Engaging with intelligent voice assistants for wellbeing and brand attachment (original) (raw)
Related papers
Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation, 2020
AI-based voice assistant (VA) technologies are facing an unprecedented growth. VA are available as a standalone device like Amazon Echo dot or Google home and also as an extension such as Google maps and OK Google. Extant research has mostly focused on the device specific characteristics to explain the adoption of VA. In this research, we take a different approach and examine the psychological determinants of VA adoption. We look at how factors such as playfulness, escapism, anthropomorphism, and visual appeal of VA influence the attitudes (hedonic and utilitarian) of consumers. Moreover, we also examine the effects of psychological characteristics of VA on usage intentions and satisfaction, which lead to a favorable word-of-mouth (WOM) behavior that is critical for adoption of a technology. Using a structural equation modeling approach, our results suggest that psychological factors have a significant positive influence on both attitudes. Hedonic attitude further influences satisfaction and utilitarian attitude positively impacts usage and satisfaction, which have a positive association with WOM. Our research offers useful insights to marketers to increase the VA adoption and makes contributions to the literature.
Computers in Human Behavior, 2019
Artificial Intelligent (AI) In-home Voice Assistants have seen unprecedented growth. However, we have little understanding on the factors motivating individuals to use such devices. Given the unique characteristics of the technology, in the main hands free, controlled by voice, and the presentation of a voice user interface, the current technology adoption models are not comprehensive enough to explain the adoption of this new technology. Focusing on voice interactions, this research combines the theoretical foundations of U> with technology theories to gain a clearer understanding on the motivations for adopting and using in-home voice assistants. This research presents a conceptual model on the use of voice controlled technology and an empirical validation of the model through the use of Structural Equation Modelling with a sample of 724 in-home voice assistant users. The findings illustrate that individuals are motivated by the (1) utilitarian benefits, (2) symbolic benefits and (3) social benefits provided by voice assistants, the results found that hedonic benefits only motivate the use of in-home voice assistants in smaller households. Additionally, the research establishes a moderating role of perceived privacy risks in dampening and negatively influencing the use of in-home voice assistants.
Consumer trust and perceived risk for voice-controlled artificial intelligence: The case of Siri
Journal of Business Research, 2021
Artificial intelligence (AI) has the potential to change consumer behavior. However, despite billions of consumers using mobile smart phones, adoption of (artificial) intelligent voice assistants, like Siri, is relatively low. A conceptual model was constructed to determine the influence of consumer trust, interaction, perceived risk, and novelty value on brand loyalty for AI supported devices. Using the MTurk platform, data was collected from a sample of 675 Apple iPhone-using respondents. The findings showed perceived risk seems to have a significantly negative influence on brand loyalty; however, other factors were found to have a significantly positive influence on brand loyalty. The influence of novelty value of using Siri was found to be moderated by brand involvement and consumer innovativeness in such a way the influence is greater for consumers who are less involved with the brand and who are more innovative.
Psychology & Marketing, 2023
Voice assistants (VAs), such as Alexa, Siri, and Google Assistant, are instruments increasingly used by consumers to perform daily tasks. The objectives of the present study are to examine the antecedents of consumers' continuance intention to use VAs and the moderating effects of personal innovativeness and experience. Based on behavioral reasoning theory, a research model is proposed to provide insights into the drivers of continuance intention to use. Two empirical studies, based on data collected via online surveys, were conducted. The model was analyzed through partial least squares structural equation modeling. The findings of the studies showed that emotional value and performance expectancy were key antecedents of continuance intention to use, which in turn positively influenced actual use and word-of-mouth intention. In contrast, the quality value was a significant antecedent of continuance intention to use in only one of the two studies, and the influence of price value, social value, effort expectancy, and privacy risk was not found to be significant. However, the second study showed that several of these relationships are moderated by the consumer's experience and personal innovativeness; specifically, less innovative users are sensitive to quality value, and experienced users are sensitive to social value.
Routledge eBooks, 2022
Digital assistants (e.g., Apple's Siri, Amazon's Alexa, Google's Google Assistant) are highly complex and advanced artificial intelligence (AI) based technologies. Individuals can use digital assistants to perform basic personal tasks as well as for more advanced capabilities. Yet, the functional and topical use of a digital assistant tends to vary by individual. This study reflects the contextual experiences of the respondents. At present, there is little empirical evidence of customer satisfaction with digital assistants. PLS-SEM was used to analyse 244 survey responses to examine this research gap. The results confirmed that expectations and confirmation of expectations have a positive and significant relationship on customer satisfaction with digital assistants. This study provides evidence that customer expectations are being satisfied through the digital assistant interaction experience. As firms integrate digital assistants into their operations, they must help customers properly define what to expect from the firm's interactive experience.
