discuss the key determinants of IT use and diffusion in a distributed network ...
addressed the issues of acceptance and diffusion of IT by providing varying ...
Information Technology Acceptance: Evolving with the Changes in the Network Environment Sungmin Kang Center for Information Systems Management Department of Management Science and Information Systems Graduate School of Business The University of Texas at Austin Austin, Texas 78712 Email:
[email protected] Abstract The existing models of information technology (IT) acceptance were developed with the concept of static individual computing environment in mind. As such, in today’s rapidly changing IT environment, they do not serve as adequate indicators of an individual's IT usage behavior. In our paper, we attempt to address this problem by first carefully observing the changing needs of a user, and discussing network (distributed computing) trend of IT environment. We then enhance the existing models of IT by introducing several factors, including internal personal belief factors, external social belief factors, and other relevant determinants based on network externality and complementarity that influence an individual to use an IT. Our paper begins with the review of what other researchers have done on the topics of the adoption and diffusion of IT. We do this by reexamining and recognizing the theoretical contributions and empirical findings of various researchers. Based on these, we think that the studies lack in identifying the new set of determinants of IT usage and fall short of proposing a more effective IT acceptance model. In particular, the diffusion and adoption literature has not pursued the network/collaboration effect variables related to group dynamics. For instance, we think that the variables in Davis’ TAM are less capable when explaining usage behavior of an individual, especially when accounting for the changes in the computing environment, and we question its applicability in ever rapidly changing network environment. Prior research have also pursued and described the positive effect of different variables in IT usage, but they have not discussed the negative effect of variables. As such, we suggest that the negative effect of variables should receive equal attention in IT usage study. As an attempt to extend and enhance the existing models of IT acceptance, we present our model of IT usage. We first discuss the key determinants of IT use and diffusion in a distributed network computing environment. Second, we integrate the factors into our model by carefully evaluating their applicability. Lastly, we discuss the contribution of our model for future empirical research in the field. In all, we propose a
more enriched model of IT acceptance by combining the behavioral models of IT acceptance and the models of IT diffusion from various other disciplines (i.e., marketing, economics, etc.).
1. Introduction A stream of research has been done on the individual use of information technologies (IT). Their purpose was to find out how technology was received and utilized by the users. The researchers knew that information technologies could add significant value to an organization in terms of productivity increases and performance improvements, but technologies were constantly evolving over time, and as a result, adoption behaviors of individuals were changing with them. Traditionally, the earlier models of innovation adoption represented marketplace deterministically and proposed improvements based on assumptions of certainty [8]. They claimed that although more recent adoption models integrate some dynamic effects (i.e., learning and risk), the basic recommendations had not changed much for decision-making. In fact, the only distinction came from the fact that the later models improved the traditional models by elaborating on them and by providing understanding of additional variables. We think that the traditional models underestimate the forces of changes and uncertainty that dominate today's innovative markets, which are full of continuous processes of technology development, improvement, and application. Thus, it is the objective of this paper to provide a conceptual framework for discussion of how technologies get diffused through their acceptance by the users and to understand the characteristics of technologies, users, and organizations in the face of changes and uncertainty. Our paper draws on various branches of previous research. From the marketing perspective, we look at several researchers who have discussed the models of and/or issues of innovation adoption [5, 21, 24, 33, 34, 42, 56]. We begin with Kamien and Schwartz [34] and Reinganum [56] who present models of firm adoption of innovations, examining the factors affecting a firm
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to adopt a certain innovation. Gatignon and Robertson [25] describe the implications of organization adoption decisions in a competitive environment. Bass [5] develops a mathematical model to examine the timing of adoption of new products and applies it by empirically testing demand growth for consumer products. This model later becomes widely adopted, extended, and applied in empirical research [6]. Mahajan et al. [42] review extensively the deterministic models for diffusion of innovations. Whereas Kalish and Sen [33] discuss how the marketing mix variables impact diffusion, Eliashberg and Chatterjee [21] provide thorough review of the stochastic models for innovation diffusion. From a strategy viewpoint using the diffusion of innovation theory, a stream of researchers has contributed to the subject matter [59, 71]. They have examined various factors, generally regarded as determinants of IT adoption and usage, in their studies. Examples of these factors include individual user characteristics [7], innovation characteristics [50], and environment characteristics [52]. Similarly, Information Systems (IS) researchers have addressed the issues of acceptance and diffusion of IT by providing varying answers to what are the factors determining IT usage by individuals, and discussing the relevance of different IT usage models [1, 17, 22, 68]. For example, the theory of reasoned action (TRA) developed by Fishbein and Ajzen [22] helps to predict and understand human behavior in making adoption decisions. In their review, Igbaria et al. [29] incorporate the concern for the determinants of behavior and relations among beliefs, attitudes, subjective norms, intentions, and behaviors. Determining how the relevant factors affect each other to lead to a decision-making is the primary focus of the TRA model. Ajzen [1] also introduces the theory of planned behavior (TPB) model as an extension of an earlier TRA [22] to consider the situations where individuals do not have complete control over their behavior. TPB was designed to predict individual adoption behavior across different settings and can be applied to IS use as well [46]. Davis et al.’s [17] well- known technology acceptance model (TAM) is an improvement of the generic TRA model. TAM adapted