What Factors Do Affect the Adoption of Internet of Things (Smart Home) in Indonesia
Novia Dwi Andini1*, Karto Adiwijaya2
1 Master of Management School, Faculty of Economics and Business, Universitas Indonesia, Jakarta, Indonesia
2 Department of Management, Faculty of Economics and Business, Universitas Indonesia, Jakarta, Indonesia
*Corresponding Author: [email protected]
Accepted: 15 June 2021 | Published: 1 July 2021
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Abstract: The aim of this study is to evaluate Indonesians' intent to adopt the Internet of Things for smart homes. In Indonesia, smart home adoption is still relatively low, even though it provides many benefits to residents who use smart homes and can also support the implementation of industry 4.0 in Indonesia. We suggest an integrative theoretical model focused on the unified theory of acceptance and use of technology (UTAUT2), which has been expanded to include variables relating to perceived risk and awareness. This research surveyed 175 respondents in Indonesia and quantitatively examined the influence from each variable on the behavioral intention of a smart home. Purposive sampling techniques were used, and the data collection was conducted via google forms. In this study, there are 8 hypotheses to be tested with SPSS 25. The technique of multiple regression was employed to determine the model with the best predictive ability for the dependent variable. The findings indicate that awareness has been the most important factor influencing intention to adopt smart home technology, accompanied by social influence, price value, performance expectancy and hedonic motivation. Finally, some managerial implications were suggested to the marketers such as making content marketing about benefit, how to use, and awareness to enhance the adoption intention of smart home in Indonesia.
Keywords: UTAUT, Internet of Things, Smart Home, Adoption Intention, Indonesia
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1. Introduction
The Indonesian digital market is currently developing rapidly in line with the increase of internet users in Indonesia. The internet connectivity network that continues to be improved and the existence of trending digital markets affect internet users' growth in Indonesia (Foila, 2018). To support the government's vision of making Indonesia 4.0, one of the factors that will aid in the adoption of Indonesia 4.0 is the internet of things (IoT) (Ministry of Industry, 2018).
The Ministry of Industry is also proactive in encouraging the development of the Internet of Things to strengthen Indonesia's digital technology structure. The internet of things, artificial intelligence (AI), human-machine interfaces, robotics and sensor technology, and 3D printing technology are five critical developments that support the advancement of Industrial System 4.0. (Adi, 2020).
According to data from the Indonesian Internet of Things Forum, approximately 400 million sensor devices are embedded, of which 16% are employed in retail, 15% in healthcare, and 11% in insurance, 10% in banking and securities, as well as some of the retail, beauty and computer maintenance industries which around 8%. Also, about 7% in government, 6% in
transportation, 5% in public utilities, 4% in real estate and commercial and agricultural services, and the remaining 3% is used for housing (Ministry of Industry, 2018). This demonstrates that smart home adoption is still relatively poor in Indonesia, even though it provides many benefits to residents who use smart homes and can also assist Indonesia in implementing industry 4.0.
The UTAUT2 (the unified theory of acceptance and use of technology) model may utilize to investigate adoption intention aspect for smart home technologies. UTAUT2 model was employed for determine the chance of a modern invention reaching acceptance so that the determining factors adoption of these technologies can be examined. The UTAUT2 model is composed of ten variables in all, including Performance Expectancy, Effort Expectancy, Social Influence, Hedonic Motivation, Price Value, Facilitating Condition, Habit, Behavioural Intention, and Use Behaviour (Gultom, 2020).
People are unfamiliar with smart homes and have a little knowledge about how to incorporate them in the smart home sense seeing as smart homes were an totally novel paradigm. One of the possible obstacles to smart home service implementation has been discovered to be a insufficient familiarity with smart home technology (Balta-Ozkan et al., 2014). Previous study discovered the technological good understanding is a crucial factor in determining whether or not to adopt smart home systems. As a result, consumer awareness is projected to have a vital position to perform in the adoption of IoT decisions (Coughlan et al., 2012)
The Internet of Things is a physical entity made up of intelligent sensors and networking technology. These sensors amass vast amounts of complex and private information. According to a new survey by PricewaterhouseCoopers (PwC) on smart home systems, customers are concerned about data protection. As a result, consumer estimation of privacy risk is therefore certain to play a major role in IoT adoption decisions.
