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  1. Effect of Technology on Sales Performance: Progressing from Technology Acceptance toTechnology Usage and ConsequenceAuthor(s): Michael Ahearne, Narasimhan Srinivasan, Luke WeinsteinSource: The Journal of Personal Selling and Sales Management, Vol. 24, No. 4, CustomerRelationship Management: Strategy, Process, and Technology (Fall, 2004), pp. 297-310Published by: M.E. Sharpe, Inc.Stable URL: http://www.jstor.org/stable/40471971 .Accessed: 22/10/2011 10:57Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at .http://www.jstor.org/page/info/about/policies/terms.jspJSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range ofcontent in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new formsof scholarship. For more information about JSTOR, please contact support@jstor.org. M.E. Sharpe, Inc. is collaborating with JSTOR to digitize, preserve and extend access to The Journal of Personal Selling and Sales Management.http://www.jstor.org
  2. EFFECT OF TECHNOLOGY ON SALES PERFORMANCE: PROGRESSING FROM TECHNOLOGY ACCEPTANCE TO TECHNOLOGY USAGEAND CONSEQUENCE Michael Narasimhan Ahearne, Srinivasan, LukeWeinstein and Technology an ever-increasing in personal plays role selling customer and relationship management (CRM). Overthe past decade,many models examining theacceptance technology beenproposed refined, of have and contributing signifi- cantly ourknowledge technology to of An adoption. implicit assumption insuchmodels that made is the increasing usage is - oftechnologybetter that more is, usage better lessusage. is than Thisis a critical assumption hasnotbeentested that Whatiftechnology diminishing in theliterature. has returns? propose We that is timeto progress a Technology it to Performance Model(TPUM) andtolookfor Usage usage levels leadtooptimum that effect performance. model on Our is testedusinga sample 131 salespeople an operational of in CRM context. Results showa curvilinear relationship a between salespersons task prime performance (measured salespercent quota)and their as to usageofthe"enabling" CRM technology. theCRM technologyenabling salesperformance, diminishing Initially, is on but return in,and sets beyond a a point, disabling on effect sales performance canbe observed. finding This offersvaluable a topractitio- insight ners and provides strategic a - directionachieving maintainingparticular of technology and a level usageto optimize task prime performance. inThe growth customerrelationship management (CRM) sues surrounding implementation CRM, we research of thesoftware has the deployment paralleled transition from trans-relationship between operational the usage of a CRM system toactionalmarketing relationship In marketing. organiza- and its effect the objectiveperformance a salesperson. on oftions,a go-to-market strategy reliesheavilyon salespeople, We lookfor optimal pointsofusageto maximize performance. hasCRM management primarily been the responsibilityof This shouldbe an issuethatis of tremendous importance tothesalesforce, and research traditionally its rootsin has had practitioners looking to maximize theirreturn investment onunderstanding sales forceautomation(SFA) (Tanner et al. in CRM technology.2005). Now SFA research being supplantedby broader, is Most research date has focusedon technology to accep-enterprise-wide CRM research. While research frameworks tance, and not on theproductive effect technology of usage.have been developedforunderstanding adoption issues or Deservedly, researchers examined acceptance tech- have the ofgeneraltechnology acceptanceissues,therehas been no re- nology, and several modelshave been proposedin thelitera-searchintotheperformance of efifect thetechnology afterthe ture.These modelsincludetheTechnology Acceptance Modeltechnology is installed and trained. Further,theadoptionre- (ΤΑΜ) (Davis 1986), and itsextension (TAM2) (Venkateshsearchhas, to date,workedundertheassumption thatmore and Davis 2000), and models based on theTheoryof Rea-overallusage is of technology better. Consistentwiththerec- soned Action (Davis, Bagozzi,and Warshaw1989), Innova-ommendations Leigh and Marshall(2001) to examineis- of tion Diffusion Theory(Moore and Benbasat1991), Triandis model (Thompson,Higgins,and Howell 1991), Motivation (Davis et al. 1992), Theoryof PlannedBehavior(TaylorandMichaelAhearne (Ph.D. Indiana Associate University), Professor Todd 1995), Social Cognitive Theory(Compeau and HigginsofMarketing, of & DepartmentMarketing Entrepreneurship, Bauer 1995; Compeau, Higgins, and Huff1999), and, recently, the ofCollege Business, of UniversityHouston, mahearne@uh.edu. Unified Theory of Acceptance and Use of TechnologyNarasimhan Srinivasan(Ph.D., StateUniversity NewYork of at (UTAUT) (Venkatesh al. 2003). Each modelhas thesame etBuffalo),Associate of ProfessorMarketing, UniversityofConnecti- dependentvariable,usage, but uses variousantecedents tocut,narasimhan.srinivasan@uconn.edu. understand acceptance of technology.LukeWeinstein (MBA,M.S. Engineering, UniversityofPennsyl- An implicit assumption all the above modelsis a posi- in Doctoralvania), in Candidate Marketing, UniversityConnecti- tiveand linearrelationship of betweenperformance usage andcut,luke.weinstein@uconn.edu. (Figure 1). There is an underlying assumption thattechnol-Authors listed are alphabetically madeequalcontributions. and The ogyutilization a proxy itsperceived is of effectiveness.Some-authorsthank Special the IssueGuest Editors thethree and review- times, it is even explicitlystated.To quote Heine, Grover,ersfortheir comments suggestions. and and Malhotra,"It is assumedthatincreasedutilization a is Journal Personal of & Management, XXIV, 4 (fall Selling Sales vol. no. 2004),pp. 297-310. © 2005 PSE National Educational All Foundation. rights reserved. τςςΜ ηβ«ς_λΐ-ά ι 9ππς to ςη j. π ηη
