Smart home: Highly-educated students' acceptance

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Smart home: Highly-educated students' acceptance Patricia Baudiera, , Chantal Ammib, Matthieu Deboeuf-Rouchonc ⁎

a b c

Léonard De Vinci Pôle Universitaire, Research Center, 12, avenue Léonard De Vinci, 92916 Paris La Défense Cedex, France Institut Mines-Télécom Business School, 9, rue Charles Fourier, 91011 Evry Cedex, France Altran Expertise Center Transformation Digitale, 14bis Terrasse Bellini, 92807 Puteaux, France

ARTICLE INFO

ABSTRACT

Keywords: Smart cities Smart living Smart home Digital natives TAM UTAUT2

In the coming years, cities face an urban transition in order to manage their resources, public administration, safety, regional economics, education, innovation, health, culture, and entertainment an efficient way. The Smart City concept includes several smart dimensions relating to the environment, mobility, the economy, governance, people, and living. This study explores the impact of Smart home dimensions on highly-educated students, drawn from what is known as the “digital native” population, one of the key components of the smart living concept. As digital natives are already engaged with the adoption of new technologies and sustainable development, we have postulated that they would be keen to use smart technologies in the home that could improve their daily life while preserving the environment. This study tests a scale developed to measure consumer perception of the Smart Home Concept (SHC) and the impact on “Performance Expectancy” and “Habit”. The model was built using some of the constructs of existing technology acceptance models, such as the UTAUT2 and TAM2 models. Based on our findings, digital natives seem ready to adopt the SHC and our results highlight the fact that Smart Home products could be targeted at this specific population.

1. Introduction According to the United Nations (2015), 70% of people will live in cities by 2050, and this exponential growth and high population density will generate potential issues (Dameri, 2013). The city will be considered as one of the main contributors of pollution (Almirall et al., 2016). Organizations (for example, cities, government, business) will have to introduce innovations such as urban mobility system (Spickermann et al., 2014) to solve all kind of complex technical, physical, and social issues (Gil-Garcia et al., 2015; Grimaldi and Fernandez, 2017). To date, innovations such as Internet connection, smartphones, WIFI and components miniaturization have contributed to the development of smart objects. For example, cameras deployed in certain sensitive areas can reduce the level of crime (Coleman and Sim, 2000), and sensors can contribute to controlling the quality of air (Morreale et al., 2011) or the quality of water (Quinn et al., 2010). Cities must now face up to the challenge of hyper-connectivity. Even if the Smart City Concept is considered by some researchers as the utopia of the twenty-first century (Datta, 2015), we note a growing interest in the concept from different sources and origins. For the success of Smart technologies' implementation, the cooperation and active participation of people is a key factor (Zhang et al., 2014). Therefore, Smart cities

must address the needs of both businesses and citizens. A lot of cities, worldwide, are dedicating huge budgets to getting smarter, working in partnership with companies such as Cisco, Siemens, IBM, Orange etc. around different topics such as Energy management, Environment, Security, Safety, Transportation, Health or Education. For example, smart education using Information and Communication Technology could change the way of teaching and learning (Roslina et al., 2017). According to Alelaiwi et al. (2015) the spread of 3G and 4G technologies and the development of devices such as Smartphones or tablets promote the implementation of smart class environment. Learners can be connected from home and Smart education can provide them with more comfort, efficiency and flexibility. Khatoun and Zeadally (2016) suggest that Smart Cities consist of six components which they define as urban characteristics impacting the: (1) environment; (2) governance; (3) living; (4) mobility; (5) people and (6) economy (Fig. 1). For this study, the Smart Home dimension, one of the dimensions of the smart living concept, has been retained (Aldrich, 2003). Based on Fuselli et al. (2013), buildings represent 45% of the emission of CO2 and by changing behaviors and using Smart Home technologies from 10 to 30% of energy consumption can be saved (Yohanis et al., 2008). Nevertheless, the Smart Home penetration rate is still very low, US is leading the market with 3.7% followed by Japan, Germany, Sweden and Norway at

Corresponding author. E-mail addresses: [email protected] (P. Baudier), [email protected] (C. Ammi), [email protected] (M. Deboeuf-Rouchon). ⁎

https://doi.org/10.1016/j.techfore.2018.06.043 Received 3 January 2018; Received in revised form 25 May 2018; Accepted 26 June 2018 0040-1625/ © 2018 Elsevier Inc. All rights reserved.

Please cite this article as: Patricia Baudier, Chantal Ammi and Matthieu Deboeuf-Rouchon, Technological Forecasting & Social Change, https://doi.org/10.1016/j.techfore.2018.06.043

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is considered as successful when systems used are integrated without problem in individuals' daily life providing them with comfort & ease of use and when users can rely and trust the technology. Domestication implies that individuals will first learn about (1) the technology including the benefit of using it, (2) the use of the technology and then (3) the symbolic behind the use of technology. Several researches applied the domestication theory to the SHC especially in the context of energy management (Isaksson, 2014; Juntunen, 2014). Digital natives, born after 1980 (Prensky, 2001), also called “the Net generation” or “the Millennials”, have grown up with digital solutions. They are considered as multi-taskers, early adopters of new communication technologies (Howe and Strauss, 2000) and to be addicted to innovations (Bennett et al., 2008). According to Rogers (2003), early adopters can influence others in their decision on whether or not to adopt a new technology. He demonstrates that innovation acceptance is linked to both the innovative technology proposed and individual's reaction regarding the acceptance of the technology. Based on Prensky (2013), digital natives think and react in quite a different way to the rest of the population and are more concerned with global issues, such as sustainable development (Williams and Page, 2011). Given the gaps in research on the SHC for digital natives, this study aims to evaluate their level of acceptance of smart home technologies. The purpose of our study is to consider users' acceptance of Smart Home technologies, especially users within the digital native population, with a high level of education. Questions that we will consider include what are the dimensions of the acceptance models that directly impact the intention to adopt SHC? What are the variables of SHC that impact “Performance Expectancy” or “Habit” two of the dimensions of the UTAUT2 model? This document is organized as follows. First, we present the Smart City (Khatoun and Zeadally, 2016) and Smart Living (Barlow and Venables, 2003; Lorente, 2004) concepts. Following this brief introduction, we go into further detail about the SHC (Ahn et al., 2016; Balta-Ozkan et al., 2013; Gram-Hanssen and Darby, 2018). Next, we will focus on the four dimensions of the Smart Home: (1) Convenience and Comfort; (2) Health Care; (3) Safety and Security; and (4) Sustainability as identified by Chen et al. (2009). There are several models that can be used to test the level of acceptance of new technologies: TAM, TAM2, UTAUT, and UTAUT2. UTAUT2 has been developed to more accurately predict ICT usage within a consumer context. For this article, we employed some of the variables of the UTAUT2 (Venkatesh et al., 2012) and TAM2 (Davis et al., 1989; Hsu and Lin, 2015). Our research was developed using the scales of the seven dimensions of the UTAUT2 model (Fig. 2), which was used by Yang et al. (2017) to measure the impact on the intention to live in a Smart Home. The “Personal Innovativeness” concept – the propensity of people to use an innovative technology (Agarwal and Prasad, 1998; Schillewaert et al., 2005) – was also integrated into our model. Furthermore, we argue that the moderating variables of gender and type of education will impact the relationship of all variables in our model related to “Intention to Use” the SHC, and therefore we integrate them in our model. The research design, including our hypotheses, is described in Section 2.4. We have applied Churchill Jr (1979)'s paradigm for the pre-test of the new scale developed to measure the SHC, and the final survey was carried out with highly-educated students from the digital native population, which was our target audience. In Sections 4 & 5, data are analyzed, and findings are presented. Our results identify the impact of “Comfort/ Convenience”, “Health and Safety/Security” on “Performance Expectancy” and “Habit”, but, surprisingly, the impact of “Sustainability” was unproven. Our research highlights the importance of “Performance Expectancy” and “Habit” on “Intention to Use”. Using our findings, ways to surmount Smart Home barriers and managerial implications regarding Smart Home technology to digital natives are suggested.

Fig. 1. Smart City model (reproduced with the authors' permission). Source: Khatoun and Zeadally (2016).

0.8%1. Hopefully, the worldwide market size at 24.1 billion US dollars in 2016 should double within the next years to reach 53.45 billion US dollars in 2022.1 Wong and Leung (2016) note that Smart Home technologies could simplify daily life and reduce stress by improving safety and security. Smart homes and smart devices have the integrated technologies in infrastructure, platform, software and services, together with machine learning, data analytics, and user-friendly high-tech services (Qureshi et al., 2017). Some factors may influence the diffusion of innovation, such as the willingness to use innovative products and the accessibility of new technology (i.e. low price). The diffusion of innovation depends on adopters' profiles and can be classified into five categories (Rogers, 2003): (1) Innovators, who are the first to adopt innovation; they usually do not have financial issues and are ready to take risks by adopting innovative technology; (2) Early adopters, who are often considered as having a high degree of opinion leadership; (3) Early majority – the third category to adopt a technology after the innovators and the early adopters; (4) Late majority, who start to use a new technology when the majority of people are already using it; (5) Laggards, who are the last to use a new technology. Innovators and Early Adopters, with high technology readiness, are more likely to accept new technology as they understand all benefits and issues such as discomfort and security (Parasuraman, 2000). Attracting them is crucial for sales and marketing strategy (Moore, 2002), their greater knowledge of smart technologies reinforces their perceived benefits (BaltaOzkan et al., 2013). Nevertheless, to be successful companies shouldn't ignore late adopters that usually acquire mature products (Lam and Venkatesh, 2017). Jahanmir and Lages (2016) developed a late adopters' scale measuring their behavior: the slowness of adoption (Late adopters need time to adopt a new technology), the resistance to innovation (Late adopters are reluctant to change), and finally the skepticism (Late Adopter have some doubts regarding the added value of new technology). Based on Jahanmir and Lages (2016), late adopters are sensible to low prices. According to Lehtonen (2003), the diffusion of innovations concept implies a more passive role of individuals. Hargreaves et al. (2018) rather prefer to use the domestication theory where users are more involved as they will buy and implement smart solutions within their home. Based on Sørensen (1994), domestication 1 https://www-statista-com.devinci.idm.oclc.org/statistics/682204/globalsmart-home-market-size/.

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Fig. 2. UTAUT2 (Venkatesh et al., 2012).

2018). Statista (2006),2 segmented the Smart Home market share of smart appliances into five categories (1) Cooking appliances (26.4%), (2) Room air conditioners (20.1%), (3) Home laundry appliances (19.1%), (4) Dishwashers and disposals (18.6%) and finally (5) Fridges and freezers for 15.6%. Depending on smart devices sold, several international brands have invested in smart technologies such as Whirlpool, Electrolux, Bosh/Siemens, General Electric, Samsung, LG, AT&T and Philips. Smart Home services have started to attract providers proposing smart metering tools to manage the consumption of energy (Indrawati and Tohir, 2016; Peters et al., 2018), developing solutions to monitor health and therefore allow elderly or disabled people to carry on living at home in a safe environment (Ehrenhard et al., 2014; Kim et al., 2013; Peine and Moors, 2015; Pham et al., 2018), and companies providing entertainment services, for example, audio or video systems (Nikayin et al., 2011). To adopt smart systems, individuals must understand all proposed benefits (Kumar et al., 2018). The main perceived benefits of Smart Home are savings of energy, money and time (Wilson et al., 2017). But the prices are still a barrier for the adoption of SHC including potential installation costs, high repair and maintenance costs (Balta-Ozkan et al., 2013). Customers need to compare the investment versus benefits including savings (Hosek et al., 2017). Therefore, lower deployment costs and personalized support are needed to leverage the SHC to a larger audience especially late adopter (Balta-Ozkan et al., 2013). Cost is not the only barrier for users, the market growth is directly linked to the perceived benefits and the acceptance of potential risks of adopting a Smart Home such as privacy (GfK, 2016). Furthermore, SHC is considered as non-essential luxuries that will make the household lazy (Wilson et al., 2017) and will increase the dependency on technology and outside experts (Wilson et al. (2017). Business actors, such as software & hardware vendors, infrastructure providers or telecom operators (Hosek et al., 2017) promote the SHC’ benefits to make the smart home business more attractive and understandable. They work all together to propose turnkey solutions, solving the interoperability and privacy issues that could impact the adoption of smart appliances in Smart Homes (Maddulety et al., 2017; Hosek et al. (2017). Professionals increasingly propose smart solutions for new buildings or renovation. Several cities worldwide develop smart

2. Theoretical background and hypotheses 2.1. Smart home concept Based on Khatoun and Zeadally (2016), Smart Cities consist of six components defined around urban characteristics (1) Smart Environment, (2) Smart Mobility, (3) Smart Economy, (4) Smart Governance, (5) Smart People and (6) Smart Living (Fig. 1). One of these components, the Smart Living Concept, provides users with different functionalities, such as home automation and entertainment solutions (i.e., domotica), and the capability to monitor and improve health (Chang et al., 2018) or manage energy (Barlow and Venables, 2003; Lorente, 2004). This concept, which impacts the individual's life both in their personal (home) and professional environments, is based on integrated and secured systems that will intuitively, simply, and automatically manage spaces. Smart Living solutions can be easily set up in existing environments (Aldrich, 2003). As our research focused on measuring the acceptance of smart technologies in the user's personal life, we narrowed the scope of the Smart Living Concept to the Smart Home Concept, which we also identified as the interactive, intelligent, or networked home. Although no fixed definition of the concept of Smart Home exists, Gram-Hanssen and Darby (2018) define it as a home that includes digital sensing and communication devices and propose a segmentation into four categories: (1) security and control; (2) activities; (3) relationships and continuity; (4) reflection of identity and social status. Since 2011, the number of publications on Smart Cities has increased due to the growing interest of researchers (Hajduk, 2016), but few of them have focused on the potential users of Smart Home technologies. According to Wilson et al. (2015), from a survey of 150 articles regarding the SHC, 61% were published in engineering and technical sciences journals, 19% in medicine/health journals and 20% in social sciences journals, including economics, psychology, and energy. Alaa et al. (2017) identified 229 articles covering the topics of Smart Home applications based on the Internet of Things. At the outset, Smart Home solutions were developed mainly to provide users with support in controlling environmental systems (lights and ambient temperature), and managing devices such as refrigerators (Fensel et al., 2017), driers or washing machines (Parag and Butbul,

