Interactive voice assistants – Does brand credibility assuage privacy risks?

Jain, Shilpi; Basu, Sriparna; Dwivedi, Yogesh K.; Kaur, Sumeet

doi:10.1016/j.jbusres.2021.10.007

Cited by 84 publications

(53 citation statements)

References 111 publications

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“…In general, the latest generation of voice assisted devices offers better-quality tools for services providing added space for personalization with regard to previous interactions (McLean and Osei-Frimpong, 2019 ; Pantano and Pizzi, 2020 ). Accordingly, digital adaptations in voice assisted devices extend on the expectation for performance and productivity in the workplace, so their link to hedonic pleasure and utility derived from usage has an impact on the balance that its users attain in their personal lives (Mishra et al, 2021 ; Jain et al, 2022 ). Popular personal assistant devices in the present-day marketplace, such as, Siri, Alexa, Cortana, and Bixby, are integrating common every day-use devices in consumer technology, such as speakers, autonomous vehicles and mobile devices, by integrating voice recognition into AI, so users interact with smartphones from a creative, novel, and more immediate interface.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Application and extension of the UTAUT2 model for determining behavioral intention factors in use of the artificial intelligence virtual assistants

2022

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Virtual Assistants, also known as conversational artificial intelligence, are transforming the reality around us. These virtual assistants have challenged our daily lives by assisting us in the different dimensions of our lives, such as health, entertainment, home, and education, among others. The main purpose of this study is to develop and empirically test a model to predict factors that affect users' behavioral intentions when they use intelligent virtual assistants. As a theoretical basis for investigating behavioral intention of using virtual assistants from the consumers' perspective, researchers employed the extended Unified Theory of Acceptance and Use of Technology (UTAUT2). For this research paper, seven variables were analyzed: performance expectancy, effort expectancy, facilitating conditions, social influence, hedonic motivation, habit, and price/value. In order to improve consumer behavior prediction, three additional factors were included in the study: perceived privacy risk, trust, and personal innovativeness. Researchers carried out an online survey with 304 responses. The obtained sample was analyzed with Structural Equation Modeling (SEM) through IBM SPSS V. 27.0 and AMOS V 27.0. The main study results reveal that factors, such as habit, trust, and personal innovation, have a significant impact on the adoption of virtual assistants. However, on the other side, performance expectancy, effort expectancy, facilitating conditions, social influence, hedonic motivation, price/value, and perceived privacy risk were not significant factors in the users' intention to adopt this service. This research paper examines the effect of personal innovation, security, and trust variables in relation to the use of virtual assistants. It contributes to a more holistic understanding of the adoption of these intelligent devices and tries to fill the knowledge gap on this topic, as it is an emerging technology. This investigation also provides relevant information on how to successfully implement these technologies.

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Section: Theoretical Frameworkmentioning

confidence: 99%

Application and extension of the UTAUT2 model for determining behavioral intention factors in use of the artificial intelligence virtual assistants

2022

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“…Hedonic value refers to the esthetic, experiential, emotional, and pleasure‐related benefits perceived by individuals (Chitturi et al, 2007; Jain et al, 2022). Lin et al (2020) reported that when customers decide to adopt artificial intelligence, they evaluate whether artificial intelligence adoption is pleasing and enjoyable.…”

Section: Literature Reviewmentioning

confidence: 99%

Artificial intelligence in marketing: A meta‐analytic review

Mehta¹,

Jebarajakirthy

Maseeh

et al. 2022

Psychology and Marketing

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This study synthesizes the artificial intelligence literature into a Meta-analytic framework based on the theory of reasoned action and the unified theory of acceptance and use of technology 2, and examines concrete relationships between the constructs of this framework. This meta-review also performed a moderation analysis to investigate the possible reasons for inconsistent findings across studies.The findings suggest that three methodological moderators (sample type, gender dominance, and publication type), and one contextual moderator (level of country's technology advancement) lead to inconsistencies in the relationships between study constructs. Academically, this review synthesizes the artificial intelligence literature and resolves inconsistencies in the literature and also adds constructs to both theory of reasoned action and the unified theory of acceptance and use of technology 2.

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“…A branded product may lessen consumers' anxiety because of the minimized perceived risk and accessibility of the product from past experience (Shen and Chiou, 2010). A recent study found that brand credibility moderates the negative impacts of privacy concerns on the overall perceived value of AI-EVAs (Jain et al, 2022). Using a branded (vs off-brand) AI-EVA may ease consumers' anxiety concerning a service, thereby mimicking the important relational feeling in the service context.…”

Section: Name Brand Versus Off-brand Perceptionsmentioning

confidence: 99%

“…Using a branded (vs off-brand) AI-EVA may ease consumers' anxiety concerning a service, thereby mimicking the important relational feeling in the service context. In addition, prominent brands of AI-EVAs are more accessible and easier to control with their compatible smartphone apps, therefore positively impacting consumers' intention to use (Jain et al, 2022). As consumers are more likely to purchase and use products with well-established brand names (Aghekyan-Simonian et al, 2012), the brand of the product (e.g.…”

Section: Name Brand Versus Off-brand Perceptionsmentioning

confidence: 99%

Customers’ perceptions of hotel AI-enabled voice assistants: does brand matter?

Rui-ying

Cain

Jeon

2022

IJCHM

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Purpose Extending the technology acceptance model (TAM) to a new context, the purpose of this paper is to propose an integrative model of the brand of artificial intelligence-enabled voice assistants (AI-EVA) and customers’ perceptions and behavioral intentions of using AI-EVA in hotels. Moderating effects of construal levels and hotel scales were examined. Design/methodology/approach This paper adopted a mixed method approach. A qualitative and phenomenological methodology was adopted in Study 1 to explore hotel customers’ experience with AI-EVA. Study 2 applied experimental design to investigate the effects of the brand of AI-EVA and construal level on customers’ perceptions and behavioral intentions of using AI-EVA. Based on Studies 1 and 2 results, Study 3 examined how the brand of AI-EVA and hotel scale affect customers’ perceptions and behavioral intentions of using AI-EVA during hotel stays. Findings This research found that customers perceive a higher level of perceived usefulness, perceived ease of use and anthropomorphism when AI-EVA is branded (vs off-brand). Perceived usefulness positively affects customers’ intention to use and to spread positive word-of-mouth. Anxiety of using AI-enabled devices and privacy concerns inhibit customers’ intention to use AI-EVA. Anthropomorphism increases customers’ willingness to spread positive word-of-mouth. Construal level moderates the effect of the brand of AI-EVAs on perceived ease of use and anthropomorphism. Hotel scale moderates the effect of brand on perceived usefulness. Originality/value This paper is one of the first attempts to uncover and integrate different factors underlying customers’ perceptions of using AI-EVA in an extended TAM in hotel settings. This paper provides an integrative model extending the TAM to a new context by deploying a mixed-method approach across three studies.

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Interactive voice assistants – Does brand credibility assuage privacy risks?

Cited by 84 publications

References 111 publications

Application and extension of the UTAUT2 model for determining behavioral intention factors in use of the artificial intelligence virtual assistants

Application and extension of the UTAUT2 model for determining behavioral intention factors in use of the artificial intelligence virtual assistants

Artificial intelligence in marketing: A meta‐analytic review

Customers’ perceptions of hotel AI-enabled voice assistants: does brand matter?

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