3094111 Conceptual Maps of the Leading MBA Programs

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Conceptual Maps of the Leading MBA Programs in the United States: Core Courses,Concentration Areas, and the Ranking of the SchoolAuthor(s): Eli Segev, Adi Raveh, Moshe FarjounSource: Strategic Management Journal, Vol. 20, No. 6 (Jun., 1999), pp. 549-565Published by: John Wiley & SonsStable URL: http://www.jstor.org/stable/3094111

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Strategic Management JournalStrat.Mgmt.J., 20: 549-565 (1999)CONCEPTUALMAPS OF THE LEADINGMBAPROGRAMSIN THE UNITEDSTATES: CORECOURSES, CONCENTRATION REAS, AND THERANKINGOF THE SCHOOLELI SEGEV1"*,ADI RAVEH2 AND MOSHE FARJOUN11Facultyof Management, Tel-AvivUniversity, Ramat-Aviv,Tel Aviv, Israel2School of Business Administration and Statistics Department, The Hebrew Univer-sity of Jerusalem, Jerusalem, IsraelThis paper captures the structureof MBAprograms in 25 leading U.S. business schools at thebeginning of the revolution these programs are undergoing.It is a study of strategic groupsin the MBA industry, and a baseline for examining adaptation and strategic change ineducational institutions.We use the Co-plot method to map the schools according to the 1993structureof their core courses and existing areas of concentration.Themaps indicate similaritiesamong business schools and shed light on their 1994 ranking. Each of the five top schoolshas been found to be in a different cluster of MBAprogram structures. The findings suggestthat program structure content-the particular mix of core and concentration areas-in itselfis not a source of superior performance.Copyright? 1999 John Wiley & Sons, Ltd.

STRATEGIC CHANGE IN BUSINESSSCHOOLSIn recent years the environment of businessschools has been undergoing major changes.Organizations are becoming more global anddynamic and less hierarchical (Wind, 1991). Vastimprovements in quality, productivity, and com-petitiveness are being made. Downsizing, reengi-neering, networking, information basing, andlower costs through quality have become com-monly held concepts (Roberts, 1990). Greateremphasis is placed today upon human factorssuch as team effort, employment stability, andflexibility (Roberts, 1990). In addition, the roleof manager has been redefined. Managers todayneed more insight into global, political, economic,

Key words: strategic groups; business schools; stra-tegic change; business level strategy*Correspondence to: E. Segev, Faculty of Management, TelAviv University,PO Box 39010, Ramat Aviv, Tel Aviv69978, IsraelCCC0143-2095/99/060549-17 $17.50Copyright 1999 JohnWiley & Sons,Ltd.

and cultural environments; they probably needsome experience across disciplinary functions,and stronger interpersonal skills (Wharton, 1991;Wind, 1991). These changes in the business worldhave seriously challenged the relevance of theways managers have been traditionally developedand been educated. Studies on the state of man-agement education have indicated that the MBAprograms are not consistent with the new man-agement paradigm and require major rethinkingand redesign (Roberts, 1990; Wharton, 1991;Wind, 1991).In addition to changes in the subject matterof business itself, business schools have beendrastically affected by a key institutional change:the introduction of business school ranking inpublications such as Business Week and U.S.News and WorldReport. Prior to 1988, a businessschool relied mainly on its own data and percep-tions to assess its position relative to otherschools. The introduction of an external and amore objective evaluation has created a new met-ric and resulted in new positional ranking. Thepublic ranking has established itself as a measurethat is difficult to ignore and as an important

Received3 February1997Final revisionreceived28 October1998

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550 E. Segev, A. Raveh and M. Farjounconsideration in schools' adaptation and redesign(Elsbach and Kramer, 1996).The new and broad-ranging demands haveforced business schools to adapt their strategiesand operations or risk becoming obsolete andlosing the support of their stakeholders (Roberts,1990; Wharton, 1991). One of the main toolsused by business school faculties to deal withchanges in the business world and the institutionalenvironment is curriculum redesign. Many busi-ness school faculties view curriculum redesign asa key aspect that can have a major impact onthe ranking of the school's MBA program.In thestruggle for improved ranking, the policies andpractices of top schools in particularhave oftenbecome a model for emulation.The changes occurring in the MBA field canbe viewed as a 'naturally occurring experiment'which presents a research opportunity to traceand explain adaptation and performance effectsof strategic change in educational institutions. Thepresent study focuses on capturing and comparingthe MBA program structures of leading businessschools near or at the beginning of the currentrevolution, and examining a possible relationshipbetween certain program templates and perform-ance rankings. It therefore helps establish anempirical and conceptual baseline for subsequentstudies of these changes. The first of our objec-tives is to find out whether there is an identifiablegrouping of business school strategies as reflectedin the structure of their MBA programs. Thesecond, which is closely related to the first, is tofind out whether there is a relationship betweenthe curriculum design of MBA programs andschool rank. Specifically, do highly ranked pro-grams differ from other schools in the structureof their MBA programs?

THEORETICAL FRAMEWORKMBA programs represent a competitive industryin which business schools compete for both inputs(e.g., students) and outputs (e.g., placement). TheAmerican market in particular is fairly homo-geneous and closely knit in terms of studentand faculty mobility (D'Aveni, 1996). Differentbusiness schools and their MBA programs canbe viewed broadly as rivals in terms of bothresources and market served (Chen, 1996). MBAprograms in particular are the school's flagship

