User acceptance of wearable devices: An extended perspective of perceived value

Accepted Manuscript User acceptance of wearable devices: An extended perspective of perceived value Heetae Yang, Jieun Yu, Hangjung Zo, Munkee Choi PII: DOI: Reference:

S0736-5853(15)00106-9 http://dx.doi.org/10.1016/j.tele.2015.08.007 TELE 723

To appear in:

Telematics and Informatics

Received Date: Revised Date: Accepted Date:

23 April 2015 20 July 2015 13 August 2015

Please cite this article as: Yang, H., Yu, J., Zo, H., Choi, M., User acceptance of wearable devices: An extended perspective of perceived value, Telematics and Informatics (2015), doi: http://dx.doi.org/10.1016/j.tele.2015.08.007

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User acceptance of wearable devices: An extended perspective of perceived value

Heetae Yang Department of Business and Technology Management Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea Email: [email protected]

Jieun Yu Economics and Management Research Lab KT Corporation 90 Bulljeong-ro, Bundang-gu, Seongnam 463-711 Republic of Korea Email: [email protected]

Hangjung Zo@ Department of Business and Technology Management Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea Voice: +82-42-350-6311 Fax: +82-42-350-6339 Email: [email protected]

Munkee Choi Department of Business and Technology Management Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea Email: [email protected]

Submitted to the Telematics and Informatics on April 23, 2015 Revised and resubmitted to the Telematics and Informatics on July 20, 2015 Please do not quote without permission of the authors. @

Please direct future correspondence to the identified authors.

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User acceptance of wearable devices: An extended perspective of perceived value

Abstract This study develops a research model for analyzing customers’ perceived value of wearable devices. It investigates the impact of each component of perceived benefit and risk on the perceived value of wearable devices, and explores how their attributes affect customers’ perceived benefit. Partial least squares was employed to test the proposed model and corresponding hypotheses on data collected from 375 survey samples (273 potential and 102 actual users). The results show that perceived value is a clear antecedent of adoption intention. Perceived benefit—including perceived usefulness, enjoyment, and social image—seems to have a greater impact on perceived value than perceived risk. Specifically, a significant difference was observed between potential users and actual users. This study discusses a number of implications and contributes useful insights for researchers as well as practitioners.

Keywords: wearable device; perceived value; perceived benefit; perceived risk; user acceptance

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1. Introduction As information technology (IT) continues to develop, mobile devices are getting smarter and have become essential tools for communication (Wang et al., 2014). Mobile device types are diversifying into classes such as smartphones, tablet PCs, and wearable devices. Wearable devices are attracting much attention as the next generation of portable electronics. Nine out of ten smartphone vendors have already entered the wearable device market or are about to ship their first product, whereas, two years ago, only two vendors were at that stage (Gartner, 2014). The global wearable device market is expected to grow by 78% each year, from 19 million units in 2014 to 112 million in 2018 (IDC, 2014). Wearable devices are used external to the body, either attached as an accessory or embedded in clothes (Raskovic et al., 2004). They can be used in various applications equipped with sensors, internet connections, processors, and operating systems as well as user-friendly interfaces with touch pads/screens. Watch-type wearable device users can receive e-mail, text messages, and phone notifications on their wrists without having to pull out their cellphones. Wristband or necklace-type wearable devices are mainly used to track the user’s health and fitness status in real time. Head-mount display-type wearable devices are suitable for virtual reality content and 3D games. Despite the positive prospects and functionality of wearable devices, little research has been done on user acceptance and behaviors concerning them because they are still in the very early stage of commercialization. This study focuses on customers’ perceived value of wearable devices as well as its determinants. Its research model is developed based upon previous research that has studied perceived value by incorporating perceived usefulness, perceived enjoyment, and social image. This study examines users’ perceived value of wearable devices to investigate the impact of each component of perceived benefit and risk on perceived value and to explore how the attributes of wearable devices affect customers’ perceived benefit. This study divides users into potential users and actual users to compare the significant factors influencing perceived value.

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2. Literature review 2.1. Perceived value Schechter (1984) explained that perceived value is composed of qualitative and quantitative as well as objective and subjective factors that jointly form a buyer’s experience. Dodds et al. (1991) defined perceived value as the ratio of perceived benefit relative to perceived sacrifice. Woodruff and Gardial (1996) described perceived value as a trade-off between desirable attributes and sacrifice attributes. The most widely accepted definition of perceived value is that in Zeithaml (1988), who found that consumers defined value in four terms: value is (1) “low price,” (2) “whatever I want in a product,” (3) “the quality I get for the price I pay,” and (4) “what I get for what I give.” Zeithaml (1988) thus synthetically defined perceived value as the consumer’s overall assessment of the utility of a product based on the perception of what is received and what is given. Thus, this study refers to the perceived value of wearable devices as a potential customer’s overall perception of wearable devices based on their benefits and sacrifices.

2.2. Perceived benefit Extrinsic benefits are functional and utilitarian, while intrinsic benefit perceptions result from fun and playfulness for their own sake (Holbrook, 1999). Rogers (1995) found that both extrinsic and intrinsic factors influenced perceived value and behavioral intention. Several studies have also shown that perceived usefulness and perceived enjoyment are main components of extrinsic and intrinsic benefits, respectively (Davis et al., 1992; Kim et al., 2007). Park and Chen (2007) found that perceived usefulness and enjoyment are representative benefits by adopting innovative IT products. Social image, the extent to which peers in a user’s social network respect and admire the user because of IT usage (Lin and Bhattacherjee, 2010), is another important component of perceived benefit because people want to improve their social status or differentiate it from those of others in their social system.

