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Barriers in the adoption of information systems

Barriers in the adoption of information systems

Barriers in the adoption of information systems




            In a global business environment increasingly fraught with uncertainty, challenge and opportunity, those involved with formulating corporate strategy continue to strive to achieve competitive advantage. In the 1990s the Internet really blossomed, Dot-Coms were the ephemeral rage, and information systems became increasingly more crucial to the formation of successful strategies. Over the course of time since the dot-com era, it has become clear that the information system world is one where human, social, and organizational factors are as important as the technological ones (Avison, Fitzgerald, and Powell, 2001). This study explores some of the potential barriers in successful implementation of IS in organizations. For this purpose, I will examine the core assumptions underlying the Diffusion of Innovations (DOI; Rogers 2003) theory for Willingness to Use new technology, and core assumptions from the Technology Acceptance Model (TAM; Davis, Bagnozzi, Warshaw, & Paul, 1989; Venkatesh, Speier, & Morris, 2002), arriving at a hybrid model that seeks to test attributes conceptually common to both.

            In 1986, Bakos and Treacy suggested that the use of information technology (IT) as a competitive weapon in business organizations had become a popular cliché; but there was still a lack of understanding of the influences of a given information technology on any specific organization. They also pointed out difficulty in the processes and communication within an organization that allow a smooth coordination of technology and corporate strategy. In recent years, the literature has intensively re-examined the relationship between strategy and information technology. Within the literature, the terms information systems and information technology are used to describe different information systems applications. In 2002, Davis, Azjen, Saunders, and Williams found evidence of the positive relationship between strategic information technologies, (as opposed to standard operating systems), and market differentiation strategy, vis-à-vis a differentiation-based competitive market position. The fundamental relationship between strategy and information technology was studied by Tan (1996), and Palmer and Markus (2000), among others. Many authors including Mahmood and Mann (1993), Kettinger, Grover, and Segars (1995), Palvia (1997), and Li and Ye (1999) suggest that the relationship between an information technology and firm performance should be investigated within a strategic context. Weill (1992), Mahmood and Mann (1993) and Dans (2001) found links between information technology and organizational performance. Similarly, Chan, Huff, Barclay and Copeland (1997) reported that firm performance is enhanced when information systems are aligned with organization’s overall strategic orientation.

The literature seems to indicate that information technologies are crucial to corporate strategy and firm performance. But there remains a lack of study regarding how to determine problems with IT implementations early on, and the successful implementation of solutions to those problems. Without such knowledge, the complete benefits of information technologies to firm strategy and performance cannot be reaped. Discovering what determines successful attitudes toward usage of such technologies at the individual level is critical to firm performance. There already exists an abundance of literature regarding information technology and various aspects of organizational performance (Akkermans, & van Helden, 2002; Chan, Huff, Barclay, & Copel, 1997; & Hitt, Wu, & Zhou, 2002). What is lacking is an analysis of how IT innovations are most productively adopted at the individual level, and how recognition of the critical success factors to usage of these technologies affects attitudes toward using them.

In a global and increasingly fast-paced business environment, Willingness to Use IT innovations and the speed with which they are adopted can significantly affect competitive advantage. This study is designed to examine some key determinants of individual Willingness to Use a new technology prior to acquisition. I use central tenets from Rogers’s Diffusion of Innovations (DOI) Theory and TAM to identify key critical success factors for individual Willingness to Use new information technologies, using measures developed by Moore and Benbasat (1991) to assess such willingness.

In his seminal work Diffusion of Innovations, Rogers (2003) posed theories about attributes of innovations that are involved in the diffusion of innovations at the individual level, but he and others have only empirically tested “Rate of Adoption” (Rogers, 1995; Holloway, 1977; Moore and Benbasat, 1991). Rogers defines Rate of Adoption as the relative speed with which an innovation is adopted by members of a social system. Although Rate of Adoption is a very useful measure for assessing adoption across a group of individuals over time, it does not allow researchers to assess an individual’s attitude toward using new technology from the outset because it requires a longitudinal comparison of multiple individuals. Thus, existing research mostly studies adoption after the fact.

