Factors Influencing Consumer Buying Behavior for Smart Home Technologies

Abstract

Smart home technologies (SHT) offer numerous benefits to consumers. This study explored the relationship between the perceived benefits of and the likelihood of subsequently purchasing SHT among Taiwanese consumers. The study conducted a survey in May 2024 and collected data from 424 respondents of various ages, educational backgrounds, and income levels. Data on the perceived benefits of SHT, the perceived challenges of adopting these technologies, current methods for managing household tasks and energy consumption, and the likelihood of purchasing SHT were collected. The perceived benefits of SHT include enhanced comfort, security, and energy efficiency. Comfort and energy efficiency but not enhanced security were significant predictors of adoption. Proficiency in online research but not general technical proficiency also significantly predicted adoption. Consumers dissatisfied with current home energy management methods were more likely to adopt SHT. Positive perceptions of benefits and dissatisfaction with current methods drive the adoption of SHT. As there is increasing environmental awareness in Taiwan, this study verifies that environmentally conscious consumers affect their buying decisions positively. This study highlights how SHT can improve quality of life while promoting sustainable development. The study offers valuable insights into consumer buying behaviors and contributes to the SHT industry with actionable suggestions for improving product design, incorporating more green technology into their products, enhancing user interfaces, strengthening security protocols, and upgrading interoperability between different smart home devices that may facilitate users to embrace SHT.

1. Introduction

A smart home is a home equipped with technology to enhance comfort, security, and entertainment by managing devices within the home and with the outside world [1]. Ghaffarianhoseini et al. [2] showed that smart homes help users maintain independence while enhancing safety, security, and comfort. Smart homes monitor and regulate activities, behavior patterns, and health status, adapting automatically to user needs and preferences. Smart homes also manage energy consumption, contributing to energy-efficient living environments that are essential for sustainable urban development.

Rock et al. [3] stated that smart homes are used for real-time remote control, surveillance, sensing, home automation, entertainment, and family communication. They helped users save time and improved security, safety, fun, convenience, and comfort within users’ homes. Smart homes also provide caregiving to enable aging-in-place and independence [4]. In Taiwan, the “Remote Health Care Services Development Plan”, which integrates information and communications technologies, enhances smart healthcare in home care [5]. The adoption of SHT is primarily driven by the above-mentioned desires to enhance personal comfort and convenience. This trend highlights a fundamental human tendency to seek solutions that simplify one’s daily routines and improve one’s living standards. Across different cultures, individuals gravitate toward smart home technologies not just for the technology itself but for the tangible benefits of comfort, convenience, and efficiency.

The SHT market has been growing substantially due to advances in Internet of Things technology and increasing consumer demands for connected living and energy consumption management. According to the prediction of Statista [6], the smart home market in Taiwan is expected to reach a revenue of US$611.2 million in 2025, and the projected revenues will achieve US$796.9 million by 2029. This growth is driven by several factors. First, the integration of SHT into daily life has increased consumer demand for home automation solutions. As awareness and acceptance of smart home devices grow, adoption rates increase, fueling market expansion. Second, technological advancements and the proliferation of Internet of Things devices are enabling more sophisticated and interconnected smart home ecosystems. As these devices evolve, consumers have more options to enhance their living space. Third, government initiatives and supportive policies, such as subsidies for energy-efficient appliances and smart city projects, are boosting market growth. This alignment between SHT adoption and personal comfort reflects a convergence of consumer priorities across cultures.

The projected consumer interest in SHT aligns with market trends. Data consistently show increased demand for these technologies, highlighting their growing value among consumers worldwide. Indicators such as sales figures, adoption rates, and product launches suggest that personal comfort and lifestyle benefits are driving market dynamics. A gap in the literature is the limited focus on sociopsychological factors that affect consumer attitudes toward SHT. Aspects such as perceived value, trust, and psychological motivations have received relatively little scrutiny. Understanding how these factors interact with technology can provide insights into why certain consumers embrace SHT while others remain skeptical or hesitant.

Pliatsikas and Economides [7] highlighted the benefits of SHT, such as monitoring and managing sick individuals and older adults, saving money, and managing energy consumption. Several studies on the topic have explored consumer preferences and adoption patterns. The factors that influence consumer buying behavior in this domain are not well understood. A comprehensive analysis of the drivers and barriers affecting purchasing decisions is needed.

Some studies have suggested that younger consumers are more likely to adopt SHT due to their familiarity with digital devices, whereas other studies have argued older individuals may show more interest due to their need for convenience and safety. Zhou et al. [8] argued that smart homes need to focus not only on the basic needs of the elderly, such as material life and home safety, but also on the spiritual needs of elderly users. Children or caregivers shall actively guide the elderly to use smart homes to help them realize their self-worth. Several studies have examined how demographic factors such as age, income, and education influence the adoption of SHT, although a consensus on the significance and directionality of these factors has not been established. Understanding these demographic nuances is essential for developing effective marketing strategies and product designs.

Additionally, the influence of contextual factors, such as cultural differences and regulatory frameworks, on consumer buying behavior is not well understood. Cultural norms can affect perceptions of privacy and security, affecting the desirability of smart home technologies across different regions. Similarly, regulatory environments and industry standards can influence consumer trust and willingness to invest.

The above-mentioned research gaps are crucial to understanding consumer buying behavior; this study hereby addresses these gaps by exploring the factors affecting consumer buying behavior for SHT in Taiwan. The study conducted an online survey to examine the interplay between technological, sociopsychological, demographic, and contextual factors that shape consumer attitudes and preferences.

On the basis of the aforementioned studies, the following research questions were formulated: RQ1.1: how do users perceive the benefits of SHT in terms of enhanced comfort, security, and energy efficiency? RQ1.2: to what extent do user perceptions of these benefits correlate with their likelihood of purchasing smart home services and products? RQ2.1: what is the level of technical proficiency among individuals in managing Internet-connected devices and navigating information? RQ2.2: how does technical proficiency influence an individual’s inclination to adopt smart home services and products? RQ3.1: what are the current methods used by individuals for managing home energy consumption and household tasks? RQ3.2: how do perceived inadequacies in these methods drive users to seek solutions that enhance their home energy management capabilities through SHT adoption?

These questions explore the factors identified in the hypotheses and provide insights into the relationships between user perceptions, technical proficiency, perceived inadequacies of current methods, and buying behaviors related to SHT.

2. Literature Review and Research Hypotheses

2.1. SHT Literature

The SHT field has attracted a lot of research since 1997 [9] and has raised a lot of research questions in recent years.

From a technological aspect, SHT comprise hardware, sensors, and switches, enabling the creation of novel appliances and services for home use that can perform a myriad of functions, such as alerting residents to open gates or extreme temperatures, providing security for individuals with physical disabilities, and reminding older adults to take their medication. SHT combine various technologies and services within home networks to improve our quality of life [10]. Alam et al. [11] stated that SHT represent a modern form of ubiquitous computing, embedding intelligence and automation into homes to address needs related to comfort, control, security, safety, healthcare, and energy conservation. Users can control home appliances and devices remotely; the ambient intelligence system can optimize households’ electricity usage.

Today, the Internet has become an integral part of our homes, bringing convenience and automation to enhance everyday routines. Smart homes are interconnected ecosystems that include various devices and services that operate independently and in tandem, using Internet of Things technologies to communicate and having intuitive and interactive designs. Equipped with Internet-connected devices, individuals can easily manage their home environments using a smartphone or tablet.

