Ideational Influence, Connectedness, and Venue Representation: Making an Assessment of Scholarly Capital

Abstract

Assessing the research capital that a scholar has accrued is an essential task for academic administrators, funding agencies, and promotion and tenure committees worldwide. Scholars have criticized the existing methodology of counting papers in ranked journals and made calls to replace it (Adler & Harzing, 2009; Singh, Haddad, & Chow, 2007). In its place, some have made calls to assess the uptake of a scholar’s work instead of assessing “quality” (Truex, Cuellar, Takeda, & Vidgen, 2011a). We identify three dimensions of scholarly capital (ideational influence (who uses one’s work?), connectedness (with whom does one work?) and venue representation (where does one publish their work?)) in this paper as part of a scholarly capital model (SCM). We develop measurement models for the three dimensions of scholarly capital and test the relationships in a path model. We show how one might use the measures to evaluate scholarly research activity.

Full text

J
ournal of the
A
I
S
ssociation for
nformation
ystems
Research Paper ISSN: 1536-9323
Volume 17
Issue 1
pp. 1 – 28
January
2016
Ideational Influence, Connectedness, and Venue
Representation: Making an Assessment of Scholarly
Capital
Michael J. Cuellar
Information Systems, Georgia Southern University, USA
mcuellar@georgiasouthern.edu
Hirotoshi Takeda
Département des systèmes d'information
organisationnels, Université Laval, Quebec, Canada
hirotoshi.takeda@fsa.ulaval.ca
Richard Vidgen
UNSW Business School, University of New South
Wales, Australia
r.vidgen@unsw.edu.au
Duane Truex
CIS, Georgia State University, USA
dtruex@gsu.edu
Abstract:
Assessing the research capital that a scholar has accrued is an essential task for academic administrators, funding
agencies, and promotion and tenure committees worldwide. Scholars have criticized the existing methodology of
counting papers in ranked journals and made calls to replace it (Adler & Harzing, 2009; Singh, Haddad, & Chow,
2007). In its place, some have made calls to assess the uptake of a scholar’s work instead of assessing “quality”
(Truex, Cuellar, Takeda, & Vidgen, 2011a). We identify three dimensions of scholarly capital (ideational influence
(who uses one’s work?), connectedness (with whom does one work?) and venue representation (where does one
publish their work?)) in this paper as part of a scholarly capital model (SCM). We develop measurement models for
the three dimensions of scholarly capital and test the relationships in a path model. We show how one might use the
measures to evaluate scholarly research activity.
Keywords: Affiliation Network Analysis, Bibliometrics, Citation Analysis, Connectedness, Hirsch Family Indices,
Ideational Influence, IS Research Evaluation, Scholarly Capital, Scientometrics, Social Network Analysis, Venue
Representation.
John Mingers was the accepting senior editor. This paper was submitted on October 2, 2013 and went through three revisions.

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1 Introduction
In most academic institutions, judgments about scholars’ capability to produce and publish research at a
sufficiently high level make up a key part of decisions about recruiting, retaining/re-appointing, promoting,
funding, and granting tenure to them. This evaluation process has pragmatic consequences when
comparing the abilities of one researcher or of a set of researchers to others. Indeed, scholarly evaluation
impacts the very survival of academic programs, academic departments, and individual faculty.
Historically, stakeholders have evaluated information systems (IS) researchers—as individuals and as
collectives—by counting the number of publications they have published in “quality” venues with
publications in the highest-ranked journals carrying the heaviest weighting. This journal-ranking approach
seems logical. Those researchers active in a field, together with the editors and editorial board members
of the field’s journals, have a sense of the relative quality of the many publication venues relevant to the
field. Certainly, people in the field know it better than someone from outside it who may be charged with
evaluating a scholar to, for example, hire them or decide if they should receive tenure. Someone making
such a judgment may reasonably seek a warrant or indicator as to the value of a scholar’s work. Thus,
many institutions refer to informed and composite journal rankings. Relying on such journal rankings
saves evaluation committees from having to examine and judge individual scholars’ merits in detail
themselves (i.e., journal ranking lists provide them with a ready evaluative shorthand).
However, two issues arise in adopting this ranking approach. First, one can question the methods for
determining what constitutes the “best” journals. Scholars have proposed various ways of establishing
journal quality (e.g., journal impact factor and citation counts), but perhaps the most widely used
technique in use today is the journal ranking mechanism. Journal rankings are typically determined via
surveys of researchers or by relying on the opinions of expert panels, such as the Academic Journal
Quality Guide that the Association of Business Schools produces (Harvey, Kelley, & Rowlinson, 2010). A
body of literature concerned about the subjectivity of such rankings exists, and this literature has voiced a
growing concern over ways in which these “received” measures are biased or are merely schemes to
preserve power regimes already in place (Chua, Cao, Cousins, & Straub, 2002; Gallivan, 2009; Hardgrave
& Walstrom, 1997; Singh et al., 2007; Truex, Cuellar, Vidgen, & Takeda, 2011b). In a recent paper,
Mingers and Willmott (2013) detail the consequences of using journal lists “to correct the biases ascribed
to evaluators of research quality” (p. 2) and describe the controversy these lists have generated in the UK
and Commonwealth countries because using such lists somewhat “shoehorns horizontal diversity of
research and scholarship into a single, seemingly authoritative vertical order…, privileging the research
agendas of those journals and devaluing research published elsewhere, irrespective of its content and
contribution” (p. 2).
Second, current research has shown that the concept of quality itself is one which has not been well
theorized. To date, no field has adopted a “theory of academic literature quality”, nor has anyone even
proposed one (Dean, Lowry, & Humpherys, 2011; Locke & Lowe, 2002; Straub & Anderson, 2010). As a
consequence, the literature has become a battleground over developing a gold standard for academic
venues. The situation has become one in which different groups use different surrogate measures to
compare the “quality” of one venue to another. They use measures such as rejection rates, citation
counts, impact factors, and other bibliometrics to assert the supremacy of a particular publication venue,
all of which scholars have shown to be biased measures (Chua et al., 2002; Hardgrave & Walstrom,
1997). Even then, once one decides on the venue list, studies have shown these top journals are not
effective at identifying papers that their respective field will highly use (Singh et al., 2007). In short, the
discourse is one in which journal “quality” becomes self-enforcing via a kind of reification by repetition
(Truex, Cuellar, & Takeda, 2009). Reviewers and editors expect that manuscripts under review include
those publication venues featured in ranking lists in their citations and bibliographies, which further
cements these journals’ position.
Therefore, in this paper, we argue that—given that we lack a theory of academic quality or clearly
accepted criteria of academic quality and the problems that highly ranked venues have in identifying
papers that go on to become highly influential—one should base hiring, promotion, and tenure decisions
not on whether a scholar’s work is of sufficient quality but whether the scholar possesses sufficient
scholarly capital to enable the scholar’s organization to achieve its research goals. This change allows us
to move from the quixotic quest to identify quality in scholarly output to the more realizable and pragmatic
exploration of what measurable scholarly capital an academic brings to their organization.