TAM is one model used to predict information system usage. Davis et al. (1989) developed the Technology Acceptance Model that does so by using the independent variables of Perceived Usefulness and Perceived Ease-of-Use. However, these two perceptual measures, while valuable, are somewhat limited in their ability to explain the user’s initial attitude toward adopting innovations. In addition, the work of Rogers and Davis et al., and all subsequent research employing their models, leaves a critical gap in the existing knowledge of this important subject. 


This study of factors that influence individual attitudes toward using technical innovations will begin with an examination of the existing literature regarding these factors. The two primary theories that emerge are Rogers’s Diffusion of Innovations and Davis’s Technology Acceptance Model, both of which are widely cited in the literature. However, the study of the organizational literature will lead to a better understanding of two important factors: 1) why IT innovations succeed or fail, and 2) what steps may be taken to ensure success.


Diffusion of Innovations – an Overview

French sociologist Gabriel Tarde first engaged in the original diffusion research in 1903. He plotted the original S-shaped diffusion curve, which indicates time on the X axis and adoption on the Y axis. Interestingly, most innovations still follow an S-shaped Rate of Adoption (Rogers, 2003). The variance lies in the slope of the “S.” Some innovations diffuse rapidly creating a steep S-curve; other innovations have a slower Rate of Adoption, creating a more gradual slope of the S-curve. The Rate of Adoption, (or diffusion rate,) has become an important area of research to sociologists, and advertisers alike.

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Everett Rogers (2003) defined diffusion as the process by which an innovation is communicated through certain channels over time among the members of a social system and implemented. He is one, if not the only innovation scholar to pose diffusion theories at both the organizational and individual levels. Indeed, more than 5000 diffusion publications had referenced his theory (Rogers, 2004). Certainly, not all of these publications specifically cover IT innovations, but even so, the extent of usage and acceptance of Rogers’s theory is impressive and speaks to its value.

Rogers identified five perceptual attributes that determine an individual’s attitudes toward usage of innovations: Relative Advantage, Complexity, Trialability, Observability, and Visibility.


An Overview of TAM

Although I focus on DOI, I also use some key aspects of The Technology Acceptance Model (TAM) to reinforce DOI hypotheses. TAM details how users come to accept and use a technology (Davis et al., 1989). The model suggests that when users receive a new software package, two factors influence their decision about how and when they will use it. They are Perceived Usefulness (PU) and Perceived Ease-of-Use (EOU; Davis et al., 1989). This model is one of the most widely employed in the IT literature in terms of predicting behavioral intent to use technologies.

There are also several other theories with a logical relationship to TAM. Though I will briefly review those theories for completeness, my focus is on TAM, and especially on TAM’s implications for DOI. These other behavioral intention models include the Theory of Reasoned Action (Ajzen and Fishbein, 1980), the Theory of Planned Behavior (Ajzen, 1991), Social Cognitive Theory (Bandura, 1977a; 1986) and The Unified Theory of Acceptance and Use of Technology (Venkatesh, Morris, and Davis, 2003).


Diffusion of Innovations Theory

According to Rogers (2003), Diffusion is the process in which an innovation is communicated and adopted through certain channels over time among members of an organization, in order to reach a mutual understanding. Diffusion in this case is the systems and processes that provide the infrastructure for diffusion based information to occur, in that the messages are concerned with new ideas.

There is a wide body of research regarding the concept of diffusion, (internal vs. external, and other variants), however few authors offer specific definitions of the concept (Brancheau and Wetherbe, 1990; Jensen, 2002; Mustonen-Ollila, and Lyytinen, 2003). Drury and Farhoomand (1999) defined it as meaning the spread of an innovation through the set of potential adopters. A great deal of past research findings have centered on the identification of innovation attributes that affect diffusion and the classification of adopters with different characteristics (Tornatzky and Fleisher, 1990).

