Decision Sciences
`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:
`Hillol Bala
`Operations and Decision Technologies, Kelley School of Business, Indiana University,
`Bloomington, IN 47405, e-mail:
`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.
`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.


`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,


`Venkatesh and Bala
`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.
`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,


`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.
`Social Influence
`Ease of Use
`Technology Acceptance Model (TAM)


`Venkatesh and Bala
`Table 1: Determinants of perceived usefulness.
`Subjective Norm
`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


`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).
`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


`Venkatesh and Bala
`Table 2: Determinants of perceived ease of use.
`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


`Technology Acceptance Model 3 and a Research Agenda on Interventions
`Figure 2: Technology acceptance model 3 (TAM3)a.
`Subjective Norm
`Job Relevance
`Output Quality
`Computer Self-
`Perceptions of
`External Control
`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.


`Venkatesh and Bala
`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


`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.
`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
`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


`Venkatesh and Bala
`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

This document is available on Docket Alarm but you must sign up to view it.

Or .

Accessing this document will incur an additional charge of $.

After purchase, you can access this document again without charge.

Accept $ Charge

Still Working On It

This document is taking longer than usual to download. This can happen if we need to contact the court directly to obtain the document and their servers are running slowly.

Give it another minute or two to complete, and then try the refresh button.


A few More Minutes ... Still Working

It can take up to 5 minutes for us to download a document if the court servers are running slowly.

Thank you for your continued patience.

This document could not be displayed.

We could not find this document within its docket. Please go back to the docket page and check the link. If that does not work, go back to the docket and refresh it to pull the newest information.

Your account does not support viewing this document.

You need a Paid Account to view this document. Click here to change your account type.

Your account does not support viewing this document.

Set your membership status to view this document.

With a Docket Alarm membership, you'll get a whole lot more, including:

  • Up-to-date information for this case.
  • Email alerts whenever there is an update.
  • Full text search for other cases.
  • Get email alerts whenever a new case matches your search.

Become a Member

One Moment Please

The filing “” is large (MB) and is being downloaded.

Please refresh this page in a few minutes to see if the filing has been downloaded. The filing will also be emailed to you when the download completes.

Your document is on its way!

If you do not receive the document in five minutes, contact support at

Sealed Document

We are unable to display this document, it may be under a court ordered seal.

If you have proper credentials to access the file, you may proceed directly to the court's system using your government issued username and password.

Access Government Site

We are redirecting you
to a mobile optimized page.

Document Unreadable or Corrupt

Refresh this Document
Go to the Docket

We are unable to display this document.

Refresh this Document
Go to the Docket