2 Statista Survey (Global Consumer Survey) Home/household appliances in the U.S.

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buildings such as Issy-les-Moulineaux, a city located in the South of Paris in France, who launched in 2017 the first smart building where all devices are 100% connected and managed by the voice using an Apple ‘application. By proposing smart homes, suppliers demonstrate to household all the benefits of using such solutions. Furthermore, costs are included in the global acquisition price. According to Balta-Ozkan et al. (2013), Smart solutions devices are more and more visible on shelves with competitive prices at retailers' level and are now part of individuals' daily life. The key characteristics of a Smart Home are easy access to the system and the capability to remotely control home appliances. Energy management is one of the top priorities of the European Commission (2015) that funded projects that involve and convince consumers to adopt smart technologies with the purpose to reduce their energy consumption and peak demand (Gangale et al., 2013). According to Paetz et al. (2012), individuals living in a Smart Home and using smart metering could have a better understanding of their energy consumption (smart metering), and smart objects inside their home could be used in an optimal way (smart appliances) to optimize energy consumption and for individuals to achieve cost savings, depending on the energy pricing policy of their provider. For example, a washing machine could turn itself on automatically when the electricity price was at its cheapest. By scheduling, electric home appliance, in an efficient way, using algorithm such as the Minimum Cost Maximum Power, users can reduce consumption cost without impacting comfort (Singaravelan and Kowsalya, 2017). An automated home system, based on mobile applications, offers low costs and flexible solutions to control and optimize energy consumption, and to improve security, such as intruder and fire detection (Kumar, 2014). BaltaOzkan et al. (2013) identified other services offered by the SHC, such as improving communication, assisted home living to improve health, comfort, or convenience, and, finally, entertainment solutions. Smart Home development was possible thanks to technological improvement, the digitalization of daily life (Smartphone, Internet, Internet of Things), and big data collection and analysis (Moreno et al., 2017) but also as a result of political support and calls for a sustainable economy (Thite, 2011; Winters, 2011; Zygiaris, 2013). SHC can also improve the quality of life, reduce costs, and manage waste/resource consumption for a large community of key players, such as residents in a building (Snow et al., 2016). Equipped with sensors connected to devices that allow users to remotely control smart object or networks, the Smart Home is considered to be the home of the future, using various innovative appliances and technologies (hardware and/or software) that provide residents with a comfortable living space (Jiang et al., 2004). Different smart objects, developed to understand residents' behavior, autonomously suggest the appropriate services that answer the residents' needs and perform certain specific tasks in an intuitive way. According to Mital et al. (2017), individuals will be prepared to adopt new smart devices in their houses when the devices can be seen to simplify their daily activities. In addition, based on Lee et al. (2017), a home is a “social space” where social connectedness between users and Smart Home devices can improve perceived social support in a Smart Home context. They defined two categories of social connectedness (SC), the inner SC (connection inside the home between smart devices and inhabitants) or the outer SC (connection between the inhabitants or devices and people or devices outside the home). Chi et al. (2007) classified smart objects according to their functional and interactional relations between non-digital and digital products. Digitalization should enhance the conventional function of domestic objects: the improved productivity and living experience they offer should be met by addressing specific needs from different kind of users. The developed interfaces should be simple to use and meet users' requirements, such as the automatic management of the house, for example, by informing the user if a door is not closed, or by adjusting lights or thermostats (Chen et al., 2009; Strickland, 2011). According to Yang et al. (2017), certain variables such as security, risk privacy, and trust can influence the decision on the adoption of a Smart Home. Singh Sohal et al. (2018)

present a cybersecurity framework to prevent potential attacks of IoTs. SHC also need to fit in with current and changing lifestyles (BaltaOzkan et al., 2013) and individuals will use Smart Home technologies if they are able to perceive value or benefits compared to potential sacrifices (Kim et al., 2017). The implementation of a Smart Home enables the real-time collection and analysis of big data to enhance services by providing accurate information to decision-makers (Hashem et al., 2016). According to Hashem et al. (2016), big data analysis will play a key role in smart city ‘development. The main challenge is to make sure that all smart objects in the Smart Home are connected and are managed remotely to collect data to help with the prediction of subsequent actions by analyzing previous actions and by offering a maximum of comfort (Mital et al., 2017). Finally, Wilson et al. (2017) identified nine main reasons for adopting the SHC: (1) activities require less effort; (2) energy saving; (3) time saving; (4) money saving; (5) improve security; (6) improve quality of life; (7) provide residents with comfort; (8) peace of mind; (9) and care. 2.2. Unified Theory of Acceptance and Use of Technology: UTAUT2 UTAUT developed by Venkatesh et al. (2003) and UTAUT2 (Venkatesh et al., 2012) provide a comprehensive synthesis of theories and models on technology acceptance as presented in Table 1. In the UTAUT2 model, Venkatesh et al. (2012) propose measuring the level of acceptance of new technologies by analyzing the impact of (1)“Performance Expectancy” (PE), (2) “Effort Expectancy” (EE), (3) “Social Influence” (SI), (4) “Facilitating Conditions” (FC), (5) “Hedonic Motivation” (HM), (6) “Price Value” (PV), and (7) “Habit” (HT) on “Intention to Use” moderated by Age, Gender, and Experience. (1) PE is defined by the belief that using a specific system or technology can enable users to enhance their performance (Venkatesh et al., 2012). Several researchers highlighted the importance of PE in the adoption of digital services or technologies (e.g., Chong et al., 2012; Luo et al., 2010). (2) EE, equivalent to the perceived ease of use (Venkatesh et al., 2012), is directly linked to the level of complexity of the technologies involved. However, for the digital native generation this variable is less critical for the acceptance of new technologies as they already have a lot of experience of using digital technologies such as computers, tablets, and Smartphones (Wang et al., 2014). (3) SI is degree to which people around you influence you to adopt a system or a technology (Sun et al., 2013). SI includes several subconstructs such as social factors and subjective norms (Venkatesh et al., 2003). Previous research demonstrates that the effect of SI is stronger when users adopt specific technologies such as healthcare wearables (Gao et al., 2015) or mobile health services (Sun et al., 2013). (4) FC defines the perception of individuals of the level of help provided from a technological or organizational (e.g., support, training) point of view to support them in utilizing a system or object (Teo et al., 2008; Venkatesh et al., 2003). FC, including Table 1 Models and theories on acceptance. Model or theory 1 2 3 4 5 6 7 8

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Theory of Reasoned Action (TRA) by Fishbein and Ajzen, 1975 Technology Acceptance Model (TAM) by Davis, 1986 The Motivational Model (MM) Theory of Planned Behavior (TPB) by Ajzen, 1991 The Model combining the TAM and TPB The Model of PC Utilization The Innovation Diffusion Theory (IDT) by Rogers, 1962 The Social & Cognitive Theory

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personal competencies (personal resources or knowledge), directly impact customers' intention to use technologies, as the level of user competencies is a key factor in the acceptance of new technologies (Venkatesh et al., 2012). (5) HM, considered as an intrinsic value, is a key factor for the adoption and usage of innovations (Brown et al., 2006). Venkatesh et al. (2012, p161) defined the HM as “the fun or pleasure derived from using a technology”. The proposed system or technologies must arouse the user's emotions and make the experience enjoyable and positive in order to motivate users to adopt them (Poong et al., 2017). (6) PV may impact significantly on consumers' adoption and usage of new technology. In marketing research, the perception of price/cost is determined by the quality of proposed products or services (Tam, 2004; Venkatesh et al., 2012; Zeithaml, 1988) and the benefits perceived from using the technology. PV has a positive and direct impact on intention to use when the perception of benefits by potential consumers is greater than the monetary cost. (7) HT, defined by Limayem et al. (2007), is the capacity of individuals to perform behaviors automatically as they learn how to use a new technology. Habit, considered as the result of previous repeated experiences (Kim and Malhotra, 2005), is linked with the level of interactions and developed familiarity (Alalwana et al., 2016).

Habit directly, significantly, and positively. 2.4.1.2. Health/Wellbeing (H). Health-oriented Smart Homes (Rialle et al., 2002) aim to monitor individuals' parameters to improve them and, if necessary, provide users with medical assistance, especially people with permanent or even temporary disabilities (Roy et al., 2011). Real-time information can be provided to healthcare professionals. Agent-based Smart Home technologies can monitor functional measurements to detect unusual situations and provide prompt first aid. Some specific platforms offer the capability to monitor blood sugar levels for diabetes patients. Other physiological parameters, such as blood pressure, temperature, and heartbeat, can be controlled. Those controls could help to reduce the expense of clinicbased assessments (Wild et al., 2008). These Smart Home models can integrate technologies adapted to patients (Alaiad and Zhou, 2017) and allow them to remain in their homes (Morris et al., 2013). Health monitoring is possible thanks to some wearable devices already available, such as clothing (Brauner et al., 2017). Chang et al. (2018) have also investigated the intention to use hearing aids in the context of the Smart City. Based on Batalla et al. (2017), the growth in the development of these devices directly impacts the concept of the Smart Home. We hypothesize that: Hypothesis 2a. Smart Home health variable impact Performance Expectancy directly, significantly, and positively.

This research was built using the scales of the seven dimensions of the UTAUT2, as shown in Fig. 2, to measure their impact on the intention to live in a Smart Home.

Hypothesis 2b. Smart Home health variable impact Habit directly, significantly, and positively.

2.3. Personal Innovativeness in the domain of IT (PI)

2.4.1.3. Safety and Security (SS). Smart Home objects provide users with real-time information that can improve safety and security by sending messages, for example, to alert the user in the event of unauthorized intrusion (surveillance cameras, breaking glass detector, door and window contacts), to detect smoke and gas detectors (trigger the sprinklers in the event of fire), and to maintain home security (Dewsbury et al., 2001; Morris et al., 2013; Robles et al., 2010). We postulate following hypotheses:

Customers' acceptance of innovation is key for the success of the implementation of innovative technologies. The PI, developed by Agarwal and Prasad in 1998, is the propensity of individual to adopt technologies, quicker than others and to test innovative technology (Schillewaert et al., 2005). The PI is an antecedent of innovation (Yi et al., 2006) and is considered as a personal trait that could directly impact adoption of new technologies (Agarwal and Prasad, 1998).

Hypothesis 3a. Smart Home safety and security variable impact Performance Expectancy directly and positively.

2.4. Hypotheses The main characteristic of a Smart Home is the capability to remotely and easily control home appliances (Balta-Ozkan et al., 2013). As previously mentioned, Chen et al. (2009) identified four dimensions of the Smart Home: (1) Convenience and Comfort; (2) Health Care; (3) Safety and Security; and (4) Sustainability, which we describe in detail in the following section. By living in a Smart Home, inhabitants may believe that their performance regarding these four dimensions will be enhanced and that this will impact positively their “Performance Expectancy”. In addition, we postulate that daily interaction with Smart Home objects may also influence their “Habit” (Gómez Mármol et al., 2012).

Hypothesis 3b. Smart Home safety and security variable impact Habit directly and positively. 2.4.1.4. Sustainability/Home energy management (SD). One of the original objectives of the implementation of Smart Homes was the management of energy consumption but also the development of renewable energy management (Batalla et al., 2017). Energy consumption and energy cost can be reduced by using Smart Home technologies, such as, the Internet of things, home automation, or by offering variable tariffs (Paetz et al., 2012). Room temperatures will vary according to whether inhabitants are at home, and the system can consider their lifestyle and habits. Consumer behavior could be impacted by sustainable household technologies (Heiskanen et al., 2005). According to Dalmas and Lima (2016), digital natives are interested in sustainable development: they want to live in harmony with the environment which must, from their perspective, be protected. We postulate following hypotheses:

2.4.1. Smart home 2.4.1.1. Convenience and Comfort (CC). Home automation of smart objects allows consumers to remotely control home systems by using devices such as Smartphones (Belimpasakis and Stirbu, 2014) to create a convenient and comfortable environment. Inhabitants can turn off or control the intensity of their heating, air conditioning, or lights, and create a specific atmosphere, for example, by dimming lights. Entertainment smart objects aim to entertain people, for example, by remote-controlling of media devices such as DVD player or TVs (Aldrich, 2003; Wilson et al., 2015). We suggest that:

Hypothesis 4a. Smart Home sustainability variable impact Performance Expectancy directly, significantly, and positively. Hypothesis 4b. Smart Home sustainability variable impact Habit directly, significantly, and positively.

Hypothesis 1a. Smart Home comfort and convenience variable impact Performance Expectancy directly, significantly, and positively.

2.4.2. UTAUT2 2.4.2.1. Performance Expectancy (PE). Several researchers demonstrate

Hypothesis 1b. Smart Home comfort and convenience variable impact 5

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the direct, positive, and significant impact of PE on the adoption of smart technologies such as sustainable household technologies (Ahn et al., 2016), electronic health records (Pulidindi et al., 2016), egovernment services (Rodrigues et al., 2016), healthcare telemedicine equipment (Kohnke et al., 2014) and the Internet of Things (Mital et al., 2017). Based on Leong et al. (2017), the “Intention to Use” the Internet of Things in the context of the Smart City can be influenced by “Performance Expectancy”. According to Gao et al. (2015), PE has a direct, positive, and significant impact on “Intention to Use” wearable technology for medical reasons. However, the impact is not significant for fitness purposes. Other studies on the intention to adopt mobile or internet technologies confirm this impact, such as for remote mobile payments (Slade et al., 2015), mobile health services (Sun et al., 2013), online information services (Oh and Yoon, 2014), internet banking usage (Al-Qeisi et al., 2014), and social networks sites (SNS) for sharing user-generated content (Herrero et al., 2017). Our postulate is:

to live in a Smart Home.

Hypothesis 5. PE has a direct, positive, and significant impact on Intention to live in a Smart Home.

