a comprehensive conceptualization of postadoptive behaviors associated with information technology...

A Comprehensive Conceptualization of Post-Adoptive Behaviors Associated with InformationTechnology Enabled Work SystemsAuthor(s): 'Jon (Sean) Jasperson, Pamela E. Carter and Robert W. ZmudSource: MIS Quarterly, Vol. 29, No. 3 (Sep., 2005), pp. 525-557Published by: Management Information Systems Research Center, University of MinnesotaStable URL: http://www.jstor.org/stable/25148694 .Accessed: 01/01/2014 07:15

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Jasperson et al./Post-Adoptive Behaviors & IT-Enabled Work Systems

^ m.C[!\M \^fc_r^ _[^^ Research Article

A Comprehensive Conceptualization of Post-Adoptive Behaviors Associated with Information Technology Enabled Work Systems1

By: 'Jon (Sean) Jasperson Mays Business School Texas A&M University 4217 TAMU College Station, TX 77843-4217 U.S.A.

jjasperson@[email protected]

Pamela E. Carter

College of Business Florida State University Tallahassee, FL 32306-1110 U.S.A.

[email protected]

Robert W. Zmud Michael F. Price College of Business

University of Oklahoma 307 W. Brooks, Room 307E Norman, OK 73019-4006 U.S.A.

[email protected]

1 Jane Webster was the accepting senior editor for this

paper. Anitesh Barua was the associate editor. Terri Griffith served as reviewer.

Abstract

For the last 25 years, organizations have invested

heavily in information technology to support their work processes. In today's organizations, intra

and interorganizational work systems are in

creasingly IT-enabled. Available evidence, how ever, suggests the functional potential of these installed IT applications is underutilized. Most IT users apply a narrow band of features, operate at

low levels of feature use, and rarely initiate exten sions of the available features. We argue that organizations need aggressive tactics to en

courage users to expand their use of installed IT enabled work systems.

This article strives to accomplish three primary research objectives. First, we offer a compre hensive research model aimed both at coalescing existing research on post-adoptive IT use be haviors and at directing future research on those factors that influence users to (continuously) exploit and extend the functionality built into IT

applications. Second, in developing this compre hensive research model, we provide a window (for researchers across a variety of scientific disci

plines interested in technology management) into the rich body of research regarding IT adoption, use, and diffusion. Finally, we discuss implications

MIS Quarterly Vol. 29 No. 3, pp. 525-557/September 2005 525



and recommend guidelines for research and

practice.

Keywords: IT adoption, IT use, post-adoptive behavior, IT value

Introduction _--HH-_--H_-_-__-_-_-_!

Organizations have made huge investments in information technology over the last 25 years, resulting in many, if not most, intra-organizational

work systems being IT-enabled. Further, organi zations are increasingly depending on IT-enabled

interorganizational value chains as the backbone of their commerce with clients, customers, sup

pliers, and partners (Davenport 1998; Mabert et al.

2000,2001). However, existing evidence strongly suggests that organizations underutilize the func tional potential of the majority of this mass of installed IT applications: users employ quite narrow feature breadths, operate at low levels of feature use, and rarely initiate technology- or task related extensions of the available features

(Davenport 1998; Lyytinen and Hirschheim 1987; Mabert et al. 2001; Osterland 2000; Rigby et al.

2002; Ross and Weill 2002).

Investments in enterprise resource planning implementations nicely illustrate this phenomenon.

The costs of an ERP implementation are high: it is not unusual for large organizations to spend over $100 million on their ERP implementations (Robey et al. 2002; Seddon et al. 2003), with an estimated $300 billion worldwide on ERP systems during the 1990s (James and Wolf 2000). How ever, approximately one-half of ERP implemen tations fail to meet the implementing organization's expectations (Adam and O'Doherty 2003). An

explanation for an organization's failure to realize

expectations regarding an ERP implementation might lie in the fact that most ERP life cycle models lack an explicit post-adoption stage. Pragmatically, the post-adoption stage is the

longest phase of the ERP project life cycle, and the phase during which benefits from the investment begin to accrue. Thus, without explicit plans for realizing benefits through the software,

the organization falls short of its implementation expectations (Rosemann 2003). Most explana tions of ERP implementation failures are invariably traced to inadequate training (Duplaga and Astani 2003; Kien and Soh 2003; Robey et al. 2002) and/or inadequate change management (Adam and O'Doherty 2003; Bagchi et al. 2003; James and Wolf 2000; Robey et al. 2002; Ross et al.

2003). Training and change management inter ventions are critical in the post-adoptive context; they allow the organization to benefit from previous learning and adjust to ongoing changes in the work

system. Yet, because we have not systematically defined and examined the post-adoptive (in this case, ERP) context, information systems researchers and practitioners often overlook the

potential of these and other post-adoptive inter ventions.

In general, organizations may be able to achieve considerable economic benefits (via relatively low incremental investment) by successfully inducing and enabling users to (appropriately) enrich their use of already-installed IT-enabled work systems during the post-adoption stage. For example, Lassila and Brancheau (1999) report that com

panies in expanding and high-integration utilization

states, where users had more freedom to adjust both software features and the organizational

processes that could take advantage of those fea tures, realized greater benefits than companies in standard adoption and low-integration utilization states.

The goals of this paper are to conceptualize the

post-adoptive behavior construct, to provide a

synthesis of the factors shown in prior research to influence post-adoptive behavior, and to situate these factors within a nomological net to facilitate future research in this domain. To guide this

effort, we focus on the following research question: What influences current users of installed IT appli cations to learn about, use, and extend the full

range of features built into these applications? We

organize the paper as follows. First, we present a view of post-adoptive behavior within the larger context of IT adoption and use. We identify three

aspects of post-adoptive behavior that have not been fully addressed in prior research: prior use,

526 MIS Quarterly Vol. 29 No. 3/September 2005



habit, and a feature-centric view of technology. Next, we develop a conceptualization of post adoptive behavior characterized by ongoing, dyna mic interactions between two levels: one level

representing individual cognitions and the other

representing organizational drivers that stimulate these individual cognitions. Finally, we conclude with implications for future research and practice.

Post-Adoptive Behavior -_-_ _

The research stream examining the adoption and use of new IT has evolved into one of the richest and most mature research streams in the information systems field (Hu et al. 1999; Venka tesh et al. 2003). Much of this research has been framed around stage models that represent the decisions and activities associated with the

adoption and diffusion of IT applications (see Cooper and Zmud 1990; Kwon and Zmud 1987; Rogers 1995). While these stage models typically incorporate three high-level stages (i.e., pre adoption activities, the adoption decision, and

post-adoption activities) (Rogers 1995), the

majority of prior research has focused on the reflective cognitive processing (e.g., resulting in

cognitions regarding a technology's usefulness and ease of use) associated with individuals' pre adoption activities, the adoption decision, and initial use behaviors.

Where research attention does address post adoptive behavior, such behaviors have generally been modeled (explicitly or implicitly) as being influenced by the same set of factors that lead to

acceptance and initial use (Bhattacherjee 2001; Kettinger and Grover 1997; Thompson etal. 1994; Venkatesh et al. 2000; Venkatesh et al. 2003). Often, researchers conceptualize post-adoptive use of an IT application as increasing (e.g., more use, greater frequency of use, etc.) as individuals

gain experience in using the application. In reality, post-adoptive behaviors not only intensify, but may also diminish over time, as the various features of an IT application are resisted, treated with indifference, used in a limited fashion, routinized

within ongoing work activities, championed, or

extended (Hartwick and Barki 1994; Hiltz and Turoff 1981; Kay and Thomas 1995; Thompson et al. 1991, 1994). Understanding the factors and

dynamics that influence these behaviors is central to this work.

We agree that the cumulative tradition of research on technology acceptance and initial use should enrich our understanding of individual post adoptive behaviors. Indeed, because of the path dependent nature of IT adoption and use pro cesses in general (Gersick 1991; Rogers 1995)? and post-adoptive IT behaviors in particular?post adoptive behavior must be framed within this

larger context. However, distinctions have been observed between pre-adoption and post-adoption beliefs and behaviors (Agarwal and Karahanna 2000; Karahanna etal. 1999; Oliver 1980), and the IS literature has argued that political and learning

models might better explain post-adoptive behaviors while rational task-technology fit models

might better explain pre-adoption and adoption behaviors (Cooper and Zmud 1989, 1990; Kling and lacono 1984; Markus 1983; Robey et al.