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To assess a scholar’s capital, we propose the scholarly capital model (SCM). The SCM is based on the
idea that, when evaluating a scholar’s research capabilities as part of making hiring, promotion, and
tenure decisions, organizations should consider three things: 1) the extent to which other scholars take up
the scholar’s work (ideational influence), 2) who the scholar works with (connectedness), and 3) how well
the scholar publishes in venues in the scholar’s field (venue representation). These three dimensions
make up the capital that a scholar possesses to perform scholarly activity. Scholars need favorable results
in each dimension to impact their field and, thus, provide their institution(s) with increased research
capability and prestige. Taken together, these three dimensions represent the extent to which one can say
a researcher has scholarly capital, which we argue provides a more rounded and democratic view of a
scholar’s impact than a simple count of papers in a small set of highly ranked journals.
As such, in this paper, we explain and test the SCM via rigorously developing the model’s concepts and
relationships and empirically examining these relationships by testing a set of hypotheses that we draw
from the model. A useful practical outcome of this research is a method for profiling the capital of
individual scholars that one can automate.
This paper proceeds as follows. In Section 2, we define and describe the SCM and the sub-constructs of
scholarly capital: ideational influence, connectedness, and venue representation. In Sections 3 through 7,
we show how one can operationalize and automate each sub-construct, and we illustrate these
relationships by applying them to a set of scholars drawn from the information systems research field.
Finally, in Sections 8, we show how one can use the measures of ideational influence, connectedness,
and venue representation to compare and contrast individual scholars in terms of their relative capital. In
Section 9, we conclude with the research’s limitations and potential for future work.
2 Scholarly Capital
With this paper, we continue an on-going academic discourse exploring the nature of academic
scholarship and the way it is evaluated. Previous work has identified that the current method of evaluating
scholarly output (i.e., counting publications in ranked journals) is problematic (Singh et al., 2007; Truex et
al., 2009; Truex et al., 2011a). Quality as a concept is under theorized (Locke & Lowe, 2002; Straub &
Anderson, 2010) and has, therefore, been used in the context of evaluating scholarly output in an implicit
manner. The method individuals choose to select and rank journals is often biased (Chua et al., 2002;
Hardgrave & Walstrom, 1997), and top-ranked journals often fail to publish what the field believes are
important papers (Singh et al., 2007). We further recognize that journal lists have performative effects.
Research becomes “homogenized” to meet the standards of the highly ranked journals to the detriment of
substantive contributions and diversity. Originality is no longer as important as the ability to produce
research that conforms to the standards of normal science (Mingers & Willmott, 2013).
As a result, some have called for changing how we evaluate scholars. As Adler and Harzing document:
Lawrence (2003, p. 261) unambiguously recommends that “we can all start to improve things by
toning down our obsession with the journal. The most effective change by far would be…to place
much less trust in a quantitative audit that reeks of false precision.” Lawrence (2002, p. 835) urges
academia to “stop measuring success by where scientists publish and [to] use different criteria, such
as whether the work has turned out to be original, illuminating and correct.” Starbuck (2005, p. 205)
likewise concludes that those evaluating scholars for promotion and tenure need to stop ignoring the
randomness of article placement in journals, and more importantly, stop basing evaluations “on one
myopic measure.” Bennis and O’Toole (2005) similarly worry that the current emphasis on journal
rankings is directly responsible for retarding the publication of relevant management knowledge.
Scholars seeking to publish in top journals “tend to tailor their choice of topics, methods, and theories
to the perceived tastes of these journals’ gatekeepers. A likely result…is stagnation in the
advancement of management knowledge and a disconnection from the needs of the business
community”. (Adler & Harzing, 2009, p. 78)
Given the present system’s deleterious effects, we recommend that we should evaluate scholars’
scholarly capital instead of publication quality. As we show in Section 3, the SCM provides a well-defined
set of concepts and measures that makes it amenable to automating when one needs to evaluate
scholars’ research ability. SCM moves the evaluation mechanism beyond one myopic measure to a profile
of multiple measures that represent scholarly capital in terms of a scholar’s ideational influence,
connectedness, and representation in their field’s venues.

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2.1 Scholarly Capital
As we discuss in Section 3, scholarly capital is the collection of capabilities and reputational assets that a
scholar brings to an organization. It represents the bank of capital that the scholar has to develop and
spread their ideas throughout a field. Truex et al. (2011a) identifies two forms of capital: ideational
influence (passive: who is using one’s work?) and what they term social influence (active: who does one
work with?). Yan and Ding (2009) also observe a correlation between citation counts and centrality
measures and suggest that citations measure papers’ impact while network centrality measures authors’
impact. In this paper, we rename “social influence” to “connectedness” to better describe the construct. To
these two forms of capital we add a third type: venue representation (where is one’s work published?).
The scholarly capital model (see Figure 1 below) shows the relationship of these three types of capital.
Figure 1. Scholarly Capital Model (SCM)
One should view the SCM as embedded in a larger and dynamic context (see Figure 2 below). The inner
part of the causal map diagram represents the reciprocal relationships between the three parts of the
SCM. The outer part of the diagram shows research funding, impact on practice, and impact on career.
Although the outer part of the diagram is outside our scope here, it is useful in showing the SCM’s
boundaries and in identifying areas for further development (e.g., how can we measure a scholar's impact
on practice?). The figure also shows the ability to attract research funding, impact practice, and scholarly
impact as enablers of academic advancement. The influence diagram shows all relationships as positive
in the sense that, as levels in one rise or fall, levels in the connected concept rise or fall in the same
direction. Although beyond our scope, by extending the diagram to incorporate negative relationships, one
could model virtuous and vicious circles in the dynamics of academic life.

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Figure 2. Scholarly Capital Model (SCM) in Context
For our purposes here, we focus on the inner part of Figure 2 (i.e., scholarly capital).
3 Model Development
In this section, we further develop the SCM by providing a theoretical account of scholarly capital, how it arises,
and how one can measure it. The model starts from defining the field (i.e., those scholars who are most
concerned with developing the IS paradigm) (Kuhn, 1996). Having defined the field, one can discuss the
theoretical roots of each of the three types of capital, how they arise, and how one can operationalize them.
3.1 Defining the IS Field
To define the field, we begin by establishing publication venues as institutions that are the field’s
“outposts”. We view publication venues—principally journals and conference proceedings—as institutions
because, as North (1991) indicates, the venues are human-devised structures that enable and constrain
interaction in fields. The idea of the “institution” has a well-established literature and theoretical base (see
Delbridge and Edwards (2007) for a review). North (1991) defines institutions as:
…the humanly devised constraints that structure political, economic and social interaction. They
consist of both information constraints (sanctions, taboos, customs, traditions, and codes of
conduct), and formal rules (constitutions, laws, property rights). Throughout history, institutions
have been devised by human beings to create order and reduce uncertainty in exchange.
In our research, publication venues represent the major repository of explicit knowledge in a research field
and serve to reduce uncertainty through peer review and editorial processes, which allow one to address
questions such as: “can this research be trusted?”, “is it credible?”, “is it relevant?”, and, “is it well
executed?”. Venues provide these benefits to scholars in exchange for those scholars accepting the
constraints that the peer-review and editorial processes provide.
Publication venues as academic institutions both constrain and enable the actions of organizations and
individual actors. As institutions, publication venues constrain because, when individual researchers and
departments seek to gain legitimacy by aligning themselves with specific venues, they are subject to
coercive, normative, and mimetic institutional pressures (DiMaggio & Powell, 1983). Coercive pressures
arise from standards and processes (such as the review process), such as when editors and reviewers
request certain changes in the authors’ proposed publication. Normative pressures arise from
professionalization in networks of researchers with similar educational backgrounds and aspirations.
Mimetic pressures arise as researchers and departments model themselves on other researchers and on
Connected
-ness
Ideational
influence
Venue
representation
Career
advancement
Ability to attract
research funding
Impact on
policy and
practice
Scholarly
capital

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departments that they see as successful (e.g., those capable of publishing in the venues that the research
field values). Following this argument, publications venues both legitimize and define a field’s boundaries.
Accordingly, we propose that one way to define a field is by identifying the set of publication venues—
typically journals and conferences—that constitute that field’s institutions that disseminate knowledge. A
field’s research paradigm (Kuhn, 1996) describes what the field believes about the nature of reality, the
prescribed methods for researching that reality, and the approved/preferred venues for publishing
research findings. Publishing in these venues confers legitimacy on the research findings of academics
wishing to contribute to their field’s body of knowledge. Through publishing in their field’s venues, IS
academics both establish themselves as part of the IS field and simultaneously create that field.
3.1.1 Operationalizing the IS Field
Although no definitive list of IS venues exists, one can identify venues by considering sources such as the
IS Senior Scholars’ journal recommendations (www.aisworld.org), listings produced by national research
councils (e.g., the Australian Research Council gives each journal a “field of reference” code or codes for
evaluating research output by subject area), and other journal listings (e.g., the U.K. Association of
Business Schools listing includes an IS section).
However, such an approach runs counter to our mission of developing a data-driven approach that one
can automate. Mingers and Leydesdorff (2014) show that one can identify academic fields by analyzing
the cross-citations between journals. Using factor analysis, Mingers and Leydesdorff identify clusters of
journals that correspond with different subfields in business and management. We propose that one can
use such an approach to automate the selection of the venues that constitute a field such as IS.
Having defined and operationalized the research field (i.e., the publication venues that constitute that
field), we now consider three different forms of scholarly capital.
3.2 Ideational Influence
One may measure a scholar’s productivity in terms of how many papers they publish. Raw publication
counting is, however, an incomplete measure because, although having been published (i.e., being
productive) is a necessary prerequisite to being cited, a scholar's capital derives not just from publishing
their ideas alone or even in having a continuing stream of ideas published and available to others but in
having those ideas considered (taken up) and acknowledged by others in the form of citations. If a
scholar’s research is rigorously executed and flawlessly written but is unknown, then it is as if the research
were never done. Therefore, part of a scholar’s capital derives not just from the number of works
published but more importantly if (and how) others acknowledge and use those works. Truex et al. (2009)
define ideational influence as a field’s uptake of a scholar's ideas. We limit the concept of ideational
influence to mean the uptake of a scholar’s ideas via published research. This definition distinguishes it
from social influence or other means of spreading their ideas. It follows that, if a scholar’s research is
influential or speaks to a topic in a way other scholars deem relevant, other scholars will draw on it in their
research (and, presumably, cite it in that research). Thus, the extent to which a field takes up a scholar’s
ideas is a key pointer to the field’s direction and what the field considers to be important.
Therefore, one finds ideational influence in a field’s use of published literature, and the process by which
ideational influence arises is at the heart of the research process. In the course of their work, a scholar
accesses previously published work perhaps as part of a literature review, perhaps as part of an effort to
support their arguments in developing their paper, or perhaps in responding to a review. Latour (1987)
argues that scholars use published literature for just this reason: to buttress one’s arguments against
those who argue against their point. He further suggests that scholars proactively use published work to
preempt and ward off attacks to their papers’ argument and premises, which is one reason why
practitioners find it so difficult to read scientific papers. Scholars also cite literature in critiquing previous
research (e.g., to argue that a previous paper is incorrect and should be refuted). Indeed, a solid literature
review is a necessary requirement for any academic publication (Rowe, 2014).
The process by which one selects and includes literature in a paper is key to ideational influence’s
development (Rowe, 2014; Sutton & Staw, 1995; Webster & Watson, 2002; Weick, 1989, 1995). First,
previous research must be visible to the author; that is, the author must know that the research exists and
where it is. One can accomplish visibility in various ways. If the literature is published in a “notable” venue
(i.e., one that is well known to the field), it will be more visible than one that is published in an obscure