2.4.2.6. Price Value (PV). As researchers do not always use the PV variable of the UTAUT2 in their model, the literature is incomplete on the subject. Leong et al. (2017) demonstrate that PV impacts, in a moderated way (β = 0.095; t-value = 1.96; p-value = 0.05), the intention to adopt the Internet of Things in the context of the Smart City. This result is aligned with the analysis provided by Venkatesh et al. (2012). According to Wang et al. (2018), “Price Value” could influence individual behavior in the case of the acceptance of electric vehicles, and Gao et al. (2015) note that PV has a direct, positive, and significant impact on “Intention to Use” wearable technology for fitness usage but not for medical reasons. We hypothesize that:

2.4.2.5. Hedonic Motivation (HM). “Hedonic Motivation” could directly impact the acceptance of the Smart Home (Ahn et al., 2016), the Internet of Things in the context of the Smart City (Leong et al., 2017), and SNS to share user-generated content (Herrero et al., 2017). HM impacts directly, positively, and significantly behavioral “Intention to Use” a technology (Venkatesh et al., 2012), results confirmed by Gao et al. (2015) in the case of wearable technology for fitness usage but not for medical reasons. The effect of enjoyment on the intention to play online games has been demonstrated by Wu and Liu (2007). We suggest that: Hypothesis 9. HM has a direct, positive, and significant impact on Intention to live in a Smart Home.

2.4.2.2. Effort Expectancy (EE). Ahn et al. (2016) find that EE doesn't impact the “Intention to Use” a sustainable household technology, also confirmed for remote mobile payments (Slade et al., 2015), or the intention to adopt SNS for sharing user-generated content (Herrero et al., 2017). EE has no direct and significant impact on the “Intention to Use” wearable technology for fitness purposes, but its impact is significant for medical reasons (Gao et al., 2015). However, some researchers find that EE impacts the “Intention to Use” new technologies such as electronic health record (Pulidindi et al., 2016), e-government services (Rodrigues et al., 2016), online information services (Oh and Yoon, 2014), healthcare telemedicine equipment (Kohnke et al., 2014), mobile health services (Sun et al., 2013) and the Internet of Things in the context of the Smart City (Leong et al., 2017). We suggest that:

Hypothesis 10. PV has a direct, positive, and significant impact on Intention to live in a Smart Home. 2.4.2.7. Habit (HT). “Habit” usually reflects the consequences and results of previous experience with a technology. In our analysis, we have adapted the scale to measure the fact that using a SHC could become a “Habit” (Escobar-Rodriguez and Carvajol-Trujillo, 2013). The greater the habit of inhabitants to use the Internet of Things or smart technologies, the greater will be their intention to adopt a SHC. HT impacts directly, positively, and significantly behavioral “Intention to Use” a technology (Venkatesh et al., 2012), and has a direct, positive, and significant impact on the adoption of SNS for sharing usergenerated content (Herrero et al., 2017) or for tagging photos on SNS (Dhir et al., 2018). Given these previous results, we consider that:

Hypothesis 6. EE has a direct, positive, and significant impact on Intention to live in a Smart Home. 2.4.2.3. Social Influence (SI). Some researchers find no impact of SI on “Intention to Use” a Smart Home technology (Ahn et al., 2016), SNS to share user-generated content (Herrero et al., 2017), or to adopt the Internet of Things in the context of the Smart City (Leong et al., 2017). Nevertheless, SI has a positive, direct, and significant impact on the “Intention to Use” healthcare telemedicine equipment (Kohnke et al., 2014), remote mobile payments (Slade et al., 2015), online information services (Oh and Yoon, 2014), mobile health services (Sun et al., 2013), wearable technology for both fitness and medical usage (Gao et al., 2015), and m-commerce (Chong et al., 2012). According to Lee (2014), college students are influenced by their peers and family in the adoption of a smartphone. We hypothesize that:

Hypothesis 11. HT has a direct, positive, and significant impact on Intention to live in a Smart Home. 2.4.3. Personal Innovativeness in the domain of IT (PI) PI can, in an effective way, determine the level of acceptance of innovative products or concepts by measuring the tendency to adopt a new technology. Ahn et al. (2016) find that “Innovativeness” directly impacts the “Intention to Use” a sustainable house, and Schweitzer and Van den Hende (2016) suggest that “Innovativeness” can moderate the intention to adopt smart products. Gupta et al. (2011) demonstrate that PI can significantly impact the “Intention to Use” new technological products, such as mobile location-based services and remote mobile payments (Slade et al., 2015). As this research analyses the level of acceptance of SHC by digital natives with high levels of education, we propose the following hypothesis:

Hypothesis 7. SI has a direct, positive, and significant impact on Intention to live in a Smart Home. 2.4.2.4. Facilitating Conditions (FC). According to Herrero et al. (2017), FC does not impact the intention to adopt SNS to share user-generated content or the Internet of Things in the context of the Smart City (Leong et al., 2017). However, FC has a direct, positive, and significant impact on “Intention to Use” wearable technology for fitness usage but not for medical reasons (Gao et al., 2015). “Intention to Use” a new technology can be determined by FC in the context of e-government services (Rodrigues et al., 2016), healthcare telemedicine equipment (Kohnke et al., 2014), or mobile health services (Sun et al., 2013). Therefore, we postulate following hypothesis:

Hypothesis 12. PI impact positively, directly, and significantly on Intention to live in a Smart Home. 2.4.3.1. Moderating effects. However, regardless of whether users are drawn from the digital native generation, or whether they have an engineering or business school profile, we argue that the moderating

Hypothesis 8. FC has a direct, positive, and significant impact on Intention 6

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variables of type of education and gender will impact the relationship of all variables within our model related to intention to use.

All the Average Variance Extracted (AVE) of each construct, above the recommended threshold of 0.5 (Table 3), validates the convergent validity (Fornell and Larcker, 1981). The composite reliability and Cronbach's Alpha, with all values above 0.7 (Evrard et al., 2009) confirm the reliability (Table 4). All the items of the new scale measuring the SHC have been accepted. The pre-test confirms the validity and reliability of our new scales.

2.4.3.2. Type of education. Schoonenboom (2014) found that highly educated people adopt innovation quicker than the less educated (Wozniak, 1987). All respondents of our study had a high level of education (at least a License or Master's Degree) but with different profiles (engineers, designers, or managers). A direct relation has been established between personality trait, vocation, and choice of studies (Lee and Ashton, 2004, 2006). Individuals tend to select their academic curriculum accordingly to their centers of interest, and their learning experience will impact their expectations (Lent et al., 1994), in their daily life. In addition, Decman (2015) demonstrated that previous education of highly educated students could impact their acceptance of e-learning. Given these earlier results, we formulate that type of education will moderate the effect of “Performance Expectancy” (CC(H13a), H(H13b), SS(H13c), SD (H13d)), and “Habit” (CC(H13e), H (H13f), SS(H13g), SD (H13h)) on the four dimensions of Smart Home and on the “Intention to use” of the UTAUT2 dimension (PE (H13i), EE (H13j), SI (H13k), FC (H13l), HM (H13m), PV (H13n) and HT (H13o)) and “Personal Innovativeness” (H13p).

3. Sample and data collection Since the introduction of the SHC, the number of academic articles published on the topic has increased, but mainly with a focus on technical aspects (Ahvenniemi et al., 2017), and topics relating to customers' needs are not yet mature. However, a gap in consumer behavior has been identified, especially in relation to the digital native population. Therefore, our survey was administered to highly educated students from the digital native population. Digital natives, also defined as the “Net generation”, Generation Y and Z or the Millennials, have a strong interest in new technologies and this generation is the first to be raised with access to the Internet and computers. They are often considered as more comfortable with and more knowledgeable about innovation than the previous generations. Digital natives are open to change. Born between 1982 and 2004, with the Information and Communication Technology revolution (Howe and Strauss, 2000), digital is part of their daily life. Their commitment to new technologies is critical, as their reaction could impact positively or negatively on the launch of new technical concepts (Prensky, 2001). Therefore, for our research, we decided to focus on the digital native population and to analyze how the SHC should address their specific needs. To measure the acceptability of SHC by digital natives, we selected a population of French students with different profiles, from business schools and a Graduate School of Engineering, who volunteered to participate in the survey. We sent them a link, in October 2017, using collaborative tools such as Yammer or their school e-mail addresses to an online questionnaire. Within one month, we collected 316 valid answers (Table 5).

2.4.3.3. Gender. The Technology Acceptance Model theorizes the influence of gender. According to Venkatesh et al. (2003), PE effect is stronger for young men whereas EE and SI effect are stronger for women with limited experience. Venkatesh et al. (2003) didn't find an effect for gender on “Facilitating conditions”. Sumak et al. (2010) demonstrate that gender does not influence the relationship between EE and PE on Intention to use e-learning solutions in those with a highlyeducated profile, but that SI impacts Intention to use. Whereas Decman (2015), also studying the acceptance of e-learning services, found no impact on all relation between the variables of the UTAUT2. Some researchers found that gender has no significant impact on technology acceptance in the case of Smart Watches (Wu et al., 2016) or mobile entertainment (Leong et al., 2013). Therefore, we hypothesize that the effect of “Performance Expectancy” (CC (H14a), H (H14b), SS (H14c), SD (H14d)) and “Habit” CC (H14e), H (H14f), SS (H14g), SD (H14h)) on the four dimensions of the Smart Home will not be moderated by gender and that the effect on Intention to use of seven dimensions of the UTAUT2 model and “Personal Innovativeness” (H14): PE (H14i), EE (H14j), SI (H14k), FC (H14l), HM (H14m), PV (H14n) and HT (H14o) and PI (H14p) will not be moderated by gender.

4. Results Structural Equation Modeling (SEM) analysis with a Partial Least Square approach was conducted to understand relationships between the constructs of the model for each school in order to highlight potential differences. Chin (1998) and Hair et al. (2011) define the minimum size of the sample, when using a PLS approach, by multiplying by 10 the number of items of the variable with a maximum of items. Therefore, the minimum size of our model to run an analysis by subgroups was 50 (Table 5). In a study carried out by Hair et al. (2012) on 311 of the models published in academic journals, 24% had a sample of below 100. SmartPLS 3 software was used to test our hypotheses. Regarding the global sample characteristics of the SHC, men represent 41% of respondents and women 59%. All respondents are < 25 years old and live mainly in cities of > 35,000 inhabitants (63%). In Table 6, the differences highlighted are due to the student profile. For example, the female population within the luxury school is higher than within the engineering school when the gender distribution resembles the global sample for EMLV and TEM. Age (between 17 and 25) and size of city (> 35,000 inhabitants) are consistent.

2.5. Research model Our research model (Fig. 3) was developed using seven constructs from UTAUT2 (Venkatesh et al., 2012) adapted to test our hypotheses. Venkatesh is one of the most cited authors and the UTAUT2 scale is one of the most frequently used scales to measure users' acceptance of technologies. In addition, we decided to select and adapt the “Intention to use” variable extracted from TAM (Davis et al., 1989; Hsu and Lin, 2015). Items use a five-point Likert scale from “strongly disagree” to “strongly agree” (Appendix B). A self-developed scale based on our literature review was developed to measure the four dimensions of the SHC (Chen et al., 2009). We have applied the paradigm of Churchill Jr (1979), first by studying the literature to build scales that have been submitted to 10 experts (Appendix A). Then, the questionnaire was administrated to a representative sample of 84 students (46 women and 38 men), mainly from three business schools (Burgundy Business School, Bordeaux, and Amiens University-IAE), to test our new scales before submitting them to the targeted audience. The cross loadings of each indicator show that no indicator loads higher on an opposite construct (Table 2) and the Average Variance Extracted (AVE) of the four constructs is higher than the squared inter correlations of other constructs (Table 3), confirming the discriminant validity (Fornell and Larcker, 1981; Hair et al., 2012).

4.1. Outer model 4.1.1. Reliability To measure the reliability of our global model, we controlled the variable loadings to be above the threshold of 0.7. The recoded item PI4 of “Personal Innovativeness” with a reversed scale “In general, I am hesitant to try out new information technologies” was removed. The reliability was also controlled by verifying that Composite Reliability and Cronbach's Alpha were above the threshold of 0.7 (Table 7). With a 7

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Fig. 3. Research model. Table 2 Cross loadings.

Table 4 Convergent validity and reliabilities.

Convenience/Comfort CC1 CC2 CC3 CC4 CC5 H1 H2 H3 H4 H5 SD1 SD2 SD3 SD4 SD5 SS1 SS2 SS3 SS4

0.792 0.737 0.801 0.830 0.729

Health

0.827 0.851 0.792 0.885 0.746

Substainability

Safety/Security Convenience/Comfort Health Safety/Security Substainability

Convenience/Comfort Health Safety/Security Sustainability

0.779 0.692 0.574 0.615

Health

0.822 0.596 0.528

Composite reliability

Average Variance Extracted (AVE)

0.841 0.879 0.824 0.890

0.885 0.912 0.879 0.920

0.607 0.675 0.647 0.696

Table 5 Template explaining number of students per campus.

0.788 0.854 0.855 0.859 0.812

Ecole International du Marketing du Luxe (EIML) Ecole de Management Léonard De Vinci (EMLV) Ecole Supérieure d'Ingénieurs Léonard de Vinci (ESILV) Télécom Ecole de Management (TEM) Total

0.892 0.876 0.718 0.714

Safety/ Security

0.804 0.639

77 85 64 90 316

24% 27% 20% 28% 100%

Cronbach's Alpha at 0.654, the construct “Facilitating Conditions” has been removed.

Table 3 AVE and squared intercorrelations of other constructs. Convenience/ Comfort

Cronbach's Alpha

4.1.2. Discriminant validity We accessed discriminant validity of the constructs by ensuring that the cross loading of each indicator loads higher on an opposing construct (Hair et al., 2012) and by verifying in Table 8 that the square root of the AVE of the construct was superior to the correlations of this construct with the other constructs (Fornell and Larcker, 1981).

Sustainability

0.834

4.1.3. Convergent validity The convergent validity was verified by controlling the Average Variance Extracted (AVE) of each construct for the four models studied,

8

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Table 6 Sample characteristics. Total

%

EIML

%

EMLV

%

ESILV

%

TEM

%

Gender Female Male Total

186 130 316

59% 41% 100%

65 12 77

84% 16% 100%

46 39 85

54% 46% 100%

27 37 64

42% 58% 100%

48 42 90

53% 47% 100%

Age 17–20 21–25 > 25 Total

171 122 23 316

54% 39% 7% 100%

30 39 8 77

39% 51% 10% 100%

58 19 8 85

68% 22% 9% 100%

29 29 6 64

45% 45% 9% 100%

53 35 2 90

59% 39% 2% 100%

City < 2000 2001–5000 5001–15,000 15,001–35,000 > 35,000 Total

16 24 36 42 198 316

5% 8% 11% 13% 63% 100%

3 8 4 6 56 77

4% 10% 5% 8% 73% 100%

6 9 7 13 50 85

7% 11% 8% 15% 59% 100%

0 2 11 9 42 64

0% 3% 17% 14% 66% 100%

7 5 14 14 50 90

8% 6% 16% 16% 56% 100%

Table 7 Reliability.

