Studies examining the interaction between consumers and AI
| Authors, Theoretical foundation and research aim | Context and key variables | Findings | Limitations |
|---|---|---|---|
| Canbek and Mutlu (2016) examine the potential use of intelligent personal assistants for learning | Education context Use | Intelligent personal assistants such as Siri, Cortana and Google Now can be used to support learning in educational context | Focus on smartphone-based assistants only |
| Based on the technology acceptance model (TAM), Moriuchi (2019) examines the effect of AI integration into e-commerce on customer engagement and loyalty | Online shopping context Perceived ease of use Perceived usefulness | Strong support for the TAM variables perceived usefulness and perceived ease of use on loyalty. Engagement serves as mediator in this relationship | Functional antecedents only |
| Based on expectations confirmation theory, Brill et al. (2019) examine customer satisfaction with digital assistants | No context specified Expectations Satisfaction | Expectations and confirmed expectations have a positive influence on satisfaction with digital assistants | Study only included consumers who continued to use digital assistants or had no experience. Discontinuance was not considered. Impact on consumer behaviour is not examined |
| Buhalis and Sinarta (2019) analyse value co-creation through big data, real-time data mining and contextual data by smart technology and its effect on customer experience | Tourism context Interaction Co-creation | Interaction based on real-time data and contextual information as well as nowness are ways to co-create value with the consumer. Real-time service adds to a firm’s competitiveness | Research is based on “best practices” within tourism context |
| Based on the uncanny valley theory, Kim et al. (2019) examine the effects of anthropomorphism of robots on consumer judgements and attitudes | Consumer robots Anthropomorphism Attitude Uncanniness | Anthropomorphism positively influences perceptions of warmth. Study also supports the uncanny valley theory by showing that too-humanlike features lead to uncanniness and negative evaluations | Study only examines the effect on attitudes, not how this attitude influences behaviour |
| Based on the unified theory of acceptance and use of technology (UTAUT2), Lu et al. (2019) conceptualise and test a service robot integration willingness scale to determine key dimensions in consumer willingness to adopt AI-based technology into service encounters | Service context Adoption Anthropomorphism | Performance efficacy, intrinsic motivation, facilitating conditions and emotions are positive determinants of acceptance Anthropomorphism is a barrier to adoption of service robots due to perceived threat to human identity | Study does not consider cultural or individual differences. Study relies on “overused” adoption theory |
| Based on the service robot acceptance model (sRAM), Fernandes and Oliveira (2021) examine consumer motivations to adopt AI-based digital voice assistants into service encounters | Service context Adoption | Functional, social and relational elements are drivers for adoption of AI-based digital voice assistants. Experience has a moderating role on acceptance | Convenience sample focused on young users of the Millennial generation. Potential inhibitors of acceptance are not included |
| Based on UTAUT2 and privacy calculus, Vilmakumar et al. (2021) examine the perception of consumers towards privacy concerns and its influence on the adoption of voice based digital assistants | Indian consumer context Privacy Adoption | Consumer perceived privacy risk does not influence adoption intentions directly but indirectly through trust | No individual consumer variables considered Limited to Indian consumers |
| Belanche et al. (2021) validate the humanness-value-loyalty model to examine how a robot’s perceived human likeness, competence and warmth affect service value expectations and loyalty | Restaurant service context Human likeness Competence | Human likeness positively affects utilitarian, social, monetary and emotional value expectations. Competence influences utilitarian value expectations | Limited to specific service robots within restaurant context |
| Authors, Theoretical foundation and research aim | Context and key variables | Findings | Limitations |
|---|---|---|---|
| Education context Use | Intelligent personal assistants such as Siri, Cortana and Google Now can be used to support learning in educational context | Focus on smartphone-based assistants only | |
| Based on the technology acceptance model (TAM), | Online shopping context | Strong support for the TAM variables perceived usefulness and perceived ease of use on loyalty. Engagement serves as mediator in this relationship | Functional antecedents only |
| Based on expectations confirmation theory, | No context specified Expectations | Expectations and confirmed expectations have a positive influence on satisfaction with digital assistants | Study only included consumers who continued to use digital assistants or had no experience. Discontinuance was not considered. Impact on consumer behaviour is not examined |
| Tourism context | Interaction based on real-time data and contextual information as well as nowness are ways to co-create value with the consumer. Real-time service adds to a firm’s competitiveness | Research is based on “best practices” within tourism context | |
| Based on the uncanny valley theory, | Consumer robots | Anthropomorphism positively influences perceptions of warmth. Study also supports the uncanny valley theory by showing that too-humanlike features lead to uncanniness and negative evaluations | Study only examines the effect on attitudes, not how this attitude influences behaviour |
| Based on the unified theory of acceptance and use of technology (UTAUT2), | Service context | Performance efficacy, intrinsic motivation, facilitating conditions and emotions are positive determinants of acceptance Anthropomorphism is a barrier to adoption of service robots due to perceived threat to human identity | Study does not consider cultural or individual differences. |
| Based on the service robot acceptance model (sRAM), | Service context | Functional, social and relational elements are drivers for adoption of AI-based digital voice assistants. Experience has a moderating role on acceptance | Convenience sample focused on young users of the Millennial generation. |
| Based on UTAUT2 and privacy calculus, | Indian consumer context | Consumer perceived privacy risk does not influence adoption intentions directly but indirectly through trust | No individual consumer variables considered |
| Restaurant service context | Human likeness positively affects utilitarian, social, monetary and emotional value expectations. Competence influences utilitarian value expectations | Limited to specific service robots within restaurant context |
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