Desautels Faculty of Management

THEORY & REVIEW
COLLECTIVE INFORMATION SYSTEMS USE:
A TYPOLOGICAL THEORY1
Bogdan Negoita
HEC Montréal, 3000 Chemin de la Côte-Sainte-Catherine,
Montréal, Québec, CANADA H3T 2A7 {[email protected]}
Liette Lapointe
Desautels Faculty of Management, McGill University, 1001 Sherbrooke Street West,
Montréal, Québec, CANADA H3A 1G5 {[email protected]}
Suzanne Rivard
HEC Montréal, 3000 Chemin de la Côte-Sainte-Catherine,
Montréal, Québec, CANADA H3T 2A7 {[email protected]}
As the nature of information systems (IS) has evolved from primarily standalone, to enterprise, and distributed
applications, the need for a better understanding of collective IS use has become a research and practical
necessity. In view of contributing to this understanding, we conceptually define collective IS use as a unit level
construct, rooted in instances of individual-level IS use within the context of a common work process. Its
emergence from the individual to the unit level is shaped by different configurations of task, user, and system
interdependence between instances of individual-level IS use. On the basis of this definition, we propose a
typology of collective IS use that comprises four ideal types, namely siloed use, processual use, coalesced use,
and networked use. For each ideal type, we theorize on the emergence process from the individual to the unit
level and we consider the measurement implications for each.
Keywords: Collective IS use, IS use, multilevel theory, typological theory, interdependence, task interdependence, user interdependence, system interdependence, theory building
Introduction 1
As information systems (IS) have evolved from standalone, to
enterprise, and distributed applications, the study of IS use
has followed a similar trajectory. Indeed, studying technologies such as distributed applications (Larson et al. 2009),
enterprise solutions (Leonardi 2013; Volkoff et al. 2005),
online forums (Nan and Lu 2014), or wikis (Aaltonen and
Seiler 2016) calls for moving from a focus on individual IS
use to collective IS use, conceptualized as a multilevel
construct rooted in instances of individual-level use and their
interdependence. This is akin to the study of social cognition,
where the consideration of interdependence between individuals’ cognition proved seminal to the rise of novel and
inherently multilevel concepts, such as collective mind and
transactive memory.
A multilevel conceptualization of collective IS use, rooted in
interdependence, carries significant implications. Indeed,
interdependence between instances of individual IS use plays
a role in differentiating types of collective IS use. For
example, collective IS use in distributed applications, such as
[email protected] (Larson et al. 2009), reflects limited interdependence between instances of individual IS use. Here, any
individual’s use of the application is non-consequential for
others’ use. In contrast, collective IS use in a complex collaboration setting (Majchrzak et al. 2000) involves instances of 1
Youngjin Yoo was the accepting senior editor for this paper.
DOI: 10.25300/MISQ/2018/13219 MIS Quarterly Vol. 42 No. 4, pp. 1281-1301/December 2018 1281
Negoita et al./Collective IS Use: A Typological Theory
individual IS use that are mutually dependent on one another.
An individual’s use of the application will shape others’ use,
reflecting a stronger interdependence between instances of
individual IS use.
In the literature, IS use at the level of the collective, that is, “a
collection of bodies bound together by interdependence”
(Martin 2002, p. 329), be it a group, a team, or an organization, has received less attention than at the individual level.
Furthermore, the few extant studies of IS use by a collective
have conceptualized the construct primarily from a singlelevel perspective (Devaraj and Kohli 2003; Doll and Torkzadeh 1998). It has been argued that not accounting for the
emergence process of the collective construct from its constituent individual-level instances potentially leads to contradictory results (Burton-Jones and Gallivan 2007; Kane and
Labianca 2011). It is in this context that Kane et al. (2014)
advocate that IS researchers investigate and theorize IS
phenomena, including IS use, across a range of collective
contexts. It is the desire to address “the growing gap between
the rich ways of using IT and its representation and measurement” (Grover and Lyytinen 2015, p. 283) that motivates our
theorization of collective IS use.
A pioneering work in the study of collective IS use, BurtonJones and Gallivan (2007) leveraged multilevel theorization
principles (Morgeson and Hofmann 1999) to conceptualize
system usage as a multilevel construct. In doing so, they proposed interdependencies-in-use, that is, “dependencies among
members of a collective that relate to their use of a system”
(p. 663), as a constitutive element of IS use at the unit level.
They further suggest that collective IS use emerges along one
of two possible patterns, shared or configural, depending on
similarity or dissimilarity in instances of individual IS use.
Notwithstanding these contributions, important opportunities
for research remain. First, we still lack a conceptual definition of what is collective IS use. Second, although the role of
interdependencies-in-use in shaping IS use at higher levels of
analysis has been established, we lack an understanding of
why and how they shape the emergence process of collective
IS use. Last but not least, we lack a fine-grained conceptualization of how collective IS use emerges along patterns
different from the duality of similarity or dissimilarity in
instances of individual IS use. Indeed, we suggest that given
the complexity of modern IS use, there is more variety in
underlying instances of individual IS use than only similar or
dissimilar. Hence, the importance of understanding how
collective IS use emerges under a broader range of conditions.
We contribute to extant literature on IS use by addressing
these outstanding issues and challenges by asking: From a
multilevel perspective, what is collective IS use and what is its
emergence process from the individual level to that of the
collective? First, we conceptually define collective IS use as
a unit level construct that is rooted in instances of individuallevel IS use within the context of a common work process. Its
emergence is shaped by different configurations of task, user,
and system interdependence between instances of individuallevel IS use. This theory-rooted conceptual definition explains what the construct is and makes clear its key identifiable characteristics (Suddaby 2010; Wacker 2004). Second,
we leverage typological theory guidelines (Bailey 1994; Doty
and Glick 1994) to explain why task, user, and system interdependence shape four ideal types of collective IS use, namely
siloed use, processual use, coalesced use, and networked use.
Finally, we draw on multilevel theory (Klein and Kozlowski
2000) to explain how the different types of collective IS use
emerge as composition (in the case of siloed use), minimum/
maximum (for processual use), variance of contribution (in
the case of coalesced use), or compilation (for networked
use). We also inform practice and facilitate its management
by bringing conceptual clarity to collective IS use (Suddaby
2010).
The paper first reviews the IS use literature to highlight where
and why extant literature can be enriched with a multilevel
explanation of collective IS use. Key concepts related to
multilevel theory and task, user, and system interdependence
are then presented to inform our development of a conceptual
definition and a typological theory of collective IS use. Following a theorization of the emergence process of collective
IS use, the paper outlines our theoretical and practical contributions and possible ways of extending its ideas.
IS Use Background
A review of extant literature on individual IS use reveals that
the conceptual and operational definitions of the construct
have evolved over time. Early conceptualizations operationalized IS use as a dichotomous variable (Alavi and Henderson
1981), self-reported frequency of use (Davis 1989), the extent
of use (Hartwick and Barki 1994), or the duration of use
(Venkatesh and Davis 2000). Other work focused on the IT
artifact and measured IS use as the number of system features
used (Saga 1994) or the diversity of applications used
(Thompson et al. 1994). Some studies went beyond the IT
artifact to include user-related (Agarwal and Karahanna 2000)
and task-related elements (Goodhue and Thompson 1995;
Taylor and Todd 1995).
Based on this sustained research activity, individual IS use
has been reconceptualized and defined as the “extent to which
an individual user employs a system to carry out a task”
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(Burton-Jones and Straub 2006, p. 233; emphasis added).
Viewing system, user, and task as orthogonal dimensions,
formative operationalizations of the construct have been
proposed. For instance, IS use-related activity (ISURA)
operationalizes IS use by taking into account the technology
interaction, the task-technology fit, and the individual adaptations that take place when a user interacts with a system to
perform a task (Barki et al. 2007).
IS use has also been considered at higher levels of analysis.
