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Voice Commerce: Understanding Shopping-Related Voice Assistants and their Effect on Brands

Authors:

Abstract

Voice Commerce (or voice shopping) is rapidly becoming a focal point in academic, business, and industry research because of its swift adoption and disruptive potential in buying dynamics. As voice assistants become better at learning consumer preferences and habits, they will increasingly influence consumer behaviors. In doing so, voice assistants may assume a central relational role in the consumer market and progressively mediate market interactions. These fast-changing market dynamics within the context of voice shopping may have a severe impact on consumer brands and retailers. Loss of brand visibility, the increased relevance of retailers’ private labels, and the growth in advertising costs are just some of the consequences anticipated by marketing and technology experts. In light of these potential dynamics, researchers are called to study the interplay between consumers, brands, and retailers’ behaviors in response to “machine behaviors”. Providing structure and guidance to researchers and marketers in order to further explore this emerging stream of research is fundamental.
Voice Commerce
Understanding shopping-related voice assistants and their
effect on brands
Alex Mari
University of Zurich, Switzerland
Alex Mari (alex.mari@business.uzh.ch) is a Research Associate and Ph.D. candidate at the Chair for Marketing and
Market Research, University of Zurich.
Introduction
Artificial intelligence (AI) technologies have left the server room to enter the lives of billions of consumers. AI enables
objects to perform activities that resemble cognitive functions associated with the human mind, such as learning and
problem solving (Russell & Norvig, 2009). AI-powered smartphones, smart homes, and smart speakers connect the
various nodes of consumers’ lives into one ubiquitous experience that seamlessly accompanies them in every routine.
Every intelligent object, from cars to toothbrushes, is expected to collect relevant information that helps to identify
consumption patterns and predict future individual behaviors (Hoffman & Novak, 2017). Within the Internet of Things
(IoT) market, the fast adoption and rising performance of voice platforms like Amazon Echo, Apple HomePod, Google
Home, Alibaba Tmall Genie, Xiaomi Xiao AI, and Baidu Xiaodu suggest that in-home voice assistants will be central
to the development of smart homes. The voice touchpoint is rapidly becoming a focal point in academic, business,
and industry research because of its swift adoption and disruptive potential in buying dynamics (Dawar & Bendle,
2018). Given its interdisciplinary nature, the research on voice assistants is highly fragmented with contributions from
a variety of disciplines (Knote et al., 2018). Recent studies have produced insights on the functional characteristics of
voice assistants (Sciuto et al., 2018; Gollnhofer & Schüller, 2018, Hoy, 2018), their adoption and social roles (Han &
Yang, 2018; Purington et al., 2017; Schweitzer et al., 2019), attitudes towards the technology (Moriuchi, 2019;
Ahmadian & Lee, 2017; Brill, 2018), and applications for marketing (Kumar et al., 2016). However, these
investigations have not led to a deeper understanding of consumer judgment and behavior towards brands. At the same
time, studies on consumer technologies for shopping, such aspersonal computers, smartphones, tabletsseem
insufficient to understand the unique nature of this new channel and shopping method. Although exemplary research
on consumer behavior and media possess insights that are likely transferable to voice assistants, the peculiarities of
this technology require new theories that are not yet fully developed (Kumar et al., 2016). This study sheds light on
the potential impact that the diffusion of shopping-related voice assistants has on consumer brands. The main
contribution is to reconcile existing interdisciplinary literature and review how voice assistants may alter market
dynamics as emerged during in-depth interviews with 31 AI-aware executives. Key conceptual nodes related to the
dual agency/mediator role of voice assistants and their anticipated effects on consumer brands are explored.
Keywords: Voice assistants (VA), voice commerce, artificial intelligence, machine behavior, brand management.
Citation: Mari, A. (2019). Voice Commerce: Understanding shopping-related voice assistants and their effect on
brands. In: IMMAA Annual Conference. Northwestern University in Qatar, Doha (Qatar). October 4-6, 2019.
The rise of voice assistants for shopping
The term voice assistant (VA) refers to conversational agents that perform tasks with or for an individual,
whether of functional or social nature and own the ability to self-improve their understanding of the interlocutor and
context. This software, embedded in smart objects, leverages a combination of AI techniques, such as automatic
speech recognition (ASR), text-to-speech synthesis (TTS), and natural language understanding (NLU), to engage in
natural conversational interactions with humans (Gaikwad, Gawali & Yannawar, 2010). Such category of IoT goes
under various names that include but are not limited to smart speaker (Bentley et al., 2018), AI assistant (Dawar &
Bendle, 2018), intelligent personal assistant (Han & Yang, 2018), personal digital assistant (Milhorat et al., 2014),
voice-controlled smart assistant (Schweitzer et al., 2019), voice-activated intelligent assistant (Jiang et al., 2015), and
conversational agent (Lee & Choi, 2017).
