Can the reputation of an established business school change?

Abstract

A business school is a university-level institution that confers degrees in Business Administration. This paper examines the causes of business school reputation using two competing perspectives: the meritocratic and the institutional. The meritocratic perspective is based on the belief that reputation is an outcome of the business school’s achievements, which act as signals of organisational reputation. The institutional perspective highlights the importance of the institutional context in explaining reputation (business school past reputation and parent university reputation), minimising the effect of outcomes as reputation signals. The two perspectives are tested on a sample of US business schools via the use of multiple regressions and path analysis. The results lend weight to the institutional thesis, which brings into doubt the efficiency of short-term strategies that look to increase business school reputation.

Keywords

reputation business schools universities SEM techniques USA

Introduction

One of the most important intangible assets an organization can possess is reputation (Hall, 1992), particularly if it sells a product that is hard to assess in terms of quality and performance, operates within a global market and aims to adopt a differentiation strategy, as in the case of many US business schools¹ (Argenti, 2000; Boyd et al., 2010; Engwall, 2007; Shenkar & Yuchtman-Yaar, 1997).

Explanations of business school (hereafter b-school) reputation have classically relied on various signals generated within the b-school or emanating from it (e.g. research performance, student quality or presence in media rankings) (Rindova et al., 2005). This perspective, in the study of reputations of a meritocratic nature, has overlooked the ‘institutional context’ within which schools are located (parent university), which may result from similar experiences in the organisational culture, and economic and financial linkages (Keith, 1999), and the overwhelming inertia that exists in the estimations and attitudes of the stakeholders of a b-school with the passage of time (Keith, 2001).

Consequently, and despite recent efforts on the part of researchers to examine the antecedents and effects of b-school reputation (Boyd et al., 2010; Rindova et al., 2005; Safón, 2009; Vidaver-Cohen, 2007), little is known about the stability of the perceptions of stakeholders over time, that is to say how much current reputation depends upon past reputation, while there is an equal paucity of information on the relation between the reputations of b-schools and their parent universities. To date, no study has simultaneously analysed the influence of b-school past reputation and parent university reputation on b-school current reputation, nor compared the predictive capacity of these antecedents of an institutional nature with those antecedents normally identified by the literature as being meritocratic. The objective of this study is to bridge that gap.

The article is set out as follows. In the theory section, I indicate what is meant exactly by b-school reputation. This definition is followed by a discussion of the causes of reputation, presenting the two alternative perspectives. The section ends with the proposal for a framework that will serve to assess the explanatory capacity of the perspectives analysed. The methods section presents the data, variables and analysis of the empirical study. I use ordinary least square (OLS) regression analysis, with the intention of controlling the potential problems of collinearity between reputation variables and their lagged variables, and structural equation modelling (SEM) techniques given the existence of two dependent variables: b-school and parent university reputations. I then go on to provide the results of my study, which clearly indicate the superiority of the institutional perspective in explaining b-school reputation, followed by a discussion of the possible repercussions of the results.

Theory

A reasonable consensus currently exists on considering organisational reputation as an overall and final addition to the perceptions of all stakeholders over time (Fombrun, 1996; Helm, 2006; Yang et al., 2008). The concept encompasses other related factors such as image, i.e. how others see us, and indentity, i.e. how we see ourselves (Chun, 2005), and is basically created from the perceptions of external stakeholders (Elsbach, 2006; Rindova et al., 2010).

In line with the generic perspective of this concept, Argenti (2000) conceives b-school reputation as the sum of individual perceptions which are derived from how students, recruiters, faculty, alumni and the parent university view the school. Similarly, Vidaver-Cohen (2007) defines b-school reputation as ‘the level of trust, admiration/respect and good feeling observers experience for the target school, as well as their perception of the school’s overall public esteem’ (p. 283), highlighting individual experience and overall public admiration in forming stakeholder perceptions.

In order to understand the construct, it is vital to distinguish between the antecedents or causes of organizational reputation and the social cognitions (perceptions) that constitute organisational reputation (Rindova et al., 2010). The former (i.e. the causes of reputation) ‘make up’ the construct, while the latter (i.e. stakeholder perceptions) ‘reflect’ the construct (Safón, 2009). The following section looks at the causes of b-school reputation and also those of university reputation. In the method section, the paper goes on to explain how the reputation measure is constructed from stakeholder perceptions.

