an application of the balanced scorecard in retailing

An application of the balancedscorecard in retailing

Rhonda Thomas Myron Gable and Roger Dickinson

Abstract

Retailers have sought measures of performance that go beyond the traditional Thisarticle reports on the application of the balanced scorecard We summarize efforts togo beyond traditional measures in retailing and conduct a study to test their value fora large multi-unit retail chain The results indicate that the balanced scorecard offersa highly individualized and exible technique for improving the performance ofretailers

KeywordsStrategy planning balanced scorecard performance measures retailing

In a world where information is relatively scarce and where problems fordecision are few and simple information is almost always a positive good Ina world where attention is a major scarce resource information may be anexpensive luxury diverting our attention from what is important to what isunimportant

(Simon 1978)

The purposes of this article are to explore the concept of the balanced scorecardand to develop a methodology for the implementation of this concept in a retailsetting Speci cally the authors will describe an empirical study in which theysecured the co-operation of a large national multi-store retailer The empiricalstudy will detail

1 the co-operating retailer2 factors operationalizing each of the four quadrants of the balanced score-

card (the input variables)3 core retail measures ndash the output variables

Rhonda Thomas is Visiting Assistant Professor at the Sellinger School of BusinessLoyola College Baltimore MD 21210 USA Tel 301 251 4711 Fax 301 251 4710E-mail Rhondatmindspringcom Myron Gable is Professor of Marketing EmeritusCollege of Business Shippensburg University Shippensburg PA 17252 USA Tel 941359 1926 Fax 941 351 4448 Roger Dickinson is Professor of Marketing College ofBusiness Administration University of Texas at Arlington Arlington TX 76019 USATel 817 272 2284 Fax 817 272 2854 E-mail Rogerdutaedu

Copyright copy Routledge 1999 0959ndash3969

The International Review of Retail Distribution and Consumer Research 91 Jan 1999 41ndash67

4 the results of the study including a description of the statistical treatmentand

5 a discussion of the contribution of this study to the retailing sector

Introduction

Over the years many conceptual frameworks and measurement classi cationschemes have attempted to match measurement with business strategy TheBoston Consulting Grouprsquos (BCG) portfolio planning model devised categoriesfor strategic business units (SBUs) based on market attractiveness and strengthof the rm in that market The GEMcKinsey Matrix and the TechnologyPortfolio Matrix followed with more theories (eg strategic gameboard andcontingency perspective) for expanding strategic measurement and managementof complex organizations (Bettis and Hall 1982 Coe 1981 Hapeslagh 1982Farguhar and Shapiro 1983 Hamermesh 1986) However they were limited bywhat they measured and how they evaluated the market environment thebusiness opportunity and competitive pressures (Abell 1980 Wind and Mahajan1981 Wernerfeldt and Montgomery 1986 Varadarajan 1989 Kerin et al1990)

Much has been written about factors that in uence the success of retail rmsBuzzell and Dew (1980) attempted to develop statistical relationships betweenstrength in the market and attractiveness of the market as CSFs in retailingManagement Horizons (1987) identi ed ve themes for success market respon-siveness and focused market dominance professional management and entrepre-neurial are innovative highly programmed resource relationships a leadershipposition in productivity and technology and a high value offer A Wall Streetreport summarizing other studies indicates that outstanding retailers holddominant market positions emphasize their merchandising uniqueness possessmanagement depth emphasize effective store location and invest in systems(Varadarajan 1991) Bell and Salmon (1996) enumerate six critical success factorsin retailing but they are addressed in a very general manner Lusch and Jaworski(1991) suggest that in retailing measurable output is not created until atransaction with a customer occurs and the customer exists in an uncontrollableexternal environment The ef ciency of service businesses such as retailing maybe more dif cult to evaluate than manufacturing business ef ciency in that it isdif cult to determine the (appropriate) amount of resources required to produceservice outputs (Sherman 1984) Schmenner (1986) suggests that concreteoperational measures are dif cult to put in place due to the variability ofemployeendashcustomer interaction related to services (such as in the retail trans-action process)

There is building consensus that traditionally de ned nancial performancemeasures such as return on investment models are not enough to evaluate acompanyrsquos competitive position and understand lsquohow we are doingrsquo in anincreasingly complex environment (Sherman 1984 Schmenner 1986 Eccles1991 Webster 1992 Stalk et al 1992 Kaplan and Norton 1992 1993 1996a)Financial measures alone are too simplistic in their interpretation of servicebusinessesrsquo performance (Bharadwaj and Menon 1993)

42 The International Review of Retail Distribution and Consumer Research

Dunne and Rothenberg (1993) point out the limitations of many of theindicators of performance in retailing For example while knowledge of marketshare by management is important it is very dif cult to measure in mostretailing environments How does a Wal-Mart determine its market share Acommon way of calculating this measure is market share 5 Wal-Mart salesdivided by total discount department store sales However this method ofcalculation does not include other competitors such as traditional departmentstores apparel shops hardware stores or any other type of store that handles aproduct Wal-Mart carries

The fundamental managerial challenge for retailers in developing strategicmodels is shifting from the treatment of nancial gures as the foundation forperformance measurement to their treatment as one among a broader set ofmeasures (Eccles 1991) In a recent article Kamakura et al (1996) indicate thata major problem in assessing the ef ciency of multiple retail outlets is securingmeasures of customer satisfaction

Kaplan and Norton (1992 1993 1996a 1996b 1996c) introduced theconceptual framework of the balanced scorecard for designating evaluating andmeasuring multiple factors that drive a rmrsquos performance The balance is seenas between long- and short-term objectives nancial and non- nancial meas-ures lagging and leading indicators and external and internal performanceperspectives (Kaplan and Norton 1996c viii) Managers do not have to rely onshort-term nancial measures as the sole indicators of the companyrsquos perform-ance In this model they link nancial and operational measures with abusinessrsquos strategic goals A scorecard should be based on a linked series ofcause-and-effect relationships derived from strategy The objectives and meas-ures should be both consistent and mutually reinforcing (Kaplan and Norton1996c 17 29 30) It is more than a collection of critical success factors

The Kaplan and Norton balanced scorecard framework (see Figure 1) isdifferent from previous strategic models in that it integrates and balancesmeasurements of customer satisfaction internal business processes at the man-ager level and external nancial measures tied to long-term corporate strategiesand shareholder wealth In addition it values the organizationrsquos ability toinnovate and improve The scorecard provides a balanced picture of currentperformance as well as making the executive aware of the possible drivers offuture performance

As suggested above the balanced scorecard has the capability of integratinglong-range strategic plans with short-term measurable objectives thereby unit-ing a companyrsquos planning and budgeting processes during its scal yearrsquosoperations (Kaplan 1994) The balanced scorecard is by-and-large presentedconceptually It provides a means for linking the measurable objectives of aretailer to its strategy and corporate vision (Kaplan and Norton 1996c)

The authors investigated the literature relating to each quadrant shown inFigure 1 and this is discussed in a later part of the article When these aspectsare addressed speci c input measures operationalizing the balanced scorecardare described and de ned For example instead of stating as a goal theimprovement of customer satisfaction the authors will identify quantitative waysof measuring customer satisfaction Later they will test their ef cacy Factors ineach aspect of the scorecard are discussed Speci c input measures or proxiesoperationalizing these factors are described and de ned in the next section

Rhonda Thomas et al An application of the balanced scorecard in retailing 43

The empirical study

The co-operating retailer

Data were collected from internal company records of a multi-unit multi-market publicly traded specialty retailer This company had approximately 575units in the United States and annual sales of over 680 million dollars All datawere gathered in the autumn of 1996 with the exception of the outcomevariables which were secured at the end of the year The merchandise carried inthe stores is primarily moderately priced home furnishings and furniture itemswith an emphasis on uniqueness and self-expression Individual stores vary insize and con guration based on the market and actual facility selected Stores aretypically located in high-traf c convenient locations such as malls and shoppingcentres in both urban and suburban neighbourhoods This rm maintainsstatistics and trade area characteristics for each store and market Extensiveoperating information for each store is maintained by the companyrsquos decisionsupport system

Some stores were eliminated from the research A unit was dropped fromconsideration if it met one or more of the following criteria during the examinedyear of operations it was 1) closed for thirty or more days during the year 2)opened more than fteen days after the start of the scal year or 3) closed priorto the end of the scal year Additionally one unit was eliminated because it wasclosed due to a re The resultant population of 542 store units was used for thestudy

