`Volume 39 Number 2
`May 2008
`
`C(cid:2) 2008, The Author
`Journal compilation C(cid:2) 2008, Decision Sciences Institute
`
`Technology Acceptance Model 3
`and a Research Agenda on Interventions
`†
`
`Viswanath Venkatesh
`Department of Information Systems, Walton College of Business, University of Arkansas,
`Fayetteville, AR 72701, e-mail: vvenkatesh@vvenkatesh.us
`
`Hillol Bala
`††
`Operations and Decision Technologies, Kelley School of Business, Indiana University,
`Bloomington, IN 47405, e-mail: hbala@indiana.edu
`
`ABSTRACT
`Prior research has provided valuable insights into how and why employees make a de-
`cision about the adoption and use of information technologies (ITs) in the workplace.
`From an organizational point of view, however, the more important issue is how man-
`agers make informed decisions about interventions that can lead to greater acceptance
`and effective utilization of IT. There is limited research in the IT implementation liter-
`ature that deals with the role of interventions to aid such managerial decision making.
`Particularly, there is a need to understand how various interventions can influence the
`known determinants of IT adoption and use. To address this gap in the literature, we draw
`from the vast body of research on the technology acceptance model (TAM), particularly
`the work on the determinants of perceived usefulness and perceived ease of use, and: (i)
`develop a comprehensive nomological network (integrated model) of the determinants
`of individual level (IT) adoption and use; (ii) empirically test the proposed integrated
`model; and (iii) present a research agenda focused on potential pre- and postimplemen-
`tation interventions that can enhance employees’ adoption and use of IT. Our findings
`and research agenda have important implications for managerial decision making on IT
`implementation in organizations.
`
`Subject Areas: Design Characteristics, Interventions, Management Sup-
`port, Organizational Support, Peer Support, Technology Acceptance Model
`(TAM), Technology Adoption, Training, User Acceptance, User Involvement,
`and User Participation.
`
`INTRODUCTION
`
`While great progress has been made in understanding the determinants of employ-
`ees’ information technology (IT) adoption and use (Venkatesh, Morris, Davis, &
`Davis, 2003), trade press still suggests that low adoption and use of IT by em-
`ployees are still major barriers to successful IT implementations in organizations
`(Overby, 2002; Gross, 2005). As ITs are becoming increasingly complex and central
`
`†
`Corresponding author.
`††
`
`Effective July 1, 2008.
`
`273
`
`1
`
`
`
`274
`
`Technology Acceptance Model 3 and a Research Agenda on Interventions
`
`to organizational operations and managerial decision making (e.g., enterprise re-
`source planning, supply chain management, customer relationship management
`systems), this issue has become even more severe. There are numerous examples
`of IT implementation failures in organizations leading to huge financial losses.
`Two high-profile examples of IT implementation failures are Hewlett-Packard’s
`(HP) failure in 2004 that had a financial impact of $160 million (Koch, 2004a) and
`Nike’s failure in 2000 that cost $100 million in sales and resulted in a 20% drop
`in stock price (Koch, 2004b). Low adoption and underutilization of ITs have been
`suggested to be key reasons for “productivity paradox”—that is, a contradictory
`relationship between IT investment and firm performance (Landauer, 1995; Sichel,
`1997; Devaraj & Kohli, 2003). This issue is particularly important given that recent
`reports suggest that worldwide investment in IT will increase at a rate of 7.7% a
`year from 2004 to 2008 compared to 5.1% from 2000 to 2004 (World Informa-
`tion Technology and Service Alliance, 2004). It has been suggested in both the
`academic and trade press that managers need to develop and implement effective
`interventions in order to maximize employees’ IT adoption and use (Cohen, 2005;
`Jasperson, Carter, & Zmud, 2005). Therefore, identifying interventions that could
`influence adoption and use of new ITs can aid managerial decision making on
`successful IT implementation strategies (Jasperson et al., 2005).
