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Reconsidering the Reputation–Performance
Relationship: A Resource-Based View
†
Brian K. Boyd*
W. P. Carey School of Business, Department of Management, Arizona State University, Tempe, AZ 85287
Donald D. Bergh
Daniels College of Business, Department of Management, University of Denver, Denver, CO 80208
David J. Ketchen Jr.
College of Business, Department of Management, Auburn University, Auburn, AL 36849-5241
Reputation is thought to differentiate organizations and help explain variability in their perfor-
mance. A recent study contributed to knowledge about the reputation–performance relationship
by depicting reputation as having two dimensions and linking each dimension to the prominence
and performance of U.S. business schools. The authors propose an alternative approach that
draws on the resource-based view (RBV) wherein reputation is an intangible asset that is
composed of complementary and reinforcing relationships whose synergies create causal ambi-
guities that have positive performance implications. The authors also test a direct effect of
faculty experience on prominence. Their results support the merit of the RBV model, indicating
that it offers greater explanatory power. The findings suggest that reputation cannot be bought
by additive and independent investments. Instead, enhancing a reputation requires managers to
carefully nurture interdependencies and complex relationships. The findings also provide new
insights about the determinants of business school reputation.
Keywords: reputation; resource-based view; structural modeling
1
†We appreciate Elizabeth Lim’s extensive preliminary analyses. Her findings helped motivate the development of
our framework. We also acknowledge the helpful comments of Tomas Hult, Duane Ireland, and Gerry Keim.
*Corresponding author: Tel.: 480-965-4781
E-mail address: briankboyd@asu.edu
Journal of Management, Vol. XX No. X, Month XXXX xx-xx
DOI: 10.1177/0149206308328507
© 2009 Southern Management Association. All rights reserved.
Journal of Management OnlineFirst, published on February 10, 2009 as doi:10.1177/0149206308328507
As noted in a recent Journal of Management article, an organization’s “reputation is per-
haps one of its most important strategic resources” (Flanagan & O’Shaughnessy, 2005: 445;
see also Hall, 1992). Reputation helps distinguish firms from competitors (Peteraf, 1993),
reduces information asymmetry and consumer uncertainty (Weigelt & Camerer, 1988), and
substitutes for expensive governance mechanisms (Kogut, 1988). Moreover, reputation has
been linked to organizational performance (Fombrun & Shanley, 1990; Roberts & Dowling,
2002; Shamsie, 2003). As such, reputation appears to be a key to the effort to understand why
some organizations outperform others—a quest regarded by many as the cornerstone of the
strategic management field (e.g., Hitt, Boyd, & Li, 2004; Rumelt, Schendel, & Teece, 1994).
Recent research provides new insights into how reputation may contribute to value creation.
In particular, a study by Rindova, Williamson, Petkova, and Sever (2005; hereinafter referred
to as the RWPS study or model) takes a large step forward by integrating prior research via
proposing that the construct of reputation has two dimensions—perceived quality and market
prominence—each of which has unique antecedents and performance implications. In general,
they argue that some stakeholders view reputations in terms of how firms provide quality goods
and services, whereas other stakeholders focus on reputation as an organization’s external
prominence. Reputation as perceived quality is rooted in an economic orientation and is driven
by the quality of inputs, ingredients, and productive assets. Reputation as market prominence
is based on a sociological tradition wherein antecedents include the media, expert intermedi-
aries, and affiliation with high-status actors. Drawing from signaling theory, Rindova et al.
assert that the quality of inputs influences perceived quality, which in turn improves an orga-
nization’s performance by lowering buyer costs and enabling higher prices due to enhanced
buyer confidence. They also use institutional theory to argue that the market prominence
dimension of reputation has cachet for which buyers are willing to pay a premium. Overall,
Rindova et al. provide a significant contribution by distinguishing important determinants of
reputation, providing insights into how managers might develop reputation, and increasing
understanding of how reputation may influence financial performance.
The RWPS model opens several new paths for further probing the reputation concept and
how it may be linked to performance and organizational success. However, Rindova et al.’s
definition of reputation as “stakeholders’ perceptions about an organization’s abilities to cre-
ate value relative to competitors” (Rindova et al., 2005: 1033) and characterization of past
research as economic signaling or global impression management may create limitations.
Although Rindova et al. recognize that reputation can also take the form of a valuable
resource that has performance implications, they do not consider whether the theoretical
framework for linking resources and performance offered by the resource-based view (RBV)
of the firm may represent an alternative explanation to their integrative model.
Building on Wernerfelt’s (1984) preliminary ideas, Barney (1991) laid out the basic tenets
of the RBV in a seminal Journal of Management article, one that has been cited nearly 9,000
times as of July 2008 according to scholar.google.com. Barney suggested that organizations
possessing resources that are valuable, rare, and difficult to imitate or substitute can achieve
sustained competitive advantages. In turn, these competitive advantages can create superior
performance for an organization relative to its rivals that lack resource endowments. From
1991 to 2007, at least 125 published studies examined the link between resources and perfor-
mance, and recent meta-analytic evidence reveals that a significant resources–performance
2 Journal of Management / Month XXXX
relationship exists across the more than 29,000 organizations included in those studies
(Crook, Ketchen, Combs, & Todd, 2008).
The RBV has been applied to conceptualizing reputation and linking it to performance
(Roberts & Dowling, 2002; Rumelt, 1987; Shamsie, 2003), wherein reputation is defined as
an organizational attribute and depicted as a broad, multidimensional single construct whose
value is determined through the interactions and interrelationships among multiple attrib-
utes, both internal and external to the firm (Barney, 1991; Dowling, 2001). The interplay
among these underlying determinants would combine to create a synergistic effect, where
value is produced from uniting the components and developing their mutually reinforcing
relationships. The linkages among reputation’s internal and external attributes are rare and
difficult to imitate and could lead to competitive advantage and superior performance (e.g.,
Dierickx & Cool, 1989; Hall, 1992; Roberts & Dowling, 2002). Considered collectively, the
RBV perspective represents a theoretical rival to the signaling and institutional arguments
put forth by Rindova et al. (2005), and at present, we do not know how the explanatory value
of the RBV theoretical model compares to the other perspectives. Additional attention would
help develop a more complete understanding of the reputation concept and how it influences
value creation.