program and a major generator of revenue. AnMBA program can thus be viewed as animportant business unit, and the school can beseen as competing in the market, using a parti-cular business unit strategy.While the changing practices of businessschools can be examined through different theo-retical lenses, such as social identity theory(Elsbach and Kramer, 1996) and status hier-archies (D'Aveni 1996), we anchor our theo-retical explanation in the literature on competitiveor business-level strategy. Though our study wasnot originally designed to test strategic grouptheory, and MBA programsare not the traditionalcontext for applying strategic group analysis, wefind it useful to base our theoretical reasoning onthe theory of strategic groups and the notion ofmobility barriers (Caves and Porter, 1977). Webelieve that using such an analysis in the contextof MBA programs can extend the range of itsapplication and provide insight into competitionand adaptation of educational institutions.Strategic groups are defined as groups of firmsin an industry following the same or similarstrategy along key dimensions (Porter, 1980:129). Ever since Hunt's analysis of competition(unpublished doctoral dissertation, Harvard Uni-versity, 1972), strategic group theory has pro-vided a common framework to answer questionspertaining to the existence of similarities anddifferences in organizations' strategic behavior,the effect of organizations' strategies on perform-ance, and changes in strategy and performanceover time (see, for example, Cool and Schendel,1988; Fiegenbaum and Thomas, 1990, 1995;Bogner, Thomas, and McGee, 1996). Eventhough the existence of strategic groups has beendocumented, the relationship between strategicgroup membershipand firm performancehas beenat the best inconclusive (see, for example, Cooland Schendel, 1988; Fiegenbaum and Thomas,1990; and the review by McGee and Thomas,1986).Of key importance to the business-level strat-egy literature is to understand what firm attributeshave the potential to contribute to performancedifferences, and to determine whether these attri-butes operate mainly at the firm or group level(Barney and Hoskisson, 1990). Strategic grouptheory and the notion of mobility barrier helpaddress these questions. The unique theoreticalcontributionof strategic group theory is in attribu-

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Conceptual Maps of Leading MBA Programsting sources of performance ifferences o a com-monalityof attributes e.g., shared resources orjoint action) at the group level.' In addition,strategicgroup heoryhelpsestablisha theoreticallink between firm strategyand attributes MBAprogram tructuren our case), and firmperform-ance.If thereare differences n performanceuchthat there is a group of schools with superiorperformance,and that superiorperformance anbe attributedo similarprogramstructures, henwhy do other schools not adopt this superiorstructureand improve their performance? t ishere that the notion of mobilitybarriers nters.A mobilitybarrier, ike an entrybarrierat theindustry level (Bain, 1956; Porter, 1980) andisolating mechanismsat the firm level (Rumelt,1984), is essentiallya limitationon replicabilityor imitation,or set of factorsthat deter or inhibitthe movementof an organization rom one stra-tegic position to another.Difficulty in mobilitymay stem from associatedcost, elapsed time, oroutcomeuncertaintyMcGee andThomas,1986).Mobilitybarriers erve as an explanation or dif-ferences across groups. To the extent that onecan identify a group of schools with similarprogram structures and superior performance,strategicgroup theoryestablisheswhy these dif-ferences in performance re relativelystable-itis difficultto duplicatesuccess.2

Program tructure s a reflectionof a school'sstrategy.To a considerable egree,program truc-ture conveys the school's approach o its MBAprogram: presumably the structure has gonethrough the mill of committees, examinations,and faculty decisions.3MBA programstructure'We are sensitive to the criticism that there is still no perfecttechnique to establish the exact number of strategic groupsin a given sample or population (Barney and Hoskisson,1990). However, if there are techniques that do a reasonablygood job, ours is one of them.2 Since in the current study we only examine a single pointin time, stability of group membership is assumed ratherthan tested. Implicitly we adopt the assumption of 'historicalefficiency' or steady-state equilibrium (Barnett and Burgel-man, 1996). A follow-up study could test rather than assumegroup stability.3Many of the attributesof program structure discussed belowclosely resemble attributes of organizational structure. Thetwo concepts are indeed related, as they both represent allo-cation and coordination of resources, especially knowledge.However, in business school settings, organizational structureusually covers several programs and functional departmentsor specialties (e.g., marketing). MBA programs are often aseparate departmentor area in the school-a subunit. Further-more, the same program structure can be administrated byCopyright? 1999 John Wiley & Sons, Ltd.

also represents wo aspects of competitivestrat-egy which are frequentlyexamined in strategicgroup studies: resourcedeployment (e.g., R&Dexpenditures) nd scope variables e.g., degreeofvertical integration,product range). MBA pro-gram structuremirrors how resources such asmoney,facultyskills,andadministrativeesourcesand time have been deployed. Since programsvary in the breadth,depth(and sequence)of thecourses they offer, it also reflects competitivescope. To a certainextent,program tructure lsoreflects the school's skills and capabilities.Thefaculty members, n their teaching,researchandconsulting,applyexistingknowledgeandgeneratenew knowledge.The curriculumherefore losely,though not completely, mirrors the knowledgeand areasof expertiseof the faculty.Thusschoolsthat offer finance or marketingconcentrationsemploy faculty with the relevantknowledge, oneithera permanent r special-contract asis. Pro-gram structure thus indirectly represents thedeployment of the organization's intangibleknowledgeresources.4Severalattributes f program tructuremake itdifficult o imitateand thereforea potential ourceof competitive advantage.The content of theprogram-the actual list of courses-representspublicly available and codified knowledge. Yet,there are reasons to believe that program s noteasy to imitate.Program tructure an be viewedas a configuration f courses or bodies of knowl-edge and thus may be more difficult to imitateas a whole than individual courses (Farjoun,1994; Miller,1996). This is particularlyruesincethe sequence and timing of courses may be ofimportance.Stocks of knowledge can have timecompressiondiseconomies:what one teaches ina 2-year programmay not be possible to com-press into 1 year without considerable oss invalue to the student(Dierickx and Cool, 1989).Furthermore,he content of a programpartiallyrepresents he business school's stock and distri-bution of knowledge,a resource that takes timedifferent organizational structures such as a matrix or a func-tional structure.4 As part of the curriculum revolution schools have started toconsider offering practical projects and cross-functionalcourses. These initiatives reflect the attempts to eliminatefunctional barriers in the business world and represent adifferent form of applying knowledge: by integration ratherthan by specialization. At the point in time where our studycommences these new initiatives were not widespread.