2002). It appears, thus, that factors not ade

quately explored in prior research may influence

post-adoptive user behaviors. We focus on three aspects of post-adoptive behavior that have been under-researched: prior use, habit, and a feature

centric view of technology.

Prior Use

By its nature, the study of post-adoptive behavior situates an individual's use of an IT application within a stream of use experiences, some of which

have already occurred and some of which have yet to occur. However, as can be seen from Table 1, the majority of previous studies tend to either examine IT application use immediately after

adoption or otherwise do not account for a user's

history in using a focal, much less a similar, IT

application. In studies that have considered the direct impact of prior use on post-adoptive behaviors, as might be expected, researchers found prior use to be a significant antecedent of post-adoptive behavior.

MIS Quarterly Vol. 29 No. 3/September 2005 527



Table 1. Role of Prior Use in Illustrative IT Adoption and Use Research

Prior Use Not Considered Adams et al. 1992; Agarwal and Prasad 1997; Bhattacherjee 1998; Compeau and Higgins 1995b; Compeau et al. 1999; Davis et al. 1989; Fuerst and Cheney 1982; Fulk 1993; Gefen and Straub 1997; Ginzberg 1981; Goodhue and Thompson 1995; Guimaraes and Igbaria 1997; Hartwick and Barki 1994; Howard and Mendelow 1991; Igbaria and Guimaraes 1994; Igbaria and livari 1995; Igbaria et al. 1997; livari 1996; Jobber and Watts 1986; Karahanna et al. 1999; King and Rodriguez 1981; Leonard-Barton and Deschamps 1988; Lucas 1975; Lucas and Spitler 1999; Rai et al. 2002; Robey 1979; Schewe 1976; Straub et al. 1995; Swanson 1974; Szajna 1996; Taylor and Todd 1995a, 1995b; Teo et al. 1999; Thompson et al. 1991; Venkatesh and Davis 2000

Prior Use Considered Indirectly Confirmation ? Bhattacherjee 2001

Changes in user perceptions over time ? Burkarhdt 1994

Changes in feature use over time ? Hiltz and Turoff 1981

Changes in choices and use of commands over time ?

Kay and Thomas 1995

Changes in individual, task, and social variables over time ?Kraut et al. 1998

Changes in use over time ? Orlikowski 2000; Orlikowski et al. 1995; Tyre and Orlikowski 1994;

Webster 1998; Yates et al. 1999

Changes in predictors of intention over time ?

Taylor and Todd 1995a; Venkatesh 2000; Venkatesh and Morris 2000; Venkatesh et al. 2000; Venkatesh et al. 2003; Xia and Lee 2000

Changes in predictors of use over time ?

Taylor and Todd 1995a

Prior Use Considered Directly Computer experience

? Igbaria 1990, 1993; Igbaria et al. 1995; Igbaria et al. 1996; Thompson et al.

1994 Computer skill

? Kraut et al. 1999 Extent of prior e-mail use (in months)

? Kettinger and Gover 1997

Prior use ? Kraut et al. 1999; Venkatesh et al. 2000; Venkatesh et al. 2002

Habit

During the initial use of an IT feature, individuals most likely engage in active cognitive processing in determining post-adoptive intention or behavior; however, with any repetitive behavior, reflective

cognitive processing dissipates overtime, leading to non-reflective, routinized behavior (Bargh 1989, 1994; Logan 1989; Ouellette and Wood 1998).

Psychologists have been studying the role of habit in individual behavior for many years (see Bargh 1989; Eagly and Chaiken 1993; James 1890;

Ouellette and Wood 1998; Triandis 1971, 1980). Ouellette and Wood (T998) provide an extensive

review of previous research on the role of habit in

predicting future intentions and behavior and find substantial empirical evidence supportive of a direct relationship between past behavior and intentions regarding future behavior. Most impor tant, with stable contexts, past behavior has a direct effect on future behavior over and above the effect of intention (Ouellette and Wood 1998). Connor and Armitage (1998) also find empirical evidence of a direct relationship between past behavior and intentions, as well as between past behavior and future behavior, and propose that future research applying the theory of planned behavior (TPB) in the context of frequently per formed behaviors should include past behavior as




a predictor of both intention and of future behavior.2

Feature-Centric View of Technology

In the social construction of technology (e.g., DeSanctis and Poole 1994; Griffith 1999; Griffith and Northcraft 1994; Orlikowski 1992; Walsham 1993; Weick 1990), features of a technology are

interpreted (and possibly adapted) by individual users so as to constitute a technology-in-use

(DeSanctis and Poole 1994; Garud and Rappa 1994; Griffith 1999; Orlikowski and Gash 1994).

As such,

Organizations where implementers are able to determine which features users

mentally bring to the social construction

process should ultimately be able to

improve technology design, implemen tation, use, and redesign. Without such

knowledge, technology implementation (indeed, any organizational change) pro ceeds on limited information, and organi zations, thus, can less proactively

manage the implementation process.

(Griffith 1999, p. 473)

In the post-adoptive context, after an individual has

begun to actively learn about and use the application, awareness of the existence, nature,

2Ajzen (2002) and his colleagues (Ajzen and Fishbein 2000; Bamberg et al. 2003) discuss, discount, and dis miss previous work that suggests habit should be added to TPB.

The observed correlation between frequency of prior and later behavior is no more (or less) than an indication that the behavior in ques tion is stable over time....Thus, behavioral

stability may be attributable not to habituation but to the influence of cognitive and motiva tional factors that remain unchanged and are

present every time the behavior is observed.

(Ajzen 2002, p. 110)

We echo Ajzen's (2002) call for future research that establishes a measure of habit independent of prior behavior frequency.

and potential usefulness of the application's features arise and, over time, are fleshed out.

Therefore, a feature-centric view of technology is valuable because the set of IT application features

recognized and used by an individual likely changes over time, and it is the specific features in use at any point in time that influence and determine work outcomes (DeSanctis and Poole 1994; Goodhue 1995; Goodhue and Thompson 1995; Griffith 1999; Hiltz and Turoff 1981; Kay and Thomas 1995; Tyre and Orlikowski 1994). Here, we define a technology's features as the building blocks or components of the technology (Griffith 1999; Griffith and Northcraft 1994). Some of these features reflect the core of the technology, collec

tively representing its identity. Other features, however, are not defining components and their use may be optional (DeSanctis and Poole 1994; Griffith 1999).

Although prior research has examined the use of a variety of technologies (see Table 2), most researchers tend to study IT applications as a black box rather than as a collection of specific feature sets. We found only five studies that have

empirically examined IT use at a feature level of

analysis (Bhattacherjee 1998; Ginzberg 1981; Hiltz and Turoff 1981; Kay and Thomas 1995; Straub et al. 1995). In each study, the researchers found variation in the number of technology features used. In addition, two studies found that feature selection and use varied over time. Hiltz and Turoff (1981), in their study of an electronic infor mation exchange system, found that the number of features considered "extremely valuable" or "fairly useful" varied with a user's experience in using the application. Kay and Thomas (1995) found that users of a Unix-based text editor adopted an

increasing number of commands as their use

became more sophisticated and that later-adopted features tended to be more complex and powerful than early-adopted features.

However, a simple increase in the number of features used may not necessarily correlate with an increase in performance outcomes. Individuals can apply features in nonproductive ways or they may be overwhelmed by the presence of too many features, resulting in an inability to choose among feature sets or to apply the features effectively in




Table 2. Technologies Studied in Illustrative IT Adoption and Use Research

Business Process Applications Account management system

? Venkatesh and Davis 2000

Accounting system ? Venkatesh et al. 2003

Activity report system ? Swanson 1974

Banking system ?

Bhattacherjee 2001 Batch report system

? Schewe 1976 CASE tool ? livari 1996; Tyre and Orlikowski 1994; Xia and Lee 2000 Computer systems

? Goodhue and Thompson 1995; Hartwick and Barki 1994 Customer service management system

? Venkatesh et al. 2003 Data retrieval system ? Venkatesh and Morris 2000; Venkatesh et al. 2000; Venkatesh et al. 2002 Database of product standards ? Venkatesh et al. 2003 DSS ? Bhattacherjee 1998f; Fuerst and Cheney 1982; Igbaria and Guimaraes 1994; King and

Rodriguez 1981

Expert system ? Leonard-Barton and Deschamps 1988

Interactive report system ? Schewe 1976

Market system ? Lucas and Spitler 1999

Marketing information system ? Jobber and Watts 1986

Online help desk system ? Venkatesh 2000

Portfolio management system ?