For instance, conceptualizing IS use as a group behavior, Doll
and Torkzadeh (1998) defined it as “the extent that information technology is used” (p. 174) across business units, and
operationalized it via self-assessed measures of use across
functional dimensions. Aggregated measures based upon system logs identifying frequency, breadth, and depth of use
have served to assess IS use across units (Devaraj and Kohli
2003). While not explicitly defining the construct, a number
of qualitative studies have conceptualized IS use within a
collective as a process shaped by the unfolding interaction
between actors and the IT artifact (e.g., Dennis and Garfield
2003; DeSanctis and Poole 1994; Leonardi 2013; Majchrzak
et al. 2000; Markus and Silver 2008; Oborn et al. 2011). A
common thread in this literature is that collective IS use reflects interactions between system, user, and task dimensions
of individual IS use, and accounts for interdependencies-inuse (Burton-Jones and Gallivan 2007). It is implicitly conceptualized “as a reciprocal interaction among technology,
users, and outcomes of users’ actions” (Griffith 1999, p. 486).
Nonetheless, research on collective IS use remains limited.
For instance, a ProQuest search through the Association for
Information Systems’ (AIS) basket of eight journals for keywords in the title or abstract—for example, (“use” OR
“usage”) AND (“group” OR “team” OR “collective” OR
“multilevel”)—returned 12 studies, published between 1993
and 2015, which specifically pertained to collective IS use.
Our analysis of these studies, detailed in Table 1, led to the
following conclusions. First, the literature offers few conceptual definitions of what can be construed as collective IS use,
particularly in light of its multilevel nature. Second, the very
rich conceptualization of IS use that points to a multidimensional construct combining system, user, and task dimensions
(Burton-Jones and Straub 2006) has yet to be leveraged in the
context of collective IS use. Third, the interaction aspect of
how instances of individual IS use are interdependent in the
context of a common work process has not been addressed
explicitly.
Failing to take into account the emergence process of collective IS use (i.e., how instances of individual-level IS use
combine to yield the phenomenon at a collective level), has
been said to lead to “unnatural, incomplete, and very disjointed view of how organizations function” (Burton-Jones
and Gallivan 2007, p. 658). Indeed, ecological fallacies,
which occur when inferences are made about individual IS
use based only on the analysis of IS use at a higher level, or
atomistic fallacy, which occurs when inferring about IS use at
a higher level of analysis on the basis of exceptional cases of
individual IS use, can constitute a challenge to our understanding of IS use.
Multilevel IS Use
Multilevel theory assumes that higher-level phenomena
emerge from lower-level components.
As interaction occurs within larger groups of
individuals, a structure of collective action emerges
that transcends the individuals who constitute the
collective.…Collective structures emerge, are transmitted, and persist through the actions of members
of the collective (Morgeson and Hofmann 1999, pp.
252-253).
It has been suggested that contextual characteristics operate
as constraints that influence the dynamics of interactions
between individuals, thus shaping the emergence process
from the individual level to the unit level, which varies from
compilation to composition (Klein and Kozlowski 2000).
Compilation assumes heterogeneity between lower-level
components and refers to the nonlinear combination of
individual-level components toward the higher-level phenomenon. Composition assumes homogeneity in lower-level
components, which converge in an almost linear fashion
toward the higher-level phenomenon. Other emergence process types exist, such as variance of contribution, minimum/
maximum, and pooled unconstrained, all being differentiated
by variations in the contributions of lower-level phenomena.
Recognizing its potential, IS researchers have increasingly
adopted multilevel theory in their theorizing efforts (BurtonJones and Gallivan 2007; Lapointe and Rivard 2005; Nan
2011). In the context of collective IS use, the conceptualization of the construct as multilevel is rooted in guidelines
aimed at specifying its function and structure (Burton-Jones
and Gallivan 2007). Here, function refers to the effects or
outputs of the phenomenon at different levels of analysis (e.g.,
individual, group, etc.), while structure refers to the interactions between individually enacted behaviors that form the
basis of the collective phenomenon. The structure comprises
three elements: (1) the way in which the instances of
individually enacted IS use relate to one another (e.g.,
interdependencies-in-use); (2) the way in which the collective
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Table 1. Salient Characteristics of IS Use at the Group Level of Analysis
Context of
IS Use Author(s)
Conceptual
Definition
Domain of Content Considered
User System Task Interaction
Decision
support
Gopal et al.
(1992)
“faithfulness of
appropriation, attitudes toward the
GSS, and level of
consensus on appropriation” (p. 48)
Keypads,
microcomputer
functionality
Generate and select
the optimal
alternative to two
decision-oriented
tasks
It is “the complexities of interaction
between technology, groups, and
tasks that make the
varied outcomes of
GSS use so hard to
understand” (p. 66).
Dennis
(1996)
Features Information capture,
sharing, and
interpretation
Dennis et al.
(1997)
Features Information capture,
sharing, and
interpretation
Trauth and
Jessup
(2000)
Idea generation,
idea evaluation, and
consensus building
Dennis and
Garfield
(2003)
Characteristics of
appropriation agents
(e.g., (satisfaction,
perceived effectiveness, and cohesiveness)
Features, spirit Existing, desired,
and emergent
structures
Appropriation of
structures “GSS
processes evolved
significantly from
single user mode to
multiuser mode” (p.
314).
Collaboration
Majchrzak
et al. (2000)
Technology (e.g.,
access control,
search, synchronous functionality)
Decentralization and
democratization of
the generative and
decision making
processes. The
“use the repository
to house a single
analytic geometric
model from which
the specialists could
perform their analysis. This was unprecedented, because it
meant that assumptions about parameters needed to be
adopted across
disciplines” (p. 588)
Pauleen
and Yoong
(2001)
Synchronous and
asynchronous
functionality
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Table 1. Salient Characteristics of IS Use at the Group Level of Analysis (Continued)
Context of
IS Use Author(s)
Conceptual
Definition
Domain of Content Considered
User System Task Interaction
Oborn et al.
(2011)
Specificity of
feature use:
checkboxes, free
form textbox,
document inserts,
etc.
Use, as a reflection
of specialist practices but “in ways
that enabled information sharing,
coordination, and
communication
between specialists
as they sought to be
oriented to each
other” (p. 559)
“HIT use (considers) how individuals interrelate their
enactments across
diverse practices
and align their uses
of technology with
others” (p. 549).
Individuals “collectively enact technology use through
interrelating and coorientation with
others” (p. 562).
Venkatesh
and
Windeler
(2012)
Duration of
“actual time
logged on to the
system” (p. 750)
Leonardi
(2013)
Frequency and
specificity of feature use: Check
Model, Nodeout
Request, Define
Initial Conditions,
Generate Report,
etc.
Reciprocal task
configuration as
evidenced in “using
CrashLab to move
engineering work
from modeling to
analysis” (p. 755)
Not
specified
BurtonJones and
Gallivan
(2007)
“System usage at any
level of analysis (is) a
user’s employment of
a system to perform a
task” (p. 659)
“engage with the
system with different
(or similar) cognition
(e.g., absorption) or
affect (e.g., wariness)” (p. 668)
“use different (or
similar) features
of a system than
others” (p. 668)
“employ the system
for different (or
similar) subsets of a
task” (p. 668)
“dependencies
among members of
a collective that
relate to their use of
a system” (p. 663).
Racherla
and
Mandviwalla
(2013)
Universal use is “a
collective employment of one more
features of the (information infrastructure)
to perform tasks.” (p.
724)
Interest (e.g.,
perception of
control, ownership)
Convenience
(e.g., number of
devices and
locations used
to access)
Task (e.g., number
of people contacted,
online friends, transactions completed,
tools used)
phenomenon emerges from individually enacted IS use (e.g.,
form of collective IS use); and (3) factors that influence the
emergence process of the collective phenomenon, such as
time (e.g., context of usage).
While previous work extends our understanding of collective
IS use, a number of issues remain. First, there is still a lack
of a conceptual definition as to what is collective IS use,
particularly in light of its multilevel nature. Second, although
interdependence between instances of individual-level IS use
is a defining characteristic of collective IS use, there is a
paucity of understanding as to what they are and how they are
constituted beyond that they are “interactions that relate to
usage” (Burton-Jones and Gallivan 2007, p. 665). Third, it
has been suggested that collective IS use must be conceptualized as either a shared or configural construct (BurtonJones and Gallivan 2007). This argument follows that homogeneity (or similarity) in instances of individual IS use leads
to the emergence process of collective IS use as a shared
construct, while heterogeneity (or dissimilarity) leads to the
emergence process of collective IS use as a configural construct. While this may be the case in some situations, likely
variations in IS use by individual users (DeSanctis and Poole
1994) imply that some situations will be more or less homoMIS Quarterly Vol. 42 No. 4/December 2018 1285
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geneous (or heterogeneous) than others. Therefore, additional
theorizing is needed to adequately reflect these instances of
variation in individual IS use at the level of the collective.