Causes of academic reputation

The specialized literature has been researching the causes of reputation and prestige of universities, colleges, departments, programs and schools for some time from two major competing perspectives: the meritocratic and the institutional (Keith, 2001).

The meritocratic perspective is based on the belief that reputation is an outcome of the achievements of the academic institution (Keith, 2001), which act as signals of organisational reputation (Rindova et al., 2005). This perspective is grounded in signalling theory (Spence, 1973), and appears most commonly in the literature, as well as being deeply rooted in the management practices of academic institutions, so much so that many of those in charge of universities, departments and professional schools intent on improving their reputation pursue strategies to purposively influence their institution’s performance on a set of outcomes identified as reputation signals (Keith, 2001: 495).

The meritocratic perspective has been developed at a variety of academic levels (university, school and department), at times by analysing the perceptions of a single stakeholder (e.g. Standifird, 2005; Rindova et al., 2005), at others by simultaneously examining several stakeholders (e.g. Safón, 2009). At the departmental level, reputation has typically relied on various performance indicators associated with faculty productivity, student quality and organisational size (Keith, 1999). At the undergraduate and graduate level, most studies have concluded that institutional size and admissions selectivity are the primary drivers of reputation (Sweitzer & Volkwein, 2009). At the b-school level, size, student quality and media rankings have been seen as the main signals of reputation (Rindova et al., 2005; Safón, 2007, 2009; Sweitzer & Volkwein, 2009).

The empirical work carried out within this perspective has been accompanied by important theoretical modelling. One of the most interesting and recent examples is that of Volkwein & Sweitzer (2006), who developed a framework of influences on institutional prestige among colleges (liberal arts colleges) and universities (research universities) using a single stakeholder, i.e. academics. Their framework is based on three areas of influence: structural characteristics of the institution (such as size, mission, control, …), and faculty and student resources and outcomes. Two empirical studies by these authors have proved the usefulness of the framework at the university level (Volkwein & Sweitzer, 2006) and at the school level (Sweitzer & Volkwein, 2009), and have shown that: (1) enrolment size is significantly and positively associated with reputation in Business, Education, Engineering, Law and Medical schools; (2) admissions test scores are positively related to prestige in four of the five disciplines (Education being the exception); and (3) research performance is significantly associated with reputation scores in four of the five disciplines (Business being the exception here).

Rindova et al. (2005) propose a general model of organisational reputation applied to a sample of US b-schools (see also Boyd et al., 2010; Rindova et al., 2010). These authors propose a model for b-schools based on signalling theory, according to which b-school reputation has two factors or dimensions: (1) perceived quality, whose antecedents are the quality of inputs (according to these authors, incoming students constitute the major resource input), and the quality of productive assets (they propose that the main productive asset employed in the educational process of a business school is its faculty); and (2) prominence, whose antecedents consist of the image derived from media rankings, research performance (certifications of achievement) and faculties' doctorates from prestigious universities that the business schools employ. Their empirical evidence clearly shows the superiority of the Graduate Management Admission Test (GMAT) score and media rankings in forming the perceptions of quality and prominence among recruiters. Other studies have also observed the strong influence of media rankings on b-school reputation (Argenti, 2000; Gioia & Corley, 2002; Safón, 2007, 2009), and on university reputation (Arpan et al., 2003; Theus, 1993).

Vidaver-Cohen (2007) propose a b-school reputation framework based on the RepTrack model of the Reputation Institute. The framework consists of two fundamental elements: quality signals (programmes, processes and practices) and stakeholder perceptions (trust, admiration, good feeling and public esteem). This framework is empirically supported by the study of Safón (2009), which states that the reputation of a b-school is a unidimensional construct, implying homogeneity in the perceptions of external stakeholders, and fundamentally hinges on the quality of students, the quality of the programme and the position of the school in media rankings.

In short, the literature based on the meritocratic perspective conceives the reputation of an academic institution as a construct of a perceptual nature, formed from certain reputation signals related to product quality and institution prominence (student and programme quality, research performance, size and the presence and position of the institution in the media rankings), that make up the converging perception of varying stakeholders (Argenti, 2000; Arpan et al., 2003; Boyd et al., 2010; Rindova et al., 2005; Safón, 2009; Theus, 1993; Vidaver-Cohen, 2007).

