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Understanding users’ acceptance of smart homes
Technology in Society xxx (xxxx) xxx–xxx
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Technology in Society journal homepage: www.elsevier.com/locate/techsoc
Understanding users’ acceptance of smart homes Ahmed Shuhaibera, Ibrahim Mashalb,∗ a b
College of Technological Innovation, Zayed University, Abu Dhabi, United Arab Emirates Computer Science Department, Faculty of Information Technology, Aqaba University of Technology, Aqaba, Jordan
ARTICLE INFO
ABSTRACT
Keywords: Internet of things User acceptance Smart home Personal factors Technology acceptance model
Smart homes allow owners to monitor and control wide range of home appliances remotely and intelligently. Despite its potential, the acceptance of smart homes by residents is still far from expectations. Thus, this study aims to investigate the factors that influence residents’ acceptance and usage of smart home. By extending the Technology Acceptance Model (TAM), this research incorporates more factors related to users such as trust, awareness, enjoyment, and perceived risks to study intention to use smart homes and investigate their impact quantitatively by using SEM-PLS approach. Results show that trust, awareness, enjoyment, and perceived risks, with perceived usefulness and perceived ease of use significantly influence attitude towards smart homes which, in turn, impact the intention to use smart homes.
1. Introduction The Internet of things (IoT) has been introduced as a new paradigm to expand the current Internet to physical objects or things [1,2]. IoT is a dynamic global network of billions of heterogeneous smart objects that are capable to sense, collect, share, and exchange information by communicating and interacting with each other. Smart objects include computers, mobile phones, heart beat monitors, smart phones, smart lighting, power meters, smart locks, motion detection sensors, and temperature sensors. It is expected that the IoT global market will worth between $3.9 and $11.1 trillion by 2025 and the number of connected smart objects will be 212 billion in 2020 [3,4]. IoT has created many powerful applications which allow users to interact directly with smart objects in smart environments to improve work efficiency and life quality. Potential IoT applications cover several aspects of every-day life such as health care, smart society, supply chain management and many others. For example, smart homes aim to improve residents’ quality of life and respond to their needs by equipping a residence with a communications network to connect sensors and smart devices and appliances which are remote-controlled and accessed through mobile phones or personal computers (PCs) [5,6]. In Jordan, Umniah, a mobile cellular telecommunication operator, has launched, among other companies, smart homes and buildings automation enabling home control, energy management, and security systems. Umniah company offers the customers advanced and convenient ways to manage their smart devices anytime anywhere through a mobile application which runs over phones and tablets. However, the
∗
smart home is still a new concept for Jordanian consumers in general and is far from their everyday lives. Indeed, smart homes are seen as a luxury, symbolic of wealth, and a means of enhancing their sense of self-importance in Jordan. Consequently, the adoption rates of smart homes are very low among Jordanians. For instance, LivingTech, a smart homes provider, reported that the number of their smart home projects is little more than 200. An important reason for the low adoption rate is the lack of understanding how users accept smart homes. Relatively few empirical studies have been conducted to explain users' acceptance of smart homes. Understanding how and why users accept smart homes is an important issue for their success and will boost the rate of users' adoption. Furthermore, understanding users’ acceptance helps firms marketing and promoting smart homes more effectively. Another advantage is providing reliable guidelines and significant insights smart homes providers to develop more attractive and interesting applications. To address this gap, this study aims to investigate users' acceptance of smart homes to develop a better understanding of the factors significantly influence user acceptance of them. This study contributes to adoption studies by proposing a theoretical model to examine factors that influence users' acceptance of smart homes. Based on the research model, this study examines the impact of users’ personal factors (e.g., awareness and trust) on smart homes acceptance and intention to use it. This work applies the Structural Equation Modeling (SEM) approach to assess the empirical strength of the relationships in the proposed model. To the best of our knowledge, this is the first study on smart homes acceptance in Jordan.
Corresponding author. E-mail address: [email protected] (I. Mashal).
https://doi.org/10.1016/j.techsoc.2019.01.003 Received 4 March 2018; Received in revised form 1 January 2019; Accepted 14 January 2019 0160-791X/ © 2019 Elsevier Ltd. All rights reserved.
Please cite this article as: Shuhaiber, A., Technology in Society, https://doi.org/10.1016/j.techsoc.2019.01.003
Technology in Society xxx (xxxx) xxx–xxx
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The major contributions of this paper are:
Smart homes are at an early stage of diffusion despite their broad range of potential and benefits. It is important to motivate individual users to adopt smart homes. Consequently, studying and understanding consumers’ perspectives and behaviors concerning smart home adoption are required to help providers expand their services.
• Explore the adoption of smart homes. • Develop and test an integrated model to explain users' acceptance of smart homes. • Identify the factors affecting users' acceptance of smart homes. • Understand the impact of users' beliefs about smart homes on their
2.2. Related work
acceptance.
As mentioned above, identifying the factors influencing users’ acceptance of smart homes is a relatively new research arena [7]. Only a few studies have been carried out. For example [11], examined the adoption of smart homes by combining Value-based Adoption Model (VAM) and TAM. The result showed that both perceived sacrifice and perceived benefit had an effect on perceived value with perceived benefit having a higher significance on perceived value. For perceived sacrifice, privacy risk and innovation resistance were found to have limit perceived value. In another recent work [12], proposed a theoretical model that extends the TPB to explain Korean customers’ behavioral intentions to adopt and use smart homes. The result of the study shows that mobility, security/privacy risk, and trust in providers are important factors affecting the adoption of smart homes. Another study from Korea is presented in (S [13]. where six focus group discussions were conducted. The main findings of that research were that benefits, and lifestyle determine the adoption of smart home technologies. Moreover, the study showed that concerns about customer care and privacy are critical barriers to its adoption. In Europe, a qualitative cross-country study of smart homes was conducted in three countries UK, Germany, and Italy [14]. The study reported that tangible benefits and increases of quality of live will be the drivers for smart home development. The study also revealed smart homes adoption barriers such as lack of understanding of smart home technology, concerns on technology failure or difficulties in use, privacy and/or security concern, and loss of consumer freedom. In the UK [15], conducted an exploratory study on the adoption of IoT in homes. The findings show that the perceived usefulness, ease of use, privacy and security, and knowledge of the technology are significant predictors of the adoption of IoT in homes. On the basis of the Unified Theory of Acceptance and Use of Technology (UTAUT) [16], developed a model that explains predictors of intention to adopt sustainable household technology in the USA. The results show that product attributes including performance,
The rest of the paper is organized as follows. Section 2 introduces the literature review. Section 3 presents research model and hypotheses. Section 4 depicts the methodology used in this research. Section 5 introduces results and findings, followed by a discussion in Section 6. Finally, Section 7 concludes the paper. 2. Literature review 2.1. Smart homes Among all IoT applications, smart homes are expected to have a broad range of potential and great benefits in the near future [7]. Smart homes have received considerable attention from both commercial and academic bodies in different countries. Nowadays, companies such as Google, Amazon, Samsung, AT&T and Comcast are providing smart home and their products. The global smart home market is expected to grow at a compound annual growth rate of 29.5% between 2012 and 2020. Forecasts indicate that by 2022 a typical family home could contain more than 500 smart devices [8]. A smart home is a cyber physical system where homes are equipped with interconnected sensors and smart appliances which are controlled over the Internet to facilitates monitoring of the surrounding environment to ensure home and personal safety and security [5,9]. A traditional smart home, as shown in Fig. 1, is often implemented under a centralized architecture where different home appliances are connected by an internal home network and controlled by the home gateway. Users interact with their smart home appliances and smart homes providers using mobile devices through telecommunications networks. Devices include smart fridge, smart curtains, smart oven, smart microwave, smart coffee maker, smart doors, smart light, smart fire alarm system, and other sensors for tracking and monitoring. Smart homes providers collect and analyze data from home appliances and mobile devices to provide interactive and automated services to residents [10].
Fig. 1. Smart home architecture. 2
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3.2. Personal factors 3.2.1. Perceived risk Smart homes can expose users to a high level of threats and risk including disclosure and violating of user's information privacy, high loss of data confidentiality and authentication. Perceived security risks usually include security, privacy, financial and technical risks [18]. Security and privacy risk concerns are considered major barrier facing smart homes adoption. In other words, users' acceptance of smart homes depends on the protection of their privacy and security. We define perceived risk as the degree to which users believe that using smart homes is secure, safe, and will protect their data. From our point of view, perceived risk has a direct relation and dramatically affects users' trust in smart homes. According to Ref. [19]; cyber risks are an existing thread to modern business and online applications such as smart factories and smart homes, which in turn could affect the decision-making process of corporate and individuals [20]. Therefore, we hypothesize that:
Fig. 2. Research model.
compatibleness, and hedonic expectancy as well as consumer characteristics and sustainable innovativeness significantly predicts adoption intent. But effort expectancy as well as social pressure and environmentalism are not significant predictors of adoption intention.