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journal that is known only by a small number of people. However, this issue is declining in importance with
the rise of automated search mechanisms such as Google Scholar, Web of Science, and Scopus. These
automated tools level the playing field by making all publications more visible. Second, once visible to the
author, the research must be appropriate; that is, it must add value to the citing paper (e.g., to defend or
ward off attacks on it) (Latour, 1987). Third, the previous research must be credible; it must be
appropriately argued and not easily dismissed by opponents. Credibility is derived from the source’s
trustworthiness and expertise (Pornpitakpan, 2004). Trustworthiness means that the source can be relied
on to make truthful assertions. Expertise refers to one’s having the background and skills to make an
accurate statement. In the context of academic literature, the proposed source should conform to the
canons of normal science. Kuhn (1996) argues that, for a scientific field to enter a period of normal
science requires: 1) a paradigm, an established understanding of the world’s nature, appropriate research
questions, and agreed-on methods for studying the questions. Therefore, during periods of normal
science, scientists will choose literature that conforms to their understanding of normal science in their
field to cite. Only in dealing with anomalies, findings that cast doubt on existing understandings, does the
scientist elect to cite literature that contradicts normal science. Therefore, assessing expertise is based on
how well a publication being considered for citation conforms to the methods agreed on in normal science.
Thus, for a publication to exert ideational influence, the publication must be visible, appropriate, and
credible. One can then include such a publication in their literature review to support their own arguments
or to critique and/or refute other arguments. When this process occurs, we can say that the referenced
paper influences the field.
In most academic fields, such as information systems, citation counting and citation patterns are the
primary way that the fields express ideational influence. Of course, there are times when this standard is
broken and scholars cite papers that do not meet the criteria for ideational influence. This situation occurs,
for example, when journal editors require certain citations as a condition for publication (Bjorn-Andersen &
Sarker, 2009; Crews, McLeod, & Simkin, 2009; Janz, 2009; Romano, 2009; Straub & Anderson, 2009).
While citation patterns might be distorted as a result of such practices, the distortion is sufficiently small
such that citation data is still the most appropriate proxy for a field’s uptake of a scholar’s ideas.
3.2.1 Operationalizing Ideational Influence
Scholars have operationalized ideational influence with the Hirsch family of indices (Egghe, 2006; Hirsch,
2005; Sidiropoulos, Katsaros, & Manolopoulos, 2006) for both scholars and journals (Cuellar, Takeda, &
Truex, 2008; Truex et al., 2009; Truex et al., 2011a). Scholars have used three of the Hirsch family indices
to assess scholars’ ideational influence. The first h-statistic proposed is the “native h-index” or simply “h-
index”. Hirsch (2005, p. 16569) developed the h-index to “quantify the cumulative impact and relevance of
an individual’s scientific research output”.
Although promising, naively using the “native-h” statistic is problematic, and some have challenged it as
being “biased” in several ways (Mingers, 2009; Mingers, Macri, & Petrovici, 2012; Truex et al., 2009). For
example, consider a scholar who produces a paper that garners a large number of citations but whose
other papers are poorly cited. The native h-index is insensitive to the number of citations to a paper once it
has received a number of citations higher than the h-index itself. The question asked is: when given two
scholars with the same h-index, does not the one having a higher number of citations to their papers have
greater influence? To address this concern and adjust for this difference, Egghe has proposed the “g-
index” (Egghe, 2006). The g-index gives greater weight to highly cited papers.
Some have also criticized the h-index for favoring older publications. Papers that have been in print for a
longer period of time have had more of a chance to gain citations. Newer papers may be as influential or
become more influential than older papers given sufficient time. To address this concern, scholars have
proposed the contemporary h-index or hc-index (Sidiropoulos, Katsaros, & Manolopoulos, 2006). The hc-
index weights citations to more recent papers more highly. By using the hc-index, we can compensate for
the effects of time and create comparability between papers of different ages.
By using all three of these indices—h, hc, and g—we can build a profile of scholars’ ideational influence
that one can use to compare their relative influence. One aspect that previous research consistently
highlights is that one should not rely on a single metric when assessing a researcher’s impact; rather, one
should use a set of metrics to measure the researcher’s impact (Bornmann, Mutz, & Daniel, 2008;
Mingers et al., 2012). In compliance to this call for multiple measures, we contend that using a set of h-
family measures will provide a more rounded and reliable measure of a scholar’s ideational influence.

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3.3 Connectedness
Scholars have established that the development of scientific knowledge is a social phenomenon (Bhaskar,
1997; Bourdieu, 1984; Kuhn, 1996; Pinch & Bijker, 1984). Bourdieu (1984) discusses the importance of
social interaction in as an item of capital in the domain of “the academy”. He analyzes academic sociology
and explores how social networks contribute to the building and exercising of power. For Bourdieu, the
nature and structure of these social networks is critically important. According to Field (2003, p. 1),
Bourdieu’s central thesis is that:
relationships matter. By making connections with one another, and keeping them going over
time, people are able to work together to achieve things that they either could not achieve by
themselves, …People connect through a series of networks and they tend to share common
values with other members of these networks; to the extent that these networks constitute a
resource, they can be seen as forming a kind of capital…. This stock of capital can often be
drawn on in other settings.
Thus, we see the network connections a scholar makes can be important in determining their ability to
perform in the academic arena. In interpreting findings or developing theories, scientists interact with each
other to help flesh out theories or test these theories either formally through the publication process or
informally through interactions at conferences and other meetings or through media such as telephone
and email. These interactions mold and shape the ideas of those interacting and eventually help foster the
consensus that determines what the field regards as “truth” (Habermas, 1985).
By connecting with other scholars, scholars form of their social capital, which they can draw on to interact
with and impact their field. The closer they are to key influencers, the more they are able to have their
ideas accepted and spread through the field. Through their connectedness in social networks, scholars
are able to build and leverage scholarly capital.
3.3.1 Operationalizing Connectedness
As social interactions occur, the informal interactions sometimes create formalized relationships. One may
instantiate this formalization by co-authoring a paper, becoming a doctoral student-advisor, joining a
faculty and becoming co-workers on the same faculty, or forming virtual research teams.
These formalized relationships can produce co-authored papers that report on scholars’ research
collaborations. The scholars’ joint vested interest in seeing the fruits of their shared research labor
published in the most suitable venue further cements the relationship. These papers, therefore, represent
the result of joint activity between scholars, and one can use co-authorships to represent the overall
scholarly social network and the connectedness of individual scholars.
To capture and assess connectedness, we analyze co-authorship relationships using social network
analysis (SNA) (Vidgen, Henneberg, & Naude, 2007). Among other things, SNA assesses network
centrality: the types and quantity of connections that one member of the network has to other members of
the network. By examining the centrality measures of the various members of the community, one can
arrive at a set of measures that assess the connectedness of each member of a research community.
In its simplest form, a network comprises nodes and edges. A node is a point on the network (Barbasi &
Albert, 1999; Coleman, 1988; Kleinberg, 2000; Travers & Milgram, 1969). In co-authorship networks, the
authors are the nodes. An edge in a network is a line connecting two nodes (Barbasi & Albert, 1999;
Coleman, 1988; Kleinberg, 2000; Travers & Milgram, 1969). An edge can be non-directional, directional,
or bidirectional. Co-authorship relationships are modeled as non-directional.
SNA provides three principal measures of centrality (degree, betweenness, and closeness) to analyze the
aggregate distances between one author and the rest of the network (Freeman, 1979; Wasserman &
Faust, 1994):
 Degree centrality of a node is concerned with the number of edges coming into (in-degree) or
out of (out-degree) the node. With a non-directional relationship, such as co-authorship, in-
degree and out-degree are the same. Degree centrality indicates how many co-authorship
interactions a particular scholar has with other scholars.
 Betweenness centrality represents the number of times a node intersects the shortest path
between two other nodes, which indicates the extent to which a scholar plays a linking role

