Dennis Herhausen

Over the last decade, the importance of machine learning increased dramatically in business and marketing. However, when machine learning is used for decision-making, bias rooted in unrepresentative datasets, inadequate models, weak algorithm designs, or human stereotypes can lead to low performance and unfair decisions, resulting in financial, social, and reputational losses. This paper offers a systematic, interdisciplinary literature review of machine learning biases as well as methods to avoid and mitigate these biases. We identified eight distinct machine learning biases, summarized these biases in the cross-industry standard process for data mining to account for all phases of machine learning projects, and outline twenty-four mitigation methods. We further contextualize these biases in a real-world case study and illustrate adequate mitigation strategies. These insights synthesize the literature on machine learning biases in a concise manner and point to the importance of human judgment for machine learning algorithms.

The Internet of Things (loT) promises to deliver enormous business value. More specifically, loT solutions disrupt existing business models by opening up novel service opportunities. In order to help companies understand the opportunities and challenges of this development, we shed light on different loT revenue models. Based on an inductive case study approach, we identify nine direct and indirect revenue patterns. The different types of revenue patterns all use loT-enabled services to create value for customers; the extent and the monetization of services, however, vary.

This paper investigates how and when a firm’s level of customer contact influences the collective organizational energy. For this purpose, we bridge the literature on collective human energy at work with the job impact framework and organizational sensemaking processes and argue that a firm’s level of customer contact is positively linked to the collective organizational energy because a high level of customer contact might make the experience of prosocial impact across the firm more likely. However, as prior research at the individual level has indicated that customers could also deplete employees’ energy, we introduce transformational leadership climate as a novel contingency factor for this linkage at the organizational level. We propose that a medium to high transformational leadership climate is necessary to derive positive meaning from customer contact, while firms with a low transformational leadership climate do not get energized by customer contact. We tested the proposed moderated mediation model with multilevel modeling and a multi-source dataset comprising 9,094 employees and 75 key informants in 75 firms. The results support our hypotheses and offer important theoretical contributions for research on collective human energy in organizations and its interplay with customers.

This paper identifies and explains a potential tension between a firm’s emphasis on customer orientation (CO) and the extent to which employees value CO as a success factor for individual performance. Based on self-determination theory and CO implementation research, we propose that firm CO may represent both autonomous and controlled motivations for CO, but that employees’ CO is more strongly linked to individual performance when employees experience solely autonomous motivation. Hence, we expect a substitution effect whereby the link between employees’ CO and their performance is weaker when firm CO is high. Furthermore, we examine a boundary condition for the previous hypothesis and propose that performance-contingent rewards have a positive effect on the internalization of the extrinsic motivation stemming from firm CO. Two multilevel studies with 979 employees and 201 top management team members from 132 firms support our hypotheses. Against previous research, our findings offer a new perspective on the effectiveness of CO initiatives, propose employees’ motivational states as the theoretical explanation for the heterogeneity in the link between employee CO and performance, and reappraise the role of performance-contingent rewards in CO research. We provide managerial implications for the effective implementation of customer-oriented initiatives within firms.