Dennis Herhausen
Title
Prof. Dr.
Last Name
Herhausen
First name
Dennis
Email
dennis.herhausen@unisg.ch
ORCID
117 results
Now showing 1 - 10 of 117
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Item type:Publication, Stolpersteine auf dem Weg zum Kunden(Marketing Review St.Gallen); Type:journal articleJournal:Swiss Marketing Review - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Customer Privacy Orientation: Conceptualization, Scale Development and ConsequencesAs new personal data processing capabilities are made available to more firms, demonstrating privacy friendly marketing strategies becomes an essential part of a firm’s value proposition. Yet, so far, no research has systematically conceptualized and operationalized the different organizational facets that span a firm’s customer privacy orientation (CPO). We address the resulting conceptual ambiguity by (re-)conceptualizing CPO, defining it as the extent to which an organization engages in strategic, structural, and operative privacy orientation while developing, delivering, and maintaining products and/or services that ensure favorable privacy perceptions by customers, which is determined by four component dimensions (strategic CPO, structural CPO, operative CPO, and customer perceptions of CPO). This conceptualization emerges from a two-stage multimethod conceptualizing process leveraging the existing literature and qualitative data from 39 in-depth expert interviews. We then use several studies with a total of 813 managerial participants to develop and validate a parsimonious four-factor, 12-item CPO scale that shows internal consistency, reliability, and validity. Our findings provide scholars a unifying understanding and measurement instrument of CPO and equip managers with a selfassessment tool to analyze and develop their privacy orientation.Type:journal articleJournal:Institut for Marketing and Customer Insight White Paper - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Generative AI Tools in Customer Service - Beyond the Hype of "Prompt Engineering"(Thexis Verlag, 2023-06); ; ; The adoption of artificial assistant tools (AAT) powered by generative artificial intelligence and large language models to support frontline employees (FLEs) opens up promising new use cases while also introducing changing skillset requirements and new challenges (e.g., in compliance and data security). In this paper, the authors explore AAT implementation in a customer contact center, drawing conclusions from a qualitative study of practical use cases, necessary FLE skills and data security requirements and solutions. Recommendations are provided for implementing AATs using the GPT-3.5 Turbo API.Type:journal articleJournal:Marketing Review St. GallenVolume:6/2023 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, How family CEOs affect employees’ feelings and behaviors: A study on positive emotions(2022-03-07); ; ; ; Type:journal articleJournal:Long Range Planning - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Start with why: the transfer of work meaningfulness from leaders to followers and the role of dyadic tenureType:journal articleJournal:Journal of Organizational Behavior - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Start with why: The transfer of work meaningfulness from leaders to followers and the role of dyadic tenure(Wiley, 2022-06-22); ; Scopus© Citations 32 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Believe the Hype? Herausforderungen und Herangehensweisen an das „Metaverse"(Thexis Verlag, 2022-11-04); ; ; Type:journal articleJournal:Marketing Review St. GallenVolume:2022Issue:6 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Overcoming the pitfalls and perils of algorithms: A classification of machine learning biases and mitigation methods(2022); ; 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.Type:journal articleJournal:Journal of Business ResearchVolume:Vol. 144Scopus© Citations 209 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Face Forward: How Employees’ Digital Presence on Service Websites Affects Customer Perceptions of Website and Employee Service Quality(Sage Journals, 2020-07-15); ; ; Type:journal articleJournal:Journal of Marketing ResearchVolume:57Issue:5Scopus© Citations 56 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Perspektiven für Face-Recognition im Data-Driven-MarketingType:journal articleJournal:Marketing Review St. GallenVolume:2020Issue:1