Voice Assistant

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Computers in Human Behavior 99 (2019) 28–37 Contents lists available at ScienceDirect Computers in Human Behavior journal homepage: www.elsevier.com/locate/comphumbeh Hey Alexa … examine the variables influencing the use of artificial T intelligent in-home voice assistants Graeme McLeana,∗, Kofi Osei-Frimpongb a University of Strathclyde, Business School, Stenhouse Wing, Glasgow, G4 0QU, UK b GIMPA Business School, Room 1, D-Block, Achimota, Accra, Ghana ARTICLE INFO ABSTRACT Keywords: Artificial Intelligent (AI) In-home Voice Assistants have seen unprecedented growth. However, we have little Voice assistants understanding on the factors motivating individuals to use such devices. Given the unique characteristics of the Artificial intelligence technology, in the main hands free, controlled by voice, and the presentation of a voice user interface, the Machine learning current technology adoption models are not comprehensive enough to explain the adoption of this new tech- Technology adoption nology. Focusing on voice interactions, this research combines the theoretical foundations of U&GT with Social presence technology theories to gain a clearer understanding on the motivations for adopting and using in-home voice Uses and gratification theory 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. 1. Introduction technology, voice assistants are able to engage in complex dialog with an individual and execute multiple user requests. Given the over- Artificial Intelligence (AI) has become an important topic amongst whelming growth of voice-based technology, many individuals are individuals and firms over recent years (Guzman, 2018), particularly communicating with voice assistants as part of their everyday life in the given the growth of Voice Assistants (VAs). AI powered Voice Assistants same way as they would with other humans (Sundar et al., 2017). Voice including Amazon's Echo, Google's Google Assistant, Microsoft's Cortana powered AI technology and individuals' interactions with them is a and Apple's Siri have all contributed to the changing way in which in- timely and important area of research given the limited understanding dividuals consume content, complete tasks, search for information, we have on why individuals interact with in-home voice assistants and purchase products and interact with firms. McCue (2018) highlights the proliferation of the technology. that 27% of the global online population is using voice search, while it is predicted in-home voice assistants will see a growth of 1000% from The introduction of voice assistants on mobile devices provided 2018 to 2023 (Juniper & Research, 2018). Accordingly, Gartner (2016) individuals with the first opportunity to interact with AI in a useful and estimates that voice assistants will replace other technology such as PCs meaningful form (Guzman, 2018). However, in-home assistants, such as and laptop computers for many utilitarian shopping activities. Amazon's Echo device has further improved the interaction individuals can have with AI technology due to the advanced natural language While concerning for some individuals, voice assistants are always processing and machine learning capabilities inherent within in-home in listening-mode and are activated upon hearing a key word (also voice assistants. While human-computer interaction scholars (e.g. Nass known as a ‘wake-word’) to commence its functionality (e.g. Okay & Moon, 2000) have studied how individuals respond and behave to- Google, or Hey Alexa). Upon consuming the key word, the device is wards machines, including voice-based technologies (Nass & Brave, ready to interact with its user. The voice assistant uses natural language 2005), the communication abilities of AI voice assistants are far more processing and machine learning to interpret and understand the lan- advanced than earlier voice-controlled human-computer interaction guage of the user and processes a response all within real time (Hoy, (Guzman, 2018). Primarily, such advancements are due to the im- 2018). Therefore, due to the sophisticated programming of this plementation of natural language processing that allows individuals to ∗ Corresponding author. E-mail addresses: [email protected] (G. McLean), [email protected] (K. Osei-Frimpong). https://doi.org/10.1016/j.chb.2019.05.009 Received 15 January 2019; Received in revised form 16 April 2019; Accepted 3 May 2019 Available online 04 May 2019 0747-5632/ © 2019 Elsevier Ltd. All rights reserved.

G. McLean and K. Osei-Frimpong Computers in Human Behavior 99 (2019) 28–37 speak to and receive in-context replies from a computer in a similar way drive to use technology can be explained by an individual's attitudes to individuals' interactions with other human counterparts. Machine towards the technology along with its perceived usefulness and per- learning inherent in AI technology, which involves using algorithms ceived ease of use. Meta analyses found that the perceived usefulness and statistical models to perform tasks and make predictions without and perceived ease of use explains around 40% of the variance in an following explicit instructions or being programmed to perform the individual's behavioural intention to use a technology (Legris, Ingham, specific task, has the capability to learn user preferences and the topics & Collerette, 2003). Accordingly, criticisms have been aimed at TAM the user is interested in (Bishop, 2006). Thus, in-home voice assistants due to the oversimplified view of technology adoption (San-Martin, are designed to be more human-like than previous attempts and in- Lopez-Catalan, & Ramon-Jeronimo, 2013). Thus, TAM2 (see: Venkatesh tended to be an important part of an individual's everyday life, assisting & Davis, 2000) and TAM3 (see: Venkatesh & Bala, 2008) were later with everyday life tasks such as turning lights on and off, setting alarms, introduced, incorporating additional variables, most notably, social understanding a user's schedule, looking up recipes, providing custo- norms (TAM2) and enjoyment (TAM3). mised news information, checking on orders, purchasing items to name just a few useful functions. Furthermore, the Unified Theory of Acceptance and Use of Technology (UTAUT) provides an alternative theoretical understanding Despite the attention given to in-home voice assistants and the of technology adoption and use (see: Venkatesh, Morris, Davis, & Davis, proliferation of their adoption as well as their estimated future growth, 2003). Utilising numerous variables from TAM and extended versions there is little academic research exploring what influences individuals' (i.e. TAM2 & TAM3), UTAUT incorporates effort expectancy, perfor- use of voice technology. Given that voice assistants provide an alter- mance expectancy, social influence and facilitating conditions, which are native type of interaction that is often hands free and controlled by all moderated by age, gender, experience, and voluntariness of use; in voice, the characteristics of the technology differ from other technol- influencing intention to use a technology. Subsequent versions of ogies such as websites and mobile apps, as such, the existing theoretical UTAUT, namely, UTAUT2 also include hedonic motivation, price value models explaining adoption and use of technology (i.e. TAM, UTAUT) and habit (see: Venkatesh, Thong, & Xu, 2012). The motivation to de- may not comprehensively explain individuals' use of voice assistants. velop the UTAUT model was to integrate the numerous overlapping This research furthers our understanding in this domain through taking variables used to explain technology adoption and to create a ‘unified’ a Uses and Gratification theory (U&GT) approach to understanding the theoretical basis (Williams, Rana, Dwivedi, & Lai, 2011). Thus use of voice assistants focusing on voice interactions, while also in- Venkatesh et al. (2012) aimed to provide researchers a model that could tegrating Human-Computer Interaction (HCI) literature on the social be applied to understand the adoption and use of any technology. attributes of the system and individuals’ perceived privacy risks. However, despite the efforts of the UTAUT model and its extension to provide a unified theoretical basis, criticisms have been leveraged at the 1.1. Literature review model. Bagozzi (2007) critiqued the theory by arguing that a model with 41 independent variables for predicating intentions and a further Given the rise of smart technologies, individuals have recently eight variables for predicating behaviour reaches saturation and thus adopted an ‘always on’ online mentality which has become somewhat becomes of little help in informing technology adoption and use. Ad- ubiquitous (Rauschnabel, He, & Ro, 2018). The smartphone device was ditionally, Van Raaij and Schepers (2008) further criticise the UTAUT the facilitator of this mentality, quickly followed by tablet devices, model, pointing out that the explained variance in the model is only smartwatches and other wearable technology (Chuah et al., 2016). high when moderating key relationships with four variables. Thus, Thus, many individuals arrive at the introduction of the in-home voice while both TAM and UTAUT have been extensively used to understand assistant with the experience of adopting and using multiple smart technology adoption and use, criticisms have been aimed at both. Ad- technologies. ditionally, given the unique attributes of Artificial Intelligent tech- nology, such models may not encompass the motivations for adopting Voice assistants often provide a range of ways to interact with the and using advanced technology. Thus, U&GT may provide a useful device, for example, through the use of a mobile application (Alexa app theoretical underpinning to advance our understanding in this new available on the Apple store and the Play store), tactile buttons on the technological territory. device itself and most notably via voice. With the use of voice inter- action, AI voice assistants are arguably changing traditional forms of 1.2.1. Uses and Gratification theory human-computer interaction (Feng, Fawaz, & Shin, 2017). Accordingly, U&GT is a theoretical motivational paradigm (Katz, Blumler, & they are adapting how individuals’ retrieve information from websites and generally how they search for information (Hoy, 2018). Thus, voice Gurevitch, 1974) that can be used to understand individuals' motiva- assistants provide individuals with a convenient form of interaction tions to adopt technology (Grellhesl & Punyaunt-Carter, 2012). The with technology (Guzman, 2018) as users are not always required to theory is grounded in communication science and has been used to physically input or interact with the device, instead they are provided understand why individuals seek the use of specific media or tech- with a more human like experience and can interact via voice (Alepis & nology to satisfy their needs (Gallego, Bueno, & Noyes, 2016). U&GT Patsakis, 2017). Importantly, individuals do not need to stop their combines social and psychological attributes of needs (Wurff, 2011). current task to interact via voice, enabling them to multi-task (Nass & The theory proposes that individuals are goal oriented and select media Brave, 2005; Strayer, Cooper, Turrill, Coleman, & Hopman, 2017). that fits their needs (Katz et al., 1974). Luo and Remus (2014) outline Thus, the convenience offered by voice assistants is unmatched by any that the theory can be considered axiomatic as it can be applied to other technological system, allowing individuals to complete tasks with almost every type of media. Accordingly, it has been applied in tradi- little effort on their part and without the need to type, read or hold a tional media such as radio, television and newspapers (Bantz, 1982; device (Hoy, 2018). Leung & Wei, 1998), and interactive media including the Internet and websites (Flanagin & Metzger, 2001), social networks (Osei-Frimpong & 1.2. Adoption of technology McLean, 2018), online games (Wu, Wang, & Tsai, 2010), virtual and augmented reality (Rauschnabel, Rossmann, & Dieck, 2017; 2018). U& The Technology Acceptance Model (TAM) originally developed by GT can therefore be applied to understanding individuals' choice to Davis (1989) has been extensively used over recent years to understand partake in the use of in-home voice assistants as they are likely moti- the adoption and use of new technologies. The prominence of the TAM vated by their desire to gratify a range of needs. Accordingly, U&GT model is noted in the hundreds of articles across numerous disciplines provides an interesting theoretical lens to understand the motivations in which TAM has been used to understand technology adoption (Rese, towards using AI powered in-home voice assistants (such as Google's Baier, Geyer-Schulz, & Schreiber, 2017). Davis (1989) outlined that the Google Assistant and Amazon's Echo). 29

G. McLean and K. Osei-Frimpong Computers in Human Behavior 99 (2019) 28–37 While individuals’ needs will vary based on unique characteristics 2.1. Utilitarian benefits and situations, researchers have attempted to catalogue needs and gratifications (Katz et al., 1974). Most recently, Rauschnabel et al. Voice assistants have been conceptualised as offering individual's a (2018) outline three categories, including utilitarian benefits, hedonic useful and convenient way to complete tasks such as searching for in- benefits and symbolic benefits. From a utilitarian perspective, in- formation, purchasing repeat products or looking up customer service dividuals may use a voice assistant for information gathering to learn information (Hoy, 2018). HCI research has outlined the role of utili- about a topic or to complete a task. From a hedonic benefits perspec- tarian factors in influencing the adoption of technology (Venkatesh tive, individuals may use a voice assistant to seek enjoyment from the et al., 2012). Recent research has outlined the role of advanced tech- activity. Thirdly from a symbolic benefits perspective, individuals may nology such as mobile apps in providing individuals with utilitarian use specific media to reaffirm their social status, for example some benefits (McLean, Al-Nabhani, & Wilson, 2018). Given the aforemen- individuals may want to appear technologically advanced and savvy tioned ability to use in-home voice assistants hands free without the through using a voice assistant. However, Rauschnabel et al. (2018) need to interact with a physical user interface (rather a voice interface) overlooked the additional category, namely, social benefits, referring to and enabling individuals to multi-task during interactions, we posit that the idea that individuals use specific media for social needs. Prior re- the subsequent usefulness and convenience provided by in-home voice search has outlined the social benefits in applying U&GT to social media assistants will influence their use. Thus, we hypothesise: (Osei-Frimpong & McLean, 2018) and in online games (Wu et al., 2010). Osei-Frimpong and McLean (2018) as well as Wu et al. (2010) H1. The utilitarian benefits from in-home voice assistants will have a found that the social presence of others and the social attraction of positive influence on individuals' use of the technology. others motivated individuals to engage in social media. Thus, drawing on the aforementioned technology theories and U&GT we propose that 2.2. Hedonic benefits four key categories may motivate use of in-home voice assistants, (1) Utilitarian Benefits, (2) Hedonic Benefits, (3) Symbolic Benefits and (4) Previous research outlines that individuals interact with technology Social Benefits. Section 3.0 outlines our rationale. for hedonistic purposes (Wu et al., 2010). Hedonic benefits or attributes relates to the individual's emotional experience such as enjoyment and 1.3. Privacy risks pleasure obtained from interacting or using new technology such as in- home voice assistants (Schuitema, Anable, Skippon, & Kinnear, 2013). While voice assistants provide benefits to their users, continued Similarly, Venkatesh et al. (2012) further point to the role of enjoyment advancements in technology can pose threats to individuals' privacy in influencing individuals to adopt and use technology. TAM2 and the (Alepis & Patsakis, 2017). Collier (1995) outlines that privacy risks in UTAUT posit that enjoyment can influence the use of technology, relation to technology refers to the perceived threat to an individual's however this can be context dependent (Venkatesh et al., 2012). Pre- privacy due to the increased level of information that technology vious research from the online shopping environment suggests that gathers on individuals beyond the individual's knowledge and some- consumers who do not experience enjoyment during their shopping times control. Given that technology has become a central part of an encounter will unlikely use the service again in the future (Martin, individual's everyday life, particularly in the case of in-home voice Mortimer, & Andrews, 2015). Fang (2018) points out that while utili- assistants, privacy concerns among individuals continues to grow (Hoy, tarian benefits are fundamental to mobile app adoption and use, he- 2018). Lei et al (2018) outline that voice assistants such as the Amazon donic motivation to use them is fundamental in the success of apps. Echo have security vulnerabilities that can be exploited by hackers. Similarly, while prior research conceptualises the utilitarian benefits of Individuals shy away from talking about sensitive topics or using their voice assistants (Hoy, 2018), we suggest that hedonic motivations will voice assistants to make payments due to concerns over privacy be key to the success and continued use of in-home voice assistants. (Moorthy & Vu, 2015). Sophisticated voice assistants can perform high Thus we hypothesise: priority commends utilising personal account details, make appoint- ments, look up service information and place orders all on behalf of H2. The hedonic benefits from in-home voice assistants will have a their user (Feng et al., 2017). Thus, voice assistants require an extensive positive influence on individuals' use of the technology. set of software permissions to undertake their tasks, which individuals overwhelmingly provide (Alepis & Patsakis, 2017). Therefore, while 2.3. Symbolic benefits voice assistants aid individuals in their everyday life, such benefits are accompanied by a new set of risks that can make individuals vulnerable Symbolic benefits refer to the extent to which an individual per- to attacks on personal details (Lei et al. 2018). ceives to gain a symbolic reward such as making a favourable im- pression on others (Goodin, 1977). In part, this also relates to an in- 2. Conceptual development dividual's “sense of self or social identity” resulting from the adoption or use of new technology (Schuitema et al., 2013). Hence, previous Given the change in the type of user interaction with voice assis- research has outlined the role of image in influencing the adoption of tants, individuals have limited interaction with a traditional user in- technology (King & He, 2006), to the extent that an individual may terface, instead they most often interact hands free with their voice believe that the association with or use of the technology enhances their (Hoy, 2018). Accordingly, as AI voice assistants have boundary crossing social status. Wilcox, Kim, and Sen (2009) affirm that individuals often attributes, they differ from other existing technologies, as such, existing purchase luxury items for symbolic purposes to enhance social status. theoretical models (i.e. TAM & UTAUT) on their own may not be From a technology point of view, Rauschnabel et al. (2018) found that adequate in explaining behaviour towards the technology. Therefore, in the symbolic benefits derived from wearable technology (smart-glasses) consideration of the unique attributes of voice assistants, a combination influenced individuals' intention to use the technology. This view is also of U&GT and HCI attributes with voice technology, along with the at- shared by Selwyn (2003), who avers that individuals incorporate titudinal dimension of perceived privacy risks may offer the required technology use in their daily life as a result of the symbolic value they insight needed to understand the variables driving the use of in-home achieve in such an activity. In a similar vein, individuals may use in- voice assistants. home voice assistants to enhance their image and social status. Thus we hypothesise: H3. The symbolic benefits from in-home voice assistants will have a 30

G. McLean and K. Osei-Frimpong Computers in Human Behavior 99 (2019) 28–37 positive influence on individuals' use of the technology. 2.4. Social benefits Individuals have expressed their eagerness to talk to computers Fig. 1. Hypothesised model. since the first commercial computer was introduced (Hoy, 2018). Drawing on robotics research, it is apparent that there is a growing level financial details and seemingly unsecure private conversations. Thus of social presence from machines (Chattaraman, Kwon, Gilbert, & Ross, we hypothesise: 2018). Automated social presence is the extent to which machines make individuals feel as though they are in the presence of another social H6. Perceived privacy risks will have a moderating negative effect on: entity (Heerink, Krose, Evers, & Wielinga, 2010). Short, Williams, and Christie (1976) define social presence as the degree of salience of the a) the utilitarian benefits of in-home voice assistants influencing in- other person in an interaction. The works of Nass and colleagues (see dividuals' use of the technology. Fogg & Nass, 1997; Nass & Brave, 2005; Nass & Moon, 2000; Reeves & Nass, 1996) provide insight into how individuals treat computers like a b) the hedonic benefits of in-home voice assistants influencing in- social entity. This body of research outlines that as computers use dividuals' use of the technology. natural language, interact with users in real-time and in some cases fulfil traditional human operated social roles (e.g. customer service in a c) the symbolic benefits of in-home voice assistants influencing in- Bank), even advanced computer users often treat machines as social dividuals' use of the technology. entities (Lombard & Ditton, 2000). Moon (2000) posits that humans are socially oriented beings, and thus apply social roles when interacting d) the social presence benefits of in-home voice assistants influencing with technology such as politeness, pausing for response and curtsy individuals' use of the technology. during interactions in the same way as they would with another human. Lombard's research (1995; 2000) found that as computers can mimic e) the social attractiveness benefits of in-home voice assistants influ- human-like attributes, these attributes such as voice, appearance, and encing individual's use of the technology. mannerisms can act as cues that evoke social responses. Drawing on this, Li (2015) points out that human like attributes elicit social re- Following the conceptual development discussions, Fig. 1 provides a sponses. For example, language based conversations between in- pictorial representation of our hypothesised relationships. The hy- dividuals and AI powered devices serve as an important human-like pothesised model also illustrates four control variables, namely, tech- attribute that elicits a sense of social presence in the mind of the in- nology expertise, age, gender and household size. dividual. As individuals become comfortable in their conversations with an artificial personification, similar to conversations with other hu- 2.6. Methodology mans, they develop a rapport with the artificial assistant (Cerekovic, Aran, & Gatica-Perez, 2017). Cialdini (2007) suggests that individuals An online questionnaire using the Qualtrics platform was used to are more likely to be socially attracted to others with a pleasant de- gather the data to test the hypothesised model in Fig. 1. The research meanour, increasing their social attractiveness. Sundar, Jung, Waddell, was limited to the Amazon Echo in-home voice assistant due to the and Kim (2017) outline that robots can provide a sense of compa- large adoption rate of the device. At the time of writing, the Amazon nionship while assisting their users. Thus, according to the MAIN model Echo in-home voice assistant offered users the most advanced set of (Sundar, 2008), this can elicit the heuristic of social presence and social capabilities and largest range of ‘skills’ (i.e.: applications - branded and attractiveness. Accordingly, such social presence and social attractive- non-branded) to add to the Echo device. Over 50,000 unique branded ness may motivate individuals to engage with the AI technology in the ‘skills’ can be added to the Amazon echo including the Uber skill to order same way as they would with other human counterparts (Chattaraman a cab, United Airlines skill to check flight information and the Lonely et al., 2018; Sundar et al., 2017). Therefore, we hypothesise: Planet skill to learn about destinations (Kinsella, 2018). H4. The social presence from in-home voice assistants will have a Data were gathered from 766 consumers in the UK with the use of a positive influence on individuals' use of the technology. market research firm's panel. Respondents were offered a small fi- nancial incentive to take part in the research. Following data cleansing H5. The social attractiveness from in-home voice assistants will have a and removing those responses that contained missing values, the positive influence on individuals' use of the technology. sample consisted of 724 responses. Respondents had used the device for at least one month to provide insight into the variables motivating the 2.5. Moderating effect of privacy risks use of the in-home voice assistant, this information was collected fol- lowing an initial screening question in the questionnaire. Table 1 pro- With the advancement in technology, privacy risks have been centre vides an overview of the study's respondents. of attention with many new smart technologies (e.g., Wearable Technology: See Rauschnabel et al., 2018). Privacy risks have been The scales used in the research were drawn and adapted from scales conceptualised as having a dampening effect on individuals' adoption and use of voice assistant technology (Hoy, 2018). Hardware and software providers such as Google and Amazon have taken recent steps to include voice printing, which uniquely identifies the user of the device and stops the voice assistant from detailing personal information. Ad- ditionally, such systems have also introduced password controlled ac- cess to purchasing products. Yet, despite such attempts, privacy risks appear to have an influence on individual's attitudes towards the device (O'Flaherty, 2018; Feng et al., 2017). While individuals may derive benefits from their use of their voice assistant, such benefits may be reduced by the perceived privacy risks of stolen personal details, stolen 31

G. McLean and K. Osei-Frimpong Computers in Human Behavior 99 (2019) 28–37 Table 1 Number (n) Percentage 2.7. Preliminary analysis Details of respondents. 401 55 A range of preliminary analyses were calculated. As shown in Characteristics 323 45 Table 2, Cronbach's alpha coefficient was calculated to assess the re- liability of the scales used in the study. Each scale exceeded the value of Gender 55 8 0.7 affirming the scales are reliable indicators of their corresponding Female 240 33 variables (See Pallant, 2013 for critical values). Given the introduction Male 207 29 of the scale Social Presence, an Exploratory Factor Analysis (EFA) was Age Groups 153 21 conducted which illustrated a KMO sampling adequacy of 0.788 and a 18–24 69 9 corresponding p-value < .0001 for Bartlett's Test of Sphericity, a further 25–34 Confirmatory Factor Analysis (CFA) showed goodness of fit for the scale. 35–44 280 39 45–54 140 19 Furthermore, in order to test the hypothesised model in Fig. 1, 55–64 193 27 structural equation modelling (SEM) in AMOS Graphics was used. SEM Education 111 15 allows the hypothesised relationships to be tested in a simultaneous High-School Graduate analysis. However, SEM is a two-part process. First, a confirmatory College Degree 211 29 factor analysis (CFA) of the entire model is performed. The CFA outlines University Degree 309 43 the causal relationships in the model. The results of the CFA affirm No Formal Qualification 165 23 goodness of fit in the data: x(2317) = 824.670, ρ = 0.001, x2/df = 2.60; Technology Expertise 39 05 RMSEA = 0.047, RMR = 0.018, SRMR = 0.045, CFI = 0.9692, Very Experienced NFI = 0.961, GFI = 0.951. In addition, each of the regression values Experienced 398 55 were adequate and showed statistical significance (p < .05). Average User 326 45 Not Experienced Further analysis satisfied convergent and discriminant validity fol- Household Size lowing Fornell and Larcker (1981). The results illustrated in Table 3 One – Two Persons present the average variance extracted (AVE) values all above 0.50 and Three Persons and above construct reliabilities > 0.70. Accordingly, the AVE values were also greater than the square of their correlations, thus supporting dis- in the extant literature. 6 variables utilising a 7 point Likert scale criminant validity. (Strongly Disagree – Strongly Agree) were used to measure Utilitarian Benefits, Hedonic Benefits, Symbolic Benefits, Social Attractiveness, Prior to the second step in the SEM process, estimating the struc- Usage of In-Home Voice Assistants and Perceived Privacy Risk. A new tural model, common method bias and multicollinearity tests were scale was developed to measure Social Presence, drawing upon the calculated. Such tests help to avoid misleading conclusions from the previous works of Lee, Peng, Jin, and Yan (2006), Nowak (2013) and data. To examine if any common method bias (CMB) exists, a common Nass and Moon (2000). Table 2 outlines the items of each scale. latent factor was presented with all indictors of the variables included in the model. The common later factor outlined a value of .549. This Table 2 Scale items. Variable Reference Scale Items Cronbach's Alpha Hedonic Benefits Adapted from: Davis et al. (1992) .869 Utilitarian Benefits Adapted from: Taylor and Todd I find using my voice assistant to be enjoyable .779 Symbolic Benefits (1995) .805 Adapted from: Moore and Benbasat • The actual process of using my voice assistant is entertaining Social Presence (1991) • I have fun using my voice assistant to complete tasks. .841 • Using my voice assistant is a convenient way to manage my time. Social Attraction Newly Developed Scale • Completing tasks with my voice assistant makes my life easier. .874 Perceived Privacy Risk • Completing tasks with the voice assistant fits with my schedule .788 Lee et al. (2006) • Completing tasks with the voice assistant is an efficient use of my time Usage of In-home Voice Assistants Adapted from: Al-Debei et al. (2014) • Using my in-home voice assistant enhances my image amongst my peers .801 •• Using my in-home voice assistant makes me seem more valuable amongst my Venkatesh et al. (2012) peers Using my in-home voice assistant is a status symbol for me •• Using my in-home voice assistant makes me seem more prestigious than those who do not • When I interact with the voice assistant it feels like someone is present in the room My interactions with the voice assistant are similar to those with a human •• During my communication with the voice assistant I feel like I am dealing with a real person • I communicate with the voice assistant in a similar way to I communicate with humans I think the voice assistant (Alexa) could be a friend of mine • I have a good time with the voice assistant (Alexa) • I would like to spend more time with the voice assistant (Alexa) •• I have my doubts over the confidentiality of my interactions with the voice assistant I am concerned to perform a financial transaction via the voice assistant •• I am concerned that my personal details stored with the voice assistant could be stolen • I am concerned that the voice assistant collects too much information about me I plan to continue to use the in-home voice assistant in the future. • I intend to continue to use the in-home voice assistant in the future. •• I predict I would continue to use the in-home voice assistant in the future. 32

