Discovering determinants of users perception of mobile device functionality fit

Computers in Human Behavior 35 (2014) 75–84

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Discovering determinants of users perception of mobile device functionality fit Arash Negahban a,⇑, Chih-Hung Chung b a b

College of Business, University of North Texas, 1155 Union Circle #311160, Denton, TX 76203, USA College of Information, University of North Texas, 1155 Union Circle #311068, Denton, TX 76203, USA

a r t i c l e

i n f o

Article history: Available online 18 March 2014 Keywords: Perceived mobile device-functionality fit Multifunctional use Multifunctionality Mobile device Smartphones

a b s t r a c t In recent years, there has been an explosive growth in the use of mobile devices. The ubiquitous and multifunctional nature of these devices with internet connectivity and personalization features make them a unique context to investigate what factors shape mobile users perception of their mobile device functionality fit with their needs. In order to answer this question, we proposed a research model in which we introduced multifunctional use and perceived device-functionality fit as two new constructs. The results of our study show that a significant portion of individuals’ perceived device-functionality fit can be explained by their perceived enjoyment, perceived ease of use, perceived usefulness, and symbolic value of the device. In terms of the theoretical contribution, our research suggests revamping the concept of device-functionality fit when it comes to mobile devices by accounting for both hedonic and utilitarian aspects of mobile devices. In terms of practical implications, our study highlights the importance of the social image that mobile devices create in the society for their users as well as the importance of look-and-feel aspects of mobile devices in shaping users perception of fit between functionalities of their mobile devices and their needs. Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction Today, mobile phone is an essential device in our daily lives. The propagation of mobile devices along with omnipresent internet access has significantly changed our lives by changing the essence of mobile phones from simple voice and messaging devices to highly flexible and multifunctional devices that can be used almost anytime and anywhere for a wide range of purposes, ranging from fully utilitarian to fully hedonic. Mobile technology has dramatically changed not only the way many businesses worked, but also the way we live and communicate with each other. It has reshaped our social habits, behaviors and our relationships with others. It has brought new needs to our lives that we never had before. Mobile devices, such as smartphones, support for internet connectivity, GPS, digital camera, and multimedia has nurtured the proliferation of myriad mobile applications that combine these services to enrich the functionalities of these devices. It is no longer easy to list all the functionalities that a mobile device provides. It ⇑ Corresponding author. Address: College of Business, University of North Texas, 1155 Union Circle #311160, Denton, TX 76203-5017, USA. Tel.: +1 (940) 594 1822. E-mail addresses: [email protected] (A. Negahban), chih-hungchung@ my.unt.edu (C.-H. Chung). http://dx.doi.org/10.1016/j.chb.2014.02.020 0747-5632/Ó 2014 Elsevier Ltd. All rights reserved.

seems that the scope of functionalities that mobile devices provide these days is ever growing. The ubiquitous and multifunctional essence of these devices along with their personalization features allows mobile users to add different applications to their mobile devices and customize them based on preferences as well as use them to address their hedonic or utilitarian needs. This makes the context of ubiquitous computing and mobile technology a unique area of study for academics and a boundless opportunity for the practitioners. Previous studies have shown two broad emerging factors affect acceptance of mobile phones: Interface characteristics and network capabilities (Sarker & Wells, 2003). However, in this study, we investigate how the concept of fit between users’ requirements and device functionalities can be applied into the context of mobile devices and how their unique characteristics can affect user’s perception of their mobile device-functionality fit. The remainder of the paper is structured as follows. After this introduction, we will provide a brief overview of the relevant literature and develop our research model for mobile device functionality fit. We will then discuss our research methodology, results, key findings and contributions, followed by limitations, directions for future research, and conclusion.

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2. Theoretical background

2.2. Hedonic aspects of information systems

In this section, we present an overview of the widely used theories that have been applied within the context of adoption and use of mobile technology in order to build a foundation for our research model and introduce the concepts of perceived mobile device functionality fit and multifunctional use.

Information systems (IS) can have both hedonic and utilitarian purposes. Utilitarian information systems aim to provide instrumental value to users while hedonic information systems aim to provide self-fulfilling value to users (Heijden, 2004; Sun & ZhanG, 2006). However, the utilitarian-hedonic aspects of systems are task-dependent. This can blur the boundary between hedonic and utilitarian aspects, especially for mixed systems that can be used for either purposes (Sun & ZhanG, 2006). For example, internet can be used both for finding a job (utilitarian use) and for watching movies (hedonic use). Previous studies have found that perceived enjoyment is a dominant predictor for hedonic aspects of information systems and perceived usefulness is strong predictor for utilitarian aspects of IS (Heijden, 2004). Perceived enjoyment is defined as the quality that using technology is perceived to be enjoyable by its own, regardless of performance expectations (Davis, Bagozzi, & Warshaw, 1992). Perceived enjoyment and perceived usefulness are important factors that influence users’ acceptance and use of technology (Hong & Tam, 2006; Lee & Chang, 2011; Liao, Tsou, & Huang, 2007; Thong, Hong, & Tam, 2006). Attitudinal beliefs, including perceive usability, perceive ease of use, and perceived enjoyment also significantly affect user’s hedonic attitude (Hong, Thong, Moon, & Tam, 2008). Enjoyment is also identified as a value driver of hedonic digital artifacts (Turel, Serenko, & Bontis, 2010).

