Smart Home Security: Factors Affecting Consumer Intention to Put Smart Home Security in Place

Abstract

With the adoption of new technologies accelerating, smart home security has emerged as a critical topic in the household market. This study aims to ascertain consumers’ intentions regarding smart home security technology. Smart home security adoption intention was studied using the Technology Readiness and Technology Acceptance Model. The research survey gathered data from 303 respondents for analysis. The model’s statistical test for hypotheses testing was analyzed using Confirmatory Factor Analysis and a Structural Equation Model. All hypotheses were supported. Optimism has a strong effect on perceived security while innovativeness has a positive effect on perceived ease of use. Discomfort has a positive effect on perceived usefulness but has a negative effect on perceived ease of use. Insecurity has a negative effect on perceived usefulness. Also, perceived ease of use significantly affects perceived usefulness and attitudes towards using smart home security. Perceived security has a positive effect on attitudes towards using smart home security. Finally, the consumer’s perceived usefulness and attitudes towards using smart home security have a sizable influence on the consumer’s intention to use smart home security technology. The research illustrates several implications for understanding consumer behavioral intentions regarding the use of smart home security technology.

Plain language summary

Consumer intentions and adoption of smart home security technology

By outlining the relationships between various factors, such as the positive and negative feelings that influence how consumers perceive the usefulness and ease of a product, this research aims to present the consumer intention to use smart home security systems and assist innovation developers and business sectors in effectively responding to consumers’ needs.

Keywords

perceived security smart home security technology acceptance model technology readiness

Introduction

A house is a secure place that should be safeguarded against unfavorable events or accidents, and it is also one of the first places where people look for security (Surantha & Wicaksono, 2018). As a result, the concept of home security systems becomes mandatory to ensure the residents’ safety (Chitnis, Deshpande, & Shaligram, 2016). Since the 1970s, the concept of home automation, including security systems, has been a source of concern. Throughout the advancement of technology, home automation and security have always sought to improve operating systems to make them more convenient, efficient, and secure. The role of home security has always been critical. Installing smart home security systems may be an alternative solution to deter criminals (Chitnis, Deshpande, & Shaligram, 2016). To ensure the security of smart homes, producers of smart home devices must apply the most appropriate security solutions, and users must embrace the best security practices (Hammi et al., 2022).

Smart home security is a subset of the greater landscape of new technologies aimed at enhancing the comfort, safety, and energy efficiency of residences. Smart homes have enormous potential to change the future of living, the market is maturing, and prominent technology services are related to home security to improve quality of life (Li et al., 2021). It focuses on technology and gadgets intended to safeguard homes and their residents from security threats. Several specialized technologies play a crucial role within the field of smart home security, and they have a substantial impact on contemporary household markets. For example, smart security cameras include motion detection, night vision, and mobile app-based remote access (Bhavani et al., 2016). They allow homeowners to monitor their property in real-time and provide evidence in case of a security breach. A smart doorbell security system allows homeowners to interact with and view guests remotely (Chaudhari et al., 2020). It enhances home security by discouraging potential intruders and documenting front-door activities. By enabling remote locking and unlocking of doors via a smartphone app, smart locks and intelligent doors increase access control (Sreelakshmy et al., 2023). The value of these technologies in the present residential market lies in their ability to give homeowners greater security control and increase user convenience and accessibility. As security concerns continue to rise in a world that is becoming increasingly digital and networked, smart home security systems provide safety and privacy alternatives.

In the previous era, the effects of a traditional home security solution were insufficient to provide additional security (Surantha & Wicaksono, 2018). At the moment, smart home security has benefited from new technology that has improved the quality and functionality of the system, such as Internet-of-Things (IoT) technology. IoT is intelligent internetwork; it enhances systems’ capacity to connect to the internet directly (Batalla & Gonciarz, 2019). IoT refers to devices and objects that collect data, store it, and analyze it using technical product components and domestic electronics to support rapid societal revolutions (Srinivas et al., 2021). Consumers can now manage and monitor their homes remotely, and the systems can also communicate with other devices such as a smartphone. Smart home security was developed to be more efficient, improve the overall quality of safety in the home environment, and meet consumers’ needs in the digital era. Technologies have evolved into a means of enhancing users’ quality of life. The smart home market has grown in popularity and importance due to the popularity and importance of smart home automation and home security and is expected to be worth more than 200,000 million USD globally by 2025 (Hubert et al., 2018). It is a massive sum, indicating that the smart home security market is becoming a hot topic.

As a subset of smart home technologies, smart home security devices demonstrate a distinct adoption pattern compared to other technologies due to a number of important considerations. By 2033, the connected home security market is projected to reach roughly US$ 52.52 billion, and it is projected to be worth $9.84 billion in 2023 and to grow at a CAGR of 18.2% between 2023 and 2033 (Global News Wire, 2023). The increased emphasis on security and privacy is one of the leading drivers. Businesses in the smart home industry constantly promote the consumer benefits of smart home technologies, such as ease, cost savings, and home security (Cannizzaro et al., 2020). Smart home security equipment is considered as a protection against actual threats. Enforcing privacy and security in smart home environments has been regarded as the most significant hurdles a smart home application can overcome (Abdullah et al., 2019). Moreover, purchasing smart home security devices frequently requires a great deal of research and planning because customers do not automatically adopt new technology into their homes and lifestyles; rather, learning and acceptance must occur (Sovacool & Del Rio, 2020). People are more likely to carefully evaluate their options, including factors like compatibility, reliability, and customer reviews. Moreover, smart home security technology is quite new aiming to improve residential quality by introducing new services and enhancing functionality (Sovacool & Del Rio, 2020). This requires research into consumer adoption intention regarding applications of these technologies.

Many smart home security studies focus on technological engineering, such as IoT layers (Touqeer et al., 2021), and the design of an IoT-based home security system (Hoque & Davidson, 2019). Previous literature provides insights into designing smart home security devices and applications to enhance the benefit of smart home security system, based on technology creation perceptions. On the other hand, previous studies on smart home applications adoption focus on different views on smart home technology regarding consumer perceptions such as gender (Nikou, 2019), older adults (Ghorayeb et al., 2021), and household use (Mulcahy et al., 2022). There are few studies conducted research on smart home security user adoptions. For instance, a study on the adoption of smart home sensors by older persons demonstrates that several elements are involved in the decision-making process and that not all of these factors could be completely analyzed in a single study (Cao et al., 2022). Also, Philip et al. (2023) found that individual’s positive feelings, such as pleasure, accomplishment, and fulfillment about implementing controls to safeguard their smart home network, are a predictor of cost-benefit analysis and intention. A study from George et al. (2021) mentioned that IoT home security systems are viewed by the public as potential sources of personal data leaks or as enablers of privacy infringements. The consumers’ adoption of smart home security equipment is distinctive and requires investigation for a number of compelling reasons, as factors influencing the adoption of smart home technology services by households remain an understudied field (Li et al., 2021). Also, as security concerns continue to rise, it is anticipated that the smart home security market will develop considerably. Although, as mentioned previously, people are increasingly focusing their attention on smart home security and demand has steadily increased, there is still a shortage of research on the consumer market for smart home security.

As a result, this study means to close this gap and expand on the concept of smart home security from the perspective of consumers. The purpose of this study is to ascertain consumer intention regarding the purchase of smart home security products by identifying critical factors using the Technology Readiness (TR) and Technology Acceptance Model (TAM). The objectives of this study are to (1) examine the acceptance of smart home security systems by consumers, and (2) positive and negative factors influencing the users’ adoption intention. The current study adopts the Technology Readiness (TR) and Technology Acceptance Model (TAM) as core research theories. TAM is considered a comprehensive framework for comprehending performance-focused technology acceptance in numerous fields, and it explains individual or organizational technology-related behavior with utilitarian motives (Chang, & Chen, 2021). On the other hand, to understand user psychology, Parasuraman (2000) introduced the concept of TR and described it as an individual’s inclination to adopt and utilize new technologies, based on both positive (favorable) and negative (unfavorable) impressions and emotions. Moreover, recent studies applied these theories to understand new technologies acceptance such as smart health wearable devices (Jeng et al., 2022), e-health and m-health technologies (Leung, & Chen, 2019), and smart shops (Chang & Chen, 2021). The combination of TR and TAM is utilized in previous research in the field of technology adoption (e.g., Kampa, 2023; Lai, & Lee, 2020).

The current study attempts to integrate TAM and TR to examine the adoption of smart home security devices, primarily because it provides a thorough picture of consumer behavior and their tendency to accept new technologies. Here are the reasons why this combination is appropriate for this investigation. First, in situations where the deployment of new technology is not mandated by organizational goals, the integrated model may be a more suitable way for monitoring technology acceptance and use (Kampa, 2023). Second, the combination of TR and TAM provides a comprehensive view of the adoption process. TAM utilizes system-specific perceptions to explain technology acceptance, whereas TR exploits the general preferences of individuals (Yi et al., 2003). In the case of smart home security equipment, it is crucial to comprehend both the psychological preparedness and the practical acceptance elements that influence adoption.

Literature Review

Technology Readiness

As suggested by Parasuraman (2000), TR is referred to as an individual’s inclination to adopt and utilize new technologies and it can determine if a person is prepared to utilize new technologies. Technology readiness is a term that refers to people’s tendency to embrace and use new technology to accomplish tasks in daily life and at work (Ali et al., 2019) and a significant predictor of an intention to use new technology (Al-ajam, 2013), regardless of their ability to use it (Pradhan et al., 2018). The concept of TR is broad; it is particularly prevalent in business marketing, where the emphasis is on the market segment consumer who is more receptive to new technology (Caison et al., 2008). The technology readiness Index (TRI) is suggested to be the suitable framework for capturing the consumer’s positive and negative dimensions in mental readiness (Flavián et al., 2022). The constructor is made up of four sub-dimensions: optimism and innovativeness, which contribute to technological readiness, and insecurity and discomfort, which act as inhibitors of technological readiness (Buyle et al., 2018).