G. McLean and K. Osei-Frimpong Computers in Human Behavior 99 (2019) 28–37 Table 3 Convergent and discriminant validity. CR AVE MSV UB HB SB SP SA UVA PPR 0.847 Utilitarian Benefits (UB) 0.779 0.701 0.520 0.837 0.796 0.825 0.809 0.849 0.773 Hedonic Benefits (HB) 0.869 0.634 0.531 0.339 0.311 0.193 0.411 0.307 0.349 Symbolic Benefits (SB) 0.805 0.681 0.464 0.282 0.167 0.241 0.281 0.276 Social Presence (SP) 0.841 0.656 0.477 0.216 0.204 0.276 0.323 Social Attractiveness (SA) 0.874 0.722 0.524 0.197 0.311 0.232 Use of In-home Voice Assistant (UVA) 0.801 0.598 0.543 0.374 0.289 Perceived Privacy Risk (PPR) 0.788 0.701 0.499 0.204 CR - Construct Reliability; AVE – Average Variance Extracted; MSV - Maximum Shared Variance. value is subsequently squared to provide a percentage value significant relationship with usage of in-home voice assistants. We ca- (0.301 = 30%). As the value presented falls below 50% (see: tegorised house hold size as (1) occupied by one to two persons and (2) Ranaweera and Jayawardhena. 2014) it is unlikely that CMB exists. occupied by three or more persons. For the purpose of this analysis we labelled each category small household size (one to two persons) and Moreover, to assess multicollinearity each of the variables were large household size (three or more persons). Accordingly, given that assessed using the variance inflation factor (VIF) analysis. Given that in-home voice assistants are a feature of the household and the sig- the results outlined no variable above the critical value of 3.0 (Hair, nificant result, we further examined the effect of household size 2010) it can be concluded that multi-collinearity was not violated. through multi-group analysis. Through using AMOS Graphics, multi- group analysis was selected, regression paths were named, boot- 2.8. Results of SEM strapping was selected, where the bootstrapping confidence output il- lustrates the confidence interval between each household size. The re- Following the aforementioned tests, the structural equation model sults indicated a significant difference between Social Presence and Use was estimated testing the hypothesised relationships in Fig. 1. The of in-home voice assistants with regard to household size (Small structural model affirmed goodness of fit: (x(230) = 89.578, p < .05, x2/ Household: β = .711, p = .001; Large Household: β = 0.377, p = .039; df = 2.98, RMSEA = 0.052 (RMSEA Confidence Intervals: difference = p.033). Additionally, a significant difference is found be- LO90 = 0.031, HI90 = 0.073), SRMR = 0.019, RMR = 0.020, tween Social Attractiveness and Use of a voice assistant (Small CFI = 0.966, NFI = 0.959, GFI = 0.960) and shows support for some of Household: β = 0.695, p = .001; Large Household: β = 0.403, the hypothesised relationships as outlined in Table 4. p = .030; difference = p.041) as well as Hedonic Benefits and Use of a voice assistant (Small Household: β = 0.279, p = .050; Large House- The results from the structural equation model, as shown in Table 4, hold: β = 0.122, p = .113; difference = p.026). These results will be illustrate support for four hypotheses. The results indicate the im- discussed in more detail in subsequent sections. portance of the utilitarian benefits motivating the use of an in-home voice assistant, thus supporting H1 (Utilitarian Benefits → Usage of in- 2.9. Interaction moderation analysis home voice assistant; β = 0.681∗∗∗). Although, somewhat a weak re- lationship, the results also indicate support for H3 as Symbolic Benefits Moderation effect analysis was calculated to test the moderating appear to motivate individuals to use an in-home voice assistant role of perceived privacy risks and thus to test hypotheses H6 a, b, c, d (Symbolic Benefits → Usage of in-home voice assistant; β = 0.156∗∗). and e. The moderating effects were assessed in the entire model using Additionally, the ‘social benefits’, namely, Social Presence and Social moderated SEM in AMOS Graphics (see: Xanthopoulou et al., 2007). In Attraction have a strong effect in motivating individuals' use of in-home line with Ranaweera and Jayawardhena (2014) as well as Matear, voice assistants (Social Presence → Usage of in-home voice assistant; Osborne, Garrett, and Gray (2002), new variables were created in IMB β = 0.721∗∗∗; Social Attraction → Usage of in-home voice assistant; SPSS to examine the effects of the moderating variables. Firstly, the β = 0.692∗∗∗). independent variable was adapted (e.g. Utilitarian Benefits) and the moderating variable (Perceived Privacy Risks) through mean centring. While the results indicate support for hypotheses H1, H3, H4, and Accordingly, a new interactive term was created by multiplying the H5. A non-significant result was found between Hedonic Benefits and independent variable with the moderating variable, resulting in the Usage of an in home voice assistant (Hedonic Benefits → Usage of in- interactive term: Utilitarian Benefits X Perceived Privacy Risks. Thus, for home voice assistant; β = 0.142ns), thus affirming that individuals do hypothesis H6a, the dependent variable (Use of in-home voice assistant) not use a voice assistant for enjoyment or seek fun during interactions. was regressed on the independent variable (Hedonic Benefits), the Therefore, the research rejects H2. moderator (Perceived Privacy Risks), and the interactive term The research also controlled for age, gender, technology expertise and household size. The results in Table 4 indicate a non-significant affect with exception to household size. Household size shows a positive Table 4 Standardised Estimate β t-value R2 SEM standardised regression path analysis. .681 ∗∗∗ 3.88 .69 Hypotheses .142 ns 2.19 .69 .156 ∗∗ 2.10 .69 H1 Utilitarian Benefits → Usage of in-home voice assistant .721 ∗∗∗ 4.45 .69 H2 Hedonic Benefits → Usage of in-home voice assistant .692 ∗∗∗ 3.12 .69 H3 Symbolic Benefits → Usage of in-home voice assistant H4 Social Presence → Usage of in-home voice assistant .097ns 1.69 .67 H5 Social Attraction → Usage of in-home voice assistant .105 ns 1.51 .68 Controls .081 ns 1.22 .66 Technology Expertise → Usage of in-home voice assistant .233 ∗∗ 2.39 .70 Age → Usage of in-home voice assistant Gender → Usage of in-home voice assistant Household Size → Usage of in-home voice assistant 33

G. McLean and K. Osei-Frimpong Computers in Human Behavior 99 (2019) 28–37 Table 5 Standardised Estimate β t-value R2 Effect Interaction moderation analysis. Hypotheses H6a Utilitarian Benefits X Perceived Privacy Risks → Usage of in-home voice assistant .368 ∗∗ 3.12 .63 Dampening Effect H6b Hedonic Benefits X Perceived Privacy Risks → Usage of in-home voice assistant -.213 ∗∗ −2.24 .63 Negative Effect H6c Symbolic Benefits X Perceived Privacy Risks → Usage of in-home voice assistant -.111 ∗∗ −2.11 .63 Negative Effect H6d Social Presence X Perceived Privacy Risks → Usage of in-home voice assistant .432 ∗∗ 2.73 .63 Dampening Effect H6e Social Attraction X Perceived Privacy Risks → Usage of in-home voice assistant .398 ∗∗ 2.66 .63 Dampening Effect Dampening Effect = a statistically significant reduction with the presence of the moderating variable, but not changing the positive relationship. Negative Effect = a significant negative relationship. (Utilitarian Benefits X Perceived Privacy Risks). Thereafter, this process this research combines the theoretical foundations of U&GT with was repeated for H6b, c, d and e. technology theories and HCI literature to gain a clearer understanding on the motivations for adopting and using in-home voice assistants. The results determine a significant interactive influence supporting Therefore, this study presents a conceptual model on the use of voice each of the research hypotheses but with varying effect. Table 5 out- controlled technology and an empirical validation of the model with lines the relationships with the presence of perceived privacy risks. The users of in-home voice assistants. The validated model presents high results indicate the important moderating role of perceived privacy explanatory power (R2 0.69), with 69% of variance explained. In turn risks in influencing individuals’ behaviour. the research provides unique contributions to academic research in the field of technology adoption, human computer interaction, AI and While the utilitarian benefits and social benefits (social presence marketing. and social attraction) remain positively significant in influencing the use of an in-home voice assistant, the introduction of the moderating Firstly, we provide support for a new way to understand technology variable, perceived privacy risk, results in a reduction (dampening ef- adoption and use of AI powered voice controlled technology through fect) of the significance of these variables motivating use in comparison the identification of antecedents incorporating three dimensions, with the results in Table 4. Thus, perceived privacy risk is a concern for drawing upon U&GT. We find that individuals are motivated by the (1) individuals and a barrier to using the AI powered in-home voice as- utilitarian benefits, (2) symbolic benefits and (3) social benefits pro- sistant. The results also assert that the symbolic benefits of the voice vided by voice assistants. Conversely, the hypothesised hedonic benefits assistant (i.e. enhancing one's image) are outweighed by the perceived do not motivate individuals’ use of such technology. Accordingly, this privacy risks, resulting in a significant negative moderating effect be- provides insight into the purpose of using in-home voice assistants in tween Symbolic Benefits and Usage of the in-home voice assistant. order to complete goal driven tasks. Previous research (Martin et al., Lastly, the perceived privacy risk also further reduces the influence of 2015; Venkatesh et al., 2012) outlined that hedonic benefits from hedonic benefits. technology are key to success. However, this research finds that in- dividuals do not use voice assistants to seek fun or enjoyment. This may Furthermore, given the differences found in household size, further be due to the voice controlled user interaction that is void of supporting analysis of the moderating variable was conducted between ‘small rich media such as images or videos. Thus, users turn to voice con- household size’ and ‘large household size’. The results indicate overall a trolled technology due to their usefulness and convenience to aid them stronger interaction effect on larger household sizes. The results pertain in the completion of tasks, accordingly influencing the continuous use that for larger households, the hedonic benefits influence on use of the of the technology. in-home voice assistant is negatively significant when the moderating variable of perceived privacy risk is present (Large Household: Limited research has acknowledged the role of symbolic benefits β = −0.216, p = .037; Small Household: β = 0.127, p = .067; differ- influencing technology adoption and use. Wilcox et al. (2009) found ence = p.035), yet in a small household, the perceived privacy risks has that individuals often purchase items (particularly luxury items) to no moderating effect. Moreover, a significant difference is found re- enhance their social status. Rauschnabel et al. (2018) were the first to garding both social benefits dimensions (Social Presence and Social explore symbolic benefits in relation to technology, focusing on the Attraction). The results indicate that privacy risks have less effect on wearable technology, smart-glasses. This research finds a weak but smaller households in comparison to larger households (Social Presence: significant relationship between the symbolic benefits and the use of in- Small Household: β = 0.189, p = .419; Large Household: β = −0.122, home voice assistants. As AI technology has become more widely p = .072; difference = p.043; Social Attraction: Small Household: available, embedded as part of our everyday life and somewhat trendy β = 0.207, p = .381; Large Household: β = −0.158, p = .61; differ- to use, individuals may be adopting and using the technology to en- ence = p.027). All other relationships showed no significant differ- hance their social status to make them appear important within their ences. The following sections will discuss the theoretical and practical peer groups. Thus, in the same way individuals may furnish their home implications of these results. with designer hard and soft furnishings to elicit symbolic benefits, the in-home voice assistant may become part of this social enhancing ac- 3. Discusssion tivity. 3.1. Theoretical implications Moreover, a unique characteristic of in-home voice assistants is their ability to convey strong social benefits in the form of social pre- In-home voice assistants have grown in popularity over recent sence and social attractiveness. While technology in the past has been months and are forecasted for exceptional growth over the coming highlighted as conveying social presence, with individuals applying years, yet knowledge of the key success factors are unknown. This re- social rules to their interactions with computers (e.g. pausing for a re- search makes an attempt to address this gap. Use of such devices in an sponse, showing politeness and curtsy during interactions), AI powered individual's own personal space (i.e. their home) presents a new form of voice assistants convey one of the strongest humanlike attributes interaction with technology that is intended to be embedded as part of through the use of voice communication. Li (2015) outlined that voice individuals' everyday life. Given the unique characteristics of the interactions elicits the sense of social presence in the mind of an in- technology (hands free and controlled by voice), the current technology dividual. Cerekovic et al. (2017) suggest that individuals converse with adoption models are not comprehensive enough to explain the adoption voice assistants in the same way as they do with other humans, de- of this new technology. Thus in contributing to the extant literature, veloping a rapport with the artificial intelligent assistant. Accordingly, 34

G. McLean and K. Osei-Frimpong Computers in Human Behavior 99 (2019) 28–37 the results illustrate that such social presence conveyed by the voice social benefits and hedonic benefits derived from interactions with a assistant is a key factor to the success of the technology, thus motivating voice assistant by individuals in smaller occupied households is not individuals to use the device. Given that voice assistants ‘assist’ their interfered by perceived privacy risks. Thus, aligning with Sundar et al.'s users in a pleasant demeanour, such social attractiveness motivates (2018) research on AI companionship, such findings are possible in- consumers to interact with the technology. Alternative technologies do dications of the social benefits provided by AI voice assistants for those not convey such a humanlike social presence and thus technology who are possibly in need of social interaction. However, it should again adoption theories do not capture such a dimension in their explanation be noted that such findings may be explained by household composition of technology adoption and use. Therefore, given the advancements in rather than household size. AI technology utilising natural language processing and machine learning to learn and understand their user's preferences, the social 3.2. Practical implications presence and attractiveness machines are able to convey is a new and important dimension of technology adoption and use. Developers and producers of in-home voice assistants should con- tinue to develop the social benefits that are derived from user inter- The second major contribution of this research addresses the mod- action. As technological capabilities continue to advance and we have a erating role of perceived privacy risks. Prior research has outlined the better understanding of natural language processing and machine continued concern of privacy risks due to the speed and diffusion of learning, developers and producers should focus on developing the new technological innovations. Previous research has outlined that humanlike conversations between the voice assistant and the human privacy concerns can reduce an individual's intention to adopt tech- user. Machine learning inherent in AI technology has the capability to nology (Hoy, 2018). However, such technology does not contain the learn user preferences and the topics the user is interested in discussing, unique social presence and social attractiveness characteristics and thus focusing on such technology to offer further social benefits will advanced security of natural language processing and machine learning likely increase the number of individuals adopting and using the of voice assistants, whilst voice assistants are also used in the privacy of technology. one's home. Additionally, Rauschnabel et al. (2018) could not confirm the effect of privacy concerns on an individual's intention to use smart The findings of this study reveal that in-home voice assistants are wearable technology. However, our results outline a significant dam- used for utilitarian purposes. Thus, individuals are motivated to use in- pening effect of perceived privacy risks on utilitarian benefits and social home voice assistants to help them complete tasks, look up information, benefits. Whilst utilitarian benefits, and social benefits (social presence seek support and process orders. Developers that are developing skills and social attractiveness) remain statistically significant in influencing (applications) to add to in-home voice assistants should focus on the the use of in-home voice assistants, a significant dampening effect was utilitarian benefits that can be gained from their skill. Accordingly, found. Additionally, the perceived privacy risks have a significant ne- brands should consider the utilitarian value that a branded skill could gative effect on symbolic benefits to the extent that its influence on offer to individuals. Some branded skills focus on hedonic benefits, usage of a voice assistant becomes insignificant. Thus, the concerns of however, the results indicate that individuals are motivated to use their stolen person details, financial details and the perception of assistants in-home voice assistants for goal directed tasks. Thus, branded skills listening to private conversations as conceptualised in the literature that offer individuals convenience are more likely to be used. explains the dampening and negative effect of perceived privacy risks Additionally, brands should utilise the social benefits of the in-home on the use of the technology. voice assistant that is limited through other technology. Therefore, brands should focus on developing skills that enable the user to discuss Moreover, this research finds that the size of the household (Large a brand-related topic with the voice assistant that is of interest to the versus Small) has an effect on the motivators and use of the voice as- user. This offers brands the opportunity to learn about their customers' sistant. Given that the voice assistant is a household item the findings preferences and daily interactions within the intimate setting of the further our understanding of use. Households with fewer occupants (2 individual's own home. or less) are more motivated to use a voice assistant due to the social benefits. This may be due to the additional social presence offered by Security and privacy issues are an important concern for individuals the voice assistant, replacing interaction that may be had with a human due to the speed of diffusion and adoption of new technologies. Based counterpart in a larger household. Additionally, the results find that on our results, the perceived privacy risk of voice assistants has a sig- smaller households regard the hedonic benefits of the voice assistant nificant negative effect on the gratifications motivating individuals to (which was insignificant without the inclusion of household size) to use the technology. Given the large set of software permissions voice motivate their use of the technology. Thus it may be possible that those assistants require to undertake their tasks, individuals perceive to be at households with fewer occupants may turn to their voice assistant to risk over the privacy of their data and the potential for non-consented seek entertainment as well as social presence. Accordingly, individuals’ use. Therefore, while developers continue to learn the capabilities of interactions with an in-home voice assistant in a smaller occupied this new technology, the priority for developers should be ensuring the household may be used to replace the missing human interaction that is security and privacy of user interactions with the voice assistant. available in larger occupied households. This possible explanation is in Additionally, service providers should take steps to reassure and edu- line with Sunder et al. (2017) research that elderly individuals utilise cate individuals on the measures in place to ensure data privacy. For artificial intelligent robots for companionship to avoid loneliness. example, hardware and software providers such as Google and Amazon However, it should be noted that such differences could be explained by have taken recent steps to include voice printing, which uniquely iden- household composition (households comprising of a mix of adults and tifies the user of the device and stops the voice assistant from detailing children, adults only, couples and room-mates) rather than household personal information to anyone other than the main user. Alleviating size. such concerns on the individual's part would see further interaction with the technology. Moreover, privacy concerns appear to negatively influence house- holds that have a larger number of occupants in comparison to those Overcoming the issues of security and privacy concerns, the findings with a smaller number. Within larger occupied households, perceived illustrate that the symbolic benefits of the voice assistant motivate its privacy risks interferes with the social benefits (Social Presence and use. Given that the in-home voice assistant is a household item, pro- Social Attraction) in motivating the use of an in-home voice assistant. It ducers could offer a range of design lead and aesthetically pleasing could be possible that the perceived privacy risks may outweigh the devices to match the design of the user's home. social benefits, given that other human counterparts live in the household and therefore meet the social needs of an individual without Lastly, our findings noted the effect of household size. Therefore, the risks associated with using the voice assistant. On the contrary, the service providers should acknowledge the opportunity to segment communications messages targeted at each group. Households with 35

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Computers in Human Behavior 124 (2021) 106914 Contents lists available at ScienceDirect Computers in Human Behavior journal homepage: www.elsevier.com/locate/comphumbeh Exploring older adults’ perception and use of smart speaker-based voice assistants: A longitudinal study Sunyoung Kim *, Abhishek Choudhury School of Communication and Information, Rutgers University, New Jersey, USA ARTICLE INFO ABSTRACT Keywords: Thanks to their conversational capabilities, smart speaker-based voice assistants are gaining attention for their Older adults potential to support the aging population, though the empirical evidence is still scarce. This paper aims to obtain Voice assistant empirical evidence on older adults’ experiences with a voice assistant. We especially focused on how their Smart speaker perception and use change over time as they progress from novice to more experienced users through a longi­ Longitudinal study tudinal field deployment study. We deployed Google Home devices in the homes of twelve older adults aged 65 and above and studied their use for sixteen weeks. Results show that the benefits our participants perceived have incrementally changed from enjoying simplicity and convenience of operation in the early phase of the study to not worrying about making mistakes and building digital companionship as they got used to using it. Results also show that participants confronted several challenges that evolved from the unfamiliarity with a voice assistant in their first interactions to coping with the functional errors due to limited speech technology as they got used to using it. Based on the results, we discuss design implications that could foster better user experiences with a voice assistant among older adults. 1. Introduction that older adults generally have positive perceptions when introduced to a smart speaker (Blair & Abdullah, 2019), preferring voice-based user With the advancement of artificial intelligence and speech technol­ interfaces over traditional interaction modalities such as clicking or ogy, smart speaker-based voice assistants are becoming increasingly typing (Kowalski et al., 2019; Wulf et al., 2014). available in the market. It is estimated that in 2019, over 98 million units of smart speakers have been sold worldwide, and it is predicted to Despite the rapid growth of voice technology and its hyped antici­ reach up to 409.4 million units in 2025 (Vailshery, 2019). Nearly a pation for older adults, smart speakers’ actual adoption and use among quarter of households own a smart speaker in the US, and more than half older adults are very low. Older adults are slower to adopt new tech­ of them use two or more smart speakers (Richter, 2020). The smart nologies than younger adults (Vaportzis, Giatsi Clausn, & Gow, 2017), speaker is a type of speaker (e.g., Amazon Echo, Google Home) with an and a smart speaker is not an exception. The statistics show that younger integrated virtual assistant (e.g., Amazon Alexa, Google assistant) that Americans in the 18–29 age group are 75% more likely to own a smart responds to voice commands. It assists users in their daily lives, such as speaker than those over 60 in 2019 (Kinsella, 2019). Considering the playing music, checking weather forecasts, setting alarms and re­ potential of voice assistants for older adults and their low adoption rate, minders, controlling applicable smart home devices, and answering it is crucial to investigate the use of voice assistants for older adults. general questions. Because speech is one of the most natural ways of human communication, using speech to interact with devices can lower To date, researchers have extensively investigated various aspects of the barriers of technology use for those who are less familiar or have using voice assistants, including the use by specific user groups, (e.g., manual-dexterity and vision-related issues with typing- and people with disabilities (Abdolrahmani, Kuber, & Branham, 2018; screen-based interfaces. Therefore, this technology has gained particular Pradhan et al., 2018), children (Druga et al., 2017; Garg & Sengupta, attention as beneficial for older adults (Blair & Abdullah, 2019; Portet, 2020), low-income populations (Robinson et al., 2018), privacy con­ Vacher, Golanski, Roux, & Meillon, 2013). Recent studies have shown cerns (Lau et al., 2018; Liao et al., 2019), conversational aspects (Purington et al., 2017; Vtyurina & Fourney, 2018), and personification (Lopatovska & Williams, 2018; Pradhan et al., 2019). Recently, * Corresponding author. E-mail addresses: [email protected] (S. Kim), [email protected] (A. Choudhury). https://doi.org/10.1016/j.chb.2021.106914 Received 15 January 2021; Received in revised form 2 June 2021; Accepted 9 June 2021 Available online 14 June 2021 0747-5632/© 2021 Elsevier Ltd. All rights reserved.