2.1. Adoption and use of information technology Technology acceptance model (TAM) (Davis, 1989) has been widely used to explain users’ acceptance and use of mobile technology (Kim & Garrison, 2009; Kim, Park, & Morrison, 2008; Negahban, 2012; Oi, Li, Li, & Shu, 2009; Son, Park, Kim, & Chou, 2012) and various mobile services including mobile internet (Chong, Darmawan, Ooi, & Lin, 2010; Chong, Zhang, Lai, & Nie, 2012; Kuo & Yen, 2009; Lee, Noh, & Kim, 2012; López-Nicolás, Molina-Castillo, & Bouwman, 2008), mobile games (Liu & Li, 2011), financial mobile services (Chen, 2008; Hsu, Wang, & Lin, 2011; Jaradat & Twaissi, 2010; Kim, Mirusmonov, & Lee, 2010; Liu, Wang, & Wang, 2011; Luarn & Lin, 2005; Teo, Tan, Cheah, Ooi, & Yew, 2012), mobile health-care services (Lin, 2011), mobile TV (Jung, Perez-Mira, & Wiley-Patton, 2009), and mobile text alert systems (Lee, Chung, & Kim, 2013). TAM posits that perceived usefulness (PU) and perceived ease of use (PEOU) are the determinants of behavioral intention to use (BI). Perceived usefulness is defined as ‘‘the degree to which a person believes that using a particular system would enhance his or her job performance’’ (Davis, 1989, p. 320). Perceived ease of use is defined as ‘‘the degree to which a person believes that using a particular system would be free of effort’’ (Davis, 1989, p. 320). Despite its widely use, TAM has some limitations in explaining acceptance and use of mobile technology (López-Nicolás et al., 2008); which were later on addressed by other complementary theories. The united theory of acceptance and use of technology (UTAUT) developed by Venkatesh, Morris, Davis, and Davis (2003) was used to evaluate the probability of success for new technology overviews. Moreover, in order to design interventions for users that may be less inclined to adopt and use new systems, it also supports them to understand the drivers of acceptance. UTAUT incorporated TAM, Theory of planned behavior (TPB), innovation diffusion theory (IDT), motivation model, social cognitive theory to develop a unified theory for technology acceptance. In addition, it tested independent variables, such as, performance expectancy, effort expectancy, social influence, facilitating conditions, to use of technology, controlling for gender, age, experience, and voluntariness of use. UTAUT also accounts for internal and external motivations. However, although the UTAUT provides a more detailed model for acceptance and use of technology, it was still has certain limitations. Therefore, Venkatesh, Thong, and Xu (2012) developed UTAUT2 and added hedonic motivation, price value, and habit to explain the model of acceptance and use of technology. UTAUT2 provides an integrated model of acceptance and use of technology, which improves TAM. UTAUT and UTAU2 provide a more detailed conceptions about the relationships between external, internal motivations, and acceptance and use of mobile technology. These two models hold that social influence (symbolic value) influences perceived usefulness. They have been used in previous research to investigate acceptance of various mobile services such as online mobile games (Chen & Kuan, 2012), mobile banking (Tan, Chong, Loh, & Lin, 2010), and other mobile services (Han, Mustonen, Seppanen, & Kallio, 2006; Rao Hill & Troshani, 2010).

2.3. Device multifunctionality Today, mobile devices are no longer a mere communication device for voice calling and text messaging, but they also provide various functionalities and services to their users such as multimedia, games, digital camera, mobile internet, navigation and GPS (global positioning system), video communication, music players (Dunlop & Brewster, 2002; Jin & Ji, 2010). By converging a large variety of functionalities, these devices are now transformed into multiplex multifunctional devices that address different needs of its users (Jin & Ji, 2010). Multifunctionality, as a key characteristic of mobile devices, has not formally pinpointed in IS literature. It is commonly associated with mobile hardware (Hoehle & Scornavacca, 2008) and the challenges it creates for Human–Computer Interaction (HCI) designers (Dunlop & Brewster, 2002). Some researchers compare mobile devices to ‘‘Swiss Army Knife’’ and discuss that trying to cram as much functionalities as possible into a single device may impair efficiency and effectiveness of those functionalities provided by mobile device (Satyanarayanan, 2005), thus reducing its perceived usefulness. The effect of multifunctional use of mobile devices on individual’s device usage behavior has been studied in previous research. In a study, Lin, Chan, and Xu (2012), tested multifunctionality within the context of smartphones by combining hedonic aspects of use and theory of planned behavior (TPB) (Ajzen, 1991) – which is a widely used theory for predicting adoption of a single functionality – to understand how it may impact adoption of multifunctional devices (Lin et al., 2012). They found that TPB and pleasure together can explain more than 50% of the variance in intention to use while the effect of pleasure varied from function to function. In another study, Hong and Tam (2006) found that adoption decision determinants for multipurpose information appliance are different from those of the utilitarian systems and are dependent on the context of use and the nature of the target technology. They defined multipurpose information appliances ‘‘as IT artifacts that (1) have a one-to-one binding with the user, (2) offer ubiquitous services

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and access, and (3) provide a suite of utilitarian and hedonic functions’’ (Hong & Tam, 2006, p. 162). They discussed that the ubiquitous and hedonic nature of multipurpose mobile devices sets them apart from other technologies in work settings. Previous research views multifunctionality of a device based on its hardware/software features and potential uses rather than the degree of its user’s multifunctional use. In other words, previous studies mostly approached the concept of multifunctionality from a device vantage point (which we name as device-perspective of multifunctionality) rather than from the user’s viewpoint (which we name as user-perspective of multifunctionality). To date, we are unaware of any study that measures multifunctionality based on user’s usage behavior and the degree to which users use various functionalities of their mobile device. To the extent of our knowledge there are also no studies that explore the effect of user’s multifunctional use of the device on device’s functionality fit with the user’s needs. In this study, we distinguish between the two views of multifuntionality (device-perspective and user-perspective) and address multifunctionality from user’s perspective. 2.4. Functionality fit Theory of task–technology fit (TTF) (Goodhue & Thompson, 1995) focuses on the fit between a task’s/user’s needs and a specific technology/functionality. TTF argues that users adopt a technology based on the fit between their task requirements and technology characteristics and how it can improve their performance (Gebauer & Ginsburg, 2009; Goodhue, 1995; Goodhue & Thompson, 1995). TTF has been widely used along with other technology adoption models such as TAM and UTAUT to explain user’s adoption of a technology (Dishaw & Strong, 1999; Yen, Wu, Cheng, & Huang, 2010; Zhou, Lu, & Wang, 2010). By combining attitudes toward use and the fit between user’s needs and a technology’s functionalities through TAM and TTF respectively, we can provide a better explanation for technology adoption (Dishaw & Strong, 1999). Previous research within the context of wireless technology adoption has shown that the fit between characteristics of task and technology along with perceived usefulness and perceived ease of use are direct determinants of user’s adoption of wireless technology in organizations (Yen et al., 2010). TTF has also been used in previous studies to explain the adoption of internet services (Shang, Chen, & Chen, 2007), location-based systems (LBS) (Junglas et al., 2008), mobile insurance (Lee, Lee, & Kim, 2007), Knowledge management systems (KMS) (Lin and Huang (2008)), mobile banking (Zhou et al., 2010). User’s characteristics can also affect the task technology fit (Lee et al., 2007). Therefore, the adoption of a technology is the product of both task’s and technology’s characteristics which consequently influence user’s performance and actual utilization (Zhou et al., 2010). This implies that a rich task technology fit will encourage user’s adoption of a technology while a poor task technology fit will negatively influence users’ intention to adopt a technology (Lee et al., 2007; Lin & Huang, 2008; Zhou et al., 2010). However, TTF falls short when it comes to operationalization of ‘‘fit’’ concept (Gebauer & Ginsburg, 2009). In an effort to operationalize the concept of ‘‘fit’’ within the context of mobile technology, Gebauer and Ginsburg (2009) identified five factors of fit for mobile IS (i.e. support for voice communication, support for mobile office, support for knowledge work, productivity support and versatility, wireless features and stability). However, these factors are more associated with utilitarian use of the device and account neither for hedonic use (enjoyment) nor for multifunctional use of the device. This is the first study within the context of mobile devices that delves into the user’s and device functionalities’ fit by accounting