Table 9 Average Variance Extracted.

Comfort/Convenience (CC) Effort Expectancy (EE) Habit (HT) Health (H) Hedonic Motivation (HM) Intention to Use (IU) Performance Expectancy (PE) Personal Innovativeness (PI) Price Value (PV) Safety/Security (SS) Social Influence (SI) Sustainability (SD)

Cronbach's Alpha

Composite reliability

0.810 0.866 0.885 0.912 0.921 0.856 0.729 0.831 0.769 0.765 0.919 0.882

0.868 0.908 0.921 0.934 0.950 0.913 0.831 0.898 0.858 0.850 0.949 0.914

Average Variance Extracted (AVE) Comfort/Convenience Effort expectancy Habit Health Hedonic motivation Intention to use Performance expectancy Personal innovativeness Price value Safety/Security Social influence Sustainability

0.569 0.712 0.744 0.739 0.863 0.778 0.551 0.747 0.670 0.588 0.860 0.681

Our evaluation confirms the validity and reliability of our outer model.

with all values above 0.50 (Table 9).

β = 0.387, t = 5.621, p = 0.000), but not by “Sustainability” (H4a: β = 0.011, t = 0.164, p = 0.869). We used the f2 to evaluate the effect size of each predictor and found a moderated effect size of “Comfort/ Convenience” at 0.141. With a R2 at 0.371, our model explains 37.1% of “Habit” determined by the same constructs that explain “Performance Expectancy” such as “Safety/Security” (H1b: β = 0.204, t = 3.523, p = 0.000), “Health” (H2b: β = 0.222, t = 3.307, p = 0.001) and Comfort/Convenience” (H3b: β = 0.342, t = 5.217, p = 0.000), but not by “Sustainability” (H4b: β = −0.028, t = 0.472, p = 0.637). The f2 (0.106) of “Comfort/Convenience” is again considered as moderated. The R2 (0.614) indicates that the model explains a significant amount of the variance in “Intention to Use” determined by both

4.2. Inner model To test the inner model, the value of R2, f2, and Q2 of the endogenous variables was verified (Fig. 4). The relations between variables were estimated by controlling the following parameters: Path coefficient (β) above 0.200, t-value > 1.96, and p-value below 0.05. The Goodness-of-Fit index at 0.57 confirms the good quality of the hypothetical model (Latan and Ghozali, 2012). We examined the explained variance (R2) of the endogenous constructs. Our model explains 39.5% of “Performance Expectancy” which was determined by “Safety/ Security” (H1a: β = 0.173, t = 2.809, p = 0.005), “Health” (H2a: β = 0.195, t = 2.848, p = 0.004) and “Comfort/Convenience” (H3a: Table 8 Discriminant validity.

CC EE HT H HM IU PE PI PV SS SI SD

CC

EE

HT

H

HM

IU

PE

PI

PV

SS

SI

SD

0.754 0.302 0.529 0.550 0.400 0.591 0.571 0.489 0.352 0.409 0.328 0.539

0.844 0.459 0.241 0.469 0.426 0.362 0.433 0.355 0.342 0.230 0.240

0.862 0.496 0.619 0.725 0.625 0.519 0.457 0.435 0.368 0.334

0.859 0.366 0.505 0.496 0.379 0.351 0.483 0.358 0.383

0.929 0.513 0.463 0.444 0.429 0.340 0.294 0.256

0.882 0.663 0.493 0.469 0.465 0.421 0.446

0.743 0.441 0.519 0.431 0.494 0.374

0.864 0.372 0.325 0.362 0.398

0.819 0.212 0.371 0.166

0.767 0.207 0.463

0.928 0.149

0.825

9

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Fig. 4. Research model with results.

“Habit” (H11: β = 0.430, t = 6.423, p = 0.000) which, with a f2 at 0.207, impacts positively and significantly the “Intention to Use”, and by “Performance Expectancy” (H5: β = 0.276, t = 4.792, p = 0.000) but not by “Effort Expectancy” (H6: β = 0.056, t = 0.283, p = 0.777), “Social Influence” (H7: β = 0.063, t = 1.341, p = 0.180), “Hedonic Motivation” (H9: β = 0.020, t = 0.298, p = 0.766), “Price Value” (H10: β = 0.050, t = 1.256, p = 0.209) and “Personal Innovativeness” (H12: β = 0.073, t = 1.446, p = 0.148). Then, we calculated the Stone-Geisser's Q2: Cross-Validated Redundancy obtained using the Blindfolding procedure (Tenenhaus et al., 2005). The Q2 of “Performance Expectancy” (0.200), “Habit” (0.256) and “Intention to Use” (0.455) were both greater than zero, indicating acceptable predictive relevance (Henseler et al., 2009). In summary, eight hypotheses were validated and seven rejected (Table 10).

4.3. Moderating effect The research model showed that both “Type of education” (H13) and “Gender” (H14) can moderate the relationship between the variables of the model. To examine their moderating effect between the four dimensions of SHC on “Performance Expectancy” and “Habit” and the six dimensions of UTAUT2 (“Facilitating condition” was rejected) and “Personal Innovativeness” on “Intention to Use”, we need to evaluate whether changes in the relationship are significant. We used the Multi Group Analysis (MGA) procedure proposed by Smart PLS3 to analyze the variability of path coefficient (β), t, and p values for each group compared to the global model using the bootstrapping process. As the variable “Facilitating Conditions” was removed from the model, Hypotheses H13l and H14l were not verified.

Table 10 Test of hypothesis (X = validated, O = rejected). Construct

Predictor variable

R2

f2

β

T-value

P-value

Q2

H

Performance expectancy

Comfort/Convenience Health Safety/Security Sustainability Comfort/Convenience Health Safety/Security Sustainability Effort expectancy Habit Hedonic motivation Performance expectancy Personal innovativeness Price value Social influence

0.395

0.141 0.039 0.034 0.000 0.106 0.049 0.045 0.001 0.006 0.207 0.001 0.096 0.009 0.004 0.007

0.387 0.195 0.173 0.011 0.342 0.222 0.204 −0.028 0.056 0.430 0.020 0.276 0.073 0.050 0.063

5.621 2.848 2.809 0.164 5.217 3.307 3.523 0.472 0.283 6.423 0.298 4.792 1.446 1.256 1.341

0.000 0.004 0.005 0.869 0.000 0.001 0.000 0.637 0.777 0.000 0.766 0.000 0.148 0.209 0.180

0.200

X X X O X X X O O X O X O O O

Habit

Intention to use

0.371

0.614

10

0.256

0.455

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Table 11 Moderating effect of “Type of education” on all relationships within the model. GLOBAL β CC>HT CC->PE EE->IU HT->IU

tpvalue value

EIML β

tpvalue value

EMLV β

ESILV

tpvalue value

β

β

tpvalue value

0.342 5.217 0.000 0.241 1.837 0.067 0.216 1.438 0.151 0.381 2.819 0.005 0.566 5.996 0.000

0.387 5.621 0.000 0.500 4.440 0.000 0.257 1.584 0.114 0.514 0.056 0.283 0.777 -0.009 0.086 0.931 0.121 1.396 0.163 0.002 0.430 6.423 0.000 0.311 2.482 0.013 0.450 3.629 0.000 0.471 H->HT 0.222 3.307 0.001 0.391 2.782 0.006 0.296 2.167 0.031 0.031 H->PE 0.195 2.848 0.004 0.142 1.106 0.269 0.347 2.544 0.011 0.040 HM0.020 0.298 0.766 0.124 1.022 0.307 0.430 0.667 0.076 >IU 0.049 PE->IU 0.276 4.792 0.000 0.185 1.474 0.141 0.446 4.600 0.000 0.368 PI->IU 0.073 1.446 0.148 0.255 2.336 0.020 0.584 0.559 0.056 0.022 PV->IU 0.050 1.256 0.209 0.111 1.409 0.159 0.299 0.765 0.083 0.031 SS->HT SS->PE SI->IU SD>HT

TEM

tpvalue value

0.204 0.173 0.063 0.028

3.523 0.000 0.137 1.12 0.263 0.327 2.995 2.809 0.005 0.069 0.486 0.627 0.210 1.869 1.341 0.180 -0.048 0.511 0.610 0.064 0.658 0.472 0.637 -0.005 0.048 0.962 1.019 0.132 SD->PE 0.011 0.164 0.869 0.093 0.807 0.420 0.478 0.060

4.3.1. Type of education The result of interaction effects to test the moderating effect of type of education is summarized in Table 11. The difference between the global model and between subgroups by school were highlighted in grey. We postulate that type of education will moderate all the relationships within the model. No major differences were found, therefore the hypotheses that type of education could moderate the relationship of “Sustainability” on both “Performance Expectancy” (H13d) and “Habit” (H13h) are not validated. The moderating effect of “type of education” between EE (H13j), HM (H13m), PV (H13n) and HT (H13o) on “Intention to Use” are not supported. “Comfort/Convenience” impacts positively and significantly “Performance Expectancy” and “Habit” for Global, EIML, ESILV and TEM models, but even if the path coefficients for EMLV at 0.216 and 0.257 are above 0.200, the t-value is below the recommended threshold of 1.96 and the p-value is above 0.05. Therefore, the hypothesis H13a and H13e on moderating effect of “type of education” on the relationship between “Comfort/Convenience” and both “Performance Expectancy” and “Habit” are validated. The positive and significant relationship of “Health” on “Performance Expectancy” was identified as positive, direct, and significant for the global model, EMLV and TEM but was rejected by EIML (β = 0.142, t = 1.106, p = 0.269) and ESILV (β = 0.040, t = 0.379, p = 0.705). Therefore, the impact of “Level of education” on “Health” H13b, is supported. Regarding the relationship of “Health” on “Habit”, similar results have been highlighted for the Global model, EIML, and EMLV but they are different for ESILV and TEM. Therefore, H13f is validated. The hypothesis that PE impacts positively on “Intention to Use” was accepted by the Global model, EMLV, and ESILV but rejected by EIML (β = 0.185, t = 1.474, p = 0.141) and TEM (β = 0.124, t = 1.012, p = 0.312). Therefore, the fact that “Type of education” could impact the relation between PE and “Intention to Use” H13i is validated. “Personal Innovativeness” only has a direct and positive impact on EIML students (β = 0.255, t = 2.336, p = 0.020). We confirm that “Type of education” moderates the relation between “Personal Innovativeness” H13p and “Intention to Use”. The positive relationship between “Social Influence” and “Intention to Use” was rejected by Global, EIML, EMLV, and ESILV models but were found to be positive, direct, and significant for TEM (β = 0.147, t = 2.130, p = 0.034), therefore the hypothesis H13k is validated. Finally, a difference was highlighted on the impact of “Security/Safety” on “Performance Expectancy” and “Habit”. Thus, the hypotheses H13c

4.000 0.000 0.322 2.803 0.005 0.024 0.981 0.098 1.055 0.292 3.927 0.000 0.511 4.580 0.000 0.260 0.795 0.109 0.982 0.327 0.379 0.705 0.266 1.956 0.051 0.679 0.498 0.652 0.515 0.058 3.468 0.001 0.124 1.012 0.312 0.321 0.748 0.097 0.938 0.349 0.907 0.365 0.078 1.264 0.207

0.003 0.266 2.200 0.028 0.080 0.792 0.429 0.062 0.066 0.527 0.598 0.230 2.042 0.042 0.511 0.049 0.670 0.503 0.147 2.130 0.034 0.309 0.071 0.591 0.555 0.054 0.506 0.613 0.633 0.180 1.290 0.198

0.164 0.870 0.019

Table 12 Validation of the hypotheses.

