Voice Commerce - Studying the Acceptance of Smart Speakers

Zaharia, Silvia; Würfel, Matthias

doi:10.1007/978-3-030-55307-4_68

Cited by 12 publications

(25 citation statements)

References 8 publications

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“…The objective is therefore to have users achieve a positive perception regarding the “degree of ease” (Venkatesh et al, 2012 ), Previously it has been shown that confidence in one's own abilities to deal with technical systems has a significant influence, directly impacting the intention to use them (Fridin and Belokopytov, 2014 ). Previous research has studied this variable to understand its influence on VAs' adoption (Chopra, 2019 ; Zaharia and Würfel, 2020 ; Mishra et al, 2021 ; Moriuchi et al, 2021 ). Therefore, based on this it is hypothesized that:…”

Section: Selected Variables For the Studymentioning

confidence: 99%

“…Previous studies have provided empirical support that evidences the impact of SI on the use of technology in different contexts (Moriuchi, 2021 ). They have also studied this variable to examine its influence on the adoption of VAs (Chopra, 2019 ; Zaharia and Würfel, 2020 ; Mishra et al, 2021 ; Moriuchi et al, 2021 ). In this context our proposed hypothesis is the following:…”

Section: Selected Variables For the Studymentioning

confidence: 99%

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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: Selected Variables For the Studymentioning

confidence: 99%

Section: Selected Variables For the Studymentioning

confidence: 99%

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

2022

View full text Add to dashboard Cite

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“…However, only a few studies investigated the antecedents of smart speakers' adoption in the consumer journey for activities related to commercial transactions. For example, interesting is the recent study proposed by Zaharia and W€ urfel (2020) in which the authors analysed the acceptance of smart speakers by German consumers, finding that they generally perceive high risk in using such new technologies. Specifically, in the model developed by Zaharia and W€ urfel (2020) the key factors positively influencing the acceptance of smart speakers to obtain information as well as make a purchase referred to performance expectancy, hedonic motivation, perceived price value and previous experience with smart speakers while perceived risk showed a negative effect on intended usage.…”

Section: Smart Speakers' Usage Intentionmentioning

confidence: 99%

How shopping habits change with artificial intelligence: smart speakers' usage intention

Aiolfi

2023

IJRDM

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PurposeThe research aims to understand how smart speakers are perceived by their actual and potential users, their attitude towards smart speakers and consequently their intention to use them.Design/methodology/approachThe authors apply a structural equation modelling (SEM) approach to test the research hypotheses through data coming from a structured questionnaire.FindingsThe results show that the higher the importance attributed to usefulness and ease of use, the higher the positive attitude that in turn positively affects the intention to use smart speakers. A significant relationship also emerged between task technology fit and attitude towards smart speakers, as well as between perceived enjoyment and attitude towards smart speakers. Perceived privacy risk, innovativeness and social attraction have been found to not significantly impact attitudes towards smart speakers.Originality/valueAlthough several academic studies have focused on various aspects of smart technologies, only a few studies discuss the factors that push consumers to use smart speakers for activities related to commercial transactions. Therefore, looking at the rapid rise of smart speakers for daily tasks and the gradual acceptance of voice interaction with digital tools, the authors proposed a study about Italian users' intention to use smart speakers. Specifically, to fill the gap in the existing literature, the authors applied a SEM approach to identify utilitarian and hedonic benefits that motivate the use of these devices.

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“…The determinants of use of VA (Burbach et al , 2019; Kessler and Martin, 2017) and more recently the adoption of VA in v-commerce (Zaharia and Würfel, 2021) have essentially been studied using the TAM (Davis et al , 1989) and the UTAUT (Venkatesh et al , 2007). These models are based on the premise that the decision on whether to use a technology depends on intention to adopt it and analyze the determinants of perceived benefits and costs and specifically the fact that a user’s positive beliefs reinforce their perception of benefits while their negative beliefs accentuate their perception of costs.…”

Section: Literature Reviewmentioning

confidence: 99%

Who are voice users? The contributions of decision-making conflict theory

Berriche

Benavent

Constantinides

2022

JCM

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Purpose This paper aims to categorize users of voice assistants and analyze decision-making conflicts to predict intention to adopt voice commerce (v-commerce). Design/methodology/approach This exploratory study used expert survey-based data collection founded on data saturation. Findings This study identifies three forms of voice systems based on senses aroused (screen first, voice only and voice first) and four profiles of voice users (passive resistant, hedonistic adopter, utilitarian adopter and active resistant), each with a different appraisal of the benefits and costs of v-commerce adoption and the experiences (positive or negative) felt during the shopping experience. This study proposes a conceptual model to predict intention to adopt v-commerce depending on voice-system and -user characteristics. Practical implications Learning from this study can help improve the marketing strategies and actions put in place by voice-assistant brands and advertisers by providing insights for adapting product recommendation algorithms to meet the needs of the identified profiles. Originality/value This paper provides an answer to the limits of classical approaches based on “one-size-fits-all” strategy by showing how voice-assistant users have different profiles that span a gradient of advance in technology adoption.

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Voice Commerce - Studying the Acceptance of Smart Speakers

Cited by 12 publications

References 8 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

How shopping habits change with artificial intelligence: smart speakers' usage intention

Who are voice users? The contributions of decision-making conflict theory

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