The non-monetary benefits of mobile commerce: Extending UTAUT2 with perceived value

International Journal of Information Management 45 (2019) 44–55

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International Journal of Information Management journal homepage: www.elsevier.com/locate/ijinfomgt

The non-monetary benefits of mobile commerce: Extending UTAUT2 with perceived value

T

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Norman Shaw , Ksenia Sergueeva Ted Rogers School of Management, Ryerson University, 350 Victoria Street, Toronto, Ontario, M5B 2K3, Canada

A R T I C LE I N FO

A B S T R A C T

Keywords: UTAUT2 Privacy concerns Perceived value Personal innovativeness mobile commerce

Consumers can conduct mobile commerce via their smartphones. They can search for products and when ready, they pay and have the products delivered to their homes. By sharing personal information, they receive faster and more customized service. Because of the risk of loss of privacy, consumers need to balance their privacy concerns against the perceived value of enhanced mobile commerce. In this empirical study, the unified theory of acceptance and use of technology (UTAUT2) is modified where perceived value replaces price value to represent the value of an IT artifact that has no direct costs attributable to it. The framework is extended to include constructs from the privacy calculus. In addition, the construct of personal innovativeness is added as a moderator with the anticipation that owners of smartphones who are more personally innovative will be more willing to share information. From an empirical study of Canadian smartphone owners, the results show that perceived privacy concerns influence perceived value and that intention to use is significantly influenced by hedonic motivation and perceived value.

1. Introduction The population of smartphone owners continues to grow, with a forecast that by 2019, the number of smartphone users worldwide will exceed 5 billion (Statista, 2017). The affordability of these devices together with cheaper Internet access has contributed to their diffusion (BCG Perspectives, 2015). In a PwC study, 36% of participants say that the mobile/smartphone will become their main purchasing tool (PwC, 2017) confirming that the smartphone in the hands of the consumer is a key enabler for mobile commerce. Mobile commerce has been defined as ‘any transaction with a monetary value that is conducted via a mobile telecommunications network’ (Okazaki, 2005, p. 160). Thakur and Srivastava (2013) included ‘potential’ commercial transactions. Mobile commerce is an extension of e-commerce, where additional capabilities are enabled through the use of a mobile platform and a wireless network (Chhonker, Verma, & Kar, 2017). Mobility allows activities to take place at any time and any place (Hillman & Neustaedter, 2017). In this paper, mobile commerce refers to any transaction taking place at any time and any place that may lead to the buying and selling of goods and services via a wireless device. One of the barriers to the growth of mobile commerce is that users feel less secure when they enter information on their mobile device rather than their laptop (Business Insider, 2016). While m-commerce

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creates a convenient way to shop, it also creates a channel for unauthorized access to data, leading to perceived risk (Cozzarin & Dimitrov, 2016). When personal information and preferences are used to directly appeal to consumers, they fear that their information may be shared with other parties without their permission (Barnes & Scornavacca, 2004). Smartphone owners therefore make trade-off decisions to determine if the risk of breach of privacy is worth the benefits of a faster, more streamlined, personalized experience. On one side, the benefits have been grouped into utilitarian, hedonic and social; on the other side, the risks are the loss of financial or personal data (Oliveira, Faria, Thomas, & Popovič, 2014). Dinev and Hart (2006) used the term ‘privacy calculus’ to represent the trade-off between benefits and risk. Although the privacy calculus has been applied in a number of studies concerning information disclosure (Keith, Thompson, Hale, Lowry, & Greer, 2013; Wang, Duong, & Chen, 2016), there are fewer studies where it has been combined with UTAUT. When the benefits outweigh the risk, there is a perceived value, which, when positive, will favor adoption and we therefore extend UTAUT2 with the privacy calculus. Smartphones are used for more than just phone calls: there are over 3 million apps in Google Play and over 2 million apps in the Apple store (Statista, 2018), with 80% of them available for downloading at no cost (Olmstead & Atkinson, 2015). The cost of the smartphone and associated network usage fees can be allocated to the most used apps, which

Corresponding author. E-mail addresses: [email protected] (N. Shaw), [email protected] (K. Sergueeva).

https://doi.org/10.1016/j.ijinfomgt.2018.10.024 Received 8 December 2017; Received in revised form 29 October 2018; Accepted 30 October 2018 0268-4012/ © 2018 Elsevier Ltd. All rights reserved.

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with the privacy concerns. In addition, Venkatesh et al. (Venkatesh, Thong, & Xu, 2012) added the construct of price value to UTAUT2 when extending UTAUT to the voluntary consumer context. In many instances of mobile commerce, there is perceived value, but no price value as the costs of the mobile device have already been sunk. In this study, we continue research into the adoption of mobile commerce by revising the UTAUT2 model with the substitution of perceived value for price value and including constructs from the privacy calculus.

include Facebook, Instagram, YouTube, Google and WhatsApp all of which are free (Harmans, 2017). If a smartphone user then decides to add mobile commerce, there are no additional costs: adoption depends on perceived value, which is the trade-off between the benefits of using the app against the risks of loss of privacy. In a meta-analysis of UTAUT, perceived value was not included in any of the studies (Dwivedi, Rana, Chen, & Williams, 2011). Similarly, in a meta-analysis of UTAUT2 applied to the adoption of mobile banking, none of the studies included perceived value (Baptista & Oliveira, 2016). We address this gap by including perceived value instead of price value to explain the adoption of an IT artefact which has no direct monetary costs associated with it. We therefore propose a theoretical model where the privacy calculus is added as an extension to UTAUT2 and price value is replaced by perceived value. Early adopters of a technology will be more likely to share their data as they have a higher tolerance for risk (Bakke & Henry, 2015). They do not wait until the technology matures, but through their personal innovativeness (Agarwal & Prasad, 1998), they are ready to enjoy the benefits associated with sharing data prior to all the safeguards being in place. This study also seeks to answer what is the moderating effect of personal innovativeness. The paper is organized as follows. The next section of this paper is a literature review of mobile commerce. The third section describes the theoretical foundation and the hypotheses. Next we outline the methodology of collecting the data and analyzing it. The fifth section is the results followed by a discussion, with limitations and suggestions for future research. The last section is the conclusion.