The aim of the study is to look at the smart home system IoT adoption intentions and answer these research question: What are the critical variables affecting customer intention against smart home technology? The study's results aim to evaluate the adoption factors that influence individual's willingness to adopt smart home services in Indonesia and to assist telecommunications companies and other businesses in developing persuasive smart home services. We begin with a literature review, build a study model, explain the process for testing the results, and analyze the results and their implications for theory and practice in the following sections.
2. Literature Review
Internet of things
The internet of things (IoT) is a term that refers to the capacity of multiple computers to communicate with one another and data transfer through the internet. The internet of things (IoT) is a technology that enables the monitoring, connectivity, and collaboration of different hardware and data through the internet. Thus, the internet of things (IoT) is described as the process of connecting non-human controlled devices to the internet (Hardyanto, 2017).
However, the Internet of Things is not only related to the control of devices through remote but also about how data and virtualizing real things to the internet form. Internet automatically becomes the connection between machines. In addition, the available user will regulate and supervise the work of the tool directly. The advantage of using Internet of Things technology is that the work that people do will be faster, easier, and efficient.
Smart Home
Smart Home is an element of IoT where all of the things or household appliances related to people's lives are "smart". This is because of the incorporation of technology in the form of versatile chips. In a smart house, IoT comes in the form of furniture found in homes and comes to make it easier for the homeowners to manage everything related to the convenience from the security to access to the equipment to be more interactive and can be handled using the gadget in the form of application in a smartphone or other.
Extended Unified Theory of Acceptance and Use of Technology 2 (Extended UTAUT2) UTAUT2 model is an expansion of the UTAUT model. The UTAUT2 model is used to assess the possibility of a new technology's success, as well as the variables that influence the technology's acceptance. (Gultom, 2020). Recognizing that the Internet of Things represents an expansion of emerging technologies into users' physical environments, which is likely to result in the blurred boundaries between physical and digital protection, as well as privacy issues, we extend the UTAUT2 model by using perceived risk as a proxy for appropriate conduct. Furthermore, since smart homes are a relatively recent paradigm, numerous individuals neglect extensive understanding about them and are inexperienced with how to utilize them. One of the possible obstacles to smart home service implementation has been discovered to be a absence of awareness of smart home technologies (Balta-Ozkan et al., 2014).
As a consequence, we consider awareness to be a potentially significant antecedent to adoption.
Performance expectancy
Performance expectancy is a measure of a person's beliefs when adopting technology to help him gain benefits in job performance. The consumers are concerned about whether the smart home can simplify and boost their everyday lives, as well as provide them with power of their personal possessions (Mashal, 2020). Performance expectations are a critical component in determining an individual's willingness to implement smart home technologies (Aldosari, 2018). Therefore, we hypothesize the following:
H1: Performance expectancy has a positive influence on behavioural intention
Effort expectancy
Effort expectancy is a measure of the user's belief that when using a smart home, it can free them from physical or mental effort. The user's intention to receive advanced technologies is not only determined by how well the technology operates, but also by how convenient it is to use and how little work is required to use it (Alalwan, Dwivedi, & Rana, 2017). In this case, the research conducted by Aldossari (2018) shows a significant influence between effort expectancy on consumer attitudes towards IoT (smart home), which affects their intention to adopt the technology. As a result, we make the following hypothesis:
H2: Effort expectancy has a positive influence on behavioural intention
Social influence
Social influence can be defined as the extent to which users view the importance of others, believing that users should use new technologies. For general customers with just rudimentary knowledge of the technology, the smart home is indeed a novel idea. As a result, their peers' and social networks' evaluations and private opinions are strongly valued. Previous studies also discovered that social influences have a substantial impact on intention to use (Aldosari, 2018).