To paraphrase Rogers definition of the study of diffusion, it is the study of how, why, and at what rate new ideas and technologies spread through cultures. According to Rogers, when the process of innovation takes place, it leads to certain consequences that affect social change. Social change in this context is defined in terms of change within the firm. Four main elements of diffusion affecting this social change are identifiable in most diffusion research studies, and in nearly every diffusion campaign or program (Rogers, 2003). They are the innovation, communication channels, time, and a social system. For the sake of completeness, here is a brief description of each element:


  1. The innovation -ideas, practices, or objects perceived as new by an individual or a group of individuals. It matters little, so far as human behavior is concerned, whether or not an idea is “objectively” new as measured by the lapse of time since its first use or discovery. The perceived newness of the idea for the individual determines his or her reaction to it. If an idea is new to an individual, it is an innovation.
  2. Communication channels – Rogers defines communication as the process by which participants create and share information with one another in order to reach a mutual understanding.
  3. Time – The time dimension is involved in diffusion in three ways: (a) The innovation-decision process by which an individual passes from first knowledge of an innovation through its adoption or rejection. (b) An innovation’s Rate of Adoption in a system, usually measured as the number of members of the system who adopt the innovation in a given time period.
  4. Social system -a set of interrelated units that are engaged in joint problem solving to accomplish a common goal.
barriers to implementing health information systems, what are the main challenges involved in implementing public health information technology projects, why is healthcare slow to adopt technology, what types of resistance to new technologies might be evident among patients, caregivers and nurses, interoperability

barriers to implementing health information systems, what are the main challenges involved in implementing public health information technology projects, why is healthcare slow to adopt technology, what types of resistance to new technologies might be evident among patients, caregivers and nurses, interoperability

Rogers (2003) stated that most of the new ideas for which diffusion has been analyzed are technological innovations, and in this context, the terms “innovation” and “technology” are often synonymous. According to Rogers’s (2003) definition, “technology is a means of uncertainty reduction that is made possible by information about the cause-effect relationships on which the technology is based” (p. 207). Most information systems are created and adopted with the explicit goal of reducing uncertainty and streamlining operations.


A Brief History of Diffusion of Innovations Research

Research on the diffusion of innovations started as independent intellectual streams during the 1940s and 1950s. Each of several disciplinary groups of diffusion researchers studied only one type of innovation; for example, rural sociologists investigated the diffusion of agrarian innovations to farmers, while educational researchers studied the spread of innovative teaching techniques among teachers and administrators. Despite the distinctive nature of these approaches to innovation research, each of these schools of thought uncovered remarkably similar findings. For example, that the diffusion of an innovation followed an S-shaped curve over time, and that innovators had higher socioeconomic status than did later adopters (Rogers, 2003).

Rogers’s stated prime motivation for writing his first book entitled, Diffusion of Innovations (1962), was to describe a general diffusion model and to argue for greater awareness among all of the various research traditions that were involved in this stream of study. By the mid-1960s, the formerly distinctive boundaries between these research traditions began to break down. The trend toward a more unified cross-disciplinary “viewpoint” continues today. Most contemporary diffusion scholars are aware of the parallel methodologies and findings of other traditions. According to Rogers (2003), all of the diffusion research traditions have now merged intellectually to form a single, large coalesced school of thought, although scholars in many different disciplines conduct diffusion research.

A host of organizational researchers have highlighted the importance of innovation to organizational competitiveness and effectiveness, resulting in multiple streams of research (Wolfe 1994). Although a number of useful insights have been made, our understanding of innovative behavior in organizations is at a relatively preliminary stage because innovation studies have produced inconclusive, inconsistent, and mixed results characterized by low levels of explanation (Bigoness and Perreault, 1981; Damanpour, 1991; Downs and Mohr, 1976; Kimberly and Evanisko, 1981; Nord and Tucker, 1987; Pennings, 1987; Rogers, 1983, 2003).

A number of influential reviews and critiques of studies employing DOI theory (Downs & Mohr, 1976; Rogers, 1983, 2003; Tornatzky and Fleisher, 1990; Tornatzky and Klein, 1982; Van De Ven and Rogers, 1988; Van de Ven et al., 1989), have concluded that the lack of clear, consistent results, in spite of a large amount of research conducted across numerous disciplines, suggests that the challenge rests in the complex, context-sensitive nature of the diffusion phenomenon itself. Namely, careful attention to the personal, organizational, technological, and environmental contexts is necessary to fully understand innovation (Tomatzky and Fleischer, 1990).