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2.3. Perceived risk Dowling and Staelin (1994) conceptualized perceived risk as the consumer’s perception of the uncertainty and adverse consequences of purchasing a product or service. While a number of risk dimensions have been suggested, performance and financial risk are the most commonly used to estimate risk (Chen and Dubinsky, 2003; Grewal et al., 1994; Sweeney et al., 1999). Financial risk is defined as the possibility of monetary loss due to a customer, including product repair or replacement and refunds (Horton, 1976). This risk also includes possible future costs as well as the perceived price at the point of purchase. Performance risk is the potential for loss incurred when a brand or product fails to meet a consumer’s expectations. Thus, a conceptual distinction between financial and performance risk is that the price of the product is an intrinsic component of financial risk, whereas price is not directly related to performance risk. Snoj et al. (2004) used perceived risk as the concept of sacrifice for mobile phone purchases. Kim et al. (2007) regarded perceived fee (monetary) and technicality (non-monetary) as components of perceived sacrifice; these constructs are similar to financial risk and performance risk, respectively.

3. Research model and hypotheses development This study develops the research model shown in Figure 1. It proposes a comprehensive framework for examining the factors of perceived value for wearable devices. In particular, the proposed model includes four antecedents (i.e., functionality, compatibility, visual attractiveness, and brand name) that reflect the characteristics of wearable devices as high-tech electronic devices and fashion items. This study defined each construct and developed a theoretical rationale for the model’s causal relationships.

3.1. Perceived value and intention to use Intention to use is the “degree of the psychological state of the people’s general minds to use specific services and systems” (Davis et al., 1989). Perceived value is an indicator of a consumer’s

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adoption intention, as it reflects utility by comparing benefits and sacrifices (Kim et al., 2007). Many IT studies have found that the perceived value of using mobile internet services on portable devices positively affects adoption intention. Chen and Lin (2015) confirmed a signigicantly positive relationship between the perceived value of blogs and continuance intention. Yu et al. (2013) have examined the perceived value dimension (i.e., hedonic, utilitarian, and social value) and its effect on behavioral intention to use location-based social networking services. Turel et al. (2010) confirmed that the overall perceived value of hedonic digital artifacts (such as MP3 songs) had a siginificant effect on behavioral intention to use in the future. Thus, to deepen the understanding of customers’ adoption behaviors around wearable devices, perceived value should be considered as a predictor of customer adoption behaviors. Therefore, this study posits the following hypothesis: H1. The perceived value of wearable devices will positively affect intention to use.

3.2. Perceived usefulness Perceived usefulness, defined as the “degree to which a person believes that using a particular system would enhance his or her job performance” (Davis, 1989), has been regarded as one of the most influential predictors of IT adoption. Park and Chen (2007) found that users’ perceived usefulness positively influenced user intention to use smartphones. Park and Kim (2013) explored user acceptance of long-term evolution (LTE) services and showed that the perceived usefulness of LTE services had positive effects on user intention to use the service. Kim et al. (2008) showed that perceived usefulness had an effect on the continued intention to use a short message service that provided utilitarian benefits to users in search of effective communication alternatives. This study defines perceived usefulness as the degree to which a person believes that using wearable devices would enhance job performance. Users of wearable devices can improve their productivity by checking their e-mail, scheduling tasks and meetings, accessing information, and communicating with colleagues anytime, anyplace. Wilson (2013) argued that wearable devices improve workers’ organizational performance by providing real-time information access and powerful data analysis. Thus, the perceived usefulness of wearable devices will positively affect overall

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perceived value as one of its benefit components. This study therefore proposes the following hypothesis: H2. The perceived usefulness of wearable devices will positively affect customers’ perceived value.

3.3. Perceived enjoyment Prior studies have considered perceived enjoyment as a critical intrinsic and hedonic motivation, adding to the extrinsic motivation for adopting IT systems and services (van der Heijden, 2004; Venkatesh, 2000). Kim et al. (2007) found that perceived enjoyment was an effective element of the percevied value of mobile internet with cognitive elements such as usefulness and fee. Mäntymäki and Salo (2011) found that perceived enjoyment was the strongest determinant of continuous intention to use social virtual services. Based on Davis et al. (1992), this study defines perceived enjoyment as the extent to which using wearable devices is perceived as enjoyable in its own right, apart from any performance consequence that may be anticipated. Market research also suggests that entertainment and fitness will create the greatest demand for smart wearable devices (Moar, 2014). Therefore, this study hypothesizes that the perceived enjoyment from using wearable devices will positively affect their overall perceived value as one of their benefit components: H3. The perceived enjoyment from wearable devices will positively affect customers’ perceived value.

3.4. Social image Individuals frequently respond to normative social influences to make a favorable impression within a reference group (Kelman, 1958). Lin and Bhattacherjee (2010) defined social image as the “extent to which users may derive respect and admiration from peers in their social network as a result of their IT usage.” Social image is more important in communication and social interaction systems, which can serve as symbolic media for the portrayal of users’ social image within their community

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(Venkatesh et al., 2003). When mobile technology was new, mobile handheld devices were treated as novel and “cool” things, which contributed to users’ perception of an enhanced sense of selfimportance (Sarker and Wells, 2003). Wearable devices are among the latest IT products, newer than smartphones and tablet PCs; therefore, wearable device users can be considered innovators due to their early adoption. This study defines social image as the extent to which users may derive respect and admiration from peers in their social communities through wearable device usage. Therefore, social image as influenced by wearable device usage will be positively related to overall perceived value as the third benefit component. This study thus proposes the following hypothesis: H4. The perceived social image of wearable devices will positively affect customers’ perceived value.

