choice framing by contrasting… Mehr anzeigen

Researchers have recently strongly questioned the robustness of the attraction effect, according to which adding a decoy option to an existing choice set affects consumers’ choice behavior. Tying in with this debate, we identify the persistent use of hypothetical choices in the domain to be a major shortcoming in attraction effect research. In an experiment on the attraction effect with a realistic choice setting that fosters external validity, we manipulate the choice framing by contrasting hypothetical choices with binding choices that entail economic consequences. We find the attraction effect to be much stronger when decisions are binding, underlining the effect’s usefulness as a marketing tool. Weniger anzeigen

Descriptive statistics and the application of multivariate data analysis techniques such as regression analysis and factor analysis belong to the core set of statistical instruments, and their use has generated findings that have significantly shaped the way we see the world today. The increasing reliance on and acceptance of statistical analysis, as well as the advent of powerful computer systems that allow for handling large amounts of data, paved the way for the development of more advanced… Mehr anzeigen

Descriptive statistics and the application of multivariate data analysis techniques such as regression analysis and factor analysis belong to the core set of statistical instruments, and their use has generated findings that have significantly shaped the way we see the world today. The increasing reliance on and acceptance of statistical analysis, as well as the advent of powerful computer systems that allow for handling large amounts of data, paved the way for the development of more advanced next-generation analysis techniques. Structural equation modeling (SEM) is among the most useful advanced statistical analysis techniques that have emerged in the social sciences in recent decades. SEM is a class of multivariate techniques that combine aspects of factor analysis and regression, enabling the researcher to simultaneously examine relationships among measured variables and latent variables as well as between latent variables. Considering the ever-increasing importance of understanding latent phenomena such as consumer perceptions, attitudes, or intentions and their influence on organizational performance measures (e.g., stock prices), it is not surprising that SEM has become one of the most prominent statistical analysis techniques today. While there are many approaches to conducting SEM, the most widely applied method is certainly covariance-based SEM (CB-SEM). Weniger anzeigen

This study investigates how firms can achieve high levels of organizational performance under different configurations of absorptive capacity and organizational innovation. The study uses partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to test relationships among dimensions of absorptive capacity, organizational innovation, and organizational performance. The results provide support for the absorptive capacity's role for… Mehr anzeigen

This study investigates how firms can achieve high levels of organizational performance under different configurations of absorptive capacity and organizational innovation. The study uses partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to test relationships among dimensions of absorptive capacity, organizational innovation, and organizational performance. The results provide support for the absorptive capacity's role for organizational innovation and performance. Furthermore, different configurations of absorptive capacity and organizational innovation conditions lead to better organizational performance. Weniger anzeigen

We replicate and extend Norton et al.’s (2012) and Mochon et al.’s (2012) studies on the IKEA effect, according to which consumers show a higher willingness-to-pay when self-assembling products. Our results support the robustness of the original effect and indicate that psychological ownership acts as a psychological mechanism that underlies the IKEA effect.

Discussions concerning different structural equation modeling methods draw on an increasing array of concepts and related terminology. As a consequence, misconceptions about the meaning of terms such as reflective measurement and common factor models as well as formative measurement and composite models have emerged. By distinguishing conceptual variables and their measurement model operationalization from the estimation perspective, we disentangle the confusion between the terminologies and… Mehr anzeigen

Discussions concerning different structural equation modeling methods draw on an increasing array of concepts and related terminology. As a consequence, misconceptions about the meaning of terms such as reflective measurement and common factor models as well as formative measurement and composite models have emerged. By distinguishing conceptual variables and their measurement model operationalization from the estimation perspective, we disentangle the confusion between the terminologies and develop a unifying framework. Results from a simulation study substantiate our conceptual considerations, highlighting the biases that occur when using (1) composite-based partial least squares path modeling to estimate common factor models, and (2) common factor-based covariance-based structural equation modeling to estimate composite models. The results show that the use of PLS is preferable, particularly when it is unknown whether the data's nature is common factor- or composite-based. Weniger anzeigen

This article critically comments Rossiter’s “How to use C-OAR-SE to design optimal standard measures“ in the current issue of EJM and provides a broader perspective on Rossiter’s C-OAR-SE framework and measurement practice in marketing in general. The paper shows that, at best, Rossiter’s mathematical dismissal of convergent validity applies to the completely hypothetical (and highly unlikely) situation where a perfect measure without any error would be available. Further considerations cast… Mehr anzeigen

This article critically comments Rossiter’s “How to use C-OAR-SE to design optimal standard measures“ in the current issue of EJM and provides a broader perspective on Rossiter’s C-OAR-SE framework and measurement practice in marketing in general. The paper shows that, at best, Rossiter’s mathematical dismissal of convergent validity applies to the completely hypothetical (and highly unlikely) situation where a perfect measure without any error would be available. Further considerations cast serious doubt on the appropriateness of Rossiter’s concrete object, dual subattribute-based single item measures. Being immunized against any piece of empirical evidence, C-OAR-SE cannot be considered a scientific theory and is bound to perpetuate, if not aggravate, the fundamental flaws in current measurement practice. While C-OAR-SE indeed helps generate more content validinstruments, the procedure offers no insights as to whether these instruments work properly in order to be used in research and practice. Weniger anzeigen

Uncovering unobserved heterogeneity is a requirement to obtain valid results when using structural equation modeling (SEM). Conventional segmentation methods usually fail in an SEM context because they account for the indicator data, but not for the latent variables and their relationships in the structural model. This research introduces a new segmentation approach to variance-based SEM using partial least squares path modeling (PLS). The iterative reweighted regressions segmentation method… Mehr anzeigen

Uncovering unobserved heterogeneity is a requirement to obtain valid results when using structural equation modeling (SEM). Conventional segmentation methods usually fail in an SEM context because they account for the indicator data, but not for the latent variables and their relationships in the structural model. This research introduces a new segmentation approach to variance-based SEM using partial least squares path modeling (PLS). The iterative reweighted regressions segmentation method for PLS (PLS-IRRS) effectively identifies and treats unobserved heterogeneity in data sets. Compared to existing alternatives, PLS-IRRS is multiple times faster while delivering results of the same quality. Researchers should therefore routinely use PLS-IRRS to address the critical issue of unobserved heterogeneity in PLS. Weniger anzeigen

Customers increasingly seek to engage with companies by actively taking part in the value creation process. Companies have reacted to this trend by integrating customers into product development processes in an effort to better fulfil their needs and simultaneously decrease costs. While research has explored various antecedents and consequences of such co-creation activities, only little is known about the psychological ownership’s role and its interaction with peer feedback. This research… Mehr anzeigen

Customers increasingly seek to engage with companies by actively taking part in the value creation process. Companies have reacted to this trend by integrating customers into product development processes in an effort to better fulfil their needs and simultaneously decrease costs. While research has explored various antecedents and consequences of such co-creation activities, only little is known about the psychological ownership’s role and its interaction with peer feedback. This research shows that psychological ownership emerges when customers engage with companies in creating the product portfolio. Furthermore, implementing feedback loops accelerates customer engagement’s positive effects in terms of product evaluations and customers’ willingness to pay. Negative feedback reverses these effects suggesting that companies should pay closer attention to feedback options when integrating customers into value creation processes. Weniger anzeigen

Single-item measures have recently become more en vogue due to studies arguing in favor of their psychometric properties vis-à-vis multi-item scales. However, their effective use requires (1) expert raters to designate the focal construct as being doubly concrete and (2) researchers to find a good single item to represent the construct. This study examines whether expert raters identify the doubly concrete nature of constructs that prior research presents as exemplary in this respect… Mehr anzeigen

Single-item measures have recently become more en vogue due to studies arguing in favor of their psychometric properties vis-à-vis multi-item scales. However, their effective use requires (1) expert raters to designate the focal construct as being doubly concrete and (2) researchers to find a good single item to represent the construct. This study examines whether expert raters identify the doubly concrete nature of constructs that prior research presents as exemplary in this respect. Furthermore, the study compares the efficacy of a broad range of selection mechanisms based on expert judgment and statistical criteria for identifying the best item in a scale. The results show that expert raters do not share the commonly held belief that researchers can validly measure constructs such as attitude toward the ad, or brand, with single items. Further analyses show that neither rater assessments nor statistical criteria prove valuable regarding identifying an appropriate single item from a set of candidate items. Weniger anzeigen

Over the past decade, the cultural intelligence construct and its underlying dimensions have been used in a number of studies. Prior research has tested the determinants and outcomes of cultural intelligence, using pooled data from different countries and cultures, and has compared the results across contexts. However, these studies often disregarded measurement invariance, which is a necessary requirement for such analyses. We assess the measurement invariance of the commonly used… Mehr anzeigen

Over the past decade, the cultural intelligence construct and its underlying dimensions have been used in a number of studies. Prior research has tested the determinants and outcomes of cultural intelligence, using pooled data from different countries and cultures, and has compared the results across contexts. However, these studies often disregarded measurement invariance, which is a necessary requirement for such analyses. We assess the measurement invariance of the commonly used four-dimensional cultural intelligence scale across five countries (China, France, Germany, Turkey, and the U.S.) by means of a composite model logic, using partial least squares structural equation modeling (PLS-SEM). Our results question the scale's dimensionality concerning China and France, and reveal an item set that is invariant across the other countries. Our findings indicate that researchers should be aware of the potential lack of measurement invariance regarding the standard measurement of cultural intelligence. They should therefore be cautious when comparing the results of cross-country and cross-cultural research. Weniger anzeigen

Bergvist (2016( and Rossiter (2016) claim that the conclusions of Sarstedt, Diamantopoulos, Salzberger, and Baumgartner (2016) are unwarranted as the study's methodology is flawed. This paper begs to differ.