The diffusion of an innovation refers to its spread through a population of potential adopters. The objective of DOI research is to explain or predict rates and patterns of innovation adoption over time and/or space. DOI research analysis focuses upon the fit of hypothesized innovation diffusion models to actual diffusion empirical examples (Fischer and Carroll, 1986; Tolbert and Zucker, 1983). Diffusion rates and explanatory variable data used in DOI studies have been collected by survey questionnaire (Attewell 1992; Teece, 1980), expert judgment (Souder and Quaddus, 1982), and archival data collection (Fischer and Carroll, 1986; Tolbert and Zucker, 1983).


Diffusion of Innovations Across Individuals Within An Organization

            The dependent variable in the Rogers (2003) DOI model is the Rate of Adoption. Rate of Adoption is the relative speed with which members of a social system adopt an innovation, compared with other individuals within that social system. According to Rogers, it is measured in terms of the number of individuals who adopt a new idea in a specified period. As such, the measure is reliant on time and only has meaning for individuals in reference to the adoption rate of other individuals within an organization. Therefore, the Rate of Adoption is a numerical indicator of the steepness of the adoption curve for an innovation (Rogers, 2003).

Although Rate of Adoption clearly has value for organizations in predicting the timing of adoption across a large number of individuals, it is not suitable as an individual-level measure of Willingness to Use new technology at the outset of that technology’s introduction. Fortunately, the large literature associated with TAM provides a very good measure for assessing an individual’s attitudes toward adopting an innovation–Willingness to Use.

The perceived attributes of an innovation have a crucial impact on the adoption of an innovation. According to Rogers, most of the variance in the Rate of Adoption of innovations at the organizational level (from 49 to 87 percent) is explained by five attributes: Relative Advantage, Compatibility, Complexity, Trialability, and Observability (Rogers 1995). He explained this proportion in empirical testing in the fourth edition of Diffusion of Innovations (2003).

Relative Advantage (RA)

Rogers defines Relative Advantage as the degree to which an innovation is perceived as superior to the innovation it supersedes. The degree of Relative Advantage is often expressed as economic profitability, as conveying social prestige, or in other ways (Rogers 2003). The nature of the innovation determines what specific type of Relative Advantage (economic, social, and so on) is important to users, although the characteristics of the potential adopters may also affect which specific subdivisions of Relative Advantage are most important. In the context of electronic commerce adoption, Kendall, Tung, Chua, Hong, Ng, and Tan (2001) refined Relative Advantage as the benefit to SMEs (small to medium enterprises) in terms of lower business costs, wider market coverage, preference to upgrade other business ventures than to adopt electronic commerce, and importance of doing business on the internet in the future. In the case of Braak and Tearle (2007), Relative Advantage (RA) was altered to include the use of a computer as improving the quality of learning.

Within the same context of learning, Lewis and Orton (2000) added that the advantage may be viewed in terms of profitability, speed, social prestige, effectiveness, or any of many other potential positive outcomes. Relative Advantage has also been defined in the context of e-mail adoption as significant in the ability to contact someone who is otherwise difficult to reach, the ability to send/receive messages anytime, day or night, and the ability to use an economical means of communication (Shelly 1998).

In the case of electronic health innovations, RA was defined in relation to economic, social status or other factors (Atkinson 2007). In the context of broadband-enabled learning, Murphy (2005) declared that Relative Advantage refers to the objective ratio of benefits to the cost of the innovation. She added that Relative Advantage means that the innovation is better than the idea that it supersedes, or at the social level, that it is status-conferring. It includes economic profitability, low initial cost, a decrease in discomfort, social prestige, a saving of time and effort and immediacy of reward.

Rogers’s Relative Advantage is clearly the most generalizable of any of Rogers’s five attributes, having a positive effect regardless of the type of technological innovation under study.

Complexity (CX)

Complexity can be considered the same as the inverse of Ease-of-Use as discussed in many of the behavioral intent models including the TAM (Wu and Wu, 2005; Lewis and Orton, 2000). Specifically, it is the degree to which an innovation is perceived as relatively difficult to understand and use. Any innovation can be classified on a complexity-simplicity continuum (Rogers 2003) because some innovations are clear in their meaning to potential adopters and some are not.