We propose that testing an RBV conception of the reputation–performance relationship
extends the explanation contributed by Rindova et al. (2005). Such a model would build on
the foundation laid by Rindova et al. to consider the viability of another explanation of rep-
utation and its effects. Comparing the two approaches also offers normative implications. If
the RWPS model prevails empirically, then the returns on managerial investments into rep-
utation would be greatest when they involved independent investment purchases, each of
which sends a signal about quality that may influence perceptions within an organization’s
institutional field. By contrast, if the RBV approach receives stronger support, then man-
agers should build reputations by investing in and managing complementary and potentially
reinforcing relationships.
Accordingly, we develop and test an RBV of reputation and performance. We reanalyze
the RWPS study dataset and find that the RBV model is both more parsimonious and has
greater explanatory power than the original signaling and institutional approach. Our study
thus builds on the study by Rindova et al. (2005) by indicating that the reputation and per-
formance relationship may be predicated more on interactive and reinforcing relationships
that create more value when combined than when considered separately and iteratively. This
finding contributes to the reputation literature more generally by indicating that developing
and exploiting reputation may be more complex than previously suggested. Finally, our
analyses emphasize the importance of considering alternate configurations of variables when
using structural equation modeling (SEM).
Theoretical Background and Hypotheses
Rindova et al. (2005) predict that perceived quality will both independently and indirectly
influence performance. Market prominence is directly linked to the performance variable.
Figure 1 shows their results. Tests using data from 107 U.S. business schools mostly support
Boyd et al. / Reputation and Performance 3
their hypotheses. Although they considered several alternative models, all the models treated
each indicator as a unique construct. Many constructs are ideally represented with multiple
indicators (Boyd, Gove, & Hitt, 2005; Hitt et al., 2004), and it remains unknown whether a
multiple indicator model may better represent the construct of reputation. In the following
section, first we review the literature on university reputation. Next, we use the RBV model
to reconceptualize the model in Figure 1 into the model shown in Figure 2.
The Role of Reputation in Interuniversity Competition
The nature of competition among colleges and universities, in general, and graduate busi-
ness programs, in particular, has changed substantially over the past several decades.
Previously, colleges and universities drew on local populations for their student base. As
modern higher education institutions evolved, “colleges themselves were struggling to find
available models and competing on terrain that was not yet stable or structured” (Washington
& Ventresca, 2004: 93). The period following World War II, however, was a time of geo-
graphic integration and the subsequent stratification of colleges and universities (Hoxby,
1997). What emerged from these developments was the bifurcation of university pricing
strategies that typify Porter’s generic strategies: Smaller, local institutions compete on price,
whereas regional and national institutions compete based on differentiated features. This
4 Journal of Management / Month XXXX
Quality
Prominence
Salaries
R
2
= .11
R
2
= .36
.33*
Faculty Exp
Degree Prestige
Faculty Pubs
Media Ranking
GMAT
R
2
= .69
.03
–.01
–.51*
.17*
.18*
.59*
.13*
Figure 1
Results of Model Tested by Rindova, Williamson, Petkova, and Sever (2005)
Note: Significant paths are noted with an asterisk.
dichotomy is clearly illustrated in tuition norms: Tuition rates for the top decile of Financial
Times master of business administration (MBA) programs is approximately $80,000,
whereas the bottom decile is approximately $37,000 (Peters, 2007).
Organizational reputation is the keystone of an institution’s differentiation strategy. In aca-
demic settings, a stronger reputation is associated with a lower acceptance rate (i.e., better
selectivity), higher yields (i.e., more admitted students choose to attend), better students, and
the ability to charge higher tuition (Monks & Ehrenberg, 1999). Because the elements of an
academic reputation are resource intensive (Peters, 2007), this can lead to a “rich get richer
and poor get poorer” cycle (Corley & Gioia, 2000). Similarly, Pfeffer and Fong (2002: 91)
characterized competition between business schools as “a self-reinforcing system.”
Research on academic reputation is consistent with a view of reputation as a broad con-
struct represented by multiple elements. Academic reputation has been labeled variously as
quality or prestige and has been linked with phenomena such as the presence of graduate
programs, faculty research productivity, faculty size, and affiliation with an elite university
(Baldi, 1997; Gomez-Mejia & Balkin, 1992; Howard, Cole, & Maxwell, 1987; Keith &
Babchuk, 1994). The case for reputation as a broad, multimeasure construct is enhanced by
recognition that two of the predictors used by Rindova et al. (2005) have been included in a
multi-indicator factor model of academic reputation (Boyd, Finkelstein, & Gove, 2005).
Furthermore, the use of reflective indicators is a valuable tool for establishing the presence
of intangible assets, such as reputation (Barney & Mackey, 2005).
More broadly, the Fortune reputation measure is a composite of eight elements. In prior
research, the individual components have been found to covary significantly (Brown &
Perry, 1994). Fombrun and Shanley (1990) reported that a single factor explained about 85%
of the variance in these reputation indicators. Similarly, we ran a confirmatory factor model
of the eight Fortune indicators reported by Brown and Perry. Our supplementary analysis
(omitted for brevity; results available from the authors) found that a single-dimension solu-
tion had a coefficient of determination, or explained variance, of 94%. Thus, the available
evidence indicates that although the model offered by Rindova et al. adds value to the liter-
ature, there is a viable alternative view. Although the specific reputation indicators vary in
our study relative to the Fortune measures, logic and findings suggest support for a depic-
tion of reputation as a broad construct represented by multiple elements.