The compromise effect, according to which consumers tend to prefer options positioned as a compromise in a given set of extreme options, ranks among the most prominent context effects in marketing research. Tying in with the recent debate on the robustness of the effect, this research shows that the effect is robust in terms of durable goods when using real branded products, including real payments, the possibility of a pre-choice evaluation, and no-buy options. The results of a comparative… Mehr anzeigen

The compromise effect, according to which consumers tend to prefer options positioned as a compromise in a given set of extreme options, ranks among the most prominent context effects in marketing research. Tying in with the recent debate on the robustness of the effect, this research shows that the effect is robust in terms of durable goods when using real branded products, including real payments, the possibility of a pre-choice evaluation, and no-buy options. The results of a comparative analysis based on previous studies' effect sizes suggest that, compared to decisions on fast-moving consumer goods (FMCG), the amount of cognitive effort spent on decisions regarding durables fosters the compromise effect. A second study supports this notion by showing that, regarding choices between durables, the compromise effect diminishes under a serotonin-deficiency-induced cognitive impairment, but its decrease is not as pronounced as with FMCG. Weniger anzeigen

Psychological and physiological states such as mood, hunger, stress, and sleep deprivation are known to affect decision-making processes and therefore crucially influence consumer behavior. A possible biological mechanism underlying the observed variability of consumer behavior is the context-sensitive variation in the levels of neuromodulators in the brain. In a series of four experimental studies, the authors pharmaceutically reduce the levels of the neurotransmitter serotonin in the brain to… Mehr anzeigen

Psychological and physiological states such as mood, hunger, stress, and sleep deprivation are known to affect decision-making processes and therefore crucially influence consumer behavior. A possible biological mechanism underlying the observed variability of consumer behavior is the context-sensitive variation in the levels of neuromodulators in the brain. In a series of four experimental studies, the authors pharmaceutically reduce the levels of the neurotransmitter serotonin in the brain to diminish the availability of subjects’ cognitive resources. In doing so, they study how serotonin brain levels influence (1) subjects’ tendency to avoid buying and (2) consumers' preference for product options positioned as a compromise in a given choice set rather than for more extreme alternatives (i.e., the compromise effect). Using realistic product choice scenarios in a binding decision framework, they find that a reduction of brain serotonin levels leads to choice deferral and decreases the compromise effect, both as a within-subjects and as a between-subjects choice phenomenon. As such, this study provides neurobiological evidence for the assumption that the compromise effect is the result of deliberate and demanding thought processes rather than intuitive decision making. Weniger anzeigen

Purpose - The purpose of this paper is to introduce the importance-performance map analysis (IPMA) and explain how to use it in the context of partial least squares structural equation modeling (PLSSEM). A case study, drawing on the IPMA module implemented in the SmartPLS 3 software, illustrates the results generation and interpretation. Design/methodology/approach - The explications first address the principles of the IPMA and introduce a systematic procedure for its use, followed by a… Mehr anzeigen

Purpose - The purpose of this paper is to introduce the importance-performance map analysis (IPMA) and explain how to use it in the context of partial least squares structural equation modeling (PLSSEM). A case study, drawing on the IPMA module implemented in the SmartPLS 3 software, illustrates the results generation and interpretation. Design/methodology/approach - The explications first address the principles of the IPMA and introduce a systematic procedure for its use, followed by a detailed discussion of each step. Finally, a case study on the use of technology shows how to apply the IPMA in empirical PLS-SEM studies. Findings - The IPMA gives researchers the opportunity to enrich their PLS-SEM analysis and, thereby, gain additional results and findings. More specifically, instead of only analyzing the path coefficients (i.e. the importance dimension), the IPMA also considers the average value of the latent variables and their indicators (i.e. performance dimension). Research limitations/implications - An IPMA is tied to certain requirements, which relate to the measurement scales, variable coding, and indicator weights estimates. Moreover, the IPMA presumes linear relationships. This research does not address the computation and interpretation of non-linear dependencies. Practical implications - The IPMA is particularly useful for generating additional findings and conclusions by combining the analysis of the importance and performance dimensions in practical PLS-SEM applications. Thereby, the IPMA allows for prioritizing constructs to improve a certain target construct. Expanding the analysis to the indicator level facilitates identifying the most important areas of specific actions. These results are, for example, particularly important in practical studies identifying the differing impacts that certain construct dimensions have on phenomena such as technology acceptance, corporate reputation, or customer satisfaction. Weniger anzeigen

Research on international marketing usually involves comparing different groups of respondents. When using structural equation modeling (SEM), group comparisons can be misleading unless researchers establish the invariance of their measures. While methods have been proposed to analyze measurement invariance in common factor models, research lacks an approach in respect of composite models. This research presents a novel three-step procedure to analyze the measurement invariance of composite… Mehr anzeigen

Research on international marketing usually involves comparing different groups of respondents. When using structural equation modeling (SEM), group comparisons can be misleading unless researchers establish the invariance of their measures. While methods have been proposed to analyze measurement invariance in common factor models, research lacks an approach in respect of composite models. This research presents a novel three-step procedure to analyze the measurement invariance of composite models (MICOM) when using variance-based SEM, such as partial least squares path modeling (PLS). Weniger anzeigen

Mixture regression models are an important method for uncovering unobserved heterogeneity. A fundamental challenge in their application relates to the identification of the appropriate number of segments to retain from the data. Prior research has provided several simulation studies that compare the performance of different segment retention criteria. Although collinearity between the predictor variables is a common phenomenon in regression models, its effect on the performance of these… Mehr anzeigen

Mixture regression models are an important method for uncovering unobserved heterogeneity. A fundamental challenge in their application relates to the identification of the appropriate number of segments to retain from the data. Prior research has provided several simulation studies that compare the performance of different segment retention criteria. Although collinearity between the predictor variables is a common phenomenon in regression models, its effect on the performance of these criteria has not been analyzed thus far. We address this gap in research by examining the performance of segment retention criteria in mixture regression models characterized by systematically increased collinearity levels. The results have fundamental implications and provide guidance for using mixture regression models in empirical (marketing) studies. Weniger anzeigen

Mixture regression models are an important method for uncovering unobserved heterogeneity. A fundamental challenge in their application relates to the identification of the appropriate number of segments to retain from the data. Prior research has provided several simulation studies that compare the performance of different segment retention criteria. Although collinearity between the predictor variables is a common phenomenon in regression models, its effect on the performance of these… Mehr anzeigen

Mixture regression models are an important method for uncovering unobserved heterogeneity. A fundamental challenge in their application relates to the identification of the appropriate number of segments to retain from the data. Prior research has provided several simulation studies that compare the performance of different segment retention criteria. Although collinearity between the predictor variables is a common phenomenon in regression models, its effect on the performance of these criteria has not been analyzed thus far. We address this gap in research by examining the performance of segment retention criteria in mixture regression models characterized by systematically increased collinearity levels. The results have fundamental implications and provide guidance for using mixture regression models in empirical (marketing) studies. Copyright Springer Science+Business Media New York 2015 Weniger anzeigen

The purpose of this paper is to provide an overview of unobserved heterogeneity in the context of partial least squares structural equation modeling (PLS-SEM), its prevalence and challenges for social science researchers. Part II – in the next issue (European Business Review, Vol. 28 No. 2) – presents a case study, which illustrates how to identify and treat unobserved heterogeneity in PLS-SEM using the finite mixture PLS (FIMIX-PLS) module in the SmartPLS 3 software… Mehr anzeigen

The purpose of this paper is to provide an overview of unobserved heterogeneity in the context of partial least squares structural equation modeling (PLS-SEM), its prevalence and challenges for social science researchers. Part II – in the next issue (European Business Review, Vol. 28 No. 2) – presents a case study, which illustrates how to identify and treat unobserved heterogeneity in PLS-SEM using the finite mixture PLS (FIMIX-PLS) module in the SmartPLS 3 software. Design/methodology/approach – The paper merges literatures from various disciplines, such as management information systems, marketing and statistics, to present a state-of-the-art review of FIMIX-PLS. Based on this review, the paper offers guidelines on how to apply the technique to specific research problems. Findings – FIMIX-PLS offers a means to identify and treat unobserved heterogeneity in PLS-SEM and is particularly useful for determining the number of segments to extract from the data. In the latter respect, prior applications of FIMIX-PLS restricted their focus to a very limited set of criteria, but future studies should broaden the scope by considering information criteria, theory and logic. Research limitations/implications – Since the introduction of FIMIX-PLS, a range of alternative latent class techniques have emerged to address some of the limitations of the approach relating, for example, to the technique’s inability to handle heterogeneity in the measurement models and its distributional assumptions. The second part of this article (Part II) discusses alternative latent class techniques in greater detail and calls for the joint use of FIMIX-PLS and PLS prediction-oriented segmentation. Originality/value – This paper is the first to offer researchers who have not been exposed to the method an introduction to FIMIX-PLS. Based on a state-of-the-art review of the technique in Part I, Part II follows up by offering a step-by-step tutorial on how to use FIMIX-PLS in SmartPLS 3. Weniger anzeigen

Accounting for heterogeneity in tourism studies remains important to avoid parameter bias (e.g., Mazanec, 2000; Mazanec, Ring, Stangl, & Teichmann, 2010) when employing analysis techniques such as regression (e.g., Ye, Zhang, & Yuen, 2013), partial least squares structural equation modeling (PLS-SEM) (e.g., Song, van der Veen, Li, & Chen, 2012), or covariance structural equation modeling (CB-SEM) (e.g., Jurowski & Gursoy, 2004). Heterogeneity can come in two forms. First, heterogeneity can be… Mehr anzeigen

Accounting for heterogeneity in tourism studies remains important to avoid parameter bias (e.g., Mazanec, 2000; Mazanec, Ring, Stangl, & Teichmann, 2010) when employing analysis techniques such as regression (e.g., Ye, Zhang, & Yuen, 2013), partial least squares structural equation modeling (PLS-SEM) (e.g., Song, van der Veen, Li, & Chen, 2012), or covariance structural equation modeling (CB-SEM) (e.g., Jurowski & Gursoy, 2004). Heterogeneity can come in two forms. First, heterogeneity can be observable in that differences between two or more groups of data relate to observable characteristics (e.g., Dolnicar, 2004). Researchers can use these observable characteristics to partition the data into separate groups of observations and compare the group-specific estimates by means of multigroup comparisons. Second, heterogeneity can be unobserved in that it does not depend on one specific observable characteristic or combinations of several characteristics (e.g., Mazanec, 2000, 2001). To identify and treat unobserved heterogeneity, researchers can draw on a variety of latent class techniques. For instance, Assaf, Oh, and Tsionas (2015) employ Bayesian finite mixture modeling within CB-SEM, and Marques and Reis (2015) finite mixture modeling within PLS-SEM. It is the latter approach that this commentary focuses on. Weniger anzeigen

