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Factors Influencing Technology Adoption: A Review
The growth of e-commerce has provided an opportunity to understand why people participate in e-commerce activities and adopt information technology. Researchers from various disciplines have analyzed the reasons from different perspectives supported by theories such as; diffusion of innovation, theory of reasoned action, and theory of planned behavior. Various models have been designed and validated to explain the factors responsible for technology adoption of e-commerce. The purpose of this study is to review the literature on technology adoption and to critique a number of key models that are frequently applied by researchers in their efforts to examine the factors that predict the adoption of technologies. This paper shows the importance of social factors and characteristics of adopters that affect their behavior to adopt technology. This paper explores research possibilities beyond the central theme of technology adoption literature that focuses on attributes of innovation.
Computers and information technologies have widespread presence in today's organizations and have considerably expanded in scope and application. Since the 1980s about 50 percent of all new capital investment in organizations has been in information technology (Westland and Clark, 2000). In order for that investment to improve productivity these technologies must be accepted and used by employees in organizations. As a result, understanding the factors that influence or inhibit technology adoption has received considerable attention from researcherstransforming it into one of the mature research areas of contemporary information systems (IS) literature (Hu et al. 1999) and resulting in the development of a number of theoretical models with roots in sociology, psychology, and information systems (e.g. Davis et al. 1989; Taylor and Todd 1995; Venkatesh and Davis 2000). The purpose of this paper is to provide a brief review of the literature in this area and to outline a number of key factors that emerge as important considerations for technology adoption researchers interested in examining the construct.
2. Technology adoption: A review of the literature
2. a. Diffusion of Innovation
To understand the factors responsible for technology adoption, it is important to examine the factors that influence adoption of an innovation. Rogers (1995) explains that adoption of innovation is a time consuming process and the rate at which diffusion of innovation takes place becomes significant for individuals or organizations that are concerned with adoption of innovation. Rogers defines diffusion as “the process by which an innovation is communicated through certain channels over time among the members of a society” (Rogers 1995, p.5). He defines innovation as an idea, practice, or object that is perceived as new by an individual or other unit of adoption' (Rogers, 1995, p.11). It has been suggested (Prescott 1995) that diffusion of innovation theory (DOI) provides a “rewarding base for expanding our understanding of IT adoption, implementation, and infusion” (p.19). His diffusion of innovation theory focuses on the adoption of innovation from a sociological perspective and has been successfully applied in the Information Systems (IS) context to explain the adoption of innovations (Moore and Benbasat, 1991; Tornatzky and Klein, 1982). In this theory the factors that affect the rate of adoption of innovation are relative advantage, compatibility, complexity, trialability and observability; explained as follows:
- Relative advantage: “the degree to which an innovation is perceived as better than the idea it supercedes” (Rogers 1995, p.15). Rogers (1995) suggests that greater the perceived relative advantage of an innovation, the more rapid its rate of adoption.
- Compatibility: “the degree to which an innovation is perceived as being consistent with the existing values, past experiences, and needs of potential adopters” (Rogers 1995, p.15). Tornatzky and Klein (1982) found that innovation was more likely to be adopted when it was found to be compatible with potential adopter's job responsibilities and the value system (Ndubinsi and Sinti, 2006).
- Complexity: “the degree to which an innovation is perceived as difficult to understand and use'” (Rogers 1995, p.16). He suggests that new ideas that are simpler to understand are adopted rapidly than the ones that require the adopter to develop new skills and understanding. Cheung et al. (2000) found that complexity could negatively influence the adoption of Internet. Lederer et al. (2000) considered complexity, as a construct, exactly opposite to the ease of use construct, which has been found to directly impact the adoption of Internet.
- Trialability: “the degree to which an innovation may be experimented with on a limited basis” (Rogers 1995, p.16). It measures the extent to which potential adopters perceive an opportunity to experiment with the innovation prior to committing to its usage (Agarwal and Prasad, 1998). Tan and Teo (2000) suggested that trialability helps minimizing unknown fears and customers (in banking context) realize that the mistakes could be rectified.