3.5. Performance risk and financial risk Trying any new product or service involves some risk, as all actions have unanticipated consequences, some of which are likely to be disagreeable. Risk may cause consumers to delay or cancel the purchase of a new product (Dhebar, 1996). Prior studies have suggested that perceived risk is an important variable that needs to be examined in regards to perceived value (Chen and Dubinsky, 2003; Teas and Agarwal, 2000); this view has been widely adopted in the IT field. Sarin et al. (2003) suggested that customers perceive the decision to purchase new high-tech products as risky because these products and their industries exhibit pervasive technological and market uncertainties. Snoj et al. (2004) showed that perceived risks were negatively related to the perceived value of using mobile phones. Chen and Dubinsky (2003) used performance risk and financial risk as the main perceived risk components and found that they were negatively associated with the perceived customer value of online shoppers. Wearable devices are new high-tech products, and their performance, price, and maintenance expenditure may be critical factors in estimations of their value as useful computing devices. Thus, perceived performance risk is defined as the possibility that the wearable devices will not function as expected, and perceived financial risk is defined as the probability of monetary loss incurred from buying or maintaining wearable devices. This study thus

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proposes the following hypotheses that performance and financial risks are negatively related to perceived value: H5. The perceived performance risk of purchasing wearable devices will negatively affect customers’ perceived value. H6. The perceived financial risk of purchasing wearable devices will negatively affect customers’ perceived value.

3.6. Functionality Convergence is a dominant paradigm in the high-technology electronics industry (Yoffie, 1997) that has enabled the addition of seemingly disparate functionalities to base products (e.g., the ability to watch television on a cellphone, accessing the internet on a personal digital assistant [Gill, 2008]). In previous studies, functionality refers to the set of potential benefits that a product can provide consumers (Ratneshwar et al., 1999; Ziamou and Ratneshwar, 2002). Ahn et al. (2007) showed that system quality, for which functionality is a measurement item, had a positive effect on the users’ perception of the usefulness of online retailing. Kim and Sim (2012) developed the acceptance model of tablet PCs and found that functionality significantly influenced perceived usefulness. The availability of hardware and software functions are considered among the perceived advantages of multimedia mobile service adoption (Pagani, 2004). This study defines functionality as hardware and software functionalities such as battery life, memory capacity, operating systems, and content and applications. As wearable devices are representative new convergence products, their functionality features can provide effective support for messaging with others, searching for information, watching various forms of media content, and using other work-related applications. Therefore, this study posits the following hypothesis: H7. The functionality of wearable devices will positively affect customers’ perceived usefulness.

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3.7. Compatibility Some studies have considered the compatibility construct as “technical compatibility,” measuring how a technology is compatible with existing software and hardware systems. Pagani (2004) examined the determinants of intention to use a mobile multimedia service and found that technical compatibility was a factor influencing perceived usefulness. Bradford and Florin (2003) revealed a positive relationship between compatibility with retained technical systems and ERP implementation success. Other studies have used compatibility to measure task and business requirements and personal lifestyles. Web 2.0 services’ (e.g., social networking services, video sharing) compatibility with user needs has been found to have a positive effect on intensity of usage (Corrocher, 2011). Chen et al. (2009) measured compatibility within a business context and found that compatibility was a clear antecedent of perceived usefulness and attitude to smartphone adoption. Thus, this study defines compatibility as the degree to which wearable devices comply with other products’ (e.g., smartphones, PCs) technical functionalities, users’ business needs and lifestyles, and proposes the following hypothesis: H8. The compatibility of wearable devices will positively affect customers’ perceived usefulness.

3.8. Visual attractiveness Schmitt and Simonson (1997) stated that styling or changing the appearance of products could spur customer demand in an increasingly competitive market. Moreover, many studies on the user acceptance of IT products and services have emphasized the importance of products’ visual attributes to users’ emotional attachment to them (Cyr et al., 2006; Hsiao, 2013; Nanda et al., 2008). Nanda et al. (2008) indicated that users’ emotional reaction and preferences were influenced by a mobile phone’s aesthetic design. Hsiao (2013) found that visual design aesthetics significantly impacted users’ attitudes to smartphones, while Cyr et al. (2006) showed that design aesthetics positively influenced perceived enjoyment. van der Heijden (2003) extended the TAM to explain individuals’ acceptance and usage of websites using the new “perceived visual attractiveness”

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construct, an antecedent of PU, PEOU, and perceived enjoyment. Consumers also acquire beautiful products and use attractive services to differentiate themselves from others. Tzou and Lu (2009) argued that possessing a consumer electronics product with an attractive appearance and a wellexecuted design could allow individuals to show differentiation. This study thus defines “visual attractiveness” as an aesthetic product design expressed through shapes, colors, and materials and user interfaces such as device menus and the mobile applications of wearable devices. This study posits the following hypotheses: H9. The visual attractiveness of wearable devices will positively affect customers’ perceived enjoyment. H10. The visual attractiveness of wearable devices will positively affect customers’ social image.

3.9. Brand name Prior studies have found that brand name is one of the most important extrinsic signals customers use to evaluate products across cultures when they face uncertainty about them (Dawar and Parker, 1994; Richardson et al., 1994). Moreover, the role of brand as a social signal is widely accepted as a key motivational factor in consumer choice (Lannon and Cooper, 1983). Tian et al. (2001) argued that consumers pursue social image enhancements by using the symbolic meanings of the products they purchase and that brands (along with product categories, versions, and styles) are used to fulfill consumers’ need for uniqueness. del Río et al. (2001) evaluated the symbolic benefit of brand names by measuring brands’ capacity to enable individuals to identify themselves with them and express social status. Park and Chen (2007) found that purchasing intention for global luxury brands was positively related to a strong belief in social recognition. Wearable device consumers may consider not only the device’s brand name but also its OS provider and application development companies, as they are relatively new smart devices, and their brand names are easily exposed. Therefore, this study hypothesizes the following: H11. The brand name of a wearable device will positively affect customer’s social image.