Prior to selecting the input variables an extensive review of the literature wasundertaken Further sensitive to the needs of the participating rm a series ofnine meetings with eight senior executives and twenty-six regional managers was

Figure 1 The balanced scorecard


held The result of these initial meetings was the development of a list of fty- ve possible input measures incorporating the four perspectives of the balancedscorecard At later meetings measures were eliminated for reasons such as thelack of archival data or of means adequately to measure a factor eg days ofsunshine Other measures were combined into a single variable The nalmeeting on the selection of input variables reduced the number that topmanagement believed to be most important to the success of a store from fty- ve to fourteen These factors are now discussed within the context of the fouraspects of the balanced scorecard

The customer quadrant

The lsquocustomerrsquos perspectiversquo re ects how a business is performing from itscustomersrsquo perspectives regarding time quality performance and service(Kaplan and Norton 1992) Speci c performance measures are to be derivedfrom customer goals The scorecard boxes and their respective measures for thisstudy are enumerated and de ned in Table 1

Constituency-based theory suggests that for a rm to be pro table the long-term needs of its customers must be satis ed (Anderson 1982) Indeed Kohliand Jaworski (1990) found that those rms that were marketing-oriented (versusselling-oriented) were also more likely to be pro table This is especiallyimportant in people-intensive service-based businesses such as retailing Mana-gerial decision making should re ect expectations that customer-oriented ex-penditures are likely to lead to long-term customer satisfaction which ultimatelyleads to increased pro tability (Webster 1992)

Customers do not typically purchase many products or services solely on theircharacteristics or price (Bharadwaj and Menon 1993) Many researchers haveproposed that service quality is a critical determinant of business success and nancial performance (Donovan and Rossiter 1982) According to Berry (1986)perceptions of service quality in the retail experience have become the mostimportant purchase-determining condition Many other empirical studies havealso underscored the relationship between perceived higher service quality andhigher pro ts (Lusch 1986 Thompson et al 1986 Buzzell and Wirsema 1981Gale and Branch 1982) For this study customer service is being de ned in away that indicates the number of sales personnel in the store Because of the typeof merchandise sold there is a need for a measurement of this type We nowdiscuss the key input variables that were used in the customer quadrant

Number of employees per square foot Ingene and Lusch (1980) used the numberof employees per square foot as a surrogate measure of service quality Theyfound a positive linear relationship between sales increases and higher ratios ofemployees to square footage of retail space This led them to conclude that withmore employees personal selling opportunities increase customers get moreassistance locating the merchandise they want and sales assistance is moreprompt reducing delays and facilitating sales Service level in the retailenvironment as seen from the customerrsquos perspective was measured as theaverage number of full-time employees per square foot of selling space(FTSERVC)


Full- and part-time employees Full-time employees have been shown to be moreknowledgeable and demonstrate greater selling skills experience and motivationin servicing and assisting customers than part-time employees This experienceis re ected in enhanced perception of quality and service by the customerSalespeople have an important impact on the customerrsquos perception of the storeand its quality (Bitner 1990) They provide tangible evidence of the storersquosatmospherics (Kotler 1988) and are often the primary source of information(tangibility) about the storersquos service quality A study in the retailing sector in1982 (Gable and Hollon) indicated that differences existed in compensation andsupplemental bene ts coverage between full-time and part-time salespeopleperhaps leading to greater job dissatisfaction among part-timers

Full-time employees tend to be better informed have more experience andtherefore should be more effective in generating sales (Ingene and Lusch 1980Darden et al 1993) due to factors such as higher levels of training commitmentand job satisfaction than part-time employees According to Wotruba (1990)part-time workers may not follow suggested procedures or sales approaches asfrequently as full-time employees Thurik and Van der Wijst (1984) examined theuse of part-time employees to deal with uctuations in demand and the in uencethese employees have over retail labour productivity They reported weakpositive in uences on productivity for a sample of European-based retailingorganizations Long-term customer relationships foster a positive image of thecompany its reputation and its merchandise

Another surrogate measure of customer service would be the ratio of full-timeto part-time personnel (FTPT) Since full-time sales associates are in the store forlonger periods than the part-timers the former have a better opportunity tobuild better customer relationships resulting in a greater ratio As mentionedearlier this ratio was secured in the autumn of the year not a period of highpart-time employment

Salaries and wages Expenditures on sales staff have been shown to be positivelyrelated to sales (Szymanski et al 1993 Ingene and Lusch 1980 Gatignon andHanssen 1978 Johnston and Futrell 1989) Churchill et al (1979) found thatcompensation is the most important reward used to motivate salespeople Inattempting to evaluate what determines a salespersonrsquos performance Churchill etal (1985) found that only a few studies investigated the impact of organizationenvironment factors on performance Bitner (1990) further supported therelationship of internally derived determinants (eg role skill and motivation)with organizational factors but no one factor has been shown to dominate indetermining rm effectiveness Ingene and Lusch (1980) suggested that betterpart-time employees are apt to be hired as full-time employees at higher pay Assuggested above full-time salespeople produce greater sales

The salary and wages measure of customer service can be operationalized byhourly wages More experienced satis ed and seasoned personnel typicallycommand higher salaries Satis ed experienced salespeople are thought to bemore motivated to satisfy a customerrsquos needs develop customer loyalty andcommunicate positive information about the merchandise and retailer than areless experienced or less satis ed personnel Increased worker satisfaction andexperience are expected to bring an increase in sales Excessive expenditures onwages may have a negative impact on pro tability but seldom on sales Thus


another surrogate measure of service is higher salaries and wages per payrollhours (COSTHOUR)

The rst part of Table 1 illustrates the measures label de nitions mean scoresand standard deviations representing the customer lsquoboxrsquo

The internal business quadrant

Kaplan and Norton (1992) de ne the quadrant as re ecting those businessprocesses that have the greatest impact on customer satisfaction and retention interms of cycle time quality employee skill and productivity These measures

Table 1 Input and output measures de nitions mean scores and standard deviations(N 5 542)

Measure De nitionMeanscore SD

The customer perspectiveFTSERVC Average number of full-time sales associates

per square foot of selling space0005 0002

FTPT Ratio of the average number of full-timesales associates to part-time sales associates

076 089

COSTHOUR Total of annual dollars of salaries and wagesper total payroll hours

739 067

The internal process perspectiveTRANS Total annual number of transactions

completed31705 6619

TURNOVER Percentage turnover of store personnel 997 534INV Total average quarterly dollars of inventory

on hand191878 29690

The nancial perspectiveSURRHOUS Number of surrounding households in two-

mile radius20913 12978

POPSTOR Population per store in market 287410 135219PROXIM Distance in miles to nearest other company

store1383 2671

OTHEROP Dollars of other operating expenses perstore such as in-store promotion andtraining but not occupancy costs

102784 27723

FACICOST Total annual occupancy costs per square footof selling space

2580 1134

The innovation and learning perspectiveSTOREAGE Age of store in years 785 687HRLYEXP Average tenure in years of hourly personnel 153 071MGREXP Average tenure in years of store managers 592 433

The output variablesSALESQFT Net annual sales per square footage of store 14008 4128OPPROFIT Pro t before taxes for a store (only direct

expenses to a store are considered)netannual sales

0047 0095

GMROI (Gross marginnet annual sales) (net annualsalesaverage inventory) 5 gross marginaverage inventory

29881 6762


bond managerial judgement about internal process and (strategy) competenciesto the actions and behaviour of individuals at lower levels of the organizationElements in the internal business quadrant in retailing are tied to regional orlocal managerial (store unit) control as opposed to corporate mandate Thesemeasures of business process re ect managerial aspects of performance withinthe control of the store manager as shown in the second part of Table 1

Number of transactions Transaction ef ciency is mostly within a store man-agerrsquos control Employees need suf cient training motivation and rewards toengage effectively in suggestive selling and close more sales As suggested byDickinson et al (1992) stores that experience a greater unit sales would beexpected to produce higher dollars of sales (and pro ts) per square foot witheverything else being equal Lusch et al (1993) note that suggestive selling is aneffective way for the retailer to increase sales and pro ts They assert thatperformance standards for salespeople should include measures such as sales perhour