- Observability: “the degree to which the results of an innovation are visible to the others” (Rogers 1995, p.16). According to Agarwal and Prasad (1998), the characteristics of observability, identified by Rogers was segregated by Moore and Benbasat (1991) as: result demonstrability- 'the tangibility of the results of using an innovation' (p.203) and visibility- the extent to which innovation is visible to the potential adopter, in the context of adoption of innovation.
Tornatzky and Klein (1982) assert that relative advantage, compatibility and complexity are the three most relevant constructs for the adoption of innovation. Other researchers such asMoore and Benbasat (1991) have successfully extended the model and added image, result demonstrability, visibility and ease of use. The five elements of Rogers' DOI theory have considerable domination in the innovation diffusion studies and have been successfully adapted to study the diffusion of technological innovation (Tung and Reick 2005).
2. b. Theory of Reasoned Action (TRA)
To understand the factors influencing adoption and acceptance of technology, information systems research has taken a wider perspective to study the factors affecting adopter's behavior to adopt the technology. Fishbein and Ajzens's (1975) theory of reasoned action (TRA) provides a firm theoretical foundation for the stream of information systems research with an objective to predict behavior of individuals to adopt a particular technology. TRA is concerned with determinants of consciously intended behaviors (Malhotra and Galletta (1999) and has influenced conceptualization of models predicting IT acceptance (e.g. Technology Acceptance Model -TAM). Drawn from social psychology, TRA states that beliefs influence attitude, which lead to intentions, and finally to behaviors. TRA introduced two core independent construct: attitude toward behavior and subjective norm, which are tied to behavioral and normative beliefs.
Attitude toward a behavior is defined as "an individual's positive and negative feelings (evaluative affect) about performing the target behavior" (Fishbein and Ajzen, 1975, p. 216). Subjective Norm is defined as "the person's perception that most people who are important to him think he should or should not perform the behavior in question" (Fishbein and Ajzen, 1975, p.302). According to TRA, individuals' attitude toward behavior is determined by their most important beliefs and consequences of performing specific behavior. Fishbein and Ajzen (1975) demonstrated through their theory that behavior is best predicted by intentions, and "intentions are jointly determined by the person's attitude and subjective norm concerning the behavior" (p.216). TRA has been successfully modeled for empirical support in predicting behavior and tested in various disciplines such as marketing and sociology (Agarwal and Prasad 1998). A meta-analysis of 87 empirical studies strongly supports the predictive power of this model (Sheppard, Hartwick and Warshaw, 1988). Expanding on the research in domains of marketing and sociology, TRA has been empirically tested and supported, in context of acceptance of information technology (Taylor and Todd, 1995a; Davis, Bagozzi and Warshaw 1989). Agarwal and Prasad (1999) observed that TRA makes an implicit and assumption that the intended behavior is under volitional control of individuals. According to Ajzen and Fishbein (1980), volitional control is achieved when a person is able to express his/her will, measured in the form of intention to perform the given action. In the context of IT adoption, TRA fails to predict the behavior of individuals with low volitional control in mandatory and non-mandatory situation. An example of behavior related to mandatory IT usage may imply behavior concerning use of new e-mail software mandated by the management of an organization. TRA provides an opportunity of expansion of its theoretical structure to incorporate beliefs affecting varying degree of volitional control of an individual.