for hedonic aspect of mobile device use (perceived enjoyment) as well as multifunctional use of the device based on user’s usage behavior (user-perspective of multifunctionality) rather than mobile device’s hardware/software characteristics (device-perspective of multifunctionality). 3. Research model and hypotheses As discussed in the literature review section, hedonic aspect of IS is an important factor in user’s acceptance and use of the technology (Heijden, 2004). These findings propose that providing a fun and enjoyable environment can favorably increase users’ perceptions toward adoption of a technology (Davis et al., 1992; Venkatesh, 1999) and encourages the usage of innovative technologies, especially for mobile technology and services. The users who have experienced enjoyment from utilizing a technology demonstrate positive attitudes toward using that technology (Davis et al., 1992). Previous research has also found that perceived enjoyment is a positive determinant of perceived usefulness (Liaw, 2002; Liaw & Huang, 2003; Norman, 2002) and perceived ease of use (Sun & ZhanG, 2006; Venkatesh, 2000). We should note that sometimes the boundary between utilitarian and hedonic systems is not very clear. This is particularly true for mixed systems, which can be used for both hedonic and utilitarian purposes (Sun & ZhanG, 2006). Multifunctional mobile devices are an instance of mixed systems that can be used for both hedonic and utilitarian purposes. The enjoyment associated with using mobile devices can affect the perceived ease of use and perceive usefulness of these devices. We also believe that the hedonic aspects of mobile device can create a perception of fit between user’s hedonic needs and their device’s functionality. Therefore, we posit that: H1a. Perceived Enjoyment positively influences Perceived Ease of Use. H1b. Perceived Usefulness.

Enjoyment

positively

influences

Perceived

H1c. Perceived Enjoyment positively influences Perceived Functionality Fit of mobile device. TAM and UTAUT models have been widely used to explain and discuss users’ acceptance and use of various technologies. These models propose that perceived usefulness and perceived ease of use are important determinants of adoption and use of a technology (Venkatesh et al., 2003; Verkasalo, López-Nicolás, MolinaCastillo, & Bouwman, 2010). Previous studies have found that that functionality fit is also a determinant of perceived usefulness and ease of use (Dishaw & Strong, 1999; Larsen, Sørebø, & Sørebø, 2009). In our study, we argue that this is not a unidirectional relationship. Perceived ease of use and perceived usefulness can also influence users perception of device functionality fit with their needs. We build our argument upon the process of imbrication that Leonardi (2011) discusses in his proposed framework of sociotechnical adaptations for flexible technologies and routines. He argues that individuals construct a perception of a technology that either constrains or affords their ability to complete their routines and achieve their goals. When individuals perceive a higher degree of affordance in a technology that helps them complete their routines, they may even change their behavior in order to imbricate the technology into their routines (Leonardi, 2011). Multifunctional mobile devices (such as Smartphones) are flexible technologies that enable the users to personalize the device

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functionalities based on their needs. Mobile users are no longer limited to the functions that the device manufacturers build into the device. There are a huge number of mobile applications (‘‘mobile apps’’) available to the users that enable them to add various functions to their mobile devices and customize them to achieve their goals. This turns mobile devices into multifunctional devices with flexible functionalities that users tailor based on their requirements. If the users find their mobile devices useful, they tend to imbricate them into their tasks and routines. As the mobile device integrates into the users routines, users tend to perceive a higher degree of fit between the functionalities of device and their needs. Users may even change the way they performed certain routines because they perceive a higher degree of affordance in the mobile device, which in turn leads to a higher degree of perceived fit between their mobile device functionalities and their needs. For instance, a user that used to note down his shopping list on paper and refer to the list on paper while doing grocery shopping, may now save the list in his mobile device and use the mobile device instead of the list on paper. As the user imbricates the mobile device into various routines, he finds that the device more aligned with his needs. Thus, he perceives a higher degree of fit between his mobile device functionalities and his needs. We hypothesize that: H2. Perceived Ease of Use positively influences Perceived Functionality Fit of mobile device.