Denning et al. [12] noted that as technology becomes more integrated into homes, the desire for impenetrable security across devices increases. Improving the effectiveness, compatibility, and user-friendliness of home security solutions is becoming increasingly important.

In the field of SHT, exploring consumer motivations, perceptions, and adoption factors to understand individual acceptance and use of information technology is crucial. Research has shown that consumers view SHT positively and appreciate the time and cost savings. Lashkari et al. [13] emphasized that smart homes attempt to integrate smartness into homes to guarantee the residents’ convenience, safety, and security while conserving energy consumption along with an effective energy management system. Deploying intelligent energy management systems in buildings will lead to energy and cost savings. SHT can also provide companionship and support for those in need, improving human well-being.

Research into information systems often uses theoretical frameworks to explore the attitudes and intentions of potential users toward adopting these technologies. Boer et al. [14] and Alexander et al. [15] highlighted the importance of Internet skills, such as information navigation, in using Internet of Things technology. Kowalski et al. [16] demonstrated that individuals with higher technical proficiency were more likely to adopt SHT services and products, suggesting that technical proficiency empowers users to control their home environment.

Peek et al. [17] examined factors such as social influence and perceptions of technology, focusing on older adults and their acceptance of technologies that support aging in place. They found that older adults value benefits such as increased safety and independence more than just perceived usefulness, suggesting that acceptance involves factors beyond the technology acceptance model (TAM) theory.

Bradfield and Allen [18] argued that current methods for managing home energy consumption are ineffective and advocated for SHT to improve management capabilities. They concluded that homeowners need to be aware that if they do not implement SHT to improve their home energy management, they will, in all likelihood, end up paying more or even facing resource shortages due to the inefficiencies of the current methodologies used for managing their home.

Alam et al. [11] emphasized that SHT facilitate users to reduce energy wastage for energy conservation and further advocated that SHT can provide real-time energy consumption feedback to users, such as useful details about the energy consumption of home appliances during different times of the day and the total consumption of each section within the home, such as kitchen, bedroom, etc. This information will be an economic motivation for consumers to improve their energy efficiency and change their consumption habits. The smart home concept has attracted great attention because of its potential to provide comfort and energy management to users.

Wang et al. [19] assessed both positive and negative aspects of smart home devices, noting that performance expectancy and compatibility significantly influence adoption intentions. Marikyan et al. [20] describe smart homes as dwellings equipped with intelligent technologies that provide personalized services and suggest that future research take a longitudinal approach to explore how perceived values, risks, technology fit, and performance affect usage behavior and satisfaction.

Regarding the challenges faced by SHT, users’ initial investments in SHT can be expensive, which is the first factor that hinders users’ adoption. Consumers are expected to gradually adopt smart home solutions by integrating existing devices, including smart lights, shutters, and thermostats. Vrancic et al. [21] identified the high costs associated with SHT, their complex nature, and ethical concerns surrounding privacy and autonomy.

Research into SHT for health and social care has focused mainly on energy management. Household appliances need to be compatible with energy management systems and SHT systems to function synergistically as an integrated whole. To capitalize on this potential, utilities and manufacturers of smart home energy management systems must collaborate to establish interfaces that integrate components and facilitate interaction between utility providers and households [22].

Ethical and legal issues, such as a lack of legal frameworks for SHT, also present challenges. Korneeva et al. [23] demonstrated that users generally felt inclined to use smart technologies and perceived them as a plausible means for safety and security improvement in their lives. However, users also expressed their concerns over the cybersecurity and technology dependence issues associated with SHT. Specifically, younger users were more worried about their personal data being monitored and analyzed. This study concluded that consumers should be informed of potential risks, including hacking, eavesdropping, data theft, and privacy breaches, that exist in smart home ecosystems.

Valid measurement scales for predicting user acceptance are crucial in SHT research. Several studies have examined the adoption of SHT by employing well-established frameworks, such as the TAM. Davis [24] developed the conceptual framework of TAM—from system features and capabilities to users’ motivation to use the system to actual system use. Davis et al. [25] validated scales for two specific variables—perceived usefulness (PU) and perceived ease of use (PEOU)—to evaluate user acceptance. This research concluded that these scales exhibited high convergent, discriminant, and factorial validity.

Zin et al. [26] explored the TAM in SHT acceptance and advocated that perceived usefulness, perceived ease of use, and facilitating conditions have a significant impact on the elderly’s attitudes towards the use of a smart health watch.

FakhrHosseini et al. [27] reviewed nine prominent technology adoption theories and found that perceived usefulness, ease of use, perceived control or self-efficacy, affect and enjoyment, and perceived risks are the common factors across their study explaining the adoption of intelligent environments.

Martin et al. [28] concluded that their review highlights the current lack of empirical evidence to support or refute the use of SHT within health and social care. In research into health monitoring, studies often lack theoretical frameworks to explain user intentions and behaviors. Usability studies should be conducted that incorporate such frameworks to identify key adoption factors for smart homes and health-monitoring technologies.

Most usability studies on home health technologies have used qualitative methodologies. In contrast, quantitative research could provide evidence on user adoption. Additionally, few studies have examined household perspectives regarding the acceptance and adoption of such technology.

2.2. Developing Research Hypotheses

The above-mentioned reviews and research questions have highlighted the diverse effects of SHT, showcasing their potential to transform modern living by enhancing comfort, security, energy efficiency, and social connectivity. Challenges such as high initial costs, ethical considerations, and legal constraints must be addressed for successful adoption. The promise of increased personal comfort and lifestyle convenience also plays a crucial role in the adoption of SHT.

This study draws on findings from Alam et al. [11] and FakhrHosseini et al. [27] regarding the perceived benefits of SHT, which comprise comfort, control, security, safety, healthcare, energy conservation, perceived usefulness, ease of use, perceived control, etc. to develop hypothesis 1.

H 1.

User perceptions of the benefits of SHT, including comfort enhancement, security improvement, and energy efficiency, positively correlate with their likelihood of purchase.

The arguments proposed by Boer et al. [14] and Alexander et al. [15] that including Internet skills, such as information navigation, and individuals with higher technical proficiency were more likely to adopt home services and products, etc., are utilized to formulate hypothesis 2.

H 2.

Individuals with higher technical proficiency, demonstrated by their ability to manage Internet-connected devices and navigate information, are more likely to adopt SHT services and products.

On the basis of findings from Bradfield and Allen [18], Alam et al. [11], and Lashkari et al. [13], the current methods for managing home energy consumption are ineffective and propose the adoption of SHT to improve management capabilities. The SHT facilitate the user to reduce energy wastage for energy conservation. The study develops hypothesis 3 as follows:

H 3.

The perceived inadequacy of current methods for managing home energy consumption and household tasks drives the adoption of SHT as users seek solutions that improve their home energy management capabilities.

These hypotheses respond to research questions described prior to the literature review. The present study employs TAM and extends the aforementioned previous research results for further investigation to hopefully contribute to the understanding and use of SHT, particularly in Taiwan.

3. Methodology

3.1. Research Model

FakhrHosseini et al. [27] stated that TAM is the most commonly used and empirically supported theory. It was built to study the use of IT systems in organizations and suggests that users are the mediators between the external variables and intention to use. Two key beliefs, PU and PEOU, predict behavioral intention in this model. Fauzi et al. [29] further commented that by adding other factors, many studies use this model as the basis for their research.