2. c. Theory of Planned Behavior (TPB)
To address the shortcomings of the TRA model, Ajzen (1985) expanded on the theoretical framework of TRA and proposed the theory of planned behavior (TPB).TPB modifies TRA by including the construct perceived behavioral control (PBC) to address situations in which individuals lack substantive control over a specific behavior (Ajzen 1991). As the TPB is a modification of TRA, the determinants attitude and subjective norm are defined in TPB, just the way they were defined in TRA. The TPB suggests that behavior can be explained by behavioral intention, which is influenced by attitude, subjective norms and perceived behavioral control. Perceived Behavioral Control (cf. Venkatesh et al. 2003) is “the perceived ease or difficulty of performing the behavior” (Ajzen 1991, p.188) and in context of IS research, “perceptions of internal and external constraints on behavior” (Taylor and Todd 1995b, p.149). The extent to which an individual perceives to have necessary resources to perform the behavior is measured by perceived importance of that resource to successful performance of the behavior (Agarwal and Prasad 1999). An example in the usage of an IT such as Internet might be beliefs related to the extent to which an individual perceives to have access to high speed Internet connection measured by beliefs related to perceived importance of high speed connection to use Internet (Agarwal and Prasad 1998).
Rawstorne et al. (1998) mentions the need to include PBC in models that try to identify the determinants of mandated usage of Information Systems and implied that the mandated usage is a type of non-volitional behavior. However, it is a different type of non-volitional control as discussed by Ajzen, (1985: 1991), when he described the influence of internal and external factors on volitional control. Rawstorne et al. (1998) note that the major difference between the Ajzen's (1985) volitional control and the volitional control associated with mandatory behavior is that, the absence of volitional control, in the former category, hinders a person's will to perform the behavior; whereas, in the latter category, mandatory use of technology hinders a person's will not to perform the behavior. Their study also highlighted the subtle but remarkable distinction that becomes important because, Ajzen (1991) added the variable PBC to TRA, to take into account the non-volitional behavior, which measures the extent to which an individual feels control over performing the behavior, rather than not performing the behavior. Despite the distinction that has raised doubts over the usefulness of TPB for explaining and predicting mandated IS usage, there is lack of empirical support in favor of the argument presented in their study. In an attempt to generalize the impact of belief structures on behavior, in a variety of research settings, Taylor and Todd (1995) proposed decomposed theory of planned behavior (DTPB) that provides a greater insight into the factors influencing IT usage by decomposing the attitudinal, normative and control beliefs that are generalizable across situations and not specialized to each context (Fu, Farn, and Chao 2005)
Owing to its ability to predict behavior in context specific situation, the theory of planned behavior has received broad support in empirical studies of social psychology (Ajzen 1991; Ajzen and Madden 1986; Taylor and Todd 1995), marketing (Chiou 1998), and information technology (Fusilier and Durlabhji 2005; Pavlou and Fygenson 2006).
2. d. Technology Acceptance Model (TAM)
To study the factors affecting the acceptance of information technology in organizations and the usage behavior of individuals adopting the information technology, Davis (1989) proposed Technology Acceptance Model (TAM). In the field of information systems, this model has been used to predict and explain user acceptance of various information technologies. According to Davis, Bagozzi, and Warshaw (1989), "the goal of TAM is to provide an explanation of the determinants of computer acceptance that in general is capable of explaining user behavior across broad range of end-user computing technologies and user populations, while at the same time being both parsimonious and theoretically justified" (p.985). TAM is an adaptation
Figure1. Technology Acceptance Model (TAM) (source Davis 1996, p.20)
of the Theory of Reasoned Action and was designed to “understand the causal chain linking external variables to its user acceptance and actual use in a work place. External variables such as objective system characteristics, training, computer self-efficacy, user involvement in design, and the nature of implementation process are theorized to influence behavioral intention to use, and ultimately usage, indirectly via their influence on perceived usefulness and perceived ease of use” (Davis 1996, p. 20).
In an attempt to answer the question as to why perceived ease of use operates through perceived usefulness (Figure 1), Davis (1993) suggested that perceived usefulness construct may reflect considerations of “benefits” and the “costs” of using the target system. Although the subjective norm construct should take into account social influence, the authors of TAM observed that conceptualization of social norm based on TRA has theoretical and psychometric problems (Malhotra and Galletta, 1999). The final TAM model excluded the social norm construct, as Davis et al. (1989) observed the difficulty in distinguishing the cause of usage behavior: whether by influence of referents on an individual's intent or by individual's own attitude (Malhotra and Galletta, 1999). However, in an attempt to predict the usage behavior, in case of mandatory settings, TAM was extended to include the subjective norm as an additional predictor of intention to use (Venkatesh and Davis 2000; Venkatesh et al. 2003). According to Robey (1996), TAM's theoretical contribution has helped researchers understand information systems usage and acceptance behaviors. As noted by Malhotra and Galletta (1999) the TAM model has emerged as one of the most influential models in the stream of research in IS acceptance and usage. Table 1 in Appendix provides a list of numerous TAM studies that have been undertaken since 1989.