Voice assistants can take various forms of in-place and mobile devices such as Bluetooth speakers (e.g.,
Amazon Echo) or built-in software agents for smartphones and computers (e.g., Apple Siri). With over 70 million
U.S. owners, in-home voice assistants currently see a faster adoption rate than smartphones and tablets (Newman,
2018). Their most popular functions are playing music, controlling smart home appliances, providing weather
information, answering general knowledge questions, and setting alarms (Sciuto et al., 2018). However, from a
commercial standpoint, digital assistants represent a novel touchpoint that allows for new forms of interaction between
consumers and brands (Sterne, 2017).
Voice commerce (or voice shopping) identifies the act of placing orders online using voice assistants. This
topic captures mainstream media headlines (e.g., Creswell, 2018; Chaudhuri & Terlep, 2018) and is often used to
speculate about the dominance of U.S. tech giantsGoogle, Amazon, Apple (see Galloway, 2017). Although the
number of consumers who have completed at least one purchase through a smart speaker is rising fast, the percentage
of buyers using VAs varies widely among product categories. A report suggests that 21% of U.S. smart speaker owners
have purchased entertainment such as music or movies, 8% household items, and 7% electronic devices (eMarketer,
2019). Meanwhile, Alexa’s users can order items like household products and fresh produce from a local Whole Foods
and receive delivery within two hours.
Functional characteristics of voice assistants
Unique from other consumer applications, VAs can converse with users naturally, interpret and handle
requests contextually, expand their knowledge, and learn from mistakes.
Natural conversation represents the main difference in this new communication channel. Voice assistants are
built to mimic human-to-human interactions. Similar to interpersonal relationships, VAs react to the interlocutor when
their name is called (Sacks & Schegloff, 1979). VAs can “memorize” relevant facts from previous conversations,
giving a sense of continuity from past interactions. Also, they assume a persona and refer to themselves as “I.” For
instance, when asking Google Home, “Okay Google, what do you think about Alexa?” the answer is, “I like her blue
light.” VAs’ ability to naturally dialog with users as well as the sense of “spontaneity” that originates from unexpected
answers can facilitate the emergence of closeness feelings (Han & Yang, 2018).
Context-awareness is a constituting factor of VAs (Knote et al., 2018). The context of a device is represented
by any information that can be used to characterize a situation relevant to its users (Abowd et al., 1999). Context-
aware computing collects and processes information about the context of a device in order to customize services to
the particular contextual clues such as the identity of the user, location of the device, time and date, purchasing history,
and declared user preferences (Kwon, 2003). Ultimately, a VA becomes context-aware if its interactions with the
human, and other machines, are personalized to the current context. Contextual information is necessary to precisely
learn personal preferences and automate routines (Milhorat et al., 2014).
Self-learning allows VAs to interpret customers’ words better and reduce friction during interactions
(Sarikaya, 2017). With the recent introduction of unsupervised systems, which operate without manual human
annotation, VAs can detect unsatisfactory interactions or failures of understanding and automatically recover from
these errors. For instance, if the user says “Play ‘Good for What’” but meant to say “Nice for What” by Drake, the
VA corrects the error and initiates a successful song request (Sarikaya, 2018). The system learns how to address these
accuracy issues and deploys updates shortly after. Automatically applying corrections to a large number of queries
each day using self-learning techniques allows VAs to develop at a faster pace.
Interactional characteristics of voice assistants
The uniqueness of VA technologies brings up a new set of interaction rules modeled after the active (and
proactive) nature of these smart devices (Rijsdijk & Hultink, 2009). In contrast to traditional media, voice touchpoints
emphasize a bidirectional interaction with consumers. VAs are designed to process one request at a time and on a turn-
by-turn basis to decrease the speech recognition error rate coming from a possible voice overlap (Hansen, 1996). This
style of interaction represents a radical difference compared to sensorially richer devices like computers or
smartphones, which present multiple pieces of information on a screen concurrently. As such, voice channels present
both challenges and opportunities for the diffusion of voice commerce.
On the positive side, e-commerce has paved the way for voice shopping (Labecki, Klaus & Zaichkowsky,
2018). With the rise of the Internet, users have learned to deal with a combination of social, cultural, economic, and
technical barriers. In doing so, they have needed to overcome the initial diffidence of buying without directly seeing,
touching, or smelling an object. Voice technologies further limit the users’ senses; besides, consumers are asked to
make shopping decisions without browsing photos, videos, or any other animated content. Another celebrated feature
of voice shopping is the ease of making low involvement purchases. VAs are “always on” devices that can access a
user’s personal information upon request (Clark, Dutta & Newman, 2016). With a simple “yes” and without providing
additional information such as credit cards or address details, VAs can process orders, or even automate them.