As a result, we make the following hypothesis:
H3: Social influence has a positive influence on behavioural intention
Facilitating conditions
Facilitating conditions are the level of individual confidence in the company's infrastructure and technical support for the use of technology (Venkatesh et al., 2012). Adopting emerging technologies requires the acquisition of specific abilities, expertise, and technological infrastructure. Consumers' intention to implement IoT at home would be greater because they have a sufficient level of facility requirements, including in terms of time and resources, access to goods, enabling technology at home, and advice in times of need. Research conducted by Sinaga (2018) states that facilitating conditions have a positive effect on the intention to adopt a smart home. Therefore, we hypothesize the following:
H4: Facilitating conditions have a positive influence on behavioural intention
Hedonic motivation
Hedonic motivation is individual motivation in shopping where they believe that shopping is an activity that creates happiness, so they do not attach much importance to the benefits of the products or services they buy. It means that someone has fun un suing smart home technology (Venkates, 2012). According to Aldosari (2018), smart home technology was introduced for hedonistic purposes in addition to technical reasons. Users who find smart home technology exciting build a positive outlook about it. Users should appreciate being innovative when they embrace smart home technologies, while smart home technology is also emerging. Therefore, we hypothesize the following:
H5: Hedonic motivation has a positive influence on behavioural intention
Price Value
Price Value is a person's understanding of the expense of using smart home services in comparison to the advantages they perceive. Generally, the price value influences user behavioural intentions, so that the higher price value leads to stronger behavioural intentions towards certain information systems. Research conducted by Adlsosari (2018) demonstrates that the price of a smart home has a favorable effect on a person's decision to implement smart home technology. That is, the higher the perceived market value, the more consumers believe that the advantages they get are greater, which increases their desire to embrace a smart house.
As a result, we propose the following:
H6: Price value has a positive influence on behavioural intention
Perceived Risk
Perceived risk is characterized as the chance of experiencing a failure as consumers use information technology resources to accomplish a goal (Featherman & Pavlou, 2003). Apart from unmet requirements, they experience risk while they are unfamiliar with the product or technologies they are using. Perceived risk refers to the assumption that customers are unaware of the repercussions of their decisions as a result of their inexperience in such behaviors (Sung
& Jo, 2018). Research conducted by Aldosari (2018) and Wilson (2015) supports the argument that the higher the perceived risk faced by users in a smart home, the lower their intention to adopt a smart home. As a result, we make the following hypothesis:
H7: Perceived risk has a negative influence on behavioural intention
Awareness
Awareness is a condition where a person knows the stimulus/object first, in this case, smart home technology. It is generally accepted that a consumer's decision to approve or reject a technology is taken after the user is informed about the breakthrough (Rogers, 2010). Since the smart home is a very recent paradigm, many people lack credible knowledge and have little experience of smart homes or how to use them. A lack of awareness of smart home technologies
has been described as a possible impediment to the introduction of smart home services (Balta- Ozkan and colleagues, 2014). Coughlan et al. (2012) discovered that having a firm grasp on technology and becoming knowledgeable of it are critical components of embracing smart home services. It is supported by research conducted by Mashal (2020), which states that The knowledge aspect has a sizable impact on the decision to implement smart home technologies.
It means that someone tends to adopt smart home technology if they already have the knowledge and are familiar with the technology. Therefore, we hypothesize the following:
H8: Awareness has a positive influence on behavioural intention The following diagram illustrates the research framework of this study.
Figure 1: Research Framework
3. Methodology
Data Collection
This analysis relies on primary data, which is information gathered directly from researchers.
The researchers collected data primarily by distributing quantitative questionnaires. The data collection technique is by distributing questionnaires online to respondents in Indonesia through various social media platforms, namely Twitter, Instagram, Line, and WhatsApp group. Closed questions with scaled-response questions are the forms of questions included in the survey. The sampling technique in determining the sample in this study was purposive sampling. This study surveyed 175 Indonesian respondents from middle and upper-class residents where they live in big cities or are touched by the internet of things and know IoT, and have never used IoT (smart home).
Respondents were instructed to read an overview outline of the smart home technology. To maintain a working knowledge of IoT and smart home technology and to respond to many questions in the distributed questionnaire. Respondents were asked to complete questionnaires about their gender, age, education, household spending, and place. This information is used to determine the demographics of the respondents in this survey. The demographics of the respondents will have an effect on the study's outcome.
