Smart Homes as Catalysts for Sustainable Consumption: A Digital Economy Perspective

Abstract

The green living issues that arise as a result of smart home use in the context of sustainability consumption, at a time when smart homes are being built that can improve the management of electricity, water, gas consumption, and when their use offers the opportunity to raise awareness of caring for health and achieving wellbeing, became the basis for writing this article. This paper explores the intersection of smart home technologies, sustainable consumption, and the digital economy, offering insights into how digital advancements can foster environmentally responsible consumer behaviors. The motivation behind this study is the growing environmental concerns and the need for sustainable solutions in consumer behavior. Despite the advancements in smart home technologies, there is a significant gap in the literature regarding their role in promoting sustainable consumption. The research employs an extended unified theory of acceptance and use of technology (UTAUT2) model, integrating factors such as convenience, health and wellbeing, and environmental impact to assess the determinants influencing the adoption of smart home technologies. This study follows a comprehensive research process involving a survey of 795 individuals and the use of structural equation modeling (SmartPLS 4). The empirical findings reveal that factors such as performance expectancy and personal innovativeness are critical in shaping the adoption of smart home technologies. Additionally, this study highlights the significant positive influence of smart homes on sustainable consumption behaviors, underscoring their potential in driving the digital economy towards sustainability goals. The significance of these findings lies in their contribution to the understanding of how digital technologies, particularly smart homes, can enhance sustainable consumption, offering implications for policymakers, developers, and stakeholders in the digital economy seeking to promote sustainability through technological innovations.

1. Introduction

In recent years, environmental issues have become more prevalent in society due to human influence. Problems such as global warming, ozone layer depletion, loss of biodiversity, scarcity of natural resources, and pollution of water, air, and soil are harmful to the environment and sustainability. Consumers are significant users of resources, and the energy sector is one of the most impactful on the environment. Therefore, smart homes have become a priority area of strategic energy planning and European policy. In the Strategic Energy Technology Plan, smart homes are one of the European Union’s 10 priority action areas. According to an European Commission document, “Create technologies and services for smart homes that provide smart solutions to energy consumers”. Behind this strategic policy objective lies the Commission’s vision for the electricity market, which aims to deliver a new deal for consumers, smart homes, and network data management and protection (EC 2015).

Smart homes, equipped with advanced technologies that facilitate remote control and automation, have emerged as significant tools in promoting energy efficiency and reducing resource waste. These technologies not only provide convenience and enhanced quality of life but also play a crucial role in sustainable consumption—minimizing environmental impacts and supporting the needs of current and future generations.

Despite advancements in smart home technologies, there is a significant gap in the literature regarding their role in promoting sustainable consumption. Therefore, this study aims to explore whether smart homes can act as catalysts for sustainable consumption within the framework of the digital economy. The research employs an extended unified theory of acceptance and use of technology (UTAUT2) model, integrating factors such as convenience, health and wellbeing, and environmental impact to assess the determinants influencing the adoption of smart home technologies.

This article addresses the following research questions:

• What are the main determinants influencing the intention to adopt smart home technologies?
• How do smart home technologies impact sustainable consumption behaviors?
• What are the significant differences in the perceived benefits of smart home technologies across different demographic groups?

To answer these research questions, a survey was conducted among 795 individuals, and structural equation modeling (SmartPLS 4) was used as the analytical method. The scope of the article includes an introduction to the topic and a literature review, research methods, empirical findings, discussion, and conclusions.

2. Literature Review

2.1. Smart Homes

Smart homes, also known as connected homes [1] or automated homes, are places of residence equipped with advanced technologies that enable them to be controlled, monitored, and automated remotely through the use of smart devices like speakers, thermostats, and security systems [2]. The idea of a smart home has existed for some time, but it has recently gained widespread popularity due to the advances in technology and the growing demand for convenience and efficiency [3,4]. One of the main benefits of a smart home is the ability to control and monitor various aspects of the home remotely through the use of a smartphone or other device [5]. Homeowners can use their smart devices to turn off the lights, adjust the thermostat, or lock the doors when they are not home [6]. This not only saves energy and money, but it also provides an added layer of security and peace of mind [7]. In addition to convenience and efficiency, smart homes have the potential to improve our daily lives in numerous other ways [8]. Smart home technology is applicable to assist with tasks such as meal planning, grocery shopping, and even healthcare [9]. Smart appliances can be programmed to alert homeowners when they are running low on certain items, and smart home systems can be integrated with medical equipment to keep an elderly or disabled person’s health under surveillance [10].

Despite the numerous advantages of smart homes [11], there are a number of issues and restrictions that need to be resolved. One of the biggest concerns is privacy and security [12]. As the number of internet-connected devices is growing, the risk of cyber-attacks and data breaches increases [13]. It is important for homeowners to ensure that their smart home devices are secured and personal data are protected [14]. Interoperability is another challenge of smart homes, or the ease with which various systems and devices can cooperate [15]. With so many different brands and technologies available, it can be difficult for homeowners to find devices that are compatible with their existing systems [16]. This can lead to frustration and a lack of integration between devices, which can hinder the overall effectiveness of a smart home [17]. Smart homes rely on internet connectivity, which can be disrupted by power outages or other issues. This can cause problems with the functionality of smart devices, which can be frustrating for homeowners [18]. Despite these challenges, the smart home industry is expected to continue growing in the coming years [19]. The incorporation of artificial intelligence (AI) and machine learning is one of the primary forces behind this expansion, which will enable smart home devices to become more intelligent and adaptable over time [20]. In addition, combining smart home technology with other platforms, such as the Internet of Things (IoT) and smart cities will create new opportunities for innovation and improvement [21]. Smart homes have the potential to increase sustainability and energy efficiency [22,23]. Smart thermostats, for example, can learn a homeowner’s schedule and adjust the temperature accordingly to save energy, while smart appliances can be programmed to turn off when they are not in use [24].