Productivity in the retail process is measured by the number of transactionscompleted (TRANS) This also re ects process ef ciency employee trainingselling skills and inventory matching to the market ndash all of which can bein uenced by the store and regional manager The greater the number oftransactions per year relative to other stores in the organization the greater theunitrsquos overall expected sales and pro ts except in instances where the averagetransaction amount declines

Turnover Employee turnover can have positive and negative organizationalconsequences (Boudreau and Berger 1985 Hollenbeck and Williams 1986Jackofsky 1984) Turnover is costly to organizations mainly because it requirestime and cost in the recruiting hiring training and retaining of salespeople(Futrell and Parasuraman 1984 Weitz 1979) Turnover can also be bene cial andfunctional to the organization (Gable 1983 Dalton et al 1982 Johnston andFutrell 1989) partly because it keeps employee salaries and fringe bene ts low

Studies have found relationships between performance and turnover incon-sistent Futrell and Parasuraman (1984) maintain that few organizations havemanagerial systems that assist the manager in keeping turnover under controlStudies by Gable and Hollon (1984) and Gable et al (1985) indicate thatturnover for retail trainees decreases when they have an understanding of the joband have prior retail experience Thus turnover can be in uenced by manage-ment

Ef ciency in the work-force is measured by the percentage of turnover at theunit level (TURNOVER) Turnover serves as a proxy measure for the jobsatisfaction of workers effective internal marketing appropriate training andsound management hiring practices

Inventory Inventory levels in uence both the revenue side and the cost sides ofa retailerrsquos bottom line Revenues and costs are affected by such elements asappropriate levels of quality quantity assortment of merchandise created for thecustomer targets distribution advertising and discounting of excess merchan-dise to make room for new incoming goods

Inventory co-ordination and compatibility are also important (Marcus 1987)Few studies or measures have related the merchandise mix to retail sales andor


pro ts (Bultez et al 1989) Dickinson et al (1992) propose an empirical modelof merchandise compatibility (crossover sales stimulated by the merchandisemix) positively related to sales per square foot in evaluating retail performance asmeasured in sales dollars per square foot

Average inventory is another surrogate measure of internal business processeffectiveness A greater amount (dollars) of inventory on hand suggests moreavailable merchandise to satisfy consumers which then suggests either largersales potential or higher costs because of the higher inventory levels The moreinventory (INV) the greater the investment and the greater managementrsquosexpectations of sales and pro ts for that unit

The nancial quadrant

Financial measures re ect outputs at the corporate level They are related toexternal reporting issues such as the creation of shareholder wealth and returnson capital deployed These nancial measures found in the third section of Table1 re ect corporate decision-making ability and managerial quality in directinggrowth and creating shareholder value

The decision regarding which markets offer the most attractive long-terminvestments is made at the corporate level of multi-unit retailing organizationsAccording to Mahajan et al (1988) retailers have historically pursued growththrough rapid store expansion and increased market penetration assuming thata linear relationship existed between these and market share They found that anS-shaped growth model was a more accurate representation of market-growthopportunities and was easier for managers to utilize in their market evaluationassessments than previous models This model includes market variables such asnumber of company outlets competitor outlets market potential companymarket sales company outlet share expected market share untapped salespotential and sales potential before saturation These variables are measured inorder to assess market penetration opportunities for multi-store retailers anddetermine the number of outlets to be added or deleted in a market

Location costs are an important decision criterion of management when decidingto invest company resources in an outlet in a speci c market location Locationdecisions re ect long-term nancial commitments which affect sales as well aspro ts The cost will be measured as the actual total occupancy cost divided bythe selling space of the unit and will therefore be used as the proxy for locationcost (FACICOST) Other operating expenses (OTHEROP) is another means formeasuring location costs These include corporate directives and practices thatdirectly in uence a unitrsquos operating expenses and hence its pro tability Costreductions and operating ef ciencies can be obtained by corporate initiativessuch as through the centralized purchasing of supplies However corporateefforts such as training and marketing are expected to increase sales in the shortterm while increasing pro tability long term

Market attractiveness Mahajan et al (1985) re ect market attractiveness withmeasures of market size annual growth rate pro t margin and competitiveintensity Operating expenses are seen as a function of location strength and


demographic and socioeconomic characteristics of the site Therefore theattractiveness of individual site locations will generally be re ected in increaseddollar operating and occupancy costs Levels of market share do not measurepro tability as the costs of doing business vary from one market to the next

The attractiveness of a market area may also be estimated by saturation theory(Applebaum and Cohen 1961ndash2) This concept evaluates differences in con-sumer demand and retail supply Demand can be determined by the number ofrelevant or target households in the market and the anticipated average ex-penditures per household for the relevant goods and services while supply oftenconsiders total square feet of retail space serving or that will serve thisdemand

Population density is a characteristic of the community and often de ned bythe number of households per square mile Ingene and Lusch (1981) found thatthe greater the population density the larger the average store square footageand thus the fewer the number of stores that will be needed to serve apopulation of a particular size

Two measures of market attractiveness will be used as proxies to be evaluatedby management

1) Number of surrounding households (SURRHOUS) which measures marketsize As the market size increases the sales and pro t potential should undermany circumstances also increase This measure does not directly measurepopulation size

2) Number of people (population) per store (POPSTOR) which measures thenumber of potential customers per store in the market The greater thepopulation per store the higher sales in general are expected This is a directmeasure of population size

Distance from nearest competing store In terms of accessibility to a particularmarket location and site selection theory has focused on de ning retail tradingareas This is a geographically delineated area within which households wouldgenerally be willing to travel to a retail location (Ingene 1984 Ingene and Lusch1980 LaLonde 1961 Applebaum and Cohen 1961ndash2) Christallerrsquos (1966)central place theory ranks communities according to the assortment of goodsavailable in each location Other studies such as Mahajan et al (1985) utilizeddemographic pro ts of market segments to determine site locations in theirportfolio approach Variables used in this study included number of householdschange in number of households average household income and median age

Cannibalization can occur in particular markets and should be monitored andevaluated relative to performance expectation market opportunity against thereality of competitive pressures Cannibalization of sales from a store within thesame retailing organization is most easily measured and captured by the distanceto the retailerrsquos next closest outlet (PROXIM) Everything else being equal as thedistance increases sales and pro ts should increase

The innovation and learning quadrant

The innovation and learning quadrant re ects the companyrsquos ability to innovateimprove and learn and is related directly to the companyrsquos value (Kaplan andNorton 1992)


McGill and Slocum state

any company today with a sustainable strategic advantage ndash an ability to ensurea competitive edge over the long run via protection perpetuation andorreplacement ndash has achieved that position through dedicating its peoplepolicies and practices to learning from experience

(McGill and Slocum 1993 74)

The organizationrsquos achieved experience in the market quality of managementtalent and longer employee tenure have all been linked to improved ef ciencyand business processes

Retailers must continually explore new ways of servicing their customers andcontinually monitor their merchandise mix in order to differentiate themselvespositively and sustainably in todayrsquos competitive market The adoption ofinformation innovations by large retailers (such as Home Depot The Limitedand Wal-Mart) requires employee involvement and commitment to learning newskills as well as a willingness to work towards time and cost savings This meanshaving the right culture to attract and the right managerial skills to identify (andkeep) the right type of people It also means facilitating communication andparticipation both vertically and horizontally among all levels of the organiza-tion The above elements are generally seen as relatively new bases for attainingstrategic advantage in the market and are gaining attention in performanceassessment of successful businesses (Senge 1990 Bharadwaj and Menon 1993Eccles 1991)

Manager and employee tenure McEvoy and Cascio (1987) in their meta-analysisof performance and employee turnover found that low turnover tends to occuramong good performers while high turnover tends to occur among lowperformers Turnover is a dichotomization of the continuous variable referred toas tenure Typically performance improves with tenure (Price and Mueller1981) As managerial and employee tenure increases an organizationrsquos learningpotential is expected to improve

Gable Hollon and Dangello (1992) indicate that tenure at the job is also apredictor of performance Bowman (1963) suggests that managers identifythrough experience the crucial variables associated with a decision and learn howto weigh those variables in making decisions introduced by short-term organiza-tional pressures and conditions Bowman (1963) and Kunreuther (1969) providea series of studies in which signi cant cost savings were achieved by applying adecision makerrsquos model consistently further supporting the notion that manage-rial tenure should be positively linked to organizational opportunities forlearning and innovation Individuals are expected to learn with experience(Senge 1990) Since rms are a collection of individuals their capabilities are are ection of the cumulative experience and abilities of their membership (Hastie1986)

