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Journal of Education for Business

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

An Efficiency Comparison of MBA Programs: Top

10 Versus Non-Top 10

Maxwell K. Hsu , Marcia L. James & Gary H. Chao

To cite this article: Maxwell K. Hsu , Marcia L. James & Gary H. Chao (2009) An Efficiency Comparison of MBA Programs: Top 10 Versus Non-Top 10, Journal of Education for Business, 84:5, 269-274, DOI: 10.3200/JOEB.84.5.269-274

To link to this article: http://dx.doi.org/10.3200/JOEB.84.5.269-274

Published online: 07 Aug 2010.

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he฀ average฀ total฀ cost฀ of฀ attend-ing฀ a฀ top-10฀ MBA฀ program฀ in฀ the฀ United฀ States฀ is฀ approximately฀ $198,300,฀versus฀the฀non-top-10฀coun-terparts’฀average฀total฀cost฀of฀$123,700฀ (Holtom฀&฀Inderrieden,฀2007).฀Recent฀ findings฀ from฀ the฀ Graduate฀ Manage-ment฀Admission฀Council฀(GMAC)฀data฀ show฀that฀“students฀who฀attend฀lower-ranking฀ schools฀ experience฀ a฀ better฀ return฀ on฀ investment฀ than฀ those฀ who฀ attend฀higher-ranking฀schools”฀(Holtom฀ &฀ Inderrieden,฀ p.฀ 36).฀ To฀ review฀ this฀ striking฀ finding฀ from฀ another฀ angle,฀ the฀present฀study฀compares฀the฀cohort฀ group฀of฀top-10฀MBA฀programs฀in฀the฀ United฀States฀with฀their฀lower-ranking฀ counterparts฀on฀the฀basis฀of฀their฀value-added฀efficiency.

Print฀ media฀ such฀ as฀Business฀ Week,฀ Financial฀Times฀(“Financial฀Times ฀pub-lishes฀ 2006฀ global฀ MBA฀ rankings,”฀ 2006),฀the฀Wall฀Street฀Journal,฀the฀ Econ-omist,฀ and฀U.S.฀ News฀ &฀ World฀ Report฀ (“Schools฀ of฀ Business,”฀ 2006)฀ all฀ pro-vide฀their฀own฀versions฀of฀the฀B-school฀ rankings.฀ Hiring฀ competent฀ instructors,฀ maintaining฀smaller฀class฀sizes,฀and฀set-ting฀ competitive฀ entrance฀ criteria฀ are฀ ways฀ top฀ MBA฀ programs฀ have฀ used฀ to฀ improve฀their฀rankings.฀However,฀critics฀ point฀out฀that฀many฀MBA฀programs฀shift฀ the฀balance฀of฀power฀from฀assessment฀of฀ learning฀ outcomes฀ and฀ academic฀ schol-arship฀ to฀ obsession฀ with฀ ranking฀ sta-tus฀ (Association฀ to฀ Advance฀ Collegiate฀

Schools฀of฀Business฀International,฀2005;฀ Policano,฀2005).฀It฀is฀worse฀that฀because฀ of฀ varying฀ ranking฀ methodologies฀ and฀ data-collection฀processes,฀these฀rankings฀ may฀not฀reflect฀the฀overall฀performance฀ and฀ uniqueness฀ of฀ an฀ MBA฀ program.฀ As฀ Tracy฀ and฀ Waldfogel฀ (1997)฀ point-ed฀ out,฀ one฀ serious฀ problem฀ with฀ the฀ aforementioned฀B-school฀rankings฀is฀that฀ they฀do฀not฀differentiate฀program฀inputs฀ from฀ outputs.฀ Thus,฀ we฀ believe฀ that฀ in฀ conjunction฀with฀the฀published฀B-school฀ rankings,฀findings฀from฀the฀present฀study฀ could฀ help฀ the฀ MBA฀ program฀ adminis-trators฀and฀applicants฀confidently฀obtain฀ a฀ more฀ comprehensive฀ guideline฀ when฀ they฀assess฀top฀U.S.฀MBA฀programs.

Why฀ do฀ students฀ enroll฀ in฀ an฀ MBA฀ program?฀ Bickerstaffe฀ and฀ Ridgers฀ (2007)฀ identified฀ the฀ following฀ four฀ factors:฀ new฀ career฀ opportunities,฀ personal฀ development฀ and฀ educational฀ experience,฀ increased฀ salary,฀ and฀ networking.฀ However,฀ if฀ the฀ absolute฀ values฀ of฀ those฀ factors฀ are฀ focused฀ on,฀ MBA฀applicants฀may฀fall฀into฀a฀trap฀such฀ as฀ a฀ blind฀ trust฀ in฀ B-school฀ rankings.฀ Top฀ MBA฀ programs฀ can฀ recruit฀ the฀ best฀ students฀ who฀ are฀ more฀ likely฀ to฀ outperform฀students฀from฀the฀other฀MBA฀ programs.฀ This฀ does฀ not฀ necessarily฀ mean฀that฀the฀top฀MBA฀programs฀have฀ done฀ their฀ best฀ to฀ train฀ their฀ students.฀ To฀ better฀ gauge฀ an฀ MBA฀ program’s฀ performance,฀ researchers฀ should฀ resort฀ to฀the฀efficiency฀measurement.

An฀Efficiency฀Comparison฀of฀MBA฀

Programs:฀Top฀10฀Versus฀Non-Top฀10

MAXWELL฀K.฀HSU฀ MARCIA฀L.฀JAMES

UNIVERSITY฀OF฀WISCONSIN–WHITEWATER฀ UNIVERSITY฀OF฀WISCONSIN–WHITEWATER

WHITEWATER,฀WISCONSIN฀ WHITEWATER,฀WISCONSIN฀

GARY฀H.฀CHAO

KUTZTOWN฀UNIVERSITY KUTZTOWN,฀PENNSYLVANIA

T

ABSTRACT.฀The฀authors฀compared฀the฀ cohort฀group฀of฀the฀top-10฀MBA฀programs฀ in฀the฀United฀States฀with฀their฀lower-rank- ing฀counterparts฀on฀their฀value-added฀effi-ciency.฀The฀findings฀reveal฀that฀the฀top-10฀ MBA฀programs฀in฀the฀United฀States฀are฀ associated฀with฀statistically฀higher฀average฀

technical฀and฀scale฀efficiency฀and฀scale฀ efficiency,฀but฀not฀with฀a฀statistically฀higher฀ average฀pure฀technical฀efficiency .฀By฀calcu-lating฀the฀efficiency฀measures,฀the฀proper฀ decision฀variables฀of฀the฀MBA฀programs฀ can฀be฀identified฀and฀improvements฀to฀their฀ efficiency฀can฀be฀made.฀In฀addition,฀the฀ findings฀can฀assist฀prospective฀students฀in฀ selecting฀the฀best฀MBA฀programs฀for฀their฀ educational฀investment.