  3. Selling& SalesManagement298 Journal Personal of Figure 1 command-driven character-based interfaces. Therewerelim- ExpectedPerformance PlottedAgainst itedapplications available. Applications weregenerally expen- TechnologyUsage Time sive, in and thecompetition anyapplication was field relatively limited. Indeed,manyoftheempirical studies used in testing the earlyacceptancemodels measuredthe acceptanceand usage of word-processing applications(Davis 1989; Davis, Bagozzi, and Warshaw 1989, 1992). Today,the typewriter has becomea relic, and there pervasive ofwordproces- is use sors,and thistoo has long ceased to be an innovation. ci s Over the past couple of decades,technology evolved has and is significantly different than when acceptancemodels werefirst introduced. Today,PCs have become pervasive inI / business, withan installed base ofapproximately million201 (Juliussen 2002). Usersinteract withfriendly graphical inter- facesthatare increasingly easierto use. Many vendorspro- ^-- > videa variety application of and software, prices keepdropping Usage Key dramatically. competitive factors a userschoiceof an in - applicationincludeproductfunctionality ease of use and two keyantecedents TAMs. With increasing of adoptionof new technology just to staycompetitive, businesses have todesirable behavior and impliesbetter performance" (2003, p. necessarily invest heavily in training their employees withnew191). But this criticalassumptionthat usage is a proxyto technology (Marshallet al. 2000). inperformance not been tested theliterature. has Although havecertainly we learned great a deal from TAMs We recognize thatthe focusof past research been the has in theliterature, specific the technologies examined havebeenacceptance of technological innovations, and the objective evolving, of and we can stillfindexamples technology imple-was to accelerate acceptance technology. whathap- the of But, mentationfailuresin recentCRM literature (e.g., Jones, ispensafter technology accepted? any Would increased usage Sundaram,and Chin 2002; Riversand Dart 1999; Speieralways be desirable? Does technology alwaysimprove perfor- and Venkatesh 2002). Beyondacceptance, productivethe ef-mance?Initially, thereare acquisition and setupcosts,train- fectof technology usage on performance a veryimportant isingcosts,and maintenance costs.The positive returns would issue today.start in flowing onlyafter some time.How does technology A major contribution this studyis the criticalassess- ofusage affectperformance overan extended period of timeaf- mentof theimplicit assumption a positive of and linearrela-terthe initialsetupperiod,training completed, is and users tionship betweentechnology usage and performance. First,are now usingthe system regularly? it possibleto evaluate Is every salesperson knowsthattimeis a scarceresource be tothereturns from specific a technology? These important ques- judiciouslyallocatedamong varioustasks.Second, technol-tionsarevery relevant managers. to The purposeofthisstudy ogy cannot substitute severalnecessary tasks,such as travelis to examinethe effect operational of CRM technology us- and personal discussion. Third, can a linearrelationship onlyage on salespersons performance after theyhave been using holdfor limited a range, substitutability otherwise as will elimi- -the technology at leastsix months.It is consistent for with nate all expensive inputswith low marginal productivitythe research in priorities sales strategy performance and re- that is, everyresourcehas diminishing returns. Otherwise,cently identified Leighand Marshall(2001): information technology be used increasingly, salespeoplecan be by can andtechnology (IT), sales/service/support systems, selection/ eliminated. and However, thisis not likely happen.There is a todevelopment of salespeople. limitto whattechnology do, and we recognize can thattech- nology is only a tool, and the substanceof personalselling BACKGROUND stillresides withsalespeople. Whatiftherelationship between technology usageand per-When TAMs were first in introduced the literature around formance curvilinear? Figure A meta-analysis time is (See 2.) ofthemid-1980s,the businessenvironment verydifferent and performance was relationships showssimilar curvilinear pat-thanit is today. The computer was industry mainframe ori- terns (Sturman 2003). BefDre can testforpossiblemedia- weented, withan installed base ofapproximately millionper- 19 torsand moderators affect relationship, mustfirst that the wesonalcomputers (PCs) in theUnitedStates(Juliussen 2002). understand how and whentheuse ofsalestechnology leadstoUsersgenerally interacted with computers using unfriendly increased and effectiveness efficiency (Tanneret al. 2005).
  4. Fall2004 299 Letus consider operational an - CRM context a term coined Figure 2by Tanneret al. (2005) to describe thatportionof CRM us- Proposed Curvilinear Relationship Performance of andage forsales processmanagement, such as lead generation, Technology UsageTimecontact, scheduling, performance tracking, and otherfunc- Enablingtionsin a salescontext. Intuitively, is reasonable expecta it to f ,. ν λ a CRM system improve or her per- to his j Disablingsalesperson usingformance effectively by increasing or her usage of CRM histechnology from, say,threeto fourhours per week. But if ι ν thatsamesalesperson increased or herusageto 30 hoursa hisweek,thatsalesperson would be increasing or her CRM hisusage at the expenseof otherworktasksthatmust be per-formed. with any businessthatattempts optimizeits As to > ause of resources, salesperson needs to optimizehis or her - -allocation a keyresource time amongvarioustasks, of inorderto maximize or hersalesperformance. his It is natural thatthere in would be heterogeneity technol- Usageogyusage across employees. Anecdotal evidence suggested hasthatsalespeople amongthemosttechnophobic resis- are and Note:Assumes is technologyinstalled, training over, CRM is in is andtantof all white-collar workers(Mills 1995; Parthasarathy use.and Sohli 1997), thoughthisshouldbe changing technol- asogy becomesmorepervasive. Some people takeenthusiasti-callyto new technologies and are innovators. Othersprefer to better understand how technology acceptanceand usagethe old way of doing businessand are laggards. Hence, it is leadsto improved performance. TAMs provide witha The usquite that conceivable salespeople may often