Employee experience can be measured in average hourly employee tenure(HRLYEXP) Satis ed workers stay with a rm longer provide positive andmore extensive information to customers and are motivated to see the rmsucceed The expectation is that the longer tenured employees will expend more


effort towards satisfying customer needs than dissatis ed workers Therefore asexperience increases the ability to enhance the organization image and percep-tion of quality will tend to increase

Manager experience can be measured by average store manager tenure(MGREXP) As store managersrsquo experience increases their ability to understandthe customer market inventory and human resource skills and needs within thatstore should improve All of these aspects should re ect learning and experiencein the organization and should lead to improved performance and value for thecompany The average tenure of a manager was 592 years whereas the averagefor hourly workers was 153 years

Store age Market tenure (presence in the market) generally leads to improvedcompany product and brand recognition among consumers in the marketKnowledge of the companyrsquos merchandise quality along with its price and valueare regarded as key factors in shaping buying behaviour (Sawyer and Dickson1984 Doyle 1984) Word-of-mouth referrals credibility and retailer reputationmay be enhanced Experience curve effects are also expected to produceimprovements internally in the storersquos operations (Abernathy and Wayne 1974Yelle 1979)

A companyrsquos experience and the information available to customers about abusiness shape the companyrsquos reputation Allen (1988) found that a companyrsquosreputation becomes more valuable as competition gets more intense Companyreputation has long been recognized as a key factor in successfully marketing aservice (Thomas 1978 Zeithaml 1981 Lewis and Booms 1983) A companyrsquosreputation re ects the history of its past actions (Rosenthal and Landau 1979Kreps and Wilson 1980) and affects the buyerrsquos expectations with respect to thequality of its offerings (Nelson 1970 Shapiro 1983) Lusch et al (1993) suggestthat in retailing successful strategic planning leads to market adaptation andsurvival by understanding customersrsquo needs and competition in the marketplaceStore experience is operationalized as tenure in a market or store age (STORE-AGE)

The innovation and learning measures are shown in the fourth section ofTable 1

Core retail measures output variables

Commonly used measures to assess performance in the retail setting are sales persquare foot operating pro t margin and gross margin return on inventory(GMROI) At a meeting with senior executives of the rm these measures wereapproved Earlier analysis by the authors suggested that the input variableswould have an impact on performance For this reason output data were securedat a later point in time than the input variables

Net annual sales per square foot is a measure of the productivity of store space thatpermits easy comparison of retail units

Operating pro t margin The ratio of net pro t divided by net sales indicateshow much a retailer is making on each dollar of sales after all expenses have been


considered While this measure does not show how effectively a retailer isutilizing the capital at its disposal it is another well accepted predictor of retailperformance

GMROI This measure incorporates two important retailing variables togetherin a single statistic ndash The gross margin percentage is expressed as a percentageof sales and the annual turnover rate of the inventory It is computed in thefollowing manner (gross marginnet sales) (net salesaverage inventory) 5(gross marginaverage inventory)

Thus if a particular store has a gross margin of 40 per cent and an annualturnover rate of 5 the GMROI is 200 That is for each dollar invested ininventory the store may be seen as obtaining $200 in gross margin annuallyRetailers who interrelate gross margin percentage and inventory turnovereffectively will be able to achieve higher performance results The last part ofTable 1 presents relevant data about the output variables

Results

An intercorrelation matrix of the fourteen input variables is presented in Table2 It reveals a high degree of collinearity between OTHEROP with INV (074)and TRANS (084) and INV with TRANS (074) Tabachnick and Fidell (1989)recommend that any variables included in the same analysis be removed if theyhave a correlation of 070 or higher To diminish the impact of multicollinearityand increase con dence in the results the variables OTHEROP and INV wereremoved from further analysis Twelve variables remained and are employed inthe current research

Table 3 displays correlations of each of the twelve input variables with thethree output variables Nine of the twelve input variables correlate signi cantlywith SALESQFT Similar results emerged for OPPROFIT and GMROI Twoinput variables TURNOVER and HRLYEXP failed to correlate signi cantlywith any of the output variables There were mixed results for POPSTOR andPROXIM In three instances COSTHOUR SURRHOUS and FACICOSTinverse correlations emerged with OPPROFIT

While correlation analysis provides insights into the relationships betweeninput and output variables there is still a need to select an appropriate analyticaltechnique that can distinguish high-performing stores from low-performingstores as measured by the output variables making use of the input variablesDiscriminant analysis is an appropriate statistical technique when the inputvariables are metric and the output variables lend themselves to being categor-ized

Table 4 reports the statistical analyses predicting performance as measured byOPPROFIT Canonical discriminant analysis (SASSTAT Userrsquos Guide 1990)was employed The CANDISC procedure as described in the guide performs acanonical discriminant analysis that is a dimension-reduction technique relatedto principal component analysis and canonical correlation In order to get a morebalanced grouping of stores median cut-off values were used to assign a score toeach of the 542 observations If a score was above the median response it wasplaced in one group and termed a lsquohighrsquo performer The others were labeled


Tab

le 2

Int

erco

rrel

atio

n m

atri

x of

inp

ut v

aria

bles

(N

554

2)

INV

TU

RN

OV

ER

TR

AN

SS

TO

RE

AG

EH

RLY

EX

PM

GR

EX

PF

TSE

RV

CF

TP

TC

OS

TH

OU

RS

UR

RH

OU

SP

OP

ST

OR

PR

OX

IMO

TH

ER

OP

FAC

ICO

ST

INV

100

01

74

15

01

26

33

06

20

12

07

06

74

28

TU

RN

OV

ER

100

05

04

ndash41

ndash

13

07

04

ndash23

0

30

10

40

12

7T

RA

NS

100

13

02

29

54

11

18

34

17

09

84

44

STO

RE

AG

E1

002

11

71

10

61

41

40

3ndash

080

6ndash

23

HR

LYE

XP

100

27

02

04

38

04

03

ndash07

01

ndash05

MG

RE

XP

100

16

08

27

ndash07

09

ndash02

24

01

FT

SERV

C1

004

61

63

21

7ndash

045

13

7F

TPT

100

03

06

ndash02

ndash01

10

ndash04

CO

STH

OU

R1

001

81

2ndash

27

22

34

SUR

RH

OU

S1

000

6ndash

15

30

45

POPS

TO

R1

000

32

52

4PR

OX

IM1

000

71

6O

TH

ER

OP

100

07

FAC

ICO

ST1

00

Si

gni

cant

at

05

or l

ower


lsquolowrsquo performers Further 80 per cent of each group was used as a classi cationsample the balance being employed as a hold-out sample Therefore thediscriminant analysis is performed using a fraction of the data set to validate therule generated by the balance of the dataset The discriminant function isquadratic that is the variance-covariance matrix is not assumed to be equalbetween groups

Table 4 indicates the standardized coef cients F values and probabilities (p)for the 12 input variables (the complete model) The complete model has aR-square of 48 with an accompanying likelihood ratio of 52 (F 5 330p 5 0001) Of the twelve predictor variables of OPPROFIT nine are signi cantat the 05 level or lower The decision was then made to make use of a reducedmodel using only those nine variables

Further this table provides standardized coef cients for the nine inputvariables F values and probabilities are not shown because they are identical tothose for the complete model They remain the same even if the model isreduced This model has a R-square of 47 with an accompanying likelihood ratioof 53 (F 5 429) allowing rejection of the null hypothesis that there was norelationship between the dependent variable and explanatory variables at the0001 level of signi cance Of the nine variables three (FTPT COSTHOURand FACICOST) have negative loadings The discriminant analysis ndingsindicate that higher-performing stores are more likely than lower-performingstores to have 1) higher TURNOVER TRANS STOREAGE MGREXPFTSERVC and PROXIM and 2) lower FTPT COSTHOUR and FACI-COST

The question of classi cation accuracy is of substantial importance If theclassi cation accuracy were not greater than could be expected by chancedifferences in score pro les would provide no meaningful information foridentifying group membership The question is then how much of the classifica-tion accuracy should be relative to chance Hair et al (1987 90) recommend thatthe classi cation accuracy should be at least 25 per cent greater than thatachieved by chance Chance classi cation accuracy is approximately 50 per centfor the sample employed to develop the discriminant function Therefore the