Davis (1989) on examination of the email system and a file editor application, found that PEOU and PU were significantly correlated to self-reported use of both the systems. However, in a follow-up study of 40 MBA students, the results showed that only PU determined their intention to use the system (Gefen and Straub 2000). TAM has been since applied widely across various contexts and cultures [for a detailed list of forty five TAM studies from 1989-2000, see Gefen and Straub (2000)]. In a majority of studies examining the nature and relationship of PU and PEOU to behavioral intention, PU has been found consistently to have a direct impact on the behavioral intention to use (Mathieson 1991; Adams et al. 1992; Hendrickson et al. 1993; Straub et al. 1995; Gefen and Straub 1997; Karahanna and Straub 1999; Gefen 2000). In contrast, few studies have found that PEOU directly affected the behavioral intention to use along with PU (Moore and Benbasat 1991; Thompson et al. 1991; Venkatesh and Davis 1994; Chin and Gopal 1995; Venkatesh 1999). Gefen and Straub (2000) pointed out the inconsistency of PEOU in relation to its correlation with usage behavior. The explanation for the inconsistency was related to the intrinsic and extrinsic aspect of tasks related with Information Technology (IT). The findings of this study suggest that it is the type of task that seems to determine whether PEOU directly affects use-intention.
Expanding on the original TAM studies, Davis et al. (1992) suggested that user intention to adopt a new IT is affected by extrinsic and intrinsic motivations. According to the motivation theory of McGuire (1974) consumers are motivated by extrinsic and intrinsic motivations. Extrinsic motivations refers to the drive to perform a behavior to achieve specific goals or rewards, while intrinsic motivations refer to the perceptions of pleasure and satisfaction derived from performing the behavior itself (Deci and Ryan 1985; Vallerand 1997). The characterization of utilitarian (extrinsic) and hedonic (intrinsic) motivations, is well supported in previous retail literature (Childers et al. 2001). Consumers are motivated by utilitarian aspects of shopping, achieving their shopping tasks with a minimum of efforts (Babin et al. 1994). They are also motivated by hedonic aspects of shopping that relates to fun and playfulness rather than task completion (Hirschman and Holbrook 1982).
According to Davis et al. (1992) "extrinsic motivation refers to the performance of an activity because it is perceived to be instrumental in achieving valued outcomes that are distinct from the activity itself" (p.1112). Also in this study, "intrinsic motivation refers to the performance of an activity for no apparent reinforcement other than the process of performing the activity per se" (p.1112). This study suggests that PEOU would affect IT adoption indirectly through its effect on PU, as the easier the system is to use, the more useful it can be (Venkatesh and Davis 2000). The impact of other external variables employed to study their influence on behavioral intention is fully mediated by these beliefs of PU and PEOU (Davis et al. 1989). In TAM, extrinsic motivation is clearly captured by the PU construct (Davis et al. 1989:1992; Venkatesh and Davis, (2000) as it refers to time saving and shopping effectiveness (Childers et al. 2001). However, most TAM researchers have argued that PEOU, which refers to the process of leading to an outcome (Childers et al. 2001) does not fully capture the intrinsic motivations (Davis et al. 1992; Monsuwe et al. 2004; Pavlou 2003). Further research in TAM studies have included the 'perceived enjoyment' construct to capture the pleasure and satisfaction derived by performing a behavior and its effect on use-intention.