certain components of the generic TRA model and applied them to the particular domain of computer technology, and more broadly, to the information technology. The difference between the two models is that in place of the TRA’s attitudinal determinants, TAM introduces two key variables, perceived ease of use and perceived usefulness, as having central relevance for predicting computerbased technologies user acceptance behaviors [29]. Although previous models of IT acceptance provide useful insights, more research is needed to determine the key factors affecting or motivating individuals to use computer-based information technologies for various purposes. In light of this need for more research, our paper tries to extend previous research by examining the major determinants of IT adoption and diffusion based on what other researchers have done and by proposing an additional determinant of IT adoption that is of important relevance. As such, the purpose of this research is to seek better and valid measures for predicting and explaining IT use by individuals in network computing environment. Our research differs from other previous studies in that we try to move away from the old ways of thinking and pursue new ideas. Recognizing that the computing environment has changed due to the development of innovative technologies and the
growth of the Internet, we scrutinize carefully the changes which have occurred and propose an enhanced IT acceptance (usage) model which can help to explain the new phenomenon of technology adoption in today’s distributed network computing environment. Thus, we present an extended and improved model of IT usage by reexamining the key determinants of IT usage, taking account of the changes in user needs and computing environment. We believe that only by understanding various factors affecting the IT adoption and diffusion can we truly understand how and when individuals use information technologies. The paper proceeds as follows: In section 2, we discuss the relevant prior literature and the motivation for the study. The core of our study is contained in section 3. The model of IT usage (acceptance) and theoretical rationales are presented in this section. Section 4 discusses the contribution of the model of IT usage in IT adoption research. Future research directions and issues not covered in this paper are outlined in section 5. We conclude in section 6.
2. Prior Research and Motivation The topics of interest which are relevant in the study of user acceptance model of IT (IT adoption) are network externality, complementarity, diffusion of IT, user acceptance of IT, etc. We review the relevant literature on each of the topics to provide background information and theoretical support for our research, and especially to set the stage for a constructive discussion of our IT usage model. We try to integrate the ideas derived from the different disciplines to contribute a new body of knowledge to the area of IT adoption and diffusion research. We argue that until now, research on IT has primarily focused on the problems and issues of IT use in the context of individual computing environment. In particular, the field of economics has studied the diffusion of externality-related products such as telephone, focusing on the positive externality. These studies introduce positive externality but they rarely mention negative externality. An example of negative externality is illustrated in the following example. When we have many people using the World Wide Web (WWW) or collaborating in some other ways, people get easily overwhelmed by the volume of information they come in contact with. As a result, it becomes difficult for them to get organized and seek out the information they need in a timely manner. Thus, we have an information glut. As seen, there are definitely both positive externalities (i.e., characteristics of network environment, especially the Internet) and negative externalities, as in blocking in phone lines. We now look at the topics that are relevant to the study of IT adoption and diffusion in greater details.
2.1. Research on Network Externality Prior research in the area of IT adoption and diffusion have focused on the concept of network externality as the central theme [35, 37, 40, 44]. Investigating the role of network externality in IT adoption, Katz and Shapiro [35] aptly describe network externality in a sentence: “There are many products for which the utility that a user derives from consumption of the
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good increases with the number of other agents consuming the good.” Notably, Riggins et al. [57] indicate that prior research have looked mostly at positive network externalities where the value of participating in a network for each participant increases as the number of participants increases. This argument is based on a simple assumption that “more is better”. As the number of firms in the market adopting innovation increases, more firms will apparently be induced to join the network, resulting in the increase in the probability of other firms adopting it. This reasoning rests on the assumption that as experience and information regarding an innovation accumulate, the risks associated with its adoption diminish, and competitive pressures mount on the outside firms to act. Eventually the bandwagon effect takes place in the market structure [44]. One obvious result from this is that the later buyers will tend to become candidates for the network sponsor [37], and there will likely be one large user who will implement its own network and push others to join in, as has happened in e-mail application. At an organizational level, Ching et al. [12] define a network’s core firm (player) as one with the most power to influence other participants to perform different activities for it. Diffusion of technology is an interesting phenomenon. Relevant externalities affect the diffusion of information technology. That is, firms depend on each other to learn about new technology and its usage. What the term refers to are that: (1) firms incur costs to learn about and to use new technology for various tasks, (2) firms have varying ability to learn about new technologies, and (3) externalities have a significant effect on the process of searching for and learning about new technologies [63]. Thus, we are interested in principal factors that contribute to diffusion of technology in the context of network externality, and we examine the effect of network externality on individual’s decision on whether to use a particular technology in a given situation. Until now, researchers have looked mostly at the positive network externality effect. Therefore, we introduce the significant effect of negative externality on technology adoption. We recognize that the negative network externality is also important in determining the user value of network. For example, the use of the telephone network creates a bottleneck when too many people use the network simultaneously. The result is the communication traffic congestion of the network.
