Institutt for markedsføring

Journal of Service Management (JOSM) s. 1-17 Doi: https://doi.org/10.1108/josm-04-2026-0291

Purpose: Although service research is an applied discipline, much of the field has emphasized confirmatory inquiry in relatively ordered settings. While such research remains valuable, it is often less well suited to emerging, relational and poorly understood service phenomena. This article therefore argues for an empirics-first approach to problem formulation in service research. Design/methodology/approach: Drawing on the Cynefin framework, the article distinguishes among clear, complicated, complex and chaotic service problems. Using a conceptual approach, it extends Cynefin to service research methodology by showing how different problem contexts call for different forms of theorizing, methodological fit and research design. Findings: The article shows that what constitutes a meaningful contribution in service research depends on the nature of the problem being studied. In relatively ordered contexts, contributions often involve refinement, replication and the specification of contingencies. In complicated contexts, they involve disentangling mechanisms and clarifying trade-offs. In complex contexts, contributions center on sensemaking and explaining emergent practices and processes. In chaotic contexts, contributions often lie in understanding stabilization, adaptation and the restoration of order under turbulent conditions. Originality/value: The article challenges service researchers to move beyond an overreliance on abstract literature gaps and theory-led confirmatory designs. Instead, it advocates careful empirical diagnosis of focal problems as the foundation for producing methodologically rigorous, theoretically meaningful and practically relevant research contributions.

Journal of Business Research 207 s. 116015-116015 Doi: https://doi.org/10.1016/j.jbusres.2026.116015

Journal of Marketing Analytics Doi: https://doi.org/10.1057/s41270-026-00466-2

This tutorial and case study provides a comprehensive, step-by-step guide to conducting multiple and logistic regression analyses using SmartPLS 4. Although SmartPLS is primarily known for partial least squares structural equation modeling (PLS-SEM), its latest version includes features that enable researchers to perform and visualize regression analyses effectively. The tutorial introduces the theoretical foundations of both standard multiple and logistic regression, outlines the main stages of model specification and estimation, and demonstrates how to implement these analyses within the SmartPLS environment. Emphasis is placed on key analytical decisions, such as model design, assumption testing, interpretation of coefficients, and goodness-of-fit measures. Practical examples and graphical outputs are included to illustrate the implementation process and to enhance understanding of the results.

Journal of Business Research

Psychology & Marketing