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Journal of Education for Business

ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20

A Value-Added Approach to Selecting the Best

Master of Business Administration (MBA) Program

Dorothy M. Fisher , Melody Kiang & Steven A. Fisher

To cite this article: Dorothy M. Fisher , Melody Kiang & Steven A. Fisher (2007) A Value-Added Approach to Selecting the Best Master of Business Administration (MBA) Program, Journal of Education for Business, 83:2, 72-76, DOI: 10.3200/JOEB.83.2.72-76

To link to this article: http://dx.doi.org/10.3200/JOEB.83.2.72-76

Published online: 07 Aug 2010.

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any฀ researchers฀ have฀ tried฀ to฀ identify฀the฀best฀master฀of฀busi-ness฀ administration฀ (MBA)฀ programs฀ by฀using฀a฀variety฀of฀data฀and฀method-ologies.฀The฀most฀notable฀studies฀have฀ been฀those฀that฀BusinessWeek฀(BW)฀and฀

U.S.News฀&฀World฀Report฀(USN&WR)฀ have฀conducted.฀The฀rankings฀of฀MBA฀ programs฀in฀these฀studies฀are฀based฀on฀ weighted฀scores฀of฀selected฀attributes฀or฀ measures.฀ One฀ problem฀ with฀ research-ers’฀ using฀ weighted฀ scores฀ is฀ that฀ the฀ scores฀ rely฀ on฀ arbitrary฀ weightings.฀ In฀ addition,฀some฀of฀the฀selected฀attributes฀ are฀ derived฀ from฀ subjective฀ survey฀ responses฀ of฀ graduates,฀ recruiters,฀ and฀ peers.฀ As฀ a฀ consequence,฀ academics,฀ employers,฀ and฀ prospective฀ students฀ often฀ question฀ the฀ value฀ of฀ business฀ school฀ rankings฀ (Dichev,฀ 1999;฀ Hol-brook,฀ 2004;฀ Schatz,฀ 1993).฀ Neverthe-less,฀students,฀employers,฀and฀educators฀ watch฀ these฀ rankings.฀ In฀ the฀ short฀ run,฀ these฀rankings฀provide฀MBA฀programs฀ with฀immediate฀prestige.฀In฀the฀long฀run,฀ a฀more฀tangible฀result฀of฀these฀rankings฀ for฀individual฀programs฀is฀greater฀abil-ity฀to฀raise฀external฀funds฀and฀to฀attract฀ high-quality฀ students.฀ These฀ rankings฀ also฀provide฀incentive฀for฀other฀institu-tions฀to฀improve฀their฀MBA฀programs.฀

To฀differentiate฀the฀quality฀of฀an฀MBA฀ program฀from฀the฀quality฀of฀the฀incom-ing฀ students฀ and฀ to฀ also฀ exclude฀ sub-jective฀ responses฀ of฀ various฀ constituent฀

groups,฀Tracy฀and฀Waldfogel฀(1997)฀pro-posed฀a฀methodology฀for฀ranking฀MBA฀ programs฀ by฀ using฀ data฀ on฀ the฀ labor฀ market฀ performance฀ of฀ each฀ program’s฀ graduates.฀The฀authors฀regressed฀starting฀ salary฀ on฀ measures฀ of฀ student฀ quality฀ and฀derived฀the฀salary฀residual฀as฀a฀mea-sure฀of฀program฀value฀added.฀Their฀study฀ identified฀ several฀ high-quality฀ programs฀ that฀ were฀ not฀ ranked฀ by฀ either฀ BW฀ or฀ USN&WR฀ because฀ these฀ programs฀ did฀ not฀attract฀higher฀tier฀students.฀

In฀ this฀ study,฀ we฀ followed฀ the฀Tracy฀ and฀Woldfogel฀(1997)฀approach฀to฀eval-uating฀ MBA฀ programs:฀ We฀ evaluated฀ based฀on฀value฀added฀to฀students.฀How-ever,฀ we฀ used฀ a฀ methodology฀ called฀

data฀ envelopment฀ analysis฀ (DEA)฀ to฀ connect฀ student฀ quality฀ (i.e.,฀ inputs)฀ with฀ employment-related฀ benefits฀ (i.e.,฀ outputs)฀ to฀ evaluate฀ value฀ added฀ by฀ individual฀MBA฀programs.฀In฀addition,฀ we฀included฀cost-related฀attributes฀such฀ as฀tuition,฀cost฀of฀living,฀and฀length฀of฀ time฀necessary฀to฀complete฀the฀program฀ as฀inputs฀to฀identify฀which฀schools฀pro-vided฀students฀with฀the฀best฀value.฀DEA฀ does฀ not฀ require฀ a฀ set฀ of฀ preassigned฀ weights฀ for฀ inputs฀ and฀ outputs฀ and,฀ therefore,฀overcomes฀the฀deficiency฀that฀ subjective฀ weights฀ introduce.฀ In฀ addi-tion,฀ DEA฀ is฀ an฀ extreme฀ method฀ that฀ compares฀ an฀ individual฀ MBA฀ program฀ with฀ a฀ peer฀ group฀ of฀ only฀ the฀ best฀ or฀ most฀ efficient฀ MBA฀ programs฀ on฀ the฀ basis฀ of฀ the฀ input฀ and฀ output฀ attributes฀ that฀DEA฀identifies.฀