The popularity of TAM is attributed to the most important belief constructs PU and PEOU, making it parsimonious in comparison to other models that have been tested and examined in other domains of IT research. Another advantage of this model is the flexibility with which it has been adapted to examine the social, psychological and cultural factors that influence the usage behavior of information technologies. From the list of TAM related studies in Table 1 in Appendix, it is evident in numerous replications, adaptations, and extensions of TAM model; PU and PEOU are central to the research purpose of these studies. However, in a comparative study of TAM with TPB, Mathieson (1991) observed that TAM is easy to use but TPB provides a richer understanding of factors influencing individual's behavior towards IT. In another comparative study of TAM, TPB and DTPB, Taylor and Todd (1995) observed that DTPB provided increased explanatory power for intentions as compared to TAM and TPB. Despite TAM's prediction abilities in comparison to other models, researchers in IT have taken the advantage of its parsimonious nature and successfully adapted it to achieve empirical results.
2 .d. (i). Application of TAM
Several researchers have used TAM to examine the factors influencing web technology adoption by consumers (Chen et al. 2003; Childers et al. 2001; Gefen, Karahanna and Straub 2003; Gefen and Straub 2000; Lederer et al. 2000). Childers et al. (2001) investigated the effects of PEOU, PU and enjoyment in utilitarian (grocery shopping) and hedonic (gift giving) context and found that PEOU and PU as the utilitarian aspect of online shopping, were equally important as enjoyment, a hedonic aspect of online shopping. Lederer et al. (2000) found that PU and PEOU predicted web use for work related tasks. Teo et al. (1999) also found that perceived enjoyment was an important antecedent. Lee et al. (2000) applied TAM for the study of consumer web adoption and included perceived risk to predict individual purchasing behavior on line. They showed that perceived transaction risk negatively affects PU and purchase behavior and perceived product performance risk only negatively impacts purchase behavior. However, they acknowledged the limitation of their model as lacking important factors like demographic and type of product measures.
Research studies that examine additional belief constructs such as perceived risks, perceived enjoyment, perceived access barriers, perceived behavioral control, perceived innovativeness (see Table 1 in Appendix) along with the generic belief constructs of PU and PEOU; enhances our understanding of their relative importance in influencing the IT acceptance behavior. Among the models that have been extended, TAM is the most influential model that has wider acceptability because of empirical support it has received from research pertaining IT adoption literature.
2. d. (ii). Extension of TAM
Most TAM related studies focus on the psychological, social and technological factors that influence the individual usage behavior. Very few studies have attempted to broaden the scope of inquiry beyond these factors and explore the influence of demographics on IT usage behavior. Porter and Donthu (2006) have attempted to explain the role of demographics in acceptance of Internet, in American context. In their study, they considered an extended TAM model, with an additional construct of perceived access barriers, along with the fundamental constructs: PU and PEOU, mediating the influence of external demographic variables on attitude towards Internet use, finally influencing the actual Internet usage. The rate of growth in acceptance of Internet use was found to be conveniently increasing, among the individuals who are older, less educated, belonging to minority and with low income. Contrastingly, the Internet usage rate of these demographic groups was lower than that of general population (Lenhart et al. 2003). In this study they tried to explain the differential rates of Internet usage based on external demographic variables of age, education, income and race. Taking into account that cost to access Internet could explain the demographic based differences (Hoffman et al. 2000), they tried to analyze the argument that cost might not be the only causal factor (Lenhart et al. 2003).
In the previous research, access barrier, such as cost was considered as an external variable, influencing the use of personal technology (Hoffman et al. 2000; Venkatesh and Brown 2001). In this study, perceived access barrier has been included in the model as a belief variable, influencing attitude towards Internet use. Perceived access barriers, in the context of Internet usage was conceptualized as a belief that Internet is expensive to use and difficult to access. Their findings suggested that perceived access barriers had significantly negative effect on attitude. In context of Internet usage, this belief can significantly influence consumer attitudes, thereby affecting consumers representing diverse segments of the population. Though, the Internet usage has been operationalized in this study as “personal use”, the type of personal use, such as: communication, entertainment, information seeking, socializing, purchasing needs to be investigated to understand the influence of demographic group on Internet usage behavior.