Complexity was defined simply as the difficulty perceived by the SMEs (small to medium enterprises) in adopting electronic commerce by Kendall et al. (2001). Factors included: one’s level of technical knowledge/expertise, the level of electric commerce security, and the now dated Y2K problem. They posed that the more knowledge and expertise one has, the less complex the system will be, and the more secure the system is perceived to be, the less complex it will appear. This may appear to be counter-intuitive, especially in the case of ERP (Enterprise Resource Planning) systems which although secure, may certainly not be easy to use.

Lewis and Orton (2000) took an approach similar to Davis (1989) by naming their attribute ‘Simplicity,’ which is, of course, the opposite of Complexity. Adding to Rogers’s definition, they refined the attribute to include plug-in modules as just one example of an impediment to the usage of online learning. In terms of e-mail, Shelly (1998) added difficulty in obtaining e-mail access and learning how to use a network as factors adding to Complexity.

Atkinson (2007) stated that innovations that are perceived as more complex are less likely to be adopted. Complexity is the only attribute negatively related to adoption. Some researchers have used the term ‘simplicity’ so that the attributes would have the same directionality as the other attributes in terms of their relationship with adoption.


Trialability (TR)

Trialability is the degree to which an innovation will be available for trial usage before adoption (Rogers, 2003). Innovations available for trial for a period of time are generally more acceptable to individuals than those simply thrust upon them. Personally trying out an innovation is one way for an individual to give meaning to an innovation and to find out how it works under one’s own conditions. A personal trial can dispel uncertainty about a new idea (Rogers 2003). Given the potential complexity and requisite business model alterations inherent in PWS systems usage, Trialability may be a very critical factor.

Innovations that can be tried before adoption are adopted more rapidly than those that cannot, especially among those who adopt earlier relative to the majority of potential adopters. Later adopters use the experience of peers as a vicarious trial of the innovation (Atkinson 2007).

Weiss and Dale (1998) stated that Trialability is simply the ability to “try out” an innovation before adopting it. Within the framework of studying the usage of e-mail, hands on instruction, Trialability, and allowing participants to experiment with using e-mail may have reduced the difficulty for them of using e-mail (Shelly 1998). In addition to Rogers’s definition, Lewis and Orton (2000) added that the ability to try an innovation with no requirement or expectation of continued use gives users a chance to see how it works under their own situations and conditions, and also helps to dispel uncertainty about the new practice.

In the context of electronic commerce usage in SMEs, Kendall et al. (2001) defined Trialability as the ability to engage in electronic commerce without incurring high start-up costs. They saw the perceived high cost of implementing electronic commerce as a detriment. Also, availability and awareness of possible grants was seen as relevant to this factor. When grants are given, businesses are able to reduce high start-up costs, thus reducing the pain associated with failed initiatives. In short, grants will enhance the Trialability of an innovation.


Willingness to Use

Willingness to Use is the dependent variable of interest in this study, and as such is determined by manipulations of the independent variables. Davis (1989) defined the behavioral intent to use technologies as ‘User Acceptance,’ and Venkatesh et al. (2003) utilized the term ‘Use Behavior.’ Rogers (2003) uses the term ‘Rate of Adoption’ to describe his expected behavioral outcome, which is his dependent variable in DOI theory. A Rate of Adoption measure is not suitable for drawing conclusions about an individual’s Willingness to Use new technology at a point in time, irrespective of the adoption decisions of other comparison individuals. Therefore, in the current study, I assess individuals’ attitudes toward adoption using Willingness to Use measures widely used in the TAM literature (Purao, and Storey 2008: Son, Kim, and Riggins 2006; Liu and Ma 2005; and Shih, H. 2004). I define Willingness to Use as the extent to which an individual has a positive attitude toward using a new technology.


Results of Prior Research with DOI

Over the last three decades, numerous studies have sought to measure either some or all of Rogers’s five attributes. In 1987, Midgely performed a metaanalysis of 95 studies that employed DOI theory for predicting the Rate of Adoption outcome. Of these, 43 studied Relative Advantage, 27 studied Compatibility, 16 studied Complexity, and 13 studied Trialability. Midgely found that 67% of the studies found statistically significant support for both Relative Advantage and Compatibility, while Complexity and Trialability received 56% and 67% respectively. Interestingly, Midgely made no mention or deference to the Observability attribute.