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552 E. Segev, A. Raveh and M. Farjounto duplicate. Finally, the content of a programcoexists with complementary assests such as thebusiness school's culture, identity, and organi-zational structure. Inasmuch as these assets areintangible, socially complex, and often associatedwith the particular and unique history of theschool, they may be difficult to imitate (Barney,1991). Moreover, the actual process of deliveryof the knowledge in the classroom or in othermedia may be difficult to imitate because itinvolves tacit knowledge such as teaching skills(Polanyi, 1967) and social complexity (Barney,1991).A different aspect of program structure whichmakes it difficult to imitate is its 'stickiness' andresistance to change. However, unlike contentand delivery, resistance to change is an adoptionbarrier-it indicates the difficulties of the would-be imitator organization to successfully adopt anew structureeven when the content and deliveryare fairly well known and understood. Adoptionbarriersoften pose difficulties in knowledge trans-fer (Powell, 1995; Szulanski, 1996) because theadopting organization does not have the requiredabsorptive capacity (Cohen and Levinthal, 1990).Programstructure,especially in academic settings,is also one of the most difficult things to change.The addition of new programs and courses, andcurriculumchanges and redesign, often entail fac-ulty recruitment and new resources allocation,with all the political strife, conflict, and resistancethat these processes entail. The change is oftenin the core of the organization (Hannan andFreeman, 1984; 149). It may indicate a moveaway from current goals and policies and requireconcomitant changes in organizational structure(Kraatz and Zajac, 1996), culture, and identity(Elsbach and Kramer, 1996). Such changes aredifficult to implement, especially in academic set-tings, with their well-known tendency to inertia.They may require substantial learning and takeseveral years to implement-a period in whichschool performance may destabilize and suffer.Thus program structuresrepresent not only majorresource investments but also 'sticky' or difficult-to-reverse commitments (Ghemawat, 1991).The notion of strategic groups and mobilitybarrier (or isolating mechanism) thus help framethe study's results and relate them to the literatureon business-level strategy. If the results of thestudy reveal a group of schools with similarprogram structures and superior performance,

strategic group theory establishes program struc-ture as a potential explanation of why these dif-ferences in performance are relatively stable.Alternatively, the results may indicate that eachtop school has a unique structure but there areother lower rankedschools with similar structures.In this case, one can safely reject the idea ofprogram structure as a mobility barrier(or isolat-ing mechanism) and leave open the possibility ofan unspecified attribute at either the group or thefirm level. Finally, each top school may have aunique structure not shared by any other school.In this case, while strategic group arguments arenot supported, program structure is a potentialisolating mechanism at the firm level. Therefore,the results for the study's two research questionsare related. Furthermore, they can help resolvetwo theoretical concerns: (a) does program struc-ture contribute to the school's competitive posi-tion; and (b) if it does, is it a barrier at thegroup or firm level?The above discussion clarifies what factorsother than the content of a program may bepotential sources of competitive advantage. It alsosuggests that program structure reflects resourcedeployment and is related to other organizationalresources and attributes such as identity and cul-ture. To be sure, there are other indicators ofbusiness school strategy and distinctive com-petence such as its faculty, endowment money,quality of students, synergy between programs,and location. However, program structure is aprimaryindicator of strategy and can be capturedusing measures that are much more objective andreplicable than most other potential indicators.

METHODTerms and definitions: The 'classical'programThe 'classical' structure s a 2-year MBA programof courses that can be broadly divided into twocategories: core and elective. The core coursesnormally cover the first year of study and aremainly introductorycourses covering many fields.Accounting, microeconomics, macroeconomics,information systems, organizational behavior,finance, marketing, and operations managementare the most commonly required core courses.Electives include a very large number of coursesfrom various fields and are usually divided into

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Conceptual Maps of Leading MBA Programstwo categories: major courses, of which studentsare required to take a minimum number in aspecific concentration area of their choice, andfree electives, which students select from all theavailable fields of study to complete the totalcredit requirements of the program (Appendices1 and 2).5Focus of the studyThe major purpose of the current study is tocapture and compare the program structure andinternal configuration of the MBA programs in25 leading U.S. schools at the beginning of thecurrent revolution. As already noted, these pro-grams are mostly 2 years in duration, the firstyear being devoted to core courses, and thesecond year to electives-usually in an area ofconcentration (major). Thus the MBA programis determined by its mix of courses and theconcentration areas it offers. By focusing on merestructure, however, we deliberately ignore theinherent nature of the program: the reputation ofthe school or the university; the quality of thefaculty; teaching methods; special features suchas groups, blocks, and teamwork; and specialcourses such as the Chicago LEAD program.Moreover, as our study shows, core courses orconcentration areas with identical titles may varyin content among schools (Appendices 1 and 2).Nevertheless, to enable analysis and comparison,this study focuses on core courses and existingconcentration areas only, i.e., the structure of theprogram. Going back to our theoretical dis-cussion, our measures capture primarily the con-tent of the programand not the way it is deliveredor adopted.The end of 1993 is a very appropriate pointfor a baseline study such as ours. The winds ofchange were perceptible at that time, but theactual modifications of the MBA programs werestill minimal. It was clear that in the subsequentyears most schools would experience instability,and that it would take a while before strategyand its reflection in program structure would beonce more reconciled.

5Appendices 1 and 2, describing the core courses, concen-tration areas, specializations and majors are available uponrequest from the authors. See also http://www.tau.ac.il/-segeva/book/index.html.Copyright? 1999 John Wiley & Sons, Ltd.

PopulationOur sample of 25 universities was culled fromthe lists of top business schools that are publishedperiodically (in U.S. News and World Report,1992, and Business Week, 1988, 1990, 1992,1994, for example), on the basis of criteria suchas deans' ratings, graduates' ratings, graduates'salaries, and industry evaluations. Tables 1 and 2list the business schools included in this study,arranged in alphabetical order. We should alsonote that, even though ranking is also discussedin this paper, all 25 are leading schools.DataProgram structureData on core courses and concentration areaswere collected from the 1993 school bulletins,catalogues, and curriculum guides (see Appendix3).6 In many cases, a complete set of the corecourse syllabi was available. Table 1 presents abinary matrix of all core courses, by school, inwhich '1' indicates that the school required thespecific core course. In U.S. business schools,core and required courses constituted about 50percent of the total MBA program requirements,ranging between 40 percent and 55 percent. Theexception is Pittsburgh,which offers an 11-monthprogram rather than the usual 2-year program,with core courses constituting 80 percent of theprogram. Appendix 1 presents summary descrip-tions of the core courses. Table 2 is a binarymatrix of all concentration areas, by school, inwhich '1' indicates that the school offered thegiven concentration area. Accounting, marketing,information systems, operations management,economics, finance, human resources, organi-zational behavior, and internationalbusiness wereoffered by the majority of the schools. Otherconcentration areas were more atypical. Appendix2 presents summary descriptions of the concen-tration areas.PerformanceWe used the 1994 Business Week ranking formeasuring performance. The performance rank-