Optimism

The first dimension of technological readiness is optimism, which can motivate or contribute to technological readiness. Optimism is the perception that technology brings greater control and competency, and innovativeness is the individual predisposition to accept technology at an early stage (Alhammadi et al., 2023). Additionally, it is defined as a person’s positive tendency to use technology and the belief that technology has a beneficial effect on their lives (Ali et al., 2019). Optimistic individuals have an appropriate attitude toward technology acceptance and risk perception (Buyle et al., 2018). In other words, optimism refers to the widespread belief among the public that technology is a positive force. The benefits of smart home security systems include intelligent situation analysis, automatic alerts when a pessimistic scenario is detected, and remote control of the home via a smartphone application (Hoque & Davidson, 2019). Thus, smart home security is a valuable technology that can improve one’s quality of life in terms of safety. Users with more optimistic perspectives are less prone to be worried by technology and are more adapted than the ordinary user (Munyoka & Maharaj, 2019). Thus, optimistic people will accept and trust smart home security technology, and they will perceive security in its operation. In other words, optimism is associated with a sense of security.

H1: Optimism positively affects perceived security.

Innovativeness

Innovativeness refers to a person’s level of enthusiasm for experimenting with technology, being on the cutting edge of efforts to try the latest advanced technology (Nugroho & Andryzal Fajar, 2017), having tendency to embrace technology in its early stages (Alhammadi et al., 2023) before most of their social group (Jarrar et al., 2020) and is a human characteristic that generally indicates a personal characteristic and is not situational (Ali et al., 2019). It is a driving force behind technological readiness (Buyle et al., 2018). Individuals with a high level of innovativeness typically receive a high score. They are typically early adopters, eager to accept and experiment with new technology. In other words, innovativeness refers to an inclination to become a technology innovator (Nugroho & Andryzal Fajar, 2017). Smart technology can be defined as a creative process applied to systems that collect data about their operation to design and analyze intelligent systems capable of proving a more significant advantage (Caison et al., 2008). Individuals who are not part of the smart community or who have a low innovativeness score are aware of the benefits of smart systems. There is an insignificant association between innovation and perceived usefulness in such underdeveloped nations due to a lack of awareness of technology (Ullah et al., 2020). Thus, the research predicts that due to individuals’ innovativeness, people will believe that utilizing smart home security is simple. In other words, perceived ease of use is positively related to innovativeness.

H2: Innovativeness positively impacts perceived ease of use.

Discomfort

Discomfort is a term that refers to a person who lacks control over technology and has a sense of being involved in technology (Blut & Wang, 2020). A technological product with a high level of complexity may affect the product’s evaluation and the user’s learning cost (Buyle et al., 2018) and is the sense of being threatened by or lacking control over technology (Alhammadi et al., 2023). However, Yang et al. (2022) showed no significant effect of discomfort on perceived usefulness and perceived ease of use, such as a study in VR technology. While, some technology might require control and a sense of being overwhelmed which might negatively affect perceived usefulness (Alarafee et al., 2022). Unlike, smart devices are not designed to be simple for everyone due to their complex functions (Pradhan et al., 2018). As a result, those uncomfortable with technology frequently regard it as a complicated issue that is difficult to resolve (Blut & Wang, 2020). However, smart home technologies such as smart home security have continuously evolved to make homes more secure and comfortable to live in (Hoque & Davidson, 2019). The design of systems and functions was enhanced to provide a broader range of services and increase the efficiency of the system’s operation. Additionally, smart home security can assist and enhance the home environment’s safety (Kodali et al., 2017). A person’s level of discomfort reveals their attitude toward using modern technologies. A person’s choice of a certain service or good is often motivated by their emotions (Suhartanto et al., 2020). As a result, this research predicts that individuals who lack a sense of connection with complex technology will have a pessimistic attitude toward adoption. They continue to believe, however, that smart home security is advantageous.

H3: Discomfort negatively affects the perceived ease of use.

H4: Discomfort positively impacts perceived usefulness.

Insecurity

Insecurity is defined as a lack of trust in technology and a belief that it will fail to function appropriately (Nugroho & Andryzal Fajar, 2017). It emphasises an individual’s feelings about how technology operates through products or services (Sophonthummapharn & Tesar, 2007). More insecure individuals frequently tend to perceive the risk associated with new technology (Ali et al., 2019). Although smart home security has several advantages and is extremely useful (Kodali et al., 2017), individuals with a high degree of insecurity are likely to feel uneasy about utilising smart home technology (Ali et al., 2019). The fundamental difficulty lies in the fact that many of the most significant obstacles facing IoT applications in smart cities are related to security (Heidari et al., 2022). However, security features such as when the user needs to download any software should be considered based on its function of it (Rafdinal & Senalasari, 2021). As a result, this research predicts that individuals who have felt unsafe while utilizing technology will negatively perceive smart home security in terms of perceived usefulness.

H5: Insecurity adversely affects perceived usefulness.

Technology Acceptance Model

Regarding the adoption of advanced information technologies, The technology acceptance model (TAM) has been widely used in academic studies for decades (Davis et al., 1989a; Davis et al., 1989b). TAM is a generally used theoretical framework for forecasting and describing user acceptance of new technology (Diop et al., 2019). According to TAM, personality influences an individual’s technology adoption behavior. There are two fundamental factors namely, perceived ease of use and perceived usefulness, playing as antecedents of the intention to embrace technologies (Davis et al., 1989b). Perceived ease of use (PEOU) refers to the extent to which people perceive that using a particular technology can be effortless, while perceived usefulness (PU) refers to the extent to which people believe that employing a particular technology would enhance their performance. PU and PEOU principally influence the attitudes toward particular technology and the actual acceptance of it (Hubert et al., 2018). Additionally, the attitude may influence an individual’s intention to adopt the technology, where intention refers to a person’s perception that they will use the technology (Diop et al., 2019). TAM attends as a conceptual foundation for IS study, has been implemented and expanded across other domains, and is one of the most extensively used theoretical frameworks for explaining technological acceptance such as metaverse (Aburbeian et al., 2022), voice assistance (Acikgoz & Vega, 2022), and AR-supported mobile applications (Oyman et al., 2022). TAM has been shown to be effective in explaining acceptance of IoT such as smart home security however, consumers’ acceptance of innovative technology is closely associated with their individual qualities (Hussin et al., 2023). As a result, perceived usefulness and perceived ease of use are considered factors in consumers’ decision to purchase smart home security.

Perceived Security

Perceived security is a psychological concept that significantly influences users’ technological behavior (Zhang et al., 2019) and is the perceived ability to send sensitive information over the Internet safely (Salisbury et al., 2001). Perceived security, according to this definition, refers to an individual’s view of their capacity to communicate sensitive data over the internet securely. A person with low perceived security has a negative perception of safety. It would affect their decisions and behaviors, as well as their overall quality of life (Mahrous et al., 2018). A house is a location where most people are concerned about home security; they want to ensure that their residence is the safest possible (Surantha & Wicaksono, 2018). As a result, smart home security may be a viable option for mitigating risk and enhancing their perceived security. As a result, if a person has a high likelihood of having security awareness, they can use perceived security as one of the factors affecting their intention to use smart home security. Moreover, there are studies in other technology products like m-payment that show that if a corporation affirms the security and reliability of client data, it will also result in a good attitude from the customer (Laksamana et al., 2023). Additionally, this study predicts that perceived security has a beneficial effect on consumer attitude.

H6: Perceived security positively affects consumers’ attitude towards smart home security.

Perceived Ease of Use

Perceived ease of use is the primary variable in studies about technology adoption, and researchers have discovered a positive correlation between perceived ease of use and technology adoption (Gu et al., 2019). It is not only the primary contributing factor to a technology’s adoption intention but also investigates user behavior directly or indirectly via perceived usefulness (Nouri & Soltani, 2019). Perceived ease of use affects perceived usefulness (Nugroho & Andryzal Fajar, 2017). It also has a beneficial effect via perceived usefulness (Diop et al., 2019). Making a product suitable for the function can start with design and how to make it easy to learn how to use can enhance a sense of happiness in users (Zheng & Shan,2021). Consumers who perceive those products are easy to use are inclined to believe that they can improve their quality of life (Zhang & Liu ,2022).

H7: Perceived ease of use positively affects consumers’ attitude toward smart home security.

H8: Perceived ease of use positively impacts perceived usefulness toward using smart home security.

Perceived Usefulness

Perceived usefulness is defined as a person’s belief that technology will improve their job performance (Davis et al., 1989b). It is regarded as an external incentive for users. Additionally, user intention is influenced by usefulness (Sánchez & Hueros, 2010). Perceived usefulness, directly and indirectly, affect behavioral intention (Wong et al., 2013). It is the primary variable for examining the adoption intention of innovation from the user’s perspective in a study. A previous home service technology study also explained that the likelihood of consumers adopting technology is better if they at least acknowledge that this innovation improves the quality of life (Zhang & Liu ,2022). This study predicts that perceived value will increase a customer’s intention to purchase smart home security. Thus, this research indicates that individuals who believe smart home security is beneficial also have a favorable view of their intention to use smart home security.

H9: Perceived usefulness positively affects consumers’ intention toward using smart home security.