A Resource-Based View of Reputation
The RBV portrays reputation as an intangible resource that is derived from combinations
of internal investments and external appraisals (Dowling, 2001; Roberts & Dowling, 2002;
Shamsie, 2003). From this view, reputation can be defined as “a general organizational
attribute” (Roberts & Dowling, 2002: 1078) whose value is derived from interconnections of
factors that lead to causal ambiguities, competitive advantage, and ultimately, performance
superiority (cf. Barney, 1986a; 1991). More specifically, the underlying determinants of rep-
utation are complex, oftentimes embedded within the firm, and likely to be associated with
a high degree of ambiguity, the combination of which limits replication, creating the oppor-
tunity for sustained profitability (Roberts & Dowling, 2002), making reputation one of the
Boyd et al. / Reputation and Performance 5
organization’s most important resources (Hall, 1992). Furthermore, external and internal
factors can combine to create a virtuous cycle that develops, enhances, and further differen-
tiates reputation (Podolny, 1993). The bundling of these elements can produce a synergistic
value and rarity and make replication difficult (cf. Barney, 1991; Peteraf, 1993). Although
reputation can also reduce information asymmetry through conveying information about
quality, its internal origins, such as leadership and culture, can serve as the foundation for
abnormal performance (Weigelt & Camerer, 1988).
The RBV logic aligns the determinants of perceived quality to competitive advantage,
which is reflected in the firm’s market prominence. Thus, instead of perceived quality and
market prominence having two separate effects on performance (as in the RWPS model),
RBV reasoning offers an alternative model wherein reputation leads to market prominence
and then to performance. We suggest that RBV offers an explanation for how reputation and
market prominence might influence performance (Dowling, 2001; Roberts & Dowling,
2002; Rumelt, 1987) and that reputation is part of a chain of integrated relationships that cre-
ate unique value and position an entity in its industry. This model is shown in Figure 2.
We propose that prominence is a distinct construct from reputation and that prominence
actually mediates the effect of reputation on performance. The distinction between prominence
and reputation is that prominence refers to the degree to which an organization is visible and
well known, whereas reputation involves an assessment of being good, bad, or somewhere in
between (cf. Roberts & Dowling, 2002). Our expectation is that business schools that possess
strong reputations will be highly salient to key partners, such as corporate recruiters. Surveys
of corporate recruiters have identified limited time and limited resources as the most important
barriers for firms trying to recruit MBA students (Graduate Management Admissions Council,
2007). Faced with this constraint, recruiters must prioritize their time at the programs deemed
most likely to fill their hiring needs. Prior studies have identified variables such as student char-
acteristics, media rankings, and program quality as influencing recruiters’ choices in selecting
business schools to visit (Olkin, 2004; Safon, 2007).
In considering the elements of the RWPS model, student and faculty quality would repre-
sent two of several tangible attributes that reflect an intangible resource (i.e., reputation),
which in turn influences a school’s prominence. Influential third parties would also offer opin-
ions and views that would bear upon the school’s position. None of these is independent; the
qualities of the students and faculty shape perceptions by external constituencies, and vice
versa. The combination of student, faculty, and external member views could coalesce to cre-
ate causal ambiguity and thereby shape the school’s position or prominence in its market
(Reed & Defillippi, 1990). Higher quality students, an energetic and successful faculty, and
positive assessments by external parties would likely produce complementary relationships
that are unique, valuable, and difficult to imitate and that lead to higher performance in terms
of graduate salaries and school rankings. Overall, the synergistic bundles of student, faculty,
and constituent factors would be related to a school’s prominence and ultimately to its perfor-
mance. Based on this rationale, we expect that MBA programs with a strong reputation will
be more visible in the eyes of corporate recruiters:
Hypothesis 1: Reputation will have a positive effect on prominence.
6 Journal of Management / Month XXXX
Faculty Experience and Prominence
The RWPS model depicts faculty experience as a proxy of the quality of a school’s faculty.
Rindova et al. (2005) hypothesized that experience would have a positive effect on one aspect
of reputation and a positive indirect effect on prominence. However, the literature highlights an
alternate scenario wherein faculty experience is unrelated to quality metrics such as research
productivity and influence on knowledge creation and may actually have a negative effect on
prominence. Drawing from recent RBV research, resources and capabilities can vary over
time. Capabilities evolve through several life cycle stages, and resources can erode as they get
older (Helfat, 2000; Helfat & Peteraf, 2003). Applying this logic to a university setting, it
follows that the value of faculty experience could decline over time. Specifically, faculty expe-
rience covaries strongly with personal age, academic rank, and the proportion of faculty
members who are tenured (Gomez-Mejia & Balkin, 1992). Although experienced academics
have the potential to accrue much knowledge—both in content areas and pedagogy—there is
Boyd et al. / Reputation and Performance 7
Quality
GMAT
MediaRank
FacPubs
DegPrestige
Reputation
Prominence
Salaries
R
2
= .79
R
2
= .45
.96
(12.1)
.66
(8.2)
.37
(3.4)
−.94
(10.5)
1.0
.92
(11.2)
.93
(11.1)
Faculty Exp
–.20
(2.9)
.05
(0.1)
Figure 2
Results of Revised Model
Note: Certain terms (e.g., theta and phi matrices) are omitted for ease of representation. The t values of parameters
are noted in parentheses. The significance levels are as follows: t = 2.0, p ≤ .05; t = 2.7, p ≤ .01; t = 3.5, p ≤ .001.
also the potential to have lost touch with current research and managerial practice. In addition,
faculty with longer tenure may become more committed to the status quo and exhibit inertia
(Miller, 1991).