The research model of this study is shown in the Figure 1. This model was developed on the basis of the above-mentioned previous studies and the identified hypotheses and by taking the characteristics of the Taiwan market into account to investigate the factors influencing the adoption of SHT. The model consists of three main constructs, each representing a hypothesis, along with their respective indicators.

Perceived benefits refer to how individuals subjectively evaluate the advantages of using SHT. These benefits include the following:

• Comfort enhancement. This is the perception that SHT improves daily comfort and convenience by automating tasks, adjusting environmental settings, and providing personalized experiences.
• Security improvement. This belief centers on the idea that SHT improves home security through features such as surveillance cameras, motion sensors, smart locks, and burglar alarms, thereby ensuring the feeling of safety.
• Energy efficiency. This perception involves the belief that SHT aids in energy conservation by optimizing usage, monitoring consumption, and integrating renewable energy sources, leading to cost savings and environmental benefits.

Perceived benefits are subjective and vary among individuals, preferences, and experiences. Understanding these perceptions is crucial for predicting and explaining buying behaviors related to SHT.

Technical proficiency refers to how skilled and knowledgeable individuals are in handling and understanding Internet-connected devices and information related to SHT. This includes the following:

• Skill in managing Internet-connected devices. This involves the ability to set up, configure, troubleshoot, and operate various smart home devices such as thermostats, lighting systems, security cameras, and appliances. This proficiency indicator includes the ability to use applications or platforms to control and customize these devices.
• Proficiency in navigating information. This refers to the ability to access, evaluate, and use information related to SHT, such as product specifications, user manuals, troubleshooting guides, and online resources. This proficiency involves effectively searching for and interpreting information to make informed decisions and resolve technical issues.

Technical proficiency is crucial for determining how ready and able individuals are to adopt smart home services and products. Higher technical proficiency is likely to positively correlate with SHT adoption by making it easier to integrate and use smart home technologies. Understanding the technical proficiency of users helps in designing user-friendly interfaces, providing effective technical support, and tailoring marketing strategies to different user segments.

Perceived inadequacy of current methods refers to how individuals view the limitations and inefficiencies of current approaches for managing home energy consumption and household tasks. This perception suggests that traditional methods are not meeting the evolving needs and expectations of modern users. Indicators of this inadequacy include the following:

• Dissatisfaction with current home energy management. This involves feelings of dissatisfaction or frustration with how effective, convenient, or cost-effective current methods are for monitoring, controlling, and optimizing energy use. Challenges may include difficulty tracking energy consumption, limited control over energy-intensive devices, and inefficiencies in managing energy expenses.
• Perceived difficulty in managing household tasks. This refers to the perception that traditional methods for managing tasks such as cleaning, organizing, scheduling, and coordinating activities are complex, inefficient, or time-consuming. Individuals may find these methods cumbersome, labor-intensive, or inflexible in responding to changing needs and preferences.

This perception of inadequacy motivates individuals to seek alternative solutions, such as SHT, to improve home energy consumption management. Understanding these perceptions is essential for identifying innovation opportunities, designing effective interventions, and promoting the purchasing of SHT that offer tangible benefits and address unmet needs.

3.2. Research Design and Questionnaire Form

The study is a cross-sectional research study that employed a questionnaire survey to obtain primary evidence. The survey questions were formulated mainly based on concepts advocated and survey forms used by previous empirical studies, as follows:

The PU, PEOU, usage intentions, perceived value, perceived risk, and related measurement scales for variables were employed by Zhou et al. [8] in a questionnaire survey conducted in China in a paper titled “A study on smart home use intention of elderly consumers based on TAMs”.

The PU, PEOU, skills using IoT, and skills navigating Internet information were in a survey form based on TAM theory adopted by Boer et al. [14] for the paper “Accepting the IoT in our homes: the role of user skills” and survey activities in the Netherlands.

The PU, PEOU, and perceived characteristics of innovation (PCIs) were utilized by Yuen et al. [30] in the questionnaire form for the paper titled “Factors influencing autonomous vehicle adoption: an application of the TAM and innovation diffusion theory”, a survey conducted in South Korea.

Energy consumption management studies were used by Lashkari et al. [13] in a paper titled “Energy management for smart homes—state of the art” and Bradfield et al. [18] in a paper titled “User perceptions of and needs for SHT in South Africa”.

This study reorganized, restructured, and updated all concepts and questions employed in the above literature and changed wordings and phrasings to meet the object of this research and fit in with cultural differences in Taiwan. Finally, the survey was designed to collect data primarily related to the following key areas:

Perceived Benefits of SHT. Perceptions of comfort, security, and energy efficiency.

Technical Proficiency. Skills in managing Internet-connected devices and navigating information.

Current Methods for Managing Home Tasks. Current methods for managing energy consumption and household tasks.

Likelihood of Adoption. Likelihood of purchasing SHT.

Consumer demands, concerns, buying role, and environmental awareness. Respondent’s attitude to supporting energy-saving and global sustainability issues.

The detailed questionnaire survey form is in Appendix A. This form started with an informed consent statement and a brief introduction to products that use SHT to provide participants with a basic understanding of the subject under investigation. This form consisted of both categorical questions and a Likert scale to obtain a comprehensive view of respondent attitudes and behaviors. Survey data were quantitatively analyzed.

Data analysis involved both descriptive and inferential statistics: For descriptive statistics, mean scores and standard deviations were calculated for key variables, including perceived benefits, technical proficiency, and likelihood of purchasing. Regarding inferential statistics, correlation and regression analyses were conducted to test the hypotheses. Correlation coefficients were used to assess the strength and direction of relationships between variables. Multiple regression analysis was performed to examine the effect of perceived benefits and technical proficiency on the likelihood of adoption. All three hypotheses were tested using regression analysis.

3.3. Pilot Study and Data Collection

The questionnaire form was sent to a local online survey platform company for expert validation first, and then a pilot study of forty copies was carried out to test respondents’ feedback prior to the formal commencement of the survey activity.

An open invitation to potential respondents and the formal survey were conducted in May 2024. Respondents were recruited using convenience sampling. Assuming a confidence level of 95%, a standard deviation of 0.5, and an error margin of ±5% (confidence interval), the calculation indicated that the number of participants should be no less than 385 [31].

Students of an EMBA course, alumni at a university in Taiwan, and individuals from the local community were invited to participate. Interested respondents were then sent a link to an online survey that collected responses to items about various aspects of SHT adoption, including perceived benefits, technical proficiency, current methods for managing home tasks, overall satisfaction, and consumer demands, concerns, and environmental awareness.

3.4. Sample Description and Respondent Demographic Characteristics

In total, 424 valid responses were collected from a diverse sample of respondents of various ages, educational backgrounds, and income levels. The demographic characteristics of the respondents are summarized in

Table A1. Respondent demographic characteristics.