Attitude

Attitude refers to an individual’s persistent psychological tendency toward a particular behavior (Li et al., 2019). It is associated with assessing the possible consequences of an individual’s behavior and will result in different decisions based on their assessments of the behavior (Hua & Wang, 2019). A person’s attitude is any negative or good emotion related to a specific conduct (Hameed, & Nigam, 2023). Thus, to forecast consumer behavior toward the intention to purchase smart home security systems, one can consider attitude as a factor in determining an individual’s intention (Xu et al., 2019). Additionally, prior research indicates that attitude is a significant factor in determining a consumer’s intention to purchase a product (Ahmed et al., 2019).

Intention

Behavioral intention refers to an individual’s motivation and effort to carry out actions (Chin et al., 2018). The intention is thought to encourage the factors that influence behavior. It measures an individual’s willingness, how hard they try, and how much effort they exert to perform the behavior (Collins et al., 2011). Intention presupposes that people will act following their intentions (Nystrand & Olsen, 2020). The relationship between attitude and behavior intention to use specific technology has thus been discovered in empirical research, and a positive association has been discovered (Al-Emran et al., 2022; Alanazi & Soh, 2019; Aldossari & Sidorova, 2020; Maswadi et al., 2022). However, this study utilized a technology acceptance model to forecast an individual’s intention. In the current study, the term “intention to use” refers to a consumer’s intention to use smart home security.

H10: Attitude positively impacts consumers’ intention of using smart home security.

Hypothesis

As illustrated in Figure 1, the researcher proposes the following hypothesis:

Figure 1.

Research Conceptual Framework.

Methodology

Sample and Data Collection

This study seeks to ascertain consumers’ intention to use smart home security devices. Two sections are included in the questionnaire survey: demographic data and question items. The item content is prepared, and the questionnaire content is examined using seven scales in accordance with pertinent literature. The questionnaire was first created in the English language based on related studies and later translated in the Thai language, as the native language of the Thai people is the Thai language. The question items are checked for content understanding before launching the questionnaire. Before conducting the questionnaire survey, researchers conducted a pre-test to determine the questionnaire’s clarity. The study used a questionnaire to collect data from a small group of 30 individuals. The pre-test emphasised the importance of changing to increase the questionnaire’s inclusiveness (Tommasetti et al., 2018).

This study used an online questionnaire to elicit data on the key factors influencing consumer intention. The questionnaire was distributed among homeowners in Thai online communities where each homeowner joined to contribute information about their home-related issues. Participation is entirely voluntary. To qualify for this study, respondents needed to be at least 18 years old and have at least one experience with smart home security devices. Finally, there were 304 respondents in total. The structure equation (SEM) model was used to estimate the research hypothesis contained in the questionnaire (Bentler & Weeks, 1980). The sample size should be between 250 and 500 individuals, the maximum number of individuals eligible to apply to the SEM (Bentler & Chou, 1987). This research utilized Covariance-based Structural Equation Modelling (CB-SEM) since the current study aimed to apply TAM and TR theories to the adoption intention for smart home security. If the existing hypothesis needs to be confirmed, covariance-based CB-SEM is the appropriate technique because the inquiry is more confirmatory than exploratory (Hair, Matthews, et al., 2017; Hair, William, et al., 2017).

SPSS statistical package was applied to analyze the data to reveal the questionnaire survey’s reliability. The data was then analyzed using the responses from respondents who completed the survey. The results are shown in Table 1. According to the respondents’ demographics, 35.5% were male, and 64.5% were female. The majority of age respondents (75%) were between the ages of 20 and 35. Additionally, 43.8% of their income was spent on 10,000 to 25,000 Baht. Finally, the accommodation area revealed that urban areas accounted for 78.6% of the total, while rural areas accounted for 21.4%. The majority of respondents in terms of accommodation area is in line with the report from World Bank (2023) that currently, over 56% of the population in the world resides in urban areas and this trend is anticipated to continue, with the urban population more than tripling by 2050. Moreover, the majority of respondents in terms of age is in line with previous research regarding digital natives, including Generation Y and Z, or the Millennials, who have a great interest in new technology and are the first generation to have grown up with Internet and computer access. It is commonly believed that they are more familiar with and aware of innovation than earlier generations and are open to change (Baudier et al., 2020). Therefore, the demographic distribution illustrated in this study is reflective of users of smart home security devices.

Table 1.

The Demographic Data of Respondents.

The demographic data	Frequency	%
Gender
Male	108	35.5
Female	196	64.5
Age
20–35	228	75
36–50	52	17.1
50 above	24	7.9
Income level (THB)
10,000–25,000	133	43.8
25,001–40,000	116	38.2
40,001 above	55	18.1
Accommodationarea
Urban	239	78.6
Rural	65	21.4

Reliability Test

To minimize biases and errors in data collection, pilot test was conducted before full scale of data collection. The sample size was greater than 30 and less than 500, which is the most appropriate for studies (Roscoe, 1975). Meanwhile, the sample size or the number of respondents should be ten times the number of observed variables or instrument items (29 questions × 10 = 290) (Hair, Matthews, et al., 2017). As a result, the sample size in this study was adequate, at 303 samples according to both theories. The test reliability of each item was used to determine the questionnaire’s validity, and Cronbach’s alpha coefficient was used to ensure that the questionnaire was acceptable (Fraenkel et al., 2019). Cronbach’s alpha should be between .70 and .99, which is considered acceptable. All questionnaires used in this study had an acceptable Cronbach’s alpha of .90. Additionally, prior to conducting the questionnaire survey, the pre-test questionnaire was validated by an expert in the field. A pilot test with a sample size of 30 was conducted to determine the questionnaire’s validity using Cronbach’s alpha, as shown in Table 2.

Table 2.

Cronbach’s Alpha for the Pilot Test.

Pilot test
Respondents	N = 30
Number of items	29
Cronbach’s alpha	.903

Variables and Measurement

The questionnaire structure was adapted from previous literature to collect data on 7-point Likert scale is well-suited for usability questionnaires distributed electronically or without supervision. It is widely recognized in the literature as a standard psychometric scale to gauge responses because it provides a more precise evaluation of a participant’s genuine assessment (Finstad, 2010; Li, 2013). with one representing “strongly disagree” and seven representing “strongly agree.” The questionnaire includes an example of smart home security devices and briefly describes the features of the methods to ensure that respondents have a firm understanding of smart home security devices. The questionnaire items have been adapted in light of prior research as shown in Table 3. The technology readiness constructs were determined by Oukes et al. (2019). There are four factors with 12 items that are used to determine whether a user is innovative and on the cutting edge of technology use. The TAM questionnaire adapted three items based on prior research to assess perceived ease of use (Aslam et al., 2017; Cho & Son, 2019; Hua & Wang, 2019). Perceived usefulness altered four questionnaire items from the preceding study (Hua & Wang, 2019). Then, three items of perceived security are discussed, which were adapted from earlier literature (Aggarwal & Rahul, 2018; Aslam et al., 2017; Ong & Lin, 2015). Attitudes and intentions were adapted from previous literature (Aslam et al., 2017; Hua & Wang, 2019).

Table 3.

Questionnaire Items/References.

Questionnaires	Sources
Optimism	(Oukes et al., 2019)
OP1: The newest technologies of products and services are more convenient to use
OP2: I would like to use the most advanced technology
OP3: I am confident that machines will be able to follow through with what I give them to do
Innovativeness	(Oukes et al., 2019)
INNO1: Normally, I often figure out new high-tech of products and services by myself
INNO2: I am interested and follow up with the latest technology developments in my interest field
INNO3: I have fewer problems when I make technology work for me
Insecurity	(Oukes et al., 2019)
INSE1: Many new technologies still have a safety risk that has not been found until after people use them
INSE2: The machine’s performance is less reliable than people
INSE3: New technological innovations continuously improve their skills. It seems to hurt many people by making human skills is obsolete
INSE4: Using too high-tech technology, it can be a risk that there is no equipment or replacement parts.
Discomfort	(Oukes et al., 2019)
DIS1: I think that sometimes technology systems might not be designed for use by familiar people
DIS2: I think that the written language on manual of high-tech products or services is hard to understand
DIS3: The new technology might be breakdown or disconnected while I am using it to replace important people-tasks
Perceived Usefulness	(Hua & Wang, 2019)
PU1: I think smart home security helps me to improve the quality of my life
PU2: I think smart home security is useful
PU3: I think smart home security has working efficiency than guard
PU4: I think smart home security helps me to reduce paying guard cost
Perceived ease of use	(Aslam et al., 2017; Cho & Son, 2019; Hua & Wang, 2019)
PEOU1: I think using smart home security products is clear and understandable easily
PEOU2: I think the function of smart home security is not complicated
PEOU3: I think it is easy for me to have skilful in using smart home security
Perceived security	(Aggarwal & Rahul, 2018; Aslam et al., 2017; Ong & Lin, 2015)
PS1: I have enough information about the security systems of smart home security
PS2: I think installing smart home security is secure
PS3: I feel secure when installing smart home security in my house
Attitude	(Aslam et al., 2017)
AT1: Installing smart home security is a good idea
AT2: Installing smart home security is beneficial
AT3: I have a positive attitude related to smart home security
AT4: Installing smart home security is interesting
Intention	(Aslam et al., 2017; Hua & Wang, 2019)
INT1: I will buy smart home security if I need my house safety
INT2: I would like to recommend smart home security to others
INT3: I am willing to buy smart home security at a higher price if I need it

Finding and Result

Descriptive statistics

The descriptive statistic table contains the mean and standard deviation values for each construct and item. The range of the average mean for each item is shown in Table 4. The mean is between 4.62 and 6.14. The standard deviation is in the range of 0.72 to 1.37. These consequences described the data distribution and the difference between the observed values and the sample mean.