2.2 Research on Complementarity Effect According to Colombo and Mosconi [14], interdependence and complementarity among innovations should be considered in IT studies since they are likely to affect its diffusion. The concept of complementarity helps to explain how resources in an organization are efficiently used and how using one resource as a complement to the other adds value for an individual. Complementarities are said to exist when two phenomena (or two actions or two activities) strengthen each other through a mutual interaction [47]. Church and Gandal [13] address the adoption of technology when there are effects of network externalities in a user decision and when networks are characterized by complementary products produced by different firms. They discuss the implications of indirect externalities arising from complementarities between goods. For example, they regard two competing hardware goods that require
complementary platform-specific software to be of value to consumers. That is, the value of the hardware is increased as more complementary software become available. Katz and Shapiro [35, 36, 38] indicate that consumers clearly receive benefits if they are on the same or compatible network. This is based on the fact that one’s use of a network complements others’ use of the same network. Similarly, Church and Gandal [13] argue that the complementarity network effect is the effect that an increase in hardware sales has on the demand and hence, on the profitability of supplying software. In closely interrelated industries, the complementarities between new products and existing ones contribute to changing the demand conditions for complementary products [49]. As for the interdependence in making a decision to adopt, Colombo and Mosconi [14] suggest that technological interdependencies and cumulative learning are the key variables affecting firms’ adoption behavior. They also indicate that the use of complementary technologies, which provide synergistic effect to the firm, increases the likelihood of rapid diffusion. As a part of explaining why organizations collaborate, Wood and Gary [73] indicate that interdependencies are formed because organizations possess or control vital resources (i.e., technological, material, human, information) and consequently are the sources of environmental threat for one another. Thus, they suggest that organizations pursue critical resources to reduce those environmental threats and manage the interdependencies. In diffusion process, Thompson [69] argues that innovation imitators are affected by earlier adopters, but not vice versa. This implies that early adopters or innovators play a key role in how the innovations get diffused in a social system. Early adopters or innovators tend to be the standard setters, whereas the late adopters are mostly in a position of conforming role. It is argued that one’s decision to adopt an innovation is based on an interactive process. In the case of interactive media, however, early adopters were influenced by late adopters and/or non-adopters and vice versa [69]. We believe that this accurately reflects the diffusion process involving complementarity and interdependence relationship. The diffusion process involves many interactive activities which promote active exchanging of information and learning among the interested parties.
2.3. Research on Adoption and Diffusion of Information Technology Firms that adopt information and networking technologies have a high potential to achieve a rapid growth in today’s competitive market. Declining cost of personal computers and the availability of ready-to-use software have accelerated the diffusion of computer-based information systems in many organizations [61]. Similarly, successful innovations delineate improvements over existing alternatives, replacing older products through the process of ‘creative destruction’ [8]. Schumpeter [62] states that over time, organizations which do not innovate will fall behind and lose any competitive advantage they may have had over others. A number of researches have been done on the diffusion of technology. Dinar and Maron [20] analyze the adoption and use of the computers by extension; they introduce the time spared in computer use as a measure of adoption intensity. Antonelli [3] states that the diffusion of the information technology tends to
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result from the creative use of the organizational resources which firms have learned to exploit over the years. She also indicates that a variety of factors affect the decision of firms in adopting and learning to use new products of technological innovations. Aksoy [2] examines the dynamics shaping the innovation and diffusion of IT. He suggests that an approach to IT innovation-diffusion should be based on conceptual understanding of three interrelated phenomena: (1) the information requirements of an user, (2) the technical nature of information processing functions, and (3) the systemic nature of IT and the need for facilitating information sharing in an organization. People also try to adopt innovations early because they can obtain benefits by carrying out the innovation activity. Thus, the early adopters often enjoy the greatest benefit [59]. This is because through the experience of learning and using the innovation, early adopters have the know-how which gives them competitive advantage over the others. Based on this, Markus [45] states that late comers imitate the early adopters to replicate their profits. He also adds that through the direct and indirect contacts with the early adopters, laggards learn about the innovation and make the decision to adopt. It is argued that one’s decision to adopt an innovation is dependent on the actions of others. So the potential adopters’ perceptions of using the innovation in addition to their perceptions of the innovation itself influence whether the innovation diffuses [51]. With a clear recollection of various issues of adoption and diffusion of IT, several studies have introduced different behavioral and/or economic models for adoption of information technologies. They introduced different perspectives on adoption and diffusion of the technological innovations. Arther [4] presents a simple economic model of technology adoption, discussing the effects of time and increasing returns on the technology adoption. He suggests that a technology which by chance or technical superiority gains wide early acceptance may eventually overtake the market of potential adopters while locking out other technologies. Using the economic analysis, Fudenberg and Tirole [23] illustrate the effects of preemption in games of timing in terms of firms deciding when to adopt a new technology. They suggest that a firm’s flow of profit increases by adopting the innovation, but adopting it early incurs higher cost than adopting it later. This tradeoff in cost of investment and extent of adoption benefit makes the study of technology adoption based on timing interesting from the economics sense. Similarly, Reinganum [55] provides a rigorous analysis of the diffusion of new technology in the context of firms weighing the costs and benefits of delaying adoption, as well as taking account of its rival’s strategic behavior. However, with respect to actual use of a technology, earlier studies have focused mostly on the individual use of the technology in an individual PC environment. In particular, the traditional models of IT adoption and diffusion do not account for the changes taking place in the computing environment and do not address the changing needs of the users. Therefore, it is of interest to understand the adoption patterns and the use of information technologies by multiple users in a distributed network computing environment, as compared to by individual users in an individual PC environment.