3. Theoretical foundation and research model 3.1. The progression of technology acceptance theory One of the earliest theories of adoption is the Diffusion of Innovations (Rogers, 1983). Rogers was a professor of rural sociology and he analyzed the diffusion of agricultural advances. His conclusions were that an innovation would be adopted when it had a relative advantage over what was in current use, it was compatible, it was simple to use, it could be trialed and the results could be observed. As the use of information and communication technologies (ICT) advanced, researchers added theoretical frameworks to explain why users would adopt a technological innovation. In 1989, Davis (1989) published an article in MIS Quarterly introducing the Technology Acceptance Model (TAM), which posits that an ICT artifact is adopted when it is useful and easy to use. Benbasat and Barki (2007) criticized TAM and suggested that there should be more emphasis on the antecedents of perceived usefulness and perceived ease of use. Various authors followed this suggestion by extending TAM in different contexts with antecedents: for example, trust in the context of online banking (Manochehri & Sundarraj, 2011), perceived risk and perceived benefit, also in the context of online banking (Lee, 2009), habit and convenience in the context of online shoppers (Yoon & Kim, 2007). Venkatesh, Morris, Davis, and Davis, (2003) performed a comprehensive analysis of eight theories of adoption. They synthesized the variables into four constructs: performance expectancy, effort expectancy, social influence and facilitating conditions. Their resulting theoretical framework, Unified Theory of Adoption and Use of Technology (UTAUT), replaced TAM in many studies. In a meta-analysis, Dwivedi et al. (2011) reported that not all of the studies replicated the findings of the original article. A further criticism is that TAM and UTAUT were formulated to explain the adoption of ICT within an organizational setting, where use is mandated, but with personal computers and hand-held devices, consumers are free to select applications voluntarily. The variable, voluntariness of use, was added to UTAUT in a study of the use of open data by government policy makers (Zuiderwijk, Janssen, & Dwivedi, 2015). Rana, Dwivedi, Williams, and Weerakkody, (2016) synthesized constructs from nine theories of adoption and validated the core constructs of UTAUT. Although Venkatesh et al. (2003) proposed that UTAUT should be empirically tested with the moderators of age, gender, experience and voluntariness of use, Dwivedi, Rana, Janssen et al. (2017); Dwivedi, Rana, Jeyaraj, Clement, and Williams, (2017) found that many studies had not tested for them. Venkatesh et al. (2012) modified UTAUT to explain voluntary usage by adding hedonic motivation, price value and habit. Alalwan, Dwivedi, and Rana, (2017) applied UTAUT2 to mobile banking and Slade, Dwivedi, Piercy, and Williams, (2015) applied UTAUT2 to mobile payments, extending the model with perceived risk and trust. Although studies have found that attitude mediates some of the paths influencing behavioural intention (Dwivedi, Rana, Jeyaraj et al., 2017; Rana, Dwivedi, Lal, Williams, & Clement, 2017), our foundational model for our theory building is UTAUT2 without the construct of attitude. UTAUT2 has become well established and addresses the consumer context, where consumers adopt systems on their own volition. They may be persuaded by the influence of others or they may be

2. Literature review With the growth of smartphone users and the desire of business to engage with them via mobile commerce, related research has sought to understand the factors that inhibit or enhance adoption. From a metaanalysis of 201 articles published between 2008 and 2016, the Technology Adoption Model (TAM) (Davis, 1989) was the theoretical foundation for over half of the studies (Hew, 2017). The most common constructs that were added to the research models were perceived risk, perceived enjoyment and trust. The major influencing variable was perceived usefulness, which, in the case of mobile commerce, is represented by the convenience of being able to engage anytime and anyplace (Bendary & Al-Sahouly, 2018). Choi (2018) focused on smartphone based m-commerce. With the help of TAM, he showed that perceived usefulness, with the dimensions of ubiquity and location based services, was the most influential variable. Although ease of use was significant, it had a weaker effect than usefulness. Many studies have used UTAUT as their foundational theory (Hew, 2017). Marinkovic and Kalinic (2017) focused on the mobility of the smartphone and found that trust, social influence, usefulness, enjoyment and mobility were all significant. Hillman and Neustaedter (2017) compared shopping in-person to shopping online and found that consumers are more concerned about sharing personal information with online entities than with physical stores. Groß (2016) found that customers are concerned about the risk of a transaction malfunctioning resulting in financial loss. In a study in Singapore, consumers took into account the reputation of the online company to offset their perception of risk (Chandra, Srivastava, & Theng, 2010). Information collected often includes address, payment details and preferences. Consumers want to control who accesses their data. They make a trade-off between the negative consequences of unauthorized access and the added benefits of convenience (Zhang, Chen, & Lee, 2013). As more data is being collected via the smartphone, privacy concerns influence intention to engage in m-commerce (Lee & Rha, 2016; Zhang et al., 2013). This conflict of balancing the risks of sharing personal information in order to gain defined benefits has been termed the privacy-calculus paradox (Dinev & Hart, 2006; Milne & Gordon, 1993). There has been little research where UTAUT2 has been extended 45

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motivation will be more influential. Previous studies of UTAUT2 found hedonic motivation to have a significant influence on consumers’ behavioral intention to adopt mobile banking (Baptista & Oliveira, 2016). Therefore, we include hedonic motivation in our model and our next hypothesis is:

dissuaded by the perceived difficulty. We modify UTAUT2 by including privacy concerns and replacing price value with perceived value. The following paragraphs describe the constructs within our modified model. 3.2. Social influence

Hypothesis 3. Hedonic motivation positively influences consumers’ intentions to use mobile commerce

Social influence is the ‘degree to which individuals perceive that important others believe they should use the new system’ (Venkatesh et al., 2003). In social settings, they wish to be accepted by the group and follow group norms (Cooper, Kelly, & Weaver, 2001). When developing UTAUT, Venkatesh et al. (2003) included subjective norms, as this construct had been included within the frameworks of the Theory of Reasoned Action (Fishbein & Ajzen, 1976), TAM2 (Venkatesh & Davis, 2000) and the Theory of Planned Behavior (Ajzen, 1991). When extending UTAUT to UTAUT2 to explain voluntary use, social influence was retained as one of the constructs of the model (Venkatesh et al., 2012). Studies of mobile commerce have supported the significance of social influence on usage (Blaise, Halloran, & Muchnick, 2018). Although mobile commerce can be conducted as a solo activity, individuals may be persuaded to engage in it because their friends and family are doing so. In addition, they may wish to impress others by stating that they bought an item with their smartphone. Hence, we formulate our first hypothesis:

3.5. Habit Habit is ‘learned actions that have become automatic responses to cues’ (Lankton, Wilson, & Mao, 2010, p. 300). Prior use is the precursor to habit and as actions are repeated and learned over time, habitual behavior is repeated without conscious intentions (de Guinea & Markus, 2009). In a study of e-commerce, Liao, Palvia, and Lin, (2006) added habit to TAM and found that as consumers developed habitual behaviors with respect to a particular website, they were more inclined to continue visiting that same website. Venkatesh et al. (2012) added habit in UTAUT2 arguing that behavioral intention is influenced by unconscious actions as well as conscious intentions. Over 75% of the US adult population own smartphones (Pew Research, 2017) with the average ownership of smartphones being two to three years (eMarketer, 2016). Surveys have shown that usage averaged 2.3 h per day in 2017 (Comscore, 2017). Chou, Chiu, Ho, and Lee, (2013) defined habit as the extent to which users use their mobile apps automatically. Amoroso and Lim (2017) found that habit is positively correlated with continued use of mobile phones, which can be explained by many smartphone apps being designed in a similar manner due to the common characteristics of a small touch screen coupled with the expectation that learning will be intuitive. Users who engage in mobile commerce will have developed some habitual behaviors depending upon the length of time they have owned their phone. Although these are related to the use of their smartphone in general, they are also applicable to other applications including mobile commerce. In the context of smartphone usage, we follow Zhang, Sun, Yang, and Wang, (2018) example and assume that smartphone usage over a number of years will lead to more habitual behavior. We have therefore specified habit as a formative construct with the number of years of ownership as its sole indicator. Our next hypothesis is:

Hypothesis 1. Social influence positively influences consumers’ intentions to use mobile commerce 3.3. Facilitating conditions TPB (Ajzen, 1991) extended TRA (Fishbein & Ajzen, 1976) for those conditions where individuals do not have complete control due to external conditions. These ‘facilitating conditions’ (FC) represent the ‘conceptualized knowledge, resources and opportunities to perform a specific behavior’ (Venkatesh, 2000, p. 346). Studies have shown FC to be significant (Hossain, Hasan, Chan, & Ahmed, 2017; Patnasingam, Gefen, & Pavlou, 2005). When working in an organization, using a mandated system, there is a help desk. In contrast, consumers using their smartphone turn to multiple support channels, as there may be one help desk to solve problems with their smartphone and a different help desk if the network is not functioning properly. Nevertheless, consumers still expect their smartphone app to work flawlessly (Hossain et al., 2017). Our second hypothesis is:

Hypothesis 4. Habit positively influences consumers’ intentions to use mobile commerce

Hypothesis 2. Facilitating conditions positively influence consumers’ intentions to use mobile commerce

3.6. Effort expectancy According to Davis (1989), TAM posits that technology adoption depends upon both perceived usefulness and perceived ease of use (PEOU). Kim, Yoon, and Han, (2016) confirmed that PEOU significantly influences intention to use mobile apps. In UTAUT, Venkatesh et al. (2003) defined effort expectancy as ‘the degree of ease associated with the use of the system’ (Venkatesh et al., 2003, p. 350). Ease of use was significant for consumers engaged in online shopping (Teo, Tan, Ooi, Hew, & Yew, 2015; Yang, 2010). Some studies have found that effort expectancy has a significant effect on performance expectancy but not on intention to use (Dwivedi et al., 2011). However, Oliveira, Thomas, Baptista and Campos (2016) found that effort expectancy significantly influenced the adoption of mobile payments. Similarly, Shaikh, GlaveeGeo, and Karjaluoto, (2018) found that users of mobile banking were influenced by effort expectancy. In a meta-analysis, the influence of effort expectancy on behavioural usage was significant (Faaeq, Ismail, Osman, Al–Swidi, & Faieq, 2013). We therefore hypothesize that smartphone owners expect the app to be designed such that it is easy to use:

3.4. Hedonic motivation Motivation can be divided into extrinsic and intrinsic, where extrinsic refers to the utilitarian outcome and intrinsic refers to the selffulfillment while engaged in the activity (Vallerand, 1997). Van der Heijden (2004) extended TAM and found that adding an element of enjoyment to an instrument leads to prolonged use. When considering the use of an IT artifact, individuals make rational and emotional decisions (Koo, Chung, & Nam, 2015). Davis, Bagozzi, and Warshaw, (1992) found that intrinsic motivation was a positive influence and that users were more eager to adopt a system when it had both extrinsic (utilitarian) value and intrinsic (i.e. hedonic) value. TAM has been applied to online shopping with the added construct of intrinsic motivation, and although there are extrinsic benefits, such as convenience and time-saving, consumers are more engaged when the website leads to enjoyment (Shang, Chen, & Shen, 2005). Venkatesh et al. (2012) added the construct of hedonic motivation into UTAUT2 to capture the emotion of enjoyment, arguing that for voluntary systems, hedonic 46

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smartphone usage, price is not relevant when the smartphone has already been paid for and its costs have been sunk. Kim, Kim, and Wachter, (2013) found that consumers are motivated to use their mobile phones because of the utilitarian value, hedonic motivation and the social dimension; Wang et al. (2016) identified benefits of convenience and time saving and sacrifices of security and effort. Monetary value was not mentioned in either of these studies. Smartphones have many uses and their ubiquity means they are used frequently throughout the day: phone calls, text messaging and multiple apps satisfying different needs. These various uses provide benefits in the dimensions of utilitarian, social and hedonic, while the sacrifice is the cost of the phone, any usage fees and the perceived privacy risk (Liu, Zhao, Chau, & Tang, 2015; Wang & Wang, 2010). Assuming that the consumer pays for a block of monthly usage, there is no additional price to be paid by adding a free app. Therefore, the construct of Price Value in UTAUT2 is not relevant. In the context of mobile commerce, where the costs of the smartphone are sunk costs, perceived value replaces price value.

Table 1 Dimensions of Perceived Value. Yr.