Table 4.

Descriptive Statistic.

Constructs and scale items	Mean	SD
Optimism	6.087	0.69
OP1:	6.06	0.92
OP2:	6.37	0.72
OP3:	5.82	0.92
Innovativeness	5.64	0.86
INNO1:	5.63	1.01
INNO2:	5.67	1.05
INNO3:	5.63	1.00
Insecurity	4.80	1.22
INSE1:	4.62	1.54
INSE2:	4.78	1.37
INSE3:	4.92	1.37
INSE4:	4.89	1.37
Discomfort	5.13	1.06
DIS1:	4.82	1.06
DIS2:	5.43	1.37
DIS3:	5.15	1.16
Perceived usefulness	5.76	0.79
PU1:	5.74	0.87
PU2:	5.68	0.94
PU3:	5.80	0.89
PU4:	5.81	0.91
Perceived ease of use	5.92	0.81
PEOU1:	6.00	0.87
PEOU2:	6.10	0.88
PEOU3:	5.66	1.12
Perceived security	5.83	0.87
PS1:	5.75	0.99
PS2:	5.80	0.96
PS3:	5.93	0.94
Attitude	6.09	0.678
AT1:	6.11	0.73
AT2:	6.14	0.75
AT3:	6.04	0.77
AT4:	6.07	0.81
INT: intention	5.92	0.87
INT1:	5.94	0.92
INT2:	5.86	1.01
INT3:	5.97	0.95

The proposed hypotheses are analyzed in two steps: The Confirmatory Factor Analysis (CFA) and the Structural Equation Modelling (SEM) test. CFA was used to analyze a statistical test for construct validity to ensure the measurement models’ reliability and validity. There are three types of goodness-of-fit indices used to assess model fit: perfect fit, parsimonious fit, and incremental fit. Each type requires the use of at least one sub-index (Asyraf & Afthanorhan, 2013). The goodness-of-fit statistic quantified the model’s fit. RMSEA, GFI, AGFI, CFI, and Chi-square/df were all used in this study. The overall measurement model indices were significant, as shown in Table 5. The chi-square/df value was 2.167 (Passed), the GFI value was 0.846 (Passed), the AGFI value was 0.806 (Passed), the CFI value was 0.925 (Passed), and the RMSEA value was 0.6. (Passed).

Table 5.

Measurement Model Indices.

Fit index	Thresholds	Value	Assessment
Chi-square/df	<3.00	2.167	Passed
GFI	>0.80	0.846	Passed
AGFI	>0.80	0.806	Passed
CFI	>0.80	0.925	Passed
RMSEA	<0.08	0.062	Passed

Convergent Validity

Convergent validity was used to assess the reflective measurement model in this study, which involves evaluating the model’s reliability and validity to ensure their consistency. Consider construct reliability to be composite reliability, reliability is a measurement model used to determine latent constructs’ robustness. In this research, factor loading, average variance extracted (AVE), and composite reliability (CR) were taken during the research analysis. These metrics assist determine the quality and trustworthiness of convergent data. Factor loading scores were utilized to represent the weight and association of each factor. To evaluate structure reliability, Cronbach’s alpha and CR values were computed. Cronbach’s alpha requires a value greater than 0.7 to indicate that the measurement model is reliable. A CR value of .60 to .70 is considered satisfactory; for more advanced research, values between .7 and .9 are reflected as satisfactory (Asyraf & Afthanorhan, 2013). According to Hair et al. (2017), the AVE value should be greater than 0.5, and the CR value should be greater than the AVE value. However, AVE values below .5 are acceptable if other convergent validity reliability criteria are met (Ayaz, & Yanartaş, 2020; Fornell & Larcker, 1981). Convergent validity demonstrated the importance of factor loading, composite reliability (CR), and average variance extracted (AVE) in Table 6. Cronbach’s alpha is (.726–.909) and CR is (.730–.909), exceeding the critical threshold. AVE, on the other hand, is between .476 and .734, and all elements except for OP and DIS earned a value above the critical threshold of 0.5. Consequently, even though the AVE value of OP and DIS factors are less than .5, it can be acceptable because the CR values of these factors are high, and the AVE values of both factors are smaller than the CR value.

Table 6.

Convergent Validity.

Constructs and scale items	Factor loading	Cronbach’s Alpha	CR	AVE
OP: Optimism		.726	.747	0.496
OP1:	0.723
OP2:	0.642
OP3:	0.744
INNO: Innovativeness		.796	.804	0.579
INNO2:	0.737
INNO3:	0.855
INNO4:	0.681
INSE: Insecurity		.882	.884	0.655
INSE1:	0.776
INSE2:	0.875
INSE3:	0.808
INSE4:	0.775
DIS: Discomfort		.728	.730	0.476
DIS2:	0.717
DIS3:	0.629
DIS4:	0.719
PU: Perceived Usefulness		.899	.890	0.670
PU1:	0.836
PU2:	0.739
PU3:	0.878
PU4:	0.814
PEOU: Perceived ease of use		.797	.827	0.619
PEOU1:	0.881
PEOU2:	0.831
PEOU3:	0.624
PS: Perceived security		.750	.891	0.734
PS1:	0.775
PS2:	0.937
PS3:	0.850
AT: Attitude		.909	.909	0.715
AT1:	0.824
AT2:	0.868
AT3:	0.822
AT4:	0.867
INT: Intention		.882	.883	0.715
INT1:	0.836
INT2:	0.844
INT3:	0.857

Each item’s factor loading value should be greater than 0.5; items with factor loading values less than 0.5 should be removed. The average variance extracted from this section should be 0.5, indicating that the degree of convergent validity is sufficient (Belhekar, 2019). However, when the composite reliability is greater than 0.06 and the average variance extracted is 0.4, the construct's validity is still sufficient (Fornell & Larcker, 1981). As shown in Table 6, each item had a factor loading greater than 0.5 and a composite reliability value greater than 0.7. Additionally, the AVE for all constructs was within a reasonable range. As a result, the construct and its items were validated for validity.

Discriminant Validity

Discriminant validity refers to a measurement construct that is distinct from other constructs in terms of its correlation with other constructs and in which the measured variable demonstrates only this single construct (Hair et al., 2017). The data in Table 7 demonstrate the model's fitness and discriminant validity result indicating that the factor loading for each item was greater than 0.5. (Cabrera-Nguyen, 2010). The AVE from each construct should be greater than the squared correlation between the construct and the other constructs to determine discriminant validity. Alternatively, the discriminant validity value is derived from the AVE square root (Asyraf & Afthanorhan, 2013).

Table 7.

Discriminant Validity Matrix.

	INT	ATT	PS	PEOU	PU	DIS	INSE	INN	OP
INT	0.846
ATT	0.714	0.846
PS	0.611	0.779	0.857
PEOU	0.644	0.774	0.769	0.787
PU	0.575	0.579	0.55	0.677	0.819
DIS	0.094	0.021	0.162	0.087	0.096	0.690
INSE	0.131	-0.038	0.079	0.039	0.213	0.604	0.810
INN	0.295	0.245	0.283	0.372	0.536	0.225	0.285	0.761
OP	0.426	0.462	0.555	0.603	0.585	0.064	0.131	0.639	0.704

Structure Equation Modelling

This research utilized CB-SEM because the objective of the current study is to apply TAM and TR theories to the smart home security adoption intention. SEM is a statistical model that defines the relationship between multiple variables (Hair et al., 2017). To ensure reliability and validity, a structural equation model was used. The path coefficients indicate the degree of influence on the dependent constructs and interpret the conceptual model (Dash & Paul, 2021). Regarding Hair, Matthews, et al. (2017), CB-SEM is employed if the existing theory requires to be checked and confirmed because the investigation is more confirmatory than exploratory. Hence, if the purpose of the research is theory testing and confirmation, then CB-SEM is the right approach (Hair, Matthews, et al., 2017). The CB-SEM is conducted using IBM Amos because it is an appropriate tool based on covariance (Hair, Matthews, et al., 2017).

The hypothesized path’s outcome is shown in Table 8 and Figure 2. A P-value supported each hypothesis expressed as a level of significance. It was demonstrated in the first hypothesis that optimism has a beneficial effect on perceived security (Standard path = 0.645, p-value .001). The outcome of H2 demonstrated that innovativeness has a beneficial effect on perceived ease of use (Standard path = 0.649, p-value .001). In H3 and H4, discomfort has a negative effect on perceived ease of use (Standard path = −0.146, p-value = .024 .05), whereas it has a positive effect on perceived usefulness (Standard path = 0.253, p-value .001). As a result of H5, insecurity has a negative effect on perceived usefulness (Standard path = −0.111, p-value = .0320.005). For H6, the result supports the hypothesis that perceived security has a beneficial effect on attention (Standard path = .261, p-value .001). In H7 and H8, the results indicated that perceived ease of use has a positive effect on perceived usefulness (standard path = 0.682, p-value .001) and attitude (standard path = .347, p-value .001). Perceived usefulness has a positive relationship with intention to purchase; the result indicates that H9 is supported (standard path = 0.253, p-value .001). The final hypothesis, as a result of the results, established that attitude is positively related to purchase intention (standard path = 0.74, p-value .001).

Table 8.

Hypothesized Paths.