While the attraction effect has received considerable attention in consumer research, recent research concludes that the effect is restricted to artificial choice settings, which questions its relevance for marketing practice. This paper takes a broader perspective on the issue of the generalizability of research results and introduces a set of background factors, which, if neglected, have adverse consequences for such generalizability. The results of our extensive review of the literature on… Mehr anzeigen

While the attraction effect has received considerable attention in consumer research, recent research concludes that the effect is restricted to artificial choice settings, which questions its relevance for marketing practice. This paper takes a broader perspective on the issue of the generalizability of research results and introduces a set of background factors, which, if neglected, have adverse consequences for such generalizability. The results of our extensive review of the literature on this topic, published during the last four decades in the top 30 marketing journals, show that context effect studies have routinely neglected these background factors. In light of our results, we propose guidelines for implementing context effect experiments in future consumer research. These guidelines allow for a more realistic analysis of the attraction effect and related context effects in consumer research. Weniger anzeigen

While the attraction effect has received considerable attention in consumer research, recent research concludes that the effect is restricted to artificial choice settings, which questions its relevance for marketing practice. This paper takes a broader perspective on the issue of the generalizability of research results and introduces a set of background factors, which, if neglected, have adverse consequences for such generalizability. The results of our extensive review of the literature on… Mehr anzeigen

While the attraction effect has received considerable attention in consumer research, recent research concludes that the effect is restricted to artificial choice settings, which questions its relevance for marketing practice. This paper takes a broader perspective on the issue of the generalizability of research results and introduces a set of background factors, which, if neglected, have adverse consequences for such generalizability. The results of our extensive review of the literature on this topic, published during the last four decades in the top 30 marketing journals, show that context effect studies have routinely neglected these background factors. In light of our results, we propose guidelines for implementing context effect experiments in future consumer research. These guidelines allow for a more realistic analysis of the attraction effect and related context effects in consumer research. Weniger anzeigen

Psychological ownership has emerged as an important predictor of workplace motivations, attitudes, and behaviors. While pieces of psychological ownership theory have been recently adopted to marketing contexts as well, much is left to be done. With a more comprehensive application and use of psychological ownership theory in marketing research, additional understanding and explanation could be provided on many of the key phenomena in marketing, such as customer satisfaction, loyalty… Mehr anzeigen

Psychological ownership has emerged as an important predictor of workplace motivations, attitudes, and behaviors. While pieces of psychological ownership theory have been recently adopted to marketing contexts as well, much is left to be done. With a more comprehensive application and use of psychological ownership theory in marketing research, additional understanding and explanation could be provided on many of the key phenomena in marketing, such as customer satisfaction, loyalty, word-of-mouth, willingness-to-pay. In this paper, we focus on individual psychological ownership associated concepts and evidence with implications for marketing research. Our work offers multiple useful avenues for future research focused on, but not limited to marketing contexts. Weniger anzeigen

Psychological ownership has emerged as an important predictor of workplace motivations, attitudes, and behaviors. While components of psychological ownership theory have been recently adapted to marketing contexts as well, much remains to be done. With a more comprehensive application and use of psychological ownership theory in marketing, additional understanding and explanation could be provided for many of the key phenomena, such as customer satisfaction, loyalty, word-of-mouth, and… Mehr anzeigen

Psychological ownership has emerged as an important predictor of workplace motivations, attitudes, and behaviors. While components of psychological ownership theory have been recently adapted to marketing contexts as well, much remains to be done. With a more comprehensive application and use of psychological ownership theory in marketing, additional understanding and explanation could be provided for many of the key phenomena, such as customer satisfaction, loyalty, word-of-mouth, and willingness to pay. In this article, we focus on individual psychological ownership– associated concepts and evidence with implications for research in marketing. Our work offers multiple avenues for future research focused on, but not limited to, marketing contexts. Weniger anzeigen

Research on international marketing usually involves comparing different groups of respondents. When using structural equation modeling (SEM), group comparisons can be misleading unless researchers establish the invariance of their measures. While methods have been proposed to analyze measurement invariance in common factor models, research lacks an approach in respect of composite models. This research presents a novel three-step procedure to analyze the measurement invariance of composite… Mehr anzeigen

Research on international marketing usually involves comparing different groups of respondents. When using structural equation modeling (SEM), group comparisons can be misleading unless researchers establish the invariance of their measures. While methods have been proposed to analyze measurement invariance in common factor models, research lacks an approach in respect of composite models. This research presents a novel three-step procedure to analyze the measurement invariance of composite models (MICOM) when using variance-based SEM, such as partial least squares path modeling (PLS). Weniger anzeigen

Discriminant validity assessment has become a generally accepted prerequisite for analyzing relationships between latent variables. For variance-based structural equation modeling, such as partial least squares, the Fornell-Larcker criterion and the examination of cross-loadings are the dominant approaches for evaluating discriminant validity. By means of a simulation study, we show that these approaches do not reliably detect the lack of discriminant validity in common research situations. We… Mehr anzeigen

Discriminant validity assessment has become a generally accepted prerequisite for analyzing relationships between latent variables. For variance-based structural equation modeling, such as partial least squares, the Fornell-Larcker criterion and the examination of cross-loadings are the dominant approaches for evaluating discriminant validity. By means of a simulation study, we show that these approaches do not reliably detect the lack of discriminant validity in common research situations. We therefore propose an alternative approach, based on the multitrait-multimethod matrix, to assess discriminant validity: the heterotrait-monotrait ratio of correlations. We demonstrate its superior performance by means of a Monte Carlo simulation study, in which we compare the new approach to the Fornell-Larcker criterion and the assessment of (partial) cross-loadings. Finally, we provide guidelines on how to handle discriminant validity issues in variance-based structural equation modeling. Weniger anzeigen

Anecdotal evidence suggests that older individuals behave more emotionally and less cognitively due to their decreasing biological, cognitive, and/or social abilities, or a combination thereof. However, in the psychology and aging literatures, recent research indicates that the sense of future time is a better predictor of consumer perceptions, attitudes, and behaviors than chronological age. Tying in with these research streams, this paper introduces individuals' future time perspective (FTP)… Mehr anzeigen

Anecdotal evidence suggests that older individuals behave more emotionally and less cognitively due to their decreasing biological, cognitive, and/or social abilities, or a combination thereof. However, in the psychology and aging literatures, recent research indicates that the sense of future time is a better predictor of consumer perceptions, attitudes, and behaviors than chronological age. Tying in with these research streams, this paper introduces individuals' future time perspective (FTP) as a moderator of the well-known satisfaction–loyalty relationship. More precisely, this paper demonstrates that FTP influences the satisfaction–loyalty relationship by (1) driving customer loyalty, and (2) moderating the relationship between these two constructs. Besides contributing an important concept to the business research literature, the findings provide evidence that explains the previous heterogeneous results of chronological age-related research. This concept allows for a more nuanced analysis of aging's impact on the perceptions, attitudes, and behaviors of consumers. Weniger anzeigen

While the question of how community design influences user behavior in online communities has recently attracted considerable research, few studies have empirically evaluated the influencing factors of specific user behavior. Building on a conceptual framework of identity-based vs. bond-based attachment in online communities, this study evaluates the influence of several antecedents on user attachment as well as attachment's mediating role for explaining consumer behavior. Results of a survey… Mehr anzeigen

While the question of how community design influences user behavior in online communities has recently attracted considerable research, few studies have empirically evaluated the influencing factors of specific user behavior. Building on a conceptual framework of identity-based vs. bond-based attachment in online communities, this study evaluates the influence of several antecedents on user attachment as well as attachment's mediating role for explaining consumer behavior. Results of a survey reveal that network effects, intergroup comparison, and social categorization have a positive and significant effect on common identity attachment, whereas this is not the case with in-group interdependence. Conversely, collectivism, interpersonal similarity, and social interaction drive common bond attachment, while personal information has no effect. Most importantly, the results show that common identity attachment is the primary driver of user behavior in online communities. Weniger anzeigen

We comment on a recent article by Chong (2013) on the roles of demographic and motivation variables in mobile commerce usage. Drawing on recent research on the service-dominant logic, socioemotional selectivity theory, and data from a first empirical study, we argue that a broader discussion on the value relevance of mobile commerce activities and the consideration of consumers’ future time perspectives would provide a richer, potentially more appropriate picture of the drivers of mobile… Mehr anzeigen

We comment on a recent article by Chong (2013) on the roles of demographic and motivation variables in mobile commerce usage. Drawing on recent research on the service-dominant logic, socioemotional selectivity theory, and data from a first empirical study, we argue that a broader discussion on the value relevance of mobile commerce activities and the consideration of consumers’ future time perspectives would provide a richer, potentially more appropriate picture of the drivers of mobile commerce usage. Furthermore, using data from a second empirical study, we highlight several validity issues of the used scales. We hope to motivate a replication and extension of Chong’s model and also provide recommendations for future research on this area. Weniger anzeigen

Discriminant validity assessment has become a generally accepted prerequisite for analyzing relationships between latent variables. For variance-based structural equation modeling, such as partial least squares, the Fornell-Larcker criterion and the examination of cross-loadings are the dominant approaches for evaluating discriminant validity. By means of a simulation study, we show that these approaches do not reliably detect the lack of discriminant validity in common research situations. We… Mehr anzeigen