or overuse under- of richfoundation theory and important constructs regard-use CRM technology. Overusemay come at the expenseof ing usage,as outlinedin Table 1. The nextlogicalstepis toother salestasks that mayhavea greater effect performance. studythe effect technology on of usage varianceon actualper-Underutilization would implythatthe salesperson less ef- is formance.fective thanhe or shecould be, ifonlyhe or sheused thetools The major contribution this research to examineif of isof theCRM technology a greater to degree. technology, such as capitaland labor,has diminishing mar- If thereis a curvilinear relationship betweentechnology ginalproductivity in thecorporate production process.Timeusage and performance, has veryimportant it implications of salespeople not unlimited is and has to be allocatedacrossforthe effective of technologies personalselling.To use in various tasks,withdiffering effects performance. on Currently,what degreeof usage is technology enabling? What are the thedifferential allocationof timeon technology voluntar- isfactors thatcan help us predict when technology usage will ilydecidedbyeach salesperson, withno recommendations orhavea positive effect performance? on Does technology have processtraining how to optimizetheallocations. on Histori-diminishing returns? What are the factors thatwill predict cally,time management salespeoplefocusedon the best forwhen usagewill have a disablingeffect performance? on To way to rationa salespersons time among the accountsthatanswer suchquestions, researchers to movebeyondtech- have makeup hisor herterritory Zoltners, (e.g., Sinha,and Chongnology acceptance and usagecontexts. need to study We situ- as has 1979). Today, competition intensified, moreis expectedationsafter particular any technology is adoptedand installed of salespeople(e.g., Nonis and Sager2003), leadingto moreby an organization, salespeoplehave been trained ef- and to in stress salespeople. Time management now be seenas a canfectively thesystem. use cluster behaviors of thatare deemedto facilitate productivity and alleviatestress(Lay and Schouwenburg1993). In this study, deal onlywithtimespenton specific we CRM tech- THEORETICAL FOUNDATIONS AND MODEL nology on and its effect performance. This is not a broad timemanagement study.Improving humanperformance organizations a primary in is In theearly 1990s,Microsoft chairman Gatespredicted Billgoal fororganizations increase to competitiveness (Marshall thatbusiness technology eventually would allowpeopleto doet al. 2000). Organizationsspend significant amounts of a fulldaysworkin fouror fivehours,freeing themto spendmoneyon technology implementation look to get in- and moretimeon leisure or and pursuits withfriends family. In-creased and productivity performance from their investments stead,bythemid-1990s,theaverage was workweek nearly an(Marshall et al. 2000). To meetthesegoals,researchers need hourlongerthanin theearly1980s, and a survey foundthat
  5. 300 Journal Personal of Selling& SalesManagement Table I Review ofTechnology Acceptance Models in the LiteratureTopic Reference Key Conclusions AcceptanceModel (ΤΑΜ)Technology Davis ( 1989); Usage can be predicted fromintentions. Davis,Bagozzi, Warshaw( 1989); and Keyantecedentsof usage are: Taylor and Todd ( 1995) · perceivedease of use, • perceivedusefulness.Theoryof ReasonedAction(TRA) Davis,Bagozzi, Warshaw( 1989); and Subjectivenormfrom TRA is examined, mixed but(an acceptancemodel) Jones,Sundaram, Chin (2002) and resultson significance.Triandis acceptance model of (an Thompson,Higgins, Howell (1991) and Key constructs thatwere significant antecedentsPC utilization) of usage were: • social factors(similar subjectivenorm), to • complexity use (similar ease of use), of to • job fit(similar perceivedusefulness), to • long-term consequences.Innovation Diffusion Theory (IDT) Moore and Benbasat(1991) Looked onlyat adoptionversusnonadoptionand(an acceptancemodel) added new constructsof • observability, • trialability, • voluntariness.MotivationalModel (MM) and Warshaw (1992) Davis,Bagozzi, Enjoyment an antecedentof intention usage. is and(an acceptance model)Theoryof PlannedBehavior(TPB) TaylorandTodd ( 1995) Subjectivenormsignificant,made up of(an acceptancemodel) · peer influence, • superiorsinfluence. Adds perceivedbehavioral controlwithantecedents of • self-efficacy, • facilitating conditions.Social Cognitive Theory (SCT) Compeau and Higgins (1995); Self-efficacyan antecedentof usage,both directly is(an acceptancemodel) Compeau, Higgins, Huff and (1999) and mediatedthrough anxietyand affect.ΤΑΜ extendedwithTask-Technology Fit Dishaw and Strong(1999); TTF does not directly usage but is mediated affect(TTF) Mathiesonand Keil (1998) through ease of use. AcceptanceModel ExtendedTechnology Sundaram, Chin (2002); Jones, and Adds to theΤΑΜ (Davis 1989) by including(TAM2) Venkateshand Davis (2000) antecedentsof perceivedusefulness subjective of norm, image, relevance, job outputquality, result and Moderatorsof intention use demonstrability. to are experienceand voluntariness.Unified TheoryofAcceptanceand Venkatesh al. (2003) et Summarizes literature the nicelyand proposes aUse ofTechnology (UTAUT) unifying framework. antecedentsof intention Key and thus usage are: • performance expectancy(usefulness), • effort expectancy (ease of use), • social influence(subjectivenorms), • facilitating conditions. Moderatorsare: • gender, •age, • experience, • voluntariness. AddictionTechnology Brown( 199 1; 1993); Charlton(2002); Findsthe existenceof a formof behavioral Griffiths (1998) addictionin the use of computersand the Internet. Addiction has six components: salience,mood modification,tolerance, withdrawalsymptoms, and conflict, relapse.LearningOrientationand Performance Ahearneet al. (2004); Shows the linkbetween learning orientation andOrientation and Kumar(1994) Sujan,Weitz, performance orientation the adoptionof to technologyand performance an SFA context. inComplexity Teo and Lim(1996) Studyof factorsassociated withusage.Adds complexity in the contextof beingdifficult but to use,the opposite of ease of use.
