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We are happy to announce that the paper “Prediction-oriented model selection in partial least squares path modeling,” authored by Pratyush N. Sharma (University of Delaware), Galit Shmueli (National Tsing Hua University), Marko Sarstedt (OVGU), Nicholas Danks (National Tsing Hua University), and Soumya Ray (National Tsing Hua University) has been accepted for publication in Decision Sciences. In our paper, we compare the performance of standard PLS-SEM-based model evaluation 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. Based on our results, we identify a set of criteria that researchers should use when not having the luxury of a holdout sample, and the goal is selecting correctly specified models with low prediction error. We also illustrate the model selection criteria’s practical utility using a well-known corporate reputation model.
Der Lehrstuhl für Marketing lädt zum Gastvortrag ein:
um 11:15 Uhr
im Gebäude 26-Hörsaal 1
Zu Gast sein wird Verena Schödel, Head of Advertising von Carglass.
Das Thema ihres Vortrages lautet:
„Das Prinzip Carglass - Konsistente Markenführung mit System“
Weitere Informationen finden Sie hier!
We are happy to announce that the paper titled “PLS-based Model Selection: The Role of Alternative Explanations in Information Systems Research” has been accepted for publication in Journal of the Association for Information Systems, the flagship journal of the Association for Information Systems. Authored by Pratyush N. Sharma (University of Delaware), Marko Sarstedt (OVGU), Galit Shmueli (National Tsing Hua University), Kevin H. Kim (University of Pitsburgh), and Kai O. Thiele (Hamburg University of Technology), the 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 a 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, the study then compares 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. The 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, the paper introduces a five-step procedure that delineates the roles of model selection and statistical inference, and discusses misconceptions that may arise in their use.
The article titled “Addressing Endogeneity in International Marketing Applications of Partial Least Squares Structural Equation Modeling,” authored by G. Tomas M. Hult (Michigan State University), Joseph F. Hair (University of South Alabama), Dorian Proksch (HHL Leipzig Graduate School of Management), Marko Sarstedt (OVGU), Andreas Pinkwart (HHL Leipzig Graduate School of Management), and Christian M. Ringle (Hamburg University of Technology) has been accepted for publication in Journal of International Marketing. In this paper, we discuss the issue of endogeneity, which has largely been overlooked by users of partial least squares structural equation modeling (PLS-SEM), a popular method for analyzing complex inter-relationships between observed and latent variables. To identify and treat endogeneity, and create awareness of how to deal with this issue, we introduce 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.
Die Selbstoptimierungstechnologie boomt. Eine gigantische Industrie lockt ständig mit neuen Apps zur Vermessung des Körpers. Die Arbeit am „perfekten Menschen“ scheint das große Projekt unserer Zeit zu sein. Ist die fortschreitende Selbstoptimierung eine Chance oder bereits Gebot? Welches Menschenbild liegt diesem Optimierungsgedanken zugrunde?
Diesen Fragen geht die ARTE-Dokumentation „Du sollst Dich optimieren!“ nach und trifft Menschen, die in allen Lebensbereichen, wie Ernährung, Arbeit und Kindererziehungen die aktuellsten Methoden der Selbstoptimierung verinnerlicht haben. Im Rahmen der Dokumentation beschreibt beispielsweise Prof. Dr. Marko Sarstedt, wie junge Eltern Optimierungsmethoden bei der Kindererziehung anwenden. Darüberhinaus analysieren Soziologen und Philosophen den Trend der zunehmenden Selbstbeschau.
Die Dokumentation kann via Youtube abgerufen werden, den Link finden Sie hier.
We’re happy to announce that the paper “Methodological research on partial least squares structural equation modeling (PLS-SEM): An analysis based on social network approaches“ has been accepted for publication in Internet Research, a leading journal in the Information Systems field. Authored by Gohar F. Khan (The University of Waikato), Marko Sarstedt, Wen-Lung-Shiau (Ming Chuan University), Joe F. Hair (University of South Alabama), Christian M. Ringle (Hamburg University of Technology), and Martin P. Fritze (University of Cologne), we explore 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.
Die Klausureinsicht für die Veranstaltungen Marketing, Marketing Performance Management und Marketing Management findet am 19.04.2018 von 8 bis 9 Uhr im Raum G22A-362 statt.
The exam inspection for the courses Marketing, Marketing Performance Management, and Marketing Management will be held on April 19, 2018 between 8 and 9 a.m. in room G22A-362.