Discriminant validity assessment has become a generally accepted prerequisite for analyzing relationships between latent variables. For variance-based structural equation modeling, such as partial least squares, the Fornell-Larcker criterion and the examination of cross-loadings are the dominant approaches for evaluating discriminant validity. By means of a simulation study, we show that these approaches do not reliably detect the lack of discriminant validity in common research situations. We therefore propose an alternative approach, based on the multitrait-multimethod matrix, to assess discriminant validity: the heterotrait-monotrait ratio of correlations. We demonstrate this approach’s superior performance by means of a Monte Carlo simulation study, in which we compare the new approach to the Fornell-Larcker criterion and the assessment of (partial) cross-loadings. Finally, we provide guidelines on how to handle discriminant validity issues in variance-based structural equation modeling. Weniger anzeigen

Rigdon’s (2012) thoughtful article argues that PLS-SEM should free itself from CB-SEM. It should renounce all mechanisms, frameworks, and jargon associated with factor models entirely. In this comment, we shed further light on two subject areas on which Rigdon (2012) touches in his discussion of CB-SEM and PLS-SEM. Rigdon (2012) highlights ways to make better use of PLS-SEM’s predictive capabilities, for example, by reverting to set correlations. We discuss this issue in more detail… Mehr anzeigen

Rigdon’s (2012) thoughtful article argues that PLS-SEM should free itself from CB-SEM. It should renounce all mechanisms, frameworks, and jargon associated with factor models entirely. In this comment, we shed further light on two subject areas on which Rigdon (2012) touches in his discussion of CB-SEM and PLS-SEM. Rigdon (2012) highlights ways to make better use of PLS-SEM’s predictive capabilities, for example, by reverting to set correlations. We discuss this issue in more detail, highlighting the need to examine the predictive capabilities of models when developing and testing theories, and broach the issue of confirmatory versus exploratory modeling. As a result of our discussion, we call for the continuous improvement of the PLS-SEM method to uncover its capabilities for theory testing while retaining its predictive character. Weniger anzeigen

This article introduces and motivates an exchange of thoughts on the paper by Edward E. Rigdon in thefirst of twoLong Range Planningspecial issues on partial least squares structural equation modeling (PLS-SEM) in strategic management published in 2012 and 2013. For30 years, there has been a heated debate on the benefits and drawbacks of PLS-SEM versus those of its sibling, the covariance-basedstructural equation modeling (CB-SEM) approach. Edward E. Rigdon’s paper is a milestone that proposes… Mehr anzeigen

This article introduces and motivates an exchange of thoughts on the paper by Edward E. Rigdon in thefirst of twoLong Range Planningspecial issues on partial least squares structural equation modeling (PLS-SEM) in strategic management published in 2012 and 2013. For30 years, there has been a heated debate on the benefits and drawbacks of PLS-SEM versus those of its sibling, the covariance-basedstructural equation modeling (CB-SEM) approach. Edward E. Rigdon’s paper is a milestone that proposes a change of thought and en-courages the long-required emancipation of the PLS-SEM method from CB-SEM. These developments will have a pronounced impact onthe proper application of SEM as a key multivariate analysis method in the strategic management discipline, further enhancing thepotential it has as a research tool. Weniger anzeigen

To define research and statistical methods needed for relevant research and the development of the family business field, this article reviews the past 30 years of analytic and statistical methods used by family business researchers. The article explores the many reasons for studying research methods, especially in family business, and examines the progression and development of methodologies, sample sizes and related methodological issues, as well as theories and topics studied in family… Mehr anzeigen

To define research and statistical methods needed for relevant research and the development of the family business field, this article reviews the past 30 years of analytic and statistical methods used by family business researchers. The article explores the many reasons for studying research methods, especially in family business, and examines the progression and development of methodologies, sample sizes and related methodological issues, as well as theories and topics studied in family business research. Directions for future research highlight methods that we believe should be used to advance family business theory and practice. Weniger anzeigen

This paper extends prior research on the reputation of nonprofit organizations (NPOs) by investigating the moderating role of socio‐demographic characteristics in forming NPO reputation and reputation's effects on donating and volunteering behavior. The findings offer new insights into the role an NPO's reputation plays and its effects on key outcomes such as willingness to donate and work as a voluntary member in specific subgroups. The results show that successful reputation management is… Mehr anzeigen

This paper extends prior research on the reputation of nonprofit organizations (NPOs) by investigating the moderating role of socio‐demographic characteristics in forming NPO reputation and reputation's effects on donating and volunteering behavior. The findings offer new insights into the role an NPO's reputation plays and its effects on key outcomes such as willingness to donate and work as a voluntary member in specific subgroups. The results show that successful reputation management is specifically important for male, older, highly educated, and affluent respondents. Communicational measures aimed at strengthening an organization's social responsibility are particularly promising regarding triggering favorable donor behavior and voluntary support. Copyright © 2014 John Wiley & Sons, Ltd. Weniger anzeigen

This paper addresses Rönkkö & Evermann’s (2013) criticisms of the partial least squares (PLS) approach to structural equation modeling (SEM). We contend that the alleged shortcomings of PLS are not due to problems with the technique, but instead to three problems with Rönkkö & Evermann’s (2013) study: (1) the adherence to the common factor model, (2) a very limited simulation designs, and (3) over-stretched generalizations of their findings. Whereas Rönkkö & Evermann (2013) claim to be… Mehr anzeigen

This paper addresses Rönkkö & Evermann’s (2013) criticisms of the partial least squares (PLS) approach to structural equation modeling (SEM). We contend that the alleged shortcomings of PLS are not due to problems with the technique, but instead to three problems with Rönkkö & Evermann’s (2013) study: (1) the adherence to the common factor model, (2) a very limited simulation designs, and (3) over-stretched generalizations of their findings. Whereas Rönkkö & Evermann (2013) claim to be dispelling myths about PLS, they have in reality created new myths that we, in turn, debunk. By examining their claims, our paper contributes to re-establishing a constructive discussion of the PLS method and its properties. We show that PLS does offer advantages for exploratory research and that it is a viable estimator for composite factor models. This can pose an interesting alternative if the common factor model does not hold. Therefore, we can conclude that PLS should continue to be used as an important statistical tool for management and organizational research, as well as other social science disciplines. Weniger anzeigen

The authors aim to present partial least squares (PLS) as an evolving approach to structural equation modeling (SEM), highlight its advantages and limitations and provide an overview of recent research on the method across various fields.

As a central element of the socioemotional selectivity theory, individuals’ future time perspective (FTP) has recently attracted considerable attention in marketing. Lang and Carstensen (2002) provide a measurement scale for FTP, which has since been used by many academic researchers who univocally accept its original unidimensional reflective operationalization. Challenging this assumption, we draw on data from three studies to systematically explore the scale’s psychometric properties. We… Mehr anzeigen

As a central element of the socioemotional selectivity theory, individuals’ future time perspective (FTP) has recently attracted considerable attention in marketing. Lang and Carstensen (2002) provide a measurement scale for FTP, which has since been used by many academic researchers who univocally accept its original unidimensional reflective operationalization. Challenging this assumption, we draw on data from three studies to systematically explore the scale’s psychometric properties. We find that the FTP scale comprises three dimensions rather than one. In a second step, the nature of the relationships between these dimensions and the more abstract higher-order FTP construct as well as between the dimensions and their items is explored. Our assessment of the scale’s predictive validity shows that the unidimensional operationalization misleads researchers because dimension-specific effects become confounded in a composite effect. As such, this study takes a step toward advancing the FTP’s measurement and understanding its role in different research settings. Weniger anzeigen

When applying the partial least squares structural equation modeling (PLS-SEM) method, the assumption that the data stem from a single homogeneous population is often unrealistic. For the full set of data, unobserved heterogeneity in the PLS path model estimates may result in misleading interpretations. This research presents the PLS genetic algorithm segmentation (PLS-GAS) method to account for unobserved heterogeneity in the path model estimates. The results of a simulation study guide an… Mehr anzeigen

When applying the partial least squares structural equation modeling (PLS-SEM) method, the assumption that the data stem from a single homogeneous population is often unrealistic. For the full set of data, unobserved heterogeneity in the PLS path model estimates may result in misleading interpretations. This research presents the PLS genetic algorithm segmentation (PLS-GAS) method to account for unobserved heterogeneity in the path model estimates. The results of a simulation study guide an assessment of this novel approach. PLS-GAS allows for uncovering unobserved heterogeneity and identifying different groups within a data set. In an application on customer satisfaction data and the American customer satisfaction index model, the method identifies distinctive group-specific PLS path model estimates which allow for a further differentiated interpretation of the results. Weniger anzeigen

When applying the partial least squares structural equation modeling (PLS-SEM) method, the assumption that the data stem from a single homogeneous population is often unrealistic. For the full set of data, unobserved heterogeneity in the PLS path model estimates may result in misleading interpretations. This research presents the PLS genetic algorithm segmentation (PLS-GAS) method to account for unobserved heterogeneity in the path model estimates. The results of a simulation study guide an… Mehr anzeigen

When applying the partial least squares structural equation modeling (PLS-SEM) method, the assumption that the data stem from a single homogeneous population is often unrealistic. For the full set of data, unobserved heterogeneity in the PLS path model estimates may result in misleading interpretations. This research presents the PLS genetic algorithm segmentation (PLS-GAS) method to account for unobserved heterogeneity in the path model estimates. The results of a simulation study guide an assessment of this novel approach. PLS-GAS allows for uncovering unobserved heterogeneity and identifying different groups within a data set. In an application on customer satisfaction data and the American customer satisfaction index model, the method identifies distinctive group-specific PLS path model estimates which allow for a further differentiated interpretation of the results. Weniger anzeigen

With economic considerations exerting an ever-increasing influence on soccer clubs’ activities, fan satisfaction becomes an essential strategic management objective for these institutions. Despite this topic’s obvious relevance, the literature has paid little attention to the measurement of fan satisfaction. Based on a thorough literature review, as well as interviews with soccer fans and industry experts, this paper develops an analytical model for measuring soccer fan satisfaction (FANSAT). A… Mehr anzeigen

With economic considerations exerting an ever-increasing influence on soccer clubs’ activities, fan satisfaction becomes an essential strategic management objective for these institutions. Despite this topic’s obvious relevance, the literature has paid little attention to the measurement of fan satisfaction. Based on a thorough literature review, as well as interviews with soccer fans and industry experts, this paper develops an analytical model for measuring soccer fan satisfaction (FANSAT). A large-scale sample of soccer fans permits subsequent application of the FANSAT approach. The impact-performance map results of the driver analysis show that stadium features, aspects of the stadium, club management, and fan-based support for the club are the most important determinants of fan attendance. The results of this study provide implications of major relevance for international sports organizations, national sports organizations, and clubs. FANSAT is useful beyond this application, and other sports disciplines can adopt this tool for measuring and improving their fans’ satisfaction. Weniger anzeigen

We present partial least squares (PLS) as an evolving approach to structural equation modeling (SEM), highlight its advantages and limitations and provide an overview of recent research on the method across various fields.