While the concept of a smart home is relatively straightforward, the specific features and capabilities of a smart home can vary significantly depending on the devices and systems that are included. Some common features of smart homes include the following: (1) Voice control: many smart home devices and systems can be controlled through voice commands, using devices such as “Amazon Echo” or “Google Home” [25]. This allows homeowners to control their home using natural language, making it easier to use the various systems and appliances [26]. (2) Smart lighting: These systems allow homeowners to control the lighting in their home remotely, including turning lights on and off, dimming them, and changing their color. Some systems also include features such as automatic scheduling, which can turn lights on and off based on the homeowner’s schedule [27]. (3) Smart appliances: Smart appliances such as refrigerators, washing machines, and ovens can be controlled remotely through a smartphone app or a central hub. This can allow homeowners to monitor and control their appliances from a location with an internet connection, anywhere [28]. (4) Smart security: Smart security systems can include features such as door and window sensors, motion detectors, and security cameras, which can be monitored and managed remotely via a smartphone app or central hub. These systems can alert homeowners to any unusual activity and allow them to take appropriate action, such as contacting the police or activating an alarm [29]. (5) Smart thermostats: Smart thermostats can learn a homeowner’s schedule and adjust the temperature accordingly, helping to save energy and reduce utility costs. Some thermostats can also be controlled remotely through a smartphone app or central hub, allowing homeowners to adjust the temperature [30]. (6) Smart home entertainment systems: Smart home entertainment systems can include features such as smart TVs, sound systems, and remotely accessible and controllable streaming devices through a smartphone or tablet app or through a smart speaker or smart watch. Some smart home entertainment systems can also be integrated with other smart home devices, allowing for the seamless control of multiple systems and functions [31]. (7) Sensors and monitoring devices: Smart homes can also include a variety of sensors and monitoring devices that can detect and alert homeowners to potential hazards, such as water leak detectors, smoke detectors, and carbon monoxide detectors. These devices can help to ensure the safety and security of a home [32]. The features and capabilities of a smart home can vary significantly depending on the specific devices and systems that are included. However, these devices and systems can offer a number of advantages, such as comfort, energy efficiency, security, and increased safety [33].

A smart home is a home that is fitted with technology that enables for the automation and control of various household functions and systems [34]. Smart home technology has been around for a while, but in recent years it has become more advanced and affordable, making it more accessible to the average consumer [35,36]. One of the main benefits of a smart home is energy efficiency [37]. Smart home technology can help homeowners save energy and reduce their energy bills by automating energy-efficient actions, such as turning off lights when they are not needed, adjusting the thermostat to more energy-efficient temperatures, and turning off appliances when they are not in use [38]. This not only saves money, but it reduces our carbon footprint in addition and protects the environment [39]. Home automation technology can also improve home safety and security. Smart security systems can be remotely controlled using a smartphone or tablet, allowing homeowners to monitor their home from anywhere [40]. These systems can include features such as door and window sensors, motion detectors, and security cameras, which can alert homeowners to any potential threats [41,42]. Smart locks can also be controlled remotely, allowing homeowners to lock or unlock their doors from anywhere, and even grant access to friends or family members remotely [43]. The technology for smart homes could also be improved in terms of safety by detecting and alerting homeowners to any potential hazards, such as leaks or electrical issues [44]. The technology for smart homes can also enhance convenience and comfort. Smart lighting systems can be controlled remotely, allowing homeowners to turn lights on or off, dim them, or change their color from their smartphone or tablet [45]. Smart appliances, such as dryers, ovens, and washing machines, can be controlled remotely, allowing homeowners to start or stop them from anywhere. This can be especially useful for busy homeowners who may not have the time to go home to start or stop an appliance [46]. Smart home technology can additionally enhance comfort by allowing homeowners to control the temperature and humidity in their home remotely, ensuring that it is always comfortable and conducive to sleep or relaxation [47]. Smart home technology can also improve accessibility for those with disabilities or mobility issues [48]. Smart home technology can include features such as voice control, which allows for the hands-free operation of various household functions [49]. Smart home technology can also include features such as automatic door openers, which can make it easier for those with mobility issues to enter and exit their home [50].

Smart homes have a long history dating back to the early 20th century. While the idea of a smart home has changed over time, the underlying idea has remained the same: to make our lives easier, more efficient, and more convenient through the use of technology [51]. One of the earliest examples of a smart home was the “Home of Tomorrow”, a house that was built in the 1930s as part of the Chicago World’s Fair. The house was equipped with a number of futuristic technologies, including automatic doors, a central vacuum system, and even a “smart” kitchen that could cook and serve meals without human intervention. While many of these technologies were ahead of their time and never became widely adopted, they helped to inspire the idea of a smart home and set the stage for future developments [52]. In the 1950s and 1960s, the idea of a smart home began to take shape with the development of home automation systems [53]. These systems allowed homeowners to control various household functions, such as lighting and heating, remotely using switches and timers. These systems were expensive and complex, and were mainly used in high-end homes and commercial buildings. In the 1980s and 1990s, introduction of personal computers and the internet paved the way for the modern smart home. Homeowners could now control their home’s functions and systems remotely using their computers or cellular phones, and could even access and control their home from anywhere in the world. The development of technology for smart homes also began to focus on energy efficiency, with the introduction of smart thermostats and energy management systems [54]. Today, smart home technology has become more advanced and affordable, making it accessible to the average consumer. Smart home technology can now include a wide range of functions and systems and can also be integrated with smart devices [55].

2.2. Development of Research Hypotheses

Modern scientific theories on technology acceptance are models that aim to explain how people decide whether or not to adopt and use new technologies [56]. These theories are important tools for understanding how people interact with and make decisions about technology, and they can be useful for technology designers, developers, and marketers who are looking to increase the adoption and usage of their products.

The UTAUT and UTAUT2 models developed by Venkatesh et al. [57] give a thorough overview of the models and theories on technology acceptance already discussed. The UTAUT2 model specifically recommends measuring the degree of acceptance of new technologies by examining the effects of variables like “Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Hedonic Motivation, Price Value, and Habit on Intention to Use”.

“The Unified Theory of Acceptance and Use of Technology (UTAUT2)” is a model developed by Venkatesh, Thong, and Xu in [57] that aims to explain how people decide whether or not to adopt and use new technology. UTAUT2 builds upon and expands upon the previously discussed theories and includes seven factors that influence technology acceptance:

• “Performance expectancy”: This is the degree to which individuals believe that using the technology will improve their job performance. If people believe that the technology will help them perform better, they are more likely to adopt and use it [58].
• “Effort expectancy”: This is to what extent do people think that the technology will be easy to use. If people believe that the technology is easy to use, they are more likely to adopt and use it [59].
• “Social influence”: This is a measure of how much individuals believe that others around them are using the technology. If people believe that others are using the technology, they are more likely to adopt and use it themselves [60].
• “Facilitating conditions”: This is the degree to which people believe that the necessary resources and support are available for them to use the technology. If people think that the required resources and support are available, they are more inclined to use and adopt technology [61].
• “Hedonic motivation”: The extent to which people are motivated to use the technology because they believe it will be enjoyable or fun [62].
• “Price value”: The degree to which individuals think that the price they paid for the technology is reasonable given the benefits they receive from using it [63].
• “Habit”: It is possible that the development of a habit of using a particular technology could influence a person’s actual usage behavior [64].

UTAUT2 has been widely tested and found to be a useful predictor of technology adoption and use in many different contexts. It is a valuable tool for understanding how people decide whether or not to adopt and use new technologies, and it can be useful for technology designers, developers, and marketers who are looking to increase the adoption and usage of their products. The intention to live in smart homes was measured in this research using the scales of the UTAUT2’s seven dimensions.