Item
Factor	Sample (n = 424)	Percent (%)
Gender
Male	231	54.48
Female	193	45.52
Age
≤30 years	41	9.67
31–45 years	168	39.62
46–60 years	165	38.92
>60 years	50	11.79
Education
High school or below	58	13.68
Associate bachelor’s degree	62	14.62
Bachelor’s degree	181	42.69
Master’s degree	114	26.89
Doctoral degree	9	2.12
Personality
Extroverted	41	9.67
Agreeable	193	45.52
Conscientious	105	24.76
Emotionally stable	53	12.50
Neurotic	20	4.72
Open to experiences	12	2.83
Occupation
Executive/top management	27	6.57
Middle management	56	13.63
First-line management	53	12.89
Non-managerial staff/employee	230	55.96
Housewife, retiree	43	10.46
Student	2	0.49
Annual personal income
US$ ≤23,000	204	48.11
US$ 23,001–33,000	102	24.06
US$ 33,001–50,000	70	16.51
US$ 50,001–70,000	20	4.72
US$ >70,000	28	6.60
Number of cohabitants
0	41	9.67
1	60	14.15
2	69	16.27
3	95	22.41
4	159	37.50
Annual household income
US$ <33,000	155	36.56
US$ 33,000–46,000	108	25.47
US$ 46,001–63,000	69	16.27
US$ 63,001–83,000	58	13.68
US$ >83,000	54	8.02
Ownership of house
Leasing, your landlord	58	13.68
Your relative or friend	22	5.19
Your grandparents or parents	130	30.66
Your spouse or yours	214	50.47
Others	0	0

, Appendix B.

The average age of the respondents was 46.32 years old. The average number of cohabitants was 2.64. The average annual household income was US$52,503.85. The average personal income was US$32,394.68. Most respondents lived with 3 or more cohabitants, with 37.50% reporting 4 cohabitants. A substantial proportion (36.56%) of the respondents had an annual household income of US$ < 33,000, with a declining percentage in higher income brackets. Only 8.02% reported household incomes of US$ > 83,000. Half (50.47%) of the respondents owned or co-owned their houses with their spouses, and 30.66% resided in houses owned by their parents or grandparents.

4. Respondent Answers and Analysis

4.1. Answers and Analysis of Each Construct

Answers to each of the research constructs are listed in the summary of respondent answers in

Table A2. Summary of respondent answers.

Construct
Question (Variable)	Sample (n = 424)	Percent (%)
A. Perceived Benefits
How do you perceive the benefits associated with SHT in terms of enhanced comfort, security, and energy efficiency?
1. For comfort enhancement
Strongly Disagree	5	1.18
Disagree	2	0.47
Neutral	84	19.81
Agree	255	60.14
Strongly Agree	78	18.40
2. For security improvement
Strongly Disagree	4	0.94
Disagree	4	0.94
Neutral	88	20.76
Agree	251	59.20
Strongly Agree	77	18.16
3. For energy efficiency
Strongly Disagree	5	1.18
Disagree	24	5.66
Neutral	136	32.07
Agree	191	45.05
Strongly Agree	68	16.04
4. The perceived benefits of SHT correlate with your likelihood of adoption.
Strongly Disagree	10	2.36
Disagree	68	16.04
Neutral	163	38.44
Agree	146	34.43
Strongly Agree	37	8.73
B. Technical Proficiency
1. What is your level of technical proficiency in managing Internet-connected devices?
Never touched before, difficult	18	4.24
Novice	18	4.24
Intermediate	138	32.55
Heavy User	232	54.72
Advanced	18	4.25
2. How proficient are you in navigating information related to SHT?
Never touched before, difficult	16	3.77
Novice but shall be able to	63	14.86
Intermediate	167	39.39
Familiar	147	34.43
Advanced	32	7.55
3. How do your technical skills influence your inclination to purchase smart home services and products?
Not at all	25	5.90
Slightly	91	21.46
Moderately	166	39.15
Very much	117	27.59
Extremely	25	5.90
C. Perceived Inadequacy of Current Methods
1. What methods do you currently use for managing home energy consumption? (Select all that apply)
Traditional light and gas switches	231	54.48
Conventional home appliances	164	38.68
Manual thermostat adjustment	185	43.63
air conditioner
Energy-saving home appliances	224	52.83
SHT products	78	18.40
(e.g., automatic lighting, gas detection, etc.)
Others	0	0
2. How satisfied are you with your current methods of home energy management?
Very Dissatisfied	2	0.47
Dissatisfied	45	10.61
Neutral	248	66.98
Satisfied	90	21.23
Very Satisfies	3	0.71
3. Do you perceive any operating difficulties in managing household tasks by using current methods?
Not at all	133	31.37
Slightly	215	50.71
Moderately	70	16.51
Very much	4	0.94
Extremely	2	0.47
D. Consumer demands, concerns, buying role, and environmental awareness.
1. Which aspect of smart home technologies do you find the most useful?
Security Management	101	23.93
Intelligent Household Appliance and Energy Management	208	49.29
Smart Medicine and Healthcare	60	14.22
Digital Video and Multimedia	53	12.56
Entertainment
Others	0	0
2. Do you have any privacy concerns about SHT? For example, are you worried about unauthorized access to Internet-connected cameras?
Extremely	26	6.13
Very Much	150	35.38
Moderately	205	48.35
Slightly	37	8.73
Not at all	6	1.41
3. Which purchasing role do you play at your home regarding smart home products? (Select all that apply)
Initiator, suggesting purchase	157	37.03
Analyzer, inquiry about quotation	123	29.01
Gatekeeper, information provider	141	33.25
Influencer, professional knowledge	111	26.18
Decider, payer	107	25.24
Buyer, purchasing handler	95	22.41
User, beneficiary	123	29.01
4. How concerned are you about energy-saving and global sustainability issues?
Not at all	10	2.36
Slightly	183	43.16
Moderately	141	33.25
Very much Supportive	80	18.87
Extremely Supportive	10	2.36

in Appendix B. Analysis of the answers is as follows:

4.2. Construct A of Perceived Benefits

Question 1: The preference for SHT to enhance comfort was strongly supported by the respondents, with a combined 78.54% either agreeing (60.14%) or strongly agreeing (18.40%) with the statement that SHT are for enhanced comfort. Few respondents did not support this preference.

Question 2: The idea that SHT are a means to improve security was agreed to by 59.20% of the respondents and strongly agreed to by 18.16% of the respondents. Few respondents disagreed with this idea.

Question 3: In total, 61.09% of the respondents either agreed (45.05%) or strongly agreed (16.04%) with the statement that SHT are for energy efficiency. Few respondents disagreed with this statement.

Question 4: In total, 38.44% of the respondents had a neutral perception of the perceived benefits and likelihood of purchasing SHT. Furthermore, 34.43% agreed with this statement, and 8.73% strongly agreed with this statement. Overall, 43.16% of the respondents had a positive perception. In total, 18.40% of the respondents had a negative perception, with 2.36% strongly disagreeing and 16.04% disagreeing with the statement.

4.3. Construct B of Technical Proficiency

Question 1: Regarding technical proficiency in managing Internet-connected devices, 54.72% of the respondents identified as heavy users, indicating a high level of familiarity and comfort with technology. Only a small percentage of respondents classified themselves as novice or advanced users, and 8.48% reported having minimal experience.

Question 2: In total, 39.39% of the respondents classified themselves as having an intermediate level of familiarity, 34.43% of the respondents classified themselves as familiar, and 3.77% of the respondents classified themselves as never having touched the technology and finding it difficult.

Question 3: Regarding the influence of technical skills on the adoption of SHT, 39.15% of the respondents reported that technical skills have a moderate influence, indicating that a foundational level of technical knowledge plays a key role in smart home adoption. Additionally, 27.59% of the respondents stated that technical skills have a very significant effect, and 21.46% of the respondents indicated only a slight influence. The smallest groups, 5.90% each, believed that technical skills either had no influence or were extremely influential. Overall, most respondents (66.74%) fall within a moderate to very significant range of reliance on these skills for smart home adoption.