Table 1: Respondent Profile
Criteria (%)
Gender Man 37%
Woman 63%
Age 18-25 years old 86%
Monthly expenditure Rp3.000.000 - Rp5.000.000 74%
Rp5.000.000 - Rp10.000.000 22%
Domicile Jabodetabek 42%
Palembang 23%
Married Status Single 91%
Occupation Government employees 31%
Employee / private employees 35%
stundent 19%
Number of family members 2-3 26%
4-5 50%
Table 1 shows the characteristics of the research respondents. It can be concluded that the respondents of this study are mostly woman, aged 18-25 years, with monthly expenses of IDR 3,000,000-IDR 5,000,000, domiciled in Jabodetabek, with unmarried status and most of them work as private employees / employees, with number of family members 4-5 people.
Measurement
Before being used in the main study, a pretest was carried out on the questionnaire that had been made. Before doing the pretest, it was preceded by a wording process to ensure that the language and sentence structure in the questionnaire could be fully understood by the respondent. In this study, a pretest was conducted two times on the research object. The first two respondents are lecturers who are experts in the field of marketing, then followed by five respondents who meet the criteria in the study. The wording test is done by asking respondents for help to read, understand and provide input to revise the questionnaire questions. The variables in this research must be evaluated for their accuracy and applicability so that they can be justified scientifically. The evaluation that needs to be done includes an assessment of reliability and validity.
This validity analysis was carried out on 35 indicators represented in each question in the research questionnaire and in accordance with the construct of operational research variables (performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, perceived risk, awareness, and behaviour. intention) of each of these variables. Analysis of the validity of the questionnaire was then carried out through the factor analysis method. To determine the reliability of each variable, Cronbach Alpha was used. The variable is considered reliable if it has a Cronbach Alpha reliability coefficient of more than 0.6.
Multiple regression models are used in this study to assess the influence of the independent variable on the dependent variable. The linear regression model can be said to be good if it is able to fulfil several assumptions, which are commonly referred to as classical assumptions.
The classical assumptions in this model that need to be fulfilled are that the residuals are normally distributed, there is no multicollinearity, and the data does not have heteroscedasticity in the regression model. This classic assumption test aims so that the regression model can show unbiased results and reliable test results. Multiple linear regression analysis aims to analyze the predetermined hypothesis. The analysis will be carried out using SPSS software.
The coefficient of determination was used to calculate the influence of the independent variable on the dependent variable and hence the study's viability. The coefficient of determination R2 is used to calculate how much difference in the dependent variable can be interpreted by changes in the independent variable.
Then, the F test or Simultaneous Test is carried out, which aims to determine simultaneously whether the independent variable has a significant effect on the dependent variable. T-test or individual testing tests the regression coefficient partially by determining the statistical formula to be tested. The T-test is used to determine whether a variable has a significant or not partial effect.
4. Result and Discussion
Instrument Validity and Reliability
In this chapter we will present the analysis result of 35 questions determined by each variable to ensure the consistency of each construct. From the computation process's output as shown in Table 2, for the Kaiser-Meyer-Olkin (KMO) Validity test, the Loading Value is above 0.5, whereas the Barlett’s test of spercity is below 0.5. This means it is acceptable and suitable to use in the modeling.
The composite data reliability of Cronbach’s Alpha per construct is more than 0.7, which means that the items have indicated reliability in measuring the research model. This also means that the questionnaire is suitable to be used as a survey instrument.
Table 2: Instrumental Validity and Reliability test
No. Variable indicator KMO Sig, factor loading Cronbach’s Alpha 1. Performance
Expectancy
PE1 PE2 PE3 PE4
0.782 0.000 0.794 0.873 0.843 0.792
0.844
2. Effort Expectancy
EE1 EE2 EE3 EE4
0.838 0.000 0.868 0.873 0.857 0.832
0.878
3. Social Influence
SI1 SI2 SI3 SI4 SI5
0.845 0.000 0.848 0.898 0.882 0.841 0.734
0.897
4. Facilitating Conditions
FC1 FC2 FC3 FC4
0.758 0.000 0.688 0.818 0.762 0.758
0.744
5. Hedonic Motivation
HM1 HM2 HM3
0.719 0.000 0.908 0.861 0.875
0.855
6. Price Value PV1 PV2 PV3
0.653 0.000 0.730 0.861 0.861
0.743
7. Perceived Risk
PR1 PR2 PR3 PR4
0.781 0.000 0.812 0.882 0.852 0.692
0.824
8. Awareness AW1 0.820 0.000 0.876 0.877
AW2 AW3 AW4
0.892 0.805 0.852 9. Behavior
Intention
BI1 BI2 BI3 BI4
0.812 0.000 0.846 0.864 0.852 0.755
0.848
Model Testing
Before performing multiple linear regression tests, a classical assumption test is conducted.