Overall, these issues make empirical study, irrespective of
epistemological stance, difficult. Without a clear conceptual
definition and granular understanding of key building blocks,
such as interdependence and the emergence process, qualitative researchers may find it unnecessarily challenging to
engage in meaningful sensemaking and interpretation of collective IS use. Similarly, quantitative researchers will have
the difficult task of operationalizing and testing, within nomological nets, a collective IS use construct that lacks conceptual
clarity.
Interdependence in Collective IS Use
Before introducing task, user, and system interdependence,
we state the key conceptual assumptions that underlie our
work (Rivard 2014). First, we conceptualize collective IS use
as a multilevel construct, more precisely as a unit level construct that emerges from the individual level. Second, we
build on the very rich definition of IS use at the individual
level, which comprises the task, user, and system dimensions
(Burton-Jones and Straub 2006). Third, we conceptualize
task, user, and system interdependence as structural features
of work, which are manipulable and can be designed to be
performed at different degrees of interdependence (Wageman
1995). Fourth, we assume task, user, and system interdependence as orthogonal. For instance, organizational theory
(e.g., Thompson 1967; Van de Ven et al. 1976; Wageman
2001) construes task interdependence irrespective of user
goals and rewards or of technology. In social psychology, it
is often assumed that “high and low degrees of (user) interdependence may exist independent of the degree of task
interdependence” (Van Der Vegt et al. 1998, p. 130).
Task Interdependence
Task interdependence refers “to features of inputs into the
work itself that require multiple individuals to complete the
work” (Wageman 2001, p. 198). We borrow from Van de
Ven et al.’s (1976) extension of earlier work (Thompson
1967), to suggest four possible interdependence-based task
configurations: pooled, sequential, reciprocal, and team.
These four basic arrangements, detailed in Table 2, were
originally conceptualized at the organizational level (Thompson 1967) but later applied to a group context (Bell and
Kozlowski 2002; Van de Ven et al. 1976).
In a pooled configuration, task interdependence is at its
lowest, yet it still exists. To perform a given task does not
require inputs from any other task. However, minimum interdependence exists because, for a given process to be completed, all the individual tasks have to be accomplished. This
situation is best described as “one in which each part renders
a discrete contribution to the whole” (Thompson 1967, p. 54).
An example would be the creation of a library database
(Lankes et al. 2007). Task design could imply that one task
is to enter data about books, another to enter data about journals, and yet another about corporate annual reports. Each
task is done independently from the others and each makes a
discrete contribution to the whole database.
In a sequential configuration, the input to a given task is the
output of another task, which has to be completed before the
subsequent task may be undertaken. Here, “work and
activities flow uni-directionally” (Bell and Kozlowski 2002,
p. 9) and there is a direct interdependence between functionally adjacent tasks. An example can be found in business
expense processing, which requires the completion of tasks in
a particular order (Grover and Malhotra 1997). First, an
expense form is completed detailing all transactions; second,
the relevant original receipts are filed; third, the management
approvals take place, etc.
A reciprocal configuration is one “in which the outputs of
each (task) become inputs for the others” (Thompson 1967, p.
55), inducing stronger task interdependence. In contrast with
the sequential configuration, there is a reciprocity aspect,
whereby “each unit involved is penetrated by the other …
[thereby] each unit posing contingency for the other”
(Thompson 1967, p. 55). The dependence within the overall
flow of the work process is bidirectional in that work and
activities flow back and forth between tasks, over time (Bell
and Kozlowski 2002). An example is the set of tasks
involved in the breast cancer patient encounter phase
described in Oborn et al. (2011). First, there is the request of
biological samples. Second, the pathology task of analyzing
the evidence for diagnostic purposes. Finally, given the
diagnostic, the oncology task that establishes a course of
treatment. All tasks exhibit a degree of flexibility in that their
enactment can be modified based on feedback from a
subsequent task. For instance, a new requisition of imagery
or biological samples can be prompted by a pathology task
lacking sufficient evidence for diagnosis.
Researchers have extended Thompson’s work by suggesting
team arrangement as a fourth type of task interdependence,
referring “to situations where the work is undertaken jointly”
(Van de Ven et al. 1976, p. 325) and concurrently, across all
tasks. This arrangement displays the highest degree of task
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Table 2. Types of Task Interdependence
Type Representation
Coordination
Mechanism Example
Pooled Standardization Creating a database of
documents held in a library
(Lankes et al. 2007).
Sequential Planning Business expense
processing, whereby first,
expense forms are
completed, second, receipts
are filed, third, management
approvals are enacted, etc.
(Grover and Malhotra 1997).
Reciprocal Feedback and
mutual adjustment
The enactment of evidence
requisition, pathology, and
oncology tasks during the
breast cancer patient
encounter phase (Oborn et
al. 2011).
Team Scheduled or
unscheduled
meetings
Complex collaboration
aimed at developing a
revolutionary rocket engine
requiring the enactment, in
almost real-time, of
diagnosis, problem-solving,
and implementation tasks
(Majchrzak et al. 2000).
interdependence. It is illustrated in Majchrzak et al.’s (2000)
study of complex collaboration required for developing a
rocket engine, where the innovative nature of the work process called for “highly interdependent iterative virtual brainstorming sessions” (p. 574). Diagnosis, problem-solving, and
implementation were enacted in a joint, simultaneous, and
iterative manner to complete the work process. Quasi realtime task coordination was achieved through scheduled or unscheduled meetings or impromptu face-to-face communication.
User Interdependence
User interdependence pertains to the extent to which “consequences of work are contingent on collective performance”
(Wageman 2001, p. 201). It reflects the mutual dependence
of members of a collective in terms of goals and rewards, and
“can exist without any interdependence in the task inputs”
(Wageman 2001, p. 201). Indeed, research has shown that
groups with similar patterns of inputs and outputs necessary
to complete a task can vary widely in their interdependence in
terms of goals and rewards (Campion et al. 1996; Stewart and
Barrick 2000).
Goals are the level of performance-related outcomes that
actors aim to achieve (Aubé and Rousseau 2005). Collective
goals and individual goals may or may not depend on one
another. For instance, the data entry clerks contributing to the
creation of a library database (Lankes et al 2007) may share
the goal of entering x documents on a given day. Or each
individual may have their own target to reach. Similarly, the
extent to which rewards accrue to an individual depending on
the efforts and performance of one’s coworkers can vary. In
the same example, a bonus depending on collective performance would represent high interdependence, while a bonus
based on individual performance would represent low interdependence.
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Table 3. Types of User Interdependence
Type Representation
Coordination
Mechanism Example(s)
Low Bureaucratically
planned goal
setting and reward
structure
Rewards earned “based solely on individual
performance, such as a commission for an
individual salesperson” (Wageman 2001, p.
201), or goals, such as “hours billed by a
member of a consulting team” (Wageman
2001, p. 201)
High Bilateral or multilateral interactions
enabled by
(a)synchronous
feedback and
mutual adjustment
“A room full of telemarketers … held
accountable for a collective goal” (Wageman
2001, p. 201), whereby “rewards accrue …
and are distributed … in shares that are
independent of the performance of specific
individuals” (Wageman 2001, p. 201)
We suggest two basic arrangements—low and high—of user
interdependence, shaped by varying goal and reward structures (see Table 3). User interdependence is low when goals
and compensation levels are based solely on individual performance (Wageman 2001). In contrast, it is high when there
is a unique goal for all the members of a collective and the
reward is based solely on collective performance (Wageman
2001).
System Interdependence
System interdependence has been conceptually defined as the
“technologies’ interaction with and dependence on one
another” (Bailey et al. 2010, p. 714). The extent of interdependence is then determined by
the “space between two interdependent technologies:
a technology gap signals a transfer of work product
from one technology on which an operation has been
completed to another on which the next operation is
to be carried out” (Bailey et al. 2010, p. 714).
The lower the amount of time and the number of distinct steps
or actions required to transform the output of a system into the
input of another system, the higher the system interdependence, and vice versa.
We refine this conceptual definition that reflects key identifiable characteristics of system interdependence (i.e.,
interaction and dependence between technologies) by providing a more detailed description of what is meant by
dependence between systems. To do so, we focus on the
technology-related aspects of interdependence, specifically
how systems are physically and functionally linked together.