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

Copyright฀©฀2009฀Heldref฀Publications

How฀ can฀ a฀ program฀ improve฀ its฀ efficiency?฀ One฀ way฀ is฀ to฀ reduce฀ its฀ inputs฀ while฀ improving฀ its฀ outputs.฀ Should฀ a฀ program฀ minimize฀ all฀ inputs฀ and฀ maximize฀ all฀ outputs?฀ Not฀ neces-sarily.฀ A฀ program฀ may฀ only฀ need฀ to฀ improve฀ its฀ efficiency฀ by฀ focusing฀ on฀ some฀variables฀rather฀than฀all฀of฀them.฀ Specifically,฀ the฀ boundary฀ constraints฀ that฀ correspond฀ to฀ the฀ input฀ or฀ output฀ variables฀ for฀ each฀ MBA฀ program฀ can฀ be฀ identified.฀ This฀ information฀ offers฀ valuable฀guidelines฀for฀each฀MBA฀pro-gram฀to฀enhance฀its฀efficiency.

The฀ purpose฀ of฀ the฀ present฀ article฀ is฀ threefold:฀first,฀to฀identify฀less฀efficient฀ MBA฀programs฀using฀the฀data฀envelop-ment฀analysis฀(DEA)฀technique;฀second,฀ to฀ fill฀ the฀ gap฀ of฀ the฀ current฀ literature฀ in฀ examining฀ whether฀ differences฀ in฀ efficiency฀ exist฀ among฀ the฀ often฀ more฀ expensive฀ top-10฀ U.S.฀ MBA฀ programs฀ and฀ other฀ non-top-10฀ U.S.฀ MBA฀ pro-grams;฀and฀third,฀to฀help฀MBA฀program฀ administrators฀ identify฀ sources฀ of฀ rela-tive฀inefficiency฀so฀that฀they฀can฀improve฀ their฀ programs’฀ value-added฀ efficiency.฀ As฀a฀result,฀this฀proposed฀method฀offers฀ MBA฀ program฀ administrators฀ a฀ useful฀ means฀when฀they฀develop฀strategic฀plans฀ to฀ achieve฀ market฀ competitiveness.฀ In฀ addition,฀the฀findings฀can฀offer฀prospec-tive฀ MBA฀ students฀ another฀ venue฀ to฀ evaluate฀ MBA฀ programs฀ before฀ they฀ submit฀their฀applications.

Literature฀Review

In฀the฀education฀literature,฀a฀number฀ of฀research฀studies฀have฀investigated฀the฀ relative฀ efficiency฀ of฀ various฀ decision-making฀ units฀ (DMUs)฀ at฀ the฀ adminis-trative฀levels฀(Ahn,฀Charnes,฀&฀Cooper,฀ 1988;฀ Chen,฀ 1997;฀ Haksever฀ &฀ Mura-gishi,฀1998;฀McMillan฀&฀Datta,฀1998).฀ Bradley,฀ Jones,฀ and฀ Millington฀ (2001)฀ used฀DEA฀to฀evaluate฀the฀efficiency฀of฀ all฀secondary฀schools฀in฀England฀during฀ 1993–1998.฀ Mizala,฀ Romaguera,฀ and฀ Farren฀ (2002)฀ used฀ the฀ stochastic฀ pro-duction฀ frontier฀ method฀ to฀ assess฀ the฀ technical฀efficiency฀of฀schools฀in฀Chile,฀ but฀it฀is฀worthy฀to฀note฀that฀the฀stochas-tic฀production฀frontier฀method฀can฀only฀ deal฀with฀single฀outputs฀(Aigner,฀Lovell,฀ &฀ Schmidt,฀ 1977).฀ Recently,฀ Gimenez,฀ Prior,฀and฀Thieme฀(2007)฀exploited฀the฀ DEA฀ method฀ to฀ analyze฀ the฀ technical฀

and฀ managerial฀ efficiency฀ of฀ education฀ systems฀across฀31฀countries.

Focusing฀ on฀ the฀ U.S.฀ MBA฀ educa-tion,฀ Haksever฀ and฀ Muragishi฀ (1998)฀ used฀ DEA฀ to฀ measure฀ value฀ added฀ in฀ an฀ MBA฀ program,฀ and฀ they฀ found฀ that฀ the฀ top-20฀ MBA฀ programs฀ do฀ not฀ necessarily฀ outperform฀ the฀ second-20฀ MBA฀ programs.฀ Colbert,฀ Levary,฀ and฀ Shaner฀ (2000)฀ used฀ DEA฀ to฀ determine฀ the฀relative฀efficiency฀of฀24฀top-ranked฀ U.S.฀ MBA฀ programs,฀ and฀ they฀ argued฀ that฀ the฀ ranking฀ of฀ MBA฀ programs฀ on฀ the฀ basis฀ of฀ DEA฀ would฀ “more฀ com-pletely฀ and฀ accurately฀ represent฀ MBA฀ programs”฀ than฀ the฀ publicized฀ rank-ing฀ of฀ MBA฀ programs฀ by฀ well-known฀ magazines฀ such฀ as฀Business฀ Week฀ (p.฀ 668).฀ Colbert฀ et฀ al.฀ also฀ extended฀ their฀ study฀ to฀ include฀ foreign฀ MBA฀ pro-grams.฀ Using฀ 7฀ top฀ MBA฀ programs฀ in฀ the฀United฀States฀and฀3฀renowned฀MBA฀ programs฀ outside฀ the฀ United฀ States,฀ Colbert฀et฀al.฀found฀only฀1฀of฀the฀top-10฀ MBA฀programs฀(i.e.,฀Columbia฀Univer-sity)฀ to฀ be฀ relatively฀ inefficient.฀ More฀ recently,฀Fisher฀and฀Kiang฀(2007)฀evalu-ated฀ the฀ U.S.฀ MBA฀ programs฀ with฀ a฀ value-added฀ approach.฀ They฀ compared฀ the฀ DEA฀ efficiency฀ rankings฀ with฀ the฀ Business฀Week฀and฀U.S.฀News฀&฀World฀ Report฀ (“Schools฀ of฀ Business,”฀ 2006)฀ rankings฀and฀discussed฀the฀discrepancy฀ found฀ between฀ them.฀ However,฀ Fisher฀ and฀ Kiang’s฀ study฀ did฀ not฀ identify฀ the฀ source฀of฀inefficiency฀related฀to฀the฀less฀ efficient฀MBA฀programs.