Ginzberg 19811; Venkatesh and Davis 2000; Venkatesh et al. 2003 Property management system

? Venkatesh 2000 Sales information system

? Lucas 1975; Robey 1979

Scheduling system ? Venkatesh and Davis 2000

Student information system ? Rai et al. 2002

Communications and Collaboration Systems Computer conferencing system

? Orlikowski et al. 1995; Yates et al. 1999 Electronic information exchange system ? Hiltz and Turoff 1981 +

Electronic mail ? Adams et al. 1992; Fulk 1993; Gefen and Straub 1997; Kettinger and Grover 1997; Kraut et al. 1999; Szajna 1996

Lotus Notes ? Orlikowski 2000 Online meeting manager

? Venkatesh et al. 2003 Video telephone system

? Kraut et al. 1998; Webster 1998 Voice mail system

? Adams et al. 1992; Straub et al. 1995*

Computers Computing resource center

? Taylor and Todd 1995a, 1995b

Computers ?

Igbaria 1990, 1993; Igbaria et al. 1995; Igbaria and livari 1995; Igbaria et al. 1996 PC ? Compeau and Higgins 1995b; Compeau et al. 1999; Howard and Mendelow 1991; Igbaria et

al. 1997; Thompson et al. 1991; Thompson et al. 1994

Office Applications Graphics

? Adams et al. 1992 Office systems

? Lucas and Spitler 1999; Tyre and Orlikowski 1994 Spreadsheet

? Adams et al. 1992 Text editor ? Kay and Thomas 1995* Word processing

? Adams et al. 1992; Davis et al. 1989




Table 2. Technologies Studied in Illustrative IT Adoption and Use Research

(Continued)

System Software Client/Server system

? Guimaraes and Igbaria 1997 In-house LAN ? Burkhardt 1994

Mainframe systems ? Lucas and Spitler 1999

Windows operating system ? Karahanna et al. 1999; Venkatesh 2000; Venkatesh and Davis 2000

World Wide Web/Internet Internet ? Kraut et al. 1999; Teo et al. 1999

WWW ? Agarwal and Prasad 1997

Examined feature level use.

their work (Silver 1990; Trice and Treacy 1988). Positive performance benefits are most likely to occur when individuals recognize a match between the requirements of a work task and an appli cation's features and subsequently alter their post adoptive behaviors by selectively applying features to leverage the synergy offered by this fit between the task and the technology (Goodhue 1995; Goodhue and Thompson 1995; Todd and Ben basat 2000). By examining individual post-adop tive behavior both at a feature level of analysis and over time, researchers may increase our under

standing of why different users evolve very differing patterns of feature use and, as a result, extract differential value from an IT application.

In summary, despite more than 20 years of research examining IT adoption and use, we believe our collective understanding of post adoptive behavior is at an early stage of develop ment. Further, the three shortcomings just iden tified resonate through the existing literature and

impede the intellectual development of the post adoptive behavior construct. Because of these

shortcomings, prior research has, for the most

part, inhibited penetrating examinations of how individuals selectively adopt and apply, and then

exploit and extend the feature sets of IT appli cations introduced to enable organizational work

systems. Recognition of these three deficiencies has greatly influenced the lens applied here in

developing a fresh conceptualization of post adoptive behavior.

The Phenomenon of Post-Adoptive Behavior

We define post-adoptive behavior as the myriad feature adoption decisions, feature use behaviors, and feature extension behaviors made by an individual user after an IT application has been installed, made accessible to the user, and applied by the user in accomplishing his/her work activities.3 Figure 1 situates post-adoptive be havior, at the individual level of analysis, within a broader three-stage model of IT adoption and use.

Stage one reflects an organization's decision to

adopt a technology. This decision might be volun

tary or mandatory,4 with a mandatory decision

reflecting situations where regulators, competitors, and/or partners induce the organization to both

adopt a technology and force organization mem bers to apply the technology (Hartwick and Barki

1994). After the organization has adopted and installed the IT application, stage two occurs when intended, as well as unintended, users make indi

3Through the remainder of this article, our use of the term post-adoptive behavior denotes an individual's use of a single feature (or a select subset of features) available in an IT application.

4Some researchers have applied the terms discretionary or nondiscretionary use (see Howard and Mendelow

1991) to represent the same idea represented by our use of the terms voluntary or mandatory use.




Organizational Application Adoption Decision

(voluntary or mandatory)

\ Individual Application Adoption Decision

(voluntary or mandatory) _

J^^^ Post-Adoptive Behaviors ?s.

/ Individual Feature \ / Adoption Decision s. \

/ (voluntary or mandatory) \. , \

/ N, Individual Feature \ I Extension j \ ^ (voluntary) /

\ Individual Feature Use ^^^ / \ (voluntary or mandatory) /

Figure 1. Feature-Centric View of IT Adoption and Use

vidual decisions to adopt the technology (Leonard Barton and Deschamps 1988). This secondary adoption decision reflects an explicit acceptance by an individual that s/he will use the technology to

carry out assigned work tasks, and it may also be

voluntary or mandatory. A mandatory decision reflects the situation where an organization embeds the IT application within a work system, thus forcing the user to adopt the application to

complete his/her work assignments.

After an individual commits to using an IT applica tion during stage two, stage three occurs as the individual actively chooses to explore, adopt, use,

and possibly extend one or more of the appli cation's features. These tertiary feature-level

decisions may occur voluntarily or, particularly with

initial use experiences, as an organizational

mandate; typically, though, IT applications have many more features than those mandated for work

accomplishment. After some individuals have

gained experience in using a specific feature (or set of features), they may discover ways to apply the feature that go beyond the uses delineated by the application's designers or implementers, thereby engaging in feature extension behaviors

(Cooper and Zmud 1990; Goodhue and Thompson 1995; Kwon and Zmud 1987; Morrison etal. 2000; Saga and Zmud 1994). By definition, feature extensions are always voluntary.5 In our concep

5ln general, we believe that feature extensions are

always voluntary; however, we recognize that after one individual's voluntary feature extension, the organization




tualization, feature adoption, use, and extension all

fall within the realm of post-adoptive behaviors.

Although the IT adoption and use literature has

primarily focused on voluntary use contexts, the

conceptualization developed here applies to both

mandatory and voluntary contexts. Even when an

organization mandates the use of an IT appli cation, individuals retain considerable discretion

regarding their use of the features of the appli cation (Hartwick and Barki 1994).

A Two-level Model of Post Adoptive Behavior _ __-_

_ _ _

Organizations are "social systems of collective action that structure and regulate the actions and

cognitions of organizational participants through rules, resources, and social relations" (Oscasio

2000, p. 42). As such, the rich and dynamic inter

play that occurs within systems of collective action

(i.e., the organizational context) shapes and influences individuals' cognitive processing and

cognitive content (Bandura 1986, 1995; Weick 1979a, 1979b, 1995). This desirability to accom

modate both organizational and individual levels of

analysis is particularly important with complex IT enabled work systems, such as ERP systems, as noted by others.

Although people described individual ad

justments to ERP's technical complexity and changes in jobs, learning was not concentrated at the individual level. Rather, the structures and processes of

entire divisions needed to change, and occasional references to cultural change reflected the organizational scope of the

learning process. (Robey et al. 2002, p. 38)

may realize the value of the extension and subsequently mandate use of the extension for other users. In such situations, the organization has redefined the feature

(i.e., enacted a technology-in-use definition of the feature; see Orlikowski 2000); therefore, use of this feature by individuals other than the innovator would not be considered a feature extension.

Applying such notions, our conceptualization of

post-adoptive behavior involves two levels of

analysis (see Figure 2): one operating at the level of an individual's cognitions and behaviors

regarding feature adoption, use, and extension;

and the other operating at the level of the

organizational context within which these individual

cognitions are situated. Here, the individual cogni tions that determine post-adoptive intentions or behaviors are seen as becoming stabilized

(resulting in routinized behaviors) unless stimu lated by interventions emanating from the organi zation level (i.e., work system interventions), the individual level (i.e., user-initiated interventions), or both. By modeling individual cognition and organi zational action separately but interdependent^, the exercise of accommodating the multiple threads of behavior involved becomes conceptually less

complex.