Furthermore, research on faculty publication and influence is supportive of the possible
decline in the value of the experience resource. Research productivity typically dips shortly
after tenure, typically around 6 or 8 years post-PhD (Park & Gordon, 1996). This pattern has
been explained by the declining marginal utility of additional publications: Because publi-
cation norms are low in many fields, faculty members accrue more utility from their 1st or
2nd articles than from their 10th (Tuckman & Leahey, 1975). For example, in the strategic
management field, having just one A-tier article is enough to keep a professor ranked in the
75th percentile, even 10 years postdegree (Boyd et al. 2005). In addition, years of experience
has inconsistent ties with faculty influence (Diamond, 1988; Helmreich, Spence, Beane,
Lucker, & Matthews, 1980), as measured by frequency of citation (Bergh, Perry, & Hanke,
2006). Overall, although there are certainly exceptions, on average, it appears that most fac-
ulty members become less engaged with research as time passes. This lack of involvement
will weaken their ability to accrue or retain prestige and will also increase the likelihood that
their content knowledge may become obsolete.
A business school composed mainly of more experienced faculty could experience a
decline in the value of its faculty resource relative to other business schools that have higher
numbers of less experienced faculty. We expect that business schools that are top heavy with
highly experienced faculty will have lower prominence and be of lesser interest to recruiters.
Hypothesis 2: Faculty experience will have a negative effect on prominence.
Prominence and Performance
Our final hypothesis concerns the starting salaries for MBA graduates. Although we agree
with Rindova et al. (2005) that prominence leads to higher starting salaries, RBV offers some
different reasons for this link. First, higher quality resources that lead to higher values of
prominence represent an intangible advantage that cannot be easily replicated. Buyers pay a
premium to the more prominent organization when its services are perceived as more dif-
ferentiated and valuable. Second, buyers may seek spillover benefits through purchasing a
prominent product or brand and are thereby willing to pay a premium. Finally, a supply-and-
demand imbalance can arise in favor of prominent providers—for example, leading business
schools produce far fewer graduates than employers want. This disparity allows the gradu-
ates of such programs to extract premium prices for their labor. Hence, the RBV approach
proposes that the resources of the firm translate into a prominence advantage that influences
value creation.
Higher starting salaries are an important metric of institutional success. From a student’s
perspective, starting salary helps to drive lifetime earnings potential: In a survey of 1,126
MBA graduates, starting salary was found to be a key determinant of current salary, even 10
years postdegree (Olson, Frieze, & Good, 1987). Although completion of an MBA is associ-
ated with higher wages (Dreher, Dougherty, & Whitely, 1985; Pfeffer, 1977), this effect is
8 Journal of Management / Month XXXX
more pronounced for graduates of elite institutions (Dugan, Grady, Payn, & Johnson, 1999).
Presumably, prospective students will seek out the most prominent program that they can be
admitted to in order to maximize future income. From an employer’s perspective, the decision
to pay higher starting salaries to graduates of certain programs is a metric of the human cap-
ital of these students: “Given the competitive labor market for MBAs, salaries will reflect the
willingness of employers to pay for the attributes embodied in a program’s graduates” (Tracy
& Waldfogel, 1997: 4). In fact, companies that actively recruit graduate business students (i.e.,
via on-campus interviews) offer 24% higher starting salaries for MBA graduates than firms
that do not actively recruit on campus (Graduate Management Admissions Council, 2007).
Finally, higher alumni salaries are desirable to the institutions themselves. Partly, higher
salaries are desired simply because they are seen as indicators of a successful program (e.g.,
Williams & Harrell, 1964). More important, higher starting salaries enable institutions to
charge higher levels of tuition (Tracy & Waldfogel, 1997), which in turn can be used to under-
write initiatives to boost program reputation (Pfeffer & Fong, 2002). Therefore,
Hypothesis 3: Prominence will have a positive effect on starting salary.
Support for our model and our specific predictions would shed new light on the role of
reputation. More generally, such support would have theoretical implications for the RBV.
In an article that was fiercely critical of the RBV’s conceptual rigor, Priem and Butler (2001)
asserted that the RBV suffers from a significant “black-box” problem in that the question of
how resources shape performance remains unanswered. As Ketchen, Hult, and Slater (2007:
962) note, “A simple resources–performance link obviously lacks face validity. At the risk of
being flippant, customers do not mail checks to a company just because the company pos-
sesses certain resources.” If our hypotheses are supported, this would mean that prominence
plays an intervening role between reputation and performance. Recent conceptual work on
what is in the black box between resources and performance centers on internal factors such
as structuring a resource portfolio, bundling resources, and leveraging resources (Ketchen
et al., 2007; Sirmon, Hitt, & Ireland, 2007). Our predictions augment these views by pointing
toward the potential role of an external factor—the degree to which an organization is
viewed as prominent—in the resources–performance link.
Method
Sample and Measurement
Our analyses were based on the correlation matrix reported by Rindova et al. (2005). The
use of their study data provided the ability to compare directly their model with an RBV
approach. The sample was composed of 107 business schools that had accredited MBA
programs and had annual enrollments of at least 50 students in their full-time MBA plat-
forms. The original data were collected from multiple sources, including faculty profiles,
multiple published sources, and surveys of 1,600 corporate recruiters. Descriptive statistics
are reported in Table 1.
Boyd et al. / Reputation and Performance 9
Prominence. Each recruiter was asked to identify three schools that he or she would like
to rate. Prominence was measured as the number of nominations for each university.
Starting salary. This was the mean starting base salary of MBA graduates at a given school,
excluding bonus pay or other benefits. Salaries were adjusted to reflect the difference in cost
of living across regions. Data for this item were collected from U.S. News & World Report.
Perceived quality. Recruiters evaluated MBA students on 13 criteria (e.g., strategic think-
ing, team skills, interpersonal skills), which were then aggregated into a composite index
score. Interrater reliability was .95, and Cronbach’s alpha was .98 for this measure.
GMAT scores. The mean score of entering MBA students was collected from U.S. News &
World Report.