Given฀ that฀ the฀ recent฀ GMAC฀ find-ing฀(Holton฀&฀Inderrieden,฀2007)฀draws฀ new฀ attention฀ to฀ differences฀ between฀ the฀ top-10฀ U.S.฀ MBA฀ programs฀ and฀ the฀ non-top-10฀ U.S.฀ MBA฀ programs,฀ it฀ is฀ time฀ to฀ revisit฀ the฀ MBA฀ rank-ing฀issue฀by฀comparing฀the฀value-added฀ efficiency฀ between฀ these฀ two฀ cohort฀ groups฀using฀the฀DEA฀technique.฀Sub-sequently,฀ proper฀ decision฀ variables฀ of฀ the฀MBA฀programs฀could฀be฀identified,฀ and฀improvements฀can฀be฀made. METHOD

General

DEA ฀refers฀to฀an฀optimization฀meth-od฀of฀linear฀programming฀to฀generalize฀ Farrell’s฀(1957)฀single-input฀and฀single-output฀ technical฀ efficiency฀ measure฀ to฀ a฀ more฀ complicated฀ case฀ in฀ which฀ a฀

single฀efficiency฀score฀can฀be฀calculated฀ as฀a฀result฀of฀multiple฀inputs฀and฀outputs฀ related฀to฀the฀DMUs.฀DMUs฀often฀refer฀ to฀units฀of฀organizations฀such฀as฀banks,฀ post฀offices,฀nursing฀homes,฀courts,฀and฀ MBA฀ programs,฀ which฀ typically฀ per-form฀the฀same฀function฀and฀try฀to฀attract฀ the฀ same฀ type฀ of฀ customers฀ or฀ clients.฀ A฀DMU฀commonly฀uses฀a฀set฀of฀inputs฀ (e.g.,฀ labor,฀ capital)฀ to฀ produce฀ a฀ set฀ of฀ outputs฀ (e.g.,฀ products,฀ profits)฀ to฀ satisfy฀ the฀ needs฀ of฀ its฀ customers.฀The฀ DEA฀method฀was฀originally฀developed฀ by฀Charnes,฀Cooper,฀and฀Rhodes฀(1978)฀ with฀ a฀constant฀ return฀ to฀ scale฀ (refers฀ to฀ the฀ situation฀ in฀ which฀ the฀ propor-tional฀output฀changes฀are฀subject฀to฀the฀ same฀proportional฀input฀changes),฀and฀it฀ was฀later฀advanced฀by฀Banker,฀Charnes,฀ and฀ Cooper฀ (1984)฀ to฀ include฀ a฀ vari-able฀return฀to฀scale฀(refers฀to฀allowing฀ each฀ DMU฀ to฀ maximize฀ its฀ level฀ of฀ efficiency฀ without฀ subjecting฀ the฀ pro-portional฀ output฀ changes฀ to฀ the฀ same฀ proportional฀input฀changes).฀As฀a฀credit฀ to฀their฀developers,฀the฀two฀fundamental฀ DEA฀ models฀ are฀ known฀ as฀CCR฀ and฀ BCC.฀The฀CCR฀and฀BCC฀formulas฀are฀ provided฀below:

CCR฀Model

Max Subject to

Θ Subject to

π ith฀ input฀ consumed฀ and฀ the฀ amount฀ of฀ the฀rth฀output฀generated฀by฀the฀jth฀MBA฀ program.฀In฀addition,฀m฀is฀the฀number฀of฀

input฀variables,฀whereas฀s฀is฀the฀number฀ of฀ output฀ variables,฀λ_j฀ is฀ the฀ weight฀ of฀ variables,฀and฀n ฀is฀the฀number฀of฀obser-vations฀(n฀=฀58฀in฀the฀present฀study).฀Θ฀ and฀π฀are฀the฀efficiency฀results฀of฀MBA฀ programs฀under฀investigation฀from฀CCR฀ and฀BCC฀models,฀respectively.

DEA฀has฀become฀increasingly฀impor- tant฀as฀a฀managerial฀tool,฀and฀new฀appli-cations฀ with฀ more฀ variables฀ and฀ more฀ complex฀ models฀ are฀ being฀ developed.฀ Nonetheless,฀the฀main฀advantage฀of฀the฀ DEA฀ technique฀ remains฀ the฀ same;฀ it฀ allows฀several฀inputs฀and฀outputs฀to฀be฀ considered฀simultaneously฀to฀determine฀ the฀ relative฀ performance฀ of฀ a฀ specific฀ DMU฀to฀that฀of฀its฀peers.

In฀ DEA฀ estimation,฀ any฀ input฀ use฀ greater฀than฀the฀optimal฀amount฀is฀con-sidered฀ unnecessary,฀ and฀ such฀ a฀ DMU฀ would฀ be฀ classified฀ as฀ inefficient.฀ For฀ all฀ DMUs,฀ overall฀technical฀ and฀ scale฀ efficiency฀ (TSE)฀ refers฀ to฀ the฀ extent฀ to฀ which฀ a฀ specific฀ unit฀ achieves฀ the฀ best฀ overall฀ productivity฀ attainable฀ in฀ the฀ most฀ efficient฀ manner฀ (Banker฀ et฀ al.,฀ 1984),฀and฀it฀can฀be฀further฀decomposed฀ into฀ pure฀ technical฀ efficiency฀ (PTE)฀ and฀ scale฀ efficiency฀ (SE).฀ In฀ the฀ con-text฀ of฀ MBA฀ programs,฀PTE฀ refers฀ to฀ how฀ efficiently฀ MBA฀ programs฀ use฀ the฀ employed฀resources฀such฀as฀the฀average฀ GPA,฀the฀average฀GMAT฀score,฀tuition,฀ and฀the฀enrolled฀MBA฀students’฀average฀ salary฀ before฀ entering฀ the฀ MBA฀ pro-gram.฀ Alternatively,฀ SE฀ represents฀ how฀ productive฀the฀scale฀size฀is.฀It฀is฀the฀ratio฀ of฀TSE฀from฀the฀constant฀return฀to฀scale฀ to฀PTE฀from฀the฀variable-return-to-scale฀ constraint.฀ All฀ efficiency฀ indexes฀ range฀ from฀0฀to฀1,฀and฀the฀upper฀limit฀means฀ that฀the฀DMU฀operates฀more฀efficiently฀ than฀its฀peers.฀After฀determining฀the฀effi- ciency฀measurement฀from฀DEA,฀the฀effi- ciency฀scores฀of฀the฀more฀expensive฀top-10฀ U.S.฀ MBA฀ programs฀ and฀ their฀ less฀ expensive฀ non-top-10฀ counterparts฀ are฀ compared฀ using฀ a฀ nonparametric฀ Kol-mogorov-Smirnov฀Z฀test.฀Because฀DEA฀ does฀ not฀ have฀ any฀ planned฀ functional฀ form฀relating฀inputs฀to฀outputs,฀it฀would฀ be฀more฀appropriate฀to฀examine฀the฀pro-posed฀ hypothesis฀ with฀ a฀ nonparametric฀ method฀in฀the฀present฀study฀than฀to฀use฀a฀ parametric฀measure฀such฀as฀a฀t฀test.

