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Item type:Publication, Access status: with files , Looking at the bigger picture: on the relevance of null effects of model differences across timeLongitudinal research traditionally aims to detect and quantify change in outcomes such as attitudes, behaviors, or performance over time. Nonsignificant results, particularly in regards to longitudinal research designs, are however often overlooked, despite their potential theoretical and managerial relevance. This research contributes to addressing this gap by examining the stability of a theoretically grounded model across five independent time points. Adopting a model-logic approach to longitudinal analysis, time is conceptualized as a contextual dimension (e.g., a moderator) rather than as a predictor. Multigroup PLS-SEM is employed to assess the difference between the theoretically established relationships across years. The results do not reveal statistically significant differences in key structural paths, indicating a high degree of temporal stability. These null results provide evidence for the robustness and generalizability of the underlying theoretical mechanisms across varying economic and societal contexts. The study furthermore situates its design in the broader open science movement by framing longitudinal model comparisons as a form of structured temporal replication. Implications and guidelines for theory development, longitudinal modeling, and future research on replication and robustness in structural equation modeling are discussed.Publicationtype: Journal Article15 4 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Multiple linear and logistic regression analysis: a SmartPLS 4 software tutorial(Palgrave Macmillan, 2026-03-02); ; ; ; This tutorial and case study provides a comprehensive, step-by-step guide to conducting multiple and logistic regression analyses using SmartPLS 4. Although SmartPLS is primarily known for partial least squares structural equation modeling (PLS-SEM), its latest version includes features that enable researchers to perform and visualize regression analyses effectively. The tutorial introduces the theoretical foundations of both standard multiple and logistic regression, outlines the main stages of model specification and estimation, and demonstrates how to implement these analyses within the SmartPLS environment. Emphasis is placed on key analytical decisions, such as model design, assumption testing, interpretation of coefficients, and goodness-of-fit measures. Practical examples and graphical outputs are included to illustrate the implementation process and to enhance understanding of the results.Publicationtype: Review Article30 27 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Covariance-based structural equation modeling (CB-SEM): a SmartPLS 4 software tutorial(Springer, 2025-06-07); ; ; ; Covariance-based structural equation modeling (CB-SEM) enables researchers to estimate models with hypothesized cause-effect relationships between latent variables (i.e., constructs), each of which is operationalized by several items (i.e., indicators). To conduct CB-SEM analyses, researchers can rely on a range of software applications. However, many of these applications require researchers to engage in sometimes complicated and error-prone programming tasks. While IBM SPSS AMOS provides a graphical user interface (GUI), it does not fully meet the expectations of contemporary software. In order to address these challenges, the statistical SmartPLS 4 software has recently introduced a new CB-SEM module, which improves the user experience through a modern and intuitive graphical interface and comprehensive result reports. This tutorial describes the key CB-SEM analysis steps (i.e., model setup, estimation, and results evaluation) using the SmartPLS software.Publicationtype: Journal Article23 47 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Combined importance–performance map analysis (cIPMA) in partial least squares structural equation modeling (PLS–SEM): a SmartPLS 4 tutorial(Palgrave Macmillan, 2024-06-04); ; ; Recent research on partial least squares structural equation modeling (PLS–SEM) extended the classic importance–performance map analysis (IPMA) by taking the results of a necessary condition analysis (NCA) into consideration. By also highlighting necessary conditions, the combined importance–performance map analysis (cIPMA) offers a tool that enables better prioritization of management actions to improve a key target construct. In this article, we showcase a cIPMA’s main steps when using the SmartPLS 4 software. Our illustration draws on the technology acceptance model (TAM) used in the cIPMA’s original publication, which features prominently in business research.Publicationtype: Journal Article29 39 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Does culture matter? Corporate reputation and sustainable satisfaction in the Chinese and German banking sectorCorporate reputation is important for all types of banks across the world, despite these countries differing culturally. Building on an extended corporate reputation model, we identify the key drivers of customer-based reputation and sustainable customer satisfaction in two culturally different countries, namely China and Germany. We also consider two reputation dimensions—perceived competence and likeability—and their effects on the target construct. Empirical data from 625 German and 734 Chinese commercial bank customers allow us to estimate the corporate reputation model with the partial least squares structural equation modeling (PLS-SEM) method, and by substantiating the relationships by means of a necessary condition analysis (NCA) and a predictive power analysis. By comparing the two countries’ results, we identify their cultural differences. Overall, we confirm the model’s relevance for the two cultures, finding that banks’ perceived attractiveness is the most important driver of both cultures’ customer-perceived bank reputation. By means of an importance-performance map analysis, we identify a large overlap between the two cultures’ set of important constructs, likeability’s much greater importance in Germany, and the perceived quality construct’s relevance in both countries. We contribute to research and scientific knowledge about corporate reputation models by identifying the similarities in and differences between two countries’ markets with respect to the banking sector, all of which have implications for international banks’ management.Publicationtype: Journal ArticleScopus© Citations 4 53 117 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , What’s important for relationship management? : the mediating roles of relational trust and satisfaction for loyalty of cooperative banks’ customersBuilding on the corporate reputation model, this study investigates the drivers of customer-based corporate reputation. We consider two corporate reputation dimensions (i.e., the cognitive dimension competence and the affective dimension likeability, and their effects on customer satisfaction and loyalty). Adapting the model to the banking sector, we theoretically extend this model by reasoning that customer satisfaction and relational trust are mediators of the relationship between the two corporate reputation dimensions and loyalty. Studying a sample of 675 customers and members of cooperative banks in Germany, we find perceived attractiveness to be the most important driver of corporate reputation. Furthermore, we confirm a positive relationship between corporate reputation and loyalty, and a mediating effect of both satisfaction and relational trust. With our study, we give support for the proposition of customer satisfaction's as well as relational trust’s role as mediators of the relationship between corporate reputation and loyalty. With this research, we expand our knowledge on the well-known corporate reputation model, which has high relevance and important implications for marketing research and relationship management practice.Publicationtype: Journal ArticleScopus© Citations 26 164 344 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Cutoff criteria for overall model fit indexes in generalized structured component analysisGeneralized structured component analysis (GSCA) is a technically well-established approach to component-based structural equation modeling that allows for specifying and examining the relationships between observed variables and components thereof. GSCA provides overall fit indexes for model evaluation, including the goodness-of-fit index (GFI) and the standardized root mean square residual (SRMR). While these indexes have a solid standing in factor-based structural equation modeling, nothing is known about their performance in GSCA. Addressing this limitation, we present a simulation study’s results, which confirm that both GFI and SRMR indexes distinguish effectively between correct and misspecified models. Based on our findings, we propose rules-of-thumb cutoff criteria for each index in different sample sizes, which researchers could use to assess model fit in practice.Publicationtype: Journal ArticleScopus© Citations 132 247 389