H13a H13b H13c H13d H13e H13f H13g H13h H13i H13j H13k H13m H13n H13o H13p

Education Validated Validated Validated Not supported Validated Validated Validated Not supported Validated Not supported Validated Not supported Not supported Not supported Validated

CC->PE H->PE SS->PE SD->PE CC->HT H->HT SS->HT SD->HT PE->IU EE->IU SI->IU HM->IU PV->IU HT->IU PI->IU

and H13g are supported. Table 12 summarizes the validation of hypotheses regarding the impact of “Type of education” on the relationships of our model: nine hypotheses were validated and six rejected. 4.3.2. Gender We postulate that the model will not be moderated by gender. No major differences were found regarding following relationships:

• “Comfort/Convenience” (H14 ), “Safety/Security” (H14 ), and “Sustainability” (H14 ) on “Performance Expectancy” • “Comfort/Convenience” (H14 ), “Health” (H14 ), “Safety/Security” (H14 ), and “Sustainability” (H14 ) on “Habit” • PE (H14 ), SI (H14 ) HM (H14 ,), PV (H14 ), and HT (H14 ) on a

c

d

e

f

g

h

i

“Intention to Use”.

k

m

n

o

Therefore, the hypotheses listed above are supported. The “Effort Expectancy” didn't impact positively and significantly on the “Intention to Use” for the Global and the female models, but we found a positive, direct, and significant impact for male (β = 0.162, t = 2.125, p = 0.034). Therefore, the “Effort Expectancy” H14j is not validated (Table 13). Two other relationships for female are different from the 11

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Table 13 Moderating effect of gender. GLOBAL

CC → HT CC → PE EE → IU HT → IU H → HT H → PE HM → IU PE → IU PI → IU PV → IU SS → HT SS → PE SI → IU SD → HT SD → PE

Male

Female

β

t-Value

p-Value

β

t-Value

p-Value

β

t-Value

p-Value

0.342 0.387 0.056 0.430 0.222 0.195 0.020 0.276 0.073 0.050 0.204 0.173 0.063 −0.028 0.011

5.217 5.621 0.283 6.423 3.307 2.848 0.298 4.792 1.446 1.256 3.523 2.809 1.341 0.472 0.164

0.000 0.000 0.777 0.000 0.001 0.004 0.766 0.000 0.148 0.209 0.000 0.005 0.180 0.637 0.869

0.393 0.363 0.162 0.473 0.217 0.395 −0.087 0.347 −0.062 0.030 0.312 0.207 0.093 −0.115 −0.169

3.729 3.575 2.125 4.432 2.324 3.503 0.956 3.837 0.881 0.440 2.933 2.003 1.292 1.097 1.602

0.000 0.000 0.034 0.000 0.021 0.001 0.340 0.000 0.379 0.660 0.004 0.046 0.197 0.273 0.110

0.313 0.436 −0.005 0.417 0.217 0.049 0.054 0.235 0.160 0.067 0.154 0.163 0.031 0.006 0.115

4.097 5.491 0.081 5.723 2.545 0.669 0.695 3.451 2.456 1.465 2.253 2.318 0.539 0.085 1.581

0.000 0.000 0.935 0.000 0.011 0.504 0.488 0.001 0.014 0.143 0.025 0.021 0.590 0.933 0.114

natives may be less impacted than the elderly by health issues, “Health” is one of the antecedents of “Performance Expectancy” and “Habit” of the SHC. Digital natives expect Smart Homes to provide them with information to increase their health awareness in order to live in a healthier way. As regards the moderating effect of gender on the relationship between “Health” and “PE”, and the fact that we found no significant impact for women, one of the explanations could be that females generally have better knowledge about their health status than males, and are more likely to check it on a regular basis (Nathanson and Lorenz, 1982; Waldron, 1988). Therefore, they may not feel the need to use Smart Home technologies to monitor their health. This relationship has also been rejected by EIML students, where females represent 84% of respondents. Our findings on “Safety/Security” confirm previous research highlighting “Security” as one of the main motivations for using SHC (Aldrich, 2003; Koskela and Väänänen-Vainio-Mattila, 2005). Residents expect SHC to enhance their security (Strengers and Nicholls, 2017) and Smart Home users are concerned about their “Safety” (Balta-Ozkan et al., 2013). Paradoxically, digital natives, usually interested in Sustainable development (Dalmas and Lima, 2016) do not consider “Sustainability” to be a predictor of “Performance Expectation” of SHC. This absence of a relationship is an interesting result, given that some recent studies have investigated the positive impact of Sustainable development of smart technologies on Smart Cities (Alkhalidi et al., 2018; Sarma and Sunny, 2017). Nevertheless, our results confirm the study done by Ahn et al. (2016) on the acceptance of sustainable household technology to reduce the impact on the environment. They found that a concern with sustainability does not predict “Intention to Use”. We retained the UTAUT2 scale (Venkatesh et al., 2012) to measure acceptability. Of the seven constructs proposed, one construct was removed – “Facilitating conditions” – as it was considered not to be valid and reliable. This study demonstrates that only two of the UTAUT2 constructs, “Performance Expectancy” and “Habit”, have a significant impact on “Intention to Use”, confirming a SHC study done by Yuan et al. (2015) on the usage of Health applications. The construct of “Habit” has the strongest effect on “Intention to Use”, as confirmed in the study carried out by Brauner et al. (2017) on Smart Interactive Textiles in Home Environments. This strong relationship between “Habit” and “Performance Expectancy” confirms results found by Venkatesh et al. (2012) and a study done by Lewis et al. (2013) on the acceptance of established and emerging technologies in higher education. Digital natives feel comfortable using new technologies, such as the smart technologies used in SHC. The more individuals use a technology, the stronger is the habit (Hew et al., 2015; Nikou and Bouwman, 2014). This could therefore strengthen their interest in adopting a new technology (Marhaeni and Indrawati, 2015; Pahnila et al., 2011). Dhir et al. (2018) found that “Habit” was one of

Table 14 Validation of hypotheses.

H14a H14b H14c H14d H14e H14f H14g H14h H14i H14j H14k H14m H14n H14o H14p

CC->PE H->PE SS->PE SD->PE CC->HT H->HT SS->HT SD->HT PE->IU EE->IU SI->IU HM->IU PV->IU HT->IU PI->IU

Gender Validated Not supported Validated Validated Validated Validated Validated Validated Validated Not supported Validated Validated Validated Validated Not supported

Global and male models for “Health” on “Performance Expectancies” H14b (β = 0.049, t = 0.669, p = 0.504) and “Personal Innovativeness” on “Intention to use” H14p (β = 0.160, t = 2.456, p = 0.014). The Hypotheses H14b and H14p are not supported (Table 13). Table 14 summarizes the validation of hypotheses regarding the impact of gender on the relationships within our model: 12 hypotheses were validated and three rejected. 5. Discussion Our study sheds light on the behavior of digital natives with high levels of education with regards to the acceptance of the Smart Home Concept. Of the four constructs of the SHC, three – “Comfort/ Convenience”, “Health”, and “Safety/Security” – influence “Performance Expectancy” and “Habit”, being two constructs of the UTAUT2 model. Digital natives perceive significant benefits from Smart Home technologies with a huge focus on “Comfort/Convenience”. “Comfort/Convenience” is the key motivator for using a Smart Home, emphasizing the utilitarian perception of the concept and confirming that Smart Home technologies can offer users the capability to better understand consumption by collecting information and by taking the appropriate decisions without human intervention. Our results are in line with other researchers who have concluded that Smart Homes offer a good understanding of consumption (Gram-Hanssen and Darby, 2018; Strengers and Nicholls, 2017). The more individuals that use smart technologies, the stronger the “Habit” will become. Even though digital 12

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the two variables of UTAUT2 with “Hedonic Motivation”, explaining the intention to tag photos on social networking sites. Nevertheless, these results differ from the study of Leong et al. (2017), who found no direct and significant impact of “Habit” on behavioral “Intention to Use” IoT's in the Smart City context, confirming results from Segura and Thiesse (2015) on persuasive information systems. This could be explained by the fact that the Smart Home is a more personally impactful subject than Smart Cities which are more common or global projects. PE, in our model, is the other strong predictor of “Intention to Use”. These results support findings from Venkatesh et al. (2003) and confirmed results from previous research done on technology acceptance in projects such as health applications or services (Jang et al., 2016; Yuan et al., 2015), m-commerce (Alkhunaizan and Love, 2012), instant message application (Marhaeni and Indrawati, 2015), e-services (Lin et al., 2011) but also findings from Oye et al. (2011) on Information Communication Technology acceptance by highly educated people. The perception of “Usefulness” is considered as a key factor in adoption intention (Leong et al., 2013; Luo et al., 2010). The result of “Performance Expectancy” suggests, in the case of SHC, that individuals will adopt functional technology. Another major result is that “Social Influence” has no significant impact on “Intention to Use” a SHC. Individuals who are willing to adopt the SHC are mainly early adopters, with a highly educated profile, who are not influenced by others (Leong et al., 2017). Our findings confirm the study done by Ahn et al. (2016), who found no relationship of social pressure on the adoption of sustainable household technology. Lu et al. (2005) confirm that users are not influenced by other people to adopt a wireless internet services solution via a mobile technology or a mobile application (Hew et al., 2015). For Yuan et al. (2015), “Social Influence” does not predict users' “Intention to Use” health applications. The Smart Home is a new technology not yet widely adopted and the “Intention to Use” an innovation is stronger when the innovation is already used by others (Kulviwat et al., 2009). Once the concept becomes part of daily life, the impact of “Social Influence” could be higher. We found no relationship between “Price Value” and “Intention to Use”; our results differed from Venkatesh et al. (2012) and Indrawati and Tohir (2016) on smart metering acceptance. Nevertheless, our findings confirm the research done by Hew et al. (2015) and Tavares and Oliveira (2016), who found no relationship between “Price Value” and “behavioral Intention to the Use” electronic health portals, and Wong et al. (2014) in the context of Mobile TV. This lack of impact may be due to the sample, as students have lower budgets and are may not yet be thinking about investing in a Smart Home as they often live with their parents (Albouy et al., 2003). Another construct within the UTUAT2 model, “Effort Expectancy”, did not perform in predicting the “Intention to Use” of SHC, confirming other research (Ahn et al., 2016; Yuan et al., 2015). Respondents considered that using a SHC would require some effort to learn or interact; the more complex the technology is felt to be, the less the individual would be interested in it (Taiwo and Downe, 2013). This result is a surprise given that smart technologies usage should be simple and obvious, particularly for the targeted audience of students with a high level of education who normally feel more comfortable with such innovations. In these circumstances, we suggest that the SHC was not well understood by the users, they did not have sufficient information and they were not familiar with the technology behind the concept. The level of information provided could reduce the perceived complexity, as the less the technology is perceived as complex, the more likely it is that the innovation will be adopted (Wallace and Sheetz, 2014). Nevertheless, the relationship is positive and significant for males, who are often considered to be more interested in technology than females (Van Slyke et al., 2002). Our research showed that the “Hedonic Motivation” doesn't impact “Intention to Use” SHC. Results are in line with previous research on the use of emerging technologies in higher education (Lewis et al., 2013), on the adoption of mobile payment (Oliveira et al., 2016) and electronic health portal (Tavares and Oliveira, 2016) but contradict results from a study on IoT adoption in the context of Smart

Cities (Leong et al., 2017) or the adoption of mobile banking by the generation Y part of the digital population (Boonsiritomachai and Pitchayadejanant, 2017). In summary, SHC is perceived by digital natives as more of a utilitarian solution rather than a hedonic one. Finally, the “Personal Innovativeness” construct has no significant impact on “Intention to Use”. This finding could be due to the fact that the respondents had not yet experimented with the SHC, therefore they did not have experience of and perhaps insufficient knowledge about the concept. Our result confirms the study done by Lu et al. (2005) on the adoption of wireless mobile technology. Nevertheless, impact of “Personal Innovativeness” is direct, positive, and significant for females. Other researchers found that gender can moderate consumer innovativeness (Ahuja and Thatcher, 2005; Melnyk et al., 2009). Our results confirmed findings from Goldsmith et al. (1987), indicating that the level of innovativeness leads a positive attitude on “Intention to Use” for females with higher impact than for males. This result is also validated by EIML students, composed mainly of women (84%). Finally, we found that both “Type of Education” and “Gender” could impact some relationships within our model. Zhang et al. (2017) found no difference between males and females regarding the impact of Perceived Usefulness (equivalent of performance expectancies) on behavioral intention to use healthcare wearables. Other researchers found that gender had no influence on highly educated profiles for EE and PE on BI in the case of e-learning system acceptance (Decman, 2015; Sumak et al., 2010). 6. Conclusion, contribution, and limits The main motivation for writing this article was to explore an ongoing transformation in response to technological changes and the development of Smart Homes. The growing interest in the Smart Home Concept promises to change the way individuals approach their personal daily life. Indeed, it seems that it will be more difficult to continue to manage the home without using smart technologies. The implementation of Smart Homes relies on user acceptance and the benefits it accords; therefore, the commitment of individuals is essential, and determining the key factors of acceptability will be crucial to better understand the predictors of the “Intention to Use”. In this study, we aim to address the acceptability of the SHC to the digital native population with a high level of education by providing some of the first empirical evidence on the role that Smart Home dimensions play in digital natives' behavior. This study aims to answer to one of the important questions in the field of smart technologies is how the Smart Home is perceived by digital natives, considered to be more susceptible to the implementation of new technologies. Our findings contribute to the emerging marketing literature on smart technologies. Understanding the phenomenon of the adoption of such technology is key for this growing market. Our results offer some theoretical and managerial implications. The first major theoretical contribution of our study was the development of a new scale measuring the Smart Home dimension. We have followed the Churchill Jr (1979) paradigm, and began our theoretical inquiry with an analysis of the emergent stream of literature on the SHC before proposing a new measurement scale, validated by experts. This scale was pre-tested and submitted to our final sample. Second, our results add to existing knowledge in the new field of Smart Home acceptance by not employing the most frequently used models of TAM or UTAUT, but the predictive value of the updated UTAUT2, which expands its applicability in a consumer context (Venkatesh et al., 2012). In addition, the “Personal Innovativeness” construct was mobilized to measure the antecedents of adoption regarding the SHC. This variable is important, particularly in relation to the targeted audience. A better understanding of acceptance levels in the information systems field is key for the development of smart technology. Third, our study, using French, highly educated young adults, contributes to the expansion of the geographical scope of existing research on the relevant topic of the Smart Home and brings new insights into the behavior of digital natives in relation to Smart Homes. 13