H3. Perceived Usefulness positively influences Perceived Functionality Fit of mobile device. Multifunctional mobile devices help people manage daily activities covering work, communication, and entertainment. Multifunctionality factor has a significant relationship with intention to use of mobile technology (Lin et al., 2012). Multifunctionality factor played an important role to explain people how to evaluate and adopt a product (Sääksjärvi & Samiee, 2010). In other words, a multifunctional mobile device would facilitate the acceptance and use of mobile technology. Contextual factors are important predictors of adoption and use of mobile technology (Liu & Li, 2011). These factors can be associated with location, situational and social contexts. Different functions may be preferred by users in different contexts, for instance users tend to play mobile games in situations in which they are bored, have nothing else to do, or want to kill time (Liu & Li, 2011). Multifunctional devices can to be used in more situations

and contexts compared to single function devices. As a result, they may fit better with users’ needs in a larger variety of contexts. We believe exploring the multifunctionality factor could help us develop a comprehensive mobile device-functionality fit model. Thus, we hypothesize that: H4. Multifunctional Use positively influences Perceived Functionality Fit of mobile device. Mobile devices allow people maintain their access to various services as well as keep being connected to other people while on the move. Nowadays, symbolic value has become a vital factor for adoption and use of new mobile devices. For instance, iPhone fans may purchase and use iPhone not necessarily because of its utilitarian functionalities but also because of the descriptive norms and the social image that owning an iPhone creates. According to the study of Pagani (2004), personalization is an important perceived benefit of mobile services. Interactions among informal social groups influence user’s opinions, decisions, and behavior (López-Nicolás et al., 2008). People adopted mobile technologies and services for either functional or nonfunctional reasons (Pedersen, 2005). Mobile technology has been regarded as a symbol of social value and being upto-date, which can subsequently urge users adopt and use a mobile device. Viewing symbolic value as a feature of mobile device allows us to posit that the alignment between the perceived image created by the device and the image the users intend to build for themselves in the society can influence the degree of users’ perceived functionality fit of the device. Thus, we posit that: H5. Symbolic Value positively influences Perceived Functionality Fit of mobile device. Our proposed research model is shown in Fig. 1. 4. Methodology In order to validate our research model, we developed a survey instrument. Survey method has been widely used in previous research to measure user’s intention to use, continuance of use, and satisfaction, as well as service and system quality within information systems discipline. The focus of our study is to identify factors associated with user’s perceived device functionality fit. We used student samples, which are widely used by previous studies to investigate factors associated with adoption and use of mobile devices (Liao et al., 2007; Negahban, 2012; Phan & Daim, 2011).

Fig. 1. Research model.

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4.1. Measurement We adapted the items for perceived enjoyment, perceived ease of use, perceived usefulness, and symbolic value from prior research and modified them to fit the context mobile of our study in order to increase the reliability and validity of our survey instrument. We developed the items for perceived device functionality fit construct. To measure multifunctional use of the device, we asked the respondents to answer the frequency that they use certain features of their Smartphone as a mobile device. We listed 16 functionalities provided by Smartphones which were: voice calling, video calling, text messaging, instant messaging, checking (receiving) emails, sending emails, web browsing, playing music, watching video, playing games, checking social network sites, posting on social network sites, reading electronic documents, creating/ editing electronic documents, online shopping, using specialized software applications. For each of the functionalities, respondents answered how often they use that certain functionality ranging from Never to every time. The degree of multifunctional use of the device was then calculated based on the number of functionalities that the respondents used frequently or more (scored greater than or equal to 5) and the mean of all the scores for various functionalities. All the survey items were measured using 7-point Likert scale. A copy of the survey instrument is included in Appendix A.

The results of our analysis showed that the composite reliabilities of all constructs were above 0.89, the Cronbach’s alpha for all constructs were greater than 0.83, and they all loaded highly under their respective construct with more than 0.75, which are indicating the reliability of our multi-item constructs (Hair, Ringle, & Sarstedt, 2011). The AVE’s (Average Variance Extracted) for all constructs were than 0.75. This is an evidence for convergent validity (Hair et al., 2011). Checking Fornell–Larcker criterion for our study, AVE’s for all constructs were greater than the square of their associated correlations, which satisfies the requirements for discriminant validity (Fornell & Larcker, 1981). The result of confirmatory factor analysis (CFA) also shows that all the items have the highest loading under their respective construct (see Table 2). Table 1 presents the descriptive statistics, AVE’s, correlations, Cronbach’s alpha, composite reliability, and square root of AVE’s (bolded) for each of the constructs. 5.2. Structural model

We used Partial Least Squares (PLS) to test our research model using Smart-PLS software. PLS is a preferred method for multi-item constructs and small sample sizes (Chin, 1998; Hulland, 1999). In this section, we test our model in a two-step process. First, we assess the measurement model (Outer model) in order to examine validity and reliability of our measurement instrument. Second, we evaluate the structural model (Inner model) estimates in order to test the significance of our hypotheses and the predictive power of our model.

In order to address common methods variance (CMV), we used Harman’s one-factor test, which is one of the most widely used techniques to verify whether common (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). The basic assumption of this technique is that if a substantial amount of common method variance is present, the non-rotated factor analysis solution will result in one general factor that accounts for the majority of covariance among measures (Podsakoff et al., 2003). After conducting Harman’s single-factor test for our study a single dimension accounted for 0.37 total of variance explained which indicates that common method bias is not problematic in our study. Checking for multicollinearity, we computed VIF’s (Variance Inflation Factor) for different constructs in our model and we found that all the VIF’s were less than 3, which suggests that multicollinearity is not a major issue in our study (Hair et al., 2011). The result of the path analysis with a bootstrap sample number of 5000 shows that all of our hypotheses, except H4, are supported at 0.05 confidence interval. Fig. 2 summarizes the results of path analysis for our proposed model. The R-Square for perceived usefulness and perceived ease of use constructs were 28% and 30% respectively, which shows that perceived enjoyment can explain about one-third of variance in these constructs. This also confirms that hedonic and utilitarian aspects of these devices affect each other. The resulted R-Square for perceived device functionality fit as the dependent construct was 0.57, which indicates that perceived enjoyment, perceived ease of use, perceived usefulness, and symbolic value of mobile device can explain 57% of variance in perceived device functionality fit of the mobile device.

5.1. Measurement model

6. Discussion

We analyzed the reliability, validity, correlations, and factor loadings to assess our measurement model. We dropped out one of the items associated with perceived ease of use because of its low loading as well as high cross loading with another construct.

Our paper contributes to the IS literature by introducing the concept of perceived device-functionality fit. We intended to identify factors associated with individual’s perception of the extent to which their mobile device meets their needs and requirements.

4.2. Data collection Data were collected over a five-month period using an online survey. The respondents were undergraduate and graduate students in a southern university of the United States. The survey link was sent to about 600 students out of which 430 responses were collected (71% response rate). After discarding 85 invalid/incomplete responses and 9 outliers, 336 responses were used for data analysis. In terms of gender, 45% of respondents were male and 55% were female. The respondents’ age ranged from 18 to 65 while about 80% of respondents were between 20 and 26 years old. 5. Data analysis and results

Table 1 AVE’s and reliabilities.

