Jan-Michael Becker is an associate professor in the department of marketing at the BI Norwegian Business School. He has received his doctorate degree from the University of Cologne where he was also an assistant professor in the German habilitation system (“Akademischer Rat”). He has been a visiting scholar at Georgia State University, Atlanta, USA and at University of Waikato, Hamilton, New Zealand as well as an external lecturer at the University of Tübingen, Germany.
His research interest and expertise focus on how the continuing digital transformation and new technologies affect marketing activities, consumer behavior, and society. In addition, he is doing research on methodological topics such as marketing analytics, behavioral research methods, causal inference, machine learning, and computational statistics.
His research has been published in several premier academic journals, including Journal of the Academy of Marketing Science (JAMS), International Journal of Research in Marketing (IJRM), Nature Human Behavior, Information Systems Research, MIS Quarterly, Journal of Interactive Marketing, Psychometrika, Multivariate Behavioral Research, Long Range Planning, Journal of Business Research, and Marketing Letters.
He is a co-developer and co-founder of the statistical software SmartPLS (www.smartpls.com).
For more information on publications and current research activities please visit:
ORCID: https://orcid.org/0000-0003-3013-3739
Google Scholar: https://scholar.google.de/citations?user=6-gGk0UAAAAJ
Web of Science: https://www.webofscience.com/wos/author/rid/L-8838-2018
Scopus: https://www.scopus.com/authid/detail.uri?authorId=55471403200
Researchgate: https://www.researchgate.net/profile/Jan_Michael_Becker
Companies spend large amounts of money to induce word of mouth (WOM) and spread it among consumers. This research introduces the concept of “WOM relevance,” which measures the importance of WOM for consumers’ purchase-decision process in a specific category. It uses three studies to develop and validate a parsimonious scale to measure WOM relevance at the consumer level across various product categories and different types of WOM, and applies the scale in an additional set of five studies. Specifically, this research disentangles the consumer-level and category components of WOM relevance; shows that the consumer-level variation is (much) larger than the category-level variation; and provides insights into differences in WOM relevance across categories, consumers, and WOM types. It also empirically shows that electronic WOM relevance relates to consumers’ search behavior and consideration-set formation in an online-shopping environment. Furthermore, it demonstrates that the proposed scale predicts choices as well as a more sophisticated choice model does. Finally, this research shows that WOM relevance influences not only consumers’ own purchase-decision process but also their intentions to retransmit others’ WOM messages.
Karagür, Zeynep; Becker, Jan-Michael, Klein, Kristina & Edeling, Alexander (2022)
How, Why, and When Disclosure Type Matters for Influencer Marketing
Consumers’ changing media consumption behaviors and skepticism toward traditional forms of advertising have prompted the growth of influencer marketing. Even as regulatory authorities call on brands and influencers to disclose the posts as advertising, no consistent guidelines exist. The distinct effects of self-generated versus platform-initiated disclosures also remain unclear, nor has research addressed the interplay of key influencer characteristics and marketing disclosures. This article reports on findings from the first academic field study of influencer marketing disclosures, as well as three experimental studies, which indicate that disclosure is a double-edged sword. When provided through a platform-initiated branded content tool, disclosure consistently exerts the strongest effect on perceptions of advertising, negatively relating to influencer trustworthiness and consumer engagement. The effects of disclosure type also depend on the number of followers and number of previously endorsed products (i.e., influencer characteristics). Yet consumers also express appreciation for transparency when influencers disclose posts as advertising, which increases perceived trustworthiness of the influencer and engagement with the post. The implications of these findings should inform choices by public policy makers, brand managers, and influencers.
Becker, Jan-Michael; Proksch, Dorian & Ringle, Christian M. (2022)
Revisiting Gaussian copulas to handle endogenous regressors
Marketing researchers are increasingly taking advantage of the instrumental variable (IV)-free Gaussian copula approach. They use this method to identify and correct endogeneity when estimating regression models with non-experimental data. The Gaussian copula approach’s original presentation and performance demonstration via a series of simulation studies focused primarily on regression models without intercept. However, marketing and other disciplines’ researchers mainly use regression models with intercept. This research expands our knowledge of the Gaussian copula approach to regression models with intercept and to multilevel models. The results of our simulation studies reveal a fundamental bias and concerns about statistical power at smaller sample sizes and when the approach’s primary assumptions are not fully met. This key finding opposes the method’s potential advantages and raises concerns about its appropriate use in prior studies. As a remedy, we derive boundary conditions and guidelines that contribute to the Gaussian copula approach’s proper use. Thereby, this research contributes to ensuring the validity of results and conclusions of empirical research applying the Gaussian copula approach.
Rigdon, Edward E.; Sarstedt, Marko & Becker, Jan-Michael (2020)
The behavioral sciences underestimate the uncertainty of research fndings and thus overestimate replicability. Metrologists in the physical sciences quantify all material components of uncertainty, even if some components must be quantifed using non-statistical means. Behavioral science should follow suit.