Hypothesis	Endogenous Variable	Exogenous Variable	Standard path	P-value	Results
H1	Optimism	Perceived security	0.645	***	Supported
H2	Innovativeness	Perceived ease of use	0.649	***	Supported
H3	Discomfort	Perceived ease of use	−0.146	*.024	Supported
H4	Discomfort	Perceived usefulness	0.253	***	Supported
H5	Insecurity	Perceived usefulness	−0.111	*.032	Supported
H6	Perceived security	Attitude	0.261	***	Supported
H7	Perceived ease of use	Attitude	0.347	***	Supported
H8	Perceived ease of use	Perceived usefulness	0.682	***	Supported
H9	Perceived usefulness	Intention of using	0.253	***	Supported
H10	Attitude	Intention of using	0.74	***	Supported

Note. p-value <.001 = ***, p-value < .01 = **, p-value < .05 = .*

Figure 2.

Structure equation modelling.

Discussion and Conclusion

The purpose of this research is to ascertain the possible factors that influence consumers’ intentions to use smart home security. TR and TAM are extensive concept models for the subject of technology adoption based on existing research (Sophonthummapharn & Tesar, 2007). As a result, these two concepts were applied to the study's fundamental model. Additionally, perceived security was incorporated into the concept model to aid in investigating the research's key factors. The above theoretical model was used to develop the hypothesis in this study. Hence, this study conducted an analysis of individuals’ intention to use smart home security by applying TAM and TR in order to gain a better understanding of how individuals’ views about technology influence their behavioral intentions. The current study’s conclusions provide both theoretical and practical implications.

In TR theory, there are two favorable inhibitors: namely optimism (OP) and innovativeness (INN). To begin, optimism is positively related to perceived security. This finding is consistent with prior research (Ferreira et al., 2014), indicating that optimistic individuals have a positive attitude and are comfortable bringing technology into their daily lives to facilitate and accomplish their goals such as their quality of life in terms of safety. Additionally, they may perceive their security as more approachable. Also, this result is in line with Kim et al. (2020) that customers who are optimistic would improve the connection between perceived performance and attitude. Therefore, it is crucial for providers of smart home security technology to focus more on disseminating sufficient information to provide customers with knowledge and a positive attitude and perception that smart home security technology would provide additional benefits, including perceived security. Based on the respondents in this research, they have a positive perception of new and advanced technology, and they are confident on the features about the security and convenience it offers compared to the current practice.

Second, there is a positive correlation between innovativeness and perceived ease of use. This hypothesis supports the previous review of the literature (Caison et al., 2008). Individuals desire to use new technology because it appears to be more convenient (Nugroho & Andryzal Fajar, 2017). Hence, innovative consumers enjoy the pleasure of investigating smart home security solutions and have positive easy-using perceptions of smart home security technologies. Therefore, it is essential to approach groups of people with a high preference for innovation with up-to-date smart home security technology information. In the meanwhile, it would be effective to illustrate the simple processes application of smart home security systems to encourage customer innovation.

In TR theory, there are two unfavorable inhibitors: namely discomfort (DIS) and Insecurity (INSE). Results from the current study found that discomfort has a significant negative effect on perceived ease of use. On the other hand, the relationship between discomfort and perceived usefulness is positive. Discomfort can be considered a barrier to technological readiness (Blut & Wang, 2019) and dissuades consumers from developing a favorable attitude toward technology-based smart home security and diminishes their perceived ease of use (Ali et al., 2019). The considerable link between discomfort and perceived ease of use in this study suggests that persons with a higher level of discomfort toward technology may not find smart home security solutions easy to use. Even though they have less trust in their ability to control it, individuals with a high level of discomfort may find smart home security useful. Users may recognize the use of the technology since they understand the benefits of intelligent home security solutions. In the instance of home security equipment, this conclusion offers insight to research on technology adoption by suggesting that users may still regard the device as useful even if they lose control over it.

Next, insecurity is the degree to which people with a higher risk perception have doubts about the technology’s operation (Nugroho & Andryzal Fajar, 2017). The result of this study found that insecurity negatively affects perceived usefulness. This result is in line with the study from Pillai et al. (2020). Many consumers are unfamiliar with smart home security solutions, so they may feel insecure when using them for the first time. New users may be apprehensive about insecurity, which may reduce their likelihood of perceiving the usefulness of smart home security technology. Later on, this study found that perceived security positively affects attitudes toward smart security technology. Perceived security plays an intriguing role in consumers' attitudes and intentions, as it inspires consumers’ confidence in the success of all transaction procedures on security protocols. Therefore, the consumers may translate it into trusting the system and have a positive attitude which leads to willingness to use the smart home security system. This study result conforms with previous studies in the area of technology adoption such as mobile food delivery applications (Belanche et al., 2020), mobile wallet (Chawla, & Joshi, 2019), mobile fintech payment services (Lim et al., 2019). According to Batalla and Gonciarz (2019), a concerned person with perceived risk related to their safety has a high chance of using the smart home application to solve their problem. It is consistent with the previous literature (Mahrous et al., 2018), perceived risk influences an individual’s perceived security.

Later, this study confirms that the ease with which smart home security can be used is sufficient to elicit consumer attitudes towards the technologies, which in line with Buyle et al. (2018). Additionally, this study is also in line with previous studies in current technologies such as smart shops (Chang & Chen, 2021), and mobile payment (Li et al., 2019) that perceived ease of use positively affects perceived usefulness as well as attitude towards using specific technologies. Also, this study demonstrated that consumers perceive smart home security to be beneficial in terms of enhancing their safety, in line with Diop et al. (2019) that perceived usefulness is the primary predictor of a consumer's intention to use new technology. As a result, this study confirms a significant relationship between perceived usefulness and intention to use smart home security technologies. Finally, attitudes strongly correlate with the intention to use smart home security. Consumers who have a favorable attitude toward a product/service positively correlate with their intention to use that product or service (Ali et al., 2019). This study demonstrated that perceived ease of use and perceived usefulness have a substantial impact on consumers’ attitudes and intentions to adopt smart home security technologies, indicates that when the smart home security technology is easy to use and the users recognize the technology's considerable benefits for them, they tend to have a positive attitude toward its use and are more inclined to employ it.

Technology becomes a potential assistant tool for humans to improve their quality of life, and most respondents in this study are between the ages of 20 and 35 living in urban area. This group is regarded as having a high degree of digital-native. As a result, they understand how to use smart home security. This finding is consistent with previous research (Ali et al., 2019), indicating that innovativeness is typically associated with a favorable attitude toward technology among residents who have a favorable perception of technology based on smart home security and a favorable attitude toward consuming such products. The demographic characteristics of our respondents certainly shaped their perceived ease of use and usefulness, as they are primarily young adults living in urban regions with high digital proficiency. This group may possess a superior foundational comprehension of technology and exhibit more ease in assimilating smart devices into their lives compared to older populations or individuals in less urbanized regions, both within Thailand and in foreign nations with varying degrees of digital penetration.

Moreover, the main respondents are the Thai population who present the cultural value by Hofstede et al. (2005) to be a high cultural dimension in collectivism and power distance which suggested that users tend to adopt the technology based on subjective norms and unified ingroups, which throughout people’s lifetimes continue to guard them for unquestioning loyalty. This result may imply that in collectivist societies, social norms and the perspectives of close-knit groups exert significant impact. The findings suggest that when one’s social networks endorse or embrace these technologies, it enhances their perceived usefulness and ease of use. On the other hand, Thailand has a high value for avoiding uncertainty, clear rules, and a formal way of living. Therefore, it can be explained that it is likely for them to try to avoid and reduce risks by employing the tools for their security (Phonthanukitithaworn et al., 2015). This implies that countries or societies that prioritize avoiding uncertainty likely reinforce the positive associations between people's sense of security, their perception of the usefulness of smart home security systems, and their intention to implement them. The inclination to mitigate potential risks corresponds effectively with the perceived usefulness of smart home security systems. Conversely, cultures characterized by reduced uncertainty avoidance may demonstrate increased tolerance for ambiguity and reflect diminished concern for security in the adoption of new technology. Consequently, cultures that emphasize privacy may exhibit reluctance in embracing smart home security systems, particularly if they recognize possible threats to personal data. In addition, cultural differences in trust toward technology may additionally affect adoption rates. Societies exhibiting greater trust in technology are more inclined to embrace smart home security systems.

This study enhances the current body of knowledge by using TAM and TR theories to the field of smart home security. The inclusion of perceived security as a mediating variable between TR and TAM provides a more nuanced comprehension of how consumer perceptions of safety and security impact their decisions to adopt. This study also highlights discomfort and fear as factors that obstruct TR, offering vital insights into the psychological obstacles that could hamper its adoption. Furthermore, the study illustrates the correlation between perceived security and positive attitude toward the technology, emphasizing the significance of addressing customer apprehensions around safety and privacy. Lastly, this study enhances comprehension of individual variances in technology adoption by analyzing the significance of optimism and innovativeness in TR. This research makes a significant contribution to the existing body of knowledge by presenting a framework that provides understanding on how consumers adopt smart home security systems.

Implication

Smart home security has dominant functions and features to ensure the safety of people who need protection. Consumers probably seek tools for providing more safety. Besides, security systems and personal insurance are the priority factors that all new users would consider installing smart home security systems. Moreover, the superior quality of product experiment and implementation confirmed that the smart home system product is not harmful to customers regarding personal information data used in the system. Based on the research findings, it is recommended that individuals’ characteristics of technology readiness, such as optimism and innovativeness, may enable consumers to have a positive attitude toward and perceived convenience with smart home security. It would affect their willingness to adopt smart home security in their daily lives. Additionally, this study demonstrated that perceived ease of use, perceived usefulness, and perceived security all contribute to an increase in the attitude and intention to use smart home security. This knowledge could assist businesses in improving their products.