`The theme of interventions as an important direction for future research is
`documented in recent research. For instance, Venkatesh (2006) reviewed prior re-
`search on IT adoption and suggested three avenues for future research that are
`pertinent to the editorial mission of Decision Sciences: (i) business process change
`and process standards; (ii) supply-chain technologies; and (iii) services. Within
`each of these three avenues, he noted interventions as a critical direction for future
`research that had significant managerial implications and the potential to enhance
`IT implementation success. More recently, other researchers have provided new
`directions in individual-level IT adoption research with a particular focus on inter-
`ventions that can potentially lead to greater acceptance and effective utilization of
`IT (Benbasat & Barki, 2007; Goodhue, 2007; Venkatesh, Davis, & Morris, 2007).
`Our objective is to present a brief literature review, propose an integrated model
`of employee decision making about new ITs, empirically validate the model, and
`present a research agenda that identifies a set of interventions for researchers and
`practitioners to investigate to further our understanding of IT implementation.
`The research on individual-level IT adoption and use is mature and has pro-
`vided rich theories and explanations of the determinants of adoption and use deci-
`sions (e.g., Venkatesh et al., 2003; Sarker, Valacich, & Sarker, 2005 for group-level
`IT adoption research). Notwithstanding the plethora of IT adoption studies, there
`has been limited research on the interventions that can potentially lead to greater
`acceptance and use of IT (Venkatesh, 1999). The most widely employed model
`of IT adoption and use is the technology acceptance model (TAM) that has been
`shown to be highly predictive of IT adoption and use (Davis, Bagozzi, & Warshaw,
`1989; Adams, Nelson, & Todd, 1992; Venkatesh & Davis, 2000; Venkatesh &
`Morris, 2000). One of the most common criticisms of TAM has been the lack of
`actionable guidance to practitioners (Lee, Kozar, & Larsen, 2003). Many leading
`researchers have noted this limitation in interviews reported in Lee et al. (2003).
`For example, Alan Dennis, a leading scholar in the field of information systems,
`
`2
`
`
`
`Venkatesh and Bala
`
`275
`
`commented, “imagine talking to a manager and saying that to be adopted technol-
`ogy must be useful and easy to use. I imagine the reaction would be ‘Duh!’ The
`more important questions are what [sic] makes technology useful and easy to use”
`(Lee et al., 2003, p. 766). Some work has been done to address this limitation by
`identifying determinants of key predictors in TAM, namely, perceived usefulness
`and perceived ease of use. Some researchers have developed context-specific de-
`terminants to the two TAM constructs—for instance, Karahanna and Straub (1999)
`for electronic communication systems (i.e., e-mail systems), Koufaris (2002) for
`e-commerce, Hong and Tam (2006) for multipurpose information appliances, Rai
`and Patnayakuni (1996) for CASE tools, and Rai and Bajwa (1997) for executive
`information systems—that have immense value in theorizing richly about the spe-
`cific IT artifact (type of system) in question and identifying determinants that are
`specific to the type of technology being studied. Others have developed general
`and context-independent determinants that span across a broad range of systems
`(e.g., Venkatesh, 2000; Venkatesh & Davis, 2000). While each of these approaches
`has merits, and it is not our goal to debate generality versus context specificity
`in theorizing (Bacharach, 1989; Johns, 2006), in this article, we are choosing the
`general set of determinants of TAM as a basis for the identification of broadly
`applicable interventions that can fuel future research.
`Venkatesh and Davis (2000) identified general determinants of perceived
`usefulness and Venkatesh (2000) identified general determinants of perceived ease
`of use. These two models were developed separately and not much is known about
`possible crossover effects—that is, could determinants of perceived usefulness
`influence perceived ease of use and/or could determinants of perceived ease of
`use influence perceived usefulness? Investigating and theorizing about potential
`crossover effects or ruling out the possibility of these effects is an important step
`in developing a more comprehensive nomological network around TAM. Further,
`interventions, based on the determinants of perceived usefulness and perceived
`ease of use, hold the key to helping managers make effective decisions about
`applying specific interventions to influence the known determinants of IT adoption
`and, consequently, the success of new ITs (Rai, Lang, & Welker, 2002; DeLone
`& McLean, 2003; Sabherwal, Jeyaraj, & Chowa, 2006). Given this backdrop, this
`article presents an integrated model of determinants of perceived usefulness and
`perceived ease of use, empirically validates the model, and uses the integrated
`model as a springboard to propose future directions for research on interventions.