Reference

Context

Dimensions

1988

(Zeithaml, 1988)

Purchase of beverages

1998

(Sweeney et al., 1998)

Pre-purchase of retail items

2000

(McDougall & Levesque, 2000) (Pura, 2005)

Services: dentist, auto, restaurant, haircut Mobile services

(Wang & Wang, 2010)

Mobile hotel reservations

Price Product attributes Quality ‘Get’ for what is ‘given’ Emotional Social Quality Price Utilitarian Price Where When Circumstances Epistemic (novelty) Emotional Social Convenience Monetary Information quality System quality Service quality Technological effort Perceived fee Perceived risk Benefits Sacrifices Fees Perceived privacy risk Novelty

2005

2010

2015

(Liu, Zhao et al., 2015)

Mobile coupons

Hypothesis 6. Perceived value positively influences consumers’ intentions to use mobile commerce 3.8. Performance expectancy influence on perceived value Achieving desired outcomes, as represented by perceived usefulness, is one of the two key influencing variables of TAM (Davis, 1989). Not surprisingly, as recorded by Benbasat and Barki (2007), a system will be used if its results are useful. Similarly, Taylor and Todd (1995) included perceived usefulness in their combined model of TPB and TAM. In the Diffusion of Innovations, Rogers (1983) noted that an innovation had to have a relative advantage in order to be adopted. Venkatesh et al. (2003) synthesized these constructs into ‘performance expectancy’. In Section 3.7, we explored prior literature that defined perceived value as the trade-off between benefits received versus sacrifices given. The benefits correspond to performance expectancy: time can be saved and the user can be more productive. In our research model, we propose that performance expectancy is an antecedent of perceived value, instead of having a direct effect on intention to use. As an example, Amazon has a one-click purchase option, such that when consumers agree to store their credit card details with Amazon, one click effects a charge to the credit card and the purchase is complete. This is useful, accomplishes the purchase more quickly and increases productivity, all of which are indicators of performance expectancy. However, consumers must be willing to make a sacrifice by sharing their credit card data, otherwise they will not perceive they are receiving value. Following Zeithaml’s definition (1988), the get of usefulness is traded against the give of credit card data, with its perceived risks. In our theoretical model, we posit that performance expectancy directly influences perceived value, which leads to our next hypothesis:

Hypothesis 5. Effort expectancy positively influences consumers’ intentions to use mobile commerce 3.7. Perceived value versus price value In a qualitative study, Zeithaml (1988) asked consumers about their perception of value, which was that a product had to have the desired attributes and be of good quality for the price paid. She summarized that perceived value is the tradeoff between ‘what I get for what I give’ (Zeithaml, 1988, p. 13). Dodds, Monroe, and Grewal, (1991) rephrased this as the tradeoff between benefits and sacrifices, emphasizing that if the price is too high, there is no net perceived value. McDougall and Levesque (2000) found that customers ‘receive value for money’ when the benefits they receive are perceived to be greater than the costs. Perceived value has multiple dimensions: there are benefits, such as features and quality, and sacrifices, such as effort and price (Sweeney, Soutar, & Johnson, 1998). Perceived value has been decomposed into dimensions of ‘get’ and ‘give’. See Table 1. As an example, wearables, such as Fitbits, offer the benefit of monitoring health, but some consumers consider their price to be too high (Dwivedi, Shareef, Simintiras, Lal, & Weerakkody, 2016; Sergueeva & Shaw, 2017). In extending UTAUT to the consumer context, Venkatesh et al. (2012) added the construct of ‘price value’ to UTAUT2 to represent ‘the cognitive tradeoff between the perceived benefits of the applications and the monetary cost for using them’ (Venkatesh et al., 2012, p. 161). When deciding to use mobile services, convenience is the benefit, and the monetary outlay is the sacrifice (Pura, 2005). Table 1 shows that Value has many dimensions, both positive and negative. Where price is relevant, price may influence the perception of value. For example, when purchasing an item, the benefits of quality and comfort are weighed against the price to be paid. However, there are contexts where price is not relevant. A walk in fresh air has no price attached to it: there are benefits of healthy exercise and sacrifices of time that could be spent on other activities as well as risks of injury from a fall or an accident. In the context of

Hypothesis 7. Performance expectancy positively influences perceived value 3.9. Perceived privacy concerns Perceived privacy concerns relate to the ‘willingness to provide personal information to transact’ (Dinev & Hart, 2006, p. 65). Consumers are reluctant to divulge personal information due to their privacy concerns negatively influencing their propensity to make online purchases (Dinev & Hart, 2006). Consumers perform a cost-benefit evaluation which has been termed the ‘privacy calculus’: the negative consequences associated with sharing personal information are traded off against the benefits to be gained by sharing (Dinev & Hart, 2006; Milne & Gordon, 1993). This aligns with our definition of perceived value, which is the trade-off between benefits and sacrifices. For mobile 47

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of perceived privacy concerns as a second order construct, perceived transaction risk is a first order construct that represents the security associated with the transaction when private financial data is being shared.

commerce, the benefits are represented by performance expectancy and the sacrifice is the sharing of personal data, represented by perceived privacy concerns. We therefore hypothesize: Hypothesis 8. Perceived privacy concerns negatively influence perceived value

3.10. The dependent variable: Intention to use

3.9.1. Specification of perceived privacy concerns Information privacy is the desire of individuals to control their own data by determining the extent to which it is communicated to others (Culnan & Armstrong, 1999). There is an element of risk when sharing personal information (Martins, Oliveira, & Popovič, 2014) due to the lack of control of who will have access to this information and how it will be used (Zhang et al., 2013). Sheehan and Hoy (2000) suggested that consumers want to be aware of what data is collected and how it will be used beyond the original transaction. Their study confirmed other dimensions such as sensitivity of information, familiarity with the entity collecting the data and what are the associated benefits of sharing this information. In our model, we condense these past findings by specifying perceived privacy concerns as a second level construct with three dimensions: awareness, control and collection. The first order constructs that represent these dimensions are perceived privacy protection, perceived privacy risk and perceived transaction risk.

The dependent variable for this study is the intention to use mcommerce (ITU). ‘Intentions are assumed to capture the motivational factors that influence behavior. They are indications of how hard people are willing to try, of how much of an effort they are planning to exert in order to perform the behavior.’ (Ajzen, 1991, p. 181). Intentions are a measure of the perceived likelihood that the respondent will use the innovation. With theories of adoption, such as TAM, UTAUT and UTAUT2, the dependent variable is actual use, whose antecedent is intention to use. However, actual use limits the participation to those who have already adopted the IT artifact, whereas intention can introduce features that are not yet widely available. Past studies have shown that intention to use is a strong predictor of actual use (Schepers & Wetzels, 2007; Venkatesh et al., 2012). Therefore, given that a number of studies have only measured behavioral intention (Oliveira et al., 2016; Slade et al., 2015), our model has intention to use as the dependent variable. 3.11. Personal innovativeness

3.9.2. Perceived privacy protection Perceived privacy protection refers to consumer’s awareness that the service provider with whom they are sharing information has a responsibility to protect their data from accidental disclosure or unauthorized use by third parties (Kim, Ferrin, & Rao, 2008). Now that many apps ask for details such as names, location, credit card information, shopping preferences and browsing history, many smartphone users want to be confident that their privacy is protected (PwC, 2017). In our specification of perceived privacy concerns as a second order construct, perceived privacy protection is a first order construct that represents consumer awareness of the need for websites to protect private data.