  6. Fall2004 301morethan68 percent respondents at leastsomewhat of felt and used a CRM system 30-plus hoursper week clearly formore overwhelmed work today (Carey 1996). With the at would have littletimeto make sales calls and close transac-speedof technology, managers expectmoretasksto be com- tions. This would have a severe negative effecton thepletedbyemployees thesameamountoftime.At themost in salespersons performance. thesame salesperson Yet usingthebasic level,employees have two resources, timeand energy, CRM technology say,three for, hoursperweek,might an seeand theseresources undertheemployee control(Brown are s increasein his or her productivity, per week statistics, callsand Leigh1996). So withincreased and expectations demands, and salesas a percent quota performance. of Maybethissamesalespeoplemust manage theseresources optimizetheir to salesperson usingthe CRM forfourhoursper week wouldeffectiveness, with few,if any,tools to make objective but see evenfurther in increases theirperformance. theques- Soobservations decisionsin this resourcemanagement. or In tion is, at whatpointof usage does performance to de- startthe CRM world,othershave touchedon thisissue,such as cline? To capture the enabling and disabling aspects ofEngleand Barnes(2000), suggesting too much use and that technology usage,we proposea quadraticmodel thatcan beemphasis on theplanning function CRM systems of maynot expressed asbe productive. * * - Performance (X + βλ Usage + β2 Usage2. Similarly, self-regulatory capabilities critical success are forin boundary-spanning such as personalselling(Brown, roles The above model is a generalized way of capturing the andLeigh, Jones 2005). Self-regulatory effectiveness is defined enabling of if effect technology β{ is positive and significant,as theability to monitor and control ones own goal-directed is and and if/?2 negative significant, similar theBass (1969) to andbehaviors performance (Bagozziand Dholakia 1999). In in model. It is powerful thatit captures neteffect vari- the ofcomplex taskenvironments such as sales, individualsmust ous factors thatcan influence and ßr βχpursuemultiple goalssimultaneously (Austinand Vancouver There would be a minimallevelof sales performance ex-1996; Bagozziand Dholakia 1999). Attention effort and can pectedforeach salesperson whenthere little is use ofthespe-be channeled from mostproductive the tasks thosethatare to cific technology under investigation. The coefficient is βχ forless instrumental attaining centralgoals (Brown,Leigh, to be positive, theenabling - effect thatis, expected denotingand Jones2005). While research shownthatself-regula- has initially using technology improves performance. However,tory skillscan be enhancedthrough training areassuch as in technology expectedto have diminishing is returns. Thus,timemanagement (Leach 1999), how do we train salespeople thecoefficient is expected be negative, β2 to denoting thedis- - point,moreusagewithself- regulatory on theusageofCRM systems, skills when ablingeffect thatis, beyonda particularwe do notunderstand effect suchusage the of on their - would detractfromperformance because otheressential perfor-mance? taskswith greater marginalproductivity would have to be of We claimthatone possiblebenefit CRM implementa- neglected or sacrificed.High levels convenience of usingtech-tionswould be more effective of the salespersons use time nology or highlevelsof "relative enjoyment" to (relative other(Tanneret al. 2005), butwe do not have sufficient empirical tasksperformed salespeople)using technology, by self-effi-data to support thisclaim,or how it maybe moderated a by are cacy,ease of use, usefulness, social factors additional orsalespersons technology expertise. Absenttoolsto objectively facilitating factors thatmay increaseusage of technology inoptimize their management timeand effort of resources, sales- sales situations.persons will makesubjective to estimates prioritize opti-and Certaintasksare moreconvenient perform to thanothermize usage of these resources.We theorizethat affective tasks.For example,a salesperson might findit moreconve-orientations tasksfrom to whichsalespeopleget internal re- nientto workwithhis or her operational CRM technologywards couldhaveenabling in accepting usingtech- effects and insteadofgetting intoa carand driving visita customer. to Itnology, whereasresistance avoidingtasksthatare not as to could be even moreconvenient the salesperson tiredor if isenjoyable others as could contribute a disablingeffect, to as expects to encounter objections from prospective a customer.would the limitation time.This theory only being of- of is This increases positiveeffect working the of withthe CRMferedas a way of understanding why different individuals technology, taskthatan employer a would typically re- stillmight havedifferential of utilization technology. Furthermore, quire the salesperson perform, allowsthe salesperson to andthisexplanation offered is only as a background under- for to partially avoid an important alternate taskwith possiblestanding affinity to or disaffinity technology and is not the negative effect.focusoftheresearch. Our current study dealswiththeexplo- The userwilltendto spendmoretimeon thosetasks fromrationof thediminishing of returns technology usage. whichhe or she getsmoreenjoyment, insteadof beingever To assumethatperformance would continueto increase vigilant aboutwhichtasks willcontribute mostto perfor- thelinearly with technology usage is intuitively appealing. not for mance. Because timeis a scarceresource anysalesperson, aTo illustrate, successful outside salesperson who adopted a salesperson spending lotoftimeon an enjoyable would a task
