Department of Business and Management

Master’s Degree in Marketing Analytics & Metrics Chair of Marketing Metrics

Smart speakers’ adoption: Technology Acceptance Model and the role of Conversational Style

Prof. Michele Costabile Prof. Maria Giovanna Devetag

SUPERVISOR CO-SUPERVISOR

Flavia Pigliacelli ID 712581 CANDIDATE

Academic Year 2019/2020

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A mio padre, tra le cui braccia calde anche l’ultima paura morì. A mia madre, forte e debole compagna. A mia sorella Claudia, vento e magica corrente. Alla mia famiglia, carburante del mio fare e paracadute del mio disfare.

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“Do not be fobbed off with mere personal success or acceptance. You will make all kinds of mistakes; but as long as you are generous and true, and also fierce, you cannot hurt the world or even seriously distress her.” Winston Churchill

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TABLE OF CONTENTS

INTRODUCTION ...... 5 CHAPTER ONE ...... 7 1.1 Smart speakers: home voices paving the way to the Internet of Things ...... 7 1.2 Artificial Intelligence and Machine Learning in voice search: disruptive ingredients for the IoT business revolution ...... 13 1.3 Voice is the new touch: how smart speakers may overturn marketing strategies ...... 17 1.4 Missing pieces of the smart speakers’ revolution puzzle ...... 25 CHAPTER TWO...... 30 2.1 Smart Objects: building blocks of the Internet of Things ...... 30 2.1.2 Drivers and Barriers of IoT adoption ...... 33 2.1.3 Focus on Smart Speakers: Amazon Alexa and Google Home ...... 36 2.1.4 Smart Home: an application of Virtual Vocal Assistants within a familiar environment ...... 39 2.2 Conceptual Framework ...... 41 2.2.1 Theoretical roots of IoT acceptance: a review of Technology Acceptance Model ...... 44 2.2.2 Leveraging on User Experience induced by Interaction to improve Smart Speakers’ adoption: Pragmatist Aesthetics ...... 46 2.2.3 Leveraging on User Experience induced by Interaction to improve Smart Speakers’ adoption: Anthropomorphic cues ...... 50 2.2.4 Leveraging on User Experience induced by Interaction to improve Smart Speakers’ adoption: Conversational Style (and its gaps) ...... 54 CHAPTER THREE ...... 58 3.1 Conceptual Model: Variables and Hypotheses ...... 58 3.2 Research Method: Measures and Data Collection ...... 61 3.2.1 Pre-testing Scenarios for Independent and Moderator Variables ...... 61 3.2.2 Main-Test ...... 64 3.3 Data Analysis and Findings ...... 66 3.3.1 Pre-Test Results ...... 66 3.3.2 Main-Test Results ...... 67 3.3.2.1 Descriptives, Reliability and Validity of the scales, Manipulation Check ...... 67 3.3.2.2 Main and Moderation Effects on Perceived Usefulness and Attitude ...... 68 3.3.2.3 Linear Regressions of measured variables ...... 69 3.3.2.4 Chi-square associations between measured variables ...... 71 3.4 Sentiment Analysis: Google Home Mini vs Amazon Echo Dot 3rd Generation ...... 74 3.5 Main Results Summary ...... 77 CONCLUSIONS...... 78 APPENDIX ...... 82 BIBLIOGRAPHY ...... 93

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INTRODUCTION

Internet Of Things, understood as the billions of physical devices around the world connected to the Internet, is being adopted worldwide by almost all sectors. In business and industry, there are several IoT use cases and real-life deployments. In the consumer space there are many thousands of devices and applications for a broad variety of purposes, for using which people do not necessarily need to be experts despite their great and complex technological power. Given that IoT promises to make our environment, our homes and offices smarter, more measurable and social, predictions regarding its economic impact keeps evolving. IoT success is pushing firms to design and deliver new services, raging from product functions regulation to user experience personalisation. Not surprisingly, for consumers, the smart home is probably the place where they are more likely to come into contact with internet-enabled things and represents the principal area where the big tech companies are competing hard. Besides smart bulbs, smart fridges, smart tv and thermostats, smart speakers are the most obvious objects driving this trend since they easily make real and spontaneous the remote control of other items as well as the performance of daily routines through natural language. Explicitly, smart speakers are devices integrated with Virtual Assistants, declensions of Artificial Intelligence, that assure interaction and hands-free activation by means of a wake-word. As such, they are becoming pervasive in homes due to their suitability in readily carrying out voice commands aimed at different use-cases: asking weather forecasts, performing Internet searches, playing media content, shopping online, supervising home automation. Briefly, smart speakers, serving as hubs of a connected lifestyle, are profoundly influencing consumer behavior transformation inside the home. Furthermore, their proliferation is remodelling the way businesses communicate with customers across a variety of spaces. Although smart speakers are sketched to improve efficiency and convenience, as well as comfort and entertainment, usability and accessibility issues are still open questions since these items are not always appreciated and their functionalities are not always thoroughly harnessed. For instance, eMarketer’s predictions about adoption rate have undergone a slight downward adjustment for this year (eMarketer, 2020), signalling that smart speakers usage is not reaping the expected success. Indeed, while sales are rising, ownership largely converges on a minority of households that are generally heaviest users of connected home technologies and does not seem to reach broader portions (Williams, 2020). This circumstance represents a challenge for marketers that are trying to make the voice commerce take-off real and for smart home technologies developers that aspire to expand the market beyond early adopters. Accordingly, companies need to find a way to boost and expand adoption of smart speakers because, beyond everything, they still constitute a hot market on the threshold of their early majority stages of adoption (Olson, 2019; Voicebot, 2019). These initial stages of their lifecycle will eventually have a massive impact on future profits as well as on tech companies’ plan of connecting customers to their ecosystems, whose first executive point rests precisely on home smart speakers success (Routley, 2019). Considering the urgency of making humans satisfied users of smart speakers, it constitutes an imperative to discover the right recipe of their implementation. Being homes places where people search for a break from a hectic lifestyle, users’ 5 acceptance and consequent adoption of such IoT devices could be affected, other than functionality, by the level of comfort and amusement expressed by them during interaction. Consequently, this research aims at assessing the eventual role of smart speaker conversational style in the context of their adoption.

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CHAPTER ONE

1.1 Smart speakers: home voices paving the way to the Internet of Things Nowadays, technology increasingly exerts a profound impact on our lives, by strongly involving every aspect of it: professional, private and social. Internet of Things (IoT) is a galaxy born from the idea of delivering, through the Internet, objects of our daily experience to the digital world. The IoT is the interconnection of devices embedded with sensors and network connectivity that collect and exchange data to support communication between (Reply, 2020): • objects and manufacturers • objects and end users • objects and objects

Many items that up to a few years were considered static can become smart, thanks to the integration of small technological components like RFID tags and sensors (Stefani, 2020). IoT essence resides in the interconnection of intelligent items that exchange information with each other. This paradigm potentially does not know any application boundaries: cars that dialogue with road infrastructure to prevent accidents, coordinated household appliances that optimize the power commitment, production plants that exchange data with products for the management of their life cycle, medical devices located in an emergency room, skis that send information on the state of snow (Osservatori, 2020). In a nutshell, IoT is a network of physical devices embedded with software, sensors and actuators that together model their intelligence (Engineering, 2018). Currently, there exists a plethora of devices that acts as an integral part of the IoT: headphones, smartphones, wearable devices (e.g. smartwatches and Fitbit), laundry machines, coffee makers, enabled vehicles, lamps, virtual assistants (e.g. Alexa and Google Home) (Digital, 2020). While it is true that all objects can become smart by connecting to the network and exchanging information about themselves and their surroundings, it is equally true that this process does not happen in all areas at the same speed: it depends on the existence of established technological solutions, on the competitive balance in a given market and, ultimately, on the symmetry between the value of the information and the cost of creating the network of intelligent objects. It is a fact that IoT plays a central role in the digital development of our country (Osservatori, 2020). According to “Il Digitale in Italia” annual report, IoT sector was the driving digital force in Italy during 2019 (Tiot, 2020). Overall, smart technologies grew at double-digit rates and saw their share of the entire digital market grow to 19.5% from 13.4% in 2018. As a result of the crisis prompted by the Covid-19 emergency, it is expected that in 2020 the digital market dynamics will experience losses and a minus-sign growth rate: ICT Services -3.7%, ICT Software and Solutions -1.1%, Devices and Systems -3.5%, Network Services -3.9%, Digital Content and Digital Advertising -1.5%. Despite these negative forecasts, IoT technologies will continue to ride on the wave of success (Tiot, 2020). Actually, it has been estimated that the number of connected objects will reach $125 billion mark by 2030, four times the value registered in 2017, thus blurring the boundaries that have made the IoT not thoroughly a reality up to these times (Parikh, 2019). Consumers represent the largest users of IoT 7 things, accounting for nearly two-thirds of the overall applications in use (Parikh, 2019). IoT applications for consumers are very diverse and often overlap with those employed in more industrial and business-oriented situations. For instance, some wearables are made for personal usage, others, which can look pretty similar, are built for professional or industrial usage (i-scoop, 2017). Namely, IoT possible applications for consumers are not limited to a single field but are extended to several branches of interest: smart home, smart vehicles, smart health to name a few. Especially the first one is the IoT system most frequently searched on Google because it guarantees savings in time, energy and money (Baswani, 2018). First and foremost, apart from fitness bands, smart home devices have captured consumers’ attention since they are seen as the answer for making certain aspects of life easier, more personalized and experiential (Parikh, 2019). Home automation is a navigated concept in the market, but thanks both to the development of new technologies and to the emergence of new saving requirements for homeowners it has been experiencing a rapid growth in the last years (Gong, 2016). Indeed, the smart home connotation has ubiquitously become a measure of standard of living and a goal to be achieved even for the less well-off (Engineering, 2018). Smart home refers to the possibility to manage automatically or remotely systems and devices inside a home, in order to save energy, simplify domestic life and ensure the safety of tenants (Salvadori, 2020). In this contemporary context, home can be considered as a microcosm in which it is possible to observe all the elements, both subjects and objects, that are part of an IoT project: end-users, technological layers, interoperability elements, privacy and security issues (Della Mura, 2020). Smart home portrays new challenges and opportunities both for producers of connected devices and for service companies, that will cause significative benefits (Reply, 2018): • enrichment of the product experience • insights on customers’ approaches to smart home products • expansion of products use cases • interoperability with third party devices • commerce of things through objects capable of being transaction-enabling platform

By 2025 more than 50% of the internet traffic delivered to households will be used for the operation of connected appliances (Schwab, 2016). Thus, home automation seems to be the wave of the future: connected products are abounding in possibilities to make our lives easier, more convenient and more comfortable. Looking back to a recent past, throughout 2019 the smart home has been one of the areas in which initiatives and alliances between companies have multiplied with the aim of creating integrated roots that, thanks to digital, can provide users with value-added services and can contribute to market growth beyond the more traditional sectors of the IoT, such as industry 4.0 (Salerno, 2020). Given the proliferation of a series of new solutions for the connected home, Insider Intelligence expects the total number of connected devices on the planet to reach 64 billion by 2025, including (Intelligence, 2020): • smart appliances (washers, dryers, refrigerators, etc.) • smart home safety and security systems (sensors, monitors, cameras, alarm systems)

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• smart home energy equipment (thermostats, lighting)

Surely, smart home can be regarded as a sector in full growth especially thanks to the services component which is destined to represent the real differential in the up-coming years (Della Mura, 2020). The offer is maturing not only in terms of quantity but also and above all in terms of quality, finally going beyond the mere idea of connected objects and embracing the talk about integration between products and services (Della Mura, 2020). The relevance of smart home is remarkably highlighted by numbers: in Italy it is worth €530 million, equal to a growth of 40% year after year (Della Mura, 2020; Corcom, 2020). In particular, 2018 was a turning point for this sector in Italy: not only the market grew by 52% compared to 2017, but the long-awaited smart home speakers have finally been introduced in our Country (Salvadori, 2020). In 2018 smart home was chosen by 41% of Italians, understood as the percentage of people who have purchased at least one smart device dedicated to their connected home (Tiot, 2019). Then, 2019 confirmed the phenomenon with a further 40% increase (Salvadori, 2020). Half of smart home market leverages on certain needs that Italians have for their habitations and relies on high-tech devices to satisfy them (Tiot, 2019): smart services for the control and management of domestic utilities (water, electricity and gas) are examples of such necessities. Definitely, the Italian turnover of products and services for the smart home is driven by security solutions, speakers and appliances, that together cover 60% of the market (Corcom, 2020). This growth tendency is comparable to that of the main European countries, although the void to fill is still wide with respect to Germany and the United Kingdom (2.5 billion each) or France (1.1 billion) (Corcom, 2020; Della Mura, 2020). In line with the market trend, consumer awareness of smart home items is increasing as well as citizens' fears about the risks linked to cyber security and the violation of privacy (Corcom, 2020). Besides such a greater awareness, which is inevitably more rooted in the younger segments of the population compared to the senior ones, there has been a significant increase in the number of users who have declared themselves autonomous in the installation and configuration of the purchased devices (Della Mura, 2020). Manufacturers, designers, architects, electrical installers and builders continue to cover an essential role in the development of smarter homes but have been less involved starting from 2018 (50%), recording a loss of twenty percentage points compared to the previous year (70% in 2017) (Tiot, 2019). These instruments are so important that a real war has started between the giants of technology to gain market share in this specific sector and to make room for them in Italians’ homes (Stefani, 2020). Currently it is estimated that 38% of Italians have at least one smart object at home and the trend is set to increase (Stefani, 2020). As pointed out in a recent survey, Italy is today in the middle of the development phase of IoT solutions (Barilli, 2019): • market growth continues at a fast pace, in terms of value and maturity of the offer • sensors are evolving • start-ups are proliferating • new market opportunities are emerging (e.g. In-Thing purchase and design-driven approaches)

Thus, a full Artificial Intelligence-related bloom alongside with criticalities unavoidably takes place and 9 companies must be ready to take up in order to profit from IoT. The leap in quality is imputable to the stimulation guided by smart speakers landed in Italy in 2018, which currently weigh 18% of the market value. From a practical point of view, smart speakers are smart objects equipped with microphones and wi-fi connections, which, thanks to Artificial Intelligence and voice recognition, are able to carry out orders received from people. Nonetheless, smart speakers’ main purpose does not consist in taking away people’s curiosity by simply carrying out very easy tasks (Benedetti, 2020): one of the main interesting peculiarities of these items is that they wear the garments of the gateway to the smart home, the most domestic and familiar form of IoT (Jones, 2018). Smart speakers go loudest to find a balance between reducing energy consumption and seeking new forms of comfort: they are the main characters in shaping solutions for intelligent heating and room monitoring. This circumstance explains why their diffusion has been providing big benefits to smart home appliances’ sales (Tiot, 2019). Big retailers, already web giants like Amazon and Google, have been able to harness the phenomenon of smart speakers driving smart home popularity, while traditional operators have struggled more (Tiot, 2019). By way of illustration, the idea of Amazon has always been that of a constantly evolving service that can continuously be enriched and that, through interactions with the user, is able to learn new skills, new types of answers and new dialectical nuances with a view to increase empathy between the machine and the person (Benedetti, 2020). It is also interesting to highlight how, thanks to the ability to perform operations based on voice commands and the ability to interact with home automation devices, smart speakers can meet many needs of people with disabilities and can simplify many aspects of elderlies’ daily life (e.g. remembering to take medication, make emergency calls, prepare the shopping list). In America, Amazon and Google’s virtual agents have represented the gateway to the IoT life concept: they have been making every day more real the possibility of a future in which every action will be at the tip of the tongue (Molla, 2018). Alexa and Google Assistant are contributing to bringing smart home technologies closer to the mainstream to such an extent that voice control is considered the primary driver for the success of this market: currently, the former spreads its artificial voice through more than 20.000 devices and the latter trough more than 10.000 (Molla, 2018). It is believed that by 2023 the voice assistant market will be worth $7.8billion and the number of voice devices placed in homes will reach 275 million (White, 2019). Amazon has been the pioneer company in introducing smart speakers and in achieving success in the market. This circumstance offered Amazon not only the first mover advantage but also the benefit of earlier integrating with many partners capable of furnishing additional and exclusive services compared to Google, its main competitor in this sector. For instance, as early as 2016 Bosch has developed a line of connected home products embedded with Alexa intelligent assistant. By the way, nowadays many firms are inclined to opt for a more neutral and inclusive strategy to create their hands-free products and services, that consists of integrating with both Amazon and Google’s vocal assistants, in order to get in touch to as many customers as possible. According to Idc's forecasts, the adoption of smart speakers will continue to expand rapidly and in 2023 there will be 48 million units sold: curiosity and impulse purchases will be replaced by the need to have several smart devices in the homes (Corcom, 2019). In such a scenario, a new alliance to get the market of connected homes off the 10 ground has been created between technology giants Amazon, Google and Apple. The main objective consists in making it easier for manufacturers to create smart home products compatible with smart speakers such as Amazon Echo and Google Home (Dini, 2019). Hence, considering either the constant refining of IoT technologies by virtue of new Machine Learning techniques and the spread of smart home products as something one cannot do without in the name of simplification, competition for the leadership in this market is far from over (White, 2019). Amazon and Google’s voice assistants have proven to be useful and unique in modifying human-machine interactions: in particular streaming music is the most successful current use case, pinpointing that the way people consume media has changed since the introduction of these devices in the market (Molla, 2018). Although hitherto smart speakers have demonstrated to be good popular partners to stream music, to schedule personal agenda and to get weather forecasts, their capabilities are far greater and need a new breakout application: in CEO and co-founder of August Home Jason Johnson’s opinion, once people buy smart speakers, they want to exploit them in their entirety and look for objects that allow them to do so (Molla, 2018). Hence, being readily accessible and available to most consumers in reason of their affordable price, in a not so distant future they are going to represent the bridge to centralize and control items (e.g. refrigerators, televisions, thermostats) embedded with intelligence inside a home (Corcom, 2017). Heretofore, home speakers are the smart products that have aroused the greatest interest among Italian consumers, who have bought them online or in store, relying on a trusted installer only in 19% of cases (Barilli, 2019): they are therefore beginning to spread, be known and used in an increasingly independent way even though they are not exploited to their full functionality. The ability of these items to smoothly integrate with the smart home devices has been significantly boosting the growth of their market (Allied, 2019), since it has proved to be a key factor in their adoption and in the development of partnerships between smart home manufacturers and creators of smart speakers. Vice versa, in a sort of endless loop, increase in usage of smart home devices drives the sale of smart speakers in the market as well (Allied, 2019). Despite the growth still lags significantly behind other European countries, Italy is perfectly aligned with its counterparts in terms of smart speakers (Della Mura, 2020). Only six years have passed since Amazon introduced Alexa to the American market and almost two in the Italian one, paving the way for smart speakers marketed later by other technology giants (e.g. Google). Smart voice assistants not only have introduced great innovations in terms of solutions for users, but also have been acting as a driving force for the entire sector, which has seen an increase in sales of various smart home devices (Salvadori, 2020). Not surprisingly, smart speakers are the second best- selling smart home solution after security systems and are worth €96 million. According to a report published by eMarketer, which analyses the sector by both comparing data concerning the main players from 2017 to today and forward-looking at the prospects until 2021, Amazon is currently the market leader (Stefani, 2020). Although many people probably know someone who owns a Google smart speaker, recent research from eMarketer indicates they’re far more likely to encounter people who have Amazon Alexa speakers (Matthews, 2020; Stefani, 2020): • in 2019, 73 out of 100 smart speakers were Amazon, 31 Google 11

• in 2020, a slight contraction is expected for Amazon since Google and other smaller brands will gain market share • in 2021, 68 American smart speakers’ owners will have an Amazon device, 32 Google

The research is limited to the United States but permits to have a fairly clear picture of the situation and the implications of companies’ strategic choices. Analysis that can only start from a given: the smart speaker trend is in good health and it is evident that the smart speakers (along with headphones and earphones) drive the entire sector (Stefani, 2020). So, the interesting evolution of smart home segment is above all linked to voice control: both the number of devices and the number of third-party devices into which voice control systems are integrated are rising. Given their potential role of hub in smart homes, their market value is expected to grow rapidly (CAGR 2018-2023 = 34.44%) and almost hit the threshold of $12 billion in 2023. Producers of smart speakers strive to allow people interact with these devices as if they were dealing with friends, making almost disappear the access boundaries of conversation. As for the players, the market seems to be divided between the big companies, to which about 600 product families are ascribed, and start-ups (Della Mura & Costa, 2020). Above all, it is interesting to underline that many start-ups after raising funds have reached a turnover of over $5 million. According to PwC study, more than £10 billion is expected to be spent in the UK on technologies related to digital assistants, while according to a study by Capgemini, over the next three years 40% of users will prefer to use a voice assistant rather than apps and websites. Such statistics explain how these technologies are now increasingly incorporated into everyday life (Benedetti, 2020). However, while it is true that these solutions can make life more efficient, it is also true that it may be necessary to invest time to learn all their skills so that people can make the most of them. Since vocal smart technology is not regarded as a luxury option only affordable by the middle-upper classes, the battle will take place on a scenario dissimilar from the expenditure perspective: it is necessary to leverage on other distinctive and groundbreaking factors for winning (White, 2019), among which user experience must be primarily taken into account (Salvadori, 2020). Moreover, there is still a significant portion of the population that either does not buy smart objects or make the purchase without using them (Della Mura, 2020). In fact, people may feel intimidated by the perceived technological complexity of these devices or may be unable to perceive their usefulness and positive impact on their lives. There is also no lack of fears about security and privacy, which continue to be a brake both for those who own the devices and those who have not yet purchased them. Albeit a major boost coming from voice assistants’ expansion, few consumers are really interested in buying smart home products in 2020: for instance, only 8% possess a vocal assistant (Corcom, 2020). The most frequent reasons that still hinder the use are excessive complexity (18%), lack of perception of benefits (10%) and the difficulty in using Apps for management (6%) (Corcom, 2020). In fact, it frequently happens that smart speakers’ functions are perceived as being too complex, futuristic or not indispensable (Barilli, 2019). To increase sales, not only installation services need to be improved but it is also urged to work on user experience, communicate the benefits and reassure consumers about the use of the data collected (Corcom, 2020). In such a context, the

12 development of user-friendly devices could represent the flywheel for their profound acceptance, dragging behind equally fascinating solutions connected to the home automation management of other smart solutions such as heating or alarm systems. Additionally, right now there are fewer connected objects than expected partly because not all equipment manufacturers have yet made their products smart. This is actually a knock- on effect: objectively not all end users are yet fully aware of the value that connected devices can offer and therefore, at the time of purchase, the choice is not always oriented towards a smart product. It is therefore important that companies that can play a role in the IoT ecosystem work, with a collaborative approach, to make as many devices smart as possible and to develop services that are relevant to end users (Della Mura, 2020). The risk, otherwise, is that companies and users are waiting for each other and that the potential of IoT is not expressed. Furthermore, although growth is positive and in line with that of the main Western countries, in absolute terms Italy is still one of Europe's rear lights: it seems that many consumers are still in the mood to manage their homes in a more traditional and less smart way. As a result, it is still needed to work on several aspects: from the number of objects that can be managed with voice to communication with consumers, which must focus more on showing the real benefits of smart products (Salvadori, 2020).

1.2 Artificial Intelligence and Machine Learning in voice search: disruptive ingredients for the IoT business revolution Disruptive innovations generally have a high social impact because of the peculiarity they possess of generating new market opportunities and profoundly overturning occurring ones (Petrovic, 2017). The pawns in this chessboard of novelties are mainly moved by afresh players that own unusual ways of thinking and the audacity to act in the market without the burden of past success. IoT runs along the edges of such a drawing and, in itself, holds the potential to provide several opportunities to businesses (Reply, 2020): • Improved inventory management • Total or partial automation of specific activities • Long-term relationship with final consumers • Access to more data to influence consumers’ decisions • More personalized experiences • Improved customer experience • Overall improved effectiveness

Internet is evolving in a network of various communicating devices constantly connected, that makes large amount of information about consumers readily available to managers in a steady stream and shapes new ways of gaining competitive advantage (Marek, 2017). In the Marketing 4.0 generation, exploded as a consequence of Internet evolution and IoT technologies blossoming, people no longer just want to satisfy their basic needs but also want to express their creativity as an integral part of a product. Thereby, they wish to be able to interact with products, participate and share their experiences (Jara, 2012). In our country, the IoT market is worth €6.2 billion, with a growth of 24% compared to the previous year, which corresponds to €1.2 billion, more 13 than the consolidated numerals of 2018. An interesting fact, which puts Italy in perfect alignment with the main Western economies, where the rates of increase range between 20% and 25%. But this figure, that undoubtedly underlines the good health of the segment, must be flanked by another one, which perhaps defines its relevance even more: the value of the data generated by the IoT (Della Mura & Costa, 2020). The whole potential of IoT, between $3.9 trillion and $11.1 trillion in 2025, will definitely explode when interoperability issues between multiple systems are overcome and the enabling technology is sufficiently developed (Maier, 2016). The opportunity offered to marketing by IoT technologies, regardless of their domain classification, resides in the possibility of collecting large amount of data concerning people habits. Following challenges are mostly linked to strategical marketing choices, from advertisement to customer service. IoT meets the conditions not only to make individuals’ lives easier but also to change the face of businesses and marketing intelligence because of the digital disruption it is currently causing. Thus, although it is not so obvious how intertwined our routines and smart technologies will become, marketers must know the dynamics of the IoT market and the associated risks (Digital, 2020). Smart technology has blurred the line between online and offline, thus profoundly redrawing how people interact with the environment on a daily basis. IoT true value stays in the ability to enhance opportunities at the intersection between supply and demand, filling the gap of information seeking (Caro, 2019). Smart devices are of particular interest for marketing theory because their product analytics and remote access functionalities open up a wide range of opportunities for marketing management (Decker, 2017). The first one, which constitutes an extension of the well-established web and mobile analytics approaches, consists in the autonomous collection related to usage habits, whereas the second one provides the possibility to remotely adjust product parameters or attributes. The combination of the two represents a virtually continuous customer touch line along which it would be smoother for companies to detect product failures and eventually repair them even before customers’ realization. Considering that market intelligence is of outmost importance in product design success, insights about IoT frequencies and parameters of consumption can be employed for molding new stimulating product design that can not only promote consumers’ interest but also help organizations gain and maintain a competitive advantage in the marketplace, consequently sustaining high levels of profits (Taylor, 2020). Information collected from operational habits is beneficial for the product design optimisation since it makes it possible to create systems that better-fit actual usage patterns (Taylor, 2020). Traditional products are commonly marketed with a transaction-dependent revenue model. Instead, apart from profits, revenues, retention and satisfaction rates, IoT products make available new indicators of companies’ performance in customer relationship management, particularly linked to intensity and type of product usage: number of features used, frequency of product usage, share of customers that correctly use the product, number and duration of usage terminations. The result of the integration of IoT in marketing concepts is called participative marketing, which enables new interaction and participation models based on the ubiquitous identification. Indeed, in participative marketing, different crucial constructs are built by customers through their interactions with products experienced in different occasions (Jara A. J., 2014): 14

• brand reputation, regarded as the result of feelings and values that a brand stands for • brand identity, intended as the positioning of a brand in consumers’ mind • brand image, considered as the way a brand get into individuals’ mind and conquer recognition • brand integrity or reputation, intended as the credibility of a firm and consisting in the establishment of people trust towards it by virtue of fulfilling what is claimed through positioning and brand value

The main driver of interest in data collected by IoT devices is their potential usefulness in prompting participative marketing and alerting consumers of product changes or in replacing surveys for informing marketing decisions and executing marketing campaigns (Taylor, 2020). Keeping in mind that data is the fuel that runs the marketing engine, from shaping more personalized offers to creating entertainment content for humans (Petrovic, 2017), thanks to Artificial Intelligence and marketing-based analytic data models marketers can drive better marketing performance and properly understand Return On Investment (ROI) (Salesforce, 2020). Indeed, it has been shown that AI can be profitably applied to several strategies (Mitić, 2019): • cost leadership → to reduce costs while maintaining quality • differentiation → to provide the highest quality among all the products in the market • alliance → to increase marketing efficiency • diversification → to enter into a new business unit or a new sector of activity • direct marketing → to interact directly with final consumers without commercial intermediaries, thanks to a comprehensive customer database

Thus IoT devices support growth, construed as the sum of marketing campaign performance linked to sales and revenues across segments and customer experience enhancement across the customer journey. This is imputable to the sensible time frame reduction for the optimization of marketing efforts offered by Artificial Intelligence combined with Machine Learning. It is crucial to emphasise that AI and ML are not substituting human creativity or reasoning ability but are making marketers smarter by providing them the chance to rapidly adapt, scale and sweep along every step of the marketing process (Figure 1: Artificial Intelligence and Machine Learning fitting in marketing at every step of the process (Salesforce, 2020)).