On the negative side, an effortless decision-making process does not guarantee an optimal level of consumer
satisfaction. Shopping-related voice assistants offer a limited set of items for each product category based on their
understanding of the consumer and context. This simplified representation of the marketplace reduces consumers’
visibility of product alternatives and emphasize the critical role of ranking algorithms. The algorithm that ranks the
information represents a “black box” for the VA user, and often for its developers (Voosen, 2017). Such visual
limitations may increase product (and brand) polarization while enhancing the risk of the so-called filter bubble or
echo-chamber effects (Colleoni, Rozza & Arvidsson, 2014).
Methodology
A total of 31 semi-structured in-depth interviews were conducted in December 2018 to supplement an
interdisciplinary literature review. During the interviews with international AI-aware corporate executives and
consultants, theoretical perspectives were not employed to facilitate the emergence of insights (Avis, 2003). Interviews
were audio-taped, and transcriptions analyzed adopting an inductive line-by-line coding approach. This process
followed a constant comparative data analysis according to the grounded theory (Glaser & Strauss, 1967). Using
NVivo 12 for Mac, codes were grouped into themes and then re-evaluated to ensure that they reflect data extracts.
Through conceptualization, relationships among categories and sub-categories were established (Figure 1). The
emerging conceptual nodes were related to the dual agency and mediator roles of VAs as well as their main anticipated
effects on consumer brands.
Figure 1. Key conceptual nodes emerged during qualitative investigation.
The agency role of voice assistants
In their recommender agent role, VAs attempt to predict which items a target user will like based on expressed
preferences or implicit behaviors (Shen, 2014). This form of recommender system may replace traditional decision-
making when consumers feel time constraints or recognize the referrer as a particularly knowledgeable source
(Olshavsky & Granbois, 1979). End-users typically evaluate a virtual agent on its ability to personalize suggestions
that satisfy their needs. Consumers adopt algorithmic recommender systems if they are believed to match their interests
(Abdollahpouri et al., 2019). Higher accuracy of suggestions from a platform translates into not only an increase in
consumer satisfaction but also their overall trust in the technology (Li & Karahanna, 2015). In this context,
recommendation outcomes may correspond to consumer preferences more closely than if they had chosen
independently (André et al., 2018).
Due to their central role in a complex business network (Snehota & Hakansson, 1995), VAs do not consider
users as the only stakeholders benefiting from the recommendation outcome. The strategic goals of the retailer,
merchant, advertiser, and voice assistant itself, may differ from those of end-users. Thus, the user is not the sole focus
of a recommendation in almost every transaction on the VA. For instance, a VA might recommend a private label
over a consumer brand following the retailer’s objective to swiftly grow its shares in a specific product category. Thus,
the objectives of several parties need to coexist (Abdollahpouri et al., 2019).
The ultimate goal of recommendation personalization is the automation of the buying experience. Throughout
the collection of significant volumes of personal and behavioral information, VAs can push users to automate
repurchase, for instance, via “subscribe & save” promotional activities, increasingly popular on the e-commerce
websites. According to André et al. (2018), this power of attorney towards VAs goes at the expense of higher-order
psychological processes such as emotions and moral judgments. In the context of purchase automation, consumers
might have aspirational preferences that differ from the preferences suggested by their past behavior. These meta
preferences, also called preferences over preferences (Jeffrey, 1974), are apparent in the case of an environmentally
aware person who wants to use less bottled water but is regularly reminded to buy plastic bottles. The inherent tension
between the actual-self and the ideal-self represents a boundary for those consumers who follow VAs’ suggestions to
automate repurchases.
There is a brand of soap that my wife loves. One day the Amazon says, “Hey, you buy this all the time, why don't you
subscribe?”. Now, we have a subscription to soap, and every six months we get a bunch. If we have more than we need, we
adjust the delivery frequency. This product automatically shows up, and we are definitely going to buy the same brand. We are
locked in.
- Jim Sterne, Emeritus Director of the Digital Analytics Association (DAA), Author of “AI for Marketing.”
While functioning as a salesperson, VAs are redefining relationships among consumers, brands, and retailers
(Figure 2). As consumers’ relationships with VAs shift from limited influence to steadfast dependency, brands need
to understand which elements influence consumer choices and how to redesign their value chain (Mandelli, 2018).
Consumer brands feel threatened by the rapid adoption of VAs as the bargaining power is shifting in favor of VA
technology owners (Dawar & Bendle, 2018; WSJ, 2018). In the case of Amazon’s Alexa, the VA manufacturer is also
the retailer behind the most advanced voice shopping functionality, accounting for nearly 45% of the total U.S. retail
e-commerce (eMarketer, 2018).
