{"title":"Permissibility vs. Feasibility: AI in service from a CX perspective","authors":"Michael Giebelhausen, T. Andrew Poehlman","doi":"10.1108/jsm-06-2023-0210","DOIUrl":null,"url":null,"abstract":"<h3>Purpose</h3>\n<p>This paper aims to provide researchers and practitioners with a consumer-focused alternative for considering the integration of artificial intelligence (AI) into services.</p><!--/ Abstract__block -->\n<h3>Design/methodology/approach</h3>\n<p>The paper reviews and critiques the most popular frameworks for addressing AI in service. It offers an alternative approach, one grounded in social psychology and leveraging influential concepts from management and human–computer interaction.</p><!--/ Abstract__block -->\n<h3>Findings</h3>\n<p>The frameworks that dominate discourse on this topic (e.g. Huang and Rust, 2018) are fixated on assessing technology-determined feasibility rather than consumer-granted permissibility (CGP). Proposed is an alternative framework consisting of three barriers to CGP (experiential, motivational and definitional) and three responses (communicate, motivate and recreate).</p><!--/ Abstract__block -->\n<h3>Research limitations/implications</h3>\n<p>The implication of this research is that consistent with most modern marketing thought, researchers and practitioners should approach service design from the perspective of customer experience, and that the exercise of classifying service occupation tasks in terms of questionably conceived AI intelligences should be avoided.</p><!--/ Abstract__block -->\n<h3>Originality/value</h3>\n<p>Indicative of originality, this paper offers an approach to considering AI in services that is nearly the polar opposite of that widely advocated by e.g., Huang et al., (2019); Huang and Rust (2018, 2021a, 2021b, 2022b). Indicative of value is that their highly cited paradigm is optimized for predicting the rate at which AI will take over service tasks/occupations, a niche topic compared to the mainstream challenge of integrating AI into service offerings.</p><!--/ Abstract__block -->","PeriodicalId":48294,"journal":{"name":"Journal of Services Marketing","volume":null,"pages":null},"PeriodicalIF":3.8000,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Services Marketing","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1108/jsm-06-2023-0210","RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose
This paper aims to provide researchers and practitioners with a consumer-focused alternative for considering the integration of artificial intelligence (AI) into services.
Design/methodology/approach
The paper reviews and critiques the most popular frameworks for addressing AI in service. It offers an alternative approach, one grounded in social psychology and leveraging influential concepts from management and human–computer interaction.
Findings
The frameworks that dominate discourse on this topic (e.g. Huang and Rust, 2018) are fixated on assessing technology-determined feasibility rather than consumer-granted permissibility (CGP). Proposed is an alternative framework consisting of three barriers to CGP (experiential, motivational and definitional) and three responses (communicate, motivate and recreate).
Research limitations/implications
The implication of this research is that consistent with most modern marketing thought, researchers and practitioners should approach service design from the perspective of customer experience, and that the exercise of classifying service occupation tasks in terms of questionably conceived AI intelligences should be avoided.
Originality/value
Indicative of originality, this paper offers an approach to considering AI in services that is nearly the polar opposite of that widely advocated by e.g., Huang et al., (2019); Huang and Rust (2018, 2021a, 2021b, 2022b). Indicative of value is that their highly cited paradigm is optimized for predicting the rate at which AI will take over service tasks/occupations, a niche topic compared to the mainstream challenge of integrating AI into service offerings.
期刊介绍:
■Customer policy and service ■Marketing of services ■Marketing planning ■Service marketing abroad ■Service quality Capturing and retaining customers in a service industry is a vastly different activity to its product-based counterpart. The fickle nature of today"s consumer is a vital factor in understanding the factors which determine successful holding of market share - and the intense competition within the sector means practitioners must keep pace with new developments if they are to outwit competitors and develop customer loyalty.