4.4. Construct C of Perceived Inadequacy of Current Methods

Question 1: Regarding methods used for managing energy consumption, traditional approaches dominated. Traditional light and gas switches were the most commonly used methods, with 54.48% of the respondents relying on them. Similarly, energy-saving home appliances were popular, used by 52.83% of the respondents, suggesting a growing awareness of energy-efficient products. Manual thermostat adjustment for air conditioners was also a frequent method that was used by 43.63% of the respondents. Conversely, SHT products for energy management were the least common, with only 18.40% adoption. No respondents selected the “Other” category, showing a preference for conventional and emerging smart technology methods. This highlights a balance between traditional energy management methods and the gradual adoption of newer, more advanced solutions.

Question 2: Most respondents (66.98%) had a neutral sentiment regarding energy consumption management. A combined 10.61% of the respondents reported feeling dissatisfied, and 21.23% of the respondents expressed satisfaction, indicating a relatively small but notable group who were pleased with their energy management strategies. The percentages for “Very Dissatisfied” and “Very Satisfied” were minimal, at 0.47% and 0.71%, respectively. Overall, the findings show that a significant portion of the respondents were neutral. Improvement in energy consumption management to enhance user satisfaction is warranted.

Question 3: Regarding perceived operating difficulties in managing household tasks with current methods, most of the respondents experienced at least some level of difficulty. Specifically, 50.71% of the respondents reported feeling “slightly” challenged, indicating a moderate concern regarding their current management methods. Additionally, 16.51% of the respondents expressed a “moderate” level of difficulty, and only small fractions reported being “very much” (0.94%) or “extremely” (0.47%) affected by these difficulties. By contrast, 31.37% of the respondents indicated they do not face difficulties at all. Overall, the findings suggest that many individuals find household task management manageable, and a considerable number find it challenging, highlighting an opportunity for improving current methods to enhance energy efficiency and user experience.

4.5. Construct D of Consumer’s Demand, Concern, Buying Role, and Environmental Awareness

Question 1: The primary purpose of using SHT was for intelligent household appliance and energy management, with 49.29% of the respondents saying this was their main motivation. Security management followed as a significant priority for 23.93% of the participants, reflecting concerns about safety in the home environment. By contrast, areas such as smart medicine and healthcare and digital video and multimedia entertainment attracted less interest, highlighting the dominant focus on energy management and security in SHT adoption.

Question 2: Concerns around privacy were prevalent, with 48.35% of the respondents expressing a moderate level of worry and 35.38% indicating they were very much concerned. Only a small percentage, 1.42%, reported feeling not at all concerned, suggesting that privacy remains a significant issue for the majority of participants in relation to SHT.

Question 3: The role of initiator was the most prominent in purchasing smart home technologies, with 37.03% of the respondents identifying as such. Other significant roles included gatekeeper (33.25%) and analyzer (29.01%), reflecting a collaborative decision-making process where multiple stakeholders are involved in the purchasing journey.

Question 4: Concerns about energy saving and sustainability were evident among respondents, with 43.16% expressing a slight level of concern and 33.25% indicating a moderate level of concern. Although only a small percentage felt very much or extremely supportive (21.23% combined), the data suggest that a significant portion of participants recognize the importance of energy efficiency and sustainability, highlighting a growing awareness in this area.

5. Hypothesis Testing and Interpretation

5.1. Hypothesis 1

User perceptions of the benefits associated with SHT, including comfort enhancement, security improvement, and energy efficiency, positively correlated with their likelihood of purchase. For this testing, data from questions 1 to 4 from Construct A of the perceived benefits were utilized.

We calculated the correlation coefficients between the likelihood of purchase and each of the three perceptions of benefits. We used the average scores provided to perform this calculation. For simplicity, we assumed the data points for the perceptions of benefits and likelihood of purchase were as follows:

Comfort enhancement (X₁): 3.94

Security improvement (X₂): 3.93

Energy efficiency (X₃): 3.69

Likelihood of purchase (Y): 3.31

Comfort enhancement had the highest average score (3.94), indicating that respondents felt most satisfied with how SHT improve their comfort levels. Security improvement followed closely, with an average score of 3.93, showing strong agreement on the perception that SHT improve security. Energy efficiency had a slightly lower average score (3.69), indicating that although respondents recognize the potential for energy savings, their satisfaction with this aspect is slightly less than that for comfort and security. Likelihood of adoption had the lowest average score (3.31), suggesting that although respondents acknowledge the benefits of SHT, they have a more neutral or reserved attitude toward actually purchasing these technologies.

5.1.1. Interpretation of Dependence

The data suggest a positive but partial dependence of purchase likelihood on satisfaction with the perceived benefits. Although higher satisfaction with comfort, security, and energy efficiency aligns somewhat with a higher likelihood of purchase, this dependence is not absolute. Despite respondents’ positive perceptions of comfort and security, their likelihood of purchasing SHT remains moderate.

This indicates potential barriers, such as financial considerations, perceived ease of use, or privacy concerns, that may affect the final decision to purchase SHT. For instance, users may appreciate the benefits but feel hesitant due to installation costs, complexity, or maintenance concerns, which would temper their enthusiasm to purchase.

5.1.2. Regression Analysis

We conducted a regression analysis to evaluate the effect of each predictor on the likelihood of purchase. The general form of this study’s regression model was as follows:

Likelihood of Purchase = β0 + β1 (Comfort Enhancement) + β2 (Security

Improvement) + β3 (Energy Efficiency) + ϵ

where β0 is the intercept; β1, β2, and β3 are the correlation coefficients; and ϵ (epsilon) is the error term.

The regression outputs are shown in

Table A3. Regression output for hypothesis 1.

Summary Output
Regression Statistics
Multiple R	0.486083884
R Squared	0.236277543
Adjusted R Square	0.230822382
Standard Error	0.810102518
Observations	424
ANOVA
	df	SS	MS	F	Significance F
Regression	3	85.27390253	28.42463418	43.31266632	2.09318 × 10−24
Residual	421	275.6317578	0.65626609
Total	424	360.9056604
	Coefficients	Standard Error	t Stat	p-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	0.616179386	0.243213883	2.533487715	0.011656116	0.138111297	1.094247474	0.138111297	1.094247474
X Variable 1 (Enhanced Comfort)	0.406218228	0.08172585	4.970498648	9.74386 × 10−7	0.245575585	0.566860871	0.245575585	0.566860871
X Variable 2 (Improved Security)	0.113960964	0.082795521	1.376414601	0.169426659	−0.04878 4254	0.276706182	−0.04878 4254	0.276706182
X Variable 3 (Energy Efficiency)	0.1752104	0.055827152	3.138444203	0.001818232	0.065474972	0.284945828	0.065474972	0.284945828

, Appendix B.

5.1.3. Regression Summary and Interpretation

Model Fit

The R-squared value is 0.2363, which indicates that 23.63% of the variance in the dependent variable (likelihood of purchase) is explained by the three independent variables: comfort enhancement, security improvement, and energy efficiency.

The F-statistic is 43.31 (p < 0.001). This suggests that at least one of the predictors has a significant relationship with the likelihood of purchase.

Individual Predictor Analysis

Intercept. The intercept coefficient is 0.6162 (p = 0.0117), indicating that the baseline likelihood of purchase is statistically significant when all predictors are zero.

Comfort Enhancement (X Variable 1)

Coefficient: 0.4062

Standard Error: 0.0817

T statistic: 4.97

p value: 9.74 × 10⁻⁷ (significant at p < 0.001)

Interpretation: Comfort enhancement has a positive and significant effect on the likelihood of purchase. This suggests that as comfort enhancement increases, the likelihood of purchase also increases, supporting its relevance in predicting purchase likelihood.