Figure 2. Triadic relationship between brand, retailer, and consumer mediated by a voice assistant.
The impact of market mediation on consumer brands and retailers
VAs’ increasing mediation of consumer interactions with the market does affect the path to purchase
dynamics. The main concerns from consumer brands around VAs’ diffusion are related to brand visibility via organic
search results, the rise of retailers’ private labels, and the potential increase in advertising spending.
Search algorithms represent the gatekeeper for modern companies and retailers. Compared to display-
enabled smart devices, the optimization of voice search results on VAs presents three structural challenges due to the
nature of consumer interactions and information framing. First, during voice shopping users can review one to three
options before they start forgetting information such as price or quantity of the mentioned products. Reduced attention
span and short-term memory can negatively influence the satisfaction towards this shopping system, especially when
the user is required to search for products in an explorative way extensively. Second, VAs deliver search results to
users in the form of recommendations. The assistive nature of the interaction with VAs implies a delegation of
responsibility, at least in the absence of explicit requests by the user. Whenever a user directly asks for a specific brand
or product, VAs respond with the closest option available to them. However, when shopping for items without
specifying a brand, VAs are more likely to recommend their private labels, if available. In the case of Alexa, when a
brand name is not proactively mentioned by the user, the private label, under the name of Amazon’s Choice, appears
as the first recommendation in over 50% of instances (Cheris, Rigby & Tager, 2017). Third, the search engine results
continuously adapt to the user’s purchase history and the evolving understanding that VA acquires about its
interlocutor. However, after a user has purchased a product, for example, Nespresso coffee capsules, the subsequent
suggestions for coffee start from the same manufacturer. As such, this dynamic might reduce variety seeking in
shoppers.
Alexa does commoditize entire product categories, all the way from diamonds to detergents. During a product search, by the
time you get to the third item, you have forgotten what the first was and what the price of the second one was. You’re done
beyond the third results. You’ve become a commodity fighting for air space.
- Dr. A. K. Pradeep, CEO at MachineVantage, Co-author of “AI for marketing and product innovation.”
Private label development is seen as particularly dangerous by national brands (see Quelch & Harding,
1996). In utilitarian product categories characterized by low purchase involvement, the parallel expansion of private
labels and VAs represent a risk for category “commoditization” (Pradeep, Appel & Sthanunathan, 2018). An
emblematic example of this process comes from the battery business. A few years after its launch in 2009, Amazon’s
private label “AmazonBasics” accounts for 31% of the overall battery sales online by large margins from national
brands such as Duracell (21%) and Energizer (12%) (Neff, 2016). The price of private labels is reported to be 20%-
30% lower than national brands, on average (Collins & Metz, 2018). With Amazon’s private label portfolio growing
to 135 brands and more than 330 Amazon exclusive brands, similar trends gradually become visible in a variety of
product categories such as skincare, home improvement tool, and golf equipment (Jumpshot, 2018). The limited
“shelf space” available to merchants on in-home smart devices strengthens the private brands’ position. According
to Cheris, Rigby, and Tager (2017), in categories in which Amazon offers private-label products, Alexa recommends
the private-label products 17% of the time, although these products represent only about 2% of the total volume sold.
Amazon’s biased placement on VAs of its private labels against national brands challenges the traditional retail
marketing practice that expects a distribution of a given brand, “share of shelf,” proportional to its sales, “market
share.” Furthermore, consumers can decide to automate fully (e.g., subscription) or semi-automate (e.g., product
added to the shopping list) their purchases creating self-established lock-in mechanisms.
If I ask Alexa to send me twenty AA batteries, I will probably get Amazon’s branded batteries. However, if I explicitly ask for
Duracell, I receive my preferred brand, provided it is available on the platform. Thus, companies have to invest in branding
even more than they did before so that consumers asked for a product by the name.
- Jim Sterne, Emeritus Director of the Digital Analytics Association (DAA), Author of “AI for Marketing.”
For decades, advertising represented the primary tool to generate brand awareness, improving both recall
and recognition. With the rise of the Internet, the concept of advertising transmuted to search engines where
advertisers buy promotional spaces in response to a set of keywords searched by the user. Within digital advertising,
“search advertising” represents the most successful format, accounting for 45% of the total spending (IAB & PWC,
2018). Advertisers face an overall cost increase of search ads with a particular impact on highly competitive consumer
products. For instance, the cost per click on the search term “laundry detergent liquid” reached $17 on Amazon in a
given period (Koksal, 2018). Search advertising in the form of voice has a paramount role in voice commerce
marketing. Although brands are generally positive towards this new form of investment, the peculiarities of the voice
channel induce concerns. Compared to web browser navigation where search engines can display ten results per page
and up to five advertisements, VAs can only suggest a few results with limited space for sponsored messages. This
scarcity of space might increase competition among advertisers with a consequent rise in advertising costs.