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4. Research method 4.1. Data An online survey was conducted and validated for two weeks in June 2015 before it was used to test the research model and hypotheses. A total of 375 samples (273 potential and 102 actual users) were retained for study after samples with missing or erroneous data were removed. Detailed descriptive statistics for the respondents’ demographic characteristics are presented in Table 1. The samples’ demographic characteristics resemble the Korean Population Statistics collected for Koreans from 10 to 69 years old in January 2015, in which 50% of the population was female, and the nation was broken down into the 10 to 19 age group (13.1% of the population), the 20 to 29 age group (16.5%), the 30 to 39 age group (21.1%), the 40 to 49 age group (21.9%), the 50 to 59 age group (17.3%), and the 60 to 69 age group (10.1%).



4.2. Instrument development The measurement items in this study were developed based on previous studies and checked for reliability and validity. The 42 measurement items describe 11 latent constructs: functionality, compatibility, visual attractiveness, brand name, perceived usefulness, perceived enjoyment, social image, performance risk, financial risk, perceived value, and intention to use.

5. Data analysis and results 5.1. Measurement model This study employed the partial least squares (PLS) method to test the proposed model and corresponding hypotheses using Smart PLS 2.0, an appropriate method given the sample size, the

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focus on each path coefficient, and the focus on variance explained rather than overall model fit (Chin et al., 2003). Confirmatory factor analysis was conducted to investigate the convergent validity of each construct. Table 3 shows the cross-loadings of all items, indicating that they load highest on their respective construct. Table 4 summarizes the measurement item and construct statistics. Convergent validity was assessed by examining the factor loadings for each item in the measurement model, the significance level for each loading, the reliability, and the average variance extracted (AVE) for each construct. All factor loadings exceeded 0.60, the minimum requirement for confirming the convergent validity of the constructs (Anderson and Gerbing, 1988). Cronbach’s alphas for all 11 constructs were above the recommended reliability level (0.70). The AVE for each construct exceeded 0.50 (Fornell and Larcker, 1981), establishing convergent validity. For discriminant validity, the square root of the AVE for each construct should be greater than the correlation values between any two constructs. The inter-construct correlation matrix (see Table 5) demonstrated that all values met these recommendations for discriminant validity.

5.2. Hypotheses testing The structural equation model results are summarized in Figure 2 and Table 6. For potential users, all proposed causal connections were statistically supported by the results. Perceived value was a significant factor influencing intention to use (β=0.724, t-value=22.79, p<0.001), supporting hypothesis H1 and explaining 52.5% of the variance. All proposed constructs of perceived benefit (i.e., perceived usefulness, perceived enjoyment, social image) and perceived risk (i.e., performance risk, financial risk) significantly influenced perceived value (R2=0.585). Among the determinants of perceived value, social image had a stronger positive effect on perceived value (β=0.303, tvalue=4.66, p<0.001) than did perceived usefulness (β=0.242, t-value=3.39, p<0.001) or perceived

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enjoyment (β=0.223, t-value=2.67, p<0.01). Performance risk had a slightly stronger negative effect on perceived value (β=-0.143, t-value=2.14, p<0.05) than did financial risk (β=-0.139, t-value=2.23, p<0.05). Thus, hypotheses H2, H3, H4, H5, and H6 are all supported. All four of the constructs reflecting the attributes of wearable devices were also supported. The functionality and compatibility of wearable devices positively affected perceived usefulness (H7, β=0.381, t-value=5.70, p<0.001; H8, β=0.338, t-value=5.30, p<0.001). Visual attractiveness had a positive influence on perceived enjoyment (H9, β=0.585, t-value=13.68, p<0.001) and social image (H10, β=0.417, t-value=5.50, p<0.001). Brand name had a significant effect on social image (H11, β=0.157, t-value=2.22, p<0.05). The results for actual users are slightly different. Of the 11 proposed hypotheses, nine are supported. Perceived value was found to influence intention to use and explained 51.1% of the variance, supporting H1 (β=0.715, t-value=20.79, p<0.001). Perceived usefulness, perceived enjoyment, and social image directly influenced perceived value (β=0.213, t-value=2.77, p<0.01; β=0.333, t-value=3.56, p<0.001; β=0.311, t-value=4.98, p<0.001; R2=0.515), while the paths from performance risk and financial risk to perceived value were insignificant. Perceived enjoyment was the strongest factor affecting perceived value. Thus, H2, H3, and H4 are supported, but H5 and H6 are not. Functionality and compatibility showed positive effects on perceived usefulness (H7, β=0.212, tvalue=3.64, p<0.001; H8, β=0.436, t-value=7.07, p<0.001). Visual attractiveness was a significant factor influencing both perceived enjoyment and social image (H9, β=0.528, t-value=12.09, p<0.001; H10, β=0.169, t-value=2.45, p<0.05). Brand significantly influenced social image (H11, β=0.354, tvalue=5.74, p<0.001).