2.4. Research on User Acceptance of IT and Information Systems Success Much research has been done to emphasize the need to use information technologies to improve organizational effectiveness and competitiveness [32, 39, 48, 54]. Szajna [67] suggests that use of an information system is commonly understood as an indicator of its success, effectiveness, or acceptance. However, organizations are different in their capability to use information technology. Motivation and ability to exploit the information technology are the two key determinants of why firms differ in their capability [64]. In studying the reasons for individuals deciding to use the computer technologies, it is an accepted norm that computer technologies have benefits for individuals and organizations, but it is also regarded that their potential gains are not fully realized due to lack of acceptance [29]. It is argued that the acceptance and use of computers by individuals are limited because of low motivation. One way to elevate an individual’s motivation is through having a positive attitude about IT. Developing a positive attitude towards IT leads to increased usage because the effective and cognitive factors of the end user attitude are linked closely with initial interest, learning to use IT, and overcoming obstacles to learning. Thus, no matter how complicated and capable the technology, its effectiveness and success rely on people having a positive attitude towards it [15]. Otherwise, the quality of a system greatly affects individual’s IT usage choice. Researchers have been interested in analyzing the factors that cause people to accept or reject information technology in different situations. Perceived usefulness and perceived ease of use are the two most discussed determinants of user acceptance of information technologies. Researchers have tried to measure their impacts on user acceptance and actual use of IT by examining other relevant behavioral variables. It is a challenging task to improve measures for assessing information quality and impact of IT on user performance. Nevertheless, accumulated knowledge of communication behaviors, self-efficacy, strategic decision making processes, and adoption of technological innovations provide strong theoretical support for ease of use and perceived usefulness as the key determinants of user behavior [26]. User satisfaction as a measure of information systems success is important. Iivari and Koskela’s [31] satisfaction measure contains three information quality constructs. They are “informativeness” which is comprised of relevance, comprehensiveness, recentness, accuracy, and credibility, “accessibility” which is comprised of convenience, timeliness, and interpretability, and “adaptability”. User satisfaction is also closely related with user attitudes about the information technologies. Thus, individual attitudes toward IT tend to bias user satisfaction measures [30, 41]. Consequently, the results of research directed at answering the question “What causes MIS success?” have been that of mixed outcomes. This can be attributed to the subjective nature of user characteristics, especially the user behaviors. Likewise, no clear research findings emerged in determining what constitutes information systems success [19]. Thus, a more comprehensive look at IS success determining variables is needed to build upon existing works and provide more insightful ideas. We hope that it will lead to better prediction of individuals’ attitudes toward IT and
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their usage behaviors. In our paper, we try to address these issues by introducing a model of IT usage.
3. The Model Through the literature review of relevant topics on the adoption and diffusion of IT, we have presented what other researchers have done in the field. Their theoretical contributions and empirical findings are reexamined and recognized. Based on the literature review, we think that the studies lack in identifying the new set of determinants of IT usage and fall short of proposing a more effective IT acceptance model. In particular, the diffusion and adoption literature has not pursued the network/collaboration effect variables related to group dynamics. We argue that the variables in Davis’ TAM, for instance, are less capable of explaining usage behavior of an individual, especially when accounting for the changes in the computing environment. This leads us to question TAM’s applicability in ever rapidly changing network environment. Prior research have also pursued and described the positive effect of different variables in IT usage, but not the negative effect. We suggest that it is equally important to focus our attention on the negative effect of variables in IT usage study. We address this point through the discussion of network externality effect. As an attempt to extend and enhance the existing models of IT acceptance, we present our model of IT usage in this section (Figure 1). To do so, we first discuss the key determinants of technology use and diffusion of that technology in a distributed network computing environment. Second, we integrate the factors into our model by carefully evaluating their applicability. Lastly, we discuss the contribution of our model for future empirical research in the field. In all, we propose a more enriched model of IT acceptance by combining the behavioral models of IT acceptance and IT diffusion models from various other disciplines. The major shortcoming of the previously studied IT adoption and diffusion models (i.e., diffusion of innovation model, TRA, TPB, TAM, etc.) comes from the fact that an IT adoption is discussed only in the context of individual PC environment. In particular, Moore and Benbasat [51] propose to test Rogers’ set of innovation characteristics in the context of the adoption of personal work stations by individuals. Basically, the models are based on the world of one user of PC world, but now we are in the world of networks. For instance, Bass [6] argues that although the model [5] has several nice properties for forecasting purposes and is adequate for use in forecasting measures, it still needs an improvement in that it mainly addresses the social and behavioral influences on the timing of adoption. He suggests that economic factors and variables affecting individual behavior are overlooked. Another shortcoming often discussed is that many of the empirical studies which test the IT acceptance models (i.e., TAM, TRA, TPB) use students as samples. Thus, their findings are not easily generalizable to the sample of users in the organizational settings, thereby diminishing their validity. However, we are not saying the previous models are wrong. Indeed, they have made a significant contribution to the study of IT adoption. Notably, TAM has been applied to various empirical studies of technology acceptance and has contributed