Figure 1: Artificial Intelligence and Machine Learning fitting in marketing (Salesforce, 2020)

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Specifically, AI embedded in smart objects enriches traditional marketing with tools that make way for personalized and relevant content brought at the right time to impact conversion rates. Indeed, it allows to predict customers’ future by referring to their past (Roy, 2019). AI tools are increasingly affecting a marketer’s journey by: • assessing past behavioural patterns, like purchase trends and queries • collecting customer data • further identification of the best possible way to use customer data

Hence, marketers can access and analyse what will most frequently cause people reactions and how it will attract them, thus creating the best possible path towards sales either in terms of incentives or product recommendation strategies. All of this is feasible on a real-time ongoing basis. AI technologies affect companies since they partly facilitate the process of defining competitive and productive businesses. Thanks to the advances provided by this modern technology, marketing has evolved from traditional via digital into intelligent. Inter alia, AI and ML embedded in smart devices enable an in-depth understanding of all activities related to the purchasing process. Hence, marketers can take a closer and shrewd look at consumer behaviour and either forecast it in order to make micro segmentation and accordingly guarantee long-lasting relationship with the brand (Mitić, 2019). IoT devices connect the various nodes of consumers’ lives into one ubiquitous experience and help collect relevant information for identifying consumption patterns and predict consumers’ future behaviours (Mari, 2019). Maximizing customer’s value over time is the main imperative for modern businesses. In this frame, smart products represent the means of delivering that value through an ongoing dialogue. Indeed, they help dampening the focus on one-time transaction relationships with customers because they provide firms the possibility to literally remain connected to clients and to track products’ real using shifts in the long run (Porter, 2015). As a result, they open up opportunities for marketing primarily linked to much finer segmentation and customization: data collected by IoT devices provide a sharper picture of product usage, revealing, for instance, which features customers appreciate the most and which ones they fail to use. A valuable knowledge like this is truly helpful for tailoring special offers or after-sale services, modelling features in accordance with segments’ preferences and developing more sophisticated pricing strategies. Customization is another value of connected products and, from this point of view, data provided by connected products adds value to their design and life management logic (Bellini, 2019). Intelligent products equipped with software allow continuous upgrades: by providing data on use specifications they send designers information and suggestions on how to best approach an evolutionary update that can meet, in the best possible way, users’ needs. A contribution by Porter and Heppelmann has brought attention to the service transformation, meaning the evolution from product to service. In order for this metamorphosis to take place, several steps must be taken (Bellini, 2019):

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• an appropriate product design • a review of the product according to the use made by consumers (data coming from the products are fundamental) • a vision of the possible scenarios and use cases (e.g. the same product could be used by more than one person and could provide more services).

Connected products are intended to radically change the relationship between manufacturers, networks and sales partners. Obviously, the shift from product to service changes the way in which customers relate to manufacturers and the way in which the product itself arrives at its final configuration according to customization preferences (Bellini, 2019). The risk factors may switch depending on the context in which a product is used or on the geographical area in which it performs its functions. The arrival of new applications can direct the end user to new ways of use rather than to the resolution of new needs. So just as a product, when it becomes a connected product, can solve new problems, so companies that undertake this path can evolve not only towards a new business model (from selling products to selling services), but also towards new markets (from selling devices to selling data or services that can be realized thanks to the collected data) (Bellini, 2019). The servitisazion principle applies also to spaces and comes to life in environments capable of increasing their intelligence, building knowledge and returning it in the form of services shaped according to the objectives of those who live it. Thus, environments, whether private or public, are enriched with intelligence and offer a growing availability of data and tools to understand and enhance them. Internet of Things and Big Data have made the difference and allow to reach the threshold of a new phase that combines digital innovation and service transformation (Bellini, 2019). Especially voice search is one of the current most popular AI applications for marketing, which permits both to deliver right messages at the right time and to get closer to customers by offering more personalized experiences. According to the “Osservatorio IoT del Politecnico di Milano”, there is talk of a total value of €30 billion, attributable to the new services that the IoT enables, from pay per use to new methods of advertising (Della Mura & Costa, 2020). Appropriately, marketers need to understand how customers will be affected by these technologies in order to build a strategical advantage on insights, especially keeping in mind that smart objects are able to produce unprecedent access to a wider range of tremendously helpful instruments (Digital, 2020).

1.3 Voice is the new touch: how smart speakers may overturn marketing strategies The prevalence of IoT devices in the homes, combined with the availability of constantly updated information on lifestyle trends, will allow marketers to perform better predictive analysis about targets of interest’s evolving expectations and reach niche audiences too (Della Mura & Costa, 2020). It is important to note that vocal assistants, marked by sophistication in engineering architecture and simplicity in use, are at the centre of such a behavioural revolution of humans interacting with the network that cannot be relegated to mere voice search activities (Loiacono, 2019). Voice technology, whose rate has beaten the one that accompanied the

17 spread of television back to time (Corcom, 2017), has introduced unprecedented opportunities that brands can apply to reach consumers in their home and hopefully establish emotional connection with them (Adams, 2018). This last circumstance becomes real if companies are able to think how their items embedded voices can extend into real vocal experiences that do not just stop superficially on impressions but successfully fullfill functional benefits too (Adams, 2018). Smart speakers are the latest innovation powered by Artificial Intelligence currently dominating the consumer technology market (Allied, 2019). At the same time, voice assistants can represent relevant marketing tools because in terms of business intelligence they provide opportunities for identifying customer needs: the combination of embedded sensors and constant connectivity to the Internet permits to easily collect data about consumption habits and consequently propose a more personalized offer (Ramamurty, 2017; Zheng, 2018). Briefly, smart speakers provide personalized services to users by collection and analysis of the data (Allied, 2019). Today the impact of voice technology is very wide and concerns not only physical devices but also Artificial Intelligence, Machine Learning and the whole ecosystem of voice apps available. The global smart speaker market size was valued at $4,358 million in 2017, and is projected to reach $23,317 million by 2025, registering a Compound Annual Growth Rate (CAGR) of 23.4% till 2025. North America constituted the highest smart speaker market share of 36.9%: in the United States about 74.2 million Americans used a smart speaker as of 2019 , and the number exceeded 85 million in China (Matthews, 2020). Not by chance, the smart speaker market growth rate is the highest in Asia-Pacific delivering a CAGR of 24.93% (Allied, 2019). Albeit by 13%, in 2019 smart speakers market penetration in Italy was on the rise, recording a clear preference for Amazon Echo (53%) compared to Google Home (43%) (Loiacono, 2019). The success of these tools, according to a survey by Statista, is imputable to their easier and faster support in accomplishing multitasking functions. Indeed, of those who own a smart speaker (Loiacono, 2019): • 82% tend to search for information on weather, news, recipes, appointments, offers • about 67% play music and streaming videos • about 35% use smart speakers to make purchases and to ask for brand support

Additionally, Business Insider’s “The Smart Speaker Report” indicated that smart speakers may be growing faster than many other tech gadgets, including smartphones. It is possible to assert that they are not mere fun fads but rather groundbreakers in how people address everyday needs. Speech is the simplest and most human communication method, that due to its innate simplicity, intuitiveness, naturalness and hands-free control enables people to readily interact with companies by virtue of smart speakers (Iñiguez-Carrillo, 2019). The attention virtual assistants are gaining is notable considering that it is widely believed that “Voice is the new Operating System”, therefore revealing that access to information, entertainment and different types of content will be handled by these IoT robberies (Jones, 2018). Such a circumstance unavoidably impacts on marketing dynamics, making its inherent activities constantly running, highly individualized and adaptive by means of true ongoing bidirectional conversations. In particular, digital assistants represent the right-hand men of brands

18 in making them more popular and in assuring a new way of building stronger relationships with customers in virtue of the constantly updated insights about their preferences, derived from massive amounts of heterogeneous captured data (Baswani, 2018). This means that clients’ higher expectations can be satisfied in a quicker way, resulting in a reinforced loyalty, through personalized offerings, optimized marketing campaigns and improved pricing precision. Furthermore, these devices offer unprecedent opportunities to build competitive advantage, by virtue of customer experience management molded through the whole journey: from the first contact with a brand up to the entire life cycle of a product after its purchase (Marek, 2017). Customer experience refers to consumers’ internal and external impressions resulted from multiple direct or indirect contact with a company: its purpose is to prevent products and services from losing their distinction. Ordinarily, deep insights about customer experience are gained at numerous touchpoints and are precious for building comprehensive knowledge about consumers as well as anticipating their future behaviours or needs. Smart speakers can also be exploited to establish relationships and emotional ties with customers, deliver products that meet expectations, improve product service and reinvent marketing communications (Marek, 2017). Such a scenario will result in a greater availability of products, advertising messages and sales promotions tailored to individual requirements. Most of all, these IoT devices enable companies to limit potentially negative experiences resulting from receiving standardized marketing messages. In the voice era, digital marketing spend will reach $146 billion by 2023, growing at a 9% CAGR, according to a Forrester report shared with Marketing Dive (Sweeney, 2019). The evolution of digital marketing towards new shores, including voice recognition, has been predicted by the constant collapse of the Google’s cost per click for almost 4 years (Forbes, 2019). A recent study by Forrester Research has identified the transition of digital marketing away from traditional text-based advertising and more towards voice queries, thus transforming both the way people search and things are advertised (Sweeney, 2019). Search ad budgets are predicted to shift to voice-activated queries through Amazon Echo and Google Home, becoming mainstream in few years. Actually, it is believed that advertising could represent a good revenue opportunity for smart speakers’ providers given the nearly $19 billion expected to be globally spent for it by 2022 (Juniper, 2017) and accounted that people like voice assistants even when they convey advertising messages (Strategy, 2019; La Rosa, 2019): • 30% of consumers would be willing to hear an ad if they are asked before it plays • 25% of consumers would be willing to hear an ad if the it is personalized • 18% of consumers would be willing to hear an ad embedded into the speaker’s answer • 23% of consumers would be willing to hear an ad if it connects to a brand that they’ve liked on a social media platform • 38% of consumers who heard a campaign on a smart speaker found it less intrusive than TV, print, online and social advertising • 39% of consumers who heard a campaign on a smart speaker found it more engaging

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Consumers are tired of seeing generic ads that may have nothing to do with them: they often switch channel when advertisements are on air or pay extra money to stream entertainment services just to avoid advertising. Traditional and generic advertisements are less effective and personalization is in loud demand also in this sector: 90% of consumers said they are interested in personalised content and 80% said they are more likely to buy from a company that offers personalised experiences (Casarin, 2019). This explains why, not by chance, voice is regarded as the new touch. Smart speakers delineate a market, valued $13 billion by 2024, doomed to assume a critical role in the management of connected devices (Corcom, 2017): marketers will adapt their search strategies moving from pay-per-click to voice skills, SEO and branding to better capitalize on voice (Sweeney, 2019). Nonetheless, for the time being, ads through smart speakers currently provide little range to marketers for adjusting their strategies because of the few vocal-based interactive options available (Sweeney, 2019). As voice technology continues to grow and become more popular among people, it will be increasingly crucial for companies to take a voice search strategy into account in their digital marketing plan (Loiacono, 2019). Anyway, some farsighted brands (e.g. Capital One, Domino's, Humana, L'Oréal) have yet begun to incorporate voice search in their marketing strategies, instead of merely using it to answer questions and provide customer service (Forbes, 2019). Indeed, as the number of consumers adopting the technology increases, new opportunities to tap into voice will arise and potentially offer greater sources of revenue. For instance, Amazon has created its own influencer program, a more exclusive version of its affiliate marketing system that is open only to high-end influencers on Twitter, Facebook, YouTube and Instagram (Forbes, 2019). Another reason why it is important for companies to integrate voice technology into marketing strategies is because voice search is radically changing Search Engine Optimisation (SEO) practices (Digital, 2020). In fact, users interact with queries in a different way than when text searches are performed: voice queries tend to be longer than text ones and therefore more plentiful words should be incorporated into marketing contents. Similarly, voice queries tend to be done by asking complete questions. Moreover, in virtue of the IoT landscape, SEO Industry will experience a shift from honing on keywords-based content to intention-based one: web pages will not be ranked simply on the inclusion of specific words but on the natural language modality that is contained within them (Digital, 2020). This allows companies to start using relevant keywords that really get a sense of what potentially customers might ask. Provided that 20% of all Google search queries are done by voice search, the optimization of business content for voice search is a digital marketing trend to take absolutely into account for 2020, through making communication contents more confidential. In other words, a tone of voice set in a colloquial way and close to the way people communicate is the real key to achieve a perfect optimization of a brand’s marketing strategy for voice searches (Casarin, 2019). Hence, it could happen that in a not too distant future firms will, along the same lines of Search Engine Advertising, pay to make their products the most suggested ones by vocal assistants, especially keeping in mind that people are less prone to skip ads on smart speakers because of the inconvenience of such a task (Molla, 2018). For the time being, Alexa and Google Assistant do not accept monetary compensations for guaranteeing visibility to specific brands and prefer to suggest products options in line with other criteria (e.g. items’ availability and 20 proximity, Prime subscription, purchase history and user preferences) (Molla, 2018). Thus, it is pivotal for companies to get their online presence coherent for smart speakers, without sticking to only one model, also because these devices allow them to build awareness around their products through voice marketing. The future is dotted with smart assistants and assuming that the level of accuracy will be quite the same among different brands, it is urged to find the possible levers that really will drive consumers’ preferences (Molla, 2018). Top Of Mind Awareness (TOMA), placed in the context of the advertising ecosystem, shows whether a brand or a product comes first in consumers’ relevant decision-making set when thinking of a particular industry. The padding of the existing time gap between needs recognition and buying actualization is a core goal of TOMA (Petrovic, 2017). However, smart speakers are able to independently alter this time span impacting on the role of TOMA: through them, purchase choices can be made immediately after noticing a certain demand. Since progressively the starting point of a buying decisional process is a generic research, for companies being highly ranked in the organic results of search engines is just impressing as being positioned on top of consumers’ mind. Furthermore, both recommendation engines and user reviews systems profoundly influence individuals’ interests and judgements, striking TOMA. Embedded vocal assistants represent the link between consumers and companies that are approaching the IoT market: for instance, right now it is possible to purchase products on Amazon through an Echo device. As this field matures, it is expected that people will be able to directly interact with firms through open networks working on pre-established protocols (Verhoef, 2017). As a consequence, smart speakers are not just completely reshaping the way consumers work, play and live but they are strengthening business intelligence too by offering big amounts of data that companies can harness to improve operations and customer service (Gregory, 2015). They are disruptive also for the retail industry since they provide opportunities to develop a wide ecosystem that connects the physical world to the digital one for the sake of improving customer experience (Gregory, 2015). Moreover, they represent a new sales channel and thus a new source of revenues. The smart retail sector is particularly promising because it allows the collection of data about user behaviour in virtual stores, thus molding the opportunity to provide personalised offers and useful information for designers involved in the development of new collections (Della Mura & Costa, 2020). So, not surprisingly, the aspect of our lives in which IoT devices will have the greatest impact is that of purchasing and e-commerce (Reply, 2020). IoT has already begun to spread to retail stores and technologies will continue to revolutionise traditional sales processes in the years to come. IoT devices will change e-commerce by turning inanimate everyday objects into potential sales channels: beacons, smart mirrors, smart shelves, commerce buttons, robots, face recognition, virtual assistant and more are revolutionizing retail, creating new opportunities in customer experience, logistics, efficiency and customer relationships. Thanks to their disruptive nature and driving force in redefining mobile search, they are writing a brand-new chapter in the contemporary Internet trends. Chiefly, smart speakers can be viewed as a new touchpoint in the customer journey, based on bidirectional interaction between humans and technologies (Gollnhofer, 2018). Remarkably, smart speakers are tools able of reshaping the quality of interaction between retailers and consumers, offering good opportunities of creating engagement and hopefully sell more stuff 21

(iAdvize, 2018). Indeed, since they are setting new habits in digital interactions and in the customer path-to- purchase, their natural kid “voice commerce” is expected to explode and is predicted to reach $40 billion in USA by 2022 (Bentahar, 2019). Given that people find voice commerce convenient and fascinating by virtue of personalization, it truly holds the power of being a game changer for e-commerce and retail: smart speakers seem to be really compelling for the desirable plan that people will make use of them to effortlessly purchase everything they want. From a commercial viewpoint, voice applications may represent a new source of revenue not stuck in the borders of mere transactions but drawn-out to product searching, reviews listening and customer service access, thus potentially revising all the stages of the customer journey (Mari, 2020). Customers are certainly enthusiastic about conversational interfaces and are increasingly growing comfortable with buying products by using them. Bringing a human touch to e-commerce transactions that otherwise tend to be highly process-driven and impersonal improves customers’ experience with a brand. Conversational commerce is much more than just a hot trend: brands should seriously think about embracing it since, as reported by Capgemini, firms offering a vocal support in this context have benefitted from a 25% boost in Net Promoter Score (NPS) (Haptik, 2020). Due to their ability to simulate human conversation and provide an engaging customer experience, fulfilling the related demands of speed, convenience and friendly service, smart speakers can significantly boost website conversion rates (Haptik, 2020). On the one side, they have the intelligence to identify what customers wants and promote products or services that match those requirements. On the other, they can analyse a customer’s past behavior and pinpoint opportunities that are likely to drive up-selling and cross-selling. As vocal assistants mitigate information overload and search complexity, they hold the power to enhance decision-making quality. Ultimately, they can contribute to year-on-year improvements in customer lifetime value and even annual company revenues (Haptik, 2020). Together with chatbots they define the boundaries of the current “Conversational Marketing”. Built on real-time conversations, it consists of guiding customers through the marketing funnel as quickly as possible removing the appeal to fill forms or sending emails and waiting hours for getting an answer (Chatbotize, 2019). Thus, with communication taking place hic et nunc, conversational marketing has the power to boost sales and build authentic relationships ongoing through speech. It is a good palliative for the inefficiencies of e-commerce, which certainly offers convenience and almost unlimited choice but lacks human touch consequently producing low conversion rates (2% on average vs 40% of in-store commerce). Especially at a time in history when it is hard to beat big players, such as Amazon and ASOS, on price, online retailers are in danger of missing the opportunity to hit customer lifetime value, satisfaction rate and brand identity KPIs because of their inability to engage, convert and retain consumers through real-time humanlike interactions, that people love experiencing in physical stores (Chatbotize, 2019). Therefore, they need to set up their strategies on superior quality services to build strong relationships with customers by providing authentic and meaningful conversations. Nevertheless, to this day there are still some remnants with regards to the possibility that smart speakers could become a new touchpoint, given the predominantly tactile and visual experiential nature of shopping (Molla, 2018). Still, for the Fast-Moving Consumer Goods (FMCG) industry the ground of shopping 22 through smart speakers can be more easily explored and offers household consumables companies the opportunity to either impose themselves as potential leader in this channel or to increase their sales. Perhaps it is too early to talk about real voice commerce, construed as a completely voice-optimised shopping experience but it is expected to reach 40 billion dollars by 2022 (Loiacono, 2019). Global brands such as Starbucks, Domino's, Johnnie Walker and Nestlé have already begun to integrate voice technology into their marketing strategies to create a better connection with customers. Since voice is definitely the most natural way to communicate, it is able to create personal connection between brand and potential customers. It is up to firms the decision to develop creative marketing strategies that use voice as any form of content marketing, whether it is in line with their missions. By way of illustration, recently Oreo used Alexa to launch a new taste of its cookies through the "Mystery Oreo" contest, a clue game in which those who guess the taste can win $50.000 (Loiacono, 2019). Smart speakers are meaningful for the marketing field because they do not only help users perform a plethora of tasks related, for instance, to research queries, which are currently their most exploited functions, but they also learn consumer preferences over time opening up possible personalization scenarios (Taylor, 2019). For firms, the opportunity of smart speakers’ personalization lies in allowing consumers to make this choice within the decision-making spectrum of their own brand tone and style. Thus, each company needs to develop its own voice strategy focused on use cases, rather than Return On Investments, and measured in terms of its impact on customer usage. Furthermore, it is pivotal to stress that consumer acceptance of these devices is mirrored by the value that companies place on them: luckily, most organizations truly believe that voice assistants are critical catalysts of their customer engagement strategies and more extensively of their businesses. Consumers feel comfortable using smart speakers either for finding tailored solutions for their lives or for interacting with companies: by 2022, as voice becomes the dominant preferred medium, 70% of them may replace their visits to physical stores because they would not miss the presence of salespeople for reaching information about products or reach out to customer service thanks to virtual agents’ 24/7 accessibility (Taylor, 2019). For each activity across the consumer journey, the consumer uptake of voice is expected to grow. However, this conjecture will become reality if virtual agents’ interaction skills are humanly refined. Indeed, as people become more proficient in using smart speakers, it will turn crucial for these devices to be more life-like. Especially an improvement in terms of fun and engagement would firstly boost consumers’ experience as well as satisfaction and secondly reinforce both consumers’ propensity to spend and loyalty towards the company (Taylor, 2019). Benefits brought by smart speakers vary across operational advancements and customer experience. Regarding the first facet, hey have generated efficiencies in handling more customers and in creating insights that fuel product development, drastically drowning the time required for solving problems. Regarding the second one, organizations have experienced an increase of the NPS as a consequence of reductions on delays and progresses in responsiveness (customer experience), from which satisfaction levels have inexorably taken advantage. When adopted in the appropriate situation, smart speakers might have a huge impact on revolutionizing customer experience landscape. Companies must avoid missing the opportunity to build valuable relationships with customers by means of 23 these devices: in other terms, they need to find customer centred approaches to these technologies and to understand their evolving implicit dynamic of use. Nonetheless, it is not only a matter of employing a new medium: it is a matter of being customer centric over time. A possible means by which such a goal can be achieved is thinking about ways of conveying tone and emotions as well as adapting them. It is possible to talk about virtual agents’ personalization when they are able to maximize their chance of success by operating within a specific user’s context, which means assuming a conscious attitude, anticipating needs and adapting to change (Moussawi, 2018). Many studies in the field assert that the success of technological application can be only determined through their actual use and their interaction with users. Last but not least, their interoperability with other compatible objects helps them stay relevant over time. Not by chance, even if IoT concept has been around for about twenty years, masses have not expanded the requirement of networkable things until the advent of smart speakers, that have swept away such a hassle especially thanks to their capability of acting as an extensions of users and consequently maintaining a relationship with them (Jones, 2018). However, recent reports have highlighted that most of interviewed sample has never used a smart speaker for controlling other smart devices (McCaffrey, 2018), thus there is still room for improvement. When consumers change the way they interact with companies, marketing need to be prepared and transform its way of both listening and sending significant messages to them (McCaffrey, 2018). Given that currently brands spend imposing funds on visual communications (e.g. logos, packaging) to create customer experiences and that, differently from other touchpoints, smart speakers mainly appeal the auditory sense, a significant change in consumer-brand interaction is urged. Smart speakers have skills to reshape business landscape by reconfiguring dynamics between customers and companies: allegiance will shift from trusted brands to trusted AI assistants, with unavoidable implication for strategical marketing (Dawar, 2018). The more consumers use smart speakers, the better they will understand their habits and meet their needs, increasing their satisfaction in a self-reinforcing cycle. Especially those that are able to operate reliably and well, have more probability to increase users’ loyalty. Hence, brands should be ready to respond to virtual assistants’ revolution so as not to be crushed. However, since voice assistants are not yet extensively adopted, or at least below their full capacities, the plethora of marketing advantages that they bring are not thoroughly exploited. People actual usage of smart speaker is restricted to simple navigational tasks, whereas more sophisticated ones tend to be avoided (Dubiel M. H., 2018). In order to achieve gratifying results in customer acquisition, satisfaction and retention they should strive to understand how AI algorithms work when recommending products that fit each consumer’s expectations and how to optimise their positions on AI platforms (Dawar, 2018). With regards to the first challenge, firms can either choose to pay for preferential positioning or either sharpen their differentiation by continually innovating offerings so that they are in line with consumers’ needs. Besides this, brands should appraise the worth of maintaining direct and personalised ties with consumers by leveraging on smart products that provide an efficient matching in the marketplace at any given time as well as compute retention metrics instantly for either each customers or only some of them (Dawar, 2018). How the world of marketing will change in view of smart speakers’ diffusion is still an open question, partially because it is not 24 given to know whether their use, either for individual purposes or as a true digital marketing medium, will be de facto exponential or liable to decay (Baldelli, 2019). However, if there is one thing widely known about voice searches, it is that they are almost completely conversational, meaning that longtail keywords will not be the only thing to take into account: content must be quickly accessible and the conversational style must be colloquial (Baldelli, 2019). It appears that if smart speakers’ producers want to have a stronger showing in the market, they’ll need to implement some serious differentiation strategies (Matthews, 2020).