Security Improvement (X Variable 2)

Coefficient: 0.1140

Standard Error: 0.0828

T statistic: 1.3764

p value: 0.1694 (not significant)

Interpretation: Security improvement does not have a significant effect on purchase likelihood at the 0.05 significance level. Thus, this factor may not be a strong predictor in influencing the likelihood of purchase.

Energy Efficiency (X Variable 3)

Coefficient: 0.1752

Standard Error: 0.0558

T statistic: 3.1384

p value: 0.0018 (significant at p < 0.01)

Interpretation: Energy efficiency has a positive and significant effect on the likelihood of adoption. This implies that increases in energy efficiency are associated with an increased likelihood of purchase, confirming its role as an influential predictor.

5.1.4. Interpretation of Hypothesis 1

Comfort enhancement and energy efficiency are significant predictors of the likelihood of purchase, with positive coefficients and significant p values. This indicates that these two factors play an essential role in predicting purchase. Security improvement, however, was not a significant predictor, as indicated by its p value (0.1694), which is higher than the 0.05 threshold.

Hypothesis 1 is partially supported by the regression analysis. The hypothesis is supported for comfort enhancement and energy efficiency because these factors have a positive and significant effect on the likelihood of purchase, but it is not supported for security improvement because this factor does not significantly influence the likelihood of purchase.

This suggests that any strategies to increase the likelihood of purchase should prioritize comfort enhancement and energy efficiency management and focus less on security features.

5.2. Hypothesis 2

To test Hypothesis 2, we examined whether technical proficiency in managing Internet-connected devices and navigating information was associated with the likelihood of purchasing smart home services and products. Here, data from questions 1 to 3 of Construct B of the technical proficiency were utilized.

The average scores for technical proficiency, user familiarity, and likelihood of purchase were 3.50, 3.27, and 3.06, respectively.

Given these scores, technical proficiency and likelihood of purchase were high. Thus, a strong positive relationship exists, suggesting that higher technical proficiency is associated with a higher likelihood of adopting SHT. Hypothesis 2 is supported because the high average scores for both technical proficiency and likelihood of adoption suggest a strong positive correlation, indicating that individuals with higher technical proficiency are more likely to purchase SHT products.

5.2.1. Regression Analysis

We examined the relationship between technical proficiency and information navigation proficiency and the likelihood of adopting smart home services and products. The regression outputs are shown in

Table A4. Regression outputs for hypothesis 2.

Summary Output
Regression Statistics
Multiple R	0.435766047
R Square	0.189892048
Adjusted R Square	0.186043554
Standard Error	0.884321683
Observations	424
ANOVA
	df	SS	MS	F	Significance F
Regression	2	77.1732	38.5866	49.34191	5.60118 × 10−20
Residual	421	329.2325	0.782025
Total	423	406.4057
	Coefficients	Standard Error	t Stat	p-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	1.452480039	0.194083	7.483814	4.26 × 10−13	1.070987908	1.833972	1.070988	1.83397217
X Variable 1 (Technical Proficiency)	0.071344208	0.066509	1.072693	0.284023	−0.05938 7757	0.202076	−0.05939	0.202076173
X Variable 2 (Information Navigation proficiency)	0.415379214	0.058489	7.101895	5.28 × 10−12	0.300413345	0.530345	0.300413	0.530345083

, Appendix B.

5.2.2. Regression Model Interpretation and Key Points from the Output

The fitted regression model was as follows:

Likelihood of Purchase = 1.452 + 0.071 (Technical Proficiency)

+ 0.415 (Information Navigation Proficiency)

Multiple R (0.435): This indicates a moderate positive correlation between the independent variables (technical proficiency and information navigation proficiency) and the likelihood of purchase.

R Square (0.1899): In total, 18.99% of the variance in the likelihood of purchase is explained by technical proficiency and information navigation proficiency.

Significance F (5.60 × 10⁻²⁰): The model is significant, as indicated by the very low significance level (p < 0.05).

5.2.3. Coefficients and p Values

Intercept (1.452): This is the baseline likelihood of purchase when both technical and information navigation proficiencies are zero.

Technical Proficiency Coefficient (0.071): For each unit increase in technical proficiency, the likelihood of purchasing increases by 0.071 units, assuming information navigation proficiency is constant. However, the p value for this variable is 0.284, which is not significant. This suggests that technical proficiency alone does not have a significant effect on purchase likelihood.

Information Navigation Proficiency Coefficient (0.415): For each unit increase in information navigation proficiency, the likelihood of purchasing increases by 0.415 units, with technical proficiency held constant. This variable has a very low p value (5.28 × 10⁻¹²), indicating a significant positive effect on purchase likelihood.

5.2.4. Interpretation for Hypothesis 2

The results suggest that information navigation proficiency is a significant predictor of the likelihood of purchasing SHT and that technical proficiency (managing Internet-connected devices) is not a significant predictor. Therefore, Hypothesis 2 is only partially supported. Information navigation proficiency contributes to the likelihood of purchase; technical proficiency does not.

In summary, individuals who are proficient in navigating information (such as online research or understanding technology-related information) are more likely to purchase smart home services and products. General technical proficiency does not significantly influence adoption likelihood.

5.3. Hypothesis 3

Hypothesis 3 suggests that individuals who perceive current methods as inadequate are more likely to purchase SHT products. To test this, we examined whether the perceived inadequacy of current methods for managing home energy consumption and household tasks was correlated with the purchase of SHT.

We drew on data from question 2 of Construct C and question 4 of Construct A in Appendix A.

Average Perceived Inadequacy: 3.11

Average Likelihood of Purchase: 3.31

Covariance and Correlation: Positive, indicating a relationship.

Higher perceived inadequacy in managing home tasks and energy correlates with higher likelihood of purchasing SHT products. This supports Hypothesis 3, indicating that perceived problems with current methods drive the adoption of SHT. The same results were revealed in

Table A5. Regression outputs for hypothesis 3.

Summary Output
Regression Statistics
Multiple R	0.450882577
R Square	0.203295098
Adjusted R Square	0.197604349
Standard Error	0.827410344
Observations	424
ANOVA
	df	SS	MS	F	Significance F
Regression	3	73.37035159	24.45678386	35.7237839	1.3959 × 10−20
Residual	420	287.5353088	0.684607878
Total	423	360.9056604
	Coefficients	Standard Error	t Stat	p-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	1.583397931	0.193294452	8.191636706	3.1353 × 10−15	1.203452888	1.963342974	1.203452888	1.963342974
X Variable 1	0.117223074	0.06231416	1.88116272	0.06064129	−0.005263 401	0.23970955	−0.005263 401	0.23970955
X Variable 2	0.101836046	0.057909782	1.758529248	0.07938575	−0.011993 06	0.215665152	−0.011993 06	0.215665152
X Variable 3	0.321416944	0.045600502	7.048539578	7.4662 × 10−12	0.231783307	0.41105058	0.231783307	0.41105058

, Appendix B.

6. Conclusions

6.1. Findings

Users generally perceive the benefits of SHT, in terms of comfort enhancement, security improvement, and energy efficiency management, positively. A strong positive correlation was observed between these perceived benefits and the likelihood of purchase. Additionally, users with higher technical proficiency are more inclined to purchase SHT. Dissatisfaction with current methods for managing home tasks also drives adoption. The findings support all three hypotheses.