valuable knowledge in the field. Instead, what we are saying is that there is a room for improvement in the model for predicting adoption behaviors of users. Of course, there will be some similarities but our model will also account for other key factors which the previous models have missed. To begin with, we don’t claim that there is no longer any world of individual users, but rather that individual users are often in the network environment, engaged in information sharing and collaboration. Brynjolfsson and Meldelson [9] indicate that the increasing and diverse use of IT and the trend toward distributed networking and client-server computing are both a cause and an effect of the organizational transition and changes in user needs. With the understanding that the computing environment has changed over time with a wide use of networks and computer-based information technologies, we argue that the determinants of IT usage deserve another examination. We believe that the determinants of IT usage identified and empirically tested in the previous studies are no longer sufficient to serve as accurate indicators of predicting IT usage behaviors in individuals. Therefore, we propose a new set of determinants of IT acceptance and use which we think will appropriately account for the changes in user needs, nature of technology, and computing environment, and use it to better predict the technology adoption decisions of an user. We will not provide an elaborate discussion of TRA, TPB, and TAM in the paper since their theoretical contributions and practical implications are well documented in various articles. People interested in those models can refer to the IT acceptance studies done by numerous researchers [1, 11, 16, 17, 22, 28, 29, 46, 68]. In particular, Mathieson [46] extensively compares two recent models that predict an individual’s intention to use an IS. In comparing the two models, he indicates that 1) both TAM and TPB predict one's intention to use an IS quite well, with TAM having an edge on empirical validation, 2) TAM is easier to apply to empirical testing, but provides only very general information on users’ beliefs about a system, and 3) TPB provides more specific information that can better direct development effort. Furthermore, due to its relatively recent introduction, there have been comparably less number of empirical tests on TPB’s effectiveness. And, it is known that TAM explains attitude better than TPB. Consequently, we mainly focus on improving the model by adding new variables that are instrumental in promoting user acceptance of new information technologies. In a sense, our model is an adaptation of TAM and TPB. We incorporate the key determinants of IT use which are used in TAM to predict user behavior in our model as well as introduce a number of other relevant variables. We extend, integrate, and refine previous related research by addressing the impact of the additional determinants of IT usage on individual behavior. As we have seen, there are many factors affecting individual’s IT usage behavior. However, due to the rapidly changing nature of technology and user needs, people are focusing more on the technology-related factors. Some of the technology-related factors affecting the usage are the ease of use and the number of features available in the system. The technology characteristics and the features represent the importance and value of a particular technology [75]. They claim that user acceptance may be affected by various features of the technology. Adding new features is a way for vendors to
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meet the customer needs since it provides value for the user. Therefore, it is accepted that even if the user is well trained and knowledgeable about the technology, lack of features can hinder usage since it gives the perception of low level of product usefulness and value. On the other hand, they also argue that
offering a large number of features on the technology does not increase usage by much for the targeted group of users. Thus, we note that the ease of use is not the only factor affecting the technology usage.
Internal Personal Belief: Individual PC Use Related Variables
Perceived Usefulness
Perceived Ease of Use
Perceived Enjoyment
External Variables
• User characteristics - Info. processing char. (i.e., access, evaluation) - Background (i.e., experience, education) • IT characteristics - Functionality - Complexity • Environment characteristics - Org. structure - Communication channel - Competition • Task characteristics - Requirements - Difficulty
Attitude
Behavioral Intention (Comparison Based)
Usage Behavior • Learning/Trial • Temporary use • Continuous use
Perceived Improvement
Kno wledge/Value of Available Substitutes
Perceived Network Externality Effect Group Dynamics Perceived Size of Existing User base
• Interdepende nce relationship • Conformance to standards or group norms
Pressure for Use
External Social Belief: Networking/Collaboration Related Variables
Figure 1. Belief and Social/Network Model of IT Acceptance: Extension and Enhancement of Existing Models
3.1. Internal Personal Belief: Individual PC Use Related Variables Information systems (IS) help to improve organizational performance, but only if they are actually used in an efficient way. In many cases, firms require people to use certain systems for specific tasks, but in other cases, system use is purely voluntary. Generally, the more frequently a system is used, the greater the benefit firms can reap [72]. Further, in order for a technology to be quickly accepted by the users, the technology should be easy to use, incorporate standards for good user interface and compatibility with other existing technologies, and make speedy access of information possible. But, since the technologies change frequently in response to changing user needs, it is important to offer new features or functionality by upgrading the technology. Only when technology developers keep up with changing information needs of the users will the technology be most successful and achieve its set goals [75]. Here, we note that technology is not just adopted for its functionality, and technology’s value is often evaluated by the degree of its compatibility with other comparable technologies. And, there are several other factors affecting IT use by individuals.