6 Available upon request from the authors.Strat. Mgmt.J., 20: 549-565 (1999)

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554 E. Segev, A. Raveh and M. FarjounTable 1. Core courses by schoolSchool/Core courseBerkeleyCarnegie MellonChicagoColumbiaCornellDartmouthDukeHarvardIndianaMichiganMITNorth CarolinaNorthwesternNYUPittsburghRochesterStanfordTexasUCLAUSCVanderbiltVirginiaWashington U.WhartonYale

1 2001 01 01 0001 01 00 10000001 1001 01 01 01 0000000000 11 01 01 0

1. Decision Science2. Ethics3. Finance4. Financial Accounting5. Human Resource Management6. International Business7. Macroeconomics8. Management Communication9. Management Information Systems

31111111111111101111111111

41101111111111101111111111

5 600001 000000 10000000 11 0000000000000001 0000000000000

71111111100100110111110111

80000010100100000000011000

9 10 111 1 10 1 10 1 10 0 11 0 10 1 10 1 11 1 11 0 11 1 11 0 10 1 11 0 11 1 11 1 01 0 11 0 11 0 11 0 11 1 11 1 10 0 11 1 10 1 1

12111111111111111101111111

13000000000000000010000000

14 15 16 171 1 0 01 1 0 11 1 0 11 1 0 11 1 0 01 1 0 11 1 0 11 1 0 01 1 0 01 1 0 11 1 0 01 0 0 01 1 0 11 1 0 00 1 0 11 1 0 11 1 0 01 1 0 11 1 0 11 1 0 11 1 0 11 1 0 11 1 0 11 1 0 1

180111000101101100110110010 0 1 1 0 0 1 1 1 0

10. Managerial Accounting11. Marketing Management12. Microeconomics13. Nonmarket Environment14. Operations Management15. Organizational Behavior16. Political Analysis for Management17. Statistics18. Strategic Management (Business Policy)

order measures are less suitable for deriving schools in core courses and concentration areasgroup means and variances as has been done both as a composite and as individual categories.with the continuous measures used in most stra- Additionally it allows us to elicit the structure oftegic group studies, but they are satisfactory for the correlation among the core courses and theestablishing general tendencies as we have done concentration areas (i.e., cones) which reflectshere. Despite its limitations, the Business Week the different bodies of knowledge deployed in theranking is a relatively objective performancemea- program itself. The relationshipbetween differentsure based on multiple indicators and is known configurations of program structuresand the per-to be taken very seriously by business school formance ranking are reported as part of the Co-administrators(Elsbach and Kramer, 1996). plot analyses and not as a separate analysis.Classical multivariateanalysis methods, such asThe Co-plot method principal component analysis or cluster analysis,usually analyze either variables or observationsThe Co-plot method (see also Raveh, 1986; Gil- separately. The Co-plot method analyzes the twoadi, Spector, and Raveh, 1996; Weber, Shenkar, simultaneously. Using this new graphic displayand Raveh, 1996) is a novel technique that we technique, we are thus able to locate each busi-use to capture the configuration of courses (both ness school (observation) within a two-core and concentration) at any given school. It dimensional space, the location of each obser-allows us to elicit the similarity among business vation being determined by all variablesCopyright? 1999 John Wiley & Sons, Ltd. Strat.Mgmt.J., 20: 549-565 (1999)


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Conceptual Maps of Leading MBA ProgramsTable 2. Concentration areas by schoolSchool/Concentration 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Berkeley 1 0 1 1 0 0 1 1 0 0 0 1 1 0 1 0 1 1 0 O 0Carnegie 1 1 1 0 0 0 0 1 0 1 1 1 1 1 1 0 0 1 1 0 0Chicago 1 1 1 1 0 1 10 0 1 0 1 1 0 1 0 0 0 0 0 0Columbia 1 1 1 1 0 1 0 0 0 1 1 1 1 0 0 1 1 0 1 0 0Comell 1 0 1 1 0 1 0 1 0 1 0 0 1 0 1 0 0 0 0 0 0Dartmouth O O O O O O O O O O O O O O O O 0 0 1 0 0Duke 1 1 1 1 1 0 0 0 O 0 0 1 1 0 1 0 0 0 0 0 0Harvard O 0 1 1 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0Indiana O 0 1 1 0 1 0 1 0 1 0 1 1 0 0 0 1 0 0 0 0Michigan 1 1 1 1 0 0 0 1 0 1 1 1 1 0 1 0 1 1 1 0 0MIT 1 1 1 1 0 1 1 1 0 1 0 1 1 1 0 0 0 0 1 1 0N. Carolina 1 1 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0Northwestern 1 1 1 1 1 1 1 1 0 1 0 1 1 0 1 1 0 1 1 0 1NYU 1 1 1 0 0 0 1 0 1 0 1 1 1 1 0 0 0 1 0 0Pittsburgh 1 0 1 1 0 1 0 1 0 1 0 1 1 0 0 0 1 0 1 0 0Rochester 1 0 0 1 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 0Stanford 1 0 1 1 0 0 0 1 0 1 0 1 1 0 1 0 1 0 1 0 0Texas 1 0 0 1 0 0 0 1 0 0 0 1 1 1 1 0 0 0 1 0 0UCLA 1 1 1 0 1 0 1 0 1 0 1 1 0 0 1 1 1 1 0 0USC 1 0 0 1 1 1 0 1 0 1 0 1 1 0 0 0 0 1 0 0 0Vanderbilt 1 0 0 1 0 1 0 1 0 0 0 1 1 1 0 0 0 0 0 0 0Virginia 1 0 0 1 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0Washington U. 1 0 1 1 0 1 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0Wharton 1 1 0 1 1 0 0 0 1 0 1 1 0 0 0 0 1 1 1 0 1Yale 0 1 1 1 0 0 0 0 0 1 0 1 1 0 1 1 1 0 0 0 0