The second perspective for explaining academic reputation is the institutional perspective. This approach underlines the importance of the institutional context in explaining reputation by minimising the effect of the signals seen, particularly in the case of established institutions. According to Keith (2001), the institutional perspective assumes that academic institutions, as organisations, exist within an institutionalised environment and to survive must have legitimacy, which forces them to be structured in ways that allow for similar outcomes. With the passage of time, institutions become increasingly homogenous and their reputation gradually bears less and less relation to what they are actually doing and achieving (internal criteria), in order to reflect to a greater extent other criteria that arise from external sources, such as the perceived value of educational credentials. These external perceptions become more embedded and consolidated in the institutional context, generating beliefs about reputation that are maintained over time and remain unaltered, which explains why academic reputation is an institutional characteristic that is almost static for long periods of time (for example, Keith (2001) suggests that it takes more than 30 years for there to be a relevant modification in the reputation of a university).

This perspective has been present in the literature on organisational reputation for some time (for example, Shapiro (1983) modelled corporate reputation as a construct explained by two antecedents – lagged reputation and product quality), although it has not received a great deal of attention from researchers. In the case of the literature on academic reputation, it is not the predominant perspective either, although in recent years, some studies have appeared that have considered this approach and have come up with robust empirical evidence on the influence of past reputation on current reputation in the case of academic departments (Keith, 1999; Toutkoushian et al., 1998), b-schools (Morgeson & Nahrgang, 2008) and universities (Keith, 2001).

The institutional context has another implication from the viewpoint of the reputations of academic institutions. Departments and professional schools include parent universities within their institutional context, with which they share emotional, cultural, economic and financal ties and with whom they maintain a certain relationship of subordination, for example large-scale restructuring is normally carried out at the university level (Keith, 1999). This suggests a relation between the reputations of b-schools and their parent universities. This perspective is also supported by the idea that the association between two elements provides the individual with information, as the individual compares the quality of the lesser known element with the reputation of the more widely known element (Rindova et al., 2005).

The idea that the reputation of the different parts (divisions, departments, units, …) is determined by the reputation of the whole (corporation) and vice versa has not received a great deal of attention in the literature on management and marketing, and is only lightly touched upon in the literature on academic reputation. In management and marketing, the relation between CEO reputation and firm reputation has been scrutinised, and a positive relation has been reported (Gaines-Ross, 2000; Helm, 2006). It has also been observed that franchisees take advantage of the reputation of the franchiser and that international subsidiaries can benefit from the reputation of the parent firm (Montero, 2010). In the case of academic reputations, the relation between the reputations of the various parts and the whole is widely recognised in terms of departments and the parent university. A number of studies have observed strong correlations between both reputations (for example Zhelyazkov (2006) reports a correlation of higher than 0.70), having found indications that departmental reputation is basically a result of the ‘halo effect’ brought about by its affiliation with a prominent university (Webster, 1992; Keith, 1999). Other studies propose the possibility that university reputation is caused, in part, by professional schools' (business, law and medical schools) reputations (Devinney et al., 2006; Graham & Diamond, 1997), and by the behaviour of the off-shore campuses (McDonald, 2006; Kim & Zhu, 2010). Some research, such as that of Lock (1999), has come up with an inverse relation, observing that, in the years prior to the reform induced by the reports published in the US by Gordon and Howell, and that of Pierson in 1959, the prestige of an individual b-school is largely based on the reputation of the parent university, an idea that Argenti (2000) also subscribes to.

All this leads to the assumption that the institutional context is fundamental to understanding academic reputation in the case of established b-schools, and that both past reputation and parent university reputation (in terms of b-schools) and b-school reputation (with regard to the parent university) are important antecedents of the reputation of the institution. Figure 1 shows the hypothesised model of b-school and university reputations. The model is based on the two aforementioned perspectives and is shaped by the need to help construct a measure of b-school and university reputations that is applicable to more than one entity, which demands that the model be simplified as much as possible.

Figure 1.