Linking physically and functionally distinct information
systems to act as a coordinated whole is a function of having
compatible interfaces (the means to exchange data successfully), as well as data in a compatible format (the content of
data exchange is mutually meaningful) (Markus 2000).
System interdependence is therefore a function of the ability,
first, to relay data and, second, to understand the data that are
being exchanged. Excluding systems that are completely
independent, and thus show no system interdependence whatsoever, we suggest two basic arrangements as illustrations of
loose and tight system interdependence (see Table 4).
System interdependence is low under a loose configuration,
as systems interact and depend on one another on a case-bycase basis and have their own data sources. Given the distributed nature of the data sources, one cannot assume
standardized data. As a result, successful interaction depends
on the standardization of interface interoperability so that, at
the very least, the output of a given system would be automatically translated into a format that would satisfy the input
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Table 4. Types of System Interdependence
Type Representation Coordination mechanism Example
Loose Standardization for compatible
means of data exchange and
data formats
Electronic Medical Records
(EMRs) implementations in
community clinics (Lapointe
et al. 2012).
Tight Planning of data specification to
ensure the content of data
exchange is mutually meaningful
ERPs and other similar
enterprise systems sharing
data, functionality, and
output (Soh et al. 2000).
requirements of a subsequent system. Under such a configuration, the interface interoperability between the various
systems could be enabled by the use of an open standard file
format (e.g., XBRL) or through the use of application
programming interfaces (APIs) to expose the data structure
behind a particular system’s boundary. EMR implementations used in Canadian community clinics are an example of
loose interdependence (Lapointe et al. 2012). Given the
multitude of vendors, different medical jurisdictions, and no
standards regarding data exchange and data formats, this
landscape of EMR implementations is fragmented. There are
many point-to-point connections without a built-in interface
to allow for automatic data exchange between EMR implementations. The disappearance of one implementation from
such an ecosystem does not prohibit the functioning of
another, although data quality issues may arise.
Under a tight configuration, system interdependence is high
since systems share a common data source, a common, possibly centralized, hardware and network infrastructure, and so
on. Systems are highly interdependent when, for example,
changes to the underlying data brought about by any system
will directly and immediately influence all the others. Concerns related to compatible interface formats that encompass
the communication specifications of each system (e.g., the
means of data exchange) are minimal since the underlying
data are hosted in a unique, centralized location, shared by the
various systems. Such a high degree of system interdependence is predicated on planning of data specifications
since all data elements are managed concomitantly. For
example, within an ERP, a module that holds patient management data and a module that stores billing and collection data
(Soh et al. 2000), the billing and collection module cannot
function without the patient management data to provide
detail on medical procedures having been conducted.
Similarly, the patient management module needs the billing
and collection module to reconcile all charges and credits
related to the provision of care.
An Interdependence-Based Definition
of Collective IS Use
Reflecting on the overall conceptual development of interdependence between instances of individual-level IS use, we
define collective IS use as a unit level construct that is rooted
in instances of individual-level IS use within the context of a
common work process. Its emergence is shaped by different
configurations of task, user, and system interdependence
between instances of individual-level IS use.
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A Typological Theory of
Collective IS Use
We propose a typological theory of collective IS use.
Typological theories comprise a typology (Doty and Glick
1994). Unlike taxonomies, which begin empirically and serve
to put empirically observed entities into categories, typologies
are primarily conceptual (Bailey 1994). A typology is conceptualized from first-order constructs, that is, dimensions the
researcher combines into ideal types. Because of its conceptual nature, the values taken on by a typology’s first order
constructs do not have to be “mutually exclusive and
exhaustive sets” (Doty and Glick 1994, p. 232). For instance,
in Mintzberg’s (1980) typology of organizational structure,
the values of first order constructs related to technical system
complexity and automation do not vary exhaustively across
the typology’s five ideal types.
The term ideal refers to the notion that a given category is the
“clearest example of the type” (Bailey 1994, p. 19) and does
not necessarily represent a state to strive for. For instance, in
Miles and Snow’s typology of strategy, “the Reactor [ideal
type] is a form of strategic ‘failure’” (Miles et al. 1978, p.
550), one that “exhibits a pattern of adjustment to its environment that is both inconsistent and unstable” (Miles et al. 1978,
p. 557). Because ideal types are conceptual entities that are
extreme in their nature, they may not be perfectly reflected in
reality (Weber 1949). While ideal types can be thought of as
ostensive in nature, in practice, enactments of collective IS
use will approach a particular ideal type, yet show a certain
deviation from that conceptually pure exemplar.
Ideal Types of Collective IS Use
We propose four ideal types of collective IS use (detailed in
Table 5) shaped by unique configurations of the three firstorder constructs introduced earlier, namely task, user, and
system interdependence. In this typology, each ideal type is
shaped around the nature of task interdependence. Indeed,
because the raison d’être of an organization is to accomplish
tasks linked to the production of goods and services (Leavitt
1965; Van de Ven et al. 1976), the organizational design
literature points to task interdependence as the key contingency to be managed in an organizational context, including
departments and work units (Thompson 1967). As such, each
ideal type was conceptualized by establishing which values of
user interdependence and system interdependence provide a
better fit (Leavitt 1965) with a particular value of task
interdependence.
Siloed use reflects pooled tasks conducted in a context of low
user interdependence and loosely interdependent systems.
Given the strict division of labor in pooled task interdependence, combinations other than low user interdependence and
loose system interdependence would suggest a suboptimal use
of resources, such as time and money. This contention is
supported by research that found that higher levels of user
interdependence, in the case of pooled task interdependence,
will see
costs outweigh … benefits. Much energy may be
expended coordinating members and regulating collective behavior that might otherwise be expended
on task performance itself (Wageman 1995, p. 149).
A similar argument can be made with regards to higher levels
of system interdependence, where effort spent on planning
and centralizing hardware, software, and data may be unwarranted, in light of the individual and self-contained nature of
the task.
Distributed projects, such as [email protected] (Larson et al.
2009), approximate siloed use. Tasks reflect a pooled configuration, with each task unfolding in the same way. The task
involves the simulation of new proteins. The result of the
simulation is submitted to the project’s master server. The
standardization built into the specifications of the simulation
makes it possible for the integration of all distributed tasks.
User interdependence is low as individual goals and rewards
are independent of collective goals and rewards. While, the
goal of the project was to develop drug therapies (Larson et
al. 2009), the goal of each participant was to engage in prosocial behavior (Jarvenpaa and Staples 2000). Loose system
interdependence is exhibited, as the distributed applications
that receive and analyze data packets do not interact with each
other, and no data are received nor sent between applications.
Processual use is shaped by sequential task interdependence,
with low user interdependence and tight system interdependence. While the number of tasks in a work process may
vary, the pattern of sequential task interdependence always
involves a pair of tasks and a one-way output/input flow.
Under these conditions, the planning of task sequences and
output/input flows ensures coordination (Thompson 1967).
Here again, high user interdependence may be unjustified cost
wise (Wageman 1995), with research reporting that high user
interdependence does not influence the performance of such
tasks (Wageman and Baker 1997). In this context however,
tight system interdependence is warranted, as loose interdependence could create delays and backlogs, as output from
one task may not become input to a subsequent task in a
timely manner (Bailey et al. 2010).
An approximation of this ideal type is that of a sequential
business process supported by an ERP. The sequential nature
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Table 5. Typology of Collective IS Use
Collective IS Use
Interdependence Siloed Use Processual Use Coalesced Use Networked Use
Task Pooled Sequential Reciprocal Team
User Low Low High High
System Loose Tight Loose Tight
of task interdependence is particularly important since it
reflects the order in which discrete tasks, composing a
sequential business process, need to be executed. For
instance, in a materials management process, procurement,
inbound logistics, and warehousing tasks need to take place
in that specific order so as to ensure the availability of raw
materials before they can be used in manufacturing (Volkoff
et al. 2005). At any point in time, a task is dependent on the
successful completion of its immediate predecessor in the
workflow. User interdependence is generally low in a typical
ERP implementation of such a process. Given that employees
operate in the specific contexts of their business units, they
accomplish their individual tasks “in response to their local
experiences and needs” (Boudreau and Robey 2005, pp. 4-5),
with individual goals and rewards typically not contingent on
collective performance (Sharma and Yetton 2003). The tight
system interdependence is a key element reflected in the value
proposition of an ERP system. It is the shared data repository
and common functionality supporting an entire business process that helps reduce data redundancy, increase data
visibility, and enhance data quality (Shang and Seddon 2002).
