Customer engagement, consumption and firm performance in a multi-actor service eco-system: The moderating role of resource integration

Ho, Mia Hsiao-Wen; Chung, Henry F.L.; Kingshott, Russel; Chiu, Chun-Chen

doi:10.1016/j.jbusres.2020.02.008

Cited by 62 publications

(41 citation statements)

References 55 publications

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“…We draw on customer engagement, consumption and firm performance literature (Ho et al, 2020), transactive relationship theory (Tanioka et al, 2019), and AI in service literature (Hui et al, 2001; Huang and Rust, 2018; Davenport and Ronanki, 2018) to show that as organisations experiment with new touch-points along the customer journey, they are looking to AI-enabled technologies to provide effective and engaging customer service (Bitner, 2001, 2017; Meuter et al, 2000). In particular, through online customer service, customers are able to contact organisations at their own convenience, a capability that creates value through time-saving and convenience, increases customer satisfaction, and enhances brand loyalty (Colwell et al, 2008; Negash et al, 2003).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

AI Customer Service: Task Complexity, Problem-Solving Ability, and Usage Intention

Shieh²,

Esch

et al. 2020

Australasian Marketing Journal

187

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Artificial intelligence (AI) in the context of customer service, we define as a technology-enabled system for evaluating real-time service scenarios using data collected from digital and/or physical sources in order to provide personalised recommendations, alternatives, and solutions to customers’ enquiries or problems, even very complex ones. We examined, in a banking services context, whether consumers preferred AI or Human online customer service applications using an experimental design across three field-based experiments. The results show that, in the case of low-complexity tasks, consumers considered the problem-solving ability of AI to be greater than that of human customer service and were more likely to use AI while, conversely, for high-complexity tasks, they viewed human customer service as superior and were more likely to use it than AI. Moreover, we found that perceived problem-solving ability mediated the effects of customers’ service usage intentions (i.e., their preference for AI vs. Human) with task complexity serving as a boundary condition. Here we discuss our research and the results and conclude by offering practical suggestions for banks seeking to reach customers and engage with them more effectively by leveraging the distinctive features of AI customer service.

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Section: Theoretical Backgroundmentioning

confidence: 99%

AI Customer Service: Task Complexity, Problem-Solving Ability, and Usage Intention

Shieh²,

Esch

et al. 2020

Australasian Marketing Journal

187

View full text Add to dashboard Cite

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“…Reflecting CE's dimensionality, its constituent operant resource investments comprise customers' cognitive (thought-based), emotional (affective), and behavioral (activity-based) investments in their brand interactions, thus reflecting the concept's multidimensional nature (Harrigan et al, 2018;Calder et al, 2009). However, some authors limit their view to behavioral CE (Verleye et al, 2014;Ho et al, 2020), including through direct (i.e. purchases) and indirect contributions (e.g.…”

Section: Customer Engagementmentioning

confidence: 99%

Friend or foe? Customer engagement’s value-based effects on fellow customers and the firm

Clark

Lages

Hollebeek

2020

Journal of Business Research

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Customer engagement (CE) research has rapidly developed in recent years, with insight being gleaned in such areas as CE conceptualization, valence, measurement, and theoretical associations. However, despite the development of understanding of CE's impact on the focal customer (e.g. by cultivating his/her brand attachment/loyalty), much less remains known regarding CE's effects on other actors, particularly on multiple actors simultaneously. Based on the premise that differing actors tend to have different goals/interests, as advanced in stakeholder theory, we deduce that positive/negative CE can yield differing perceived valuebased effects across actors. To address this issue, we examine the particular case of positive/negative CE's value-creating/eroding effects as perceived by the actors of fellow customers and the firm, which we classify in a multi-actor typological framework. After introducing and discussing the typology, we conclude by outlining key implications that arise from this research, limitations, and avenues for further research on CE's multi-actor effects.

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“…Capar and Kotabe (2003) implemented a sample consisting of 81 major German service firms. Ho et al (2020) conducted a recent research based on a sample of eticketing service companies in Taiwan from 2013-2018. Also, several studies adopted a comparative study between industrial and service companies, for example, Raymond et al (2014) used a mixed sample of 347 companies, 93 French and 254 Canadian, 286 of which were in the industrial sector and 61 belonged to the service sector.…”

Section: Type Of Industrymentioning

confidence: 99%

Investigating the Effects of Board and Firm Characteristics on Firm Performance: An Agency Theory Perspective

Yassin

2021

المجلة العلمیة للدراسات والبحوث المالیة والتجاریة

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Customer engagement, consumption and firm performance in a multi-actor service eco-system: The moderating role of resource integration

Cited by 62 publications

References 55 publications

AI Customer Service: Task Complexity, Problem-Solving Ability, and Usage Intention

AI Customer Service: Task Complexity, Problem-Solving Ability, and Usage Intention

Friend or foe? Customer engagement’s value-based effects on fellow customers and the firm

Investigating the Effects of Board and Firm Characteristics on Firm Performance: An Agency Theory Perspective

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