H6. Perceived risk will have a significant negative influence on trust smart homes.
3. Research model and hypotheses As illustrated in Fig. 2, the research model is developed on the basis of the literature review. The model consists of four technology factors (perceived usefulness, perceived ease of use, attitude towards use, and intention to use); three personal factors (awareness, trust, and perceived enjoyment) and finally perceived risk. The model indicates that perceived usefulness, attitude towards use, and trust influence intention to use smart homes. In addition, perceived ease of use affects perceived usefulness. Moreover, perceived usefulness, perceived ease of use, awareness, trust, perceived enjoyment affects attitude towards uses smart homes. Finally, perceived risk affects trust.
3.2.2. Trust Users' trust of smart homes is important to help people overcome concerns about risks and mitigate uncertainty facing adoption of smart homes. Intuitively, when users trust a technology, they will have a high positive attitude towards using this technology. For the purpose of this paper, we define trust as users' confidence in the level of reliability of smart homes that make the users meet their expectations toward the systems. In smart homes context [21], showed that residents' perceived trust in smart homes positively relates to their attitude towards smart homes. In the same line [22], reported that trust of smart homes was positively related to the users’ intention to use smart homes. Thus, we incorporate trust in our research model as one of the important factors influencing both attitude towards and intention to use smart homes, which leads to the following hypothesis:
3.1. TAM factors Technology Acceptance Model (TAM) [17] is the most commonly used model and is generally preferred for predicting and explaining user behavior toward new technology acceptance and usage. The TAM defines four main constructs are used, namely, intention to use, attitude towards, perceived usefulness, and ease of use. Perceived usefulness and attitude determine intention to use. The attitude toward using a technology is affected by the perceived ease of use and usefulness. Moreover, perceived ease of use also positively influences perceived usefulness. In the smart homes context, we define these constructs as follows. Perceived usefulness as the degree to which a user believes that using smart homes will enhance user life quality. Perceived ease of use is the degree to which a user believes that using smart homes would be free of physical and mental effort. Attitude towards using smart homes is defined as the degree of user's positive or negative evaluation or feelings about using smart homes. Intention to use smart homes is defined as an indication or a measure of the strength of a user's readiness to use smart homes. Based on this, we present the following hypotheses:
H7. Trust will have a significant positive influence on attitude towards using smart homes. H8. Trust will have a significant positive influence on intention to use smart homes. 3.2.3. Perceived enjoyment We define perceived enjoyment in the context of smart homes as the extent to which the activity of using smart homes is perceived to be enjoyable in its own right, apart from any performance consequences that may be anticipated. In smart homes context, perceived enjoyment has been positively associated with the perceived ease of use [22]. However, the relation between enjoyment and attitude towards using smart homes has never been tested. Thus, we hypothesize that: H9. Perceived enjoyment will have a significant positive influence on attitude towards using smart homes.
H1. Perceived usefulness will have a significant positive influence on intention to use smart homes. H2. Perceived usefulness will have a significant positive influence on attitude towards using smart homes.
3.2.4. Awareness It is widely agreed that decisions to accept or reject a technology take place after the users are made knowledgeable of the innovation [23]. Awareness is defined by Ref. [24] as an understanding that allows the user to reduce uncertainty in a subjective manner. Awareness with respect to smart homes is certainly associated with learning about the smart devices connected and how they communicate but might not be associated with past personal experience despite the strong relationship between them, because there are many resources for delivering knowledge about it to the individual, thus awareness in this context is different than familiarity. It has been found that one of the potential
H3. Perceived ease of use will have a significant positive influence unperceived usefulness of smart homes. H4. Perceived ease of use will have a significant positive influence on attitude towards using smart homes. H5. Attitude towards using smart homes will have a significant positive influence on intention to use smart homes.
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barriers of smart homes adoptions is lack of awareness of smart home technology [14]. Similarly, Coughlan et al. [15] reported that awareness of the technology are important factors in adopting smart homes. We further extend the original TAM by incorporating the awareness as an additional factor and propose a relation between awareness and the attitude to use smart homes.
uncompleted for most of the variables, and two respondents were found to have bias in a certain answer. As recommended by Creswell [26], these 27 responses were eliminated from the data analysis. Consequently, 258 responses were deemed valid for the research and showed readiness for analysis. 5. Data analysis and results
H10. Awareness will have a significant positive influence on attitude towards using smart homes.
In order to test and validate the research model, Structural Equation Modeling (SEM) was applied in this research. SEM is “a family of statistical models that explain the relationships among multiple variables” [27]. SEM permits complicated variable relationships to be expressed and gives a more complete picture of the entire model [28], it is being extensively used in behavioral science research [27] and in marketing, information technology and systems research [28]. PLS is the preferred approach for causal-predictive analysis and is better suited for theory development than for theory testing [29]. It is also suitable when small samples are employed for estimation and testing [30]. Another supportive point is that PLS can be applied to complex structural equation models with a large number of constructs [29]. Therefore, SEM using the PLS technique was used in the current study to test the overall structure of the research model. PLS is an iterative process that “provides successive approximations for the estimates, subset by subset, of loadings and structural parameters” [31]. The PLS model is usually analyzed and interpreted in two stages; firstly, by assessing the reliability and validity of the measurement model (constructs and items); and secondly, by assessing the structural model through interpreting the path coefficients and identifying the adequacy of the research model [27]. The following sections discuss the results of these two stages, by using SmartPLS 2.0 software.
4. Methodology The research paradigm followed in the current research is positivism, as it is believed that factual knowledge could be gained through observations and measurements, by collecting data and interpretation through objective approaches and that the research findings are usually observable and quantifiable. Thus a quantitative method was employed in this research to answer the research questions. Specifically, the data gathered were prepared for analysis, and then the preliminary quantitative data analysis was conducted by applying and presenting the demographic analysis regarding the research sample. Afterwards, the advanced quantitative analysis is presented through the use of SEM-PLS regression analysis, along with the revised research model and the research hypotheses. Details about each phase are presented in the subsequent sections. 4.1. Instrument development A questionnaire instrument was employed in the current research and is adapted using the conceptualization and development work found in smart homes literature. The questionnaire contained four measuring items for variables such as perceived enjoyment, awareness, trust, perceived risks, perceived ease of use, and intention to use smart homes. Perceived usefulness and attitude towards smart home applications had five measuring items each. Six demographic variables were recorded: gender, age range, educational level, occupation, marital status, and family-member category. A seven-point Likert scale was used to measure the constructs presented in the model (scores ranged from 1 = ‘strongly disagree’ to 7 = ‘strongly agree’, with the ‘neutral’ score = 4). This scale would effectively allow respondents to express their opinions in this research as it offers a wider range of agreement levels to a statement than the traditional five-point scale. The survey was available in two languages (Arabic and English). When translating the questionnaires from English to Arabic, the researchers ensured that the meaning of the source language statement was preserved to achieve the semantic equivalent [25]. The survey instrument was refined during a pre-test to ensure the internal consistency of the measured instrument, with the involvement of 18 respondents. Consequently, only one item associated with the construct ‘Awareness’ (AW3) was rephrased. Afterwards, a pilot sample was conducted using 30 respondents to assure the reliability and validity of the instrument. As a result, all resulting measuring items were clear and usable (See appendix A).
5.1. PLS measurement (outer) model results Firstly, the values of the outer loadings were examined in order to view the correlations between the latent variable and the reflective indicators in its outer model. According to Ref. [27], indicators with outer loading above 0.6 were retained, whereas indicators with outer loadings between 0.4 and 0.6 were “considered for removal from the scale only when deleting the indicator leads to an increase in the composite reliability (or the Average Variance Extracted) above the suggested threshold value”. Indicators with very low outer loadings (below 0.4) were eliminated from the scale. By performing the outer model testing, all the items were found above the acceptable level of (0.6), thus demonstrating reliable items. In total, 34 items were validated to measure the dependent and independent variables, as shown in Table 1. Additional testing of the quality and the scales was conducted, which evaluates item cross loadings. In order to examine the discriminant validity across the items, the pattern of item loadings across constructs in the model was examined (called cross loadings). Specifically, an item loading on the associated construct should be greater than all of its loadings on other constructs [27]. In this research, the discriminant validity of all items was demonstrated, since cross loadings among different constructs were greater than the determined cut off point (as shown in Table 1). Another measurement involved in the outer model testing is construct validity. Construct validity assesses whether the measures chosen are true measures of the constructs describing the event, and that these measures are actual tools for representing or measuring the construct being investigated [27,28]. For the current study, construct validity was established, including both convergent and discriminant validity. Convergent validity refers to the extent to which a measure correlates, or converges, with other measures of the same construct [27]. Convergent validity is demonstrated when the Average Variance Explained (AVE) value between the constructs is equal to, or exceeds, 0.5 [27,32]. As seen in Table 2, the AVE scores for all constructs in the model were more than 0.5, which meets the first requirement of achieving
4.2. Data collection and preparation Data were collected using a convenience sampling approach via an online self-administered survey and a snowball technique was employed to increase the response rate. Participants were meant to be older than 16 years, and to have awareness of the concept of smart homes. In total, 285 Jordanian respondents took the survey within a two-month period (Nov–Dec 2017). Then, the data were downloaded from the SurveyMonkey.com portal into SPSS23.0 data file. Afterwards, a code was assigned to each question; all questions were coded by using letters and numbers on the basis of abbreviation and sequence. By going through the cases, 25 responses (out of 285) were detected as 4
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Table 1 Items loading and Cross loading.