`
`BACKGROUND
`
`TAM was developed to predict individual adoption and use of new ITs. It posits
`that individuals’ behavioral intention to use an IT is determined by two beliefs:
`perceived usefulness, defined as the extent to which a person believes that using
`an IT will enhance his or her job performance and perceived ease of use, defined
`as the degree to which a person believes that using an IT will be free of effort. It
`further theorizes that the effect of external variables (e.g., design characteristics) on
`behavioral intention will be mediated by perceived usefulness and perceived ease
`of use. Over the last two decades, there has been substantial empirical support in
`favor of TAM (e.g., Adams et al., 1992; Agarwal & Karahanna, 2000; Karahanna,
`
`3
`
`
`
`276
`
`Technology Acceptance Model 3 and a Research Agenda on Interventions
`
`Agarwal, & Angst, 2006; Venkatesh et al., 2003, 2007). TAM consistently explains
`about 40% of the variance in individuals’ intention to use an IT and actual usage.
`As of December 2007, the Social Science Citation Index listed over 1,700 citations
`and Google Scholars listed over 5,000 citations to the two journal articles that
`introduced TAM (Davis, 1989; Davis et al., 1989).
`Theoretical Framework
`Prior research employing TAM has focused on three broad areas. First, some stud-
`ies replicated TAM and focused on the psychometric aspects of TAM constructs
`(e.g., Adams et al., 1992; Hendrickson, Massey, & Cronan, 1993; Segars & Grover,
`1993). Second, other studies provided theoretical underpinning of the relative im-
`portance of TAM constructs—that is, perceived usefulness and perceived ease of
`use (e.g., Karahanna, Straub, & Chervany, 1999). Finally, some studies extended
`TAM by adding additional constructs as determinants of TAM constructs (e.g.,
`Karahanna & Straub, 1999; Venkatesh, 2000; Venkatesh & Davis, 2000; Koufaris,
`2002). Synthesizing prior research on TAM, we developed a theoretical framework
`that represents the cumulative body of knowledge accumulated over the years from
`TAM research (see Figure 1). The figure shows four different types of determinants
`of perceived usefulness and perceived ease of use—individual differences, system
`characteristics, social influence, and facilitating conditions. Individual difference
`variables include personality and/or demographics (e.g., traits or states of indi-
`viduals, gender, and age) that can influence individuals’ perceptions of perceived
`usefulness and perceived ease of use. System characteristics are those salient fea-
`tures of a system that can help individuals develop favorable (or unfavorable)
`perceptions regarding the usefulness or ease of use of a system. Social influence
`captures various social processes and mechanisms that guide individuals to formu-
`late perceptions of various aspects of an IT. Finally, facilitating conditions represent
`organizational support that facilitates the use of an IT.
`Determinants of Perceived Usefulness
`Venkatesh and Davis (2000) proposed an extension of TAM—TAM2—by identify-
`ing and theorizing about the general determinants of perceived usefulness—that is,
`subjective norm, image, job relevance, output quality, result demonstrability, and
`
`Figure 1: Theoretical framework.
`
`Individual
`Differences
`
`System
`Characteristics
`
`Social Influence
`
`Facilitating
`Conditions
`
`Perceived
`Usefulness
`
`Perceived
`Ease of Use
`
`Behavioral
`Intention
`
`Use
`Behavior
`
`Technology Acceptance Model (TAM)
`
`4
`
`
`
`Venkatesh and Bala
`
`277
`
`Table 1: Determinants of perceived usefulness.
`
`Determinants
`
`Definitions
`
`Subjective Norm
`
`Image
`
`Perceived Ease of Use The degree to which a person believes that using an IT will be
`free of effort (Davis et al., 1989).
`The degree to which an individual perceives that most people
`who are important to him think he should or should not use the
`system (Fishbein & Ajzen, 1975; Venkatesh & Davis, 2000).
`The degree to which an individual perceives that use of an
`innovation will enhance his or her status in his or her social
`system (Moore & Benbasat, 1991).
`The degree to which an individual believes that the target system
`is applicable to his or her job (Venkatesh & Davis, 2000).
`The degree to which an individual believes that the system
`performs his or her job tasks well (Venkatesh & Davis, 2000).
`Result Demonstrability The degree to which an individual believes that the results of
`using a system are tangible, observable, and communicable
`(Moore & Benbasat, 1991).
