Sara Leroi-Werelds

PLS FAC-SEM: an illustrated step-by-step guideline to obtain a unique insight in factorial data

Purpose The purpose of this paper is to provide an illustrated step-by-step guideline of the partial least squares factorial structural equation modeling (PLS FAC-SEM) approach. This approach allows researchers to assess whether and how model relationships vary as a function of an underlying factorial design, both in terms of the design factors in isolation (i.e. main effects) as well as their joint impact (i.e. interaction effects). Design/methodology/approach After an introduction of its building blocks as well as a comparison with related methods (i.e. n-way analysis of variance (ANOVA) and multi-group analysis (MGA)), a step-by-step guideline of the PLS FAC-SEM approach is presented. Each of the steps involved in the PLS FAC-SEM approach is illustrated using data from a customer value study. Findings On a methodological level, the key result of this research is the presentation of a generally applicable step-by-step guideline of the PLS FAC-SEM approach. On a context-specific level, the findings demonstrate how the predictive ability of several key customer value measurement methods depends on the type of offering (feel-think), the level of customer involvement (low-high), and their interaction (feel-think offerings×low-high involvement). Originality/value This is a first attempt to apply the factorial structural equation models (FAC-SEM) approach in a PLS-SEM context. Consistent with the general differences between PLS-SEM and covariance-based structural equation modeling (CB-SEM), the FAC-SEM approach, which was originally developed for CB-SEM, therefore becomes available for a larger amount of and different types of research situations.

Dealing with Nonlinearity in Importance-Performance Map Analysis (IPMA): An Integrative Framework in a PLS-SEM Context

Importance-performance map analysis (IPMA) combines PLS-SEM estimates, indicating the importance of an exogenous construct’s influence on another endogenous construct of interest, with an additional dimension comprising the exogenous construct’s performance in a two-dimensional map. From a practical point of view, IPMA contributes to more rigorous management decision-making. The basic principles of IPMA are well understood, yet the inter-construct relationships are typically modeled as being linear. An abundance of empirical literature indicates that this may lead to erroneous conclusions. In an IPMA context, this can lead to false conclusions regarding an exogenous construct’s importance. Although several approaches exist to account for nonlinear inter-construct relationships, these approaches are characterized by drawbacks impeding their applications in practice. Overall, this serves as a backdrop for the current chapter which aims to contribute to (PLS-SEM) IPMA theory in the following ways. First, we provide an integrative framework to guide IPMAs using PLS-SEM. Second and synergistically with the first contribution, we introduce a so-called log-log model that allows to capture the most common functional forms (i.e., both linear and nonlinear) without the need to make a priori assumptions about the correct functional form specification. Third, a comprehensive empirical application is provided that illustrates our proposed IPMA framework as well as the proposed log-log model to more adequately capture the nature of the PLS-SEM relationships ultimately defining the IPMA’s importance dimension.

Does communicating the customer’s resource integrating role improve or diminish value proposition effectiveness?

Purpose The purpose of this paper is to examine whether explicitly communicating the customer’s resource integrating role in value propositions improves or diminishes value proposition effectiveness. Design/methodology/approach Based on existing research on value propositions, three effectiveness criteria are used: role clarity, expected customer value, and purchase intention. Two experiments manipulating the presence of the customer’s resource integrating role in value propositions test the conceptual model in both an indirect interaction (Study 1, toothpaste, n=207) and a direct interaction context (Study 2, fitness program, n=228). Additionally, Study 2 includes the moderating role of resource availability. Findings Explicitly communicating the customer’s resource integrating role in value propositions increases customers’ role clarity, which in turn influences customer’s attitude toward the service and purchase intention through a service-related (i.e. expected benefits and expected efforts) and an ad-related (i.e. ad credibility and attitude toward the ad) route. However, these results only hold for customers high in resource availability. Originality/value This research provides initial empirical support for the often-stated claim that value propositions should include the (potential) value of the offering as well as the (resource integrating) role of the customer. Taking a broader perspective, this research provides initial empirical support for recent calls to develop marketing communication practices that facilitate value-in-use. This paper’s findings show that adopting service logic in marketing communications seems to improve value propositions’ effectiveness.

The impact of customer value types on customer outcomes for different retail formats

Willems, K (reprint author), Vrije Univ Brussel, Dept Business, Fac Econ & Social Sci, Brussels, Belgium. kim.willems@vub.ac.be

FAC-SEM using PLS-SEM: an empirical illustration in a customer value measurement context

FAC-SEM is a special type of multigroup analysis where the groups are structured as a factorial design. The aim of FAC-SEM is to assess how the path model parameters vary as a function of the dimensions of the underlying factorial design. As such, FAC-SEM offers researchers a unique opportunity to further understand their experimental data. This paper explains how the FAC-SEM methodology can be used in a PLS path modeling context and demonstrates the procedure using empirical data from a customer value measurement study.