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Fourth, most of the existing research focuses on technical aspects, and, as far as we know, this is one of the first study to investigate residents' acceptance of the SHC. Finally, our empirical data contradicted some results issued by previous researchers regarding the concerns of young adults about sustainability. The possible explanation for the lack of relevance of this variable could be due to the specific characteristics of the technology under examination: The SHC as students do not directly link home-based systems with the environment. Our study proposes several implications for practice. Companies in the Smart Home business confront different challenges as some of the functionalities proposed are already used on a daily basis (i.e., home automation, security) whereas some Smart Home solutions are still at an exploratory level; they have been developed and are therefore available but not yet totally implemented or adopted (i.e., health). Therefore, companies will have to continue to improve existing technology while promoting the newer ones. Thus, they need to identify users' expectations in order to propose accurate solutions. Based on our results, they should focus on utilitarian functionalities and social concepts for the digital native population. In fact, it emerges from our results that “Performance Expectancies” (Utilitarian) and “Habit” (Social) are the two dimensions of the UTUAT2 model that explain the “Intention to use” a SHC. Our findings suggest that companies should focus on “Performance Expectancies” as one of the utilitarian values of the UTAUT2 model. Solutions proposed should make residents' daily lives easier, more comfortable, and meet their expectations in terms of productivity, as customers need to receive some performance gain by using SHC. We suggest that developers and designers should also adapt their products to suit other consumer profiles, for those who have less experience with smart products. Finally, marketers should emphasize the key role of SMC in facilitating daily life and task completion. Our results are in line with previous studies confirming the importance of PE in information system adoption theory (Venkatesh et al., 2012). One of the major contributions of this study is the influence of “Habit”, found to be the strongest of all variables in predicting the intention to adopt an SHC. Therefore, marketers should emphasize and market the use of existing smart technologies to make sure individuals will adopt proposed innovations. Marketers should link technologies already used or recognized by individuals such as wearables and home automation or energy management systems to the SHC. They might, for example, gradually update products' hardware and software systems to make them more convenient and easier to use. By stimulating the use of all kinds of smart technologies, they will facilitate the adoption of other smart technologies. Marketers, key players should promote the SHC concept heavily, using social media for the younger population, focussing on TV for the older generation, to explain the benefits of the SHC. The fact that young adults already use IoT or smart technologies will help companies to convince them regarding the benefits of using a SHC, and use them to convince the older generation. Despite finding that Social Influence has no impact on the digital native population, as they are already familiar with the technology behind smart technology, especially highly educated students, they can play the role of ambassador for the adoption of the Smart Home within their family. The model's strength lies in identifying variables of SHC considered as important for digital natives. The results indicate clearly that digital native consumers have a greater interest in the factors that help them to improve their daily life (such as “Comfort/Convenience”, “Health”, and “Safety/Security”) and that constructs not directly focused on their personal satisfaction, such as “Sustainability”, seem less relevant. Therefore, marketers should pay more attention to consumer attributes to conduct accurate marketing strategies to integrate interestingness and novelty of the Smart Home Technology. Practitioners should, in particular, focus on “Comfort/Convenience”, as this study underscores the importance of this variable for the targeted audience. Health

concerns have gained more attention in recent years, and according to our research model and empirical results, females' health beliefs significantly influence, even indirectly, their intention to use Smart Home technologies. To improve public health knowledge, we suggest developing more pertinent information pertaining to the opportunities of added-value services for healthcare thanks to Smart Home usage. New insurance products could be designed to encourage users to develop good habits using a scientific and personalized product portfolio. “Sustainability” was not validated in our research, although ecological concerns are part of the daily life of the digital native. It seems unclear whether this is a genuine driving factor in purchasing decisions, even if respondents are used to paying attention to energy efficiency and perceive the opportunity to achieve additional savings from a Smart Home. Marketers and designers should focus on the fact that the Smart Home can be an effective way to increase consumer awareness of the benefits that smart technologies could bring to their everyday lives in term of sustainability. The relatively high cost of Smart Home products also acts as a usage barrier for low income groups such as young adult consumers. Therefore, in order to lower the initial purchase burden for new consumers, companies should introduce subsidy programs, offers for certain age groups, or product bundling. This study also analyzed two moderate variables: “Gender”, which is very often used in this type of research concerning technological acceptance; and “Type of Education”. As we found that gender moderates some relationships within our model, marketers should also consider gender when promoting the SHC. To conclude, our results suggest that Business strategists should not ignore this type of disruptive innovation. 6.1. Limitations and future research Although, our article aims to cover a key research gap by focussing on the emerging topic of Smart Home acceptance, some limitations must be mentioned. First, we focus on digital native students with a high level of education. Their preferences, their lifestyles, and their degree of acceptance of technological products can differ from that of the digital native without little education or a lower level of education. The specific audience targeted in the study could explain the strong results of the two constructs “Performance Expectancies” and “Habit” explaining “Intention to Use”. Second, we did not take into account other characteristics of digital native users that might influence their perception of the above-mentioned system characteristics, for example, whether they were active users, users with different levels of income, or responsible for their family finances. Finally, it would be a more accurate representation if we were to include other categories of the population, such as digital immigrants born before 1981, as Kumar and Lim (2008) found that age could influence the acceptance of mobile technology, especially where generation Y (born between 1980 and 2000) and baby boomers (born between 1945 and 1974) were compared. Therefore, we plan to integrate different profiles for the digital native population as well as digital immigrants in future research. These modifications will offer a more accurate understanding of the relevant variables determining the acceptance of Smart Home technologies and most likely produce different results. Finally, the SHC scale that we have developed should be tested by other researchers and perhaps further developed in the near future. Acknowledgement The authors would like to thank the schools who agreed to administer the questionnaire (Pôle Universitaire Léonard De Vinci, MinesTélécom Institute and Ecole Internationale du Marketing du Luxe) and the Altran research lab for their collaboration. We also acknowledge the experts for their review and approval of the Smart city scale.

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Appendix A. List of experts 1 2 3

Aug-17 Aug-17 Sept17 Sept17 Sept17 Sept17 Oct-17 Oct-17

4 5 6 7 8

Professor Researcher Assistant Professor

Bordeaux University Research Laboratory in technology Hanyang University

Bordeaux Sejong Seoul

France Korea Korea

Professor

Hanyang University

Seoul

Korea

Strategy and Planning Director

CISCO/Smart City

Paris

France

Associate director and Dean

Hanyang University

Seoul

Korea

Professor Researcher

Ecole Management Léonard de Vinci Altran Research/Smart City

Paris Paris

France France

Appendix B. Questionnaire: Performance expectancy PE.1. PE.2. PE.3. PE.4.

“I find Smart home objects useful in a daily life”. “Using Smart home objects increases the chances of achieving things that are important”. “Using Smart home objects helps to accomplish things more quickly”. “Using Smart home objects increase productivity”.

Effort expectancy EE.1. EE.2. EE.3. EE.4.

“Learning how to use Smart home objects is easy for me”. “Interaction with Smart home objects is clear and understandable”. “I find Smart home objects easy to use”. “It is easy for me to become skillful at using Smart home objects”.

Social influence SI.1. “People who are important to me think that I should use Smart home objects”. SI.2. “People who influence my behavior think that I should use Smart home objects”. SI.3. “People, whose opinions that I value, prefer that I use Smart home objects”. Facilitating conditions FC.1. FC.2. FC.3. FC.4.

“I have the resources necessary to use Smart home objects”. “I have the knowledge necessary to use Smart home objects”. “Smart home objects are compatible with other technologies I use”. “I can get help from others when I have difficulties Smart home objects”.

Hedonic motivation HM.1. “Using Smart home objects is fun”. HM.2. “Smart home objects are enjoyable”. HM.3. “Using Smart home objects is very entertaining”. Price value PV.1. “Smart home objects are reasonably priced”. PV.2. “Smart home objects are a good value for the money”. PV.3. “At the current price, Smart home objects provide a good value”. Habit HT.1. HT.2. HT.3. HT.4.

“The use of Smart home objects could become a habit for me”. “I could become addict using Smart home objects”. “I could use Smart home objects”. “Using Smart home objects could become natural to me”.

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Intention to use ITU.1. “Using smart home service is worthwhile”. ITU.2. “I intend to use smart home service in the future”. ITU.3. “I predict I would use smart home services in the future”. Safety/Security Using a smart home object can enhance my safety and Security by: SS.1. “Controlling that the doors and windows are closed”. SS.2. “Informing you in case of unauthorized intrusion”. SS.3. “Detecting Gas emission and Smoke”. SS.4. “Informing directly people outside about life accident (fall…)”. Health/Well being A smart home objects can if necessary: H.1. “Increase your chances to engage in a healthier way of living”. H.2. “Increase awareness of your health and wellness behavior”. H.3. “Provide you with information that help you make better decisions for your health and wellness”. H.4. “Smart home could give you greater control over your health and wellness”. H.5. “Smart home could improve your self-tracking activity if you are using a wearable device”. Control It's convenient that Smart home objects can: CC.1. “Help you as a resident proactively without human intervention”. CC.2. “Provide auto-adjusted control”. CC.3. “Control every electrical apparatus through simple operation”. CC.4. “Enable access to a lot of information”. CC.5. “Help you take better decisions”. Sustainable development People who live in a smart home: SD.1. “Know exactly your consumption in energy, water… (time, duration, expenses, used quantity…)”. SD.2. “Can save resources (energy, water…)”. SD.3. “Can better manage wastes”. SD.4. “Can do cost savings”. SD.5. “Have an eco-friendly attitude”. Personal innovativeness PI.1. “I like to experiment with new information technologies”. PI.2. “If I heard about a new information technology, I would look for ways to experiment with it”. PI.3. “Among my peers, I am usually the first to try out new information technologies”. PI.4. “In general, I am hesitant to try out new information technologies*”. *reversed scale.

Alalwana, A.A., Dwivedib, Y.K., Williams, M.D., 2016. Customers' intention and adoption of telebanking in Jordan. Inf. Syst. Manag. 33 (2), 154–178. Albouy, V., Murat, F., Roth, N., 2003. Les aides aux jeunes aduldes: reflexions sur les concepts et éléments de chiffrage. Economie & prévision. 4 (160–161), 1–22. Aldrich, F.K., 2003. Smart homes: past, present and future. In: Harper, R. (Ed.), In-side the Smart Home, 1st ed. Springer, Heidelberg, pp. 17–39. Alelaiwi, A., Alghamdi, A., Shorfuzzaman, M., Rawashdeh, M., Hossain, M.S., Muhammad, G., 2015. Enhanced engineering education using smart class environment. Comput. Hum. Behav. 51, 852–856. Alkhalidi, A., Qoaider, L., Khashman, A., Al-Alami, A.R., Jiryes, S., 2018. Energy and water as indicators for sustainable city site selection and design in Jordan using Smart grid. Sustain. Cities Soc. 37, 125–132. Alkhunaizan, A.M., Love, S., 2012. What drives mobile commerce? An empirical evaluation of the revised UTAUT model. Int. J. Manag. Market. Acad. 2 (1), 82–99. Almirall, E., Wareham, J., Ratti, C., Conesa, P., Bria, F., Gaviria, A., Edmondson, A., 2016. Smart cities at the crossroads: new tensions in city transformation. Calif. Manag. Rev. 59 (1), 141–152. Al-Qeisi, K., Dennis, C., Alamanos, E., Jayawardhena, C., 2014. Web site design quality and usage behavior: unified theory of acceptance and use of technology. J. Bus. Res. 67 (11), 2282–2290.

References Agarwal, R., Prasad, J., 1998. A conceptual and operational definition of personal innovativeness in the domain of information technology. Inf. Syst. Res. 9 (2), 204–215. Ahn, M., Kang, J., Hustvedt, G., 2016. A model of sustainable household technology acceptance. Int. J. Consum. Stud. 40 (1), 83–91. Ahuja, M.K., Thatcher, J.B., 2005. Moving beyond intentions and toward the theory of trying: effects of work environment and gender on post-adoption information technology use. MIS Q. 29 (3), 427–459. Ahvenniemi, H., Huovila, A., Pinto-Seppä, I., Airaksinen, M., 2017. What are the differences between sustainable and Smart cities? Cities 60, 234–245. Ajzen, I., 1991. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50 (2), 179–211. Alaa, M., Zaidan, A.A., Zaidan, B.B., Talal, M., Kiah, M.L.M., 2017. A review of smart home applications based on internet of things. J. Netw. Comput. Appl. https://doi. org/10.1016/j.jnca.2017.08.017. Alaiad, A., Zhou, L., 2017. Patients' adoption of WSN-based Smart home healthcare systems: an integrated model of facilitators and barriers. IEEE Trans. Prof. Commun. 60 (1), 4–23.