`
`Job Relevance
`
`Output Quality
`
`perceived ease of use—and two moderators—that is, experience and voluntariness.
`The first two determinants fall into the category of social influence and the remain-
`ing determinants are system characteristics as per the theoretical framework shown
`in Figure 1. Table 1 provides the definitions of the determinants of perceived use-
`fulness. TAM2 presents two theoretical processes—social influence and cognitive
`instrumental processes—to explain the effects of the various determinants on per-
`ceived usefulness and behavioral intention. In TAM2, subjective norm and image
`are the two determinants of perceived usefulness that represent the social influence
`processes. Drawing on Kelman’s (1958, 1961) work on social influence and French
`and Raven’s (1959) work on power influences, TAM2 theorizes that three social
`influence mechanisms—compliance, internalization, and identification—will play
`a role in understanding the social influence processes. Compliance represents a
`situation in which an individual performs a behavior in order to attain certain re-
`wards or avoid punishment (Miniard & Cohen, 1979). Identification refers to an
`individual’s belief that performing a behavior will elevate his or her social status
`within a referent group because important referents believe the behavior should
`be performed (Venkatesh & Davis, 2000). Internalization is defined as the incor-
`poration of a referent’s belief into one’s own belief structure (Warshaw, 1980).
`TAM2 posits that subjective norm and image will positively influence perceived
`usefulness through processes of internalization and identification, respectively. It
`further theorizes that the effect of subjective norm on both, perceived usefulness
`and behavioral intention will attenuate over time as users gain more experience
`with a system.
`In TAM2, four constructs—job relevance, output quality, result demonstrabil-
`ity, and perceived ease of use—capture the influence of cognitive instrumental pro-
`cesses on perceived usefulness. Drawing on three different theoretical paradigms—
`that is, work motivation theory (e.g., Vroom, 1964), action identification theory
`(e.g., Vallacher & Wegner, 1987), and behavioral decision theory (e.g., Beach &
`Mitchell, 1996, 1998), Venkatesh and Davis (2000) provided a detailed discussion
`of how and why individuals form perceptions of usefulness based on cognitive
`
`5
`
`
`
`278
`
`Technology Acceptance Model 3 and a Research Agenda on Interventions
`
`instrumental processes. The core theoretical argument underlying the role of cogni-
`tive instrumental processes is that individuals “form perceived usefulness judgment
`in part by cognitively comparing what a system is capable of doing with what they
`need to get done in their job” (Venkatesh & Davis, 2000, p. 190). TAM2 theorizes
`that individuals’ mental assessment of the match between important work goals
`and the consequences of performing job tasks using a system serves as a basis for
`forming perceptions regarding the usefulness of the system (Venkatesh & Davis,
`2000). TAM2 posits that perceived ease of use and result demonstrability will have
`a positive direct influence on perceived usefulness. Job relevance and output quality
`will have a moderating effect on perceived usefulness such that the higher the out-
`put quality, the stronger the effect job relevance will have on perceived usefulness.
`Venkatesh and Davis found strong support for TAM2 in longitudinal field studies
`conducted at four organizations.
`
`Determinants of Perceived Ease of Use
`Building on the anchoring and adjustment framing of human decision making,
`Venkatesh (2000) developed a model of the determinants of perceived ease of
`use. Table 2 presents the definitions of the determinants of perceived ease of
`use. Venkatesh (2000) argued that individuals will form early perceptions of per-
`ceived ease of use of a system based on several anchors related to individuals’
`general beliefs regarding computers and computer use. The anchors suggested by
`Venkatesh (2000) are computer self-efficacy, computer anxiety, and computer play-
`fulness, and perceptions of external control (or facilitating conditions). The first
`three of these anchors represent individual differences per Figure 1—that is, gen-
`eral beliefs associated with computers and computer use. Computer self-efficacy
`refers to individuals’ control beliefs regarding his or her personal ability to use
`a system. Perceptions of external control are related to individuals’ control be-
`liefs regarding the availability of organizational resources and support structure to
`facilitate the use of a system. Computer playfulness represents the intrinsic mo-
`tivation associated with using any new system. Venkatesh (2000) suggested that
`while anchors drive initial judgments of perceived ease of use, individuals will
`adjust these judgments after they gain direct hands-on experience with the new
`system. Two system characteristics–related adjustments—that is, perceived enjoy-
`ment and objective usability—were suggested by Venkatesh (2000) to play a role
`in determining perceived ease of use after individuals gain experience with the
`new system. Venkatesh (2000) theorized that even with increasing experience with
`the system, the role of two anchors—computer self-efficacy and perceptions of
`external control—will continue to be strong. However, the effects of the other two
`anchors—computer playfulness and computer anxiety—were theorized to dimin-
`ish over time. Venkatesh (2000) further theorized that the effects of adjustments on
`perceived ease of use were stronger with more hands-on experience with the sys-
`tem. Although longitudinal studies were conducted, the specific moderating role
`by experience was not tested in Venkatesh (2000).