Coalesced use reflects reciprocal tasks conducted in the
context of high user interdependence and loosely interdependent systems. Reciprocal task interdependence implies
that an initial task must be completed before the subsequent
task can begin, with the subsequent task potentially constituting a contingency for the initial task. While the number
of tasks composing a given work process may vary, the pattern of task interdependence always involves a pair of tasks at
any one time and a bidirectional output/input flow. High user
interdependence is well suited to such a task configuration as
research shows that it contributes to the performance of
groups involved in reciprocal tasks (Wageman 1995). In this
context, loosely interdependent systems are better suited, as
the cost of high system interdependence may be prohibitive.
Indeed, the cost of planning which data elements are needed
to complete a work process increases exponentially with the
number of tasks and data flows involved (Thompson 1967).
Also, tight system interdependence might engender vulnerabilities to unexpected changes when interacting with any
part(s) of the system(s), “leading to abrupt and unexpected
nonlinearities, poor system performance, or even disastrous
results” (Barki and Pinsonneault 2005, p. 174).
An example is the use of medical technology by clinical
specialists for the diagnosis and treatment of breast cancer
patients (Oborn et al. 2011). Tasks, such as collecting biological samples, examining evidence, and providing treatment, are reciprocal since at any point the outcomes of one
task may affect a preceding or subsequent task. User interdependence is high, with the collective goal of providing
quality care linked to individual goals to secure the appropriate tissues (e.g., surgeons), to identify accurately the nature
of the samples (e.g., pathologists, radiologists), and to
establish an adequate course of treatment (e.g., oncologists).
It is inferred from the case that the collective reward of an
enhanced reputation for the hospital as the “flagship for comprehensive, unified breast cancer treatment programs” (Oborn
et al. 2011, p. 550) is connected with the stakeholders’
intrinsic rewards. Finally, system interdependence is loose.
As Oborn et al. (2011) observe, “the patient encounter often
extended into several intervals, exam rooms, and technologies” (p. 554), with no, or very limited compatible means
for data exchange.
Networked use is shaped by team task interdependence, with
high user interdependence and tight system interdependence.
In the context of team task interdependence, where task
interdependence is exhibited concurrently across all tasks,
high user interdependence helps motivate individuals to
engage with one another (Wageman 1995), hence providing
a better fit (Leavitt 1965). Similarly since the nature of the
task interdependence is simultaneous (Van de Ven et al.
1976), the speed of data exchange across systems has to be
real time. Tight system interdependence is thus necessary to
ensure the availability of data to support the various tasks.
An illustration is Majchrzak et al.’s (2000) account of the use
of collaborative technology by a team involved in the development of a rocket engine. The team arrangement of tasks
involves designing, prototyping, and testing solutions that are
“highly interdependent, iterative” (Majchrzak et al. 2000, p.
574) tasks. They are coordinated by way of unscheduled
meetings, which allow for low latency feedback (Majchrzak
et al. 2000), and strengthen the case for team task interdependence (Van de Ven et al. 1976). The user interdependence
is high, as the engineers’ shared goal and reward are linked to
the achievement of the shared objective “to develop (but not
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build) a concept and drawings for a revolutionary and highly
complex rocket design that could be marketed by the three
companies” (Majchrzak et al. 2000, p. 574). The technology
shows tight system interdependence in support of real-time,
synchronous collaboration. Data flow automatically between
different modules handling e-mail, conferencing, electronic
whiteboards, and version control for libraries of solutions and
practices.
Explaining the Emergence Process
of Collective IS Use
No conceptualization of a multilevel construct is complete
unless the bottom up process through which the lower-level
entities aggregate into the unit level phenomenon is explained
(Klein and Kozlowski 2000; Kozlowski et al. 2013; Morgeson
and Hofmann 1999). The emergence process is not a mere
synonym for appearance or growth (Corning 2002). Instead,
it refers to the “dynamic interaction processes among lower
level entities (i.e., individuals)—(which) over time—yield
phenomena that manifest at higher levels.…[It] is multilevel,
process oriented, and temporal” (Kozlowski et al. 2013, pp.
582-583).
First, the emergence process is the link between the lower
level, from which a collective phenomenon originates, and the
higher level, at which it manifests (Goldstein 2000). For
instance, in the IS literature, group-level resistance to IT
implementation has been shown to originate from individual
resistive behaviors (Lapointe and Rivard 2005), and collective
IS use is deemed to emerge from instances of individual IS
use (Burton-Jones and Gallivan 2007).
Second, the emergence process shows explanatory mechanisms that “drive the dynamic interactions among entities
(e.g., individuals) that yield the emerged property” (Kozlowski et al. 2013, p. 585). For instance, the emergence process
has been shown to be shaped by whether there is independence or convergence in individual-level behaviors giving rise
to group-level resistance as a result of compilation or composition (Lapointe and Rivard 2005). In the context of IS use,
explanations of the emergence process have rested on how
similar or dissimilar instances of individual IS use are, leading
to collective IS use through either a shared or a configural
process (Burton-Jones and Gallivan 2007).
Finally, the emergence process is time sensitive and this
“temporal scope … affects the apparent origin and direction
of many phenomena” (Klein and Kozlowski 2000, p. 23).
The time lapse between the enactment of lower-level interactions and the higher-level manifestation can be shorter or
longer (Crutchfield 1994; Kozlowski et al. 2013).
With this in mind, we next describe the emergence process of
each ideal type of collective IS use. Before doing so, however, it is important to note that regardless of the ideal type,
researchers will want to operationalize and measure individual
IS use with measures such as time of IS use, the extent to
which a system is used to perform a task, number of features
used, etc. In addition, they will need to assess task, user, and
system interdependence. These measures are crucial in giving
researchers and practitioners the means to assess the separation, or the Euclidean distance (e.g., Sabherwal and Chan
2001), between readings of task, user, and system interdependence observed in practice and the task, user, and system
interdependence values ostensively associated with a given
ideal type. The distance will determine the proximity to a
specific ideal type and its associated emergence process and
quantitative assessment.
Extant literature offers many alternatives to measure task,
user, and system interdependence. For instance, the assessment of task interdependence can be rooted in the concept of
handoffs (Pentland et al. 2016, 2017), as means of ascertaining the sending and receiving of task outputs and inputs,
respectively. The assessment of user interdependence can
rely on the assessment of users’ beliefs that contingent outcomes were influenced by their peers (e.g., Janssen et al.
1999; Wageman 1995). Finally, the operationalization of
system interdependence can be adapted from Bailey et al.
(2010) and take into account the time it takes for data transfer
between systems and the extent or the number of interventions
necessary to transfer data between systems. Table 6 presents
the emergence processes we theorized, along with examples
that, although they did not explicitly study IS use, nonetheless
detailed contexts where each collective IS use ideal type
could have applied. Subsequent vignettes, inspired by these
examples, illustrate each ideal type, its emergence process,
and quantitative assessment.
Siloed Use, the Emergence
Process as Composition
The pooled task interdependence, low user interdependence,
and loose system interdependence suggest that individual IS
use is done independently with no significant feedback between the instances of individual IS use. Siloed use is characterized by standardized use and a lack of interaction between
individual behaviors. In such circumstances, standardization
acts as a constraint, “shaping composition forms of emergence
that are characterized by stability, uniformity, and convergence” (Klein and Kozlowski 2000, p. 57). Thus, collective
IS use is best represented through a linear combination of
instances of individual IS use. Since every instance of individual IS use represents a discrete contribution, collective IS
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Table 6. Collective IS Use: the Emergence Process and Assessment
Collective IS Use Siloed Use Processual Use Coalesced Use Networked Use
Emergence process Composition Minimum/maximum Variance of
contribution
Compilation
Combination rule Linear Non-linear Non-linear Non-linear
Quantitative
assessment
Sum, average;
standard deviation
Minimum and maximum
values; coefficient of
variation
Modes; withingroup variance
Cluster analysis; centroid
and centroid distance
Example(s) Distributed computing project
(Larson et al. 2009)
Business process
integration (Volkoff et al.