The logic underlying the conceptual model

depicted in Figure 2 captures the dynamic inter actions between the two sub-models (i.e., individual cognition model and organizational action model). Three major theoretical lenses lend

support for this two-level model of post-adoptive behavior.

First, psychologists argue that cognitive scripts (derived from prior cognitions) drive habitualized individual behavior (Bargh 1989, 1994; Logan 1989; Ouellette and Wood 1998; Triandis 1971, 1980). Individuals may alter habitual behavior in situations in which the individual deliberates her/his actions (Louis and Sutton 1991). Such deliberations lead to changes in cognitions which in turn lead to novel behaviors (Ajzen 2002; Louis and Sutton 1991). Over time, the new behaviors become routinized and the individual returns to a state of habitual behavior (Bargh 1989, 1994). If individuals do not encounter situations which induce them to significantly alter their cognitions, the ingrained cognitive script will only reinforce these habitual behaviors (Bargh 1989; Logan 1989; Louis and Sutton 1991; Ouellette and Wood

1998).

Second, punctuated equilibrium theory proposes that deep structures (i.e., deep, less-reflective




j Organizational Action Model WlM


new structural entities?the constitution of organi zational and technology structures (Giddens 1979, 1984; Orlikowski 1992; Orlikowski and Robey 1991).

In summary, central to our conceptualization of

post-adoptive behavior is the notion that, over

time, post-adoptive behaviors become habitualized unless interventions occur to disrupt the formation of these deep, non-reflective mental scripts. When individuals attend to these interventions, the interventions produce periods of substantive

technology use, defined as a state in which an individual reflectively engages with one or more features of an IT application.6 In the absence of a substantive period of technology use, post adoptive behavior likely transitions to a state of habitual behavior in which an individual engages in a recurring pattern of using a selected subset of

technology features in his/her work (Bargh 1989, 1994; Conner and Armitage 1998; Edmondson et al. 2001; Limayem et al. 2001; Logan 1989; Ouellette and Wood 1998; Venkatesh et al. 2000; Venkatesh et al. 2002). Where these habitual behaviors lead to satisfactory outcomes and where the work context is stable, such behaviors might very well be viewed as appropriate. Often, however, these two conditions do not jointly hold

(Edmondson etal. 2001).

Organizational Action Model of Post-Adoptive Behavior

The organizational action model of post-adoptive behavior situates an individual's use of an IT

application's features within a complex set of

organizational actions that, when attended to, induce episodes of substantive technology use.

The work system represents the context within which organizational members perform their

assigned work (Gibson et al. 1994; Schippmann 1999). Thus, the work system includes organiza

6Through the remainder of this article, our use of the term substantive technology use denotes an individual's reflective consideration to use a single feature (or a select subset of features) available in an IT application.

tional members, the work tasks undertaken by members, work processes, technology features that enable or support work tasks and processes, and social structures that direct organizational members both in their work-related behaviors and in their interactions with each other. Social struc tures include both performance-related (e.g., performance evaluation and feedback, promotion, merit pay, bonuses, etc.) and personal-related (e.g., social recognition, reputation, social inter

action, etc.) incentives and disincentives that prior research suggests are likely to influence individual behaviors, including IT use (Ba et al. 2001; Bhattacherjee 1998; Eisenhardt 1989; Howard and Mendelow 1991; Stajkovic and Luthans 2001). An

organization's members are obviously core

elements of the work system, both in performing work-related roles and as users of work-enabling technologies. Most important, given that an

organization's members continuously interpret their work context (Brousseau 1983; Dunham etal. 1977; Gibson et al. 1994; Orlikowski 2000), their

work system sensemaking becomes an especially critical subcomponent of the work system.

Work system sensemaking occurs via observa

tions regarding work system outcome expectation gaps (as perceived by users, by peers of these users, by technology or work system experts, or by managers).7 Organizations and their members introduce new IT applications with the expectation that certain work system outcomes?again, characterized as being performance-related,

personal-related, or both?will occur (Zuboff 1988). In this specific context of post-adoptive behaviors,

we are concerned with work system outcomes that arise, either intentionally or unintentionally, as a result of applying IT application features in the conduct of organizational work, such as performing a task in a more effective and/or efficient manner, enhancing power (for an individual or group) through control of a critical information resource,

7The focal actor of the organizational action model could be one of any number of individuals employed by the

organization. Here we mention four specific organiza tional roles (user, peer, expert, or manager) that might be

played by these individuals. These organizational roles

correspond to intervention sources to be discussed later.




etc. The work system outcome expectation gap represents the difference between desired and

perceived work system outcomes?a difference that, if sufficiently large, triggers a need to resolve the dissonance caused by the expectation outcome conflict. To resolve expectation gaps,

organizational members engender interventions that have the potential to induce work system changes, which in turn directly influence work

system outcomes.

All such interventions have a source and a target. Intervention sources include the individual user, the user's peers, work and technology experts, and managers.8 The interventions that induce

periods of substantive technology use target reshaping existing cognitions regarding IT appli cation features, cognitions regarding work

systems, or cognitions regarding both. In essence, such interventions induce, or perhaps mandate,

the individual to apply unused features, to apply already-used features at higher levels of use, to discover new uses of existing features, or to

identify the need to incorporate new features into the IT application. In other words, these inter ventions pick up the pace in the mutual adaptation of organizational structures, task structures, and

technology structures that accompanies organi zational life and that, invariably, produce both

intended and unintended consequences (DeSanctis and Poole 1994; Leonard-Barton 1995; Majchrzak et al. 2000; Orlikowski 1992; Tyre and Orlikowski 1994).

Although many types of work system interventions

might be initiated, the interventions of primary interest here are those that represent either

purposeful or emergent actions directed at

disrupting established patterns of technology feature use (or nonuse) (Orlikowski et al. 1995; Yates et al. 1999). For the sake of simplicity, we do not attempt to develop a complete taxonomy of

possible interventions or to model the complex

8Although technology itself might be considered an intervention source (e.g., built-in wizards, online help, etc.), it is our belief that the initial impetus of such an intervention lies with these four identified intervention sources.

relationships that might exist between and among interventions and their outcomes. Table 3 references articles that provide further descriptions of each intervention source and provides examples of interventions undertaken by each source.

Individual Cognition Model of Post-Adoptive Behavior

The individual cognition model contains two

distinctly different feedback loops directly asso ciated with post-adoptive behavior. One loop (characterized by reflective thought and repre sented by the solid line relationships in Figure 2) contains the series of relationships from individual

cognitions to technology sensemaking and back. The logic of this feedback loop is founded in reflective consideration whereby an individual com mences reflection with a preexisting set of

cognitions and then mindfully considers and pro cesses surrounding informational cues regarding an IT application's features (Langer 1989; Langer et al. 1978; Langer and Piper 1987; Louis and Sutton 1991). This reflective cognitive processing may modify the individual's (already existent) post adoptive intentions, which then direct future post adoptive behaviors. Subsequent to these be

haviors, the individual again engages in reflection

(i.e., technology sensemaking) regarding this most recent post-adoptive experience. Then, based on

the strength of confirmation or disconfirmation associated with this technology sensemaking, the individual either adjusts his/her cognitions about

technology features accordingly (weak confir

mation) or initiates a work system intervention and/or a personal technology-learning intervention

(strong confirmation).

The second feedback loop in the individual

cognition model (characterized by non-reflective

thought and represented by the dashed line

relationships in Figure 2) consists of the direct

relationships between use history and post adoptive behavior. In this loop, reflective consi deration does not drive post-adoptive behavior.