A฀Value-Added฀Approach฀to฀Selecting฀the฀

Best฀Master฀of฀Business฀Administration฀

(MBA)฀Program

DOROTHY฀M.฀FISHER฀ MELODY฀KIANG฀ CALIFORNIA฀STATE฀UNIVERSITY–DOMINGUEZ฀HILLS฀ STEVEN฀A.฀FISHER฀

CARSON,฀CALIFORNIA฀ CALIFORNIA฀STATE฀UNIVERSITY,฀LONG฀BEACH฀

฀ LONG฀BEACH,฀CALIFORNIA฀

M

ABSTRACT.

฀Although฀numerous฀stud-ies฀rank฀master฀of฀business฀administration฀ (MBA)฀programs,฀prospective฀students’฀ selection฀of฀the฀best฀MBA฀program฀is฀a฀for-midable฀task.฀In฀this฀study,฀the฀authors฀used฀ a฀linear-programming-based฀model฀called฀ data฀envelopment฀analysis฀(DEA)฀to฀evalu- ate฀MBA฀programs.฀The฀DEA฀model฀con-nects฀costs฀to฀benefits฀to฀evaluate฀the฀value฀ that฀MBA฀programs฀added.฀The฀findings฀ may฀assist฀prospective฀students฀in฀selecting฀ programs฀that฀have฀the฀best฀market฀value.฀

Keywords:฀DEA,฀efficiency฀scores,฀MBA,฀ ranking,฀value฀added

฀

Copyright฀©฀2007฀Heldref฀Publications

Existing฀Rankings฀of฀MBA฀ Programs

In฀ general,฀ researchers฀ construct฀ rankings฀ of฀ MBA฀ programs฀ by฀ using฀ weighted฀ scores฀ of฀ selected฀ attributes.฀ In฀1988,฀BW฀published฀the฀first฀article฀ that฀ ranked฀ full-time฀ MBA฀ programs฀ and฀ started฀ the฀ ranking฀ frenzy.฀ BW฀ believes฀ that฀ customer฀ satisfaction฀ is฀ the฀ key฀ indicator฀ of฀ performance.฀ On฀ the฀one฀hand,฀recent฀BW’s฀rankings฀of฀ the฀ MBA฀ programs฀ are฀ based฀ on฀ the฀ weighted฀average฀scores฀of฀the฀student-satisfaction฀score฀(45%),฀the฀recruiters’฀ rankings฀ of฀ the฀ top฀ 20฀ schools฀ (45%),฀ and฀an฀intellectual-capital฀score฀(10%).฀ On฀ the฀ other฀ hand,฀ recent฀ USN&WR฀ rankings฀ of฀ the฀ MBA฀ programs฀ are฀ based฀ on฀ the฀ weighted฀ average฀ scores฀ of฀ quality฀ assessment฀ (40%),฀ place- ment฀success฀(35%),฀and฀student฀qual-ity฀(25%;฀Morse฀&฀Flanigan,฀2005).฀

However,฀ researchers’฀ using฀ students฀ to฀estimate฀the฀quality฀of฀a฀program฀leads฀ to฀ debatable฀ accuracy฀ (Rapert,฀ Smith,฀ Velliquette,฀ &฀ Garretson฀ 2004).฀ More฀ important,฀BW฀and฀USN&WR฀do฀not฀dif-management฀ admissions฀ test฀ (GMAT)฀ scores,฀ top฀ business฀ schools฀ are฀ likely฀ ensuring฀ the฀ success฀ of฀ their฀ graduates.฀ To฀distinguish฀the฀quality฀of฀the฀program฀ from฀the฀quality฀of฀its฀student,฀Tracy฀and฀ Waldfogel฀used฀a฀market-based฀approach฀ to฀ measure฀ the฀ value฀ added฀ by฀ a฀ pro-gram.฀This฀study฀complements฀the฀work฀ of฀ Tracy฀ and฀ Waldfogel฀ by฀ using฀ the฀ DEA฀methodology.

DEA฀Methodology

Charnes,฀Cooper,฀and฀Rhodes฀(1979)฀ developed฀ DEA฀ to฀ evaluate฀ the฀ perfor-mance฀ of฀ multi-input฀ and฀ multi-output฀ production฀ operations.฀ The฀ analytical฀ and฀ computational฀ capacities฀ of฀ DEA฀ are฀ firmly฀ based฀ on฀ mathematical฀ the-ory.฀ DEA฀ has฀ become฀ an฀ increasingly฀ popular฀management฀tool฀(Trick,฀2004)฀ and฀ has฀ been฀ successfully฀ applied฀ as฀ a฀ decision฀support฀tool฀to฀(a)฀improve฀the฀ productivity฀of฀bank฀branches฀(Charnes,฀ Cooper,฀Huang,฀&฀Sun,฀1990;฀Soteriou฀ &฀ Zenios,฀ 1999)฀ and฀ health฀