Personal innovativeness: The incorporation of new information technologies by those who are intending to use them remains a significant concern for professionals and researchers in the information systems field. Many models were created to better comprehend the process of adopting new information technologies. A new concept that further clarifies the connections outlined in the models for technology adoption and provides a practical measure for this concept that has favorable psychometric characteristics has been introduced. This concept, referred to as personal innovativeness, developed by Agarwal and Prasad [65] in the realm of information technology, is believed to have moderating effects on both the antecedents and outcomes of an individual’s perceptions regarding a new information technology. Personal innovativeness was used in UTAUT2 by Kourouthanassis et al. [66] to measure augmented reality travel apps for mobile adoption and by Juaneda-Ayensa et al. [67] to measure shopping with the intention of buying a journey.

2.3. Hypotheses

According to Chen et al. [68], the smart home can be characterized by four dimensions: “Convenience and Comfort, Health Care, Safety and Security, and Sustainability”. Guhr et al. [69] added a fifth dimension, privacy concerns. It is believed that living in a smart home can enhance an individual’s performance in these areas, leading to a positive impact on their performance expectancy. Additionally, it is hypothesized that frequent interaction with smart home technology may shape one’s habit.

Convenience and comfort (CC) are key aspects of living in a smart home, as they can significantly improve an individual’s quality of life [70]. Smart home technology allows the automation and personalization of various aspects of the home, such as temperature, lighting, and appliances, which can make daily tasks more efficient and convenient [71]. Additionally, smart home technology can provide personalized experiences through the use of voice assistants and smart home security systems, as well as the use of smart appliances that can make household tasks easier and more enjoyable [72]. We suggest the following hypotheses:

H1a. 

“Smart home comfort and convenience variable has significant positive influence on performance expectancy”.

H1b. 

“Smart home comfort and convenience variable has significant positive influence on habit”.

Health/wellbeing (HW): Smart home technology has numerous applications for promoting health and wellbeing [73]. These devices can track and monitor various health indicators, such as sleep patterns, physical activity, and nutrition, allowing individuals to make informed decisions about their health [74]. Smart home devices can also assist with managing chronic conditions and provide alerts for emergencies, allowing caregivers to remotely monitor the wellbeing of loved ones [75]. In addition, air and water quality monitors can alert homeowners to any potential health hazards in their home environment [76]. Smart home technology can greatly enhance health and wellbeing by providing individuals with the tools and information necessary for them to decide on their health in an informed manner, as well as assistance with managing chronic conditions and aging in place [77]. The following hypotheses are proposed:

H2a. 

“Smart home health variable has significant positive influence on performance expectancy”.

H2b. 

“Smart home health variable has significant positive influence on habit”.

Safety and security (SS) are important considerations in any home, and smart home technology can provide a number of features to enhance the safety and security of a home [78]. Smart home security systems can include door and window sensors and video cameras and alarms that can alert homeowners to any potential threats and allow them to remotely monitor their home [79]. Smart locks can also provide an extra layer of security by allowing homeowners to remotely lock and unlock their doors and track who has access to their home [80]. Smart home technology can also provide safety features such as smoke and carbon monoxide detectors, which can alert homeowners to potential hazards in their home environment [81]. Additionally, smart home technology can assist with emergency situations by providing alerts for fires or other emergencies and allowing individuals to call for help through the use of voice assistants or other devices [82]. We postulate the following hypotheses:

H3a. 

“Smart home safety and security variable has significant positive influence on performance expectancy”.

H3b. 

“Smart home safety and security variable has significant positive influence on habit”.

Sustainable development (SD). Smart home technology can also assist with sustainability and home energy management [83]. Smart thermostats, for example, can be programmed to adjust the temperature of a home based on the time of day or the presence of people in the home, reducing energy usage and potentially saving money on utility bills [84]. Smart appliances, such as refrigerators and washing machines, can also be more energy efficient and have the ability to be controlled and monitored remotely, allowing homeowners to optimize their energy usage [85]. Smart lighting systems can also be programmed to turn off when a room is unoccupied, further reducing energy consumption [86]. Additionally, smart home technology can assist with the management of renewable energy sources, such as solar panels, by providing real-time monitoring and data on energy production and usage [87]. The following hypotheses are proposed:

H4a. 

“Smart home sustainable development variable has significant positive influence on performance expectancy”.

H4b. 

“Smart home sustainable development variable has significant positive influence on habit”.

Privacy and data preservation (PD) are important considerations when it comes to smart home technology. Smart home devices often collect and transmit data about an individual’s home environment and activities, which can raise concerns about privacy [88]. To address these concerns, it is important for smart home companies to implement strong privacy policies and secure data storage practices [89]. This can include measures such as encrypting data, securing data transmission with secure protocols, and obtaining explicit consent from users before collecting or sharing data [90]. Smart home technology can also provide features that allow individuals to manage their privacy, such as the ability to disable data collection or delete data that have been collected [91]. Additionally, individuals can take steps to protect their privacy by carefully researching and selecting smart home devices that have strong privacy policies and by being mindful of the data that they share through smart home technology [92]. We propose the following hypotheses:

H5a. 

“Privacy and data preservation variable has significant positive influence on performance expectancy”.

H5b. 

“Privacy and data preservation variable has significant positive influence on habit”.

Performance expectancy (PE). Numerous studies have shown that PE has a positive and significant impact on the adoption of accessing electronic health records [93], learning in computer-supported classrooms [94], mobile app usage for digital wellness [95], mobile social network site usage by consumers [96], accessing social media by consumers [97], and the adoption and use of intermodal travel systems [98]. Other research on the intention to use internet or mobile technologies has confirmed the impact of PE on intention to use, such as for the use of smartphones and tablets for various activities [99], accessing technology that enables smart banking [100], marketing through the internet [101], mobile messaging [102], home digital services [103], and purchasing via social media platforms [104]. We suggest the following hypothesis:

H6. 

“PE has significant positive influence on intention to live in a smart home”.

Effort expectancy (EE): Several researchers have found that the adoption of various technologies, including electronic health records [93], computer-supported learning [94], digital wellness apps [95], consumer use of mobile social networks [96], consumer access to social media [97], and the adoption and use of intermodal travel systems [98], are significantly influenced by EE. Other studies have also confirmed the impact of EE on the intention to use technologies such as smartphones and tablets for various activities [99], smart banking [100], internet marketing [101], mobile messaging [102], home digital services [103], and purchasing through social media platforms [104]. We postulate the following:

H7. 

“EE has significant positive influence on intention to live in a smart home”.

Social influence (SI): SI has a positive and significant impact on the adoption of technologies, including electronic health records [93], computer-supported learning [94], digital wellness apps [95], consumer use of mobile social networks [96], consumer access to social media [97], and the adoption and use of intermodal travel systems [98], according to various researchers. Additionally, studies on the intention to adopt mobile or internet technologies have confirmed the impact of SI on the intention to use technologies such as smartphones and tablets for various activities [99], smart banking [100], internet marketing [101], mobile messaging [102], home digital services [103], and purchasing through social media platforms [104]. As a result, we postulate the following hypothesis:

H8. 

“SI has significant positive influence on Intention to live in a smart home”.

