(Baskerville and Myers, 2009). Since such a vehicle does not exist in the very best journals at this point, the following literature study draws primarily on other IS journals and conferences.
The adoption and use of mobile technologies has long been at the core of mobile research in the IS field. Although closely linked conceptually, research on the adoption and use of mobile devices is typically pursued independently with only a few exceptions studying both (e.g. Al-Natour and Benbazat, 2009; Cambell and Russo, 2003; Sarker and Wells, 2003). However, in practice, the two concepts are continuous: once a decision has been made to adopt a mobile device, the user is naturally prompted to use the services offered.
After using the device for a period of time, most users decide to upgrade their current device to a newer model that fits their needs better. Figure 10 portrays the reciprocal relationship between the two research streams.
Figure 10: Relationship between Mobile Adoption and Use
Source: Own creation
In the following, research in the field of mobile adoption and use is reviewed, focusing on studies that investigate the decision to adopt mobile devices in a consumer context and studies that investigate consumer usage behavior in relation to mobile devices. Driving this inquiry is the realization that mobile services yield disappointing adoption levels especially frequently (Anil, 2003; Constantiou et al., 2006). While the two research streams are investigated separately, the same factors seem to influence the streams, and hence the two streams are discussed jointly.
Use prompts
induces Adoption
The Role of the Artifact
It is by now a common assumption that in IS research the IT artifact has tended to be taken for granted (Orlikowski and Iacono, 2001). It has been either “black-boxed” or treated as being a stable element without researchers taking notice of it (Latour, 1987). In traditional models of IT adoption, such as the TAM (Davis 1989), artifacts have furthermore commonly been viewed as productivity-oriented tools. Recently, however, Benbazat and Zmud (2003, p. 186) conceptualized the IT artifact more broadly to be “the application of IT to enable or support some task(s) embedded within a structure(s) that itself is embedded within a context(s)” and proposed that factors and phenomena closely associated with the IT artifact should come into play as central elements of an IS study.
In mobile adoption research, Hong and Tam (2006) refer to multipurpose information appliances such as mobile devices as IT artifacts that have a one-to-one binding with the user, offer ubiquitous services and access, and provide a number of utilitarian and hedonic functions. They develop and empirically test an adoption model that incorporates specific perceptions of the device. Their results show that users’ technology-specific perception are important determinants of adoption, including service availability, which is the extent to which an information appliance is perceived as being able to provide pervasive and timely connections, and perceived value for money, which is a cognitive trade-off between perceptions of quality and sacrifice that results in a balanced perception of monetary value (Hong and Tam, 2006, p. 166). Similarly, Bruner and Kumar (2005) find that when accessing the mobile Internet, the fun of using a specific device should not come at the expense of the device being easy to use. Their basic notion is that a specific device used to access the Internet may provide greater intrinsic motivation to consumers. Finally, Al-Natour and Benbazat (2009), who investigate both adoption and use, propose that understanding a user’s relationship with an IT artifact is essential to understanding whether the user will decide to reuse the same artifact, the nature of such usage, and the choice to switch to another artifact. Their results support findings in previous literature that users not only view their interactions with IT artifacts
as social and interpersonal, but also attribute to them human-like behaviors and personalities. Thus, depending on how an IT artifact is appropriated, the cues manifested and perceived will be different as, in social relationships, behavioral and relationship beliefs affect choices made in future interactions. In mobile use research, Cambell and Russo (2003) investigate factors that affect perceptions and use of mobile devices and include the degree to which the device is perceived as being an artifact of personal display, and they find support for the argument that perceptions and uses of mobile devices are socially constructed.
While the artifact is gaining increased attention in recent mobile adoption and use research, the new types of devices that continually evolve and offer new services and applications constantly add new considerations. Carr’s (2003) claim that IT systems and services, along with becoming ubiquitous, have also become commodities and are no longer differentiable from each other (Carr, 2003, p. 6) is being challenged. As Hong and Tam (2006) state, “there is an intrinsic force from the demand side to intensify the extent and nature of personalization of information appliances and their supporting services”.