1. Accounting2. Decision Science3. Economics4. Finance5. Healthcare6. Human Resource Management7. Industrial Relations8. Information Systems9. Insurance and Risk Management10. InternationalBusiness11. Legal Studies

12. Marketing13. Operations Management14. Operations Research15. Organizational Behavior16. Public and Nonprofit Management17. Public Policy18. Real Estate19. Strategic Management20. Systems Dynamics21. Transportation

(existence of core courses or concentration area). goodness-of-fit is computed and associated forWe call this space the 'core and concentration each variable separately.areas' space. Co-plot is useful for visual inspec- Co-plot is based on the integration of mappingtion of such data matrices as Y,,n. The sample concepts with a variant of regression analysis. Itunits are labeled as n points (n = 25 universities starts with a data matrix Y,, of n rows and pin our study), and the variables are labeled as p columns; the rows are the p-variate observationsarrows (p = 39 in our study, 18 for core courses and the columns are the variables. The plottingand 21 for concentration areas) relative to the is carried out in four stages: two preliminarysame axes and origin. Co-plot maps the obser- treatments of the data matrix Y,, and two sub-vations (rows of the matrix) in such a way that sequent stages. The first two stages are as follows:similar observations are closely located to eachother on the map, and represents each variable Stage 1: In order to treat the variables equally,individually by an arrow. It thus enables the we normalize Yp,, in the usual way into Zp,,.simultaneous study of observations and variables The elements of matrix Z,, are deviations fromfor a set of data-hence its name. A measure of the column means Y, divided by their standard

555

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556 E. Segev, A. Raveh and M. Farjoundeviations (Sj), that is, Zj = (Yi - Yj)/Sj. Inmatrix notation: Z = (I - nll')YD-', where 1' isthe row vector of units and D is the diagonalmatrix containing the reciprocal of the standarddeviations.Stage 2: We choose a measure of dissimilaritySik>0 between each pair of observations(universities-rows of Znp), a symmetric nxnmatrix (Sik) being produced from the () differentpairs of observations. One possible measure isthe Minkowski metric:

p I/rSik= -i - Zilr -, (1 i,kn;r 1)

-j-=1

For our purposes, we chose r = 1, which is knownas the city-block distance (the sum of the absolutedeviations). Obviously, the diagonal elementsvanish (Sii = 0). The next two stages of Co-plotyield, for each matrix, two superimposed graphs.Stage 3: Here, the matrix (Sik) is mapped bymeans of some form of multidimensional scaling(MDS). Thus, observations are represented as npoints PI, i = 1, ..., n in a Euclidean space (of,say, m=2 dimensions). We chose the GuttmanSmallest Space Analysis (SSA) as a particularform of a non-metric MDS. SSA provides agraphic presentation of pairwise interrelationshipsof a set of objects (in our case, n = 25 schools)and uses the coefficient of alienation 0 as ameasure of goodness-of-fit (see Guttman, 1968,for details of this technique).7 In summary, fora two-dimensional space, this stage yields 2ncoordinates (Xli,X2i)i=l,...,n, where each rowZ =(Zil,...,Zip) mapped onto a point in two-dimensional space (XliX2i).Stage 4: At this stage, p arrows (Xjj= 1,...,p) are7The coefficient of alienation 0= (1-/2)I/2 varies between 0and 1, where ,/ is a coefficient of monotonicity (see Raveh,1986). Perfect fit is represented by the value 0, and the worstpossible fit by the value 1. Intermediate values of the coef-ficient represent intermediate degress of goodness-of-fit. Thenumber 0 expresses the extent to which distances betweenpairs of points in the two-dimensional space do not adhereto the rule regarding the monotone relationship between inputcoefficients and output distances. As a rule of thumb, analienation coefficient of less than 0.15 is considered a goodcandidate for being satisfactory.Copyright? 1999 John Wiley & Sons, Ltd.

drawn on the Euclidean space obtained in stage3. Each variablej is represented by an arrowjemerging from the center of gravity of the pointsPi. Each arrowXj is chosen so that the correlationbetween the actual values of variablej and theirprojections on the arrow are maximal. Thus, thearrows associated with highly correlated variablespoint in approximately the same direction, andthe cosines of the angles beween arrows areaccordingly approximately proportional to thecorrelations between their associated variables.The goodness-of-fit of Co-plot is assessed bytwo types of measures: one for stage 3 andanother for stage 4. In stage 3, a general (single)coefficient of goodness-of-fit for the configurationof n observations is obtained by MDS. For theSSA method, the coefficient of alienation 0 isused. For stage 4, p individual measures arecomputed for each of the p variables separately.These are the magnitudes of the p maximal corre-lations rj = 1,...,p that measure the goodness-of-fit of the p regressions, which might be helpfulin deciding whether to eliminate (or add) vari-ables. Variables that do not fit the graphic display,namely those which have a low rJ, should, inour opinion, be eliminated, thus obviating theneed to fit all the 2P subsets of variables, as inother methods that have a general coefficient ofgoodness-of-fit. The higher the correlation r, thebetter Xi represents the common direction andorder of the projections of the points along therotated axis Xj (arrow j).Co-plot is based on two graphs that are super-imposed sequentially. The first graph maps therows by n points. The second is conditionedon the first, and consists of p arrows that areportrayed individually.

FINDINGSCore and concentrationIn Figures 1-4, the 25 universities are located ina two-dimensional 'core and concentrationspace.'In this stepwise analysis, we first used all 18core course variables and 21 concentration areavariables in the analysis. Because the generalgoodness-of-fit obtained by coefficient of alien-ation 0=0.25 is rather low, and the goodness-of-fit of 15 (out of the 39) variablesare quite low,namely rJ, we refrained rom drawingthe variables(arrows) on the map depicted in Figure1.

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Conceptual Maps of Leading MBA Programs

Figure1. Similarityamongn = 25 leadingbusiness schools (p = 39 variables, oefficientof alienation0= 0.25)

Then, in each step, we used the individualmeasures computed for each of the variables (themagnitudes of the p maximal correlations rj=1,...,p that measure the goodness-of-fit of theregressions, i.e., the arrows) in deciding whichof the variables to eliminate (variables that donot fit the graphic display, namely those whichhave a low rj, were eliminated).Thus, in Figure 2 (coefficient of alienationCopyright? 1999 John Wiley & Sons, Ltd.