`
`DEVELOPMENT OF TAM3
`
`We combine TAM2 (Venkatesh & Davis, 2000) and the model of the determinants
`of perceived ease of use (Venkatesh, 2000), and develop an integrated model of
`
`6
`
`
`
`Venkatesh and Bala
`
`279
`
`Table 2: Determinants of perceived ease of use.
`
`Determinants
`
`Definitions
`
`Computer Self-Efficacy
`
`Computer Anxiety
`
`The degree to which an individual believes that he or she
`has the ability to perform a specific task/job using the
`computer (Compeau & Higgins, 1995a, 1995b).
`Perception of External Control The degree to which an individual believes that
`organizational and technical resources exist to support
`the use of the system (Venkatesh et al., 2003).
`The degree of “an individual’s apprehension, or even
`fear, when she/he is faced with the possibility of using
`computers” (Venkatesh, 2000, p. 349).
`“. . .the degree of cognitive spontaneity in
`microcomputer interactions” (Webster & Martocchio,
`1992, p. 204).
`The extent to which “the activity of using a specific
`system is perceived to be enjoyable in its own right,
`aside from any performance consequences resulting
`from system use” (Venkatesh, 2000, p. 351).
`A “comparison of systems based on the actual level
`(rather than perceptions) of effort required to
`completing specific tasks” (Venkatesh, 2000,
`pp. 350–351).
`
`Computer Playfulness
`
`Perceived Enjoyment
`
`Objective Usability
`
`technology acceptance—TAM3, shown in Figure 2. TAM3 presents a complete
`nomological network of the determinants of individuals’ IT adoption and use.
`We suggest three theoretical extensions beyond TAM2 and the model of the de-
`terminants of perceived ease of use. In this section, we discuss these theoretical
`extensions and the rationale for the integration.
`
`Crossover Effects
`We expect the general pattern of relationships suggested in Venkatesh and Davis
`(2000) and Venkatesh (2000) to hold in TAM3. Further, we suggest that the de-
`terminants of perceived usefulness will not influence perceived ease of use and
`the determinants of perceived ease of use will not influence perceived usefulness.
`Thus, TAM3 does not posit any cross-over effects.
`As noted earlier, two theoretical processes explain the relationships between
`perceived usefulness and its determinants: social influence and cognitive instrumen-
`tal processes. The effects of the various factors—that is, subjective norm, image,
`job relevance, output quality, and result demonstrability—on perceived usefulness
`are tied to these two processes. We have no theoretical and empirical basis to ex-
`pect that these processes will play any role in forming judgments about perceived
`ease of use. Perceived ease of use has been theorized to be closely associated with
`individuals’ self-efficacy beliefs and procedural knowledge, which requires hands-
`on experience and execution of skills (Davis et al., 1989; Venkatesh, 2000; Davis
`& Venkatesh, 2004). Further, Venkatesh (2000) suggested that individuals form
`perceived ease of use about a specific system by anchoring their perceptions to
`the different general computer beliefs and later adjusting their perceptions of ease
`
`7
`
`
`
`280
`
`Technology Acceptance Model 3 and a Research Agenda on Interventions
`
`Figure 2: Technology acceptance model 3 (TAM3)a.