Traditional adoption and use models that “black-box” the artifact are not able to entirely explain adoption and use of the new type of app phones.
The Role of User Psychographics
Demographics are the typical characteristics of users, such as gender (Nysveen et al., 2005; Riquelme and Rios, 2010) and age (Carroll et al., 2002), that have been applied to qualify effects in studies, both as moderators as well as general demographics as main empirical evidence of mobile adoption (Rice and Katz, 2001). By contrast, psychographics, which originates from marketing, is the study of personality, values, attitudes, interests, and lifestyles (Demby, 1971).
Identifying pre-adoption criteria remains a critical issue, and several researchers have either applied or emphasized psychographics in their mobile adoption studies. User characteristics that go beyond simple demographics may help categorize mobile users, and Constantiou et al. (2007) conduct statistical analyses of empirical data on mobile
service users to segment mobile adopters. The authors suggest that core characteristics among different adopter types should be supplemented with user behavior and variations in user requirements and attitudes. Several studies apply general demographics and psychographics for different purposes. Haghirian and Madlberger (2005) investigate antecedents of attitude toward advertising via mobile devices and Al-Natour and Benbazat (2005) seek to determine final intention to adopt artifacts. In mobile use studies, Constantiou et al. (2006) examine how basic mobile users can become advanced mobile users, and Bina and Giaglis (2005) identify early adopters' profiles based on gender, age, education, and income.
Since Rogers’ (2003) classification of individual’s into adopter categories, innovativeness has been a prevalent psychographic attribute in adoption research. As innovators are willing to take risks, have high social status, great financial lucidity, and interact frequently with other innovators, they are more willing to adopt new technologies that may or may not ultimately succeed (Rogers, p. 282). For early adoption, decision-making is exposed to variables other than those incurred by the technology itself and users may possibly be more influenced by those variables (e.g. Ajzen and Fishbein, 1980;
Karahanna and Straub, 1999; Rogers, 1983). Lu et al. (2008) apply social influence and personal innovativeness to TAM to explain intention to adopt wireless Internet services via mobile technology, and Yang (2010) similarly applies self-efficacy and innovativeness to TAM to explain intention to adopt mobile data services in the US and in Korea respectively. Lu et al.’s (2008) study reveals strong causal relationships between social influences, personal innovativeness, and perceptual beliefs such as usefulness and ease of use, which in turn impact adoption intentions. Providing a cultural perspective, Yang’s (2010) results indicate that that the effect of technology self-efficacy on perceived ease of use of mobile data services was stronger for American consumers than Korean consumers, and that the effect of innovativeness on behavioral intention to use mobile data services was stronger for Korean consumers than American consumers. Finally, Bauer et al. (2005) find that innovativeness increases knowledge about mobile
communication, which in turn positively influences users’ attitude towards mobile marketing.
Other examples of studies showing that psychographic attributes influence adoption behavior include studies on social influence, where the focus shifts from individual choice to socially constructed patterns of adoption and usage decisions (Bauer et al., 2005; Dickinger et al., 2008; Lu et al., 2008; Nysveen et al, 2005), trust-based constructs in the context of mobile commerce (Lin and Wang, 2005; Luarn and Lin, 2005), and broad attitudinal, social, and perceived behavior control factors (Teo and Pok, 2003).
Pedersen and Nysveen (2003) apply self-expressiveness to TAM to explain intention to adopt mobile parking services. They find that self-expressiveness contributes considerably to the explanatory power of the extended TAM. Finally, a number of studies on value-based adoption of mobile services have been conducted. Yang and Jolly (2009) apply perceived value, such as functional, social, monetary, and emotional, to attitude toward adopting mobile data services in the US and Korea, and find that emotional value has the most significant effect on using mobile data services for consumers in the two countries. Kim et al. (2005) develop the Value-based Adoption Model to explain mobile Internet adoption and demonstrate that consumers’ perception of the value of mobile Internet is a principal determinant of adoption intention.