Several online shopping studies have investigated the importance of utilitarian and hedonic shopping orientation of consumers by including perceived enjoyment in the TAM model (e.g. Childers et al. 2001; Teo et al. 1999). Lee, Fiore and Kim (2005) have broadened their research to investigate the impact of Image Interactivity Technology (IIT) of a web site on the attitude towards using the technology, based on the extension of TAM model with perceived enjoyment as a significant construct along with PU and PEOU. “Image Interactivity has been described as interactivity from the web site features that enable creation and manipulation of product or environment images to simulate (or surpass) actual experience with the product or environment” (cf. Lee, Fiore and Kim 2005, pp.622; Fiore and Jin, 2003). This study has attempted to identify factors influencing consumers' attitude using the TAM model. The basic conceptualization of this research emerges from Li, Daugherty and Biocca's (2002) suggestion to investigate the impact of utilitarian and hedonic shopping orientations on information processing styles during IIT usage. Web site interactivity has been recognized as significant in luring the consumer to visit the site, purchase, be satisfied and revisit the site (Gehrke and Turban, 1999; Li et al. 2002; Mathwick 2002). In the context of examining, web site interactivity for simple technologies, empirical research has shown to have positive effects on consumers' attitudes (Klein 2003; Schlosser 2003).
According to Li et al. (2002) IIT's are the most visited features of some online stores and suggested that IIT enhances enjoyment from interacting with the virtual products. This study attempts to measure the impact of IIT by extending the basic TAM model by including the belief construct perceived enjoyment. Davis et al. 1992, proposed the concept of perceived enjoyment, along with PU and PEOU, as a significant determinant of attitude toward adoption of a technology. Perceived enjoyment is defined as “the extent to which the activity of using the computer is perceived to be enjoyable in its own right, apart from any performance consequences that may be anticipated” (Davis et al. 1992, pp. 1113). Enjoyment has been studied extensively, and it has positive effects on consumer attitude toward buying intentions (Jarvenpaa and Todd 1997; Mathwick 2002). Heijden and Verhagen (2004) found that enjoyment and trustworthiness significantly affect attitude towards an online store. Koufaris (2002) found that enjoyment influences intentions to return. Moon and Kim (2001) found that perceived playfulness affected attitude and behavior towards use of worldwide web. In this study: PU, PEOU and perceived enjoyment are modeled to moderate the influence of shopping orientation, utilitarian and hedonic, along with level of IIT, on the attitude towards online retailer and positively affecting behavioral intention toward the online retailer. The results of this study show that utilitarian shopping orientations have a significant effect on PU and PEOU, whereas hedonic shopping orientation had a significant effect on perceived enjoyment related to IIT. Besides, the web site with higher level of IIT was perceived as more useful, easy to use and enjoyable than the website with the lower level of IIT.
Few TAM related studies have considered social influence on individual's IT usage behavior (Hassanein and Head 2004; Malhotra and Galletta 1999; Segrest et al. 1998). In an innovative approach to examine the social influence in an organizational setting, Schepers, Wetzels and Du Ruyter (2005) applied TAM model to analyze the influence of leadership styles on acceptance of technology. Their study has considered extending the TAM model to include two distinctive styles of leadership: transformational and transactional. These leadership styles were conceptualized by Burns (1978) and extended the notion of these styles in an organizational setting (Bass 1985). Transformational style of leadership is characterized by charisma, individual consideration, intellectual stimulation, and inspirational motivation. According to Judge and Piccolo (2004), charisma is defined as a degree to which the leader admirable behavior allow followers to identify with the leader or the amount of faith, respect , and inspiration, a leader instills in followers (Bass, 1985). Individual consideration refers to the ability to consider subordinates individually, delegating projects to stimulate people, create learning experiences, and paying attention to personal needs of these followers. Intellectual stimulation refers to the act of encouraging ways of thinking, reasoning before acting, and enabling subordinates to analyze problems from different perspectives (Avolio and Bass, 1988). Inspirational motivation refers to empowering and inspiring followers to pursue challenging goals and a mission (Bass, 1985). Transactional leadership is characterized by contingent reward and management-by-exception. Contingent reward refers to leader's ability to reward his subordinates when they act in accordance to contracts, rules, objectives, or expend necessary efforts on certain tasks (Howell and Avolio, 1993).