Psychology & Marketing , 2024
While consumer engagement (CE) in the context of artificially intelligent (AI-based) technologies (e.g., chatbots, smart products, voice assistants, or autonomous cars) is gaining traction, the themes characterizing this emerging, interdisciplinary corpus of work remain indeterminate, exposing an important literature-based gap. Addressing this gap, we conduct a systematic review of 89 studies using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) approach to synthesize the AI-based CE literature. Our review yields three major themes of AI-based CE, including (i) Increasingly accurate service provision through AI-based CE; (ii) Capacity of AI-based CE to (co)create consumer-perceived value, and (iii) AIbased CE's reduced consumer effort in their task execution. We also develop a conceptual model that proposes the AI-based CE antecedents of personal, technological, interactional, social, and situational factors, and the AI-based CE consequences of consumer-based, firm-based, and human-AI collaboration outcomes. We conclude by offering pertinent implications for theory development (e.g., by offering future research questions derived from the proposed themes of AIbased CE) and practice (e.g., by reducing consumer-perceived costs of their brand/firm interactions).
Consumers Acceptance of Artificially Intelligent (AI) Device Use in Service Delivery
International Journal of Information Management, 2019
This study develops and empirically tests a theoretical model of Artificially Intelligent (AI) Device Use Acceptance (AIDUA) that aims to explain customers’ willingness to accept AI device use in service encounters. The proposed model incorporates three acceptance generation stages (primary appraisal, secondary appraisal, and outcome stage) and six antecedents (social influence, hedonic motivation, anthropomorphism, performance expectancy, effort expectancy, and emotion). Utilizing data collected from potential customers, the proposed AIDUA model is tested. Findings suggest that customers go through a three-step acceptance generation process in determining whether to accept the use of AI devices during their service interactions. Findings indicate that social influence and hedonic motivation are positively related to performance expectancy while anthropomorphism is positively related to effort expectancy. Both performance and effort expectancy are significant antecedents of customer emotions, which determines customers’ acceptance of AI device use in service encounters. This study provides a conceptual AI device acceptance framework that can be used by other researchers to better investigate AI related topics in the service context.
Hey Alexa, why do we use voice assistants? The driving factors of voice assistant technology use
Communication Research Reports, 2021
ABSTRACT This study identified three factors to predict the use of the artificial intelligence communication devices known as voice assistants such as Amazon’s Alexa or Apple’s Siri: technological factors (usefulness and perceived ease of use), social influence factors (personal and societal norms), and risk factors (privacy concerns and perceived security). Considering these factors as significant antecedents, this study proposed an extended Technology Acceptance Model. To examine the proposed model, an online survey was conducted with 558 respondents. The findings indicate that perceived usefulness, personal norms, and perceived security have positive relationships with attitudes toward using voice assistants. Additionally, privacy concerns had a negative relationship with attitudes toward using voice assistants, which in turn has a positive relationship with the behavioral intention. Practical and theoretical implications are discussed.
Journal of Retailing and Consumer Services, 2021
A voice assistant (VA), a type of voice-enabled artificial intelligence, is no longer just a character in science fiction movies. Currently, voice is embedded in a variety of products such as smartphones (mobile applications) and smart speakers in consumers’ homes. Furthermore, voice assistants are becoming integral to our daily lives. While human personalities shape the way we interact with the world, voice assistant personalities can also impact everyday interactions with our environment. This study identifies seven voice assistant personality traits (VAP) of three commonly used mobile applications: Microsoft’s Cortana, Google’s Assistant, and Amazon’s Alexa. To examine the effect of VAP on consumer experience, this study applies and extends flow theory to uncover why VAP has the effects it has and what facets of VAP drive the voice interaction flow experience that can influence consumers’ attitudes and behavioral intentions. Our study shows that voice interaction with a VA that incorporates functional intelligence, sincerity, and creativity empowers consumers to take control of their voice interactions with the VA, focus on their voice interaction, and engage in exploratory behavior. Consumers’ exploratory behavior leads to consumer satisfaction and consumers’ willingness to continue using voice assistant.
Servant, friend or master? The relationships users build with voice-controlled smart devices
Journal of Marketing Management, 2019
This paper investigates the different relationships consumers build with anthropomorphised devices and how these relationships affect actual and intended future usage. An exploratory, three-week empirical study of 39 informants using voice controls on their smartphone uncovered a diversity of relationships that the informants built with such devices. We complement anthropomorphism theory by drawing on extended-self theorising to identify three primary roles that emerge from consumers' interactions with these devices. Our findings theorise the distinct ways in which consumers perceive the object agency of anthropomorphised smart devices and how these perceptions impact the consumers' engagement and future use intentions.