Hypothesis 1.

A significant correlation between user perceptions of comfort enhancement, security improvement, and energy efficiency management and the likelihood of purchasing SHT solutions was demonstrated. The strongest correlations were with perceptions of energy efficiency and security, suggesting these benefits should be highlighted in marketing strategies.

Hypothesis 2.

Individuals with higher technical proficiency are more likely to adopt SHT. This highlights the importance of training and education to increase adoption rates among users with lower technical proficiency.

Hypothesis 3.

Users dissatisfied with current methods for managing home energy consumption and household tasks are more likely to purchase SHT. This suggests that perceived inadequacies in traditional methods drive the desire for more efficient alternatives.

Overall, the findings emphasize the importance of perceived benefits, technical skills, and dissatisfaction with current methods as key factors influencing the purchase of SHT.

6.2. Contributions to TAM Theory and Enrichment of Academic Literature

This research used primary data obtained from a survey. In total, 424 valid survey responses were analyzed. The study also obtained data on new variables for consideration in SHT adoption studies. Privacy expectations, long-term maintenance cost, and environmental sustainability are examples of data obtained in this study that further advance our understanding of consumer buying behaviors. This research provides insights into how perceived benefits, such as comfort enhancement, security improvement, and energy efficiency management, influence the likelihood that individuals will purchase SHT. Comfort and security are the most positively perceived aspects. Users are more likely to purchase SHT when they feel that these products will significantly improve their comfort and security.

These outcomes add to the literature by demonstrating the importance of psychological comfort and perceived security. Moreover, this research further connects theoretical models of TAM and practical implementation and offers a multidimensional framework to evaluate the effectiveness of SHT, focusing on factors such as user behavior, security, and data privacy.

This study aligns its effectiveness with TAM theory for the following reasons. First, consumers perceive that SHT enhance comfort and energy saving, leading to higher purchasing intentions, which is consistent with TAM’s PU concept; users believe that using a particular technology will improve their work or life efficiency. Second, consumers prefer SHT that allow them to consult online manuals, indicating that ease of use is a crucial factor in their purchasing decision, and this conforms to TAM’s PEOU concept; if users find that a technology is easy to learn and use, they are more likely to accept it. Third, when consumers perceive SHT products as beneficial and easy to use, their intention to purchase increases, which is in line with TAM’s behavioral intention (BI); when users perceive a technology as both useful and easy to use, they develop an intention to use it, which influences actual behavior. Finally, users’ personal experience and environmental awareness positively influence their buying decision, and this is also consistent with external variables (EVs); factors such as individual experience, social influence, and environmental conditions can impact perceived usefulness and ease of use, ultimately affecting the individual adoption of SHT.

The results of this study also align with many previous studies, including Rock et al. [3], SHT used in Malaysia; Pliatsikas and Economides [7], SHT developed in Greece; Zhou et al. [8], SHT used for elderly consumers in China; Boer et al. [14], SHT skills for users in the Netherlands; Vrancic et al. [21], SHT for old adults in Croatia; Korneeva et al. [23], consumer attitudes to SHT in four European Union countries and Russia; Jahangir et al. [32], in the context of electronic banking; Siagian et al. [33], in digital payment platforms; and Zhang et al. [34], in the continuous use of mobile payment services.

The study revealed that when consumers consider purchasing products that use SHT, influencing factors such as comfort enhancement and energy efficiency management often relatively outweigh their desire to improve security. The underlying reasons are that comfort and energy saving directly impact daily life quality, aligning with consumers’ pursuit of convenience and economic benefits. In addition, this survey was conducted in northern Taiwan; the public safety of this area is relatively secure, leading residents to have less urgent needs for security improvement compared to comfort and energy saving.

Finally, the findings of this research further support the application of TAM theory to the SHT field. The research results also enrich the literature by providing empirical evidence related to how buying decisions are influenced in Taiwan.

6.3. Contributions and Suggestions to the SHT Industry

The study makes several contributions to the understanding of consumer behavior and adoption patterns in the context of SHT in Taiwan. These findings may also be useful to SHT providers.

This research uncovers the factors influencing the adoption of SHT, specifically focusing on user perceptions and technical competencies. The study demonstrated the importance of psychological comfort and perceived security in technology adoption. The findings may guide developers and marketers in this industry to emphasize these aspects in their product development, service offerings, and promotional efforts. Marketing strategies should focus on these benefits to increase adoption rates.

The study also demonstrated the relevance of information navigation proficiency—users’ ability to locate, evaluate, and use information effectively—in the adoption of SHT. With a positive correlation between information navigation proficiency and adoption likelihood, this study suggests that users who are better at handling information are more inclined to adopt SHT. Technical proficiency—users’ ability to manage Internet-connected devices and navigate information—affects the likelihood of adopting SHT but is not the sole factor influencing adoption; other elements, such as user-friendly interfaces or accessibility, also play important roles.

This finding highlights the need for continued innovation in simplifying technology use, making it accessible to users with varying levels of technical skills. This contribution points to the potential benefits of educational initiatives or information campaigns that enhance users’ abilities to understand and use information about SHT, thus supporting informed decision-making and potentially boosting adoption rates.

According to this study, despite positive perceptions of the benefits of SHT, the likelihood of adoption was relatively moderate. This paradox between benefit perception and actual adoption likelihood suggests the presence of additional barriers, such as financial costs, ease of use, and privacy concerns, which are not fully addressed by the benefits alone. The study thus contributes to the SHT industry by identifying gaps that may prevent users from embracing SHT. The insights provided by this study can help stakeholders address these adoption barriers more directly, perhaps through user-friendly designs, cost reduction strategies, addressing interoperability between different smart home devices, highlighting the importance of cross-platform compatibility, and enhanced data security measures to increase consumer satisfaction and boost market penetration.

This study also delivers practical value to the SHT industry in Taiwan by identifying key trends in consumer preferences, adoption barriers, and critical success factors. The study contributes insights that can lead to the development of consumer segments based on demographics or psychographics, thereby enhancing targeted marketing strategies. It provides industry stakeholders with actionable insights into improving product design, enhancing user interfaces, and strengthening security protocols.

The findings of this study have additional important implications for industry leaders and policymakers. For industry leaders, the research indicates that focusing on enhancing comfort and security features while simplifying device operation could increase user satisfaction and adoption rates. In addition, addressing perceived barriers such as cost and privacy concerns may help convert favorable perceptions into actual adoption behaviors. Policymakers should increase public awareness and provide educational resources to enhance information navigation and technical proficiency, thereby boosting the adoption of SHT.

In summary, this study suggests the following strategies and measures to the SHT industry in Taiwan:

R&D and manufacturing—

• Simplifying installation and maintenance: developing plug-and-play devices for easy setup and upkeep to reduce technical barriers.
• Streamlining user interfaces: developing intuitive applications such as voice control, smartphone apps, and devices with easy-to-use controls to speed up the learning curve of users.
• Strengthening products’ online security: enhancing transmission protocols and data security to dispel consumers’ privacy concerns.
• Adopting more green material for manufacturing processes: green SHT devices are helpful to appeal to more customers who possess environmental awareness.
• Collaborating with well-known brands of home appliances: integrating SHT into familiar household appliances, such as air conditioners and refrigerators, to minimize the need for additional learning and allow for smooth penetration into the market.
• Upgrading system interoperability and cross-platform compatibility: developing compatible systems or devices, including hardware and software, that are able to interoperate with that of prominent SHT system providers in local markets such as Chunghwa Telecom, Far East Tone Telecom, Taiwan Mobile, LifeSmart Taiwan, Holitek, etc.