Facilitating conditions (FC): According to various researchers, FC has a positive and substantial effect on the adoption of technologies including electronic health records [93], computer-supported learning [94], digital wellness apps [95], consumer use of mobile social networks [96], consumer access to social media [97], and the adoption and use of intermodal travel systems [98]. In addition, the research on the intention to use internet or mobile technologies has confirmed the impact of FC on the intention to use technologies such as smartphones and tablets for various activities [99], smart banking [100], internet marketing [101], mobile messaging [102], home digital services [103], and purchasing through social media platforms [104]. We hypothesize the following:

H9. 

“FC has significant positive influence on intention to live in a smart home”.

Hedonic motivation (HM): HM has a positive and significant impact on the adoption of technologies including electronic health records [93], computer-supported learning [94], digital wellness apps [95], consumer use of mobile social networks [96], consumer access to social media [97], and the adoption and use of intermodal travel systems [98], according to various researchers. Furthermore, research on the intention to use internet or mobile technologies has confirmed the impact of HM on the intention to use technologies such as smartphones and tablets for various activities [99], smart banking [100], internet marketing [101], mobile messaging [102], home digital services [103], and purchasing through social networking sites [104]. We hypothesize that the following:

H10. 

“HM has significant positive influence on intention to live in a smart home”.

Price value (PV): PV has a positive and significant impact on the adoption of technologies including electronic health records [93], computer-supported learning [94], digital wellness apps [95], consumer use of mobile social networks [96], consumer access to social media [97], and the adoption and use of intermodal travel systems [98], according to various researchers. In addition, research on the intention to use internet or mobile technologies has confirmed the impact of PV on the intention to use technologies such as smartphones and tablets for various activities [99], smart banking [100], internet marketing [101], mobile messaging [102], home digital services [103], and purchasing through online social networks [104]. We suggest the following hypothesis:

H11. 

“PV has significant positive influence on intention to live in a smart home”.

Habit (HT): HT has a positive and substantial effect on the adoption of technologies including electronic health records [93], computer-supported learning [94], digital wellness apps [95], consumer use of mobile social networks [96], consumer access to social media [97], and the adoption and use of intermodal travel systems [98], according to various researchers. The research on the intention to use internet or mobile technologies has also confirmed the impact of HT on the intention to use technologies such as smartphones and tablets for various activities [99], smart banking [100], internet marketing [101], mobile messaging [102], home digital services [103], and purchasing through online social networks [104]. These earlier findings have led us to believe the following:

H12. 

“HT has significant positive influence on intention to live in a smart home”.

Personal innovativeness (PI): The tracking of the propensity to use new technology and the degree of acceptance of novel ideas or products can be effectively assessed using PI. The “intention to use” new technological innovations, like remote mobile payments and mobile location-based services, has been shown through research to be significantly impacted by PI [105]. Ahn et al. [106] also discovered a direct relationship between “innovativeness” and the “intention to use” a sustainable home, and Schweitzer and Van den Hende [107] hypothesized that “innovativeness” can moderate the intention to adopt smart products. Given that this study examines the level of adoption of smart homes, we suggest the following hypothesis:

H13. 

“PI has significant positive influence on intention to live in a smart home”.

3. Method and Research Sample

3.1. Test Model

The model research proposition in Figure 1 was based on seven variables from the UTAUT2 scale [57], a widely known measure of technology acceptance. We also incorporated the “intention to use” variable from the TAM [108] using a seven-point Likert scale ranging from “strongly disagree” to “strongly agree”. In addition, we adopted a scale determined by the literature review; therefore, we can quantify each of the five aspects of the smart home concept [68,69].

3.2. The Research Process

Between March and May 2022 in Poland, data were gathered through a questionnaire available on the SurveyMonkey platform. SurveyMonkey offers free customizable surveys online and a suite of paid back-end programs for data analysis, sample selection, bias elimination, and data representation [109].

The respondents were recruited for the study using the snowball sampling method on social media (primarily on Facebook), with the researchers being aware that the main limitation of snowball sampling as a method of sample selection is its representativeness. Convenience sampling by definition is usually neither random nor representative [110].

3.3. Questionnaire Structure

We took inspiration for our model (Figure 1) from the work of Baudier et al. [111]; they used the UTAUT2 scale, along with four external dimensions:

Health and Wellbeing

Smart home devices can, when needed, achieve the following:

• HW1: “Increase your chances of a healthier lifestyle”;
• HW2: “Increase health and wellness awareness”;
• HW3: “Provide information to help you make better health and wellness decisions”;
• HW4: “Give you more control over your health and well-being”;
• HW5: “Improve self-tracking if you use a wearable device (Smartwatch)”.

Sustainable development

For people living in a smart home, these devices can achieve the following:

• SD1: “They will allow you to know exactly the consumption of energy, water… (expenditure, amount of water used, heat, etc.)”;
• SD2: “They will save resources (energy, water…)”;
• SD3: “They will allow for better management of waste (garbage)”;
• SD4: “They will reduce costs”;
• SD5: “They will allow for an ecological approach to life”.

Safety and Security

Using a smart device at home can increase my security by performing the following:

• SS1: “Controlling that doors and windows are closed”;
• SS2: “Informing in case of unauthorized intrusion”;
• SS3: “Gas and smoke emission detection”;
• SS4: “Informing others directly outside of the accident (fainting, etc.)”.

Convenience and comfort

It is convenient that smart home devices can do the following:

• CC1: “Actively help residents without human intervention”;
• CC2: “Provide automatic temperature control in your home”;
• CC3: “Control any electrical apparatus through simple operation”;
• CC4: “Provide access to a lot of information”;
• CC5: “Help you make better decisions”.

Privacy and data preservation

• PD1: “Consumers are more concerned about disconnecting from smart devices than about losing control of them”;
• PD2: “Before buying smart devices, consumers want to know where the data collected about them by this device will be stored”;
• PD3: “Before buying smart devices, consumers want to know who will have access to personal data collected about them by this device and whether these data will be safe”;
• PD4: “Consumers are more likely to accept the uncertainty of losing control of their personal data collected by these devices than the risk of disconnecting from these devices”.

Performance expectancy

• PE1: “I believe that smart home items are useful in everyday life”;
• PE2: “Using smart home devices increases your chances of achieving important things”;
• PE3: “Using smart home devices helps you get things done faster”;
• PE4: “Using smart home devices increases productivity”.

Effort expectancy

• EE1: “Learning to use smart home devices is easy for me”;
• EE2: “Interaction with smart home devices is clear and understandable”;
• EE3: “I find smart home devices easy to use”;
• EE4: “It is easy for me to deftly use smart home devices”.

Social influence

• SI1: “People who are important to me think I should use smart home devices”;
• SI2: “People who influence my behavior believe that I should use smart home devices”;
• SI3: “People whose opinions I value prefer me to use smart home devices”.