mainte-nance฀ organization฀ services฀ (Chilinge-rian฀ &฀ Sherman,฀ 1990;฀ Maniadakis฀ &฀ Thanassoulis,฀ 2000),฀ (b)฀ select฀ mutual฀ funds฀ (McMullen฀ &฀ Strong,฀ 1998),฀ (c)฀ evaluate฀software฀and฀software฀projects฀ (Fisher,฀ Kiang,฀ Fisher,฀ &฀ Chi,฀ 2004;฀ Fisher฀&฀Sun,฀1996),฀and฀(d)฀select฀the฀ best฀MBA฀program฀(McMullen,฀1997).฀ The฀Appendix฀presents฀the฀DEA฀model฀ that฀we฀used฀in฀the฀study.

The฀ DEA฀ model฀ that฀ we฀ used฀ (see฀ Appendix)฀ evaluated฀ a฀ production฀ unit฀ called฀ decision-making฀ unit฀ (DMU)฀ and฀ generated฀ an฀ efficiency฀ score฀ for฀ the฀ unit.฀ In฀ our฀ study,฀ we฀ treated฀ an฀ MBA฀ program฀ as฀ a฀ production฀ unit฀ in฀ which฀inputs฀were฀measures฀of฀students’฀ fies฀ input฀ wastes฀ and฀ output฀ deficien-cies.฀The฀main฀advantage฀of฀DEA฀is฀that฀ it฀does฀not฀require฀preassigned฀weights฀ for฀ inputs฀ and฀ outputs฀ and,฀ thus,฀ over-comes฀ the฀ deficiency฀ that฀ subjective฀ weights฀ introduce.฀ By฀ overcoming฀ the฀ deficiency,฀ DEA฀ minimizes฀ the฀ com-plexity฀ of฀ analysis฀ by฀ simultaneously฀ evaluating฀the฀attributes฀of฀interest฀and฀ presenting฀ a฀ single,฀ composite฀ score,฀ referred฀to฀as฀an฀efficiency฀score .฀More- over,฀input฀wastes฀and฀output฀deficien-cies฀generated฀by฀DEA฀help฀to฀identify฀ the฀ weaknesses฀ and฀ strengths฀ of฀ a฀ par-ticular฀program.฀

Evaluation฀of฀MBA฀Programs

The฀data฀sources฀for฀this฀study฀were฀ the฀ USN&WR’s฀ online฀ data฀ set฀ from฀ the฀publication’s฀2005฀survey฀(USNews฀ .com,฀2007;฀U.S.News฀and฀World฀Report,฀ 2005),฀ BusinessWeek฀ (Merrit,฀ 2004),฀

Peterson’s฀MBA฀Programs฀2005 ฀(Peter-son’s,฀ 2004),฀ and฀ the฀ College฀ Board฀ Web฀ site฀ (CollegeBoard.com,฀ 2005).฀ These฀data฀provided฀us฀with฀two฀broad฀ categories฀ of฀ attributes:฀ inputs฀ (x_j)฀ and฀ outputs฀ (y_j).฀We฀ chose฀ inputs฀ to฀ reflect฀ the฀qualities฀of฀students฀and฀the฀costs฀of฀ a฀program,฀whereas฀we฀chose฀outputs฀to฀ reflect฀graduates’฀achievements.฀

To฀ measure฀ value฀ added,฀ we฀ con-sidered฀ mean฀ GMAT฀ scores฀ and฀ mean฀

undergraduate฀GPA฀as฀inputs฀and฀mean฀ starting฀ salaries฀ and฀ bonuses,฀ ment฀ rates฀ at฀ graduation,฀ and฀ employ-ment฀ rates฀ three฀ months฀ after฀ gradua-tion฀ as฀ outputs.฀ To฀ measure฀ best฀ value฀ of฀ programs,฀ we฀ included฀ tuition,฀ cost฀ of฀ living,฀ and฀ length฀ of฀ a฀ program฀ as฀ additional฀inputs.฀

Our฀ first฀ goal฀ was฀ to฀ measure฀ the฀ value฀added฀of฀the฀programs.฀We฀select-ed฀an฀input–output฀mix฀to฀give฀credit฀to฀ programs฀that฀produced฀graduates฀with฀ average฀ starting฀ salaries฀ and฀ employ-ment฀rates฀similar฀to฀those฀of฀their฀peer฀ schools฀but฀that฀recruited฀students฀with฀ lower฀ average฀ GPA฀ and฀ GMAT฀ scores฀ or฀ that฀ charged฀ lower฀ tuition฀ and฀ fees.฀

Inputs฀ measured฀ the฀ quality฀ of฀ incom-ing฀ students,฀ and฀ outputs฀ measured฀ the฀ quality฀ of฀ a฀ program’s฀ graduates฀ as฀ determined฀ by฀ the฀ market.฀ Because฀ student฀quality฀was฀treated฀as฀input,฀our฀ first฀ study฀ distinguished฀ the฀ quality฀ of฀ a฀ program฀ from฀ the฀ quality฀ of฀ its฀ stu-dents฀and฀used฀a฀value-added฀approach฀ and฀thus฀generated฀a฀true฀market-based฀ performance฀evaluation฀(Tracy฀&฀Wald-fogel,฀1997).฀