MFU PDFF PEOU ENJ PU SV

AVE

Composite reliability

R-square

Cronbach’s alpha

MFU

PDFF

PEOU

ENJ

PU

SV

1 0.78 0.75 0.80 0.68 0.78

1 0.93 0.90 0.94 0.90 0.95

0 0.57 0.30 0.00 0.28 0.00

1 0.91 0.84 0.92 0.84 0.93

1 0.22 0.19 0.25 0.22 0.21

0.88 0.69 0.56 0.59 0.20

0.87 0.55 0.53 0.05

0.90 0.53 0.30

0.83 0.21

0.88

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Table 2 PLS loadings. Construct

Items

Mean

Std. dev

ENJ

PDFF

PEOU

PU

SV

MFU

Perceived enjoyment

ENJ1 ENJ2 ENJ3 ENJ4

5.79 6.14 5.68 6.18

1.11 0.94 1.15 0.89

0.88 0.93 0.89 0.89

0.43 0.53 0.51 0.52

0.42 0.54 0.49 0.51

0.43 0.48 0.48 0.50

0.31 0.24 0.26 0.26

0.21 0.25 0.23 0.19

Perceived device functionality fit

PDFF1 PDFF2 PDFF3 PDFF4

6.24 6.02 6.24 6.13

0.92 1.13 0.92 1.04

0.49 0.41 0.54 0.52

0.89 0.87 0.86 0.90

0.62 0.56 0.64 0.61

0.54 0.44 0.60 0.49

0.25 0.13 0.11 0.21

0.26 0.15 0.19 0.18

Perceived ease of use

PEOU1 PEOU3 PEOU4

6.28 5.92 6.25

0.95 1.20 0.83

0.49 0.34 0.57

0.64 0.43 0.68

0.92 0.75 0.93

0.44 0.37 0.54

0.05 0.01 0.07

0.17 0.14 0.18

Perceived usefulness

PU1 PU2 PU3 PU4

6.00 5.73 6.53 6.25

1.10 1.39 0.71 0.91

0.49 0.35 0.45 0.44

0.49 0.40 0.51 0.54

0.43 0.29 0.50 0.48

0.85 0.76 0.82 0.87

0.23 0.15 0.16 0.15

0.22 0.15 0.17 0.18

Symbolic value

SV1 SV2 SV3 SV4 SV5

4.84 4.83 4.30 4.23 4.37

1.61 1.65 1.60 1.58 1.64

0.30 0.32 0.20 0.22 0.25

0.20 0.17 0.15 0.17 0.16

0.10 0.06 0.01 0.04 0.00

0.21 0.19 0.20 0.18 0.14

0.83 0.89 0.89 0.90 0.89

0.18 0.18 0.15 0.22 0.19

Multifunctional use

MFU

9.08

3.75

0.25

0.22

0.19

0.22

0.21

1.00

Fig. 2. Path analysis results.

The result of our study shows that about 60% of the variance in individuals’ perceived device-functionality fit can be explained by four factors: perceived enjoyment, perceived ease of use, perceived usefulness, and symbolic value of the device. Among the four factors, perceived ease of use and perceived usefulness are the most significant and according to their coefficients, the effect of a specific degree of change in individuals’ perceived ease of use on individuals’ perceived device-functionality fit is twofold that of perceived usefulness. One justification for this may be due to the fact that if individual’s cannot easily use their mobile device, they will not be able to benefit from many functionalities that their mobile device provides them with, and this includes those features that address part of their expectations and needs. Thus, low degrees of perceived ease of use can significantly decrease the fit between the device functionalities and the user’s requirements. Perceived enjoyment is a significant predictor of perceived ease of use and perceived usefulness, explaining about 30% of variance in each of these constructs. This confirms the results of previous research and suggests that perceived enjoyment is affecting users’ perceived usefulness and perceived ease of use in the context of multifunctional mobile devices. It also confirms that for mixed sys-

tems (i.e. systems that can be used for both hedonic and utilitarian purposes), the boundary between utilitarian and hedonic use are blurry and the hedonic and utilitarian aspects affect each other. Perceived enjoyment and symbolic value are also significant predictors of perceived device-functionality fit with less significance compared to perceived ease of use and perceived usefulness. The effect of a single unit change in individuals’ perceived enjoyment on their perceived device-functionality fit is slightly more than half of that of perceived usefulness and slightly less than twice of that of symbolic value. This implies that when it comes to individual’s perception of the fit between functionalities of the device and their needs, perceived ease of use is the most important factor for the device to fit the user’s needs. This may be because mobile devices are not purely used for utilitarian purposes. On one hand, the mobility aspect of mobile devices along with their versatility in terms of its functionalities allows users to use these devices for hedonic purposes. As the result of our study implies, part of the fit between the user’s needs and the device’s functionalities is associated with the perceived enjoyment. On the other hand, the ubiquitous nature of the mobile devices can also shape part of individual’s image among their peers or the people around

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them. As a result, individuals may perceive mobile devices as a constituent of their social status and try to use a device that is aligned with the social image they expect from themselves. This forms part of their expectations and needs from their mobile device. The results of our study also confirm this by showing that symbolic value of the device is an antecedent of individuals’ perceived device-functionality fit. Our study has several implications for both practitioners and researchers. In terms of the theoretical contribution, our research introduces the concept of perceived device-functionality fit for mobile devices by accounting for both hedonic and utilitarian aspects of these devices. Previous studies mainly focused on the concept of task and technology from the utilitarian perspective. However, mobile devices combine both utilitarian and hedonic aspects in one device. We also suggested that the multifunctionality of mobile devices is not just an artifact of their hardware and software support. That is why we introduced the concept of multifunctional use by measuring usage patterns of different mobile device functionalities by their users. We suggested that these patterns vary from person to person which can create different levels of multifunctional use of mobile device per every single user. In terms of practical implications, our study shows that in order to manufacture mobile devices that fit best with the users requirements, mobile device manufacturers should not only focus on enhancing the hardware and software specifications of their mobile devices. Our study shows that ease of use, enjoyment, and symbolic value of the device are key factors that affect users’ perceptions of the degree to which the device addresses their needs. Mobile phone manufacturers need to pay special attention to the look-and-feel aspects of their productions such as the user interface of their mobile devices, as well as the image their brand conveys to their product users. This confirms the recent trends in Smartphone market, which shows that although Smartphones produced by companies such as HTC and Samsung have higher hardware and software configurations compared to iPhone by Apple company, but still iPhone enjoys a larger growth in sales. The reason may be because iPhone has established a higher symbolic value in the society and many people perceive it as a symbol of social status as well as it is providing a friendlier and an easierto-use user interface to its users.