ATT1 ATT2 ATT3 ATT4 ATT5 AW1 AW2 AW3 AW4 IU1 IU2 IU3 IU4 PE1 PE2 PE3 PE4 PEOU1 PEOU2 PEOU3 PEOU4 PR1 PR2 PR3 PR4 PU1 PU2 PU3 PU4 PU5 TR1 TR2 TR3 TR4
Attitudes
Awareness
Intention to Use
Enjoyment
Ease of Use
Risks
Usefulness
– > Trust
0.8421 0.9399 0.9157 0.9338 0.9438 −0.5613 −0.6223 −0.4139 −0.5169 0.6703 0.6812 0.6802 0.6709 0.6641 0.6864 0.6428 0.7001 0.6458 0.6884 0.7136 0.7244 −0.4422 −0.4044 −0.4006 −0.4138 0.7327 0.6366 0.7184 0.7164 0.7264 0.5317 0.5694 0.6462 0.6994
−0.5164 −0.5106 −0.5769 −0.5866 −0.5551 0.936 0.9312 0.7976 0.9047 −0.6112 −0.6456 −0.6993 −0.7334 −0.6593 −0.5922 −0.63 −0.6442 −0.5302 −0.6073 −0.6432 −0.664 0.4004 0.3323 0.3227 0.3742 −0.6678 −0.5525 −0.6391 −0.6755 −0.605 −0.5577 −0.6027 −0.6517 −0.6009
0.5994 0.7223 0.7409 0.7031 0.728 −0.6985 −0.7093 −0.4507 −0.6209 0.9506 0.9681 0.9601 0.9427 0.7863 0.7087 0.7265 0.7253 0.646 0.6673 0.6461 0.6845 −0.4228 −0.4041 −0.4223 −0.4335 0.7872 0.5776 0.7064 0.7259 0.7546 0.5859 0.6186 0.7108 0.7463
0.6141 0.6959 0.6773 0.7125 0.7685 −0.6743 −0.7427 −0.4455 −0.5834 0.7532 0.7395 0.7086 0.7503 0.9401 0.9064 0.9282 0.914 0.5752 0.6664 0.6264 0.649 −0.4322 −0.4038 −0.4372 −0.4767 0.6922 0.5794 0.6803 0.7268 0.7124 0.4567 0.5062 0.6266 0.6724
0.683 0.7408 0.6844 0.7436 0.7445 −0.6158 −0.6693 −0.5093 −0.6537 0.7007 0.698 0.7463 0.6923 0.7154 0.5787 0.6406 0.6705 0.8747 0.9106 0.9093 0.869 −0.4398 −0.3865 −0.3877 −0.4595 0.716 0.7106 0.6816 0.7185 0.7402 0.5038 0.5307 0.6049 0.6541
−0.4251 −0.4532 −0.3544 −0.3878 −0.4002 0.3754 0.3881 0.2725 0.3034 −0.4262 −0.437 −0.458 −0.3785 −0.4425 −0.3416 −0.4514 −0.4721 −0.3119 −0.363 −0.4138 −0.481 0.9234 0.9602 0.9572 0.9456 −0.5154 −0.4405 −0.5393 −0.6166 −0.546 −0.232 −0.1961 −0.2754 −0.3296
0.6415 0.7817 0.7353 0.7079 0.7087 −0.6988 −0.7252 −0.4654 −0.5789 0.7656 0.7755 0.784 0.7038 0.7076 0.6789 0.7238 0.7703 0.6312 0.7417 0.696 0.7534 −0.5943 −0.568 −0.5397 −0.5468 0.9219 0.8149 0.91 0.9124 0.9165 0.4782 0.5271 0.6332 0.6865
0.5241 0.659 0.6427 0.6624 0.6085 −0.6334 −0.6652 −0.466 −0.696 0.6271 0.7091 0.7595 0.7167 0.5926 0.6084 0.5225 0.5965 0.4563 0.4952 0.6145 0.6826 −0.2779 −0.2773 −0.2367 −0.2936 0.5304 0.4971 0.6222 0.6094 0.6319 0.861 0.9201 0.9144 0.9431
satisfying the second requirement of convergent validity. The amount of variance explained by R2 provides an indication of the model fit [27] as well as the predictive ability of the endogenous variables [30]. Hair et al. [27] suggest that the minimum level for an individual R2 should be greater than a minimum acceptable level of 0.10. The R2 value of almost all endogenous variables: ‘perceived Usefulness’, ‘Attitudes towards Smart Home’, and ‘Intention to Use’ were accepted and found relatively high (63.2%, 77.2%, and 76.9% respectively). However, the R2 value of ‘Trust’ was below the cutoff point, indicating that the variable ‘perceived risks’ explains8.3% of the variation of the indigenous variable ‘Trust’. This is expected as perceived risks, solely, could not be major contributor of trust as trust is multifaceted and multidisciplinary term. Overall, the model is valid, and it was appropriate to examine the significance of the paths associated with these variables. Another validity measurement, discriminant validity examines the extent to which a latent variable is truly distinct from other latent variables in predicting the dependent variable [27]. One popular approach to assess the discriminant validity followed in the current research was through examining the correlation matrix among constructs. Specifically, the AVE of each latent construct should be higher than the construct's highest squared correlation with any other latent construct [27]. The square roots of the AVE values of all constructs are calculated and compared with correlations between constructs. The results in Table 3 indicate that all constructs in the research model achieved this criterion as none of the off-diagonal elements exceeded the respective diagonal element. Thus, discriminant validity was demonstrated.
Table 2 Validity and reliability estimates of the constructs.
Attitudes Awareness Ease of Use Enjoyment Intention to Use Perceived Risks Trust Usefulness
AVE (> 0.5)
Composite Reliability (> 0.6)
Cronbach's Alpha (> 0.7)
R Square
0.8388 0.7995 0.794 0.8506 0.9129
0.9629 0.9408 0.9391 0.9579 0.9767
0.9516 0.9159 0.9135 0.9414 0.9682
0.7721 0 0 0 0.7692
0.8962
0.9719
0.9614
0
0.8284 0.8029
0.9507 0.9531
0.9309 0.9381
0.0833 0.6319
convergent validity. An alternative approach to assess the convergent validity of the constructs is to examine the composite reliability of the constructs [32]. All constructs exhibited acceptable to high scores of composite reliability by exceeding the 0.60 threshold recommended by Ref. [27]. In order to assess the internal consistency, Cronbach's alpha measures need to be examined. Internal consistency is achieved when reliability estimates are greater than 0.70 [27,33]. The 0.07 threshold is regarded in the social sciences data to be the most commonly accepted cut off point [30]. Those measurements that demonstrate low reliability levels should not be further investigated, as convergent validity would not be achieved [27]. As presented in Table 2, all scores exhibited acceptable to high reliabilities, with Cronbach's coefficient alpha exceeding the 0.70 threshold recommended by Refs. [27,33], thereby,
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Table 3 Correlation Matrix among Construct Scores (Discriminant validity).