Faculty publications. Data were collected for faculty publications at each institution, over a
5-year window. Publications were counted using the Financial Times listing of premier journals,
which includes a total of 36 scholarly and practitioner outlets across business disciplines.
Degree prestige. This item was measured as the average prestige level of the degree-granting
institution for a school’s set of faculty. Scores were based on the Gourman Report.
Faculty experience. This variable was defined as the mean number of years since a
school’s faculty members had completed their doctorates.
Analysis
We tested the hypotheses using LISREL, as did Rindova et al. (2005). We used generalized
least squares (GLS) estimation: GLS is more robust than maximum likelihood estimation in
10 Journal of Management / Month XXXX
Table 1
Descriptive Statistics
1 23 4 5678
1. Starting salary 1
2. Quality .22* 1
3. Prominence .60* .32* 1
4. GMAT scores .50* .33* .70* 1
5. Faculty experience –.17 –.07 –.17 .20 1
6. Media ranking –.38* –.21* –.79* –.67* .04 1
7. Faculty publications .39* .17 .71* .71* –.03 –.76* 1
8. Faculty degree prestige .49* .31* .68* .73* –.04 –.66* .69 1
Mean 61,449.29 7.70 21.35 614.76 16.34 49.02 62.81 4.34
Standard deviation 15,827.66 1.13 24.97 46.16 2.35 20.29 62.50 0.29
*p < .05.
the face of departures from normality, and there were large interitem correlations between
several indicators. Multiple fit measures are desirable when comparing alternate structural
models (Medsker, Williams, & Holahan, 1994). To conduct a robust assessment of our model
relative to that of Rindova et al., we included eight summary measures of model fit in our
analysis. Our first three items—chi-square, goodness of fit (GFI), and root mean square residual
(RMSR)—are typically the most widely used indicators of model fit. These measures have
the potential for upward bias in models that utilize more parameters (Kline, 1998), so our
next three items adjust for model complexity—χ
2
/df, Akaike information criterion (AIC),
and standardized chi-square. Finally, we include two items that measure fit relative to a base-
line null model—the comparative fit index (CFI) and the incremental fit index (IFI). Each of
these eight indicators has unique strengths and weaknesses. Therefore, by using a broad
array of indicators, we can provide a comprehensive comparison of the two models.
Results
Table 1 reports descriptive statistics for the variables examined. We followed a two-stage
process with our hypothesized model: We started with a separate confirmatory factor analysis
(CFA) of our multi-indicator model of reputation. Consistent with our RBV-based expecta-
tion that reputation is a unidimensional multi-indicator construct, a CFA of our five indica-
tors reported a nonsignificant chi-square of 9.09 and had significantly better fit than an
alternate model treating indicators as independent. GFI was 0.97 and RMSR was 0.05 for the
submodel. All indicators loaded in the expected direction and at significance levels of .01 or
.001. Media ranking is reverse scored, so its negative loading is consistent with expectation.
Strong support for the CFA allowed us to proceed to the second stage: hypothesis testing.
Hypothesis Tests
Figure 2 shows the results of the model used to test our hypotheses. To facilitate com-
parison, we reran the RWPS model using GLS estimation to create comparable statistics.
Summary fit indices for both models are shown in Table 2. Our hypothesized model reported
superior fit on all eight of these measures. The differences in fit were minor for GFI and CFI
and far more substantial for all other measures. In addition, comparisons should go beyond
summary fit measures to include levels of explained variance, parsimony, and the signifi-
cance and interpretability of individual parameters (Henley, Shook, & Peterson, 2006).
Parsimony is often viewed as a desirable attribute for structural models. Variable configura-
tions that have more parameters have the potential for better fit simply through chance
(Mulaik, 1998) and inflated fit scores (Bollen, 1989; Kline, 1998). Consequently, if two
competing models have comparable fit index scores, the model with more degrees of free-
dom is considered preferable (Raykov & Marcoulides, 1999: 293). Because our model
reports better fit on all eight indices and does so with fewer parameters, this configuration of
variables appears to be advantageous. Finally, in terms of explained variance, the R
2
for our
dependent variables is .79 for prominence and .45 for starting salary. Rindova et al. (2005)
reported an R
2
of .69 for prominence and .36 for starting salary.
Boyd et al. / Reputation and Performance 11
Hypothesis 1 stated that reputation would have a positive effect on prominence. The path
coefficient was .96, with a highly significant (p < .001) t value of 12.1. Not surprisingly,
MBA programs that have strong reputations are seen as more prominent by corporate
recruiters. Hypothesis 2 stated that faculty experience would have a negative effect on promi-
nence. This hypothesis was also strongly supported. The path coefficient was –.20, with a t
value of 2.9 (p < .01). In other words, schools of business whose faculty have served for
longer periods of time are considerably less prominent in the eyes of corporate recruiters.
Hypothesis 3 stated that prominence would have a positive effect on starting salary. This
hypothesis was supported, with a path coefficient of 0.66 (p < .001). In summary, the results
supported all three hypotheses.
Collectively, the findings suggest that our model represents an advance over its predeces-
sor. The extant model included eight causal paths between variables, with two of those paths
being nonsignificant. In contrast, our model has only three causal paths, more degrees of
freedom, and substantial improvements in explained variance. Combined with improve-
ments in summary fit measures, our model is advantageous due to its parsimony, greater
explanatory power, and lack of nonsupported hypotheses.
Tests of Alternative Models
To reduce the likelihood that our model is misspecified, we tested several alternate, plau-
sible models. These models adopt different plausible assumptions regarding both causal
ordering and dimensionality. Table 2 shows summary fit indices for these models.