6. Discussion This research offers a number of findings. Perceived value had a significant influence on both potential and actual customers’ intention to use, supporting the generally accepted belief that perceived value is a very important factor in consumers’ decision to adopt new products or services

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(Chen and Dubinsky, 2003; Dodds et al., 1991; Zeithaml, 1988). Therefore, customers must be made to perceive value fully in order to ensure that they will adopt and continuously use wearable devices. This study found that the impact of all perceived benefit components was stronger than those of the perceived risk components, implying that potential and actual customers’ perceptions of the benefits are more influential than are their concerns about the risks of wearable devices because many individuals have experienced innovative mobile devices such as smartphones and tablet PCs and have sufficient information about them. Interestingly, social image is a strong factor for both potential and actual users, affecting perceived value among the benefit components. As wearable devices are in an early stage of diffusion, consumers may expect to improve their social image just by purchasing these new IT products as early adopters. Furthermore, as wearable devices are incorporated into items of clothing and accessories, which can be worn on the body, they can be exposed to peers more easily than other mobile devices, allowing wearable device adopters to show off their innovativeness during the course of their everyday lives. For potential users, perceived usefulness was shown to have a slightly stronger effect on perceived value than perceived enjoyment while, for actual users, perceived enjoyment was most influential in affecting perceived value. These results indicate that actual users receive pleasure from adopting wearable devices, while potential users want wearable devices more for utilitarian purposes than for fun. In fact, wearable devices can be adopted by almost every industry for business purposes. Watch-type wearable devices can substitute for or complement smartphones and tablet PCs because users can receive e-mail, text messages, and phone notifications for business without having to pull out their mobile devices. Virtual reality and head-mounted wearable devices allow doctors performing surgery to simultaneously monitor a patient’s vital signs and react to changes without having to take their eyes off the patient. Schools may adopt wearable devices to improve higher education by providing students with simulated experiences of intense environments such as an operating room, an athletic field, or outer space. Therefore, it is important to ensure not only that current users feel more

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enjoyment but also that potential customers perceive usefulness thoroughly in order to increase their adoption intention for wearable devices. This study also demonstrated that perceived risk is an important factor only for potential users, not for actual users. This result supports the finding that early adopters are more willing to take risks (Rogers, 1995) and are less price-sensitive than later adopters (Golder and Tellis, 2004). For potential users, performance risk seems to have a greater impact than financial risk on perceived value, perhaps because, though investing in unfamiliar devices entails financial risk, this risk is less critical than the malfunction risk, as consumers expect not only a range of prices for different models but also contract discounts from manufacturers and telecommunication service providers. However, potential customers will not risk adopting wearable devices without having some assurance of their performance. The achievement of the performance expected of wearable devices should dilute perceived performance risk and lead to their widespread adoption. Functionality and compatibility both had a positive influence on perceived usefulness for potential and actual users. Perceptions of the usefulness of IT systems have traditionally been linked to the functionality the systems offer to help their users achieve their goals (Davis, 1989). Therefore, wearable devices that offer high-quality mobile applications, high-speed internet access, minimized delay, and long-lasting battery life are perceived as more useful than wearable devices that offer less functionality. Furthermore, wearable devices have to work well with the user’s existing IT products, such as smartphones, tablet PCs, and PCs for synchronizing data or transferring information. For example, the reliability of telemedicine examinations depends on accurate information transfer from the wearable device, which gathers the user’s biometric health data, to the diagnostic applications on the smartphone or the doctor’s PCs. In terms of non-technical compatibility, wearable devices should match users’ business needs and become an integral part of their everyday lives. Visual attractiveness was the strongest exogenous variable positively affecting perceived enjoyment and also positively affect social image for both user groups. J.P. Power and Associates (2007) found that consumers selected physical design as the most important factor over other factors such as operation and features in their overall satisfaction with mobile handsets. Since wearable

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devices are easily exposed to other people in various forms (such as clothes and glasses), users can enjoy them as new fashion items. The positive relationship between visual attractiveness and social image is consistent with previous studies’ findings that innovative products with a fancy and differentiated design allow their owners to show differentiation and provide information about social image (Park et al., 2008; Tzou and Lu, 2009). Wearable devices are among the latest IT products, and their consumers can show themselves as innovators. Along with the visual attractiveness construct, brand name had a positive influence on social image. Given wearable devices’ visible nature, brand name is more important for them than for general IT products as a way to express or increase consumers’ self-image. Consumers’ belief that their ostentatious brands will attract respect could play a crucial role in their decision to purchase wearable devices.

7. Conclusions This research makes several theoretical contributions. This is the first empirical study to examine the user acceptance of wearable devices, which are at an early stage of diffusion. Most IT adoption studies based on perceived value address mobile service adoption, satisfaction, and loyalty or continuance intention (Kim et al., 2007; Turel et al., 2010). This study extends the application of perceived value to the convergence IT device domain by developing a theoretical model based on customer value perceptions in order to shed light on wearable device adoption and empirically test the proposed model. Moreover, previous studies lacked a comprehensive approach to both perceived value and the characteristics of new products. This study investigated the relationships between the value of wearable devices and their product characteristics. Finally, this research showed that perceived value is a clear antecedent of adoption intention (Chen and Dubinsky, 2003; Kim et al., 2007; Sweeney et al., 1999; Zeithaml, 1988). This framework can be applied to future studies on the adoption of new convergence devices and service that examine customers’ perceived value.

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The findings of this study also provide meaningful insights for the manufacturers of wearable devices. To increase their adoption, manufacturers should develop a product portfolio that addresses the relationship between the different types of consumer value and product characteristics. As wearable devices are both high-tech electronic devices and fashion items, manufacturers should focus not only on well-differentiated functionality and high compatibility but also on attractive design and brand name.