1.4 Missing pieces of the smart speakers’ revolution puzzle Even if there is still widespread enthusiasm for IoT technologies, devices and companies, currently further steps must be taken to ensure that this sector develops and acquires the value it deserves (Tiot, 2020). Although expectations about IoT devices are really high, adoption levels and ROI are lower than many projections (Cognizant, 2019). It is believed that this circumstance is imputable to the failure of realizing promised benefits or expected human-centred value: IoT solutions are designed without paying too much attention to human experiences. Considering that IoT technologies have already been tested, new challenges related to their future being regard how they can help both people and companies in saving resources and profiting from them (Tiot, 2020): notably, it will be needed to switch from proof of concept to proof of value. The former means proving that a certain process, a certain device or a new technology actually works as it has been believed. The latter consists in demonstrating the real value of a product or service. Smart products’ most relevant value resides in their power of dramatically cutting across and transcending the boundaries of market research, since they enable marketers to identify usage patterns in terms of time (when devices are used), space (where devices are used) and modalities (how devices are used) (Taylor, 2020). Moreover, individuals can have a wider control of their personal world in less time thanks to smart devices when they are well designed, otherwise they would simply add overwhelming complexity (Thompson, 2005). However, for all these benefits to take place, both for organizations and consumers, IoT technologies must first of all be successfully developed, adopted and used. In order for these goals to be achieved, some road must still be travelled. Indeed, although some categories are sustaining a mainstream consent (e.g. Amazon Echo and Google Home), some people still have difficulties in finding valuable reasons, beyond the novelty factor, for replacing their traditional appliances especially considering that they worry about introducing additional technological complication in their daily routines (Hoffman, 2018). To overcome such obstacles, marketers need to understand the ways in which human and machines can interact inside of a given home in a state of balance between ease of use and rule complexity, without imposing them from the top and instead capturing them from customers’ bottom discovery that takes place through usage. Therefore, the single device should be marketed and its related purchase should be stimulated not in virtue of what it is possible to accomplish trough it rather in virtue of the experience that may rise through human-machine interaction thanks to its usage (Hoffman, 2018). Namely, marketers need to find ways of nudging consumers to experiment and go a step further with IoT devices, in order to fostering early adoption, habitual use and retention. For these reasons, more empirical research 25 focused on user experience of the product is required. The possibility of connecting consumers to Internet through their voice is certainly not a novelty. Nevertheless, the development of devices that base their operation mainly on voice commands has in recent years provided an exceptional springboard for this interaction modality, which thus becomes an opportunity for suppliers of products and services to meet the tastes and desires of their customers, as well as to increase their target audience (22hbg, 2019). Taking advantage of this transformation becomes essential to avoid being overtaken by competitors, because voice technology opens a new path to corporate marketing, allowing to reach customers through a range of digital devices such as smart speakers (22hbg, 2019). Albeit smart speakers seem to be appealing to consumers, it has been shown that a fair percentage (30%) of owners do not use these devices at least once a week (Kinsella, 2019). For smart speakers to be adopted successfully it is necessary that besides being regarded as useful and usable, the interaction adequately satisfies consumers, which is strongly affected by task difficulty and task completion (Bogers, 2019). Since vocal agents like Amazon Alexa and Google Assistant will become the principal channel through which people will search for information, goods and services, they will be the primary transformation driver in how companies will connect with their customers (Dawar, 2018). Given that they can potentially provide an overwhelming number of choices to different requests with unprecedent convenience, both for simpler purchases and for more sophisticated ones, by learning consumers’ criteria and optimizing people’s trade-offs, they will represent the future battlefield of marketing (Dawar, 2018). Considering that currently smart objects adoption mainly takes place in niche segments of technologically trained individuals, it is urged to clearly deliver their value for the sake of reaching a broader range of users (Hoffman, 2018). This is partially due to the fragmented marketing approaches embraced until now, which have been mainly focused on selling individual products instead of an experience. As an alternative, people should be encouraged in experimenting the use of IoT devices so as to create the experience they actually seek and integrate it into daily routines. Specifically, on one side managers have to better understand individual usage behaviour, construed as for what task, in which way and to what extent a smart object might be applied (Aunkofer, 2018) (Pavlou, 2018). On the other, they need to better personalize the interaction with these devices by fine-tuning emotional and situation-specific experiences. Indeed, if customers will endure the real value of IoT technologies and feel at the core of products’ way to relate to humans, several types of smart products have good chances to replace classical ones by 2030 (Aunkofer, 2018). Specifically, virtual assistants represent a modern way of both interacting with other smart devices and modifying people’s information seeking behaviour. Market forecasts have shown that, because of their natural and intuitive interaction modality, by 2022 smart speakers’ market will reach the value of about 5.5$ billion although the dropout rate will be quite high with about 70% of users stopping using them (Zhao, 2018). As these IoT devices are spreading in popularity, more research about their design, development and interface evaluation is urged (Taylor, 2020), especially because consumers exhibit avoidance behaviour towards them. Furthermore, when this last-mentioned circumstance does not apply human interaction is still irregular, sporadic and constrained to basic simple tasks (e.g. search queries) (Dubiel, 2018): even though AI-based voice assistant are extensively 26 purchased, people do not intend to use them in some cases. In such a perspective, it is urged to evaluate peculiar features, like the quality of interaction, of these devices besides the more traditional ones of technology adoption models (Nasirian, 2017). Thus it is pivotal to clarify: • what are the reasons why reaching the maximum technical possibilities of voice assistance services has not happened yet • what are the steps missed to complete this path • what needs to be developed to overcome users' reservations about digital assistants

The answers can be found in the empathic gap between human beings and artificial intelligence, that it is necessary to bridge. Such an assignment must be performed without falling into poorly construed, unpleasant and frustrating comparisons like the ones early approaches with machine learning-based interfaces have suffered with human beings relationships (Reply, 2020). In seeking to capture interest and therefore be accepted by its audience, a vocal assistant should possess a real pinch of human moods so that it could smoothly and unconditionally become a key element of an individual’s daily routine (Reply, 2020). In view of the fact that best ranked companies in the Global Empathy Index are able to at least double the value of the worst ranked ones, competitive advantage can be built on consciously developed empathy, delivered to customers by firms through conversational agents (Haptik, 2020). In other words, brands should be able to understand the turbulence of emotions customers are going through and communicate coherently with them, making them feel comfortable. This explains why empathy must be regarded as one of the keystones of conversation design and as the mantra needed to avoid robotic soundings during human-machine interactions. The reasons of such an imperative reside in the consequent chances to: • enhance customer experience by means of gathered data • establish better customer relationships • increase customers’ likeability, loyalty and word of mouth towards a brand

In order to successfully capitalize on smart speakers, organizations have to understand their strengths, their best use cases, the related opportunities and what is needed for them to be adopted with confidence. Working for rapidly enhance adoption rates of these IoT devices is one of the current top objectives of businesses, not only because they enable to reach an increasing number of consumers but also because a significant shopping experience completed with a smart speaker can mean deeper loyalty, greater trust, more intense word of mouth and more money spent for a brand (Rzepka, 2020). Albeit vocal assistants have been growing in popularity and commonplace in commercial market, they have been experiencing many usability issues that still exists as shown by recent research (Murad, 2019). Probably on account of their interaction modality, their wide adoption has been prevented. On the one hand, compared to text, speech is more intuitive and requires less mental workload, allowing more personal interactions and fostering warmer user attitudes especially in quite simple task. On the other, for high complex ones it is still not the preferred medium. People expect companies to refine smart speakers’ capabilities according to specific use cases, which could be supported by IoT 27 products’ gathered usage data. In order for users to easily activate any action through these smart devices, what they need is a good interface through which they can actively control IoT devices both individually and as part of a system, interact with them and get useful responses (Engineering, 2018). Albeit not so easy to translate into reality, consumers constantly need to experience simplicity, enjoyment and satisfaction while using smart objects. Additionally, discovering the actual use that make these technologies truly indispensable is the next big thing about them. For this to happen, a tangible analytical approach, deeply anchored in rigorous research into actual human wants, to product conception should be embraced (Cognizant, 2019). Indeed, such smart technological solutions are bound to become a thorough success only when human needs will represent the first piece of the internet of us puzzle. It has been predicted that 8 billion digital voice assistants will be used by 2023, despite the fact that many Human Computer Interaction researchers believe that right now they are still basic and boring in their essence (World, 2020). Probably, their future will be less boring if they accommodate more styles and flexibility of speech: when molding the details of a smart speaker, conversation quality should be delivered in a highly calibrated way. Nonetheless, the conversational aspect does not seem to capture the attention it actually deserves when speaking of voice assistants and their essence of extremely advantageous tools for businesses (Troisi, 2020). Besides performing tasks in a brilliant way, smart speakers should provide entertainment by introducing themselves as unique, engaging and interesting while chatting. Metaphorically speaking, it is not acceptable for such items to respond with anonymous and cold links, which is instead admissible for regular web search, as opposed to fashionable vocal answers. In this landscape, it is essential to find ways of preventing consumers from feeling a sense of detrimental frustration with technology: getting impatient with an interface, particularly if it behaves in a human way, may lead to anger, disappointment and even abandonment (Reply, 2020). The real challenge now sticks to keeping the smart speaker market attractive to consumers around the world, with must-have innovations that will convince them either to buy new models or to bring these devices into the homes of more and more families, either in the shape of entertainment items or managing hubs (22hbg, 2020). Forrester’s analysts believe that it could be challenging to persuade users to do more complex tasks than checking the weather with their smart speakers (Matthews, 2020). If people mainly use voice-enabled assistants to get basic questions answered, it may be difficult to convince them they need smart speakers that take care of more advanced needs. On the contrary, for the market to continue to grow it is ineluctable to focus on applications that go beyond the simple use for streaming music or controlling weather forecasts (Freddi, 2018). The biggest barrier to overcome, for further and definitive market growth to take hold, consists in convincing users of voice recognition’s aptitude to be more useful than expected, more often and for more applications (Freddi, 2018). The growing popularity of voice assistants offers a chance for brands to enter the homes of target audiences and become part of their daily lives in real time (Eldeman, 2019). The real revolution will come in the next years as soon as the use of smart speakers increases (Eldeman, 2019). The translation of a tone, which in many companies is an integral part of brand identity, into a sound becomes necessary. Brands’ voices must be consciously chosen, staying is in line with the company's values and remaining in the mind of smart speakers’ segments of interest (Eldeman, 28

2019). In a world of voice assistants, an organization and its voice representation must assist the target audience and ensure recognition and trust without being too servile. What truly matters is to differentiate, ensuring an artificially intelligent design with a human quality: in this a strong connection with consumers might be established (Eldeman, 2019).

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CHAPTER TWO

2.1 Smart Objects: building blocks of the Internet of Things Internet of Things is an evolving umbrella term (Ziegeldorf, 2014), firstly introduced to society by Ashton (1998) in a presentation about RFID and supply chain (Pinochet, 2018), that encloses the interrelation of physical objects with concepts like constant connectivity and remote-control ability. Certainly, IoT has been partially inspired by the success of RFID technology, widely used to track objects, but afterwards it has moved on a wider scenario characterised by a higher flexibility embodied in proactive objects able to perceive, memorize, interpret and act (Giordano, 2014). Specifically, it refers to dynamic systems of interconnected items able of playing the role of active co-characters in business, logistics, information and social processes thanks to their peculiarity of being “smart” (Fortino, 2017). This feature can be practically explained as IoT systems ability of (Sundmaeker, 2010): • interacting and communicating both among themselves and with the environment, while reacting autonomously to it • influencing the physical reality through processes that prompt actions and create services

Since research in this field is still in its infancy, there is not a strict definition of IoT. Nonetheless, leveraging on European Commission report “Internet of things in 2020 road map for the future” (Working Group RFID of the ETP EPOSS, 2008), it may be asserted that it consists of ubiquitously interconnected objects uniquely addressable and based on standard communication protocols. As such, IoT can be considered as a further step into the Internet Evolution that we are currently experiencing, which has the potential to create a better world for human beings by finding right solutions for satisfying their needs without explicit instructions (Figure 2: Evolution of the Internet in five phases). IoT is going to build a world where physical objects are smoothly integrated into information networks in order to offer advanced and intelligent services (Yan, 2014). Indeed, it molds new forms of linkages between people and objects at any time and place, ideally using any path and service, and builds a global network infrastructure where integrated objects have not only physical attributes but also virtual personality and intelligence (Figure 3: Definition of the Internet of Things).

Figure 2: Evolution of the Internet in five phases (Perera C., 2014) Figure 3: Definition of the Internet of Things (Perera, 2013)

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Thus, IoT is making even vaguer the traditional perspective of inanimate things, which are undoubtedly running into a shift in their capabilities switching from learning to do to learning to think, perceive, sense and respond (Perera, 2013). Practically speaking, in the IoT, real world and virtual one merge with the aim of providing customers with brand new experiences when they approach the usage of its building blocks: the so called “Smart Objects”, widgets connected to the Internet, capable of manage information (García, 2017) and augmented with sensing-processing-actuating skills (Fortino, 2012). Hence, Smart Objects and IoT are two notions that walk together and complement each other: the former can expand their intelligence to unprecedent limits not only by virtue of their inner characteristics but also of network communication protocols able of connecting them ubiquitously (García, 2017). Jointly they shape an infrastructure that enables extraordinary product capabilities (Porter, 2015): • goods can help to generate previously inaccessible information about their usage, by both monitoring and reporting on their own performance and surrounding environment • users can manage complex operations through various remote-access options • the combination of data management and remote-control technology provides new optimisation opportunities.

Since the adjective “smart” is generally used to describe a person’s ability to make clever decisions for herself in virtue of all the available information, in the IoT context it is adopted to transfer for analogy its meaning to physical objects (López, 2011). In this case, the intelligence comes from augmentation of computing skills, regardless of whether it is perceived in terms of awareness or autonomy of action (Kawsar, 2009). Actually, the difference between smart objects can be traced back to three aspects, namely their abilities of (Giordano, 2014): understanding events and human activities that they supervise in the physical world (i.e. Awareness), abstract programming (i.e. Representation) and conversing with users in terms of inputs, outputs and feedbacks (i.e. Interaction). Even if they can differ a lot from one another, they share similarities on three dimensions, that represent qualities of their intelligence (Figure 4: Meyer’s classification of Smart Objects Intelligence) (Meyer, 2009): 1. Level of intelligence → how smart an object can be, in ascending order, given its capability of: • managing gathered information (information handling) • notifying when out of the ordinary events occur (notification of the problem) • taking decisions autonomously (decision making) 2. Location of the intelligence → where an object’s intelligence resides, whether it depends on: • an external agent, like a network or a server (intelligence through the network) • the object itself, since it is able of processing information on its own (intelligence in the object) • both external and internal intelligences (combined intelligence)

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3. Aggregation level of the intelligence → what are the levels of aggregation and consequent grades of indivisibility or independency among components of a smart object: • components must not be independent, since they contribute to shape the entirety of an object able of handling information, notifications and decisions (intelligence in the item) • components may be separated, since their removal does not irreversibly affect the functionality of a smart object (intelligence in the container) • components may be independent but for some tasks they urge to work as a whole (distributed intelligence)

Figure 4: Meyer’s classification of Smart Objects Intelligence (Meyer, 2009)

Actually, smart objects can be viewed as agents equipped with sensors that provide them with autonomy, social ability and responsiveness: features required to perform specific tasks for users, to react to stimuli coming from the surroundings and to communicate both with other electronic devices and human beings (Fortino, 2012). Previous research has postulated a model (I-S-A-D-N) which highlights five main characteristics that variously combine and form a smart object (López, 2011): • unique Identity and storage capability of any relevant data (each smart object has its own digital presence, identifiable through a unique ID) • Sensing of its physical condition and surrounding environment • ability to send Actuation commands to other devices • Decision Making on the basis of available information • Networking, which stands for the ability of exploiting wired or wireless communications to improve own functionalities In addition, it has been assessed that smart objects possess other peculiar features (Kawsar, 2009): • Self-Awareness: they are able of knowing their operational and situational state • Sociality: they are able of communicating with other smart objects and computing entities • Autonomy: they are able of carrying out either operational (e.g. switching on and off) or complex actions (e.g. self-organizing) depending both on their typology and environment

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• State-fulness: they are able of maintaining a local memory to manage their states. Accordingly, these devices may be judged as a revolutionary turn in the original product design because of their smart and connectivity components: sensors that perceive the state of the physical reality, actuators that enable actions and network communication (Wi-Fi, Bluetooth or RFID) (Mani, 2017). Depending on whether they perform functions individually or collectively, smart objects can be distinguished into two main different categories (Kawsar, 2009). The former contains devices capable of perceiving, reasoning and acting independently from any infrastructure (i.e. stand-alone smart objects). Instead, the latter contains devices that share their own self-awareness with the purpose of creating a cooperative ecosystem (i.e. cooperative smart objects). Likewise, there exists also a third hybrid category which includes objects unable of working individually (i.e. infra-structured smart objects). Regardless of the possible IoT fields of application (e.g. wearables, smart homes, smart cities, industrial automation), connected objects are always linked to the same type of architecture: a local environment where they communicate through wireless links for the purpose of interacting with each other and with users (Dorsemaine, 2015). Furthermore, they embed the potentiality of extending the existing human–application interaction to the point of enabling people and objects to be almost constantly connected and exchange information (Lu, 2018). Given their interoperability with users, some product specifications are precisely devoted to clarifying the terms of the direct interaction with them so that their employment can be handily accomplished: consumers can work together with smart objects either in an active (e.g. pushing buttons), passive (e.g. communication takes place through voice, screen, lights, vibration) or hybrid way (Dorsemaine, 2015). Moreover, the behaviour of a smart object is strictly dependent on the context in which it operates because, pursuant to information exchange, it dynamically adapts to the physical environment around it (Siegemund, 2004). Notwithstanding the nature of its responses, what is especially relevant is that it is always guided by a simultaneous linkage with the real world and the virtual one, due to its duality of physical and digital entity (Giordano, 2014). For this reason, smart objects should not be evaluated only as various IoT technical declensions, but also as human-centred interactive tools developed to help people carry out tasks day-by-day. This implies that their design patterns should go beyond hardware and software specifications, which are obviously ineluctable, to embrace in addition interactive input and output capabilities as well as social aspects (Kortuem, 2009). From people’s perspective, IoT can have a huge effect on the ways they face consumption because it extends the opportunities for multiple interactions with devices from which experiences arise (Balaji, 2017).

2.1.2 Drivers and Barriers of IoT adoption Even if IoT technologies seem to represent an open door toward a smoother lifestyle, consumers’ resistance to them is a major current concern that needs to be understood and solved in order to successfully accelerate the pace of smart objects adoption. Adoption linked to IoT solutions can be viewed as the first approach or attempt for acceptance of new technologies, which is affected by a variety of determinants: organizational, individual and environmental (Padyab, 2020). Within the complex process that leads to the adoption of 33 innovations, drivers and barriers can be related to all of its five concatenated steps that have been identified (Rogers, 2010): 1. Knowledge → occurs when an innovation becomes known for both its existence and functionality 2. Persuasion → occurs when a favourable or unfavourable attitude towards an innovation is molded in such a way that a potential end-user results prone to be persuaded of its value 3. Decision → occurs when activities of mental cogitation are undertaken in order to establish whether to adopt or reject an innovation 4. Implementation → occurs when the innovation is actually used 5. Confirmation → occurs when the decision about whether to adopt or reject an innovation is deemed correct

Resistance is construed in literature as “ a form of reaction or negative attitude to new products and services that triggers change or upset the status quo” (Mani, 2018). Undoubtedly, innovations might dramatically overturn people’s daily lifestyle starting from their established habits to the roots of their deepest beliefs (Mani, 2018) and this is especially true in presence of product features (connectivity, intelligence and ubiquity) that have the potential to lay the groundwork of consumers’ reticence (Figure 5: Potential sources of resistance to smart products) (Mani, 2017).

Figure 5: Potential sources of resistance to smart products (Mani Z. &., 2017)

With regards to this circumstance, status quo bias theory (Samuelson, 1988) clearly displays that humans are almost naturally predisposed to turn down the acceptance of any novelty that, from their viewpoint, could potentially generate more losses than gains. Certainly, individuals feel choked by difficulty when they have to learn how to adopt innovations and, since they tend to be strongly attached to their routines, they easily experience a profound sense of unsatisfaction (Heidenreich, 2013). Consistent with Ram’s perspective (Ram, 1987), adoption and resistance coexist in the life cycle of an innovation and definitely the first can be achieved only overcoming the second one. Since quite often and not by chance innovations fail to become commercial successes (Heidenreich, 2013), primarily because of human hostility to transformations (Claudy, 2015), literature has focused on analysing which factors might represent a source of acceptance or refusal in this

34 specific context. Indeed, it is crucial to determine the obstacles to the diffusion and acceptance of technological innovations like IoT devices (Wiedmann, 2011), because of the urgency of helping organisations to either avoid or at least reduce the probability of rejection and failure. Previous research (Heidenreich, 2013; Ram, 1989) has shown that people may be reluctant to use innovations because of both functional (product characteristics: usefulness, novelty, price, device intrusiveness) and psychological (consumer characteristics: self-efficacy, dependence, privacy concerns) barriers (Mani, 2017). The former arise because innovations pose potential radical changes related to usage, value and risk, meaning that users would be willing to accept them only if they are able to significantly redress habits, provide substantial economic benefits and reduce risks (economic, physical, performance and social) compared to the status quo (Ram, 1989). Whereas, the latter, which are related to daily routines and the image of innovation in contrast with tradition, arise when consumers’ prior beliefs are threatened. Moreover, consumers seem to deny the utility of a smart object, perceived as non-essential or gadgetry, when its image and their ones walk in opposite directions (Mani, 2018; O'Cass, 2008; Hosany, 2012). Mani and Chouk (Mani, 2018) proposed an extension of Ram and Seth’s previous theory, adding to functional and psychological barriers three more types: ideological, individual and technological vulnerability. Particularly, the last one is regarded as a combined state of dependence and anxiety, caused both by the growing importance of technology in society and the sense of unpreparedness in using it. Furthermore, consumers may act skeptically towards innovations because beforehand, in virtue of their prior beliefs and values, they do not trust the promised capabilities mentioned by commercial sources (Banikema, 2014). Assuming that both drivers and barriers are simply two sides of the same coin, another distinction in the field of IoT acceptance is given by the VICINITY Project (Mynzhasova, 2017), which divide technical aspects from non-technical ones. The first group of hurdles encloses the need for finding solutions that may simplify and secure not only the access but also the intelligibility to what is intended as a complex environment: end-to-end encryption, cloud technologies, a general framework for transparent users’ consent and ad hoc front-end interfaces. The second one, instead, aims special attention to the importance of leveraging on incentives that may increase trustworthiness and consequently expand the chances of citizens’ first contact with novel solutions: promotional offers, security certifications, transparency and a common framework for accessing information. Trying to combine findings of the studies mentioned so far, it is possible to get a fairly complete list of the main obstacles to the adoption of smart objects, taken into account in literature until now. Privacy, trust and intrusiveness concerns → are chiefly related to data processing by both manufacturers and providers, because they might give access to information that people prefer to keep to themselves (Mani, 2018; Padyab, 2020). Besides that, these barriers are fed with a deep fear of easily making mistakes when consumers feel to have little control on some smart objects abilities, especially when they represent a “big leap” (Fraedrich, 2016). These aspects, exacerbated by lack of awareness, often push individuals to believe that IoT devices are not completely secure and can be easily misused. User interfaces, physical and aesthetics design → pertain to both the way in which information is disclosed to users and the way smart objects aesthetics can generate such strong appeal to lead to their adoption (Padyab, 35

2020). Specifically, with regards to the first case, there is a tendency to suggest that users’ cognitive overload, caused by a flow of useless and inaccurate information or by a mechanical and complicated lexicon, should be avoided. Whilst, with regards to the second one, it is believed that not only external design specifications like shape, material and colour (Jung, 2016) but also software-related ones like interaction style might have a huge impact on IoT adoption tendencies (Padyab, 2020). Indeed, well designed interfaces (Adapa, 2018), especially user-friendly ones, boost ease of use and users’ experience (Pustiek, 2015). Complexity → it represents the “degree to which an innovation is perceived as difficult to understand and use” (Rogers, 2010). In other words, end-users active participation in managing smart objects could be profoundly affected when they experience a higher level of difficulty compared to traditional products or older IoT devices handling (Padyab, 2020). It is consistently mitigated by usability, ease of use and UX aspects (Perera, 2014). Perceived usefulness → it is an excellent palliative to consumers’ resistance, as widely discussed in TAM and UTAUT literature (Mani, 2018). Perceived price → currently, it represents one of the main sources of resistance to smart objects adoption since customers seem reluctant to invest sensible amount of money for something that they do not know or either trust (Mani, 2018) (Padyab, 2020). Furthermore, they are worried about either additional costs (installation, maintenance, repair) or economic and informational benefits of IoT devices, as explained in a recent qualitative research (Touzani, 2018). Perceived value → consists in the sum of smart objects unequivocal advantages recognized by consumers with respect to other items that convey same or similar functions. It is associated to ease of use and functional elements of IoT devices (Padyab, 2020). Perceived novelty → when smart objects are perceived to be “different and unique” (Mani, 2017) (Wells, 2010), people are less hesitating to embrace their adoption.

2.1.3 Focus on Smart Speakers: Amazon Alexa and Google Home The modern contingency that sees smart speakers as objects that are imposing themselves in everyday life is part of a wider trend that more generally concerns voice computing and smart voice assistants. Specifically, Voice User Interface has been described as “a user interface that uses speech input through a speech recognizer and speech output through speech synthesis” (Schnelle-Walka, 2011). Namely, this kind of technology adopts voice as a control modality and, since it can be smoothly included in objects, it has become ubiquitous and growing (Corbett, 2016). Accordingly, voice recognition technology has come to be an integral part of a multiplicity of devices, such as smartphones, tablets, PCs, cars and home appliances, although its implementation is not yet close to extensively embracing its full potential (Tuzovic, 2018). Considering that voice-controlled devices exclusively leverage on voice-based commands, as their nature suggests, they largely facilitate individuals’ efforts. In fact, they do not only leave people free of moving hands and eyes while physically exploring the environment but also add more intuitiveness, speed and flexibility to their 36 experiences, thanks to their understanding of a large amount of qualitatively useful information (Pyae, 2018). Hence, such systems might mitigate the feeling of difficulty when faced with rather complex tasks, lower either time or energy expenditures and promote satisfaction (Oviatt, 2002). Recently, virtual vocal assistants have been introduced to the market in a broad assortment of hardware platforms and are quickly filling homes with their voices (Dawar, 2018; Hoy, 2018). Smart speakers, IoT devices that embed this artificial intelligence and hence “can be activated through voice commands” (Smith, 2018), have been designed with the clear purpose of supporting humans in daily routines by providing them information not already as text but as voice, integrated in a simulated conversation. As a matter of fact, Artificial Intelligence in the form of virtual assistants represents the heart and brain of smart speakers due to its understanding and execution of required actions. For instance, aiming attention at the most currently purchased smart speakers, Google Home devices are based on Google Assistant AI software whereas Amazon Echo ones are based on Alexa AI software. Although each assistant has its own unique features, the main difference between the two product ranges does not lie in what they let users do, which is more or less comparable, but rather in how they exhibit and execute the array of operational options available. Either way, this last facet is strongly influenced by the services Google Home and Amazon Echo rely on for certain activities (e.g. Google Assistant takes advantage of its own search engine when drafting proper answers, instead Alexa relies on Bing). Generally speaking, smart speakers act in response of queries expressed in natural language, without the users being requested to learn specific commands, and over time they tend to be more and more efficient on account of machine learning algorithms (Jones, 2018). Breakthroughs in computational linguistics (i.e. natural language processing) that led to the current state of the art are a result of (Hirschberg, 2015): • a vast increase in computing power • the availability of very large amounts of linguistic data • the development of highly successful machine learning (ML) methods • a much richer understanding of the structure of human language and its deployment in social contexts

Precisely because of their constant two-way conversational attitude, smart speakers are able to track users’ preferences, act on their behalf and provide a customized service (Kim, 2018). In order to promptly catch and adequately reply to audience’s requests, smart speakers continuously wait for listening to their specific wake words (e.g. “Alexa”, “Hey Google”) and then consistently react through either virtual, physical or audio feedbacks (Lau, 2018). This is made possible by the multiple embedded microphones which, as typical IoT sensors, have the ability to analyse the status of the surrounding environment, process information and perform coherent actions (Bugeja, 2016) by relying on the steady support of a Wi-Fi connection besides the software within. Therefore, being regularly connected to the Internet, unless turned off by users, smart speakers widely differ from older voice-activated objects insofar they can go beyond a smaller set of rough “built-in” questions and answers (Burkett, 2017; Hoy, 2018). Admittedly, once prompted with the wake-up call, they send user’s recorded voice to a server that feed them back with proper information. Afterwards, data is arranged by voice

37 assistants in order to coherently perform whatever has been previously asked by users. Furthermore, dialogue systems, regardless of their specific classification based on control dialogue methods (Finite State, Frame Based or Agent Based systems), generally share the same six structural components that define their whole essence (Kepuska, 2018): Speech Recognition (ASR), Spoken Language Understanding (SLU), Dialog Manager (DU), Natural Language Generation (NLG), Text to Speech Synthesis (TTS), Knowledge Base (Figure 6: The structure of general dialogue systems). Sketching their structure and covering it with meaning, smart speakers technically work in this way: firstly they perceive and interpret users’ utterances (Speech Recognition and Language Understanding), then they coherently select an appropriate action to perform (Dialog Manager) and finally deliver it to users through vocal explanation (Natural Language Generation and Speech Synthesis) (Cowan, 2017). In the last recent years, smart speakers available on the market have been outperforming both the popularity and the adoption rate of smartphones and tablets (Smith, 2018), in virtue of the variety of tasks that could be accomplished by simply pronouncing vocal commands (Hoy, 2018): • playing music or other multimedia content from connected services (e.g. Amazon, Google Play, Netflix, Spotify) • conducting a search (about weather, traffic, curiosities, recipes, real-time news) • setting alarms, managing calendar entries and reminders, making lists • making calls and sending messages or e-mails • placing e-commerce orders

Figure 6: The structure of general dialogue system (Kepuska, 2018)

Besides this, smart speakers’ abilities can be enriched with additional skills or tools, developed by third parties. These supplemental facets guarantee virtual assistants stay relevant (Jones, 2018) and enlarge the perspective of their possible implementations. In this way, smart speakers hold the concrete chance of becoming even more personalized and embedded in daily life, to the point of significantly increasing the ecosystem of connected and compatible products (Jones, 2018). Indeed, they are pervasively imposing themselves in home

38 environments all over the world because they “enable users to issue a broad set of commands on a wide range of topics” (Bentley, 2018). Such a circumstance strengthens the idea that vocal assistants might represent the new preferred kind of operating systems in the near future, implying that informational, practical and entertainment content will be yielded and accessed through voice (Jones, 2018). Hence, it is important to discover whether this perceived facility in carrying out duties might be partially influenced by the nature of the interaction with the voice assistant. Although only a small portion of smart speakers’ skills are actually exploited, what is especially relevant about them is that they can be the keystone for the spread of smart homes, not only because they are able to respond to human voices but also because they are getting even autonomously finer at interacting directly with other devices (Jones, 2018). Thus, the most fascinating feature of these IoT devices is their potential to support humans in household life (Hoy, 2018).