Alternative Model 1 considered whether prominence would fit better as an indicator of repu-
tation than as a mediator of reputation and starting salary. This model offers a supplementary
12 Journal of Management / Month XXXX
Table 2
Summary Fit Statistics for Alternate Models
Model
Rindova, Williamson, Figure 2
Petkova, and Hypothesized
Sever (2005) Model Figure 3a Figure 3b Figure 3c
χ
2
46.95 (4) 43.03 (2.5) 55.63 (5) 39.30 (1) 43.03 (2.5)
df 14 19 19 17 18 (3)
χ
2
/df 3.36 (5) 2.26 (1) 2.92 (4) 2.31 (2) 2.39 (3)
AIC 18.98 (5) 5.03 (1) 17.63 (4) 5.30 (2) 7.03 (3)
Standardized χ
2
6.23 (5) 3.90 (2) 5.94 (4) 3.82 (1) 4.17 (3)
GFI 0.89 (4) 0.90 (2.5) 0.87 (5) 0.91 (1) 0.90 (2.5)
RMSR 0.12 (5) 0.09 (2.5) 0.11 (4) 0.08 (1) 0.09 (2.5)
CFI 0.57 (4) 0.59 (3) 0.47 (5) 0.62 (2) 0.69 (1)
IFI 0.63 (4) 0.72 (2) 0.58 (5) 0.74 (1) 0.71 (3)
Note: Akaike information criterion (AIC) is calculated as χ
2
– 2df. Standardized χ
2
is calculated as (χ
2
– df)/√(2df).
Scores in parentheses represent rank score on each item. Lower score indicates better fit. GFI = goodness-of-fit
index; RMSR = root mean square residual; CFI = comparative fit index; IFI = incremental fit index.
Boyd et al. / Reputation and Performance 13
Quality
GMAT
MediaRank
FacPubs
DegPrestige
Reputation
Salaries
Faculty Exp
Prominence
Figure 3
Alternate Models
Figure 3a
Prominence as Exogenous Term
Quality
GMAT
MediaRank
FacPubs
DegPrestige
Reputation
Prominence
Salaries
Faculty Exp
Figure 3b
Direct Effects on Starting Salaries
test of Hypothesis 2: If the logic of prominence as a distinct mediator is flawed, the factor
structure of this alternative should offer a better fit. This simplified model has two exogenous
terms, reputation and faculty experience, and starting salary as the sole endogenous term.
Prominence is shifted from being an endogenous term to a sixth indicator on the reputation
factor model. A graphic representation of this model is shown in Figure 3a. This model
reported worse fit than our proposed model on all eight summary indicators. For example,
although this model has the same degrees of freedom as our hypothesized model, the χ
2
is
55.63—a difference of 11.64. Thus, our model is significantly better than Model 3a.
Alternative Model 2 considered whether reputation and faculty experience would have
direct effects on starting salary, independent of the mediated effect through prominence. This
question was explored through a fully saturated alternative to our hypothesized model and is
shown in Figure 3b. Because this model and our hypothesized model are hierarchical, or
nested (i.e., they differ only in the presence or absence of one or more paths), a chi-square
difference test is the most powerful comparison, as it determines whether one model is a sta-
tistically significant improvement over the other (Bollen, 1989). The change in chi-square
was not a significant improvement, forcing us to reject this alternative.
Alternative Model 3 considered whether prominence and starting salary would better fit
as indicators of a common construct versus sequential endogenous terms. Because on-campus
recruiting is associated with higher wages, it is possible that prominence and salaries are
aspects of the same phenomenon, versus the causal chain that we predict. This simplified
model has reputation and faculty experience as exogenous terms. We created a multi-indicator
endogenous construct, termed elite, composed of two indicators: prominence and starting
salary. This model is shown in Figure 3c. Overall, most indices reported that this alternative
Quality
GMAT
MediaRank
FacPubs
DegPrestige
Reputation
Elite
Faculty Exp
Prominence
Salaries
Figure 3c
Prominence and Starting Salaries as Common Factor
14 Journal of Management / Month XXXX
fit the data more poorly than did our hypothesized model. In addition, this alternative also
reported a R
2
greater than 1 for one of the y indicators.
Discussion
Reputation plays a key role in the effort to understand why some organizations outper-
form others. A recent study by Rindova et al. (2005) advanced knowledge of the reputation–
performance relationship by proposing that reputation consists of separate dimensions that
have independent value-adding effects. They characterize the reputation literature as having
two streams, one that reflects an economic-signaling, perceived quality aspect and another
that resembles an institutional stream centered on prominence. Although Rindova et al.
(2005) acknowledge that reputation can act as a resource that has performance implications,
they do not conceptualize and test a resource-based explanation. Yet, based on the meta-
analytic evidence presented by Crook et al. (2008), there is reason to believe that such an
explanation has significant merit. The present study reexamines the approach of Rindova et al.,
adding the RBV conceptualization as an attempt to build on their findings and produce a
more complete understanding. The results indicate that the RBV approach is empirically
more viable and that the effects of reputation’s dimensions are stronger when it is considered
as a broad, multiple-indicator concept. The findings further develop the link between reputation
and performance and provide another direction for developing explanation and knowledge
of this relationship. Below, we discuss the implications of our findings.
Implications
We tested a central theoretical premise that distinguishes between the RWPS model sig-
naling and institutional framework and an RBV model: whether reputation is better con-
strued as multiple constructs or as a single construct. Although our findings indicate that the
signaling and institutional approach has impressive explanatory power and insights, the
results more strongly supported the RBV’s expectations, with all five indicators loading on
a single factor. Consistent with RBV logic, reputation is perhaps best viewed as an intangible
asset that is revealed via a confluence of internal factors and external perceptions (cf. Barney &
Mackey, 2005). In the RBV approach, internal and external factors would have comple-
mentary relationships that create causal ambiguities that contribute to a valuable reputation
and competitive advantage (Dowling, 2001; Reed & Defillipi, 1990; Roberts & Dowling,
2002). Moreover, these findings may pinpoint an uncertainty in the RWPS integrated model:
It is theoretically unclear how any of its antecedents could create value together, as managers
ultimately have to manage interdependencies among the determinants and conditions that
create reputation.