Instead, habitual behavior, captured in use history, determines post-adoptive behavior. In this routin



Jasperson et a I./Post-Adoptive Behaviors & IT-Enabled Work Systems

Table 3. Description of Intervention Sources and Illustrative Intervention Actions

Intervention Source Description/Citations Intervention Actions

Users Community of users associated with an Self-orchestrated learning such as IT application formal/informal training, external

documentation, observations of others,

Bagchi et al. 2003; Hartiwck and Barki experimentation with IT features, 1994; Igbaria and Guimaraes 1994; King experimentation with work tasks and Rodriguez 1981; Manning 1996; Direct actions taken toward modifying McKersie and Walton 1991; Morrison et or enhancing the IT application and/or

al. 2000 work tasks/processes

Peers Coworkers from the same or different Designing, leading, or directing formal work units and workers in other and informal training sessions organizations Direct actions taken toward modifying

or enhancing the IT application and/or Contractor et al. 1996; Fulk 1993; Fulk et work tasks/processes al. 1990; Kraut et al 1998; Lucas and Joint actions taken with users toward

Spitler 1999; Markus 1990 modifying or enhancing the IT applica tion and/or work tasks/processes

Experts Internal and external professionals (i.e., Designing, leading, or directing formal (Work and consultants, contractors, or technologists and informal training sessions

Technology) in partner firms) housed in central or Direct actions taken toward modifying distributed work units or enhancing the IT application and/or

work tasks/processes Boynton and Zmud 1987; Earl 1993; Joint actions taken with users toward Markus and Bj0rn-Andersen 1987; modifying or enhancing the IT applica Nelson and Cheney 1987; Venkatesh tion and/or work tasks/processes and Speier 1999; Venkatesh et al. 2002; Xia and Lee 2000; Yates et al. 1999

Managers Direct supervisors, middle managers, Indirect Actions and senior executives Sponsoring or championing

Providing resources Ba et al. 2001; Bhattacherjee 1998; Issuing directives and/or mandates Guimaraes and Igbaria 1997; Howard and Mendelow 1991; Igbaria 1990, 1993; Direct Actions

Igbaria and Guimaraes 1994; Igbaria and IT application feature use livari 1995; Igbaria et al. 1996; Leonard- Work task/process involvement barton 1988; Orlikowski 2000; Orlikowski Incentive structures et al. 1995; Purvis et al. 2001; Stajkovic Inputs/influence into design of user, and Luthans 2001; Yates et al. 1999 peer, or technologist interventions

Directing modification or enhancement of IT application, incentive structures, or work tasks/processes




ized mode of IT application use, individuals use

only those IT application features they have

previously used (Bargh 1989, 1994; Conner and

Armitage 1998; Logan 1989; Ouellette and Wood

1998). In the absence of a period of substantive

technology use, this non-reflective loop becomes the primary driver of an individual's post-adoptive behavior.

The individual cognition model in Figure 2 applies both to explaining a single instance of post adoptive behavior (e.g., cognitions, intentions, behavior, technology sensemaking, and use

history relative to a specific IT application feature) and to understanding the evolution over time of individual post-adoptive behavior (e.g., the rich

portfolio of cognitions, intentions, behaviors, technology sensemaking, and use history relative to an IT application). Here, it is most critical to

recognize that each individual exposes a unique pattern of post-adoptive behavior represented by the collection of IT application features that, over

time, the individual has adopted, used, dropped, and extended.

The logic of the reflective feedback loop depicted in Figure 2 draws liberally from prior research on IT adoption and use, in particular from the unified theory of acceptance and use of technology

(UTAUT) (Venkatesh et al. 2003).9 The underlying premise of UTAUT?here, applied to post-adoptive behavior?suggests that, given a particular time and context, an individual's intentions to engage in

post-adoptive behavior are the best predictors of that individual's actual post-adoptive behaviors

(Davis et al. 1989; Taylor and Todd 1995b; Venkatesh et al. 2000; Venkatesh et al. 2003). Individual cognitions, which comprise the core of

UTAUT, can be conceptualized as encompassing two domains: cognitive process and cognitive content (Blumenthal 1977). Cognitive processing involves both the mental processes used in

9The collective results of IT adoption and use research, which has applied eight different theories to explain both intention to use and actual use behavior, was reviewed and incorporated into the development of UTAUT. We refer the reader Venkatesh et al. (2003) for a more

comprehensive discussion of these other theories.

perceiving, learning, remembering, thinking, and

understanding, and the mental activity of applying those processes (Ashcraft 1998). Cognitive con tent consists of the collection of mental structures formed as a result of cognitive processing; typically, researchers refer to instances of cogni tive content as cognitions.

But what exactly is the nature of these cognitions with regard to post-adoptive behavior? While a

large number of cognitions may play a role in

influencing individuals' adoption and use behaviors

(see Table 4), Venkatesh et al. (2003) have

synthesized and integrated these into a single set of cognitions: performance expectancy, effort

expectancy, social influence, and facilitating conditions.10 Drawing from UTAUT, we suggest these four cognitions as being most likely to influence post-adoptive intentions.

UTAUT also proposes that individual demographic characteristics moderate the relationship between

cognition and intention (Venkatesh et al. 2003). Previous research identifies not only demographic characteristics but also cognitive styles and

personality characteristics as individual differences

likely to impact post-adoptive behavior (Zmud 1979). Table 5 contains an overview of various individual differences considered by illustrative IT

adoption and use research that has examined use after adoption. Again, following the logic of UTAUT, the individual cognition model of post adoptive behavior includes such individual differences as moderators of the relationship between an individual's IT application feature

cognitions and the individual's post-adoptive intentions.11

I Venkatesh et al. (2003) define facilitating conditions as

cognitions regarding the technical and organizational infrastructure that supports system use.

II UTAUT proposes a direct relationship (moderated by age and experience) between the facilitating conditions

cognition and use. Because we have grouped all four

cognitions proposed by Venkatesh et al. (2003) into a

single construct, we have not modeled this relationship in Figure 2.




Table 4. Cognitions Studied in Illustrative IT Adoption and Use Research

Cognition Example Study

Compatability Agarwal and Prasad 1997; livari 1996; Karahanna et al. 1999; Taylor and Todd 1995b; Xia and Lee 2000

Complexity Igbaria et al. 1996; livari 1996; Thompson et al. 1991, 1994

Computer anxiety* Compeau and Higgins 1995b; Compeau et al. 1999; Howard and Mendelow 1991; Igbaria 1990, 1993; Igbaria and livari 1995; Venkatesh 2000

Ease of use Adams et al. 1992; Agarwal and Prasad 1997; Davis et al. 1989; Gefen and Straub 1997; Igbaria et al. 1995; Igbaria and livari 1995; Igbaria et al. 1997; Karahanna et al. 1999; Kettinger and Grover 1997; Lucas and

Spitler 1999; Rai et al. 2002; Straub et al. 1995; Szajna 1996; Taylor and Todd 1995a, 1995b; Teo et al. 1999; Venkatesh 2000; Venkatesh and Davis 2000; Venkatesh and Morris 2000; Venkatesh et al. 2002; Xia and Lee 2000

Effort expectancy Venkatesh et al. 2003

Facilitating conditions Taylor and Todd 1995b; Thompson et al. 1991, 1994; Venkatesh et al. 2003

Image Agarwal and Prasad 1997; Karahanna et al. 1999; Schewe 1976; Venkatesh and Davis 2000

Job-fit Thompson et al. 1991, 1994

Job relevance Venkatesh and Davis 2000

Outcome expectations Compeau and Higgins 1995b; Compeau et al. 1999; Lucas 1975; Thompson et al. 1991, 1994

Output quality Venkatesh and Davis 2000

Perceived behavioral Taylor and Todd 1995a, 1995b; Venkatesh et al. 2000 control

Performance expectancy Venkatesh et al. 2003

Relative advantage Agarwal and Prasad 1997; livari 1996; Xia and Lee 2000

Result demonstrability Agarwal and Prasad 1997; Karahanna et al. 1999; Venkatesh and Davis 2000; Xia and Lee 2000

Richness Fulk 1993; Kettinger and Grover 1997

Self-efficacy* Burkhardt 1994; Compeau and Higgins 1995b; Compeau et al. 1999; Igbaria and livari 1995; Taylor and Todd 1995b; Venkatesh 2000;

Webster 1998




Table 4. Cognitions Studied in Illustrative IT Adoption and Use Research (Continued)

Social influence (peer Compeau and Higgins 1995b; Fulk 1993; Guimaraes and Igbaria 1997; influence, management Howard and Mendelow 1991; Igbaria 1990, 1993; Igbaria et al. 1995; support, social pressure, Igbaria et al. 1996; Igbaria et al. 1997; Karahanna et al. 1999; Kraut et al.

etc.) 1999; Leonard-Barton and Deschamps 1988; Lucas 1975; Schewe 1976; Taylor and Todd 1995b; Thompson et al. 1991, 1994; Venkatesh et al. 2003; Webster 1998

Subjective norm Davis et al. 1989; Hartwick and Barki 1994; Lucas and Spitler 1999; Taylor and Todd 1995a, 1995b; Venkatesh and Davis 2000; Venkatesh and Morris 2000; Venkatesh et al. 2000

Trialability Agarwal and Prasad 1997; Karahanna et al. 1999; Xia and Lee 2000

Usefulness Adams et al. 1992; Bhattacherjee 2001; Davis et al. 1989; Fulk 1993; Gefen and Straub 1997; Hiltz and Turoff 1981; Howard and Mendelow 1991; Igbaria 1993; Igbaria et al. 1995; Igbaria and livari 1995; Igbaria et al. 1996; Igbaria et al. 1997; Karahanna et al. 1999; Kettinger and Grover 1997; Lucas 1975; Lucas and Spitler 1999; Rai et al. 2002; Robey 1979; Schewe 1976; Straub et al. 1995; Szajna 1996; Taylor and Todd 1995a, 1995b; Teo et al. 1999; Venkatesh 2000; Venkatesh and Davis 2000;

Venkatesh and Morris 2000; Venkatesh et al. 2002

Visibility_Agarwal and Prasad 1997; Karahanna et al. 1999; Xia and Lee 2000

Although some suggest these constructs represent individual differences, we include them as cognitions because most researchers measure them as individual perceptions.