  7. 302 Journal Personal of Selling& SalesManagementhave less time to spend on tasksthatmay have a relatively technology usage and its relationship performance, to andgreatereffect performance. on Hence, thereexistsa need to greaterhomogeneity withinthe sample would ensurethatbalance use of any resource with its marginal productivity, extraneousfactors not confound results. do the Therefore,inthatis, use technology long as it has a positiveeffect as on thisstudys context, having a singlefirm an advantage. isperformance, but not beyondthat.For thisto happen,onehas to first a establish curvilinear then a feed- relationship, Measuresbacksystem, a and, finally, correctivemechanism thatguidesa salesperson optimally any technology. to use The testing the proposedrelationship of betweenIT usage and salesperformance depends on twocritical variables: tech- nologyusage In and performance. past research technol-on METHODOLOGY ogy usage, researchers have relied almost exclusivelyon self- reported of measures technology usage(Jones, Sundaram,Sample and Site Selection and Chin 2002; Speierand Venkatesh 2002). In ourstudy, weWe conducted our research withinthefemale health-care di- sought an objective measure salesrepresentative of technologyvisionof a mid-sized pharmaceutical firm. This divisionof usage.In an effort accurately to track technology the usageofthecompany a SiebelCRM system placefor had in morethan salesrepresentatives, worked we witha company thatspecial-one year to prior theimplementation our research. of Siebelis izesin Siebeltechnology in implementation thepharmaceuti-widely considered be themarket to leaderin CRM systems in cal industry. In cooperationwith the IT specialistsat thethe pharmaceutical industry,holdinga dominating shareof mid-sizedpharmaceutical company, software a productwasthemarket. configuration theSiebelsystem this The of in study installedthatruns alongsidethe Siebel CRM software thatis typical the pharmaceutical for industry. The Siebel system tracks usage of technology sales representatives. soft- by Theincluded salestoolsdesigned facilitate planning, to call postcall wareprogram placesa timestampwhena salesrepresentativerecordkeeping, information gathering, intrafirm communi- beginsusingthe Siebel software a timestampwhen the andcation,and customer accountmanagement. salesrepresentative completes his or hersession. The software Our analysis sample consists 131 sales representatives. also timestamps of and track exactscreen usagewithin 90-plus 54 ofApproximately percent themare female, and the me- screens availableto thesalesrepresentative. Every timea salesdian age is in the rangeof 26-35 years. The averageexperi- representative synchronizes with the companysystem re- toence in a salesjob is 8.5 years(s.d. = 5.7), theaverage tenure port their activity to downloadnew customer call or data,thewithinthe companyis 5.8 years(s.d. = 4.4), and the sales- software transfers salesrepresentatives informa- also the usagepeopleworked their in territory average 2 A years(s.d. = an of tionto thecompanys database. The usagefigures not in- did 1.4). All salesrepresentatives collegegraduates, were withap- clude tracking timespentin administrative activities(suchasproximately 15 percent having advanceddegrees. the upload/download functions), but focusedon screens for We considered two majorconditions necessary our as for territory analysis activities, precall planning and preparing ac-research: theuse of technologies voluntary (1) was such that tivities,and postcallplanningand reporting activities. The invariance IT usageamongsalesrepresentatives existed, and CRM system had been in place formorethanone year, and(2) thetechnology been in place long enoughto provide had the data capturesystem had been in place formorethansixsufficient familiarity establish and stable of patterns useamong months without any majorproblems. onlyused data for Wethesalesforce. These werenecessary becauseour primary in- salespeople who had been usingtheCRM system at least forvestigation was to measurethe consequenceof technology sixmonths aftertraining to eliminate confounding the any onusage on sales performance. wanted to ensurethat the We data frominitialtraining. noted earlier, As system usage is wastechnology adopted,salespeoplehad been trained, and voluntary, usageis not evenrequired minimal and for report-therewas heterogeneity usage among the salespeople. of we ing purposes.However, eliminated sevenusersfromourChoosingtherespondents from single a firm opposedto a (as samplewithfewer than 50 hitson the system the three- incross-sectional studyusing variousfirms) an advantagein is monthperiodmeasured, orderto eliminate in users who onlythe present as setting, it controlsforconfounding external accessedthe system minimally who chose not to use the oreffects to the variability market due in contexts (e.g., com- system all.Adoptionofthetechnology a precursor our at is topetitive situations) and organizational factors (e.g., informa- studyabout usage,and thisfitsour requirement. This leftationsystems salesmanagement and practices). The limitation sampleof 131 salespeople conducting analysis. for ourof investigating salespeoplefromany singlefirmcan, how- Sales representatives unawarethatthe parentcom- wereever, lead to a questionof representativenessthe firm of and pany was tracking, had the abilityto track,theirCRM orthegeneralizability results. of However, what is beingtested usage patterns. This was the companypolicy.It was quitein our modelis thetheoretical relationship between degree of clear,based on discussions withseniorsalesmanagement and