16

Technological Forecasting & Social Change xxx (xxxx) xxxx

P. Baudier, et al. Balta-Ozkan, N., Davidson, R., Bicket, M., Whitmarsh, L., 2013. Social barriers to the adoption of smart homes. Energy Policy 63 (1), 363–374. Barlow, J., Venables, T., 2003. Smart Home, Dumb Suppliers? The Future of Smart Homes Markets, Inside the Smart Home. Springer, London, pp. 247–262. Batalla, J.M., Vasilakos, A., Gajewski, M., 2017. Secure smart homes: opportunities and challenges. ACM Comput. Surv. 50 (5), 1–32 (Art75). Belimpasakis, P., Stirbu, V., 2014. A survey of techniques for remote access to home networks and resources. Multimedia Tools Appl. 70 (3), 1899–1939. Bennett, S., Maton, K., Kervin, L., 2008. The ‘digital natives’ debate: a critical review of the evidence. Br. J. Educ. Technol. 39 (5), 775–786. Boonsiritomachai, W., Pitchayadejanant, K., 2017. Determinants affecting mobile banking adoption by generation Y based on the unified theory of acceptance and use of technology model modified by the technology acceptance model concept. Kasetsart J. Soc. Sci. https://doi.org/10.1016/j.kjss.2017.10.005. Brauner, P., Van Heek, J., Ziefle, M., 2017. Age, Gender, and Technology Attitude as Factors for Acceptance of Smart Interactive Textiles in Home Environments Towards a Smart Textile Technology Acceptance Model In Proceedings of the 3rd International Conference on Information and Communication Technologies for Ageing Well and eHealth (ICT4AWE). 13–24. Brown, S.A., Venkatesh, V., Bala, H., 2006. Household technology use: integrating household life cycle and the model of adaption of technology in households. Inf. Soc. 22, 205–218. Chang, V., Wang, Y., Wills, G., 2018. Research investigations on the use or non-use of hearing aids in the smart cities. Technol. Forecast. Soc. Chang. https://doi.org/10. 1016/j.techfore.2018.03.002. Chen, S.Y., Chang, S.F., Chia, F., Chi, M., 2009. A review of smart living space development in a cloud computing network environment. Comput.-Aided Des. Applic. 6 (4), 513–527. Chi, P.Y., Chen, J.H., Liu, S.Y., Chu, H.H., 2007. Designing smart living objects enhancing versus distracting traditional human-object interaction. In: Proceedings of HCI International (HCII). 88–797. Chin, W., 1998. Issues and opinion on structure equation modeling. MIS Q. 22 (1), 7–16. Chong, A.Y.L., Chan, F.T.S., Ooi, K.B., 2012. Predicting consumer decisions to adopt mobile commerce: cross country empirical examination between China and Malaysia. Decis. Support. Syst. 53, 34–43. Churchill Jr., G.A., 1979. A paradigm for developing better measures of marketing constructs. J. Mark. Res. 16, 64–73. Coleman, R., Sim, J., 2000. You'll never walk alone: CCTV surveillance, order and neoliberal rule in Liverpool city centre. Br. J. Sociol. 51 (4), 623–639. Dalmas, M., Lima, M., 2016. Génération Y, Génération postmoderne ? Les enjeux pour la GRH. Revue management et avenir. 90. pp. 151–174. Dameri, R.P., 2013. Searching for Smart city definition: a comprehensive proposal. Int. J. Comput. Technol. 11 (2), 2544–2551. Datta, A., 2015. A 100 Smart cities, a 100 utopias. Dialogues Hum. Geogr. 5 (1), 49–53. Davis, F.D., 1986. A Technology Acceptance Model for Empirically Testing New End-user Information Systems: Theory and Results. Doctoral dissertation. MIT Sloan School of Management, Cambridge, MA. Davis, F.D., Bagozzi, R.P., Warshaw, P.R., 1989. User acceptance of computer technology: a comparison of two theoretical models. Manag. Sci. 35 (8), 982–1003. Decman, M., 2015. Modeling the acceptance of e-learning in mandatory environments of higher education: the influence of previous education and gender. Comput. Hum. Behav. 49, 272–281. Dewsbury, G., Taylor, B., Edge, M., 2001. Designing safe smart home systems for vulnerable people. In: Dependability and Healthcare Informatics Workshop, pp. 65–70. Dhir, A., Kaur, P., Rajala, R., 2018. Why do young people tag photos on social networking sites? Explaining user intentions. Int. J. Inf. Manag. 38, 117–127. Ehrenhard, M., Kijl, B., Nieuwenhuis, L., 2014. Market adoption barriers of multi-stakeholder technology: Smart homes for the aging population. Technol. Forecast. Soc. Chang. 89, 306–315. Escobar-Rodriguez, T., Carvajol-Trujillo, E., 2013. Online drivers of consumer purchase of website airline tickets. J. Air Transp. Manag. 32, 58–64. European Commission, 2015. Communication from the Commission C (2015) 6317 final. In: Towards an Integrated Strategic Energy Technology (SET) Plan: Accelerating the European Energy System Transformation. Brussels, Belgium. 1–17. Evrard, Y., Pras, B., Roux, E., 2009. Market: fondements et méthodes des recherches en marketing, 4th ed. DUNOD (704 pages). Fensel, A., Tomic, D.K., Koller, A., 2017. Contributing to appliances energy efficiency with internet of thing, smart data and user engagement. Futur. Gener. Comput. Syst. 76, 329–338. Fishbein, M., Ajzen, I., 1975. Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Addison-Wesley Publishing Company, Don Mills. Fornell, C., Larcker, D.F., 1981. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 18 (1), 39–50. Fuselli, D., De Angelis, F., Boaro, M., Squartini, S., Wei, Q., Liu, D., 2013. Action dependent heuristic dynamic programming for home energy resource scheduling. Int. J. Electr. Power Energy Syst. 48, 148–160. Gangale, F., Mengolini, A., Onyeji, I., 2013. Consumer engagement: an insight from smart grid projects in Europe. Energy Policy 60, 621–628. Gao, Y., Li, H., Luo, Y., 2015. An empirical study of wearable technology acceptance in healthcare. Ind. Manag. Data Syst. 115 (9), 1704–1723. GfK, 2016. Smart Home: A Global Perspective. CES 2016. Las Vegas, USA. Gil-Garcia, R., Pardoa, T.A., Nam, T., 2015. What makes a city smart? Identifying core components and proposing an integrative and comprehensive conceptualization. Inf. Polity 20, 61–87. Goldsmith, R.E., Stith, M.T., White, J.D., 1987. Race and sex differences in self-identified innovativeness. J. Retail. 63 (4), 411–425.

Gómez Mármol, F., Christoph Sorge, C., Ugus, O., Martínez Pérez, G., 2012. Do not snoop my habits: preserving privacy in the smart grid. IEEE Commun. Mag. 50 (5), 166–172. Gram-Hanssen, K., Darby, S.J., 2018. “Home is where the smart is”? Evaluating smart home research and approaches against the concept of home. Energy Res. Soc. Sci. 37, 94–101. Grimaldi, D., Fernandez, V., 2017. The alignment of university curricula with the building of a Smart City: a case study from Barcelona. Technol. Forecast. Soc. Chang. 123, 298–306. Gupta, S., Xu, H., Zhang, X., 2011. Balancing privacy concerns in the adoption of locationbased services: an empirical analysis. Int. J. Electron. Bus. 9 (1/2), 118–137. Hair, J.F., Ringle, C.M., Christian, M., Sarstedt, M., 2011. PLS-SEM: indeed a silver bullet. J. Mark. Theory Pract. 19 (2), 139–151. Hair, J.F., Sarstedt, M., Ringle, C.M., Mena, J.A., 2012. An assessment of the use of partial least squares structural equation modeling in marketing research. J. Acad. Mark. Sci. 40, 414–433. Hajduk, S., 2016. The concept of a smart city in urban management. Bus. Manag. Educ. 14 (1), 34–49. Hargreaves, T., Wilson, C., Hauxwell-Baldwin, R., 2018. Learning to live in a smart home. Build. Res. Inf. 46 (1), 127–139. Hashem, I.-A.T., Chang, V., Anuar, N.-B., Adewole, K., Yaqoob, I., Gani, A., Ahmed, E., Chiroma, H., 2016. The role of big data in smart city. Int. J. Inf. Manag. 36 (5), 748–758. https://doi.org/10.1016/j.ijinfomgt.2016.05.002. Heiskanen, E., Kasanen, P., Timonen, P., 2005. Consumer participation in sustainable technology development. Int. J. Consum. Stud. 29, 98–107. Henseler, J., Ringle, C.M., Sinkovics, R., 2009. The use of partial least squares path modeling in international marketing. Adv. Int. Mark. 20, 277–319. Herrero, A., San Martín, H., Del Mar Garcia-De los Salmones, M., 2017. Explaining the adoption of social networks sites for sharing user-generated content: a revision of the UTAUT2. Comput. Hum. Behav. 71, 209–217. Hew, J.J., Lee, V.H., Ooi, K.B., Wei, J., 2015. What catalyses mobile apps usage intention: an empirical analysis. Ind. Manag. Data Syst. 115 (7), 1269–1291. Hosek, J., Masek, P., Andreev, S., Galinina, O., Ometov, A., Kropfl, F., Wiedermann, W., Koucheryavy, Y., 2017. A SyMPHOnY of integrated IoT businesses: closing the gap between availability and adoption. IEEE Commun. Mag. 55 (12), 156–164. Howe, N., Strauss, W., 2000. Millennials Rising: The Next Great Generation. Vintage Books, New York, NY. Hsu, C.-L., Lin, J.C.-C., 2015. What drives purchase intention for paid mobile apps? An expectation confirmation model with perceived value. Electron. Commer. Res. Appl. 14 (1), 46–57. Indrawati, Tohir, L.M., 2016. In: Indonesia, Smart Metering, Modified UTAUT2, Intention, Acceptance Information and Communication Technology (ICoICT). 4th International Conference on IEEE 25–27 May. Isaksson, C., 2014. Learning for lower energy consumption. Int. J. Consum. Stud. 38, 12–17. Jahanmir, S.F., Lages, L.F., 2016. The late-adopter scale: a measure of late adopters of technological innovations. J. Bus. Res. 69, 1701–1706. Jang, S.H., Kim, R.H., Lee, C.W., 2016. Effect of u-healthcare service quality on usage intention on a healthcare service. Technol. Forecast. Soc. Chang. 113, 396–403. Jiang, L., Liu, D.Y., Yang, B., 2004. Smart home research. In: Proceedings of 2004 International Conference on Machine Learning and Cybernetics. 2. pp. 659–663. Juntunen, J.K., 2014. Domestication pathways of small-scale renewable energy technologies. Sustain. Sci. Pract. Policy 10 (2), 28–42. Khatoun, R., Zeadally, S., 2016. Smart cities: concepts, architectures, research opportunities. Commun. ACM 59 (8), 46–57. Kim, S.S., Malhotra, N.K., 2005. A longitudinal model of continued IS use: an integrative view of four mechanisms underlying postadoption phenomena. Manag. Sci. 51 (5), 741–755. Kim, M.J., Oh, M.W., Cho, M.E., Lee, H., Kim, J.T., 2013. A critical review of user studies on healthy smart homes. Indoor Built Environ. 22, 260–270. Kim, Y., Park, Y., Choi, J., 2017. A study on the adoption of IoT smart home service: using value-based adoption model. Total Qual. Manag. Bus. Excell. 28 (9), 1149–1165. Kohnke, A., Cole, M.L., Bush, R., 2014. Incorporating UTAUT predictors for understanding home care patients' and clinician's acceptance of healthcare telemedicine equipment. J. Technol. Manag. Innov. 9 (2), 29–41. Koskela, T., Väänänen-Vainio-Mattila, K., 2005. Evolution towards smart home environments: empirical evaluation of three user interfaces. Pers. Ubiquit. Comput. 8 (3–4), 234–240. Kulviwat, S., Bruner, G.C.I.I., Al-Shuridah, O., 2009. The role of social influence on adoption of high tech innovations: the moderating effect of public/private consumption. J. Bus. Res. 62, 706–712. Kumar, S., 2014. Ubiquitous Smart home system using android application. Int. J. Comput. Netw. Commun. 6 (1), 33–43 (IJCNC). Kumar, A., Lim, H., 2008. Age differences in mobile service perceptions: comparison of generation Y and baby boomers. J. Serv. Mark. 22 (7), 568–577. Kumar, H., Kumar Singh, M., Gupta, M.P., Madaan, J., 2018. Moving towards smart cities: solutions that lead to the smart city transformation framework. Technol. Forecast. Soc. Chang. https://doi.org/10.1016/j.techfore.2018.04.024. Lam, S.Y., Venkatesh, S., 2017. Asymmetries in the effects of drivers of brand loyalty between early and late adopters and across technology generations. J. Interact. Mark. 28, 26–42. Latan, H., Ghozali, I., 2012. Partial Least Squares: Concept, Technique and Application Using Program SmartPLS for Empirical Research, BP UNDIP. Lee, S.Y., 2014. Examining the factors that influence early adopter's smartphone adoption: the case of college students. Telematics Inform. 31, 308–318. Lee, K., Ashton, M.C., 2004. Psychometric properties of the HEXACO personality

17

Technological Forecasting & Social Change xxx (xxxx) xxxx

P. Baudier, et al. inventory. Multivar. Behav. Res. 39, 329–358. Lee, K., Ashton, M.C., 2006. Further assessment of the HEXACO personality inventory: two new facet scales and an observer report form. Psychol. Assess. 18, 182–191. Lee, B., Kwon, O., Lee, I., Kim, J., 2017. Companionship with smart home devices: the impact of social connectedness and interaction types on perceived social support and companionship in smart homes. Comput. Hum. Behav. 75, 922–934. Lehtonen, T.-K., 2003. The domestication of new technologies as a set of trials. J. Consum. Cult. 3, 363–385. Lent, R.W., Brown, S.D., Hackett, G., 1994. Toward a unifying social cognitive theory of career and academic interest, choice, and performance. J. Vocat. Behav. 45, 79–122. Leong, L.Y., Ooi, K.B., Chong, A.Y.L., Lin, B., 2013. Modeling the stimulators of the behavioral intention to use mobile entertainment: does gender really matter? Comput. Hum. Behav. 29 (5), 2109–2121. Leong, G.-W., Ping, T.-A., Muthuveloo, R., 2017. Antecedents of behavioural intention to adopt internet of things in the context of Smart City in Malaysia. Glob. Bus. Manag. Res. 9 (4), 442–456. Lewis, C.C., Fretwell, C.E., Ryan, J., Parham, J.B., 2013. Faculty use of established and emerging technologies in higher education: a unified theory of acceptance and use of technology perspective. Int. J. Higher Educ. 2 (2), 22–34. Limayem, M., Hirt, S.G., Cheung, C.M.K., 2007. How habit limits the predictive power of intention: the case of information systems continuance. MIS Q. 31 (4), 705–737. Lin, C.P., Tsai, Y.H., Wang, Y.J., Chiu, C.K., 2011. Modeling IT relationship quality and its determinants: a potential perspective of network externalities in e-service. Technol. Forecast. Soc. Chang. 78, 171–184. Lorente, S., 2004. Key Issues Regarding Domotic Applications, Translated by IEEE. pp. 121–122. Lu, J., Yao, J.E., Yu, C., 2005. Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. J. Strateg. Inf. Syst. 14, 245–268. Luo, X., Li, H., Zhang, J., Shim, J.P., 2010. Examining multi-dimensional trust and multifaceted risk in initial acceptance of emerging technologies: an empirical study of mobile banking services. Decis. Support. Syst. 49 (2), 222–234. Maddulety, K., Sharma, S., Venkatesh, K.P., Seetharaman, A., 2017. Factors influencing purchase of smart appliances in Smart homes. J. Account. Bus. Manag. 24 (1), 21–42. Marhaeni, G.A., Indrawati, 2015. In: Measurement for analyzing instant messenger application adoption using a unified theory of acceptance and use of technology 2. International Conference on Multidisciplinary Trends in Academic Research. 1. GTAR, pp. 259–268. Melnyk, V., Van Osselaer, S.M.J., Bijmolt, T.H.A., 2009. Are women more loyal customers than men? Gender differences in loyalty to firms and individual service providers. J. Mark. 73 (4), 82–96. Mital, M., Chang, V., Choudhary, P., Papa, A., Pani, A.K., 2017. Adoption of Internet of things in India: a test of competing models using a structured equation modeling approach. Technol. Forecast. Soc. Chang. https://doi.org/10.1016/j.techfore.2017. 03.001. Moore, G., 2002. Crossing the Chasm. Harper Collins, New York. Moreno, V.M., Terroso-Saenz, F., Gonzalez-Vidal, A., Valdes-Vela, M., Skarmeta, A.F., Zamora, M.A., Chang, V., 2017. Applicability of big data techniques to Smart cities deployments. IEEE Trans. Indust. Inform. 13 (2), 800–809. Morreale, P., Qi, F., Croft, P., 2011. A green wireless sensor network for environmental monitoring and risk identification. Int. J. Sens. Netw. 10 (12), 73–82. Morris, M.E., Adair, B., Miller, K., Ozanne, E., Hansen, R., Pearce, A.J., Said, C.M., 2013. Smart-home technologies to assist older people to live well at home. J. Aging Sci. 1 (101), 1–9. Nathanson, C., Lorenz, G., 1982. Women and health: The social dimensions of biomedical data. In: Giele, J. (Ed.), Women in the Middle Years. 37–87. Nikayin, F., Skournetou, D., De Reuver, M., 2011. Establishing a common service platform for smart living: challenges and a research agenda. In: Toward Useful Services for Elderly and People With Disabilities, pp. 251–255. Nikou, S., Bouwman, H., 2014. Ubiquitous use of mobile social network services. Telematics Inform. 31 (3), 422–433. Oh, J.-C., Yoon, S.-J., 2014. Predicting the use of online information services based on a modified UTAUT model. Behav. Inform. Technol. 33 (7), 716–729. Oliveira, T., Thomas, M., Baptista, G., Campos, F., 2016. Mobile payment: understanding the determinants of customer adoption and intention to recommend the technology. Comput. Hum. Behav. 61, 404–414. Oye, N., Iahad, N., Rabin, Z.A., 2011. A model of ICT acceptance and use for teachers in higher education institutions. Int. J. Comput. Sci. Commun. Netw. 1 (1), 22–40. Paetz, A.G., Dütschke, E., Fichtner, W., 2012. Smart homes as a means to sustainable energy consumption: a study of consumer perceptions. J. Consum. Policy 35 (1), 23–41. Pahnila, S., Siponen, M., Zheng, X., 2011. Integrating habit into UTAUT: the Chinese eBay case. Pac. Asia J. Assoc. Inf. Syst. 3 (2), 1–30. Parag, Y., Butbul, G., 2018. Flexiwatts and seamless technology: public perception of demand flexibility through smart home technology. Energy Res. Soc. Sci. 39, 177–191. Parasuraman, A., 2000. Technology Readiness Index (TRI) a multiple-item scale to measure readiness to embrace new technologies. J. Serv. Res. 2 (4), 307–320. Peine, A., Moors, E.H.M., 2015. Valuing health technology – habilitating and prosthetic strategies in personal health systems. Technol. Forecast. Soc. Chang. 93, 68–81. Peters, D., Axsen, J., Mallett, A., 2018. The role of environmental framing in socio-political acceptance of smart grid: the case of British Colombia, Canada. Renew. Sust. Energ. Rev. 82, 1939–1951. Pham, M., Mengistu, Y., Do, H., Sheng, W., 2018. Delivering home healthcare through a cloud-based Smart home environment. Futur. Gener. Comput. Syst. 81, 129–140. Poong, Y.S., Yamaguchi, S., Takada, J.I., 2017. Investigating the drivers of mobile learning acceptance among young adults in the World Heritage town of Luang