Table 3 Correlation of input measures with output variables (N 5 542)

Input measure SALESQFT OPPROFIT GMROI

TURNOVER 01 05 01TRANS 74 40 73STOREAGE 17 31 09HRLYEXP 08 04 03MGREXP 27 22 29FTSERVC 60 21 47FTPT 09 14 12COSTHOUR 24 ndash19 17SURRHOUS 21 ndash16 15POPSTOR 24 01 23PROXIM 01 20 03FACICOST 44 ndash44 32

Signi cant at 01 or lower Signi cant at 05 or lower


classi cation accuracy should be a minimum of 625 per cent The actual level ofclassi cation accuracy of the analysis sample is 81 per cent far exceeding theminimum requirement For the hold-out sample the classi cation accuracy of 79per cent is substantially higher than the criterion of 625 per cent Thediscriminant function therefore can be considered a valid predictor of perform-ance as measured by OPPROFIT The data for this section are provided in thebottom section of Table 4

In order to get more precise parameter estimates and better estimates of theerror rates a re-sampling model was used The entire analysis was performed1000 times using a different 02 fraction for the hold-out sample on each runOnly the input variables used in the reduced model are used in this analysisCategorization accuracy of the classi cation sample of 433392 is 79 per cent forthe hold-out sample of 108608 classi cation accuracy is also 79 per cent Theseresults con rm the ndings of the reduced model The standardized coef cientscalculated in this last procedure parallel those in the reduced model These arealso displayed in Table 4 In addition lower and upper con dence limits arepresented in the last two columns of Table 4 These results indicate that thewidth of the con dence interval is small con rming the accuracy of theestimates in the re-sampling model Because of the re-sampling technique thesecoef cients are more robust than those for the reduced model The methodologyused for OPPROFIT is replicated for SALESQFT and GMROI

Table 4 Discriminant analysis with OPPROFIT as the dependent variableA Standardized coef cients

Inputvariable

Complete modelStandardized

coef cient F value P

Reduced modelStandardized

coef cient

Resampling modelStandardized

coef cient Lower Higher

TURNOVER 014 763 006 070 054 052 056TRANS 1074 7324 0001 1078 1063 1059 1067STOREAGE 179 3723 0001 151 139 137 142HRLYEXP -166 182 179 mdash mdash mdash mdashMGREXP 030 1219 0005 004 040 038 043FTSERVC 363 2457 0001 364 251 247 255FTPT ndash026 743 007 ndash040 ndash035 ndash038 ndash032COSTHOUR ndash219 1751 0001 ndash258 ndash172 ndash174 ndash169SURRHOUS ndash059 083 350 mdash mdash mdash mdashPOPSTOR 135 005 821 mdash mdash mdash mdashPROXIM 022 1479 0001 031 033 031 035FACICOST ndash1136 4776 0001 ndash1123 ndash1055 ndash1061 ndash1050

B Classi cation matrices ndash reduced model

Analysis sample(N 5 445)

Lowperformers

Highperformers

Hold-out sample(N 5 97)

Lowperformers

Highperformers

Predicted by discriminant analysisLow performers 204 20 38 8High performers 63 158 12 39


The data in Table 5 display the discriminant analysis predicting performanceas measured by SALESQFT Again a median split was used The completemodel has a R-square of 39 and a likelihood ratio of 61 (F 5 227 pr 5 0001)Of the twelve input variables eight are signi cant at the 05 level or lower Theseare used in the reduced model which has a R-square of 38 with an accompany-ing likelihood ratio of 62 (F 5 334 pr 5 0001) Two of the signi cant variables(SURRHOUS and FACICOST) have negative loadings Classi cation accuracyfor the analysis and hold-out samples are 76 per cent and 77 per centrespectively See the bottom section of Table 5 for those data

When the re-sampling method of the discriminant analysis was performed1000 times the classi cation accuracy of the analysis sample of 433392 is 77 percent and the hold-out prediction rate is 76 per cent for the sample of 108608In both instances chance categorization accuracy was far exceeded Thestandardized coef cients are shown in Table 5 as well as the lower and uppercon dence levels The extent of the con dence interval is narrow verifying theaccuracy of the standardized coef cients in the resampling model The dis-criminant analysis coef cients for SALESQFT reveal that high-performingstores are more likely than low-performing stores to have 1) higher TRANSSTOREAGE MGREXP FTSERVC COSTHOUR and POPSTOR and 2)lower SURRHOUS and FACICOST

Table 6 reports the statistical analyses predicting performance as measured byGMROI Median cut-off values are assigned to each of the 542 observations

Table 5 Discriminant analysis with SALESQFT as the dependent variableA Standardized coef cients

Inputvariable




coef cient



TURNOVER 121 330 070 mdash mdash mdash mdashTRANS 1130 22199 0001 1133 1148 1145 1151STOREAGE 048 693 009 090 096 094 098HRLYEXP 086 076 384 mdash mdash mdash mdashMGREXP 223 689 009 224 220 217 221FTSERVC 465 10604 0001 394 394 392 397FTPT ndash138 283 093 mdash mdash mdash mdashCOSTHOUR 113 1017 002 132 144 140 148SURRHOUS ndash274 953 002 ndash267 ndash264 ndash271 ndash256POPSTOR 098 1278 0004 098 080 079 082PROXIM ndash093 010 756 mdash mdash mdash mdashFACICOST ndash156 2542 0001 ndash112 ndash105 ndash109 ndash102



Lowperformers

Highperformers


Lowperformers

Highperformers



resulting in 273 high performers and 269 low performers The R-square of 38for the complete model has a likelihood ratio of 62 (F 5 218 pr 5 0001)Seven of the twelve input variables are signi cant at the 05 level or lower andare used in the reduced model Discriminant values for the reduced model havea R-square of 36 with an accompanying likelihood ratio of 64 (F 5 349 pr 50001) Only FACICOST has a negative loading paralleling the results forOPPROFIT and SALESQFT Categorization accuracy for the analysis andhold-out samples is 75 per cent and 76 per cent respectively surpassing theacceptable level criterion

The re-sampling model indicates a classi cation prediction rate of 74 per centfor the analysis sample (320908 of 433392 cases) and 76 per cent sample (82055of 108608 cases) exceeding the level for chance recommended by Hair et al(1987) Con dence intervals for the standardized coef cients are small Thecoef cients reveal that high-performing stores are more apt than low-performingstores to have 1) higher TRANS STOREAGE MGREXP FTSERVCCOSTHOUR and POPSTOR and 2) lower FACICOST

The data in Table 7 indicate that TRANS STOREAGE MGREXPFTSERVC COSTHOUR and FACICOST are signi cant discriminators for allthree resampling models TRANS has the highest standardized coef cient in allinstances and FACICOST has a negative coef cient for OPPROFITSALESQFT and GMROI STOREAGE MGREXP and FTSERVC are sig-ni cant discriminators for all three models However MGREXP has a low

Table 6 Discriminant analysis with GMROI as the dependent variableA Standardized coef cients

Inputvariable




coef cient



TURNOVER 118 235 126 mdash mdash mdash mdashTRANS 1306 23456 0001 1250 1181 1179 1184STOREAGE 083 493 027 031 029 027 031HRLYEXP 033 038 537 mdash mdash mdash mdashMGREXP 103 2364 0001 114 125 122 128FTSERVC 137 4335 0001 112 111 110 114FTPT 070 142 234 mdash mdash mdash mdashCOSTHOUR 032 741 007 022 066 063 069SURRHOUS ndash305 382 052 mdash mdash mdash mdashPOPSTOR 166 1212 0005 191 197 194 199PROXIM ndash068 082 367 mdash mdash mdash mdashFACICOST ndash057 2568 0001 ndash072 ndash073 ndash081 ndash065