In addition to the focus on an IT application's fea tures, our conceptualization involves three exten

sions to UTAUT: the influences of technology sensemaking, of use history, and of an individual's attention to introduced interventions. We discuss each of these in the remainder of this section.

Technology Sensemaking

Technology sensemaking occurs as an evaluative

cognitive process that transpires when an individual contrasts the outcomes of a post adoptive behavior episode with those expected from pre-episode cognitions (Weick 1979a, 1990, 1995). We postulate that during a substantive

period of technology use, an individual engaged in

reflective, rather than habitual, use of an IT

application feature implicitly triggers technology sensemaking which confirms (disconfirms) the

cognitions that existed prior to the active use

experience (Bhattacherjee 2001; Bhattacherjee and Premkumar 2004; Oliver 1980; Weick 1990, 1995). Weak confirmation (disconfirmation) out comes will likely lead directly to modifications in

prior-held cognitions. Strong confirmation (discon firmation) outcomes, on the other hand, will likely lead to user-initiated learning interventions and/or user-initiated work system interventions.

User-initiated technology learning interventions affect post-adoptive behaviors not only through their influence on technology cognitions but also

through their influence on an individual's interpre tations of other work system elements (Orlikowski et al. 1995). Post-adoptive intentions derive from an individual's understanding both of how to use an IT application's features and how these fea tures complement other work system elements

(Swanson 1974). Thus, self-orchestrated learning




Table 5. Individual Difference Categories Studied in Illustrative IT Adoption and Use

Individual Difference Example Study

Age Burkhardt 1994; Fuerst and Cheney 1982; Fulk 1993; Howard and Mendelow 1991; Igbaria 1990, 1993; Kettinger and Grover 1997; Kraut et al. 1999; Kraut et al. 1998; Lucas 1975; Schewe 1976; Teo et al. 1999; Venkatesh et al. 2003

Cognitive Style Fuerst and Cheney 1982; Lucas 1975

Education Burkhardt 1994; Fuerst and Cheney 1982; Fulk 1993; Howard and Mendelow 1991; Igbaria 1993; Kettinger and Grover 1997; Lucas 1975; Schewe 1976; Teo et al. 1999; Venkatesh et al. 2003

Gender Burkhardt 1994; Fuerst and Cheney 1982; Fulk 1993; Gefen and Straub 1997; Howard and Mendelow 1991; Igbaria 1990, 1993; Kraut et al. 1999; Kraut et al. 1998; Teo et al. 1999; Venkatesh and Morris 2000; Venkatesh et al. 2000; Venkatesh et al. 2003

Organizational level Howard and Mendelow 1991; Igbaria 1990

Personality Jobber and Watts 1986

Technology experience Fulk 1993; Howard and Mendelow 1991; Igbaria 1990, 1993; Igbaria et al. 1995; Igbaria and livari 1995; Igbaria et al. 1996; Kettinger and Grover 1997; Kraut et al. 1999; Schewe 1976; Taylor and Todd 1995a;

Venkatesh and Davis 2000; Venkatesh and Morris 2000

Training Howard and Mendelow 1991; Igbaria 1990, 1993; Igbaria et al. 1995; Igbaria et al. 1996; Igbaria et al. 1997; Leonard-Barton and Deschamps and Deschamps 1988; Venkatesh et al. 2002; Webster 1998; Xia and Lee 2000

Voluntariness of use* Agarwal and Prasad 1997; livari 1996; Karahanna et al. 1999; Venkatesh et al. 2003

Work experience Burkhardt 1994; Fuerst and Cheney 1982; Howard and Mendelow 1991; Schewe 1976

Although many researchers study voluntariness of use as a cognition, UTAUT proposes voluntariness of use as an individual difference which modifies the relationship between cognitions and intentions (Venkatesh et al. 2003). We include voluntariness of use as an individual difference to be consistent with UTAUT.




about the IT application's features, the potential use of those features, and the work system within which the IT application is situated constitute

crucially important means by which individuals

modify their use cognitions. Examples of such

learning interventions undertaken by an individual user include taking advantage of formal or informal

training opportunities (Fuerst and Cheney 1982), accessing external documentation (Brancheau and Wetherbe 1990), observing others (Bandura 1986; Gioia and Manz 1985), and experimenting with IT

application features (DeSanctis and Poole 1994) and/or new approaches for handling work assign ments (McKersie and Walton 1991).

Use History

Existing evidence suggests that as individuals gain experience with what was initially a novel behavior, they tend to engage less frequently in reflective consideration of this behavior and rely instead on

previous patterns of behavior to direct future behaviors (Bargh 1989; Conner and Armitage 1998; Langer 1989; Lassila and Brancheau 1999; Louis and Sutton 1991; Majchrzak et al. 2000; Ouellette and Wood 1998; Triandis 1980; Tyre and Orlikowski 1994; Venkatesh et al. 2000; Venkatesh et al. 2003; Venkatesh et al. 2002). It thus seems reasonable that, as an individual routinely applies an IT application feature within her/his work con

text, the ever-accumulating prior-use experiences

imprint these use behaviors within the cognitive (and organizational) scripts that direct the indi vidual (or the individual's work unit) in task

accomplishment (Bargh 1989; Logan 1989; Louis and Sutton 1991; March and Simon 1958; Triandis

1971; Triandis 1980; Tyre and Orlikowski 1994). Accordingly, much post-adoptive behavior, over

time, is likely to reflect a habitualization of action where the decision to use the IT application feature occurs more or less automatically via a subconscious response to a work situation (Bargh 1989, 1994; Eagly and Chaiken 1993; Limayem and Hirt 2003; Limayem et al. 2001; Logan 1989; Ouellette and Wood 1998; Thompson et al. 1994; Venkatesh et al. 2000). In some mandatory use

environments, such routinized behaviors likely develop through the mindless following of policy, procedures, methodologies, or other codified

organizational scripts (Langer et al. 1978). In

voluntary and other mandatory use environments, however, such routinized behaviors are more likely to reflect the scripting of once-active personal decision processes (Bargh 1989; Bargh 1994;

Langer etal. 1978; Langer and Piper 1987; Logan 1989; Louis and Sutton 1991; Ouellette and Wood

1998).

A key facet of post-adoptive behavior is the strong influence of an individual's use history on post adoptive intentions and post-adoptive behaviors

(encompassing both reflective thought and the

deep mental scripting that results in and from habitual use). An individual's past use behavior

generally produces a tendency (e.g., post-adoptive intention) for the individual to act in a particular

manner (i.e., applying a common set of IT appli cation features) given a particular context (i.e., a

specific work task) (Eagly and Chaiken 1993; Ouellette and Wood 1998; Triandis 1971, 1980). During the initial use of an IT feature, individuals most likely engage in active cognitive processing in determining post-adoptive intention or behavior; however, with repetition, the reflective cognitive processing dissipates, leading to automatic and routinized behavior (i.e., habit) (Bargh 1989,1994;

Logan 1989; Ouellette and Wood 1998). We define use history to include both an individual's

past use behavior (i.e., a collective, systematic

account of an individual's prior use of an IT appli cation and its features) and an individual's use habits (i.e., learned situational-behavior sequences

with respect to an IT application and its features that have become automatic [Triandis 1980]). Thus, during substantive technology use periods, use history as past behavior plays a role in pre

dicting an individual's post-adoptive intentions to

engage in post-adoptive behavior (i.e., solid-line

relationships in Figure 2). However, during periods of non-reflective, post-adoptive behavior, use history as habit becomes the dominant pre dictor of an individual's post-adoptive behavior

(i.e., dashed-line relationships in Figure 2).