From the voice commerce perspective, VAs pose a challenge to advertisers. They bring up a “real estate” problem. While I can
display several ads on the same Google Search results page, I don’t have the same ad space on smart speakers. Thus, I expect
the cost of voice ads to be more than two times higher than regular search ads. Am I able to justify this cost increase?
- Maurizio Miggiano, Head of Digital at Generali (Ex Mediacom).
Conclusions
As voice assistants become better at learning consumer preferences and habits, they will increasingly influence
consumer behaviors (Simms, 2019). In doing so, VAs may assume a central relational role in the consumer market
and progressively mediate market interactions. These fast-changing market dynamics within the context of voice
shopping may have a severe impact on consumer brands and retailers. Loss of brand visibility, the increased relevance
of retailers’ private labels, and the growth in advertising costs are just some of the consequences anticipated by
marketing and technology experts. In light of these potential dynamics, researchers are called to study the interplay
between consumers, brands, and retailers’ behaviors in response to “machine behaviors” (Rahwan et al., 2019).
Providing structure and guidance to researchers and marketers in order to further explore this emerging stream of
research is fundamental.
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... Compared to e-commerce, voice commerce interactions are still very much bound to the pre-designed channel, hence the manufacturer of the voice assistant. There is not yet a "common ground" like the web or search engine for conventional e-commerce activities, where users can freely access websites independent from the device they navigate with [3,42]. In other words, it is not yet possible to use any existing voice assistant to access an online retailer and complete a purchase transaction. ...
... In other words, it is not yet possible to use any existing voice assistant to access an online retailer and complete a purchase transaction. The main shopping channel is predetermined by the manufacturer as in the case of Amazon Alexa, which is the only manufacturer besides Google Assistant that offers a purchase option via voice, although for Google, a third-party application has to be manually installed [42]. Furthermore, the purchase functions for both models are still extremely limited to a few retailers through third-party applications that are mainly active in the United States such as Walmart or Carrefour. ...
... Interestingly, they also found that the consumer-brand relationship is somehow disturbed mainly because customers would establish a relationship with the voice assistant or agent -pointing towards a role of the anthropomorphic design in these technologies [66]. Mari [42] also phrased this phenomenon as the market-mediating role of voice assistants because they function like an agent that represents the brand. Ju et al. [28] and Kim et al. [30] investigated advertising methods with voice interfaces and thereby collected evidence for higher effectiveness in an interactive setting of voice commerce compared to the traditional one-way communication. ...
... Moving beyond interactions via keyboard, mouse, or touch interface, a new type of interaction via voice has become increasingly popular through the proliferation of voice assistants. These are voice-controlled devices that are able to perform a wide variety of tasks with or for humans (Mari, 2019). Voice interaction starts with a voice request from the user's side. ...
... Moreover, specific data (e.g., how the algorithm works and what it includes) are only available to the manufacturer of the smart speakers, such as Amazon with Alexa. Consequently, the hardware and software provider becomes a gatekeeper, with some brands not being chosen by a particular voice assistant (Mari, 2019). Firms need to be aware of this potential negative effect on their "presentation" to consumers. ...
... Due to the limited capacity of memory in the auditory sense, consumers might simply forget the content they have heard, particularly if several options are presented to them. These restrictions make advertising more difficult, potentially increasing advertising costs (Mari, 2019). ...
Chapter
Over the course of digitization, many innovative marketing technologies have emerged that—theoretically speaking—promise firms gains in efficiency and/or effectiveness. However, a central task for marketing is not to allow the use of these technologies to become an end in itself, but to preserve the guiding principle of marketing, namely customer orientation. This means that the new technologies only offer added value for firms if they also offer (perceived) added value for consumers. Using three specific application areas as examples (chatbots, voice assistants, and data privacy management), we show how firms can combine innovative marketing technologies and consumer interests in a purposeful manner.
... Customer Satisfaction is another area where few studies by (McLean & Osei-Frimpong, 2019;Hwang, 2018;Kraus, Reibenspiess, & Eckhardt, 2019;Mari, 2019;Moriuchi, 2019;Son & Oh, 2018) have put efforts to get a better understanding of the factors helping or factors that could improve consumer delight. ...
... Impact of voice on commerce was reviewed by (Kraus, Reibenspiess, & Eckhardt, 2019) to observe consumers having higher expectations in convenience for voice commerce than they have for e-commerce. Further, (Mari, 2019) construct was focus group interviews to understand role of voice as an influencing agent or a mediator with ability to increase brand awareness metric. ...