Framework

Methods

Data

With a view to isolating the sample from external factors that are hard to control, and to gauge the effect of the institutional context, only well-established universities were selected, which also have consolidated b-schools and are located within a single country (the US), present in secondary sources that cover several years and are of recognised prestige (the data for this study come from four different well-known secondary sources: Business Week, U.S. News and World Report, Quacquarelli Symonds (QS) World University Rankings and University of Texas at Dallas Top 100 Business School Research Rankings). In order to reduce the problem of missing data as much as possible, cases that required the estimation of more than one of the variables considered in the analysis were discarded, which produced a final sample of 80 b-schools (the youngest of which was 26 years old, with an average age of 75) with a parent university (the youngest being 45 years old, with an average age of 160).

Reputation measures

According to Jarvis et al. (2003: 215), indicators that capture a stakeholder’s overall level of satisfaction are reflective in nature. I model b-school reputation as a one-factor reflective construct, measured using the overall assessments of two different stakeholders: employers/recruiters and administrators from other b-schools. This b-school reputation measure is consistent with the notion of a general reputation (Helm, 2006), fundamentally based on external stakeholder perceptions (Elsbach, 2006) which have received positive empirical evidence in other studies (Arpan et al., 2003; Safón, 2009). Moreover, this form of measuring reputation allows for antecedents to be separated from the measures of stakeholders' reputation cognitions (see Figure 2 ), and is recommended both due to its consistency with the theory and its empirical application (Rindova et al., 2010).

Figure 2.

Formative and measurement model

The recruiter assessment score and the peer assessment score were applied, provided by U.S. News and World Report, where a subjective assessment of the quality of programmes, the b-schools and their students is carried out using a sample of recruiters as well as deans and directors of business schools. These measurements have been applied in previous studies (e.g. Standifird, 2005). Following Shapiro (1983), b-school reputation is measured at two consecutive moments in time: 2007 (called lagged or past reputation) and 2008 (current reputation).

University reputation was measured in the same way as b-school reputation, i.e. a one-factor reflective construct, measured using the overall assessments of the same stakeholders. The recruiter assessment score and the peer assessment score were applied, provided by QS World University Rankings. These indicators are based on an online survey distributed to academics and employers worldwide. Geographical weightings are applied to ensure fair representation from the different regions of the world. Parent university reputation is also measured at two moments in time: 2007 and 2008.

Table 1 shows the reliability and validity of the four multi-item constructs employed. The multi-item constructs were subjected to confirmatory factor analysis (CFA) with lagged correlated measurement errors. This gave a model fit for the proposed factor structure, whose values are considered acceptable (X² = 14.32, df = 10, p = 0.16, AGFI = 0.85, CFI = 0.99, RMSEA = 0.07). Convergent validity is suggested when items load significantly on their designated latent constructs. All items loaded on their respective constructs and were statistically significant (p < 0.001). I assessed discriminant validity of the measures by first constraining the interfactor correlation between reputations of b-schools and universities to unity and performing chi-square difference tests. A significantly lower chi-square (ΔX² _[1] > 3.84) for the model without restrictions on the interfactor correlation demonstrates the existence of discriminant validity. I also examined the confidence interval for the interfactor correlation and none include a value of 1.00, which means there is sufficient evidence to assume the existence of discriminant validity.

Table 1.

Construct measures and reliabilities based on CFA

Construct	Measures	Standardized coefficient
B-school past reputation (α = 0.94)	Recruiter assessment score	0.95*
B-school past reputation (α = 0.94)	Peer assessment score	0.95*
B-school current reputation (α = 0.96)	Recruiter assessment score	0.97*
B-school current reputation (α = 0.96)	Peer assessment score	0.98*
University past reputation (α = 0.79)	Recruiter assessment score	0.90*
University past reputation (α = 0.79)	Peer assessment score	0.73*
University current reputation (α = 0.85)	Recruiter assessment score	0.91*
University current reputation (α = 0.85)	Peer assessment score	0.81*
Measures of fit: X² = 14.32, df = 10, p = 0.16, AGFI = 0.85, CFI = 0.99, RMSEA = 0.07

*p < 0.001

Quality and visibility measures

Quality measures

In the case of b-schools, I gauged product quality using two measures: quality of students and programme value, as these are the most powerful signals according to a recent study by Safón (2009), carried out on a sample of top US b-schools. Quality of students was measured using the average of GMAT scores of full-time students that entered the Master of Business Administration (MBA) programme in each b-school. This means of measuring the quality of students has been previously justified and used (D’Aveni, 1996; Rindova et al., 2005; Turban & Cable, 2003). The concept of programme value added proposed by Tracy & Waldfogel (1997) was used in order to measure the education and training received at b-schools.