According to Venkatesh (1999), training, education and technical support can affect an individual's ability to accept the use of a technology. Based on this premise and the empirical results produced by the studies of (Frambach and Scillewaert 2002), this study also attempts to study the influence of ‘organizational facilitators' on the actual technology usage. Organizational facilitators refer to the concrete actions implemented by the leader in an organization corresponding to the conditions and events that create a positive environment for technology adoption. Training, education and technical support can be regarded as the organizational facilitator elements (Frambach and Scillewaert 2002). Consistent with previous research (Venkatesh et al. 2002), the organizational facilitator elements displayed a strong influence on individual's perceived ease of use of technology. This study suggests training and general end-user support is of importance in enhancing technology acceptance. Besides, this study highlights that relationship between transformational leadership and perceived usefulness is significant as 34 percent of variance in perceived usefulness is explained. The influence of transactional style of leadership was found to be non-significant for acceptance of technology.
2 .d. (iii). Criticism of TAM
Gefen and Straub (2000) noticed that most TAM studies, including the study by Davis, have not found a direct influence of PEOU on IT adoption. Davis (1989) suggested that "ease of use operates through usefulness" (p. 332), asserted by various research (Adams et al.1992; Chau 1996; Gefen and Straub 1997; Karahanna and Straub 1999; Keil et al. 1995). Based on their findings, Keil et al. (1995) has questioned the overall importance of PEOU in IT adoption. However, according to Gefen and Straub (2000) the role of PEOU in TAM remains controversial as some studies show that PEOU does affect IT use (Moore and Benbasat 1991; Thompson et al. 1991; Venkatesh and Davis 1994).
Broekhuizen (2006) has pointed out a few shortcomings of TAM in his research aimed at examining the determinants of online purchasing. Some of the arguments presented in his study are mentioned as follows:
- TAM is designed to explain the use of technology. It does not relate the technology in question to the competing alternatives which consumers choose. TAM deals with Internet in isolation of the off-line channel. Though PU refers to relative advantage of using Internet, TAM fails to explain the trade-offs consumers have to make for time savings and shopping effectiveness.
- TAM implicitly assumes that e-tailers do not differ in their performance as focus is on perceptions of using the technology itself.
- Perceived usefulness does not distinguish between improving outcome quality and/or saving time and effort.
- Though TAM's key variables in the online context have been extensively studied, little is relatively known about what constitutes PU, PEOU and enjoyment.
Baron, Patterson and Harris (2006) state that key TAM construct definitions are inadequate for technology based services, where consumers have co-created the value of the service. Their findings are based on the research that contributes to the understanding of consumer technology-based service (text messaging using mobile phones) usage. Their study suggests (p.112)
- inadequacy of acceptance of technology where technology is embedded in consumer community practice
- inadequacies in measurement of PU and PEOU, where consumers devise coping strategy to deal with technology paradoxes
- evidence of subtle differences of "social influence" and "perceived behavioral control"
Several researchers have highlighted the shortcomings of TAM model. As TAM studies are evolving, there are enormous opportunities to adapt TAM with the unexplored factors (independent variables) that influence the adoption intention/usage of technology.