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There are multiple studies establishing the importance of Business Intelligence (BI), in the Big Data Analytics context. Voice is yet to be seen as a contributing channel. Voice enabled assistants are at the forefront of conversational AI advancement. As humans speak to devices, brands and business are investing in engagement through voice channel. This voice engagement is resulting in both intangible and tangible benefits and generating voice commerce. The resultant voice data should be integral to BI, leading to Voice BI. This paper proposes a conceptual framework from engagement to intelligence, with support of five propositions to realise voice business intelligence. Type of applications and their engagement characterisation is segregated to create better understanding using Cross-Cases Observation Technique. Along with future research agenda to strengthen the propositions, this investigation observes building voice business intelligence by tracking relevant metrics which enable informed decisions.
... Le terme "assistant vocal" désigne, quant à lui, les agents conversationnels, qui dialoguent avec l'utilisateur principalement par la voix, tant en entrée qu'en sortie (Mari, 2019;Tuzovic & Paluch, 2018). Dès que l'assistant vocal est activé sur base d'un mot-clé ou de son nom (exemples : « Ok Google », « Alexa »), il répond en utilisant la première personne du singulier, via le « je ». ...
... Dès que l'assistant vocal est activé sur base d'un mot-clé ou de son nom (exemples : « Ok Google », « Alexa »), il répond en utilisant la première personne du singulier, via le « je ». Il traite ensuite la requête vocale de l'utilisateur en exécutant une tâche ou un service (Hoy, 2018;Mari et al., 2020) et possède la capacité de s'améliorer dans sa compréhension de l'interlocuteur et du contexte (Mari, 2019). ...
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Avec une utilisation mondiale estimée à 2,5 milliards d’assistants vocaux fin 2018, le marché est en pleine croissance et pourrait encore tripler d’ici 2024 (Juniper Research, 2018). Notre étude, toujours en cours, menée suivant la Méthode par Théorisation Ancrée, vise à identifier les raisons permettant d’expliquer le maintien de l’utilisation d’un assistant ainsi que les contreparties sous-jacentes. Sur base de nos résultats actuels, nous aborderons les aspects du contrôle, de la facilitation de la vie quotidienne, de la curiosité technologique, du besoin de différentiation, de la fluidité des échanges ainsi que de la recherche d’adéquation avec ses propres valeurs. Les résultats de cette recherche pourront permettre de faciliter l’utilisation des assistants vocaux en tenant compte du ressenti des utilisateurs ainsi que leur intégration dans un parcours client.
... Virtual assistants are conversational agents that can understand human dialogue (Hoy, 2018), have agency skills to execute tasks and learning techniques that allow them to adapt to consumers' behaviors (Mari, 2019). Smart assistants such as Amazon Echo, Google Home, Apple iPhone and, more recently, the Apple HomePod speaker have an impact on how people perform daily tasks such as checking the calendar, interacting with other apps to play music or read the news, finding nearby reference points and having a spontaneous conversation (Sujata et al., 2019). ...
Article
Purpose The influence of technology on marketing communications is rising in both applications and value created. Artificial intelligence (AI) and, as a result, smart speakers are benefiting both brands and customers at many levels. In particular, AI opens up the possibility to establish human-like dialogs with customers and to advertise brands in a new and engaging way. Therefore, the purpose of this paper is to understand why and how consumers would accept receiving advertising (ad) via AI-enabled devices such as smart speakers. Design/methodology/approach A total of 326 individuals participated in a study that explored the factors influencing ad acceptance in smart devices. A partial least squares-structural equation model technique was used to validate the results. Findings The findings show that customer acceptance of ads via smart assistants is influenced by smart assistant usefulness and hedonic motivations. However, privacy risk moderates the relationship between smart speaker ease of use and smart speaker usefulness. Originality/value This paper explores the main drivers of ad acceptance via smart speakers and goes beyond the existing knowledge of smart speaker acceptance to further explore how this can become an important channel for brands to communicate.
... If AI offers users higher levels of perceived usefulness, their shopping intention increases in AI-powered stores (Pillai et al., 2020). AIPA may play an important role in purchasing decisions by mediating relationships between brands, retailers, and consumers (Mari, 2019). The more useful help users get from AIPA, the higher they will assess. ...