MBA฀ programs฀ can฀ be฀ compared฀ solely฀ on฀ their฀ performance฀ (i.e.,฀ the฀ output฀ factors฀ in฀ this฀ study),฀ and฀ it฀

is฀ possible฀ to฀ use฀ a฀ simple฀ approach฀ to฀ determine฀ which฀ MBA฀ programs฀ helped฀ their฀ students฀ acquire฀ a฀ higher฀ salary.฀ However,฀ as฀ we฀ have฀ discussed฀ previously,฀ this฀ simple฀ approach฀ does฀ not฀ shed฀ light฀ on฀ the฀ other฀ part฀ of฀ the฀ equation฀ (i.e.,฀ the฀ input฀ factors).฀ After฀ all,฀top฀business฀schools฀that฀admit฀stu-dents฀with฀high฀GPA฀and฀GMAT฀scores฀ are฀ more฀ likely฀ to฀ generate฀ successful฀ graduates.฀ Thus,฀ we฀ contend฀ that฀ the฀ best-performing฀ MBA฀ program฀ should฀ be฀the฀one฀that฀can฀outperform฀its฀peers฀ with฀ the฀ same฀ level฀ of฀ inputs.฀ In฀ other฀ words,฀ the฀ MBA฀ programs฀ should฀ be฀ examined฀in฀terms฀of฀their฀value-added฀ efficiency,฀ a฀ relative฀ index฀ resulting฀ from฀the฀comparison฀of฀the฀inputs฀with฀ the฀outputs.฀The฀highest฀efficiency฀score฀ that฀ a฀ DMU฀ (i.e.,฀ an฀ MBA฀ program฀ in฀ the฀ present฀ study)฀ can฀ possibly฀ obtain฀ is฀ 1,฀ which฀ means฀ the฀ MBA฀ program฀ being฀ compared฀ outperforms฀ its฀ peers฀ and฀can฀be฀considered฀as฀a฀higher฀value-added฀program.

Variables

The฀ major฀ function฀ of฀ MBA฀ pro- grams฀can฀be฀viewed฀as฀a฀learning฀inter-mediary฀institution฀that฀bridges฀or฀links฀ MBA฀ students฀ to฀ their฀ future฀ dream฀ careers.฀Such฀a฀viewpoint฀can฀reflect฀the฀ relative฀value-added฀efficiency฀of฀MBA฀ programs฀ in฀ the฀ increasingly฀ competi-tive฀higher฀education฀environment.฀The฀ inputs฀related฀to฀MBA฀programs’฀major฀ production฀ sources฀ include฀ (a)฀ aver-age฀ undergraduate฀ GPA,฀ (b)฀ averaver-age฀ GMAT฀ score,฀ (c)฀ out-of-state฀ tuition฀ and฀ fees,฀ and฀ (d)฀ salary฀ before฀ enter-ing฀ the฀ MBA฀ program.฀ We฀ selected฀ these฀ variables฀ as฀ they฀ were฀ perceived฀ to฀be฀what฀the฀typical฀MBA฀applicants฀ would฀care฀most฀about.฀The฀effect฀of฀the฀ program’s฀gender฀division฀and฀diversity฀ factors฀may฀not฀be฀perceived฀as฀impor-tant฀ to฀ an฀ MBA฀ applicant฀ because฀ not฀ many฀human฀resources฀managers฀would฀ consider฀ these฀ as฀ key฀ hiring฀ variables.฀ The฀ business฀ schools฀ can฀ identify฀ the฀ unique฀ characteristics฀ of฀ the฀ incoming฀ students฀ and฀ determine฀ how฀ to฀ satis-fy฀ the฀ students’฀ expectations฀ that฀ can฀ become฀the฀output.฀In฀the฀present฀study,฀ outcomes฀ of฀ MBA฀ programs฀ are฀ mea-sured฀by฀(a)฀average฀starting฀salary฀and฀ bonus฀immediately฀after฀graduation,฀(b)฀

employment฀rate฀3฀months฀after฀obtain-ing฀ the฀ MBA,฀ and฀ (c)฀ aims-achieved฀ ratio.฀Data฀related฀to฀the฀inputs฀and฀out-puts฀are฀available฀from฀the฀2006฀issues฀ of฀U.S.฀News฀&฀World฀Report฀(“Schools฀ of฀ Business,”฀ 2006)฀ and฀ Financial฀ Times฀(“Financial฀Times฀publishes฀2006฀ global฀MBA฀rankings,”฀2006).฀Only฀the฀ MBA฀programs฀with฀a฀complete฀set฀of฀ selected฀ input฀ and฀ output฀ factors฀ were฀ incorporated฀ into฀ the฀ analysis;฀ there-fore,฀58฀programs฀were฀used.

One฀notable฀limitation฀of฀the฀present฀ study฀concerns฀the฀data฀used฀for฀analy-sis.฀ Though฀ several฀ additional฀ factors฀ (e.g.,฀industries,฀extracurriculum฀activi-ties,฀ professional฀ licenses,฀ national฀ or฀ international฀ competition฀ experiences)฀ may฀ influence฀ the฀ value-added฀ effi-ciency฀ of฀ the฀ MBA฀ program,฀ they฀ are฀ not฀ easily฀ quantifiable฀ and฀ thus฀ were฀ excluded฀from฀the฀model.