We฀ used฀ computer฀ software฀ that฀ implemented฀ the฀ DEA฀ model฀ as฀ a฀ lin-ear฀ programming฀ problem฀ in฀ Equation฀ 1฀(see฀Appendix)฀to฀evaluate฀the฀MBA฀ programs฀ consecutively.฀ The฀ software฀ compared฀each฀MBA฀program฀with฀the฀ other฀programs฀to฀generate฀an฀efficiency฀ score฀ (µT_฀y

o)฀ for฀ the฀ MBA฀ program฀

under฀ evaluation.฀ The฀ software฀ also฀ generated฀ input฀ wastes฀ (S−_{)฀ and฀}

out-put฀deficiencies฀(S+_{)฀in฀Equation฀2฀(see฀}

Appendix).฀ The฀ software฀ generated฀ a฀ high฀ efficiency฀ score฀ for฀ each฀ school฀ with฀ high฀ output฀ values฀ and฀ low฀ input฀ values฀relative฀to฀its฀peers.฀An฀efficient฀ MBA฀ program฀ had฀ an฀ efficiency฀ score฀ of฀1.฀An฀MBA฀program฀with฀an฀efficien-cy฀score฀of฀less฀than฀1฀was฀less฀desirable฀ relative฀ to฀ a฀ reference฀ set฀ of฀ programs฀ with฀ efficiency฀ scores฀ of฀ 1.฀ The฀Value฀ Added฀ columns฀ of฀ Table฀ 1฀ show฀ the฀ DEA฀ results฀ for฀ our฀ first฀ study.฀ The฀ results฀should฀help฀prospective฀students฀ to฀ identify฀ which฀ MBA฀ programs฀ add฀ the฀most฀value฀to฀their฀students.

For฀ some฀ students,฀ the฀ cost฀ of฀ an฀ MBA฀program฀is฀an฀important฀factor฀in฀ the฀ selection฀ process.฀ Therefore,฀ next,฀ we฀included฀tuition,฀cost฀of฀living฀in฀the฀ program’s฀ location,฀ and฀ the฀ length฀ of฀

time฀that฀was฀necessary฀to฀complete฀the฀ program฀ as฀ inputs฀ to฀ determine฀ which฀ programs฀ had฀ the฀ best฀ value.฀ The฀ Best฀ Value฀columns฀in฀Table฀1฀show฀the฀DEA฀ results฀of฀our฀using฀tuition,฀cost฀of฀living,฀ and฀the฀length฀of฀a฀program฀as฀additional฀ inputs.

As฀presented฀in฀Table฀1,฀DEA’s฀value-added฀ rankings฀ are฀ quite฀ different฀ from฀ the฀ BW฀ and฀ USN&WR฀ rankings.฀ This฀ difference฀ is฀ due฀ partly฀ to฀ the฀ fact฀ that฀ DEA฀ allows฀ flexible฀ weights฀ and฀ treats฀ student฀quality฀features฀such฀as฀GPA฀and฀ GMAT฀ scores฀ as฀ inputs.฀Therefore,฀ if฀ a฀ program฀accepts฀students฀with฀relatively฀ low฀GPAs฀and฀GMAT฀scores฀and฀is฀able฀ to฀produce฀graduates฀who,฀at฀graduation,฀ have฀ jobs฀ with฀ salaries฀ higher฀ than฀ do฀ such฀graduates฀of฀other฀schools,฀the฀MBA฀ program฀ is฀ efficient฀ in฀ terms฀ of฀ value฀ added.฀ On฀ the฀ contrary,฀ given฀ the฀ same฀ output฀ values,฀ USN&WR฀ gave฀ schools฀ higher฀rankings฀if฀they฀accepted฀students฀ with฀higher฀GPA฀or฀GMAT฀scores.฀