Applications of the partial least squares (PLS) path modeling approach—which have gained increasing dissemination in business research—usually build on the assumption that the data stem from a single population. However, in empirical applications, this assumption of homogeneity is unrealistic. Analyses on the aggregate data level ignore the existence of groups with substantial differences and more often than not result in misleading interpretations and false conclusions. This study introduces a… Mehr anzeigen

Applications of the partial least squares (PLS) path modeling approach—which have gained increasing dissemination in business research—usually build on the assumption that the data stem from a single population. However, in empirical applications, this assumption of homogeneity is unrealistic. Analyses on the aggregate data level ignore the existence of groups with substantial differences and more often than not result in misleading interpretations and false conclusions. This study introduces a genetic algorithm segmentation method for PLS path modeling (PLS-GAS) that accounts for the critical issue of unobserved heterogeneity in the path model's estimates of relations. The results from computational experiments allow a primary assessment to substantiate that PLS-GAS effectively uncovers unobserved heterogeneity. Significantly distinctive segment-specific path model estimates further foster the development of differentiated results that render more effective recommendations. Weniger anzeigen

Corporate reputation has become one of the most important intangible assets for maintaining and enhancing firms’ competitiveness in the global marketplace. Researchers have shown considerable interest in measuring the corporate reputation construct, resulting in a lack of consensus on valid measurement approaches. Against this background, we discuss commonly used reputation measures from a conceptual as well as theoretical perspective, and empirically compare them in terms of convergent… Mehr anzeigen

Corporate reputation has become one of the most important intangible assets for maintaining and enhancing firms’ competitiveness in the global marketplace. Researchers have shown considerable interest in measuring the corporate reputation construct, resulting in a lack of consensus on valid measurement approaches. Against this background, we discuss commonly used reputation measures from a conceptual as well as theoretical perspective, and empirically compare them in terms of convergent validity and criterion validity. By examining the measures’ psychometric properties, both theoretically and empirically, this study provides guidance for their reasonable application in business research and practice. Weniger anzeigen

Management consulting firms play a crucial role in companies' strategic and organizational activities. In business practice, however, they are often criticized for providing inferior solutions due to the deficiencies in the client-consultant interactions. In this paper, we study the effects of team competences and team processes on the performance of consulting teams. We specifically examine processes related to consulting teams' interaction with their clients; i.e., the communication with… Mehr anzeigen

Management consulting firms play a crucial role in companies' strategic and organizational activities. In business practice, however, they are often criticized for providing inferior solutions due to the deficiencies in the client-consultant interactions. In this paper, we study the effects of team competences and team processes on the performance of consulting teams. We specifically examine processes related to consulting teams' interaction with their clients; i.e., the communication with their clients during the project and teams' adaptability to their task. Our analysis shows that the relationship between consulting teams' competences and their performance is sequentially mediated – first, by client communication; and second, by team adaptability. The results of this study contribute to research on teams in organizations, and consulting teams in particular, by disentangling the complex influences of team competences and team processes on team performance. Weniger anzeigen

We replicate Mogilner and Aaker's (2009) study on the “time vs. money effect,” according to which subjects' evaluation and enjoyment of experiential products tend to be more (less) favorable when the concept of time (money) is made salient via an add sign. Our experimental study provides a strong support for the original findings. Furthermore, our analysis reveals only few interactions, thereby indicating the robustness of the effect with respect to variations in demographic background factors.

A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), by Hair, Hult, Ringle, and Sarstedt, provides a concise yet very practical guide to understanding and using PLS structural equation modeling (PLS-SEM). PLS-SEM is evolving as a statistical modeling technique and its use has increased exponentially in recent years within a variety of disciplines, due to the recognition that PLS-SEM’s distinctive methodological features make it a viable alternative to the more popular… Mehr anzeigen

A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), by Hair, Hult, Ringle, and Sarstedt, provides a concise yet very practical guide to understanding and using PLS structural equation modeling (PLS-SEM). PLS-SEM is evolving as a statistical modeling technique and its use has increased exponentially in recent years within a variety of disciplines, due to the recognition that PLS-SEM’s distinctive methodological features make it a viable alternative to the more popular covariance-based SEM approach. This text includes extensive examples on SmartPLS software, and is accompanied by multiple data sets that are available for download from the accompanying website (www.pls-sem.com). Weniger anzeigen

This paper discusses a recent development in partial least squares (PLS) path modeling, namely goodness-of-fit indices. In order to illustrate the behavior of the goodness-of-fit index (GoF) and the relative goodness-of-fit index (GoF_rel), we estimate PLS path models with simulated data, and contrast their values with fit indices commonly used in covariance-based structural equation modeling. The simulation shows that the GoF and the GoF_rel are not suitable for model validation. However, the… Mehr anzeigen

This paper discusses a recent development in partial least squares (PLS) path modeling, namely goodness-of-fit indices. In order to illustrate the behavior of the goodness-of-fit index (GoF) and the relative goodness-of-fit index (GoF_rel), we estimate PLS path models with simulated data, and contrast their values with fit indices commonly used in covariance-based structural equation modeling. The simulation shows that the GoF and the GoF_rel are not suitable for model validation. However, the GoF can be useful to assess how well a PLS path model can explain different sets of data. Weniger anzeigen

This second Long Range Planning special issue on PLS-SEMin strategic management research and practice seeks to further progress towards this goal. The journal received 41 articles for its special issue on PLS-SEM, twelve of which completed a thorough review process successfully. Based on the number of high quality manuscripts, a decision was made to split the special issue. In the first Long Range Planning special issue on PLS-SEM in strategic management (Hair et al., 2012a; Robins, 2012), the… Mehr anzeigen

This second Long Range Planning special issue on PLS-SEMin strategic management research and practice seeks to further progress towards this goal. The journal received 41 articles for its special issue on PLS-SEM, twelve of which completed a thorough review process successfully. Based on the number of high quality manuscripts, a decision was made to split the special issue. In the first Long Range Planning special issue on PLS-SEM in strategic management (Hair et al., 2012a; Robins, 2012), the focus was on methodological developments and their application (Becker et al., 2012; Furrer et al., 2012; Gudergan et al., 2012; Hair et al., 2012a,b,c; Money et al., 2012; Rigdon, 2012). This second special issue provides a forum for topical issues that demonstrate the usefulness of PLS-SEM by piloting applications of this method in the field of strategic management with strong implications for strategic research and practice. As such, the special issue targets two audiences: academics involved in the fields of strategy and management, and practitioners such as consultants. The six articles in this issue are summarized in the following paragraphs. Weniger anzeigen

Researchers and practitioners appreciate the various advantageous features that PLS-SEM, as a component-based approach to SEM, offers in practical applications. Although strategic management research relatively early on recognized PLS-SEM's flexibility regarding handling various modeling problems in studies (e.g., Hulland, 1999), its usefulness is still not well established amongst many management and strategy researchers. Against this background, this Long Range Planning special issue on… Mehr anzeigen

Researchers and practitioners appreciate the various advantageous features that PLS-SEM, as a component-based approach to SEM, offers in practical applications. Although strategic management research relatively early on recognized PLS-SEM's flexibility regarding handling various modeling problems in studies (e.g., Hulland, 1999), its usefulness is still not well established amongst many management and strategy researchers. Against this background, this Long Range Planning special issue on PLS-SEM in strategic management research and practice seeks to provide a forum for topical issues that demonstrate its usefulness in this field. Descriptions of the method, its empirical applications, and methodological advancements that increase its usefulness in research and practice are specifically emphasized. As such, the special issue aims at two audiences: academics involved in the fields of strategy and management, and practitioners such as consultants. Accordingly, theoretical, methodological, and empirical manuscripts with strong implications for strategic research and practice are included in this special issue. Weniger anzeigen

Every discipline needs to frequently review the use of multivariate analysis methods to ensure rigorous research and publications. Even though partial least squares structural equation modeling (PLS-SEM) is frequently used for studies in strategic management, this kind of assessment has only been conducted by Hulland (1999) for four studies and a limited number of criteria. This article analyzes the use of PLS-SEM in thirty-seven studies that have been published in eight leading management… Mehr anzeigen

Every discipline needs to frequently review the use of multivariate analysis methods to ensure rigorous research and publications. Even though partial least squares structural equation modeling (PLS-SEM) is frequently used for studies in strategic management, this kind of assessment has only been conducted by Hulland (1999) for four studies and a limited number of criteria. This article analyzes the use of PLS-SEM in thirty-seven studies that have been published in eight leading management journals for dozens of relevant criteria, including reasons for using PLS-SEM, data characteristics, model characteristics, model evaluation and reporting. Our results reveal several problematic aspects of PLS-SEM use in strategic management research, but also substantiate some improvement over time. We find that researchers still often do not fully make use of the method's capabilities, sometimes even misapplying it. Our review of PLS-SEM applications and recommendations on how to improve the use of the method are important to disseminate rigorous research and publication practices in the strategic management discipline. Weniger anzeigen