  8. Fall2004 303salesrepresentatives, thesalespeople notknowabout that did ure 2, two regressions wererun. First, simplelinearregres- atheusagetracking. sion withtechnology usage as the independent variableand Data from system this werecollected a three-month for pe- job performance as the dependent variable was run.Second,riodfor salesrepresentatives. all A measure thetotalnumber of a multiple regression as withboth usageand usage1 indepen-of system (or totalscreens hits used in a three-month period) dentvariables and job performance thedependant as variablewasusedas a measure salesperson of technology utilization(av- was run. (See Table 2 fortheregression results.)eragehits= 283; s.d. = 228). We used hitsrather thantotal First, observethatthe linearrelationship we betweenITtimebecause, withtotaltimeon thesystem, thereis a possi- usageand job performance not significant > 0.05). This is (pbility of sales representatives leaving a screenrunning with- clearly calls into questionthe assumptionmade in past re-out usingthesoftware. One shouldnote,however, these that searchthat the relationship linearand providesevidence ismeasures hitsand timeare highly of correlated (0.98). Per- forits negation.Second, supportis foundfora curvilinearhaps we did not have to worry thataccount.An added on relationship. effect IT usage, is positive signifi- The of βχ, andadvantageto using hits is that it capturesthe wider usage cant (B = 0.64, ρ < 0.05), and theeffect oiusage2^^ is nega-acrossmultiple screens and, also, multiple accessto thesame tive and significant = -0.55, ρ < 0.05), supporting (B ourfunctional screens, it was seen to be beneficial. if Further, we hypothesis. including quadraticterm, fortechnol- By the /?2,ran our analysis using hitsand separately using timeas our ogy usage, the varianceexplainedin salesperson perfor- jobmeasure usage.Our results of weresignificant irrespective of mancesignificantly increased < 0.05) from1.2 percent (p towhether measured we usagewithusinghitsor time. 4.2 percent. Although the amount of varianceexplainedin We obtainedthesalespersons performance from company job performance (percent quota) is rather low,the followingrecords. Salesperson performance was operationalized using points should be considered when interpreting relation- thisthepercent quota persalesrepresentative on achieved of based ship.First, theeffect "robust," is becauseitlinkstwodifferentsaleslevelsoverthethree-month periodcorresponding the to data sources(both of whichare objective).Second, this4.2technology usage data collection. did not attempt con- We to of in percent variation salesperson is performance explainedtrolforsalesperformance priorto theimplementation the of only by IT usage.Third, thisis the incremental increase inCRM system fortworeasons. First, sincetheSiebelCRM sys- performance beyond whatwas accomplished theolderSFA bytemwas implemented, there havebeen territory realignments technology thatwas replacedwith the current technology.along with addition many the of newsalespeople. Second,there Fourth, amountofvariance the explained in salesperson per-wasa different CRM system placeprior theSiebelsystem in to formance compares favorably theindividual to contributionsand thuspriorperformance could only allow us to look at made by othervariablesin previoussales studies.The vari-incremental ofthenewsystem gain versus old system. the How- is ance explained consistent withprior literature findings thatever, Churchill al. (1985) noted,percent quota is a as et of "no singledeterminant explaina very can largeproportion ofstrong measure performance, it controls externalities of as for thevariancein objective salesperformance" (Churchill et al.such as differences acrossterritories, including territory size, 1985, p. 116). Furthermore, Churchill al. foundthat"the etmarket potential, and economicconditions, thepercent- and to abilityof individualdeterminants predictperformanceto-quota measure in should, part, for control prior performance. seems rather unimpressive" (1985, p. 110). Our studyfo-Sales quota was calculatedbased on the volume of product cused on the incremental difference performance to in duesold(prescriptions) customers by in (physicians) a salespersons theupdating theCRM technology. does notcapture of It theterritory (a company-defined of physicians), compared set as entire effect technology onlythecapacity theupdate of but ofto a target quota thatis setat thebeginning theyearbyan of (in the CRM technology studied)to improve performance.external organization in specializing salesforce compensation. The 4.2 percent variance explained compares wellto theotherBecause prescription in information the pharmaceutical in- findingsin the literature,ranging from 3 to 9 percentdustry is accurately tracked thephysician to level(due in large (Churchill al. 1985; Vinchuret al. 1998). etpartto the factthatthe industry heavily is regulated the by By examining plot of the effects IT usageon sales- the ofFood and Drug Administration), withthevastmajority all of personjob performance in Figure3a, one can see thatthepharmacies reporting customer-prescribing to twomajor data idealsystem usage(488 hits), thepointatwhichusagemaxi- orsecondary data houses (NDC and IMS), this represents an is mizesjob performance, higher thantheaverage current sys-accurate picture of a salesrepresentatives performance. tem usage (283 hits).A histogram IT usage is shownin of Figure One shouldnotethatabout20 percent thesales- 3b. of people in the sampleuse the system above the ideal levelofResults 488 hits. One has to be cautiouswith using "hits,"as it isIn orderto testforthepresence a curvilinear of relationship possible,but not probable,thatif the sales training effec- is IT as inbetween usageand job performance, represented Fig- tive,salespeople having more"hits" navigating lot before are a
  9. 304 s s s? /NI Ο O* ■D 8 Ή îg gpSpqp Ö Ö Ö Ö Ö M *C ΰ (Λ κ υ υ - ή **■ rs - t oo η - 00 ΙΛ On ^ +, η M 0 Ν Ov <N - - <N - m cm | g - <N φο (Λ II ^ 3 S3 ι 1 «8 Sie, ik{ ^ "ü *£+ "g 0>ft> *λ - Oro - Ο SS Dr) S « cfc^pppp loI^TJ Cjujrooi^OO Si 8S 8ΰ *λ ^^ ^ ö ö c :g φ + + < ΐ II II g îg ^J ÎÎ „ ϊ? κ Su 3 si ÎÎ CC §§ öS«,-! flyON^<Nt*vO»^> I <u<u Loproppio II ** °* £ Ί-Ι- - ^ 2î ^ y ΣΣ -g -SoSoSS-p LUli: Σ - (Ν Σ - CM Τ