Attention to Introduced Interventions

Ouellette and Wood (1998, p. 66) indicate that

when behavior is a function of conscious decision making and deliberation, inten tions directly predict behavior perfor mance, and the effects of past behavior are likely to be mediated through conscious intents.

Louis and Sutton (1991) suggest that conscious

processing occurs as a result of three types of stimuli: when a situation is novel (i.e., the initial use of a technology feature), when an individual senses a discrepancy between reality and expec tation, and when individuals are induced to deliberate regarding their behavior (i.e., an inter vention is attended to). Bandura (1986) proposes attention as the first stage in his observational

learning model.

As shown in Figure 2, the extent to which an individual attends to an intervention will moderate the relationship between the intervention and individual cognitions. For an intervention to induce the individual to engage in conscious cognitive processing, the intervention must be sensed, interpreted, and considered (Bandura 1986; Yi and Davis 2003). One researcher explains why people often disregard signals directed toward them:

People find noninteresting those propo sitions that affirm their assumption ground (that's obvious), that do not speak to their assumption ground (that's irrele

vant), or deny their assumption ground (that's absurd) (Weick 1979b, p. 51).

But, what is it about an intervention that would increase the likelihood a targeted individual would attend to the intervention? Weick (1995) suggests individuals are more likely to attend to signals that are prominent and promise to disrupt the work

system context. Two intervention attributes are

suggested as particularly relevant: the salience of the work system elements likely affected by an intervention (Beach 1997; March 1994) and the

power of the intervention source (Jasperson et al.

2002). Here, power refers to the intervention source's ability to influence others to think or to act

(Emerson 1962; Frost 1987; Hall 1999; Jasperson et al. 2002).

Implications for Research: Theory -_-_-H----H-B----_H-B--H-l

We urge researchers to develop and apply richer and more complex research models in examining the variation within and across individuals' post adoptive behavior. Such research models should tap into the dynamic interplay between the organi zational action and individual cognition levels and, therefore, must collect data at multiple points-in time and account (control) for changes in the IT

application via its features, individual cognitions regarding the IT application via its features, and the work system(s) being enabled. In particular, we advocate future programs of research that

systematically (1) explore the outcomes of indi vidual post-adoptive behaviors and the resulting feedback that impacts organizational action and individual cognitions and (2) focus on work system interventions and the manner in which those interventions prompt individuals to engage in substantive technology use. We caution against future research efforts that merely replicate existing IT adoption and use research at a feature level of analysis or in a post-adoptive context; and

we implore researchers examining post-adoptive behaviors to discontinue the practice of studying post-adoption intentions as the final outcome variable?such research would have limited value in furthering our collective understanding of the dynamics of post-adoptive behavior. Below we

suggest specific programs of research designed to

investigate the dynamic nature of our two-level model.

Post-Adoptive Behaviors and Work

System Outcomes

We know little about the patterns of feature adop tion, use, and extension that occur throughout the




post-adoptive stage of diffusion or the cumulative

impacts of those patterns on work system performance over time. We urge scholars to further investigate this domain, as theory develop ment in this area is likely to illuminate the

relationships between diffusion microprocesses that occur at the individual level and macro behavioral outcomes at the organizational level.

Example research questions include

Are there consistent patterns of feature

adoption, use, and extension, and how do

such patterns evolve over time?

Are specific patterns within particular contexts

predictive of positive (negative) work system outcomes?

What aspects of feature adoption, use, and extension differentially explain impacts on various elements of a work system?

Technology Sensemaking

Given the limited amount of research examining post-adoptive behaviors at a feature level of

analysis, we have insufficient understanding of the

technology sensemaking processes that transpire

during the post-adoptive context. A deeper under

standing of these dynamic processes will allow us to better predict and explain what influences current users of installed IT applications to learn about, use more fully, and extend the feature sets

made available through these applications. Relevant research questions include

What types of post-adoptive behaviors trigger technology sensemaking?

What aspects of technology sensemaking most distinguish between and influence weak and strong confirmation (disconfirmation)?

What is the nature of the tipping point leading to strong confirmation (disconfirmation)?

What situational factors induce individuals, as a result of strong confirmation (discon

firmation), to engage in self-learning inter vention as opposed to an intervention directed at other work system elements?

Use History

Previous IT adoption and use researchers have found past use behavior to be a significant predictor of future use behavior (Igbaria 1990, 1993; Igbaria et al. 1995; Igbaria et al. 1996; Kettinger and Grover 1997; Limayem and Hirt 2003; Thompson et al. 1994; Venkatesh et al. 2000; Venkatesh et al. 2002). However, for the most part, these researchers have examined prior use quite simplistically in terms of the frequency, or level, of use of the whole technology rather than

capturing users' patterns of use regarding the

technology's features (or feature sets). We en

courage programs of research that move beyond such simplistic views of use history in order to

(1) expose the sufficiently rich depictions of use

history required to surface, study, model, and

understand the path-dependent episodes of use

leading to routinized or habitual use of an IT appli cation and, then, to (2) systematically examine the roles of both aspects of use history (past behavior and habit) in influencing post-adoptive behavior.

Suggested research questions include

What are typical patterns of feature adoption, use, and extension, and which of these

patterns lead to routinized or to habitual use?

How, when, and why do individuals engage in reflective versus non-reflective use of IT

application features?

What are the necessary conditions required to

trigger periods of substantive technology use that disrupt states of routinized or habitual feature use?

Attention to Interventions

Users must actively attend to an intervention if it is to be effectual (Beach 1997; March 1994; Weick




1979b, 1995; Yi and Davis 2003). Thus, we advocate that scholars studying post-adoption behaviors undertake efforts to increase our

understanding of the situational, intervention, and individual attributes associated with an intervention

being attended to by targeted users. Previously, we identified two such attributes: the salience of the work system element(s) targeted by an intervention and the power of the intervention source. Related research questions include

What are the key factors that influence individuals to attend to work system inter ventions, and do these factors vary in different situational contexts or with different user

groups?

What theoretical models adequately portray how these factors come together in triggering an individual's attention to an intervention?

Organizational Interventions and Substantive Technology Use

While prior literature has discussed work system interventions (Orlikowski et al. 1995; Yates et al.

1999), this important domain of IT implementation research merits more systematic study. Most

importantly, it is paramount for researchers studying post-adoptive behaviors to apply research

designs that enable them to discover, identify, and account for salient interventions directed at all of the work system elements associated with the focal IT application. Research studies that fail to account for such interventions will likely observe considerable unexplained variance. In particular, we see the following issues associated with

organizational interventions and substantive

technology use as crucial to understanding post adoptive behavior.

Training Interventions

The critical role served by training in successful IS implementation is well understood (Duplaga and

Astani 2003; Robey et al. 2002). While the findings by scholars studying IT adoption and use

consistently support the importance of training (e.g., Compeau and Higgins 1995a; Nelson and

Cheney 1987; Venkatesh and Speier 1999), such research generally has focused on training associated with initial adoption and use behaviors

(e.g., Venkatesh 1999; Venkatesh and Davis

1996). Prior IS studies indicate that the influence of ease of use on intentions (and indirectly adoption and use) diminishes over time (Davis et al. 1989; Venkatesh 2000; Venkatesh and Davis 1996, Davis 2000).

Consequently, little understanding exists of when and how an organization should orchestrate

training interventions within the post-adoptive context?regardless of whether such interventions are formal or informal, scheduled or just-in-time, or human- or technology-enabled. It seems obvious

that, as individuals' understandings of an IT

application (with its associated features) and a work context evolve over time, training strategies (i.e., learning objectives and modes of delivery) need to evolve as well. Therefore, we strongly

encourage scholars studying the post-adoptive context to develop rich conceptualizations of post adoptive training strategies, within which training tactics account for the dynamic behaviors reflected in our reconceptualization of post-adoptive behavior. Example research questions include

What are or should be the key components of

post-adoptive training strategy-making and

budgeting, and who is or should be involved in the development of those key components?

What types of processes are involved in best

practice implementations of post-adoptive training interventions, and when and how

during the post-adoptive stage of the tech

nology life cycle should each of these process types be applied?