In their study of the diffusion of innovations, Rogers (1983) defined early adopters as ‘innovative’: they are comfortable with high levels of unfamiliarity and are willing to take more risks (Rogers, 1983). Agarwal and Prasad (1998) evaluated theories of adoption and concluded that personal innovativeness (PI) was a construct that should be included on the assumption that individuals who were more innovative would be more willing to adopt an IT innovation. Extant literature has shown that PI is related to risk (Dai, Luo, Liao, & Cao, 2015). We follow Agarwal and Prasad’s suggestion and include PI as a moderator (Agarwal & Prasad, 1998). It represents the risk-taking trait of an individual: an individual with a higher level of PI would be expected to have a greater intention to use a new technology. M-commerce involves the sharing of personal and financial information, which creates the risk of privacy loss. We posit that the influence of privacy concerns on perceived value will be moderated by personal innovativeness: consumers who are more innovative will be more willing to take risks with their privacy. We also posit that the influence of performance expectations on perceived value will be moderated by personal innovativeness: consumers who are more innovative will be willing to try mobile commerce even when they do not perceive the value to be high. As early adopters, they are willing to accept less features and take more risks. Hence, we hypothesize:

3.9.3. Perceived privacy risk Perceived privacy risk represents the fear of the potential losses that would be incurred if personal information is disclosed without permission (Featherman & Pavlou, 2003). Privacy risk negatively influences intention to disclose personal information (Dwivedi, Rana, Janssen et al., 2017; Malhotra, Kim, & Agarwal, 2004). In a study of mobile coupons, perceived privacy risk had a negative influence on perceived value (Liu, Cao, & Yang, 2015). Today, not only do mobile apps request personal information explicitly, they also automatically capture such information as location, time of day and past activities. There is therefore increasing vulnerability due to the volume of data being collected and the intrusion into one’s personal life once the data is aggregated and analyzed. In our specification of perceived privacy concerns as a second order construct, perceived privacy risk is a first order construct that represents the loss of control when private data is disclosed.

Hypothesis 9a. Personal innovativeness will negatively moderate the influence of perceived privacy concerns on perceived value Hypothesis 9b. Personal innovativeness will positively moderate the influence of performance expectancy on perceived value 3.12. Research model

3.9.4. Perceived transaction risk Perceived transaction risk is defined as the losses that may occur when personal information is being collected during an online transaction (Biswas & Biswas, 2004). Perceived transaction risk is higher in online environments due to the use of external networks when making an online purchase (Shankar, Urban, & Sultan, 2002; Yang, Lu, Gupta, Cao, & Zhang, 2011). Risk perception also depends on the consumer’s previous online shopping experience and the retailer’s reputation (Biswas & Biswas, 2004). Consumers have fewer concerns about sharing name and address, but have more concerns when providing financial information due to the potential of financial harm. In our specification

The research model is shown in Fig. 1. 4. Methodology 4.1. Survey Quantitative methods were selected as their results can be generalized, albeit with limitations. A questionnaire was developed using specialized software from Qualtrics (2017). To prepare the questions, 48

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Fig. 1. Research model – Source: adapted from (Venkatesh et al., 2012).

ensure that they converged. Then discriminant analysis was carried out using the Fornell-Larcker table and the heterotrait-monotrait (HTMT) ratio of correlations. Finally, the path coefficients and their significance were calculated. PLS allows for a moderating variable by defining paths in the model specifically to measure the significance of the moderator and its effect.

subject matter experts were interviewed, an initial questionnaire constructed and then sent back to the subject matter experts for their feedback. The questions for each construct were based on extant literature, with the use of Likert scales to measure agreement with the indicators (see Appendix). With the aid of the Qualtrics software the survey was user-friendly with some built-in attention filters, where participants had to answer a question with a very specific answer. The survey was distributed to a sample of Canadian consumers by a company that provides panels for research. Participants were offered a small incentive. The selection criteria were simply that participants had to be 18 years or older and owners of a smartphone. There was no upper age limit.

5. Results 5.1. Descriptive statistics 526 completed questionnaires were returned. They were analyzed to ensure that responses were not just simply completed to receive the incentive offered by the recruiting organization. Those responses that were completed too quickly (less than one third of the average time) and those responses, which failed specific attention filters within the questionnaire, were eliminated. The remaining 287 (55%) responses were included in the analysis. The age ranges and genders are shown in Table 2. The sample of 287 cases was split 54% male and 46% female. Over 50% had owned a smartphone for five years or more. See Table 3. Only 9% were owners

4.2. Analysis PLS-SEM was the selected tool. PLS was originally developed by Herman Wold (1985) and was applied to models with constructs having multiple dimensions and fewer paths. It has since been developed further and offers many statistical tools to evaluate outer loadings and path coefficients. We used SmartPLS (Henseler, Ringle, & Sarstedt, 2015). It has recently been enhanced to include moderator analysis and heterotrait-monotrait (HTMT) ratio of correlations for discriminant analysis (Henseler et al., 2015). It has been used for hierarchical models (Wetzels, Odekerken-Schröder, & Van Oppen, 2009). The second order construct, perceived privacy concerns, was specified with three dimensions. PLS requires indicators for all constructs. However, in the case of second order constructs, there are no indicators as the variable is defined by its first order constructs. We used the repeated indicator approach, which evaluates all constructs simultaneously and has become the most popular approach for hierarchical constructs (Wilson & Henseler, 2007). Various reports were created by running the PLS algorithm. First the outer model was analyzed where the outer loadings were tabulated to

Table 2 Descriptive statistics of sample.