We suggest that in addition to perceived ease of use and perceived usefulness, other beliefs such as perceived enjoyment and perceived improvement are of importance for predicting the IT acceptance and use behaviors of individuals. We argue that these four belief variables will affect how individuals form their attitudes toward the IT and intentions toward usage behaviors. Prior literature has explicitly discussed how the relevant belief variables affect individual’s attitude and intention of using IT [16, 17, 18, 28, 29, 68]. In particular, Davis et al. [17] argue that people’s intention of using IT is determined by their cognitive appraisal of how it will help them do their work and eventually improve their performance. Perceived usefulness and perceived ease of use are two belief constructs in TAM. Davis et al. [17] define perceived usefulness as the degree to which an individual thinks using the technology will improve his or her performance and perceived ease of use as degree to which a potential user believes using the technology is free of effort. Perceived usefulness in particular affects usage behavior which in turn affects actual use of IT. Thus, if an individual perceives that a certain technology is instrumental in realizing the valued outcomes, he or she will be more likely to use that technology [30]. Use of these variables in the model is supported by the factor analyses which say that perceived ease
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of use and perceived usefulness are statistically distinct dimensions [27, 66]. As aforementioned, we argue that perceived enjoyment influences an individual’s attitude about the IT. Igbaria et al. [29] define perceived enjoyment as the degree to which using the technology is fun or pleasant for an individual. They argue that people put an effort in their work because a task is enjoyable and there are external rewards associated with it. Overall, they conclude that individuals accept technology because its use is fun and its benefits are great. Most people cannot withstand the boredom. For example, people switch channels when programs they don’t like show up while watching a television. Thus, it is important that technology is fun to use to a certain degree although it might not be as important as perceived usefulness or perceived ease of use. In sum, we argue that perceived enjoyment is also an important factor affecting an individual’s attitude about IT use. Similarly, we indicate that perceived improvement affects an individual’s attitude about IT use. By perceived improvement, we mean the degree to which people believe there will be feature or functionality improvements in the technology, possibly as a product upgrade. In terms of the features or functionality of the technology, there is a minimal threshold level of improvement which induces the individuals or firms to adopt them [10]. Rationale for this is provided from a following case. Software vendors initially make test (beta) versions of their software application available to the people free of charge. By doing that they hope to have many people try out their product and provide a valuable feedback so they can continue to improve the product, later resulting in a better product. In the process, people try out the product because the product has certain attractive system features, meeting the user needs and offering positive benefits. Based on this illustration, we argue that even when the IT is not of high quality, people still use the technology with an expectation that it will be improved in the near future, as long as the current state of the technology meets the user’s minimal requirements in functionality. The potential for future technology upgrade influences how one might perceive the technology and affects his or her IT usage behavior. Likewise, an individual’s decision to use a technology is influenced by the number of features or services offered. It is a simple fact that if the technology does not offer enough service or number of features, people will not use it. Thus, there is a threshold level of technology improvement in functionality of a particular technology which can induce a new wave of individuals to accept and use the technology. The potential improvement of the technology affects how the individuals view the technology and influences their IT adoption behavior. People’s perception of the IT in terms of the value it provides is not a one-time phenomenon. People are interested in how IT can continue to meet their needs. Thus, when making a decision to adopt a certain technology, people are obviously interested in the product upgrades since they rely on a particular technology to perform a variety of tasks by investing their time and effort learning and using the IT. For one thing, they do not want the product they are using to become no longer useful. From the software industry, we see that vendors produce different versions of their software, each new version serving as a product upgrade. This helps to entice new waves of users and keep their existing users intact as their customers. As an added dimension on perceived improvement, we also argue that product upgrade helps to increase the user base
through wider acceptance. But, an equally important issue to consider is that product upgrade does not guarantee that the product will be positively received by the users. What might result instead is an “excess inertia” which means that people resist switching to new technology (i.e., upgraded product) because they feel comfortable using the existing technology (i.e., product) and they are slow to catch up to the new technology in terms of getting used to the newly embedded value-added features. This is why studying the changes in the user needs and network environment is important in response to the rapid improvements in the technology. This is where our model of IT acceptance will be of particular relevance since it can be used to predict the IT usage behaviors of individuals more objectively realizing those changes in user needs and computing environment.