Marketing managers should consider various strategies for increasing actual users’

continuous adoption intention and potential users’ intention to use wearable devices. Fun features should be emphasized in marketing strategies targeting actual users, and the superior usefulness of wearable devices must be stressed in advertising to potential users. Specifically, marketing managers should not underestimate the huge potential of wearable devices in the B2B/enterprise context. In the long run, such devices could significantly improve employees’ productivity through, for example, relatively simple functions such as augmented reality presentation notes and real-time translation as well as sophisticated functions such as remote virtual collaboration, virtual surgery, and real-time toxic chemical detectors for the defense, utilities, aerospace, and aviation markets. Research and development (R&D) should continuously strive to realize the technical advances required for wearable device diffusion, such as the always-on battery, low-power/highperformance processor modules, and the always-connected environment with other smart devices. Furthermore, technology manufactures and fashion brands need to reinforce and collaborate with each other to address the visual attractiveness and brand name issues. Though some luxury brands and sports companies have eagerly embraced collaboration with a host of technology companies, wearable devices have not yet caught on with mainstream consumers. They need to work together at the initial stage to establish an appropriate design concept and branding strategy for improving customers’ enjoyment and social image. Although the findings of this study provide meaningful insights into wearable device adoption, several issues should be taken into consideration in future research. First, future studies may investigate other variables specific to wearable devices to better explain their perceived value. Constructs such as mobility and flow experience are appropriate to consider. Second, this research

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considered only performance risk and financial risk as risk components. Other factors, such as security risk, should be included in future research. Third, the individual differences among the survey respondents were not examined in this study. Future studies may extend and refine our findings by investigating the moderating effects of individual differences such as gender, age, and race. Despite its limitations, this study contributes to a more systematic understanding of wearable device adoption. It will provide the foundation for future research on related topics.

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Characteristics Gender Male Female Age 10-19 20-29 30-39 40-49 50-59 60+ Education Less than high school College or university Advanced degree Monthly Income($) Less than 1,000 1,000~2,000 2,000~3,000 3,000~4,000 4,000~5,000 5,000 + Occupation Official worker Service worker Professional/Researcher Self-employer Public service worker Student Housewife Other

Construct Perceived Usefulness (PU)

Perceived Enjoyment (PE)

Item PU1 PU2 PU3 PU4 PU5 PE1 PE2 PE3 PE4

SI1 Social Image (SI)

SI2 SI3 SI4

Table 1 Characteristics of the respondents Respondents (n=375) Potential users Actual users

Percentage

134 139

50 52

49.1 50.9

32 43 47 62 55 34

17 19 32 20 10 4

13.1 16.5 21.1 21.9 17.3 10.1

69 178 26

7 72 23

20.3 66.7 13.1

21 48 68 57 42 37

7 11 26 27 18 13

7.5 15.7 25.1 22.4 16.0 13.3

139 24 15 6 13 63 8 5

45 14 11 3 5 22 2 -

49.1 10.1 6.9 2.4 4.8 22.7 2.7 1.3

Table 2 Survey items used in this study Measurement items Wearable devices are very useful to my life in general. Wearable devices provide very useful service and information to me. Using Wearable devices improve the quality of the work I do. Using Wearable devices increase my productivity. Using Wearable devices enhances my effectiveness on the job. Using Wearable devices is truly fun. I know using Wearable devices to be enjoyable. The use of Wearable devices gives me pleasure. The use of Wearable devices makes me feel good.

The fact that I use Wearable devices makes a good impression on other people. Using Wearable devices improves my image within the organization. Because of my use of Wearable devices, others in my organization see me as a more valuable employee. The use of Wearable devices gives me social approval.

References (Davis, 1989; Shin, 2007) (Sweeney and Soutar, 2001; Venkatesh, 2000) (Moore and Benbasat, 1991; Sweeney and Soutar, 2001)

25

PR1 Performance Risk (PR)

PR2 PR3 FR1

Financial Risk (FR)

FR2 FR3

Perceived Value (PV)

Intention to Use (IU)

Functionality (FUNC)

PV1 PV2 PV3 PV4 IU1 IU2 IU3 IU4 FUNC1 FUNC2 FUNC3 COM1

Compatibility (COM)

COM2 COM3 COM4 VA1

Visual Attractiveness (VA)

VA2 VA3 VA4 BN1 BN2

Brand name (BN)

BN3 BN4

I worry about whether Wearable devices will not provide the level of benefits I expect. It is uncertain that Wearable devices will work satisfactorily. It is uncertain that Wearable devices will perform the functions that were described in the advertisement. Using Wearable devices would lead to financial risk for me because of the possibility of such things as higher maintenance and/or repair costs. Considering the potential investment involved, Purchasing and using Wearable devices is risky. Given the whole potential financial expenses associated with Wearable devices, Purchasing and using Wearable devices is risky. Using Wearable devices offers value for money. Compared to the effort to put, using Wearable devices is beneficial to me. Compared to the time to spend, using Wearable devices is worthwhile to me. Overall, using Wearable devices delivers me good value. Using wearable devices is worthwhile. I intend to use Media tablet in the future. I predict I would use Wearable devices in the future. I recommend others to use Wearable devices. The performance of features on Wearable devices is stable. Using wearable device is good to access contents and service (SMS, e-mail, mobile app, etc.). Wearable device is efficient to easily navigate relevant information. Wearable devices are compatible with existing hardware (smartphone, etc.). Wearable devices are compatible with legacy OS, S/W and application. Using Wearable devices is compatible with all aspects of my work. Using Wearable devices fits into my life style. The user interface of Wearable devices (i.e., colors, boxes, menus, etc.) is attractive. Wearable devices looks professionally designed. The overall look and feel of Wearable devices is visually appealing. Overall, Wearable devices look attractive. The brand name of wearable device manufacturer is considerable because quality depends on that. The brand name of wearable device manufacturer influences purchasing decision if all manufacturers provide same features. The brand name of wearable device manufacturer influences purchasing decision if all manufacturers are not different in any way. Reliable brand name is one of key factors to choose wearable devices.