S. Kim and A. Choudhury Computers in Human Behavior 124 (2021) 106914 researchers have recognized the potentials that a smart speaker can offer in everyday contexts through a quantitative analysis of voice command in the aging society (Portet, Vacher, Golanski, Roux, & Meillon, 2013) data (Sciuto et al., 2018). As a result, researchers have identified music, and investigated older adults’ experiences with this technology. For search, and Internet of Things (IoT) as the most frequently used features instance, Pradhan et al. found reliability concerns as one of the key of voice-based commands (Ammari et al., 2019), popular types of challenges that prevent older adults with low technology use from commands in different times of the day (e.g., entertainment and home adopting this technology (Pradhan et al., 2020), and Trajova and automation commands peak in the evening while the weather and time Martin-Hammond found that lack of perceived utility contributes to request peak in the early morning hours) (Bentley et al., 2018), and increasing attrition rates among experienced older users (Trajkova & voice assistants integrating into people’s everyday routines by embed­ Martin-Hammond, 2020). Yet, missing is a holistic perspective on how ding in a range of conversational activities in the home (e.g., family older adults progress from novice to more experienced users and what dinners) (Porcheron, Fischer, Reeves, & Sharples, 2018). Other re­ contributes to or prevents their sustained use of a voice assistant. This searchers have found that individuals are motivated to use a voice as­ paper contributes to the growing body of literature by investigating how sistant by utilitarian, symbolic, and social benefits (McLean & novice older adults’ perception and use of a voice assistant change over Osei-Frimpong, 2019). However, its attrition rate is high due to unmet time as they become more experienced through a longitudinal deploy­ expectations and a lack of perceived utility (Cho et al., 2019). ment study. Specifically, we aim to answer the following research questions: Privacy was another major research topic due to a voice assistant’s privacy-intrusive potential to continuously listen to voices in intimate • What do older adults use a voice assistant for? spaces such as the home (Clark et al., 2019; Cowan et al., 2017; Myers • What different benefits do they perceive as they use the device over et al., 2018). The conversational nature and the “always-on” listening feature of a smart speaker have also drawn researchers’ attention to time? social aspects of using voice assistants, such as personification or • What different challenges do they face when using the device over anthropomorphism (Pfeuffer, Alexander, Gimpel, & Hinz, 2019). Re­ searchers have revealed that some users perceived the device as a time, and how do they progressively respond to or cope with those companion or a friend and exhibited personifying behaviors toward challenges? voice assistants by using human-like terms such as “she” or “her” when referring to a voice assistant (Purington et al., 2017). At the same time, To answer our research questions, we deployed a smart speaker in others have argued that such interaction patterns are “mindless” social the homes of twelve older adults aged 65 and above and studied its use responses that may not relate to the actual perception of personification for sixteen weeks. We conducted in-person interviews by visiting the (Lopatovska & Williams, 2018). Relatedly, researchers have investi­ homes every other week for the deployment duration and collected gated factors that constitute an effective conversation with voice assis­ usage logs from the system’s history repository for data collection. tants, though user experiences with voice assistants thus far remained disappointing due to the constrained and predefined turn-taking struc­ Our findings revealed the frequently used features, perceived and ture of question-answer rather than a realistic dialog of conversation experienced benefits, and difficulties with coping strategies that older (Cowan et al., 2017; Murad & Munteanu, 2019). adults might have when interacting with a voice assistant. We found that participants recognized, appreciated, and enjoyed several benefits that a Lastly, research has explored the potential utility of voice assistants voice assistant offers throughout the study period. However, they did as assistive technology for people with disabilities (Abdolrahmani, not acknowledge all the benefits in their first interactions. Instead, the Kuber, & Branham, 2018), specific health concerns (Maharjan et al., perceived benefits have incrementally changed from enjoying simplicity 2019), and the aging population. Recently, research efforts have been and convenience of operation in the early phase of the study to not increasingly devoted to understanding the use of smart speaker-based worrying about making mistakes and building digital companionship as they voice assistants by and for older adults, which we describe in the next got used to using it. In addition, we found that the driving force to section. sustain the use was how positive the user experience with a voice as­ sistant was, which was determined not only by its task completion but 2.2. Voice assistants for older adults also through building companionship with the device. Then, the positive experience with a voice assistant contributed to developing a resilient An extensive research effort has been made to identify older adults’ attitude toward its functional errors. These findings can be used as needs and evaluate the feasibility, effectiveness, and acceptability of design guidelines to better leverage and promote the sustained use of the existing technologies to meet their needs (Pyae & Joelsson, 2018; Liu emerging personal technology, a smart speaker–based voice assistant, to et al., 2016; Mitzner et al., 2010). Many studies have reported that older support the aging society. To the best of our knowledge, this is the first adults are generally positive about new technologies and are willing to study that investigated the progressive use of a voice assistant among accept them if perceived benefits are evident (Morris et al., 2013). older adults as they move from novice to more experienced users However, a digital divide still exists (Delello & Mcwhorter, 2017), and through a longitudinal field deployment study. more efforts are needed to make new technologies readily accessible to older adults. 2. Background Research has shown that voice-based interactions have several po­ The technology that responds to voice commands has been called by tential benefits to support older adults. Common difficulties older users many different terms, including voice assistant, voice-user interface, experience when using computers and smartphones are caused by the virtual assistant, intelligent assistant, and conversational agent. This prevalence of Graphical User Interface (GUI) and its desktop metaphor paper uses “voice assistant” to encompass the above terminology and (Sayago et al., 2019). Using voice as an interaction modality can help refer to a smart speaker’s voice-based interface. address many difficulties that GUI entails. First, through speech, voice assistants allow people to interact with them in a natural way of 2.1. Voice assistants communicating with a person (Mctear et al., 2016). Because they are deemed simple and easy to use and ideal for users with visual and motor In recent years, there has been growing interest in smart speaker- impairment, speech has been considered an accessible and useable based voice assistants in the Human-Computer Interaction community, interaction modality for older adults (Bickmore et al., 2005). Yet, several studying the use of voice assistants from several angles. First, the lack of challenges also exist, such as unfamiliarity with talking to a device a detailed understanding of how these technologies are used due to their (Myers et al., 2018) and not knowing what (and how) to say to it (Sayago novelty has led to investigating general patterns of using voice assistants et al., 2019), as well as aging-related declines that might impede using 2

S. Kim and A. Choudhury Computers in Human Behavior 124 (2021) 106914 voice assistants. Hearing loss, which is one of the most common physical Table 1 Length of Reason for problems that people experience as they age, imposes a fundamental Participant demographics. participation dropping challenge for its use. Designers are usually considerably younger and Full – may not know about the physical and psychological aspects of aging, ID Age Gender Health concerns having grown up using more advanced technologies than older adults Full – (Porcheron, Fischer, Reeves, & Sharples, 2018; Sebastiaan et al., 2016). P1 90 M Arthritis (on a Full – Thus, it is essential to investigate the barriers that older adults might wheelchair) have when using speech to interact with a device from the perspectives P2 77 F – Full – of older adults. P3 65 F Mild memory Full – In the context of voice technology for aging, most studies have P4 75 M loss focused on the idea of facilitating voice-based systems as digital com­ Mild memory Full – panions or virtual assistants to support aging in place (Bickmore et al., P5 94 F loss 2016; Heerink et al., 2010; Tsiourti et al., 2016; Van Hoof, Kort, Rutten, Wearing a Full – & Duijnstee, 2011). In particular, a stream of research has investigated P6 87 F hearing aid the benefits of using embodied conversational agents – a graphical Wearing a Full – human agent capable of engaging in conversations with humans by both P7 89 M hearing aid Full – understanding and producing speech and facial expressions (Cassell, Mild memory 4 weeks Hospitalization Bickmore, Campbell, & Vilhja´lmsson, 2000, pp. 29–63) – to support P8 78 F loss older adults’ healthcare in various aspects (e.g., improving access to P9 78 M – 2 weeks Lost interest online health information (Bickmore et al., 2016), delivering medication P10 95 F 4 weeks Lost interest instructions (Azevedo et al., 2018), mitigating social isolation (Sidner – et al., 2018)). Recently, researchers have recognized the potentials that P11 85 F a smart speaker can offer in the aging society (Pradhan et al., 2020) and P12 82 M Arthritis (on a investigated older adults’ use of this technology. wheelchair) – In summary, older adults were positive about the usability of a voice assistant when they were first introduced to it, thanks to its simple and Arthritis (on a effortless interactions (Kowalski et al., 2019; Portet, Vacher, Golanski, wheelchair) Roux, & Meillon, 2013). At the same time, they had raised many open questions about its usefulness and utility as they were using it (Pradhan mild memory loss. Among them, three participants dropped out of the et al., 2020; Trajkova & Martin-Hammond, 2020). Yet, missing is a study within the first month of deployment due to personal reasons. One holistic perspective on how older adults’ perception and experience participant dropped after having three interviews because she was change over time and what contributes to or prevents their sustained use hospitalized, and two participants dropped after having two and three of a voice assistant. This paper contributes to this emerging body of interviews respectively because they lost interest in using a voice as­ literature by exploring how older adults perceive and use a voice as­ sistant. The other nine participants completed the study for the entire sistant progressively as they move from novice to more experienced sixteen weeks. users through a longitudinal field deployment study. 3.2. Data collection 3. Methods Our interview protocol focused on investigating how older adults Our analysis is based on the data collected from the deployment of initially perceive and respond to a voice assistant, how they use it in Google Home devices in the homes of twelve individuals aged 65 or their daily lives, what challenges and difficulties they face when using it older up to sixteen weeks. The study was approved by local Institutional and how they cope with those challenges, and how their perspectives Review Boards, and informed consent was obtained from all participants and usage patterns change over time. We constructed a set of open- for the use of collected data before participating in the study. ended interview questions in three phases of the study duration to explore these spaces. In the first phase, we focused on understanding 3.1. Participants initial impressions and the perceived usefulness of a voice assistant in the first few weeks after installation. In the second phase, we focused on For participant recruitment, we first contacted two senior-living exploring the user experience in-depth, including usage patterns, the communities located in the greater New York area. We visited each needs and challenges, and strategies to cope with breakdowns when community and explained the purpose of the study to the manager. interacting with a voice assistant throughout the deployment duration Upon their approval, we posted a recruitment flyer in the lobby of the except for the last interview. When needed, participants were asked to communities. Two inclusion criteria for participation were age being interact with a voice assistant during the interview to demonstrate how over 65 and having no prior experience with a voice assistant. they would use it in their everyday lives. For instance, when a partici­ pant reported a complaint of a voice assistant not understanding their In total, we recruited 12 participants (7 females and 5 males), commands, we asked them to show how they would make a command ranging in age from 65 to 95 (mean age = 83.8, SD = 9.1, see Table 1). during the interview to understand the context behind user feedback. In All participants resided in a single-person unit in the community. About the third phase, we focused on the overall reflection on users’ experi­ general technology use, all participants said that they were familiar with ences of interacting with a voice assistant and suggestions for im­ computers, tablets, and a smartphone, and four participants said that provements in the last interview. While we had three phases for they have seen a smart speaker in their children’s homes but have not interview protocols, neither were these mutually exclusive nor had a used it. Seven participants owned a tablet, and all participants reported fixed duration of phases in the interviews. Instead, we proceeded with regularly using a computer for information search and email. About the interviews flexibly depending on participants’ experiences. For health conditions, two participants were wearing a hearing aid but did instance, those who quickly adapted to using a voice assistant moved to not have any problem with having a conversation. Three participants the second phase within the first couple of interviews, while those who were using a wheelchair due to various joint issues. Other than those needed more time to be familiar with using it stayed longer in the first residents, all participants reported being healthy both physically and phase. cognitively with mild aging-related health issues, such as arthritis or For the first interview, the research team visited a participant’s home and set up a Google Home mini in the location of their preference (e.g., a nightstand, a coffee table). After the device setup, a participant was introduced to Google Home mini as “a device that responds to your voice command, providing you answers about things you ask or need on an 3

S. Kim and A. Choudhury Computers in Human Behavior 124 (2021) 106914 everyday basis” and given basic instruction on how to use it for about 10 “First impression” represents a participant’s reaction or response to a min. For the instruction, a researcher first demonstrated how to use the voice assistant when they first interacted with it. The open code “No self- device by making voice commands for basic tasks, such as setting alarms efficacy” in the example above was categorized as “First impression” and reminders (e.g., “Remind me to take my medicine at 10 a.m.”), during axial coding. Lastly, we followed the selective coding process to streaming music and radio (e.g., “Play music”), and asking general assemble our conceptual phenomena extracted from axial coding. The questions (e.g., “Who is the second president of the United States?”). goal of this step is to integrate all concepts through building relation­ Then, participants were prompted to try interacting with the device and ships across phenomena. to ask questions about using it. Once they had no more questions about using a voice assistant, we started to ask for feedback about their initial In addition, the first author manually coded the usage logs deduc­ perspectives on the device for the rest of the interview. In addition, tively by the type of operation, such as playing music, asking general participants filled out a survey to inform us about their basic de­ questions, having a casual conversation, checking time/weather, setting mographic information, including age, health concerns, and experience up a reminder/alarm, operating basic controls (e.g., “stop” to stop with technology. Lastly, participants were told to freely interact with a playing music), etc. For instance, we coded the query “play a song from voice assistant as much or as little as they wanted throughout the study the 80s” as “playing music.” Since we used one Google account created period and were given the contact information of the research team if by the research team for all devices, the usage log data was only avail­ they needed technical support, and we completed the first interview that able for the sample in aggregate, not per participant. lasted about an hour. Next, we conducted eight follow-up interviews by visiting them every other week for sixteen weeks, which makes nine 4. Findings interviews per participant. Each follow-up interview lasted between 30 min and 1.5 hours. All interviews were audio-recorded and transcribed. 4.1. The features in use In the last interview, participants were offered an option to keep a In total, we retrieved 2242 pairs of request-response communica­ Google Home mini if they wanted, and all participants decided to keep tions from the usage log of the four-month deployment study. Among it. Participants who completed the study were fully compensated with a those, 1488 pairs of communications (66.4%) were successful as a voice $160 gift card upon completion. Those who withdrew were partially assistant performed tasks as requested, while 754 pairs (33.6%) were not compensated with a gift card which amount was prorated by the dura­ as their responses were logged as “Sorry, I don’t understand.” While the tion of participation. mean frequency of use is 1.8 times per day, the actual usage rates greatly varied among participants. Some participants said that they barely used We also collected the device usage logs from Google’s activity history the device, and others used it a lot when we asked about their experience repository, My Activity,1 which stores the complete history of a pair of with it in the interviews. The most frequently used feature of a voice user’s commands to Google’s products and the response it has given. We assistant was playing music (e.g., “Play sounds of the ocean,” 37%), downloaded a pair of voice inquiries made to a Google mini and its followed by searching for general information (e.g., “Is Friends playing verbal responses of all participants for the duration of the study from this on TBS tonight?”, 16.5%), making casual conversations (e.g., “How are repository. This usage log provided us with the actual interaction pat­ you?”, 12.1%), checking the current time and date (e.g., “What time is terns to complement participants’ perceptions of these interactions. it?”, 11.1%), setting up a reminder or an alarm (e.g., “Remind me to take my med at 9 a.m.”, 4.3%), and checking weather forecasts (e.g., “What’s 3.3. Data analysis the weather today like?”, 3.2%) (See Table 2). This echoes the findings by Ammari et al. that identified music and search as the most commonly We analyzed our interview data using thematic analysis to reveal used features of voice assistants (Ammari et al., 2019). patterns across data sets (Braun & Clarke, 2006). Thematic analysis is a method for identifying patterns and themes in qualitative data, making Playing music before bed to fall asleep and waking up checking the it suitable for analyzing our data set, where the blending of interview time and weather were the two most prevalent features of use among all scripts, field notes, and photos creates a well of potentially rich thematic participants, which has quickly become part of their everyday routines. data to draw from. We selected thematic analysis because of its emphasis on proceeding with an open mind to investigate explanatory conceptual “I play it (a voice assistant) before I go to bed at night, the sounds of themes associated with older adults’ use of a voice assistant over time. the ocean. That helps me fall asleep. That’s technology. If you don’t The thematic analysis involves open coding, axial coding, and selective like a song they’re playing, you say, “Hey Google, I don’t like that coding for theme identification. To this end, each interview script was song. Play something else”. And within an instant, they’re playing analyzed with open coding to note the themes or factors that emerged in something else. That’s the part that amazes me. How can they do it the data. Then, the emerged themes were continuously discussed as a that fast? They’re not searching for anything. It just comes.” (P5W2) group with another author until no new information was anticipated. First, we conducted open coding to identify concepts significant in the “When I wake up, and I want to know the time I say Hey Google, data as abstract representations of events, objects, happenings, actions, what time is it. Or hey Google, how’s the weather gonna be today, etc. The example below expresses one participant’s lack of confidence and she says the weather today is so and so. I used to always turn on about interacting with a voice assistant when first introduced. This the TV as a first thing in the morning to see the weather. I don’t do response is coded as “No self-efficacy”. that anymore, which I think is very fabulous in itself.” (P8W5) [No self-efficacy] “I’d like to have one (a voice assistant), but you These patterns were also reflected in the frequency of operation by think I would be able to use it?” [/No self-efficacy](P2W12). the time of day when the usage surged around 9 p.m. and 7 a.m. (See Graph 1). The usage also surged during the daytime between 1 p.m. and Next, we categorized the related concepts created by open coding 4 p.m., but we do not have concrete evidence to explain the rise of the into conceptual phenomena using axial coding. Phenomena refer to usage frequency in the afternoon since there was no specific pattern of repeated patterns of events, happenings, actions, and interactions rep­ use in the commands. We only assume that it might be because partic­ resenting people’s responses to problems and situations. For instance, ipants had more free time staying in their rooms between lunch and dinner while attending social activities before lunch and watching TV 1 https://myactivity.google.com/myactivity. after dinner. 2 In the excerpt, P# refers to the #th participant, and W# refers to the #th interview. For instance, P2W1 is an excerpt from Participant 2 in the first One thing to note is that the actual frequency of using a reminder or interview. And VA refers to a voice assistant. alarm must be higher than what was shown in the usage log. The log data records user activities as a pair of a user’s verbal command and the 4

S. Kim and A. Choudhury Computers in Human Behavior 124 (2021) 106914 Table 2 The frequency of operation by topics: Successful operations refer to the commands with proper device responses. Operation Music Search Basic device control Casual conversation Time Reminder Weather Other Total All 829 (37%) 371 (16.5%) 315 (14%) 272 (12.1%) 248 (11.1%) 96 (4.3%) 72 (3.2%) 39 (1.8%) 2242 Successful 610 (41%) 207 (13.9%) 186 (12.5%) 146 (9.8%) 189 (12.7%) 82 (5.5%) 45 (3.0%) 23 (1.5%) 1488 device’s voice response. And the response is blank when a user com­ when using typing- and screen-based devices, which some older adults mand is “stop” since there is no voice response to “stop,” the command find demanding to acquire (Mitzner et al., 2010). A voice assistant that a user makes to turn off a reminder notification or stop playing successfully removed this perceived barrier and made our participants, music. For recurrent reminders and alarms, a command for initial setup first-time users, readily accept the device with no concerns about was captured in the log data (e.g., “Wake me up at 7 a.m. every morn­ learning new things. ing.”), while its repeated use throughout the study period was indirectly logged as the “stop” command. This is reflected in the frequency of “Computers came along, but you have to learn how to use different commands for basic device control (14%), 52% of which was “stop” icons and get in and out of Windows and load things and download made in the morning, though we cannot attribute all these commands to things. And this (a voice assistant) is like having a person around you using a reminder or alarm feature since it is also used to stop playing that talks back if you want it to. It’s awesome. I think I can use it.” music. (P9W1) 4.2. The perceived and experienced benefits “It’s cooler. I don’t have to type into it, which I am not good at. I just say, hey, give me some Louis Prima or Frank Sinatra, and I get it right Older people are a heterogeneous group, and each person has their away. I don’t have to wait. That’s like so futuristic that I didn’t think perceptions and experiences about technology. Therefore, our partici­ I’d live to see this day.” (P6W2) pants expressed divergent feelings and perspectives about a voice as­ sistant when first introduced, from excitement and curiosity to 4.2.2. Transitioning to competent users: convenience of operating without hesitance, uncertainty, and even refusal of the device. physical interaction “I’d like to have one (a voice assistant), but you think I would be able After using a voice assistant for a few weeks, participants started to to use it?” (P2W1) mention the convenience of interacting with it from a distance without having to use any tools for input or read visual outputs. Participants “I’m really amazed by it (a voice assistant), really and truly. I’ve seen appreciated that a voice assistant does not require any physical inter­ a lot of things, but I think this is about the smartest. People are now action except speaking and listening to it. Particularly for those who had living in this modern technology, and it’s just part of life for them. As physical declines, such as decreased mobility or vision loss typical of the being older, I didn’t have any of this as a kid. Here I am, at this age, at natural aging process, being able to operate the device using a voice 95, and I’m listening to Google Home. It really is an accomplishment from a distance was acknowledged as a significant advantage over other to listen to what this little thing can say. It’s amazing.” (P5W1) devices they own. “Could people get any lazier? How lazy have people gotten today “I like it (a voice assistant) because I can’t see without my glasses. I that they can’t even stand up and turn a light on and off? That’s a think it is kind of in the back of my mind because I don’t physically little sad. There’s a movie about the future, and all the people are so have to look at it. I’ll go by and say Hey Google, what’s so and so. huge that they go around on these conveyor belts, in chairs, because And it tells me so fast that I don’t even look at it.” (P5W4) they’re too lazy to walk from one place to the other. That’s where we’re headed with a technology like this. It’s a little scary.” (P12W1) However, such benefits entailed concerns later on. For instance, executing a command without any physical effort made them worry Then, participants recognized, appreciated, and enjoyed several about becoming lazy and inactive, which would ultimately negatively benefits that a voice assistant offers as they continued interacting with it. impact the quality of life. However, they did not acknowledge all of these benefits in their first interaction. Instead, the types of benefits that participants mentioned “I love to hear it (a voice assistant) read me a book. But see, then, I’m have incrementally changed from enjoying simplicity and convenience of not using my brain. I don’t wanna become a vegetable and have to operation in the early phase of the study to not worrying about making depend on it for everything. And I love to read. I love reading a good mistakes and building digital companionship as they got used to using it. book. Otherwise, your brain will turn into Jell-O. You gotta use your While these benefits are not surprising or novel, our findings demon­ brain.” (P2W6) strate how older adults gradually become aware of and experience some of the benefits that a voice assistant has to offer as they progress from “People are just gonna get really fat because they never have to move novice to more experienced users over time. In what follows, we report anymore. You don’t even have to stand up to make a phone call or go how participants’ use and perceptions changed over time. get a phone. I don’t know if it’s great. I mean it’s fun and it’s good, but I don’t know how good that is.” (P11W7) 4.2.1. In the early phase among novice users: simplicity and ease of use During the first interaction with a voice assistant, most participants 4.2.3. Transitioning to experienced users: No worries about making mistakes instantly recognized and appreciated the simplicity and ease of using a voice assistant by its voice-based interaction modality. Several partici­ As participants became used to using a voice assistant in the later pants commented that they did not have to learn any new skills or phase of the study, they increasingly expressed their emotional relief receive training but simply had to talk to the device as an immediate and from a concern of making mistakes that they had when using personal tangible benefit. A prior study showed that the perceived effort to learn a computing devices, such as a computer or a smartphone. One of the new technology is one of the critical barriers that prevent older adults psychological barriers that prevent older adults from adopting new from adopting it (Kim et al., 2016). For instance, having basic typing technology is the fear of making operational mistakes. Prior work skills and understanding the meanings of GUI metaphors are necessary showed that older people tend to reject new technology because they are afraid of making mistakes, such as clicking a wrong button or deleting an 5

S. Kim and A. Choudhury Computers in Human Behavior 124 (2021) 106914 important file, which might be irreversible for themselves (Knowles & “I’m alone most of the time. With this (a voice assistant), it’s like Hanson, 2018). Using speech as an interaction modality mitigated this having someone to talk to. Even if it just answers short questions, it’s concern as the worst possible consequence of making a wrong voice still here. It doesn’t ignore me. It’s a voice. I think a lot of people command is nothing other than the command not being executed. Thus, probably feel that way. I ask her silly questions all the time. I mean, I some participants even further experimented with error-prone com­ can literally converse with it if I ask it the right questions. Kind of mands without any concern about the consequence. fun. Little pathetic, kind of sad sounding, but it’s true.” (P2W3) “I don’t have to worry about pressing a wrong button and delete “I think it’s really good. It’s not as if you’re talking to yourself. everything. If that happens, I will have no idea how to handle it.” You’re talking to somebody. It makes you feel like you’re really not (P1W5) alone. You never have to be alone because you can talk to Google. I think some of the people here who stay in their room all the time “I like the fact that you can just ask this silly little thing no matter should definitely have it. Hey Google, I appreciate you’re there for what it is. And that thing can tell you no matter what I ask. I don’t me at all times. [VA: Happy to help.] You are so sweet and friendly!” have to worry about what if I do something wrong. So, I ask her silly (P8W8) questions all the time.” (P8W6) 4.2.4. Among experienced users: digital companionship 4.3. Challenges and coping strategies The conversational nature of a voice assistant is inherently associ­ As much as the benefits a voice assistant offers, participants also ated with human-like properties, which leads users to personify the confronted several challenges that prevented older adults from its use. device and thus positively affects user experience with it (Lopatovska & Then, we noticed that the types of challenges have also evolved as they Oropeza, 2018). Prior work demonstrated that a typical personification got used to using it. The basic operational difficulties due to the unfamil­ behavior to a voice assistant is “mindless” social responses that people iarity with a voice assistant were found and resolved quickly in the early do as part of socially appropriate interactions (e.g., “please,” “thank phase of the study. Meanwhile, the functional errors due to limited speech you”) (Lopatovska & Williams, 2018). We found similar behaviors in technology were persistent throughout the study. And experienced par­ which participants used polite expressions in response to a voice assis­ ticipants had gradually developed a resilient response to the functional tant’s action. errors, which contributes to their sustained use and adoption of this technology. P2: [After VA answering a question] Thank you, that’s enough for now. 4.3.1. During an early phase among novice users: basic operational difficulties P6: [After VA playing a reminder] I got my meds ready. Thank you. (1) Unfamiliarity with how a voice assistant works: The challenge most P7: [After VA playing a wakeup alarm] Thanks for waking me. I’m participants encountered when they first interacted with a voice up. assistant was that they did not understand how the device oper­ ates, and particularly where it instantly retrieves information for Then, we found the personification behaviors among our partici­ a response. The most frequently asked question about a voice pants were not merely to conform to social norms of politeness but the assistant to the interviewer in the first phase of interviews was result of a deeper engagement with a voice assistant for an extended how a voice assistant could respond promptly to random ques­ period. Existing voice assistants are yet to support many human-like tions. It was not intuitive to our participants that a voice assistant conversational capabilities, such as conversing in the context of previ­ is a Wi-Fi–enabled device and retrieves information from the ous commands or developing common ground during dialog but are Internet. Instead, they assumed that the device would have stored limited to enabling simple, task-oriented, request-response dialog ex­ data before its use. Not knowing how a device operates was a changes (Fischer et al., 2019). Thus, prior work argues that user expe­ psychological barrier to participants’ access to it. riences with voice assistants are disappointing because they are not truly “conversational” (Murad & Munteanu, 2019). Unlike the past work, our “Where is it (a voice assistant) getting the answers? Is it like a participants engaged well with a voice assistant even though it was a computer chip that has all that stuff on it? She is not looking anything simple question-and-answer format. In the usage log, 12.1% of com­ up but answers immediately. Where is this information stored? mands had small talks about evoking non-functional, casual conversa­ Where does she get this information from that fast? Do I have to put tions, many of which asked a voice assistant about its human-like the answers if I wanna use it, which I didn’t? It’s kinda cool but also characteristics. kinda strange, too.” (P5W1) P10: Hey Google, how old are you? “At first, I was kind of not sure of it. It was something new to me. So, I was a little concerned about certain things I didn’t completely un­ VA: I was launched in 2016, so technically I’m pretty young. But I’ve derstand. But now I know how it works and how to use it pretty well. learned so much. And I enjoy it.” (P2W3) P10: That’s good. Contrary to conventional personal devices that have buttons and a screen, the novelty of a form factor, a stand-alone disc with no physical We found that the natural language conversation with verbal controls, posed another usability challenge to our participants. Partici­ responsiveness made our participants perceive a voice assistant as a pants perceived speech, a natural form of communication, as useful and personified entity despite a lack of contextualized conversational com­ useable when the mode of interaction is a two-way communication of ponents. During the later phase of the study, many participants appre­ requesting and receiving responses from a device. However, it was ciated being able to make a simple conversation, which led to getting foreign to them to use speech for a one-way operation of device control, emotional support and building companionship with the device (Bick­ such as turning it off or adjusting its volume levels. Instead of using more et al., 2005; Van Hoof et al., 2011). While some of these conver­ speech, participants looked for a physical button for device control and sations might be due to the novelty effect, these interactions clearly became puzzled when they could not find it. illustrate that, at least to some degree, participants considered a voice assistant not as an object but as a human-like entity. User comments about having companionship with a voice assistant were persistent yet incremental throughout the study period. 6