Attitudes Awareness Ease of Use Enjoyment Intention to Use Perceived Risks Trust Usefulness
Attitudes
Awareness
Ease of Use
Enjoyment
Intention to Use
Perceived Risks
Trust
Usefulness
0.9158 −0.5996 0.7856 0.7599 0.8119 −0.4395 0.6782 0.7981
0.8941 −0.689 −0.6972 −0.7048 0.3794 −0.6949 −0.7019
0.8910 0.7082 0.7427 −0.444 0.6356 0.7749
0.9222 0.7767 −0.463 0.6302 0.6101
0.9554 −0.4448 0.6371 0.819
0.9466 −0.2885 −0.5949
0.9101 0.6469
0.896047
Table 4 Influence paths and hypotheses results. Path
H#
Original Sample (β) (> 0.1)
T Statistics (|O/STERR|) (> 1.96)
P-Value (< 0.05)
Result
Perceived Usefulness → Intention to Use Perceived Usefulness → Attitudes towards Smart Home Perceived Ease of Use → Perceived Usefulness Perceived Ease of Use →Attitudes towards Smart Home Attitudes towards Smart Home → Intention to Use Perceived Risks → Trust Trust → Attitudes towards Smart Home Trust → Intention to Use Perceived Enjoyment → Attitude towards Smart Home Awareness → Attitude towards Smart Home
H1 H2 H3 H4 H5 H6 H7 H8 H9 H10
0.3898 0.4114 0.7949 0.3074 0.2966 −0.2885 0.2275 0.2838 0.2078 0.2039
5.3571 6.5383 31.5967 5.7157 3.5595 4.2367 4.3217 4.9756 4.2801 3.9344
.000 .000 .000 .000 .000 .000 .000 .000 .000 .000
Supported Supported Supported Supported Supported Supported Supported Supported Supported Supported
using them. Originally, this path (trust → intention) had not been studied before in smart homes. This finding is considered an original finding in the smart home arena. In general, trust is found to have a strong positive significant influence on attitude towards smart homes. The higher the trust in smart homes the more positive the attitude towards them. Trust here is the intrinsic feeling of smart homes to be reliable, controllable and competent, which expresses user's trust beliefs on smart homes. This finding is consistent with what have reviewed in the smart home arena [21]. Therefore, researchers should give more effort towards studying trust in smart homes (as a dependent variable) and what factors could affect trust in this context. In addition, vendors of smart homes should pay attention on how to establish consumer trust in smart homes. Interestingly, ‘perceived risks’ was found to have a significant negative impact on users' trust in smart homes. Whereas a previous study indicated that security and privacy risk negatively affected attitude toward using smart homes [12], this research points out that perceived security and privacy risks could hinder users' trust in smart homes, in a way that some issues regarding breaching or loosing personal data by using smart homes can lower trust levels in smart homes, which in turn impact their attitudes towards smart homes or directly influence their intention to use them. Several independent variables have been discovered to have significant influences on attitude towards using smart homes. For instance, people's awareness about smart homes could significantly impact their attitudes towards them. In specific, the higher the awareness in smart homes and how they work, the higher the attitude towards them. It is worth mentioning that this path (awareness → attitude) has not been investigated yet in smart homes literature, given that the direct impact of awareness on PEOU had been studied in smart homes [15]. Therefore, this finding could enrich the relevant literature with an influential new path. Therefore, smart homes providers are advised to educate consumers in order to increase their awareness about them. Perceived enjoyment is also found to have a significant influence on attitude towards smart homes. Many people perceive working on smart homes as fun and joy, which in turn could elevate their attitude towards
5.2. PLS structural (inner) model results An assessment of the structural model was undertaken to determine the significance of the paths and the predictive power of the model through the PLS algorithm, then by considering a bootstrapping process that involved random resamples from the original data set to determine the significant levels of path coefficients [27]. Firstly, a systematic assessment of the structural model was conducted to assess the significance of path coefficients by examining the Standard Error, T-statistics, and confidence interval [27]. Table 4 highlights the hypotheses of the study and shows the path coefficient between the latent variables and bootstrap critical ratios. The bootstrap T-Statistics determine the stability of the estimates; considered acceptable above 1.96 at 95% confidence interval [27], by using 5000 samples as suggested by Ref. [27]. As a result, all research hypotheses were supported. The results of each path are interpreted in next section. Fig. 3 below also shows the representation of the research model tested and validated, as it appeared on SmartPLS2.0 software. These results provide a foundation for the discussion in the next section, where an additional interpretation of the results is presented, along with a discussion of the implications of the results. 6. Discussion In agreement with TAM postulates, the results in the current research found that people's perceived usefulness of smart homes significantly and positively influence their intention to use. This path was found as the most influential on intention to use, among other paths associated with intention to use, such as attitude towards smart homes, and trust. In detail, ‘attitude towards’ indeed has a significant positive effect on intention, unsurprisingly, confirming the TAM model. The third most influential factor on intention, trust, is found to be a crucial factor that influences people's intention to use smart homes. Indeed, the more the people feel that smart homes are reliable, competent and controllable, the higher the intention to use them. Thus, perceiving smart homes as trustworthy could certainly increase the possibilities of
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Fig. 3. Research model – tested and validated.
them and increase their intention to use them. Although perceived enjoyment is gaining increasing popularity in technology acceptance, this study is the first to investigate the influence of this factor on attitude towards smart homes. As a practical matter, providers of smart homes should consider improving user enjoyment through interactive functions that include fun and excitement elements.
could make a significant shift to the civilian society and achieve an improved technology awareness levels and a higher quality of life. This research has some limitations. At the method level, this research (as any research applying the survey-based method) was prone to the inherent limitation of measurement errors [28,34]. Specifically, the limitation concerns the type of questionnaire used in this research. Recording the opinions, observations and perceptions of a subset of Jordan population at a defined time means that the causality of consumer in smart homes in Jordan can only be inferred but cannot be proven. As a consequence, this limits the statistical capability to estimate a greater range of conditional probabilities of consumer acceptance of smart homes in Jordan. Nevertheless, the measurement errors were minimized, as indicated by the study's good validity and reliability results reported, and a future study could be conducted in a longitudinal fashion, which would make possible stronger causal conclusions. In addition, a qualitative method could be applied to investigate emerging acceptance conditions and dimensions, or to gain in-depth insights on some inter-relationships among factors. Secondly, at the model level, this study has focused on the users' characteristics. However, some other factors might be included and tested, such as attributes of smart homes and subjective norms. In addition, researchers are encouraged to include the culture factor in the model for future research to achieve better understanding of consumers' perspectives towards the technology of smart homes.
7. Conclusion, limitation and future direction The results confirm the research model, in that users' attitude towards accepting smart homes is heavily affected by users’ awareness, perceived enjoyment and trust which the former is impacted negatively by perceived risks which might be associated with those homes. Thus, it is strongly recommended, for both practitioners and researchers, to consider focus on these factors for better understanding and higher acceptance and usage rates of smart homes. By identifying these factors, it will be easier for smart devices manufacturers to consider fulfil the consideration associated with the factors, especially providing a joyful experience with smart home settings and trustworthy infrastructure and components to a better smart home platform. In addition, residents in general and smart devices specifically should be well educated and acknowledged about operating the devices of a smart home to preferably achieving the shift from awareness to familiarity by providing a real experience either virtually or in the reality world. By doing so, this
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Appendix A Items
Symbol
Perceived Usefulness (PU) PU1 Using Smart Homes would enable me to accomplish home tasks more quickly PU2 Smart Homes would make be useful for me to control home expenses and bills. PU3 Using Smart Homes would enhance the quality of my life. PU4 Using Smart Homes would enable me to accomplish home tasks more easy. PU5 Overall, I would find using Smart Homes to be advantageous. Perceived Ease of Use (PEOU) PEOU1 I feel using Smart Homes would be easy. PEOU2 I feel learning to use Smart Homes would be easy for me. PEOU3 I feel my interaction with the Smart Homes would be clear and understandable. PEOU4 I feel I would find it easy to get the Smart Home objects to do what I want it to do. Attitudes Toward Smart Home (ATT) ATT1 In my opinion, it is desirable to use Smart Homes. ATT2 I feel using Smart Homes would be a good idea. ATT3 I feel I would have a generally favorable attitude toward using Smart Homes. ATT4 I feel using Smart Homes would be beneficial for me. ATT5 I like the idea of using Smart Homes. Intention to Use (IU) IU1 By offering, I intend to use Smart Homes. IU2 I am willing to use Smart Homes in the near future. IU3 I would recommend Smart Homes to others. IU4 If I have smart home objects, I would subscribe in Smart Homes. Perceived Enjoyment (PE) PE1 Using Smart Homes would be fun. PE2 Using Smart Homes would be pleasurable. PE3 Using Smart Homes would give enjoyment to me. PE4 I feel excited towards using Smart Homes. Awareness AW1 I am aware of Smart Home objects. AW2 I am aware of Smart Homes. AW3 I am familiar with Smart Home objects. AW4 I understand what Smart Homes do. Trust TR1 I feel Smart Homes to be trustworthy. TR2 I feel Smart Homes to be reliable. TR3 I feel Smart Homes to be controllable. TR4 I feel Smart Homes to be competent. Perceived Risks PR1 I have privacy concerns associated with Smart Homes. PR2 I am anxious about my personal data by using Smart Homes. PR3 I have security concerns associated with Smart Homes. PR4 I am anxious about the data security of the Smart Homes.