0=0.18), we used 24 variables; 10 variables inFigure 3 (coefficient of alienation 0= 0.11), andonly three in Figure4 (very high goodness-of-fit,coefficient of alienation 0 = 0.02).Based on Figures 1-4, three kinds of obser-vations can be made: about groups of schools,about high correlations among variables (conesof arrows), and about the ranking of the lead-ing schools.Strat.Mgmt.J., 20: 549-565 (1999)

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558 E. Segev, A. Raveh and M. Farjoun

PITTSBURGH0

ScienceFigure2. Co-plot graphicdisplayfor leadingbusinessschools,measured n p = 24 variables 0=0.18)

Clusters of schoolsThere is a basic grouping in schools whichrepeats itself in all the conceptual maps. Within-group distances vary with the gradual decreasein the number of the variables (with someoutliers), but the basic structure prevails. Use ofthe minimum number of variables (Figure 4, threevariables: one core course-Management Infor-mation Systems, and two concentration areas-International Business and Decision Science)Copyright? 1999 John Wiley & Sons, Ltd.

crystallizes this basic structure into the followingsix groups:* Group 1. Berkeley, Harvard, Rochester, Texas,Vanderbilt, Washington University* Group 2. Carnegie Mellon, Chicago, Colum-bia, Yale* Group 3. Corell, Indiana, Pittsburgh, Stan-ford, USC* Group 4. Dartmouth, Virginia* Group 5. Duke, North Carolina, Wharton

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Conceptual Maps of Leading MBA Programs?)IWHARTON

N. CAF

DARTMOUTH -EthVRGAthics

VIRGINIA

Insurance,and RiskCARNEGIE

ROLINA

HARVARD Management) Communicationcor

Business -

ROCHESTERTEXAS

e) ManagementVANDERBILT Information((

Figure3. Co-plot graphicdisplayfor leadingbusinessschools,measured n p = 10 variables 0=0.16)

*Group 6. Michigan, MIT, Northwestern,NYU, UCLA8The same six group structure can be seen inFigures 1 to 3. Note the very thin line connectingDartmouth/Virginia (Group 4) and Northwestern(Group 6). This line bisects all four figures. Twoother groups (Group 2, e.g., Chicago, and Group5, e.g., Wharton) appear on one side of the line,and the last two groups (Group 1, e.g., Harvard,8 Note that the clusters may not correspond exactly to whatschools administrators believe to be their close rivals. Thispotential discrepancy between external and internal evaluationof strategic groups is discussed in Farjoun and Lai (1997).

and Group 3, e.g., Stanford) appear on the otherside. Because the locations of the schools in theframework (the x and y axes) are arbitrary, thedifferent figures exhibit rotations and mirrortransformations of the same structure. However,the basic configuration remains. On the basisof the six figures, we can make the followingobservations about within-group variations:* Group 1: Harvard and Washington Universitystand apart from the other four schools.* Group 2: Carnegie Mellon is an oddball.These two variations, albeit with smaller spatialdiscrepancies, can also be readily discerned at

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560 E. Segev, A. Raveh and M. Farjoun

NYUMICHIGAN

ManagementInformationcore)ROCHESTER BERKELEY

VANDERBILT? (( HARVARDWASH. U. TXASEXAS

NORTHWESTERN%Ur&A InternationalI*MrrBtsiness

/VIGINIADecision / DARTMOUTtr-'Science/

CHICAGO) YALE

COLUMBIA 0 CARNEGIE

DUKEON. CAROLINAWHARTON

Figure4. Co-plot graphicdisplayfor leadingbusinessschools,measured n p = 3 variables 0= 0.02)

earlier (Figures 1-3) stages of the analysis, butthey totally disappear later (Figure 4). Thus, theycan be attributed to the inclusion (Carnegie) orexclusion (Harvard and Washington) of the Man-agement Information Systems core course intheir curricula.* Group 3: Pittsburgh is different (Figures 1 and2), with its 11 month program and 80 percentcore.* Group 6: MIT is a standalone category(Figures 1-3), mainly because of its uniqueSystem Dynamics concentration area, which inthe early stages of analysis shows a greaterCopyright ? 1999 John Wiley & Sons, Ltd.

resemblance to Group 1, attributable to theManagement Communications and HumanResource Management core courses, and theIndustrial Relations concentration area.

Cones of core courses and concentrationareasOf the 39 variables used in this analysis, 24create six cones-clusters of variables with highcorrelations (see Figure2):* Cone 1: Communications/Ethics. The Oper-ations Management (course), and Ethics are

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Conceptual Maps of Leading MBA Programshighly correlated. Management Communi-cations is very close.* Cone 2: Finance/Marketing. The Finance andMarketing (courses) are presented on the samearrow; Financial Accounting is very close.* Cone 3: Decision/Public. The three corecourses-Decision Science, Political Analysisfor Management, and Statistics-and the sixconcentration areas-Decision Science, LegalStudies, Public and Nonprofit Management,Public Policy, Real Estate, andTransportation-are highly correlated.* Cone 4: Economics/International. The two con-centration areas-Economics and InternationalBusiness-are correlated.* Cone 5: Industrial/Human. The IndustrialRelations, Human Resources Management, andOperations Management concentration areas arecorrelated (note the high negative correlationbetween the Operations course andconcentration).* Cone 6: Information Systems. The InformationSystems course and concentration are both cor-related. Insurance and Risk, and SystemsDynamics are both somewhat secluded, the firstbeing close to Cone 3 and the second toCone 6.