Moreover, marketers and companies in the smart home security market may place a greater emphasis on optimists to contribute to their markets or sales, such as increasing demand for smart home security and increasing consumer awareness of its benefits. The companies involved with these products should emphasize this point and promote the smart home security market. To avoid and minimize discomfort associated with smart home security, marketers may develop awareness or training programs to make consumers more comfortable with smart home technology. However, despite their discomfort, people have a favorable attitude toward perceived usefulness. Smart home security provides numerous benefits, including increased safety in the home environment (Hoque & Davidson, 2019) and systems that enable consumers to maximize their utility efficiency. Thus, among the discomfort group, the advantage of smart home security is that it maintains the consumer’s perception of usefulness. In conclusion, the various aspects from a discomfort perspective have both negative and positive effects. Although discomfort is a deterrent to technology readiness, smart home security successfully got into the discomfort segment. To expand the market segment, the marketer or company may create additional images and awareness to absorb to capture the entire segment quietly. In addition to the benefit, economic situations and economic inequality can also affect consumers’ perceptions of the benefit of smart home security. During periods of economic instability, customers may prioritize essential requirements over non-essential expenditures on technology. Economic inequalities can result in a digital divide and obstruct individuals’ capacity to adopt smart home security. This is due to the cost of devices and installation.

The research findings offer valuable recommendations for practitioners. In terms of product creation, businesses should prioritize the creation of user-friendly interfaces in product design to reduce learning curves. The application with visual indicators and explicit directives can improve user interface accessibility. Furthermore, interoperability is essential for enhancing users’ favorable perceptions. Consequently, firms ought to build products that are interoperable with various devices and platforms to improve convenience. In terms of marketing practices, marketers may launch campaigns to raise awareness and emphasize the importance of refocusing people’s attention away from insecurity and toward smart home security. Businesses and marketing teams may invest in educational media aimed at enlightening consumers regarding the advantages of smart home security systems, correcting misunderstandings, and mitigating potential worries. By demonstrating that a manual and training program are viable options for explaining and guaranteeing that smart home security systems are beneficial and user-friendly, consumers will be more receptive. These materials can be a part of customer service section, to enhance customer support service at the same time. Lastly, the conclusion implies that businesses that provide smart home security systems must ensure that users’ and consumers’ expectations for secure information and transactions are met.

Policy-making should prioritize the enhancement of uniformity within the industry to ensure consumer trust and boost confidence. Furthermore, consistent with other information technology devices, the educational resources and public awareness initiatives offered by IT policy-making institutions to improve public knowledge should be augmented about cybersecurity. The IT policy-making organization can provide a regulatory framework that integrates innovation with consumer protection, addressing concerns such as data privacy, cybersecurity, and emergency response.

Key findings from the current study are as follows. First, perceived security plays a crucial role in influencing consumer attitudes toward smart home security. Also, TAM constructs, including perceived ease of use and perceived usefulness, significantly impact consumer attitudes and intentions. Lastly, TR factors, optimism and innovativeness, positively influence adoption, while discomfort and insecurity hinder it. From the mentioned key findings, there are suggestions for implications as follows. First, marketing and product development departments could emphasize the security benefits of smart home systems to address consumer concerns while design user-friendly interfaces to enhance perceived ease of use. They could also highlight the practical benefits of smart home security to increase perceived usefulness and to target consumers with high levels of optimism and innovativeness. Policy and regulation makers could develop clear guidelines and regulations to address privacy and security concerns. To conclude, this study provides insights into the factors influencing consumer adoption of smart home security systems.

The implications should be emphasized to technology developers to increase technology adaptation. This can be achieved by considering a user-friendly interface, simplified installation, regular feature updates, and cross-device compatibility. Moreover, for policymakers, an essential factor to consider is a data privacy policy to ensure the protection of user data recording. Only the account owner should have access to this information. Lastly, users can also create a community to share their experiences and the usefulness of the smart home security system, which can foster security awareness.

Limitation and Future Research

While the preceding section discusses several implications of this research, several limitations persist. The questionnaire survey was conducted exclusively in Thailand by the sample group. Considering the specific demographic focus, where the majority of respondents are aged between 20 and 35 and reside in urban areas and are likely to be comfortable and familiar with technological adaptation, perceptions of younger individuals might differ from those of older age groups. The researcher suggests further studies to incorporate a multigroup analysis based on demographic factors such as generation and living areas.

However, the external factors influencing consumer behavior such as social class, cultures, sub-cultures, or reference groups have not been studied. Future studies can focus more on the influence of external influences to provide more insights. Further research should be conducted to examine the external variables and cultural aspects. Socioeconomic factors such as income level, education, and age might exert an influence on adoption rates. Furthermore, customer behavior can be influenced by cultural values and attitudes towards technology, while consumer trust can be affected by cultural norms concerning privacy. Additional investigation could involve longitudinal studies to monitor changes in consumer attitudes and behavior over an extended period. Furthermore, it can include cross-cultural analyses to understand the impact of cultural foundations on adoption movements. Also, this study exclusively examines the context of Thailand. Subsequent research may apply the study in several nations or compare findings across diverse cultural contexts. Lastly, future study might arrange the study of emerging technologies to proactively predict upcoming trends and issues in the field of smart home security.

Footnotes

Acknowledgements

The researchers would like to acknowledge all of the anonymous respondents who took the time to complete questionnaires, as well as their colleagues who helped them in completing this study.

ORCID iDs

Nathathenee Gebsombut

Phaninee Naruetharadhol

Lakkana Hengboriboon

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

References

Abdullah

T. A.

Ali

Malebary

Ahmed

A. A.

(2019). A review of cyber security challenges, attacks and solutions for Internet of Things based smart home. International Journal of Computer Science and Network Security, 19(9), 139–147.

Aburbeian

A. M.

Owda

A. Y.

Owda

(2022). A technology acceptance model survey of the metaverse prospects. AI, 3(2), 285–302.

Acikgoz

Vega

R. P.

(2022). The role of privacy cynicism in consumer habits with voice assistants: a technology acceptance model perspective. International Journal of Human–Computer Interaction, 38(12), 1138–1152.

Aggarwal

Rahul

(2018). The effect of perceived security on consumer purchase intensions in electronic commerce. International Journal of Public Sector Performance Management, 4(1), 1–20. https://doi.org/10.1504/IJPSPM.2018.10009501

Ahmed

R. R.

Vveinhardt

Streimikiene

Pilinkienė

(2019). Application of the theory of planned behaviour model for examining customers’ intentions towards islamic hire purchase financing. Engineering Economics, 30(2), 236–245. https://doi.org/10.5755/j01.ee.30.2.21589

Al-ajam

A. S.

(2013). Challenges of adoption of internet banking service in Yemen. International Journal of Bank Marketing, 31(5), 317–333. https://doi.org/10.1108/IJBM-01-2013-0001

Al-Emran

Al-Maroof

Al-Sharafi

M. A.

Arpaci

(2022). What impacts learning with wearables? An integrated theoretical model. Interactive Learning Environments, 30(10), 1897–1917.

Alanazi

Soh

(2019). Behavioral intention to use IoT technology in healthcare settings. Engineering, Technology & Applied Science Research, 9(5), 4712–4717.

Alarafee

Sallabi

O. M.

Altriki

Maatuk

A. M.

(2022, July). A framework for exploring factors affecting the usability of electronic payment systems. In 2022 International Conference on Engineering & MIS (ICEMIS) (pp. 1–6). IEEE.

10.

Aldossari

M. Q.

Sidorova

(2020). Consumer acceptance of Internet of Things (IoT): Smart home context. Journal of Computer Information Systems, 60(6), 507–517.

11.

Ali

Ullah

Akbar

Akhtar

Zahid

(2019). Determinants of consumer intentions to purchase energy-saving household products in Pakistan. Sustainability, 11(5), 1–20. https://doi.org/10.3390/su11051462

12.

Alhammadi

Marashdeh

Hussain

(2023). Assessing the effect of innovation diffusion and technology readiness theories on attitude, behavioral intention and implementation of smart learning. Cross Cultural & Strategic Management, 30(4), 820–843.

13.

Aslam

Ham

Arif

(2017). Consumer behavioral intentions towards mobile payment services: An empirical analysis in Pakistan. Market-Tržište, 29(2), 161–176. https://doi.org/10.22598/mt/2017.29.2.161

14.

Asyraf

W. M.

Afthanorhan

B. W.

(2013). A comparison of partial least square structural equation modeling (PLS-SEM) and covariance based structural equation modeling (CB-SEM) for confirmatory factor analysis. International Journal of Engineering Science and Innovative Technology (IJESIT), 2(5), 198–205.

15.

Ayaz

Yanartaş

(2020). An analysis on the unified theory of acceptance and use of technology theory (UTAUT): Acceptance of electronic document management system (EDMS). Computers in Human Behavior Reports, 2, 100032.

16.

Batalla

J. M.

Gonciarz

(2019). Deployment of smart home management system at the edge: mechanisms and protocols. Neural Computing and Applications, 31(5), 1301–1315. https://doi.org/10.1007/s00521-018-3545-7

17.

Baudier

Ammi

Deboeuf-Rouchon

(2020). Smart home: Highly-educated students’ acceptance. Technological Forecasting and Social Change, 153, 119355.

18.

Belanche

Flavian

Perez-Rueda

(2020). Mobile apps use and WOM in the food delivery sector: the role of planned behavior, perceived security and customer lifestyle compatibility. Sustainability, 12(10), 4275.

19.

Belhekar

V. M.

(2019). Factor analysis and structural equation modeling. In Statistics for psychology using R (pp. 314–361). SAGE Publications. https://doi.org/10.4135/9789353282493.n10

20.

Bentler

P. M.

Chou

C. P.

(1987). Practical issues in structural modeling. Sociological methods & research, 16(1), 78–117.

21.