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Contents lists available at ScienceDirect
Technology in Society journal homepage: www.elsevier.com/locate/techsoc
Understanding users’ acceptance of smart homes Ahmed Shuhaibera, Ibrahim Mashalb,∗ a b
College of Technological Innovation, Zayed University, Abu Dhabi, United Arab Emirates Computer Science Department, Faculty of Information Technology, Aqaba University of Technology, Aqaba, Jordan
ARTICLE INFO
ABSTRACT
Keywords: Internet of things User acceptance Smart home Personal factors Technology acceptance model
Smart homes allow owners to monitor and control wide range of home appliances remotely and intelligently. Despite its potential, the acceptance of smart homes by residents is still far from expectations. Thus, this study aims to investigate the factors that influence residents’ acceptance and usage of smart home. By extending the Technology Acceptance Model (TAM), this research incorporates more factors related to users such as trust, awareness, enjoyment, and perceived risks to study intention to use smart homes and investigate their impact quantitatively by using SEM-PLS approach. Results show that trust, awareness, enjoyment, and perceived risks, with perceived usefulness and perceived ease of use significantly influence attitude towards smart homes which, in turn, impact the intention to use smart homes.
1. Introduction The Internet of things (IoT) has been introduced as a new paradigm to expand the current Internet to physical objects or things [1,2]. IoT is a dynamic global network of billions of heterogeneous smart objects that are capable to sense, collect, share, and exchange information by communicating and interacting with each other. Smart objects include computers, mobile phones, heart beat monitors, smart phones, smart lighting, power meters, smart locks, motion detection sensors, and temperature sensors. It is expected that the IoT global market will worth between $3.9 and $11.1 trillion by 2025 and the number of connected smart objects will be 212 billion in 2020 [3,4]. IoT has created many powerful applications which allow users to interact directly with smart objects in smart environments to improve work efficiency and life quality. Potential IoT applications cover several aspects of every-day life such as health care, smart society, supply chain management and many others. For example, smart homes aim to improve residents’ quality of life and respond to their needs by equipping a residence with a communications network to connect sensors and smart devices and appliances which are remote-controlled and accessed through mobile phones or personal computers (PCs) [5,6]. In Jordan, Umniah, a mobile cellular telecommunication operator, has launched, among other companies, smart homes and buildings automation enabling home control, energy management, and security systems. Umniah company offers the customers advanced and convenient ways to manage their smart devices anytime anywhere through a mobile application which runs over phones and tablets. However, the
∗
smart home is still a new concept for Jordanian consumers in general and is far from their everyday lives. Indeed, smart homes are seen as a luxury, symbolic of wealth, and a means of enhancing their sense of self-importance in Jordan. Consequently, the adoption rates of smart homes are very low among Jordanians. For instance, LivingTech, a smart homes provider, reported that the number of their smart home projects is little more than 200. An important reason for the low adoption rate is the lack of understanding how users accept smart homes. Relatively few empirical studies have been conducted to explain users' acceptance of smart homes. Understanding how and why users accept smart homes is an important issue for their success and will boost the rate of users' adoption. Furthermore, understanding users’ acceptance helps firms marketing and promoting smart homes more effectively. Another advantage is providing reliable guidelines and significant insights smart homes providers to develop more attractive and interesting applications. To address this gap, this study aims to investigate users' acceptance of smart homes to develop a better understanding of the factors significantly influence user acceptance of them. This study contributes to adoption studies by proposing a theoretical model to examine factors that influence users' acceptance of smart homes. Based on the research model, this study examines the impact of users’ personal factors (e.g., awareness and trust) on smart homes acceptance and intention to use it. This work applies the Structural Equation Modeling (SEM) approach to assess the empirical strength of the relationships in the proposed model. To the best of our knowledge, this is the first study on smart homes acceptance in Jordan.
Corresponding author. E-mail address: [email protected] (I. Mashal).
https://doi.org/10.1016/j.techsoc.2019.01.003 Received 4 March 2018; Received in revised form 1 January 2019; Accepted 14 January 2019 0160-791X/ © 2019 Elsevier Ltd. All rights reserved.
Please cite this article as: Shuhaiber, A., Technology in Society, https://doi.org/10.1016/j.techsoc.2019.01.003
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The major contributions of this paper are:
Smart homes are at an early stage of diffusion despite their broad range of potential and benefits. It is important to motivate individual users to adopt smart homes. Consequently, studying and understanding consumers’ perspectives and behaviors concerning smart home adoption are required to help providers expand their services.
• Explore the adoption of smart homes. • Develop and test an integrated model to explain users' acceptance of smart homes. • Identify the factors affecting users' acceptance of smart homes. • Understand the impact of users' beliefs about smart homes on their
2.2. Related work
acceptance.
As mentioned above, identifying the factors influencing users’ acceptance of smart homes is a relatively new research arena [7]. Only a few studies have been carried out. For example [11], examined the adoption of smart homes by combining Value-based Adoption Model (VAM) and TAM. The result showed that both perceived sacrifice and perceived benefit had an effect on perceived value with perceived benefit having a higher significance on perceived value. For perceived sacrifice, privacy risk and innovation resistance were found to have limit perceived value. In another recent work [12], proposed a theoretical model that extends the TPB to explain Korean customers’ behavioral intentions to adopt and use smart homes. The result of the study shows that mobility, security/privacy risk, and trust in providers are important factors affecting the adoption of smart homes. Another study from Korea is presented in (S [13]. where six focus group discussions were conducted. The main findings of that research were that benefits, and lifestyle determine the adoption of smart home technologies. Moreover, the study showed that concerns about customer care and privacy are critical barriers to its adoption. In Europe, a qualitative cross-country study of smart homes was conducted in three countries UK, Germany, and Italy [14]. The study reported that tangible benefits and increases of quality of live will be the drivers for smart home development. The study also revealed smart homes adoption barriers such as lack of understanding of smart home technology, concerns on technology failure or difficulties in use, privacy and/or security concern, and loss of consumer freedom. In the UK [15], conducted an exploratory study on the adoption of IoT in homes. The findings show that the perceived usefulness, ease of use, privacy and security, and knowledge of the technology are significant predictors of the adoption of IoT in homes. On the basis of the Unified Theory of Acceptance and Use of Technology (UTAUT) [16], developed a model that explains predictors of intention to adopt sustainable household technology in the USA. The results show that product attributes including performance,
The rest of the paper is organized as follows. Section 2 introduces the literature review. Section 3 presents research model and hypotheses. Section 4 depicts the methodology used in this research. Section 5 introduces results and findings, followed by a discussion in Section 6. Finally, Section 7 concludes the paper. 2. Literature review 2.1. Smart homes Among all IoT applications, smart homes are expected to have a broad range of potential and great benefits in the near future [7]. Smart homes have received considerable attention from both commercial and academic bodies in different countries. Nowadays, companies such as Google, Amazon, Samsung, AT&T and Comcast are providing smart home and their products. The global smart home market is expected to grow at a compound annual growth rate of 29.5% between 2012 and 2020. Forecasts indicate that by 2022 a typical family home could contain more than 500 smart devices [8]. A smart home is a cyber physical system where homes are equipped with interconnected sensors and smart appliances which are controlled over the Internet to facilitates monitoring of the surrounding environment to ensure home and personal safety and security [5,9]. A traditional smart home, as shown in Fig. 1, is often implemented under a centralized architecture where different home appliances are connected by an internal home network and controlled by the home gateway. Users interact with their smart home appliances and smart homes providers using mobile devices through telecommunications networks. Devices include smart fridge, smart curtains, smart oven, smart microwave, smart coffee maker, smart doors, smart light, smart fire alarm system, and other sensors for tracking and monitoring. Smart homes providers collect and analyze data from home appliances and mobile devices to provide interactive and automated services to residents [10].
Fig. 1. Smart home architecture. 2
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3.2. Personal factors 3.2.1. Perceived risk Smart homes can expose users to a high level of threats and risk including disclosure and violating of user's information privacy, high loss of data confidentiality and authentication. Perceived security risks usually include security, privacy, financial and technical risks [18]. Security and privacy risk concerns are considered major barrier facing smart homes adoption. In other words, users' acceptance of smart homes depends on the protection of their privacy and security. We define perceived risk as the degree to which users believe that using smart homes is secure, safe, and will protect their data. From our point of view, perceived risk has a direct relation and dramatically affects users' trust in smart homes. According to Ref. [19]; cyber risks are an existing thread to modern business and online applications such as smart factories and smart homes, which in turn could affect the decision-making process of corporate and individuals [20]. Therefore, we hypothesize that:
Fig. 2. Research model.
compatibleness, and hedonic expectancy as well as consumer characteristics and sustainable innovativeness significantly predicts adoption intent. But effort expectancy as well as social pressure and environmentalism are not significant predictors of adoption intention.
