Department of marketing

The journal of business & industrial marketing p. 14-14 Doi: https://doi.org/10.1108/jbim-05-2025-0499

Purpose This study aims to increase our understanding of resilience in small firms as they navigate a series of interconnected crises – that is, a poly-crisis context. It focuses on various types of resilience, such as entrepreneurial, operational, strategic, financial and relational, and how these are connected. Design/methodology/approach The empirical part of this study is based on a qualitative case study of 13 microbreweries in the Nordic countries of Denmark, Finland, Norway and Sweden. Personal interviews were conducted, and the empirical material was analyzed from a dynamic perspective based on crisis, type of resilience, and the connection between resilience types. Findings The study highlights the interconnected nature of crises and emphasizes the importance of adaptations and diverse types of resilience in coping within a poly-crisis context. The findings show the nature and importance of relational resilience in connection with other types of resilience. Originality/value This study contributes to the authors’ understanding of resilience by emphasizing the importance of a multidimensional and adaptive framework. It adds new insights into resilience as a dynamic process in small firms by showing the flow of resilience types and their connectedness over time in a poly-crisis context.

Psychology & Marketing Doi: https://doi.org/10.1002/mar.70190

Journal of Service Management (JOSM) p. 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 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.

Food Quality and Preference Doi: https://www.sciencedirect.com/science/article/abs/pii/S0950329326001850?via%3Dihub