2.1.4 Smart Home: an application of Virtual Vocal Assistants within a familiar environment Creating technologies and services for smart homes that can progressively shape an efficient energy system in the near future has been elected as a duty of paramount importance by the European Commission as early as 2015 (Wilson, 2017). Smart speakers’ expansion represents a valuable means in the adoption of smart home systems, one of the highest ranked IoT applications that are growing steadily, albeit with room for improvement (Radhika, 2016). Indeed, smart speakers’ sales volumes, which are raising at a very fast pace, have been dragging smart home sector’s ones either straightforward or not. They represent a flywheel for the entire market, since consumers seem keen to use them to manage other smart items such as household appliances, boilers, thermostats, lights and burglar alarms 1. Actually, either Amazon Echo or Google Home can work as a hub connected to other directly or indirectly (i.e. using IFTTT as intermediary) compatible devices with the aim of controlling their functionality not just in terms of their individuality but as part of a whole (Lau, 2018) (Figure 7: Functions of smart speakers as home control hub for energy management) (Strengers, 2017)).

Figure 7: Functions of smart speakers as home control hub for energy management (Strengers Y. &., 2017)

1 https://www.corrierecomunicazioni.it/digital-economy/boom-del-mercato-della-smart-home-59-a-380-milioni-di-euro/

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Smart home has been widely described as the next digital disruptor that can deeply subvert time and energy consumptions, leveraging on simplification and streamlining (Strengers, 2017). In a schematic representation, these designed intelligent buildings can be depicted as assemblages of heterogeneous smart objects that constantly exchange information among themselves and with the outside world (Ricquebourg, 2006). Nonetheless, their overall value goes well beyond the sum of their single components and neither is imputable to the parts taken individually (Hoffman, 2015), since each of them can be easily added or removed at any given time. According to the assemblage theory, this happens thanks to the on-going interaction among devices. Interaction is most importantly what gives smart homes their identity: without it they would be simple collections of objects equipped with Artificial Intelligence. Admittedly, although properties, capacities and tendencies reside in each item, they do not by themselves qualify smart homes. Smart homes’ functionality and efficiency are defined by the interaction operating through paired capacities of several components, which, by mutually affecting each other, generate new skills of the assemblage throughout (Figure 8: Interactions operate through pairs of capacities). Moreover, as the number of objects involved in the interaction grows, so does the value consumers ascribe to the concept of smart home because they develop the capacity to feel a tangible sense of presence. As a result, these assemblages are dynamic and in constant nonlinear change (DeLanda, 2011).

Figure 8: Interactions operate through pairs of capacities (Hoffman D. L., 2015)

The idea of smart home has been defined in research and marketing literature in different ways over time, that can overall be grouped in two broad categories (Darby, 2018). The first one stresses the concept of home as a sum of communication network, sensors and appliances which provides services that carefully fulfil its inhabitants’ needs, thus placing a focus on the relationship between home and user (Balta-Ozkan, 2014). The second one instead is more generally linked to the capability of innovative building systems of producing, storing and consuming energy efficiently, hence leaving openly aside their connection with occupants (De Groote, 2017). Although they walk on parallel paths, these theoretical categorizations share the same direction toward the fundamental role of communication networks in linking subsystems with each other and in assuring both remote access and control to services provision (Darby, 2018). Therefore, a core element of smart homes

40 is certainly the application of uniquely addressable interconnected objects able of optimally scheduling energy consumptions (Stojkoska, 2017). However, it alone is not enough to describe throughout the concept of smart home, that over time has changed its very essence from a method of monitoring some electronic applications (e.g. lighting and heating mechanisms) to a system that incorporates every single interactional component within the home (Ricquebourg, 2006) and outlines a proper ecosystem (Hoffman, 2015). The result of this shift is that IoT intelligence in such an environment is not restricted to switching devices on and off but encompasses also an effort of supervising occupants’ activities for the sake of simplifying their livelihood in a fast-paced world (Strengers, 2017). Namely, generating people’s wellness profile is another key factor. This last aspect, that can be summarized as “convenience”, has always been a long-run concern in consumer marketing to the point of becoming synonymous of “lack of complication and easy lifestyle” (Vannini, 2014). Traditionally, home has always been portrayed as the centre of everyday life, a place where people’s routines and ideas are warmly hosted and mostly performed (Gullestad, 1984). Thus, the combination of humans’ identity, participation and engagement constitute a kernel part in household settings. Wanting to transpose this idea to modern times and enrich it with the potential of Artificial Intelligence, it is possible to assert that smart home solutions do not represent an exception in such sense, being collections “of dynamic nonlinear experiences that emerge from consumers’ interactions with devices that also interact with each other, all on a regular basis” (Hoffman, 2015). On that account, smart homes in addiction to usual reliability have the ambition to offer flexibility to their residents in the form of digital and connected consumerism (Gram- Hanssen, 2018; Strengers, 2017). Indeed, their final goal consists of promoting comfort, security, entertainment and effortless energy efficiency for individuals through a thoughtful management of technology and connections to the outside world (Strengers, 2017; Shilpa P., 2017). Ultimately, adding intelligence to home environment improves consumers’ quality of life.

2.2 Conceptual Framework Accounting that research about consumers’ adoption behavior of smart speakers is currently poor (Haug, 2020), it is pivotal to clarify how these devices can be increasingly integrated without giving the impression to threaten human nature (Russo, 2017). The theoretical roots can be borrowed from the Theory of Acceptance Model, since it has been largely ratified as a reliable model for analysing information technologies and smart services (Gao, 2019). However, users’ acceptance and consequent adoption of IoT could be affected by other factors beyond perceived ease of use and perceived usefulness, especially in case they lack necessary knowledge and skills. To our knowledge, there are few studies that analyse factors affecting intention to use smart speakers and that explore user interactions with them. IoT devices success can be modelled through a wider perspective that accounts for customers’ susceptible needs and reflects them in product features (Dominici, 2016). For instance, the design and development of technological devices should be particularly attentive to all those product specifications that go beyond the more technical and functional ones and may represent the leading ingredients of a fruitful subject-object interaction (Aurigi, 2005). This reasoning is 41 especially applicable to the specific case of smart objects, since, in virtue of their proactivity, they can be more readily perceived as anthropomorphic machines rather than cold ones (Wu, 2017). The spread of IoT devices has raised the compulsory question of how customers will be able to relate to an embedded smartness that significantly impacts on domestic scenarios both on a technical and emotional level, consequently reshaping their lives and behaviours. The answer might be found by focusing on aesthetic paradigms linked to relational communication, pleasurable experiences and perceptions (Russo, 2017). Accordingly, when dealing with smart objects it is urged to focus their relationship issues with humans on physical dynamics that define subject-object instrumental dialogue on an experiential frame (Pragmatism Aesthetics) (Spadafora, 2016). Human-agent interaction should be based on human needs as well as on contextual understanding in order for IoT technologies to be accepted and integrated within daily routines (Shin, 2017). In this context, the theory of aesthetics of interaction fits with the purpose of overcoming the questionable duality efficiency-aesthetics in the design field of interacting items, since, embracing Norman’s viewpoint, actually “attractive things work better” (Norman, 2002). Considering that it has been proved that different atmospheres (e.g. hedonic vs utilitarian usage environment) (Childers, 2001) and beliefs about strength of enjoyment (Wu, 2007) may alter perceived ease of use and perceived usefulness, it can be inferred that people would like to be exposed to a certain level of involvement while interacting with smart objects (Gao, 2014). Although how to create a situation in which users may feel as comfortable and amused as possible when using IoT devices is still an open question, exploiting interaction style might represent a valid way. Communication modalities and skills, which are the ways the system replays to user’s inputs in a sort of feedback loop (Figure 9: The Human- Machine interaction model based on feedback loop), are vital not only for making the interaction immediate and easily understandable but also for triggering individuals’ subsequent actions (Conversational Style) (Rosini, 2016). Generally, when communicating with audiences, smart objects can take on two distinct approaches: friend-like style and engineering-like style. Consequently, different impressions, on which individuals will rely the arrangement of their evaluations and reactions (Carli, 1989), disclose. Furthermore, the effect of interaction style intensifies eminently when individuals have little knowledge of smart objects. Customarily, friend-like style triggers customers’ sweet dispositions because they attribute to IoT devices not only affable and caring intentions but also authenticity and honesty, thus anthropomorphizing them. Most importantly, smart objects that contemplate friend-like interactions contribute to creating delightful experiences and warm relationships with users. Conversely, engineering-like interacting devices, despite conveying the idea of professionality, efficiency and sophistication, barely give rise to worth remembering experiences. Still, results of previous research regarding interaction style were merely related to variables like brand warmth and brand attachment (Wu, 2017).

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Figure 9: The Human-Machine interaction model based on feedback loop (Rosini, 2016)

Pondering the unique features of smart speakers, that deeply differ from other innovations, the existing technology adoption models represent a good basis to develop a dissertation about them although being insufficient alone and so requiring a more comprehensive approach (McLean, 2019). Smart speakers are able to influence and be influenced through interaction (Hoffman, 2018): undoubtedly interactive patterns are charming elements of IoT devices’ essence. For this reason, such objects should be developed going well beyond hardware and software paradigms to include fundamental social aspects. TAM affirms that consumers’ attitudes about a new technology are chiefly shaped by perceptions of easiness and usefulness. However, these perceptions are a result of people’s processing information about the product, which could be more or less tough and could be dilute in its difficulty through human resemblance (Anthropomorphism) (Goudey, 2016). Moreover, experience design in marketing mindset has become fundamental for building satisfaction, stable customer relationships, reliable word of mouth and competitive advantage (Murphy, 2019). This circumstance is perfectly in line with consumers’ current pursuits: seeking for experiences obtainable through products and services consumption rather than simply buying something (Neuhofer, 2015). With this mind, objects’ aesthetics should be judged not much for external qualities as it should for its unique capacity to generate new experiences quickly and innovatively (Folkmann, 2015). Keeping in mind that “traditional notion of aesthetics as narrowly associated with beauty is obsolete” (Folkmann, 2015), experience shaped through interaction is invested with a double value: utilitarian, given its role of eliciting functionality, and hedonic, given its role of causing aesthetic pleasure (Cila, 2015). Because there is little knowledge of what influences individuals in their decisions to purchase and use these IoT devices (McLean, 2019), this study combines the theoretical foundations of TAM with Pragmatist Aesthetics and Anthropomorphism emphasizing the role of Conversational Style in the adoption process of smart speakers.

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2.2.1 Theoretical roots of IoT acceptance: a review of Technology Acceptance Model Consumers’ reservation towards smart speakers can hinder the successful implementation of this technology either alone or as a hub in smart home systems, consequently resulting in a failure to capture all their potential benefits. One of the main implementing performance measure in this frame of reference is attaining the expected degree of usage of the devices, which represents the mirror of consumers’ acceptance of the enabled technology (Amoako-Gyampah, 2004). A wide variety of theoretical models related to technologies acceptance have been developed over the years. Among them, Technology Acceptance Model (TAM) results particularly relevant, powerful and well-known for the purpose of predicting and measuring adoption in the IoT field (tom Dieck, 2018). The aim of this model, introduced by Davis in 1989, was to enhance not only the understanding but also a method for assessing users' likelihood acceptance, apart from providing theoretical guidance for the implementation of new technological systems (Lunney, 2016; Liu, 2018). In its vital core, it advocates that the key to technology usage resides firstly in assessing its acceptance by investigating people’s future intentions about it. Hence, it struggles to provide guidelines for tracking the influence of external factors on beliefs, attitudes and intentions (Legris, 2003). When the factors accountable for molding one’s intentions are extrapolated, organizations can manipulate them to promote acceptance and increase use (Holden, 2010). TAM indicates that when a new technology is introduced to consumers, a variety of variables influence their judgements about how and when they will be using it. Going backwards to its origins, this influential framework is an adaptation of the Theory of Reasoned Action (TRA), according to which an actual behaviour is the final result of a complex process developed by rational decision makers that incorporates intentions determined both by subjective norms and by a person’s attitude towards the behaviour itself (Fishbein, 1975). In TRA, attitude is delineated as “an individual’s positive or negative feelings (evaluative affect) about performing the target behaviour” and comes from the evaluations of one own’s beliefs (Li, 2010). Even if the above-mentioned models share common elements (i.e. attitude toward using and behavioural intention to use) and measures, they diverge for three aspects (Legris, 2003; Chen, 2011; Dong, 2017): 1. TRA is a general theory of human behaviour, which is regarded as a function of personal attitudes and subjective norms, instead TAM is explicitly dedicated to information systems usage 2. TAM introduces two new constructs which can foretell an individual’s attitude concerning the use of an application 3. TAM does not consider subjective norms as determinants of intention, because Davis estimated that they had negligible effects on behavioural intentions However, not only assuming that decisions are based on rational self-interest motivations but also incorporating users’ attitudes and beliefs into the intention to adopt, TAM surmises that consumers would be willing to embrace a new technology if they believe that it is easy to use and useful for achieving personal purposes. Indeed, the degree to which the use of a particular technology is believed both to enhance one’s job or life performance (perceived usefulness) and to be easy and effortless (perceived ease of use) is of utmost value for determining users’ intention and successful acceptance, in that order (Toft, 2014; Lin, 2007).

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Specifically, perceived usefulness can be deemed as the extent to which an innovative technology is better than its precursors, reflecting relative advantage; whereas ease of use can be deemed as the extent to which an innovative technology is viewed as convenient or difficult to understand and use, reflecting relative complexity (Huang, 2015). Accordingly, these two benefits are the factors most importantly responsible for acceptance or rejection of an information technology. These beliefs are assumed to settle a consumer’s attitude towards adopting a technology, which becomes a real intention only if this circumstance is evaluated favourably (i.e. attitude toward adoption is positive). Then, once they led to individual behavioural intention, they push consumers to actual behaviour (Kapadia, 2016) (Figure 10: The Technology Acceptance Model (TAM)). In other words, a user’s attitude, regarded mainly as the product of perceived ease of use and perceived usefulness, towards using a technology is the primary driver of actual use (Liu, 2018). Moreover, the theory postulates that perceived ease of use is a direct determinant of perceived usefulness and that external variables (e.g. social influence and cognitive instrumental processes among others), impact the one and the other, extending the boundaries of acceptance (Venkatesh, 2000; Lin, 2007; Tsai, 2017).

Figure 10: The Technology Acceptance Model (TAM) (Davis, 1989)

Afterwards, Davis himself applied the Motivation Model to TAM for suggesting that human behaviour is influenced both by extrinsic and intrinsic motivations also in the information technology adoption and use. Specifically, perceived ease of use, perceived usefulness and subjective norms belong to the first group because they are “instrumental in achieving valued outcomes that are distinct from the activity itself, such as improved job performance, pay, or promotions” (Davis, 1992). Whereas, factors related to perceptions of pleasure and satisfaction, such as playfulness and enjoyment, belong to the second one because they encourage users to act “for no apparent reinforcement other than the process of performing the activity per se” (Davis, 1992). In the course of time, a plurality of studies has supported the importance of the two TAM major beliefs in assessing users’ intent and predicting real usage, either directly or indirectly. In fact, although TAM has been subjected to criticism, it serves as a useful theoretical groundwork given its consistency in a plethora of previous investigations in users’ technology acceptance (Charness, 2016) including mobile services, online shopping, car navigation systems, tourism industry, smart wearable devices and augmented reality among others (Evans, 2015; tom Dieck, 2018; Gao B. &., 2019). Furthermore, the preference for TAM as a research model to explain consumers’ acceptance of technology-related products is due to its clear ability to explain a

45 significant portion of the discrepancies between behavioural intention and actual behaviours, besides its high interpretability (Tsai, 2017) and generalizability (Lim, 2012). Albeit several models have been developed as an alternative to TAM (e.g. Theory of Planned Behaviour) or as an upgrade (e.g. Unified Theory of Acceptance and Use of Technology) in order to overcome its limitations, it is often employed because of its parsimony and robustness which help justify substantial variation by using only two antecedents (Plouffe, 2001). Nevertheless, individual responses to emerging technologies are likely to differ depending on the factors contributing to their acceptance, in the specific context within which they are encountered. Consequently, identifying additional variables for different research contexts is particularly relevant in order to take account of specific technological features that can extent or reduce the degree to which the combination of old and new constructs impacts the actual system use (tom Dieck, 2018; Evans, 2015; Chen, 2011). Indeed, the addition of such variables marginally aids the understanding of variance in the use of specific devices and simultaneously pinpoints prompted actions to strengthen adoption, by specifying what respectively influences perceived usefulness and perceive ease of use (Legris, 2003).

2.2.2 Leveraging on User Experience induced by Interaction to improve Smart Speakers’ adoption: Pragmatist Aesthetics Products are generally conceived responding to usability principle, such that they let users perform tasks and achieve goals in the most convenient way. This trend must be absolutely respected and shared, even if at the same time it needs to be enriched, especially with regards to smart objects, by working on the aesthetics of interactive experiences (Spadafora M. , 2015). Previous literature underlines that once utilitarian attributes have been accomplished, consumers search for hedonic qualities: the former satisfy functional and practical benefits, instead the latter concern aesthetic and experiential ones (Alex, 2012). In addition to instrumental elements, variables such as visual qualities and emotional experiences play a significant role in explaining why people choose certain systems over others (Thüring, 2007). Well-established knowledge in marketing as well as in design and social psychology underlines that aesthetic qualities of a product can influence consumers’ attitudes in an even deeper way than traditional operational usability and are a major determinant of its success (Hartmann, 2008). Not surprisingly, satisfaction that equally originates from functional and aesthetical assets may drive the great success of a product in the marketplace. Additionally, consumption has suffered a move from being the consequence of cherishing tangible possessions to be the outcome of sensing experiences, which is particularly topical in the case of smart speakers. Experience is a story that needs to be told by a proper narrator: in smart speakers’ adoption, interaction is what gives aesthetic narrative to the immateriality of human-agent relationship, thereby pushing its pragmatic realization (Pham, 2015). Although designing good, pleasurable and enjoyable interaction is crucial for thoughtful experiences the route for pursuing its successful creation is not so predictable. Since IoT devices incorporate digital technology, it is not possible to speak of static products, whose aesthetical perceptions are restricted to material properties. Even if smart objects are generally endowed with materiality, what makes them uniquely relevant is their

46 immateriality. Far from Industrial Modernism’s principle of “form follows function”, most of smart speakers are discreet items that rely less on appearance and more on functionality (Folkmann, 2015). Given that tangible qualities of smart objects can be deemed as a mere mediator that provides access to intangible ones, the bridge between these two different sides of the same coin and consumers is actually interaction. In fact, consumers cannot have direct access to all smart objects specifications but can only interact with them (Hoffman, 2017). In the marketing perspective, interaction design adds attractiveness and thereby value to products. Moreover, being also a dimension of user experience of the product, it can enhance engagement by eliciting both emotions and cogitation: interaction can reinforce individuals’ psychological and behavioural positive responses in the adoption process because they may perceive greater usability especially when its aesthetics is well structured (Balaji, 2017). Hence, aesthetics of information systems is by no means marginal both to the performance and the usage of IoT technologies (Nasirian, 2017). The main purpose of applying aesthetics to interactive objects is to overcome the efficiency paradigm as the one and only landmark of interest, so as to embrace the broader concept that utilitarian and hedonic elements are actually strongly correlated (Spadafora, 2016). Therefore, taking into account aesthetic aspects in the design of interaction is becoming even more relevant for the purpose of modelling assumptions associated to users’ behaviour when they employ technological systems. Aesthetics of interaction is strictly related to expressions generated by human-object interaction, refers to IoT technology's overall appealing impressiveness and is closely linked to the emotional dimensions of customers’ attraction. This evocative process, that originates both from the mechanism or archetype of interaction and the affordances available to the user because of device’s own nature, may lead to opinions and attitudes shifts. Sticking to this, it is important to explore ways of shaping significant experiences through smart speakers’ interactive specifications that could generate possible ways of engagement as a result of a compromise between two equally valid aspects: the functional and the aesthetical ones. Notably, interaction is the medium through which a smart object can elicit an aesthetic experience, hence gratifying a user’s senses and triggering her emotions, by means of the way it expresses its essence. Thus, aesthetics should be at the core of these IoT devices so as to lay the groundwork of remarkable and positive experiences through principles of good interaction (Hassenzahl, 2015). Accordingly, Pragmatist Aesthetics has been embraced in the current study as a promising theoretical path to follow since it clearly stresses the importance of focusing on aesthetics of use rather than aesthetics of appearance (Petersen, 2004). Being well beyond traditional products appearance, Pragmatist Aesthetics draws its attention on performance and impression of interaction related to emotional and experiential qualities (Petersen, 2008). Hence, aesthetics here is not mainly linked to immediate visual impressions but to the results obtainable when individuals use their body, intellect and senses to engage to interactive systems. According to pragmatist stream, aesthetics springs up from surprise and provocation, offering new insights of the world as a gift and encouraging people to view encountered smart objects from a different perspective. The resulting aesthetic pleasure is the reflection of physical engagement with the product, in virtue of which the boundaries between aesthetics of interaction and pleasurable user experience become blurred. Consistently with pragmatist notion, the elicitation of sensorial, mental and emotional 47 dimensions as well as social standards forms the aesthetic experience in its whole. Aesthetics of interaction is not limited to creating involvement, but it stretches to the point of promoting users’ expression through bodily experiences as well as symbolic representations. In a nutshell, the aesthetics of smart speakers focuses on the multiple ways in which a user-product interaction can be pleasurable. Considering that people are looking for products that assure more than functionality only, design methodology should enable meaningful experiences in ecosystems’ propositions capable of reflecting, at least partially, their personalities and preferences (van Kollenburg, 2015). Aesthetic experiences molded through interaction intensely enhance cognitive and emotional spheres to the point of bringing about major changes in mankind complexity (D'Angelo, 2012). Moreover, they can be regarded as the balancing outcomes of two main opposite and complementary features of human processing, which represents a source of pressure in product usage: 1. need for safety, that is satisfied when the choices made facilitate perceptual understanding and efficient processing 2. need for accomplishment, that is satisfied when the choices made facilitate extending capabilities and prompt exploratory behaviour

By offering solutions for satisfying human needs, IoT technology has a great impact on the simplification of everyday life (Özcan, 2015). Mostly for this reason, previous research has focused on investigating qualities and meanings of human-technology interaction in order to individuate solutions for increasing desirability of technological devices by eliciting cognitive and emotional reactions during user experience (Mariani, 2015). In view of the fact that smart objects are proactive and context-aware items, experiences should not be depicted as static over time but as constantly evolving dialogues between two parties (Spadafora, 2015), finally becoming relevant and coherent (Cila, 2015). As such, balancing efficiency with aesthetic value in IoT devices design has been promoted as the right solution to provide a desirable experience when they are used (Spadafora, 2015). Endowed design specifications enrich both the external and internal significance of a product, increasing accordingly and hopefully its acceptance. In this view, smart objects should be not only useful but also attractive in a way that empowers their designed skills through affect (Norman, 2002). In the specific case of smart speakers, aesthetic quality relates on intangible formal product specifications like mechanism of interaction, cause-effect dynamics and styles of dialogue. Admittedly, voice interaction, as an efficient and easy to use input modality, is set to play an important role in the enrichment of user experience (Rodrigues, 2019). Notwithstanding, Pragmatist Aesthetics, which is tightly coupled to the experience users live in virtue of the dynamic aspects of the dialogue with the object, lays the foundations of smart speakers’ pleasurable liveliness in the blend of intangible formal attributes of interaction (Spadafora, 2016). Therefore, aesthetic discussion in smart speakers’ field is about the feasible ways of intending quality in interaction design, focusing on the kind of experience that may be consequently produced through vocal cues, and its potential to find new ways of investigating adoption (Pillan, 2015). It must be noted that the means of a dynamic interaction is led to life by the voice of the embedded Vocal Assistant and the way in which it reaches

48 out for people’s engagement relies also on its design. As a result, the way a smart speaker behaviour is designed and communicated becomes extremely important in determining the overall user experience. In this view, the aesthetic approaches that support the genesis of a valuable experience, drawn exploiting certain interactive attributes instead of others, can be summarised in (Spadafora, 2015; Spadafora, 2016): • What-level → the function of the object • How-level → the concrete way interaction is arranged to put functionality into action (e.g. vocal commands) • Why-level → what makes adoption meaningful to people (e.g. feeling close, being stimulated)

According to this method, the How-level consists of the interaction design, while the Why-level represents the experiential level of the interaction (Spadafora, 2016). Although interaction is the fuel of experience, which in turn evokes emotion, it has been analysed under the lens of the applicable design techniques to precisely configure it, leaving aside the inspection of its conceivable effects on users’ acceptance of smart devices. Nevertheless, given the fact that smart objects tend to assure functionality and usability for granted, what especially triggers users’ intentions to adopt is the possibility to fulfil a deeper level of appreciation through experience, which is the soil where emotional fondness and pleasure grow fruitfully (Tung, 2015). In former times, the majority of research streams in people’s interaction with technology was narrowly focused on usefulness and usability’s declensions. Nowadays, especially with reference to IoT devices, a broader perspective should be embraced by compounding the interplay of perceived instrumental qualities (e.g. effectiveness, efficiency and satisfaction) and perceived non-instrumental qualities (e.g. aesthetics and experience). The deepest motivations of such a direction are explained by the fact that technical systems’ appreciation is related to three main facets (Thüring, 2007): • Instrumentality → pragmatic features (usefulness and usability) • Symbolism → associations elicited by products in consumers’ minds • Aesthetics → sensory experiences fitting individual goals and preferences In a nutshell, the influence of usability and aesthetics should be analysed in a common design. Thus, a question that needs to be answered regards not only the attributes that prompt consumers’ perceptions of IoT devices being easy, useful and safe but also how they can alter attitudes and intentions through an experience component. Actually, there are no publications in marketing literature that study the combination of TAM constructs with aesthetics of interaction from users’ perspective and offer an integrated framework of smart objects acceptance (Hsu, 2016). For this reason, it is necessary to understand how perceptions of ease of use, usefulness and risks can be improved through interaction specifications for the sake of evoking individuals’ intention to use IoT technologies (Gao, 2014). Previous literature has mainly focused on analysing the role of aesthetics in the creation of experiences linked to concepts such as reflection, harmony, novelty, unity and the relationship between outer appearance and inner function. Our concern regards the possibilities enabled by the use of smart speakers with different declinations of voices, here referred to as "conversational styles", to

49 modulate the outcome of the relationship between TAM’s precursors and attitude. Indeed, voice can produce an aesthetic effect not only in the way people understand a smart speaker and in challenging the pertaining habits of use but also in the way it modifies its own character during interaction for the purpose of eliciting its own functionality. Even if design research and development cannot stop after the adoption of a proposition, due to smart speakers’ evolving behaviour not relegable to stillness (Spadafora, 2016), the relevant issue for this particular study concerns primarily how to deal with initial introductions. Specifically, we want to analyse smart speakers’ adoption in smart home domains where actions related to work, learning and leisure take place very often and require elements that go beyond the mere transparency and efficiency matters to properly encounter human needs and desires. In this context, user experience is the final result of a consumer interacting with a smart speaker: as such, it is deeply modulated by object elements (e.g. interaction mode) that can affect users’ evaluation and in turn it influences attitude toward behaviour and purchase intention (Gao, 2019; Novielli, 2010).