RESULTS

The฀ analysis฀ of฀ MBA฀ program฀ effi-ciency฀ includes฀ four฀ input฀ and฀ three฀ output฀ variables.฀ One฀ unique฀ value฀ of฀ the฀ DEA฀ results฀ is฀ its฀ ability฀ to฀ offer฀ a฀ relatively฀ objective฀ benchmark฀ (i.e.,฀ efficiency฀ indexes;฀ see฀ Table฀ 1)฀ that฀ can฀ help฀ MBA฀ program฀ administrators฀ recognize฀ the฀ value-added฀ efficiency฀ of฀ their฀ program฀ by฀ comparing฀ it฀ with฀ other฀competing฀MBA฀programs.

Table฀2฀sheds฀light฀on฀the฀main฀sourc-฀ es฀of฀each฀MBA฀program’s฀inefficiency฀ (to฀save฀space,฀the฀MBA฀programs฀that฀ are฀located฀on฀the฀efficiency฀frontier฀are฀ not฀ shown฀ in฀ Table฀ 2).฀ If฀ the฀ variable฀ is฀ an฀ output฀ factor,฀ the฀ administrator฀ may฀ want฀ to฀ enhance฀ the฀ performance฀ of฀ that฀ output฀ factor.฀ If฀ the฀ variable฀ is฀ an฀ input฀ factor,฀ the฀ administrator฀ may฀ ease฀ the฀ required฀ standard฀ to฀ a฀ certain฀ degree.฀ For฀ example,฀ the฀ DEA฀ results฀ indicate฀ that฀ the฀ University฀ of฀ California–Irvine฀ could฀ improve฀ its฀ value-added฀ efficiency฀ score฀ by฀ maintaining฀ the฀ same฀ level฀ of฀ outputs฀ while฀ relaxing฀ the฀ input฀ requirements฀ for฀ the฀ average฀ undergraduate฀ GPA฀ or฀ the฀ average฀ salary฀ prior฀ to฀ entering฀ its฀ MBA฀ program฀ for฀ potential฀ students.฀ Notably,฀ it฀ is฀ not฀ suggested฀ that฀ MBA฀ program฀ administrators฀ lower฀ their฀ entrance฀ criteria.฀ Instead,฀ the฀ more฀ appropriate฀ interpretation฀ is฀ that฀ an฀

MBA฀program฀may฀consider฀setting฀up฀ a฀ strategic฀ recruiting฀ plan฀ on฀ the฀ basis฀ of฀ factors฀ other฀ than฀ GPA฀ or฀ salary.฀ There฀ are฀ many฀ other฀ criteria฀ to฀ shape฀ the฀uniqueness฀of฀the฀program,฀such฀as฀ the฀ diversity฀ in฀ work฀ and฀ professional฀ experiences,฀ cultures,฀ and฀ special฀ leadership฀skills.

We฀ used฀ the฀ nonparametric฀ Kol-mogorov-Smirnov฀Z฀test฀to฀determine฀if฀ the฀mean฀efficiency฀measures฀related฀to฀ the฀ top-10฀ MBA฀ programs฀ are฀ statisti-cally฀ higher฀ than฀ those฀ related฀ to฀ the฀ non-top-10฀ MBA฀ programs.฀ The฀ Kol-mogorov-Smirnov฀Z฀scores฀showed฀that฀ the฀average฀overall฀TSE฀score฀and฀aver-age฀SE฀score฀related฀to฀the฀top-10฀U.S.฀ MBA฀programs฀were฀higher฀than฀those฀ of฀their฀counterparts฀for฀the฀non-top-10฀ U.S.฀MBA฀programs฀at฀the฀.05฀signifi-cance฀level฀(see฀Table฀3).฀Alternatively,฀ although฀ the฀ average฀ PTE฀ score฀ in฀ the฀ top-10฀U.S.฀MBA฀programs฀was฀higher฀ than฀ that฀ of฀ the฀ non-top-10฀ U.S.฀ MBA฀ programs,฀ the฀ one-tailed฀ difference฀ is฀ not฀ statistically฀ significant฀ (p฀ =฀ .125).฀ That฀is,฀the฀hypothesis฀that฀top-10฀U.S.฀ MBA฀programs฀have฀a฀higher฀efficiency฀ score฀ including฀ higher฀ TSE,฀ PTE,฀ and฀ SE฀ scores฀ than฀ their฀ non-top-10฀ coun-terparts฀was฀only฀supported฀partially.฀

Though฀ the฀ findings฀ of฀ higher฀ mean฀ TSE฀ and฀ SE฀ for฀ the฀ top-10฀ U.S.฀ MBA฀ programs฀ do฀ offer฀ supportive฀ evidence฀ to฀the฀thought฀that฀the฀top-10฀U.S.฀MBA฀ programs฀operate฀in฀a฀relatively฀more฀effi-cient฀manner฀than฀those฀outside฀the฀top-10฀ list,฀it฀is฀worthy฀to฀note฀that฀the฀mean฀effi-ciency฀score฀differences฀are฀small.฀Thus,฀ potential฀ MBA฀ students฀ should฀ conduct฀ a฀ cost฀ and฀ benefit฀ analysis฀ among฀ the฀ competing฀MBA฀programs฀and฀then฀give฀ more฀ weight฀ to฀ the฀ programs฀ offering฀ opportunities฀to฀excel฀in฀a฀particular฀area฀ of฀ interest฀ (e.g.,฀ accounting)฀ rather฀ than฀ basing฀ their฀ application฀ decision฀ solely฀ on฀ published฀ ranking฀ reports฀ or฀ on฀ the฀ present฀ study.฀ This฀ way,฀ future฀ MBA฀ students฀can฀maximize฀the฀value฀of฀their฀ MBA฀education฀investment.