7. Limitations and future research In this section, we discuss the limitations we that may impact the results of our study and what the research questions future research can address to provide a more comprehensive understanding of perceived device functionality fit. The first limitation of our research concerns with the generalizability of our findings. Our samples were limited to undergraduate and graduate students, mainly aged between 20 and 26. Although student samples have widely been used previous research, the extents to which they represent the general public have always been a question. We can argue that the young generation may have lower resistance toward adopting new technologies. They are also more confident and efficient in using the various features mobile devices provided. Thus, they may perceive a higher level of ease of use and enjoyment using their mobile devices. Moreover, their perception of symbolic value may not only be limited to their image and how important they look if using the mobile device. The youth may also consider the extent to which using the device is a hip or trendy among their peers. These may slightly skew the results of our study. Future study can investigate how age and generational differences may affect individual’s perception of ease of use, enjoyment, symbolic value and multifunctionality of mobile devices.

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The second limitation of this study is associated with the operationalization of mobile device. In our study, we focused on Smartphones as multifunctional mobile devices. However, there are other multifunctional mobile devices such as tablets and notebook computers. We need to study if there are significant differences between these devices in terms of the degree their functionalities fit individual’s needs. Finally, many features in mobile devices are dependent on internet access as well as the quality of service provided by mobile service carriers. Whether the user has mobile internet service available and the quality of that service can affect users’ perceived usefulness of their mobile device, which based on the results of our study, is the most significant predictor for individual’s perceived device functionality fit. Future research can study the impact of mobile carrier’s service quality on the perceived usefulness of mobile devices. We also believe that we need to investigate how individual’s multifunctional use of mobile device can influence the fit between device functionality and user’s requirements. We believe that as devices become more mobile and versatile as well as more personalizable in terms of allowing users to add various mobile applications to their mobile devices, the better they fit with the users’ expectations and needs. However, future research needs to develop valid and reliable scales for measuring multifunctional use of mobile (or even non-mobile) devices and test its impact of users’ device-functionality fit. Previous research have found technology-fit to be a significant predictor of technology adoption and use (Dishaw & Strong, 1999; Larsen et al., 2009; Lin, 2012). In our study, we focused on the antecedents of users’ perception of fit between their needs and functionalities of their mobile devices. Future research can investigate the mediating effect of perceived functionality fit and user’s adoption and use of mobile devices.

8. Conclusion In recent years, there has been a sharp increase in the use of mobile devices. The ubiquitous and multifunctional nature of these devices provides their users with versatile functionalities, omnipresent internet connectivity, and personalization features. This raises the question that what factors shape mobile users perception of their mobile device functionality fit with their needs. In order to answer this question, we proposed a research model incorporating four constructs from technology adoption and use literature (perceived enjoyment, perceived ease of use, perceived usefulness, and symbolic value) and introducing multifunctional use and perceived device-functionality fit as two new constructs in our model. The degree of multifunctional use of mobile device measures user’s frequency of use of various functionalities of his/her mobile device. The perceived device-functionality fit measures the degree to which functionalities of mobile device fits with user’s needs. The results of our study shows that more than half of the variance in individuals’ perceived device-functionality fit can be explained by their perceived enjoyment, perceived ease of use, perceived usefulness, and symbolic value of the device. This has several implications for both practitioners and researchers. In terms of the theoretical contribution, our research introduces the need to revamp the concept of device-functionality fit when it comes to mobile devices by accounting for both hedonic and utilitarian aspects. In terms of practical implications, our study highlights the importance of brand equity for mobile device manufacturers and the image their productions create in the society as well as the importance of look-and-feel aspects mobile devices in shaping users perception of fit between functionalities provided by their mobile device with their needs.

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Appendix A. Survey items We asked the respondents to answer the following questions for their Smartphones. Construct and its anchors

Items

Perceived Enjoyment (ENJ) (Strongly disagree, disagree, somewhat disagree, neither agree nor disagree, somewhat agree, agree, strongly agree)

ENJ1.

Using the device makes me feel good

ENJ2.

Using the device is enjoyable

ENJ3.

Using the device gives me a lot of joy

ENJ4.

I enjoy using the device

PEOU1.

The device is easy to use

PEOU2.

Using the device is frustrating. (DROPPED)

PEOU3.

Using the device does not require a lot of effort

PEOU4.

It is easy for me to use this device

PU1.

Using the device enhances my effectiveness

PU2.

Using the device enhances my productivity

Perceived Ease of Use (PEOU) (Strongly disagree, disagree, somewhat disagree, neither agree nor disagree, somewhat agree, agree, strongly agree)

Perceived Usefulness (PU) (Strongly disagree, disagree, somewhat disagree, neither agree nor disagree, somewhat agree, agree, strongly agree)

Symbolic Value (SV) (Strongly disagree, disagree, somewhat disagree, neither agree nor disagree, somewhat agree, agree, strongly agree)

Perceived Device-Functionally Fit (PDFF) (Strongly disagree, disagree, somewhat disagree, neither agree nor disagree, somewhat agree, agree, strongly agree)

PU3.

I find the device useful in my daily life

PU4.

The device helps me accomplish things that I want

SV1.

Using the device enhances my image

SV2.

Using the device is a sign of status

SV3.

Using the device makes me look more important

SV4.

People who use the device have a high profile

SV5.

Using the device gives me a high profile among my peers

PDFF1.

The functionality of the device meets my needs

PDFF2.

The device has all the functionality that I find necessary

PDFF3.