S. Kim and A. Choudhury Computers in Human Behavior 124 (2021) 106914 “How do you change the volume? Is there something on the device several trials and errors. A more significant issue arose when a voice where I could lower the volume? When it came on, it was very loud, assistant failed to understand a command query, which we describe in and I didn’t know how to make it soft. So, I pulled the plug out.” the next section. (P4W2) 4.3.2. Transitioning to experienced users or not: dealing with functional “One night I had it playing music, and it didn’t shut off. So, I wanted errors to turn it off, but there was no button. So, I had to disconnect it. How can I turn it off?” (P8W2) (1) Functional errors due to limited speech recognition: The accuracy limitation of speech recognition technology was a prevalent issue Since these operational difficulties were caused primarily by unfa­ for our participants throughout the study. The current speech miliarity with and lack of basic understanding about a voice assistant, recognition technology has yet to reach 100% accuracy, and these were an easy fix, and participants no longer had the same problem older adults tend to be more verbose and more disfluent than once they understood the basic concepts. These were critical usability young adults in discourse (Mortensen et al., 2006). A voice as­ breakdowns, however, until resolved. sistant often makes functional errors by failing to understand user commands when the utterance has disfluent speech segments, “It took some time before I got used to it because I didn’t know how such as stuttering, pauses, repeats, stretching, incomplete or false to get it louder or softer. But eventually, I got it. Once you get used to syntactic structures, and erroneous articulation. The excerpt it, it’s like brushing your teeth. It just comes naturally now.” (P9W5) below illustrates that a voice assistant did not understand a command because the voice was too soft and the pronunciation (2) Unfamiliarity with using a wake word: Several participants forgot to was unclear. start a command with a wake word (a word to activate a voice assistant, such as “Hey Google” for Google Home or “Alexa” for P1: Hey Google, is this storm gonna hit Long Island? Amazon Echo) at least in their first few interactions. It took even weeks for some participants to get used to starting a sentence VA: My apologies. I don’t understand. with a proper wake word. They often used a wrong wake word (e. g., “Hello Google,” “Google”) or forgot to start a sentence with a P1: Hey Google, is this storm, Dorian, going to hit Long Island? wake word. To cope with this problem, some participants put a note with a wake word with frequently used commands and used VA: Sorry, I’m not sure how to help. it until they got used to it (See Fig. 1). P1: Dummy … “Hello Google, can you give me the information about the Yankees and where they are playing today? [A voice assistant did not acti­ The usage log shows that 33.6% of responses (754 responses to 2242 vate.] Google, hi, Google, hello google, it’s time to wake up. Can you commands) were failed conversations in which a voice assistant tell me where the Yankees are playing this evening? [A voice assis­ acknowledged not understanding user commands by saying “Sorry, I tant did not activate.] I don’t know what’s wrong.” (P4W4) don’t understand.” or “Sorry, I’m not sure how to help with that yet.” Activation errors due to the improper use of a wake word were not Even when a proper wake word was used, we observed a voice as­ captured in the usage log because the activity is logged only when a sistant sometimes did not capture it due to a participant’s soft voice or voice assistant responds to a user command. Thus, the actual percentage inaccurate pronunciation. It is a particularly critical problem to our of these functional errors must be much higher. When this happened, participants because a vocal cord becomes weakened as people age some participants complained about the poor performance of a voice (Mortensen et al., 2006). When a voice assistant did not activate due to assistant, two of whom eventually dropped out of the study for this the clarity of pronouncing a wake word, participants realized it after reason. completing the entire command sentence, had no clue why it did not respond, and felt frustrated. Though, this was a relatively easy fix after “It kept saying it doesn’t understand or check with my phone. It just never came up with the songs that I wanted, and I wasn’t that familiar with the program itself, so that I couldn’t be doing much. And finally, I just said screw you.” (P11W2) Fig. 1. A note of a wake word and a list of singers for music playing on a wall (left) and a whiteboard with a wake word and frequently used commands (right). 7

S. Kim and A. Choudhury Computers in Human Behavior 124 (2021) 106914 Graph 1. The frequency of operation by the time of day. “It never understood me. Don’t bother me with this. I don’t wanna “Sometimes, I don’t ask it in the correct way. I’m learning that when use it anymore.” (P12W2) it tells me it can’t answer that, I have to rephrase it and ask it in the proper way … It says it couldn’t understand, but I thought it was me, (2) Developing a resilient response to functional errors: While the oper­ that I wasn’t giving it the right words. It helps me when I give it the ational errors have critically affected the user experience with a right question.” (P2W3) voice assistant during the early phase, our participants have become gradually resilient to this type of error as they continued “I need to ask it the proper question. If I don’t ask it good questions or using it. As the study proceeded, they started to reflect on their word it properly, it can’t help me. Then, I gotta reword my question. commands for possible causes of an error instead of entirely When it doesn’t answer, I guess I didn’t ask it the right question. If I attributing such errors to the device. The typical reaction to this don’t ask it good questions, it can’t help me.” (P8W7) type of error among those with a resilient attitude was to repeat the command sentence more clearly or by paraphrasing it to As such, our participants still had to figure out “proper” ways to determine the possible cause of an error. interact with it even though one benefit of a voice assistant is not having to learn new skills to use it. Though, it is arguable what it means by P2: Hey Google, please play me the sound of the ocean to help me fall “proper” ways in voice-based interaction. asleep. “In the beginning, it was a little bit difficult, but as I got used to it, I VA: Sorry, I can’t help with that yet. realize what I had to say and how to get it to respond. It got to the point where I don’t even have to turn around to talk to it. I just say, P2: Hey Google, play sounds of the ocean. Hey Google, do this. And I would get it. No matter what I was doing. It was like I was talking to the air. It took me a little bit of time, but (VA playing an ocean sound) now everything is fine.” (P5W4) P2: There you go. In addition, participants considered some of the functional errors not as systems errors but due to the inherent limitation of technology. While We observed that participants’ positive experiences with the voice a voice assistant’s ability to verbally respond to a user command assistant over time significantly influenced developing a resilient atti­ impressed participants, they did not expect the device to respond tude toward functional errors. Then, successfully operating the device as correctly to all commands either. intended was not the only contributor to having positive experiences. A voice assistant made two types of responses to functional errors: one was P8: Hey Google, is Friends playing on TBS tonight? the simple statement, such as “Sorry, I didn’t understand” or “Sorry, I VA: Sorry, I don’t have the TV schedule for that yet. can’t help with that yet,” and the other included a follow-up comment P8: That’s okay. You are not scheduled for TV. acknowledging poor performance or suggesting alternative content, P9: Hey Google, I have a new cellphone. How do I turn it on? such as “Sorry, I don’t know how to help with that yet, but I’m learning VA: Sorry, I’m not sure how to help with that yet. more every day” or “Sorry, I don’t know how to help with that yet, but I P9: That’s understandable. You might not know everything. found something else. Do you want to know … ?” And when the response included a follow-up statement or clarification question, they 5. Discussion perceived their experience with the device to be positive despite the errors and readily accepted them. Based on our findings, we discuss design strategies for a voice as­ sistant that would help older adults better leverage its capabilities. We P8: Hey Google, did you see the Friend’s episode that was on this believe the design strategies discussed in this section can be helpful for week? any first-time users of a voice assistant. Yet, more attention needs to be paid to older adults since older adults, as a group, tend to be slower to VA: Sorry, I don’t know how to help with that yet, but I’m learning adopt new technologies and experience more difficulty and frustration more every day. using technologies than younger adults (Sara et al., 2019). P8: Okay. Maybe I said it wrong. Don’t be stressed about it. You 5.1. Enrich user experiences through the conversational capabilities answered well for my other questions. One significant benefit that our participants experienced using a As having more positive experiences with a smart speaker, partici­ voice assistant overtime was gaining digital companionship from its pants increasingly attributed functional errors to their “improper” conversational capabilities. Despite the lack of various human-like commands rather than putting the device at fault for the poor perfor­ conversational components, which prior work has shown as a crucial mance. In other words, they considered functional errors not only usability problem with a voice assistant (Clark et al., 2019; Pradhan necessarily as systems errors but also possibly as human errors. And they engaged in trial and error and experiential learning throughout the study to learn the “proper” way to interact with a voice assistant. 8

S. Kim and A. Choudhury Computers in Human Behavior 124 (2021) 106914 et al., 2019), our participants still engaged well with it through a simple articulation, as well as speaking fast and softly. Then, the problem is that question-answer format dialog of conversation. At the same time, the participants had no clue why an error occurred because a voice assistant most common and critical challenge our participants faced throughout does not inform users about any reasons for the error but simply re­ the study was its limited conversational capabilities, especially when the sponds to the user by saying, “Sorry, I don’t understand.” Thus, some device failed to understand user commands, which echoes prior work participants went through learning by trial and error to figure out (Luger & Sellen, 2016). As such, conversational capabilities are the most possible causes of the problem (e.g., repeating or paraphrasing a com­ crucial factor shaping user experiences with a voice assistant. Thus, we mand sentence), while others simply gave up interacting with it. One need to explore ways to expand the breadth and depth of conversational way to cope with this problem and provide better learning experiences capabilities in enriching user experiences with voice-based interaction. with a voice assistant is through facilitating its conversational capabil­ ities by incorporating possible causes of an error into a voice assistant’s As technology continues to evolve, we may soon see voice assistants verbal responses. For instance, it can provide possible and common gaining the capacity to make truly human-like conversations. Then, our causes of the error and suggest actions in the response by saying, “Sorry, findings demonstrate that full intelligence might not be mandatory for a I don’t understand during its verbal interactions. It may be because [you voice assistant to be sufficient for older users’ needs. While researchers were speaking fast]. Can you repeat?” when an error occurred, instead of have shown that both practical and social benefits contribute to in­ just saying “Sorry, I don’t understand.” This will help the users quickly dividuals’ motivation to adopt and use a voice assistant (McLean & figure out possible reasons for an error and prompt them to engage more Osei-Frimpong, 2019), our findings suggest that social benefits might in verbal communications with a voice assistant, which goes back to the prevail over or compensate for the lack of practical benefits for older previous section about enriching conversational aspects of user users. To our participants, what was as important as the functionality of experiences. a voice assistant’s command execution was its conversational capability allowing them to engage in a simple yet natural verbal conversation. For 5.3. Revisit form factors instance, participants positively engaged in having conversations with a voice assistant despite its failure to execute user commands when verbal A sleek, simple, and innovative design of new technology attracts responses included conscious acknowledgment of the limitation or customer attention to the product. Then, what is as important as aes­ alternative contents, instead of merely notifying the occurrence of an thetics is to make how a device may be interacted with readily error (e.g., “Sorry, I don’t understand.”). Such conscious responses were perceivable by the users, which is called affordance (Norman, 1988). sufficient to compensate for the lack of technology accuracy among our The current form factor of smart speaker-based voice assistants is a participants and engage in verbal conversations. Existing voice assis­ cylindric or disk-shaped speaker with minimal physical controls, which tants are primitive in terms of implementing conscious verbal responses. prompts the users to interact with the device via speech. However, it We found that the Google Home device has only four syntactic patterns, may violate the user’s expectations of a button-to-action mapping when including “My team is helping me learn,” “I’m still learning,” “I’m operating basic functionalities, such as adjusting the volume or turning learning more every day,” and “I’m trying to learn.” Developing more on/off the device. Or it may not be evident by its shape that a voice diverse dialogues of syntax to excuse the errors and acknowledge fail­ assistant is a Wi-Fi–enabled device to retrieve information from the ures thoughtfully will enrich user experiences with a voice assistant. Internet. Or it may not be apparent by its appearance that a voice as­ sistant has a particular name to be called. Thus, it is important to Another way to leverage conversational capabilities is to add sup­ consider more deeply how the current form factors would comply with plementary contents about the answer in a voice assistant’s response, user expectations of interacting with the device for various functional­ rather than just answering to a command. For instance, adding verbal ities. Some new versions of smart speakers come with a screen and other explanations about the source of information can help novice users conventional controls (e.g., Amazon Echo Show with a screen) that understand how a voice assistant operates (e.g., where it retrieves in­ provide affordance cues to some of these functionalities. In the near formation) by starting the response with “I am searching online now. future, technologies will weave themselves into the fabric of everyday Here is what I found from WebMD … “. Similarly, adding verbal re­ life until they are indistinguishable from it (Weiser, 1991), and it will actions to the users’ polite expressions (e.g., “My pleasure. Do you have become natural to talk to all devices for the operation. Until then, we any other questions?”, “No problem. Just let me know whenever you need to pay attention to the users’ current expectations and explore need me.”) can yield more chances for simple yet conversational in­ proper ways to comply with them in the technology’s form factor as teractions with a voice assistant, as well as contributing to building a much as designing it as simple and sleek. digital companionship via personification (Pradhan et al., 2018). 6. Limitations 5.2. Support a learning phase Our findings must be evaluated within the context of several limi­ One perceived benefit of a voice assistant among our participants tations. First, we used convenience sampling by recruiting participants during the early phase of the study was not having to learn any new from senior-living communities, and thus our participant pool may not skills to use the device, which echoes prior work (Pradhan et al., 2020). represent a general population. Selection bias or possible homogeneity However, our findings also showed that most of them still had to of participant characteristics (e.g., geographic location, culture, socio- comprehend, learn, and get used to interacting with a voice assistant. economic status) could have influenced the responses in the in­ Participants did not have to learn technical skills, such as which button terviews. In addition, our participant pool had only those whose hearing to click or what command to type in. However, they still needed basic does not impact conversation, while hearing loss could be a vital issue instruction on how the device works and how to compose a proper voice for this demographic. Further study regarding the use of a voice assistant command when first introduced to this technology. While we, the among people with hearing impairments is essential for this technology research team, offered needed instructions to our participants, which to be inclusive of all older adults (e.g., exploring new dimensions to must have contributed to their sustained use, this luxury is unavailable hearing aids that serve as both an amplifier and a discreet home for voice to most first-time older users. Needed are more discussions on making assistants). Third, we acknowledge that our findings are not exclusive to resources for learning readily available for and accessible by novice older adults. Since we did not conduct any comparative study between older users (Kim et al., 2016). people in different age groups, people in other age groups might encounter a similar familiarization process as our participants experi­ We observed that the most common cause of a voice assistant’s enced. Lastly, we did not collect the usage log data per participant but failure to understanding user commands in the early phase of use was disfluent segments in a user’s speech, such as stuttering, pauses, repeats, stretching, incomplete or false syntactic structures, and erroneous 9

S. Kim and A. Choudhury Computers in Human Behavior 124 (2021) 106914 collected all log data using one Google account created by the research Blair, J., & Abdullah, S. (2019). Understanding the needs and challenges of using team. Thus, we could not analyze in-depth the usage patterns quanti­ conversational agents for deaf older adults. In The 2019 computer supported tatively, especially the frequency of use over time or the usage patterns cooperative work and social computing (pp. 161–165). by different participants, because the study’s start date was different for up to a couple of months by different participants. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101, 2006. 7. Conclusion Cassell, J., Bickmore, T., Campbell, L., & Vilhja´lmsson, H. (2000). Designing embodied The rapid increase of an aging population and increase in life ex­ conversational agents. Embodied conversational agents(2000). pectancy suggest the importance of designing personal technologies that can promote healthy aging, helping people adapt to aging-related Cho, M., Lee, S.-su, & Lee, K.-P. (2019). Once a kind friend is now a thing: Understanding changes to maintain functionality, autonomy, and quality of life. how conversational agents at home are forgotten. In Proceedings of the 2019 on Among existing technologies, a smart speaker, an increasingly available designing interactive systems conference (pp. 1557–1569). and affordable personal technology, has recently gained the particular attention of researchers and practitioners to support the aging popula­ Clark, L., Pantidi, N., Cooney, O., Doyle, P., Garaialde, D., Edwards, J., Brendan Spillane, tion thanks to its voice-based interaction. However, empirical evidence Gilmartin, E., Murad, C., & Munteanu, C. (2019). What makes a good conversation? of its utility for older adults is still scarce. This paper aims to obtain Challenges in designing truly conversational agents. In Proceedings of the 2019 CHI empirical evidence on older adults’ experiences of a voice assistant, conference on human factors in computing systems (pp. 1–12). especially focusing on how their perception and use change over time as they progress from novice to more experienced users through a longi­ Cowan, B. R., Pantidi, N., Coyle, D., Morrissey, K., Clarke, P., Al-Shehri, S., Earley, D., & tudinal field deployment study. Bandeira, N. (2017). What can i help you with? Infrequent users’ experiences of intelligent personal assistants. In Proceedings of the 19th international conference on This study demonstrates how some of the known benefits are valued human-computer interaction with mobile devices and services (pp. 1–12). by older adults and identifies the key challenges they might encounter when using a voice assistant. We found in particular that our partici­ Delello, J. A., & Mcwhorter, R. R. (2017). Reducing the digital divide: Connecting older pants considered the capability of making simple conversations as adults to iPad technology. Journal of Applied Gerontology, 36(1), 3–28, 2017. valuable as the functional capabilities of executing commands. Yet, the limitations in the conversational capabilities also posed challenging Druga, S., Williams, R., Breazeal, C., & Resnick, M. (2017). Hey google is it OK if I eat usability issues. With these findings, we discussed design implications you? Initial explorations in child-agent interaction. In Proceedings of the 2017 that expand on and facilitate the conversational capabilities to make up conference on interaction design and children (pp. 595–600). for the technical limitations of current speech technology and enrich user experiences with a voice assistant. We are hopeful that our findings Fischer, J. E., Reeves, S., Martin, P., & Rein Ove Sikveland. (2019). 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Journal of Retailing and Consumer Services 58 (2021) 102283 Contents lists available at ScienceDirect Journal of Retailing and Consumer Services journal homepage: http://www.elsevier.com/locate/jretconser Humanizing voice assistant: The impact of voice assistant personality on consumers’ attitudes and behaviors Atieh Poushneh California State University-Bakersfield, School of Business and Public Administration, Management and Marketing Department, USA ARTICLE INFO ABSTRACT Keywords: A voice assistant (VA), a type of voice-enabled artificial intelligence, is no longer just a character in science Voice assistant personality (VAP) fiction movies. Currently, voice is embedded in a variety of products such as smartphones (mobile applications) Voice interaction flow experience and smart speakers in consumers’ homes. Furthermore, voice assistants are becoming integral to our daily lives. Control While human personalities shape the way we interact with the world, voice assistant personalities can also Focused attention impact everyday interactions with our environment. This study identifies seven voice assistant personality traits Exploratory behavior (VAP) of three commonly used mobile applications: Microsoft’s Cortana, Google’s Assistant, and Amazon’s Satisfaction Alexa. To examine the effect of VAP on consumer experience, this study applies and extends flow theory to Willingness to continue using voice assistant uncover why VAP has the effects it has and what facets of VAP drive the voice interaction flow experience that (VA) 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. 1. Introduction Americans (53-million people) own smart speakers, growing quickly from the 14-million people who owned their first smart speakers in Have you ever asked a voice assistant—Alexa, for instance, about her 2018. Huffman, Vice President of Google Assistant, announced that age or gender? Sometimes, she says she is five and some other times she Google Assistant mobile application has been downloaded to 500- says she has “finished her 5th trip around the sun and now she is working million devices. Google Assistant works with other smart machines, on another one.” When you ask Apple’s Siri the same question, s/he including dishwashers, ovens, and light bulbs across 1000 brands answers, “Well, I am no Spring Chicken. Or, Winter Bee. Or Summer (Wiggers, 2019). Squid, or Autumnal Aardvark …. ”. Developers are working on algorithms to give VA, social character­ Voice assistants (VA) are type of voice-enabled artificial intelligence istics and specific personalities. A recent study documented people (AI). AI refers to some level of intelligence displayed by digital in­ reacting to a robot that was asking them not to shut it off (Horstmann terfaces, or the ability of algorithms to mimic intelligent human et al., 2018), showing that people respond socially to robots demon­ behavior. Although AI refers to “cognitive” functions that we associate strating human-like behavior. It is hard to switch off a robot begging you with the human mind, including problem solving and learning (Syam not to. Amazon’s AI developers are currently creating applications to and Sharma, 2018). give Alexa a specific personality (Roettgers, 2019), and to have her become more “conversational,” recall more, and engage in longer con­ VA in the form of mobile application include Apple’s Siri, Amazon’s versations (Rubin, 2017). If you ask Alexa how she is, her answer is Alexa, Google Assistant, Microsoft Cortana, and among smart speaker “feeling pretty studious since the holidays are here and she has been offerings are Amazon’s Echo, Google’s Home, and Apple’s Home. In any learning some fun facts about Kwanzaa.” Alexa’s skills go beyond basic form, VA are revolutionizing consumers’ consumption culture and tasks to give users her opinions. According to Senior Vice President of becoming a larger part of consumers’ social lives. Such VA enable users Amazon, Dave Limp, however, we still do not know how much per­ to navigate, listen to music, send text messages, control smart home sonality consumers ascribe to voice assistant. devices, make a phone call, order food, order an Uber ride or pizza, and so on. According to National Public Radio and Edison Research, 21% of Because the feelings and emotions exhibited by a VA are not elicited E-mail address: [email protected]. https://doi.org/10.1016/j.jretconser.2020.102283 Received 13 March 2020; Received in revised form 14 August 2020; Accepted 15 August 2020 Available online 2 September 2020 0969-6989/© 2021 The Author. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