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between other scholars. Scholars with high betweenness centrality are likely to be necessary
conduits linking scholars in disparate parts of the network.
 Closeness centrality is the reciprocal of farness, which is the sum of a node’s shortest
distance to all other nodes. The higher a scholar’s closeness centrality, the lower its total
distance to all other nodes. Scholars with high closeness centrality may be able to spread their
ideas more quickly.
By computing each of these centrality measures, we can arrive at a profile of connectedness that is useful
for comparing scholars one to another.
3.4 Venue Representation
The place in which a scholar’s work is published is a further source of capital for scholars. We refer to the
kind of resource that arises from the publishing venues in which a scholar's work appears as venue
representation. A scholar accrues venue representation through publishing in venues that are central to
their field’s body of knowledge; the more central a venue to the research field, the greater the capital
accruing to a scholar who publishes in that venue. Venue representation derives from the visibility that a
research artifact receives by virtue of being published in a research venue. Visibility and accessibility of
research artifacts increases with the number of scholars who publish in that venue; as a venue gains more
scholars, it gains more visibility to other scholars and, thus, a paper has more opportunity to be seen and
the ideas taken up.
3.4.1 Operationalizing Venue Representation
We operationalize venue representation using the affiliation network. An affiliation network is a two-mode
network with a single set of actors where the second mode is a set of events, such as a club or a social
gathering to which the actors belong. In the affiliation network, the links are not between the actors but
between actors and events: an affiliation network is a network in which actors join together by being
members in a group (Sasson, 2008). A subset of actors engage in each of the events, and, thus, the event
describes the subset of actors and the actors describe the subset of events to which they belong
(Wasserman & Faust, 1994). One can then define the group by the events and the interrelatedness of the
events and actors.
By using an affiliation network, one can classify actors by events, and the actions of those actors define
the value of the affiliation. Mediating organizations facilitate the events and create value for the affiliates
by enabling the events. The structural embeddedness of mediating organizations (Granovetter, 1985)
affects the value that accrues both to affiliates and to mediators. The duality of the nested structure of
affiliation implies that the behavior and performance of actors in the affiliation affects the behavior and
performance of mediators and vice versa (Breiger, 1974; Sasson, 2008).
Mediators, in the case of this research, are publishing venues—typically academic journals and
conferences. Events occur when actors (an academic or a group of academics) publish in a venue. The
duality of this nested arrangement is apparent: venues are sustained by the academics that publish in
those venues, while academic profiles are created by those same venues without which there would be no
h-indices or co-authorship arrangements. Therefore, the affiliation network is a simple but effective way of
capturing the idea of publishing venues as the institutions that create and are created by the academic
community of a research field.
3.4.2 Assessing Venue Representation
One can picture an affiliation matrix as one in which the rows represent scholars and the columns
represent publication venues, which is clearly not the same as the square co-authorship matrix. For
illustration purposes, Figure 3 shows an affiliation matrix comprising six scholars (S1 through S6) and four
publication venues (V1 through V4) in which, for example, scholar S2 has published three times in venue
V2 and once in venue V4. The affiliation matrix is represented graphically on the right side of Figure 3 in
which the scholars are represented by circles and the publication venues by squares. The repetition of ties
between scholar and venue are labeled with the number of times the scholar has published in that venue.

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Figure 3. Illustration of Affiliation Network Matrix and Graph
To analyze the affiliation matrix to find out which scholars are closest to the publication venues that are
central to the field, one transforms the affiliation matrix into a bipartite matrix in which the scholars and
venues are kept together in a square matrix that one can then subject to a range of standard network-
analysis techniques. Although one can represent any affiliation network as a bipartite graph, Borgatti and
Everett (1997) note that, in this case, normalizing the scores would not be valid. Borgatti and Everett
(1997) propose a routine that provides appropriately scaled results by normalizing the scores against the
maximum possible scores in an equivalently sized connected affiliation network. This algorithm produces
scaled measures of degree, closeness, and betweenness for affiliation networks and is implemented in
the UCINET social network analysis package (Borgatti, Everett, & Freeman, 2002) (and is the set of
measures we use in this research). Although we adopt the same types of measures of network centrality
for venue representation as we use for connectedness, we apply them to different networks (scholars and
publication venues rather than co-authorships (scholars with scholars)) and to different network forms (an
affiliation, two-mode network, rather than the one-mode network used in co-authorships).
4 Research Approach
In this section, we test the scholarly capital model’s (SCM) internal linkages and measurement model. Based
on the SCM, we propose three hypotheses of how the three constructs are related, and we test them through a
PLS (partial least squares) analysis of a sample of data drawn from the IS field. As we use PLS path modeling,
we cannot model the reciprocal associations and dynamic relationships of Figure 2. As such, we develop our
hypotheses by arguing for connectedness as an antecedent, ideational influence as an outcome, and venue
representation as an intermediary. However, a benefit of using PLS is that one can report correlations between
the three constructs and evaluate the measurement model’s validity and reliability.
5 Research Model and Hypotheses
In Section 3, we suggest that the visibility and legitimacy that arises by becoming central to the field by
publishing in many venues of a field constitutes venue representation. Research has shown that
publications are increasingly moving from being solo authored to co-authored (Peffers & Hui, 2003). To
achieve publication requires the ability to frame arguments in forms that a field accepts. Those scholars
who have high levels of connectedness are more likely to have access to the innermost researchers in
their community (i.e., those researchers who have a demonstrated ability to publish according to the field’s
norms). This connection to other researchers can also open up more opportunities to publish in the form
of invitations and can create a positive disposition on the part of editors and reviewers toward their
papers. Thus, we would expect those scholars with high levels of connectedness to have good access to
the publishing venues that are core to the knowledge base of the field. Therefore, we hypothesize that:
H1: Scholars with higher levels of connectedness are associated with higher levels of venue
representation.
In Section 3, we propose that ideational influence is partially created by a belief that the scholar’s research
is credible. Prior research has shown that belief in the credibility of the research is formed by an
impression of the author’s trustworthiness and expertise (Pornpitakpan, 2004). The extent to which a
scholar is “well connected” to central authors in their field provides an indication of their reputation,
trustworthiness, and expertise. Higher levels of connectedness further help a scholar to increase the

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volume of their scholarly output (as compared with working alone), which reflects the trend toward multi-
authored research (Peffers & Hui, 2003). Therefore, we hypothesize that:
H2: Scholars with higher levels of connectedness are associated with higher levels of ideational
influence.
We argue in Section 3 that, in addition to credibility, a paper needs to be visible for it to have ideational
influence. One promotes their visibility by publishing in higher-viewed venues. Venue representation
measures the centrality of scholars to the venues of their field. A scholar who publishes in venues central
to their field will tend to have higher visibility than scholars who do not do so. Additionally, scholars have
found that scholars who publish in venues that are central to their field’s knowledge base to be those who
have high ideational influence (Truex et al., 2009). Scholars who published in both North American- and
European-based journals have tended to be those with high h-indices (Truex et al., 2009). Therefore, we
expect that scholars publishing in the venues that are most central to their field will be more visible to the
field and, thus, that their papers will tend to have more citations. Therefore, we hypothesize that:
H3: Scholars with higher levels of venue representation are associated with higher levels of
ideational influence.
Figure 4. Research and Measurement Model
Figure 4 shows the research model with hypotheses, constructs, and measures. The model shows
ideational influence as the dependent variable; that is, while connectedness and venue representation are
important, their ultimate relevance is in their contribution to ideational influence.
Further, there is a pragmatic motivation for treating ideational influence as the outcome: the leading
methodologies for the ranking of universities use citations as a measure of research influence. The QS
world ranking of universities (www.topuniversities.com) gives citations a 20 percent weighting when
calculating its ranking, while the Times Higher Education (THE) weights “research influence” at 30
percent; the latter argues:
Our research influence indicator is the flagship. Weighted at 30 per cent of the overall score, it is
the single most influential of the 13 indicators, and looks at the role of universities in spreading
new knowledge and ideas. (Times Higher Education, 2012)
It also states that:
The citations help show us how much each university is contributing to the sum of human
knowledge: they tell us whose research has stood out, has been picked up and built on by other
scholars and, most importantly, has been shared around the global scholarly community to push
further the boundaries of our collective understanding, irrespective of discipline. (Times Higher
Education, 2012)
The research model also shows the measurement items. For connectedness and venue representation,
we use the most widely adopted network measures (closeness centrality, betweenness centrality, and
degree centrality), which we describe in Section 3. For ideational influence, we use the three most