H6. Perceived risk will have a significant negative influence on trust smart homes.
3. Research model and hypotheses As illustrated in Fig. 2, the research model is developed on the basis of the literature review. The model consists of four technology factors (perceived usefulness, perceived ease of use, attitude towards use, and intention to use); three personal factors (awareness, trust, and perceived enjoyment) and finally perceived risk. The model indicates that perceived usefulness, attitude towards use, and trust influence intention to use smart homes. In addition, perceived ease of use affects perceived usefulness. Moreover, perceived usefulness, perceived ease of use, awareness, trust, perceived enjoyment affects attitude towards uses smart homes. Finally, perceived risk affects trust.
3.2.2. Trust Users' trust of smart homes is important to help people overcome concerns about risks and mitigate uncertainty facing adoption of smart homes. Intuitively, when users trust a technology, they will have a high positive attitude towards using this technology. For the purpose of this paper, we define trust as users' confidence in the level of reliability of smart homes that make the users meet their expectations toward the systems. In smart homes context [21], showed that residents' perceived trust in smart homes positively relates to their attitude towards smart homes. In the same line [22], reported that trust of smart homes was positively related to the users’ intention to use smart homes. Thus, we incorporate trust in our research model as one of the important factors influencing both attitude towards and intention to use smart homes, which leads to the following hypothesis:
3.1. TAM factors Technology Acceptance Model (TAM) [17] is the most commonly used model and is generally preferred for predicting and explaining user behavior toward new technology acceptance and usage. The TAM defines four main constructs are used, namely, intention to use, attitude towards, perceived usefulness, and ease of use. Perceived usefulness and attitude determine intention to use. The attitude toward using a technology is affected by the perceived ease of use and usefulness. Moreover, perceived ease of use also positively influences perceived usefulness. In the smart homes context, we define these constructs as follows. Perceived usefulness as the degree to which a user believes that using smart homes will enhance user life quality. Perceived ease of use is the degree to which a user believes that using smart homes would be free of physical and mental effort. Attitude towards using smart homes is defined as the degree of user's positive or negative evaluation or feelings about using smart homes. Intention to use smart homes is defined as an indication or a measure of the strength of a user's readiness to use smart homes. Based on this, we present the following hypotheses:
H7. Trust will have a significant positive influence on attitude towards using smart homes. H8. Trust will have a significant positive influence on intention to use smart homes. 3.2.3. Perceived enjoyment We define perceived enjoyment in the context of smart homes as the extent to which the activity of using smart homes is perceived to be enjoyable in its own right, apart from any performance consequences that may be anticipated. In smart homes context, perceived enjoyment has been positively associated with the perceived ease of use [22]. However, the relation between enjoyment and attitude towards using smart homes has never been tested. Thus, we hypothesize that: H9. Perceived enjoyment will have a significant positive influence on attitude towards using smart homes.
H1. Perceived usefulness will have a significant positive influence on intention to use smart homes. H2. Perceived usefulness will have a significant positive influence on attitude towards using smart homes.
3.2.4. Awareness It is widely agreed that decisions to accept or reject a technology take place after the users are made knowledgeable of the innovation [23]. Awareness is defined by Ref. [24] as an understanding that allows the user to reduce uncertainty in a subjective manner. Awareness with respect to smart homes is certainly associated with learning about the smart devices connected and how they communicate but might not be associated with past personal experience despite the strong relationship between them, because there are many resources for delivering knowledge about it to the individual, thus awareness in this context is different than familiarity. It has been found that one of the potential
H3. Perceived ease of use will have a significant positive influence unperceived usefulness of smart homes. H4. Perceived ease of use will have a significant positive influence on attitude towards using smart homes. H5. Attitude towards using smart homes will have a significant positive influence on intention to use smart homes.
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barriers of smart homes adoptions is lack of awareness of smart home technology [14]. Similarly, Coughlan et al. [15] reported that awareness of the technology are important factors in adopting smart homes. We further extend the original TAM by incorporating the awareness as an additional factor and propose a relation between awareness and the attitude to use smart homes.
uncompleted for most of the variables, and two respondents were found to have bias in a certain answer. As recommended by Creswell [26], these 27 responses were eliminated from the data analysis. Consequently, 258 responses were deemed valid for the research and showed readiness for analysis. 5. Data analysis and results
H10. Awareness will have a significant positive influence on attitude towards using smart homes.
In order to test and validate the research model, Structural Equation Modeling (SEM) was applied in this research. SEM is “a family of statistical models that explain the relationships among multiple variables” [27]. SEM permits complicated variable relationships to be expressed and gives a more complete picture of the entire model [28], it is being extensively used in behavioral science research [27] and in marketing, information technology and systems research [28]. PLS is the preferred approach for causal-predictive analysis and is better suited for theory development than for theory testing [29]. It is also suitable when small samples are employed for estimation and testing [30]. Another supportive point is that PLS can be applied to complex structural equation models with a large number of constructs [29]. Therefore, SEM using the PLS technique was used in the current study to test the overall structure of the research model. PLS is an iterative process that “provides successive approximations for the estimates, subset by subset, of loadings and structural parameters” [31]. The PLS model is usually analyzed and interpreted in two stages; firstly, by assessing the reliability and validity of the measurement model (constructs and items); and secondly, by assessing the structural model through interpreting the path coefficients and identifying the adequacy of the research model [27]. The following sections discuss the results of these two stages, by using SmartPLS 2.0 software.
4. Methodology The research paradigm followed in the current research is positivism, as it is believed that factual knowledge could be gained through observations and measurements, by collecting data and interpretation through objective approaches and that the research findings are usually observable and quantifiable. Thus a quantitative method was employed in this research to answer the research questions. Specifically, the data gathered were prepared for analysis, and then the preliminary quantitative data analysis was conducted by applying and presenting the demographic analysis regarding the research sample. Afterwards, the advanced quantitative analysis is presented through the use of SEM-PLS regression analysis, along with the revised research model and the research hypotheses. Details about each phase are presented in the subsequent sections. 4.1. Instrument development A questionnaire instrument was employed in the current research and is adapted using the conceptualization and development work found in smart homes literature. The questionnaire contained four measuring items for variables such as perceived enjoyment, awareness, trust, perceived risks, perceived ease of use, and intention to use smart homes. Perceived usefulness and attitude towards smart home applications had five measuring items each. Six demographic variables were recorded: gender, age range, educational level, occupation, marital status, and family-member category. A seven-point Likert scale was used to measure the constructs presented in the model (scores ranged from 1 = ‘strongly disagree’ to 7 = ‘strongly agree’, with the ‘neutral’ score = 4). This scale would effectively allow respondents to express their opinions in this research as it offers a wider range of agreement levels to a statement than the traditional five-point scale. The survey was available in two languages (Arabic and English). When translating the questionnaires from English to Arabic, the researchers ensured that the meaning of the source language statement was preserved to achieve the semantic equivalent [25]. The survey instrument was refined during a pre-test to ensure the internal consistency of the measured instrument, with the involvement of 18 respondents. Consequently, only one item associated with the construct ‘Awareness’ (AW3) was rephrased. Afterwards, a pilot sample was conducted using 30 respondents to assure the reliability and validity of the instrument. As a result, all resulting measuring items were clear and usable (See appendix A).
5.1. PLS measurement (outer) model results Firstly, the values of the outer loadings were examined in order to view the correlations between the latent variable and the reflective indicators in its outer model. According to Ref. [27], indicators with outer loading above 0.6 were retained, whereas indicators with outer loadings between 0.4 and 0.6 were “considered for removal from the scale only when deleting the indicator leads to an increase in the composite reliability (or the Average Variance Extracted) above the suggested threshold value”. Indicators with very low outer loadings (below 0.4) were eliminated from the scale. By performing the outer model testing, all the items were found above the acceptable level of (0.6), thus demonstrating reliable items. In total, 34 items were validated to measure the dependent and independent variables, as shown in Table 1. Additional testing of the quality and the scales was conducted, which evaluates item cross loadings. In order to examine the discriminant validity across the items, the pattern of item loadings across constructs in the model was examined (called cross loadings). Specifically, an item loading on the associated construct should be greater than all of its loadings on other constructs [27]. In this research, the discriminant validity of all items was demonstrated, since cross loadings among different constructs were greater than the determined cut off point (as shown in Table 1). Another measurement involved in the outer model testing is construct validity. Construct validity assesses whether the measures chosen are true measures of the constructs describing the event, and that these measures are actual tools for representing or measuring the construct being investigated [27,28]. For the current study, construct validity was established, including both convergent and discriminant validity. Convergent validity refers to the extent to which a measure correlates, or converges, with other measures of the same construct [27]. Convergent validity is demonstrated when the Average Variance Explained (AVE) value between the constructs is equal to, or exceeds, 0.5 [27,32]. As seen in Table 2, the AVE scores for all constructs in the model were more than 0.5, which meets the first requirement of achieving
4.2. Data collection and preparation Data were collected using a convenience sampling approach via an online self-administered survey and a snowball technique was employed to increase the response rate. Participants were meant to be older than 16 years, and to have awareness of the concept of smart homes. In total, 285 Jordanian respondents took the survey within a two-month period (Nov–Dec 2017). Then, the data were downloaded from the SurveyMonkey.com portal into SPSS23.0 data file. Afterwards, a code was assigned to each question; all questions were coded by using letters and numbers on the basis of abbreviation and sequence. By going through the cases, 25 responses (out of 285) were detected as 4
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Table 1 Items loading and Cross loading.