Since the input matrices are binary, Table 3summarizes salient patterns of the six groupsof schools (Yes-indicates that the cone is aclear characteristic of the group; No-indicatesthat the characteristic is clearly absent in thegroup).These cones represent the deployment of vari-ous bodies of knowledge. Except for the Oper-ations Management courses which do not corre-

Table3. Salientpatterns f businessschoolgroupingsCore/Concentration Groups1 2 3 4 5 6Communications/Ethics Yes No - Yes - NoFinance/Marketing Yes - - YesYes -Decision/Public No Yes No -Economics/International No - - No NoYesIndustrial/Human - - Yes No No YesInformation ystems Yes No Yes - - -Note: Insurance nd Risk is offeredonly at Wharton; ystemsDynamicsonly at MIT.

spond directly to the other courses in Cones 1and 5, each cone represents certain disciplinaryand thematic knowledge. Cone 1 represents skillacquisition or development courses. Cone 2 rep-resents relatively quantitative courses. Cone 3combines knowledge related to the public sectorwith applied decision sciences. Cone 4 is con-cered with economics and its application to theinternational domain. Cone 5 is concerned withapplication of behavioral sciences and the humanaspect of the firm. Cone 6 is concerned with(information and dynamic) systems analysis. TheOperations Management courses do not fall logi-cally in any of the cones where other functionalareas (marketing, accounting, finance, humanresources, etc.) belong. Therefore, their member-ship in cones 1 and 5 while odd does not comeat the expense of a more natural home forthese courses.Adding the latest ranking appearingin BusinessWeek (1994) to the conceptual maps elicits animportant observation: the first five leadingschools (Wharton, Northwestern, Chicago, Stan-ford, and Harvard) all belong to different groups(Figure 4). In Figure 1, these five schools appearon the outer rim of the map, while four of thesix schools that were not included in the 1994list appear at the center (except for Pittsburghand Yale).9

DISCUSSIONThe study captured and compared the structureof and the interrelationships among the MBAprograms in leading U.S. schools using the Co-plot method. The structure of an MBA programwas defined as the required mix of core coursesand the concentration areas offered by the school.Analysis of the 'core and concentration space'(18 core courses and 21 concentration areas) ledto a division of the 25 leading U.S. businessschools into six different clusters of schools.Observation of these clusters indicates that 24out of the 39 variables (core courses and concen-tration areas) create cones of high correlations;9Though our basic analysis was carried out on the completeMBA program, comprising both core courses and concen-trationareas, we also investigated the structure of the first yearof the program-defined as core courses-and the structureofthe second year-concentration areas. We found that rankingis not explained by core courses or by concentration areas.

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562 E. Segev, A. Raveh and M. Farjounand that each of the top five schools (BusinessWeek, 1994) belong to a different cluster, i.e.,they differ in the structure of their core coursesand concentration areas.

Analyses of core courses only, and concen-tration areas only, indicated that some of thesephenomena may be partially traced to the struc-ture of the first year of the MBA program (corecourses), or to the structure of the second yearof the MBA program(concentration areas). How-ever, the main results are the product of theanalysis of the structureof the MBA program asa whole, consisting of both core courses andconcentration areas, as defined in this paper.Two important issues should be noted in con-nection with the present research data andmethod: (1) all findings are based on roughbinary data (the existence or nonexistence ofspecific core courses and concentration areas);nevertheless, we achieved quantitative 'look alike'mapping by using the innovative Co-plot method;and (2) in the various maps there are differ-ences among the members of a given groupingof similar leading business schools. However,in all the maps the basic configuration is main-tained, since it is an inherent property of thedata we used.The discussion thus relates three importantfindings to the main objectives of the study.The clusters of schools, cones, and courses andconcentration shed light on our first objective: todetermine and identify the potential existence ofgroups of program structures reflecting strategicchoices of the different schools. The findingsabout program structure and school's rankinganswer our second question regarding the effectof program structure differences on performanceranking.Our first objective was to compare and identifydifferent MBA program structuresdetermined bythe configurations of core and concentrations andreflecting strategic choices of the various schools.Some business schools refer to themselves as the'Harvard of the South' or as being in the'Chicago tradition.' While each school certainlyhas a unique approachto determining its requiredcore courses and the way its faculty forms con-centration areas, our analysis reveals a basicstructure that repeats itself in all the conceptualmaps. The similarity and dissimilarity of programstructures suggests that there are six relativelyhomogeneous different groups rather than one

group of the same basic structure or 25 differ-ent structures.10The maps, especially Figure 2, also indicatethe business schools with MBA programs thatdiffer structurallyfrom the norm (e.g., Pittsburgh,Yale, Wharton, Harvard,and MIT); these schoolsare located on the outer rim, whereas schoolswith a standardor balanced structure(e.g., NYU,Stanford, Washington) appeartowards the center.These schools are typical of the average on theentire group of variables. Since we analyzed avery unique subpopulation of business schools(the leaders in the field), it is more than likelythat analysis of all business schools, or any othersubpopulation,will elicit somewhat different clus-ters. At the same time, because we chose to focuson the leading schools, which all other schoolsaspire to emulate, we believe that basically wehave capturedthe importantstructuresof a viableMBA program. Turning to our theoretical dis-cussion, this first finding indicates that a structureof business schools MBA curricula exists. Thisstructure is based on particular cones or con-figurations of courses. These configurations alsorepresent the school's deployment of differentbodies of applied knowledge, be it its quantitativeorientation, its competence or emphasis on theacquisition of particularskills, or its disciplinaryorientation.We believe that the most important finding ofthis paper is that each of the top five leadingMBA programsin the United States is located ina different cluster. First, it is clear that there isno one 'best' structurefor a good MBA program.Six different viable program structures, and thusstrategies, of MBA programs were found in thispopulation of leading schools. Similarly to anyother industry, and competitive environment,there is no one way to compete and attain goals(Miles and Snow, 1978; Miller and Friesen, 1978;

'0 Thus, for example, the relative proximity of Vanderbilt'sprogram to that of Harvard, Dartmouth, and Virginia, orCornell and Stanford, is easily discernible. Since our inputmatrix is a binary one, it is relatively easy to compare therelevant factors of any two schools. For those interested inparticular comparisons, this analysis will supply additionalinformation on pairwise similarities or dissimilarities. It shouldbe remembered, too, that to facilitate analysis we standardizedcourse and concentration definitions. These definitions arefurther discussed in Appendices 1 and 2 (available from theauthors), where it can be seen that at Stanford, for example,Information Systems is included in the Operations and Tech-nology concentration area.Copyright? 1999 John Wiley & Sons, Ltd. Strat.Mgmt. ., 20:549-565(1999)


