Sample- and segment-size specific Model Selection in Mixture Regression Analysis

As mixture regression models increasingly receive attention from both theory and practice, the question of selecting the correct number of segments gains urgency. A misspecification can lead to an under- or oversegmentation, thus resulting in flawed management decisions on customer targeting or product positioning. This paper presents the results of an extensive simulation study that examines the performance of commonly used information criteria in a mixture regression context with normal data. Unlike with previous studies, the performance is evaluated at a broad range of sample/segment size combinations being the most critical factors for the effectiveness of the criteria from both a theoretical and practical point of view. In order to assess the absolute performance of each criterion with respect to chance, the performance is reviewed against so called chance criteria, derived from discriminant analysis. The results induce recommendations on criterion selection when a certain sample size is given and help to judge what sample size is needed in order to guarantee an accurate decision based on a certain criterion respectively

Sample- and segment-size specific Model Selection in Mixture Regression Analysis

As mixture regression models increasingly receive attention from both theory and practice, the question of selecting the correct number of segments gains urgency. A misspecification can lead to an under- or oversegmentation, thus resulting in flawed management decisions on customer targeting or product positioning. This paper presents the results of an extensive simulation study that examines the performance of commonly used information criteria in a mixture regression context with normal data. Unlike with previous studies, the performance is evaluated at a broad range of sample/segment size combinations being the most critical factors for the effectiveness of the criteria from both a theoretical and practical point of view. In order to assess the absolute performance of each criterion with respect to chance, the performance is reviewed against so called chance criteria, derived from discriminant analysis. The results induce recommendations on criterion selection when a certain sample size is given and help to judge what sample size is needed in order to guarantee an accurate decision based on a certain criterion respectively.Mixture Regression; Model Selection; Information Criteria

Modellselektion in Finite Mixture PLS-Modellen

Der folgende Beitrag befasst sich mit dem Problem der Modellselektion im Finite Mixture Partial Least Squares (FIMIX-PLS)-Ansatz. Dieser Ansatz, welcher der Methodengruppe der Mischverteilungsmodelle zuzuordnen ist, ermöglicht eine simultane Schätzung der Modellparameter bei gleichzeitiger Ermittlung von Heterogenität in der Datenstruktur. Ein wesentliches Problem bei der Anwendung ist die Bestimmung der Anzahl der zugrunde liegenden Segmente, welche a priori unbekannt ist. Neben diversen statistischen Testverfahren wird zur Handhabung dieser Modellselektionsproblematik häufig auf so genannte Informationskriterien zurückgegriffen. Ziel des vorliegenden Beitrags ist es herauszuarbeiten, welches Informationskriterium für die Modellselektion in FIMIX-PLS besonders geeignet ist. Hierzu wurde eine Simulationsstudie initiiert, welche die Performanz gebräuchlicher Kriterien vor dem Hintergrund diverser Einflussfaktoren untersucht. Im Rahmen der Studie konnte mit dem Consistent Akaike’s Information Criterion (CAIC) ein Kriterium identifiziert werden, das die übrigen Kriterien in nahezu allen Faktorstufenkombinationen dominiert

INFLUENCE OF COMMUNITY DESIGN ON USER BEHAVIORS IN ONLINE COMMUNITIES

While the question how community design influences user behaviors in online communities has recently attracted considerable research, few studies empirically evaluate the influencing factors of specific user behaviors. Building on Ren et al.’s (2007) conceptual framework of identity-based vs. bond-based attachment in online communities, this study evaluates the influence of numerous antecedents on user attachment as well as attachments’ mediating role for explaining consumer behavior. Using data from a large-scale survey, we find that network effects, intergroup comparison and social categorization have a positive and significant effect on common identity attachment while this is not the case with in-group interdependence. Conversely, common bond attachment is driven by collectivism, interpersonal similarity and social interaction, while personal information has no effect on common bond attachment. Most importantly, the analysis results show that common identity attachment is the primary driver for user behaviors in online communities

Mapping the jungle: A bibliometric analysis of research into construal level theory

Construal level theory (CLT) offers a valuable framework to explain the mechanisms that trigger evaluations, predictions, and behaviors by linking the degree of mental abstraction (the construal level) to psychological distance. CLT-related research has produced numerous publications in a variety of domains, impeding an ongoing overview of the research field and limiting its advancement. Addressing this concern, our paper presents the results of a comprehensive bibliometric analysis of CLT-related research. This analysis identifies leading authors and the networks in which they operate. We find that a well-connected, stable core of prominent authors predominantly shaped CLT research and was responsible for its expansion. In addition, we used topic modeling to identify latent topics and research trends, with the results showing that CLT research has expanded into more interdisciplinary and applied contexts. Specifically, although CLT's relevance for consumer research has amplified and applications in areas such as climate change and sustainability have surged, the classic areas of CLT research, such as planning fallacy and impulse control, have lost momentum. Building upon the results of our topic analysis, we identify future research paths and specifically call for a more comprehensive societal focus in CLT-related research

Goodness-of-fit indices for partial least squares path modeling

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 (GoFrel), 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 GoFrel 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 dat

Prediction in HRM research–A gap between rhetoric and reality

There are broadly two dimensions on which researchers can evaluate their statistical models: explanatory power and predictive power. Using data on job satisfaction in ageing workforces, we empirically highlight the importance of distinguishing between these two dimensions clearly by showing that a model with a certain degree of explanatory power can produce vastly different levels of predictive power and vice versa—in the same and different contexts. In a further step, we review all the papers published in three top-tier human resource management journals between 2014 and 2018 to show that researchers generally confuse explanation and prediction. Specifically, while almost all authors rely solely on explanatory power assessments (i.e., assessing whether the coefficients are significant and in the hypothesised direction), they also derive practical recommendations, which inherently result from a predictive scenario. Based on our results, we provide HRM researchers recommendations on how to improve the rigour of their explanatory studies

Partial least squares structural equation modeling using SmartPLS: a software review

In their effort to better understand consumer behavior, marketing researchers often analyze relationships between latent variables, measured by sets of observed variables. Partial least squares structural equation modeling (PLS-SEM) has become a popular tool for analyzing such relationships. Particularly the availability of SmartPLS, a comprehensive software program with an intuitive graphical user interface, helped popularize the method. We review the latest version of SmartPLS and discuss its various features. Our aim is to offer researchers with concrete guidance regarding their choice of a PLS-SEM software that fits their analytical needs

Estimating Moterating effects in PLS-SEM andPLSc-SEM: interaction term gerneration*data treatment

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

Perceived Software Platform Openness: The Scale and its Impact on Developer Satisfaction

Application developers are of growing importance to ensure that software platforms (e.g. Facebook, Android) gain or maintain a competitive edge. However, despite calls for research to investigate developers’ perspective on platform-centric ecosystems, no research study has been dedicated to identifying the facets that constitute developers’ perception of platform openness. In this paper, we develop a scale of platform openness as perceived by third-party application developers. Using both qualitative and quantitative methods, we conceptualize perceived platform openness as a second-order construct. Empirical evidence from a survey of Android application developers (N=254) support this construct’s validity. Furthermore, we identify perceived platform openness as a major driver of complementors’ overall satisfaction with the platform. Our study thus contributes to a better understanding of platform openness in particular and the management of platform-centric ecosystems in general