49

Age Range

Male

Female

Total

Smartwatch

< 20 20–29 30–39 40–49 50–59 60–70 > 70 Grand Total

6 28 34 30 28 25 5 156

11 41 27 15 20 14 3 131

17 69 61 45 48 39 8 287

0 11 6 5 0 1 2 25

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5.4. Moderating effect of personal innovativeness

Table 3 Smartphone years of ownership. Years owned

Number

Percent

1 2 3 4 5 6+ Grand Total

14 20 29 53 47 124 287

5% 7% 10% 18% 16% 43% 100%

After analyzing the data without the moderating variable, personal innovativeness was added as a moderator. SmartPLS creates an interaction term by multiplying all the indicators of the moderating variable with all the indicators of the variable affected by the moderation. To test each hypothesis with the moderating variable, the interaction term was created and the model run, first, calculating the path coefficients with the PLS algorithm and then, with bootstrapping, to calculate the t statistic and the significance of the path. See Table 7. The only interaction term that was significant was the moderating influence of personal innovativeness on performance expectancy. The path coefficient for the interaction term was -0.09 with a t-statistic of 2.506 (p = 1.2%). The effect size was calculated according to the formula:

of smartwatches.

5.2. Measurement model

f2 = (R2 with moderator – R2 without moderator) / (1 – R2 with moderator)

The first stage is the testing of the measurement model for reliability, convergence and discriminant validity (Hair, Hult, Ringle, & Sarstedt, 2016). The PLS algorithm was run and the outer loadings were calculated for each of the indicators. All loadings were greater than 0.7 (Henseler, Ringle, & Sinkovics, 2009). Construct reliability and validity were tested by calculating Cronbach’s alpha, composite reliability and average variance extracted. Cronbach’s alpha was greater than 0.8 (Cronbach & Meehl, 1955), composite reliability was greater than 0.6 and average variance extracted was greater than 0.5 (Henseler et al., 2009). See Table 4. These results confirmed construct reliability and validity. A study by Henseler et al. (2015) found that the heterotrait-montotrait (HTMT) ratio of correlations is more sensitive than the FornellLarcker criterion. Following their suggestions, we calculated the HTMT ratio for the correlations of each constructs and all cross-correlations between constructs. Table 5 shows the HTMT ratios. Further, we ran a complete bootstrap with 1000 samples and calculated the confidence intervals. In all cases, the 97.5% confidence interval did not exceed 0.85, thereby establishing discriminant validity (Hair, Ringle, & Sarstedt, 2011).

R2 for perceived value with moderator was 0.6 and R2 without moderator was 0.571. These values gave an f2 = 0.07, which is considered weak (Henseler & Fassott, 2010). 6. Discussion Our research model modifies UTAUT2 by including constructs from the privacy calculus and replacing price value by perceived value. We tested our model on a random sampling of Canadian consumers 18 years and older, who owned smartphones. We excluded price value on the assumption that costs of the smartphone and its usage were sunk costs because the owners were deploying primary tasks such as phone calls, text messaging, social media updates and gaming. We postulated that they were still motivated by value, which in our model is represented by perceived value. In our results, we found that the influence of this factor on intention to use was significant and its path coefficient had the highest value compared to the other variables that influenced intention to use, concluding that consumers engage in mobile commerce so long as it has value for them. This is consistent with past studies of users of mobile services (Liu, Zhao et al., 2015; Pura, 2005). Borrowing from the privacy calculus, perceived value was specified as the trade-off between concerns about privacy and performance expectancy. Both paths (perceived privacy concerns to perceived value and performance expectancy to perceived value) were significant. Perceived value motivates customers to use m-commerce, and shapes perceptions as they evaluate the trade-off they are making. As past meta-analyses of adoption have shown, performance expectancy (which is equivalent to perceived usefulness in TAM) is the dominant influencing variable (Dwivedi et al., 2011; King & He, 2006). Our finding indicates that consumers will adopt mobile commerce if it is useful, which by itself is not a deep insight (Benbasat & Barki, 2007). However,

5.3. The structural model The PLS algorithm calculates the coefficient of determination, R2, which is the proportion of the dependent variable explained by the influencing variables. R2 for intention to use is 0.659. This is in the moderate range from 0.5 to 075 (Hair et al., 2011). R2 for perceived privacy concerns is 0.571, which is also moderate. Significance was determined by running the bootstrapping calculations with 5000 samples and no sign change. Four paths were significant as shown in Table 6.

Table 4 Construct reliability and validity. Construct

Cronbach's Alpha

Composite Reliability

Average Variance Extracted

Effort expectancy EE Facilitating conditions FC Habit HB (specified as formative) Hedonic motivation HM Intention to use ITU Perceived value PV Perceived privacy concerns PPV Perceived privacy protection PPP Perceived privacy risk PPR Perceived transaction risk PTR Performance expectancy PE Social influence SI

0.911 0.856 1 0.864 0.925 0.97 0.932 0.945 0.953 0.909 0.877 0.952

0.944 0.911 1 0.918 0.943 0.978 0.942 0.965 0.966 0.938 0.925 0.969

0.849 0.774 1 0.789 0.77 0.916 0.597 0.902 0.876 0.791 0.804 0.912

50

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Table 5 HTMT ratio of correlations.