  10. Fall2004 305 Figure3a PlotofEffects IT Usageon Salesperson Performance of Job use Avg. Usage(4g8^ Qptjma| / -ι 150 ( y- 140 -/- 130 jf-I 120 ^^^^ »^^0 110 -^S~£ 100 -N^Ü xc 900 80i™0n 60 50 J 1 1 h 1 1 1 1 1 1 1 1 « 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 Total overThree-Month Usage(Hits Period) Figure3b Histogram IT Usage of 401 Iliiiii- %%% %%% _ %%%o "·% XVVVVVVVW ITUsage
  11. 306 Journal Personal of Selling& SalesManagementfinding whatthey want,and there stilllearning-curve are ef- Alternately, underusers be identified can and sent in forfectsthathave not stabilized. as has However, training been additionaltraining thattheyare able to get to the opti- soprovided, and a yearhas passed,we do not expectthispos- mum point faster. Overusers can also be identified sent and viewpoint be likely.siblealternative to fortraining reducetheirtechnology to usage, and evaluate whether thereare sufficient productivity gainsfromthe ex- MANAGERIALIMPLICATIONS tentof technology A feedback use. mechanism a guiding and system arefuture developments thatcould vastly assist im- in ofMost providers CRM software havetheir rootsin SFA, and provingoverallperformance. recognizethat individual Weso "thebulk of CRM functionality designedto enhance is firms will have theirown policies,both in the opennessofsalesand salesmanagement functionality" (Shoemaker 2001, monitoring, well as in human resources as training and de-p. 178). Our research showsthatthere a positive is payoff to velopment. Also,sometrainees be quicklearners com- will andSFA/CRM, but thatthe enablingeffect tapersoff,and the fortable withtechnology, whereas someothers maybe slowerrelationship betweentechnology usage and performance is in acquiringand usingtechnology improvetheirperfor- tocurvilinear.This is a powerfulimplication. For in our par- mance.Would theoptimum pointbe reached quicker the forticularsample, average, appearsthatincreasing usage on it the fast learners withgreater technology expertise? Would there- willoftechnology havea positive effect performance. on Tech- be lationship different different for salespeople basedon their usageshouldbe encouraged the until optimum point- technology expertise? Only a close monitoring technology ofnology is, -that thepointofinflection where positive the effecttapers and itseffect performance yieldusableresults on will to usageoffto zero. How can we identify optimumpoint?We this and managers, periodicupdating thestudy of suggested hereneedto keeptrack technology of usageand updatethesystem would be extremely useful. address issueoftheeffects We theto incorporate changes to the expert system thatmay be in of heterogeneous technology expertise acrossthe sales forceuse, orto provide valuable information themanager guide to to in further thenextsection.salespeople about their technology usage. To achievethe above recommendation identifying of the Tracking technology usageposesan interesting conflictfor optimumpoint of usage,it is conceivablethatduringperi-managers between confidentialmonitoring of technology use odic sales meetings, salespeopleat or nearthe optimum theversusprivacy concerns theirusers.Althoughoutsidethe of be provideda forumto discusstheiruse of technology, soscope of thepresent we study, recognize thatit is an impor- thatthey becomeexemplars theothers emulate. for to Behav-tantissue,and firms will need to decide how to respondto ioralmodification through such recognition be quite ef- canthis The conflict. salesmanagement literature underscored has fective. anecdote,instances wheretechnology By of playedatheimportance trust of amongsalespersons salesmanag- and positiverole can be identified used in training and sessions.ers (e.g., Flaherty and Pappas 2000; McNeilly and Lawson Each firm may use itsown antecedents technology that of use1999; Rich 1997). If a firm had an open policyof tracking it observes itssalespeople. in Equallyimportant is theidenti-technology use, the salesperson would knowwhen the opti- of fication factors thatcontribute the disablingeffect, to somum point is reached,but thereis also the possibility that thatpeople are persuadednot to overusetechnology when -thesituation be "gamed" thatis, ifone is rewarded can for productivity declines. marginallogging on a certain numberof times, keepingthe screen or Although we havea situation whichthebenefits tech- in ofon for certain a amountoftime, these evenbe automated. can nologyusage are capturedthrough performance, Rivers andThe potential salespersons for manipulating use of tech- the Dart (1999) findthatformal quantification ofcostsand ben-nology, when thereis the open policyof tracking such use, is efits uncommon.Erffmeyer Johnson and (2001) statethatassumesan adversarial relationship betweenemployees and systematic formal planning and evaluation practices werenotmanagement. Similarly, advisingsalespersons increaseor to widelyadopted. Positiveoutcomeswere improved commu-decreasetheir of technology use surrenders confidential- the nication withclients and accessto information. Negativeex-ityof monitoring such use and mayadd to the lack of trust periences included underestimation cost and amount of ofbetween and salespersons salesmanagers. training required.In thisstudy, thoughwe did not specifi- If a firm wishesto keep the monitoring confidential, the cally look at quantification costs and benefits opera- of ofmanagercan use the information periodic discussions for tional CRM, a long-termstrategicpayoffis improvedabout how particular salespeoplecan improvetheirperfor- performance, such returns recurring. and aremanceby usingtechnology a greater lesser to or degree.This Productivity as measured payback in periodofoperationalway the overusers also be identified, can and, in theircase, CRM (SFA) can be found in the literature. Gillan (1992)themanager could counselthemto perhapsspend less time a reported paybackperiodof 18 monthsfora $1.5 millionwithtechnology to improve some otheraspectof their or on SFA; Taylor(1993) foundabout two-thirds respondents ofjob requirements. in a survey reported a payback period lessthaneightmonths.