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common H-family statistics: h, hg, and hc (see Section 3). The measurement model is particularly
important in this research since we seek to create scholarly capital profiles; as such, our measurement
model should be reliable and three constructs in Figure 4 should have discriminant validity.
6 Data Collection and Analysis
In Table 1, we summarize the six steps required to collect data and to analyze scholarly capital. The data-
collection and analysis method (labeled “ideal”) has the following steps: 1) define the academic field with
reference to its knowledge base, 2) identify scholars in that field, 3) calculate h-family indices for the
scholars, 4) conduct co-authorship social network analysis, 5) conduct venue-affiliation analysis, and 6)
produce reports. This is the ideal method—one that would be implemented as a commercial or community
development project for production use by a field. Such a task is beyond our resources and scope here,
and, thus, in the column labeled “proof of concept”, we show the method used in this paper to first
demonstrate the SCM and its measures and second test the research hypotheses in Figure 4.
6.1 Step 1: Define the Academic Field
In Section 3, we define an academic field as comprising a set of publication venues that represent the
body of knowledge in that field. We test our hypotheses with the information systems (IS) field. The
academic community has recognized the IS field since the 1970s when researchers from various fields
recognized there was a space where technology, people, and organizations meet.
As Table 1 shows, one method of identifying the venues that constitute a field would be to follow Mingers
and Leydesdorff’s (2014) proposed approach in which one subjects journal cross-citations to factor
analysis to identify clusters of journals that correspond with different subfields in business and
management (in our case, IS). However, because this method is not automated at this point in time, we
adopt here a smaller and simpler method using a convenience sample since we establish a “proof of
concept” rather than a fully operational application of the SCM.
Table 1. Data Collection and Analysis
Ideal
Proof of concept
Step 1
Define the academic field
Apply Mingers & Leydesdorff’s (2014)
approach to identify those venues
(journals and conferences) that
constitute the IS field.
Convenience sample of all the IS
venues (journals and conferences)
that are on existing lists and for which
an Endnote database of papers was
available. We identified 17,049
authors (this includes variations on
author names).
Step 2
Identify scholars
Include any scholar who has
published in a venue that step 1
identifies as part of the IS field.
We reduced the 17,049 authors from
step 1 to the set of 448 leading
scholars that Clarke et al. (2009)
identify.
Step 3
Calculate h-family indices
Analyze all scholars identified in step
2 for h indices.
We analyzed the 448 scholars from
step 2 for h indices.
Step 4
Conduct co-authorship social network
analysis
Calculate network scores for all authors
identified in step 2 who are part of the
main component. Those who have only
sole authorships will have a
connectedness score of zero for the
academic field defined in step 1.
Once we excluded sole authors, we
reduced the 448 authors from step 2
to 390. Of these, the SNA included the
384 authors that comprised the main
component.
Step 5
Conduct venue affiliation analysis
Include all venues from step 1 and all
scholars from step 2 in the venue-
affiliation analysis.
We analyzed 438 authors jointly with
the venues in Table 2. This number is
more authors than step 4 because we
included sole authors. It is less than
step 2 because step 1 does not
include all of Clarke et al.’s (2009)
venues.
Step 6
Produce reports
Produce scholar and venue profiles
and rankings.
We produced scholar and venue
profiles and rankings.

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We consulted existing lists of IS venues and collected publication and authorship data where that data
was available (e.g., data in the form of an Endnote database). Table 2 shows the resulting coverage of IS
venues and incorporates major journals and conferences. We consolidated the individual Endnote
databases into a single file of publications.
We then exported the Endnote database to text and wrote a conversion program to break the records into
their constituent parts to load them into a database. The database is fully normalized to allow one to
flexibly use the publication data. Unsurprisingly, several the Endnote records were inaccurate (e.g., fields
were missing and author names mistyped). The conversion routine successfully loaded 18,747
publications and identified 17,049 distinct author identifiers, although, as we show, these do not
necessarily reflect a one-to-one mapping of author identifier with individual researcher.
Table 2. Publication Sources (Venues)
Journals
Conferences
 Communications of the Association for
Information Systems (1999-2010)
 European Journal of Information Systems
(1993-2007)
 Information Systems Journal (1991-2010)
 Information Systems Research (1990-2009)
 Journal of the Association for Information
Systems (2000-2010)
 Journal of Information Technology Theory
and Application (1999-2010)
 Journal of Management Information
Systems (1984-2009)
 The Journal of Strategic Information
Systems (1991-2009)
 Management Information Systems Quarterly
(1977-2010)
 Scandinavian Journal of Information
Systems (1989-2009)
 Australian Conference on Information
Systems (2001-2008)
 AIS Transactions on Human-Computer
Interaction (2009)
 Americas Conference on Information
Systems (1998-2009)
 Bled Conference on E-Commerce (2001-
2009)
 International Conference on Information
Resources Management (2008)
 European Conference on Information
Systems (1993-2009).
 ICT and Global Development (2008)
 International Conference on Decision
Support Systems (2007)
 International Conference on Information
Systems (1994-2009)
 International Research Workshop on IT
Project Management (2006-2009)
 Mediterranean Conference on Information
Systems (2007-2008)
 Midwest Association for Information
Systems Conference (2006-2009)
 Pacific Asia Journal of the Association for
Information Systems (2009)
 Pacific Asia Conference on Information
Systems (1993-2009)
 Revista Latinoamericana Y Del Caribe De
La Associacion De Sistemas De
Informacion (2008-2009)
 Special Interest Group on Human
Computer Interaction Conference (2003-
2009)
6.2 Step 2: Identify Scholars
The second step is to identify uniquely the authors (scholars) in the database. This step proved to be a
non-trivial task given that the database contained 17,049 authors. Closer inspection revealed that an
author could be entered into Endnote in many different ways (e.g., with a single initial, with first and
middle initials, with variations such as “Bob” for “Robert”, and with various misspellings). For example, if
a researcher’s name was John Quincy Public, we would find entries such as John Quincy Public, J. Q.
Public, John Q. Public, John Public, J. Quincy Public, J. Public, and John Q. P. Disambiguating all the
author names in the database was neither feasible nor essential given we wanted to develop a set of
measures and to test our hypotheses. Therefore, we decided to use a subset of IS scholars. To do so,
we took the most prominent 448 IS scholars that Clarke, Warren, and Au (2009) identify. We do not
claim that this table is a definitive list of the top IS scholars but that it represents scholars from both the
US and Europe who have high venue prominence in the IS field. For the high-scoring 448 authors, we

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cleaned the data in the database by searching for parts of their family name and then combining the
variants into a single author code.
6.3 Step 3: Calculate H-Family Indices
We computed the h-index for each of the “top 448” scholars using the Publish or Perish (PoP) tool (Harzing,
2011). PoP is a tool that one can use to measure a researcher’s bibliometric properties, including the h-
index. While the tool is proficient at finding the bibliometric measures, we did find similar data errors as in
step 2 (i.e., the disambiguation of author names). As with cleaning the Endnote data, we manually corrected
authors’ names. We collected the h-family index measures (h, hg, hc) in May and June, 2010.
PoP uses the Google Scholar (GS) database as its data source. Some have criticized GS on the basis of
inaccurate data, incorrect citations, and missing data. However, recently, several studies have addressed
GS’s suitability as a source for citation data. Mingers and Lipitakis (2009) found that GS was superior to
Web of Science (WoS). While having reasonable coverage in social science, WoS found less than half of
the papers that GS identified. Similarly, Harzing (2013) found that GS displayed considerable stability over
time, that it compared fields in a less-biased way, and that poorly represented areas (e.g., physics and
chemistry) were rapidly improving. In a follow on study, Harzing (2014, p. 1) found that “[t]he increased
stability and coverage might make Google Scholar much more suitable for research evaluation and
bibliometric research purposes than it has been in the past”.
6.4 Step 4: Conduct Co-authorship Social Network Analysis
We extracted all the publications in the database that matched the high-scoring 448 authors and had two
or more authors from the database to input them into the social networking-analysis software UCINET.
Not all authors had co-authored with others in the top 448, which resulted in our extracting 390 authors.
We then extracted the main component to arrive at a population of 384 authors (six authors were not
connected to the main group of authors), which formed the basis for the subsequent analysis.
We operationalized the concept of connectedness by using three SNA centrality measures as we describe
in Section 3, degree, betweenness, and closeness. In this research, the social network is the set of
authors who have co-authored papers in the IS field. The network takes the set of all papers submitted to
IS journals and conferences in which there is co-authorship. Sole-authored papers are, therefore, not part
of the network, and, thus, we did not include them in analyzing connectedness.
6.5 Step 5: Conduct Venue Affiliation Analysis
We constructed an affiliation matrix for the 448 authors representing the venue where they had
published. We extracted 438 authors—more than in the co-authorship network since the affiliation
network captured the sole authorships. To ensure consistency in the path analysis, we used only the
384 authors that were common to both co-authorship and venue affiliation analysis. In UCINET, we
used the affiliation network centrality analysis feature, which calculates degree, closeness, and
betweenness for the authors and their affiliations to venues. Although we were primarily interested in
the author affiliation centrality scores, the venue analysis is also of interest since it shows which
publication venues are most central to the information systems field (i.e., it can provide a novel journal
ranking based on the closeness of a venue to the IS field).