(Grewal et al., 1994; Stone and Grønhaug, 1993)

(Grewal et al., 1994)

(Sirdeshmu kh et al., 2002) (Davis et al., 1989; Hsu and Lin, 2015) (Bauer et al., 2006; Kim and Sim, 2012) (Bradford and Florin, 2003; Moore and Benbasat, 1991)

(Cyr et al., 2006)

(Brucks et al., 2000; Lau and Lee, 1999)

26

FUNC1 FUNC2 FUNC3 COM1 COM2 COM3 COM4 VA1 VA2 VA3 VA4 BN1 BN2 BN3 BN4 PU1 PU2 PU3 PU4 PU5 PE1 PE2 PE3 PE4 SI1 SI2 SI3 SI4 PR1 PR2 PR3 FR1 FR2 FR3 PV1 PV2 PV3 PV4 IU1 IU2 IU3 IU4

FUNC 0.792 0.865 0.865 0.503 0.513 0.569 0.551 0.506 0.545 0.507 0.501 0.306 0.434 0.321 0.308 0.475 0.521 0.494 0.505 0.524 0.445 0.454 0.525 0.531 0.476 0.527 0.527 0.515 -0.146 -0.172 -0.143 -0.057 -0.135 -0.215 0.601 0.603 0.629 0.642 0.561 0.573 0.546 0.582

COM 0.592 0.521 0.512 0.800 0.761 0.887 0.865 0.532 0.495 0.566 0.550 0.332 0.507 0.351 0.408 0.501 0.496 0.515 0.499 0.563 0.497 0.558 0.574 0.591 0.447 0.497 0.512 0.487 -0.135 -0.147 -0.133 -0.114 -0.166 -0.204 0.636 0.614 0.612 0.674 0.642 0.621 0.659 0.663

VA 0.553 0.445 0.438 0.463 0.471 0.499 0.541 0.903 0.869 0.917 0.920 0.365 0.522 0.280 0.312 0.373 0.450 0.346 0.366 0.386 0.431 0.479 0.544 0.566 0.427 0.382 0.394 0.430 -0.123 -0.188 -0.174 -0.060 -0.125 -0.170 0.557 0.533 0.524 0.549 0.433 0.458 0.426 0.473

Table 3 Construct cross-loadings BN PU PE SI 0.344 0.475 0.497 0.538 0.333 0.481 0.437 0.412 0.374 0.482 0.441 0.444 0.473 0.414 0.492 0.296 0.431 0.412 0.459 0.284 0.373 0.552 0.538 0.538 0.371 0.533 0.556 0.562 0.401 0.384 0.499 0.356 0.388 0.373 0.481 0.354 0.429 0.383 0.530 0.431 0.434 0.426 0.527 0.440 0.812 0.371 0.372 0.238 0.884 0.401 0.433 0.440 0.857 0.320 0.313 0.257 0.852 0.321 0.293 0.258 0.388 0.834 0.697 0.489 0.415 0.844 0.683 0.416 0.358 0.901 0.587 0.458 0.353 0.915 0.618 0.524 0.346 0.926 0.642 0.539 0.339 0.634 0.797 0.366 0.334 0.628 0.899 0.525 0.428 0.702 0.952 0.602 0.420 0.670 0.944 0.617 0.314 0.466 0.508 0.902 0.361 0.542 0.554 0.938 0.334 0.538 0.550 0.941 0.378 0.491 0.600 0.928 0.002 -0.074 -0.088 -0.146 0.042 -0.052 -0.072 -0.099 -0.010 -0.026 -0.072 -0.073 0.064 0.009 0.044 -0.095 0.033 -0.069 -0.049 -0.121 -0.059 -0.177 -0.166 -0.142 0.348 0.545 0.564 0.502 0.364 0.553 0.603 0.581 0.330 0.548 0.560 0.580 0.420 0.593 0.587 0.611 0.380 0.571 0.587 0.541 0.410 0.598 0.611 0.548 0.479 0.613 0.604 0.562 0.368 0.586 0.638 0.617

PR -0.233 -0.078 -0.113 -0.118 -0.049 -0.138 -0.181 -0.191 -0.117 -0.155 -0.167 0.085 -0.034 0.032 -0.005 -0.020 -0.008 -0.065 -0.048 -0.106 0.001 -0.086 -0.095 -0.108 -0.110 -0.130 -0.121 -0.077 0.900 0.928 0.918 0.573 0.581 0.518 -0.241 -0.226 -0.238 -0.227 -0.142 -0.100 -0.072 -0.137

FR -0.257 -0.075 -0.077 -0.120 -0.104 -0.170 -0.201 -0.127 -0.106 -0.113 -0.152 0.059 -0.042 0.035 -0.002 -0.087 -0.086 -0.089 -0.069 -0.116 0.000 -0.099 -0.092 -0.081 -0.144 -0.140 -0.131 -0.086 0.557 0.550 0.560 0.872 0.944 0.910 -0.256 -0.258 -0.246 -0.229 -0.208 -0.159 -0.183 -0.194

PV 0.667 0.519 0.520 0.453 0.441 0.680 0.675 0.522 0.480 0.558 0.562 0.307 0.409 0.293 0.310 0.560 0.533 0.508 0.529 0.570 0.428 0.530 0.639 0.644 0.553 0.587 0.600 0.567 -0.254 -0.222 -0.218 -0.195 -0.222 -0.296 0.893 0.928 0.918 0.924 0.685 0.664 0.634 0.704

IU 0.646 0.460 0.425 0.456 0.469 0.649 0.684 0.434 0.370 0.443 0.479 0.321 0.477 0.316 0.306 0.638 0.519 0.525 0.527 0.591 0.441 0.574 0.638 0.671 0.542 0.564 0.581 0.570 -0.121 -0.109 -0.103 -0.139 -0.172 -0.218 0.610 0.662 0.696 0.669 0.947 0.938 0.939 0.910

27

Table 4 Loadings of indicator variables Construct Functionality (FUNC) Compatibility (COM) Visual Attractiveness (VA) Brand Name (BN)