2005)
Patient encounter
process (Oborn et
al. 2011)
Complex collaboration
initiative (Majchrzak et al.
2000)
Temporal scope Instantaneous Lagged
use should be reducible to the sum or average of the
individual-level instances. In addition, the standard deviation
could help qualify the extent of convergence between
instances of individual IS use.
Vignette 1. Emergence of Siloed Use as
Composition
Assuming that individual-level IS use is measured as
“time” that an application has been used, in minutes, by
an individual (tn), a sample data set [t1, t2, t3, t4, t5, t6]
would show values of [5, 1, 2, 9, 4, 1]. Given that siloed
use in the case of a distributed application (Larson et al.
2009) reflects individual-level IS use that is standardized
with no interaction between the instances of individuallevel IS use, the measure of collective IS use could be the
total number of minutes an application has been used,
namely 3tn = 22. The average number of minutes an
application has been used could be computed, such as µ
= 3.67.
Beyond siloed use, for conjunctive task structures which form
the basis of the other three collective IS use types, the
emergence process is nonlinear (Klein and Kozlowski 2000;
Kozlowski and Chao 2012), with collective IS use emerging
as a result of interaction between the instances of individual
IS use.
Processual Use, the Emergence Process
as Minimum/Maximum
The sequential task interdependence, low user interdependence, and tight system interdependence characteristic of
processual use means that there is a direct interaction and oneway dependence between functionally adjacent instances of
individual IS use. An individual’s IS use is dependent upon
the outcome (or the output) of somebody else’s IS use. Under
the constraints imposed by such dependence, should an
instance of IS use fall short, collective IS use is affected. It is
a case where the collective effort is as weak (or as strong) as
the weakest (or the strongest) of its individual contributions.
As Klein and Kozlowski (2000) put it, “the standing of one
individual on the phenomenon in question, determines the
standing of the collective” (p. 71). As a result, collective IS
use representative of processual use is described by a minimum/maximum emergence process, in that it is determined by
the minimum (or maximum) value of individual IS use under
consideration. Operationalizing such collective IS use would
therefore rely on assessing the minimum and maximum values
of individual IS use.
Vignette 2. Emergence of Processual Use as
Minimum/Maximum
We assume that individual-level IS use is measured as the
extent to which a system is used to perform a task. For
instance, individual A completes 95% of the task through
her IS use, individual B completes 10%, and individual C
completes 50%. For instance, within a procurement process, whereby a part has to be created with a serial number, then the business requirement for the part detailed,
and finally, the order for the part made (Volkoff et al.
2005), individual-level IS use will vary across individuals,
as task sizes are not necessarily uniform. As a result, the
measure of collective IS use would be the minimum and
maximum values of individual-level IS use, namely 10%
and 95%, respectively.
In addition, the notion of processual use implies sequence, or
particular order. A measure of smoothness could be relevant
to account for the relative discrepancies between the minimum and maximum values, on the one hand, and the rest of
the values, on the other hand. The coefficient of variation is
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one such standardized measure of dispersion that can be used
to reflect the smoothness in a series of measurements.
Coalesced Use, the Emergence Process
as Variance of Contribution
The reciprocal task interdependence, high user interdependence, and loose system interdependence nature of coalesced
use implies a direct interaction and two-way dependence
between functionally adjacent instances of individual IS use.
These constraints on the emergence process are such that
“individuals may make (elemental) contributions that are
similar or different, but the substantive focus is on the variance of contribution” (Klein and Kozlowski 2000, p. 72). The
implications with regard to elemental contribution are twofold. First, the type of contribution can and will vary from
those that engage in individual IS use to those that do not
engage in individual IS use. Second, the amount of contribution can vary, sometimes significantly, whereby those that do
engage in individual IS use can and will do so to various
degrees, for instance, to include those actively engaged versus
lurkers, free-riders, etc. As such, while collective IS use is
still rooted in multiple instances of individual IS use, these
individual-level behaviors can show considerable variation in
the type and extent of IS use.
With regard to the measurement of collective IS use resulting
through emergence based on variance of contribution,
“variance, of course, is a key operationalization” (Klein and
Kozlowski 2000, p. 72). In addition, the concept of
coalescing implies that there is something around which to
coalesce (i.e., surgeons use technology in a similar way,
which is different from radiologists, etc.). Given a distribution of individual IS use scores, the values that appear most
often in the data (i.e., the modes, as suggested by Chan 1998)
could constitute those points of coalescence. As such, multimodality in individual-level IS use scores would indicate the
presence of substantively meaningful areas of convergence.
Within such subgroups, the variance would be low, while
across subgroups the variance should be high, reflecting possible differences in the type and amount of individual-level
contribution. Accordingly, the operationalization of coalesced use would involve the assessment of both the modes
and the within-group variance exhibited across individual IS
use measures.
Vignette 3. Emergence of Coalesced Use as
Variance of Contribution
Assuming that individual-level IS use is measured as
‘frequency of use’, a sample data set could include values
of [1, 1, 2, 1, 2, 1, 5, 4, 5, 5, 5]. Research has shown that
practitioners may show different frequencies of use, for
instance, with surgeons’ (S) extremely limited use of
SubSys reflected in particularly low IS use values, while
radiologists’ (R) frequent interaction with a web-based
clinical information system translating into higher IS use
values, etc. (Oborn et al. 2011) With the dispersion in the
type and amount of elemental contribution related to IS
use across specialist groups (Sn, Rn) reflected in the
individual-level IS use scores, the measure of collective
IS use would be the modes of a data set (i.e., 1 and 5).
The modes represent the points of coalescence that most
closely reflect the diversity of IS use in a particular
context. In addition, according to Chan (1998), withingroup variance such as the index rwg, “could serve as an
operationalization of a focal construct” (pg. 239) at the
unit level by helping to quantify the degree of homogeneity around a particular mode.
With coalesced use, the operationalization of collective IS use
moves away from yielding one particular value, such as an
average, or a set of values, like a minimum and a maximum.
Instead, its operationalization should encompass, without
complete aggregation, a collection of values of interest. Such
operationalization would reflect more closely the conceptual
richness associated with the increased diversity in the underlying instances of individual-level IS use.
Networked Use, the Emergence
Process as Compilation
The team task interdependence, high user interdependence,
and tight system interdependence characteristics of networked
use imply a direct interaction and a real-time, two-way dependence between all instances of individual IS use. Under
networked use, the ease of coordination of individual IS use
through ad hoc, often unplanned, communication across a rich
channel encourages divergent and complementary instances
of individual IS use. In addition, the high user interdependence encourages risk taking and role specialization, which
sees individuals use technology in new and different ways.
Finally, the tight system interdependence facilitates timely
data flows across systems, unlocking new combination and recombination opportunities between instances of individual IS
use. All of these processes actively encourage variation in
individual IS use. With these constraints, collective IS use
emerges as a result of compilation (Klein and Kozlowski
2000).
Given the dynamic interaction between instances of individual
IS use, it is likely that collective IS use will show evidence of
synergistic effects (Corning 2002), in addition to meta1294 MIS Quarterly Vol. 42 No. 4/December 2018
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structuring roles enacted in a spontaneous and organic manner
(Orlikowski et al. 1995). It is thus impossible, particularly for
this type of collective IS use, to be reducible solely to its
instances of individual IS use. As a result, measuring collective IS use is particularly challenging, in light of its nonlinear
nature. Despite this difficulty in quantifying the outcome of
the emergence process, an understanding of the boundary
conditions, constituent elements, and their interactions can
enhance measurement by serving to “delimit its possible
states” (Goldstein 2000, p. 11) and to provide a measure of
retrospective coherence (Snowden and Boone 2007). Given
these circumstances, measuring collective IS use could be
done by way of cluster analysis. The results of a cluster
analysis would reveal similar patterns of individual-level IS
use, for instance feature use, and help group the instances of
individual IS use on the basis of similarity. Similar instances
of individual IS use will find themselves in one cluster while
dissimilar instances of individual IS use will be located in
different clusters. Because of considerable variations in
individual IS use, there is the need to use a clustering
algorithm that shows robustness to noise and outliers. The
density-based spatial clustering of applications with noise
(DBSCAN) (Ester et al. 1996) algorithm would be particularly useful. The algorithm allows for the discovery of nonlinear clusters of uneven sizes and does not require a priori
specification of the desired number of clusters. Instead,
DBSCAN takes into account the minimum distance between
two points to be part of the same cluster to compute stable
configurations of clusters of varying sizes.
