Sep 2026· Asia Pacific Journal of Marketing and Logistics· 49 references
AI in Service Interactions
Abstract
Purpose This study examines whether generative AI chatbot features are associated with user autonomy and competence and whether these psychological states are, in turn, associated with GenAI misuse intention, conceptualized here as an empowerment backfire mechanism. Design/methodology/approach Drawing primarily on the Self-Determination Theory, this study develops an empowerment-based model of GenAI chatbot misuse and examines the Neutralization Theory as an alternative account of moral rationalization. Data from 736 GenAI users were analyzed using structural equation modeling (SEM). Findings Results show that GenAI capability and accessibility were positively associated with autonomy and competence, which were positively associated with GenAI misuse intention. Situational anxiety moderated only the competence–GenAI misuse intention relationship. By contrast, neutralization mechanisms showed mixed associations, suggesting that empowerment-related perceptions may play a more central role than conventional rationalization in explaining ethically questionable GenAI use. Research limitations/implications Although the cross-sectional design limits causal inference, the findings highlight the unintended ethical consequences of empowerment-related psychological states in AI-enabled service settings. Originality/value This study introduces the empowerment backfire mechanism, showing that GenAI capability and accessibility may increase misuse intention through autonomy and competence.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
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