Specifically,฀ the฀ DEA฀ value-added฀ results฀ in฀ Table฀ 1฀ indicate฀ that฀ seven฀ schools฀have฀efficiency฀scores฀of฀1.฀BW฀ and฀ USN&WR฀ did฀ not฀ rank฀ Bentley฀ College—which฀ was฀ one฀ of฀ the฀ seven฀ schools—in฀the฀top฀50฀because฀it฀received฀ a฀ low฀ quality฀ assessment฀ from฀ recruiters฀ and฀peer฀schools.฀Our฀closer฀examination฀ of฀the฀data฀indicated฀that฀compared฀to฀its฀ peers,฀Bentley฀College฀accepted฀students฀ with฀relatively฀low฀average฀GMAT฀scores,฀ whereas฀the฀employment฀rate฀of฀its฀gradu- ates฀shortly฀after฀graduation฀was฀relative-ly฀ high.฀ Researchers฀ can฀ make฀ a฀ similar฀ conclusion฀ for฀ Texas฀ A&M฀ University,฀ College฀Station.฀BW฀and฀USN&WR฀did฀ not฀ rank฀ the฀ University฀ of฀ New฀ Hamp-shire฀ as฀ among฀ the฀ top฀ 50฀ schools,฀ but฀ DEA฀ rated฀ it฀ as฀ a฀ best฀ value฀ because฀ the฀ average฀ GMAT฀ scores฀ of฀ its฀ incom-ing฀ students฀ were฀ the฀ lowest฀ of฀ the฀ 89฀ schools฀ in฀ this฀ study฀ and฀ yet฀ the฀ value฀ added฀ was฀ among฀ the฀ best,฀ as฀ indicated฀ by฀its฀efficiency฀score.฀On฀the฀other฀hand,฀ BW฀and฀USN&WR฀ranked฀the฀University฀ of฀ Pennsylvania฀ (Wharton)฀ as฀ 3rd฀ and฀ 2nd,฀ respectively,฀ whereas฀ DEA฀ ranked฀ it฀ 18th.฀ Our฀ detailed฀ data฀ revealed฀ that฀ the฀average฀GMAT฀of฀admitted฀Wharton฀ students฀ was฀ the฀ highest฀ of฀ the฀ schools฀ evaluated฀but฀that฀its฀graduates฀were฀not฀ better฀ than฀ those฀ of฀ its฀ peers฀ in฀ terms฀ of฀ their฀ starting฀ salaries฀ at฀ graduation.฀ Furthermore,฀the฀subjective฀judgments฀of฀ TABLE฀1.฀Data฀Envelopment฀Analysis฀Rankings฀of฀BusinessWeek฀(BW)฀

and฀U.S.News฀&฀World฀Report฀(USN&WR)฀Rankings฀

฀ Value฀added฀ Best฀value

School฀ Score฀ Ranking฀ Score฀ Ranking฀ (2004)฀ (2005)

Harvard฀University฀ 1.0000฀ ฀฀1฀ 1.0000฀ ฀1฀ ฀5฀ ฀1

Stanford฀University฀ 1.0000฀ ฀฀1฀ 1.0000฀ ฀1฀ ฀4฀ ฀2

Massachusetts฀Institute฀of฀฀

฀ Technology฀ 1.0000฀ ฀฀1฀ 1.0000฀ ฀1฀ ฀9฀ ฀4

Dartmouth฀College฀ 1.0000฀ ฀฀1฀ 1.0000฀ ฀1฀ 10฀ ฀6

Texas฀A&M฀University฀ 1.0000฀ ฀฀1฀ 1.0000฀ ฀1฀ ฀—฀ 32

Bentley฀College฀ 1.0000฀ ฀฀1฀ 1.0000฀ ฀1฀ ฀—฀ ฀—

University฀of฀New฀Hampshire฀ 1.0000฀ ฀฀1฀ 1.0000฀ ฀1฀ ฀—฀ ฀— Pennsylvania฀State฀University฀ 0.9997฀ ฀฀8฀ 1.0000฀ ฀1฀ 31*_{฀ 37}