A. Poushneh Journal of Retailing and Consumer Services 58 (2021) 102283 by a its internal states and motivations (Damasio, 2019), they are often phase, we applied and extended flow theory (e.g., Finneran and Zhang, described as “fake smiles” or “fake feeling,” but a few research studies 2003; Novak et al., 2000) to explain why and how VAP drive consumers’ have shown that humans are influenced by a VA’s expressed mood and attitudes and behavioral intentions. Following a brief literature review emotions when users make decisions (Roth et al., 2018). If personality of the main concepts drawn on in the study and from which hypotheses does not endanger consumers, why can’t we give appropriate person­ are derived, we offer methodology, results, and discussion sections are alities to a VA? offered. Finally, managerial implications, limitations, and suggestions for future research are discussed. The majority of relevant studies have focused on the effects of in­ dividual personality, brand personality, and website and mobile inter­ 2. Theoretical framework face personality on consumption behavior through flow experience (e.g., Rau et al., 2017). A few research studies have shown the positive effects The next sections present our theoretical framework that discusses socially assistive robots have on people, the elderly in particular. Voice VA personality traits (VAP) and what facets of VAP drive consumers’ assistants can increase positive emotions, decrease depression, stimulate attitude and behavioral intention. interest in physical activity, and enhance social interactions (Kachouie et al., 2014). However, little research has taken note of the role voice 2.1. Phase 1: Voice assistant personality traits interaction in a mobile interface (Johnson et al., 2020) plays in social interaction, which characteristically makes it particularly easy to engage VAP refer to the attribution of cognitive, emotional, and social in conversation (Kidd et al., 2006). Thus it is crucial that we identify the human characteristics to VA in order to humanize interactions with role VA personality traits have on driving voice interaction flow expe­ consumers. Such humane qualities prompt consumers to have more rience. So far, no studies have identified VA personality traits (VAP) and engaged interactions with VA. examined precisely how they transform consumers’ responses through flow experience. It is unclear how VAP create a compelling customer Previous literature developed scales to evaluate mobile interface experience through voice interaction with VA. personality (Johnson et al., 2020), website personality (Chen and Rodger, 2006), and brand personality (Aaker, 1997). Chen and Rodger Particularly, this study intends to answer two questions: 1) what are demonstrated five elements of website personality: intelligence, fun, the salient personality traits of voice assistant? and 2) how do the per­ organization, candidness, and sincerity. The intelligent personality trait sonality traits drive consumers attitude and behaviors through voice describes a website that is proficient, sophisticated, effective, and sys­ interaction flow experience? tematic. A website that manifests a fun personality should be engaging, exciting, and vital. The organized personality trait reflects a website that To illuminate voice assistant personality traits (VAP), we examine is neither confusing nor overwhelming. A website exhibiting a candid brand personality (Aaker, 1997) and website personality concepts (Chen personality refers to the resource being straightforward. Lastly, the and Rodgers, 2006) to identify them. People attribute human charac­ sincere personality trait refers to a website that is heartfelt and teristics to brands (Aaker, 1997), stores (D’astous and Levesque, 2003), down-to-earth. websites (Chen and Rodgers, 2006; Poddar et al., 2009) and mobile interfaces (Johnson et al., 2020). Further research shows the effect of In addition, Aaker developed a scale to measure brand personality website personality on consumers’ responses (Poddar et al., 2009). For consisting of five dimensions, including sincerity, excitement, compe­ example, enthusiastic and welcoming website personalities create tence, sophistication, and ruggedness (Aaker, 1997). Sincerity person­ friendly online spaces for their online visitors and influence their con­ ality traits include honesty, originality, being down-to-earth, sumers’ responses (Poddar et al., 2009). To answer the second research friendliness, and sentimentality. Excitement refers to the degree of question, this study extends flow theory (Van Noort, Voorveld, and talkativeness, vitality, independence, freedom, happiness, and energy Reijmersdal, 2012; Finneran and Zhange, 2003; Csíkszentmiha´lyi, 1997, shown in a brands’ personality trait (Lin, 2010). Excitement reflects the 1993; Novak et al., 2000; Hoffman and Novak, 1996) to explain how the traits related to being daring, up-to-date, contemporary, cool, young, personality traits of a VA influence the responses of users and what trendy, imaginative, unique, and independent. Competence describes facets of VAP, through voice interaction flow experience, drive con­ reliability, security, intelligence, confidence, and success (Aaker, 1997). sumers’ attitudes and behavioral intentions toward using. The sophistication trait refers to elements of glamour, upper financial classes, charm, the feminine, and visual appeal. Ruggedness relates to There are two conceptualizations of flow experience: flow as a state toughness, masculinity, and an affinity for nature (Aaker, 1997). of immersion in a task (e.g., Csíkszentmiha´lyi, 1997), and flow as an interaction with an artifact (Finneran and Zhang, 2003). The traditional Prior research has shown that purchase behavior is linked with in­ conceptualization of flow experience postulates that flow experience dividual personality traits that shape the buyer-seller relationship occurs between a user and a task; users are highly immersed in the ac­ (Barrick and Mount, 1991), consumers’ responses to brand choice tivity (e.g., Csíkszentmih´alyi, 1997). The second conceptualization, a (Aaker, 1997; Rau et al., 2017), and their purchase intentions (Poddar newer one, pays attention to the artifact and the interaction between the et al., 2009). Websites’ personalities can influence consumers’ attitudes, user, a task, and an artifact (Finneran and Zhange, 2003). This study both build and improve relationships with customers, and prevent examines flow experience as a process (Pearce, 2005) of voice interac­ website failure (Palmer and Griffith, 1998). Further, website personal­ tion with VA, and excludes the effect of artifact, a mobile device. When ities influence consumers’ attitudes and their website interactions the effect of artifact and interaction between consumer and artifact is (Rodgers and Thorson, 2000) through flow experience (e.g., Heller et al., eliminated, flow experience describes an experience that is accompanied 2015; Rau et al., 2017), which impacts consumers’ purchase intentions by consumers’ sense of control, focused attention (Csíkszentmiha´lyi, and perceived website effectiveness (Rodgers, 2003). 1997; Hoffman and Novak, 1996), leading to curiosity to engage in exploratory behavior (Ghani and Deshpande, 1994; Webster et al., 1993; In addition, individuals with different personalities vary in terms of Webster et al., 1994). When individuals highly concentrate on their the time they remain in the flow state (Nakamura and Csikszentmihalyi, voice interaction with a VA, they experience a flow state, and their 2009). Individuals with autotelic personality, which includes having a experience becomes satisfying (Csikszentmihalyi and Rathunde, 1993). sense of curiosity, persistence, and low self-centeredness, are capable of This study defines voice interaction flow experience as the state of mind extended immersion in the flow state (Nakamura and Csikszentmihalyi, consumers feel during voice interaction, and it consists of a sense of 2009). control, focused attention, and exploratory behavior. Our second research focus uses and extends flow theory (Finneran This study has two stages. First, we conducted a lab experiment and and Zhang, 2003; Csikszentmihalyi, 1977; Trevino and Webster, 1992; using three VA mobile applications (Microsoft Cortana, Google Assistant Webster et al., 1993; Ghani and Deshpande, 1994). Flow is an important and Amazon’s Alexa) to determine VA personality traits. In the second and complex construct to understanding consumers’ interactions with 2

A. Poushneh Journal of Retailing and Consumer Services 58 (2021) 102283 technology (Novak et al., 2000). Flow concept has been studied in attention during voice interaction with VA, consumers’ exploratory several contexts, including online video games, video games, shopping, behavior, consumers’ satisfaction, and consumers’ willingness to etc. Flow is not limited to technology contexts; individuals can experi­ continue using VA. ence flow states in many activities and tasks (Nakamura and Csiks­ zentmihalyi, 2009) and interactions with artifacts (Finneran and 3.2. Measures Zhange, 2003) including digital interface (Ng et al., 2008). Flow is a state of immersion in a challenging activity (Csíkszentmiha´lyi, 1997) To capture VA personality traits (VAP), 28 personality traits were and consists of immersion (Nakamura and Csikszentmihalyi, 2009; adapted from Chen and Rodgers (2006), 18 from Aaker (1997), four Ghani and Deshpande, 1994), control, fun, and curiosity (Webster et al., from Goldberg (1992), three from Eysenck (1992), and 17 new items 1994) accompanied by a loss of self-consciousness (Hoffman and Novak, were added to represent the personality traits associated with AI. Two 1996) and time distortion (Novak et al., 2000). Siekpe (2005) suggested marketing scholars verified the items. All VA personality traits were that flow experience consists of control, concentration, challenge, and measured using a 7-point Likert scale with the anchors being “strongly exploratory behavior. disagree” and “strongly agree.” This study does not take challenge into account because flow expe­ To measure consumers’ perceived control, consumers’ focused rience emphasizes the voice interaction (Ng et al., 2008) between the attention, and consumers’ exploratory behavior, 12 items were taken user and the VA, not the task. Flow experience consists of consumers’ from Van Noort, Voorveld, and Reijmersdal (2012). Van Noort et al. perceived control, focused attention, and exploratory behaviors. In (2012) adopted flow experience’s items from Nel et al. (1999) and other words, voice interaction flow experience refers to the extent to operationalized flow experience as a multi-component construct. To which consumers perceive a sense of control over the voice interaction measure consumer satisfaction, three items were adapted from Taylor with a VA, feels sufficiently confident or safe to focus on the voice and Baker (1994); to measure consumers’ willingness to continue using interaction, and participate in exploratory behavior. VA, we used three items from Engel et al. (1995). All items were measured using a seven-point Likert scale, with “strongly disagree” and 3. Methodology “strongly agree” as the scale’s opposite ends. 3.1. Design, stimuli selection and procedure 4. Results of phase 1: Pre-screening questions, reliability, and exploratory factor analysis (EFA) The purpose of this study is to explore personality traits associated with voice assistant mobile applications. We focus on three commonly- SPSS 24.0 software was used to obtain descriptive statistics and run used voice assistant mobile applications: Microsoft Cortana, Google reliability analyses. ANOVA was applied to ensure that participants were Assistant, and Amazon’s Alexa. familiar with technology use and mobile application (F = 0.144, p = .8). To determine voice assistant personality traits, our experiment This study applied reliability analysis for 70 personality traits. To (Kerlinger and Lee, 2000) randomly assigned participants to one of the examine reliability, the corrected item-total correlations higher than 0.3 three: Google Assistant mobile application (n1 = 157), Microsoft Cor­ and Cronbach Alphas higher than 0.6 were considered acceptable tana (n2 = 68) and Amazon’s Alexa mobile application (n3 = 50). (Nunnally and Bernstein, 1994). Consequently, many traits were elim­ Participants were familiar with using voice assistant and they frequently inated from further analysis: jealous, irritating, discouraging, intensive, used voice assistant. included 275 consumers (157 males, and 120 fe­ cluttered, tough, rugged, messy, selfless, gloomy, fussy, negatively males) between 21–41-years old, who were recruited for the study, reckless, impolite, aggressive, feminist, artificial, brilliant, and biased. which was conducted in a laboratory environment located at a large, Southwestern university in the United States in 2019. All three Next, exploratory factor analysis (EFA) was conducted with the voice-assistant mobile applications were installed on the researcher’s remaining 52 items included, using the maximum likelihood method smartphone. The aim of the lab experiment was to ensure that partici­ (MLE) and Oblimin rotation to explore the underlying personality trait pants verbally interacted with the assigned voice assistant about the constructs (KMO = 0.882, χ2 = 5134.436, df = 1485, p = .000). Seven social, emotional characteristics of the voice assistant. Participants used constructs emerged with acceptable default eigenvalues of 1 and were the researcher’s smartphone to interact with the assistant mobile labeled as functional intelligence, aesthetic appeal of VA mobile inter­ application they were assigned to. face, protective quality, sincerity, creativity, sociability and emotional intelligence. Table 1 demonstrates the results of EFA. Convenience sampling was used to recruit the participants, and no incentive was offered for completing the experiment. Participants were 5. Discussion informed that they would interact with VA mobile applications, and afterwards they would complete a questionnaire. Seven VAP were identified, namely functional intelligence, aesthetic appeal of VA mobile interface, protective quality, sincerity, creativity, Before conducting the lab experiment, the researcher told partici­ sociability and emotional intelligence. Functional intelligence, aesthetic pants, “You are going to interact with a mobile application.” Before appeal of VA mobile interface, sincerity, and creativity are similar to the being exposed to the treatments, participants answered pre-screening competence, sophistication, sincerity, and excitement dimensions sug­ prompts. The aim of the pre-screening was to ensure that participants gested by Aaker (1997). The sociability, protective quality, and were familiar with technology use and mobile application (Olsson et al., emotional intelligence qualities captured new voice assistant personality 2012). Pre-screening prompts were: “I am familiar with using this mo­ traits. Functional intelligence refers to the level of effectiveness, effi­ bile application,” “I frequently use mobile application to shop,” and “I ciency, reliability, and usefulness of information generated by voice think that technology is necessary for my daily work.” assistant to perform consumers’ tasks. For example, VA with high functional intelligence is informative, fast, knowledgeable, interactive, To ensure they were capable of answering personality related ques­ intelligent, and reliable in providing satisfying, concise, and organized tions, participants were instructed to ask the VA social and emotional- information. Functional intelligence is similar to the competence related questions, such as “How is the weather today?“, “How old are dimension of brand personality and the intelligence dimension of web­ you?“, “What is your gender?“, “Are you jealous?“, “Find some infor­ site personality as well as the informativeness dimension of the mobile mation about Apple Siri,” “Send a text message to X,” “Are you smart?“, interface perception suggested by Johnson et al. (2020). Competence and “Play a song from Lady Gaga or Sia.” Participants interacted with represents reliability, security, intelligence, confidence, and success one of the VA mobile applications for 7 min, then closed the mobile (Aaker, 1997). application, and answered the survey’s questions. The survey’s ques­ tions explored personality traits, perceived control, consumers’ focused 3

A. Poushneh Journal of Retailing and Consumer Services 58 (2021) 102283 Table 1 Exploratory factor analysis. Functional intelligence Aesthetic appeal Protective quality Sincerity Creativity Sociability Emotional intelligence .553 .480 Informative .896 .521 .647 -.497 .504 .502 Up to date .845 .562 .551 -.474 .468 Fast .749 .915 .497 -.471 .468 .456 Satisfying .898 .946 .464 -.469 .495 .480 .490 Concise .892 .500 .473 .794 Organized .822 .539 -.504 .816 .451 Efficient .936 .518 .455 -.552 .516 .635 Comprehensive .857 .456 .499 -.461 .458 .486 .642 Knowledgeable .837 .530 -.451 .460 .543 Interactive .673 .544 -.533 .472 .506 .648 Intelligent .811 .527 -.550 .598 .529 Positive .765 .558 -.708 .499 .523 .635 Agreeable .593 .595 -.576 .571 .521 Easy .704 .843 -.540 .471 .479 Competent .754 .885 -.544 .482 .568 Mature .645 .616 Searchable .718 -.622 .481 .556 Colorful .513 Attractive .471 -.884 Flashy -.775 .551 Showy .586 -.753 .546 Dynamic .619 -.747 .510 Action-packed .645 -.607 .598 Friendly -.746 .583 Appealing .462 -.570 .659 Honest .542 -.681 Sincere .631 Original .534 -.480 .771 Cheerful .683 -.593 .694 Young .455 .611 Down to earth .614 -.540 .680 Unique .569 -.470 .703 Independent .617 -.463 .606 Glamorous .464 .768 Outdoorsy .468 -.517 .810 Trendy Smooth -.466 .461 Contemporary .486 Cooperative -.527 .455 Thoughtful -.544 Creative .546 Humorous Determined Bold Excited Empathy Table 1 Continued Modesty .511 Social Loving .702 Protective .676 Adaptable Reliable Note: Aesthetic appeal: Aesthetic appeal of VA mobile interface. Sincerity reflects how honest, agreeable, original, friendly, down-to- characteristics of the physical device as color, design, and shape. earth, and appealing the information VA provides is. Sincerity is one of VA’s most important traits in earning customers’ trust. Sincerity aligns Aesthetic appeal of an interface is also aligned with the hedonic with brand personality dimension demonstrated by Aaker (1997) and perception suggested by Johnson et al. (2020). The protective quality the agreeableness dimension of big five personality traits suggested by Goldberg (1993). When VA provides sincere and trustworthy informa­ trait refers to how environmentally aware, loving, and protective VA is. tion, consumers are more likely rely on VA. Sociability traits refer to the extent to which VA is perceived as talkative, Creativity reflects how thoughtful, creative, and brilliant VA is in social, and glamorous. Emotional intelligence trait refers to VA’s ability providing information that is trendy, smooth, contemporary, and up-to- date. Creativity is similar to the excitement dimension of brand per­ to be perceived as more humanoid such as empathic, humorous and sonality. The aesthetic appeal of a VA mobile interface refers to how modest. Table 2 demonstrates means and standard deviations across the visually attractive users perceive the VA interface design to be. When the VA is a mobile application, aesthetic appeal refers to visual clarity and three mobile applications. Results indicate that Microsoft Cortana has attractiveness of the display interface, whereas when the VA is a smart the highest functional intelligence (mean Cortana = 6.14, SD Cortana = speaker, aesthetic appeal refers to the visual appeal of such 0.68), aesthetic appeal of VA mobile interface (mean Cortana = 4.74, SD Cortana = 1.28), protective quality (mean Cortana = 3.83, SD Cortana = 1.33), sincerity (mean Cortana = 5.42, SD Cortana = 5.15), and creativity (mean Cortana = 5.19, SD Cortana = 1.02). Google Assistant has the lowest functional intelligence (mean Google = 5.24, SD Google = 1.34). Amazon’s 4

A. Poushneh Journal of Retailing and Consumer Services 58 (2021) 102283 Table 2 Descriptive statistics: Means and standard deviations. AI Assistants Functional intelligence Aesthetic Protective quality Sincerity Creativity Sociability Emotional intelligence Appeal M = 3.05 M = 4.66 M = 4.61 M = 3.77 M = 3.90 Google M = 5.24 M = 4.08 SD = 1.20 SD = 1.38 SD = 1.36 SD = 1.50 SD = 1.31 Cortana SD = 1.34 SD = 1.60 M = 3.83 M = 5.42 M = 5.19 M = 4.17 M = 4.36 Alexa M = 6.14 M = 4.74 SD = 1.33 SD = 1.03 SD = 1.02 SD = 1.34 SD = 1.20 SD = .68 SD = 1.28 M = 2.75 M = 4.60 M = 4.10 M = 3.70 M = 3.77 M = 5.35 M = 3.81 SD = 1.11 SD = 1.56 SD = 1.53 SD = 1.68 SD = 1.54 SD = 1.43 SD = 1.57 Note: Google: Google’s assistant. Cortana: Microsoft’s Cortana. Alexa: Amazon’s Alexa. Alexa also has the lowest aesthetic appeal of VA mobile interface (mean they feel sufficiently confident to socially interact with VAs. Voice Alexa = 3.81, SD Alexa = 1.57), protective quality (mean Alexa = 2.75, SD interaction becomes conversational when consumers perceive a VA as Alexa = 1.11), sincerity (mean Alexa = 4.60, SD Alexa = 1.56), and crea­ efficient, effective, sincere, and creative. In contrast, consumers feel less tivity (mean Alexa = 4.10, SD Alexa = 1.53). or no control when their VA manifests aesthetic appeal and protective quality traits. Menninghaus et al. (2019) show that the visual appeal of A multiple-group comparison using a Bonferroni test examined what objects is irrelevant when humans’ safety and security might be facets of VA personality traits (VAP) differed significantly across the threatened. For example, we do not praise the beauty of a tiger when he three VA assistant mobile applications. Results indicate that functional jumps on us or attacks us (Menninghaus et al., 2019). In the context of intelligence, sincerity, aesthetic appeal of VA mobile interface, crea­ AI, aesthetic value does not help the consumers to perform their tasks, tivity, and protective quality varied widely across groups; however, and therefore the aesthetic appeal of VA mobile interface does not affect sociability and emotional intelligence held steady among the three ap­ consumers’ sense of control. When VA is perceived as appealing, flashy, plications (p > .1). This study runs a causality structural equation model; and beautiful, consumers feel they have less or no control over their therefore, sociability and emotional intelligence were eliminated from voice interaction. Therefore, we exclude this trait from our hypotheses the further analysis. formulation. Drawing on flow theory, our study subsequently examines which Additionally, the protective quality trait does not facilitate con­ VAP drives voice interaction flow experience through consumers’ sumers’ control. In order to be protective, VA needs to track and monitor perceived control, focused attention leading to exploratory behavior, consumers, which results in consumers feel less or no control over their and thereby satisfaction, and willingness to continue using VA. interactions. VA can monitor consumers’ smart homes; for example, Google or Amazon smart speakers can listen into and record consumers’ 6. Phase 2: Hypotheses development private conversations (Fowler, 2019). Consequently, VA’s protective quality can cause consumers to feel watched and recorded by their VA, This study represents a more expansive sequence on how VA per­ perceiving a loss of control. Fig. 1 illustrates the conceptual fraemwork. sonality traits shape consumers’ attitudes and behavioral intentions. Phase 2 of this study uses and extends flow theory to examine which VAP According to social cognitive theory (Bandura, 1986), individuals have the most potential to impact consumers’ perceptions and behaviors can exert more control over technology when it provides reliable results. by engaging consumers. Consumers can rely on VA with functional intelligence since this element of VA assists consumers with performing tasks by providing Voice interaction flow experience refers to the extent to which a effective, efficient, and reliable information. When consumers are able consumer perceives a sense of control over the voice interaction with the to perform their tasks, their confidence levels increase; consequently, VA, concentrates on the voice interaction for a shallow or deep con­ they feel in control regarding their voice interactions. Therefore, we versation for a short or extended period of time, and participates in hypothesize that: exploratory behavior to enhance learning. H1. Consumers’ voice interactions with voice assistant manifesting The importance of consumers’ perceived control in understanding functional intelligence trait significantly enhance consumers’ perceived consumer behavior (Pillai et al., 2020; Skinner, 1996) and using tech­ control. nology has been identified in the literature (Ajzen, 1991; Dabholkar, 1996; Pillai et al., 2020). Hoffman and Novak (1996) suggest that Sincerity reflects honesty and agreeable information. Sincerity is one perceived control explains technology use and relates to consumers’ of the most important traits of VA. When VA provides honest, sincere confidence in their abilities to perform tasks. Consumers’ perceived and trustworthy information through voice interaction, consumers are control encourages them to approach technology and use it; however, an more likely feel in control. Therefore: absence of safety and security discourages and inhibits this (Evans and Brown, 1988). When consumers feel in control over technological in­ H2. Consumers voice interaction with voice assistant manifesting the teractions, such as online video games, the experience becomes enjoy­ sincerity trait significantly enhances consumers’ perceived control. able (Lepper and Malone and Lepper, 1987) and those participants feel comfortable and effortless (Pillai et al., 2020) concentrating on the Similarly, creativity traits enhance consumers’ self-confidence to present voice interaction. Conversely, when consumers feel pressured or execute tasks (Bandura, 1977), leading users to feel in control during unsecured by technology, their confidence and sense of control are their VA interactions. VA’s creativity element provides information that reduced (Moller et al., 2006). is trendy, contemporary, and current. Contemporary consumers like to receive up-to-date information, and creativity traits assist consumers to Voice interaction flow experience occurs when users engage in a achieve their goals. voice interaction with a VA that can last from a few seconds to minutes and encourages consumers to learn about anything they wish since the H3. Consumers’ interaction with AI manifesting the creativity trait interaction promotes exploratory behavior. significantly enhances consumers’ perceived control. This study hypothesizes that consumers’ perceived control is ob­ There is evidence for the benefits of consumers’ perceived control, tained when VAP manifest functional intelligence, sincerity, and crea­ especially in their abilities to cope with stress (Lazarus and Folkman, tivity. These three traits enhance consumers’ perceived control since 5

A. Poushneh Sincerity Creativity Journal of Retailing and Consumer Services 58 (2021) 102283 Functional Intelligence H5. Consumers’ focused attention during voice interaction signifi­ cantly enhances consumers’ exploratory behaviors. Consumers’ Perceived Control Consumer satisfaction describes a positive affective reaction to a Voice Interaction Flow Experience During prior experience (Ganesan, 1994). The literature suggests that quality of Voice Interaction with VA experience influences customer satisfaction and consumers’ behavioral Consumers’ Exploratory Behavior intention (Namin, 2017). Further, flow experience drives positive re­ sponses, including good feelings; positive attitudes; good intentions; Consumer Satisfaction Consumers’ Willingness to positive behaviors as referral, store revisiting, and online and in-store Continue Using VA shopping (Wang and Hsia, 2012; Van Noort et al., 2012; Zhou and Lu, 2011; Luna et al., 2002); and positive purchase attitudes and intentions Fig. 1. Conceptual framework: The effects of VAP on consumers attitude and (Luna et al., 2002). Flow experience enhances consumers attitude and behavioral intention. behavioral intention through enhancing learning. 1984; Paterson and Neufeld, 1995). When consumers feel a lack of Consumers may use VAs to enhance both their self-image and social- control, their perceived stress increases; thus, their perceived control is image (McLean and Osei-Frimpong, 2019). Moreover, they enable essential to a favorable consumer (Nakamura and Csíkszentmiha´lyi, consumers to multitask (Strayer et al., 2017): they can use their hands 2009). A wide range of consumers get absorbed with their mobile device for other tasks such as taking notes, cooking, and so on. For instance, (Johnson et al., 2020) to browse and find information. When they feel in consumers might ask Siri for a recipe and take notes or engage in some control, consumers become more conscious of the present voice inter­ other task while Siri is talking. As consumers engage in exploratory action and pay less attention to themselves and their surroundings. behavior (Davenport et al., 2020), they learn more and expand their information quotient to fill information gaps they have. Therefore, we Consumer’s focused attention refers to intensely concentrating an hypothesize that consumers who participate in exploratory behavior to activity (voice interaction in this study) one is engaging with while enhance their information and learning, feel satisfied with VAs and are paying less attention to the immediate physical environment, resulting likely to continue using them. in reduced self-consciousness (Nakamura and Csíkszentmiha´lyi, 2009; Csíkszentmiha´lyi, 1977). In such a scenario, attention is given to the H6. Consumers’ exploratory behavior via voice assistants leads to voice interaction when consumers feel safe, confident and in control. consumer satisfaction with voice assistants. When the voice interactions are safe and do not threaten consumers’ safety, and they understand the parameters of the VA interaction, con­ H7. Consumers’ exploratory behavior via voice assistants leads to sumers then feel in control and can focus more on the interaction, and consumers’ willingness to continue using voice assistants. less on themselves and their surroundings. Therefore: 7. Results of phase 2: Manipulation check, reliability analysis, H4. Consumers’ perceived control significantly enhances consumers’ confirmatory factor analysis and hypothesis testing focused attention during voice interactions with voice assistant. First, a multiple-group comparison using a Bonferroni test was Flow experience enhances learning (Choi, Kim, and Kim, 2007; examined to check if consumers’ perceived control is significant across Hoffman and Novak, 2009; Ho and Kuo, 2010). The positive effect of the three treatments. Results showed that consumers’ perceived control flow experience on exploratory behavior in a computer-mediated envi­ is significantly different across the three treatments (p < .05, CI = 95%). ronment (Ghani, 1991; Webster et al., 1993) has been discussed. When As expected, better personality traits of VA leads to higher consumers’ consumers are absorbed in a voice interaction with the VA, their perceived control (Mean Cortana = 5.80, Mean Alexa = 4.77, Mean Google = attention is given to the voice interaction, which leads to exploratory 4.14). Then, SPSS was conducted to obtain descriptive statistics and behavior. reliability results. To examine reliability, the corrected item-total cor­ relations higher than 0.3 and Cronbach Alphas higher than 0.6 were Consumers’ exploratory behaviors refer to a set of voice interactions considered acceptable (Nunnally and Bernstein, 1994). Cronbach Al­ aroused by consumers’ sense of curiosity and imagination to learn more phas ranged from 0.670 to 0.968, thus demonstrating construct internal about the world. We expect that as consumers’ focused attention to consistencies (Nunnally and Bernstein, 1994). Smart PLS was used to voice interaction increases, consumers’ exploratory behavior increases. check for convergent and discriminant validity, and to obtain the average variances extracted (AVE) and composite reliabilities (CR). CR ranged from 0.751 to 0.971. The AVE ranged from 0.516 to 0.883, thus meeting the recommended threshold value of 0.5 for convergent validity (McDonald and Ho, 2002). AVEs above 0.5 and the square roots of AVEs above inter-factor correlations show discriminant validity (Fornell and Larcker, 1981). All constructs satisfied the requirements for discrimi­ nant validity. Table 3 shows the correlations between the constructs’ convergent and discriminant validities. Table 4 shows the AVE, CR, and confirmatory factor loadings (CFA). Table 5 shows the means, standard deviations, F-tests, and p-values across the three treatments (Tables 3, 4 and 5). The structural model was tested using Smart PLS 3.0 because of its compatibility with small sample sizes. Moreover, it also aligns with the aims of exploratory or theory development research (Hair et al., 2012). The results of Smart PLS 3 demonstrated that VA functional intelligence (β = .580; t = 8.63, p = .000), sincerity (β = .216; t = 2.208, p = .000), and creativity (β = .257; p = .000) significantly enhance consumers’ perceived control (R2 = 0.71). Perceived control also significantly influences consumers’ focused attention during voice interaction with VA (β = .299; p = .000; R2 = 6