For universities, I used two measures of quality: incoming student quality, measured via the selectivity rate; and quality of the learning process, measured using the student/faculty ratio. These measures have some limitations and have received a certain amount of criticism (Pascarella & Terenzini, 1991; Terenzini & Pascarella, 1994), though it has been observed that they are strongly associated with university status (Keith, 2001). In addition, obtaining them is simple and homogenous when it comes to analysing a large group of universities. On another note, the quality of incoming students is one of the most commonly used dimensions for assessing quality of universities (Dill & Soo, 2005), their measures (high school grades and selectivity rate) are strongly related to the variables used to measure the quality of outputs (i.e. quality of graduates) and students are enriched by the so-called ‘peer effect’. Equally, student/faculty ratio is a commonly used measure of quality process in many evaluations and rankings around the world (e.g. the Higher Education Statistics Agency in the UK and the QS World University Rankings).

Entity visibility measures

I measured visibility using research performance, institution size and the presence and position of the institution in the media rankings, in a very similar way to how Rindova et al. (2005) measure prominence. I gauged the research performance of universities via citations, according to the data provided by QS World University Rankings. Citations are normally seen as a proxy of the impact of research in the literature (Adler & Harzing, 2009). I obtained the research performance of the b-schools from the study periodically carried out by the UT Dallas' School of Management. This school provides a tool to study research contributions based on publications in 24 leading journals in major business disciplines.

Organisational size is an extremely important source of visibility generally observed in different types of organizations (Fombrun & Shanley, 1990; Shenkar & Yuchtman-Yaar, 1997; Turban & Keon, 1993; Turban & Greening, 1997), and also in the case of academic institutions (Argenti, 2000; Theus, 1993). The literature commonly uses the number of students or graduates to measure the size of universities and schools (see, for example, Thomas & Li, 2009). In this study, I used the total number of students in figures published by U.S. News and World Report for both the universities and the schools.

Media rankings are a highly important element of visibility in academic institutions (Rindova et al., 2005; Vidaver-Cohen, 2007). In the case of b-schools, I used the Business Week b-school ranking, as this the most widely used and influential ranking (Morgeson & Nahrgang, 2008; Rindova et al., 2005). In the case of universities, I used the U.S. News and World Report National Universities Ranking. This ranking considers the 262 national universities in the country, based on categories developed by the Carnegie Foundation for the Advancement of Teaching. In this ranking, a university is understood to be an institution that offers a full range of undergraduate majors, as well as master’s and doctoral degrees.

Following the recommendation by Rindova et al. (2010), with a view to respecting the time lapse between the antecedents of reputations and reputation perceptions, all the data on quality and visibility were lagged by one year with regard to the measures of current reputation.

Analysis

The estimation of the framework of Figure 1 was carried out in two stages. Firstly, I estimated sub-frames 1 and 2 independently using ordinary least square (OLS) regression analysis, with the intention of controlling the potential problems of collinearity (Study 1). Despite the fact that the focus of this article is b-school reputation, due to the relations of reciprocity derived from the institutional perspective, the analyses are carried out in the same way for b-schools as they are for their parent universities. Secondly, I estimated the framework globally using path analysis (Study 2).

Results

Study 1: Sub-frames estimation

Table 2 shows descriptive statistics and Table 3 the results of the regressions of university current reputation on predictors (sub-frame 1, Figure 1).

Table 2.