Lowperformers

Highperformers


Lowperformers

Highperformers



Tab

le 7

A s

umm

ary

of a

naly

ses

for

OP

PRO

FIT

S

AL

ES

QF

T a

nd G

MR

OI

Inpu

tva

riab

leA

spec

t of

scor

ecar

dO

PPR

OF

ITC

oef

cien

tR

anki

ngSA

LE

SQF

TC

oef

cien

tR

anki

ngG

MR

OI

Coe

fci

ent

Ran

king

TU

RN

OV

ER

Inte

rnal

054

6mdash

mdashmdash

mdashT

RA

NS

Inte

rnal

106

31

114

81

118

11

ST

OR

EA

GE

Inno

vati

on1

395

096

70

297

HR

LY

EX

PIn

nova

tion

mdashmdash

mdashmdash

mdashmdash

MG

RE

XP

Inno

vati

on0

407

220

41

253

FT

SE

RVC

Cus

tom

er2

513

394

21

114

FT

PT

Cus

tom

erndash

035

8mdash

mdashmdash

mdashC

OST

HO

UR

Cus

tom

erndash

172

41

445

066

6S

UR

RH

OU

SF

inan

cial

mdashmdash

ndash26

43

mdashmdash

PO

PS

TO

RF

inan

cial

mdashmdash

080

81

972

PR

OX

IMF

inan

cial

033

9mdash

mdashmdash

mdashFA

CIC

OST

Fin

anci

al-1

055

2ndash

105

6ndash

073

5

St

anda

rdiz

ed c

oef

cien

ts f

rom

the

res

ampl

ing

mod

el (

data

bei

ng r

un 1

000

tim

es)

are

bein

g us

ed


coef cient for GMROI Note that COSTHOUR is signi cant for all threemodels but has a negative sign for OPPROFIT The second column shows thatthese six descriptors represent all aspects of the balanced scorecard POPSTORa measure from the nancial quadrant is a signi cant discriminator forSALESQFT and GMROI Further three variables are signi cant for one modelonly SURRHOUS for SALESQFT and PROXIM and TURNOVER forOPPROFIT The former has a high but negative coef cient while the latter haslow coef cients HRLYEXP is not a signi cant discriminator for any of themodels

Discussion

Implications and conclusions based on this empirical research must remainguarded The cross-sectional data are for only one multi-store chain at one pointin time Moreover the analysis suffers from the excluded variable bias Somevariables were purposefully omitted and some data that might have been usefulwere not available For example measures for such factors as visual merchandis-ing advertising and store atmosphere were not considered Further the in-formation generated by the research is provided to management in an lsquoafter thefactrsquo form Problems discovered by research of this kind may be remedied in thelong term but seldom in the short term In addition the fact that data weresecured from one rm may have resulted in a more homogeneous groupingpossibly masking differences existing in a wider population of retailers Becauseof these limitations this type of study should be replicated

Despite its limitations the balanced scorecard can provide assistance to busyretailing executives Some may be of the belief that retail managers cannotoperate with multiple measurements of performance For some multiple meas-ures can be both confusing and ambiguous Recent advances in technology andincreased sophistication of decision making have made it feasible to developintegrated decision frameworks The authors feel that the bene ts of bringing allparts of the rm together with structured quanti able multiple measures areworth the cost Retail managers of the twenty- rst century will need a full bodyof strategic tools if their rms are to survive and thrive The use of discriminantanalysis together with operationalizing the four aspects of the balanced score-card is capable of providing retailing managers with a highly individualized and exible technique for both predicting and improving performance By perform-ing this analysis retail management can de ne and measure performanceaccording to the rmrsquos unique strategic perspective and market position

Comparing store performance within a chain can contribute to a number ofimportant management decisions First store-management evaluations promo-tion and development rely explicitly and implicitly on assumptions about thecauses of store performance Second important resource allocations at the storelevel such as advertising budgets store expansions and store closings are madeon managementrsquos understanding of the relationships between inputs and per-formance outcomes Third adopting a best practices approach to continuousimprovement and corporate learning requires the monitoring of inputs andestimating their impact on outcomes


In a recent issue of the Harvard Business Review (1996) seven letters to theeditor by high-level executives from rms such as Mobil Oil and ChaseManhattan Bank indicate how they have incorporated the principles of thebalanced scorecard into better management of their respective organizationsThis article takes an initial step on how retailers can utilize the balancedscorecard by organizing and creating multiple measures of performance

In this study six of the measures were relevant in determining operatingpro t sales per square foot and gross margin return on investment and the sixare re ected in each of the four aspects of the balanced scorecard Theseperformance indicators are

1 number of transactions (from the internal process quadrant)2 annual occupancy cost per square foot of selling space (from the nancial

quadrant)3 age of store in years (from the innovation and learning quadrant)4 store managerrsquos tenure in years (from the innovation and learning quad-

rant)5 number of full-time employees per square foot of selling space (from the

customer quadrant) and6 wages and salaries per payroll hour (from the customer quadrant)

Number of transactions (TRANS) This is the most powerful descriptor with allthree outputs More effective merchandising including more effective displaysand in-stock conditions is important to increasing the number of transactionsSimilarly more effective sales training should contribute to such aspects as amore effective selling process and a better handling of multiple customersPoint-of-sale modi cations might get customers through the selling processmore ef ciently Waiting times might be decreased and customers more sat-is ed

Annual occupancy cost per square foot of selling space (FACICOST) This factorhas a negative impact on the three output variables In the context of the presentretail chain this appears to mean that the store has unique enough merchandiseand presentation It is what is often called a destination store Customers arewilling to travel speci cally to that outlet Thus the chain is probably overpayingin todayrsquos market for the bene ts derived from many mall locations Relatedlymany types of malls are decreasing in their abilities to bring customers in so thatmany of the rents negotiated in earlier more prosperous times for malls are toohigh for this retailer Another possible explanation is that this chain has troubledealing with the intense competition that exists in many high-rent malls

Age of store (STOREAGE) For almost all chains older retail stores should besystematically refurbished For many retailers however particularly in thebeginning years of a store unit pro ts and GMROI will rise Retailers shouldmanage this ageing process Taking the long-term view is important Experience-curve effects should improve performance and word-of-mouth advertisingshould yield long-term effects for outlets that are effectively managed If a storehas achieved maturity and sales and pro ts are not at a minimum desired level


actions may be needed including closing that store FACICOST and STORE-AGE are signi cant predictors and in combination they point to the importanceof securing desirable locations on advantageous terms over the longer term

Store managerrsquos tenure (MGREXP) Part of effective management is developingmanagers who have a concern for the culture and the long-term success of the rm In general part of the compensation of store managers should be based onlong-term performance factors perhaps the image of a particular outletFurther consideration should be given to the training career patterns and livesof those who manage stores Steps should be taken to recruit and hire individualswho will not only be good managers but also will remain with the organizationTo implement this two-step process greater use can be made of the employmentapplication form (see Gable et al 1992)

Full-time employees per square foot of selling space (FTSERVC) As suggestedabove FTSERVC should help pro tability and customer service in many waysFull-time employees should be better trained leading to such results as fewerwalkouts a more effective and pro table pattern of sales a better organizedstock and the systems of the store should be more effectively implemented andbetter understood Since customer service is a necessary condition for successfulstore performance the time taken for a customer to be served should be closelymonitored For many retailers traf c patterns should be (and are) monitored byday of the week and time of day Efforts for many retailers should also bedirected towards making part-time employees feel more like full-timeemployees

Wages and salaries per payroll hour (COSTHOUR) This factor like MGREXPand FTSERVC is associated with human resource management policies andpractices It has a positive effect on sales and GMROI but is a negativedescriptor of pro tability Therefore care is essential before giving raises orinstalling incentive programmes Costs and bene ts should be carefullymonitored

A perspective

A key bene t of this research to retailing managers is that insights generatedfrom this kind of study can usually be tested in another group of storesAdditional research is needed to determine if the input variables that weresigni cant descriptors in this study would be the same for other rms and typesof retail rms Other measures may be better predictors of sales and pro tabilityFor example the rm in this research is investigating the installation ofmechanical devices that will both count the number of customers entering astore and indicate how long they stay If implemented this has interestingimplications as an input measure Subsequent research could at some point leadto different con gurations of factors associated with the balanced scorecardFurther the input variable (INV) was broadly de ned Some measure of year-to-year change of inventory holdings could be used as another way of measuringinventory


This study provides assistance to retailing executives who are concerned withimproving their organizationrsquos performance Because of the large number ofvariables that retailers can employ (eg orientation to the customer merchandisemix service level and location) comparisons across retail units are oftendif cult Sheer size and complexity of organizational structure can add to theproblems However this study suggests that it is possible for each retailer tode ne and measure performance according to its unique strategic perspectiveand market position The information generated is surely a boon to busyexecutives operating in a dynamic environment