Vignette 4. Emergence of Networked Use as
Compilation
We assume that individual-level IS use is measured as
feature use. For instance, it may be that for some individuals (A1, A2, A3), 80% of features used are to foster
original contribution, while for others (B1, B2, B3), 65%
of features used are to make coordination possible.
Indeed, research indicates that different roles (lead engineers, specialists, and managers) engaged in a complex
collaboration project, use different features like screen
capture feature, concurrency control, distributed scan-in
drawings, and drawing palette to highlight content on
engineering diagrams (Majchrzak et al. 2000).
By way of cluster analysis, it becomes possible to
uncover and describe differentiated patterns of collective
IS use. A cluster analysis of the features used by the
individuals engaged in the project would help identify
similar types of feature use across individuals. Conceptually, the clusters could be associated with particular
roles, extent of experience, etc. The interpretation of the
theoretical meaning of the clusters provides part of the
operationalization of collective IS use.
In addition, each cluster, once established, has its own
centroid value, which is an average of the data points
within a cluster. This provides a quantitative assessment
of how extensive is the use of certain features for a particular role, for instance, use of concurrency control by
managers (cluster B).
To operationalize how clusters are related, or how the
patterns of feature use of some individuals are mutually
related to the patterns of feature use of other individuals,
measures such as centroid distance and the average
distance are useful. The centroid distance between
cluster A (i.e., lead engineers, specialists) and B (i.e.,
managers) is the distance between centroid (A) and
centroid (B). The average distance is calculated by
finding the average pairwise distance between the points
in each cluster. In other words, for every point ai
in
cluster A, one can compute dist(ai
,b1), dist(ai
,b2),…
dist(ai
,bn), where b1, b2,… bn are points in cluster B, and
average the respective distances.
The Time Dimension of the Emergence Process
Theorizing on the emergence process calls for paying attention to the temporal dimension (Kozlowski et al. 2013). As
Crutchfield (1994) notes, “some of the most engaging and
perplexing … phenomena are those in which highly structured
collective behavior emerges over time from interaction” (p.
12; emphasis added). To date, multilevel research is not sufficiently mature to suggest distinct cutoff points for differentiating the timeframes necessary for the interactions between
lower level behaviors to unfold and manifest themselves as
collective behavior. However, by focusing on the extreme
ideal types of collective IS use, cogent arguments can be
offered that differentiate siloed use and networked use from
the perspective of the time it takes for them to emerge from
their respective patterns of task, user, and system interdependence.
In the case of siloed use, the time differential for the emergence process is almost instantaneous, making the form of
this type of collective IS use become quickly established. It
is relatively easy to identify the existence of collective IS use
under this ideal type given that the multiple instances of
individual IS use show very limited dependence on one
another. Coordination between the various instances of individual IS use is achieved through standardization (Larson et
al. 2009; Thompson 1967), so there is a lack of feedback to
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shape the emergence process. In siloed use, standards constrain how instances of individual IS use can and will interact
with one another, meaning that time-demanding matters such
as change and evolution are particularly challenging and
limited in scope. With respect to collective IS use, this is
similar to an exoskeleton structure constraining change in the
way in which instances of individual IS use interact with one
another.
In the case of networked use, the time differential for the
emergence process is considerably lagged, making the form
of this type of collective IS use take a longer time to become
established. It is difficult to identify the existence of collective IS use under this ideal type given that the multiple
instances of individual IS use show very significant interaction and dependence with one another. Coordination
between the instances of individual IS use is achieved through
face-to-face scheduled or unscheduled meetings (Majchrzak
et al. 2000; Van de Ven et al. 1976), so feedback is pervasive
and an integral part of the emergence process. The presence
of informal and time sensitive feedback implies that timedemanding matters, such as change and evolution, are
facilitated. With respect to collective IS use, this is similar to
an endoskeleton structure allowing for change in the way of
interacting between instances of individual IS use.
Furthermore, drawing on dynamic network literature
(Schilling and Phelps 2007), within a particular ideal type,
there could be variations in the time it takes for collective IS
use to emerge. The specific configuration of task, user, and
system interdependence between instances of individual IS
use, will influence information flow and feedback, causing
networked use, which is more clustered (i.e., dense connectivity) and with higher reach (i.e., short path lengths) to
emerge faster than networked use that is less clustered and
with a smaller reach.
Discussion and Conclusion
As the nature of technology in organizations becomes
increasingly fragmented and distributed, there is a growing
need to understand collective IS use. In their call for blue
ocean theorizing, Grover and Lyytinen (2015) argue for the
“need to continually develop and advance contextual theories
and sound typologies of IT” (p. 287). Following this call, we
propose a typological theory of collective IS use that
leverages multilevel theory (Klein and Kozlowski 2000) and
typological theory (Bailey 1994; Doty and Glick 1994)
guidelines to bring a unique contribution to the extant literature on IS use. This paper makes a theoretical contribution
toward a better understanding of collective IS use and constitutes a fundamental piece in the development lifecycle of the
construct (Hirsch and Levin 1999). As detailed below, our
theory development process delivered on the key dimensions
of a theoretical contribution: what, how, and why (Whetten
1989).
Theoretical Contribution Criterion: What
What is an essential element of theory, and refers to the
dimensions that should be considered as part of a comprehensive and parsimonious explanation of the phenomenon of
interest (Whetten 1989). Our conceptual definition of collective IS use and its related typology address the key elements
of what is collective IS use in a way that brings conceptual
clarity to the construct.
Conceptual clarity is essential for ensuring consistency of
definition and interpretation of terms, a starting point toward
generating “good” theory (Rivard 2014; Suddaby 2010). Key
to establishing conceptual clarity is the inclusion of a good
conceptual definition, which Suddaby (2010) argues is a
statement that is “precise and parsimonious, effectively captur[ing] the essential properties and characteristics of the
concept” (p. 347). First, our definition of collective IS use
explicitly accounts for the key identifiable characteristics of
the construct (Jaccard and Jacoby 2010; Suddaby 2010;
Wacker 2004). As such, collective IS use is a unit level
construct that emerges in the context of a common work
process from instances of individual IS use and their respective task, user, and system interdependence. Second, our
conceptual definition shows parsimony (Suddaby 2010;
Wacker 2004) in that it is concisely articulated, yet it captures
the essential characteristics of the phenomenon. Third, collective IS use, as conceptually defined here, avoids tautology
(Suddaby 2010; Wacker 2004). Finally, our definition avoids
circularity or the presence of antecedent or outcome variables
in its wording (Suddaby 2010; Wacker 2004).
Contributing to conceptual clarity is to explicitly identify the
assumptions that underlie the theorization of a construct
(Suddaby 2010). We do so by clearly stating our main conceptual assumptions (Rivard 2014). First, collective IS use is
a multilevel construct that emerges from the individual level.
Second, our conceptualization is rooted in the very rich
definition of IS use at the individual level, thus comprised of
task, user, and system dimensions. Third, task, user, and
system interdependence as structural features of work are
manipulable, and can show different degrees of interdependence. Finally, we assume that task, user, and system
interdependence are orthogonal.
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All in all, our conceptual definition of collective IS use represents a revealing look into the inner workings of collective IS
use which extends our previous understanding, namely that
“system usage at any level of analysis (being) a user’s
employment of a system to perform a task” (Burton-Jones and
Gallivan 2007, p. 659).
Theoretical Contribution Criterion: How
The how criterion speaks to the way in which “a set of factors
(relate to one another in a way that) adds order to the conceptualization by explicitly delineating patterns” (Whetten
1989, p. 491).
Establishing conceptual clarity requires explaining the
semantic relationship behind a phenomenon, as “constructs
exist only in referential relationships, either explicit or implicit, with other constructs and with the phenomena they are
designed to represent” (Suddaby 2010, p. 350). Following the
work of Burton-Jones and Gallivan (2007), we assume that
collective IS use emerges from instances of individual IS use
and we acknowledge the role of interdependencies-in-use as
a precondition to collective IS use. One of our unique contributions lies in elucidating these interdependencies. Our
conceptualization of task, user, and system interdependence,
the assumption of orthogonality, and the preeminence of task
interdependence, shapes our understanding of four collective
IS use ideal types: siloed use, processual use, coalesced use,
and networked use. Informed by the organizational design
(e.g., Leavitt 1965; Thompson 1967; Van de Ven et al. 1976),
organizational behavior (e.g., Wageman 1995; Wageman and
Baker 1997), and information systems (e.g., Bailey et al.