Descriptive statistics

Variable		Mean	SD	1	2	3	4	5	6	7	8	9	10	11	12	13
1.	University current reputation	0.000	1.000
2.	University past reputation	0.000	1.000	0.967***
3.	B-school current reputation	0.000	1.000	0.782^***	0.754^***
4.	B-school past reputation	0.000	1.000	0.744^***	0.719^***	0.991^***
5.	University selectivity rate	0.485	0.223	–0.627^***	–0.594^***	–0.588^***	–0.554^***
6.	University size	23,711.938	13,767.406	0.084	0.129	0.004	0.010	0.327^**
7.	University student/faculty ratio	57.063	25.633	0.309^**	0.269*	0.401^***	0.402^***	–0.587^***	–0.420^***
8.	University research performance	73.138	19.311	0.575^***	0.605^***	0.542^***	0.538^***	–0.460^***	–0.098	0.266*
9.	University ranking	53.150	34.916	–0.743^***	–0.737^***	–0.715^***	–0.683^***	0.843^***	0.303^**	–0.537^***	–0.626^***
10.	B-school program value	948.009	13,480.206	0.444^***	0.369^**	0.245*	0.229*	–0.394^***	–0.187	0.293^**	0.180	–0.473^***
11.	GMAT score	651.525	47.179	0.761^***	0.753^***	0.859^***	0.845^***	–0.621^***	0.059	0.350^**	0.598^***	–0.674^***	0.028
12.	B-school size	339.263	352.430	0.717^***	0.670^***	0.696^***	0.688^***	–0.641^***	–0.050	0.512^***	0.446^***	–0.635^***	0.444^***	0.665^***
13.	B-school research performance	39.268	35.295	0.708^***	0.681^***	0.670^***	0.671^***	–0.511^***	0.194	0.335^**	0.501^***	–0.541^***	0.287^**	0.680^***	0.779^***
14.	B-school ranking	25.975	9.564	–0.752^***	–0.701^***	–0.712^***	–0.704^***	0.639^***	0.018	–0.451^***	–0.489^***	0.633^***	–0.461^***	–0.682^***	–0.905^***	–0.748^***

*p < 0.05

**p < 0.01

***p < 0.001

Table 3.

Regressions of university current reputation on predictors

	Model 1		Model 2		Model 3		Model 4		Model 5
Variables	Beta	Sig.	Beta	Sig.	Beta	Sig.	Beta	Sig.	Beta	Sig.
K
Product quality
Selectivity rate	–0.12		–0.07		–0.14		–0.08
Student/faculty ratio	–0.02		0.02		–0.07		0.00
Institution visibility
Research performance	0.14		–0.03		0.09		–0.04
Media ranking	–0.66	***	0.11		–0.45	**	0.05
Size	0.33	***	–0.01		0.24	**	–0.03

University past reputation			0.95	***			0.91	***	0.97	***
B-school past reputation					0.34	***	0.10	*

R²	0.67		0.94		0.72		0.94		0.93
Adjusted R²	0.65		0.94		0.70		0.94		0.93

F	30.22	***	189.12	***	31.72	***	171.67	***	1107.62	***
Maximum VIF	4.70		6.35		5.55		6.64		1.00

Note: Standardized coefficients shown.

*p < 0.05

**p < 0.01

***p < 0.001

As can be seen, the problems of collinearity are within acceptable limits as, in the five models, the maximum VIF is lower than 10 (see Hair et al., 1998). All the models are significant and the dependent variable (university current reputation) is explained by a very high percentage (adjusted R² > 0.65). In line with the institutional perspective, the majority of the variance of current university reputation (adjusted R² = 0.93) is explained by its lagged variable (past university reputation), which allows for a reasonably sound support of this perspective in explaining the causes of reputation, in light of the evidence provided in Models 2, 4 and 5. Equally, Model 4 provides supplementary empirical support to this perspective by demonstrating the influence of school reputation on parent university reputation.

Table 4 shows the results of the regressions of B-school Current Reputation on predictors (sub-frame 2).

Table 4.

Regressions of b-school current reputation on predictors

	Model 6		Model 7		Model 8		Model 9		Model 10
Variables	Beta	Sig.	Beta	Sig.	Beta	Sig.	Beta	Sig.	Beta	Sig.
K		***		**		***
Product quality
Program value	0.19	**	0.04	*	0.16	*	0.02
GMAT scores	0.81	***	0.12	**	0.76	***	0.07	*
Institution visibility
Size	–0.06		–0.02		–0.06		–0.02
Media ranking	–0.08	*	0.00		–0.08	*	0.00
Research performance	0.04		–0.02		0.03		–0.03

B-school past reputation			0.91	***			0.90	***	0.99	***
University past reputation					0.09		0.07	**

R²	0.79		0.99		0.79		0.99		0.98
Adjusted R²	0.78		0.98		0.78		0.99		0.98

F	55.97	***	814.79	***	46.62	***	777.45	***	4302.36	***
Maximum VIF	3.60		5.54		4.41		6.84		1.00

Note: Standardized coefficients shown.