Facilitating conditions

• FC1: “I have the resources necessary to use smart home devices”;
• FC2: “I have the knowledge necessary to use smart home devices”;
• FC3: “Smart home objects are compatible with the devices and technologies I use”;
• FC4: “I can get help from others when I have problems with smart home objects”.

Hedonic motivation

• HM1: “Using smart home devices is fun”;
• HM2: “Smart home devices are fun”;
• HM3: “Using smart home devices is very fun”.

Price value

• PV1: “Smart home devices come at a reasonable price”;
• PV2: “Smart home devices are good value for money”;
• PV3: “At the current price, smart home devices are a good value”.

Habit

• HT1: “Using smart home devices could become a habit for me”;
• HT2: “I could get addicted using smart home devices”;
• HT3: “I could use smart home devices”;
• HT4: “Using smart home devices can become natural for me”.

Personal innovativeness

• PI1: “I like experimenting with new information technologies”;
• PI2: “If I heard about a new information technology, I would look for ways to experiment with it”;
• PI3: “Among my family/friends, I am usually the first to try out new information technologies”;
• PI4: “In general, I do not hesitate to try out new information technologies”.

Intention to use

• ITU1: “It is worth using the smart home service”;
• ITU2: “I intend to use the smart home service in the future”;
• ITU3: “I anticipate that in the future I will use Smart Home services”.

3.4. Research Sample

The sample group for the study consisted of 795 individuals who were representative of households in Poland, with 324 men (40.7%) and 471 women (59.3%). While the sample was not representative of the overall population, it was predominantly composed of young people aged 16–20 (17.7%), aged 21–25 (61.3%), and the remaining 21% were aged 26 or older. Of the survey participants, 20.8% lived in rural regions, 27.7% lived in cities with a population of 100 thousand or less, 24.2% lived in towns where there were 100 to 200 thousand residents, and 26.9% inhabited cities with populations of 200 thousand or more. Most respondents reported that their household’s financial situation was good (63.2%), with the ability to afford some luxury items. A total of 26.9% reported a sufficient financial situation, requiring careful planning for major expenses, while 9.4% proclaimed themselves to be in very good financial standing. Just 0.5% said that the financial situation in their household was poor (

Table 1. Characteristics of respondents.

Specification		in %
Sex	Man	40.7
	Woman	59.3
Age	16–18	17.7
	21–25	61.3
	26 and more	21.0
Size of place of residence	Rural areas	20.8
	Towns 100 thousand or less	27.9
	Towns 100 to 200 thousand	24.2
	Towns 200 thousand or more	27.1
Subjective evaluation of financial situation	Very good	9.4
	Good	63.2
	Sufficient	26.9
	Poor	0.5

). A total of 71.2% of respondents claimed to have very high proficiency with smartphones, while 50% claimed to have the same level of proficiency with tablets, and 61.1% claimed to have the same proficiency with a variety of internet applications. When considering respondents who reported either high or very high skills, the percentage rose to over 90% for all categories except for the tablet, where 76.3% of respondents stated that they were proficient in these areas.

4. Results

The software used to create the SEM model was SmartPLS4 v. 4.0.8.7 [112]. The software is based on calculating PLS-SEM algorithms [113]. The default configuration settings were used to calculate the results. When a loading is above 0.70, it means that the latent variable being measured explains more than half of the variance in the indicator, which indicates that the indicator is reliable to a satisfactory level [113]. The recorded item HT2 of “Habit” was removed due to low loading.

First, we evaluated the internal consistency reliability of variables. One method for achieving this when using PLS-SEM is to calculate composite reliability (ρc). A higher value of ρc indicates a higher level of reliability. In exploratory research, scores between 0.60 and 0.70 are considered fair, while values from 0.70 to 0.95 are considered satisfactory to good. Cronbach’s alpha also measures internal consistency reliability. It has similar thresholds but typically produces lower values than ρc. When estimating reflective measurement models with PLS-SEM, Cronbach’s alpha typically has the lower value, while ρc has the higher value for internal consistency reliability. Additionally, the reliability coefficient ρA may also be calculated, which usually falls between ρc and Cronbach’s alpha (

Table 2. Assessment of measurement models using PLS-SEM.

Variable	Indicators	Convergent Validity			Internal Consistency Reliability
		Loadings	Indicator Reliability	AVE	Composite Reliability ρc	Reliability ρA (rho_A)	Cronbach’s Alpha
		>0.70	>0.50	>0.50	>0.70	>0.70	0.70–0.95
Health and Wellbeing	HW1	0.885	0.783	0.791	0.950	0.934	0.933
	HW2	0.928	0.861
	HW3	0.906	0.821
	HW4	0.912	0.832
	HW5	0.812	0.659
Sustainable development	SD1	0.816	0.666	0.748	0.937	0.920	0.916
	SD2	0.911	0.830
	SD3	0.837	0.701
	SD4	0.877	0.769
	SD5	0.880	0.774
Safety and Security	SS1	0.832	0.692	0.729	0.915	0.880	0.875
	SS2	0.908	0.824
	SS3	0.869	0.755
	SS4	0.801	0.642
Convenience and comfort	SA1	0.787	0.619	0.670	0.910	0.877	0.876
	SA2	0.822	0.676
	SA3	0.876	0.767
	SA4	0.830	0.689
	SA5	0.773	0.598
Performance expectancy	PE1	0.834	0.696	0.683	0.896	0.854	0.844
	PE2	0.742	0.551
	PE3	0.874	0.764
	PE4	0.849	0.721
Effort expectancy	EE1	0.839	0.704	0.798	0.940	0.925	0.915
	EE2	0.908	0.824
	EE3	0.905	0.819
	EE4	0.918	0.843
Social influence	SI1	0.936	0.876	0.884	0.958	0.935	0.935
	SI2	0.954	0.910
	SI3	0.931	0.867
Facilitating conditions	FC1	0.809	0.654	0.638	0.875	0.825	0.809
	FC2	0.808	0.653
	FC3	0.863	0.745
	FC4	0.707	0.500
Hedonic motivation	HM1	0.932	0.869	0.735	0.890	0.908	0.818
	HM2	0.947	0.897
	HM3	0.663	0.440
Price value	PV1	0.790	0.624	0.751	0.900	0.904	0.841
	PV2	0.901	0.812
	PV3	0.903	0.815
Habit	HT1	0.794	0.630	0.767	0.908	0.861	0.846
	HT3	0.904	0.817
	HT4	0.923	0.852
Personal innovativeness	PI1	0.893	0.797	0.730	0.915	0.899	0.877
	PI2	0.877	0.769
	PI3	0.807	0.651
	PI4	0.837	0.701
Intention to use	ITU1	0.915	0.837	0.884	0.958	0.934	0.934
	ITU2	0.958	0.918
	ITU3	0.947	0.897

) [114,115]. With a Cronbach’s Alpha at 0.435 and coefficient ρA at 0.484, the variable “Privacy and data preservation” was removed. Therefore, hypotheses 5a and 5b were not tested.