2.2.3 Leveraging on User Experience induced by Interaction to improve Smart Speakers’ adoption: Anthropomorphic cues It is becoming a prevalent trend to furnish smart objects with anthropomorphic attributes such as forms, appearances, movements and personalities so that consumers can easily add clues related to functionality to those related to personal and social significance. Indeed, soaking a smart object with anthropomorphism let customers harvest a positive and affective experience (Marek, 2017), thus explaining why humanness and friendliness of these items are often advertised and promoted with such an emphasis to describe them as “digital buddies” (Han, 2018). According to CASA (i.e. Computers Are Social Actors) paradigm, centred on the media equation theory “media = real life”, during interactions users treat computers as if they were humans, reacting socially to them (Edwards, 2013; Seeger, 2017). Substantially, since their responses are social and natural, people consider technological devices as teammates in tasks completion and evaluate their value on the basis of their vocal qualities (Lee, 2010). For instance, although smart speakers represent good companions in everyday life journey, their operation is based on advanced algorithms and engineering principles that are too complex to be fully understood by the average customer thus generating a subconscious psychological distance with her (Bahlenberg, 2019). For this reason, anthropomorphism has been depicted as a suitable instrument for making people feel closer and more comfortable while interacting with IoT devices when there is a lack of logical knowledge. Effectively, the field of human-robot interaction focuses mainly on designing artificial intelligence applications so that they can be viewed as human, both under the cognitive and affective lenses, since it is reasonable to believe that IoT devices will be more likely accepted and used in this way (Novak, 2019). Anthropomorphism is a human innate tendency that consists of attributing human-like qualities to non-human agents for the purpose of making easier the interpretation of activities carried out by particular items and decreasing the sense of discomfort experienced because of the unknown (Goudey, 2016; Pfeuffer,

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2019). Apart from being an innate ability that people start discovering during infancy and use frequently throughout their lifetime (Yuan, 2017), it constitutes a trend largely adopted in marketing in order to push customers to humanize brands and products (van Esch, 2019; Cao, 2019) or to refine advertising and offerings’ efficacy (Wang, 2019; Laksmidewi, 2017). Indeed, anthropomorphism, as a second-level factor compared to other traditional marketplace variables (e.g. purchase price, frequency of use, self-acquisition), can significantly influence personal value attributed to products (Hart, 2013). Moreover, previous marketing research has stressed the importance of anthropomorphizing offerings to boost consumers’ evaluative responses through feelings of sympathy, familiarity and comfort: the underlying hypothesis is that the more a product evokes an individual, the better customers like it and the higher its long-term sales will be (Goudey, 2016; Herak, 2020). Indeed, when anthropomorphism is successfully molded by marketers it may support the value of a product and consumers’ willingness to pay for it (Hart, 2013), since it is readily perceived as a sentient being (Schweitzer, 2019). Its arousal is more easily provoked by anthropomorphic cues (e.g. auditory aspects of the interaction) embedded in technological devices that may draw users’ conclusions in terms of familiarity towards them. In addition, it is amplified by the naturalness as well as the contextual sensitiveness of smart objects’ responses during interaction (Schweitzer, 2019) (van Pinxteren, 2019). Specifically, this last facet contributes to create trustworthy experiences which in turn increase human-likeness and positively influence users’ affect due to social or emotional connection. Ascribing human-likeness, either by referring to products and brands using personal pronouns (i.e. you, she, he instead of it) or including perceivable anthropomorphic cues (e.g. voice or facial elements) that convey dominance or friendliness, amplifies the sense of connectedness with smart objects (Kang, 2020; Novak, 2019). Put differently, it satisfies users’ desire of control and reduces psychological tension during interaction, making virtual agents more acceptable and more likely to be adopted (Kang, 2020; Novak, 2019). Furthermore, what really matters in the eyes of consumers is not so much that objects resemble mankind as that they share the same goals, which anyway can be metaphorically explained and understood through anthropomorphism (Novak, 2019). An explanation of individuals’ deepest motivations and timings in anthropomorphizing non-human entities has been summarized by the three-factor theory (Epley, 2007), according to which the main drivers of such an acting should be found in one cognitive factor, namely elicited agent knowledge, and two motivational ones, respectively effectance and sociality. Tendentially, people employ knowledge about humans as a basis to infer circumstances with which they are not acquainted because of its fast accessibility and familiarity. This reasoning is inductively applied to the process of understanding uncommon objects, thus portraying the abovementioned elicited agent knowledge. Furthermore, since people firmly crave to efficiently interact with non-human entities, they spoil anthropomorphism as an optimal way not only to clarify the mysterious nature of complex entities but also to forecast their behaviours, consequently improving effectance and personal confidence during usage. Ultimately, in virtue of their very nature, mankind is eager to socialize with the outside world and when this need cannot be satisfied from other people it could be fulfilled by other entities, like technological devices (Cao, 2019). This inclination to engage with seemingly alive items is consistent 51 with theories (Baumeister, 1995; Maslow, 1943) that humans have a basic need to belong and interact with others, in order to achieve which they are also willing to put in place compensatory mechanisms (Mourey, 2017). In a nutshell, mankind applies the most familiar assumptions available, coinciding with its very own essence, to either deal with lack of knowledge or of social boundaries with others (Hart, 2013; Yuan, 2017). Applying human characteristics and intentions to interactive objects helps reducing uncertainties especially in people who have little time or cognitive resources for solve the riddle and form their own clear judgements. Hence, leveraging on anthropomorphic features enhances users’ trust, credibility and likeability towards virtual agents because of the near-human aura that encircles them. The influence of product anthropomorphism depends on the characteristics manipulated to construct an expression, the psychological characteristics of consumers, and the impact associated with the cognitive paradigm (Goudey, 2016). Specifically, previous research has postulated that features that trigger people’s anthropomorphism can be grouped in three distinct categories (Cao, 2019): • Visual → appearance, movement, facial expressions and gestures that resemble human body • Verbal → humanlike voice, vocal cues and name to infer gender of the assistant • Psychological → involve abstract attributes since they cannot be seen or heard directly. They can be divided in task-related features, socially-related ones and various combinations of the two. The first type concerns the way a given task is carried out by non-human agents. The second one mainly regards the naturalness and the social properties of the user-object interaction. Lastly, the former and the latter together with visual and verbal signs contribute to shape the personality of the device.

Generally, although both consumers and virtual agents’ specifications endow in defining the level of perceived anthropomorphism and consequently all the individual evaluations of the products (Belk, 2016), can be considered valid the assumption that the more an object resembles and behaves like a human being the more positive the feedbacks will be (Yuan, 2017). Nevertheless, such favourable outcomes may be lost beyond a certain threshold of anthropomorphism, depicting the so called “uncanny valley” effect, if the human-like design causes discomfort by creating expectations that a smart object is not able to meet (Belk, 2016; Pfeuffer, 2019). Accordingly, humanlike designs may be not experienced as beneficial when the similarity is structured in such a way that it is perceived as uncanny, leading to non-linear users’ responses (Wagner, 2019; Murphy, 2019). Anthropomorphism has, however, primarily a positive connotation and strengthens long-term performance (Herak, 2020) since it is harnessed to augment functional as well as behavioural qualities of objects (Kontogiorgos, 2019). Thanks to advanced natural language processing and machine learning capabilities, communication skills of smart speakers are far more advanced than previous voice-controlled technologies (McLean, 2019). Likewise, they are designed to interact meaningfully with individuals and assisting them in daily tasks by assuming human-like manners in spoiling the most natural interface: language (McLean, 2019; Seeger, 2017). Design choices linked to the modalities that could be taken up by a Voice Assistant to interpersonally relate to humans encourage anthropomorphism, consequently promoting the

52 socialness of human-agent interactions (Yuan, 2017; Purington, 2017; Kang, 2020). The propensity to anthropomorphize strongly relies on the presence of details (e.g. personality, gender, voice, autonomy, interactivity, expertise) capable of firstly assigning human-likeness to an object and secondly activating a sequence of typical social rules that helps consumers evaluate the quality of the interaction as if they're actually dealing with a human being (Kang, 2020). Anthropomorphic cues, like voice, affect both the implementation and the evaluation of human-agent interactions by following patterns that are similar to human-human ones (Kang, 2020). Consequently they reduce uncertainty, lead to a strong sense of connectedness and contribute to build a sense of experiential companionship with IoT devices: shortly, they satisfy basic rules typical of mankind’s sociality and deliver trustworthiness despite the inanimate nature of smart objects (Kang, 2020) (Kim, 2016; Purington, 2017). Besides the type of voice-activated, Vocal Assistants may show different combinations of humanlike traits, such as gender, name and personality that make them so tremendously different from traditional IT devices that people may easily improve their fondness to the point of electing them as friends or family members (Cao, 2019). Indeed, there is a strong propensity to avoid the mere act of emulating human interaction systems for functional reasons in place of recreating other elements that may invest speech assistants with emotional meaning. A smart speaker should be able to change its anthropomorphic level to such an extent to maximize the efficiency of its tasks. To reach this goal, it is mandatory to keep in mind what are the features that facilitate anthropomorphic feelings: appearance, human- object interaction and the harmonious union of the two (Zlotowski, 2015). Previous research has pointed out that when consumers heavily wish to establish an affective interaction with an entity, they increasingly feel the need to anthropomorphize and to fix a frame of familiar comprehension, especially if it is complex in its nature (e.g. laptops) (Hart, 2013). Thereby, anthropomorphism may play a significant role in positively influencing the acceptance of voice assistants in natural human environments, especially considering that while interacting with them people recognize hints and apply patterns of behaviour typical of human relationships (Wagner, 2019). Over the course of time, considerable efforts have been taken in making voice assistants humanlike since anthropomorphic cues deliver feelings of naturalness and comfort in the conversation flow, enhancing user experience’s pleasure, engagement and level of trust (Chefitz, 2018) (Fadhil, 2018). Considering the actual state of the art, it might be strategical to focus also on design approaches that are not too generalized across users or contexts and account for more sophisticated characteristics of virtual agents’ speech (Chefitz, 2018). Admittedly, consumers are still exploring smart speakers and offering them optional interface modalities rather than one-fits-all systems might help adjusting their dispositions (Chefitz, 2018). Even proposing a genderless assistant could represent a substantial and differentiable novelty in the market (Chefitz, 2018). Indeed, literature has outlined that desire for customization of intelligent personal assistant through vocal characteristics (e.g. gender, accent, speed) and speech settings (e.g. types of responses, jokes, personification) has been often mentioned by users as a way to strengthen intended adoption through suiting each individual’s preferences (Lopatovska, 2020).

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2.2.4 Leveraging on User Experience induced by Interaction to improve Smart Speakers’ adoption: Conversational Style (and its gaps) Given the rapid advancement and availability of intelligent personal assistants, their technical properties have been largely investigated. Instead, efforts aimed at evaluating user experience of virtual agents have been more limited and concentrated on specific user groups, either children (Lovato, 2015) or older adults (Wulf, 2014) resulting in non-generalizable implications (Cowan, 2017). Being designed in order to coherently convey naturalness, convenience and comfort at the same time (Wagner, 2019), smart speakers are radically changing traditional forms of human-computer interaction that require further investigations (McLean, 2019). The interplay between a user and a smart speaker are strongly directed by the interface specifications, which are therefore critical for the successful employment of this IoT device (Dudley, 2018). It seems that, despite the development and prevalence of virtual assistants, individuals still do not feel completely comfortable while interacting with them, explaining why these objects are commonly used for very simple tasks and for short periods (Hoegen, 2019; Vtyurina, 2017; Cowan, 2017). This is partially due to the inability of smart speakers to both carry on a multi-turn conversation and to adapt to human behaviour, although some upgrades have been gained at least for human-like robots through smiles and other visual cues. While the ability of virtual intelligent assistants to engage in dialog was studied quite extensively (Hoegen, 2019), less attention was paid to conversational style ability to influence people’s acceptance of smart speakers. In her studies about human- to-human conversation, Tannen defined style as “the use of specific linguistic devices, chosen by references to broad operating principles or conversational strategies”, pinpointing that people can provide the same information (“what”) in different ways (“how”) and invest it with a meaning that goes well beyond the sheer semantic composition (Tannen, 2005). Moreover, she was firmly convinced that conversational style should not be regarded as something fringe or occasional because “anything that is said must be said in some way” (Tannen, 2005), but rather as a means by which speakers can signal intention and relation in talk (Shamekhi, 2016). Previously it has been asserted that, despite knowing that technologies are not humans, users behave towards them as if they actually were so, thus socially engaging with them (Cambre, 2019). Thus, considering its relevance in human-to-human relationships, it has been argued that, among other aspects of human-agent interaction, conversational style for virtual personal assistants deserves to be the object of in-depth analysis (Shamekhi, 2016). In the same vein, Norton explained that interaction style is crucial in the field of communication to determine audience’s reactions to conveyed information (Norton, 1983). Indeed, during interpersonal interaction different impressions may rise depending both on gender, social status, context and vocabulary’s tendency to be more or less friendly, precise, animated and dominant. As well as for humankind also for virtual agents, styles are molded as a coherent combination of several linguistic markers, among which pace (prosody, speech rate, pauses), expressive paralinguistics (pitch and loudness shifts), word choice, humour and gender are particularly relevant. Assuming that conversational (or interchangeably interaction) style can be viewed as a way in which users and smart objects cooperate in a harmonic way, it might play a

54 core role in directing users’ attitudes (Wu, 2017). Indeed, efforts directed to the design of realistic human-like virtual agents are addressed as critical not only for the overall quality improvement of human-agent interaction but also for its success (Shamekhi, 2016). Interaction is one of the key representative features of smart speakers that strongly impacts on the overall quality of the system and on provided information (Nasirian, 2017). Recognizing that people communicate with virtual agents following human manners (e.g. as if they were talking to a friend), interaction design can provoke consequences on trust, usage intention and user satisfaction (Nasirian, 2017). However, previous studies has solely focused on whether interactional elements of a voice assistant like frequency of interruptions, number of turns and its correspondence with an individual’s way of speaking affect preferences, performance, trust and human-agent relationships (Hoegen, 2019; Shamekhi, 2016). Bearing in mind that so far useful perspectives on voice interface design have been especially centred on what smart speakers say in conversation rather than how, this research strives to pose the accent on ways in which IoT devices should sound like (Cambre, 2019). In order for smart speakers to modulate engaging user experiences, the first step that is urged to be taken concerns delivering not only effective and efficient task completion but also enjoyable interactions, adapted to users’ cravings and context constraints, since great usability alone is not enough to create interest (Følstad, 2018). Especially the foundations of user experience emphasize the idea that objects can no longer be functional alone but emotional and pleasurable too (van Kollenburg, 2015). Indeed, users would be more inclined to avoid source of frustration, such as failure in carrying out performing duties, and to encounter at the same time playful interactions while exploiting conversational agents’ functions. Previous research has assessed how higher satisfaction can be achieved by assigning personified characteristics (e.g. gender, name) to virtual assistants and how personality traits are able to support productivity while also being fun, friendly and empathic (Spadafora, 2015; Purington, 2017; Thies, 2017). Certainly, voice interaction specifications, apart from fluency or gender, which actually can be easily chosen between and within current models in the market (e.g. Amazon Alexa is a female and Google Assistant can be either male or female), like humorous approach, can be decisive into increasing smart speakers’ adoption (Rodrigues, 2019). Design features based on social and psychological factors have the power to modulate users’ changes in behaviour (Kamilaris, 2016). Metaphorically applying a human stereotype of personality primarily coloured by extraversion, which according to the Big Five theory is given by the tendency to be sociable, fun-loving and affectionate, to smart speakers could be a good strategy for the design of their interactive behaviour (Spadafora, 2016). It has been demonstrated that interaction via voice helps users satisfy non-utilitarian needs of connection. Indeed, recently it has been disclosed that almost half of the interactions with Alexa tend to be non-utilitarian or entertainment-related (e.g. declarations of love to the agent and requests for jokes) (Moussawi, 2018). Despite the blend between productivity-oriented and relational-oriented features emerges like the key to successful interactions, actually the current major smart speakers’ producers (e.g. Amazon and Google) have combined very little of the two (Følstad, 2018). They are great examples of highly compartmentalized strong productivity and mild relational interaction. Elements of surprise, playfulness and emotion are supported for human-agent exchanges meant to be entertaining only (e.g. 55

"Alexa, pretend to be a super villain" or "Alexa, tell me a bedtime story") and not performing too: the conversational agent is able to either help or amuse you (Business or Pleasure), never both at the same time (Følstad, 2018). In other words, until now vocal assistants have been distinctively designed for hedonic and utilitarian purposes, additionally giving more breathing space to the latter since people use such systems, viewed as experts, primarily to achieve pragmatic goals (Duijst, 2017; Wu, 2017). Thus, companies tend to make a distinctive choice between pleasure/friend-like and business/engineering-like modalities when designing IoT technologies, presuming that most of the time a detached and professional approach guarantees a more competent image. However, the results of past research has showed that friend-like communication is able to produce not only better effects on constructs such as brand warmth and brand attachment but also a sense of brand competence similar to engineering-like one (Wu, 2017). Additionally, when assessing both the quality of the interaction and the reliability of the information acquired, consumers have an almost natural propensity to hastily lean on technologies’ expression modes that reveal feelings of anger and happiness to such an extent that their responses can be classified not only as social but as mindless too (Kim, 2016) (Zhao, 2018). This happens because vocal effects evoke the social presence heuristic, according to which humans do not treat agents as artificial and robotic actors (Kim K. J., 2016; Zhao, 2018). As a consequence, it could be interesting to analyse how different shades of anthropomorphic vocal cues, as robust social enhancers, influence the adoption of the object (Kim, 2016). Furthermore, user experience, which is mapped out by beliefs, perceptions, preferences, behaviours, physical and psychological responses, flourishes more readily when functional and delightful needs are equally fulfilled (Fan, 2006). For these feedbacks to raise before, during and after the use of IoT devices, emotions and affect should be conveyed as well as efficacy and efficiency (Duijst, 2017). Moreover, it has been stated that static and always identical behaviours performed by a conversational agent may easily cause persistent boredom, consequently decreasing both engagement and satisfaction (McTear, 2016). Strengthening this fact, recently it has been revealed that IoT devices embedded with multiple synthetic voices, esteemed as prominent signals rich in social information that humans are naturally accustomed to recognizing, are evaluated more positively (Kim K. J., 2016; Cambre, 2019). Moreover, it has been suggested that besides naturalness virtual agents’ voices should transmit a sense of abstraction and deliberate meaning, just like actors on stage performing and mimicking more or less real (and sometimes surreal) daily life scenes (Aylett, 2019) for the sake of shaping users’ perceptions (Cambre, 2019). Natural language conversation as well as different speaking styles should be taken in consideration to the same account, especially because smart speakers’ ability to exhibit emotions and a sense of spontaneity through casual language or jokes makes them good counterparts in a conversation (Mari, 2020). Likely, vocal cues would represent a preciously persuasive tool through which attitudes and behaviours might be non-coercively changed, insofar they outline an interaction style that fits both the task structure and individuals’ attributes (Cambre, 2019). As proposed in past studies (Cambre, 2019), it would be fascinating to evaluate whether and how IoT devices’ specifications that violate stereotypes could affect perceptions, attitudes and behaviours. Additionally, as argued by MAYA (i.e. Most Advanced Yet Acceptable) design principle, consumers privilege 56 those products that are experienced as attractively new and typical at the same time. For smart objects, novelty and familiarity must be wisely balanced in interaction and appearance in order to produce a holistic aesthetic pleasure (Cila, 2015). Hence, we propose to not consider the two existing conversational styles discussed above as specifications of distinct devices or situations, as it has been done so far, but to exploit the benefits that may emerge from their commingling. Specifically, given that consumers’ needs with respect to virtual agents are dual in their nature, the purpose of this research is to understand the relation between classical variables and pleasurable aspects in influencing smart speakers’ adoption, rather than regarding them as two types of distinctive factors that work independently from one another on behavioural intentions. Previous research has converged primarily on technological aspects, instead this study encompasses in addition an aesthetic and experiential approach for better understanding the path that leads to a fruitful acceptance of IoT devices, noted that conversational styles may be particularly relevant in influencing consumers’ judgements of smart speakers (Kim, 2016). Hence, taking inspirations from gaps and suggestions for future research addressed by previous literature (Følstad, 2018) (Wu, 2017), we would like to assess whether different conversational styles (Business vs Pleasure) can have an impact on the relation between TAM variables in an utilitarian usage frame. Put it briefly, we would like to answer the following research questions: 1. How should a smart speaker’s voice sound like? 2. How do interaction specifications (i.e. conversational styles) influence consumers’ behavioural intentions? 3. Do conversational style declensions impact engagement?

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CHAPTER THREE

3.1 Conceptual Model: Variables and Hypotheses Relying on the disquisitions exposed in previous sections, the aim of the current study is to identify the weight covered in the context of smart speakers’ adoption by (Figure 11: Research Model): • Conversational Style as a Moderator on the relationships between TAM variables (Perceived Ease Of Use- Perceived Usefulness and Perceived Ease Of Use – Attitude) • Perceived Usefulness and Conversational Style as predictors of Engagement • Attitude and Intention To Use as predictors of Purchase Intentions

Figure 11: Research Model

To this end, few respondents were firstly randomly submitted to a questionnaire geared towards the definition of the right scenarios needed for manipulating Perceived Ease Of Use and Conversational Style, respectively (3.2 Research Method: Measures and Data Collection: 3.2.1 Pre-testing Scenarios for Independent and Moderator Variables; 3.3 Data Analysis and Findings: 3.3.1 Pre-Test Results). Once Pre-Tests contents were verified in their validity for the purposes of the current study, a wider range of people were randomly exposed to 4 different scenarios, obtained through the combination of Perceived Ease Of Use and Conversational Style manipulations, and asked to complete a survey in which several variables of interest were measured: Perceived Usefulness, Engagement, Attitude, Intention To Use, Purchase Intentions (3.2.2 Main- Test; 3.3.2 Main-Test Results). Perceived Ease of Use → As discussed in previous sessions (2.2.1 Theoretical roots of IoT acceptance: a review of Technology Acceptance Model), it stands for “the degree of ease associated with the use of a technological system” (Evans, 2015). In this context, it relates to the amount of effort that consumers should

58 invest in using a smart speaker (Huang, 2015; Moriuchi, 2019) and electing it as a hub in their smart homes. Relying to TAM traditional scheme, we would like to assess both its direct and indirect (with Perceived Usefulness as a mediator) effects on attitude. H1: Perceived Ease Of Use has a positive effect on Perceived Usefulness H2: Perceived Ease Of Use has a positive effect on Attitude Conversational style → As previously discussed (2.2.4 Leveraging on User Experience induced by Interaction to improve Smart Speakers’ adoption: Conversational Style), it is not merely the manner in which conversational interactions are performed (Tannen, 2005) but also a proxy of personality in speech (Sapir, 1927). Hence, it is a matter of practically executing communication as well as an inner smart speakers’ potentiality to produce new attitude settings and patterns of engagement during human-agent interaction (Folkmann, 2015). The underlaying rationale of inspecting this variable resides in the fact that usually customers might perceive IoT technologies not only as impersonal and anonymous but also as lacking warmth and sociability in virtue of their specifications (Balaji, 2017). Past research has investigated the role of vocal features on enhancing either accessibility or usability (Mhaidli, 2020) in the contexts of video games (Harada, 2011) and musical instruments (Fasciani, 2014). However, smart speakers differ tremendously from technologies that are not based on Artificial Intelligence especially in terms of human characteristics such as language (Wagner, 2019). Working on vocal features of conversational agents’ discourse, regarded as main determinants in gaining different types of experience (Gao, 2019), might be crucial for the overall quality of human-agent interaction (Shamekhi, 2016). Thus, a consciously designed vocal approach might prevent the preclusion of a thoroughly appreciated adoption of these devices by attributing them essential human characteristics rather than superficial ones as well as increasing their credibility, liveliness and personality (Wagner, 2019; Waytz, 2014). H3: Conversational Style impacts H1 H4: Conversational Style impacts H2 H10: Conversational Style has a positive effect on Engagement Perceived Usefulness → It can be characterized as “the degree to which a person believes that using a particular system would enhance his or her performance” (Davis, 1989) (2.2.1 Theoretical roots of IoT acceptance: a review of Technology Acceptance Model). In this study, it refers to smart speakers’ ability to help consumers to exploit all virtual assistant’s abilities in improving the execution of certain tasks (Huang, 2015; Evans, 2015), including the possibility to control smart appliances (e.g. thermostats), which individually represent small pieces in the smart home picture. The greater the perceived usefulness of smart speakers, the more likely it is that they will be adopted (Yildirim, 2019). The current research aims at measuring its direct effect on Attitude and Engagement. H5: Perceived Usefulness has a positive effect on Engagement H6: Perceived Usefulness has a positive effect on Attitude Engagement → On one side it brings together customers’ commitment and emotional involvement, on the 59 other it goes beyond them: it is an iterative process centred on interactive consumer experiences capable of sustaining people’s interest toward a product or a service (Moriuchi, 2019). In this context, it draws its origins from Perceived Usefulness, as a benefit of using an IoT device (Huang, 2015), and might influence the success of human-agent interaction (Shamekhi, 2016). Furthermore, although not investigated in this research, Engagement culminates in Customer Loyalty, as suggested by the relationship marketing paradigm (Huang, 2015). Attitude → It is an individual’s subjectivity in terms of the amount of affect for or against some object (Fishbein, 1977; Liu, 2018; Spears, 2004). Thus, Attitude is a noteworthy internal state, evaluative in nature, since it consists of an “imputation of some degree of goodness or badness to the object under the lens” (Eagly, 1993; Spears, 2004). Furthermore, Attitude is an enduring state that energizes and directs behaviour as well as provides information about the external world, as opposed to feelings that are transitory and self-referent (Eagly, 1993; Spears, 2004). In other words, it consists of a person’s positive or negative disposition about performing a target behaviour (Evans, 2015): if using a new technology is evaluated favourably (Attitude is positive), the individual is expected to form an Intention To Use (Lunney, 2016). Attitude is a construct often at the centre of numerous marketing studies because it is extremely useful in predicting consumer behaviour (Spears, 2004): if Attitude brings high sales conversion and high responsiveness to marketing, the brand can experience a situation with a long-term potential (Srinivasan, 2015). Attitudinal metrics, that can be collected from both classic attitude surveys and online proxies, are able to explain sales across brands and categories, thus helping bridge the gap between marketing and finance (Srinivasan, 2015). Specifically, for the purpose of the current research, it stands for the amount of affect for or against using a smart speaker. Generally, negative attitudes might rise because of the hidden complexities of smart speakers (van Deursen, 2019). Stimulating positive attitudes toward these devices is the first step towards the likelihood of their ownership, the development of skills and a wider range of use cases (van Deursen, 2019). H7: Attitude has a positive influence on Intention to Use Intention to Use → It is the most proximal antecedent of user behaviour (Jackson, 1997; Holden, 2010), made up of a person’s apprehension when she finds herself in the circumstance of conducting a physical activity (Liu, 2018), meaning in this specific context using a technology (Evans, 2015), and of a person’s purpose to explore it (Cao, 2019). In other words, it depicts what is commonly meant for acceptance since it reliably predicts actual usage of system technology (adoption). It is a measure of customers’ stated willingness to behave in a certain way. Most of all, Intention To Use is influenced by attitude: if using a smart speaker is evaluated favourably (positive attitude), the individual who made that assessment will be willing to use it. This variable together with attitude is at the core of technologies’ adoption and actual usage (Tsai, 2017; Lunney, 2016). H8: Intention to Use has a positive influence on Purchase Intentions H9: Attitude has a positive influence on Purchase Intentions Purchase Intentions → It is a measure of an individual’s conscious plan to make an effort to purchase a brand 60

(Spears, 2004). Put differently, it is a personal action tendency towards the brand, which is fairly distinct from Attitude: whereas the latter stands for the sum of evaluations, the former portrays “the person’s motivation in the sense of his or her conscious plan to exert effort to carry out a behaviour” (Eagly, 1993; Spears, 2004). Despite differences, attitudes influence behaviour through behavioural intentions (Fishbein, 1977; Spears, 2004). Generally, Purchase Intentions is a variable employed in marketing management to forecast future demand and make strategic decisions, such as whether to increase or reduce production or whether to start a price modification, with regards to either new or existing products (Morwitz, 2007). Concerning the first case, this variable helps managers predict (Morwitz, 2007): • whether a concept deserves further development • whether a product deserves to be launched • which geographical markets deserves to be the soil of a product launch • which segments must be targeted

As a rule, Purchase Intentions is largely adopted by managers and academic as a proxy for purchase behaviour: it is widely believed that this measure is predictive of subsequent purchase (Morwitz, 2007). Willingness To Pay → Understanding consumers’ Willingness To Pay is of paramount relevance for business success, since it helps managers estimate demand and design a proper pricing strategy (Yuan, 2017). It represents “the maximum price a given consumer accepts to pay for a product or service” (Le Gall-Ely, 2009; Yuan, 2017; Adams, 2019). Since it is subjectively set by individuals, Willingness To Pay reflects people’s perceived value of a product (Yuan, 2017). Several factors might sway consumers’ Willingness To Pay, such as: product information, product image, pricing strategy and system design (Yuan, 2017). In this context, it refers to the amount of money, expressed in Euros, a person would pay in order to take possession of a smart speaker. Control variables → Gender, Age, Smart Speaker’s Ownership, Consumer Type. The latter has been modulated according to the respondent's level of technology readiness (i.e. comfort in using technologies), level of familiarity with smart speakers and possession of smart speakers, if any.