`
`Experience
`
`Voluntariness
`
`Subjective Norm
`
`Image
`
`Job Relevance
`
`Output Quality
`
`Result
`Demonstrability
`
`Anchor
`
`Computer Self-
`efficacy
`
`Perceptions of
`External Control
`
`Computer
`Anxiety
`
`Computer
`Playfulness
`
`Adjustment
`
`Perceived
`Enjoyment
`
`Objective
`Usability
`
`Perceived
`Usefulness
`
`Behavioral
`Intention
`
`Use
`Behavior
`
`Perceived
`Ease of Use
`
`Technology Acceptance Model (TAM)
`
`aThick lines indicate new relationships proposed in TAM3.
`
`of use based on hands-on experience with the specific system. Social influence
`processes (i.e., compliance, identification, and internalization) in the context of IT
`adoption and use represent how important referents believe about the instrumental
`benefits of using a system (Venkatesh & Davis, 2000). Even if an individual gets in-
`formation from important referents about how easy a system is to use, it is unlikely
`that the individual will form stable perceptions of ease of use based on the beliefs
`of referent others over and above his or her own general computer beliefs and
`hands-on experience with the system (e.g., Davis & Venkatesh, 2004). Further, the
`determinants of perceived ease of use represent several traits and emotions, such as
`computer self-efficacy, computer playfulness, and computer anxiety. There are no
`theoretical and empirical reasons to believe that these stable computer-related traits
`and emotions will be affected by social influence or cognitive influence processes.
`
`8
`
`
`
`Venkatesh and Bala
`
`281
`
`We suggest that the determinants of perceived ease of use will not influ-
`ence perceived usefulness. The determinants of perceived ease of use suggested
`by Venkatesh (2000) are primarily individual differences variables and general be-
`liefs about computers and computer use. These variables are grouped into three
`categories: control beliefs, intrinsic motivation, and emotion. Perceived usefulness
`is an instrumental belief that is conceptually similar to extrinsic motivation and
`is a cognition (as opposed to emotion) regarding the benefits of using a system.
`The perceptions of control (over a system), enjoyment or playfulness related to a
`system, and anxiety regarding the ability to use a system do not provide a basis for
`forming perceptions of instrumental benefits of using a system. For example, con-
`trol over using a system does not guarantee that the system will enhance one’s job
`performance. Similarly, higher levels of computer playfulness or enjoyment from
`using a system do not mean that the system will help an individual to become more
`effective (e.g., Van der Heijden, 2004). Therefore, we expect that the determinants
`of perceived ease of use will not influence perceived usefulness.
`
`New Relationships Posited in TAM3
`TAM3 posits three relationships that were not empirically tested in Venkatesh
`(2000) and Venkatesh and Davis (2000). We suggest that experience will moderate
`the relationships between (i) perceived ease of use and perceived usefulness; (ii)
`computer anxiety and perceived ease of use; and (iii) perceived ease of use and
`behavioral intention.
`
`Perceived ease of use to perceived usefulness, moderated by experience
`We suggest that with increasing hands-on experience with a system, a user will have
`more information on how easy or difficult the system is to use. While perceived
`ease of use may not be as important in forming behavioral intention in a later
`period of system use (Venkatesh et al., 2003), users will still value perceived ease
`of use in forming perceptions about usefulness. We base this argument on action
`identification theory (Vallacher & Kaufman, 1996) that posits a clear distinction
`between high-level and low-level action identities. High-level identities are related
`to individuals’ goals and plans, whereas low-level identities refer to the means to
`achieve these goals and plans. For instance, in the context of a word processing
`software use, a high-level identity can be writing a high quality report and a low-
`level identity can be striking keys or use of a specific feature of the software
`(Davis & Venkatesh, 2004). Perceived usefulness and perceived ease of use are
`considered high-level and low-level identities respectively (Davis & Venkatesh,
`2004; Venkatesh & Davis, 2000). We suggest that, with increasing experience, the
`influence of perceived ease of use (a low-level identity) on perceived usefulness (a
`high-level identity) will be stronger as users will be able to form an assessment of
`their likelihood of attaining high-level goals (i.e., perceived usefulness) based on
`information gained from experience of the low-level actions (i.e., perceived ease
`of use).