Summary

Relying on one or two key measures of pro tability for planning and control hasbeen a continuing problem for retailing and business rms This article offers abrief history of various attempts at multiple measures A balanced scorecardmakes it possible for each rm to de ne and measure performance according toits unique strategic perspective and market position The balanced scorecard isanalysed with data from a large multi-unit retail chain and appears to be a highlyindividualized and exible technique for improving retailer performance

References

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Abernathy WJ and Wayne K (1974)lsquoLimits of the learning curversquo HarvardBusiness Review 52(SeptemberndashOctober) 109ndash19

Allen M (1988) lsquoCompetitiveconfrontation in consumer servicesrsquoPlanning Review 17(JanuaryndashFebruary)4ndash9

Anderson PF (1982) lsquoMarketingstrategic planning and the theory ofthe rmrsquo Journal of Marketing46(Spring) 15ndash26

Applebaum W and Cohen SB(1961ndash2) lsquoTrading area networks andproblems of store saturationrsquo Journalof Retailing 37(Winter) 35ndash6

Bell DE and Salmon WJ (1996)Strategic Retail ManagementCincinnati OH South-Western pp3ndash6

Berry LL (1986) lsquoBig ideas in servicesmarketingrsquo Journal of ConsumerMarketing 3(Spring) 47ndash51

Bettis RA and Hall WK (1982)lsquoDiversi cation strategy accounting

determined risk and accountingdetermined returnrsquo Academy ofManagement Journal 25(June) 254ndash64

Bharadwaj SG and Menon A (1993)lsquoDeterminants of success in serviceindustriesrsquo Journal of ServicesMarketing 7(4) 19ndash39

Bitner MJ (1990) lsquoEvaluating serviceencounters the effects of physicalsurroundings and employee responsesrsquoJournal of Marketing 54(April) 69ndash82

Boudreau JW and Berger CJ (1985)lsquoDecision-theoretic utility analysisapplied to employee separations andacquisitionsrsquo Journal of AppliedPsychology 70 581ndash612

Bowman EH (1963) lsquoConsistency andoptimality in management decisionmakingrsquo Management Science9(January) 310ndash21

Bultez A Gusbrechts E Naert Pand Abeele PV (1989) lsquoAsymmetriccannibalism in retail assortmentsrsquoJournal of Retailing 65(2) 153ndash92

Buzzell R and Dew M (1980)lsquoStrategic management helps retailersplan for the futurersquo Marketing News13(18) 1 16


Buzzell R and Wirsema F (1981)lsquoModeling changes in market share across-sectional analysisrsquo StrategicManagement Journal 2(JanuaryndashMarch) 27ndash42

Christaller W (1966) Central Places inSouthern Germany trans CW BaskinEnglewood Cliffs NJ Prentice Hall

Churchill GA Jr Ford NM andWalker OC (1979) lsquoPersonalcharacteristics of salespeople and theattractiveness of alternative rewardsrsquoJournal of Business Research 7(June)25ndash50

Churchill GA Jr Ford NMHartley SW and Walker OC(1985) lsquoThe determinants ofsalesperson performance a meta-analysisrsquo Journal of MarketingResearch 22(May) 103ndash18

Coe BJ (1981) lsquoUse of strategicplanning concepts by marketersrsquoProceedings of the Annual MarketingEducatorrsquos Conference ChicagoAmerican Marketing Association pp13ndash16

Dalton D Todor WD andKrackhardt DM (1982) lsquoTurnoveroverstated the functional taxonomyrsquoAcademy of Management Review7(January) 117ndash23

Darden WR McKee D andHampton R (1993) lsquoSalespersonemployment status as a moderator inthe job satisfaction model a frame ofreference perspectiversquo Journal ofPersonal Selling and Sales 8(Summer)11ndash13

Darmon RY (1990) lsquoIdentifyingsources of turnover costs a segmentalapproachrsquo Journal of Marketing54(April) 46ndash56

Dickinson RA Harris F and SircarS (1992) lsquoMerchandise compatibilityan exploratory study of itsmeasurement and effect on departmentstore performancersquo InternationalReview of Retail Distribution andConsumer Research 2(October) 351ndash79

Donovan R and Rossiter J (1982)lsquoStore atmosphere an environmentalpsychology approachrsquo Journal ofRetailing 58(Spring) 34ndash57

Doyle M (1984) lsquoNew ways ofmeasuring valuersquo Progressive GrocerExecutive Report 15ndash19

Dunne P and Rothenberg MJ (1993)lsquoMeasuring long-term retailperformance using non- nancialmeasuresrsquo presented at theSymposium on Patronage Behavior andRetail Strategic Planning CuttingEdge III Lake Placid New York 7May

Eccles RG (1991) lsquoThe performancemeasurement manifestorsquo HarvardBusiness Review 69(JanuaryndashFebruary)131ndash7

Farguhar C and Shapiro SJ (1983)Strategic Business Planning in CanadaThe Use of Analytical Portfolio ModelsOttawa The Conference Board ofCanada

Futrell CM and Parasuraman A(1984) lsquoThe relationship of satisfactionand performance to sales forceturnoverrsquo Journal of Marketing48(Fall) 33ndash40

Gable M (1983) lsquoCosting andcontrolling employee turnover inretailingrsquo presentation at AnnualMeeting of the National RetailMerchants Association January NewYork

Gable M and Hollon C (1982) lsquoAcomparison of retail personnelpractices applied to full-time and part-time employeesrsquo PersonnelAdministrator 57(2) 62ndash4

Gable M and Hollon C (1984)lsquoEmployee turnover of managerialtrainees in a department store chainrsquoRetail Control 52(5) 54ndash61

Gable M Hollon C and DangelloF (1985) lsquoPredicting voluntarymanagerial trainee turnover in a largeretailing organizationrsquo Journal ofRetailing 60(4) 43ndash63

Gable M Hollon C and DangelloF (1992) lsquoIncreasing the utility of theapplication blank the relationshipbetween job application informationand subsequent performance andturnover of salespeoplersquo Journal ofPersonal Selling and Sales Management12(3) 39ndash55

Gale BT and Branch B (1982)lsquoConcentration versus market sharewhat determines performance and whydoes it matterrsquo Antitrust Bulletin27(Spring) 83ndash106


Gatignon H and Hanssen DM(1978) lsquoModeling marketinginteractions with application tosalesforce effectivenessrsquo Journal ofMarketing Research 24(August)247ndash57

Hair J Anderson R and Tatham R(1987) Multivariate Data Analysis NewYork Macmillan

Hamermesh RG (1986) lsquoMakingplanning strategicrsquo Harvard BusinessReview 64(JulyndashAugust) 115ndash19

Hapeslagh P (1982) lsquoPortfolio planninguses and limitsrsquo Harvard BusinessReview 60(JanuaryndashFebruary) 58ndash72

Harvard Business Review (1996)lsquoLetters to the editorrsquo 74(MarchndashApril) 75ndash85

Hastie R (1986) lsquoExperimental evidenceon group accuracyrsquo in Jabline FMPutnam LL Roberts KH andPorter LW (eds) Handbook ofOrganizational Communication AnInterdisciplinary Perspective BeverlyHills CA Sage

Hollenbeck JR and Williams CR(1986) lsquoTurnover functionality versusturnover frequency a note on workattitudes and organizationaleffectivenessrsquo Journal of AppliedPsychology 71 606ndash11

Ingene CA (1984) lsquoStructuraldeterminants of market potentialrsquoJournal of Retailing 60(Spring) 37ndash64

Ingene CA and Lusch R (1980)lsquoMarket selection decisions fordepartment storesrsquo Journal ofRetailing 56(Fall) 21ndash40

Ingene CA and Lusch R (1981) lsquoAmodel of retail structurersquo in Sheth J(ed) Research in Marketing 5 101ndash64

Jackofsky EF (1984) lsquoTurnover and jobperformance an integrated processmodelrsquo Academy of ManagementReview 9(1) 74ndash83

Johnston M and Futrell C (1989)lsquoFunctional salesforce turnover anempirical investigation into the positiveeffects of turnoverrsquo Journal of BusinessResearch 18(2) 141ndash58

Kamakura WA Lenartowicz T andRatchford BT (1996) lsquoProductivityassessment of multiple retail outletsrsquoJournal of Retailing 72(4) 333ndash56