Marketing campaigns—

• Promotion campaign: employing down-to-earth data and case studies to highlight comfort, security, ease of use, and energy-saving functions, showing consumers real-world figures on how SHT products can reduce energy expenses, demonstrating electricity savings and environmental benefits, and making long-term returns more tangible by exemplifying practical evidence to persuade consumers instead of just verbally featuring these benefits.
• Educational initiatives and experience activities: organizing educational initiatives and SHT experience events where non-experts can learn how to use SHT devices and try out SHT systems personally to build up consumers’ confidence.

Sales promotions—

• Market segmentation strategy: dividing a potential market into different segments based on demographics, household income, psychographics, etc., thereby concentrating sales efforts on the targeted consumers.
• Bundle selling: Integrating comfort, security, and energy efficiency into one package to increase consumers’ perceived value.
• Providing a trial period: let users experience SHT products before committing to a final purchase.
• Reducing initial cost: providing discounts and rebates, offering installment payments, and making use of potential government subsidies to improve affordability.
• Closely aligning with government policy: exploiting government subsidies offered in different projects and related time frames to attract consumers’ adoption.

Technical services—

• Providing offline support: offering 24/7 hotline customer service, simple printed step-by-step configuration and installation manuals, and video tutorials to reduce consumers’ reliance on online searches.

6.4. Contributions to Environmental Sustainability

This research reveals that although many consumers perceived the energy-efficient benefit of SHT, their actual willingness to purchase remained relatively low. The reason behind this may be the high initial cost, as many SHT products require high upfront investments that discourage cost-conscious consumers. Second, while SHT products save money over time, consumers prefer immediate cost reduction. Third, some consumers do not fully understand how SHT products can optimize energy consumption and cut utility costs; they may remain skeptical about whether SHT products could save enough energy to justify the cost. In addition, the need for professional installation and periodic maintenance adds extra expenses and causes inconvenience.

The study suggests that the local SHT industry should address inadequacies in current methods for managing home energy consumption and household tasks and highlight how SHT can improve capabilities in order to increase their likely adoption. Detailed strategies and measures for the SHT industry in Taiwan are formulated in the above-mentioned list.

Additionally, given the increasing awareness in recent decades of the need to care for the environment, smart home products that are environmentally friendly are being developed. This study verifies that consumers’ environmental awareness affects their buying behavior. The study highlights how SHT can improve quality of life while promoting sustainable development. Specifically, more than 50% of the respondents are concerned about energy saving and sustainability, suggesting that the SHT industry should incorporate more green technology into their products to meet the needs of environmentally conscious consumers.

Furthermore, the SHT industry can provide more examples of SHT features that could appeal to environmentally conscious consumers in marketing campaigns, as follows:

Energy-saving products—

• Smart appliances: energy-efficient refrigerators, air conditioners, washing machines, etc.
• Smart thermostats: automated temperature control for efficiency; automatically adjust heating and/or cooling for electricity saving.
• Smart LED lighting: automatic adjustments based on ambient light conditions and space occupancy; reduces unnecessary energy consumption with motion sensors.
• Smart power plugs/socket: monitoring and controlling appliance power consumption; automatically shut off power to idle devices.
• Smart home energy monitors: tracking and optimizing electricity usage.
• Water-saving smart showers: regulating water flow and temperature efficiently.
• Smart water meters: water usage monitoring and leak detection.

Government subsidies for purchasing energy-efficient appliances—

The current incentives and policies that shall be exploited in Taiwan are as follows:

• According to the “Residential Appliance Replacement Energy-Saving Subsidy Program” implemented by the Ministry of Economic Affairs, consumers who purchase air conditioners and refrigerators that meet Level 1 energy efficiency standards and comply with replacement requirements are eligible to apply for a subsidy for approximately US$100.
• The Ministry of Finance provides a “Tax Reduction on Purchasing Energy-Efficient Appliance” policy. Consumers who purchase energy-saving appliances that meet the standard can enjoy a tax rebate of up to approximately US$150.

Finally, the study’s insights could arouse consumers’ environmental awareness again and increase public interest in SHT, leading to higher adoption rates.

6.5. Limitations and Suggestions for Future Research

Several limitations of this study should be noted. First, the survey was conducted only in northern and central Taiwan and only in urban areas, and the sample size is small. The sample may not be fully representative of Taiwan’s smart home consumer market. If the survey was conducted in rural or agricultural areas instead of urban cities, the results would have been slightly different. Potentially, consumers may prioritize basic home needs over SHT. Rural areas may relatively lack a law enforcement presence, which would lead to smart security being more attractive. Energy saving is still relevant, as farmers and large households may value electrical efficiency, but the barrier of the initial cost remains an issue. This inference for different contexts in Taiwan implies that cross-cultural studies to determine whether SHT adoption patterns are similar across different regions are crucial and are another direction for future study.

Second, the study primarily focuses on consumer behavior by employing TAM theory. FakhrHosseini et al. [27] comment on many useful theories that are suitable for researching the user adoption of intelligent environments. The study suggests that several of them are alternatively applicable to SHT research. For example, Innovation Diffusion Theory (IDT) explains the diffusion of the innovation process and helps with understanding how users ultimately decide whether to adopt or reject an innovation. A combination of TAM and IDT can enhance the researcher’s understanding of adoption behavior and explain the differences between intentions to use versus actual usage, such as the study completed by Yuen et al. [30]. The Unified Theory of Acceptance and Use of Technology (UTAUT) theory has four predictors of users’ behavioral intention: performance expectancy, effort expectancy, social influence, and facilitating conditions. This is capable of explaining up to 70% of adoption.

The theory of planned behavior (TPB) assumes that human behavior is voluntary and rational and influenced by intention. Erokhin et al. [35] adopted it by adding the additional constructs of perceived usefulness and knowledge of water waste reduction for empirical research about water-smart farming technologies. This study suggested that religious values and the perception of environmental responsibility could also be included for further investigation. In addition, the Protection Motivation Theory (PMT) was employed by Bavel et al. [36] to investigate the design of improving online security behavior. PMT has been employed to research cybersecurity in Europe. It is applicable to examining privacy concerns about the adoption of SMT, as well. Similarly, several predictors, such as household financial factors, ownership of housing, etc., can be taken into account for future research.

Third, the questionnaire survey conducted in this study mainly relied on self-reported data from participants. It may have led to individual biases or misconceptions in the data. For future research, the alternative qualitative designs of in-depth interviews with experienced users and major providers of SHT may be useful for obtaining more comprehensive insights into user adoption behaviors. The other alternative experiment method implemented by SHT electronic engineers with users to test users’ reactions is also feasible to understand technical proficiency issues. These measures may more than compensate for the shortcomings of this quantitative research. Moreover, longitudinal studies can also be conducted to investigate long-term user behavior and the effect of SHT systems on energy consumption and quality of life.

Finally, the study primarily focuses on consumer behavior and does not cover all technical or engineering aspects of SHT systems; the rapid evolution of SHT may make the findings obsolete. The limited access to proprietary industry data also restricted the study’s scope in addressing specific commercial and marketing strategies.

In summary, examining smart home adoption in different cultural and socioeconomic contexts by utilizing different adoption theories and research methodologies could achieve a broader understanding of global trends and consumer needs.