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This study aims to identify the key predictors that affect the continuance intention of artificial intelligence personal assistant (AIPA). It proposes the theoretical framework which employs utilitarian value, hedonic value, perceived ease of use, perceived usefulness, novelty value, perceived enjoyment, and parasocial interaction. Data was collected from 257 users of artificial intelligence personal assistants. This study used partial least squares structural equation modeling (PLS-SEM) to analyze the empirical data. The results show that utilitarian value and hedonic value are significantly correlated with continuance intention. The findings reveal that perceived ease of use, perceived usefulness, novelty value have a significant effect on utilitarian value. The analysis results indicate that novelty value, perceived enjoyment, and parasocial interaction are significantly associated with hedonic value. The current study conducted multi-group analysis according to the AIPA type, gender, and use experience. The results of this study will be a useful guideline for research and business on AIPA.
... This demand is an opportunity for businesses to incorporate technology into their marketing approach. While many studies agree that the use of VAs is limited to basic functions like search, alarms, weather, and music (Mari, 2019), voice commerce is on the increase, with revenue hitting $1.8 billion in 2018 and projected to attain $40 billion by 2022 in the United States alone (Hayllar and Coode, 2018). ...
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Voice assistants have emerged as a new form of technology that can identify human speech and respond accordingly via synthesized voices and this family of technologies has helped people accomplish various requirements in their daily lives. However, despite the numerous benefits of AI-based assistants, consumers' concerns about their privacy have increased. Nevertheless, only a few studies focus on the brand loyalty of customers, which influences the intention of consumers to persist in using voice assistants. Furthermore, the impact of brand credibility on the overall perceived value receives little attention. Therefore, this study attempted to identify the mechanism through which the users of voice assistants might develop reuse intention and loyalty toward a specific service provider brand and analyze how brand credibility can influence the overall perceived value of voice assistants. The study drew on the uses & gratification theory, signaling theory, and prospect theory to develop the conceptual model and its underlying hypotheses. Using purposive sampling and an online survey, data were collected from 426 Chinese users of AliGenie, Alibaba's intelligent personal assistant. Data and the hypothesized model were analyzed using partial least squares structural equation modeling. Findings from quantitative analysis identified the perceived privacy risk as the most significant factor and obstacle influencing consumers' overall perceived value toward the usage of voice assistants. Furthermore, findings indicate that brand credibility moderates the existing relationship between the perceived privacy risk and the overall perceived value, a high brand credibility results in a much lower association between the perceived privacy risk and overall perceived value. Furthermore, the findings discovered a significant and positive relationship between brand loyalty and individuals' continued usage of voice assistants.
... Noting that AI includes a self-learning component (Mari, 2019), there may also be a feedback loop whereby the VA learns from past communications and outcomes, improving subsequent communications (see again, Figure 1). ...
Article
Purpose Artificial intelligence–enabled voice assistants (VAs), such as Amazon's Alexa, Google Assistant, and Apple's Siri, are available in smartphones, smart speakers, and other digital devices and channels. Use of these VAs is growing rapidly and are expected to significantly impact purchase intentions. This article focuses on how the communications enabled and provided by these VAs influence VA evaluations and usage intentions, contingent on the stage of the customer journey. Design/methodology/approach This paper builds from work on VAs, work on artificial intelligence (AI) and work on communications, to offer a comprehensive and up-to-date understanding of how VA evaluations and usage intentions may be impacted by the communications from VAs, contingent on the stage of the customer journey. Findings This paper proposes a model for VA enabled communications impact VA evaluations. It builds from work on VAs, AI, communications, and customer journey management. In the proposed model, VA evaluations are not only impacted by source, message and recipient characteristics (per prior communication models), but also by (1) VA/AI specific features, like perceptions of humanness and perceptions of artificiality, and (2) stage of the customer journey. Practical implications This paper provides guidance to firms, as regards how VA communications may influence VA evaluations and usage intentions. As an initial conjecture, (1) increasing perceptions of humanness, (2) decreasing perceptions of artificiality (3) a better fit between communications style (e.g. abstract vs concrete), and request type (e.g. transactional vs informational) (4) a better fit between VA communications (e.g. information vs banter), and consumer perceptions of the VA (servant vs partner) and (5) a better fit between VA communications and the stage of the customer journey may positively influence VA evaluations and VA usage intentions. Originality/value This paper provides a fresh look at the impact of VA communications, clarifying how such communications impact VA evaluations and usage intentions at various stages of the customer journey.