What types of learning experiences and post adoptive behavior outcomes should be assessed and incorporated into training activities at later time periods?



LeeHighlight

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Portfolios of Interventions

While it is both possible and desirable to design laboratory or field experiments which impose a

single intervention on a subject group or a com

munity of users, it is highly unlikely that such a controlled action would occur in practice as users are invariably subjected to multiple such inter ventions at any point in time. For example, a

single intervention source (e.g., a manager) might initiate multiple interventions targeted at a specific user group regarding a particular IT application feature; meanwhile, individuals within this user

group are also likely to be subject to multiple interventions from this same (and/or other) source(s) regarding this same (and/or other) IT

application(s) and corresponding IT application feature(s). Researchers who investigate the role of interventions in post-adoptive behavior contexts must account for the effects of interacting inter ventions. Pertinent research questions might include

Do certain interventions complement or inhibit others?

Do path-dependencies exist across portfolios of interventions over time?

For users involved with multiple work sys tems, what are the consequences?both

positive and negative?of these users' expo sure to concurrent interventions directed at more than one work system?

Substantive Technology Use Periods

For too long, scholars working in the domain of IT

implementation and use have ignored intensive studies of post-adoption life cycles. What are and what should be the ebb and flow of resources invested in an IT implementation effort after the

application is installed? Clearly much of the benefit derived from installed IT applications comes during periods of equilibrium rather than

during periods of dramatic change. However, much remains to be learned about managing a

technology's post-adoption life cycle. In particular,

we believe that future researchers should direct their interest toward examinations of appropriate patterns of substantive technology use. Some example research questions include

When should periods of substantive tech

nology use proliferate (inducing active

learning by users) and when should they diminish (enabling these users to leverage this learning)?

Is it advisable to constrain (to specific users, to specific technology features, etc.) periods of substantive technology use?

What are the dysfunctionalities of substantive

periods of technology use? Here, we have

ignored such dysfunctionalities, such as the

potential for interventions to lead to pro ductivity lost, to cognitive overload, or to

feelings of mistrust.

How likely is it that, and under what conditions

might, an intervention trigger a substantive

period of technology use that never stabilizes, ultimately ending in work system failures?

Once individuals are engaged in substantive

technology use episodes, what contextual conditions should be in place to increase the likelihood that gains in individual learning transfer to others?

Implications for Research: Methodology 1

As discussed throughout this paper, previous researchers have overlooked a significant source of variation in individual post-adoptive behaviors

by ignoring the distinct features of an IT appli cation. However, researchers who design studies that collect data at the feature level of analysis face a number of challenges. Here, we focus on

four of these challenges: core versus ancillary features, designers' versus users' views, discreet

versus bundles of features, and existing versus new instrumentation.




Core Versus Ancillary Features

A crucial first step in working at the feature level of an IT application is appropriately scoping the research project by identifying the specific features to include in the research design. IT applications associated with IT-enabled work systems are

comprised of very large feature sets consisting of both core and ancillary features. Accordingly, the researcher must decide the set of features that is to be the focus of a research design for at least two reasons. First, ancillary features, which are

optional, may be unused or unknown to a majority of an IT application's users. As a result, it may prove ineffectual or dysfunctional to incorporate such features into a research design, depending upon the goals of the study. Second, empirical studies at a feature level have the potential to utilize data collection methods and data sets that are too large and too rich for subjects/respondents (from or about whom data is collected) and the researcher (in terms of the volume of data to be

collected, analyzed, and interpreted), respectively.

A number of viable options exists in selecting those features to be the focus of a research

design, including focus on (1) the core features of a technology since those features serve to charac terize the technology as a whole (Griffith 1999), (2) those features that most clearly differentiate the

specific technology from other technologies (e.g., communication and social structure features in the

case of GSS), (3) those features most likely to be

applied in a consistent fashion over the entire post adoptive life cycle, and (4) those features most

likely to stabilize or destabilize use patterns (Griffith 1999). What is most important is that the researcher carefully considers these various

options and provides clear justification for the

approach taken.

Designers' Versus Users' Views

Also important when working at the feature level of

analysis is determining the point of view appro priate for the goals of the research. Two alter

natives are possible: the designer's view (i.e., a set of predefined features believed relevant for all users of a specific IT application) or the users' view (i.e., a social construction of the technology in-use as defined collectively by a specific user

community). Reasons might exist for selecting either view. For example, if the intent is to study a

single IT application across multiple work contexts, it would be desirable to employ the designers' view so that a consistent view is maintained across these work contexts. On the other hand, if the intent is to study over time the evolution of user

cognitions within a single user community, it would be desirable to employ these users' views (or, more likely, the views of subsets of users within the community) of the IT application's features to increase the likelihood that the nuances reflected in changes in cognitions might better be surfaced and interpreted. Regardless of the selected view, the key is that the researcher has thoughtfully examined and justified his/her selection.

Discrete Features Versus Bundles of Features

A related decision is whether to focus on an IT

application's elemental features or on meaningful bundles of these elemental features. For example, several distinct features might collectively come

together in forming a feature bundle (e.g., discreet features such as "Generate Balance Sheet,"

"Generate Income Statement," and "Generate

Statement of Cash Flows" may also exist as a feature bundle called "Generate Financial

Statements") whose functionality is generally understood by designers, by users, or both.

Again, as above, either approach is viable given a

study's research goals as well as the nature of the IT-enabled work system(s) under investigation.

Existing Versus New Instrumentation

A particularly thorny challenge when moving to the feature level of analysis is deciding whether or not

existing instrumentation from the IT adoption and




use literature is applicable. Can researchers use

slight modifications in wording to existing scales to

adequately capture the nuances of feature adop tion, use, and extension? Or do these existing scales need more extensive refinement, possibly to the point where the resultant reconcep tualization requires that they be developed anew?

We know of no current research which has examined this issue and, thus, advocate that scholars undertake research (1) to examine whether or not existing instrumentation can be

effectually ported to the feature level of analysis and, if needed, (2) to develop instrumentation

enabling researchers to measure the cognitions and use behaviors associated with the dynamic interactions reflected in our reconceptualization of

post-adoptive behavior.

Implications for Practice

Installed IT applications, particularly those that establish new IT-enabled work platforms, all too often do not meet senior managements' expec tations due to a lack of functionality customized for

unique business needs and processes; em

ployees' lack of understanding of the IT application features, the new work processes, or both; and a

lack of continual system upgrades and enhance ments. To induce managers, technical and busi

ness experts, and the users associated with the

implementation of an IT application to engage in a rich set of post-adoptive behaviors, we have

argued that periods of substantive technology use must occur among the community of users.

In our conceptualization, the primary means for

accomplishing this task is through the (direct or

indirect) orchestration of work system interventions

applied throughout the post-adoptive life cycle? interventions that induce an organization's mem bers to engage in active learning activities asso ciated with the IT-enabled work system. Ac

cordingly, we strongly believe that the technology and business managers responsible for the success of an IT-enabled work system initiative should reconsider these responsibilities in two

substantive ways: the active management of the

post-adoptive life cycle and the active collection of data on post-adoptive behaviors.

Management of the Post-Adoptive Life Cycle

All too often, the active management of the imple mentation of an IT-enabled work system essen

tially halts soon after its installation as the key principals involved with the implementation (i.e., business and project managers, IT and business

experts, etc.) are either reassigned to other

projects or move on to what they consider more

pressing activities (Ross et al. 2003). As a result, the majority of the post-adoptive life cycle is without management attention and direction. We thus advocate that organizations strongly con sider reconvening the principals associated with such implementation efforts, after installa tion, to plan for and to provide the resources for the post-adoptive life cycle. Here, active reflection (Edmondson et al. 2001) should be

engendered regarding what has so far transpired, the extent to which prior expectations regarding the new work system have been met, and current

organizational realities. Paramount in establishing this plan for the post-adoptive life cycle are decisions about when and how to induce periods of substantive technology use within the user

community. In addition, organizations must allow

sufficient time for periods of relative stability during which users might leverage the learning so gained.

Collection of Data on Post-Adoptive Behaviors

Because of the learning (as well as the unlearning) that occurs during the post-adoptive life cycle, the

principals responsible for the post-adoptive life

cycle of a newly installed IT-enabled work system will undoubtedly have to periodically adjust or otherwise refine the post-adoptive implementation plan. However, it would be very difficult to assess either the current state of the implementation effort


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