Existing research implicitly assumes that all factors known to influence customer satisfaction are likewise important for investor behavior. However, if investors do not equally value activities targeting different satisfaction drivers, managers focusing on short-term stock returns might over- or under-emphasize certain satisfaction drivers to the detriment of the long-term success of the firm. Therefore, we extend prior research on the value relevance of customer satisfaction by assessing the… Mehr anzeigen

Existing research implicitly assumes that all factors known to influence customer satisfaction are likewise important for investor behavior. However, if investors do not equally value activities targeting different satisfaction drivers, managers focusing on short-term stock returns might over- or under-emphasize certain satisfaction drivers to the detriment of the long-term success of the firm. Therefore, we extend prior research on the value relevance of customer satisfaction by assessing the relationship between the dynamics of key satisfaction drivers and contemporaneous risk-adjusted stock returns. Moreover, we compare three major markets using a dataset covering nearly the entire set of car brands sold between 2004 and 2008. Our results show that investors react to information related to perceived product quality, whereas, surprisingly, the cost of ownership and dealer service quality are unimportant despite the importance attributed to them in consumer research. Furthermore, we observe that information concerning the U.S. market dominates that of the UK and German markets. Weniger anzeigen

Most methodological fields undertake regular critical reflections to ensure rigorous research and publication practices, and, consequently, acceptance in their domain. Interestingly, relatively little attention has been paid to assessing the use of partial least squares structural equation modeling (PLS-SEM) in marketing research— despite its increasing popularity in recent years. To fill this gap, we conducted an extensive search in the 30 top ranked marketing journals that allowed us to… Mehr anzeigen

Most methodological fields undertake regular critical reflections to ensure rigorous research and publication practices, and, consequently, acceptance in their domain. Interestingly, relatively little attention has been paid to assessing the use of partial least squares structural equation modeling (PLS-SEM) in marketing research— despite its increasing popularity in recent years. To fill this gap, we conducted an extensive search in the 30 top ranked marketing journals that allowed us to identify 204 PLSSEM applications published in a 30-year period (1981 to 2010). A critical analysis of these articles addresses, amongst others, the following key methodological issues: reasons for using PLS-SEM, data and model characteristics, outer and inner model evaluations, and reporting. We also give an overview of the interdependencies between researchers’ choices, identify potential problem areas, and discuss their implications. On the basis of our findings, we provide comprehensive guidelines to aid researchers in avoiding common pitfalls in PLS-SEM use. This study is important for researchers and practitioners, as PLS-SEM requires several critical choices that, if not made correctly, can lead to improper findings, interpretations, and conclusions. Weniger anzeigen

Establishing predictive validity of measures is a major concern in marketing research. This paper investigates the conditions favoring the use of single items versus multi-item scales in terms of predictive validity. A series of complementary studies reveals that the predictive validity of single items varies considerably across different (concrete) constructs and stimuli objects. In an attempt to explain the observed instability, a comprehensive simulation study is conducted aimed at… Mehr anzeigen

Establishing predictive validity of measures is a major concern in marketing research. This paper investigates the conditions favoring the use of single items versus multi-item scales in terms of predictive validity. A series of complementary studies reveals that the predictive validity of single items varies considerably across different (concrete) constructs and stimuli objects. In an attempt to explain the observed instability, a comprehensive simulation study is conducted aimed at identifying the influence of different factors on the predictive validity of single versus multi-item measures. These include the average inter-item correlations in the predictor and criterion constructs, the number of items measuring these constructs, as well as the correlation patterns of multiple and single items between the predictor and criterion constructs. The simulation results show that, under most conditions typically encountered in practical applications, multi-item scales clearly outperform single items in terms of predictive validity. Only under very specific conditions do single items perform equally well as multi-item scales. Therefore, the use of single-item measures in empirical research should be approached with caution, and the use of such measures should be limited to special circumstances. Weniger anzeigen

Establishing predictive validity of measures is a major concern in marketing research. This paper investigates the conditions favoring the use of single items versus multi-item scales in terms of predictive validity. A series of complementary studies reveals that the predictive validity of single items varies considerably across different (concrete) constructs and stimuli objects. In an attempt to explain the observed instability, a comprehensive simulation study is conducted aimed at… Mehr anzeigen

Establishing predictive validity of measures is a major concern in marketing research. This paper investigates the conditions favoring the use of single items versus multi-item scales in terms of predictive validity. A series of complementary studies reveals that the predictive validity of single items varies considerably across different (concrete) constructs and stimuli objects. In an attempt to explain the observed instability, a comprehensive simulation study is conducted aimed at identifying the influence of different factors on the predictive validity of single versus multi-item measures. These include the average inter-item correlations in the predictor and criterion constructs, the number of items measuring these constructs, as well as the correlation patterns of multiple and single items between the predictor and criterion constructs. The simulation results show that, under most conditions typically encountered in practical applications, multi-item scales clearly outperform single items in terms of predictive validity. Only under very specific conditions do single items perform equally well as multi-item scales. Therefore, the use of single-item measures in empirical research should be approached with caution, and the use of such measures should be limited to special circumstances. Weniger anzeigen

This study investigates the customer satisfaction of airline passengers and introduces perceived safety as a satisfaction driver, which has not yet been considered in the literature. Applying structural equation modeling to data collected from a sample of airline passengers reveals that perceived safety is one of the key drivers that can explain the degree of overall customer satisfaction. This relationship is, however, strongly moderated by the purposes for which airline passengers travel… Mehr anzeigen

This study investigates the customer satisfaction of airline passengers and introduces perceived safety as a satisfaction driver, which has not yet been considered in the literature. Applying structural equation modeling to data collected from a sample of airline passengers reveals that perceived safety is one of the key drivers that can explain the degree of overall customer satisfaction. This relationship is, however, strongly moderated by the purposes for which airline passengers travel (i.e., either for business or pleasure). Perceived safety has a significantly greater impact on the overall customer satisfaction of people who travel for pleasure than on that of business travelers, which implies that airlines should more strongly emphasize safety features in advertising aimed at leisure travelers. Weniger anzeigen

This paper examines the effects of internal quality, i.e. the influence of the general and project-specific
work environment on perceived service quality and customer satisfaction within the context of consulting
projects. The expected cause-effect relationships are investigated based on dyadic data. That is,
the causal analysis comprises the perceptions of customers as well as the judgments of project managers.
Using data from one of the world leading strategic management… Mehr anzeigen

This paper examines the effects of internal quality, i.e. the influence of the general and project-specific
work environment on perceived service quality and customer satisfaction within the context of consulting
projects. The expected cause-effect relationships are investigated based on dyadic data. That is,
the causal analysis comprises the perceptions of customers as well as the judgments of project managers.
Using data from one of the world leading strategic management consulting companies, this study
reveals new insights into the determinants of customer satisfaction and perceived service quality. The
results confirm the service-profit chain’s relevance and allow originating managerial implications to establish
the long term success of a consulting company. Weniger anzeigen

Purpose – Revisiting Fornell et al.'s (1996) seminal study, this chapter looks at the evidence for observed and unobserved heterogeneity within data underlying the American customer satisfaction index (ACSI) model. Examining data for two specific industries (utilities and hotels) reveals only modest differences. However, we suppose that unobserved heterogeneity critically affects the results. These insights provide the basis for shaping further differentiated ACSI model analyses and more… Mehr anzeigen

Purpose – Revisiting Fornell et al.'s (1996) seminal study, this chapter looks at the evidence for observed and unobserved heterogeneity within data underlying the American customer satisfaction index (ACSI) model. Examining data for two specific industries (utilities and hotels) reveals only modest differences. However, we suppose that unobserved heterogeneity critically affects the results. These insights provide the basis for shaping further differentiated ACSI model analyses and more precise interpretations. Weniger anzeigen

Structural equation modeling (SEM) has become a quasi-standard in marketing and management research when it comes to analyzing the cause-effect relations between latent constructs. For most researchers, SEM is equivalent to carrying out covariance-based SEM (CB-SEM). While marketing researchers have a basic understanding of CB-SEM, most of them are only barely familiar with the other useful approach to SEM-partial least squares SEM (PLS-SEM). The current paper reviews PLS-SEM and its algorithm,… Mehr anzeigen

Structural equation modeling (SEM) has become a quasi-standard in marketing and management research when it comes to analyzing the cause-effect relations between latent constructs. For most researchers, SEM is equivalent to carrying out covariance-based SEM (CB-SEM). While marketing researchers have a basic understanding of CB-SEM, most of them are only barely familiar with the other useful approach to SEM-partial least squares SEM (PLS-SEM). The current paper reviews PLS-SEM and its algorithm, and provides an overview of when it can be most appropriately applied, indicating its potential and limitations for future research. The authors conclude that PLS-SEM path modeling, if appropriately applied, is indeed a "silver bullet" for estimating causal models in many theoretical models and empirical data situations. Weniger anzeigen

This accessible, practice-oriented and compact text provides a hands-on introduction to the principles of market research. Using the market research process as a framework, the authors explain how to collect and describe the necessary data and present the most important and frequently used quantitative analysis techniques, such as ANOVA, regression analysis, factor analysis, and cluster analysis. An explanation is provided of the theoretical choices a market researcher has to make with regard… Mehr anzeigen

This accessible, practice-oriented and compact text provides a hands-on introduction to the principles of market research. Using the market research process as a framework, the authors explain how to collect and describe the necessary data and present the most important and frequently used quantitative analysis techniques, such as ANOVA, regression analysis, factor analysis, and cluster analysis. An explanation is provided of the theoretical choices a market researcher has to make with regard to each technique, as well as how these are translated into actions in IBM SPSS Statistics. This includes a discussion of what the outputs mean and how they should be interpreted from a market research perspective. Each chapter concludes with a case study that illustrates the process based on real-world data. A comprehensive web appendix includes additional analysis techniques, datasets, video files and case studies. Several mobile tags in the text allow readers to quickly browse related web content using a mobile device. Weniger anzeigen