In 2005, Wu and Wu studied e-CRM (electronic customer relationship management) adoption in organizations by combining elements of the TAM and Innovation Diffusion Theory. Their hypothesis that Complexity would have a negative impact on attitudes toward usage was not supported. Interestingly, however, their structural equation model did find support for the hypothesis that Complexity has a negative impact on Relative Advantage. This finding would seem to support the finding of Davis (1989) that Ease-of-Use has a moderating effect on the relationship between it and Relative Advantage.

In a study covering general diffusion patterns in IT innovations and their relationship to innovation characteristics, Teng et al. (2002) found conflicting results regarding Complexity. Although it stands to reason that groups exhibiting maximal usage would find an innovation less complex than those with minimal usage, the conflicting results between groups rendered the results ambiguous. In another study regarding whether consumers assess product attributes of innovations in a stepwise process, it was determined that Complexity had very mixed effects (Faiers et al., 2007).

While studying the adoption and usage of e-mail as a learning tool, Shelly (1998) found that of an experimental group called ‘participants,’ 91% did not consider Complexity as an issue toward usage. However, 55% of the control group labeled ‘nonparticipants’ considered Complexity a problem. Shelly discovered that the participants had more invested in the usage of e-mail as a learning tool than nonparticipants, thus resulting in the divergence in perceived Complexity. In terms of Trialability, the results were more conclusive than they were for Complexity. Of the eighteen studies of Trialability, ten found that it had a positive impact on attitudes toward usage (Kendall, et al., 2001; Ardis and Green, 1998; Faiers, et al., 2007; Sakraida and Draus, 2003; Wu and Wu, 2005; Greer and Murtaza, 2003; Vollink, et al., 2002; Shelley, 1998; Atkinson, 2007; and Lee, 2004), five of the studies did not consider Trialability (Braak and Tearle, 2007; Choudhury and Karahanna, 2008; Wilson, et al. 1999; Zhu, et al., 2006; and Teng, et al., 2002), two found Trialability to have a negative impact on attitudes toward usage (Tornatzky and Klein, 1982; and Oh, et al., 2003), and one found ambiguous effects (Murphy, 2005). Given the lack of study or results differing from Rogers’s theory, Trialability is an attribute that requires further study, as explored in this research.

In a meta-analysis in 1982, Tornatzky and Klein found that overall, Trialability did not have a positive impact on attitudes toward usage of innovations. This is likely due to the nature of the 75 innovations they studied. It also might speak to the timeframe of the research. In 1982, research in IT innovations was in its infancy. While studying the adoption of broadband in Korea, Oh et al. (2003) took a slightly different approach, as they considered Rogers’s perceived attributes of innovations as antecedents to the TAM, namely Perceived Usefulness and Perceived Ease-of-Use. As a result, they found no support for the hypothesis that Trialability had a positive effect on Usefulness or Ease-of-Use. Murphy (2005) found ambiguous effects of Trialability on attitudes toward usage of broadband-enabled learning, based primarily upon environmental issues such as the speed of the connection, access to related equipment, software, and technical support.

The Technology Acceptance Model (TAM)

The Technology Acceptance Model (TAM) of Davis et.al. (1989) is an information systems theory that models how users come to accept and use a technology. In addition, research associated with the TAM has provided well-validated measures of Willingness to Use that can be deployed to assess attitudes toward adopting a new technology among individuals at the outset of that technology’s introduction without relying on Rate of Adoption measures that require longitudinal measurement and a comparison sample from which to infer rate.

After receiving criticism from other scholars that the TAM was prone to measurement bias due to grouping of scale items in order of PU and PEOU, Davis and Venkatesh (1996) performed a rigorous three-experiment study that verified the high reliability and validity of the TAM model. Interestingly, they did find a dependency relationship between Perceived Usefulness and Perceived Ease-of-Use as Davis had originally theorized.