Prabang, Laos. Inf. Dev. 1 (1), 1–15. Prensky, M., 2001. Digital natives, digital immigrants Part 1. On the Horizon. 9. pp. 1–6 (5). Prensky, M., 2013. Our brains extended. Educ. Leadersh. 70 (6), 2227. Pulidindi, V., Jinkab, S., Priyac, S.A., 2016. User acceptance of electronic health records: cross validation of Utaut model. Glob. Manag. Rev. 10 (3), 42–54. Quinn, N., Ortega, R., Rahilly, P., Royer, C., 2010. Use of environmental sensors and sensor networks to develop water and salinity budgets or seasonal wetland real-time water quality management. Environ. Model. Softw. 25 (9), 1045–1058. Qureshi, F.F., Iqbal, R., Qasim, M., Doctor, F., Chang, V., 2017. Integration of OMNI channels and machine learning with smart technologies. J. Ambient. Intell. Humaniz. Comput. 9 (44), 1–17. Rialle, V., Duchene, F., Noury, N., Bajolle, L., Demongeot, J., 2002. Health Smart home: information technology for patients at home. Telemed. J. E Health 8 (4), 395–409. Robles, R.J., Kim, T.H., Cook, D., Das, S., 2010. A review on security in smart home development. Int. J. Adv. Sci. Technol. 15, 13–23. Rodrigues, G., Sarabdeen, J., Balasubramanian, S., 2016. Factors that influence consumer adoption of e-government services in the UAE: a UTAUT model perspective. J. Internet Commer. 15 (1), 18–39. Rogers, E.M., 1962. Diffusion of Innovations. The Free Press Of Glencoe, New York. Rogers, E.M., 2003. Diffusion of Innovations, fifth edition. Free press, New York, NY. Roslina, Zarlis, M., Mawengkang, H., Sembiring, R.W., 2017. The role of information and communication technology in developing smart education. J. Phys. Conf. Ser. 890 (1), 566–570. https://doi.org/10.1088/1742-6596/890/1/012091. Roy, P.C., Bouzouane, A., Giroux, S., Bouchard, B., 2011. Possibilistic activity recognition in smart homes for cognitively impaired people. Appl. Artif. Intell. 25, 883–926. Sarma, S., Sunny, S.A., 2017. Civic entrepreneurial ecosystems: Smart city emergence in Kansas City. Bus. Horiz. 60 (6), 843–853. Schillewaert, N., Ahearne, M.J., Frambach, R.T., Moenaert, R.K., 2005. The adoption of information technology in the sales force. Ind. Mark. Manag. 34 (4), 323–336. Schoonenboom, J., 2014. Using an adapted, task-level technology acceptance model to explain why instructors in higher education intend to use some learning management system tools more than others. Comput. Educ. 71, 247–256. Schweitzer, F., Van den Hende, E.A., 2016. To be or not to be in thrall to the march of smart products. Psychol. Mark. 33 (10), 830–842. Segura, A.S., Thiesse, F., 2015. Extending UTAUT2 to explore pervasive information systems. In: ECIS 2015 Completed Research Papers, pp. 154. Singaravelan, A., Kowsalya, M., 2017. A novel minimum cost maximum power algorithm for future smart home energy management. J. Adv. Res. 8, 731–741. Singh Sohal, A., Sandhu, R., Sood, S.-K., Chang, V., 2018. A cybersecurity framework to identify malicious edge device in fog computing and cloud-of-things environments. Comput. Secur. 74, 340–354. https://doi.org/10.1016/j.cose.2017.08.016. Slade, E.L., Dwivedi, Y.K., Piercy, N.C., Williams, M.D., 2015. Modeling consumers' adoption intentions of remote mobile payments in the United Kingdom: extending UTAUT with innovativeness, risk, and trust. Psychol. Mark. 32 (8), 860–873. Snow, C.C., Håkonsson, D.D., Obel, B., 2016. A Smart City is a collaborative community: lessons from Smart Aarhus. Calif. Manag. Rev. 59 (1), 92–108. Sørensen, K.H., 1994. Technology in use: two essays on the domestication of artefacts. In: Centre for Technology and Society Working Paper. 2/94 (Trondheim, Norway). Spickermann, A., Grienitz, V., Von Der Gracht, H.A., 2014. Heading towards a multimodal city of the future? Multi-stakeholder scenarios for urban mobility. Technol. Forecast. Soc. Chang. 89, 201–221. Statista, 2006. Global Consumer Survey, Home/household appliances in the U.S. pp. 109. Strengers, Y., Nicholls, L., 2017. Convenience and energy consumption in the smart home of the future: industry visions from Australia and beyond. Energy Res. Soc. Sci. 32, 86–93. Strickland, E., 2011. Cisco bets on South Korean Smart city. IEEE Spectr. 48, 11–12. Sumak, B., Hericko, M., Polancic, G., Pusnik, M., 2010. Investigating of e-learning system acceptance using UTAUT. Int. J. Eng. Educ. 26 (6), 1327–1342. Sun, Y., Wang, N., Guo, X., Peng, Z., 2013. Understanding the acceptance of mobile health services: a comparison and integration of alternative models. J. Electron. Commer. Res. 14 (2), 183–200. Taiwo, A.A., Downe, A.G., 2013. The theory of User Acceptance and Use of Technology (UTAUT): a meta-analytic review of empirical findings. J. Theor. Appl. Inf. Technol. 49 (1), 48–58. Tam, J.L.M., 2004. Customer satisfaction, service quality and perceived value: an integrative model. J. Mark. Manag. 20 (7–8), 897–917. Tavares, J., Oliveira, T., 2016. Electronic health record patient portal adoption by health care consumers: an acceptance model and survey. J. Med. Internet Res. 18 (3), 1–17. Tenenhaus, M., Esposito, V., Chatelin, Y.M., Lauro, C., 2005. PLS path modeling. Comput. Stat. Data Anal. 48, 159–205. Teo, T., Lee, C.B., Chai, C.S., 2008. Understanding pre-service teachers' computer attitudes: applying and extending the technology acceptance model. J. Comput. Assist. Learn. 24 (2), 128–143. Thite, M., 2011. Smart cities implications of urban planning for human resource development. Hum. Resour. Dev. Int. 14 (5), 623–631. United Nation, 2015. World Urbanization Prospect: The 2014 Revision. Department of Economic and Social Affairs. Population Division, New York Available from Internet: http://esa.un.org/unpd/wup/FinalReport/WUP2014-Report.pdf. Van Slyke, C., Comunale, C.L., Belanger, F., 2002. Gender differences in perceptions of Web-Base shopping. Commun. ACM 45 (8), 82–86. Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D., 2003. User acceptance of information technology: toward a unified. MIS Q. 27 (3), 425–478. Venkatesh, V., Thong, J.Y., Xu, X., 2012. Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q. 36 (1), 157–178.

18

Technological Forecasting & Social Change xxx (xxxx) xxxx

P. Baudier, et al. Waldron, I., 1988. Gender and health-related behavior. In: Gochman, D.S. (Ed.), Health Behavior: Emerging Research Perspectives. 193–208. Wallace, L.G., Sheetz, S.D., 2014. The adoption of software measures: a technology acceptance model (TAM) perspective. Inf. Manag. 51 (2), 249–259. Wang, T., Jung, C.H., Kang, M.H., Chung, Y.S., 2014. Exploring determinants of adoption intentions towards enterprise 2.0 applications: an empirical study. Behav. Inform. Technol. 33 (10), 1048–1064. Wang, N., Tang, L., Pan, H., 2018. Analysis of public acceptance of electric vehicles: an empirical study in Shanghai. Technol. Forecast. Soc. Chang. 126, 284–291. Wild, K., Boise, L., Lundell, J., Foucek, A., 2008. Unobtrusive in-home monitoring of cognitive and physical health: reactions and perceptions of older adults. J. Appl. Gerontol. 27 (2), 181–200. Williams, K.C., Page, R.A., 2011. Marketing to the generations. J. Behav. Stud. Bus. 5 (1), 1–17. Wilson, C., Hargreaves, T., Hauxwell-Baldwin, R., 2015. Smart homes and their users: a systematic analysis and key challenges. Pers. Ubiquit. Comput. 19 (2), 463–476. Wilson, C., Hargreaves, T., Hauxwell-Baldwin, R., 2017. Benefits and risks of smart home technologies. Energy Policy 103, 72–83. Winters, J.V., 2011. Why are Smart cities growing? Who moves and who stays. J. Reg. Sci. 51 (2), 252–270. Wong, J.K.W., Leung, J.K.L., 2016. Modelling factors influencing the adoption of smarthome technologies. Facilities 34 (13/14), 906–923. Wong, C.-H., Tan, G.W.-H., Loke, S.-P., Ooi, K.-B., 2014. Mobile TV: a new form of entertainment? Ind. Manag. Data Syst. 114 (7), 1050–1067. Wozniak, G.D., 1987. Human capital, information, and the early adoption of new technology. J. Hum. Resour. 22 (1), 101–112. Wu, J., Liu, D., 2007. The effect of trust and enjoyment on intention to play online games. J. Electron. Commer. Technol. Asia Pac. J. Market. Logist. 26 (2), 211–231. Wu, L.-H., Wu, L.-C., Chang, S.-C., 2016. Exploring consumer's intention to accept smartwatch. Comput. Hum. Behav. 64, 383–392. Yang, H., Lee, H., Zo, H., 2017. User acceptance of smart home services: an extension of the theory of planned behavior. Ind. Manag. Data Syst. 117 (1), 68–89. Yi, M.Y., Fiedler, K.D., Park, J.S., 2006. Understanding the role of individual innovativeness in the acceptance of IT-based innovations: comparative analyses of models and measures. Decis. Sci. 37 (3), 393–426.

Yohanis, Y., Mondol, J., Wright, A., Norton, B., 2008. Real-life energy use in the UK: how occupancy and dwelling characteristics affect domestic electricity use. Energ. Buildings 40, 1053–1059. Yuan, S., Ma, W., Kanthawala, S., Peng, W., 2015. Keep using my health apps: discover users' perception of health and fitness apps with the utaut2 model. Telemed. e-Health 21 (9), 735–741. Zeithaml, V.A., 1988. Consumer perceptions of price, quality, and value: a means-end model and synthesis of evidence. J. Mark. 52 (3), 2–22. Zhang, D., Liu, S., Papageorgiou, L.G., 2014. Fair cost distribution among smart homes with microgrid. Energy Convers. Manag. 80, 498–508. Zhang, M., Luo, M., Nie, R., Zhang, Y., 2017. Technical attributes, health attribute, consumer attributes and their roles in adoption intention of healthcare wearable technology. Int. J. Med. Inform 108, 97–109. Zygiaris, S., 2013. Smart city reference model: assisting planners to conceptualize the building of Smart city innovation ecosystems. J. Knowl. Econ. 4 (2), 217–231. Patricia Baudier is an associate professor of Marketing at EMLV Business School - Pôle Universitaire Léonard De Vinci in Paris (France). Her research focuses on new technologies, consumers behavioral and Digital Marketing. She spent 28 years within major American companies such as Apple and Kodak, mainly at marketing positions. She has authored several papers in leading journals of innovation, management and marketing. Chantal Ammi is a full professor of Marketing at Institute Mines Telecom, Telecom Business School (France). Her research focuses on new technologies, consumers behavioral, usage and acceptability, information systems and innovation. She was the director of a Doctoral Program and a laboratory of research (Litem) for many years. She supervised many PhD students and managed many European and industrial contracts of research in the fields of innovation and technologies (Automation, robotics…). She has authored several papers in leading journals of innovation, management and marketing. Matthieu Deboeuf-Rouchon is Advanced Solution Manager within Altran Technologies' Expertise Center Digital Transformation. Passionate about the impacts of technology on people, economic organizations and society in general, he is currently preparing a PhD around Smart City and Artificial Intelligence.

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