Purpose – Partial least squares (PLS) path modeling has become a pivotal empirical research method in international marketing. Owing to group comparisons' important role in research on international marketing, we provide researchers with recommendations on how to conduct multigroup analyses in PLS path modeling. Methodology/approach – We review available multigroup analysis methods in PLS path modeling and introduce a novel confidence set approach. A characterization of each method's strengths… Mehr anzeigen

Purpose – Partial least squares (PLS) path modeling has become a pivotal empirical research method in international marketing. Owing to group comparisons' important role in research on international marketing, we provide researchers with recommendations on how to conduct multigroup analyses in PLS path modeling. Methodology/approach – We review available multigroup analysis methods in PLS path modeling and introduce a novel confidence set approach. A characterization of each method's strengths and limitations and a comparison of their outcomes by means of an empirical example extend the existing knowledge of multigroup analysis methods. Moreover, we provide an omnibus test of group differences (OTG), which allows testing the differences across more than two groups. Findings – The empirical comparison results suggest that Keil et al.'s (2000) parametric approach can generally be considered more liberal in terms of rendering a certain difference significant. Conversely, the novel confidence set approach and Henseler's (2007) approach are more conservative. Originality/value of paper – This study is the first to deliver an in-depth analysis and a comparison of the available procedures with which to statistically assess differences between group-specific parameters in PLS path modeling. Moreover, we offer two important methodological extensions of existing research (i.e., the confidence set approach and OTG). This contribution is particularly valuable for international marketing researchers, as it offers recommendations regarding empirical applications and paves the way for future research studies aimed at comparing the approaches' properties on the basis of simulated data. Weniger anzeigen

Since its first introduction in the Schmalenbach Business Review, Hahn et al.’s (2002) finite mixture partial least squares (FIMIX-PLS) approach to response-based segmentation in variance-based structural equation modeling has received much attention from the marketing and management disciplines. When applying FIMIX-PLS to uncover unobserved heterogeneity, the actual number of segments is usually unknown. As in any clustering procedure, retaining a suitable number of segments is crucial, since… Mehr anzeigen

Since its first introduction in the Schmalenbach Business Review, Hahn et al.’s (2002) finite mixture partial least squares (FIMIX-PLS) approach to response-based segmentation in variance-based structural equation modeling has received much attention from the marketing and management disciplines. When applying FIMIX-PLS to uncover unobserved heterogeneity, the actual number of segments is usually unknown. As in any clustering procedure, retaining a suitable number of segments is crucial, since many managerial decisions are based on this result. In empirical research, applications of FIMIX-PLS rely on information and classification criteria to select an appropriate number of segments to retain from the data. However, the performance and robustness of these criteria in determining an adequate number of segments has not yet been investigated scientifically in
the context of FIMIX-PLS. By conducting computational experiments, this study provides
an evaluation of several model selection criteria’s performance and of different data characteristics’
influence on the robustness of the criteria. The results engender key recommendations and identify appropriate model selection criteria for FIMIX-PLS. The study’s findings enhance the applicability of FIMIX-PLS in both theory and practice. Weniger anzeigen

Alongside structural equation modeling (SEM), the complementary technique of partial least squares (PLS) path modeling helps researchers understand relations among sets of observed variables. Like SEM, PLS began with an assumption of homogeneity – one population and one model – but has developed techniques for modeling data from heterogeneous populations, consistent with a marketing emphasis on segmentation. Heterogeneity can be expressed through interactions and nonlinear terms. Additionally… Mehr anzeigen

Alongside structural equation modeling (SEM), the complementary technique of partial least squares (PLS) path modeling helps researchers understand relations among sets of observed variables. Like SEM, PLS began with an assumption of homogeneity – one population and one model – but has developed techniques for modeling data from heterogeneous populations, consistent with a marketing emphasis on segmentation. Heterogeneity can be expressed through interactions and nonlinear terms. Additionally, researchers can use multiple group analysis and latent class methods. This chapter reviews these techniques for modeling heterogeneous data in PLS, and illustrates key developments in finite mixture modeling in PLS using the SmartPLS 2.0 package. Weniger anzeigen

As a result of the increasing adoption of private sector firms' values and concepts, non-profit organizations (NPOs) are becoming more and more aware of intangible assets' importance for achieving competitive advantages. Even though reputation can be considered an organization's central intangible asset, there is still no appropriate measurement approach for reputation in this context. In this paper, we identify the dimensions of NPO reputation and develop indices to measure these components… Mehr anzeigen

As a result of the increasing adoption of private sector firms' values and concepts, non-profit organizations (NPOs) are becoming more and more aware of intangible assets' importance for achieving competitive advantages. Even though reputation can be considered an organization's central intangible asset, there is still no appropriate measurement approach for reputation in this context. In this paper, we identify the dimensions of NPO reputation and develop indices to measure these components. We develop a model by means of a qualitative inquiry and a quantitative study using a large-scale sample from the German general public. We find support for a two-dimensional measurement approach comprising an affective and cognitive component as well as four antecedent constructs (“quality,” “performance,” “organizational social responsibility (OSR),” and “attractiveness”). The results of a second quantitative study in which we examine NPO reputation's relationship with important outcome variables, such as willingness to donate or work as an honorary member, provide support for the measurement approach's stability as well as criterion validity. Furthermore, the results reveal the affective dimension's importance regarding positively influencing donor behavior. Weniger anzeigen

Maintaining and increasing visitor satisfaction is a crucial success
factor in managing modern, multi-functional stadiums for sports, concerts,
shows and other kinds of events. Based on a typical service delivery process
when attending an event, this study identifies the relevant factors that influence
visitor satisfaction with stadiums. An analysis of this process and its service
interfaces by means of direct observation allows us to establish relationships in
a structural… Mehr anzeigen

Maintaining and increasing visitor satisfaction is a crucial success
factor in managing modern, multi-functional stadiums for sports, concerts,
shows and other kinds of events. Based on a typical service delivery process
when attending an event, this study identifies the relevant factors that influence
visitor satisfaction with stadiums. An analysis of this process and its service
interfaces by means of direct observation allows us to establish relationships in
a structural equation model. Using data from almost 2,500 visitors of a
major German multi-purpose stadium, the hypothesised relationships are
subsequently tested by means of the partial least squares (PLS) path modelling
approach. An importance-performance map-based assessment is used to derive
recommendations for improving service interface performance and hence,
visitor satisfaction. Weniger anzeigen

In the social science disciplines, the assumption that the data stem from a single homogeneous population is often unrealistic in respect of empirical research. When applying a causal modeling approach, such as partial least squares path modeling, segmentation is a key issue in coping with the problem of heterogeneity in the estimated cause–effect relationships. This article uses the novel finite-mixture partial least squares (FIMIX-PLS) method to uncover unobserved heterogeneity in a complex… Mehr anzeigen

In the social science disciplines, the assumption that the data stem from a single homogeneous population is often unrealistic in respect of empirical research. When applying a causal modeling approach, such as partial least squares path modeling, segmentation is a key issue in coping with the problem of heterogeneity in the estimated cause–effect relationships. This article uses the novel finite-mixture partial least squares (FIMIX-PLS) method to uncover unobserved heterogeneity in a complex path modeling example in the field of marketing. An evaluation of the results includes a comparison with the outcomes of several data analysis strategies based on a priori information or k-means cluster analysis. The results of this article underpin the effectiveness and the advantageous capabilities of FIMIX-PLS in general PLS path model set-ups by means of empirical data and formative as well as reflective measurement models. Consequently, this research substantiates the general applicability of FIMIX-PLS to path modeling as a standard means of evaluating PLS results by addressing the problem of unobserved heterogeneity. Weniger anzeigen

In this article, we examine the influence of cross-cultural traits on brand extension success. Drawing on prior brand extension studies from different countries, we conduct a comprehensive meta-analysis, complementing the data sets with Hofstede’ s cultural dimensions values. Our results show that all the dimensions either directly or indirectly exert a significant influence on brand extension success by not only moderating the effects of the mother brand ’ s quality, but also by moderating the… Mehr anzeigen

In this article, we examine the influence of cross-cultural traits on brand extension success. Drawing on prior brand extension studies from different countries, we conduct a comprehensive meta-analysis, complementing the data sets with Hofstede’ s cultural dimensions values. Our results show that all the dimensions either directly or indirectly exert a significant influence on brand extension success by not only moderating the effects of the mother brand ’ s quality, but also by moderating the fit between the brand and the extension. Weniger anzeigen

Mit dem After Sales-Geschäft generieren die deutschen Automobilbauer etwa die Hälfte ihrer Gewinne. Gerade vor dem Hintergrund der aktuellen gesamtwirtschaftlichen Entwicklungen, wovon insbesondere das Neuwagengeschäft betroffen ist, erscheinen Anstrengungen zur Optimierung der kundenindividuellen Ansprache im Service-, Teile- und Wartungsgeschäft aussichtsreich zu sein. Als Voraussetzung dafür kann das Wissen über die kundenindividuellen Serviceintervalle als entscheidender Wettbewerbsvorteil… Mehr anzeigen