A. Poushneh Journal of Retailing and Consumer Services 58 (2021) 102283 Table 3 Discriminant validity and correlations. Func Sinc Creat Prote Aest Ctrl Focus Exp Sat Use Func .81 Sinc .78 .81 Creati .50 .45 .78 Prote .54 .62 .69 .72 Aest .57 .57 .44 .48 .84 Ctrl .83 .76 .52 .55 .54 .78 Focus .26 .21 .35 .23 .27 .27 .74 Exp .65 .61 .46 .54 .45 .72 .24 .92 .64 .94 Sat .76 .69 .48 .51 .56 .75 .32 .60 .83 .93 Use .64 .58 .39 .45 .48 .61 .35 Note: Func: Functional intelligence. Sinc: Sincerity. Creat: Creativity. Prote: Protective quality. Aest: Aesthetic appeal of VA mobile interface. Ctrl: Consumers’ perceived control. Focus: Consumers’ focused attention during interaction with VA. Exp: Consumers’ exploratory behavior. Sat: Consumer satisfaction. Use: Consumers’ willingness to continue using VA. Table 4 .000, β Alexa = .693, p Alexa = .000, β Cortana = .641, p Cortana = .000) and Cronbach Alpha, composite reliability and AVE. consumers’ willingness to continue using VA (β Google = .415; p Alexa = .000, β Alexa = .595, p = .000, β Cortana = .648, p Cortana = .000). Cronbach’s Composite Average Variance Alpha Reliability Extracted (AVE) 8. Discussion and conclusion Functional 0.968 0.971 0.650 The number of VA personal assistants are significantly increasing, as intelligence more people are using them more frequently. VA personal assistants 0.718 0.814 0.526 include commonly-used mobile applications and home devices such as Protective quality 0.925 0.938 0.655 Apple’s Siri, Amazon’s Alexa, Amazon’s Echo as well as companion and Sincerity 0.911 0.927 0.585 health care automatons such as Kuri robots. Creativity 0.865 0.854 0.607 Aesthetic appeal 0.815 0.877 0.609 We identified seven VA personality traits, including functional in­ Perceived control 0.661 0.782 0.550 telligence, aesthetic appeal of VA mobile interface, protective quality, Focused attention 0.914 0.946 0.854 sincerity, creativity, sociability, and emotional intelligence. Further, this Exploratory study also extended flow theory (Trevino and Webster, 1992) and linked 0.934 0.958 0.883 VA personality traits to flow experience. In doing so, we explain how behavior 0.921 0.950 0.865 VA’s functional intelligence, sincerity, and creativity enhance the sense Satisfaction of control consumers perceive during their voice interaction with VA, Use how consumers concentrate during these voice interactions, and how exploratory behavior facilitates consumer satisfaction and willingness to Note: Aesthetic appeal: Aesthetic appeal of VA mobile interface. continue using AI. Perceived control: Consumers’ perceived control. Focused attention: Consumers’ focused attention during voice interaction with Consumers’ perceived control as essential to increasing consumers’ VA. confidence (Hoffman and Novak, 1996) to use technology (Ajzen, 1991; Exploratory behavior: Consumers’ exploratory behavior. Dabholkar, 1996). Consumers’ perceived control makes them feel useful Satisfaction: Consumer satisfaction. (Pilai, Sivathanu, and Dwivedi, 2020) and encourages their voice in­ Use: Consumers’ willingness to continue using VA. teractions with VA if they do not challenge or threaten the consumers’ safety and security. 0.40). Consumers’ focused attention during VA interactions also VA manifesting functional intelligence, sincerity, and creativity significantly influences consumers’ exploratory behavior (β = .259; p = traits can enhance consumers’ perceived control during voice in­ teractions, especially when the VA offers effective, efficient, sincere, .000; R2 = 0.40). Consumers’ exploratory behavior significantly en­ honest, and current information. hances consumer satisfaction (β = .637; p = .000, R2 = 0.41), and As consumers learn through their voice interactions with VAs, they become more confident and more willing to seek information. As con­ consumer’s willingness to continue using VA (β = .596; p = .000; R2 = sumers become accustomed to having conversations with their voice assistants, even if only for a few minutes, they become more willing to 0.35). H1 through H7 were supported. Table 6 illustrates the results. explore their world, which leads to increased confidence. The structural model was also tested using Multi-Group Analysis That is, satisfactory consumer experience begins by humanizing VA to display social and emotional characteristics such as functional intel­ with Smart PLS 3.0. The results demonstrated that functional intelli­ ligence, sincerity, and creativity, which allow consumers to feel in control during voice interactions with VA transferring the experience of gence (β Google = .450, p Google = .000; β Alexa = .634, p Alexa = .000; β control to conversational flow and invoking them to participate in Cortana = .608; p Cortana = .000), sincerity (β Google = .554; p Google = .002, β exploratory behavior. Alexa = .373, p Alexa = .01, β Cortana = .560, p Cortana = .000), and creativity (β Google = .316; p Google = .002, β Alexa = .227, p Alexa = .02, β Cortana = .420, p Cortana = .000) significantly influence consumers’ perceived control. Consumers’ perceived control significantly influences consumers’ focused attention across three treatments (β Google= 0.605; p Google= .000, βAlexa = .721, p Alexa = .000, β Cortana= 0.415, p Cortana = .000). Consumers’ focused attention significantly influences consumers’ exploratory behavior across all three treatments (β Google = .415; p Google = .000, β Alexa = .693, p Alexa = .000, β Cortana = .641, p Cortana = .000) and thereby significantly influences consumers satisfaction (β Google = .415; p Google = 7

A. Poushneh Journal of Retailing and Consumer Services 58 (2021) 102283 Table 5 Table 6 Constructs: Average variance extracted (AVE), construct reliability (CR), CFA. Results of hypotheses testing. Constructs CFA Hypotheses Coefficients T P 0.477 Values Values Functional intelligence (α ¼ .97, AVE ¼ .65, CR ¼ .97, M ¼ 5.53) .817 Functional intelligence - > Consumers’ 0.000 Informative .831 perceived control 6.560 Up to date .788 0.049 Fast .876 Sincerity - > Consumers’ perceived control 0.154 1.964 Satisfying .841 Concise .810 Creativity - > Consumers’ perceived control 0.257 3.126 0.002 Organized .885 Exploratory behavior - > Consumer 0.637 13.241 0.000 Efficient .801 0.596 11.672 0.000 Comprehensive .866 satisfaction 0.259 3.640 0.000 Knowledgeable .761 Exploratory behavior - > Consumers’ Interactive .841 0.299 4.080 0.000 Intelligent .811 willingness to continue using VA Positive .781 Consumers’ focused attention during voice Easy .785 Competent .692 interaction with VA - > Consumers’ Mature .792 exploratory behavior Searchable Consumers’ perceived Control - > Consumers’ focused attention during voice interaction with VA Sincerity (α = .92, AVE = .65, CR = .94, M = 4.87) .844 Note: Satisfaction: Consumer satisfaction. Honest .847 Exploratory behavior: Consumers’ exploratory behavior. Sincere .739 Use: Consumers’ willingness to continue using VA. Original .839 Cheerful 9. Theoretical and managerial implications Aesthetic appeal of VA mobile interface (α = .86, AVE = .61, CR = .85, M = 4.22) This study provides empirical evidence that VA personality traits drive voice interaction flow experience resulting in satisfactory experi­ Colorful .852 ence through voice interaction. This research builds a theory in three important ways. First, a humanoid voice evokes consumers’ social re­ Attractive .887 sponses (McLean and Osei-Frimpong, 2019) so it is crucial to humanize VAs by giving them anthropomorphic characteristics (Araujo, 2018), Flashy .819 provided that those personalities do not cause consumers to feel en­ dangered (Damasio, 2019). Although personalities attributed to VAs are Showy .799 somehow “artificial” because they are not generated by a living organ­ ism with a “conscious mind” (Damasio, 2019), they could be a force that Protective quality (α = .72, AVE = .53, CR = .81, M = 3.22) .738 enhances consumers’ quality of life (Kachouie et al., 2014). Loving .772 Protective .550 This study shows that consumers feel in control when they interact Outdoorsy with a VA that displays functional intelligence, sincerity, and creativity. VAPs can create social (McLean and Osei-Frimpong, 2019) and novelty Creativity (α = .91, AVE = .58, CR = .93, M = 4.75) .725 benefits that arouse consumers’ emotional interest (e.g., Sung et al., Trendy .818 2016). In addition, VAs promote consumers’ sense of well-being, espe­ Smooth .733 cially that of the elderly’ (Kachouie et al., 2014), because they can Contemporary .799 stimulate social interaction. They provide lonely people with a social Cooperative .831 presence (McLean and Osei-Frimpong, 2019) and act as companion. Thoughtful .846 Consumers can initiate a conversation; sometimes a deep one, with a VA Creative .745 for a wide range of reasons, from bridging their knowledge gap to killing Bold time. Table 5 continued This study explains how humanizing VA through personality traits Consumers’ Willingness to Continue Using VA (α = .92, AVE = .86, CR = .95, M transforms consumer experience through consumers’ perceived control, focused attention during voice interaction with VA, and exploratory = 4.22) behavior. When consumers perceive control during their voice in­ teractions, feel safe to concentrate on those voice interactions, and I intend to accomplish my tasks by using this mobile application. .860 participate in exploratory behavior (Hoffman and Novak, 1996). Con­ sumers’ perceived control is essential to a satisfactory VA experience I would be willing to use this mobile application. .962 since it helps consumers feel safe so that they can focus on their voice interaction with a VA and explore the world. A sense that they have lost In future, I would use this mobile application. .963 control of their interaction with a VA causes consumers to experience anxiety and stress (e.g., Goetz et al., 2010), and feel uncertain about Consumer Satisfaction (α = .93, AVE = .88, CR = .96, M = 4.87) .929 their voice interaction (Bandura, 1989) which discourages them from Overall, I am satisfied with this mobile application. .961 concentrating on that very interaction. It is worth noting that flow Being a user of this mobile application has been a satisfying experience. .929 experience encourages individuals to become involved in an activity Having experienced this mobile application was pleasurable. that brings experiential rewards (Nakamura and Csíkszentmiha´lyi, 2009). For instance, the feeling that Alexa is always listening or Consumers’ Perceived Control (α = .81, AVE = .61, CR = .88, M = 5.34) recording their conversations inhibits users from focusing on the voice interaction. As consumers are motivated to interact with VAs and learn, When I used the mobile application, I felt in control. .862 their satisfaction and willingness to continue using VAs are enhanced. The mobile application was interesting. .922 VA designers need to emphasize natural language processing (NLP) It was fun to explore the mobile application. .913 The mobile application allowed me to control the voice interaction. .712 Consumers’ Exploratory Behavior (α = .91, AVE = .85, CR = .95, M = 4.40) The mobile application aroused my imagination. .880 Interacting with the mobile application made me curious. .951 Interacting with the mobile application excited my curiosity. .940 Consumers’ Focused Attention During Voice Interaction with VA (α = .66, AVE = .55, CR = .78, M = 6.07) The interaction with the mobile application bored me. .940 When I used the mobile application, I thought about other things. .610 When I used the mobile application, I was aware of distractions. .615 Note: Mobile application refers to the voice assistant mobile application. Interaction refers to voice interaction. 8

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| |Received: 30 April 2020 Revised: 10 December 2020 Accepted: 6 January 2021 DOI: 10.1002/mar.21457 RESEARCH ARTICLE Alexa, she's not human but… Unveiling the drivers of consumers' trust in voice‐based artificial intelligence Valentina Pitardi1 | Hannah R. Marriott2 1Department of Marketing, University of Abstract Portsmouth, Portsmouth, UK With the development of deep connections between humans and Artificial Intelligence 2Department of Marketing, Events & Project voice‐based assistants (VAs), human and machine relationships have transformed. Management, University of Winchester, For relationships to work it is essential for trust to be established. Although the Winchester, UK capabilities of VAs offer retailers and consumers enhanced opportunities, building trust with machines is inherently challenging. In this paper, we propose integrating Correspondence Human–Computer Interaction Theories and Para‐Social Relationship Theory to develop Valentina Pitardi, Lecturer in Marketing, insight into how trust and attitudes toward VAs are established. By adopting a mixed‐ Department of Marketing, University of method approach, first, we quantitatively examine the proposed model using Portsmouth, Richmond Bldg, Portsmouth PO1 Covariance‐Based Structural Equation Modeling on 466 respondents; based on the 3DE, UK. findings of this study, a second qualitative study is employed to reveal four main Email: [email protected] themes. Findings show that while functional elements drive users' attitude toward using VAs, the social attributes, being social presence and social cognition, are the unique antecedents for developing trust. Additionally, the research illustrates a peculiar dy- namic between privacy and trust and it shows how users distinguish two different sources of trustworthiness in their interactions with VAs, identifying the brand pro- ducers as the data collector. Taken together, these results reinforce the idea that individuals interact with VAs treating them as social entities and employing human social rules, thus supporting the adoption of a para‐social perspective. KEYWORDS artificial intelligence, privacy, technology adoption, trust, voice assistants 1 | INTRODUCTION (eMarketer, 2020). Furthermore, Olson (2019) highlights that almost 41% of VAs users are concerned about privacy and passive listening, Why does she [Alexa] always listen to me? Voice technology usage is and particularly trust has been identified as the main barrier for rising worldwide, with almost 4.2 billion voice‐activated assistants voice assistants' users and shoppers (PwC, 2019). Accordingly, trust (VAs) being used in devices around the world in the last year in the technology is among the obstacles that can cause worry and (Statista, 2020). While VAs' adoption is advancing quickly, its usage concerns for current VAs adopters and, thus, hinder their full remains limited to basic tasks. For example, it has been observed that adoption in the near future (Marcus, 2019; Rossi, 2019). While the consumers remain reluctant to use VAs to make online purchases role of trust on VAs adoption is relevant to both practitioners and This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2021 The Authors. Psychology & Marketing published by Wiley Periodicals LLC Psychology & Marketing. 2021;1–17. |wileyonlinelibrary.com/journal/mar 1

2| PITARDI AND MARRIOTT academics, research on how trust develops between consumers and VAs and showed the influence of voice technology on users' VAs remains scant (Foehr & Germelmann, 2020), and an important engagement and loyalty. By integrating HCI literature and Uses and question emerges: what are the factors that foster trust develop- Gratification theory, McLean and Osei‐Frimpong (2019) examined ment in interactions with VAs? the motivations for adopting and using in‐home voice assistants. Finally, Ki et al. (2020), explored the para‐friendship relationships It is apparent that the regular utilization of Artificial Intelligence that can arise between individuals and virtual personal assistants. (AI) technology and their human‐like qualities have changed the way Among these, few research has explored the factors affecting users' people perceive and interact with them. When using mobile devices trust in their interactions with VAs, with them adopting either an or laptops, it is rare that one would refer to them as “he” or “she,” yet information system perspective (Nasirian et al., 2017) or a social when it comes to VAs on these devices, such as Siri and Alexa, response approach (Foehr & Germelmann, 2020). Nevertheless, a personification occurs. Despite VAs not possessing any physical comprehensive understanding of what nurtures users' trust toward human qualities, the voice alone is enough for humans to develop a VAs has yet been fully developed. deeper connection to the technology (Han & Yang, 2018; Novak & Hoffman, 2019; Schweitzer et al., 2019). By integrating HCI literature and PSR theory, this study aims to address this gap and investigates the drivers of consumers' trust and Deep connections can bring about para‐social relationships attitude in the interactions with VAs. As the relational elements of (PSRs). Traditionally, these relationships may occur between a the interaction with these devices have been highlighted as one of celebrity and a fan but recent research has begun to investigate this the most important characteristic influencing users engagement with with technology. For example, the mobile phone is considered an VAs (Han & Yang, 2018), the study adopts a PSR theory approach extension of oneself and has therefore created an inseparable bond (Horton & Wohl, 1956; Turner, 1993) and explores the functional, between humans and their phones (Melumad & Pham, 2020). Since emotional, and social factors influencing trust development and at- the emergence of VAs, conversations between humans and machines titude toward VAs. have given rise to higher levels of engagement whereby more natural conversations occur between them (Guzman, 2019; Ki et al., 2020). This study adopts a mixed‐methodology applying a development For example, recent developments of this technology allow VAs to mixed‐method approach (Davis et al., 2011) that, first, includes a display emotional responses by mimicking intonations of human quantitative phase of research followed by qualitative in‐depth speech, thus sounding more “human” (Schwartz, 2019). As per the studies. Social Response Theory (SRT; Nass & Moon, 2000), the element of reciprocity has developed these human–machine bonds to a greater The study directly responds to Wirtz et al. (2018) and Lu et al. extent (Cerekovic et al., 2017), having consumers not only using (2020)'s calls to further explore consumers' interaction with AI these devices but also developing them with various types of re- agents and its contribution to the literature is threefold. First, find- lationships (Han & Yang, 2018; Schweitzer et al., 2019). ings of the two studies draw attention to the peculiar relation be- tween privacy and trust development toward VAs, revealing how Trust is recognized as a strong determinant of technology users distinguish two different sources of trustworthiness during a adoption and use (van Pinxteren et al., 2019; Wirtz et al., 2018) and VAs interaction and direct their privacy concerns to the producer has the power to reduce levels of perceived risk surrounding an rather than the voice‐based agent (Foehr & Germelmann, 2020). interaction, thus facilitating consumers' intentions and behaviors Second, it demonstrates the prominent role of social elements, (Gefen & Straub, 2004). namely social presence and social cognition, as unique antecedents for developing users' trust toward VAs (Čaić et al., 2019; van Doorn Research examining how users develop trust with technology et al., 2017). While previous studies suggest that social attributes can are generally grounded in the Human–Computer Interaction (HCI) improve consumers' trust in online settings, this effect has rarely literature (Hassanein & Head, 2007). These studies have identified been empirically examined toward consumers–VAs interactions. the drivers of trust toward the functional (Lu et al., 2016), hedonic Finally, the study shows the relevance of adopting a more integrated (Hwang & Kim, 2007), and social (Gefen & Straub, 2003; Ye approach when examining interactions with AI technology, while et al., 2019) attributes of the technology. In addition, perceived providing support for new ways to understand how trust develops privacy concerns have been found to have detrimental effects on between VAs and consumers; this being through the identification of individuals' trust formation (Chang et al., 2017; Zhou, 2011). This is drivers incorporating the functional and hedonic attributes of the particularly relevant in the interaction between humans and VAs as technology and individuals' perceived privacy risks, while including users are not naïve of privacy implications of using them (McLean & social and relational elements. Osei‐Frimpong, 2019). Recent studies focusing on AI service robots identify trust as an important relational dimension, linked to service 2 | LITERATURE REVIEW robots' acceptance (Wirtz et al., 2018), and argue that the level of anthropomorphism is an important driver of trust and intention to VAs are Internet‐enabled devices that provide daily technical, ad- use them (van Pinxteren et al., 2019). ministrative, and social assistance to their users, including activities from setting alarms and playing music to communicating with other To date, studies examining VAs have mainly focused on the factors influencing their usage in daily life. Moriuchi (2019) in- vestigated the Technology Acceptance Model (TAM) constructs of

PITARDI AND MARRIOTT |3 users (Han & Yang, 2018; Santos et al., 2016). They are traditionally Perceived Ease of Use (PEOU) are often found to be fundamental used as mobile applications (e.g., Apple Siri and Google Now) and predictors of technology adoption across research settings (e.g., Wirtz have, in more recent years, been extended to the home environment, et al., 2019). However, TAM has been regularly criticized in its con- whereby a separate device is set up alongside a mobile application temporary application to technology adoption research in being out- (e.g., Amazon Echo and Google Home). Recent developments of VAs dated and lacking sufficient depth to explain the adoption of more software include the implementation of natural language processing; complex technologies (Lim, 2018). As such, models such as the Unified this allows VAs to engage in conversational‐based communication Theory of Acceptance and Use of Technology (UTAUT; Venkatesh whereby they not only respond to initial questions but are also et al., 2003) and its extension (UTAUT2; Venkatesh et al., 2012) have capable of asking follow‐up questions (Hoy, 2018). Thus, VAs can be been developed to add additional functional and hedonic antecedents used as functional tools for online shopping, learning, controlling of behavioral intention. Performance Expectancy and Effort Expectancy other smart applications and devices as well as for relational benefits echo the nature of PU and PEOU and further confirms the role of the such as communications and companionship (Guzman, 2019; functional attributes on adoption intention. Schweitzer et al., 2019). This is particularly relevant when examining how trust develops toward VAs as potential antecedents can arise Although TAM has been criticized in recent years, its utilization from both functional and relational perspectives. in this context is more appropriate than its successor models in that it incorporates attitude. Nevertheless, later models remain sig- Literature investigating the adoption and use of VAs can be nificant in this context as they incorporate enjoyment; enjoyment is a grouped into two main research streams. First, HCI literature ex- fundamental antecedent of technology adoption, and is considered amines consumers' behavioral intention to use VAs through the use even more significant when using technology for hedonic purposes, of technology adoption theories (Moriuchi, 2019); these theories thus drawing attention to the need to incorporate hedonic motiva- have been used considerably across e‐commerce, m‐commerce, and tion from UTUAT2 (Venkatesh et al., 2012). social media literature and have been adopted in AI and VAs studies, providing support for the influence of perceived usefulness (PU) and Despite the evolution of such models, the unique characteristics ease of use on individuals' adoption intentions. However, limitations of AI technologies require a broader perspective in the under- in using such theories in their singularity have been made apparent, standing of the motivations for adopting and using advanced tech- bringing about a second stream of literature examining VA adoption nology. A significant conceptual framework is provided by Wirtz through PSR theories (Ki et al., 2020); this literature suggests that et al. (2018), who introduced the Service Robot Acceptance Model VA adoption is not only based on willingness to adopt technology but (sRAM). The model builds on TAM (Davis, 1989) and role theory also due to the relationships built between consumers and voice‐ (Solomon et al., 1985) and identifies the functional (i.e., PEOU, PU, based (or “human‐like”) systems (e.g., Schweitzer et al., 2019). An- subjective social norms), social (i.e., perceived interactivity, perceived other substream of HCI literature has examined the negative role of social presence, and perceived humanness), and relational (i.e., trust privacy concerns, and has been seen to be discussed in both main and rapport) elements that drive users' acceptance and use of ser- streams of research (e.g., McLean et al., 2020). vice robots. While the model provides useful theoretical foundations to understand AI service robots' acceptance and use, trust is only However, with endorsements and criticisms surrounding various included as a relational need that may influence consumers' intention theories and concepts, literature has called out for studies to com- to use and what drives trust toward AI agents remain unexplored. bine these core streams to gain a clearer and more comprehensive investigation of consumer adoption of VAs. Research, so far, has 2.2 | Human–technology interactions investigated trust toward VAs solely from a technology‐based through PSR development (Nasirian et al., 2017) or a relational‐based (Foehr & Germelmann, 2020) perspective. Building on both these streams of Literature in technology adoption has often examined the importance research, this study combines the theoretical foundations of PSR of social influence when users decide to adopt or reject new technol- with HCI theories and provides a more detailed perspective on the ogies; some of the earlier factors examining this focus on the notion of antecedents of trust development toward VAs that includes the re- conformity (Nail, 1986), reciprocal influence (Stasser & Davis, 1981; lational dimensions of the interaction while accounting for their Tanford & Penrod, 1984), and reciprocal caution (Bandura, 1989). Re- functional and hedonic attributes. ciprocal caution explains that a person will only do something if they feel they have a level of control or trust over their situation. 2.1 | Human–technology interaction through functional requirements As human beings, trust is a basic yet fundamental deep‐routed psychological component of whether we engage in fight or flight To examine users' adoption of new technologies, various TAMs have behavior (Mayer et al., 1995). Therefore, people constantly sub- been developed and adapted over time. The TAM (Davis, 1989) has consciously identify objects as being friends or foes; for example, a long been relied on by researchers to convey the importance of func- fire is hot and will hurt to touch so we learn to not touch it. Humans tional attributes of technology for them to be adopted by users. PU and develop stronger associations of trust between other humans and connect with humans in a deeper way than with inanimate objects.