New฀York฀University฀ 0.9966฀ ฀฀9฀ 0.9966฀ 22฀ 13฀ 13

University฀of฀Minnesota฀ 0.9892฀ 10฀ 0.9891฀ 23฀ 31*_{฀ 23}

Columbia฀University฀ 0.9831฀ 11฀ 0.9830฀ 26฀ ฀8฀ ฀฀9

Carnegie฀Mellon฀University฀ 0.9784฀ 12฀ 0.9889฀ 24฀ 15฀ 17

University฀of฀Michigan฀ 0.9767฀ 13฀ 0.9767฀ 27฀ ฀6฀ 10

Northwestern฀University฀ 0.9754฀ 14฀ 1.0000฀ ฀1฀ ฀1฀ ฀฀4

Boston฀University฀฀ 0.9721฀ 15฀ 0.9720฀ 30฀ 31*_{฀ 48}

University฀of฀Washington฀฀ 0.9720฀ 16฀ 1.0000฀ ฀1฀ 31*_{฀ 18}

University฀of฀Chicago฀฀ 0.9677฀ 17฀ 0.9713฀ 31฀ ฀2฀ ฀฀8

University฀of฀Pennsylvania฀ 0.9674฀ 18฀ 0.9670฀ 32฀ ฀3฀ ฀฀2

Cornell฀University฀ 0.9642฀ 19฀ 0.9851฀ 25฀ ฀7฀ 15

University฀of฀Iowa฀ 0.9609฀ 20฀ 1.0000฀ ฀1฀ 31*_{฀ 37}

University฀of฀Illinois,฀฀

฀ Urbana-Champaign฀฀ 0.9605฀ 21฀ 0.9620฀ 34฀ 31*_{฀ 27}

Wake฀Forest฀University฀ 0.9584฀ 22฀ 0.9581฀ 35฀ 31*_{฀ 42}

Northeastern฀University฀ 0.9551฀ 23฀ 0.9547฀ 37฀ ฀—฀ —

Duke฀University฀ 0.9522฀ 24฀ 0.9519฀ 40฀ 11฀ 11

Vanderbilt฀University฀ 0.9503฀ 25฀ 0.9500฀ 41฀ 30฀ 45

University฀of฀California,฀฀

฀ Berkeley฀ 0.9474฀ 26฀ 0.9474฀ 42฀ 17฀ ฀6

University฀of฀Denver฀ 0.9379฀ 27฀ 1.0000฀ ฀฀1฀ —฀ 78

University฀of฀Virginia฀ 0.9271฀ 28฀ 0.9271฀ 47฀ 12฀ 14

Arizona฀State฀University฀ 0.9247฀ 29฀ 0.9364฀ 45฀ ฀฀1฀ 31

Ohio฀State฀University฀ 0.9236฀ 30฀ 0.9237฀ —฀ 31*_{฀ 21}

Purdue฀University฀ 0.9224฀ 31฀ 0.9398฀ 43฀ 21฀ 23

University฀of฀Rochester฀ 0.9204฀ 32฀ 0.9247฀ 50฀ 29฀ 23

University฀of฀Oregon฀ 0.9195฀ 33฀ 0.9999฀ 21฀ —฀ —

Georgia฀Institute฀of฀฀

฀ Technology฀฀ 0.9185฀ 34฀ 0.9266฀ 49฀ 31*_{฀ 32}

University฀of฀North฀Carolina฀ 0.9142฀ 35฀ 0.9141฀ —฀ 16฀ 21

Temple฀University฀ 0.9137฀ 36฀ 1.0000฀ ฀฀1฀ ฀฀ —

University฀of฀Maryland฀ 0.9128฀ 37฀ 0.9170฀ —฀ 28฀ 27

University฀of฀California,฀฀

฀ Los฀Angeles฀฀ 0.9109฀ 38฀ 0.9270฀ 48฀ 14฀ 11

Tulane฀University฀ 0.9093฀ 39฀ 0.9089฀ —฀ ฀—฀ 45

University฀of฀Alabama฀ 0.9064฀ 40฀ 1.0000฀ ฀฀1฀ ฀—฀ —

University฀of฀Notre฀Dame฀ 0.9032฀ 41฀ 1.0000฀ ฀฀1฀ 24฀ 32

Georgetown฀University฀ 0.9019฀ 42฀ 0.9019฀ —฀ 25฀ 27

Louisiana฀State฀University฀ 0.9012฀ 43฀ 0.9541฀ 38฀ ฀—฀ —

Rice฀University฀ 0.8992฀ 44฀ 0.8990฀ —฀ 31*_{฀ 49}

Case฀Western฀Reserve฀฀

฀ University฀ 0.8976฀ 45฀ 0.8971฀ —฀ 31*_{฀ —}

Iowa฀State฀University฀ 0.8968฀ 46฀ 1.0000฀ ฀฀1฀ ฀—฀ —

Michigan฀State฀University฀ 0.8960฀ 47฀ 0.9536฀ 39฀ 31*_{฀ 32}

Babson฀College฀ 0.8914฀ 48฀ 0.8913฀ —฀ 26฀ —

Washington฀University฀฀

฀ in฀St.฀Louis฀ 0.8900฀ 49฀ 0.8899฀ —฀ 23฀ 32

Emory฀University฀ 0.8892฀ 50฀ 0.8940฀ —฀ 20฀ 18

*_{All฀20฀schools฀in฀the฀second฀tier฀are฀given฀a฀ranking฀of฀31.}

BW USN&WR

recruiters,฀ peers฀ from฀ other฀ schools,฀ and฀ alumni฀ have฀ influenced฀ the฀ rankings฀ by฀ BW฀ and฀ USN&WR.฀ This฀ explains฀ why฀ both฀BW฀and฀USN&WR฀ranked฀the฀Uni-versity฀of฀Pennsylvania฀as฀high,฀whereas฀ it฀ was฀ ranked฀ 18th฀ in฀ value-added฀ rank-ings฀ and฀ 32nd฀ in฀ best฀ value฀ rankings.฀A฀ similar฀ explanation฀ for฀ rankings฀ of฀ the฀ University฀of฀Chicago฀is฀true.฀

The฀DEA฀best-value฀results฀in฀Table฀ 1฀ indicate฀ that฀ Iowa฀ State฀ University,฀ University฀ of฀ Notre฀ Dame,฀ University฀ of฀ Alabama,฀ Temple฀ University,฀ and฀ University฀ of฀ Denver฀ ranked฀ as฀ 46th,฀ 41st,฀40th,฀36th,฀and฀27th,฀respectively,฀ in฀ value฀ added฀ but฀ received฀ efficiency฀ scores฀ of฀ 1฀ when฀ we฀ included฀ cost฀ of฀ living,฀ program฀ length,฀ and฀ tuition฀ as฀ additional฀input฀attributes.฀It฀is฀also฀note-worthy฀that฀neither฀BW฀nor฀USN&WR฀ ranked฀Iowa฀State฀University,฀Universi-ty฀of฀Alabama,฀or฀University฀of฀Denver฀ in฀the฀top฀50฀schools.฀In฀terms฀of฀value฀ added,฀ these฀ schools฀ were฀ ranked฀ low.฀ However,฀these฀programs฀provided฀bet-ter฀value฀because฀of฀their฀lower฀tuition,฀ cost฀of฀living,฀or฀length฀of฀time฀for฀stu-dents฀to฀complete฀the฀program.