2010; Barki and Pinsonneault 2005) literature, we theorized
how, from a functional perspective, a particular combination
of user interdependence and system interdependence fits best
with each type of task interdependence. This conceptualization is finer-grained than that solely based on the similarity or
dissimilarity in instances of individual IS use (Burton-Jones
and Gallivan 2007).
Theoretical Contribution Criterion: Why
The why criterion speaks to the “dynamics that justify … the
proposed causal relationships” (Whetten 1989, p. 491). In
other words,
the construct, its definition, its scope conditions …
and its relationship to other constructs must all make
sense. That is, they must all cohere or “hang together” in a logically consistent manner (Suddaby
2010, p. 351).
We achieve that goal by rooting our theory building effort in
established approaches, such as the multilevel theory principles that helped anchor our conceptualization of collective
IS use. Just as Klein and Kozlowski (2000) explain why a
collective phenomenon emerges in the extremes as composition or compilation based on the existence of processes that
enable or constrain variation, we argue that the emergence
process, in the case of siloed use (as composition), processual
use (as minimum/maximum), coalesced use (as variance of
contribution), and networked use (as compilation), is explained by the different configurations of task, user, and
system interdependence.
Overall, our theoretical development shows that each ideal
type is fundamentally distinct from the others. A key characteristic of collective phenomena, equifinality, suggests that
the same end state can be reached by many potential means.
This implies the necessity to understand the “different
contextual constraints and patterns of interaction” (Klein and
Kozlowski 2000, p. 59). As a result, the interpretation and
empirical analysis of one type should not be compared and
contrasted to the results of a different type, without close
consideration to how instances of individual IS use interact
and depend on one another. An omnibus approach to
studying collective IS use may promote dispersion and confusion in the field, as researchers report contradictory results
when comparing between the proverbial apples and oranges.
Opportunities for Future Research
Ultimately the value of any conceptualization of collective IS
use will depend on whether it proves generative, first, by
addressing effectively outstanding issues in extant literature,
second, by drawing attention to topics that have been
neglected in extant literature, and third, by involving an
expanded and useful treatment of practice. Therefore, we
invite researchers to push the boundaries of our work.
First, bringing conceptual clarity to collective IS use is a
stepping-stone for future theory building efforts. With the
proliferation of distributed and enterprise information
systems, but also because collective IS use can possibly
extend from the group, to the organization, and to the societal
level, we believe that there are opportunities to leverage our
conceptualization of collective IS use to understand better and
to study empirically IS use by organizations or societal
networks. In extending the conceptualization of collective IS
use in such directions, particular consideration should be
given to the conceptualization of user interdependence. At
higher levels of analysis, the measure of how actors interact
and depend on one another may no longer be provided by the
goals and rewards perspective, but rather by how issues are
MIS Quarterly Vol. 42 No. 4/December 2018 1297
Negoita et al./Collective IS Use: A Typological Theory
framed in larger collectives (Benford and Snow 2000) or
collective action taken (Olson 2009).
Second, we provide a number of opportunities for empirically
exploring the typology. Recall that the ideal types were
shaped around the nature of task interdependence and conceptualized by establishing which values of user interdependence
and system interdependence provide a better fit (Leavitt 1965)
with a particular value of task interdependence. This suggests that, given a certain task configuration, the closer the
actual profile of collective IS use is to an ideal type, the better
the collective performance. Leveraging our suggested operationalizations for task, user, and system interdependence,
researchers could elaborate models to theorize about such
relationships and test them using well-established approaches,
such as the systems approach to profile deviation (Drazin and
Van de Ven 1985). They could also test whether nonideal
configurations may converge toward an ideal type over time,
and determine the circumstances under which such convergence occurs. The nature of the emergence process associated
with the four ideal types could also be studied. This work can
appeal to researchers of various epistemological dispositions
given that this paper’s conceptualization of collective IS use,
and in particular its emergence process, raises a number of
issues that can be explored further through qualitative or
quantitative methods. Qualitative researchers could pursue
additional study of the emergence process from the individual
to the unit level. Work aimed at understanding better the
micro–macro divide behind collective IS use would only
benefit from the deep insights that are typically generated by
qualitative methods (Klein and Myers 1999). This research
would have the potential to contribute valuable insights to
extant literature on IS use by bringing empirical support to
this paper’s conceptualization of collective IS use. Quantitative researchers could empirically test the emergence processes we propose. This would be a crucial step in the
direction of knowledge accumulation and an important
contribution to the IS field.
Finally, our conceptualization of collective IS use may inform
the literature on technology affordances. The concept of
affordances is by its nature relational in that it is defined in
the relationship between the user and the material features of
the IT artifact (Orlikowski and Scott 2008; Volkoff and
Strong 2013). In the context of a collective, our conceptualization being rooted in task, user, and system interdependence could focus the attention not only on the relationship between technology features and users, but also on the
relationship between instances of individual IS use. As evidenced in extant literature, collective level affordances are
prevalent when studying IS use across a wide range of collective contexts (Grover and Lyytinen 2015; Kane et al. 2014;
Leonardi 2013; Strong et al. 2014). The reconceptualization
of collective level affordances to incorporate the interdependence between instances of individual IS use could provide a
fruitful avenue for the further study of what users can (or
cannot) do with technology.
Contributions to Practice
For practitioners, our typology provides a tool for managing
collective IS use. Managers could foresee that more complex
collective IS use will emerge, over time, from certain configurations of task, user, and system interdependence. This
implies that the management of siloed use may very well rely
on best practices or particular technical and managerial
approaches that have been previously successful. Managing
networked use, however, will be a different proposition since
the team configuration of task interdependence, high user
interdependence, and tight system interdependence will render
such collective IS use more dynamic in nature. Reliance on
expert insight or the assessment of real-time simulations of
specific contexts of use should be used given the more complex nature of this type of collective IS use. Due to possible
variations in task, user, and system interdependence, interventions that may have proven beneficial at managing collective
IS use in one particular instance may not guarantee similar
results as in the past. In this case, hindsight no longer leads
to foresight. As a result, our theorization should assist practitioners to manage collective IS use more effectively.
Acknowledgments
The authors wish to thank the senior editor, Youngjin Yoo, for his
advice and guidance on our work. A heartfelt thank you to the
reviewers for an inspiring and developmental review process.
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About the Authors
Bogdan Negoita is an assistant professor of Information Technology
(IT) at HEC Montréal. He holds a Ph.D. in Management (Information Systems) from the Desautels Faculty of Management at
McGill University. His research interests include information
systems use, the distributed championing of IT implementations, and
the management of open source projects. His research has been
published in MIS Quarterly.
Liette Lapointe is an associate professor of Information Systems
and Vice Dean, Undergraduate Programs, at the Desautels Faculty
of Management of McGill University. She holds an M.Sc. in
Healthcare Administration from the Faculty of Medicine at
Université de Montréal and a Ph.D. in Administration (Information
Systems) from HEC Montréal. Her research interests include information technology implementation, user reactions to IT, IT addiction, as well as issues related to the adoption of information systems
in healthcare settings. Her research in information systems and
healthcare management has been published in such journals as
Canadian Medical Association Journal, Information Systems Journal, International Journal of Medical Informatics, Journal of the
Association for Information Systems, MIS Quarterly, and Organization Science.
Suzanne Rivard is a professor of Information Technology (IT) at
HEC Montréal and is the HEC Montréal Endowed Chair in Strategic
Management of Information Technology. She is a fellow of the
Royal Society of Canada and a fellow of the Association for Information Systems. She received her Ph.D. from the Ivey School of
Business, University of Western Ontario. Her research pertains
primarily to IT project risk management, outsourcing of IT services,
and user-related issues, such as user resistance to IT implementation.
Suzanne’s work has been published in such journals as Communications of the ACM, Journal of Information Technology, Journal of
Management Information Systems, Journal of Strategic Information
Systems, MIS Quarterly, Omega, and Organization Science.
MIS Quarterly Vol. 42 No. 4/December 2018 1301
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express written permission. However, users may print, download, or email articles for
individual use.

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