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Session Overview
12-03: Nurhafihz Noor
Saturday, 20/Jul/2019:
3:45pm - 4:10pm

Seminar Room 2-3

Chair: Chia-Lin Lee

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Investigating the Service Quality of Artificial Intelligence Service Agents

Authors: Nurhafihz Noor (The University of Adelaide, Australia), Sally Rao Hill (The University of Adelaide, Australia), Indrit Troshani (The University of Adelaide, Australia)

AI is projected to make significant impacts spanning global economy and broader society. A recent PwC survey reports that AI is expected to generate over US$15.7 trillion to the global economy by 2030. The service industry is a key sector that is expected to be affected through the proliferation of AI service agents (AISA). AISA are technology agents, such as software programs, machines and robots, that act autonomously in response to changing conditions in their environment to achieve service goals. Powered by machine learning, AISA can provide consumers with benefits such as human-like empathetic service that is effectively always available. Irrespective of these benefits, it is unclear how they contribute to consumer perceptions of service quality. The objective of this study is to develop a new scale (AISERVQUAL) for measuring the service quality delivered by AISA and investigate its impact on consumers.
Existing service quality scales are inadequate in explaining the emerging service environment in which customers experience AISA. Early service quality scales were proposed in contexts dominantly involving human service agents in the service interaction. With the rise of technologies and the internet, scale measurements have focused on electronic service systems. With this, the importance of affective components such as empathy, present in several human service quality scales, assumed less importance for online contexts. However, existing service agents are not fully comparable with AISA which can provide both human-like service experiences with the benefits of technology and internet-connected service systems. Arguably, the service quality scale for AISA should contain an adaptation of existing measures relevant for the AISA service environment as well as new attributes not captured by exiting service quality scales with human or non-AI technology service agents.
The research to develop the AISERVQUAL scale is consistent with the general guidelines for scale construction in the literature. From a preliminary literature review, 11 dimensions containing 109 measure items have been identified to be able to tap the domain of AISERVQUAL. Through a series of refinements, the final scale will be tested with US respondents who represent one of the top markets for AI applications. Chatbots and IPAs will be used as suitable AI applications for this study due to their wider consumer accessibility, high market adoption and diverse task capabilities required.
This study will contribute to the development of a new service quality scale for AISA. This is in line with the research stream in services marketing by developing service quality scales that address new technologies and environments. This new scale is also useful for future research investigating antecedents and consequences of AISERVQUAL. In addition, dimensions influencing AISERVQUAL can serve as a diagnostic tool for service professionals to develop better quality AISA.

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