Liengaard, Benjamin D.; Becker, Jan-Michael, Bennedsen, Mikkel, Heiler, Phillip, Taylor, Luke N. & Ringle, Christian M. (2024)
Dealing with regression models’ endogeneity by means of an adjusted estimator for the Gaussian copula approach
Endogeneity in regression models is a key marketing research concern. The Gaussian copula approach offers an instrumental variable (IV)-free technique to mitigate endogeneity bias in regression models. Previous research revealed substantial finite sample bias when applying this method to regression models with an intercept. This is particularly problematic as models in marketing studies almost always require an intercept. To resolve this limitation, our research determines the bias’s sources, making several methodological advances in the process. First, we show that the cumulative distribution function estimation’s quality strongly affects the Gaussian copula approach’s performance. Second, we use this insight to develop an adjusted estimator that improves the Gaussian copula approach’s finite sample performance in regression models with (and without) an intercept. Third, as a broader contribution, we extend the framework for copula estimation to models with multiple endogenous variables on continuous scales and exogenous variables on discrete and continuous scales, and non-linearities such as interaction terms. Fourth, simulation studies confirm that the new adjusted estimator outperforms the established ones. Further simulations also underscore that our extended framework allows researchers to validly deal with multiple endogenous and exogenous regressors, and the interactions between them. Fifth, we demonstrate the adjusted estimator and the general framework’s systematic application, using an empirical marketing example with real-world data. These contributions enable researchers in marketing and other disciplines to effectively address endogeneity problems in their models by using the improved Gaussian copula approach.
Söllner, Matthias; Mishra, Abhay N, Becker, Jan-Michael & Leimeister, Jan Marco (2024)
Use IT Again? Dynamic Roles of Habit, Intention and their Interaction on Continued System Use by Individuals in Utilitarian, Volitional Contexts
This paper employs a longitudinal perspective to examine continued system use (CSU) by individuals in utilitarian, volitional contexts when alternative systems are present . We focus on two key behavioural antecedents of CSU – habit and continuance intention – and theorise how the relationships between CSU and these antecedents evolve over time. In addition, we hypothesise how the interaction effect of habit and intention on CSU evolves temporally. Our theorising differs from extant literature in two important respects: 1) In contrast to the widespread acceptance of the diminishing effect of continuance intention on CSU in the information systems (IS) literature, we hypothesise that in our context, its impact increases with time; and 2) In contrast to the negative moderation effect of habit on the relationship between intention and CSU proposed in the literature, we posit a positive interaction effect. We collect longitudinal survey data on the use of a higher education IS from students in a European university. Our results suggest that the impact of continuance intention on CSU as well as the interaction effect between habit and intention are increasing over time. We further introduce a methodological innovation – the permutation approach to conduct the multi-group analysis with repeated measures – to the literature.
Lins, Sebastian; Becker, Jan-Michael, Lyytinen, Kalle & Sunyaev, Ali (2023)
Prior information system research remains inconsistent of the effects of system certifications. In their current use, certifications are often reduced to graphical seals. This approach fails to incorporate detailed assurance information emanating from the certification process. To address this gap, we adopt a design science approach and deploy a four-phase research design to clarify how to design impactful IS certification presentations. First, we identify sources of users’ limited understanding of seals and formulate a design proposal for a certification presentation by drawing upon the elaboration likelihood model. In the second phase, we formulate and validate a set of design meta-requirements and guidelines to improve certification presentation, using cognitive load theory and Toulmin’s model of argumentation as kernel theories. In the third phase, new certification presentations that comply with the proposed guidelines are developed and evaluated for their effectiveness. We show that presentations that augment seal-based certification presentations with richer assurance information improve certification effectiveness. This increases users’ assurance and trust perceptions when the presentations align with the users’ cognitive information processing needs in ways that reduce their cognitive load and enhance argument quality of assurance information.
Becker, Jan-Michael; Cheah, Jun-Hwa, Gholamzade, Rasoul, Ringle, Christian M. & Sarstedt, Marko (2023)
Purpose – Partial least squares structural equation modeling (PLS-SEM) has attracted much attention from both methodological and applied researchers in various disciplines – also in hospitality management research. As PLS-SEM is relatively new compared to other multivariate analysis techniques, there are still numerous open questions and uncertainties in its application. This study aims to address this important issue by offering guidance regarding its use in contexts with which researchers struggle. Design/methodology/approach – The authors examine the most prominent questions and answers posed in a well-known PLS-SEM discussion forum. The authors do so by using a text analysis technique to identify the most salient topics. Findings – The data analysis identifies three salient PLS-SEM topics (i.e. bootstrapping and significance testing, higher-order constructs and moderation). Research limitations/implications – The results allow us to address the PLS-SEM community’s main methodological issues. The authors discuss each area separately and provide explanations and guidelines. Practical implications – The guidelines on the most important PLS-SEM topics provide decision-making and application aids. In this way, the authors make a decisive contribution to clarifying ambiguities when applying the PLS-SEM method in hospitality management research and other disciplines. Originality/value – There has as yet been no systematic analysis of this kind in the field of PLS-SEM; the authors, therefore, present the first research results. The findings and recommendations provide guidance for PLS-SEM applications in hospitality research and practice.
Rigdon, Edward E.; Becker, Jan-Michael & Sarstedt, Marko (2019)
Parceling Cannot Reduce Factor Indeterminacy in Factor Analysis: A Research Note
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.
Rigdon, Edward E.; Becker, Jan-Michael & Sarstedt, Marko (2019)
Factor Indeterminacy as Metrological Uncertainty: Implications for Advancing Psychological Measurement
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.
Academic Degrees
Year
Academic Department
Degree
2011
University of Cologne
Dr.rer.pol
Work Experience
Year
Employer
Job Title
2020 - Present
BI Norwegian Business School
Associate professor
2011 - 2020
University of Cologne
Assistant professor in the german habilitation system