In evaluating reflective measurement models, the next step is to assess convergent validity, which refers to how well all of a construct’s indicators measure the same concept by explaining the variance in the items. This can be determined through the average variance extracted (AVE), which is calculated by taking the mean of the squared loadings for each indicator related to a construct [116].

To evaluate the reliability of the adopted measurement scales, the Cronbach’s alpha coefficient [117] was first used, which indicates what portion of the variance in the summative scale is the variance in the true value of this scale. An index below 0.7 is questionable [118], whereas a marginal value of 0.6 is acceptable in less stringent approaches [119]. Approaching an alpha value of 1 does not make the scale perfect, as it actually means that the entire scale could be replaced with a single positional scale. A Cronbach’s alpha reliability coefficient of around 0.8 is likely a reasonable goal for researchers [120].

For a better illustration of the reliability of the dimensions obtained from exploratory factor analysis, both Cronbach’s alpha and composite reliability (CR) were analyzed. Cronbach’s alpha is treated as the lower bound of the interval containing the true reliability of the scale, while composite reliability (CR) is considered the upper bound of this interval [116]. The recommended value for this coefficient, interpreted similarly to Cronbach’s alpha, is 0.7. It is worth noting that reliability between 0.6 and 0.7 is acceptable provided other indices of latent variable validity are appropriate [121].

Additionally, convergent validity can be used to analyze the internal structure of constructs. It is assumed that variables theoretically expected to measure a construct should be highly correlated with each other, while the correlation between variables measuring different constructs should be low [116]. Convergent validity was assessed using the average variance extracted (AVE) [122]. The acceptable lower limit is 0.5 [123]; values below 0.5 indicate that more error than can be explained by variance remains on average in the items constituting the latent variable structure.

In summary, the applied scales exhibited “attractive parameters”, with all considered latent variables demonstrating good reliability, as the CR coefficient exceeded 0.75. This allows for the assumption that the obtained results are valid and reliable, forming a basis for causal analyses.

To verify the discriminant validity of the reflectively measured constructs, it is necessary to examine their ability to be distinguished from other constructs both in terms of their correlation with other constructs and how specifically the indicators represent a single construct. This analysis can be conducted through the use of Henseler et al.’s [124] “heterotrait-monotrait ratio (HTMT) of correlations” in PLS-SEM. A threshold of 0.85 for the HTMT should be considered (

Table 3. Heterotrait–monotrait ratio (HTMT) values.

	Convenience and Comfort	Effort Expectancy	Facilitating Conditions	Habit	Health/Wellbeing	Hedonic Motivation	Intention to Use	Performance Expectancy	Personal Innovativeness	Price Value	Safety and Security	Social Influence
Effort expectancy	0.566
Facilitating conditions	0.514	0.737
Habit	0.755	0.659	0.609
Health/Wellbeing	0.753	0.487	0.450	0.618
Hedonic Motivation	0.733	0.686	0.593	0.802	0.698
Intention to Use	0.682	0.636	0.602	0.848	0.634	0.777
Performance expectancy	0.687	0.639	0.575	0.792	0.683	0.798	0.821
Personal Innovativeness	0.520	0.609	0.630	0.678	0.489	0.630	0.688	0.563
Price value	0.344	0.274	0.418	0.415	0.433	0.487	0.438	0.451	0.392
Safety and Security	0.793	0.521	0.425	0.634	0.672	0.599	0.616	0.624	0.370	0.267
Social Influence	0.390	0.303	0.460	0.372	0.412	0.444	0.496	0.518	0.465	0.458	0.271
Sustainable Development	0.766	0.431	0.428	0.617	0.745	0.618	0.614	0.646	0.415	0.372	0.655	0.375

) [124].

We received the results for the path coefficients listed in

Table 4. Results of the significance tests and the structural model’s path coefficient.

Hypothesis	Path	Path Coefficient	T Statistics	f2 Effect Size	p-Value	Significant ?
H1a	CC → PE	0.200	3.715	0.027	0.000	Yes
H1b	CC → HT	0.402	7.197	0.111	0.000	Yes
H2a	HW → PE	0.265	4.545	0.055	0.000	Yes
H2b	HW → HT	0.124	2.050	0.012	0.040	Yes
H3a	SS → PE	0.151	3.122	0.020	0.002	Yes
H3b	SS → HT	0.133	3.099	0.016	0.002	Yes
H4a	SD → PE	0.163	3.127	0.021	0.002	Yes
H4b	SD → HT	0.107	2.012	0.009	0.047	Yes
H6	PE → ITU	0.215	6.741	0.081	0.000	Yes
H7	EE → ITU	0.026	0.742	0.001	0.458	No
H8	SI → ITU	0.105	4.636	0.032	0.000	Yes
H9	FC → ITU	−0.001	0.023	0.000	0.981	No
H10	HM → ITU	0.092	2.433	0.014	0.015	Yes
H11	PV → ITU	0.022	1.046	0.002	0.296	No
H12	HT → ITU	0.474	13.011	0.374	0.000	Yes
H13	PI → ITU	0.131	3.937	0.038	0.000	Yes

. A total of 13 out of 16 tested hypotheses are significant at a 5% error level. The values of f² for hypotheses H2b, H4b, and H10 are not above the 0.15 criterion, thus they are reasonable. The paths for hypotheses H1b and H12 show the highest significance.

The initial evaluation of the model variables show that “habit” has the largest impact (0.474) on “intention to use” (H12), followed by “performance expectancy” (0.215) (H6), and “personal innovativeness” (0.131) (H13). Together with “hedonic motivation” (H10) and “social influence” (H8), these variables explain 76.9% (the R2 value) of the variance in “intention to use”.

All external variables to original UTAUT2 model have a significant positive effect on “performance expectancy” and “habit”. “Convenience and comfort” has the strongest effect on “habit” (0.402) (H1b) and “health/wellbeing’ has the strongest effect on “performance expectancy” (0.265) (H2a). The model explains 45.5% of the variance in “performance expectancy” and 46.3% of the variance in “habit”.

5. Discussion

The UTAUT 2 model was used by Gansser and Reich [125] to investigate the factors influencing the “intention to use” products containing artificial intelligence (AI). The focus was on studying the intention to use in three segments: “mobility, household, and health”. The authors extended this basic model to include “health, convenience, comfort, sustainability, safety, security, and personal innovativeness”. Their research showed that all added variables, with the exception of safety in the healthcare segment and price value in all segments, affected the intention to use products containing artificial intelligence. Our research also did not confirm the significance of price value.

Gultom and Asvial [126] for UTAUT2 added three additional variables that may influence the adoption of smart home service technologies: risk, trust, just like Gansser and Reich [125], and the attractiveness of alternatives. In their research, as in ours, the hypotheses regarding price value and facilitating conditions were not confirmed. What is surprising is that they did not confirm two more variables, those of “social influence” and “habit”. Our research confirmed that “social influence” and “habit” have a significant impact on “intention to use”.

