Using Information-Seeking Argument Mining to Improve Service

Skiera, Bernd; Yan, Shunyao; Daxenberger, Johannes; Dombois, Marcus; Gurevych, Iryna

doi:10.1177/10946705221110845

Cited by 7 publications

(4 citation statements)

References 51 publications

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“…(2019) also found that the existing service literature disproportionally focuses attention on the managerial consequences of new service technologies (e.g. Skiera et al. , 2022) rather than on the impact of these technologies on consumers (e.g.…”

Section: Conceptual Backgroundmentioning

confidence: 99%

“…Kunz et al (2019) also found that the existing service literature disproportionally focuses attention on the managerial consequences of new service technologies (e.g. Skiera et al, 2022) rather than on the impact of these technologies on consumers (e.g. Kipnis et al, 2022;Pantano and Scarpi, 2022), though studies of both outcomes are certainly represented.…”

Section: Conceptual Background 21 Artificial Intelligence (Ai) In Ser...mentioning

confidence: 99%

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RAISE: leveraging responsible AI for service excellence

Alkire,

Bilgihan,

Bui

et al. 2024

JOSM

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PurposeThis article introduces the Responsible AI for Service Excellence (RAISE) framework. RAISE is a strategic framework for responsibly integrating AI into service industries. It emphasizes collaborative AI design and deployment that aligns with the evolving global standards and societal well-being while promoting business success and sustainable development.Design/methodology/approachThis multidisciplinary conceptual article draws upon the United Nations' Sustainable Development Goals (SDGs) and AI ethics guidelines to lay out three principles for practicing RAISE: (1) Embrace AI to serve the greater good, (2) Design and deploy responsible AI and (3) Practice transformative collaboration with different service organizations to implement responsible AI.FindingsBy acknowledging the potential risks and challenges associated with AI usage, this article provides practical recommendations for service entities (i.e. service organizations, policymakers, AI developers, customers and researchers) to strengthen their commitment to responsible and sustainable service practices.Originality/valueThis is the first service research article to discuss and provide specific practices for leveraging responsible AI for service excellence.

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Section: Conceptual Backgroundmentioning

confidence: 99%

Section: Conceptual Background 21 Artificial Intelligence (Ai) In Ser...mentioning

confidence: 99%

RAISE: leveraging responsible AI for service excellence

Alkire,

Bilgihan,

Bui

et al. 2024

JOSM

View full text Add to dashboard Cite

show abstract

“…Belk’s article on love and sex doll as service workers addresses how AI can provide artificial emotional comfort for customers at the micro-level, Skiera et al’s information-seeking argument mining provides a high-tech approach to improving the provision of preferred service at the meso-level, and Mishra and Makridis’ article on AI service and well-being provides macro evidence for how AI drives economic growth and well-being.…”

Section: Ai Service and Emotionmentioning

confidence: 99%

AI Service and Emotion

Bagozzi

Brady

Huang

2022

Journal of Service Research

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AI in service can be for routine mechanical tasks, analytical thinking tasks, or empathetic feeling tasks. We provide a conceptual framework for the customer, firm, and interactional use of AI for empathetic tasks at the micro-, meso-, and macro-levels. Emotions resulting from AI service interactions can include basic emotions (e.g., joy, sadness, and fear), self-conscious emotions (e.g., pride, guilt, embarrassment), and moral emotions (e.g., contempt, righteous anger, social disgust). These emotions are mostly likely to occur during frontline interactions in which both firms and customers use AI, a phenomenon called “AI as customer.” The analysis level of AI service and emotion can be at the macro-level in which AI is transforming the service economy into a feeling economy, at the meso-level in which firms can use “thoughtful AI” to make the employees’ and customers’ lives a little bit better by brightening their days, and at the micro-level in which customers can experience basic, self-conscious, and moral emotions from interactions with service AI.

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“…However, the current WOM knowledge is limited to traditional two-dimensional platforms (e.g. Trust Pilot) or social media websites (Filieri et al , 2018a; Filieri, 2016; Mladenović et al , 2020; Skiera et al , 2022) and does not reflect the immersive nature of Metaverse-like environments. Given the depth and breadth of the environment, the behavior will hypothetically change (Belk et al , 2022), and the domain knowledge, although rapidly evolving, is still scattered and inconclusive.…”

Section: Introductionmentioning

confidence: 99%

MetaWOM – toward a sensory word-of-mouth (WOM) in the metaverse

Mladenović,

Ismagilova,

Filieri

et al. 2023

IJCHM

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Purpose Based on the key dimensions of the Metaverse environment (immersiveness, fidelity and sociability), this paper aims to develop the concept of sensory word-of-mouth (WOM) in Metaverse – the metaWOM. It attempts to upgrade the Reviewchain model and suggests the utilization of non-transferable tokens (NTTs) in curbing the explosion of fake WOM. Design/methodology/approach Following Macinnis’ (2011) approach to conceptual contributions, the authors browsed the currently available literature on WOM, Metaverse and NTT to portray the emergence of metaWOM. Findings By relying on Metaverse’s three building blocks, the authors map out the persuasiveness of metaWOM in the Metaverse-like environment. By incorporating NTT in the Reviewchain model, the authors upgraded it to provide a transparent, safe and trusted review ecosystem. An array of emerging research directions and research questions is presented. Research limitations/implications This paper comprehensively analyzes the implications of a Metaverse-like environment on WOM and debates on technologies that can enhance the metaWOM persuasiveness. The proposed model in this paper can assist various stakeholders in understanding the complex nature of virtual information-seeking and giving. Originality/value This is the original attempt to delineate the sensory aspect of WOM in the Metaverse based on three crucial aspects of the Metaverse environment: immersiveness, fidelity and sociability. This paper extends the discussion on the issue of fake reviews and offers viable suggestions to curb the ever-growing number of fraudulent WOM.

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Using Information-Seeking Argument Mining to Improve Service

Cited by 7 publications

References 51 publications

RAISE: leveraging responsible AI for service excellence

RAISE: leveraging responsible AI for service excellence

AI Service and Emotion

MetaWOM – toward a sensory word-of-mouth (WOM) in the metaverse

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