†p < 0.10

*p < 0.05

**p < 0.01

***p < 0.001

The results support both perspectives. As can be observed in Model 9, the institutional perspective has the greater explanatory power, as b-school lagged reputation and university lagged reputation are significant and the former explains the majority of variance in b-school current reputation on its own (adjusted R² = 0.98), as shown in Model 10. However, and despite the scant margin for the other predictors, Model 9 gives positive indications as to the explanatory capacity of the meritocratic perspective, especially with regard to student quality, measured by GMAT scores.

Study 2: Global estimation

Study 2 simultaneously examines the reputations of b-schools and their parent universities. Due to the simultaneous existence of two dependent variables, I used path analysis. Figure 3 shows the theoretical model estimated. The new results confirm the validity of the institutional perspective, as all its paths are significantly different to zero. It can also be observed that (1) past reputation has the same weighting both in the case of parent universities and b-schools; and (2) the weighting of b-school reputation on parent university reputation and vice versa (parent university reputation on b-school reputation) is approximately a tenth of that attributed to past reputation.

Figure 3.

Structural model results

In addition, Study 2 gives interesting results in relation to the reciprocity between the two reputations analysed. Out of the four theoretical possibilities – (1) b-school reputation causes parent university reputation; (2) parent university reputation causes b-school reputation; (3) the two constructs are related reciprocally; or (4) the constructs are not causally related at all and any empirical association must be a spurious one – only the third one is supported.

Discussion

The research described in this paper explores the validity of two competing perspectives for explaining b-school reputation, and clearly shows the superiority of the institutional perspective over the meritocratic perspective. The results obtained bring into question moves by b-school administrators who attempt to improve the reputation of their institutions in the short term, and recommends to them that school reputation should be seen as the result of developing their mission in the long term rather than considering it a short- or medium-term objective. Other findings and implications of the research are as follows.

Firstly, the reputations of b-schools and universities are fundamentally explained by their past reputation (Models 4 and 9), which explains more than 90 per cent of their variance. This result is in accordance with previous research on universities and department reputations (Keith, 1999, 2001), but challenges the ideas expressed by the majority of literature on b-school reputation by demonstrating the scarce influence exerted by reputation signals on current reputation in the short term. For example, media rankings have been one of the most important reputation signals according to the literature (Gioia & Corley, 2002; Rindova et al., 2005; Safón, 2007, 2009; Trank & Rynes, 2003; Vidaver-Cohen, 2007). However, my research presents different results. Model 7 (Study 1) shows how media rankings cease to be significant when past reputation acts as a predictor. How might this result be interpreted? Firstly, it must be taken into consideration that results from previous studies indicating the contrary do not control for b-school past reputation. Secondly, one must bear in mind that many b-school media rankings are constructed using perceptions (e.g. 90 per cent of the Business Week B-school Rankings are based on the perceptions of recruiters and students) and are thus, in reality, proxies of reputation. Model 9 provides additional evidence: in the short term, media rankings do not influence current reputation, as ‘new’ information (that which is not already contained in past reputation) does not significantly influence current perceptions. In short, media rankings might be considered as good proxies of past reputation, but they lack new information that could modify current perceptions.

The previous result poses an interesting question. We know that the changes in media rankings have consequences for the strategies of b-schools. For example, recently Fee et al. (2005) demonstrated that poor performance in media rankings is associated with a significant increase in the likelihood of the departure of the dean. Therefore, should changes in media rankings be taken into consideration to the extent where they might modify the strategies of b-schools? The answer is no. This study and previous ones show that reputation is a reasonably stable concept (e.g. Keith, 2001; Morgeson & Nahrgang, 2008), and that changes in the media rankings are extremely random and reversible over time (Dichev, 1999), a fact that denies their legitimacy as useful information for decision-making in the short or medium term.