`
`Computer anxiety to perceived ease of use, moderated by experience
`Experience will moderate the effect of computer anxiety on perceived ease of use,
`such that with increasing experience, the effect of computer anxiety on perceived
`
`9
`
`
`
`282
`
`Technology Acceptance Model 3 and a Research Agenda on Interventions
`
`ease of use will diminish. We expect that, with increasing experience, system-
`specific beliefs, rather than general computer beliefs, will be stronger determinants
`of perceived ease of use of a system. Venkatesh (2000) argued that system-specific
`objective usability and perceived enjoyment will be stronger determinants over time
`and the effects of general computer beliefs (e.g., computer anxiety) will diminish
`because with increasing experience, users will develop accurate perceptions of
`effort required to complete specific tasks (i.e., objective usability) and discover
`aspects of a system that lead to enjoyment (or lack thereof). Computer anxiety is
`theorized as an anchoring belief that inhibits forming a positive perception of ease
`of use of a system (Venkatesh, 2000). Research on anchoring and adjustment has
`found that while anchors influence judgments, the role of anchors declines over
`time as adjustment information becomes available (Yadav, 1994; Wasnik, Kent,
`& Hoch, 1998; Mussweiler & Strack, 2001). Drawing on this, we argue that the
`effect of computer anxiety on perceived ease of use will decline with increasing
`experience as individuals will have more accurate perceptions of the effort needed
`to use a system.
`
`Perceived ease of use to behavioral intention, moderated by experience
`We expect that experience will moderate the effect of perceived ease of use on
`behavioral intention such that the effect will be weaker with increasing experience.
`Perceived ease of use—that is, how easy or difficult a system is to use—is an initial
`hurdle for individuals while using a system (Venkatesh, 2000). However, once
`individuals get accustomed to the system and gain hands-on experience with the
`system, the effect of perceived ease of use on behavioral intention will recede into
`the background as individuals now have more procedural knowledge about how to
`use the system. Consequently, individuals will place less importance on perceived
`ease of use while forming their behavioral intentions to use the system.
`
`METHOD
`
`Longitudinal field studies were conducted to test TAM3. Data were collected from
`four different organizations—sites A through D—implementing new ITs. These
`organizations provided an opportunity to test our research model in real-world
`settings of IT implementations. The research sites represented different indus-
`tries, organizational contexts, and functional areas. Further, the types of ITs were
`different across the sites. Such variability in organizational settings and types of
`technologies adds to the potential generalization of our findings. In two of these
`organizations, the use of the new system was voluntary. In all four organizations,
`we collected data over a 5-month period with four points of measurements. In this
`section, we describe the settings, participants, measurement, and data collection
`procedure.
`
`Settings and Participants
`Site A was a medium-sized manufacturing firm that introduced a proprietary op-
`erational system to manage daily operations such as floor and machine scheduling
`and personnel assignment. These operations were conducted manually by the floor
`
`10
`
`
`
`Venkatesh and Bala
`
`283
`
`supervisors before the implementation of the new system. The users received 2 days
`of formal training on the new system. The users of the new system were 48 floor
`supervisors of whom 38 completed the survey at all points of measurement. The
`use of the new system was voluntary.
`Site B was a large financial services firm that was in the process of transi-
`tioning to a Windows-based environment from mainframe-based IT applications.
`The users were members of the personal financial services department. The system
`use was voluntary as the users were allowed to use the old systems. Formal on-site
`training about the system was conducted for 1.5 days. Out of 50 potential users
`of the system who participated in the training, 39 provided usable responses at all
`points of measurement.
`Site C was a small accounting services firm that introduced a new Windows-
`based customer account management system replacing the old paper- and DOS-
`based systems. The users were from customer service representatives. The system
`use was mandatory as the old system was phased out immediately after the new
`system implementation. On-site system training was conducted for 1 day. Out of
`51 potential users of the new system who attended the training, 43 provided usable
`responses at all points of measurement.
`Site D was a small international investment-banking firm that implemented
`a new system to assist in analyzing and creating financially sound international
`stock portfolios. The users were analysts performing different functions related
`to domestic and international stock management. While the organization had an
`existing system to perform the activities related to analyzing and creating stock
`portfolios, the new system had substantially different features and was developed
`by a different vendor. The use of the system was mandatory. The potential users
`received a 4-hour training program to become familiar with the new system. Out
`of 51 potential users of the new system, 36 provided usable responses at all points
`of me