Perceived Usefulness (PU) Perceived Enjoyment (PE) Social Image (SI) Performance Risk (PR) Financial Risk (FR) Perceived Value (PV) Intention to Use (IU)

Items

Factor loading

Std. error

t-value

FUNC1 FUNC2 FUNC3 COM1 COM2 COM3 COM4 VA1 VA2 VA3 VA4 BN1 BN2 BN3 BN4 PU1 PU2 PU3 PU4 PU5 PE1 PE2 PE3 PE4 SI1 SI2 SI3 SI4 PR1 PR2 PR3 FR1 FR2 FR3 PV1 PV2 PV3 PV4 IU1 IU2 IU3 IU4

0.792 0.865 0.865 0.800 0.761 0.887 0.865 0.903 0.869 0.917 0.920 0.812 0.884 0.857 0.852 0.834 0.844 0.901 0.915 0.926 0.797 0.899 0.952 0.944 0.902 0.938 0.941 0.928 0.900 0.928 0.918 0.872 0.944 0.910 0.893 0.928 0.918 0.924 0.947 0.938 0.939 0.910

0.029 0.021 0.020 0.038 0.042 0.015 0.017 0.016 0.024 0.012 0.020 0.036 0.017 0.027 0.028 0.026 0.029 0.016 0.015 0.012 0.038 0.015 0.006 0.007 0.016 0.010 0.009 0.010 0.026 0.017 0.019 0.031 0.017 0.019 0.017 0.010 0.013 0.011 0.008 0.009 0.009 0.013

27.181 41.924 44.298 21.217 18.005 58.863 51.670 56.152 36.705 75.295 46.047 22.819 53.111 31.693 30.559 31.801 29.179 56.590 59.454 79.595 21.130 58.265 156.160 140.286 54.736 92.337 109.543 92.944 34.936 55.511 48.377 28.198 54.459 48.226 52.933 96.621 70.219 82.757 121.214 100.618 106.241 69.837

AVE (>0.5)

Composite Reliability (>0.6)

Cronbach’s alpha (>0.7)

0.708

0.879

0.792

0.689

0.898

0.850

0.814

0.946

0.924

0.725

0.913

0.878

0.783

0.947

0.930

0.810

0.944

0.921

0.860

0.961

0.946

0.839

0.940

0.904

0.826

0.934

0.897

0.839

0.954

0.936

0.872

0.964

0.951

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FUNC COM VA BN PU PE SI PR FR PV IU

FUNC 0.841 0.643 0.569 0.417 0.570 0.545 0.552 -0.168 -0.161 0.676 0.606

Table 5 Correlations of the constructs and square root of AVE COM VA BN PU PE SI PR FR

PV

IU

0.830 0.595 0.487 0.583 0.618 0.524 -0.151 -0184 0.692 0.693

0.916 0.721

0.934

0.902 0.496 0.435 0.565 0.440 -0.175 -0.138 0.590 0.480

0.852 0.420 0.426 0.374 0.012 0.004 0.399 0.437

0.885 0.730 0.549 -0.057 -0.102 0.611 0.634

0.900 0.597 -0.085 -0.079 0.632 0.654

0.927 -0.118 -0.135 0.622 0.608

0.916 0.607 -0.254 -0.122

Table 6 Summary of hypotheses testing results Potential users Path Path S.E t-Value coefficient coefficient H1: PV → IU (+) 0.724*** 0.032 22.798 0.715*** H2: PU → PV(+) 0.242*** 0.072 3.386 0.213** H3: PE → PV(+) 0.223** 0.084 2.669 0.333*** H4: SI → PV(+) 0.303*** 0.065 4.661 0.311*** H5: PR → PV(-) -0.143* 0.067 2.141 -0.009 H6: FR → PV(-) -0.139* 0.062 2.234 -0.037 H7: FUNC → PU(+) 0.381*** 0.067 5.702 0.212*** H8: COM → PU(+) 0.338*** 0.064 5.297 0.436*** H9: VA → PE(+) 0.585*** 0.043 13.684 0.528*** H10: VA → SI(+) 0.417*** 0.076 5.505 0.169** H11: BN → SI(+) 0.157* 0.070 2.219 0.354*** Note: * p < .05; ** p < .01; *** p < .001;

Hypothesis

0.909 -0.270 -0.199

Actual users S.E

t-Value

0.034 0.077 0.094 0.063 0.055 0.051 0.058 0.062 0.044 0.069 0.062

20.786 2.770 3.558 4.980 0.173 0.720 3.640 7.074 12.090 2.448 5.738

29

Perceived Benefit Functionality

H7 Perceived Usefulness

Compatibility

H8 H2

Perceived Enjoyment Visual Attractiveness

H9

H3

H10 Social Image

Perceived Value

H4

H11 Brand Name

H5

Performance Risk

H6

Financial Risk

Perceived Risk

Figure 1 Research model

H1

Intention to Use

30

Functionality

.381*** .212***

Perceived Benefit

Perceived Usefulness R2=.431, .338

.338***

Compatibility

.436***

.242*** .213**

Perceived Enjoyment R2=.343, .279

.585*** .528***

Visual Attractiveness

.223** .333***

***

.417 **

.169

.157*

Brand Name

Social Image R2=.264, .195

Perceived Value R2=.585, .515

.303*** .311***

.724*** .715***

Intention to Use R2=.525, .511

-.143* -.009

.354***

Performance Risk

-.139* -.037

Financial Risk

Perceived Risk

Note: * p < .05; ** p < .01; *** p < .001; Bold: Potential users, Italic: Actual users

Figure 2 PLS results of the structural model

31

Highlights > This study examines potential customers’ perceived value of wearable devices. > The results show that perceived value is a clear antecedent of adoption intention. > Perceived benefit has a greater impact on perceived value than perceived risk.