Jan-Michael Becker

European Journal of Information Systems 33(1) s. 80-96 Doi: https://doi.org/10.1080/0960085X.2022.2115949

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.

International Journal of Contemporary Hospitality Management 35(1) s. 321-346 Doi: https://doi.org/10.1108/IJCHM-04-2022-0474

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.

International Journal of Research in Marketing 39(2) s. 313-335 Doi: https://doi.org/10.1016/j.ijresmar.2021.09.006

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.

Journal of the Academy of Marketing Science 50(1) s. 46-66 Doi: https://doi.org/10.1007/s11747-021-00805-y

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.

Nature Human Behaviour 4(April) s. 329-331 Doi: https://doi.org/10.1038/s41562-019-0806-0

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.

Multivariate Behavioral Research 54(3) s. 429-443 Doi: https://doi.org/10.1080/00273171.2018.1535420

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.

Psychometrika 84(3) s. 772-780 Doi: https://doi.org/10.1007/s11336-019-09677-2

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.