Thesis
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Alors que le monde était autrefois dominé par les majors du pétrole, l’ère des géants de la tech a commencé il y a plus de 15 ans. Aujourd’hui, en mai 2022, 4 entreprises technologiques sont parmi les 5 plus grosses capitalisations mondiales, à savoir Apple, Microsoft, Alphabet et Amazon. Ces mastodontes de la recherche, des magasins d’applications et de la vente en ligne sont tous capitalisés à plus du triple d’un autre acteur de la tech au business model tourné principalement vers les réseaux sociaux : Meta Platforms (ex-Facebook). La publicité sur les moteurs de recherche est en effet devenue le levier le plus important du marketing digital, représentant 42% des investissements publicitaires sur internet. Cela montre que les marketeurs considèrent le référencement, organique ou payant, comme étant le levier le plus pertinent pour leur entreprise. Savoir bien référencer son site, son application ou ses produits sur Google, Bing, l’App Store ou Amazon est ainsi devenu le nerf de la guerre. Or, depuis quelques années, une tendance de fond prend lentement forme : l’usage de la voix pour effectuer des recherches, et, dans une très faible mesure, des réservations voire des commandes. Ce serpent de mer, annoncé depuis longtemps comme une petite révolution dans le monde du référencement et du commerce en général, tarde à se concrétiser. Deux raisons à cela : la lente évolution des usages et l’immaturité technologique. Alors peut-être ce sujet de thèse est-il traité trop tôt ? Est-il pertinent de traiter le vocal comme un nouveau levier à part ? Quoi qu’il en soit, il se pourrait que la publicité, le marketing et le commerce par la voix deviennent des canaux d’acquisition ou d’interaction client primordiaux à moyen ou long terme.
Thesis
When Alan Turing formulated the Turing test in 1950, he certainly would not have thought that 70 years later, new trends and technology would change the way we experience our everyday life. Digital Voice Assistants are rapidly conquering the market and offering consumers simple, voice-based usability. Companies from various industries, such as the retail or the health sector, have recognized their potential and are already offering services digitally with the support of Digital Voice Assistants. It is only a matter of time that voice assistant will soon, at least to a certain extent, find their way into consumers' everyday lives. Against this background, the present thesis offers a good basis for better assessing the acceptance of Digital Voice Assistants and dealing more precisely with the influencing factors among the age cohorts - Millennials and older people. Three surveys of Millennials and one of older people were nearly examined under investigation of carefully selected technology acceptance models – the modified TAM and the modified UTAUT2. Those two models have proven to be reliable theories for testing the acceptance of new media. When analyzing the predictors among Millennials, Pastime is the most important aspect that influences the acceptance of Digital Voice Assistants. Moreover, Enjoyment, Image, Expediency, and Social Influence also positively impact the intention to use the system. Nonetheless, privacy concerns and the fear of being intercepted negatively affect Millennials' acceptance and the use of such new technologies. Within the second investigated group – older people, Performance Expectancy, Facilitating Conditions and Hedonic Motivation have the strongest influence on the acceptance of Digital Voice Assistants. Although, it should be noted that there are noticeable differences between individuals aged 55 to 64 years and those beyond the age of 65 years. The qualitative analysis shows that Digital Voice Assistants are very helpful while quickly looking for short information, navigating a car, traveling, or using a mobile phone, especially when manual input is impossible. People in both target groups mainly use their Digital Voice Assistants for time-saving and increase their image among friends and family, who strongly influence their decisions and behavior. Many users, especially those older ones, turn to Digital Voice Assistants when they feel lonely and need a conversation with somebody. Future studies should examine further age cohorts in different countries. A specific subdivision of older people will also be recommended. Another interesting aspect, which will certainly provide meaningful findings, is to examine differences between Behavioral Intention to Use and different education levels.
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Purpose The purpose of this paper is to develop a comprehensive research model that can explain customers’ continuance intentions to adopt and use intelligent personal assistants (IPAs). Design/methodology/approach This study proposes and validates a new theoretical model that extends the parasocial relationship (PSR) theory. Partial least squares analysis is employed to test the research model and corresponding hypotheses on data collected from 304 survey samples. Findings Interpersonal attraction (task attraction, social attraction, and physical attraction) and security/privacy risk are important factors affecting the adoption of IPAs. Research limitations/implications First, this is the first empirical study to examine user acceptance of IPAs. Second, to the authors’ knowledge, no research has been conducted to test the role of PSR in the context of IPAs. Third, this study verified the robustness of the proposed model by introducing new antecedents reflecting risk-related attributes, which has not been investigated in prior PSR research. But this study has limitations that future research may address. First, key findings of this research are based only on data from users in the USA. Second, individual differences among the survey respondents were not examined. Practical implications To increase the adoption of IPAs, manufacturers should focus on developing “human-like” and “professional” assistants, in consideration of the important role of PSR and task attraction. R&D should continuously strive to realize artificial intelligence technology advances so that IPAs can better recognize the user’s voice and speak naturally like a person. Collaboration with third-party companies or individual developers is essential in this field, as manufacturers are unable to independently develop applications that support the specific tasks of various industries. It is also necessary to enhance IPA device design and its user interface to enhance physical attraction. Originality/value This study is the first empirical attempt to examine user acceptance of IPAs, as most of the prior literature has concerned analysis of usage patterns or technical features.