Conclusions฀and฀ Recommendations

These฀days,฀“more฀and฀more฀business฀ schools฀are฀fishing฀for฀MBAs฀from฀the฀ same฀ applicant฀ pool.฀ To฀ bring฀ in฀ the฀ best฀ catch,฀ each฀ school฀ must฀ position฀ TABLE฀1.฀Pure฀Technical฀Efficiency฀(PTE),฀Technical฀and฀Scale฀Efficiency฀

(TSE),฀and฀Scale฀Efficiency฀(SE)฀฀

Rank฀ ฀ School฀ PTE฀ TSE฀ SE฀

1฀ Harvard฀University฀ 1.000฀ 1.000฀ 1.000

2฀ Stanford฀University฀ 1.000฀ 1.000฀ 1.000

2฀ University฀of฀Pennsylvania฀ 1.000฀ 1.000฀ 1.000

4฀ Massachusetts฀Institute฀of฀Technology฀ 1.000฀ 1.000฀ 1.000

4฀ Northwestern฀University฀ 1.000฀ 1.000฀ 1.000

6฀ Dartmouth฀College฀ 1.000฀ 1.000฀ 1.000

6฀ University฀of฀California,฀Berkeley฀ 1.000฀ 0.984฀ 0.984

8฀ University฀of฀Chicago฀ 1.000฀ 1.000฀ 1.000

9฀ Columbia฀University฀ 1.000฀ 1.000฀ 1.000

10฀ University฀of฀Michigan,฀Ann฀Arbor฀ 1.000฀ 0.999฀ 0.999

11฀ Duke฀University฀ 0.984฀ 0.980฀ 0.996

11฀ University฀of฀California,฀Los฀Angeles฀ 0.960฀ 0.953฀ 0.993

13฀ New฀York฀University฀ 0.999฀ 0.994฀ 0.994

14฀ University฀of฀Virginia฀ 1.000฀ 1.000฀ 1.000

15฀ Cornell฀University฀ 1.000฀ 1.000฀ 1.000

15฀ Yale฀University฀ 1.000฀ 1.000฀ 1.000

17฀ Carnegie฀Mellon฀University฀ 1.000฀ 1.000฀ 1.000

18฀ Emory฀University฀ 0.954฀ 0.947฀ 0.993

18฀ University฀of฀Texas฀at฀Austin฀ 0.999฀ 0.956฀ 0.957

18฀ University฀of฀Washington฀ 1.000฀ 1.000฀ 1.000

21฀ Ohio฀State฀University฀ 0.972฀ 0.968฀ 0.996

21฀ University฀of฀North฀Carolina฀at฀Chapel฀Hill฀ 0.994฀ 0.972฀ 0.977

23฀ Purdue฀University฀ 0.979฀ 0.960฀ 0.981

23฀ University฀Minnesota,฀Twin฀Cities฀ 1.000฀ 1.000฀ 1.000

23฀ University฀of฀Rochester฀ 0.980฀ 0.965฀ 0.985

26฀ University฀of฀Southern฀California฀ 0.980฀ 0.961฀ 0.981

27฀ Georgetown฀University฀ 0.995฀ 0.990฀ 0.995

27฀ Indiana฀University฀ 1.000฀ 0.996฀ 0.996

27฀ University฀of฀Illinois฀at฀Urbana-Champaign฀ 1.000฀ 1.000฀ 1.000 27฀ University฀Maryland,฀College฀Park฀ 0.977฀ 0.977฀ 1.000

31฀ Arizona฀State฀University฀฀ 0.979฀ 0.970฀ 0.991

32฀ Georgia฀Institute฀of฀Technology฀ 0.996฀ 1.000฀ 1.004

32฀ Michigan฀State฀University฀ 1.000฀ 1.000฀ 1.000

32฀ Texas฀A&M฀University,฀College฀Station฀ 1.000฀ 1.000฀ 1.000

32฀ University฀of฀Notre฀Dame฀ 0.978฀ 0.975฀ 0.997

32฀ Washington฀University฀in฀St.฀Louis฀ 1.000฀ 0.975฀ 0.975 37฀ Pennsylvania฀State฀University,฀University฀Park฀ 1.000฀ 1.000฀ 1.000

37฀ University฀of฀Iowa฀ 1.000฀ 1.000฀ 1.000

37฀ University฀of฀Wisconsin–Madison฀ 0.952฀ 0.907฀ 0.953

40฀ Brigham฀Young฀University฀ 1.000฀ 1.000฀ 1.000

40฀ University฀of฀Arizona฀ 1.000฀ 1.000฀ 1.000

42฀ University฀of฀California,฀Davis฀ 0.961฀ 0.934฀ 0.971

42฀ Wake฀Forest฀University฀ 1.000฀ 0.993฀ 0.993

45฀ Tulane฀University฀ 0.954฀ 0.955฀ 1.001

45฀ University฀of฀Georgia฀ 1.000฀ 0.976฀ 0.976

45฀ Vanderbilt฀University฀ 1.000฀ 1.000฀ 1.000

48฀ Boston฀University฀ 1.000฀ 1.000฀ 1.000

49฀ Rice฀University฀ 1.000฀ 0.985฀ 0.985

49฀ University฀of฀California,฀Irvine฀ 0.970฀ 0.944฀ 0.973

51฀ Babson฀College฀ 1.000฀ 0.930฀ 0.930

54฀ Boston฀College฀ 0.993฀ 0.954฀ 0.961

54฀ Southern฀Methodist฀University฀ 0.991฀ 0.921฀ 0.929

57฀ University฀of฀Pittsburgh฀ 1.000฀ 0.985฀ 0.985

58฀ Case฀Western฀Reserve฀University฀ 1.000฀ 0.965฀ 0.965

60฀ Temple฀University฀ 1.000฀ 0.945฀ 0.945

62฀ George฀Washington฀University฀ 1.000฀ 0.965฀ 0.965 68฀ University฀of฀South฀Carolina฀ 1.000฀ 0.975฀ 0.975 83฀ University฀of฀Arkansas฀at฀Fayetteville฀ 1.000฀ 1.000฀ 1.000

Note.฀Analysis฀used฀data฀from฀“Schools฀of฀Business”฀(2006).฀Only฀the฀MBA฀programs฀with฀a฀฀ complete฀set฀of฀selected฀input฀and฀output฀factors฀are฀incorporated฀into฀the฀analysis.

its฀boat฀carefully,฀cast฀a฀broad฀net,฀and฀ offer฀ more฀ tempting฀ bait฀ on฀ its฀ hook”฀ (Zupan,฀ 2005,฀ p.฀ 34).฀ That฀ is,฀ deans฀ and฀MBA฀program฀administrators฀need฀ to฀ communicate฀ effectively฀ about฀ how฀ their฀ MBA฀ programs฀ differ฀ from฀ other฀ MBA฀ programs.฀ Without฀ information฀ related฀to฀objective฀efficiency฀measures,฀ the฀existing฀rank฀reports฀from฀the฀media฀ could฀not฀help฀MBA฀program฀adminis- trators฀make฀the฀most฀appropriate฀stra-tegic฀decisions.฀The฀worst-case฀scenario฀ is฀ that฀ it฀ could฀ take฀ years฀ for฀ an฀ MBA฀ program฀to฀recover฀from฀strategic฀mis-takes฀ made฀ because฀ of฀ reallocating฀ its฀ limited฀resources฀solely฀on฀the฀basis฀of฀ competitiveness-ranking฀reports.