FC HB HM ITU PV PPV PPP PPR PTR PE SI

EE

FC

HB

HM

ITU

PV

PPV

PPP

PPR

PTR

PE

0.503 0.161 0.709 0.634 0.635 0.454 0.544 0.311 0.316 0.692 0.363

0.09 0.455 0.417 0.485 0.4 0.46 0.247 0.324 0.426 0.282

0.05 0.06 0.021 0.036 0.028 0.021 0.041 0.087 0.012

0.816 0.806 0.499 0.617 0.358 0.315 0.75 0.554

0.816 0.492 0.613 0.349 0.31 0.666 0.527

0.52 0.639 0.376 0.328 0.773 0.602

0.839 0.92 0.916 0.419 0.397

0.574 0.543 0.53 0.445

0.622 0.282 0.319

0.275 0.251

0.528

perceived value not only depends upon usefulness, it also depends upon risks associated with privacy: as the risks of sharing information increase, perceived value decreases. For example, consumers may like the usefulness of searching for product information, learning about specific features and asking to receive alerts when new products are announced. However, these benefits are tempered by privacy concerns: browsing for product information leaves a trail of searches resulting in the potential delivery of unwanted targeted advertisements and the sharing of personal information could lead to identity theft. The balance of providing limited private information with the convenience of receiving targeted promotions helps the users decide whether to opt in or not. These results are consistent with past studies of the privacy calculus (Dinev & Hart, 2006; Wang et al., 2016). Although functionality, represented by performance expectancy, is important so too is enjoyment. Hedonic motivation significantly influences intention to use. This was predicted by UTAUT2 (Venkatesh et al., 2012) and is consistent with other studies (Alalwan et al., 2017; Chun, Lee, & Kim, 2012). The design of an app is important. With a selection of apps that perform similar tasks, users prefer to use an app that is well-designed and fun to use. In contrast to hedonic motivation, effort expectancy did not have a significant influence on intention to use. Past studies of adoption have shown that perceived ease of use is significant, albeit with a weaker effect than perceived usefulness (King & He, 2006). The non-significance of ease of use in our study may be explained by the ubiquity of smartphones and the similarity of their most common features. Many applications use the touch screen for navigation and icons for guidance, resulting in designs that are intuitive and easy to use. Our empirical results show that hedonic motivation is significant whilst ease of use is not. Social influence was not significant in this study. In their study of the social influence construct in eight adoption theories, Venkatesh et al. (2003) found that this construct was significant in mandatory situations but not when use is voluntary. This suggests that the probability of people fulfilling the expectations of others is greater for users whose behavior is rewarded or penalized. Consumer adoption of mobile commerce is a voluntary action and is often conducted solo. The nonsignificance of social influence also agrees with other studies (Alalwan

Table 7 Moderating effect of Perceived Innovativeness. Moderating effect on path…

Path coeff.

t statistic

P value

Perceived privacy concerns to perceived value Perceived expectancy to perceived value

0.16 −0.09

0.253 2.506

0.801 0.012

et al., 2017; Morosan & DeFranco, 2016). Facilitating conditions were not a significant influence on intention to use. A meta-study of UTAUT had found that most studies reported a significant relationship between facilitating conditions and intention to use (Dwivedi et al., 2011). Our results agree with Venkatesh et al. (2003) who state that facilitating conditions could be confounded with ease of use. Smartphone apps are easy to use: suppliers build reliable products, network providers offer reliable connectivity and app developers make their apps intuitive, requiring very little assistance. Consequently, mobile commerce is simply just another app on the smartphone, which seldom requires the needs of a help desk, where phone calls to the service center are more about billing issues than problems with features. Although Venkatesh et al. (2012) found habit to have a significant effect, we found that it was not significant. We have included habit on the assumption that habit refers to use of smartphone apps in general rather than mobile commerce. The designs of smartphones are similar across the various brands and the navigation aids for many of the apps follow the same procedure of clicking on certain icons displayed on the screen. Most apps are designed such that the users can access features with minimal training. Once apps in general are mastered on the smartphone, there is little effort in learning to use apps for mobile commerce. We specified habit as a formative construct, defined as years of ownership. Over 80% of our sample could be considered habitual users as they had owned a smartphone for three years or more. There was not enough variation of habit to determine its significance. In a study of mobile messages, Shareef, Dwivedi, Kumar, and Kumar, (2017) also found that habit was not significant. The role of personal innovativeness as a moderator was mixed. The path for performance expectancy to perceived value was significantly

Table 6 Significance of model paths. Hypoth.

Path

Path coeff

T Statistics

P Values

Signifi-cance

1 2 3 4 5 6 7 8

Social influence - > Intention to use Facilitating conditions - > Intention to use Hedonic motivation - > Intention to use Habit - > Intention to use Effort expectancy - > Intention to use Perceived value - > Intention to use Performance expectancy - > PV Perceived Privacy Concerns - > PV

0.038 0.001 0.300 0.024 0.094 0.473 0.610 0.269

0.79 0.034 4.292 0.727 1.851 7.347 14.761 6.055

0.43 0.973 0 0.467 0.064 0 0 0

NS NS *** NS NS ***

***

p < 0.001; NS = not significant for p < 0.05. 51

*** ***

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concerns, the app needs to be structured such that the consumer sees that security is built-in (Baptista & Oliveira, 2016). Trust also depends upon reputation, which can be made visible with the help of on-line reviews (Choi, 2018) and third party certification (Thakur & Srivastava, 2014). Individuals who are more personally innovative will be early adopters. Companies could target these individuals so that the next wave of adopters sees them as ambassadors once they are satisfied with the privacy and security protection. Another finding from this study is that apps need to be enjoyable. There are many apps from which to choose. Although they may offer very similar functionality, their design is a differentiator. They can combine usefulness with enjoyment, by building in personalization, where consumers can gain the benefit of faster access to functions that they commonly use and can modify the colour scheme and the display of icons so that usage remains enjoyable (Namahoot & Laohavichien, 2018). App developers should make the extra effort to ensure that their apps are not only useful and safe, but also that consumers will enjoy using them and have a sense of security while doing so.

moderated, although the effect was weak. However, there was no significant moderation on the path for perceived privacy concerns to perceived value. One interpretation is that innovators were willing to adopt mobile commerce with a lower barrier of usefulness when assessing the trade off with their privacy concerns. They were still concerned with the risk of information disclosure, but they were willing to sacrifice some features and still use the app. Our results supported Rogers (1983), who suggested that early adopters were willing to deal with uncertainty and risk. Jeong, Yoo, and Ku, (2008) had found support for the moderating effect of personal innovativeness in a study of consumer adoption of mobile RFID. 6.1. Theoretical contribution Studies of adoption in various contexts have been based on theoretical extensions of existing technology adoption models. Junglas and Spitzmuller (2005) added privacy constructs as antecedents of perceived usefulness. When Venkatesh et al. (2012) proposed UTAUT2 as an extension of TAM, they recognized that consumers, unlike users in organizations, have to pay costs associated with their IT artifact and they added the construct of price value. Our study modifies the UTAUT2 model in two important respects. First, price value is replaced by perceived value. Consumers who use their smartphones for mobile commerce have already paid for their smartphone and are typically subscribers to a monthly service, which covers their usage of the Internet for multiple activities. Furthermore, most of the apps that they download to connect with service providers are free. Consequently, for the consumer there is no additional cost. We therefore replace price value by perceived value, where the value takes into account the non-monetary costs. Our second modification of the UTAUT2 model is the change of the path for performance expectancy. Following the privacy paradox arguments (Culnan & Armstrong, 1999), perceived value is the trade-off between benefits and privacy concerns. Benefits can be measured by increased productivity and enhanced usefulness, which are reflective measures of performance expectancy. Therefore, we substitute performance expectancy for benefits. In our model, performance expectancy does not have a direct influence on intention to use. Instead, it forms part of the privacy paradox equation and directly influences perceived value. Our theoretical contribution is the modified model of adoption, which is applicable to consumers sharing personal information via an IT artifact, which has no direct attributable costs.