The classical assumption examination gave the following results: the data is normally distributed, there is no multicollinearity, and heteroscedasticity. The results of the linear regression of the 8 independent variables (performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, perceived risk, and awareness) toward one independent variable of Behavioral Intention are shown in Table 3 below:
Table 3: Model Testing Result
Variable Unstandardized
Coefficients
Standardized
Coefficients t Sig.
B Std. Error Beta
1 (Constant) -.526 .316 -1.668 .097
Performance Expectancy .263 .076 .205 3.474 .001
Effort Expectancy -.212 .068 -.168 -3.126 .002
Social Influence .248 .060 .242 4.100 .000
Facilitating Conditions -.031 .078 -.025 -.391 .696
Hedonic Motivation .168 .071 .148 2.366 .019
Price Value .275 .064 .230 4.263 .000
Perceived Risk -.019 .021 -.040 -.869 .386
Awareness .407 .058 .401 7.082 .000
In accordance with the study model suggested, the directions from performance expectancy, social influence, hedonic motivation, price value, and awareness to behavioral intention were found to be important and pointing in the model's direction. As a result, H1, H3, H5, H6, and H8 have been sponsored. Unexpectedly, the path between EE and behavioral intention of adoption IoT technologies was found to be significant but negative. Thus, H2 was contradicted.
At the 0.05 standard, the relationship between promoting conditions and perceived danger was not significant. As a result, hypotheses H4 and H7 were rejected. The model accounts for 69.2%
of the variation in behavioral purpose. The direction coefficients and their importance are shown in Figure 3.
Discussion
The proposed theoretical model of IoT acceptance expands UTAUT2 to incorporate perceived risk and awareness and provides a high level of explanatory meaning for IoT adoption intentions. The findings indicate that performance expectancy positive striving toward behavior intention. Although UTAUT claims that performance expectancy is the most powerful factor influencing adoption intention, this research shows that this assertion changes as awareness is included. Based on the results of this study, it can be concluded that Indonesian consumers carefully considered the performance of smart home products before deciding to adopt them. Research conducted by Aldossari (2018) also provides the same result that performance expectancy has a significant positive effect on behavior intention.
Surprisingly, effort expectancy shows effects that vary from the original UTAUT model, indicating that effort expectancy has a major negative impact on behavioral intention in this study. This means that even though the effort in adopting smart home technology is quite heavy, respondents want to continue to adopt smart home technology. Based on the respondent's profile, the majority of respondents have a young age range of 18-25 years, which makes them feel the effort in adopting technology is not an obstacle in adopting technology, but they believe that the effort will be in accordance with the benefits of the results that will be provided by smart home. When viewed from the characteristics, research respondents are those who have never used a smart home, so their knowledge of how to use and how smart home works is still low, making them feel that the use of smart home technology is still difficult to use.
It was discovered that social influence has a significant beneficial impact on behavioral intention, as determined by the opinions of prominent persons and parties such as family members, employers, and the news media, both of which may have a significant impact on other people's decisions to implement the smart home program. This is obvious to Indonesians who are usually very much influenced by the advice given by those closest to them. Users who believe that those in their social group have a favorable outlook about smart home use are more prone to develop similar attitudes. Additionally, supportive encounters with smart homes exchanged by members of the user's social circle will affect the user's favorable attitude toward smart homes. This suggests that someone's decision to embrace modern technologies in terms of smart home is influenced by their proximity and confidence of others who are influential or affect their behaviour. The same results are also shown by research from Aldossari (2018), Mashall (2020), and Sinaga (2019) which states that the social influence factor is a significant predictor of behavior intention.