Using structural equation modeling, Adams, Nelson, and Todd (1992) performed two studies to evaluate the psychometric properties of the Ease-of-Use and Usefulness scales, while examining the relationship between Ease-of-Use, Usefulness, and system usage. The first provided a strong assessment of the convergent validity of the two scales measuring Usefulness and Ease-of-Use by examining heterogeneous user groups dealing with heterogeneous implementations of messaging technology. It also represented a strong test of discriminant validity of the construct measures. In 1994, Straub studied whether or not there are differences in e-mail and fax usage between Japanese and American knowledge workers utilizing TAM. Statistically speaking the study verified usage of the TAM in this unique international context. A study by Karahanna, Straub, and Chervany (1999) added the element of the subjective norm, along with elements of DOI theory to test attitudes toward use over time. They found that potential adopters base their attitudes on a richer set of innovation characteristics than existing users. Whereas pre-adoption attitude is based on perceptions of Usefulness, Ease-of-Use, Result Demonstrability, Visibility, and Trialability, post-adoption attitude is only based on instrumentality beliefs of usefulness and perceptions of image enhancements (Karahanna, Straub, and Chervany, 1999).

These findings further suggest the need for additional study of usage behaviors prior to adoption as opposed to ex post facto. A study of TAM conducted by Plouffe, Hulland, and Vandenbosch (2001) aimed to show that the TAM, although parsimonious and robust, lacked requisite explanatory power of all of the attitudes toward usage of information technology. They therefore tested the TAM directly against the Perceived Characteristics of Innovating (PCI) scale as developed by Moore and Benbasat (1991). Their results show that the PCI’s complete set of antecedents (Relative Advantage, Compatibility, Ease-of-Use, Result Demonstrability, Image, Visibility, Trialability, and Voluntariness) explain substantially more variance in attitudes toward usage than does TAM.

Other Models of Behavioral Intent

Throughout the extant literature describing the adoption process of innovations and technologies is the element of behavioral intent. As discussed within this section, these intentions are manifest in different ways. Because innovations are seldom immediate, the costs and benefits involved with adoption and implementation give decision makers cause to contemplate them completely. In the behavioral models, intentions have shown to be strong predictors of future behaviors (Fishbein and Azjen, 1975; Azjen, 1991). Accordingly, it follows that intentions would be a strong indicator of the adoption of various technologies.

IT researchers have used intention models from social psychology to explain and/or predict technology adoption and implementation. In particular, theories that have featured intent as a primary construct are the Theory of Reasoned Action (TRA), (Fishbein and Azjen, 1975; Azjen and Fishbein, 1980), the Theory of Planned Behavior (TPB) (Azjen, 1991), The Unified Theory of Acceptance and Use of Technology (UTUAT) (Venkatesh, Morris, Davis, and Davis 2003), and the Technology Acceptance Model (TAM) (Davis 1989).



The recent development of strategic IT applications that target virtually all facets of firm operations, including production and accounting, have proliferated substantially. Considering the rapid growth of these innovative technology applications, it is important to examine the extent to which existing theories can explain or predict acceptance and usage of technology. In this vein, the current study represents a conceptual replication and extension of previous works by reexamining prevalent theoretical models.

I employ scenarios to manipulate three key attributes of an innovation to examine their impact on Willingness to Use. I employ concepts form both the Technology Acceptance Model (TAM), and Diffusions of Innovations (DOI) Theory to identify key influences on attitudes toward using a new technology, and use elements of a scale developed by Moore and Benbasat (1991) to assess an individual’s Willingness to Use new technology in a manner that does not require a longitudinal comparison with other individuals. Although Moore and Benbasat (1991) developed an instrument nicely suited to testing diffusion theory, they did not endeavor to test the theory themselves. Likewise, although Rogers (2003) expanded upon his theory when discussing the Moore and Benbasat work, he did not empirically test it.

Although Rogers (2003) states that a number of investigations of the perceived attributes and the Rate of Adoption of innovations have been conducted with various types of respondents in the last decade, he does not elaborate past discussion of the scales developed by Moore and Benbasat (1991) and Tornatzky and Klein (1982). It seems there has been much work perfecting instruments to measure perceptions of attributes and Rate of Adoption, but researchers have left a great void in testing the theory in terms of Willingness to Use. It is precisely this void that the current study seeks to fill.

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