3.2 Research Method: Measures and Data Collection 3.2.1 Pre-testing Scenarios for Independent and Moderator Variables Before running the main-test, a pre-test has been distributed online in order to verify if scenarios have been correctly created and consequently the variables under the lens have been adequately manipulated. It consisted of two distinct Qualtrics surveys, respectively focused on Perceived Ease Of Use and Conversational Style. The former regarded the Independent Variable Perceived Ease Of Use, for which two randomized scenarios have been mutually exclusive submitted to respondents (Figure 12: Perceive Ease Of Use Manipulation: High-Easy Task (green) vs Low-Complex Task (red)): 1. Easy Task Scenario, molded in the context of simple weather queries

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2. Complex Task Scenario, molded in the context of weather and temperature queries through the usage of an additional smart home device

Figure 12: Perceive Ease Of Use Manipulation: High-Easy Task (green) vs Low-Complex Task (red)

Explaining it better, the Independent Variable was manipulated in the questionnaire through describing, in the first case, elementary mechanisms of functioning and, in the other, more difficult ones characterized by elaborated and numerous instructions. In both circumstances, the simulated user’s requests were not coloured by a purpose of entertainment but were aimed at completing some clear operations. Specifically, people were asked to carefully read a script in which a more or less easy task should have been carried out by means of either the sole smart speaker or the smart speaker in conjunction with another smart home appliance. Ultimately, the section of the questionnaire dedicated to the manipulation of the Independent Variable was preceded by a presentation of the smart speakers, in which these devices were briefly introduced and described. The scale for measuring the Perceived Ease Of Use was a slight adaptation of the items used by (Venkatesh, 2000), in turn tailored by (Davis, 1989). The adjustment consisted in replacing the word "system" (understood as a generic technological system) with the term “smart speaker”, besides translating all the original items in Italian. Previous studies used this same scale by replacing "system" with more specific terms (e-mail, smartphone, etc.): for example, in (Sohn, 2020) "Artificial Intelligence" has been adopted. In addition, the third item in the questionnaire was reversed: from the original "I find the system to be easy to use" to "I find it complicated to use the smart speaker". This reverse coded item was used in (Sohn, 2020). The practice embraced in both the above-mentioned studies, but also in others, according to which TAM constructs are generally measured with a 7-point Likert Scale, anchored by strongly disagree-strongly agree, has been embraced in this research too (Venkatesh, 2000). The second Qualtrics survey regarded the Moderator Variable Conversational Style, for which two 62 randomized scenarios have been mutually exclusive submitted to respondents (Figure 13: Conversational Style Manipulation: Business (blue) vs Pleasure (yellow)): 1. Business Conversational Style 2. Pleasure Conversational Style

Figure 13: Conversational Style Manipulation: Business (blue) vs Pleasure (yellow)

Explaining it better, the Moderator Variable was manipulated in the questionnaire through mimicking, in the first case, a very serious and professional smart speaker response mode and, in the other, a more frivolous one. Additionally, these simulated responses were presented in the text form in place of the vocal one, in order to isolate the pure conversation modality from any influences dictated by voice gender and to easily point to the idea of a genderless interaction, which has been elected as a way of differentiation from the competition in previous research (Chefitz, 2018). The two styles of conversation were built considering: • the vocal interactions typical of Google Assistant (really serious when used for specific tasks and ironic when used for spotless fun) • Google Assistant’s habitual suggestions • some indications coming from literature (Wu, 2017; Shamekhi, 2016; Novielli, 2010; Kim, 2019; Følstad, 2018) and discussed in previous sections (2.2.4 Leveraging on User Experience induced by Interaction to improve Smart Speakers’ adoption: Conversational Style)

Furthermore, the second type of Conversational Style is normally not used by smart speakers in tasks not aimed at pure entertainment: generally, they take on a more playful tone only when asked to fullfill requests for sheer users’ enjoyment goals. The section of the questionnaire dedicated to the manipulation of the Moderator Variable was preceded by a presentation of the smart speakers, in which these devices were briefly introduced and described. For measuring Conversational Style two scales were adopted. The first one, was a slight adaptation of the items used by (Wu, 2017). Beyond the Italian surrender, the adjustment consisted in replacing "Siri" with "smart speaker". The items were measured with a 7-Point Likert Scale, anchored by strongly disagree-strongly agree, as in the original study. The second scale was an adaptation of the one used by (Zhang, 1996) to measure, with a 9-point Semantic Differential Scale, the humour perceived in advertising. The items were made of 5 pairs of bipolar adjectives. 63

For both surveys, the Control Variable Consumer Type was measured using a scale adapted from (Schroeder, 2018), which was composed of two items respectively centred on respondents’ technology readiness, meaning the comfort they eventually experience when approaching new technologies, and familiarity with smart speakers. Beyond the Italian surrender, the adjustment consisted in replacing “virtual assistants (such as SIRI)” with “smart speaker”. The items were measured with a 7-Point Likert Scale, anchored by not at all comfortable-extremely comfortable and not at all familiar-extremely familiar, as in the original study. Finally, for both surveys three Sociodemographic Control Variables were measured: • Ownership → respondents were asked to point out, through a double choice question type (Yes/No), whether they possess a smart speaker • Age → respondents were asked to point out, through an open question, their age • Gender → respondents were asked to point out, through a double choice question type (Female/Male), their gender

3.2.2 Main-Test Participants were recruited via instant messaging and social network platforms. A causal research design was chosen to evaluate whether there exists a moderation effect of Conversational Style on the causal relationship between Perceived Ease Of Use and respectively Perceived Usefulness and Attitude. Given that four conditions were taken in consideration (High and Low Perceived Ease of Use, Business and Pleasure Conversational Style), the research model reflects a 2x2 between-subject factorial design with four different randomized scenarios mutually exclusive submitted to respondents: 1. High Perceived Ease of Use (Easy Task Scenario) x Business Conversational Style 2. High Perceived Ease of Use (Easy Task Scenario) x Pleasure Conversational Style 3. Low Perceived Ease of Use (Complex Task Scenario) x Business Conversational Style 4. Low Perceived Ease of Use (Complex Task Scenario) x Pleasure Conversational Style

Each respondent was asked to answer questions regarding the measured variables of this study: Perceived Usefulness, Engagement, Attitude, Intention To Use, Purchase Intentions, Willingness To Pay. The scale for measuring Perceived Usefulness was a slight adaptation of the items used by (Shuhaiber, 2019). The first Perceived Usefulness measurement scales developed by (Davis, 1989) first and modified by (Venkatesh, 2000) then are too anchored to the use of old technologies in the context of working environment to be applied to the present study without being profoundly distorted. The items taken from (Shuhaiber, 2019) have been translated into Italian and adapted to the specific situation by replacing “smart homes” with “smart speakers” in order to be consistent with the purposes of the current research. The items were measured with a 7-Point Likert Scale, anchored by strongly disagree-strongly agree, as in the original study. For measuring Engagement, two scales have been employed. The first one was taken from (Moriuchi, 2019), in turn adapted from previous studies. The items have been translated into Italian and tailored to the specific

64 situation by replacing “voice assistant/assistants” with “smart speakers”. The items were measured with a 7- Point Likert Scale, anchored by strongly disagree-strongly agree, as in the original study. The second scale was an adaptation of the one developed by (O'Brien, 2010) and used by the author herself in subsequent studies (O’Brien, 2010). The scale was not applied in its entirety since it originally consists of 31 items and 6 factors. This choice was made because otherwise the questionnaire would have been too long, tedious and redundant, with the risk of exponentially increasing the drop-out rate of participants. Nevertheless, in light of the contents set out in the section 2.2.2 (Leveraging on User Experience induced by Interaction to improve Smart Speakers' adoption: Pragmatist Aesthetics), Aesthetics and Felt Involvement factors of O’Brien’s scale, for a total of 8 items rated on a 7-Point Likert Scale anchored by strongly disagree-strongly agree, were deemed useful to measure as components of Engagement. Usually in research, Attitude is at least measured including three items (Chen, 2011) through bipolar affective or evaluative dimensions relative to an attitudinal object (Spears, 2004). In this study a 5-item scale taken from (Spears, 2004) was implemented after a translation and a slight adaptation coherent to the context of application had been completed. The items, made of 5 pairs of bipolar adjectives, were measured with a 7- Point Semantic Differential Scale, as in the original study. The scales for Intention To Use and Purchase Intentions were taken from (Sohn, 2020), in turn adapted respectively from (Rahman, 2017) and (Davis, 1989). These variables are tendentially measured by the subjective probability of performing a behaviour, either use or purchase (Spears, 2004). For both scales, items were measured with a 7-Point Likert Scale anchored by strongly disagree-strongly agree, translated into Italian and adapted to the specific situation by replacing “AI” with “smart speakers”. Willingness To Buy was appraised through a simple open question, in which respondents were asked what was the maximum price they would have paid for a smart speaker, given a budget of 200€. This question was a slight adaptation of (Adams, 2019), which consisted, besides the Italian surrender, in switching “smart home voice assistant” with “smart speaker”. Previous literature stressed the importance of taking account of individual variables in addition to TAM when evaluating technology acceptance (Goudey, 2016; Lin, 2007), such as visual aesthetics and technological familiarity. Coherently, in the survey applied for the Main-Test smart speakers’ functional aesthetics, instead of visual ones (not coherent with our research goals), were taken into account as a relevant component of engagement. Furthermore, technological familiarity was assessed for framing respondents as more or less comfortable with technologies in general and accustomed to smart speakers in particular (Consumer Type). Ultimately, also in the Main-Test, Sociodemographic Control Variables were measured in the same way as Pre-Test.

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3.3 Data Analysis and Findings 3.3.1 Pre-Test Results Concerning Perceived Ease Of Use, as previously explained, people were asked to carefully read a random script in which a more or less easy task should have been carried out by means of either the sole smart speaker (Easy Task Scenario) or the smart speaker in conjunction with another smart home appliance (Complex Task Scenario). Afterwards, they ranked their perceptions of ease of use of the smart speaker in the context of the randomly showed task by answering some questions on a scale from 1 to 7. Overall, 46 subjects (28 women, 18 men) with an average age of 31.28 completed the survey: 23 were randomly exposed to the Easy Task stimulus and 23 to the Complex Task one. Despite 72% of the sample (mostly women) admitted that they do not own a smart speaker, respondents simultaneously declared on average to feel comfortable when approaching new technologies (5.46) and to be familiar with the use of smart speakers (4.37). Further related results highlighted whether or not the manipulation, carried out through a descriptive text, of the task at hand had been successfully held. Specifically, an Independent t-test was run on the grouping variable “Scenario” (1=Task Facile, 2=Task Difficile) in order to test the following hypotheses: • H0: There are no mean differences for Perceived Ease Of Use between Task Facile and Task Difficile (µ Task Facile = µ Task Difficile) • H1: There are mean differences for Perceived Ease Of Use between Task Facile and Task Difficile (µ Task Facile ≠ µ Task Difficile)

Before conducting the main analyses, the Levene Statistics, that tests the assumption according to which variances of the two means are equal (H0: σ2=σ2; H1: σ2≠σ2), was checked: since p < 0.05, variances are not constant for Perceived Ease Of Use between Task Facile and Task Difficile. As a consequence, t-test with no equal variances was used to make conclusions: with a 95% confidence (level of significance α=0.05) a statistically significant difference between mean of Task Facile (5.66) and mean of Task Difficile (4.29) was demonstrated. Thereby, the Independent Variable Perceived Ease Of Use was properly manipulated through the displayed stimuli. Concerning Conversational Style, as previously explained, people were asked to carefully read a script in which two different smart speaker response modes were randomly mimicked: a very serious and professional one (Business Conversational Style) versus a more frivolous one (Pleasure Conversational Style). Afterwards, they ranked their perceptions of the conversational style of the smart speaker by answering some questions. Overall, 47 subjects (29 women, 18 men) with an average age of 30.66 completed the survey: 24 were randomly exposed to the Business Style stimulus and 23 to the Pleasure Style one. Despite 64% of the sample (mostly women) admitted that they do not own a smart speaker, respondents simultaneously declared on average to feel comfortable when approaching new technologies (5.36) and to be familiar with the use of smart speakers (4.49). Further related results highlighted whether or not the manipulation, carried out through a descriptive text, of the type of answer at hand had been successfully held. Specifically, an Independent t-test was run on the grouping variable “Scenario” (1=Business, 2=Pleasure) in order to test the following 66 hypotheses: • H0: There are no mean differences for Conversational Style between Business and Pleasure (µ Business = µ Pleasure) • H1: There are mean differences for Conversational Style between Business and Pleasure (µ Business ≠ µ Pleasure)

Before conducting the main analyses, the Levene Statistics, that tests the assumption according to which variances of the two means are equal (H0: σ2=σ2; H1: σ2≠σ2), was checked: since p > 0.05, variances are constant for Conversational Style between Business and Pleasure. As a consequence, t-test with equal variances was used to make conclusions: with a 95% confidence (level of significance α=0.05) a statistically significant difference between mean of Business (4.11) and mean of Pleasure (5.43) was demonstrated. Thereby, the Moderator Variable Conversational Style was properly manipulated through the displayed stimuli. It is important to stress that for this variable the manipulation worked successfully with regards to the data registered through the first scale, adapted from (Wu, 2017), whereas not statistically significant differences were pinpointed in terms of humour, which was measured as an addition with the second scale, adapted from (Zhang, 1996). Nevertheless, it is possible to affirm that the measurement set up by (Wu, 2017) is more suitable for making conclusions in this context, since it was designed ad hoc for capturing Virtual Assistant’s tendencies to act more or less friendly, carefully, detachedly and professionally. Instead, the scale used by (Zhang, 1996), which properly fits the advertising environment, as results showed seems to be not advisable in this context especially considering that humour is somehow also expressed through vocal pitch that was not engineered in this study for the reasons explained above (3.2.1 Pre-testing Scenarios for Independent and Moderator Variables).

3.3.2 Main-Test Results 3.3.2.1 Descriptives, Reliability and Validity of the scales, Manipulation Check The interview was extended to a sample of 45 people for each condition. Overall, 180 subjects (155 women, 25 men) with an average age of 27.62 (18 ≤ x ≤ 63) completed the survey: • 46.7% between 18 and 24 years old • 36.1% between 25 and 34 years old • 13.9% between 35 and 44 years old • 1.1% between 45 and 54 years old • 2.2% between 55 and 63 years old

Despite 76% of the sample (mostly women) admitted that they do not own a smart speaker, respondents simultaneously declared on average to feel comfortable when approaching new technologies (µ=5.50) and to be familiar with the use of smart speakers (µ=4.19). Regarding reliability assessment, the output produced significant Cronbach’s Alpha values (>0.6) for all the

67 scales (Perceived Usefulness: α = 0.916; Engagement1: α = 0.802; Engagement2: α = 0.897; Attitude: α = 0.918; Intention To Use α = 0.930; Purchase Intentions α = 0.917), indicating their high capability of yielding consistent results. Kaiser-Meyer-Olkin’s test (KMO), which indicates the proportion of variance in variables that might be caused by underlying factors, reported a value higher than 0.8 (0.920), pinpointing a really good adequacy of the sampling and predicting that data will factor well overall. Bartlett’s test of sphericity signalled a statistically significant correlation (p<0.01) between variables. Consequently, a Factor Analysis was run in order to verify the validity of the scales. It showed that the last two scales, that measured respectively Intention To Use and Purchase Intention, could be summed up in a single factor instead of originally keeping the two of them, for a total of 5 factors, instead of 6, together explaining 74% of total variance. However, since Eigenvalue of a hypothetical sixth factor is slightly below 1 (0.949), it was retained for the purpose of this research which aims at specifically exploring the act of purchasing a smart speaker in spite of generalising the eventual consumer behaviour toward it. In the last part of the questionnaire, participants were asked to appraise the Perceived Ease Of Use and Conversational Style of the presented scenario. This evaluation aimed at ascertaining whether the stimuli were being recognized correctly as intended. The measurement was made using the two Seven-Point Likert scales adopted in the Pre-tests (Davis, 1989; Venkatesh, 2000; Sohn, 2020; Wu, 2017). Two Independent Samples t- tests were executed to ascertain the accurateness of the responses. Once checked for the Levene Statistics to determine equal variances among groups (p < 0.01 for both variables), t-tests with no equal variances were considered to draw conclusions: • With a 95% confidence (level of significance α=0.05) a statistically significant difference between mean of Task Facile (5.4778) and mean of Task Difficile (4.8639) was demonstrated • With a 95% confidence (level of significance α=0.05) a statistically significant difference between mean of Business (4.0222) and mean of Pleasure (4.3861) was demonstrated

Thus, results indicated that manipulation of both the selected variable worked well and coherently with Pre- Test results.

3.3.2.2 Main and Moderation Effects on Perceived Usefulness and Attitude For the purpose of verifying the existence of a moderating effect of Conversational Style on the relationships Perceived Ease Of Use – Perceived Usefulness and Perceived Ease Of Use – Attitude a Two-Way Anova test was run on SPSS. Concerning the first relationship, despite an interaction between factors (Independent Variable and Moderator) was not highlighted, Two-Way Anova confirmed the existence of a statistically significant effect exerted by Perceived Ease Of Use on Perceived Usefulness (F(1;176)=4.088 p < 0.05). Specifically, Perceived Usefulness is higher when the task to complete is easier (M=4.3772, SD=1.51180) compared to a more complex one (M= 3.9389, SD=1.35862).

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Concerning the second relationship, Two-Way Anova displayed that neither a main effect directly exerted by Perceived Ease Of Use on Attitude or a moderating effect exerted by Conversational Style on the relationship between these two variables exist. Particularly, even if Attitude seems to be higher on average when an Easy Task is displayed (M=4.4444, SD=1.44433) instead of a Complex one (M=4.1133, SD=1.40945), this difference is not statistically significant (F(1;176)=2,407 p > 0.05). Furthermore, despite an interesting crossover interaction between Perceived Ease Of Use and Conversational Style was showed in the graph (Attitude always increases when an Easy Task is performed and the curve results steeper when a Business Conversational Style is taken on by the smart speaker) it is not statistically significant (F(1;176)=0.747 p>0.05). Additionally, even if Two-way Anova did not showcased a direct relationship between Perceived Ease Of Use and Attitude, once Attitude was transformed in a categorical variable (if x < µ=4.2789, x=0; if x ≥ µ=4.7289, x=1), a statistically significant association among these variables was actually discovered through a Chi-square test of independence (X2(1;180)=5.692 p < 0.05). Thus, Perceived Ease Of Use and Attitude are not independent of each other. In particular, despite the weak nature of its strength (Cramer’s V=0.178), this relationship clearly displays that when a Complex Task is performed Attitude is most frequently low (52), whereas when an Easy Task is carried out Attitude is most frequently high (54).

3.3.2.3 Linear Regressions of measured variables Different simple Linear Regressions were calculated to predict: 1. Engagement based on Perceived Usefulness, by relying on the two different measurement scales previously described 2. Attitude based on Perceived Usefulness 3. Intention To Use based on Attitude 4. Purchase Intentions based on Intention To Use 5. Purchase Intentions based on Attitude 6. Engagement based on Conversational Style

The related results are shown below: 1. Regarding the first scale, in accordance with Pearson’s Correlation, a measure of linear associations strength, it was possible to assess that the two variables are moderately and positively correlated (r=0.519, p < 0.01). A significant regression equation was found (F(1,178)=65.579, p < 0.01), with an R2 of 0.269. This effect size clearly displayed that almost 27% of the variation in Engagement1 is accounted for by its relationship with Perceived Usefulness. Participants’ predicted Engagement1 is equal to 0.916+0.478*Perceived Usefulness. Hence, the outcome variable increases 0.478 for each unit of Perceived Usefulness. Concerning the second scale, it was possible to assess that the two variables are moderately and positively correlated (r=0.580, p < 0.01). A significant regression equation was found (F(1,178)=90.046, p < 0.01), with an R2 of 0.336. This effect size clearly displayed that almost 34% of the variation in Engagement2 is accounted for by its relationship with Perceived Usefulness. Participants’ 69

predicted Engagement2 is equal to 1.545+0.491*Perceived Usefulness. Hence, the outcome variable increases 0.491 for each unit of Perceived Usefulness. 2. In accordance with Pearson’s Correlation, it was possible to assess that the two variables are weakly and positively correlated (r=0.375, p < 0.01). A significant regression equation was found (F(1,178)=29.096, p < 0.01), with an R2 of 0.140. This effect size clearly displays that 14% of the variation in Attitude is accounted for by its relationship with Perceived Usefulness. Participants’ predicted Attitude is equal to 2.739+0.370*Perceived Usefulness. Hence, the outcome variable increases 0.370 for each unit of Perceived Usefulness. 3. In accordance with Pearson’s Correlation, it was possible to assess that the two variables are weakly and positively correlated (r=0.281, p < 0.01). A significant regression equation was found (F(1,178)=15.243, p < 0.01), with an R2 of 0.079. This effect size clearly displays that almost 8% of the variation in Intention To Use is accounted for by its relationship with Attitude. Participants’ predicted Intention To Use is equal to 2.113+0.321*Attitude. Hence, the outcome variable increases 0.321 for each unit of Attitude. 4. In accordance with Pearson’s Correlation, it was possible to assess that the two variables are strongly and positively correlated (r=0.897, p < 0.01). A significant regression equation was found (F(1,178)=729.522, p < 0.01), with an R2 of 0.804. This effect size clearly displays that 80% of the variation in Purchase Intentions is accounted for by its relationship with Intention To Use. Participants’ predicted Purchase Intention is equal to -0.052+0.825*Intention To Use. Hence, the outcome variable increases 0.825 for each unit of Intention To Use. 5. In accordance with Pearson’s Correlation, it was possible to assess that the two variables are weakly and positively correlated (r=0.328, p < 0.01). A significant regression equation was found (F(1,178)=21.428, p < 0.01), with an R2 of 0.107. This effect size clearly displays that almost 11% of the variation in Purchase Intentions is accounted for by its relationship with Attitude. Participants’ predicted Purchase Intention is equal to 1.350+0.344*Attitude. Hence, the outcome variable increases 0.344 for each unit of Attitude. 6. No statistically significant associations were discovered for the two scales employed for measuring the outcome variable. Nevertheless, by focusing on each item of the above-mentioned scales, it was disclosed that Conversational Style can exert an effect on one particular aspect of Engagement, namely fun (i.e. ENG2N8: “L’esperienza descritta è stata divertente”). In accordance with Pearson’s Correlation, it was possible to assess that the two variables are weakly and negatively correlated (r=-0.230, p < 0.01). A significant regression equation was found (F(1,178)=9.938, p < 0.01), with an R2 of 0.053. This effect size clearly displays that about 5% of the variation in fun, understood as an Engagement component, is accounted for by its relationship with Conversational. The outcome variable decreases 0.733 for each unit of Conversational Style.