Conclusion

Selecting฀ the฀ best฀ program฀ is฀ a฀ daunting—but฀important—task฀for฀any฀ aspiring฀ MBA฀ student.฀ The฀ selection฀ strategy฀by฀any฀prospective฀MBA฀stu-dent฀ is฀ a฀ personal-investment฀ decision฀ about฀costs,฀length,฀quality,฀reputation,฀ and฀placement.฀Therefore,฀the฀selection฀ is฀ a฀ multiple-criteria฀ decision-mak-ing฀ process฀ that฀ matches฀ the฀ student’s฀ capabilities฀and฀desires฀with฀an฀appro-priate฀MBA฀program.

In฀this฀study,฀we฀used฀the฀DEA฀model฀ to฀ evaluate฀ the฀ performance฀ of฀ individu-al฀ MBA฀ programs฀ relative฀ to฀ their฀ peer฀ group.฀ DEA฀ does฀ not฀ require฀ a฀ set฀ of฀ preassigned฀ weights฀ for฀ inputs฀ and฀ out-puts฀ and฀ thus฀ overcomes฀ the฀ deficiency฀ introduced฀ by฀ subjective฀ weights.฀ By฀ using฀nonsubjective฀assessments฀of฀MBA฀ programs,฀ DEA฀ can฀ provide฀ students฀ with฀ unbiased฀ guidelines฀ for฀ selection.฀

With฀numerous฀MBA฀programs฀available,฀ the฀ findings฀ of฀ this฀ study฀ should฀ help฀ students฀ to฀ reduce฀ time฀ and฀ cost฀ and฀ to฀ improve฀ their฀ selection฀ process.฀ At฀ the฀ same฀time,฀these฀findings฀should฀help฀the฀ MBA฀program฀directors฀in฀identifying฀the฀ strengths฀ and฀ weaknesses฀ of฀ their฀ pro-grams.฀ Moreover,฀ DEA฀ allows฀ a฀ student฀ to฀ compare฀ selected฀ MBA฀ programs฀ on฀ the฀basis฀of฀certain฀selected฀attributes.฀For฀ instance,฀a฀student฀might฀be฀interested฀in฀ universities฀ on฀ the฀ east฀ coast฀ regardless฀ of฀ tuition.฀ In฀ such฀ a฀ case,฀ DEA฀ would฀ include฀ as฀ DMUs฀ only฀ those฀ MBA฀ pro-grams฀that฀schools฀on฀the฀east฀coast฀offer.฀ Researchers,฀educators,฀and฀students฀can฀ determine฀ the฀ input–output฀ mix฀ on฀ the฀ basis฀of฀the฀students’฀objectives฀and฀goals฀ and฀ their฀ own฀ qualifications.฀ Therefore,฀ researchers,฀ educators,฀ and฀ students฀ can฀ drop฀ unimportant฀ criteria฀ while฀ adding฀ additional฀criteria฀pertinent฀to฀the฀student฀ to฀obtain฀a฀desired฀input–output฀mix.฀

The฀ findings฀ of฀ this฀ study฀ should฀ assist฀ prospective฀ students฀ in฀ select-ing฀ MBA฀ programs฀ that฀ have฀ the฀ best฀ market฀ value฀ and฀ should฀ assist฀ deans฀ in฀ identifying฀ the฀ strengths฀ and฀ weak-nesses฀of฀their฀MBA฀programs.฀

NOTES

Dr.฀Dorothy฀M.฀Fisher’s฀research฀interests฀are฀ decision-support฀systems,฀Web฀accessibility,฀soft-ware-as-a-service,฀and฀business฀education.

Dr.฀Melody฀Kiang ’s฀research฀interests฀are฀devel-opment฀ and฀ applications฀ of฀ artificial฀ intelligence฀ techniques฀to฀a฀variety฀of฀business฀problems.฀

Dr.฀ Steven฀ A.฀ Fisher’s฀ research฀ interests฀ are฀ financial฀reporting฀issues฀and฀business฀education.

Correspondence฀ concerning฀ this฀ article฀ should฀ be฀addressed฀to฀Dr.฀Dorothy฀M.฀Fisher,฀Professor,฀ Department฀ of฀ Information฀ Systems,฀ California฀ State฀ University–Dominguez฀ Hills,฀ Carson,฀ CA฀ 90747.