ATT1 ATT2 ATT3 ATT4 ATT5 AW1 AW2 AW3 AW4 IU1 IU2 IU3 IU4 PE1 PE2 PE3 PE4 PEOU1 PEOU2 PEOU3 PEOU4 PR1 PR2 PR3 PR4 PU1 PU2 PU3 PU4 PU5 TR1 TR2 TR3 TR4
Attitudes
Awareness
Intention to Use
Enjoyment
Ease of Use
Risks
Usefulness
– > Trust
0.8421 0.9399 0.9157 0.9338 0.9438 −0.5613 −0.6223 −0.4139 −0.5169 0.6703 0.6812 0.6802 0.6709 0.6641 0.6864 0.6428 0.7001 0.6458 0.6884 0.7136 0.7244 −0.4422 −0.4044 −0.4006 −0.4138 0.7327 0.6366 0.7184 0.7164 0.7264 0.5317 0.5694 0.6462 0.6994
−0.5164 −0.5106 −0.5769 −0.5866 −0.5551 0.936 0.9312 0.7976 0.9047 −0.6112 −0.6456 −0.6993 −0.7334 −0.6593 −0.5922 −0.63 −0.6442 −0.5302 −0.6073 −0.6432 −0.664 0.4004 0.3323 0.3227 0.3742 −0.6678 −0.5525 −0.6391 −0.6755 −0.605 −0.5577 −0.6027 −0.6517 −0.6009
0.5994 0.7223 0.7409 0.7031 0.728 −0.6985 −0.7093 −0.4507 −0.6209 0.9506 0.9681 0.9601 0.9427 0.7863 0.7087 0.7265 0.7253 0.646 0.6673 0.6461 0.6845 −0.4228 −0.4041 −0.4223 −0.4335 0.7872 0.5776 0.7064 0.7259 0.7546 0.5859 0.6186 0.7108 0.7463
0.6141 0.6959 0.6773 0.7125 0.7685 −0.6743 −0.7427 −0.4455 −0.5834 0.7532 0.7395 0.7086 0.7503 0.9401 0.9064 0.9282 0.914 0.5752 0.6664 0.6264 0.649 −0.4322 −0.4038 −0.4372 −0.4767 0.6922 0.5794 0.6803 0.7268 0.7124 0.4567 0.5062 0.6266 0.6724
0.683 0.7408 0.6844 0.7436 0.7445 −0.6158 −0.6693 −0.5093 −0.6537 0.7007 0.698 0.7463 0.6923 0.7154 0.5787 0.6406 0.6705 0.8747 0.9106 0.9093 0.869 −0.4398 −0.3865 −0.3877 −0.4595 0.716 0.7106 0.6816 0.7185 0.7402 0.5038 0.5307 0.6049 0.6541
−0.4251 −0.4532 −0.3544 −0.3878 −0.4002 0.3754 0.3881 0.2725 0.3034 −0.4262 −0.437 −0.458 −0.3785 −0.4425 −0.3416 −0.4514 −0.4721 −0.3119 −0.363 −0.4138 −0.481 0.9234 0.9602 0.9572 0.9456 −0.5154 −0.4405 −0.5393 −0.6166 −0.546 −0.232 −0.1961 −0.2754 −0.3296
0.6415 0.7817 0.7353 0.7079 0.7087 −0.6988 −0.7252 −0.4654 −0.5789 0.7656 0.7755 0.784 0.7038 0.7076 0.6789 0.7238 0.7703 0.6312 0.7417 0.696 0.7534 −0.5943 −0.568 −0.5397 −0.5468 0.9219 0.8149 0.91 0.9124 0.9165 0.4782 0.5271 0.6332 0.6865
0.5241 0.659 0.6427 0.6624 0.6085 −0.6334 −0.6652 −0.466 −0.696 0.6271 0.7091 0.7595 0.7167 0.5926 0.6084 0.5225 0.5965 0.4563 0.4952 0.6145 0.6826 −0.2779 −0.2773 −0.2367 −0.2936 0.5304 0.4971 0.6222 0.6094 0.6319 0.861 0.9201 0.9144 0.9431
satisfying the second requirement of convergent validity. The amount of variance explained by R2 provides an indication of the model fit [27] as well as the predictive ability of the endogenous variables [30]. Hair et al. [27] suggest that the minimum level for an individual R2 should be greater than a minimum acceptable level of 0.10. The R2 value of almost all endogenous variables: ‘perceived Usefulness’, ‘Attitudes towards Smart Home’, and ‘Intention to Use’ were accepted and found relatively high (63.2%, 77.2%, and 76.9% respectively). However, the R2 value of ‘Trust’ was below the cutoff point, indicating that the variable ‘perceived risks’ explains8.3% of the variation of the indigenous variable ‘Trust’. This is expected as perceived risks, solely, could not be major contributor of trust as trust is multifaceted and multidisciplinary term. Overall, the model is valid, and it was appropriate to examine the significance of the paths associated with these variables. Another validity measurement, discriminant validity examines the extent to which a latent variable is truly distinct from other latent variables in predicting the dependent variable [27]. One popular approach to assess the discriminant validity followed in the current research was through examining the correlation matrix among constructs. Specifically, the AVE of each latent construct should be higher than the construct's highest squared correlation with any other latent construct [27]. The square roots of the AVE values of all constructs are calculated and compared with correlations between constructs. The results in Table 3 indicate that all constructs in the research model achieved this criterion as none of the off-diagonal elements exceeded the respective diagonal element. Thus, discriminant validity was demonstrated.
Table 2 Validity and reliability estimates of the constructs.
Attitudes Awareness Ease of Use Enjoyment Intention to Use Perceived Risks Trust Usefulness
AVE (> 0.5)
Composite Reliability (> 0.6)
Cronbach's Alpha (> 0.7)
R Square
0.8388 0.7995 0.794 0.8506 0.9129
0.9629 0.9408 0.9391 0.9579 0.9767
0.9516 0.9159 0.9135 0.9414 0.9682
0.7721 0 0 0 0.7692
0.8962
0.9719
0.9614
0
0.8284 0.8029
0.9507 0.9531
0.9309 0.9381
0.0833 0.6319
convergent validity. An alternative approach to assess the convergent validity of the constructs is to examine the composite reliability of the constructs [32]. All constructs exhibited acceptable to high scores of composite reliability by exceeding the 0.60 threshold recommended by Ref. [27]. In order to assess the internal consistency, Cronbach's alpha measures need to be examined. Internal consistency is achieved when reliability estimates are greater than 0.70 [27,33]. The 0.07 threshold is regarded in the social sciences data to be the most commonly accepted cut off point [30]. Those measurements that demonstrate low reliability levels should not be further investigated, as convergent validity would not be achieved [27]. As presented in Table 2, all scores exhibited acceptable to high reliabilities, with Cronbach's coefficient alpha exceeding the 0.70 threshold recommended by Refs. [27,33], thereby,
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Table 3 Correlation Matrix among Construct Scores (Discriminant validity).
