Guest editorial

Lowe, Ben; Dwivedi, Yogesh K.; D’Alessandro, Steven

doi:10.1108/ejm-06-2019-966

Cited by 24 publications

(10 citation statements)

References 57 publications

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“…The use of innovative technologies has revolutionised the frontline interface (Lowe et al , 2019), changing the nature of service and customers’ experiences (van Doorn et al , 2017; Puntoni et al , 2021). Service providers are investing substantial financial resources in social robots to automate interactions with customers.…”

Section: Discussionmentioning

confidence: 99%

Dashed expectations in service experiences. Effects of robots human-likeness on customers’ responses

Laura

Viglia

Nunan

2023

EJM

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Purpose There is growing interest in the use of human-like social robots, able to undertake complex tasks whilst building consumer engagement. However, further exploration is needed on the optimal level of humanoid appearance for service robots. In particular, the literature is limited with respect to mitigating disconfirmed expectations for robots high in human-likeness. This paper aims to address this gap by testing the effect of robot appearance, disconfirmed expectations and warmth (vs competence) on customers’ responses. Design/methodology/approach The study adopts a mixed-method design by presenting a focus group (Study 1) that guides two laboratory experiments (Studies 2 and 3). Studies 2 and 3 test for the moderating effect of warmth (vs competence) and the mediating roles of perceived eeriness and disconfirmed expectations. Findings The findings show that a robot high (vs low) in human-likeness leads to higher negative customers’ responses, which is explained by disconfirmed expectations rather than perceived eeriness. However, when customers interact with a warm (vs competent) robot high in human-likeness, this negative effect vanishes. Research limitations/implications The paper investigates boundary conditions and underlying mechanisms that affect customers’ experiences. Although the study adopts high realistic experiments, a limitation lies in not measuring customers’ actual behaviours in the field. Practical implications This study provides new insights on how the appearance and characteristics of social robots influence the consumers’ experience. By doing so, this study offers managers actionable insights (i.e. enhancing warmth) to lessen the risk of disconfirmed expectations. Originality/value The paper offers new explanations as to why human-like robots can generate negative responses from customers. Moving beyond the “uncanny valley” hypothesis, this study shows the key role of disconfirmed expectations in explaining consumers’ negative responses towards humanoid robots. Moreover, it sheds light on the moderating role of warmth (vs competence), which can mitigate such negative effects.

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Section: Discussionmentioning

confidence: 99%

Dashed expectations in service experiences. Effects of robots human-likeness on customers’ responses

Laura

Viglia

Nunan

2023

EJM

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“…In the vein of dependent variables, most studies share similar theoretical foundations, which are primarily based on the Technology Acceptance Model (TAM) (Davis, 1989) and its subsequent modifications, such as the TAM2 (Venkatesh & Davis, 2000), TAM3 (Venkatesh & Bala, 2008), Unified Theory of Technology Use and Acceptance (UTAUT) (Venkatesh et al, 2003), and UTAUT2 (Venkatesh et al, 2012). However, although literature often refers to such renowned theories due to their adaptability and accessibility, their utility is largely context-specific (Lowe et al, 2019), and they may not be comprehensive enough to demonstrate the introduction of emerging technologies such as IAs (McLean & Osei-Frimpong, 2019). As a result, introducing them in a new context necessitates a close examination of its fundamental elements as well as empirical confirmation of key relationships.…”

Section: A Frame For Paper Analysismentioning

confidence: 99%

“…However, although literature often refers to such renowned theories due to their adaptability and accessibility, their utility is largely context-specific (Lowe et al, 2019 ), and they may not be comprehensive enough to demonstrate the introduction of emerging technologies such as IAs (McLean & Osei-Frimpong, 2019 ). As a result, introducing them in a new context necessitates a close examination of its fundamental elements as well as empirical confirmation of key relationships.…”