The findings indicate that Facilitating Conditions are insignificant as a predictor of behavioural intentions against smart homes. This unexpected result may be explained by the fact that the majority of the participants in this sample were unfamiliar with smart home technologies.
Individuals with limited to no prior experience with smart home technology may assume that it is equivalent to using comparable non-IoT objects. As a result, they disregard facilitating conditions when deciding on an adoption, as a result of extensive experience with equivalent, non-IoT artifacts. Instead, consumers with little to no knowledge could be unable to distinguish between smart home technologies and similar non-IoT objects, leading them to feel that they should not require any tools to promote their usage of smart home technology. Previous study further supports this conclusion, which shows the same results that facilitating conditions are not a significant predictor of behavior intention (Aldossari, 2018)
The findings indicate that hedonic motivation has a statistically important positive effect on behavior intention of smart home tecnologies. This discovery suggests that smart home technology is implemented for hedonic purposes in addition to practical ones. Individuals that find smart home technology enjoyable tend to form attitudes that support it. Additionally, user innovation necessary support to hedonic motivation. Due to the fact that smart home technology is still in its development, consumers will taste the thrill of innovation when they embrace new technology (smart home). This finding is also consistent with Aldossari's (2018) previous study, which found that hedonic incentive has a beneficial impact on behavior intention.
The findings indicate that price value has a positive effect on behavioral intention. This suggests that people evaluate the monetary value of smart home technologies in opposition to
the benefits. People that believe the price of smart home technologies is reasonable in comparison to the potential benefits have a greater behavioral intention to incorporate it.
Additionally, users can consider the cost of comparable non-IoT artifacts when calculating the PV of smart home technology. As consumers think smart home has a number of benefits over comparable non-IoT objects, their PV for smart home technology would be higher. The beneficial impact of price value on behavioral intentions about IT adoption confirms by previous study (Aldposari, 2018).
Perceived risks were not found to be statistically significant in relation to the intention to adopt IoT at home. However, several experiments discovered no substantial correlation among perceived risk and decision to adopt (Wang & Yi, 2012; Tan et al., 2014; Kapoor et al., 2015).
This research asserted users viewed the negligible impact because the benefits of technology outweighed the costs, certain respondents were ineligible to accept risks, and a number of users really have little fear of losing something while utilizing technology. Perceived risks were insignificant in this study since the majority of respondents were unfamiliar about the Internet of Things or smart home systems, and therefore were unaware of potential risks associated with IoT use.
The degree to which people are aware of smart home technologies has a huge impact on their behavioural decision to use smart home services. The same findings are clarified by Mashall (2018), who discovered that awareness is a major indicator of action purpose when it comes to implementing smart home technologies. In this research, the most important indicator of intention to adopt IoT at home was found to be awareness.
From the simultaneous test (f test), it can be concluded that PE, EE, SI, FC, HM, PV, PR, AW simultaneously have an effect on BI.
Table 4: F-test Result ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 92.706 8 11.588 46.703 .000b
Residual 41.189 166 .248
Total 133.895 174
a. Dependent Variable: BI
b. Predictors: (Constant), AW, HM, PV, EE, SI, PE, FC, PR
The next step is to evaluate the value of R2 to evaluate the degree to which independent variables have an impact on the dependent variable.
Table 5: R2 Test Model Summaryb
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .832a .692 .678 .49812
a. Predictors: (Constant), AW, HM, PV, EE, SI, PE, FC, PR b. Dependent Variable: BI
In light of the findings above, the value procured from the analysis of the coefficient of determination is 69.2%, which means that the variables PE, EE, SI, FC, HM, PV, PR, AW have
an effect of 69.2% on BI, while the remaining 30.8% influenced by a variety of other factors that were not examined in the study.
4. Conclusion and Research Limitation
Conclusion
The Internet of Things is a new phenomenon that is supposed to have a major impact on certain aspects of people ’s lives. The aim of this analysis is to determine the factor affecting of adoption of IoT technology in the sense of the smart home. UTAUT2 is integrated into the literature on perceived risk and awareness by the theoretical paradigm discussed and evaluated here. The expanded model provides a more complete view of the factors affecting IoT adoption.