Attitudes Awareness Ease of Use Enjoyment Intention to Use Perceived Risks Trust Usefulness
Attitudes
Awareness
Ease of Use
Enjoyment
Intention to Use
Perceived Risks
Trust
Usefulness
0.9158 −0.5996 0.7856 0.7599 0.8119 −0.4395 0.6782 0.7981
0.8941 −0.689 −0.6972 −0.7048 0.3794 −0.6949 −0.7019
0.8910 0.7082 0.7427 −0.444 0.6356 0.7749
0.9222 0.7767 −0.463 0.6302 0.6101
0.9554 −0.4448 0.6371 0.819
0.9466 −0.2885 −0.5949
0.9101 0.6469
0.896047
Table 4 Influence paths and hypotheses results. Path
H#
Original Sample (β) (> 0.1)
T Statistics (|O/STERR|) (> 1.96)
P-Value (< 0.05)
Result
Perceived Usefulness → Intention to Use Perceived Usefulness → Attitudes towards Smart Home Perceived Ease of Use → Perceived Usefulness Perceived Ease of Use →Attitudes towards Smart Home Attitudes towards Smart Home → Intention to Use Perceived Risks → Trust Trust → Attitudes towards Smart Home Trust → Intention to Use Perceived Enjoyment → Attitude towards Smart Home Awareness → Attitude towards Smart Home
H1 H2 H3 H4 H5 H6 H7 H8 H9 H10
0.3898 0.4114 0.7949 0.3074 0.2966 −0.2885 0.2275 0.2838 0.2078 0.2039
5.3571 6.5383 31.5967 5.7157 3.5595 4.2367 4.3217 4.9756 4.2801 3.9344
.000 .000 .000 .000 .000 .000 .000 .000 .000 .000
Supported Supported Supported Supported Supported Supported Supported Supported Supported Supported
using them. Originally, this path (trust → intention) had not been studied before in smart homes. This finding is considered an original finding in the smart home arena. In general, trust is found to have a strong positive significant influence on attitude towards smart homes. The higher the trust in smart homes the more positive the attitude towards them. Trust here is the intrinsic feeling of smart homes to be reliable, controllable and competent, which expresses user's trust beliefs on smart homes. This finding is consistent with what have reviewed in the smart home arena [21]. Therefore, researchers should give more effort towards studying trust in smart homes (as a dependent variable) and what factors could affect trust in this context. In addition, vendors of smart homes should pay attention on how to establish consumer trust in smart homes. Interestingly, ‘perceived risks’ was found to have a significant negative impact on users' trust in smart homes. Whereas a previous study indicated that security and privacy risk negatively affected attitude toward using smart homes [12], this research points out that perceived security and privacy risks could hinder users' trust in smart homes, in a way that some issues regarding breaching or loosing personal data by using smart homes can lower trust levels in smart homes, which in turn impact their attitudes towards smart homes or directly influence their intention to use them. Several independent variables have been discovered to have significant influences on attitude towards using smart homes. For instance, people's awareness about smart homes could significantly impact their attitudes towards them. In specific, the higher the awareness in smart homes and how they work, the higher the attitude towards them. It is worth mentioning that this path (awareness → attitude) has not been investigated yet in smart homes literature, given that the direct impact of awareness on PEOU had been studied in smart homes [15]. Therefore, this finding could enrich the relevant literature with an influential new path. Therefore, smart homes providers are advised to educate consumers in order to increase their awareness about them. Perceived enjoyment is also found to have a significant influence on attitude towards smart homes. Many people perceive working on smart homes as fun and joy, which in turn could elevate their attitude towards
5.2. PLS structural (inner) model results An assessment of the structural model was undertaken to determine the significance of the paths and the predictive power of the model through the PLS algorithm, then by considering a bootstrapping process that involved random resamples from the original data set to determine the significant levels of path coefficients [27]. Firstly, a systematic assessment of the structural model was conducted to assess the significance of path coefficients by examining the Standard Error, T-statistics, and confidence interval [27]. Table 4 highlights the hypotheses of the study and shows the path coefficient between the latent variables and bootstrap critical ratios. The bootstrap T-Statistics determine the stability of the estimates; considered acceptable above 1.96 at 95% confidence interval [27], by using 5000 samples as suggested by Ref. [27]. As a result, all research hypotheses were supported. The results of each path are interpreted in next section. Fig. 3 below also shows the representation of the research model tested and validated, as it appeared on SmartPLS2.0 software. These results provide a foundation for the discussion in the next section, where an additional interpretation of the results is presented, along with a discussion of the implications of the results. 6. Discussion In agreement with TAM postulates, the results in the current research found that people's perceived usefulness of smart homes significantly and positively influence their intention to use. This path was found as the most influential on intention to use, among other paths associated with intention to use, such as attitude towards smart homes, and trust. In detail, ‘attitude towards’ indeed has a significant positive effect on intention, unsurprisingly, confirming the TAM model. The third most influential factor on intention, trust, is found to be a crucial factor that influences people's intention to use smart homes. Indeed, the more the people feel that smart homes are reliable, competent and controllable, the higher the intention to use them. Thus, perceiving smart homes as trustworthy could certainly increase the possibilities of
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Fig. 3. Research model – tested and validated.
them and increase their intention to use them. Although perceived enjoyment is gaining increasing popularity in technology acceptance, this study is the first to investigate the influence of this factor on attitude towards smart homes. As a practical matter, providers of smart homes should consider improving user enjoyment through interactive functions that include fun and excitement elements.
could make a significant shift to the civilian society and achieve an improved technology awareness levels and a higher quality of life. This research has some limitations. At the method level, this research (as any research applying the survey-based method) was prone to the inherent limitation of measurement errors [28,34]. Specifically, the limitation concerns the type of questionnaire used in this research. Recording the opinions, observations and perceptions of a subset of Jordan population at a defined time means that the causality of consumer in smart homes in Jordan can only be inferred but cannot be proven. As a consequence, this limits the statistical capability to estimate a greater range of conditional probabilities of consumer acceptance of smart homes in Jordan. Nevertheless, the measurement errors were minimized, as indicated by the study's good validity and reliability results reported, and a future study could be conducted in a longitudinal fashion, which would make possible stronger causal conclusions. In addition, a qualitative method could be applied to investigate emerging acceptance conditions and dimensions, or to gain in-depth insights on some inter-relationships among factors. Secondly, at the model level, this study has focused on the users' characteristics. However, some other factors might be included and tested, such as attributes of smart homes and subjective norms. In addition, researchers are encouraged to include the culture factor in the model for future research to achieve better understanding of consumers' perspectives towards the technology of smart homes.
7. Conclusion, limitation and future direction The results confirm the research model, in that users' attitude towards accepting smart homes is heavily affected by users’ awareness, perceived enjoyment and trust which the former is impacted negatively by perceived risks which might be associated with those homes. Thus, it is strongly recommended, for both practitioners and researchers, to consider focus on these factors for better understanding and higher acceptance and usage rates of smart homes. By identifying these factors, it will be easier for smart devices manufacturers to consider fulfil the consideration associated with the factors, especially providing a joyful experience with smart home settings and trustworthy infrastructure and components to a better smart home platform. In addition, residents in general and smart devices specifically should be well educated and acknowledged about operating the devices of a smart home to preferably achieving the shift from awareness to familiarity by providing a real experience either virtually or in the reality world. By doing so, this
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Appendix A Items
Symbol
Perceived Usefulness (PU) PU1 Using Smart Homes would enable me to accomplish home tasks more quickly PU2 Smart Homes would make be useful for me to control home expenses and bills. PU3 Using Smart Homes would enhance the quality of my life. PU4 Using Smart Homes would enable me to accomplish home tasks more easy. PU5 Overall, I would find using Smart Homes to be advantageous. Perceived Ease of Use (PEOU) PEOU1 I feel using Smart Homes would be easy. PEOU2 I feel learning to use Smart Homes would be easy for me. PEOU3 I feel my interaction with the Smart Homes would be clear and understandable. PEOU4 I feel I would find it easy to get the Smart Home objects to do what I want it to do. Attitudes Toward Smart Home (ATT) ATT1 In my opinion, it is desirable to use Smart Homes. ATT2 I feel using Smart Homes would be a good idea. ATT3 I feel I would have a generally favorable attitude toward using Smart Homes. ATT4 I feel using Smart Homes would be beneficial for me. ATT5 I like the idea of using Smart Homes. Intention to Use (IU) IU1 By offering, I intend to use Smart Homes. IU2 I am willing to use Smart Homes in the near future. IU3 I would recommend Smart Homes to others. IU4 If I have smart home objects, I would subscribe in Smart Homes. Perceived Enjoyment (PE) PE1 Using Smart Homes would be fun. PE2 Using Smart Homes would be pleasurable. PE3 Using Smart Homes would give enjoyment to me. PE4 I feel excited towards using Smart Homes. Awareness AW1 I am aware of Smart Home objects. AW2 I am aware of Smart Homes. AW3 I am familiar with Smart Home objects. AW4 I understand what Smart Homes do. Trust TR1 I feel Smart Homes to be trustworthy. TR2 I feel Smart Homes to be reliable. TR3 I feel Smart Homes to be controllable. TR4 I feel Smart Homes to be competent. Perceived Risks PR1 I have privacy concerns associated with Smart Homes. PR2 I am anxious about my personal data by using Smart Homes. PR3 I have security concerns associated with Smart Homes. PR4 I am anxious about the data security of the Smart Homes.
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