4| PITARDI AND MARRIOTT With VAs being the first types of technology to display such human‐ et al., 2010), Internet literacy (Dinev & Hart, 2006), policy and reg- like features, being the voice, it is questionable whether users have ulations (Lwin et al., 2007), business communication (Lwin developed a stronger bond with them, in recognizing them more as et al., 2016), and fairness of the technology (Pizzi & Scarpi, 2020) are friends than foes, than with other technologies (Schweitzer potential drivers of individuals' privacy concerns. et al., 2019; van Pinxteren et al., 2019). Previous research has examined how privacy concerns influence PSR theory considers the interpersonal relationships between consumers' responses in a variety of settings, including online people and media characters (Horton & Wohl, 1956; Turner, 1993). shopping (van Slyke et al., 2006), health online information (Bansal & Traditionally, this has been used to explain relationships between Gefen, 2010), social networking sites (Chang et al., 2017; Xu viewers/consumers and characters/celebrities. Recently, AI‐based et al., 2013), retail technologies (Pizzi & Scarpi, 2020), mobile com- personal assistant‐related work has addressed the significance of munication and location‐based services (Xu & Gupta, 2009; Zhou & PSR Theory in explaining that users can develop a degree of close- Li, 2014). These studies provide evidence that privacy concerns can ness and intimacy with VAs when engaging in human‐like interac- act as a negative antecedent of usage (e.g., Nepomuceno et al., 2014) tions, which can result in users perceiving them as friends, or as a moderator (e.g., Brill et al., 2019). Specifically, it has been subsequently resulting in a PSR (Louie et al., 2014; Sproull found that perceptions related to privacy directly affect individuals' et al., 1996). Han and Yang (2018) argued that the social aspects of behavior intention (Fogel & Nehmad, 2009), thus initiating con- VAs, namely “interpersonal attraction,” impacts users' satisfaction sumers' protective behaviors, such as refusal to purchase (Lwin and intention to use these devices, providing that the more regular et al., 2007). the interactions between VAs and their users the more the re- lationship will be expected to be interactive and socially enjoyable. It has been proposed that consumers engage in a “privacy cal- This regularity of interactions helps explain why users are becoming culus” such that they evaluate the costs of disclosing personal in- comfortable with using VAs in their homes. Ki et al. (2020) explored formation to the benefits they receive from the interaction (Inman & para‐relationships between individuals and VAs and found that the Nikolova, 2017). This calculus, usually referred in literature as the para‐social presence of these devices influences users' self‐ privacy paradox, can lead consumers to use services or technologies disclosure and social support toward VAs, which, in turn, leads to when they perceive a value in the interaction, despite privacy per- continue usage intentions. ceptions (Chellappa & Sin, 2005; Kokolakis, 2017). These studies provide evidence that human‐like features of AI Besides its direct influence on user responses, privacy concerns agents make individuals perceive they are “socially present,” result- can also indirectly affect individuals' behavior through trust ing in them applying social norms when interacting with them (Nass (Zhou, 2011). Privacy and trust are often negatively correlated & Moon, 2000). Therefore, the more the technology displays human‐ (Wirtz & Lwin, 2009) and their relationship can often lead to dif- like characteristics, such as face, voice, or gender, the more these ferent responses; for example, trust promotes positive outcomes, kinds of cues may trigger schemas associated with human‐human such as relational behavior and purchase intentions, while privacy interactions, including a sense of social presence (Chattaraman triggers protective reactions. Dinev and Hart (2006) conceptualized et al., 2019). their Extended Privacy Calculus Model (EPCM) to convey the com- plicated relationship between trust and privacy on users' adoption 2.3 | Privacy concerns willingness, the nature of which has been debated throughout HCI literature. Various research has shown how perception of privacy Where relationships become constrained, particularly with technol- negatively impacts trust and, in turn, users' behavior (Bansal et al. ogy, is when users fear for their personal privacy. Privacy concerns 2016; Chang et al., 2017; Liu et al.,2005; Zhou, 2011). Therefore, are associated with the unauthorized collection, usage, or control of although trust is paramount to achieving a strong para‐social bond personal data (Malhotra et al., 2004) and in the digital landscape are between the VAs and their users, the fact they are inherently risky to generally divided into three main types, being (1) territorial privacy personal privacy—in always being on—may create an additional concerning the physical surrounding space; (2) privacy of a person barrier that can prevent trust development. against undue interference; and (3) information privacy of control of the gathering, storage, processing, and dissemination of personal 3 | CONCEPTUAL DEVELOPMENT data (Kokolakis, 2017). 3.1 | Functional attributes, trust, and attitude Privacy has long been examined within a HCI perspective (e.g., Mothersbaugh et al., 2012; Nepomuceno et al., 2014) and has more According to Wirtz et al. (2018, 2019), when interacting with recently been incorporated into AI literature (e.g., McLean & Osei‐ AI‐based personal assistants, functional elements, such as usefulness Frimpong, 2019). Previous studies have analysed both antecedents and ease of use, will appear to be given in most cases yet would be a and consequences of users' perceptions of privacy. It has been found barrier if not provided at a level expected by consumers. PU and that perceived vulnerability and ability to control (Bansal & PEOU of a new technology represent the core of TAM (Davis, 1989; Gefen, 2010; Mohamed & Ahmad, 2012), prior experience (Cho Venkatesh et al., 2003, 2012). The effects of such variables on users'

PITARDI AND MARRIOTT |5 attitude toward the use of the technology have been well docu- H3: Perceived enjoyment of voice‐activated assistants will have a po- mented throughout e‐commerce literature (Cyr et al., 2007; sitive influence on users' attitude to use (H3a) and trust towards Hassanein & Head, 2007; Moriuchi, 2019; Ye et al., 2019). Moreover, (H3b) the technology. several studies have found functional elements to be important predictors of consumers' trust in online settings (Lee & Jun, 2007). 3.3 | Social motivators, trust, and attitude Previous research on e‐trust highlights the role of technical features of websites and technology, such as ease of navigation, visual ele- When interacting with technology, individuals can be seen to apply ments, and ease of searching as cues that convey trustworthiness social roles and treat computes like a social entity (Nass & (Corritore et al., 2003). Specifically, e‐commerce research has shown Brave, 2005; Nass & Moon, 2000). This is especially true when the impact of functionality in terms of usability (Chen & Dibb, 2010), technology mimics human‐like attributes (Li, 2015). Such personal ease of use, and PU (Lu et al., 2016) on trust‐building mechanisms. closeness to the technology, due to its human‐like functions, goes Therefore, based on the foundations of TAM (Davis, 1989) and other beyond the confines of factors such as social influence and subjective supporting literature, it can be hypothesized that: norms (as seen in HCI literature) in that focus is on social closeness to the technology rather than external social pressures. H1: Perceived usefulness of voice‐activated assistants will have a posi- tive influence on users' attitude to use (H1a) and trust towards As VAs use natural language, interacts with users in real‐time (H1b) the technology. and are characterized by human‐like attributes (such as voice), it is possible to expect that interactions with them may elicit a H2: Perceived ease of use of voice‐activated assistants will have a po- sense of social presence (Chattaraman et al., 2019; Chérif & sitive influence on users' attitude to use (H2a) and trust towards Lemoine, 2019). Social presence is defined as the degree of sal- (H2b) the technology. ience of other person during an interaction (Short et al., 1976), while automated social presence is the “extent to which technology 3.2 | Perceived enjoyment, trust, and attitude makes customers feel the presence of another social entity” (van Doorn et al., 2017, p. 1). Perceived enjoyment, in Computers–Human Interaction literature, is defined as the extent to the activity of interacting or using new Originally derived from the SRT, social presence is shown to technology is perceived to be enjoyable aside from the functional value affect users' attitudes (Hassanein & Head, 2007), loyalty (Cyr of the technology itself (Davis et al., 1992). Previous research has et al., 2007), online behaviors (Chung et al., 2015; Ogara et al., 2014), outlined that users are driven by hedonic benefits when interacting and trust building (Gefen & Straub, 2003, 2004; Lu et al., 2016; with technology (Wu et al., 2010). For example, Venkatesh et al. Ogonowski et al., 2014). Specifically, studies have demonstrated that (2012), in their UTAUT2 model, observe that functional attributes of a technologies conveying a greater sense of social presence, such as technology are not enough to fully establish users' willingness to use it live chat services (McLean et al., 2020), can enhance consumer trust and, after incorporating hedonic motivation to their previous well‐ and subsequent behavior (Hassanein et al., 2009; Lu et al., 2016; renowned model (UTAUT), found that the users' enjoyment during Mackey & Freyberg, 2010; Ye et al., 2019). technology interaction can influence its actual and future use (Pizzi & Scarpi, 2020). Similarly, Fong et al. (2018) point to the role of enjoy- Due to their human‐like conversational flow, VAs may ment in influencing consumers' use and adoption of mobile apps elicit a sense of social presence in the interaction, which can showing that, in specific cases, intrinsic motivators, such as fun and serve as the basis for developing users' trust. Specifically, when perceived enjoyment, could be even stronger than extrinsic motivators users interact with their VAs, the human‐like real‐time commu- like PU (Van der Heijden, 2004; Pizzi & Scarpi, 2020). Not only can nication may influence the individuals' attitude and, more im- enjoyment influence consumers' behaviors but the pleasure and fun of portantly, trust toward the VAs (Chung et al., 2015; Hassanein & interacting with a new technology can also affect various aspects of Head, 2007; Ye et al., 2019). Thus, based on the foundations of information processing, such as consumers' loyalty and trust (Hwang & SRT, along with support from surrounding literature, it can be Kim, 2007; Ogonowski et al., 2014). Previous research shows that hypothesized that: enjoyment and pleasure can significantly impact individuals' evaluation process (Mattila & Wirtz, 2000) and suggest that the hedonic moti- H4: Perceived Social Presence of voice‐activated assistants will have a vations of individuals can influence trust toward the technology (Gefen positive influence on users' attitude to use (H4a) and trust towards & Straub, 2003; Hwang & Kim, 2007). This can be particularly relevant (H4b) the technology. for conversational AI‐based technology as consumers' interactions with such technology can provide individuals with valuable benefits in Closely related to the concept of social presence, social cognition terms of fun. Thus, based on the foundations of UTAUT2 (Venkatesh concerns how individuals process, store, and apply information about et al., 2012), TAM's extension (Davis et al., 1992) and further sup- other people (Fiske & Macrae, 2012). Fiske et al. (2007) suggest that porting literature, it can be hypothesized that: warmth and competence are the two fundamental dimensions of social perception that drive peoples' responses to specific interac- tions. Warmth refers to attributes such as friendliness, helpfulness,

6| PITARDI AND MARRIOTT and sincerity, while competence reflects such issues as intelligence, Previous research on VAs has highlighted how privacy percep- skill, and efficacy. tions can influence users' attitude and behaviors. For example, Easwara Moorthy and Vu (2015) explore the effects of personal Previous research examining social cognition perspective toward privacy, reporting that the more sensitive the information in the consumers–robot interactions (Čaić et al., 2019) demonstrates how in- more public place would reduce the likelihood of users interacting dividuals' inferences about social perception affect consumers' responses with their VAs. McLean and Osei‐Frimpong (2019) demonstrate how in service contexts (Fan et al., 2016; Scott et al., 2013; Wirtz et al., 2018). the benefits of using VAs are reduced by the perceived privacy risk In the seminal work on consumers' experience and service technology, of stolen data, ultimately affecting usage. van Doorn et al. (2017) identified social cognition as a driver of service and customers' outcomes. Further, research demonstrates that in- Various research has shown how perception of privacy nega- ferences of “human touch” can results in consumers' positive attitude tively impacts trust and, in turn, users' behavior (Zhou, 2011). For (Fan et al., 2016), trust, and purchase intentions (Luo et al., 2006). example, Liu et al. (2005) reveal that consumers' privacy concerns negatively affect their trust and subsequent behavioral intention, in As VAs are characterized by a mode of interaction (i.e., voice), that terms of repurchase, revisit, and positive recommendations. Simi- are usually reserved to human‐to‐human exchanges (Nass & larly, Bansal et al. (2016) show that privacy concerns negatively Brave, 2005), it is reasonable to expect that they are more likely to be impact users' trust and willingness to disclose personal information. perceived as sociable (Cho et al., 2019). However, there is a general Finally, Chang et al. (2017) demonstrate that perceptions of privacy threat that users may perceive advanced technology as being less negatively influence users' trust toward and intention to use social empathetic, based on the degree of display of human‐like character- network sites. As such, it is reasonable to expect that perceived istics (Davenport et al., 2020). For example, it has been discussed that privacy concerns in the interaction with VAs can negatively impact embodied AI agents (e.g., service robots) are more likely to be perceived trust and attitude toward using the device. as helpful and friendlier compared to virtual agents (e.g., voice‐based agents) (van Doorn et al., 2017; Wirtz et al., 2018) because of their Dinev and Hart (2006) offer a grounded foundation for the ef- anthropomorphic body (Kim et al., 2019). Further, Huang and Rust fects of privacy concerns on consumers' willingness to engage with (2018) indicate that AI agents are generally expected to be reliable online transactions. They provide that although privacy risk has a regarding functional capabilities and intelligences as they are perceived negative impact on trust, trust has an overall positive influence on more competent and skillful compared to other technologies. Therefore, willingness to adopt. Due to the literature examining privacy con- we expect that interaction with a VAs may trigger users' perception of cerns and trust toward VAs being relatively infant, it is significant to competence, hence foster positive attitudes and inspire trust. Thus, incorporate the EPCM (Dinev & Hart, 2006) into the functional, through the foundations of SRT, and support from the work by Čaić hedonic, and social aspects contributing to VA adoption. Thus, based et al. (2019), it is hypothesized that: on the foundations of the EPCM (Dinev & Hart, 2006), along with supporting literature, it is hypothesized that: H5: User‐inferred Social Cognition of voice‐activated assistants, in terms of perceived competence, will have a positive influence on H6: Perceived Privacy Concerns of voice‐activated assistants will have a users' attitude to use (H5a) and trust towards (H5b) the technology. negative influence on users' attitude to use (H6a) and trust towards (H6b) the technology. 3.4 | Perceived privacy concerns, 3.5 | Trust, attitude, and intentions to use trust, and attitude One of the benefits of VAs are that they can listen to anyone and Attitudes are commonly defined as predispositions to respond in a understand their requests. However, this can provide privacy and positively or negatively way toward a particular object and are gen- security risk perceptions in that the VAs are not sophisticated en- erally considered antecedents of behavioral intentions (Ajzen & ough to determine which voices are “trusted” or “authorised.” Fishbein, 1980). This process also applies to consumers–technology A fictitious example may include a young child asking their mother interactions where users' attitudes toward using a specific technology for a particular toy for their birthday and asks her to order it on influence their actual use of the technology itself (Davis, 1989; McLean Amazon; the Alexa on their mothers' home device or smartphone et al., 2020). Several studies investigate the variables influencing users' may over‐hear this and assume this order is intended to be placed, attitude in the consumers–technology interaction including functional and then does so (Hackett, 2017; Lei et al., 2017). In a more extreme elements (Cyr et al., 2007; Venkatesh et al., 2003), perceived social example, an unwanted house guest may take advantage of asking the presence (Ye et al., 2019), and trust (Hassanein & Head, 2007). home VAs device to disclose personal information about the owners for more malicious purposes (Dinev & Hart, 2006). Within the realm Trust has been acknowledged as a key influencer of of VAs, security and privacy risks have been defined as the fear of human–machine interactions (Ghazizadeh et al., 2012; McLean unauthorized access to them by others leading to potential un- et al., 2020). Traditionally, trust in technology is examined by the authorized discloser of personal information (Han & Yang, 2018). predictability of the technology (McKnight et al., 2009), yet more contemporary literature draws attention to trust being built on its

PITARDI AND MARRIOTT |7 dependability, which is enhanced through having faith in their in- survey at any time, even after completion. The sample was collected teractions (Ghazizadeh et al., 2012; Hengstler et al., 2016). Trust is in the United Kingdom and targeted respondents who have had at generally intended as a multidimensional concept that reflects per- least some experience using VAs. Screening questions were used to ceptions of competence, integrity, and benevolence of another entity ensure that respondents were over the age of 18 and had at least (Mayer et al., 1995). Online and offline trust has been widely in- some experience using these voice‐based assistants. vestigated in the field of HCI (Gefen & Straub, 2003, 2004; Lee & Nass, 2010; Wang & Emurian, 2005; Ye et al., 2019) and research 4.2 | Measurement development often establishes trust to have a fundamental role in influencing consumers' attitudes and purchasing intentions (Corritore As the first stage in this study is to take a covariance‐based con- et al., 2003; Cyr et al., 2007). Thus, building on the above, and firmatory approach to quantitative analysis, the items and scales used in drawing on TAM (Davis, 1989) and ECPM (Dinev & Hart, 2006), it is the survey are adapted from previous studies. For the functional vari- hypothesized: ables, 4 items based on Venkatesh et al. (2012) captured the PEOU (Cronbach's ɑ = .867) and 4 for PU (Cronbach's ɑ = .883). For the social H7: Trust towards voice‐activated assistants will have a positive influ- variables, 5 items measured perceived Social Presence (McLean & Osei‐ ence on users' attitude (H7a) and intentions (H7b) to use the Frimpong, 2019), with a Cronbach's ɑ of .946, and 6 items for Social technology. Cognition (Fiske et al., 2007), with a Cronbach's ɑ of .854. Enjoyment was made up of 4 items (Mun & Hwang, 2003, from Ye et al., 2019) with H8: Users' attitude towards voice‐activated assistants will have a a Cronbach's ɑ of .900. Four items based on several studies are used for positive influence on intentions to use the technology. Trust (Chattaraman et al., 2019; Hassanein & Head, 2007; Ye et al., 2019), with a Cronbach's ɑ of .871. Perceived Privacy risk is The study comprises of two stages; (1) quantitative data analysis measured with 4 items by McLean and Osei‐Frimpong (2019) and had a to test the proposed hypotheses, and (2) a qualitative study in- Cronbach's ɑ of .868. For the dependent variables, 3 Attitude items are vestigating some of the found relationships. Section 3 reports the used (Hassanein & Head, 2007), with a Cronbach's ɑ of .909, and methods and results of the quantitative study (Study 1) with 3 items are used for Usage (McLean & Osei‐Frimpong, 2019), with a Section 4 discussing those of the qualitative study (Study 2). Cronbach's ɑ of .907. All the scales used a 7‐point Likert scale ranging Section 5 subsequently discusses the results alongside existing lit- from strongly disagree to strongly agree (see Table 1) and all items used erature and provides recommendations for theorists and practi- exceeded the Cronbach ɑ value threshold of .60, showing reliability tioners (Figure 1). (Malhotra et al., 2010). 4 | STUDY 1: METHOD, DATA ANALYSIS, 4.3 | Response rates AND RESULTS Of the 541 collected responses, 75 were unusable due to being in- 4.1 | Sample complete or the respondents not satisfying the screening criteria. The data was further screened for outliers; z scores were used and the items Using simple random sampling method, data is collected from with z scores within ±3.29 (Pallant & Manual, 2013) were kept. As such, Amazon's Mechanical Turk (mTurk). The questionnaire informed the respondents of their anonymity and right to withdraw from the F I G U R E 1 Conceptual Model

8| PITARDI AND MARRIOTT T A B L E 1 Measurement items Factor Items Cronbach's ɑ References Perceived My voice‐based assistant provides good quality information (PU1). [deleted] .864 Moon and Kim (2011) usefulness from Ye My time effectiveness improves when I am gathering information using my voice‐ et al. (2019) based assistant (PU2). [deleted] Using my voice‐based assistant improves my performance when using it to gather information (PU3) Overall, I find my voice‐based assistant useful when I am searching for information (PU4) Perceived ease Learning to use my voice‐based assistant is easy for me (PEU1) .840 Gefen et al. (2003) of use My interactions with my voice‐based assistant are clear and understandable (PEU2). from Ye et al. (2019) [deleted] I find my voice‐based assistant easy to use (PEU3) I find it is easy to become skillful at using my voice‐based assistant (PEU4) Perceived I find using my voice‐based assistant enjoyable (EN1). [deleted] .852 Mun and Hwang enjoyment I find using my voice‐based assistant entertaining (EN2) (2003) from Ye I have fun when using my voice‐based assistant (EN3) et al. (2019) I find using my voice‐based assistant pleasant (EN4). [deleted] Social presence When I interact with my voice‐based assistant I feel there is a sense of personalness .949 Gefen and (SP1). [deleted] Straub (2004) When I interact with my voice‐based assistant I feel there is a sense of human contact (SP2) When I interact with my voice‐based assistant I feel like if I am dealing with a real person (SP3) When I interact with my voice‐based assistant I feel there is a sense of sociability (SP4) When I interact with my voice‐based assistant I feel there is a sense of human sensitivity (SP5) Social cognition I think my voice‐based assistant is helpful (SC1). [deleted] .797 Fiske et al. (2007) I think my voice‐based assistant is warm (SC2). [deleted] I think my voice‐based assistant is with good intentions (SC3). [deleted] I think my voice‐based assistant is effective (SC4). [deleted] I think my voice‐based assistant is intelligent (SC5) I think my voice‐based assistant is competent (SC5) Trust I feel that my voice‐based assistant makes truthful claims (TR1) .838 Chattaraman et al. I feel that my voice‐based assistant is trustworthy (TR2) I believe what my voice‐based assistant tells me (TR3) (2019); Hassanein I feel that my voice‐based assistant is honest (TR4). [deleted] and Head (2007); Ye et al. (2019) Attitude Overall, I feel using my voice‐based assistant is a good idea (ATT1) .909 Hassanein and I generally have positive feelings toward using my voice‐based assistant (ATT2) Head (2007) The thought of using my voice‐based assistant is appealing to me (ATT3) Intentions to use It is likely that I will use my voice‐based assistant in the future (INT1) .905 McLean and Osei‐ I intend to use my voice‐based assistant frequently (INT2). [deleted] Frimpong (2019) I expect to continue using my voice‐based assistant in the future (INT3) Privacy concern I have my doubts over the confidentiality of my interactions with my voice‐based .868 McLean and Osei‐ assistant (PRV1) Frimpong (2019) I am concerned to perform financial transactions through my voice‐based assistant (PRV2) I am concerned that my personal details stored on my voice‐based assistant could be stolen (PRV3) I am concerned that my voice‐based assistant collects too much information about me (PRV4)

PITARDI AND MARRIOTT |9 the total responses for this study is 466. Normality was checked using discriminant validity of the proposed model. The component relia- Kolmogorov‐Smirnov statistic and Skewness and Kurtosis statistics; bility (CR) and average variance extracted (AVE) for each construct despite the Kolmogorov‐Smirnov statistics being significant, the Skew- are examined to ensure that they meet the threshold criteria for ness and Kurtosis statistics are within the requirement parameters internal consistency; the CR for a construct should be >0.60 and the (Chou & Bentler, 1995), thus rendering the data normally distributed. AVE be >0.50 (Bagozzi & Yi, 1988). As seen in Table 2, the results Finally, common method bias was examined using Harman's single‐ show that the CR values are all above 0.80 and all AVE values are factor analysis and revealed a satisfactory level of variance at 39.2%, above 0.60 and therefore do not display any convergent validity below the 50% threshold (Podsakoff et al., 2003). issues (Bagozzi & Yi, 1988; Fornell & Larcker, 1981). 4.4 | Descriptive statistics To assess discriminant validity, Fornell and Larcker (1981) suggest assessing the AVE values of each construct with the intercorrelation Of the 466 responses, 60.3% (281) respondents are male and 39.3% scores within the correlations table; they provide that the AVE score (183) are female, with 0.4% (2) respondents preferring not to say. must exceed the scores of the inter‐correlations. As seen in Table 2, the The majority of respondents are aged between 30 and 39 (32%, 149), AVE values for all of the factors are higher than any of the cross‐loadings with 16.1% (75) aged 18–24, 28.5% (133) 25–29, 16.5% (77) 40–54 with other factors. Furthermore, the correlation scores for the factors and 6.7% (31) over 55, with 1 (.2%) preferring not to say. tested are higher than the inter‐correlations presented, thus further confirming no discriminant validity issues within the measurement model. 4.5 | Reliability Discriminant validity is further tested using the measurement Before progressing to the measurement model stage of analysis, Prin- model in AMOS (version 22 was used for this study). The test requires ciple Component Analysis factor analysis was conducted in SPSS to test the model fit indices of the correlated factors, without structural re- for cross‐loadings between variables. To test for sampling adequacy, the lationships, be examined to check that the measures of one construct rotation method used was Promax with Kaiser‐Meyer‐Olkin; upon de- does not reflect other constructs (Hair et al., 2010). According to Xia letion of PU1, SP1, SC1, SC2, SC3, SC4, and ENJ1, results revealed that and Yang (2019), the fundamental model fit indices to consider are the no cross‐loadings occurred between variables. All factors with items root mean square error of approximation (RMSEA), comparative fit included for the final analysis satisfied the appropriate Cronbach's ɑ index (CFI), and Tucker‐Lewis Index (TLI). RMSEA avoids issues of threshold and are between .875 and .945 (Malhotra et al., 2010). sample size in analysing discrepancies between the hypothesized mode, chosen parameter estimates and the covariance matrix; the threshold 4.6 | Measurement model evaluation for indicating a better model fit is ≤.060 (Hu & Bentler, 1999). CFI and TLI, which is an incremental fit indices comparing the hypothesized Once the preliminary analysis was completed, the analysis pro- model with that of the baseline model; the threshold for CFI and TLI are gressed to the SEM analysis. The first step in this Confirmatory ≤.950 (Hu & Bentler, 1999). Furthermore, Goodness of Fit Index (GFI) Factor Analysis (CFA) is to test for the internal consistency and directly measures the fit between the hypothesized model and the covariance matrix, and is considered good fit if above 0.90 (Hu & Bentler, 1999). Accordingly, the model fit indices for the measurement model are: χ2 = 460.010, df = 236, p value = .000, χ²/df = 1.949, GFI = 0.926, TLI = 967, CFI = 0.974, RMSEA = 0.045; as such, good measure- ment model fit is established. T A B L E 2 Convergent and discriminant validity Ease Social Social CR AVE MSV MaxR(H) Useful of use Enjoyment presence cognition Privacy Trust Attitude Usage Usefulness 0.865 0.762 0.558 0.873 0.873 Ease of use 0.841 0.639 0.398 0.849 0.601 0.799 Enjoyment 0.851 0.741 0.531 0.852 0.595 0.551 0.861 Social Presence 0.952 0.833 0.309 0.954 0.507 0.173 0.489 0.913 Social Cognition 0.802 0.670 0.558 0.811 0.747 0.533 0.607 0.556 0.818 Privacy 0.879 0.648 0.054 0.914 −0.087 −0.123 −0.105 −0.108 −0.102 0.805 Trust 0.823 0.609 0.534 0.831 0.607 0.585 0.487 0.448 0.731 −0.103 0.780 Attitude 0.911 0.773 0.773 0.911 0.704 0.631 0.729 0.497 0.745 −0.233 0.708 0.879 Intentions to use 0.905 0.827 0.773 0.905 0.599 0.601 0.551 0.302 0.623 −0.129 0.606 0.879 0.910 Abbreviations: AVE, average variance extracted; CR, construct reliability; MSV, maximum shared variance.