The functionality of the device is adequate for accomplishing my everyday tasks

PDFF4.

I am satisfied with the functionality of the device

Multifunctional Use (MFU) (Never, rarely, occasionally, sometimes, frequently, often, every time)

How often do you use your Smartphone for ____? Voice calling Playing games Video calling Checking social network sites Text Posting on social network sites messaging Instant Reading electronic documents messaging Checking/ Creating/ editing electronic documents receiving emails Sending Online shopping emails Web Using specialized software for browsing programming, statistics, graphics design, etc. Playing music Watching video

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Do you own a Smartphone? r Yes s No. Do you regularly use a Smartphone? r Yes s No. Gender: r Female s Male. Age: r 18–23 s 24–29 t 30–35 u 36–41 v 42–47 w More than 48. Education: r Undergraduate student s Masters student t PhD student u Certificate/non-degree program student v Other How long have you been using a Smartphone? r More than 6 years s 3–6 years t 1–3 years u Less than 1 year v Never before. References Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179–211. Chen, L. Da. (2008). A model of consumer acceptance of mobile payment. International Journal of Mobile Communications, 6(1), 32. Chen, L. S., & Kuan, C. J. (2012). Customer acceptance of playing online game on mobile phones. International Journal of Mobile Communications, 10(6), 598–616. Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.). Modern Methods for Business Research (Vol. 295, pp. 295–336). Mahwah, NJ: Lawrence Erlbaum Associates. Chong, A. Y.-L., Darmawan, N., Ooi, K.-B., & Lin, B. (2010). Adoption of 3G services among Malaysian consumers: An empirical analysis. International Journal of Mobile Communications, 8(2), 129–149. Chong, X., Zhang, J., Lai, K., & Nie, L. (2012). An empirical analysis of mobile internet acceptance from a value-based view. International Journal of Mobile Communications, 10(5), 536–557. Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. Davis, F., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111–1132. Dishaw, M. T., & Strong, D. M. (1999). Extending the technology acceptance model with task–technology fit constructs. Information & Management, 36(1), 9–21. Dunlop, M. D., & Brewster, S. A. (2002). The challenges of mobile devices for human computer interaction (editorial for special edition). Personal and Ubiquitous Computing, 6(4), 235–236. Fornell, C., & Larcker, D. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. Gebauer, J., & Ginsburg, M. (2009). Exploring the black box of task–technology fit. Communications of the ACM, 52(1), 130–135. Goodhue, D. L. (1995). Understanding user evaluations of information systems. Management Science, 41(12), 1827–1844. Goodhue, D. L., & Thompson, R. L. (1995). Task–technology fit and individual performance. MIS Quarterly, 19(2), 213–236. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. The Journal of Marketing Theory and Practice, 19(2), 139–152. Han, S., Mustonen, P., Seppanen, M., & Kallio, M. (2006). Physicians’ acceptance of mobile communication technology: An exploratory study. International Journal of Mobile Communications, 4(2), 210–230. Heijden, H. Van der (2004). User acceptance of hedonic information systems. MIS Quarterly, 28(4), 695–704. Hoehle, H., & Scornavacca, E. (2008). Unveiling experts perceptions towards the characteristics and value propositions of mobile information systems. In 7th International Conference on Mobile Business (pp. 334–343). Hong, S.-J., & Tam, K. Y. (2006). Understanding the adoption of multipurpose information appliances: The case of mobile data services. Information Systems Research, 17(2), 162–179. Hong, S.-J., Thong, J. Y. L., Moon, J.-Y., & Tam, K.-Y. (2008). Understanding the behavior of mobile data services consumers. Information Systems Frontiers, 10(4), 431–445. Hsu, C.-L., Wang, C.-F., & Lin, J. C.-C. (2011). Investigating customer adoption behaviours in mobile financial services. International Journal of Mobile Communications, 9(5), 477–494. Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2), 195–204. Jaradat, M. R., & Twaissi, N. M. (2010). Assessing the introduction of mobile banking in Jordan using technology acceptance model. International Journal of Interactive Mobile Technologies iJIM, 4(1), 14–21. Jin, B. S., & Ji, Y. G. (2010). Usability risk level evaluation for physical user interface of mobile phone. Computers in Industry, 61(4), 350–363. Jung, Y., Perez-Mira, B., & Wiley-Patton, S. (2009). Consumer adoption of mobile TV: Examining psychological flow and media content. Computers in Human Behavior, 25(1), 123–129.