6.3. Limitations and future research The use of a third-party research firm who recruits participants may have served as a limitation to this study’s findings. Such organizations recruit individuals who like to respond to survey questions in return for some form of reward. This does not represent the general population. Some participants may have completed the questionnaire with little thought to their answers simply to gain the reward. Although attention filters, speed checks and straight-line analysis were techniques that were used to filter out such cases, there may be cases that were not captured via these checks. There was no restriction on the age, other than a minimum of eighteen years, participants had to be owners of smartphones and be living in Canada. Therefore, any generalizations from this study would be limited to such smartphone owners. Mobile commerce apps request specific data, which is used to personalize interaction. Future researchers could evaluate if consumers have different considerations depending upon the type of data requested. Consumers may be more willing to share their fitness data rather than their personal health record. There may be cultural differences. Gupta, Iyer, and Weisskirch, (2010) compared consumers in India and the USA and found that sensitivity depended upon cultural background. More cross-country comparisons could be made.

6.2. Implication for practice

7. Conclusion

Consumers who wish to engage in mobile commerce want to receive value. They enjoy saving time and being able to look for products at any time/any place. By sharing personal information, they can receive alerts and location specific promotions. They can even increase convenience and minimize effort through one-click purchasing, by storing their payment card details with the merchant, obviating the need to enter card details for each transaction. Although they are willing to provide data, they are concerned their data will be shared with unauthorized others, due to security lapses or poor policy procedures. They trade-off their privacy concerns with the benefits to be gained from using the app. Storing credit card information with the merchant means that the individual is more productive because of the faster checkout. Sharing preferences results in personalized offers, which are useful and save time. Having location services turned on allows searching for places nearby. Organizations that offer commerce apps should stress the utilitarian benefits while at the same time assuring their users that they will be protected. Because privacy violation and transactions risks are concerns, app providers should highlight their legal commitments to give consumers confidence (Thakur & Srivastava, 2014). Advertising and the use of social media can make consumers aware of the benefits. To alleviate privacy

This study evaluates constructs that influence intention to use an innovation to which there is no direct cost attributable. Some apps that are used on mobile devices are offered for free. The cost of the mobile device has already been incurred and justified for other reasons, such as communicating with friends via social media. In many instances, consumers pay for a fixed amount of monthly usage and so long as they do not exceed this amount, they incur no additional costs. Therefore, their adoption of the innovative new app does not have a direct financial cost. To understand the factors that influence the adoption of these innovations, the price value construct within UTAUT2 was replaced by perceived value. The perception of value depends upon the benefits received versus the ‘costs’ incurred. In the case of mobile commerce, the ‘costs’ are the perceived privacy concerns with the associated risks. To gain the convenience of speed and time, users are asked to share personal information, which has the risks of lack of control that could lead to unauthorized access. In our revised model, perceived value is influenced by performance expectancy and perceived privacy concerns. This model was empirically tested among Canadian consumers. The results show that performance expectancy and privacy concerns both significantly influence perceived value and that perceived value and hedonic motivation have a strong effect on intention to use. Academics 52

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Perceived privacy protection

may wish to apply this model to other innovations that do not have a direct cost, replacing price value with perceived value. They could apply the same model, modifying the independent variables that influence perceived value. For practitioners, the findings show that consumers are willing to share information to gain added benefits, but will only do so if they feel assured that their data is safe. And they want to have fun at the same time. With the plethora of apps available, app providers must provide value, data security and enjoyment.

When I use a smartphone app for mobile commerce…

• …I feel that I will have enough privacy • …I am comfortable with the amount of privacy protection • …I believe that my privacy is preserved Adapted from (D. J. Kim et al., 2008)

Appendix A. Construct Measures Perceived privacy risk Personal Innovativeness When using my smartphone, I am often asked to provide personal information, such as my name, address, gender and age. I am worried that…

To what extent do you agree with the following statements about a new app from a company or a developer that you trust?

• When I hear about a new app, I look for ways to experiment with it • Among my peers, I am usually the first to try out a new app • I like to experiment with new apps

• …my privacy information could be misused • …my personal information could be inappropriately shared with others • …my personal privacy could be threatened

Adapted from (Agarwal & Prasad, 1998)

Adapted from (Dinev & Hart, 2006) Facilitating Conditions Perceived value If you were to use your smartphone for mobile commerce, to what extent would you be confident that…

Despite the risks involved in sharing my personal information and my payment data, I believe that using my smartphone for mobile commerce…

• …my smartphone will function correctly • …products and services will be available • …I can get help if I have difficulties

• …is valuable • …is worthwhile • …overall delivers good value • …is beneficial to me

Adapted from (Venkatesh et al., 2012) Performance expectancy

Adapted from (F. Liu, Zhao et al., 2015) Using your smartphone for mobile commerce would… Social influence

• …be useful in my daily life • …increase my productivity • …overall, it would be useful

Please indicate your agreement with the following statements.

• People who are important to me think that I should use mobile commerce • People who influence my behavior think that I should use mobile commerce • People whose opinions I value prefer that I use mobile commerce

Adapted from (Venkatesh et al., 2012) Effort expectancy Please indicate the degree to which you agree or disagree with the following statements.

Adapted from (Venkatesh et al., 2012)

• I would find it easy to use a smartphone for mobile commerce • My use of the smartphone for mobile commerce is clear and understandable • Overall, I would find using a smartphone for mobile commerce easy

Hedonic motivation

• Using my smartphone for mobile commerce is… • …fun • …enjoyable • …annoying

to use

Adapted from (Venkatesh et al., 2012)

Adapted from (Venkatesh et al., 2012) Perceived Transaction Risk Intention to use When paying using your smartphone, what is the degree of risk that…

With reference to using your smartphone for mobile commerce, please indicate the degree to which you agree or disagree with the following statements.

• …my payment data would be compromised • …there would be a transaction error • …hackers would access my payment data

• I expect my use of mobile commerce to increase in the future • I intend to use mobile commerce in the future • I will recommend the use of mobile commerce to friends

Adapted from (Biswas & Biswas, 2004) 53

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• I will always try to use mobile commerce where feasible • I plan to use mobile commerce frequently • It is unlikely that I will use mobile commerce

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Adapted from (Taylor & Todd, 1995) Habit

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