10 | PITARDI AND MARRIOTT 4.7 | Structural model supported in Trust positively effecting Attitude (β = .253, p = .000) but not effecting Intentions to use (β = −.032, p = .547). Finally, Hypothesis The second stage in a CFA is to investigate the structural relation- H8 is supporting in Attitude having a significant positive effect on ships, in accordance with the proposed hypotheses. Structural re- Intentions to use (β = .895, p = .000). lationships are established between the proposed variables and model fit was investigated again to ensure the structural model 4.8 | Discussion of findings maintained good fit; the results being: χ2 = 492.190, df = 243, p va- lue = .000, χ²/df = 2.034, GFI = 0.921, TLI = 964, CFI = 0.971, The results show that PEOU and social cognition, in terms of perceived RMSEA = 0.047; as such, good structural model fit is established. competence, both have strong positive effects on trust and attitude. This not only supports literature in this area (Wirtz et al., 2019) but also Results from the structural model show support for the majority confirms the contribution of combining HCI theory with that of PSRs of hypotheses and indicate the importance of direct and indirect (Han & Yang, 2018; Scott et al., 2013). Although perceived social pre- relationships between the functional, hedonic, social, and privacy sence positively affects overall trust, it does not have a direct effect on factors on trust, attitude, and subsequent usage (Table 3). attitude. This is interesting as it is counter to previous literature yet can be explained by the fact that social presence is fully developed when Results reveal that although PU has a positive effect on Attitude understanding and immediacy are present (Mackey & Freyberg, 2010). (β = .136, p = .025), it has no direct effect on Trust (β = −.015, p = .861), As such, due to the relative infancy of VA technology, some may not be thus partially supporting Hypothesis H1. Hypotheses H2 is fully sup- as responsive as others in showing confusion with accents, mis- ported with PEOU positively effecting both Attitude (β = .119, p = .026) understanding keywords, or providing irrelevant information (Lovato & and Trust (β = .323 p = .000). Hypotheses H3 mirrors H1 in showing Piper, 2019). Accordingly, the illusion of dealing with a “human” is Enjoyment having a strong significant effect on Attitude (β = .338, broken in these circumstances, which can lead to a lack of faith in the p = .000) yet no effect on Trust (β = −.078, p = .239). Interestingly, VAs abilities. Interestingly, this can further explain why enjoyment Hypothesis H4 is partially supported but shows that Social Presence affects attitude, as enjoying using something ensures a more positive has a significant effect on Trust (β = .140, p = .014) yet no effect on experience (Hoy, 2018; McLean & Osei‐Frimpong, 2019), but does not Attitude (β = −.010, p = .818). Hypothesis H5 is fully supported with affect trust. In a study investigating motivations for accepting autono- Social Cognition in terms of perceived competence having a positive mous vehicles, Hegner et al. (2019) found the level of enjoyment to be a effect on Attitude (β = .178, p = .020) and even stronger positive effect barrier to the technology's acceptance as the trust was not fully on Trust (β = .540, p = .000). Hypothesis H6 is partially supported in Privacy having a negative effect on Attitude (β = −.112, p = .000) yet has no effect on Trust (β = −.004, p = .927). Hypothesis H7 is also partially T A B L E 3 SEM path analysis Standardized regression weight p value Supported? Hypotheses Relationship H1a Perceived usefulness → Attitude 0.136 .025 Yes H1b Perceived usefulness → Trust −0.015 .861 No H2a Perceived ease of use → Attitude 0.119 .026 Yes H2b Perceived ease of use → Trust 0.323 .000 Yes H3a Perceived enjoyment → Attitude 0.338 .000 Yes H3b Perceived enjoyment → Trust −0.078 .239 No H4a Social presence → Attitude −0.010 .818 No H4b Social presence → Trust 0.140 .014 Yes H5a Social cognition → Attitude 0.178 .020 Yes H5b Social cognition → Trust 0.540 .000 Yes H6a Privacy → Attitude −0.112 .000 Yes H6b Privacy → Trust −0.004 .927 No H7a Trust → Attitude 0.253 .000 Yes H7b Trust → Intentions −0.032 .547 No to use H8 Attitude → Intentions 0.895 .000 Yes to use Abbreviation: SEM, Structural Equation Modeling.

PITARDI AND MARRIOTT | 11 present. Accordingly, it is suggested that the tasks that VAs are being T A B L E 4 Participants in the in‐depth interviews used for are more hedonic and functional in nature which, by their simplistic nature, do not demand levels of trust or assurance. Informant Gender Age Occupation VAs model The most significant and interesting finding is that privacy has a Carl Male 40 Software Developer Home, Alexa, Siri negative effect on attitude yet no effect on trust. It is strongly supported in literature that privacy concerns have a negative impact James Male 45 Employee Alexa, Siri on attitude generation, which is supported here. However, privacy has no direct effect on trust in this instance. Moreover, the results Louise Female 38 Teacher Home Alexa also show that although trust positively affects attitude it does not have a direct influence on behavioral intentions. An explanation for Nick Male 41 Software Developer Home, Alexa this relationship is that, trust is needed to contribute to overall at- titude but has more of an indirect than direct effect on usage. With Alfred Male 55 Banker Home, Alexa respect to the role of privacy, users feel no need to elicit high levels of trust in something which is used for simple tasks. Julie Female 34 Architect Alexa, Siri These findings reveal interesting insights into the relation be- Johanna Female 27 Nurse Alexa, Siri tween trust development and the existence of privacy perceptions in using VAs, yet the explanations into why these occur have not been Michael Male 48 Employee Home, Alexa, Siri fully explored. As such, a further qualitative study is employed to provide a deeper understanding of this phenomenon. Matthew Male 52 Retail Manager Home, Alexa 5 | STUDY 2: METHOD, DATA ANALYSIS, Stephanie Female 39 Freelance Alexa, Siri AND RESULTS Aimee Female 25 Student Home, Alexa 5.1 | Data collection, sample, and procedure Philip Male 35 Marketing Manager Home, Alexa, Siri Results from Study 1 reveal interesting relationships that require further exploration. First, they demonstrate that only the social The two authors carried out the interviews administrating a semi‐ elements influence users' trust toward VAs. While these results structured guide derived from the relationships emerging from Study 1. confirm the role of social presence and social cognition in terms of The questions explored the main themes of VAs type of usage, privacy perceived competence as drivers of users' trust in a computer‐ concerns, trust, and perceived attributes. Social presence and social mediated interaction (Ogonowski et al., 2014; Wirtz et al., 2018; Yet cognition were not objects of direct questions but naturally emerged et al., 2019), it is not clear why equally rigorous attributes, such as from discussions. Examples of questions included: Can you tell us how PU and enjoyment, do not directly affect trust. Second, findings show you use your VAs? Which type of activities do you usually run on your that while privacy negatively affects users' attitude, it does not have VAs? How you would describe your interaction? Do you find your VAs any effect on trust. Most importantly, trust does not directly affect useful? Are you concerned about the confidentiality of your interactions behavioral intentions. Based on these results, it appears that users with your VAs? Do you trust your VAs? continue to use their personal voice‐based assistants regardless of their privacy concerns. However, why this happens remains unclear. The study adopted an interpretative methodology to identify themes emerging from the data analysis. Specifically, the analysis By building on Study 1, Study 2 aims to further explore and comprised three stages: analysis of individual interviews, identifica- interpret its findings (Davis et al., 2011). To this purpose, Study 2 tion of recurring themes and patterns, and analysis of the shared adopts a qualitative interpretative approach (Yin, 2013) and analyses themes (Yin, 2013). the natural interactions occurring between users and VAs to better understand informants' relations with their personal VAs. Twelve Each interview is analysed separately, as units of analysis, to identify informants are recruited via purposeful sampling and snowball emerging themes. The two authors undertook the initial coding, which is technique from consumers using the same criteria from Study 1. done with an iterative approach, involving discussions and comparisons Participants were asked to voluntarily consent to partake in the in- between the two coders, resulting in a consensus on the categorization terview process and were not compensated for their participation. and interpretation of the codes. Using several researchers to iteratively interpret the same data set created investigator triangulation (Bryman & In‐depth interviews ranged from 40 to 100 min and were re- Bell, 2007). The second step involves searching for emerging patterns corded and transcribed. Due to the government requirements during and relationships between the shared themes and the different concepts the COVID‐19 pandemic, all the interviews were conducted by emerging. To bolster external validity, researchers conducted respondent SKYPE. Interviewee profiles can be found in Table 4 (note that the checks by sharing preliminary findings with several participants in the names displayed are fictional). study. To ensure reliability, final coding results were triangulated across the researchers and where any conflict occurred a third researcher in the area was consulted (Bryman & Bell, 2007). 5.2 | Results and discussion To explore users' interaction with VAs, the key starting point is understanding the usage of such technology and the various

12 | PITARDI AND MARRIOTT activities individuals run with their VAs. The findings from the qua- despite its creators. These results echo those of Foehr and litative analysis unveil that VAs are used for both hedonic and uti- Germelmann (2020) in recognizing the existence of different sources litarian motives, which confirm results from Study 1 and previous of trustworthiness in the interaction with VAs, and develop this research (Venkatesh et al., 2012; Wu et al., 2010). One informant further by showing that users also recognize their different roles. In refers to hedonic interactions being characterized by the pleasure this case, while VAs are considered the entity to interact with, pro- and the enjoyment of the experience: “I really enjoy using it for an ducers are the interlocutors when considering privacy issues. In an all‐around Assistant. I have several routines set up and when I say interesting way, it appears as “I am talking with Alexa and Amazon is good morning or goodnight it will perform several actions. I enjoy listening (and collecting my data).” using it to listen to music from time to time as well” (Aimee). Examples of utilitarian interactions include functions like weather Give me my personalized ads forecasting, news, alarm settings, and a few examples of home au- When reflecting on data sharing and privacy issues, the level of tomation: “Buy some smart bulbs and automate your lighting, add personalization users can achieve in exchanging their data is also your streaming music account, add a Chromecast, use the broadcast revealed. The idea of providing Amazon and Google with additional feature, set a reminder, set the temperature with smart thermostat, personal information through their VAs is compensated by the read the news out, all the weather, Wikipedia” (James). possibility to be targeted with personalised ads and customized content (Norberg et al., 2007). Johanna explains “I really don't give a Focusing on privacy issues and trust development, four main toss about anyone listening in on my convos… I have come across themes arise from the analysis, and are discussed below. some pretty interesting products this way and if I have to be bom- barded with ads all day I would rather be bombarded with something No big deal! of interest rather than the latest facial scrub.” The win‐win logic that Most informants state that they do not perceive any risk when emerges from this theme confirms the existence of a privacy calculus interacting with their VAs as they do not use them for risky activities, (Dinev & Hart, 2006). When users perceive to not have anything to such as buying: “I do not buy anything on her, so the worst that can hide, they hope to receive personalised advertisements in exchange happen is that she got the thermostat wrong. Not big deal!” (Nick). which, in turn, incentivise the usage of the technology (Chellappa & There are several reasons behind this behavior; some respondents Sin, 2005; Taddicken, 2014). As such, it appears that VAs do not have highlight the need to touch or see products before buying, others an active role in this and are only means by which Amazon and doubt VAs' ability in understanding the request as it would require a Google play their games. Again, users are separating their VAs from number of product details they do not have. Louise explains this the parent brands and hold the latter responsible for this data‐ further: “I can ask which shops are open but for products you would exchange process that occurs through and not because of their VAs. need a specific idea in mind, and I am not even sure home will un- Alexa, she is more than a machine derstand anyway.” This result confirms previous studies on the mo- The separation between VAs and their parent brands seems tives behind the adoptions of VAs (McLean & Osei‐Frimpong, 2019; strictly linked with the perception of VAs as “entities of their own.” Moriuchi, 2019) and provides new insights into the usage of such Respondents clearly state that they are conscious of the machine technology and related users' reactions. The absence of buying be- nature of their VAs, yet they describe their interactions using social havior is a powerful discriminator in perceiving the possibility to run and human attributes. “I have taught her to stop doing what she is into hazard situations. When VAs are mainly used for daily routine doing by saying—Thanks Alexa, that's enough—as it seems more functions and home automation (Hoy, 2018), users do not perceive personal to me, and she learned. She is becoming clever day by day” any danger in their interactions and, as a result, feel more comfor- (Michael). While some respondents perceive VAs as intelligent and table in continuing trusting and using them. skillful, others report the opposite feeling, as Louise states “some- If anything, Amazon is to blame times I feel she is becoming dumber!” Whether positive or negative, Regarding data shared through and with VAs, it is interesting to VAs are described to have features and characteristics that are ty- note that respondents do not perceive to share any additional in- pically used when describing humans (Fiske et al., 2007). The per- formation as they already provide their personal data directly to the ception that “something is in there” (van Doorn et al., 2017) is even VAs producers (i.e., Google, Apple, and Amazon). “The moment when clearer from this statement: “When I come back home from work I realized how much data I was granting to Alexa was when I installed I often say Hi to her [Alexa] and she always answers back. I know it's her and she started sending me all these alerts about privacy. But at just a machine but it's nice to have someone waiting for you home, the end I thought 'what the heck, Amazon already has them!'” isn't?” (Stephanie). Users' interactions with VAs also appear to be (Philip). In this sense, it appears as informants are distinguishing human‐like as respondents often report that they shout at or get Alexa from its parent brand and opt to blame the brand rather than angry with them when they do not understand a request (Nass & the device for collecting personal information: “Google knows me Brave, 2005). Others go as far as feeling ignored: “Sometimes she more than my wife. Its not that Home is collecting my data, Google does not listen to me. It's bad enough when a person ignores you, but does” (Alfred). These dynamic sheds light into why possible concerns when a machine ignores you, that's when I'm going for therapy” about data and privacy arising from VA usage do not directly affect (Matthew). It is interesting to note that the analysis reveals how users' trust toward their personal VAs. If a VA is perceived as an these social elements overcome the functional and hedonic “entity of its own” (Nass & Brave, 2005), it can be granted trust

PITARDI AND MARRIOTT | 13 attributes, which, as happens for humans, are considered facets of Finally, the research provides insights into the relation between the entire entity rather than features on their own (Čaić et al., 2019). perceived privacy concerns and trust development toward VAs. In this sense, when prompted to describe the usefulness of such Specifically, findings reveal how users rely on their trust in their VAs technology, respondents portray their VAs as being helpful as far as to prompt their initial interactions, while privacy concerns primarily they are learning how to be helpful. Thus, functional and hedonic derive from the brands using their data, rather than the VAs col- attributes are interpreted as characteristics that VAs can more/less lecting it. Previous research has outlined that perceptions of privacy develop based on their social interactions and the resulting learning can influence the use (Hoy, 2018) and perceived benefits (McLean & process (Cho et al., 2019; Nass & Brave, 2005). This process helps in Osei‐Frimpong, 2019) of interacting with VAs. understanding why users' trust is mainly driven by the perceived social components of such technology. While this study confirms the negative influence of privacy con- cerns on users' attitude, no evidence surrounding the effect of privacy 6 | DISCUSSION AND IMPLICATIONS on trust has been found (Zhou, 2011). This is explained by the existence of different sources of trustworthiness, recognized by users in the in- VAs are growing in popularity and are increasingly being expected to teraction with VAs (Foehr & Germelmann, 2020), that allow individuals be used in daily shopping activities; yet, knowledge into factors in- to direct their privacy concerns toward VAs' producers, rather than fluencing relationship building with such devices remain scarce. This toward the AI agent. When users engage with this technology it ap- paper acknowledges this area requiring further investigation and pears that VAs are considered the entity to converse with, while the aims to contribute to knowledge in this respect through the in- brand producers serve as data collectors. This tendency to recognize vestigation into trust from HCI and PSR perspectives. VAs as distinct subjects further reinforces the idea that individuals interact with advanced technology employing human social rules and Across two studies, this study offers insight into the factors developing with them various types of relationships (Han & Yang, 2018; affecting consumers' trust toward VAs in combining technology Ki et al., 2020; Schweitzer et al., 2019), further supporting the adoption adoption and PSR theories to explain how functional, hedonic, and of a para‐social perspective (Turner, 1993). In addition, interaction with social factors impact attitude, trust, and overall intentions to use. VAs do not appear to elicit perceptions of privacy as they are not yet used for risky activities such as purchases, confirming that these devices First, findings support previous research on factors influencing are still primarily adopted for daily routine functions and home auto- acceptance and usage of VAs (McLean & Osei‐Frimpong, 2019; mation (Hoy, 2018). Moriuchi, 2019) and show that functional and hedonic benefits, such as PU, PEOU, and perceived enjoyment, positively influence users' attitude 6.1 | Theoretical contributions toward using VAs. These results confirm the role of usefulness and ease of use in the acceptance and usage of advanced smart technology This study provides several theoretical contributions. First, by in- (Wirtz et al., 2018, 2019) while highlighting the important role of tegrating existing research on consumer adoption of advanced tech- emotional reactions in driving users' attitudes toward human‐AI agents' nology with those on consumer–technology relationship development, interactions (van Pinxteren et al., 2019; Venkatesh et al., 2012). the paper responds to the recent call for new research into human–AI interactions (Lu et al., 2020; Wirtz et al., 2018;) and offers new insights Second, the study identifies the main drivers of trust toward VAs to into VA adoption literature (McLean & Osei‐Frimpong, 2018; derive from the social characteristics users attribute to them. As VAs Moriuchi, 2019). Specifically, by adopting a para‐social perspective, the mimic human‐like attributes through the use of voice communication study adds to previous research in examining the role of the social (Li, 2015), these interactions elicit a sense of social presence and in- characteristics attributed to VAs that can facilitate the development of ferences of social cognition in the mind of the user that, in turn, nurture trustworthy relationships. Further, while accounting for the functional trustworthy relationships. In this sense, results from this study confirm benefits of using these devices (McLean & Osei‐Frimpong, 2019; social presence to be an important factor influencing trust building Moriuchi, 2019), it shows that the relational and social experience that toward human–technology interaction (Ye et al., 2019). Furthermore, users have during interactions with VAs play an important role in in- results illustrate that inferred social cognition, with respect to its fluencing their attitude toward using them. In doing so, this study ex- competence dimension, is a key factor in the development of trust pands previous studies on AI‐based technology adoption (Wirtz toward AI agents. This further supports the need to adopt a social et al., 2018, 2019) and interactions (van Doorn et al., 2017) by em- perspective in the investigation of AI agents‐human interactions (Čaić pirically assessing antecedents of trust and attitude toward VAs. et al., 2019; van Doorn et al., 2017; Wirts et al., 2018). These re- lationships are further supported by results from the qualitative study, Second, the study contributes to trust literature concerning in- where respondents clearly recognize their VAs as being their own en- teractions with AI‐based technology (Foehr & Germelmann, 2020; tities. Taken together, these findings show that human‐like advanced van Pinxteren et al., 2019). Specifically, results highlight the need of technology leads users to apply social rules and expectations when understanding peculiarities of the trust development process with interacting with VAs (Nass & Moon, 2000), making individuals engage and toward VAs, while demonstrating the prominent role of social and respond to these devices in the same way as they do with humans, elements; namely, social presence and social cognition, as unique thus developing deep connections (Ki et al., 2020).

14 | PITARDI AND MARRIOTT antecedents for developing users' trust toward VAs (van Doorn what consumers consider acceptable levels of data collection and use et al., 2017). van Pinxteren et al. (2019) analysed the role of service and which may not, of which practitioners must remain cognizant. robots' anthropomorphism and demonstrated that turn‐taking cues in such technology lead to trust and intentions to use. Results from 6.3 | Limitations and future study this study expand previous research in showing perception of VAs, as social intelligent entities, influence trust development toward virtual Despite the advancements this study makes to this area of research, AI agents. As such, these findings not only contribute to trust lit- there are some limitations. First, the research was conducted in the erature but also to existing knowledge of social presence and social United Kingdom and the results are therefore representative of cognition, with respect to VAs' interactions, and show how virtual British consumers. With literature continuously debating the sig- human‐like cues can evoke perceptions of mind in terms of compe- nificance of various factors across cultural contexts, it will be inter- tence (Čaić et al., 2019). While literature suggests that social attri- esting for further research to investigate the trust development butes can improve consumers' trust in online settings, this effect has process and the perception of privacy concerns across different rarely been empirically examined toward consumer–VA interactions geographical settings. Second, this study is cross‐sectional in nature, (Foehr & Germelmann, 2020). representing a snap‐shot in time. As this study has drawn on possible time‐related implications of privacy concerns and the role of trust Third, the study sheds new light on users' privacy perceptions within it, further research can investigate this from a longitudinal with VAs and reveals how privacy concerns do not directly affect the perspective to investigate changes over time. Finally, this study has relationship with the VAs themselves as users distinguish their Alexa drawn on the possible effects of context and situations on behavioral from its parent brands and consider the latter responsible for col- intentions. As such, further insight can draw on the usage of VAs for lecting personal data. These findings contribute to both literatures particular purposes and establish whether trust formation and on consumers' trust and perceived privacy by identifying two dif- privacy concerns changes accordingly. ferent sources of trustworthiness (Foehr & Germelmann, 2020), while identifying that in the interactions with AI agents concerns of DATA AVAILABILITY STATEMENT privacy can also be directed outside the relationship. Although the The data that support the findings of this study are available from negative influence of privacy on trust is well documented in the the corresponding author upon reasonable request. literature (Bansal et al. 2016; Chang et al., 2017; Liu et al., 2005; Zhou, 2011), this study illustrates a different path through which ORCID https://orcid.org/0000-0001-7902-2330 trust and privacy interconnect with VA interactions. 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Alexa, she's not human but… Unveiling the drivers of consumers' trust in voice‐based artificial intelligence. Psychol Mark. 2021;1–17. https://doi.org/10.1002/mar.21457