In addition, the results suggest that combined relationships among the components form
valuable, difficult-to-imitate, and rare resources, the essential building blocks of the RBV’s
explanation of performance differentials among firms (Barney, 1986a, 2001). This reasoning
extends a central premise in Rindova et al.’s (2005) logic, namely that the combination of
Boyd et al. / Reputation and Performance 15
determinants rather than their independence appears to better represent the complex nature
of the reputation concept and how it produces value. This is an important distinction between
the integrated signaling and institutional approaches and the RBV; the value-creating prop-
erties of reputation appear to be less associated with incremental and staged development
and come more from interrelationships that may have causal ambiguity, rareness, and intan-
gibility. Certainly, reputation would have some attributes that can be disentangled to repre-
sent signals, but their aggregated and combined effects seem to better explain how reputation
creates value. Although Rindova et al. motivate their model as an attempt to provide more
transparency and clarity in the reputation concept, there appears to remain much value in
considering the interdependencies among reputation’s underlying determinants.
Furthermore, although the two-stage RWPS model represents a more detailed depiction
of reputation and reflects an important evolution in the development of the concept, there
appears to be stickiness among the attributes whose value is omitted when the components
are considered independently. The determinants of reputation appear to have a synergy affect,
where their integration creates more value than when the parts are considered separately.
Rindova et al. (2005) offer a conceptual respecification and deconstruction of reputation, but
the performance implications appear to be stronger when we consider reputation’s interde-
pendencies together rather than separately.
The study findings contribute to the more general understanding of reputation and perfor-
mance. Whereas prior research has documented that reputation is important for performance
(Fombrun & Shanley, 1990; Hall, 1992) and can have enduring effects (Roberts & Dowling,
2002), we know little about how the dimensions of reputation create value. Rindova et al.
(2005) provide an important contribution by developing and testing an integrative and com-
prehensive model. Our study appears to support the addition of another perspective, as the
findings provide empirical evidence for the reasoning that the synergy of the determinants
matters; the interactions of internal and external factors could lead to complexities and
causal ambiguities that are difficult to imitate and therefore likely to be advantageous.
Reputation is an intangible resource that has financial consequence, so the RBV model
would appear to have some relevance to the more general reputation literature.
A key assumption underlying our model is that the broad, multi-indicator depiction of
reputation would have a positive effect on prominence. This is another area where the RBV
model builds on the RWPS model. In the previous conceptualization, media ranking, faculty
publications, and degree prestige directly drive prominence. In this scenario, the antecedents
are depicted, in essence, as levers—a business school seeking to enhance its prominence
could simply boost its publications, for example. We find stronger support for a conceptual-
ization that eschews this tight coupling. In the RBV model, organizations seeking to become
more prominent do so through the emergence and management of an intangible strategic
asset, reputation. Tangible measures such as publications are indicators of this asset, but the
process by which the asset evolves is characterized by loose coupling and causal ambiguity.
Because our model reflects the inherent complexity of organizations, it may offer enhanced
face validity in addition to greater predictive validity.
A key difference between our model and the RWPS model is the role of prominence. In
our model, prominence plays an intervening role between reputation and performance,
16 Journal of Management / Month XXXX
whereas in the latter prominence is an element of reputation. The results supported our
framework. To the extent that future results reinforce this conclusion, there are important
conceptual and practical implications. Conceptually, the intervening role of prominence
highlights that the often studied resource and performance link is an oversimplification. Our
study adds empirical evidence to support recent calls to reveal the conceptual black box
between resources and performance (Ketchen et al., 2007; Sirmon et al., 2007). These recent
calls highlight the roles of internal factors such as structuring a resource portfolio, bundling
resources, and leveraging resources. In contrast, our results suggest that external factors,
such as the degree to which an organization is viewed as prominent, play a role in the
resources–performance link. Among the organizations we examined, a business school that
is viewed positively by stakeholders but remains obscure among those stakeholders cannot
expect performance benefits from its positive reputation.
From a practical standpoint, organizations interested in enhancing their performance may
need to work to ensure that they remain salient in the minds of key stakeholders. Because
reputation does not have a direct effect on performance, being viewed positively is only valu-
able to the extent that an organization is also highly relevant. Human cognition operates
under an availability bias wherein information that is easy to recall (e.g., due to its recent-
ness or its vividness) shapes opinions more than it objectively should (Tversky & Kahneman,
1974). This suggests that there is potentially great value in regularly reminding stakeholders
about an organization and its accomplishments, such as many business schools do by send-
ing newsletters touting their accomplishments to deans at other business schools.
Consistent with an RBV approach, we found that faculty experience has a negative effect
on prominence. This presents normative implications for business schools. Traditionally,
some schools seeking to become more prominent have attempted to do so by hiring older,
established stars, usually at very high salaries. However, as noted in our literature review,
years of experience has reported mixed results as a proxy for influence. Our results suggest
that business schools shopping in the academic “free-agent” market need to downplay these
stars’ reputations in their decision making and focus on stars’ recent productivity. Certainly,
landing a well-known scholar who continues to produce high-quality research is very desir-
able. However, these people are rare, and they are in tremendous demand. Schools that are
unable to obtain such a person must consider other options. We suggest that they would be
wise to avoid formerly prolific, highly recognized scholars and instead target their scarce
recruiting dollars on hiring midcareer high achievers whose professional reputations are
likely to grow over time. This finding is consistent with Barney’s (1986b) strategic factor
market argument, whereby acquiring unique and valuable resources generally includes a pre-
mium that discounts their ultimate contributions.