2. e. Unified Theory of Acceptance and Use of Technology (UTAUT) model
Researchers in Information Systems (IS) are confronted with choice of different models that explain user acceptance of new technology, which is described as the one of the most mature areas of research in the IS literature (Hu et al. 1999). In an attempt to progress toward a unified view of user acceptance, Venkatesh et al (2003) conducted a study to review eight theoretical models and synthesize their findings to propose a Unified Theory of Acceptance and Use of Technology (UTAUT) model. Venkatesh et al. (2003) empirically compared the eight ‘individual acceptance models', namely: 1) Theory of Reasoned Action (TRA); 2) Technology Acceptance model (TAM); 3) Motivational model (MM); 4) Theory of Planned Behavior (TPB); 5) Combined TAM and TPB (C-TAM-TPB); 6) Model of PC Utilization (MPCU); 7) Model based on Innovation Diffusion Theory (IDT); 8) Model based on Social Cognitive Theory (SCT).
In an attempt to predict the usage behavior as a dependent variable, the comprehensive analysis of all the eight models resulted in four constructs that appeared to have a significant role as direct determinants of user acceptance and usage behavior, namely: performance expectancy, effort expectancy, social influence, and facilitating conditions
In this study, data from four organizations was used over a six-month period with three points of measurement. The eight models explained a variance between 17 percent and 53 percent. The UTAUT model when tested against the original data, explained a variance of 69 percent and significantly excelled in its performance over the eight individual models.
The UTAUT model also explains the moderating effect of demographics on the intention to use the technology. Though it has provided encouraging results in different organizational settings, it has not been tried for organizations that provide e-services to consumers. As the objective, scope and functioning of e-service providers varies with different organizations, researching the usefulness of the UTAUT model could provide a greater understanding of adoption of e-services by consumers. A relatively new field of research is emerging to study adoption of e-government services provided by the government to its citizen (G2C). Most studies examining the adoption of e-government services have used extension of TAM model to study barriers of adoption (Gilbert and Balestrini 2004), apathy to adoption (Schaupp and Carter 2005), citizen trust and adoption (Carter and Belanger 2004; Horst, Kuttschreuter and Gutteling 2007; Warkentine, Gefen, Pavlou and Rose 2002). These studies have been country specific and the UTAUT model has not been tested so far for the study of e-government services adoption. The constructs of synthesized UTAUT model can provide insight into the factors that influence the intention to use e-government services. As the G2C e-government has a large consumer base for its services, the moderating role of demographics in the UTAUT model can provide a better understanding of the perceptions important to demographic groups that may influence their adoption behavior.
The core beliefs of TAM model are 'perceived usefulness' and 'perceived ease of use' that influence the intention toward using IT that affects usage behavior. However, lack of clarity exists in defining the nature of relationship that exists between these beliefs in relation to one another and the way they influence the intention to use the technology. Most studies conclude that PEOU operates through PU and has no direct influence on IT adoption. Though Gefen and Straub (2000) maintain that nature of task (intrinsic or extrinsic) explains the varying effects of PEOU on technology adoption, there is lack of empirical research that investigates the significance of PEOU's influence on intention to use, in context of technologies that serve the information-seeking tasks and behavior. The results could be encouraging in explaining the adoption of technologies, with varying tasks related to information utility across different cultures.
One of the major constructs of TRA and TPB models, subjective norm, was initially dropped from the TAM model following problems related to psychometric properties. It has been included in TAM 2 and various extension models to capture the social influence on the intention to adopt IT. The social factors responsible for adoption of technology has been successfully accounted in various models (TRA, TAM, TPB, DOI and PC utilization) representing different theoretical background. However, their significance has been reiterated by inclusion of a construct, social influence in the UTAUT model. There is enormous potential to research the impact of social influence on adoption of technology, in a cultural context, where usage is relatively new behavior of adoption.
The overall contribution of this paper is to analyze the theories that provide groundwork for research in technology acceptance. It also analyzes the models that have been successfully developed to test the acceptance of technology by individuals. It analyzes the arguments presented in the technology adoption literature to provide a better understanding of the evolving nature of the various models that examine factors of adoption. This study concludes by addressing the necessity of UTAUT model for (G2C) e-government adoption by citizens and stresses that the merits of the result can be analyzed for future research in e-government adoption, across different nations and cultures.