The Role of Usage Objectives
Though several research studies apply user psychographics, usage objectives have played an increasingly important role in mobile adoption and use studies. While different qualities provided by mobile systems have been applied to studies, such as system and content quality (Cheong and Park, 2005; Haghirian and Madlberger, 2005), quality of service (Andrews et al., 2001), and aesthetic qualities (Cyr et al., 2006), mobile adoption and use objectives have been increasingly referred to as productivity-oriented/ utilitarian or pleasure-oriented/hedonic (Van der Heijden, 2004), terms tracing back to the motivational studies of the 1950s (Deci, 1975; Hirschman and Holbrook, 1982; Holbrook and Hirschman, 1982). Van der Hejden (2004) emphasizes the hedonic usage objectives
of IT, which he maintains provide self-fulfilling rather than instrumental value to the user, are strongly connected to home and leisure activities, focus on the fun-aspect of using devices, encourage prolonged rather than productive use, and are intrinsically motivated. In contrast, utilitarian usage of IT, which has been emphasized previously, provides instrumental value to the user, implying that there is an objective external to the interaction between user and device such as increasing task performance, and is extrinsically motivated (Van der Heijden, 2004, p. 695).
In mobile adoption research, there has been considerable work on the utilitarian-based TAM to predict whether individuals will adopt and voluntarily use a technology. TAM has consistently outperformed other theories, such as the Theory of Reasoned Action (TRA) (Ajzen and Fishbein, 1973; Fishbein and Ajzen, 1975) and the Theory of Planned Behavior (TPB) (Ajzen, 1985, 1991) in terms of explained variance (e.g., Davis et al., 1989; Venkatesh et al., 2003). Several mobile studies therefore adopt TAM and employ perceived ease of use and perceived usefulness as key independent variables while adding other variables to increase explanatory power of adoption and use (e.g. Cheong and Park, 2005, Carlsson et al., 2006; Riquelme and Rios, 2010). Several studies also extend the model with hedonic measures, such as perceived enjoyment (Dickinger et al., 2008, Hill and Troshani, 2010; Hong and Tam, 2006; Van der Hejden, 2004, 2005), fun (Bruner and Kumar, 2005), and playfulness (Cheong and Park, 2005).
In mobile use studies, Lee et al. (2009) adopt utilitarian and hedonic benefits as two key objectives for mobile data service usage and find that information quality has a stronger influence on usage increase when the main motive is utilitarian rather than hedonic.
Nysveen et al.’s (2005) study, however, suggests that social norms and hedonic, intrinsic motives are important determinants of intention to use among female users, whereas utilitarian, extrinsic motives are key drivers among men. Finally Wu and Du (2010) suggest that mobile devices can also be dual-purposed, possessing co-existing utilitarian and hedonic purposes.
The Role of Assimilation:
While research on the adoption and use of mobile devices indicates considerable impact, it has been established that the long term innovative effects and benefits occur when users subsequently assimilate technologies, make them their own, and embed them within their lives (Bar et al., 2007). Technology assimilation refers to the process of incorporating and absorbing uses of IT into an existing cognitive structure. The term is inspired by Piaget’s (1972) notion of intelligent adaptation and learning referred to by Piaget (1972) as assimilation and accommodation. Piaget (1972) states that assimilation is the process of using or transforming the environment so that it can be placed within preexisting cognitive structures, while accommodation is the process of changing cognitive structures in order to accept something from the environment. Technology assimilation, therefore, assumes that when a technology has been adopted, it will be incorporated into the adopter’s cognitive structures. However, Fichman and Kemerer (1997) found that an assimilation gap may exist and developed a measure for the difference between cumulative acquisition and deployment patterns, as technologies are not always fully assimilated. High assimilation is desirable, as assimilation and the continued usage of mobile devices may prevent undesirable costs or induce users to re-configure the device (Bar et al., 2007; Bhattacherjee, 2001).