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7 Results
7.1 Descriptive Findings
Table 3 illustrates the results. The table uses a single measure for each of the three types of scholarly
capital to identify the twenty highest-scoring scholars in each dimension. Ideational influence is
represented by the h-index (the hc and hg are also calculated), connectedness by co-author closeness
centrality (degree and betweenness centrality are also calculated), and venue representation by venue
closeness centrality (venue degree and betweenness centrality are also calculated).
Table 3. Ideational Influence, Connectedness, and Venue Representation: Top 20-Scoring Scholars
H index*
Co-author
closeness
Venue closeness
AalstWVanDer
56
WatsonRT
40.67
LyytinenKJ
0.9730
WhinstonAB
55
DavisGB
40.33
HirschheimRA
0.9709
BenbasatI
54
ZmudRW
40.29
WatsonRT
0.9689
ChenK
50
KingJL
40.00
SambamurthyV
0.9626
RobeyD
50
MarkusML
40.00
TanBCY
0.9626
GroverV
47
GalliersRD
39.51
BenbasatI
0.9606
KraemerKL
44
BaskervilleRL
39.43
WhinstonAB
0.9606
DennisAR
44
DavisFD
39.31
BaskervilleRL
0.9586
LyytinenKJ
42
WhinstonAB
39.00
HuangWW
0.9586
StraubDW
41
IvesB
38.73
LoebbeckeC
0.9586
HirschheimRA
41
ValacichJS
38.73
GeorgeJ
0.9565
KingJL
41
BeathC
38.50
HuffSL
0.9565
SmithMA
38
DennisAR
38.50
IraniZ
0.9565
MarkusML
38
GrayP
38.42
KraemerKL
0.9565
WatsonRT
38
MarchS
38.27
MarkusML
0.9565
AgarwalR
37
GeorgeJ
38.09
TeoTSH
0.9565
ValacichJS
36
HirschheimRA
38.05
DhillonG
0.9545
WalshamG
35
KraemerKL
38.01
GuptaA
0.9545
KeilM
34
ClemonsEK
37.97
IivariJ
0.9545
KauffmanRJ
34
McLeanE
37.90
LeeCC
0.9545
* As of May/June 2010

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Figure 5 shows the co-authorship network. In Figure 5, the blue squares show the core 20 researchers
identified in Table 3 as measured by closeness centrality.
Figure 5. Co-authorship Social Network (Main Component)
Figure 6 shows the affiliation of authors and publication venues. The squares represent venues and the
circles authors. One can clearly see the less influential publication venues and scholars can in the
periphery of the network.
Figure 6. Venue Representation (Squares are Venues, Circles are Authors)

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7.2 Test of the Research Model
We analyzed the data using the partial least squares (PLS) technique with reflective indicators in
SmartPLS 2.0 (Ringle, Wende, & Will, 2008). The PLS technique has become increasingly popular in
management research over the last decade or so in large part due to its flexibility. In particular, PLS does
not require a normal distribution in the data and is able to handle small- to medium-sized samples (Chin,
1998). PLS also combines the assessment of the measurement model with the structural model, which
simplifies the analytical work in comparison with ordinary least squares regression.
Following Petter, Straub, and Rai (2007), we considered the variables to be reflective rather than
formative. We make this determination first because the indicators are manifestations of the variables.
The various Hirsch indices reflect ideational influence. Changes in the latent construct ideational influence
would be reflected in the indicators rather than vice versa. Similarly, social and venue representation are
reflected in the values of the centrality measures. Second, the indicators will covary with each other. The
Hirsch indices are based on the same underlying citation pattern and, while they put different emphasis on
the nature of that pattern, they should vary in the same direction. Similarly, the centrality measures are
also based on the same underlying co-authorship and venue-affiliation patterns. Finally, the antecedents
of the Hirsch indices and the centrality measures, while they tap into different aspects of their constructs,
are the same. The Hirsch indices all stem from the same causal factors because scholars read and are
influenced by the ideas contained in the papers. The citation pattern analyzed by the Hirsch indices
indicate the casual factors. Similarly, these co-authorships, which the centrality measures analyze, reflect
the social relationships that effect co-authorships.
To test the constructs, we performed a confirmatory factor analysis and reliability analysis. The loadings
and cross-loadings in Table 4 demonstrate that the scale items exhibited high levels of convergent
validity—the extent to which theoretical scale items were empirically related. The loadings of the
measures on their respective constructs in the model ranged from 0.789 to 0.979, with all being significant
at the p < 0.1% level.
Table 4. Loadings and Cross-loadings
Ideational influence
Connectedness
Venue
representation
H
0.979
0.555
0.432
Hg
0.956
0.581
0.423
Hc
0.970
0.484
0.413
CoauthClose
0.453
0.873
0.559
CoauthBetween
0.503
0.885
0.418
CoauthDegree
0.543
0.919
0.553
VenueClose
0.273
0.404
0.789
VenueBetween
0.344
0.453
0.858
VenueDegree
0.486
0.612
0.959
To assess construct reliability of the reflectively modeled constructs, we examined composite reliability
and the average variance extracted (AVE) for each construct (see Table 5).
Table 5. Construct Reliability
Construct
Composite reliability
AVE
Ideational
.978
.938
Connectedness
.923
.797
Venue
.904
.760
The reliability measures were well above the cut-offs of 0.70 and 0.80 that scholars typically advise for
building strong measurement constructs (Nunnally, 1978; Straub & Carlson, 1989). All items were higher
than the cut-off of 0.50 for AVE that Fornell and Larcker (1981) recommend.

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Table 6. Correlations Between Constructs*
Ideational
Social
Venue
Ideational
0.969
Social
0.561
0.893
Venue
0.437
0.575
0.872
* Diagonal elements are square roots of average variance extracted (AVE)
Table 6 shows the extent to which question items measured the construct intended rather than other
constructs (i.e., discriminant validity). We used Fornell and Larcker’s (1981) standard test for discriminant
validity, which compares the square root of average variance extracted for each construct with the
correlations between it and other constructs; discriminant validity is demonstrated if the square root is
higher than the correlations. Table 4 clearly indicates each construct shared greater variance with its own
measurement items than with other constructs with different measurement items with a good margin of
difference.
As an additional test for discriminant validity, we used the cross-loading method that Chin (1998)
recommends. The method requires measurement items to load higher on a construct than the scale items
for other constructs and for no crossloading to occur. Item loadings in the relevant construct columns were
all higher than the loadings of items designed to measure other constructs; similarly, when looking across
the rows, the item loadings were considerably higher for their corresponding constructs than for other
constructs (Table 4).
Overall, the evaluation of the measurement model SCM suggests that, while the constructs were
correlated (as Figure 2 suggests), they were sufficiently distinct (discriminant validity) and their
measurement was sufficiently reliable.
7.2.1 Tests of Hypotheses
The results show support for the theoretical research model (Figure 7). We found strong support for the
association of connectedness with venue representation with H1 supported at the p<.001 level. We found
strong support for H2 (p<.001); that is, that connectedness is associated with ideational influence. H3 was
also supported (p<.01); that is, that venue representation is associated with ideational influence. Together,
connectedness and venue representation explained 33.4 percent of the variation in ideational influence.
Figure 7. Path Model Results
8 Discussion
In this paper, we propose that we should assess the scholarly capital of researchers using a combined set
of measures representing ideational influence, connectedness, and venue representation (Figures 1 and
2). Each of these different sets of indices focuses on a different area of scholarly capital. While we would