The฀ findings฀ indicate฀ that฀ an฀ MBA฀ program฀with฀a฀highly฀competitive฀rat-ing฀ tends฀ to฀ correspond฀ to฀ statistically฀ higher฀TSE฀and฀SE.฀For฀MBA฀program฀ administrators,฀there฀are฀many฀potential฀ ways฀ to฀ enhance฀ program฀ efficiency.฀ On฀the฀basis฀of฀the฀findings฀of฀the฀pres-ent฀study,฀the฀average฀starting฀salary฀is฀ one฀of฀the฀most฀important฀output฀crite-ria฀for฀MBA฀program฀administrators฀to฀ improve฀ their฀ programs’฀ value-added฀ efficiency.฀Alternatively,฀the฀variable฀฀of฀ employment฀rate฀3฀months฀after฀gradu-ation฀ may฀ not฀ be฀ as฀ essential฀ for฀ the฀ top฀ MBA฀ programs.฀ Each฀ school฀ can฀ better฀position฀itself฀after฀assessing฀the฀ source฀of฀its฀inefficiency฀and฀the฀unique฀ features฀ of฀ its฀ program฀ (see฀ Table฀ 2).฀ MBA฀ program฀ administrators฀ outside฀ the฀top-10฀list฀may฀want฀to฀spend฀more฀ time฀ in฀ building฀ strong฀ relations฀ with฀ promising฀ global฀ firms฀ that฀ hire฀ and฀ pay฀their฀MBA฀graduates฀higher฀starting฀ salaries฀and฀bonuses.฀Perhaps฀one฀of฀the฀ best฀ strategies฀ for฀ all฀ MBA฀ programs฀ is฀ to฀ pursue฀ the฀ blue฀ ocean฀ strategy,฀ in฀ which฀ MBA฀ program฀ administra-tors฀strategically฀determine฀what฀makes฀ their฀ programs฀ special฀ in฀ the฀ minds฀ of฀ the฀potential฀MBA฀students฀and฀the฀hir-ing฀ firms.฀ For฀ example,฀ Simon฀ School฀ promotes฀ its฀ full-time฀ MBA฀ program฀ “in฀economics฀and฀analysis,฀its฀position฀ as฀one฀of฀the฀smallest฀and฀most฀person-alized฀programs฀in฀the฀top฀tier,฀its฀high฀ percentage฀of฀students฀from฀abroad,฀and฀ its฀ specializations฀ in฀ technology฀ and฀ healthcare”฀(Zupan,฀2005,฀p.฀39).

The฀ present฀ study฀ focused฀ on฀ the฀ elite฀ U.S.฀ MBA฀ programs฀ identified฀ by฀ U.S.฀News฀&฀World฀Report฀(“Schools฀of฀

Business,”฀ 2006)฀ and฀Financial฀ Times฀ (“Financial฀Times฀publishes฀2006฀global฀ MBA฀rankings,”฀2006).฀Using฀the฀same฀ method฀discussed฀in฀the฀present฀article,฀ the฀European฀MBA฀program฀administra-tors฀ may฀ assess฀ their฀ programs’฀ value-added฀ efficiency.฀ In฀ addition,฀ a฀ trend฀ analysis฀that฀examines฀year-to-year฀vari-ances฀ should฀ be฀ considered฀ in฀ future฀ research฀efforts.฀It฀is฀noteworthy฀that฀the฀ DEA฀ efficiency฀ rankings฀ and฀ existing฀ rankings฀from฀the฀media฀should฀comple- ment฀each฀other฀rather฀than฀act฀as฀a฀sub-stitute.฀Instead฀of฀replacing฀the฀existing฀

rankings฀with฀the฀efficiency฀rankings,฀B-school฀administrators฀should฀emphasize฀ that฀ the฀ best฀ MBA฀ programs฀ are฀ those฀ that฀ can฀ help฀ MBA฀ students฀ develop฀ their฀career.฀Two฀informative฀indicators฀ would฀ be฀ a฀ higher฀ salary฀ after฀ gradua-tion฀and฀a฀wider฀salary฀gap฀between฀pre-฀ and฀ post-MBA฀ education.฀ However,฀ given฀ the฀ growing฀ international฀ student฀ population฀ in฀ the฀ U.S.฀ MBA฀ programs,฀ B-school฀administrators฀and฀researchers฀ may฀ want฀ to฀ use฀ a฀ purchasing-power฀ parity-weighted฀number฀to฀factor฀in฀the฀ possible฀influence฀of฀a฀weak-U.S.-dollar฀ employment฀ with฀ a฀ similar฀ opportunity฀ in฀ a฀ country฀ with฀ a฀ stronger฀ or฀ weaker฀ TABLE฀2.฀Input฀and฀Output฀Variables฀for฀Improving฀MBA฀Program฀

Efficiency฀

฀ Input฀ Output฀

Rank฀ ฀ School฀ State฀ 1฀ 2฀ 3฀ 4฀ A฀ B฀ C

6฀ University฀of฀California,฀Berkeley฀ CA฀ X฀ X฀ X฀ ฀ ฀ X฀ X 10฀ University฀of฀Michigan,฀Ann฀Arbor฀ MI฀ X฀ ฀ ฀ X฀ X฀ X฀ X