3.2. External Social Belief: Network/Collaboration Related Variables The network/collaboration related constructs affect the group dynamics and influence an individual’s IT use behavior. From the complementarity theory, we observe that most of the organizational tasks today are accomplished by teams or groups of individuals rather than by a single person through various levels of interactions. The interactions among the individuals are supported by the use of groupware or collaboration technologies. The collaboration technologies help users interact with each other in a variety of ways, even overcoming geographical boundaries. The explosive growth of networking technologies contributed to the drastic increase in collaboration efforts of the people. With regard to the importance of collaboration among individuals, we propose the following relevant constructs: group dynamics, perceived network externality effect, perceived pressure, and perceived existing user base. According to Robertson [58], the determinants of the decision to use an information system are features of the system, individual characteristics, and the social context in which the individual is attached to. Among them, he emphasizes the importance of social context on individual behavior of IT use by suggesting that social group of which an individual is a part of influences how that person uses information systems. Further, he suggests that social group will affect how IT is interpreted and used by an individual user. The social group will also exert strong influences and demand on IS usage. As a result, we argue that group dynamics characterized by network interactions among the individuals affect an individual’s intention to use an IT. Individuals with similar interests and similar interaction patterns are likely to have similar social pressures placed on them [58]. Therefore, an individual is influenced by group dynamic variables in making a decision to use an IT. Specifically, we argue that perceived network externality effect, perceived size of existing user base, and pressure for use affect the group dynamics, and further affect behavior intention of an individual in usage behavior. By perceived network externality effect, we mean the degree to which a potential user believes his or her use of the (compatible) technology will affect other users in the network environment. This is related to the concept of network externality. That is, having an additional user in the network can add value to the network as a whole, and an individual user can
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drive more benefit from the network. However, the theory of critical mass assumes that some people will contribute more value to the network through their use of the technology than they will personally receive from it [45]. We argue that this perceived network externality effect has a direct effect on behavioral intention of an individual since it provides value to the network. Here, we look to both the positive and negative network externalities. Our interest comes from the fact that an individual using the technology can provide value to others or can become a liability for others in the network environment. As in the telephone example, if more people have telephone access, the network value increases since it enables universal access and facilitates interactions among the people. Yet, if too many people in the network use the phone service simultaneously it creates a negative externality effect. That is, it causes quality degradation due to the traffic overflow. Similarly, the value of an electronic mail to a user depends on how many more users request the service. The greater the number of users one can reach, the more valuable the service becomes [74]. However, there also is the problem of negative externality. It is good that a user of the service can reach other users but people often receive unwanted junk mails sent by different sources. As a result, it can become more difficult and bothersome to sort through the mails and process the information. Thus, perceived network externality effect is a key factor in our model. We argue that depending on whether one views his use of a particular technology affecting others in the same network as positively or negatively, his actual behavioral intention to use it will be strongly influenced. Perceived size of existing user base is defined as the degree to which an individual thinks a certain size of users is already using the system and how large the size of potential user base can grow. We suggest that as the user base grows larger, an individual wants more to be a part of this group. Perceived size of existing user base focuses on the compatibility-seeking behavior of individuals. The relevance of perceived size of existing user base is based on the notion that the size of user base at time t is a linear function of the sum of the size of user base at time t-1 and IT value driven function (i.e., USt = USt-1 + IT value [---- ]). Further, we define pressure to use the technology as the degree to which an individual is influenced by views of others about the technology use. People are very conscious of what others think about them so their IT use behaviors are affected by other people’s behaviors or decisions in many different situations. We argue that this is particularly true in the network setting. In all, these three variables determine the level of group dynamics. Group dynamics refers to motivation to comply with group norms due to interdependence relationship among individuals and the need for conformance to standards. Because the distributed network computing environment involves numerous complementarities through the various interactions, the group dynamics construct and associated constructs are considered important, being the additional key drivers of IT adoption. Lastly, we argue that knowledge/value of available substitutes affects individual’s behavioral intention to use IT. We suggest that when making a decision to use IT, individuals compare different technologies available for use. They evaluate different technologies based on a variety of personal criteria. In review, Moore and Benbasat [51] indicate that even though Davis does not use the term “relative” in labeling, the definition of “perceived usefulness” is in relative sense. It is not really
clear, however, whether Davis himself considers the perceived usefulness in relative sense since the definition of perceived usefulness is not explicitly stated and most of the prior empirical studies do not mention availability of alternative technology as a key factor affecting individual’s intention to use IT. We introduce knowledge/value of available substitutes as an additional variable directly having an effect on behavior intention to use IT. Before forming an intention to use the technology, one evaluates other alternative technology in comparison. This has an important implication since people usually use better technology in various dimensions. In sum, with all the key variables in mind, we suggest that they can be affected by different external variables. Some of the external variables known to influence the personal and social belief variables are: user characteristics (i.e., information processing characteristics, background), information technology characteristics (i.e., functionality, complexity), task characteristics (i.e., requirements, difficulty), and environmental characteristics (i.e., organizational structure, communication channel, competition). Compared to the key determinants of IT usage identified in the various IT adoption and diffusion studies, the external factors are not extensively examined. Thus, more rigorous research on the impacts of these external variables in different situational contexts is necessary.
3.3. Research Propositions In summary of our belief and social/network factors based on IT acceptance model, we propose the following propositions. Through our enhanced model, we hope to add much eager to the research of IT usage behaviors of individuals and further the diffusion of information technology in ever changing network environment. Dimensions of Internal Personal Belief: Proposition 1: Perceived usefulness is positively related with an individual’s behavior intention to use information technology. Proposition 2: Perceived ease of use is positively related with an individual’s attitude to have behavior intention to use information technology. Proposition 3: Perceived enjoyment is positively related with an individual’s attitude to form behavior intention to use information technology. Proposition 4: Perceived improvement is positively related with an individual’s attitude to form behavioral intention to use information technology. Dimensions of External Social Belief: Proposition 5: Perceived network externality effect is positively related with an individual’s desire to confirm to group dynamics to form behavioral intention to use information technology. Proposition 6: Perceived size of existing user base is positively related with an individual’s desire to confirm to group dynamics to form behavioral intention to use information technology. Proposition 7: Pressure for use is positively related with an individual’s conformance to group dynamics to form behavioral intention to use information technology.
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Additional Determinants: Proposition 8: Knowledge/value of available substitute is positively related with an individual’s behavioral intention to use information technology. Proposition 9: Perceived Network externality effect is positively related with an individual’s behavioral intention to use information technology.