83

Junglas, I., Abraham, C., & Watson, R. T. (2008). Task-technology fit for mobile locatable information systems. Decision Support Systems, 45(4), 1046–1057. Kim, S., & Garrison, G. (2009). Investigating mobile wireless technology adoption: An extension of the technology acceptance model. Information Systems Frontiers, 11(3), 323–333. Kim, C., Mirusmonov, M., & Lee, I. (2010). An empirical examination of factors influencing the intention to use mobile payment. Computers in Human Behavior, 26(3), 310–322. Kim, D.-Y., Park, J., & Morrison, A. M. (2008). A model of traveller acceptance of mobile technology. International Journal of Tourism Research, 10(5), 393–407. Kuo, Y.-F., & Yen, S.-N. (2009). Towards an understanding of the behavioral intention to use 3G mobile value-added services. Computers in Human Behavior, 25(1), 103–110. Larsen, T. J., Sørebø, A. M., & Sørebø, Ø. (2009). The role of task–technology fit as users’ motivation to continue information system use. Computers in Human Behavior, 25(3), 778–784. Lee, H., & Chang, E. (2011). Consumer attitudes toward online mass customization: An application of extended technology acceptance model. Journal of ComputerMediated Communication, 16(2), 171–200. Lee, D., Chung, J. Y., & Kim, H. (2013). Text me when it becomes dangerous: Exploring the determinants of college students’ adoption of mobile-based text alerts short message service. Computers in Human Behavior, 29(3), 563–569. Lee, K. S., Lee, H. S., & Kim, S. Y. (2007). Factors influencing the adoption behavior of mobile banking. Journal of Internet Banking and Commerce, 12(2), 1–9. Lee, S., Noh, M., & Kim, B. G. (2012). An integrated adoption model for mobile services. International Journal of Mobile Communications, 10(4), 405–426. Leonardi, P. M. (2011). When flexible routines meet flexible technologies: Affordance, constraint, and the imbrication of human and material agencies. MIS Quarterly, 35(1), 147–168. Liao, C.-H., Tsou, C.-W., & Huang, M.-F. (2007). Factors influencing the usage of 3G mobile services in Taiwan. Online Information Review, 31(6), 759–774. Liaw, S.-S. (2002). Understanding user perceptions of World-wide web environments. Journal of Computer Assisted Learning, 18(2), 137–148. Liaw, S.-S., & Huang, H.-M. (2003). An investigation of user attitudes toward search engines as an information retrieval tool. Computers in Human Behavior, 19(6), 751–765. Lin, S.-P. (2011). Determinants of adoption of mobile healthcare service. International Journal of Mobile Communications, 9(3), 298–315. Lin, W.-S. (2012). Perceived fit and satisfaction on web learning performance: IS continuance intention and task–technology fit perspectives. International Journal of Human–Computer Studies, 70(7), 498–507. Lin, J., Chan, H. C., & Xu, L. (2012). A tale of four functions in a multifunctional device: Extending implementation intention theory. IEEE Transactions on Professional Communication, 55(1), 36–54. Lin, T. C., & Huang, C. C. (2008). Understanding knowledge management system usage antecedents: An integration of social cognitive theory and task technology fit. Information & Management, 45(6), 410–417. Liu, Yong, & Li, H. (2011). Exploring the impact of use context on mobile hedonic services adoption: An empirical study on mobile gaming in China. Computers in Human Behavior, 27(2), 890–898. Liu, Yuan, Wang, S., & Wang, X. (2011). A usability-centred perspective on intention to use mobile payment. International Journal of Mobile Communications, 9(6), 541–562. López-Nicolás, C., Molina-Castillo, F. J., & Bouwman, H. (2008). An assessment of advanced mobile services acceptance: Contributions from TAM and diffusion theory models. Information & Management, 45(6), 359–364. Luarn, P., & Lin, H.-H. (2005). Toward an understanding of the behavioral intention to use mobile banking. Computers in Human Behavior, 21(6), 873–891. Negahban, A. (2012). Factors affecting individual’s intention to purchase smartphones from technology adoption and technology dependence perspectives. Americas Conference on Information Systems. Seattle, WA. Norman, D. (2002). Emotion & design: Attractive things work better. Interactions, 9(4), 36–42. Oi, J., Li, L., Li, Y., & Shu, H. (2009). An extension of technology acceptance model: Analysis of the adoption of mobile data services in China. Systems Research & Behavioral Science, 26(3), 391–407. Pagani, M. (2004). Determinants of adoption of third generation mobile multimedia services. Journal of Interactive Marketing, 18(3), 46–59. Pedersen, P. (2005). Adoption of mobile Internet services: An exploratory study of mobile commerce early adopters. Journal of Organizational Computing and Electronic Commerce, 15(3), 37–41. Phan, K., & Daim, T. (2011). Exploring technology acceptance for mobile services. Journal of Industrial Engineering and Management, 4(2), 339–360. Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method bias in behavioral research: A critical review of the literature and recommended remedies. The Journal of Applied Psychology, 88(5), 879–903. Rao Hill, S., & Troshani, I. (2010). Factors influencing the adoption of personalisation mobile services: Empirical evidence from young Australians. International Journal of Mobile Communications, 8(2), 150–168. Sääksjärvi, M., & Samiee, S. (2010). Assessing multifunctional innovation adoption via an integrative model. Journal of the Academy of Marketing Science, 39(5), 717–735. Sarker, S., & Wells, J. D. (2003). Understanding mobile handheld device use and adoption. Communications of the ACM, 46(12), 35–40. Satyanarayanan, M. (2005). Swiss army knife or wallet?. Pervasive Computing, IEEE (pp. 2-3).

84

A. Negahban, C.-H. Chung / Computers in Human Behavior 35 (2014) 75–84

Shang, R.-A., Chen, Y.-C., & Chen, C.-M. (2007). Why people blog? An empirical investigations of the task technology fit model. Pacific-Asia Conference on Information Systems. Auckland, New Zealand. Son, H., Park, Y., Kim, C., & Chou, J.-S. (2012). Toward an understanding of construction professionals’ acceptance of mobile computing devices in South Korea: An extension of the technology acceptance model. Automation in Construction, 28, 82–90. Sun, H., & ZhanG, P. (2006). Causal relationships between perceived enjoyment and perceived ease of use: An alternative approach. Journal of the Association for Information Systems, 7(9), 618–644. Tan, K. S., Chong, S. C., Loh, P. L., & Lin, B. (2010). An evaluation of e-banking and mbanking adoption factors and preference in Malaysia: A case study. International Journal of Mobile Communications, 8(5), 507–527. Teo, A., Tan, G. W., Cheah, C., Ooi, K., & Yew, K. (2012). Can the demographic and subjective norms influence the adoption of mobile banking? International Journal of Mobile Communications, 10(6), 578–597. Thong, J., Hong, S., & Tam, K. (2006). The effects of post-adoption beliefs on the expectation–confirmation model for information technology continuance. International Journal of Human–Computer Studies, 64(9), 799–810. Turel, O., Serenko, A., & Bontis, N. (2010). User acceptance of hedonic digital artifacts: A theory of consumption values perspective. Information & Management, 47(1), 53–59.

Venkatesh, V. (1999). Creation of favorable user perceptions: Exploring the role of intrinsic motivation. MIS Quarterly, 23(2), 239–260. Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 1997, 342–365. Venkatesh, Viswanath, Morris, M. G., Davis, G. B., & Davis, F. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. Verkasalo, H., López-Nicolás, C., Molina-Castillo, F. J., & Bouwman, H. (2010). Analysis of users and non-users of smartphone applications. Telematics and Informatics, 27(3), 242–255. Yen, D. C., Wu, C.-S., Cheng, F.-F., & Huang, Y.-W. (2010). Determinants of users’ intention to adopt wireless technology: An empirical study by integrating TTF with TAM. Computers in Human Behavior, 26(5), 906–915. Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26(4), 760–767.