11฀ Duke฀University฀ NC฀ X฀ ฀ ฀ X฀ X฀ X฀ X

11฀ University฀of฀California,฀Los฀Angeles฀ CA฀ ฀ X฀ X฀ X฀ X฀ ฀

13฀ New฀York฀University฀฀ NY฀ ฀ X฀ X฀ X฀ X฀ ฀

18฀ Emory฀University฀ GA฀ X฀ X฀ X฀ X฀ X฀ ฀ X

18฀ University฀of฀Texas฀at฀Austin฀ TX฀ ฀ X฀ X฀ X฀ X฀ ฀

18฀ University฀of฀Washington฀ WA฀ X฀ X฀ X฀ X฀ X฀ X฀ X

21฀ Ohio฀State฀University฀ OH฀ ฀ X฀ X฀ X฀ X฀ X฀

21฀ University฀of฀North฀Carolina฀at฀Chapel฀Hill฀ NC฀ ฀ X฀ X฀ X฀ X฀ ฀

23฀ Purdue฀University฀ IN฀ X฀ ฀ X฀ X฀ X฀ ฀

23฀ University฀of฀Rochester฀ NY฀ ฀ X฀ ฀ X฀ X฀ X฀

26฀ University฀of฀Southern฀California฀ CA฀ X฀ ฀ ฀ X฀ X฀ ฀ X

27฀ Georgetown฀University฀ DC฀ X฀ ฀ ฀ X฀ X฀ ฀ X

27฀ Indiana฀University฀ IN฀ X฀ X฀ X฀ X฀ X฀ ฀ X

27฀ University฀of฀Maryland,฀College฀Park฀ MD฀ ฀ X฀ X฀ X฀ X฀ X฀ X

31฀ Arizona฀State฀University฀ AZ฀ ฀ X฀ X฀ X฀ X฀ X฀ X

32฀ Georgia฀Institute฀of฀Technology฀ GA฀ ฀ X฀ X฀ X฀ ฀ X฀

32฀ University฀of฀Notre฀Dame฀ IN฀ X฀ ฀ X฀ X฀ X฀ ฀ X

32฀ Washington฀University฀in฀St.฀Louis฀ MO฀ X฀ ฀ ฀ ฀ X฀ ฀ X 37฀ University฀of฀Wisconsin–Madison฀ WI฀ X฀ X฀ X฀ ฀ X฀ ฀ 42฀ University฀of฀California,฀Davis฀ CA฀ X฀ ฀ X฀ X฀ ฀ ฀

42฀ Wake฀Forest฀University฀ NC฀ X฀ X฀ X฀ X฀ ฀ X฀ X

45฀ Tulane฀University฀ LA฀ X฀ X฀ ฀ X฀ X฀ X฀

45฀ University฀of฀Georgia฀ GA฀ X฀ ฀ X฀ X฀ ฀ X฀ X

49฀ Rice฀University฀ TX฀ X฀ X฀ ฀ X฀ X฀ X฀ X

49฀ University฀of฀California,฀Irvine฀ CA฀ X฀ ฀ ฀ X฀ ฀ ฀

51฀ Babson฀College฀ MA฀ X฀ X฀ ฀ X฀ X฀ X฀ X

54฀ Boston฀College฀ MA฀ X฀ ฀ ฀ X฀ X฀ ฀ X

54฀ Southern฀Methodist฀University฀ TX฀ X฀ ฀ ฀ X฀ X฀ ฀

57฀ University฀of฀Pittsburgh฀ PA฀ X฀ X฀ X฀ X฀ ฀ X฀

58฀ Case฀Western฀Reserve฀University฀ OH฀ ฀ X฀ ฀ X฀ X฀ X฀ X

60฀ Temple฀University฀ PA฀ X฀ ฀ X฀ X฀ X฀ ฀

62฀ George฀Washington฀University฀ DC฀ X฀ X฀ X฀ X฀ ฀ X฀ X 68฀ University฀of฀South฀Carolina฀ SC฀ ฀ X฀ X฀ X฀ ฀ ฀ X

Note.฀Input฀1฀=฀average฀undergraduate฀GPA;฀Input฀2฀=฀average฀GMAT฀score;฀Input฀3฀=฀out-of-state฀tuition฀ and฀fees;฀Input฀4฀=฀average฀salary฀before฀entering฀MBA฀programs;฀Output฀A฀=฀average฀starting฀salary฀ and฀bonus;฀Output฀B฀=฀the฀employment฀rate฀3฀months฀after฀graduation;฀Output฀C฀=฀the฀aims฀achieved฀ ratio.฀X฀denotes฀that฀the฀corresponding฀variable฀is฀a฀bounding฀constraint฀in฀A.฀Charnes,฀W.฀Cooper,฀and฀฀ E.฀Rhodes’s฀(1978;฀CCR)฀and฀R.฀Banker,฀A.฀Charnes,฀and฀W.฀Cooper’s฀(1984;฀BCC)฀models.

currency.฀ Further฀ studies฀ are฀ needed฀ to฀ find฀ ways฀ and฀ means฀ to฀ help฀ MBA฀ program฀administrators฀identify฀a฀more฀ desired฀ input–output฀ mix฀ and฀ further฀ improve฀this฀benchmark฀process.

NOTES

Maxwell฀ K.฀ Hsu฀ thanks฀ the฀ University฀ of฀ Wisconsin–Whitewater’s฀ College฀ of฀ Business฀ &฀ Economics฀ for฀ a฀ research฀ award฀ that฀ led฀ to฀ the฀ completion฀of฀this฀article.

Maxwell฀ K.฀ Hsu฀is฀ an฀ associate฀ professor฀ of฀ marketing฀in฀the฀College฀of฀Business฀&฀Econom-ics฀ at฀ the฀ University฀ of฀ Wisconsin–Whitewater.฀ He฀ has฀ published฀ two฀ dozen฀ refereed฀ articles฀ in฀ scholarly฀journals฀such฀as฀Applied฀Economics฀Let-ters,฀Information฀ &฀ Management,฀International฀ Journal฀ of฀ Advertising,฀Journal฀ of฀ Academy฀ of฀ Marketing฀Science,฀Journal฀of฀International฀Mar-keting,฀and฀Journal฀of฀Services฀Marketing.

Gary฀H.฀Chao฀is฀an฀associate฀professor฀in฀the฀ department฀ of฀ management฀ at฀ Kutztown฀ Univer-sity.฀ His฀ research฀ interests฀ include฀ supply฀ chain฀ management,฀ the฀ decision-making฀ process,฀ and฀ performance฀evaluations.

Marcia฀L.฀James฀is฀a฀professor฀of฀information฀ technology฀ and฀ business฀ education฀ in฀ the฀ Col-lege฀of฀Business฀&฀Economics฀at฀the฀University฀ of฀Wisconsin–Whitewater.฀ She฀ teaches฀ business฀ and฀ professional฀ communication฀ in฀ the฀ MBA฀ program฀ and฀ publishes฀ in฀ the฀ areas฀ of฀ gender฀ communication,฀corporate฀propaganda,฀and฀cor-porate฀social-networking.

Correspondence฀ concerning฀ this฀ article฀ should฀ be฀ addressed฀ to฀ Marcia฀ L.฀ James,฀ 800฀ W.฀ Main฀ Street,฀Whitewater,฀WI฀53190,฀USA.

E-mail:฀jamesm@uww.edu

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