Although faculty experience is negatively related to prominence, it appears unconnected
to other study variables: Experience does not load on the reputation dimension and also has
no direct effect on MBA starting salaries. A supplementary analysis also rejected the
argument that experience was a consequence, versus a cause, of prominence. What is it, then,
about having a long-tenured faculty that leads a business school to be less salient in the
minds of recruiters? One possibility is that long-tenured faculty members are less likely to
create up-to-date, cutting-edge curricula that appeal to recruiters. In this scenario, highly
Boyd et al. / Reputation and Performance 17
experienced faculty become “stale in the saddle” (Miller, 1991) and grow to resist new ideas
and approaches to MBA education. This in turn makes their MBA programs less appealing
to recruiters. In terms of practice, this suggests that programs with long-tenured faculty
members can benefit substantially from benchmarking against other programs. It also sug-
gests the rejuvenating value of infusing new blood into a program by bringing junior faculty
into the fold. Indeed, because faculty experience is negatively linked to prominence, which
in turn is positively linked to salaries, reducing the level of faculty experience via the infu-
sion of new faculty may enhance performance. Alternatively, deans may be able to turn back
the clock with long-tenured faculty members by convincing them to be open and engaging
with novel ideas about and approaches to MBA education. One possibility is that the causal
mechanism is actually group composition versus age. With time, groups often become more
rigid and insulated from outside events and trends. A high score for experience may simply
reflect a dearth of new faculty members and reflect the calcification associated with group-
think. Overall, it is important for experienced faculty members and the administrators that
lead them to recognize that “being old” and “acting old” are two different conditions. The
former cannot be changed, but the latter certainly can.
In sum, our findings contribute to knowledge on the reputation–performance relationship
in two important ways. Viewing reputation within a sequential chain rather than as consist-
ing of independent dimensions provides a more parsimonious model. The RBV model indi-
cates that the combination of resource factors represents a stronger explanation of differences
in firm performance. In addition, the results indicate that adding resources more likely to grow
and develop has higher performance potential. By integrating several disparate resources,
reputation can become more difficult to imitate and provide a more sustainable source of
competitive advantage.
Limitations and Future Research
Our study’s findings should be viewed within the context of its limitations. In particular,
the use of U.S. business schools as the sample creates limitations. Our conclusion that the
evidence favors a depiction of reputation as unidimensional is grounded in data whose rela-
tions may not be generalizable to other industries and countries. For business schools, students
represent both products and customers. This is a fairly unique configuration of roles. As
such, we cannot dismiss past research that has portrayed reputation as two-dimensional—
such a depiction may hold in other settings. Furthermore, the above discussion pertaining to
professor contributions are based on statistical tendencies, and attention should be paid to the
full range of variance, not just means. Experienced professors vary widely in their skills and
currency. Indeed, in many cases, a few very crucial and experienced professors are the lynch-
pins to a business school’s high-quality and positive reputation. The tenure system ensures
that faculty members who are close to retirement are more likely to be less involved profes-
sionally than are junior faculty, because junior faculty fitting this bill are excused. Our results
may therefore reflect a structural arrangement as much as they reflect an experience effect.
We certainly do not advise deans to attempt to phase out senior faculty or usher them into
early retirement in an effort to boost their graduates’ salaries. Clearly, such contributions as
18 Journal of Management / Month XXXX
leadership and process management provided by senior faculty are also important to ensuring
success on other performance dimensions important to the school.
Despite its limitations, our study highlights potentially fruitful opportunities for future
research. First, we hope that our reanalysis prompts additional inquiry into the process
through which reputation influences performance. Reputation has been conceptualized as
both a firm- and industry-level factor; thus, research that examines its multitheoretical and
multilevel nature is needed. Potential interactive effects between resources need to be exam-
ined. For example, if a firm has a strong reputation and a strong culture, are the performance
advantages accrued from these two resources additive, or can synergy between them create
a multiplicative effect? Additional insight into the endurance of performance advantages,
such as those emanating from reputation, is also needed.
More broadly, we encourage researchers to reexamine other studies that use SEM. Another
approach to research that relies on leveraging published results, meta-analysis, appears to be
growing in prominence in recent years among strategic management researchers. Such
efforts have provided important conclusions to the literature, including that transaction costs
do drive decisions about organizational form (Geyskens, Steenkamp, & Kumar, 2006),
whereas capital levels do not (Combs & Ketchen, 2003). Opportunities to advance the liter-
ature by reexamining SEM studies appear plentiful, especially because most strategic man-
agement articles that use SEM do not discuss tests for alternative models (Henley et al.,
2006). Given that we found evidence that supports another explanation within a SEM study,
reevaluation of other studies may lead to further refinements in understanding. Such
approaches provide a necessary and valuable contribution by providing sharper insights into
theorized relationships (Boyd et al., 2005).
Conclusion
Reputation is an important concept in strategic management, and its role in value
creation needs to be carefully delineated. Our purpose was to reexamine the reputation–
performance relationship via the RBV. We find support for our contention that an RBV
framework provides an enhanced platform for building knowledge about reputation and its
performance effects. Importantly, the determinants of reputation appear to have synergistic
relationships that create more value when considered collectively. As managers continue to
navigate complex environments, our findings suggest that treating reputation as a broad,
multi-indicator intangible strategic asset is an approach that can help them achieve superior
performance.
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Biographical Notes
Brian K. Boyd is an associate professor of management at the W. P. Carey School of Business at Arizona
State University. His current research interests include strategic management, corporate governance, international
management, and research methods.
Donald D. Bergh is a professor in the Department of Management, Daniels College of Business, University of Denver.
His research interests include corporate strategy (mergers, acquisitions, divestitures) and applications of research
methods.
David J. Ketchen Jr. is the Lowder Eminent Scholar and Professor in the Department of Management, College of
Business, Auburn University. His research interests include entrepreneurship and franchising, methodological
issues in organizational research, strategic supply chain management, and the determinants of superior organiza-
tional performance.
22 Journal of Management / Month XXXX
