Understanding the Impact of Chatbots on Purchase Intention

Soares, Ana Maria; Camacho, Catarina; Elmashhara, Maher Georges

doi:10.1007/978-3-031-04829-6_41

Cited by 7 publications

(6 citation statements)

References 43 publications

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“…Second, we contribute to the literature on chatbots, that though being recently addressed by the academic attention (Araujo, 2018; De Cicco et al. , 2020; Soares et al. , 2022; Rese et al.…”

Section: Discussionmentioning

confidence: 99%

“…These factors and the relations among them were missing in UTAUT and UTAUT2 (Venkatesh et al, 2003;2012). Second, we contribute to the literature on chatbots, which, though being recently addressed by the academic attention (Araujo, 2018;De Cicco et al, 2020;Soares et al, 2022;Rese et al, 2020), still lacks a thorough understanding of the factors driving the usage of this technology. Then, the current study tests the enriched version of the UTAUT showing good validity and exploratory power, suggesting that the added antecedents increase its explanatory power when the intention of use.…”

Section: Theoretical Implicationsmentioning

confidence: 97%

“…Thanks to their ability to mimic interpersonal interactions, chatbots have grabbed the attention of a growing number of researchers within a wide range of contexts, including customer and information service, food service, health, education, and transportation (e.g., Araujo, 2018;Kuberkar and Singhal, 2020;Soares et al, 2022;De Cicco et al, 2021;Zarouali et al, 2018). Due to their ability to address problems and deficiencies in e-retailing, chatbots can also mitigate the impersonal nature and risks related to online purchasing (Chen et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Thanks to their ability to mimic interpersonal interactions; chatbots have grabbed the attention of a growing number of researchers within a wide range of contexts, including customer and information service, food service, health, education and transportation (e.g. Araujo, 2018; Kuberkar and Singhal, 2020; Soares et al. , 2022; De Cicco et al.…”

Section: Introductionmentioning

confidence: 99%

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Using chatbots in e-retailing – how to mitigate perceived risk and enhance the flow experience

Silva

Cicco

Vlačić

et al. 2022

IJRDM

Self Cite

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PurposeChatbots represent an undeniable player between online retailers and customers as they boost operational efficiency and bring cost savings to businesses while offering convenience for customers in terms of timing and immediacy. However, as chatbots represent a new-born online touchpoint in retailing, especially when it comes to online pre-purchase and purchase experience, this study examines whether and how effort expectation, facilitating condition, performance expectancy, social influence, trust, perceived risk and flow affect consumers' intention to use chatbots for online shopping. The purpose of this paper is to address this issue.Design/methodology/approachA total of 226 respondents participated in an online survey. Participants were asked to try a new online service and interact with a chatbot designed using Chatfuel, a platform within the Facebook Messenger setting. Structural equation modelling was used to test the proposed research model regarding the intention to use chatbots.FindingsThis study discusses the importance of offering useful and trustworthy conversational agents for online shopping and argues and explains the insignificant paths amongst other studied factors and intention to use chatbots concluding with the need to explore more drivers for such contemporary technologies. Moreover, the findings indicate that trust turns out to be an important predictor of behavioural intention towards chatbots, in addition to its role in mitigating perceived risk and enhancing flow experience.Originality/valueGiven the lack of empirical evidence related to chatbots applied for business purposes, this paper fills a gap in this research field and provides a deeper understanding of what leverages consumers' intention to use chatbots for online shopping.

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Section: Discussionmentioning

confidence: 99%

Section: Theoretical Implicationsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Using chatbots in e-retailing – how to mitigate perceived risk and enhance the flow experience

Silva

Cicco

Vlačić

et al. 2022

IJRDM

Self Cite

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“…With regards to future conversational agents, which should be working for and collaborate with humans, there seems to be no doubt that the social characteristics of these entities will play a significant role in their acceptance. Today, conversational agents are already used in clinical and therapeutic contexts [106,107], in interactive education environments [108,109], as well as in e-commerce [110,111]. They are likely to also propagate into other domains so that they will continue to engage, entertain, and potentially even enlighten us, to the point where we become used to them acting as our servants, butlers, or even 'best friends', perfectly adapted to our needs and preferences.…”

Section: Final Thoughtsmentioning

confidence: 99%

Perspectives on Socially Intelligent Conversational Agents

Brinkschulte

Schlögl

Monz

et al. 2022

MTI

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The propagation of digital assistants is consistently progressing. Manifested by an uptake of ever more human-like conversational abilities, respective technologies are moving increasingly away from their role as voice-operated task enablers and becoming rather companion-like artifacts whose interaction style is rooted in anthropomorphic behavior. One of the required characteristics in this shift from a utilitarian tool to an emotional character is the adoption of social intelligence. Although past research has recognized this need, more multi-disciplinary investigations should be devoted to the exploration of relevant traits and their potential embedding in future agent technology. Aiming to lay a foundation for further developments, we report on the results of a Delphi study highlighting the respective opinions of 21 multi-disciplinary domain experts. Results exhibit 14 distinctive characteristics of social intelligence, grouped into different levels of consensus, maturity, and abstraction, which may be considered a relevant basis, assisting the definition and consequent development of socially intelligent conversational agents.

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