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The limited focus on particular research designs, data analysis methods, and research objects frequently characterise customer research projects. However, standard practice regarding researching certain phenomena is not always correct, and, in many cases, could provide misleading results. In this paper, we call for a more holistic approach to customer research, which considers the entire research design and data analysis toolbox, while also recognising the importance of consumer groups other than costumers. At the same time, we call for using simple data analysis methods, which often suffice to show relevant effects, instead of overemphasising method complexity as is often the case in top-tier journals. Based on our discussion, we offer researchers and practitioners concrete recommendations for advancing their research design and data analyses.
The influence of trust on the adherence to investment recommendations in the context of robo-advisors is under-researched. This relationship needs to be better understood because robo-advice lacks a critical element of trust: human interaction. Theory suggests that ability, integrity, and benevolence are key factors in building trust in human advisors. Using an experimental study design, our research examines the relationship between a robo-advisor's trust attributes and the acceptance of its investment advice. The results show that trust in a robo-advisor increases the propensity to follow its recommendations. While ability and integrity are significant, benevolence is not. The study contributes to the research on technology acceptance, trust, and the adoption of technology-based recommendations by improving the understanding of the relationship between trust and the acceptance of automated investment recommendations.