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Research Hypotheses Results H1: Perceived Ease Of Use → Perceived Usefulness Confirmed H2: Perceived Ease Of Use → Attitude Not Confirmed* H3: Perceived Ease Of Use * Conversational Style → Perceived Usefulness Not Confirmed H4: Perceived Ease Of Use * Conversational Style → Attitude Not Confirmed H5: Perceived Usefulness → Engagement Confirmed H6: Perceived Usefulness → Attitude Confirmed H7: Attitude → Intention To Use Confirmed H8: Intention To use → Purchase Intentions Confirmed H9: Attitude → Purchase Intentions Confirmed H10: Conversational Style → Engagement Not Confirmed**

*not confirmed with Two-Way Anova, but Chi-Square test of independence showed a significant relationship between variables **not confirmed with Two-Way Anova run on the Engagement scales considered in their entirety, but a significant effect exerted by Conversational Style on one item of the second scale, namely fun, was discovered

3.3.2.4 Chi-square associations between measured variables Once transformed from continuous to nominal, several Chi-square tests of independence were run in order to study eventual relationships between measured variables. Variables Means (µ) Categorical Values (x) Perceived Usefulness 4.1556 if x<µ x=0 (LowPUM); if x≥µ x=1 (HighPUM) Engagement1 2.9019 if x<µ x=0; if x≥µ x=1 Engagement2 3.5854 if x<µ x=0; if x≥µ x=1 Attitude 4.2789 if x<µ x=0; if x≥µ x=1 Intention To Use 3.4852 if x<µ x=0; if x≥µ x=1 Purchase Intentions 2.8236 if x<µ x=0; if x≥µ x=1 Willingness To Pay 70.72 if x<µ x=0; if x≥µ x=1 Comfort 5.50 if x<µ x=0; if x≥µ x=1 Familiarity 4.19 if x<µ x=0; if x≥µ x=1

The existence of statistically significant associations between Comfort and some other variables was highlighted. As can be seen in the table below, Cramer’s V singled out that these relationships are all coloured by weak strengths, except for Comfort-Purchase Intention. Particularly, it was disclosed that:

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• Engagement2 tends to be most frequently above the average (68) when respondents feel comfortable in approaching new technologies and below the average (37) when they do not • Attitude tends to be most frequently above the average (69) when respondents feel comfortable in approaching new technologies and below the average (36) when they do not • Intention To Use tends to be most frequently above the average (69) when respondents feel comfortable in approaching new technologies and below the average (35) when they do not • Purchase Intention tends to be most frequently above the average (63) when respondents feel comfortable in approaching new technologies and below the average (44) when they do not

Variables Chi-Square (X2) Statistical significance Strength of association Comfort – Engagement2 X2(1;180)=5.673 p < 0.05 Weak Cramer’s V= 0.178 Comfort – Attitude X2 (1;180)=5.166 p < 0.05 Weak Cramer’s V= 0.169 Comfort – Intention To Use X2 (1;180)= 4.244 p < 0.05 Weak Cramer’s V= 0.154 Comfort – Purchase Intention X2 (1;180)= 11.465 p < 0.01 Moderate Cramer’s V= 0.252

Moreover, the existence of statistically significant associations between Ownership, was already categorical (Yes=1/No=2) without additional transformation, and some other variables was highlighted. As can be seen in the table below, Cramer’s V singled out that these relationships are mostly coloured by weak strengths, except for Ownership-Intention To Use and Ownership-Purchase Intention. Particularly, it was disclosed that: • Perceived Usefulness tends to be most frequently above the average for both smart speakers non-owners (74) and owners (35) • Engagement2 tends to be most frequently above the average (28) for smart speakers owners and below (75) the average for non-owners • Attitude tends to be most frequently above the average (28) for smart speakers owners and below (73) the average for non-owners • Intention To Use tends to be most frequently above the average (36) for smart speakers owners and below (80) the average for non-owners • Purchase Intention tends to be most frequently above the average (30) for smart speakers owners and below (89) the average for non-owners • Willingness To Pay tends to be most frequently below the average for both smart speakers non-owners (74) and owners (31)

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Variables Chi-Square (X2) Statistical significance Strength of association Ownership - Perceived Usefulness X2(1;180)=10.272 p < 0.01 Weak Cramer’s V= 0.239 Ownership – Engagement2 X2(1;180)=5.164 p < 0.05 Weak Cramer’s V= 0.169 Ownership – Attitude X2(1;180)=4.435 p < 0.05 Weak Cramer’s V= 0.157 Ownership – Intention To Use X2(1;180)=23.245 p < 0.01 Moderate Cramer’s V= 0.359 Ownership – Purchase Intention X2(1;180)=16.077 p < 0.01 Moderate Cramer’s V= 0.299 Ownership – Willingness To Pay X2(1;180)= 4.401 p < 0.05 Weak Cramer’s V= 0.156

Ultimately, the existence of statistically significant associations between Familiarity and some other variables was highlighted. As can be seen in the table below, Cramer’s V singled out that these relationships are mostly coloured by moderate strengths, except for Familiarity-Engagement1 and Familiarity-Attitude. Particularly, it was disclosed that: • Perceived Usefulness tends to be most frequently above the average (72) for people who declared to be familiar with smart speakers and below (46) for those who are not • Engagement1 tends to be most frequently above the average (55) for people who declared to be familiar with smart speakers and below (53) for those who are not • Engagement2 tends to be most frequently above the average (61) for people who declared to be familiar with smart speakers and below (54) for those who are not • Attitude tends to be most frequently above the average (59) for people who declared to be familiar with smart speakers and below (50) for those who are not • Intention To Use tends to be most frequently above the average (66) for people who declared to be familiar with smart speakers and below (56) for those who are not • Purchase Intention tends to be most frequently high (56) for people who declared to be familiar with smart speakers and below (61) for those who are not

Variables Chi-Square (X2) Statistical significance Strength of association Familiarity - Perceived Usefulness X2(1;180)=16.460 p < 0.01 Moderate Cramer’s V= 0.302 Familiarity – Engagement1 X2(1;180)=7.584 p < 0.01 Weak Cramer’s V= 0.205 Familiarity – Engagement2 X2(1;180)=13.973 p < 0.01 Moderate Cramer’s V= 0.279 Familiarity – Attitude X2(1;180)=7.943 p < 0.01 Weak Cramer’s V= 0.210 Familiarity – Intention To Use X2(1;180)=22.586 p < 0.01 Moderate Cramer’s V= 0.354 Familiarity – Purchase Intention X2(1;180)=17.761 p < 0.01 Moderate Cramer’s V= 0.314

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3.4 Sentiment Analysis: Google Home Mini vs Amazon Echo Dot 3rd Generation In order to further deepen the issue of smart speakers acceptance, a Sentiment Analysis was run on online collected reviews. In particular, the intent was to study what most often generates discussion among consumers with regards to smart speakers. People tend to buy either offline or online such devices and when the second type of purchase takes place it is not uncommon that they leave comments about them on the online retailers’ websites. This kind of judgements are largely linked to product specifications, operability and obstacles in exploring all the possible functionalities, besides problems with payment or delivery procedures which are not of interest in this research. Since individuals truly believe that Internet can freely accept all their opinions, their messages are rarely filtered. Being truly honest and immediate, as well as detailed in most cases, such shared thoughts are actually useful to identify the focus of consumers’ satisfaction or to discern what are the most and least appreciated features, understood in a broad sense, of these products. Such investigation may partially contribute to bringing to light key elements for the success of an early-stages market that is still fervent although not thoroughly burst in its potential. More precisely, Sentiment Analysis inspects people’s opinions, emotions and feelings towards entities of various nature (e.g. products, services, organizations), understood in their entirety or with a focus on specific attributes (Liu, 2012). It is a qualitative research often employed for business purposes given that it helps organizations to easily discover appraisals of their offerings as well as to adjust the shoot of their marketing strategies. Specifically, Sentiment Analysis discloses the patterns hidden in past purchases so that firms can be more successful in meeting consumers’ needs since accuracy of product demand forecasting, assortment optimization, product recommendation can be significantly improved (Chen, 2015). As a consequence, it can generate substantial value by allowing organizations to get real time, transparent and usable information that provides predictive orientation of the target audience’s expectations, ultimately boosting performance (Ahmed, 2017). Considering that evaluations, both personal and others’, chiefly influence human behaviours, Sentiment Analysis applications have reached several domains ranging from consumer products and services to social events and political elections (Liu, 2012). In this study, opinions regarding Google Home Mini and Amazon Echo Dot 3rd Generation, that share similar average prices and features, were translated in English and collected through a browser extension (i.e. Data Miner) from google.com (Google, 2020; Amazon, 2020) which groups the reviews written by consumers on various e-commerce sites (e.g. ePRICE, eBay, Best Buy). Having collected a total of 416 reviews, data were subjected to opinion mining in R Studio. Firstly, the cleanText function has been applied in order to work on a text in its purest form, since elements such as punctuations and stopwords generally do not add meaning to the emotional idea that a sentence is trying to convey. Afterwards, n-grams were generated through the getnGrams function. Then, the twenty most frequent single words and bigrams, which are sequential groups of words that communicate a different idea together than they do apart, used in Text have been graphically visualized through different histograms. Besides the name of the smart speakers under the lens, some of the most meaningful top 20 words and bigrams detected were: • light, bulb, music, devices, hub (Google Home Mini)

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• sound, music, device, home (Amazon Echo Dot 3rd Generation) • smart home, smart bulb, smart light, starter kit, hey google, play music, home automation (Google Home Mini) • sound quality, play music, smart home, ask alexa, smart devices (Amazon Echo Dot 3rd Generation)

For the sake of identifying the most judged aspects, the Bag Of Words (BOW) approach, that works on the corpus (i.e. a collection of documents), was carried out too. It extracts features from a text, simply considering words occurrence in a document regardless of the sense they assume in a common grammar use. Once the Document Term Matrixes for both Google Home Mini and Amazon Echo Dot 3rd Generation have been created and displayed in graphs through wordcloud (Figure 14: Google Home Mini and Amazon Echo Dot 3rd Generation's Wordclouds), by combining the inspection of BOW analysis’ terms frequencies and respective associations with the results of getNgrams (top words and bigrams) the following aspects have been selected for performing the Sentiment Analysis: Assistance, Home, Play, Voice, Wakeword.

Figure 14: Google Home Mini and Amazon Echo Dot 3rd Generation's Wordclouds

These aspects, that fairly synthetize and represent the features and functions of a smart speaker that most frequently trigger consumers’ involvement, have been added to the original datasets in order to highlight whether their mention in the collected reviews subsisted or not. Thereafter, Sentiment Analysis was performed at a document level. To this end, the Syuzhet package was used to compute the algebraic sum of the sentiment values attached to aspects for each review. Sentiment scores were first calculated according to the NRC emotion lexicon, which stands for a list of words and relative associations with eight emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust) and two sentiments (negative and positive) (Mohammad, 2020). Against an amount of reviews expressing mostly very positive feelings and showing a substantially unchanged level of trust for both smart speakers, Amazon Echo Dot 3rd Generation seems to perform worse on emotions like anger, disgust and fear resulting in higher negativity (Figure 15: Syuzhet Bar Plots). However, such adversities are so well tempered by nice levels of joy and surprise that the differences in terms of emotions and sentiments between the two smart speakers are substantially small.

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Figure 15: Syuzhet Bar Plots

Thereafter, sentiment scores were compared across aspects by visualizing a boxplot for each of them. A boxplot is a standardized way of displaying minimum, first quartile, median, third quartile and maximum of a distribution. In a boxplot, the central rectangle marks the 50% of the distribution (second and third quartile), the line in bold represents the median and the two outer whiskers (below and above the box) are the minimum and maximum of distribution. Results showed that (Figure 16: Boxplots for Google Home Mini and Amazon Echo Dot 3rd Generation): • Google Home Mini’s users hold more similar opinions about Wakeword and more different ones about Assistance. Moreover, for this smart speaker, lowest values were detected for Home and Voice, whereas highest ones occurred for Assistance • Amazon Echo Dot 3rd Generation’s users share more homogeneous points of view for Play. Moreover, for this smart speaker, lowest and highest values emerged for Home, conveying the idea that the sentimental spectrum is quite wide for this aspect

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Figure 16: Boxplots for Google Home Mini and Amazon Echo Dot 3rd Generation

3.5 Main Results Summary By analysing the results collected through the experimental survey and the Sentiment Analysis reported above it is possible to draw some insights. Perceived Ease Of Use directly influences Perceived Usefulness of smart speakers: the outcome variable resulted in higher value when the task, linked to the definition of the Independent Variable, was easier. According to Two-Way Anova findings, Perceived Ease Of Use does not seem to exert a direct effect on Attitude with respect to the context of this study. Regarding Conversational Style, it does not have a moderating effect on either Perceived Ease Of Use- Perceived Usefulness or Perceived Ease Of Use-Attitude relationships. Despite no direct effect of Conversational Style on Engagement was highlighted, considering the two scales adopted to measure the outcome variable in their entirety, a statistically significant result was showed when a simple Linear Regression was run on the single items of each scale. Specifically, this effect regarded an item used to quantify a component of Engagement, namely fun. Furthermore, Perceived Usefulness of smart speakers positively influences both Engagement and Attitude towards these devices. In turn, Attitude influences Intention To Use and Purchase Intentions. Ultimately, Intention To Use too impacts Purchase Intentions. With reference to Sentiment Analysis, the two types of smart speakers reported similar results in terms of overall emotions and sentiments. Going into details of the selected aspects: • Home recorded the lowest values for both smart speakers • Voice is less appreciated for Google Home Mini than for Amazon Echo Dot 3rd Generation • Focusing on the declension of Artificial Intelligence (i.e. Virtual Assistants) embedded in these items, overall Google Assistant seems to be appreciated more frequently than Alexa

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CONCLUSIONS

Inspired by a question advanced in past literature and not already solved (Følstad, 2018), this study tried to fill a gap related to the speaking modalities that could be assumed by a smart speaker and their impact on well- known TAM relationships. Explicitly, smart speakers tend to adopt a very professional intonation when asked to perform utilitarian duties and instead a more frivolous intonation when asked to perform hedonic ones. Hence, we wanted to explore whether the second type of speaking modality could have enhanced different responses, with respect to the first type, on selected outcome variables when requested actions are aimed at utilitarian purposes. Moreover, the circumstance that sees smart speakers as gateways for the smart home take- off was taken into account as well. Nevertheless, their implementation for this purpose is not yet fully explored since people tend to activate these devices to solve very quick and effortlessly requests. Thus, we wanted to appraise whether there would have been any difference on outcome variables when two different conversational styles were delineated in the context of smart speaker usage for solving either easy or complex tasks (high or low Perceived Ease Of Use), the second type of which were outlined as a smart home practical actualization. Keeping these objects in mind, two variables were manipulated and Two-Way Anova tests were run to check whether there exists a statistically significant difference between groups subjected to the 4 scenarios obtained through this technique. The results of the exploratory analysis showcased that Conversational Style does not exert a moderating effect either on Perceived Ease Of Use-Perceived Usefulness or Perceived Ease Of Use-Attitude relationships. Additionally, we wanted to verify if TAM relationships, well-established for assessing the adoption issue in the generic technological field, are sustained also in the context of the specific smart speakers framework. In past research, not always the subsistence of a direct effect carried out by Perceived Ease Of Use on Perceived Usefulness was considered. In this study, such relationship was not only confirmed but also inspected in more details. Indeed, it was interestingly discovered that Perceived Usefulness is greater when Perceived Ease Of Use is also greater. Placing it differently, consumers believe that a smart speaker is more useful when it is perceived as easy to use, as the reflection of the simple step-goals that can be achieved by means of its functionalities. Hence, while we are generally prone to think that the more a technological object is able to perform complex operations the more it is useful and valuable in its nature, the same was not corroborated in this research. This occurrence is verified regardless of the conversational style assumed by the device, despite higher values (not statistically significant) of Perceived Usefulness arise when business modality is displayed. Concerning the relationship Perceived Ease Of Use- Attitude, although the Two-Way Anova test did not uncover a statistically significant difference among groups, Chi-square test of independence exhibited that there exists an association between these two variables. This relationship clearly displays not only that the two variables are not independent from each other, but also that when a complex task is performed Attitude is most frequently below the average, whereas when an easy task is carried out Attitude is most frequently above the average. Thus, it is possible to conclude that smart speakers are largely preferred when thought as tools able to rapidly solve tasks that do not require too much 78 physical and mental efforts, namely when Perceived Ease Of Use is high. In such circumstance, consumers present more positive attitude towards these devices and regard them as more useful. Besides traditional TAM relationships (Perceived Ease Of Use-Perceived Usefulness, Perceived Ease of Use-Attitude, Perceived Usefulness-Attitude, Attitude-Intention To Use), other linkages among measured variables were outlined: Purchase Intentions are directly influenced both by Attitude and Intention To Use, respectively. This dependent variable was rarely examined in conjunction with TAM constructs: we verified that Attitude and Intentions To Use can turn into real smart speakers purchases. Besides the validation of Perceived Usefulness as a precursor of Engagement, Conversational Style seems to directly influence with statistical significance one aspect, although the only, of the composite Engagement concept: notably fun. Ultimately, with respect to control variables, it was appraised that: • values assumed by some outcome variables (Engagement, Attitude, Intention To Use, Purchase Intentions) tend to be higher when people feel comfortable in approaching new technologies • values assumed by some outcome variables (Perceived Usefulness, Engagement, Attitude, Intention To Use, Purchase Intentions) tend to be higher for people who are familiar with smart speakers • values assumed by some outcome variables (Engagement, Attitude, Intention To Use, Purchase Intentions) tend to be higher for smart speakers owners

Curiously, Perceived Usefulness has a propensity to be higher among non-owners: as a consequence, it could be deduced that advantage and convenience of using these IoT devices decreases once they have been purchased and allegedly used. Likewise, Willingness To Pay is indistinctly below 70 Euros among owners and non-owners, stating that smart speakers are not considered worthy of excessive expenditure despite the plethora of operations they can carry out. Concerning the results of the Sentiment Analysis, run on online collected reviews with the purpose of further investigating the issue of smart speakers acceptance, some fascinating insights were obtained. While reviews centred on Google Home Mini more frequently exhibit home automation as a topic of interest, the ones centred on Amazon Echo Dot 3rd Generation more frequently exhibit media playing as a subject matter. An almost linear conclusion can be drawn by these findings: Google Home Mini seems to receive more attention as a hub adopted for controlling other smart devices inside a home, whereas Amazon Echo Dot 3rd Generation is taken more into account for reproducing media content. Furthermore, the Virtual Assistant embedded in Google Home Mini collected good appreciation among users, pinpointing that it is seen as a virtual agent with a wide range of functionalities and potential in personal assistance for executing different tasks. From a theoretical point of view, this study provides additional information about the nature of TAM variables relationships in the context of smart speakers’ adoption. Expressly, apart from Perceived Usefulness and Attitude that concurrently move upwards when Perceived Ease Of Use is high, Intention To Use may not be the last stop on the smart speakers acceptance route but rather the threshold of their actual purchase. Moreover, despite Conversational Style, intended as a dimension of user experience of the product, does not modify

79 consumers’ perceptions of usability or attitudes towards smart speakers, it should be investigated further in future research. Indeed, it holds the power to alter at least one component of engagement: fun decreases when a business style of speaking is assumed by a smart speaker, if asked to perform either utilitarian easy or complex operations. However such reduction is exacerbated when the second type of task is accomplished. From a managerial point of view, this study provides several useful insights for managers who control the development and distribution of smart speakers. Companies should work on making smart speakers easier and funnier for consumers to use. Such efforts may in fact contribute to boost adoption of smart speakers, real usage, purchase actions and users engagement. Contemporarily strengthening ease of use and usefulness, especially considering their bond which leads them not to be two independent entities, should always be borne in mind since many positive effects may derive by their correct dosing: the more consumers feel that a smart speaker is easy to use the more it will be regarded as useful, generating not only positive attitudes but also engagement. Concerning the first-mentioned facet, people’s behaviours are quite consistent with attitudes since these endurable evaluations shape people’s minds, making them like or dislike an object as well as moving them towards or away from it. The last-mentioned facet is crucial too in the marketing field, since it generally prepares the ground to lasting and profitable brand-consumers relationships: briefly, it is the gateway for customer loyalty. Thus, a deeper and more meaningful dialogue would be created by bringing people to a continuous, constant and repeated use not only of the product itself but also of the additional services offered by the related brand. In this way, the overall economic and functional value attributed to these devices could optimistically achieve more satisfactory results in the future, both among owners and non-owners. By the way, once good levels of engagement are reached, the probability of conquering new portions of potential consumers by means of word of mouth and online reputation gets bigger. In turn, this circumstance may hopefully affect other smart home devices sales, opening up not only new channels but also the opportunities for strategical marketing described in the first chapter of this dissertation. Focusing on the two biggest competitors in the market place, since potential consumers generally affirm that they would like to buy a Google instead of an Amazon device (Olson, 2019), managers of the second mentioned company should try to better integrate the smart speakers functionalities in the smart home context and to better position it as a possible hub in individuals’ habitations. Indeed, smart home is a hot topic in consumers’ minds when they are asked to share judgements about smart speakers but seems to be better devised for Google models. Although this study offers fruitful knowledge into users’ adoption of smart speakers, its findings and implications are exposed to limitations. As highlighted in the method, Perceived Ease Of Use and Conversational Style were manipulated. Thus, for research purposes, the composite complexity of reality was deeply simplified. In particular, Conversational Style was presented in a text form: serious and frivolous intonations were mimicked by means of the sole word composition in order to readily isolate the pure conversation modality from any influences dictated by vocal gender. Consequently, some nuances, such as humour and speech pace, may have been lost by zero-setting vocal pitch. Hence, future research may contemplate the possibility of examining Conversational Style and its gender or genderless nature through real 80 vocal experiments, in order to gather further shades of its role in the context of smart speakers’ adoption. Moreover, it could be interesting to study the effects of Conversational Style outside the TAM theoretical framework. Furthermore, user experience dimensions, that come to life from subject-object interaction, diverse from Conversational Style could be explored hereafter. Scales of measurement, other from those used in this research, could be used to gauge Engagement and analyse it in the detail of all its numerous components, only partially taken into consideration here. Additionally, future research could address the influence of Engagement on trust issues which were not accounted in this study but still represent a source a frustration for smart speakers adopters. Ultimately, forthcoming studies should provide a better distributed sample not only across men and women but also across elderly age groups (45-63), which may be significantly illustrative of behavioural patterns. With that said, the smart speakers’ sector is entering the early majority stage of adoption (Voicebot, 2019): for marketers, new challenges linked to broad features, convenience of use and third-party applications appear on the horizon.

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APPENDIX

Appendix A (Pre-Test)

Pre-Test Perceived Ease Of Use (Task Facile = High PEOU vs Task Difficile = Low PEOU)

Pre-Test Conversational Style (Business vs Pleasure), scale of measurement (Wu, 2017)

Pre-Test Conversational Style (Business vs Pleasure), scale of measurement (Zhang, 1996)

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Appendix B (Two-Way Anova)

Two-Way Anova PEOU*Conversational Style → PU

Two-Way Anova PEOU*Conversational Style → Attitude

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Appendix C (Linear Regression)

Linear Regression Perceived Usefulness → Engagement1

Linear Regression Perceived Usefulness → Engagement2

Linear Regression Perceived Usefulness → Attitude

84

Linear Regression Attitude → Intention To Use

Linear Regression Intention To Use → Purchase Intentions

Linear Regression Attitude → Purchase Intentions

85

Linear Regression Conversational Style → ENG2N8 (fun)

Appendix D (Chi-square)

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Appendix E (Sentiment Analysis)

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SUMMARY INTRODUCTION Internet Of Things, understood as the billions of physical devices around the world connected to the Internet, is being adopted worldwide by almost all sectors. IoT success is pushing firms to design and deliver new services, raging from product functions regulation to user experience personalisation. Not surprisingly, for consumers, the smart home is probably the place where they are more likely to come into contact with internet- enabled things and represents the principal area where the big tech companies are competing hard. Smart speakers are the most obvious objects driving this trend since they easily make real and spontaneous the remote control of other items as well as the performance of daily routines through natural language. Although smart speakers are sketched to improve efficiency and convenience, as well as comfort and entertainment, usability and accessibility issues are still open questions since these items are not always appreciated and their functionalities are not always thoroughly harnessed. Being homes places where people search for a break from a hectic lifestyle, users’ acceptance and consequent adoption of such IoT devices could be affected, other than functionality, by the level of comfort and amusement expressed by them during interaction.

CHAPTER ONE - Relevance

According to “Il Digitale in Italia” annual report, IoT sector was the driving digital force in Italy during 2019 (Tiot, 2020). Overall, smart technologies grew at double-digit rates and saw their share of the entire digital market grow to 19.5% from 13.4% in 2018 (Tiot, 2020). Consumers represent the largest users of IoT things, accounting for nearly two-thirds of the overall applications in use (Parikh, 2019). Namely, IoT possible applications for consumers are not limited to a single field but are extended to several branches of interest: smart home, smart vehicles, smart health to name a few. Especially the first one is the IoT system most frequently searched on Google because it guarantees savings in time, energy and money (Baswani, 2018). By 2025 more than 50% of the internet traffic delivered to households will be used for the operation of connected appliances (Schwab, 2016). The relevance of smart home is remarkably highlighted by numbers: in Italy it is worth €530 million, equal to a growth of 40% year after year (Della Mura, 2020; Corcom, 2020). In particular, 2018 was a turning point for this sector in Italy: not only the market grew by 52% compared to 2017, but the long-awaited smart home speakers have finally been introduced in our Country (Salvadori, 2020). Then, 2019 confirmed the phenomenon with a further 40% increase (Salvadori, 2020). The leap in quality is imputable to the stimulation guided by smart speakers landed in Italy in 2018, which currently weigh 18% of the market value. One of the main interesting peculiarities of these items is that they wear the garments of the gateway to the smart home, the most domestic and familiar form of IoT (Jones, 2018). According to Idc's forecasts, the adoption of smart speakers will continue to expand rapidly and in 2023 there will be 48 million units sold: curiosity and impulse purchases will be replaced by the need to have several smart devices in the homes (Corcom, 2019). Hence, competition for the leadership in this market is far from over (White, 2019). The 111 ability of these items to smoothly integrate with the smart home devices has been significantly boosting the growth of their market (Allied, 2019). Vice versa, in a sort of endless loop, increase in usage of smart home devices drives the sale of smart speakers in the market as well (Allied, 2019). Since vocal smart technology is not regarded as a luxury option only affordable by the middle-upper classes, the battle will take place on a scenario dissimilar from the expenditure perspective: it is necessary to leverage on other distinctive and groundbreaking factors for winning (White, 2019), among which user experience must be primarily taken into account (Salvadori, 2020). Moreover, there is still a significant portion of the population that either does not buy smart objects or make the purchase without using them (Della Mura, 2020). In fact, people may feel intimidated by the perceived technological complexity of these devices or may be unable to perceive their usefulness and positive impact on their lives. Albeit a major boost coming from voice assistants’ expansion, few consumers are really interested in buying smart home products in 2020: for instance, only 8% possess a vocal assistant (Corcom, 2020). The most frequent reasons that still hinder the use are excessive complexity (18%), lack of perception of benefits (10%) and the difficulty in using Apps for management (6%) (Corcom, 2020). In such a context, the development of user-friendly devices could represent the flywheel for their profound acceptance. Additionally, the prevalence of IoT devices in the homes, combined with the availability of constantly updated information on lifestyle trends, will allow marketers to perform better predictive analysis about targets of interest’s evolving expectations and reach niche audiences too (Della Mura & Costa, 2020). Voice technology, whose rate has beaten the one that accompanied the spread of television back to time (Corcom, 2017), has introduced unprecedented opportunities that brands can apply to reach consumers in their home and hopefully establish emotional connection with them (Adams, 2018). This last circumstance becomes real if companies are able to think how their items embedded voices can extend into real vocal experiences that do not just stop superficially on impressions but successfully fullfill functional benefits too (Adams, 2018). The attention virtual assistants are gaining is notable considering that access to information, entertainment and different types of content will be handled by these IoT robberies (Jones, 2018). The evolution of digital marketing towards new shores, including voice recognition, has been predicted by the constant collapse of the Google’s cost per click for almost 4 years (Forbes, 2019). A recent study by Forrester Research has identified the transition of digital marketing away from traditional text-based advertising and more towards voice queries, thus transforming both the way people search and things are advertised (Sweeney, 2019). This explains why, not by chance, voice is regarded as the new touch: marketers will adapt their search strategies moving from pay-per-click to voice skills, SEO and branding to better capitalize on voice (Sweeney, 2019). The future is dotted with smart assistants and assuming that the level of accuracy will be quite the same among different brands, it is urged to find the possible levers that really will drive consumers’ preferences (Molla, 2018). Smart speakers are not just completely reshaping the way consumers work, play and live but they are strengthening business intelligence too by offering big amounts of data that companies can harness to improve operations and customer service (Gregory, 2015). Together with chatbots they define the boundaries of the current “Conversational Marketing”. Built on real-time conversations, it consists of guiding customers through 112 the marketing funnel as quickly as possible (Chatbotize, 2019). Thus, with communication taking place hic et nunc, conversational marketing has the power to boost sales and build authentic relationships ongoing through speech. It is a good palliative for the inefficiencies of e-commerce, which certainly offers convenience and almost unlimited choice but lacks human touch consequently producing low conversion rates (2% on average vs 40% of in-store commerce). Especially at a time in history when it is hard to beat big players, such as Amazon and ASOS, on price, online retailers are in danger of missing the opportunity to hit customer lifetime value, satisfaction rate and brand identity KPIs because of their inability to engage, convert and retain consumers through real-time humanlike interactions, that people love experiencing in physical stores (Chatbotize, 2019). Since voice is definitely the most natural way to communicate, it is able to create personal connection between brand and potential customers. It is up to firms the decision to develop creative marketing strategies that use voice as any form of content marketing, whether it is in line with their missions. For each activity across the consumer journey, the consumer uptake of voice is expected to grow. However, this conjecture will become reality if virtual agents’ interaction skills are humanly refined. Indeed, as people become more proficient in using smart speakers, it will turn crucial for these devices to be more life-like. Especially an improvement in terms of fun and engagement would firstly boost consumers’ experience as well as satisfaction and secondly reinforce both consumers’ propensity to spend and loyalty towards the company (Taylor, 2019). Last but not least, smart speakers interoperability with other compatible objects helps them stay relevant over time. However, recent reports have highlighted that most of interviewed sample has never used a smart speaker for controlling other smart devices (McCaffrey, 2018), thus there is still room for improvement. Smart speakers have skills to reshape business landscape by reconfiguring dynamics between customers and companies: allegiance will shift from trusted brands to trusted AI assistants, with unavoidable implication for strategical marketing (Dawar, 2018). The more consumers use smart speakers, the better they will understand their habits and meet their needs, increasing their satisfaction in a self-reinforcing cycle. Especially those that are able to operate reliably and well, have more probability to increase users’ loyalty. Hence, brands should be ready to respond to virtual assistants’ revolution so as not to be crushed. However, since voice assistants are not yet extensively adopted, or at least below their full capacities, the plethora of marketing advantages that they bring are not thoroughly exploited. How the world of marketing will change in view of smart speakers’ diffusion is still an open question, partially because it is not given to know whether their use, either for individual purposes or as a true digital marketing medium, will be de facto exponential or liable to decay (Baldelli, 2019). Although expectations about IoT devices are really high, adoption levels and ROI are lower than many projections (Cognizant, 2019). It is believed that this circumstance is imputable to the failure of realizing promised benefits or expected human-centred value: IoT solutions are designed without paying too much attention to human experiences. Considering that IoT technologies have already been tested, new challenges related to their future being regard how they can help both people and companies in saving resources and profiting from them (Tiot, 2020): notably, it will be needed to switch from proof of concept to proof of value. Namely, marketers need to find ways of nudging consumers to experiment and go a step further 113 with IoT devices, in order to fostering early adoption, habitual use and retention. For these reasons, more empirical research focused on user experience of the product is required. For smart speakers to be adopted successfully it is necessary that besides being regarded as useful and usable, the interaction adequately satisfies consumers, which is strongly affected by task difficulty and task completion (Bogers, 2019). Since vocal agents like Amazon Alexa and Google Assistant will become the principal channel through which people will search for information, goods and services, they will be the primary transformation driver in how companies will connect with their customers and they will represent the future battlefield of marketing (Dawar, 2018). Considering that currently smart objects adoption mainly takes place in niche segments of technologically trained individuals, it is urged to clearly deliver their value for the sake of reaching a broader range of users (Hoffman, 2018). As these IoT devices are spreading in popularity, more research about their design, development and interface evaluation is urged (Taylor, 2020), especially because consumers exhibit avoidance behaviour towards them. Furthermore, when this last-mentioned circumstance does not apply human interaction is still irregular, sporadic and constrained to basic simple tasks (e.g. search queries) (Dubiel, 2018): even though AI-based voice assistant are extensively purchased, people do not intend to use them in some cases. In such a perspective, it is urged to evaluate peculiar features, like the quality of interaction, of these devices besides the more traditional ones of technology adoption models (Nasirian, 2017). In seeking to capture interest and therefore be accepted by its audience, a vocal assistant should possess a real pinch of human moods so that it could smoothly and unconditionally become a key element of an individual’s daily routine (Reply, 2020). In order for users to easily activate any action through these smart devices, what they need is a good interface through which they can actively control IoT devices both individually and as part of a system, interact with them and get useful responses (Engineering, 2018). Albeit not so easy to translate into reality, consumers constantly need to experience simplicity, enjoyment and satisfaction while using smart objects. Probably, their future will be less boring if they accommodate more styles and flexibility of speech: when molding the details of a smart speaker, conversation quality should be delivered in a highly calibrated way. Nonetheless, the conversational aspect does not seem to capture the attention it actually deserves when speaking of voice assistants and their essence of extremely advantageous tools for businesses (Troisi, 2020). Besides performing tasks in a brilliant way, smart speakers should provide entertainment by introducing themselves as unique, engaging and interesting while chatting. Forrester’s analysts believe that it could be challenging to persuade users to do more complex tasks than checking the weather with their smart speakers (Matthews, 2020). The biggest barrier to overcome, for further and definitive market growth to take hold, consists in convincing users of voice recognition’s aptitude to be more useful than expected, more often and for more applications (Freddi, 2018). The growing popularity of voice assistants offers a chance for brands to enter the homes of target audiences and become part of their daily lives in real time (Eldeman, 2019). The real revolution will come in the next years as soon as the use of smart speakers increases (Eldeman, 2019).