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expect all three aspects of capital to be interrelated and reinforcing, with the research model’s giving
connectedness precedence (Figure 4), we recognize the fundamental role of the ability to build
connectedness and give primacy to ideational influence as the outcome. However, we also recognize the
associations are not one way: scholars with high levels of venue representation and ideational influence
are likely to be popular as co-authors and, thus, accrue further connectedness (Figure 2).
We can only speculate about why some researchers have a higher level of capital than others. One factor
may be their ability. Since scholars are not uniform in their capabilities, we can readily assume that
different scholars have different abilities to perform research, to write research, to social network, and so
on. Differences in scholars’ ability to perform these functions may explain why some researchers have
more capital than others. For example, a scholar may have a great ability to conceptualize an idea and
write research in a clear and compelling manner (Davis, 1971). These capabilities would tend to create
research that other researchers would more likely cite than researchers who could not produce such well-
written accounts of their work. These capabilities would also tend give them a greater capability to be
published in the venues central to their field. It might also increase their ideational influence because they
would socialize using their ideas, which might predispose others to accept them once in published form.
Clearly, there will be a range of further factors, such as gender, PhD supervisor, academic institution, and
access to field data, that contribute to a scholar’s influential capability.
8.1 A New Way of Ranking Journals?
We begin this paper by questioning how one can evaluate the scholarly capital that an individual
researcher possesses. During conference and colloquia presentations of this research program of which
this paper is a part, many scholars challenged us with “so what?” questions, often ones expressing
concerns about the continuing “metrification” of academic output. With this paper, we offer a more
comprehensive and fair way to assess a scholar’s research capital. In previous publications, we have
challenged the extant methods as being one dimensional and power laden. We are aware that, even
though we think a portfolio approach is better than the most common metric (i.e., tallying “hit counts” in a
small number of journals), even a portfolio approach could be abused if applied without care.
Further, we recognize the value of venue as a source of capital and seek to find a more useful and
democratic measure of this concept. The venue affiliation analysis produces a journal ranking and a
scholar ranking—those venues that are core to a field will be more highly ranked than those that are
peripheral (see the visualization in Figure 6). Those venues that emerge as most central from the
affiliation analysis will be the ones that scholars seek to publish in (which, in turn, reinforces those
scholars’ centrality). This duality of scholars and venues reflects the way scholars create the venues (as
institutions) by publishing in them and the way those same venues define the scholars as part of the IS
community by virtue of publishing their papers. Rather than relying on expert opinions or on surveys (with
problems of response rates and bias), we can automate the production of a journal ranking list through
affiliation analysis in which IS scholars can be said to “vote” for the venue of their choice simply by the act
of where they choose to submit their research.
8.2 Profiling Researchers
Returning to the paper’s original point, appropriately using the portfolio of measures proposed by the SCM
would be to use the range of measures proposed with an inclusive list of publication venues (i.e., a set of
venues) that can be said to represent the IS field. One can automatically produce this set of measures
using methods such as those proposed by Mingers and Leydesdorff (2014) and can use it to support
personal planning, individual career assessment, or the evaluation and comparing of the productivity of
collections of scholars in an academic unit. We argue that one can use the citation, co-authorship
centrality, and venue affiliation measures to create a profile of a scholar’s scholarly capital. Such a profile
provides a sense of where a researcher is in their career and how closely woven into a field they are with
regards to venues (where they publish) and co-authors (who they work with). One could use such profiles
to shape departments and to identify “strengths” and likely trajectories of potential personnel placements.
To illustrate the point, Figure 8 shows profiles for researchers with different scholarly capital outcomes.
We provide the profiles of researchers from our dataset that score well on one of ideational influence (8b),
connectedness (8c), and venue representation (8d) and also one profile that scores particularly highly on
all three dimensions (8a).
The researcher Figure 8a depicts scores well in all three dimensions: they are highly cited, publish in the
core venues, and have an extensive co-authorship network. Few researchers can achieve this profile, and

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those that do are typically highly experienced academics who have spent many years working in a field.
The researcher in Figure 8b scores as well as the one in Figure 8a with regard to h-index but has more
sole-authored papers and, thus, may have less connectedness. Researcher 8c scores well in terms of co-
authorship and venue, but their research has yet to become truly influential in terms of ideational influence
(something that may change with time).
Researcher 8d scores highly for venue representation through publishing in a wide range of IS venues; they
score well on co-authorship closeness but, in common with researcher 8c, their work scores lower on
ideational influence. If these profiles are in part the products of intentional personal choices and are
correlated with amount of time in academic life, hiring committees and departmental leaders may use such
data in considering a portfolio management approach to hiring and promotion. We suggest the portfolio
management approach because depictions, such as those illustrated in Figure 8, answer the questions: 1) is
one a part of the field (i.e., does one publish in IS venues?), 2) does one have connectedness among the
researchers in their field?, and 3) are others using one’s work and taking up their ideas? Figure 8 shows a
scholar’s relative “well-roundedness” (or not) at a given point of time in their career.
An area of concern with this methodology would be evaluating early-career scholars because it takes
time, even once one is published, for others to read and cite a published work, and there is a natural lag in
growing these measures. Accordingly, one needs to interpret the profiles produced in Figure 8 with care
for early-career scholars. Regardless, Hirsch (2007) has shown the h-index to be a good predictor of its
future values. For use in tenure cases, a faculty can compare a scholar’s profile in ideational influence to
those of well-established scholars at their point of tenure to indicate whether this particular scholar is
projected to be the kind of scholar that the faculty would desire.
Figure 8. Illustrative Researcher Profiles

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8.3 Limitations to this Research
This research has several limitations, which include how we tested the model, the data we used, and how
we operationalized the constructs. As Figure 2 shows, we tested only the model’s internal aspects, which
showed that the three components of scholarly capital are related as the model describes. We did not test
the external components, the impact of scholarly capital on career advancement, ability to attract grants,
and so on. Second, despite having good coverage of the publication venues for the IS field, the dataset
analyzed in this research only comprises the 448 leading information systems academics that Clarke,
Warren, and Au (2009) identify. Although an important group of scholars, this group clearly does not cover
all research-active scholars in the IS field. Future research should strive to analyze all IS scholars and all
relevant venues (i.e., applying the ideal method in Table 1). In this paper, we only illustrate the method to
prove the scholarly capital model concept.
We used the citation information in Google Scholar. While scholars consider this dataset to be
superior in representing the IS field compared to Web of Science or Scopus, its content it is still
subject to omissions and errors.
Also, we operationalized the dimensions of scholarly capital using a variety of measures. We
operationalized ideational influence using three h-index variants (see below) calculated from citations
drawn from Google Scholar, which have limited applicability. First, it is difficult to compare scholars across
fields. The difficulty in comparing across fields exists because fields may have differences that defeat
equivalence across them. One field may have many more scholars in it than another (e.g., management
versus IS). In this case, there are many more scholars who might cite a management paper than an IS
paper, which leads to generally higher h-indices in management than in IS.
Fields also have different authoring cultures. In the physical sciences, for example, papers may list many
more authors than in the social sciences. Similarly, the standards for what one cites can vary across
fields, which will lead to non-equivalence in h-indices across fields, which prevents direct comparison but
allows relative comparison. For instance, the spider diagram (Figure 8) shows a given scholar’s in-field
influence, which one can use to compare scholars in a field. The relative scaling (0 to 100) in the spider
diagram suggests that one might use it to compare scholars across fields, although we need further work
to investigate this application.
Another issue with the h-indices is that it takes time for citations to build up for a paper, which makes
evaluating early-career scholars difficult when using the h-index. The time lag factor, however, is not
necessarily the case because Hirch (2007) has shown the h-index to be a good predictor of itself, which
allows one to set levels of the Hirsch index to evaluate scholars for promotion and tenure purposes. As
Hirsch (p. 19197) says, “we found that the h index appears to be better able to predict future achievement
than the other three indicators—number of citations, number of papers, and mean citations per paper—
with achievement defined by either the indicator itself or the total citation count”. He concludes: “the h
index is also effective in discriminating among scientists who will perform well and less well in the future”
and “the h index is a useful indicator of scientific quality that can be profitably used (together with other
criteria) to assist in academic appointment processes and to allocate research resources” (p. 19198).
Finally, some papers are so widely cited that they become “black boxed” (Latour, 1987). For example,
consider Cronbach’s 1951 paper on his alpha (Cronbach, 1951), which was so widely cited that scholars
today automatically accept the alpha and no longer cite the original paper. As Latour notes, papers move
from being fully described and cited to being summarily described and cited to simply cited and finally not
cited at all when their assertions are accepted as facts or “black boxed”. Thus, a small number of black-
boxed papers may not be fully reflected in our scholarly-capital measures.
For connectedness, we used social network analysis. Scholars who do not co-author will not be included
in the connectedness network. Further, closeness and betweenness scores for co-authorship are
calculated only for those scholars in the main component. In a sense this fact is not a limitation; rather, it
is useful information about the connectedness of a scholar being evaluated for promotion or tenure. For
example, consider the case of Peter Checkland. Most of his influence comes through citations to his
books on soft systems methodology. He did not co-author much, and his books don’t show up in the
venue representation network as central venues. In his case, he would have a high ideational influence
but low connectedness and venue representation. Those evaluating his scholarly capital should conclude
that his capital is in his books. If they were looking for capital in the form of connectedness, then perhaps
they would need to look elsewhere. Thus, one gains valuable information for making decisions about the
capital a scholar brings by examining the profile of their measures.

