Discovering Unobserved Heterogeneity in Structural Equation Models to Avert Validity Threats

A large proportion of information systems research is concerned with developing and testing models pertaining to complex cognition, behaviors, and outcomes of individuals, teams, organizations, and other social systems that are involved in the development, implementation, and utilization of information technology. Given the complexity of these social and behavioral phenomena, heterogeneity is likely to exist in the samples used in IS studies. While researchers now routinely address observed heterogeneity by introducing moderators, a priori groupings, and contextual factors in their research models, they have not examined how unobserved heterogeneity may affect their findings. We describe why unobserved heterogeneity threatens different types of validity and use simulations to demonstrate that unobserved heterogeneity biases parameter estimates, thereby leading to Type I and Type II errors. We also review different methods that can be used to uncover unobserved heterogeneity in structural equation models. While methods to uncover unobserved heterogeneity in covariance-based structural equation models (CB-SEM) are relatively advanced, the methods for partial least squares (PLS) path models are limited and have relied on an extension of mixture regression—finite mixture partial least squares (FIMIX-PLS) and distance measure-based methods—that have mismatches with some characteristics of PLS path modeling. We propose a new method—prediction-oriented segmentation (PLS-POS)—to overcome the limitations of FIMIX-PLS and other distance measure-based methods and conduct extensive simulations to evaluate the ability of PLS-POS and FIMIX-PLS to discover unobserved heterogeneity in both structural and measurement models. Our results show that both PLS-POS and FIMIX-PLS perform well in discovering unobserved heterogeneity in structural paths when the measures are reflective and that PLS-POS also performs well in discovering unobserved heterogeneity in formative measures. We propose an unobserved heterogeneity discovery (UHD) process that researchers can apply to (1) avert validity threats by uncovering unobserved heterogeneity and (2) elaborate on theory by turning unobserved heterogeneity into observed heterogeneity, thereby expanding theory through the integration of new moderator or contextual variables

A Meta-Analysis of Brand Extension Success:The Effects of Parent Brand Equity and Extension Fit

Given the high failure rates of brand extensions, insights into the drivers of brand extension success are critical for marketing practitioners and scholars. Prior research has inferred that parent brand equity and extension fit are the two key success drivers; however, empirical findings are mixed. Drawing on signaling theory, categorization theory, and a large database of 2,134 effect sizes from research spanning 1990–2020, the authors address these mixed findings through a meta-analysis to develop empirical generalizations. The results show that parent brand equity and extension fit positively influence extension success. However, the multifaceted dimensions of these two drivers have differential effects. For example, among the fit dimensions, usage fit has the weakest effect. While the results suggest an overall positive interaction effect between the two drivers, a fine-grained perspective that considers the drivers’ various dimensions reveals differences. For example, brand familiarity appears to have a lower interaction effect with extension fit than the other dimensions of parent brand equity. Furthermore, the authors provide a comprehensive analysis of five groups of moderators: contextual factors (parent brand, extension, communication, and consumer factors) and research method factors. The authors offer managerial and future research implications for the design of brand extension strategies.</p

Managing brands in the social media environment

The dynamic, ubiquitous, and often real-time interaction enabled by social media significantly changes the landscape for brand management. A deep understanding of this change is critical since it may affect a brand's performance substantially. Literature about social media's impact on brands is evolving, but lacks a systematic identification of key challenges related to managing brands in this new environment. This paper reviews existing research and introduces a framework of social media's impact on brand management. It argues that consumers are becoming pivotal authors of brand stories due to new dynamic networks of consumers and brands formed through social media and the easy sharing of brand experiences in such networks. Firms need to pay attention to such consumer-generated brand stories to ensure a brand's success in the marketplace. The authors identify key research questions related to the phenomenon and the challenges in coordinating consumer- and firm-generated brand stories

The double-edged sword of foreign brand names for companies from emerging countries

Foreign branding-or using brand names that evoke foreign associations through, for example, spelling a brand name in a foreign language-is a popular means in both developed and emerging countries of suggesting a specific country of origin (COO) in the hope that it will evoke certain product qualities. As a result, consumers increasingly encounter products with brand names that imply a COO that differs from the actual COO (where the product is manufactured). In four experiments, the authors find support for the hypothesis that incongruence between the actual COO and implied COO decreases purchase likelihood asymmetrically. Incongruence backfires in hedonic categories but has hardly any effect in utilitarian categories. Furthermore, incongruence decreases purchase likelihood more if the actual COO is an emerging rather than developed country. The authors address the psychological process underlying the asymmetric effect of incongruence by showing that consumers apply different information-processing strategies to hedonic versus utilitarian products. These results have important implications for (foreign) branding decisions

Choice-Based Conjoint Analysis

Conjoint analysis is one of the most popular methods to measure preferences of individuals or groups. It determines, for instance, the degree how much consumers like or value specific products, which then leads to a purchase decision. In particular, the method discovers the utilities that (product) attributes add to the overall utility of a product (or stimuli). Conjoint analysis has emerged from the traditional rating- or ranking-based method in marketing to a general experimental method to study individual’s discrete choice behavior with the choice-based conjoint variant. It is therefore not limited to classical applications in marketing, such as new product development, pricing, branding, or market simulations, but can be applied to study research questions from related disciplines, for instance, how marketing managers choose their ad campaign, how managers select internationalization options, why consumers engage in or react to social media, etc. This chapter describes comprehensively the “state-of-the-art” of conjoint analysis and choice-based conjoint experiments and related estimation procedures

sj-pdf-1-jmx-10.1177_00222429231213011 - Supplemental material for Behavioral Labeling: Prompting Consumer Behavior Through Activity Tags

Supplemental material, sj-pdf-1-jmx-10.1177_00222429231213011 for Behavioral Labeling: Prompting Consumer Behavior Through Activity Tags by Martin P. Fritze, Franziska Völckner and Valentyna Melnyk in Journal of Marketing</p

sj-pdf-1-jnm-10.1177_10949968231215362 - Supplemental material for How Important Is Word of Mouth? Development, Validation, and Application of a Scale

Supplemental material, sj-pdf-1-jnm-10.1177_10949968231215362 for How Important Is Word of Mouth? Development, Validation, and Application of a Scale by Jan-Michael Becker, Franziska Völckner, and Henrik Sattler in Journal of Interactive Marketing</p

Avengers, assemble! A network-based contingency analysis of spillover effects in multi-brand alliances

Klostermann J, Hinze TK, Völckner F, Kupfer A-K, Schwerdtfeger R. Avengers, assemble! A network-based contingency analysis of spillover effects in multi-brand alliances. Journal of the Academy of Marketing Science. 2023.Brand alliances are becoming increasingly complex, as marketers have begun to combine not only two but multiple brands to foster spillover effects. A particularly complex brand-alliance strategy is team brands, which combine various brands under a team-brand name. Using data from the Marvel brand universe, we examine contingency factors of sales spillover effects between team brands (e.g., Avengers) and their constituent brands (e.g., Hulk). We investigate the moderating role of key network characteristics, describing the team-brand networks and the constituent brands’ roles within these networks from both a firm perspective (brand-brand networks reflecting managers’ decisions about which constituent brands to combine) and a consumer perspective (brand-association networks reflecting consumers’ team-brand associations). The results show that network characteristics strongly affect spillovers and, more importantly, that their effect depends on both the direction (spillover from constituent brands to team brands or vice versa) and the network (brand-brand vs. brand-association network)

The Role Of Parent Brand Quality For Service Brand Extension Success

Although substantial differences between product quality and service quality have spurred service research for the past 30 years, studies of brand extension success drivers in a services context measure the core driver of parent brand quality, using sca