Section: Research Approachmentioning

confidence: 99%

Understanding the Design Elements Affecting User Acceptance of Intelligent Agents: Past, Present and Future

et al. 2022

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Intelligent agents (IAs) are permeating both business and society. However, interacting with IAs poses challenges moving beyond technological limitations towards the human-computer interface. Thus, the knowledgebase related to interaction with IAs has grown exponentially but remains segregated and impedes the advancement of the field. Therefore, we conduct a systematic literature review to integrate empirical knowledge on user interaction with IAs. This is the first paper to examine 107 Information Systems and Human-Computer Interaction papers and identified 389 relationships between design elements and user acceptance of IAs. Along the independent and dependent variables of these relationships, we span a research space model encompassing empirical research on designing for IA user acceptance. Further we contribute to theory, by presenting a research agenda along the dimensions of the research space, which shall be useful to both researchers and practitioners. This complements the past and present knowledge on designing for IA user acceptance with potential pathways into the future of IAs.

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“…Eventually, this led to the unified theory of acceptance and use of technology (UTAUT) model and other extended versions of UTAUT with the inclusion of habit and facilitating conditions to capture the greater perspective of technology adoption behaviors (Dwivedi et al, 2019). TAM and its extended models have been widely used to understand technology adoption, but their application are highly context-based (Lowe et al, 2019), and are not able to explain novel technologies (McLean and Osei-Frimpong, 2019).…”

Section: Theoretical Frameworkmentioning

confidence: 99%

“…Moreover, most of the empirical studies have investigated the acceptance of chatbots or virtual assistants in developed economies (Rese et al, 2020;Sheehan et al, 2020;Pillai and Sivathanu, 2020;Brachten et al, 2021) and have mainly considered technological acceptance model (TAM) and theory of reasoned action (TRA) (Brachten et al, 2021;Rese et al, 2020;Pillai and Sivathanu, 2020). However, the application of these theories is context-dependent (Lowe et al, 2019), and they are inappropriate to effectively explain the acceptance of novel technologies (McLean and Osei-Frimpong, 2019). Considering this serious concern, this study seeks to determine the elements that develop the acceptance of chatbots in a developing economy by using the "Service Robot Acceptance Model (sRAM)," recently proposed by Wirtz et al (2018) for AIbased technologies.…”

Section: Introductionmentioning

confidence: 99%

Chatbots in the frontline: drivers of acceptance

Aslam

Siddiqui

Arif

et al. 2022

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PurposeBy extending the service robot acceptance model (sRAM), this study aims to explore and enhance the acceptance of chatbots. The study considered functional, relational, social, user and gratification elements in determining the acceptance of chatbots.Design/methodology/approachBy using the purposive sampling technique, data of 321 service customers, gathered from millennials through a questionnaire and subsequent PLS-SEM modeling, was applied for hypotheses testing.FindingsFindings revealed that the functional elements, perceived usefulness and perceived ease of use affect acceptance of chatbots. However, in social elements, only perceived social interactivity affects the acceptance of chatbots. Moreover, both user and gratification elements (hedonic motivation and symbolic motivation) significantly influence the acceptance of chatbots. Lastly, trust is the only contributing factor for the acceptance of chatbots in the relational elements.Practical implicationsThe study extends the literature related to chatbots and offers several guidelines to the service industry to effectively employ chatbots.Originality/valueThis is one of the first studies that used newly developed sRAM in determining chatbot acceptance. Moreover, the study extended the sRAM by adding user and gratification elements and privacy concerns as originally sRAM model was limited to functional, relational and social elements.

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Guest editorial

Cited by 24 publications

References 57 publications

Dashed expectations in service experiences. Effects of robots human-likeness on customers’ responses

Dashed expectations in service experiences. Effects of robots human-likeness on customers’ responses

Understanding the Design Elements Affecting User Acceptance of Intelligent Agents: Past, Present and Future

Chatbots in the frontline: drivers of acceptance

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