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Scholars who traverse fields will appear to be less connected and less well represented when one focuses
on a single field. For example, a scholar may have published in venues classified as marketing and
software engineering and in IS venues. One can achieve a fuller picture of a scholar’s capital by analyzing
their capital in all the fields in which they publish, which allows one to produce a field profile (e.g., this
scholar is 70% IS, 20% marketing, 5% software engineering, and 5% “other”). The availability of such a
profile would again be useful to hiring committees wanting to understand the makeup of an applicant’s
research and answer the question: “to what extent is this person an IS researcher?”.
8.4 Future Work
Future work related to developing the SCM falls into three different areas: further testing and developing
the theory, improving the operationalization of the constructs, and using design science research in
practically applying the theory.
8.4.1 Further Testing and Development of the Model
As we indicate in Section 4, we tested only the internal components of the model in Figure 2. We need
additional work to assess the impact of scholarly capital on career advancement, on attracting grants, and
on policy and practice. Further developing the model involves researching the antecedents of ideational
influence, connectedness, and venue representation. How do these constructs arise and what are the
causal relationships between them? Also, we need further theorization and empirical examination in each
of these antecedents. For example, one could research the social antecedents of scholarly capital. Other
research (e.g., Gallivan & Benbunan-Fich, 2007) has shown that women and minorities have less capital
than “old, white men”. An important extension to the current work would be to investigate the causes of
this differential lack of capital. We may hypothesize this difference exists because of the preponderance of
North American men in IS at the beginning who now control the field. We might expect that, as more
researchers originating from non-North American geographic locations, women, and minorities rise up the
ladder, the discrepancy may change. Some have also assumed that those who hold divergent views from
the majority may have less influence due to their difficulty in getting published. We need future research to
address this concern as well. Another area for investigation is the “fragmented ad-hocracy” of the IS field
(Banville, Landry, & Kling, 1989). The IS field comprises many different subfields such as design science,
management of the information resource, adoption and diffusion of technology, and so on. The implication
here is that being a member of these subfields will have an effect on one’s capital. Further research
should be done on how being in a particular subfield affects a scholar’s capital and on how to effectively
compare these researchers across the subfields (i.e., to consider not only field but intra-field capital and
inter-field (interdisciplinary) capital).
8.4.2 Improving the Operationalization of the Constructs
This area includes research into the methods of automating the analysis and additional methodologies
and measures that one might use. To automate the analysis in the ideal method such as in the one that
Mingers and Leydesdorff (2014) describe, one would uniquely identify each scholar (e.g., with a scholar ID
such as found in Scopus and Google Scholar) and reliably assign publications to scholars. This process
would allow one to benchmark all scholars with regard to their peers. Further, the resulting venue
centrality list would be a truer reflection of which journals are most important to any given field. However,
such work is beyond the reach of an academic research project and would require a commercial
organization or community project to implement. We note that Google Scholar already has much of this
data and that scholars can establish a Scholar webpage that shows their h-family indices and co-authors.
The Wirtschaftsinformatik-Genealogie German Language project (Wirtschaftsinformatik-Genealogie)
whose goal is documenting “the history of publishing in the discipline of computer science” and its
genealogical database of works spanning nearly a century is another step in this direction. Such
organizations could make a small step to uniquely identify scholars and venues in a given field and
produce all of the field-level and scholar-level profiles reported in this paper. At such a time, we would
expect to see appointment and funding decisions go beyond simple journal “hit counts” and begin to be
supplemented with automated scholarly capital profiles, particularly given the emphasis put on citations by
the world university rankings (Times Higher Education, 2012). One could also use additional methods to
assess scholarly capital. For example, one could use data-mining techniques from the Natural Language
Processing field, such as latent semantic indexing and other text mining technologies, to assess scholars’
ideational influence. Previous IS studies have used such analysis techniques. Culnan (1986, 1987) and

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Culnan and Swanson (1986) used co-citation analysis to perform a clustering analysis of the publication
topics in the IS field at that time. For a two-day conference honoring for Heinz Klein in 2007, we used
Leximancer to identify the topics on which the late Klein wrote and which scholars wrote of Klein’s work.
Alternatively, one could use developments such as the altmetrics movement (www.altmetrics.org).
Altmetrics argues for greater diversity in measuring impact in the scholarly ecosystem and in developing
new forms of filter to sift through the large volumes of research being generated and disseminated in non-
traditional media (e.g., blogging). While we used measured connectedness using SNA analysis of co-
authorships, emerging social media sites for scholars, such as ResearchGate (www.researchgate.net)
allow one to more widely assess connectedness and impact.
Some have suggested enhancing the assessment of capital outside of the academy by considering other
kinds of measures. For example, Aguinis, Suarez-Gonzalez, Lannelongue and Joo (2012) argue that we
should use measures such as references in Google pages. Fenner and Lin (2014) propose using HTML
views and PDF downloads. Bar-Ilan et al. (2012) investigate using social bookmarking sites, such as
Mendeley (www.mendeley.com), to use bookmarks to assess impact. Other measures for evaluating
research ability could be proposed: for example, research funding, such as grants (see Figure 2). All of
these different measures are valuable and can be used to give insight into impact, influence, and ability in
different ways and represent valuable future research directions for expanding this model.
8.4.3 Creating a Methodology for Using the SCM in Practice
Finally, we need to develop a specific methodology for using the SCM in practice. This effort would be a
design science activity that would result in a methodology to perform SCM analysis in different kinds of
organizations. Organizations would use the methodology to describe to practitioners how to set up an
analysis regime that: 1) accurately defines the fields of study for which the scholar will be evaluated, 2)
establishes the levels of capital required for all three constructs needed to meet the organization’s
research goals, 3) collects information for the field or fields of interest to the organization, 4) computes the
metrics, and 5) renders the portfolio for the scholar to be compared against the organization’s standards.
9 Conclusion
This work contributes to a growing discourse in many fields of inquiry about how to compare scholars’
ability in and across fields. We begin the paper by observing that evaluating scholarly research ability is a
key concern for academia. Many believe the present method, counting papers published in ranked
journals, to be suboptimal for this purpose. Consequently, we propose a method for evaluating scholarly
capital that provides a set of measures for profiling scholars. One can automate the generation of the set
of measures and use the measures to provide a fair, open, and transparent method for evaluating
scholarly research capital.
Acknowledgements.
We thank the senior editor and the anonymous reviewers for their valuable feedback, which helped us to
improve the paper.
While engaged in this research program, Duane Truex served as Professor of Industrial Economics at Mid
Sweden University (Mittuniversitetet, Sundsvall Sweden).

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28
Ideational Influence, Connectedness, and Venue Representation: Making an Assessment of Scholarly Capital
Volume 17
Issue 1
About the Authors
Michael Cuellar is an Assistant Professor in the Information Systems department of Georgia Southern
University. He received his PhD in 2009 from Georgia State University. His research interests are focused
on the areas of project management and organizational change, critical realism as applied to Information
Systems and the nature of scholarly capital. He has published in the European Journal of Information
Systems, the Journal of the Association for Information Systems, and the European Journal of Operations
Management, as well as the ICIS, AMCIS, and other conferences. He is Managing Editor for the Journal
of the Southern AIS and a Senior Editor for JISE and on the editorial boards of Database and BISE. He
has been the secretary for the AIS SIG ITPM from 2009 to present.
Hirotoshi Takeda is an Assistant Professor of Management Information Systems at Laval University in
Quebec City, Canada. He has seven years of industry experience in telecommunications, semiconductor
manufacturing, and IT consulting. After his career in industry, he graduated with a PhD in Computer
Information Systems from Georgia State University and a PhD in Management from the University of Paris
Dauphine. He has degrees in electrical engineering and computer science from UC Irvine, a Masters of
Electrical Engineering from the Georgia Institute of Technology and his MBA from Southern Methodist
University. His research interests include discourse analysis, mobile computing, bibliometrics, virtual
communities, knowledge management, supply chain management, and green IS. His research has
appeared in the Journal of the Association for Information Systems, European Journal of Information
Systems, Information Systems Educators Journal, and the proceedings of the ICIS, AMCIS, SAIS, UKAIS,
ISECON, and IFIP WG 8.2.
Richard Vidgen is Professor of Business Analytics at the University of New South Wales Business
School, Australia. Following fifteen years working in the IT industry he has held professorial positions at
the University of Bath and the University of Hull in the UK. His research interests include (1) business
analytics and data science, (2) the evaluation of technology and its use in supporting behavior change for
pro-societal benefit, and (3) the application of complex adaptive systems theory, ideas, and models to the
study of information systems and analytics.
Duane Truex holds joint appointments in the Computer Information Systems Department and in the
Institute of International Business of the J. Mack Robinson College of Business at the Georgia State
University where he also serves as the Program Director for the GSU’s University of Nantes (France)
Academic Exchange program. He has held additional academic appointments in Sweden (as Professor of
Industrial Economics at the Mid Sweden University, in France (as a Research Professor at Université de
Nantes) and in England as a former Leverhulme Fellow (at Salford University). In addition to his inquiries
into the nature of scholarly influence, his research explores the emergent and performative properties of
language as instantiated in information systems development (ISD) and in the design of enterprise
systems, the effect of organizational emergence on systems architectures and post-implementation
governance of enterprise-wide systems (ES), and the social impacts of information systems (IS) on
society.
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