E-mail:฀dfisher@csudh.edu฀

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APPENDIX฀ Data฀Envelopment฀Analysis

In฀ data฀ envelopment฀ analysis฀ (DEA),฀ a฀ production฀ operation฀ using฀m฀ inputs฀ to฀ produce฀s฀ outputs฀ is฀ called฀ a฀decision-making฀ unit฀ (DMU).฀A฀DMU฀has฀discretion฀in฀using฀an฀input฀mix฀to฀produce฀an฀output฀mix.฀In฀this฀study,฀the฀following฀linear฀programming฀formula-tion฀of฀the฀DEA฀model฀was฀used฀(Charnes฀et฀al.,฀1990;฀Sun฀&฀Gong,฀1993):฀฀

฀ Maximize฀ V_P฀=฀µT_y o฀฀ ฀ Subject฀to฀฀ µT_y

j฀–฀ωTxj฀≤฀0,฀j฀=฀1,฀.฀.฀.฀n

฀ ฀ ωT_x

o฀=฀1฀ (1)

฀ ฀ µT_,฀ωT_฀>฀0,

where฀n฀is฀the฀number฀of฀DMUs;฀x_j฀is฀the฀input฀vector;฀y_j฀is฀the฀output฀vector;฀DMU_o฀is฀the฀DMU฀currently฀being฀evaluated;฀µ฀and฀ω ฀cor-respond฀to฀x_o฀and฀y_o,฀respectively,฀and฀are฀the฀implied฀weights.฀The฀DEA฀model฀evaluates฀all฀n฀DMUs฀consecutively.฀The฀corresponding฀ dual฀of฀(1)฀takes฀the฀following฀form:฀

฀ Minimize฀

฀ Subject฀to฀

฀฀฀ ฀ ฀ (2)

฀

฀ ,

where฀m฀is฀the฀number฀of฀inputs;฀s฀is฀the฀number฀of฀outputs;฀S+_{฀is฀the฀output฀slacks;฀S}–_{฀is฀the฀input฀slacks;฀λ฀is฀a฀coefficient฀vector฀for฀}

DMUs,฀and฀ε฀is฀a฀sufficiently฀small฀number.฀It฀can฀be฀proved฀that฀there฀exist฀optimal฀solutions฀for฀(1)฀and฀(2),฀and฀V_D฀<฀V_P฀<฀1.฀ To฀define฀efficiency,฀let฀(µ*_,฀ω*_{)฀denote฀an฀optimal฀solution฀to฀(1).฀DMU}

o฀is฀said฀to฀be฀efficient฀if฀µTyo฀=฀1,฀where฀µ*฀>฀0฀and฀ω*฀>฀0.฀ Alternatively,฀the฀efficiency฀of฀DMU_o฀can฀be฀measured฀in฀terms฀of฀the฀dual฀problem฀(2).฀DMU_o฀is฀efficient฀if฀θ*_{฀=฀1,฀S}+*_{฀=฀0,฀S}–*_฀=฀0,฀

where฀(λ*_,฀θ*_,฀S+*_,฀S–*_{)฀is฀an฀optimal฀solution฀to฀problem฀(2).฀For฀an฀efficient฀performance,฀DMU}

o’s฀optimal฀inputs฀and฀outputs฀should฀be฀

(x_o*_,฀y

o*),฀where฀xo*฀=฀θ*xo–S–*฀and฀yo*฀=฀yo฀+฀S+*.฀Therefore,฀the฀input฀wastes฀are฀S–*฀and฀corresponding฀output฀shortfalls฀are฀S+*.฀ From฀the฀definition฀of฀efficiency,฀when฀θ*_{฀=฀1,฀S}+*_฀=฀S–*_{฀=฀0,฀then฀x}

o*฀=฀xo฀and฀yo*฀=฀yo฀(i.e.,฀optimal฀values฀equal฀observed฀values).฀ Otherwise,฀µT_y

o฀<฀1฀and฀DMUo฀is฀said฀to฀be฀inefficient฀and฀has฀an฀efficient฀score฀of฀less฀than฀1.฀It฀is฀inefficient฀relative฀to฀its฀peer฀group,฀

which฀consists฀of฀efficient฀DMUs.฀The฀peer฀group฀or฀reference฀set฀consists฀of฀the฀left฀side฀of฀the฀equation,฀฀฀฀฀฀฀฀฀฀฀฀฀and฀฀฀฀฀฀฀฀฀฀฀฀,฀of฀(2).฀

An฀inefficient฀DMU฀can฀thus฀improve฀its฀productivity฀by฀eliminating฀input฀wastes,฀S–_{,฀or฀decreasing฀output฀deficiencies,฀S}+_{,฀relative฀to฀its฀}

reference฀set.฀The฀resulting฀reference฀set฀may฀be฀interpreted฀as฀data฀envelopment฀because฀the฀value฀on฀the฀right฀side฀of฀an฀equation฀cannot฀ exceed฀the฀value฀on฀the฀left฀side฀(Charnes฀et฀al.,฀1991).

V S S

y S y

D i

i m

i i

S

ij j

n

j i jo

= +

=

=

+

=

=

+

∑

∑

∑

θ ε λ

– ( )

–

– 1 1

1 ,, , . . .

– – , , . . .

i s

xij S x i m

j n

j i io

j j

=

= =

=

=

∑

1

1

1 1

λ θ

λ

nn

j

S S

∑

= ≥

≥

+

1 0

0 ,

, –

λ

yj j

n

j

=

∑

1 λ j xj n

j

=

∑

1 λ