Mit dem After Sales-Geschäft generieren die deutschen Automobilbauer etwa die Hälfte ihrer Gewinne. Gerade vor dem Hintergrund der aktuellen gesamtwirtschaftlichen Entwicklungen, wovon insbesondere das Neuwagengeschäft betroffen ist, erscheinen Anstrengungen zur Optimierung der kundenindividuellen Ansprache im Service-, Teile- und Wartungsgeschäft aussichtsreich zu sein. Als Voraussetzung dafür kann das Wissen über die kundenindividuellen Serviceintervalle als entscheidender Wettbewerbsvorteil gesehen werden, denn nur hiermit lassen sich Kunden auch zielgerichtet ansprechen und potenzielle Abwanderungen vermeiden. Genau an diesem Punkt knüpft dieser Beitrag an, indem mit der Hazard-Raten-Analyse ein wissenschaftlich fundiertes und praktikables Verfahren zur Prognose kundenindividueller Serviceintervalle illustriert wird. Da dieses im betriebswirtschaftlichen Kontext ohnehin sehr junge Analyseverfahren bislang überwiegend im FMCG (Fast Moving Consumer Goods)-Bereich auf Scannerdaten zum Einsatz kam, kann dieser Beitrag als Leitfaden für eine Erweiterung im Bereich langlebiger Konsum- und Investitionsgüter gesehen werden. Die Ergebnisse zeigen, dass der Anteil korrekt geschätzter Serviceintervalle die einfache lineare Fortschreibung, die bis dato das Standardverfahren zur Prognose von Serviceintervallen darstellt, um über 20% übertrifft bzw. die Prognosegenauigkeit von ±73 Tagen auf ±38 Tage gesteigert werden kann. Das Erfolgspotenzial einer kundenindividuellen Direktansprache lässt sich mit dieser substanziellen Verbesserung der zugrunde liegenden Informationsbasis erheblich steigern. Aus der Verbesserung der Prognosegenauigkeit auf kundenindividueller Ebene (Mikroebene) resultiert schließlich auch auf der Makroebene (Unternehmensplanung- und steuerung) eine erhöhte Planungssicherheit. Weniger anzeigen

This article examines whether exposure to a company's sponsorship of cultural
activities such as "high-brow" arts—including classical music, literature, art exhibitions,
and museums—provides a long-term increase in the general public's assessment of
corporate reputation. As corporate reputation has been found by previous studies to be
composed of two primary dimensions (i.e., the likeability of the firm, the competence of
the firm), it is of particular interest to examine whether… Mehr anzeigen

This article examines whether exposure to a company's sponsorship of cultural
activities such as "high-brow" arts—including classical music, literature, art exhibitions,
and museums—provides a long-term increase in the general public's assessment of
corporate reputation. As corporate reputation has been found by previous studies to be
composed of two primary dimensions (i.e., the likeability of the firm, the competence of
the firm), it is of particular interest to examine whether sponsorship of cultural events
affects one or both of these dimensions. A two-dimensional model of image transfer
is used as the theoretical basis for a study of more than 3,000 German consumers
conducted in collaboration with 10 major multinational companies (e.g., BMW Group and
Siemens). Results show that some significant effects of culture-sponsoring activities can
be demonstrated for the likeability dimension of corporate reputation and some of its
antecedents. However, no significant link between culture sponsorships and consumer
perceptions of firm competence is found. Weniger anzeigen

When applying multivariate analysis techniques in information systems and social science disciplines, such as management information systems (MIS) and marketing, the assumption that the empirical data originate from a single homogeneous population is often unrealistic. When applying a causal modeling approach, such as partial least squares (PLS) path modeling, segmentation is a key issue in coping with the problem of heterogeneity in estimated cause-and-effect relationships. This chapter… Mehr anzeigen

When applying multivariate analysis techniques in information systems and social science disciplines, such as management information systems (MIS) and marketing, the assumption that the empirical data originate from a single homogeneous population is often unrealistic. When applying a causal modeling approach, such as partial least squares (PLS) path modeling, segmentation is a key issue in coping with the problem of heterogeneity in estimated cause-and-effect relationships. This chapter presents a new PLS path modeling approach which classifies units on the basis of the heterogeneity of the estimates in the inner model. If unobserved heterogeneity significantly affects the estimated path model relationships on the aggregate data level, the methodology will allow homogenous groups of observations to be created that exhibit distinctive path model estimates. The approach will, thus, provide differentiated analytical outcomes that permit more precise interpretations of each segment formed. An application on a large data set in an example of the American customer satisfaction index (ACSI) substantiates the methodology’s effectiveness in evaluating PLS path modeling results. Weniger anzeigen

This paper extends Festge and Schwaiger’s (2007) model of customer satisfaction with industrial goods by accounting for unobserved heterogeneity. The application of a novel response-based segmentation approach in partial least squares path modeling (PLS-PM) - the finite mixture partial least squares (FIMIX-PLS) methodology - opens the way for the effective identification of distinctive customer segments. In comparison to previous studies in this field, group-specific path model estimates… Mehr anzeigen

This paper extends Festge and Schwaiger’s (2007) model of customer satisfaction with industrial goods by accounting for unobserved heterogeneity. The application of a novel response-based segmentation approach in partial least squares path modeling (PLS-PM) - the finite mixture partial least squares (FIMIX-PLS) methodology - opens the way for the effective identification of distinctive customer segments. In comparison to previous studies in this field, group-specific path model estimates reveal each customer segment’s particular characteristics as well as other differentiated findings. Hence, this contribution demonstrates that structural equation modeling studies on the aggregate data level can be seriously misleading and makes a strong case for segment-specific customer satisfaction analyses. Weniger anzeigen

Despite their apparent practical advantages over multi-item measures, the use of single items is often
regarded as a grave error, as they are believed to be unreliable and invalid. This article provides an
evaluation of single-item measures regarding reliability and criterion validity and, thus, extends the
most prominent study in this field by Bergkvist and Rossiter (2007) whose results should be considered with caution, due to a violation of testing assumptions. Using an appropriate… Mehr anzeigen

Despite their apparent practical advantages over multi-item measures, the use of single items is often
regarded as a grave error, as they are believed to be unreliable and invalid. This article provides an
evaluation of single-item measures regarding reliability and criterion validity and, thus, extends the
most prominent study in this field by Bergkvist and Rossiter (2007) whose results should be considered with caution, due to a violation of testing assumptions. Using an appropriate testing procedure, we show that multi-item measures outperform single items to a significant degree, thus questioning recent research findings. Weniger anzeigen

Purpose
The purpose of this paper is to critically review the latest approaches for capturing and explaining heterogeneity in partial least squares (PLS) path modelling and to classify these into a methodological taxonomy. Furthermore, several areas for future research effort are introduced in order to stimulate ongoing development in this important research field.

Design/methodology/approach
Different approaches to treat heterogeneity in PLS path models are introduced… Mehr anzeigen

Purpose
The purpose of this paper is to critically review the latest approaches for capturing and explaining heterogeneity in partial least squares (PLS) path modelling and to classify these into a methodological taxonomy. Furthermore, several areas for future research effort are introduced in order to stimulate ongoing development in this important research field.

Design/methodology/approach
Different approaches to treat heterogeneity in PLS path models are introduced, critically evaluated and classified into a methodological taxonomy. Future research directions are derived from a comparison of benefits and limitations of the procedures.

Findings
The review reveals that finite mixture‐PLS can be regarded as the most comprehensive and commonly used procedure for capturing heterogeneity within a PLS path modelling framework. However, further research is necessary to explore the capabilities and limitations of the approach.

Research limitations/implications
Directions for additional research, common to most latent class detection procedures include the verification and comparison of available approaches, the handling of large data sets, the allowance of varying structures of path models, the profiling of segments and the problem of model selection.

Originality/value
Whereas modelling heterogeneity in covariance structure analysis has been studied for several years, research interest has only recently been devoted to the question of clustering in PLS path modelling. This is the first contribution which critically consolidates available approaches, discloses problematic aspects and addresses significant areas for future research.
Weniger anzeigen

Owing to their considerable potential for market segmentation studies, mixture regression models have recently received increasing attention from both academics and practitioners. One fundamental difficulty with their application is related to the problem of model selection, that is, the choice regarding the number of segments. Retaining the correct number of segments is, however, crucial as many managerial decisions depend on this decision. Since the proper number of segments is unknown in… Mehr anzeigen

Owing to their considerable potential for market segmentation studies, mixture regression models have recently received increasing attention from both academics and practitioners. One fundamental difficulty with their application is related to the problem of model selection, that is, the choice regarding the number of segments. Retaining the correct number of segments is, however, crucial as many managerial decisions depend on this decision. Since the proper number of segments is unknown in real-world applications, a thorough understanding of measures that guide the model selection decision is of fundamental importance. Based on a simulation study, this paper addresses the issue by evaluating how the interaction of the most important influencing factors for the measures’ success — sample and segment size — affects the performance of four of the most widely used criteria for assessing the correct number of segments in mixture regression models. For the first time, the quality of these criteria is evaluated with regard to a wide spectrum of possible constellations. Furthermore, relative and absolute performances are analysed in respect of outside criteria. Recommendations on criterion selection are thereafter deduced from the results when a certain sample size is given. These recommendations also help to establish the sample size that is needed in order to guarantee an accurate decision based on a specific criterion. An application based on customer satisfaction data illustrates the relevance of the findings. In conclusion, theoretical and managerial implications are provided. Weniger anzeigen

In 2002, CICEM Ltd., a worldwide recognized chemical producer located in Asia, developed a new Polyethylen-foil (HDPE-foil) suitable for packaging bitumen into plastic bags. In order to successfully introduce this product into the market, he needed a new packaging technology capable of filling hot bitumen into foil bags. Here for Mr. Tanaka, initiator of this new development at CICEM Ltd. contacted Haver and Boecker, a world-leading, medium-sized German manufacturer of packaging systems and… Mehr anzeigen

In 2002, CICEM Ltd., a worldwide recognized chemical producer located in Asia, developed a new Polyethylen-foil (HDPE-foil) suitable for packaging bitumen into plastic bags. In order to successfully introduce this product into the market, he needed a new packaging technology capable of filling hot bitumen into foil bags. Here for Mr. Tanaka, initiator of this new development at CICEM Ltd. contacted Haver and Boecker, a world-leading, medium-sized German manufacturer of packaging systems and asked them, if they were interested in developing such a system. But since the technical requirements were extensive, the development costs were fairly high considering Haver and Boecker's corporate size and the possibilities of applying the new system to other applications are limited, Haver and Boecker insisted on conducting market research on the Chinese bitumen market before making the investment decision. This quantitative case highlights the difficulties of demand estimation in an emerging market due to a lack of reliable or relevant data. Furthermore it makes clear that certain research problems can only be solved through creative improvisation where local market knowledge is a critical factor. The case is designed for business students as well as international market researchers seeking assistance concerning the overcoming of different difficulties of everyday market research, especially with respect to market potential estimation. Weniger anzeigen