Secondly, b-school quality (GMAT score) is a predictor of b-school reputation in the demanding Model 9, where neither the other quality variable (programme value) nor any of the variables related to visibility (research performance, size and the aforementioned media rankings) turn out to be significant. This result is partially coherent with previous research (Rindova et al., 2005; Safón, 2009), which indicates the strong impact of student quality on b-school reputation and leads to an interesting implication. Student quality is a manageable variable for the school, as the smaller the size, ceteris paribus, the greater the selectivity and the higher the average GMAT score. As size does not appear to have a short-term impact on reputation and it is impossible to improve university and b-school lagged reputations, if the objective of a school is to slightly improve its reputation in the short term, the only strategy remaining is to reduce the number of students at the school.

Thirdly, the previous argument is strengthened by another finding in this study. It has been shown that the reputations of schools and their parent universities are influenced reciprocally. Furthermore, they are of similar intensity, as can be seen in the standardised parameters of study 2 (see Figure 3). This result is coherent with part of the literature and brings up two thought-provoking implications. The first is that, as b-school reputation is an important source of reputation for the parent university, which does not depend on school size but does rest on the GMAT score, it stands to reason that the parent university must contain the b-school size within limits that can guarantee a high level of selectivity if its objective is to improve parent university reputation. The other implication lies in the existence of a reputational link between the b-school and its parent university; both institutions must attempt to benefit from the reputation of the other as much as they can, modifying, where possible, their communication strategies towards the outside world with a view to associating the two names.

Fourthly, traditionally, the quantity and, above all, the quality of research published has been one of the major reputation signals among academics, both in terms of the level of b-schools (Armstrong & Sperry, 1994), and other professional schools (Sweitzer & Volkwein, 2009). However, research performance has come out as a signal of neither b-school reputation nor university reputation in this study. The lack of influence of performance research on reputation among other stakeholders (recruiters, students, …) has been recently explained by the literature for the case of b-schools (Bennis & O’Toole, 2005; Morgeson & Nahrgang, 2008; Pfeffer &Fong, 2002; Safón, 2007; Sweitzer & Volkwein, 2009; Van de Ven & Johnson, 2006), but not for universities. Standifird (2005) found a positive relation between research emphasis and peer assessment when studying the reputation of colleges in rankings published in U.S. News and World Report. However, reputation is not solely the perception of one stakeholder, but rather the sum of ‘all’ the stakeholders. This research has introduced the perceptions of two stakeholders (academics and recruiters), a fact that explains results that are contrary to those of Standifird (2005), as it is among firms where research performance appears to have the least impact. This result forces us to reconsider the relation between research and academic reputation if we want these two aspects to work together in the short term. In particular, what type of research has an impact on stakeholder perceptions? How much research should be accumulated and maintained over time so that it has an impact on institutional reputation?

Limitations and future research

Due to the use of secondary sources and the need to work with homogenous data, the sample consists of US institutions with high levels of reputation, which limits the generalisation of the results. Future studies should look into whether the formation of academic reputation is different in b-schools that have low levels of reputation or are relatively new. Equally, it would be interesting to examine whether the formation of reputation is similar in other countries. The b-schools analysed are among the oldest and most highly reputed in the world. It is worth questioning whether in other countries, where b-schools are newer (e.g. are less than thirty years old), the meritocratic perspective is more important than the institutional one, as there has not been sufficient time for perceptions to become consolidated. It would also be of interest to observe the explanatory capacity of the two perspectives in countries where their b-schools do not require an entrance exam such as the GMAT, or the starting salaries of alumni that conclude their studies at the b-school are not known or the b-schools do not appear in the main international and national rankings. In such countries, objective signals would hinder the formation of perceptions, thereby reducing the chances of finding an explanation based on the meritocratic perspective.

This study measures reputation as an aggregate of the perceptions of two external stakeholders. Despite the fact that other studies analyse a similar number of stakeholders (e.g. one in the case of Rindova et al. (2005) or three in the study by Safón (2009)), a correct measurement of ‘general reputation’ demands a broader range of perspectives, although a strong correlation between the perceptions of different stakeholders would be expected (particularly external ones). Further studies might repeat these analyses with a greater number of stakeholders with a view to verifying the consistency of the relations established herein and the superiority of the institutional explanation.

Footnotes

Notes

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