An extended UTAUT2 model with risk variables, i.e., security, privacy, and trust in technology, to determine the level of consumer acceptance of the Internet of Things in the context of a smart home, was proposed by Aldossari and Sidorova [127]. Their results showed that trust and security risks play a significant role in the acceptance of the smart home, just as “security and safety” played a role in our research. With regard to the main variables in the model, both in our research and in their research, “performance expectancy”, “social impact”, and “hedonic motivation” were significant predictors of use and acceptance of a smart home, while “effort expectancy” and “price value” were indicated as significant in their research, and the hypotheses in our research indicating their importance for the use of a smart home were not confirmed. On the other hand, our research demonstrated a significant relationship for “facilitating conditions”, whereas this was not confirmed in the research of Aldossari and Sidorova.

Other studies in which the UTAUT2 model was used to diagnose the adoption of smart home technology were conducted among the Indian population [128] and confirmed that “factors such as performance expectancy, trialability, trustworthiness, psychological risk, and tech-savvy attitude, which the authors extended this model has a significant impact on the purchasing decisions of Smart Home technology by users”. They did not confirm a significant impact for “effort expectancy” and “price value” on adoption of smart homes. Our research provided a similar confirmation, as “price value” and “effort expectancy” were also not significant. Shantana Lakshmi and Deepak Gupta [128] introduced trust towards a technology into the model, indicating that “the adoption or purchase rate of smart home technology can be increased thanks to it”.

The model of Venkatesh et al. [57] also became a reference point for Iqbal and Idrees [129], who conducted research on the Pakistani market. Their study “aims to understand the reasons for IoT adoption for home automation while expanding the ambit of new technology (IoT) by incorporating novel variables of IOTA (cryptocurrency) concatenation with IoT & challenges to understand the reasons behind IoT adoption deeply”. Concerning the additional variables, they introduced two hypotheses that were supported. The study revealed that only “performance expectancy” and “facilitating conditions” have a significant influence on “intention to use” home automation. Our study differs here, since our results did not support “facilitating conditions”, only the “performance expectancy”. On the other hand, “price value”, “effort expectancy”, “hedonic motivation” and “social influence” included in the UTAUT2 model were not confirmed by Iqbal and Idress [129]. Our study differs, since our results neither supported influence of “price value” and “effort expectancy”, but significant impact was confirmed for “hedonic motivation” and “social influence”.

Our research can complement that of Große-Kreul [130] who examined the variables that affect consumers’ intention to adopt smart energy technologies. The study also explored whether the rapidly growing smart home market will encourage the adoption of smart energy technologies and whether consumer-driven diffusion will result in the realization of sustainability potentials. His study suggested “that adjustable green defaults should be introduced, and that a growing smart home market will not increase smart-energy technologies adoption”. Große-Kreul [130] had two models for smart meters and smart thermostats and the results showed that “the intention to adopt the smart thermostat is significantly and positively influenced by performance expectancy and social influence, whereas effort expectancy and hedonic motivation are not significant”. We present similar results, except for hedonic motivation, which was significantly confirmed in our study. However, for the use of smart meters, only “hedonic motivation” was significant in his study, the same as in our study, but “performance expectancy”, “effort expectancy”, and “social influence” were not confirmed in his study. Our study only confirmed “effort expectancy”, which was also not significant.

A unique complement to these presented results is the article by Canziani and MacSween [131], which describes “how voice-activated smart home devices like Amazon Alexa and Google Home influence consumers’ retail information seeking and ordering behaviors, and examining the impacts of device utility and hedonic perceptions of voice”. Based on the UTAUT2 model, they created their own adaptation of different external variables, leaving only the facilitating conditions from the original model. In their model, this variable predicted the smart home’s device utility.

In their article, Ferreira et al. [132] observed that “environmental sustainability is gaining importance in various fields including homes and that smart home technologies are increasingly contributing to more efficient energy consumption, but their adoption rate is lower than expected”. They proposed “a theoretical model based on the UTAUT2 to explore the effects of environmental awareness on individual intentions and behavior towards smart home”. Unfortunately, they did not provide detailed results for testing the UTAUT2 hypotheses, only for the moderating effects of environmental awareness on core UTAUT2 variables.

Sequeiros et al. [133] focused on “smart home services, which are a new generation of consumer services supported by IoT technology”. They mentioned that “IoT technology deliver security, comfort, entertainment, assisted living, and efficient management of the home to improve the quality of life of consumers”. The authors used an original UTAUT2 model to test smart home adoption. They identified a significant impact of “price value”, “facilitating condition”, “hedonic motivations” and “habit” on “intention to use”. Our study did not confirm the significance of “price value” and “facilitating conditions”. On the other hand, their study did not support the impact of “performance expectancy” and “social influence” on “intention to use”, whereas our study confirmed that both variables have a significant impact.

Meanwhile, Nikou [134] highlighted in his article that although smart home technology has been in existence for a while, it is still not widely used, so the potential benefits have largely been overlooked. He used an adapted TAM model, and demonstrated that “personal innovativeness” had a significant impact on “intention to use” smart home technology. Our study also confirmed that personal innovativeness was significant. Due to the fact that Nikou’s [134] research focused on developing a thorough a hypothesis that can adequately account for the variation in the “intention to use” smart home technology, their work complements our research.

The presented research results fit within the research review conducted by Hussin et al. [135] who emphasized that IoT technology is developing and has many advantages for both the environment and human life. IoT technology is therefore widely used in smart homes. Despite predictions of rising demand for IoT smart homes in the years to come, there is currently very low levels of acceptance of these smart homes.

Another unique complement to these presented results is the meta-analysis completed by Nascimento et al. [136]. They emphasized the increasing affordability of smart home technologies, the lack of understanding of the factors that drive their continued use, and the impact that the adoption of smart appliances and services in the domestic setting has on users. We describe the use of advanced technologies in smart homes, but Nascimento et al. [136] revealed a lack of knowledge of the elements influencing user acceptance.

In addition, the sample predominantly consisted of young people aged 16–25, representing a demographic that is generally more technologically adept and open to adopting new technologies. This might have influenced the strong impact of “habit” and “performance expectancy” on the intention to use smart home technologies. Additionally, the respondents’ relatively high financial status (63.2% reported a good financial situation) could explain why “price value” was not a significant factor, as this demographic might not be as sensitive to cost concerns when adopting new technologies.

Furthermore, the socio-economic conditions in Poland, where the study was conducted, might have influenced the results. Poland has been experiencing rapid economic growth and increasing internet penetration, which could have positively impacted the adoption of smart home technologies. The government’s support for digital innovation and sustainability initiatives could also have played a role in shaping the respondents’ perceptions and acceptance of smart home technologies.

In conclusion, the socio-economic and political context in Poland likely contributed to the strong influence of factors such as “habit”, “performance expectancy”, and “personal innovativeness” on the intention to use smart home technologies, while reducing the impact of “price value” and “facilitating conditions”.

