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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
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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
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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,
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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.
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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
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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
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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
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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.
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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
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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
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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.
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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
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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
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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.
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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
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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
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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.
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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).
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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
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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
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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?
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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.
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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
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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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