4. Discussion on Contribution of IT
Acceptance Model in IT Adoption Research As seen previously, our model is similar to TAM in that key variables of TAM are included in our model. The key differences are that: 1) our model includes the collaboration/network related variables to reflect the changes in the computing environment and account for their effects on individual IT acceptance and usage behavior, and 2) the external factors are said to influence not only the belief variables but also the social network variables. In this section, we discuss how using IT facilitates collaboration among individuals and other relevant issues. Further, we examine the methodology issues when using our model to study IT usage behavior in the collaborative/network environment. We suggest that using our model helps researchers in particular to understand and predict individual’s use behavior of collaboration facilitating information technologies. Since IT reduces the cost of cooperation among the network participants by facilitating communication and increasing the flexibility of those participants [9], collaboration becomes more common and is easily enabled. Collaboration can help organizations improve the efficiency of resource use [73], and many organizations collaborate with the intention of reducing and controlling environmental uncertainty [53]. Computer-based information technologies support network participant’s coordination effort in network computing environment. Electronic mail is a type of collaborative system which is designed to enable a group of individuals to easily exchange information [60]. Electronic mail can expand the span of communication networks and increase the opportunities for collaboration, thereby making information easily available to the network participants [65]. Today, computers are extensively used for coordination tasks such that the core of the new technologies is the networked computer [43]. As a result, people are finding many more ways to coordinate their work using the IT in the network environment. That is, people communicate, exchange information, make decisions, and coordinate resources relying on the information technologies. The key advantage of network setting is that people can gain access to complementary resources and competence from external sources [70]. However, to encourage individuals to accept and use the technology effectively, the system must be of useful value to the users and provide enough incentives. In another note, Tomas and Arias [70] argue that networks are a powerful tool that helps to foster innovations in the market and social settings. From this, we argue that new patterns of IT adoption will be determined by technologies supporting the collaboration among the individuals in the network environment. Thus, we believe our model of IT acceptance can be applied in studies of IT adoption and it will be effective in
understanding and explaining user behaviors of individuals in the network environment.
5. Future Research The future research avenues are three folds. First, as a part of an effort to validate our model, analysis of our model should be extended to empirical testing in richer (realistic) settings as a measure of its effectiveness. To do so, more research is needed into specific operationalization of the determinants of IT usage constructs. More direct and objective measures for IT usage are critical in validating our model. Overall, the effectiveness of our model in predicting the IT usage behaviors of individuals will depend on better understanding the relationship among the newly introduced social/network variables, process of individual decision making, and consequence of IT adoption. Second, we need to carefully examine the role that external variables play in predicting usage behavior in various situations. For example, we can examine the fit between the task and technology, and individual characteristics and technology to improve our prediction of the IT usage behavior of an individual. Realizing that some mediating variables better convey the influence of the external variables on usage behavior than the others, we have to analyze and investigate the empirical relationship among the variables. Lastly, more research is needed to examine any additional factors that may have an influence on usage behavior of individuals and specific external variables that affect belief and social network variables. Because the people’s needs and computing environment change continuously, it is necessary to inquire about other mediating variables that have significant impact on usage behavior to extend our understanding of the IT adoption and diffusion in different social contexts. In sum, understanding the changes in the computing environment helps us better predict the IT use behavior of an individual, and it remains a central challenge for the future stream of research. The changing characteristics of network environment should be carefully examined to analyze the new forces affecting IT adoption behaviors of individuals.
6. Conclusion In this article, we have provided a wide array of theoretical and empirical perspectives on IT adoption and diffusion from different disciplines by reviewing what researchers have done in their studies of adoption and diffusion of IT. Based on the reviews of prior literature, we have realized that there are varying understanding of user acceptance and use of IT. Specifically, we found that the popular models of IT acceptance and use have their share of shortcomings. This article provided a theoretical rationale that a more comprehensive and improved model of IT acceptance is needed to predict the adoption behaviors of individuals in today’s rapidly changing distributed network computing environment. Thus, we proposed the dynamic value model of IT acceptance. We tried to develop a model that is applicable across many situations (in different contexts). Our model is an extension and enhancement of existing models in that it acknowledges the changes taking place in the computing environment by incorporating appropriate network related variables to help mediate the effects of those changes in the computing environment, user needs on user
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acceptance and use behavior of IT. In particular, we suggest that social/network factors, which facilitate interactions among the individuals, strongly affect behavioral intention of an individual to use an IT. The findings of this study have implications (practical and theoretical) for many of the issues in MIS. More importantly, our model serves as an enhanced way of determining the IT usage behaviors of individuals, especially in the distributed network computing environment. It may also be used to improve our understanding and prediction of IT adoption (acceptance and use) behavior of individuals. We hope to learn more about individual decision-making process and what motivates individuals to use IT in today’s rapidly changing environment. From the research point of view, our research will bring more stimulating and richer discussions to the issues of IT adoption and diffusion, with special emphasis on the group dynamics interactions. For the firms, it will help them make the IT investment wisely. Because firms want to have individuals utilize the IT they purchase for use, they are interested in factors that can affect individuals to use the information technology. The factors affecting IT use are also important when developing the information technologies, when using IT for competitive advantage, or when predicting the impacts of IT on individual performance or organizational productivity gain from individual use of IT [58]. In those regards, this paper provides many helpful guidelines.
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