Bentler

P. M.

Weeks

D. G.

(1980). Linear structural equations with latent variables. Psychometrika, 45(3), 289–308. https://doi.org/10.1007/BF02293905

22.

Bhavani

A. N.

Jami

T. U.

Ashok

G. A.

(2016). Low cost smart security camera with night vision capability using raspberry Pi and PIR sensor. International Journal of Advanced Technology and Innovative Research (IJATIR), 8(21), 4053-4056.

23.

Blut

Wang

. (2020). Technology readiness: A meta-analysis of conceptualisations of the construct and its impact on technology usage. Journal of the Academy of Marketing Science, 48(4), 649–669. https://doi.org/10.1007/s11747-019-00680-8

24.

Buyle

Van Compernolle

Vlassenroot

Vanlishout

Mechant

Mannens

(2018). “Technology readiness and acceptance model” as a predictor for the use intention of data standards in smart cities. Media and Communication, 6, 127–139. https://doi.org/10.17645/mac.v6i4.1679

25.

Cabrera-Nguyen

(2010). Author guidelines for reporting scale development and validation results in the Journal of the Society for Social Work and Research. Journal of the Society for Social Work and Research, 1, 99–103. https://doi.org/10.5243/jsswr.2010.8

26.

Caison

Bulman

Pai

Neville

(2008). Exploring the technology readiness of nursing and medical students at a Canadian University. Journal of Interprofessional Care, 22(3), 283–294. https://doi.org/10.1080/13561820802061809

27.

Cannizzaro

Procter

Maple

(2020). Trust in the smart home: Findings from a nationally representative survey in the UK. PLoS One, 15(5), e0231615.

28.

Cao

Erdt

Robert

Naharudin

N. B.

Lee

S. Q.

Theng

Y. L.

(2022). Decision-making factors toward the adoption of smart home sensors by older adults in Singapore: mixed methods study. JMIR Aging, 5(2), e34239.

29.

Chang

Y. W.

Chen

(2021). What motivates customers to shop in smart shops? The impacts of smart technology and technology readiness. Journal of Retailing and Consumer Services, 58, 102325.

30.

Chaudhari

Gilbile

Bhosale

Chavan

Wakhare

(2020, September). Smart doorbell security system using IoT. In International conference on sciences and technology (No. 4228). International Journal of Scientific Research in Science and Technology by IJSRST

31.

Chawla

Joshi

(2019). Consumer attitude and intention to adopt mobile wallet in India–An empirical study. International Journal of Bank Marketing, 37(7), 1590–1618.

32.

Chin

Jiang

B. C.

Mufidah

Persada

S. F.

Noer

B. A.

(2018). The investigation of consumers’ behavior intention in using green skincare products: A pro-environmental behavior model approach. Sustainability, 10(11), 3922. https://doi.org/10.3390/su10113922

33.

Chitnis

Deshpande

Shaligram

(2016). An investigative study for smart home security: Issues, challenges and countermeasures. Wireless Sensor Network, 8(04), 61. https://doi.org/10.4236/wsn.2016.84006

34.

Cho

Son

(2019). The effect of social connectedness on consumer adoption of social commerce in apparel shopping. Fashion and Textiles, 6(1), 1–17. https://doi.org/10.1186/s40691-019-0171-7

35.

Collins

S. E.

Witkiewitz

Larimer

M. E.

(2011). The theory of planned behavior as a predictor of growth in risky college drinking. Journal of Studies on Alcohol and Drugs, 72(2), 322–332. https://doi.org/10.15288/jsad.2011.72.322

36.

Dash

Paul

(2021). CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting. Technological Forecasting and Social Change, 173, 121092.

37.

Davis

F. D.

Bagozzi

R. P.

Warshaw

P. R.

(1989a). Technology acceptance model. Journal of Management Science, 35(8), 982–1003

38.

Davis

F. D.

Bagozzi

R. P.

Warshaw

P. R.

(1989b). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982

39.

Diop

E. B.

Zhao

Duy

T. Van

. (2019). An extension of the technology acceptance model for understanding travelers’ adoption of variable message signs. PLoS ONE, 14(4), 1–17. https://doi.org/10.1371/journal.pone.0216007

40.

Ferreira

J. B.

da Rocha

da Silva

J. F.

(2014). Impacts of technology readiness on emotions and cognition in Brazil. Journal of Business Research, 67(5), 865–873. https://doi.org/10.1016/j.jbusres.2013.07.005

41.

Finstad

(2010). Response interpolation and scale sensitivity: Evidence against 5-point scales. Journal of Usability Studies, 5(3), 104–110.

42.

Flavián

Pérez-Rueda

Belanche

Casaló

L. V.

(2022). Intention to use analytical artificial intelligence (AI) in services – The effect of technology readiness and awareness. Journal of Service Management, 33(2), 293–320. https://doi.org/10.1108/JOSM-10-2020-0378

43.

Fornell

Larcker

D. F.

(1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312

44.

Fraenkel

J. R.

Wallen

N. E.

Hyun

H. H.

(2019). How to design and evaluate research in education (10th ed.). McGraw-Hill Education

45.

George

J. F.

Chen

Yuan

(2021). Intent to purchase IoT home security devices: Fear vs privacy. PLoS ONE, 16(9), e0257601.

46.

Global News Wire. (2023). Connected home security market surges to US$ 52.52 billion, with continued growth forecasted through 2033. Global News Wire.

47.

Ghorayeb

Comber

Gooberman-Hill

(2021). Older adults' perspectives of smart home technology: Are we developing the technology that older people want?. International journal of human-Computer Studies, 147, 102571.

48.

Khan

I. U.

Khan

S. U.

(2019). Understanding mobile tourism shopping in Pakistan: An integrating framework of innovation diffusion theory and technology acceptance model. Mobile Information Systems, 2019, Article 1490617. https://doi.org/10.1155/2019/1490617

49.

Hair

J. F.

Jr. Matthews

L. M.

Matthews

R. L.

Sarstedt

(2017). PLS-SEM or CB-SEM: updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107–123.

50.

Hair

J. F.

William

Black

B. J.

Babin

R. E.

Anderson

R. E.

(2017). Multivariate data analysis (MVDA). In Nash

R. A.

Wachter

(Eds.), Pharmaceutical quality by design: A practical approach (pp. 263–309). Wiley. https://doi.org/10.1002/9781118895238.ch8

51.

Hameed

Nigam

(2023). Exploring India’s generation Z perspective on AI enabled internet banking services. Foresight, 25(2), 287–302.

52.

Hammi

Zeadally

Khatoun

Nebhen

(2022). Survey on smart homes: Vulnerabilities, risks, and countermeasures. Computers & Security, 117, 102677.

53.

Heidari

Navimipour

N. J.

Unal

(2022). Applications of ML/DL in the management of smart cities and societies based on new trends in information technologies: A systematic literature review. Sustainable Cities and Society, 81, 104089.

54.

Hofstede

G. J.

Minkov

. (2005). Cultures and organizations: Software of the mind. McGraw-Hill. .

55.

Hoque

M. A.

Davidson

(2019). Design and implementation of an IoT-based smart home security system. International Journal of Networked and Distributed Computing, 7(2), 85–92. https://doi.org/10.2991/ijndc.k.190326.004

56.

Hua

Wang

(2019). Antecedents of consumers' intention to purchase energy-efficient appliances: An empirical study based on the technology acceptance model and theory of planned behavior. Sustainability, 11(10), 2994. https://doi.org/10.3390/su11102994

57.

Hubert

Blut

Brock

Zhang

R. W.

Koch

Riedl

(2018). The influence of acceptance and adoption drivers on smart home usage. European Journal of Marketing, 53(6), 1073–1098. https://doi.org/10.1108/EJM-12-2016-0794

58.

Hussin

S. F.

Abdollah

M. F.

Ahmad

I. B.

(2023). Acceptance of IoT technology for smart homes: A systematic literature review. In Selamat

M. H.

Abdullah

A. H.

Noor

N. S. M.

Yuhaniz

S. S.

(Eds.), Proceedings of the International Conference on Information Systems and Intelligent Applications (pp. 187–202). Springer. https://doi.org/10.1007/978-3-031-27064-6_15

59.

Jarrar

Awobamise

A. O.

Sellos

P. S.

(2020). Technological readiness index (TRI) and the intention to use smartphone apps for tourism: A focus on Indubai mobile tourism app. International Journal of Data and Network Science, 4(3), 297–304.

60.

Jeng

M. Y.

Pai

F. Y.

Yeh

T. M.

(2022). Antecedents for older adults’ intention to use smart health wearable devices-technology anxiety as a moderator. Behavioral Sciences, 12(4), 114.

61.

Kampa

R. K.

(2023). Combining technology readiness and acceptance model for investigating the acceptance of m-learning in higher education in India. Asian Association of Open Universities Journal, 18(1), 43–65. https://doi.org/10.1108/AAOUJ-07-2022-0076

62.

Kim

J. J.

Lee

M. J.

Han

(2020). Smart hotels and sustainable consumer behavior: Testing the effect of perceived performance, attitude, and technology readiness on word-of-mouth. International Journal of Environmental Research and Public Health, 17(20), 7455.

63.

Kodali

R. K.

Jain

Bose

Boppana

(2017). IoT based smart security and home automation system. Proceeding - IEEE International Conference on Computing, Communication and Automation, ICCCA 2016, 1286–1289. https://doi.org/10.1109/CCAA.2016.7813916

64.

Lai

Y. L.

Lee

(2020). Integration of technology readiness index (TRI) into the technology acceptance model (TAM) for explaining behavior in adoption of BIM. Asian Education Studies, 5(2), 10.