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Conceptual Maps of Leading MBA ProgramsMintzberg, 1973; Porter, 1980; Miller, 1986,1996). Rather, there are several viable options-in our case, different mixes of core courses andconcentration areas. Each of the leading schoolsincluded in our study belongs, according to ouranalysis, to a cluster, from the two-schoolDartmouth-Virginia cluster, to the six-schoolcluster consisting of Berkeley, Harvard, Roches-ter, Texas, Vanderbilt, and Washington Univer-sity. Had the top five (based on accepted rankingmethod) fallen into one or two clusters, we couldhave claimed a better or best structure for abusiness school. Our findings clearly show, how-ever, that each of the top five schools falls intoa different cluster. As suggested by Kumar,Thomas, and Fiegenbaum (1990), all top fiveschools may be located on the same 'efficientfrontier.' Thus, within each of the identified clus-ters there is an opportunity to excel.Viewed in light of our theoretical discussion,the findings suggest that different groups ofschools all indicate that program structure ascaptured here is not a mobility barrier or apotential source of competitive advantage. Thisis despite attributes of program structure suchas complementary resources, time compression,configuration, history, and social complexity thatmake it difficult for imitation. Moreover, ourfindings do not allow us to speculate whetherdifferences in performance are due to group-levelresources or strategies or due to firm-specificresources and strategies. Specifically, since eachof the top schools is in a different group, wecannot infer that each top performeris so becauseof a different firm-specific reason.What the findings leave unclear is what uniqueaspects of the highly ranked programs help themperform better. Other factor(s), not directlyrelated to program structure, such as the repu-tation of the host university, resources, admissionpolicies, quality of the faculty, to name a few,may explain higher ranking. However, since ourmeasures captureonly the content of the program,there is still a possibility that other factorsassociated with program structure do serve as amobility barrier.This brings us to the two otheraspects of program structure:delivery and adop-tion. It well may be that top leading schools arebetter able to deliver their program. Alternatively,other lower ranked schools may be relativelyinapt and inert in terms of their ability to adoptnew programs-they may have lower capacityto change.Copyright? 1999 JohnWiley & Sons, Ltd.

The focus on program structuresurfaces a dis-tinction between two aspects of mobility barriers:imitation barriers (e.g., program structurecontentand delivery) and adoption barriers(e.g., programadoption). Program delivery is primarily an attri-bute of the model organization which makes itdifficult for the follower to imitate (e.g., becauseof tacitness). Like program content, it locatesimmobility in the model's strength (i.e., superiordelivery). By contrast, program adoption is pri-marily an attribute of the follower organization.It locates immobility in the follower's weakness(i.e., its inert existing program structure). Whengeneralized to other firm core attributes such asits organizational structure, culture, and identity,this distinction potentially has wider implicationsfor theories of sustained competitive advantageeither at the group level or the firm level. For itsuggests that focusing on firm or group attributesas isolating mechanisms or mobility barriers(andby extension entry barriers)-and therefore asource of sustained competitive advantage-maygive only half of the performance picture. Otherfactors such as the ability of other firms to changeor their motivation to do so once net benefits areconsidered may in some circumstances providea better explanation for sustained performancedifferences. Although these factors may provedifficult to measure, they suggest the need toempirically capture firms' potential weaknesses(e.g., potential barriers to change) and not onlypotential strengths.The study's findings leave open several ques-tions for future studies and raise interesting practi-cal issues. It may be useful in particularto exam-ine them from the different adaptation paths ofschools within the same group and schools fromdifferent groups. From the first angle, one optionis for the follower school to improve is positionwithout necessarily changing the content of itsprogram. This may require improving the waythe program is implemented but does not requirethe risks and costs associated with large-scaleprogram restructuring. A second option open tothe follower is to break out of his current groupby innovating. In this case there is a possibilitythat a school will be alone or a leader in a newsuccessful strategic group, or may risk a failurewhich will worsen its position. Viewed from thesecond angle, if one of the top five leadingschools wishes to move higher in the rankingorder, factors other than programcontent may bevital. Alternatively, schools can keep the same

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564 E. Segev, A. Raveh and M. Farjounprogram structure but improve complementaryresources to protect themselves from challengersfrom within the group."Once a conceptual and empirical baseline hasbeen built, some of these propositions can be bestexplored using longitudinal data. Data collected ata later point in time may shed light on themobility of schools within and between groups,on the formation of new groups and on thedisappearance of certain program structure tem-plates. These data can also shed better light onthe performance effects of strategic group mem-bership. Interesting questions remain: Does thetrend to integrate knowledge rather than to applyspecialized bodies of knowledge differentiatebetween leading and following schools? How dorecent institutional changes (i.e., changes inaccreditation standards of the AACSB) affectschool's curriculum changes? Are there perform-ance differences between schools that drasticallychange their structures and those which do not?Are new groups being formed? Does rankingitself, through the existence of positive feedbackloops, predict mobility? These are all fascinatingissues that can be addressed in a future study. Itwill be important to analyze the leading MBAprogram again, once experience with the inno-vations at Wharton and Chicago has beenaccumulated (and the 'Hawthorne effect' hassubsided).The study highlights issues usually notaddressed in conventional strategic group analy-sis: the deployment of knowledge resources, thedistinction between barriers associated with imi-tation and adoption barriers, and the potentialcontribution of soft organizational attributes suchas delivery and adoption to performance out-comes. It also contributes by informing futurestudies of strategic change in knowledge-basedand educational institutions. Lastly, the empiricalpart of the study employs an innovative method-ology that can help strategists and strategyresearchers capture similarities and differencesin strategies.On the practicalnote, the study and the concep-tual lens of mobility and isolating barriersequip" Note thoughthat the findingthatprogram tructures nota source of competitiveadvantage oes not necessarilymeanthat it can not become so in the future.For example,it ispossible thattop schools will convergeto a 'best structure'programand will be able to protectthemselves rom otherschoolsthrough omplementaryssets.

business schools' deans and administrators witha new way to view the changes they help steer.Although the latest changes in MBA programsarose from a real need, the findings of the currentstudy suggest that the focus on structuralchanges(modifying the load and mix of the core courses,adding new concentration areas), by itself, willnot result in a better-rankedprogram.

ACKNOWLEDGEMENTThe work of the second author was partiallysupported by the Recanati Foundation.

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3094111 Conceptual Maps of the Leading MBA Programs