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CHAPTER TWO – Conceptual Framework Internet of Things is an evolving umbrella term (Ziegeldorf, 2014), firstly introduced to society by Ashton (1998) in a presentation about RFID and supply chain (Pinochet, 2018), that encloses the interrelation of physical objects with concepts like constant connectivity and remote-control ability. Practically speaking, in the IoT, real world and virtual one merge with the aim of providing customers with brand new experiences when they approach the usage of its building blocks: the so called “Smart Objects”, widgets connected to the Internet, capable of manage information (García, 2017) and augmented with sensing-processing-actuating skills (Fortino, 2012). Hence, Smart Objects and IoT are two notions that walk together and complement each other: the former can expand their intelligence to unprecedent limits not only by virtue of their inner characteristics but also of network communication protocols able of connecting them ubiquitously (García, 2017). In this case, the smartness comes from augmentation of computing skills, regardless of whether it is perceived in terms of awareness or autonomy of action (Kawsar, 2009). Accordingly, these devices may be judged as a revolutionary turn in the original product design because of their smart and connectivity components: sensors that perceive the state of the physical reality, actuators that enable actions and network communication (Wi-Fi, Bluetooth or RFID) (Mani, 2017). Regardless of the possible IoT fields of application (e.g. wearables, smart homes, smart cities, industrial automation), they embed the potentiality of extending the existing human–application interaction to the point of enabling people and objects to be almost constantly connected and exchange information (Lu, 2018). Smart objects should not be evaluated only as various IoT technical declensions, but also as human-centred interactive tools developed to help people carry out tasks day-by-day. This implies that their design patterns should go beyond hardware and software specifications, which are obviously ineluctable, to embrace in addition interactive input and output capabilities as well as social aspects (Kortuem, 2009). Even if IoT technologies seem to represent an open door toward a smoother lifestyle, consumers’ resistance to them is a major current concern that needs to be understood and solved in order to successfully accelerate the pace of smart objects adoption. Adoption linked to IoT solutions can be viewed as the first approach or attempt for acceptance of new technologies. Resistance is construed in literature as “ a form of reaction or negative attitude to new products and services that triggers change or upset the status quo” (Mani, 2018). Undoubtedly, innovations might dramatically overturn people’s daily lifestyle starting from their established habits to the roots of their deepest beliefs (Mani, 2018) and this is especially true in presence of product features (connectivity, intelligence and ubiquity) that have the potential to lay the groundwork of consumers’ reticence (Mani, 2017). With regards to this circumstance, status quo bias theory (Samuelson, 1988) clearly displays that humans are almost naturally predisposed to turn down the acceptance of any novelty that, from their viewpoint, could potentially generate more losses than gains. Certainly, individuals feel choked by difficulty when they have to learn how to adopt innovations and, since they tend to be strongly attached to their routines, they easily experience a profound sense of unsatisfaction (Heidenreich, 2013). Consistent with Ram’s perspective (Ram, 1987), adoption and resistance coexist in the life cycle of an innovation and definitely the first can be achieved only overcoming the second one. Since quite often and not 115 by chance innovations fail to become commercial successes (Heidenreich, 2013), primarily because of human hostility to transformations (Claudy, 2015), literature has focused on analysing which factors might represent a source of acceptance or refusal in this specific context. Previous research (Heidenreich, 2013; Ram, 1989) has shown that people may be reluctant to use innovations because of both functional (product characteristics: usefulness, novelty, price, device intrusiveness) and psychological (consumer characteristics: self-efficacy, dependence, privacy concerns) barriers (Mani, 2017). The former arise because innovations pose potential radical changes related to usage, value and risk, meaning that users would be willing to accept them only if they are able to significantly redress habits, provide substantial economic benefits and reduce risks (economic, physical, performance and social) compared to the status quo (Ram, 1989). Whereas, the latter, which are related to daily routines and the image of innovation in contrast with tradition, arise when consumers’ prior beliefs are threatened. Moreover, consumers seem to deny the utility of a smart object, perceived as non- essential or gadgetry, when its image and their ones walk in opposite directions (Mani, 2018; O'Cass, 2008; Hosany, 2012). Mani and Chouk (Mani, 2018) proposed an extension of Ram and Seth’s previous theory, adding to functional and psychological barriers three more types: ideological, individual and technological vulnerability. Particularly, the last one is regarded as a combined state of dependence and anxiety, caused both by the growing importance of technology in society and the sense of unpreparedness in using it. Voice User Interface adopts voice as a control modality and, since it can be smoothly included in objects, it has become ubiquitous and growing (Corbett, 2016). Considering that voice-controlled devices exclusively leverage on voice-based commands, as their nature suggests, they largely facilitate individuals’ efforts. Being regularly connected to the Internet, unless turned off by users, smart speakers widely differ from older voice-activated objects insofar they can go beyond a smaller set of rough “built-in” questions and answers (Burkett, 2017; Hoy, 2018). The most fascinating feature of these IoT devices is their potential to support humans in household life (Hoy, 2018). Actually, either Amazon Echo or Google Home can work as a hub connected to other directly or indirectly (i.e. using IFTTT as intermediary) compatible devices with the aim of controlling their functionality not just in terms of their individuality but as part of a whole (Lau, 2018). Smart home has been widely described as the next digital disruptor that can deeply subvert time and energy consumptions, leveraging on simplification and streamlining (Strengers, 2017). In a schematic representation, these designed intelligent buildings can be depicted as assemblages of heterogeneous smart objects that constantly exchange information among themselves and with the outside world (Ricquebourg, 2006). Nonetheless, their overall value goes well beyond the sum of their single components and neither is imputable to the parts taken individually (Hoffman, 2015), since each of them can be easily added or removed at any given time. Accounting that research about consumers’ adoption behavior of smart speakers is currently poor (Haug, 2020), it is pivotal to clarify how these devices can be increasingly integrated without giving the impression to threaten human nature (Russo, 2017). The theoretical roots can be borrowed from the Theory of Acceptance Model, since it has been largely ratified as a reliable model for analysing information technologies and smart services (Gao, 2019). However, users’ acceptance and consequent adoption of IoT could be affected by other 116 factors beyond perceived ease of use and perceived usefulness, especially in case they lack necessary knowledge and skills. To our knowledge, there are few studies that analyse factors affecting intention to use smart speakers and that explore user interactions with them. For instance, the design and development of technological devices should be particularly attentive to all those product specifications that go beyond the more technical and functional ones and may represent the leading ingredients of a fruitful subject-object interaction (Aurigi, 2005). The answer might be found by focusing on aesthetic paradigms linked to relational communication, pleasurable experiences and perceptions (Russo, 2017). Accordingly, when dealing with smart objects it is urged to focus their relationship issues with humans on physical dynamics that define subject-object instrumental dialogue on an experiential frame (Pragmatism Aesthetics) (Spadafora, 2016). In this context, the theory of aesthetics of interaction fits with the purpose of overcoming the questionable duality efficiency-aesthetics in the design field of interacting items, since, embracing Norman’s viewpoint, actually “attractive things work better” (Norman, 2002). Considering that it has been proved that different atmospheres (e.g. hedonic vs utilitarian usage environment) (Childers, 2001) and beliefs about strength of enjoyment (Wu, 2007) may alter perceived ease of use and perceived usefulness, it can be inferred that people would like to be exposed to a certain level of involvement while interacting with smart objects (Gao, 2014). Although how to create a situation in which users may feel as comfortable and amused as possible when using IoT devices is still an open question, exploiting interaction style might represent a valid way. Communication modalities and skills, which are the ways the system replays to user’s inputs in a sort of feedback loop, are vital not only for making the interaction immediate and easily understandable but also for triggering individuals’ subsequent actions (Conversational Style) (Rosini, 2016). Generally, when communicating with audiences, smart objects can take on two distinct approaches: friend-like style and engineering-like style. Consequently, different impressions, on which individuals will rely the arrangement of their evaluations and reactions (Carli, 1989), disclose. Furthermore, the effect of interaction style intensifies eminently when individuals have little knowledge of smart objects. Still, results of previous research regarding interaction style were merely related to variables like brand warmth and brand attachment (Wu, 2017). Pondering the unique features of smart speakers, that deeply differ from other innovations, the existing technology adoption models represent a good basis to develop a dissertation about them although being insufficient alone and so requiring a more comprehensive approach (McLean, 2019). TAM affirms that consumers’ attitudes about a new technology are chiefly shaped by perceptions of easiness and usefulness. However, these perceptions are a result of people’s processing information about the product, which could be more or less tough and could be dilute in its difficulty through human resemblance (Anthropomorphism) (Goudey, 2016). Moreover, experience design in marketing mindset has become fundamental for building satisfaction, stable customer relationships, reliable word of mouth and competitive advantage (Murphy, 2019). This circumstance is perfectly in line with consumers’ current pursuits: seeking for experiences obtainable through products and services consumption rather than simply buying something (Neuhofer, 2015). With this mind, objects’ aesthetics should be judged not much for external qualities as it should for its unique capacity to generate new experiences quickly and 117 innovatively (Folkmann, 2015). Keeping in mind that “traditional notion of aesthetics as narrowly associated with beauty is obsolete” (Folkmann, 2015), experience shaped through interaction is invested with a double value: utilitarian, given its role of eliciting functionality, and hedonic, given its role of causing aesthetic pleasure (Cila, 2015). Because there is little knowledge of what influences individuals in their decisions to purchase and use these IoT devices (McLean, 2019), this study combines the theoretical foundations of TAM with Pragmatist Aesthetics and Anthropomorphism emphasizing the role of Conversational Style in the adoption process of smart speakers.

CHAPTER THREE – Data Collection and Analysis Relying on the disquisitions exposed in previous sections, the aim of the current study is to identify the weight covered in the context of smart speakers’ adoption by (Figure 11: Research Model): • Conversational Style as a Moderator on the relationships between TAM variables (Perceived Ease Of Use- Perceived Usefulness and Perceived Ease Of Use – Attitude) • Perceived Usefulness and Conversational Style as predictors of Engagement • Attitude and Intention To Use as predictors of Purchase Intentions

To this end, few respondents were firstly randomly submitted to a questionnaire (Pre-Test) geared towards the definition of the right scenarios needed for manipulating Perceived Ease Of Use and Conversational Style, respectively. Once Pre-Tests contents were verified in their validity for the purposes of the current study, a wider range of people were randomly exposed to 4 different scenarios (Main-Test), obtained through the combination of Perceived Ease Of Use and Conversational Style manipulations, and asked to complete a survey in which several variables of interest were measured: Perceived Usefulness, Engagement, Attitude, Intention To Use, Purchase Intentions. Perceived Ease of Use→ “the degree of ease associated with the use of a technological system” (Evans, 2015). Conversational style→ a matter of practically executing communication as well as an inner smart speakers’ potentiality to produce new attitude settings and patterns of engagement during human-agent interaction (Folkmann, 2015). A consciously designed vocal approach might prevent the preclusion of a thoroughly appreciated adoption of these devices by attributing them essential human characteristics rather than superficial ones as well as increasing their credibility, liveliness and personality (Wagner, 2019; Waytz, 2014). Perceived Usefulness→ “the degree to which a person believes that using a particular system would enhance his or her performance” (Davis, 1989). Engagement→ an iterative process centred on interactive consumer experiences capable of sustaining people’s interest toward a product or a service (Moriuchi, 2019). Attitude→ an individual’s subjectivity in terms of the amount of affect for or against some object (Fishbein, 1977; Liu, 2018; Spears, 2004). Intention to Use → depicts what is commonly meant for acceptance since it reliably predicts actual usage of

118 system technology (adoption). Purchase Intentions→ a measure of an individual’s conscious plan to make an effort to purchase a brand (Spears, 2004). Willingness To Pay → “the maximum price a given consumer accepts to pay for a product or service” (Le Gall-Ely, 2009; Yuan, 2017; Adams, 2019). Control variables → Gender, Age, Smart Speaker’s Ownership, Consumer Type. The latter has been modulated according to the respondent's level of technology readiness (i.e. comfort in using technologies), level of familiarity with smart speakers and possession of smart speakers, if any. The main survey was extended to a sample of 45 people for each condition. Overall, 180 subjects (155 women, 25 men) with an average age of 27.62 (18 ≤ x ≤ 63) completed the survey. Despite 76% of the sample (mostly women) admitted that they do not own a smart speaker, respondents simultaneously declared on average to feel comfortable when approaching new technologies (µ=5.50) and to be familiar with the use of smart speakers (µ=4.19). By analysing the results collected through the experimental survey and the Sentiment Analysis reported in previous sections it is possible to draw some insights. Perceived Ease Of Use directly influences Perceived Usefulness of smart speakers (F(1;176)=4.088 p < 0.05): the outcome variable resulted in higher value when the task, linked to the definition of the Independent Variable, was easier (M=4.3772, SD=1.51180). According to Two-Way Anova findings, Perceived Ease Of Use does not seem to exert a direct effect on Attitude with respect to the context of this study (F(1;176)=2,407 p > 0.05). However, a Chi-Square test of independence showcased the existence of an association between Perceived Ease Of Use and Attitude, once they have been transformed into categorical variables, highlighting their non-independence (X2(1;180)=5.692 p < 0.05). Regarding Conversational Style, it does not have a moderating effect on either Perceived Ease Of Use-Perceived Usefulness or Perceived Ease Of Use-Attitude relationships. Despite no direct effect of Conversational Style on Engagement was highlighted, considering the two scales adopted to measure the outcome variable in their entirety, a statistically significant result (F(1,178)=9.938, p < 0.01) was showed when a simple Linear Regression was run on the single items of each scale. Specifically, this effect regarded an item used to quantify a component of Engagement, namely fun. Furthermore, Perceived Usefulness of smart speakers positively influences both Engagement (F(1,178)=65.579, p < 0.01; F(1,178)=90.046, p < 0.01) and Attitude (F(1,178)=29.096, p < 0.01) towards these devices. In turn, Attitude influences Intention To Use (F(1,178)=15.243, p < 0.01) and Purchase Intentions (F(1,178)=21.428, p < 0.01). Ultimately, Intention To Use too impacts Purchase Intentions (F(1,178)=729.522, p < 0.01). Furthermore, the table reported below shows additional and interesting findings obtained by running several Chi-Square tests of independence in order to study eventual relationships between measured variables, after transforming them from continuous to nominal. With reference to Sentiment Analysis, the two types of smart speakers reported similar results in terms of overall emotions and sentiments. Going into details of the selected aspects: home recorded the lowest values for both smart speakers; voice is less appreciated for Google Home Mini than for Amazon Echo Dot 3rd Generation; focusing on the declension of Artificial Intelligence (i.e. Virtual Assistants) 119 embedded in these items, overall Google Assistant seems to be appreciated more frequently than Alexa. Variables Chi-Square (X2) Statistical Strength of association significance Comfort – Engagement2 X2(1;180)=5.673 p < 0.05 Weak Cramer’s V= 0.178 Comfort – Attitude X2 (1;180)=5.166 p < 0.05 Weak Cramer’s V= 0.169 Comfort – Intention To Use X2 (1;180)= 4.244 p < 0.05 Weak Cramer’s V= 0.154 Comfort – Purchase Intention X2 (1;180)= 11.465 p < 0.01 Moderate Cramer’s V= 0.252 Ownership - Perceived Usefulness X2(1;180)=10.272 p < 0.01 Weak Cramer’s V= 0.239 Ownership – Engagement2 X2(1;180)=5.164 p < 0.05 Weak Cramer’s V= 0.169 Ownership – Attitude X2(1;180)=4.435 p < 0.05 Weak Cramer’s V= 0.157 Ownership – Intention To Use X2(1;180)=23.245 p < 0.01 Moderate Cramer’s V= 0.359 Ownership – Purchase Intention X2(1;180)=16.077 p < 0.01 Moderate Cramer’s V= 0.299 Ownership – Willingness To Pay X2(1;180)= 4.401 p < 0.05 Weak Cramer’s V= 0.156 Familiarity - Perceived Usefulness X2(1;180)=16.460 p < 0.01 Moderate Cramer’s V= 0.302 Familiarity – Engagement1 X2(1;180)=7.584 p < 0.01 Weak Cramer’s V= 0.205 Familiarity – Engagement2 X2(1;180)=13.973 p < 0.01 Moderate Cramer’s V= 0.279 Familiarity – Attitude X2(1;180)=7.943 p < 0.01 Weak Cramer’s V= 0.210 Familiarity – Intention To Use X2(1;180)=22.586 p < 0.01 Moderate Cramer’s V= 0.354 Familiarity – Purchase Intention X2(1;180)=17.761 p < 0.01 Moderate Cramer’s V= 0.314

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Research Hypotheses Results H1: Perceived Ease Of Use → Perceived Usefulness Confirmed H2: Perceived Ease Of Use → Attitude Not Confirmed* H3: Perceived Ease Of Use * Conversational Style → Perceived Usefulness Not Confirmed H4: Perceived Ease Of Use * Conversational Style → Attitude Not Confirmed H5: Perceived Usefulness → Engagement Confirmed H6: Perceived Usefulness → Attitude Confirmed H7: Attitude → Intention To Use Confirmed H8: Intention To use → Purchase Intentions Confirmed H9: Attitude → Purchase Intentions Confirmed H10: Conversational Style → Engagement Not Confirmed**

*not confirmed with Two-Way Anova, but Chi-Square test of independence showed a significant relationship between variables **not confirmed with Two-Way Anova run on the Engagement scales considered in their entirety, but a significant effect exerted by Conversational Style on one item of the second scale, namely fun, was discovered

CONCLUSIONS Inspired by a question advanced in past literature and not already solved (Følstad, 2018), this study tried to fill a gap related to the speaking modalities that could be assumed by a smart speaker and their impact on well- known TAM relationships. Explicitly, smart speakers tend to adopt a very professional intonation when asked to perform utilitarian duties and instead a more frivolous intonation when asked to perform hedonic ones. Hence, we wanted to explore whether the second type of speaking modality could have enhanced different responses, with respect to the first type, on selected outcome variables when requested actions are aimed at utilitarian purposes. Moreover, the circumstance that sees smart speakers as gateways for the smart home take- off was taken into account as well. We wanted to appraise whether there would have been any difference on outcome variables when two different conversational styles were delineated in the context of smart speaker usage for solving either easy or complex tasks (high or low Perceived Ease Of Use), the second type of which were outlined as a smart home practical actualization. Keeping these objects in mind, two variables were manipulated and Two-Way Anova tests were run to check whether there exists a statistically significant difference between groups subjected to the 4 scenarios obtained through this technique. The results of the exploratory analysis showcased that Conversational Style does not exert a moderating effect either on Perceived Ease Of Use-Perceived Usefulness or Perceived Ease Of Use-Attitude relationships. Additionally, we wanted to verify if TAM relationships, well-established for assessing the adoption issue in the generic

121 technological field, are sustained also in the context of the specific smart speakers framework. In past research, not always the subsistence of a direct effect carried out by Perceived Ease Of Use on Perceived Usefulness was considered. In this study, such relationship was not only confirmed but also inspected in more details: consumers believe that a smart speaker is more useful when it is perceived as easy to use, as the reflection of the simple step-goals that can be achieved by means of its functionalities. Hence, while we are generally prone to think that the more a technological object is able to perform complex operations the more it is useful and valuable in its nature, the same was not corroborated in this research. This occurrence is verified regardless of the conversational style assumed by the device, despite higher values (not statistically significant) of Perceived Usefulness arise when business modality is displayed. Concerning the relationship Perceived Ease Of Use- Attitude, although the Two-Way Anova test did not uncover a statistically significant difference among groups, Chi-square test of independence exhibited that there exists an association between these two variables. This relationship clearly displays not only that the two variables are not independent from each other, but also that when a complex task is performed Attitude is most frequently below the average, whereas when an easy task is carried out Attitude is most frequently above the average. Thus, it is possible to conclude that smart speakers are largely preferred when thought as tools able to rapidly solve tasks that do not require too much physical and mental efforts, namely when Perceived Ease Of Use is high. In such circumstance, consumers present more positive attitude towards these devices and regard them as more useful. Besides traditional TAM relationships (Perceived Ease Of Use-Perceived Usefulness, Perceived Ease of Use-Attitude, Perceived Usefulness-Attitude, Attitude-Intention To Use), other linkages among measured variables were outlined: Purchase Intentions are directly influenced both by Attitude and Intention To Use, respectively. This dependent variable was rarely examined in conjunction with TAM constructs: we verified that Attitude and Intentions To Use can turn into real smart speakers purchases. Besides the validation of Perceived Usefulness as a precursor of Engagement, Conversational Style seems to directly influence with statistical significance one aspect, although the only, of the composite Engagement concept: notably fun. Ultimately, with respect to control variables, it was appraised that: • values assumed by some outcome variables (Engagement, Attitude, Intention To Use, Purchase Intentions) tend to be higher when people feel comfortable in approaching new technologies • values assumed by some outcome variables (Perceived Usefulness, Engagement, Attitude, Intention To Use, Purchase Intentions) tend to be higher for people who are familiar with smart speakers • values assumed by some outcome variables (Engagement, Attitude, Intention To Use, Purchase Intentions) tend to be higher for smart speakers owners

Curiously, Perceived Usefulness has a propensity to be higher among non-owners: as a consequence, it could be deduced that advantage and convenience of using these IoT devices decreases once they have been purchased and allegedly used. Likewise, Willingness To Pay is indistinctly below 70 Euros among owners and non-owners, stating that smart speakers are not considered worthy of excessive expenditure despite the

122 plethora of operations they can carry out. Concerning the results of the Sentiment Analysis, an almost linear conclusion can be drawn: Google Home Mini seems to receive more attention as a hub adopted for controlling other smart devices inside a home, whereas Amazon Echo Dot 3rd Generation is taken more into account for reproducing media content. Furthermore, the Virtual Assistant embedded in Google Home Mini collected good appreciation among users, pinpointing that it is seen as a virtual agent with a wide range of functionalities and potential in personal assistance for executing different tasks. From a theoretical point of view, this study provides additional information about the nature of TAM variables relationships in the context of smart speakers’ adoption. Expressly, apart from Perceived Usefulness and Attitude that concurrently move upwards when Perceived Ease Of Use is high, Intention To Use may not be the last stop on the smart speakers acceptance route but rather the threshold of their actual purchase. Moreover, despite Conversational Style, intended as a dimension of user experience of the product, does not modify consumers’ perceptions of usability or attitudes towards smart speakers, it should be investigated further in future research. Indeed, it holds the power to alter at least one component of engagement: fun decreases when a business style of speaking is assumed by a smart speaker, if asked to perform either utilitarian easy or complex operations. However such reduction is exacerbated when the second type of task is accomplished. From a managerial point of view, companies should work on making smart speakers easier and funnier for consumers to use. Such efforts may in fact contribute to boost adoption of smart speakers, real usage, purchase actions and users engagement. By the way, once good levels of engagement are reached, the probability of conquering new portions of potential consumers by means of word of mouth and online reputation gets bigger. Focusing on the two biggest competitors in the market place, since potential consumers generally affirm that they would like to buy a Google instead of an Amazon device (Olson, 2019), managers of the second mentioned company should try to better integrate the smart speakers functionalities in the smart home context and to better position it as a possible hub in individuals’ habitations. Indeed, smart home is a hot topic in consumers’ minds when they are asked to share judgements about smart speakers but seems to be better devised for Google models. Although this study offers fruitful knowledge into users’ adoption of smart speakers, its findings and implications are exposed to limitations. As highlighted in the method, Perceived Ease Of Use and Conversational Style were manipulated. Thus, for research purposes, the composite complexity of reality was deeply simplified. In particular, Conversational Style was presented in a text form: serious and frivolous intonations were mimicked by means of the sole word composition in order to readily isolate the pure conversation modality from any influences dictated by vocal gender. Consequently, some nuances, such as humour and speech pace, may have been lost by zero-setting vocal pitch. Hence, future research may contemplate the possibility of examining Conversational Style and its gender or genderless nature through real vocal experiments, in order to gather further shades of its role in the context of smart speakers’ adoption. Moreover, it could be interesting to study the effects of Conversational Style outside the TAM theoretical framework. Furthermore, user experience dimensions, that come to life from subject-object interaction, diverse from Conversational Style could be explored hereafter. Scales of measurement, other from those used in this research, could be used to gauge 123

Engagement and analyse it in the detail of all its numerous components, only partially taken into consideration here. Additionally, future research could address the influence of Engagement on trust issues which were not accounted in this study but still represent a source a frustration for smart speakers adopters. Ultimately, forthcoming studies should provide a better distributed sample not only across men and women but also across elderly age groups (45-63), which may be significantly illustrative of behavioural patterns.

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