65.

Jin

Wang

(2019). Influence of environmental concern and knowledge on households’ willingness to purchase energy-efficient appliances: A case study in Shanxi, China. Sustainability, 11(4), 1–18. https://doi.org/10.3390/su11041073

66.

Wang

Wangh

Zhou

(2019). Mobile payment with alipay: An application of extended technology acceptance model. IEEE Access, 7, 50380–50387.

67.

(2013). A novel Likert scale based on fuzzy sets theory. Expert Systems with Applications, 40(5), 1609–1618. https://doi.org/10.1016/J.ESWA.2012.09.015

68.

Yigitcanlar

Erol

Liu

(2021). Motivations, barriers and risks of smart home adoption: From systematic literature review to conceptual framework. Energy Research & Social Science, 80, 102211.

69.

Lim

S. H.

Kim

D. J.

Hur

Park

(2019). An empirical study of the impacts of perceived security and knowledge on continuous intention to use mobile fintech payment services. International Journal of Human–Computer Interaction, 35(10), 886–898.

70.

Laksamana

Suharyanto

Cahaya

Y. F.

(2023). Determining factors of continuance intention in mobile payment: fintech industry perspective. Asia Pacific Journal of Marketing and Logistics, 35(7), 1699–1718. https://doi.org/10.1108/APJML-11-2021-0851

71.

Leung

Chen

(2019). E-health/m-health adoption and lifestyle improvements: Exploring the roles of technology readiness, the expectation-confirmation model, and health-related information activities. Telecommunications Policy, 43(6), 563–575.

72.

Mahrous

A. M.

Moustafa

Y. M.

Abou El-Ela

M. A.

(2018). Physical characteristics and perceived security in urban parks: Investigation in the Egyptian context. Ain Shams Engineering Journal, 9(4), 3055–3066. https://doi.org/10.1016/j.asej.2018.07.003

73.

Maswadi

Ghani

N. A.

Hamid

(2022). Factors influencing the elderly’s behavioural intention to use smart home technologies in Saudi Arabia. PLoS ONE, 17(8), e0272525.

74.

Mulcahy

Letheren

McAndrew

Glavas

Russell-Bennett

(2022). Are households ready to engage with smart home technology? In Letheren

Mulcahy

Glavas

(Eds.), The role of smart technologies in decision making (pp. 4–33). Routledge.

75.

Munyoka

Maharaj

M. S.

(2019). Privacy, security, trust, risk and optimism bias in e-government use: The case of two Southern African Development Community countries. South African Journal of Information Management, 21(1), a983. https://doi.org/10.4102/sajim.v21i1.983

76.

Nikou

(2019). Factors driving the adoption of smart home technology: An empirical assessment. Telematics and Informatics, 45, 101283.

77.

Nouri

B. A.

Soltani

(2019). A hybrid model of factors affecting adoption of mobile banking technology between customers of Iranian banks. Serbian Journal of Management, 14(1), 49–75. https://doi.org/10.5937/sjm14-13429

78.

Nugroho

M. A.

Andryzal Fajar

(2017). Effects of technology readiness towards acceptance of mandatory web-based attendance system. Procedia Computer Science, 124, 319–328. https://doi.org/10.1016/j.procs.2017.12.161

79.

Nystrand

B. T.

Olsen

S. O.

(2020). Consumers’ attitudes and intentions toward consuming functional foods in Norway. Food Quality and Preference, 80, 103827. https://doi.org/10.1016/j.foodqual.2019.103827

80.

Ong

C. S.

Lin

Y. L.

(2015). Security, risk, and trust in individuals' internet banking adoption: An integrated model. International Journal of Electronic Commerce Studies, 6(2), 343–356. https://doi.org/10.7903/ijecs.1428

81.

Oukes

Blauw

van Bon

A. C.

DeVries

J. H.

von Raesfeld

A. M.

(2019). Acceptance of the artificial pancreas: Comparing the effect of technology readiness, product characteristics, and social influence between invited and self-selected respondents. Journal of Diabetes Science and Technology, 13(6), 1105–1113. https://doi.org/10.1177/1932296818823728

82.

Oyman

Bal

Ozer

(2022). Extending the technology acceptance model to explain how perceived augmented reality affects consumers’ perceptions. Computers in Human Behavior, 128, 107127.

83.

Parasuraman

(2000). Technology readiness index (TRI) a multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307–320.

84.

Pillai

Sivathanu

Dwivedi

Y. K.

(2020). Shopping intention at AI-powered automated retail stores (AIPARS). Journal of Retailing and Consumer Services, 57, 102207.

85.

Philip

S. J.

Luu

T. J.

Carte

(2023). There’s No place like home: Understanding users’ intentions toward securing internet-of-things (IoT) smart home networks. Computers in Human Behavior, 139, 107551.

86.

Phonthanukitithaworn

Sellitto

Fong

M. W. L.

(2015). User intentions to adopt mobile payment services: A study of early adopters in Thailand. Journal of Internet Banking and Commerce, 20(1), 1–19.

87.

Pradhan

M. K.

Lee

(2018). Understanding travelers' behavior for sustainable smart tourism: A technology readiness perspective. Sustainability, 10(11), 1–20. https://doi.org/10.3390/su10114259

88.

Rafdinal

Senalasari

(2021). Predicting the adoption of mobile payment applications during the COVID-19 pandemic. International Journal of Bank Marketing, 39(6), 984–1002.

89.

Roscoe

J. T.

(1975). Fundamental research statistics for the behavioral sciences. Holt, Rinehart and Winston.

90.

Salisbury

W. D.

Pearson

R. A.

Pearson

A. W.

Miller

D. W.

(2001). Perceived security and World Wide Web purchase intention. Industrial Management & Data Systems, 101(4), 165–177.

91.

Sánchez

R. A.

Hueros

A. D.

(2010). Motivational factors that influence the acceptance of Moodle using TAM. Computers in Human Behavior, 26(6), 1632–1640. https://doi.org/10.1016/j.chb.2010.06.011

92.

Sophonthummapharn

Tesar

(2007). Technology readiness and propensity of cell phone users to subscribe to commercial messaning services. Marketing Management Journal, 17(2), 81–95.

93.

Sovacool

B. K.

Del Rio

D. D. F.

(2020). Smart home technologies in Europe: A critical review of concepts, benefits, risks and policies. Renewable and Sustainable Energy Reviews, 120, 109663.

94.

Sreelakshmy

Kumar

A. K.

Titus

Hemajothi

Venkatesh

Lavanya

(2023, May). Design and development of an ai based intelligent door for home security system. In 2023 International conference on advances in computing, communication and applied informatics (ACCAI) (pp. 1–8). IEEE.

95.

Srinivas

Swami Das

Malathi Latha

Y. L.

(2021). Future smart home appliances using IoT. In Innovations in computer science and engineering: proceedings of 8th ICICSE (pp. 143–151). Springer Singapore.

96.

Suhartanto

Gan

Sarah

I.S.

Setiawan

(2020). Loyalty towards Islamic banking: Service quality, emotional or religious driven?. Journal of Islamic Marketing, 11(1), 66–80, https://doi.org/10.1108/JIMA-01-2018-0007.

97.

Surantha

Wicaksono

W. R.

(2018). Design of smart home security system using object recognition and PIR. Procedia Computer Science, 135, 465–472. https://doi.org/10.1016/j.procs.2018.08.198

98.

Tommasetti

Singer

Troisi

Maione

(2018). Extended Theory of planned behavior (ETPB): Investigating customers' perception of restaurants’ sustainability by testing a structural equation model. Sustainability, 10(7), 1–21. https://doi.org/10.3390/su10072580

99.

Touqeer

Zaman

Amin

Hussain

l-Turjman

Bilal

(2021). Smart home security: challenges, issues and solutions at different IoT layers. The Journal of Supercomputing, 77(12), 14053-14089.

100.

Ullah

Alnumay

W. S.

Al-Rahmi

W. M.

Alzahrani

A. I.

Al-Samarraie

(2020). Modeling cost saving and innovativeness for blockchain technology adoption by energy management. Energies, 13(18), 4783.

101.

Wong

K. T.

bt Osman

Goh

P. S. C.

Rahmat

M. K.

(2013). Understanding student teachers' behavioural intention to use technology: Technology acceptance model (TAM) validation and testing. International Journal of Instruction, 6(1), 89–104.

102.

World Bank. (2023). Urban development. The World Bank. https://www.worldbank.org/en/topic/urbandevelopment/overview

103.

Zhang

Bao

Zhang

Xiang

(2019). A SEM-neural network approach to predict customers' intention to purchase battery electric vehicles in China’s Zhejiang Province. Sustainability, 11(11). https://doi.org/10.3390/su11113164

104.

Yang

Yan

Wang

Xue

(2022). Flow experiences and virtual tourism: The role of technological acceptance and technological readiness. Sustainability, 14(9), 1–17.

105.

Lai Tung

Lai

(2003). Incorporating technology readiness (TR) into TAM: Are individual traits important to understand technology acceptance? In DIGIT 2003 Proceedings (Paper 2). .

106.

Zhang

Luximon

Song

(2019). The role of consumers’ perceived security, perceived control, interface design features, and conscientiousness in continuous use of mobile payment services. Sustainability, 11(23), 6843.

107.

Zhang

Liu

(2022). How consumers’ adopting intentions towards eco-friendly smart home services are shaped? An extended technology acceptance model. The Annals of Regional Science, 69(2), 351–374.

108.

Zheng

Shan

(2021). Research on the differences in the intention of use of the elderly to different smart home-based elderly care technology based on TAM model. International Journal of Frontiers in Sociology, 3(18), 1–6.