  12. Fall2004 307Therearealso other returns. Verity (1993) findsthatbenefits Figure4includethe reduction errors of associatedwithmanual pro- Proposed ModeratingEffect TechnologyExpertise of on reduced and improved costs, closerates. the Relationshipof Performance andcessing, support Tay-lor(1994) reports SFA helpssalespeople that withfasteraccess TechnologyUsage Timeto information also reducesthe numberof follow-ups and Enabling ^for In moreinformation. ourpresent the study, nonquantified ! Disablingbenefitsseem veryplausible,but we primarily concentrated ofon theeffect technology usageon theprimetasksalesper-formance. 8 ! V- -<7 i LIMITATIONS AND FUTURE RESEARCHThe empirical finding thecurvilinear of relationshipbetweenprime tasksalesperformance enablingtechnology and usageis an importanttionship betweentechnology A contribution the literature. linearrela- to usage and performance notis - >tenable. Technology has diminishing returns, a curvilin- and Usage Ψearrelationship supported. need to conductfurther is We re- Technology Expertise Levelsearchto determine what the antecedentsto the optimal ExpertUserperformance usage levels rather are, to thanantecedents adop- AverageSkillUsertion (increased usage).We need to determine whattheante- Unskilled User/cedentsare forthe initialenablinglinearrelationship found Technophobein/?l5 plus the antecedents the disabling for foundin effects is training over,and CRM is in Note:Assumestechnology installed, isßr Aretheseantecedents same foreach partof thecurve, the use.unique to each partofthecurve, do they or sharesome com-mon antecedents? Once we understand theseantecedents, we towillbe able to recommend technology usersand their man-agershow to utilizemore of the technology use it more or seemscounterintuitive,is becauseexpert it usershavetheca-effectively,or we could recommend whento limitfurther us- pabilityto use moreadvancedfeatures a CRM system. of Theage ofthetechnology. TAMs haveprovided witha rich The us advanced features should also be more productive features, ofsetof antecedents acceptance, and some of theseanteced- meaning the extrausage time could be spenton usingad-entsmaybe thesamefortheperformance vanced productive usagerelationship. features the technology. of However, we The point of inflection, the optimumpoint,is not a or also hypothesize that theexpert user would geta greater rela-stationary point.As the system updatedand morepeople is in tiveimprovement his or herperformance thanan averageuse the system the optimumpoint is likelyto be effectively, skilleduser. we Further, suspect thatunskilled technophobic orapproached sooner. Thiswillcontinue perhaps, tech- until, the users would not achieveanyproductivity benefits therela- ornologygetsoutmoded.Then, therewill be a new learning tionshipwould not be significant. Futureresearch this oncurvefortheimproved technology, thecyclebeginsonce and proposedmoderating is effect needed. Such insight into theagain. In one way,themodelthatwe havetested maybe seen differing relationships betweendifferent classesof expertiseto be a testof the effectiveness the particular of userscould provideus with a basis forconductingfurther technologythatis in use at the particular research firm.However,because the into how, or if,technology productsshould inter-technology study themarket we is leaderin thepharmaceuti- facedifferently different with classesof users.Current CRMcal industry well as in many otherindustries, is very as it software software and training is marketed provided a and inlikely that thepattern results hold trueforany major of will "one size fits package.Validation our hypothesis all" of couldtechnology. lead to different interfaces, adaptiveuser interfaces, user or We also need to examinetheeffect individual of technol- based on theexpertise theuser. ofogyexpertise therelationship. do not believethatthe on We More empirical studieswill be needed to further validaterelationship withdifferent theTechnology will be the same forsalespersons Performance Usage Model (TPUM), especiallylevelsof expertise.Figure4 proposesa possiblemoderating withrespect itsexternal to in validity othertechnology areas,effect expertise therelationship. believethatexpert of on We in and acrossvariousfirms different This valida- industries.userswillreachtheir individualized optimumtechnology us- tion also includesthe development testof measures and notage byusing thesystem morethanan average While this user. currently foundin theliterature be able to characterize to and
  13. 308 Journal Personal of Selling& SalesManagement discriminate between positive negative the and phasesoftech- ifspecification other of variables does affect coefficients the in nology usage. In effect, arecallingfora greater we quantifica- anymeaningful way, a future stream research of emulating the tionof the costsand benefits technology of implementation development theBass model could well be anticipated. of and usage.Such results also assist system will in upgrade deci- Our model also does not directly A addresstraining. keysions,based on theexpected cost-benefit ratio. statedassumption is thatthetechnology in place,and sales- is The current study usescross-sectional While we pro- data. people are studiedaftertheyhave been trained.However,pose thatthecausalrelationship thatunderuse overuse is or of appropriate training could be a keymoderator theperfor- totheCRM technology leadsto reduced salesperformance, per- manceusagecurvilinear relationship.What aboutother mod-hapstechnology use is driven performance. by However, there erators, such as theuse of technology usage in evaluation, oris no obvious rationale to whyperformance as would drive in counseling? Research be conductedon how to modify cantechnology usage. It is morereasonable assumethattech- to training, reward systems, eventhetechnology optimize or tonology enhances performance. a However, longitudinal study usage of an individualuserto gain maximumperformance.is neededto validate causality therelationship. the of Further, Using appropriate Bayesiantechniques(Rossi and Allenbyresearch thespecific on effect individual of screens groups or 2003), it may be possibleto model an individual usersper-of screens (i.e., call planning versusanalysis versuscalendar- formance usagerelationship curve. Then itwould be possibleing,etc.) will enhanceour understanding the differential of to use thismodel as an inputto a software technology prod-effect the variouscomponentsof the CRM technology. of uct witha built-in expertsystem, whichoptimizes pro-theLongitudinal follow-up studiescould also moreclearly iden- ductivity performance theindividual and of user.Also,itmighttify laggedeffects any from technology usage.A longitudinal be possibleto customizethe training reward or systems forstudy thatincludesthe timeof initialinstallation a CRM of each individualuser.system could also provide valuableinsight intotheinitialdip a Finally, broadertimemanagement studythattakesintoin performance typically relatedto the introduction new of accountnot onlythe effect timespenton CRM technol- ofenterprise-wide software technologies. At thistime, do not we ogy but also the effect CRM technology differential of onhave sufficient to econometrically data determine length the timeallocationsby salespersons achievespecified to goals ofof a lag, ifany,betweentechnology usage and performance. an organization would be useful.Priorliterature another inWe feelcomfortable assumingthatmost of the effect in is area,information search studies,usinggross measures time, of incaptured our data by usinga largewindowof time. and numberof activities (Ratchford and Srinivasan1993) We could also look at breaking individual out components revealed diminishing to returns timeexpenditures. Results ofof usage,such as precallplanning, territory analysis, postcall the present studyreveala similar of pattern positivebut di-planning and reporting, so on, to see ifthere optimal and are minishing returns. Hence, a managerial studyon differentialusage points forthe individualcomponents.For example, time allocationto varioussales activities (such as prospect-Engle and Barnes(2000) have suggested thatthe sales force ing,analysis, follow-up, etc.) could be conductedto evaluatemustbe trained how to use the planningfunctions in more and improve in CRM technology orderto optimizea sales- inefficiently less time.Similarly, theysuggest thatthe focus persons time.ofusageshouldbe on their in actualselling use and customerinteraction situations. There is evidence(Erffmeyer Johnson and 2001) thatus- REFERENCESers,both management and salespeople, have higher satisfac- Ahearne, Michael,RonaldJelinek, E. 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