The findings indicate that the UTAUT2 constructs have an impact on IoT adoption (i.e., PE, EE, SI, HM, PV). Additionally, the findings indicate that understanding plays a major role in IoT adoption intention. Surprisingly, the findings indicate that FC and PR have no impact on adoption intentions in the smart house. This research adds to the body of knowledge in information systems by demonstrating the explanatory power of an existing UTAUT2 in the sense of user IoT.
Theoretical Contributions
Our paradigm expands UTATU2 by applying perceived risk and awareness. Therefore, the model considers not just the reasons for IoT adoption, but also the obstacles and customer knowledge of smart home technology. Our findings indicate that exploring risk factors expands our perception and provides a more complete view of the conditions affecting IT adoption.
Additionally, the research contributes to the literature on IT acceptance by recognizing the role of technology literacy in adoption intention of smart home technology. The results of this study suggest that customers' awareness of IoT efficiency and accessibility has a substantial impact on their decision to adopt smart homes.
Implication for practice
The study's findings have managerial implications in the form of influencing aspects, the direction of influence and the extent to which technology adoption factors influence behavior intention in adopting smart home so that later it can encourage business success. The smart home market is growing, and the study's findings assist Internet of Things companies in developing a better understanding of the factors affecting market acceptance of smart homes.
Based on the research results, the awareness factor is a factor that has a dominant influence compared to other factors. As a provider of smart home systems, it is recommended that they teach customers about all of these services and how to utilize them. The method that can be done by smart home service providers is to use various communication channels that are in accordance with the target market of Iot itself, for example the use of social media Instagram, YouTube, and also some advertisements carried out in marketplaces that provide smart home equipment.
The results also demonstrate the performance expectancy factor is one of the strongest factors affecting the intention to adopt a smart home. The message content that can be highlighted in the marketing communications carried out is to show the benefits of a smart home that can improve performance in the home and clearly illustrates that IoT at home can further accelerate their homework. Messages created can use a combination of rational and emotional appeal. By creating content that is packaged in a storytelling way, the audience can understand the benefit offered from a smart home and experience the experience of using a smart home indirectly.
Additionally, the findings of this research suggest that social influence seems to have a significant beneficial effect on the decision to adopt, which has significant consequences for the marketing campaign for smart home technologies. Smart home service providers can work with influencers or public figures who have influence and trust from consumers about technology in the form of IoT in conveying the message they want to convey to the audience.
Additionally, societal aspects must be incorporated into the creation and promotion of smart home apps. This can be accomplished by encouraging customers to exchange stories concerning their interactions via social networking networks, using smart home technology such as cost-saving analytics, which can assist IoT providers in attracting more customers, as testimonials from people who have used IoT have a direct impact on residents in the neighborhood.
In the other side, hedonic motivation's significant positive impact on behavior intention demonstrates that smart home systems are implemented for hedonic purposes. As a result, it is preferable for smart home service providers to place a priority on customer functionality while creating apps and building software, so that smart home consumers can enjoy the benefits of smart home technology adoption. One way to improve the experience can be done by adding an element of gamification to the smart home device. The addition of more interactive and innovative features in the development of a smart home can also be considered in the future.
Additionally, the findings of this research suggest that consumer assessments of the advantages of price have a substantial effect on action intention. This can be included in the content in marketing communications which are carried out by providing interesting content such as videos that show the difference when someone uses a smart home at his residence and does not use it. This can become a reference for someone in providing a price value for the money they spend when adopting smart home technology.
Research Limitations and Further Research
The study has several limitations. First, this study uses additional variables in the form of perceived risk and awareness on independent variables and sees the effect on behavior intention for the dependent variable, but not on use behavioral. In addition, this research is only limited to smart homes in Indonesia, especially big cities that are only touched by smart homes. The sample of this study only uses respondents for the IoT object in the form of a smart home in Indonesia. Further research can conduct case studies with other types of IoT such as AI, 3D printing and others to see whether the results are the same or different from this research to provide variability in interpretation and evaluation. In this study, the method used is a very quantitative method and the process of collecting research data is carried out in a short time and with a limited number of respondents. For the characteristics of the respondent, it’s only seeing the monthly expenditure, the respondent did not ask about the income of the respondent so it did not show specifically the respondent's buying ability on smart home products.
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