Finally, we would like to discuss our results in view of Baudier et al.’s [111] work. We tried to advance their model by testing it on different samples and adding one external variable “privacy and data preserving” to the model. Baudier et al. [111] supported “security and safety”, “health/wellbeing”, and “convenience and comfort” as having a significant impact on “performance expectancy” and “habit”. We confirmed the same results, but we were also able to support the hypothesis about the significancy of sustainable development. Perhaps our sample was more aware of long-term developments and sustainability, since the sample was more diversified in age. Baudier et al. [111], in the core UTAUT2 model, only confirmed “performance expectancy” and “habit” as having a significant impact on “intention to use”. We also confirmed the same results, but we were also able to support the hypothesis about the significance of “social influence” and “hedonic motivation”. We also confirmed the impact of personal innovativeness as being significant, whereas Baudier et al. [111] did not confirm this. The possible difference in the results may originate from the different samples. Their sample was comprised of 89% females, whereas our sample was 59% female. We not only had students in our sample but also people of different ages. We also used a 7-point Likert scale, when they used a 5-point scale. The main distinction between a 5-point and a 7-point Likert scale is the level of granularity or detail provided by the response options. A 7-point scale can be more precise as it provides more detailed information about the strength of agreement or disagreement than a 5-point scale. Due to these differences, we were able to obtain better results with our model. Our model explained 76.9% of the variance in “intention to use” smart homes, 45.5% of the “performance expectancy” variance, and 46.3% of habit variance. Baudier et al. [111] obtained 61.4%, 39.5%, and 37.1% for these aspects, respectively. In

Table 5. Summary of the results from testing extended UTAUT2 variables on smart home adoption and comparison of tested hypotheses.

Study/Hypothesis	PE	EE	SI	FC	HM	PV	HT	PI	SS	HW	CC	SD	PR
Our study	Yes	No	Yes	No	Yes	No	Yes	Yes	Yes	Yes	Yes	Yes	No
Baudier et al. [111]	Yes	No	No	-	No	No	Yes	No	Yes	Yes	Yes	No	-
Aldossari and Sidorova [127].	Yes	Yes	Yes	No	Yes	Yes	-	-	Yes	-	-	-	Yes
Gansser and Reich [125]	Yes	Yes	Yes	Yes	Yes	No	Yes	Yes	-	Yes	Yes	Yes	-
Gultom and Asvial [126]	Yes	Yes	No	No	Yes	No	No	-	-	-	-	-	-
Iqbal and Idrees [129]	Yes	No	No	Yes	No	No	-	-	-	-	-	-	-
Shantana Lakshmi and Deepak Gupta [128]	Yes	No	-	-	-	No	-	-	-	-	-	-	-
Canziani and MacSween [131]	-	-	-	Yes	-	-	-	-	-	-	-	-	-
Große-Kreul [130] (Heating model)	Yes	No	Yes	-	No	-	-	-	No	No	-	-	-
Große-Kreul [130] (Meter model)	No	No	Yes	-	Yes	-	-	-	No	No	-	-	-
Nikou [134]	-	-	-	-	-	-	-	Yes	-	-	-	-	-
Sequeiros et al. [133]	No	-	No	Yes	Yes	Yes	Yes	-	-	-	-	-	-

PE—“performance expectancy”; EE—“effort expectancy”; SI—“social influence”; FC—“facilitating conditions”; HM—“hedonic motivation”; PV—“price value”; HT—“habit”; PI—“personal innovativeness”; SS—“security and safety”; HW—“health/wellbeing”; CC—“convenience and comfort”; SD—“sustainable development”; PR—“privacy and data preservation”; Yes—supported; No—not supported; “-“—not tested.

, we present an overview of the recent results with the use of UTAUT2 in smart home acceptance.

This study presents a novel approach to understanding the adoption of smart home technology by incorporating five external variables into the UTAUT2 model. These variables, convenience and comfort, health and wellbeing, security and safety, sustainability, and privacy and data preservation, provide a more comprehensive understanding of the factors that influence individuals’ decision to adopt smart home technology. The inclusion of these variables is unique in comparison to previous studies which have primarily focused on other theories and variables, such as trust, risk, trialability among others. This study also included a sample group that was representative of households, different age groups, and different levels of urbanization.

6. Conclusions

This study aimed to extend the UTAUT2 model by integrating additional variables relevant to the adoption of smart home technologies. Our research addressed the primary research questions, identifying significant determinants influencing the intention to adopt smart home technologies and assessing the impact of these technologies on sustainable consumption behaviors. Out of the 16 hypotheses tested, 13 were supported, highlighting the critical roles of performance expectancy, habit, and personal innovativeness in shaping users’ intention to use smart home technologies. Additionally, the findings revealed that smart homes significantly contribute to sustainable consumption behaviors.

Theoretically, this study expands the UTAUT2 model by incorporating variables such as convenience, health and wellbeing, safety and security, sustainability, and privacy. These additions provide a more comprehensive understanding of the factors driving the adoption of smart home technologies. Practically, the results suggest that enhancing these aspects in smart home technologies can increase user acceptance and adoption. For developers and policymakers, this implies that focusing on user-friendly designs, robust security features, and clear sustainability benefits can promote the uptake of smart home technologies. Enterprises can leverage these insights to design marketing strategies that highlight the convenience, security, and environmental benefits of their smart home products.

This study had several limitations that should be taken into account when evaluating the findings. Firstly, the sample group consisted predominantly of young people aged 16–20 and 21–25, with the remaining 21% aged 26 or older, which might not adequately represent the overall population. Secondly, the sample was not representative of the overall members of society as it included respondents from only one country, potentially limiting the generalizability of the results to other cultures and societies. Additionally, the sample had a relatively high financial status, with 63.2% reporting a good financial situation and the ability to afford luxury items. These factors may have influenced the study results and may not generalize to the broader population. Furthermore, this study relied on self-reported data, which are vulnerable to recall bias and social desirability bias.

The future research should consider more diverse and representative samples, including older adults and individuals from varying financial backgrounds. Further investigations could also explore the adoption of different types of smart home technologies and their specific impacts on wellbeing, examining aspects such as physical health, mental health, and social connections. Additionally, future studies should continue to examine the role of price value, effort expectancy, and facilitating conditions in technology adoption as these variables were not significant in this study. Exploring how these elements evolve over time as smart home technologies become more widespread would also be beneficial.

Poland’s commitment to sustainable development also plays a crucial role in the context of this research. The Polish government has been increasingly focusing on policies that promote energy efficiency and sustainability, aligning with the broader European Union directives. This socio-political emphasis on sustainability may have influenced the respondents’ positive attitudes towards the sustainable aspects of smart home technologies. Future research could further investigate how national policies and the socio-economic conditions in Poland and other countries impact the adoption and perception of smart home technologies, providing a more nuanced understanding of the global and local factors driving sustainability in the digital age.