Kaplan RS (1994) lsquoDevising abalanced scorecard matched to business

strategyrsquo Planning Review23(SeptemberndashOctober) 15ndash19 48

Kaplan RS and Norton DP (1992)lsquoThe balanced scorecard ndash measuresthat drive performancersquo HarvardBusiness Review (JanuaryndashFebruary)71ndash9

Kaplan RS and Norton DP (1993)lsquoPutting the balanced scorecard toworkrsquo Harvard Business Review71(SeptemberndashOctober) 134ndash47

Kaplan RS and Norton DP (1996a)lsquoUsing the balanced scorecard as astrategic management systemrsquo TheHarvard Business Review 74(JanuaryndashFebruary) 75ndash85

Kaplan RS and Norton DP (1996b)lsquoLinking the balanced scorecard tostrategyrsquo California ManagementReview 39(1) 53ndash79

Kaplan RS and Norton DP (1996c)The Balanced Scorecard Boston MAHarvard Business School Press

Kerin R Mahajan V andVaradarajan PR (1990)Contemporary Perspectives on StrategicMarket Planning Needham HeightsMA Allyn amp Bacon

Kohli AK and Jaworski BJ (1990)lsquoMarket orientation the constructresearch propositions and managerialimplicationsrsquo Journal of Marketing54(April) 1ndash18

Kotler P (1988) Marketing ManagementAnalysis Planning and Control 6thedn Englewood Cliffs NJ PrenticeHall

Kreps DM and Wilson R (1980)lsquoTemporal resolution of uncertainty inStapleton and Subrahmanyamsmultiperiod equilibrium asset pricingmodelrsquo Econometrica 48(6) 1565ndash66

Kunreuther H (1969) lsquoExtensions ofBowmanrsquos theory of managerialdecision makingrsquo Management Science15(April) 415ndash39

LaLonde B (1961) lsquoThe logistics ofretail locationrsquo in Stevens WD (ed)Fall American Marketing ProceedingsChicago IL American MarketingAssociation

Lewis RC and Booms BH (1983)lsquoThe marketing aspects of servicequalityrsquo in Berry LL Shostack GLand Upah GD (eds) EmergingPerspective on Services Marketing


Chicago American MarketingAssociation pp 99ndash107

Lusch R (1986) lsquoThe new algebra ofhigh performance retail managementrsquoRetail Control 54(September) 15ndash35

Lusch R and Jaworski B (1991)lsquoManagement controls role stress andretail store manager performanceJournal of Retailing 67(Winter)397ndash419

Lusch R Dunne P and GebhardtR (1993) Retail Marketing CincinnatiOH South-Western

McEvoy GM and Cascio WF (1987)lsquoStrategies for reducing employeeturnover a meta-analysis of therelationship between performance andturnoverrsquo Academy of ManagementJournal 30(December) 744ndash62

McGill ME and Slocum JW Jr(1993) lsquoUnlearning the organizationrsquoOrganizational Dynamics 22(Autumn)67ndash79

Mahajan V Sharma S and KerinA (1988) lsquoAssessing marketopportunities and saturation potentialfor multi-store multi-market retailersrsquoJournal of Retailing 64(Fall) 315ndash32

Mahajan V Sharma S and SrinivasD (1985) lsquoAn application of portfolioanalysis for identifying attractive retaillocationsrsquo Journal of Retailing 61(4)19ndash34

Mahajan V Varadarajan R andKerin R (1987) lsquoMetamorphosis instrategic market planningrsquo in FrazierGG and Sheth JN (eds)Contemporary Views on MarketingPractice Lexington MA LexingtonBooks

Management Horizons (1987) lsquoLessonsfrom high achievement retailcompaniesrsquo written by Varadarajan PRajan Dublin Ohio

Marcus S (1987) as re ected inlsquoMerchant Prince Stanley Marcusrsquo IncJune 41ndash8

Milgram S (1970) lsquoThe experience ofliving in citiesrsquo Science 167 1661ndash8

Miller MD and Gibson ML (1995)lsquoThe CIO as an integrative strategistrsquoInformation Strategy The ExecutivesrsquoJournal Winter 35ndash40

Nelson P (1970) lsquoInformation andconsumer behaviorrsquo Journal of PoliticalEconomy 78(6) 729ndash56

Olsen LM and Lord DJ (1979)lsquoMarket area characteristics and branchperformancersquo Journal of BankResearch 10(Summer) 102ndash9

Price JL and Mueller CW (1981)Professional Turnover The Case ofNurses Jamaica NY Spectrum

Rosenblum J and Keller RA (1994)lsquoBuilding a learning organization atCoopers and Lybrandrsquo PlanningReview 23(SeptemberndashOctober) 2829 44

Rosenthal RW and Landau HJ(1979) lsquoTheoretic analysis of bargainingreputationrsquo Journal of MathematicalPsychology 20(3) 233ndash55

SASSTAT Userrsquos Guide Version 6(1990) 4th edn Vol 1 Cary NC SASInstitute pp 387ndash404

Sawyer AG and Dickson P (1984)lsquoPsychological perspectives onconsumer response to sales promotionrsquoin K Jocz (ed) Research on SalesPromotion Collected Papers CambridgeMA Marketing Science Institute

Schmenner RW (1986) lsquoHow canservice businesses survive andprosperrsquo Sloan Management Review27(Spring) 21ndash32

Senge PM (1990) The Fifth DisciplineNew York Doubleday Dell

Shapiro C (1983) lsquoPremiums for highquality product as returns toreputationsrsquo Quarterly Journal ofEconomics 98(November) 659ndash79

Sherman HD (1984) lsquoImproving theproductivity of service businessesrsquoSloan Management Review 25(3)11ndash23

Simon HA (1978) lsquoRationality asprocess and as product of thoughtrsquoAmerican Economic Review 68(May)13

Stalk G Evans P and ShulmanLE (1992) lsquoCompeting on capabilitiesthe new rules of corporate strategyrsquoHarvard Business Review 70(MarchndashApril) 57ndash69

Suczewski J (1994) lsquoRetailreengineeringrsquo Retail Insights 3(2)

Szymanski DM Bhjaradwaj SGand Varadarajan PR (1993) lsquoAnanalysis of the market sharendashpro tability relationshiprsquo Journal ofMarketing 57(July) 1ndash18


Tabachnick B and Fidell L (1989)Using Multivariate Statistics New YorkHarper amp Row pp 88 89

Thomas DRE (1978) lsquoStrategy isdifferent in service businessesrsquoHarvard Business Review 56(JulyndashAugust) 158ndash65

Thompson RG Singleton FDThrall RM and Smith BA (1986)lsquoComparative site evaluations forlocating a high energy physicslaboratory in Texasrsquo Interfaces 16(35)

Thurik R and Van der Wijst N(1984) lsquoPart-time labor in retailingrsquoJournal of Retailing 60(Fall) 62ndash80

Varadarajan PR (1989) lsquoMarket shareobjectives and the maximumsustainable growth of amultidimensional rm a portfoliostrategy perspectiversquo Working PaperTexas A amp M University CollegeStation TX

Varadarajan PR (1991) lsquoPerspectiveson corporate excellence in retailingrsquoJournal of Marketing Channels 1(2)29ndash52

Webster FE Jr (1992) lsquoThe changingrole of marketing in the corporationrsquoJournal of Marketing 56(October)1ndash17

Weitz BA (1979) lsquoA critical review ofpersonal selling research the need for

contingency approachesrsquo in AlbaumG and Churchill GA Jr (eds)Critical Issues in Sales ManagementState of the Art and Further ResearchNeeds Eugene OR College ofBusiness Administration University ofOregon p 76

Wernerfeldt B and Montgomery C(1986) lsquoWhat is an attractive industryrsquoManagement Science 32(October)1223ndash30

Wind YJ and Mahajan VJ (1981)lsquoDesigning product and businessportfoliosrsquo Harvard Business Review 59(JanuaryndashFebruary) 155ndash65

Wotruba TR (1990) lsquoThe relationshipof job image performance and jobsatisfaction to inactivity-proneness ofdirect salespeoplersquo Journal of theAcademy of Marketing Science 18(Spring) 113ndash22

Yelle LE (1979) lsquoThe learning curvehistorical review and comprehensivesurveyrsquo Decision Sciences 10(April)302ndash28

Zeithaml VV (1981) lsquoHow consumerevaluation processes differ betweengoods and servicesrsquo in Donnelly JHand George WR (eds) Marketing ofServices Chicago American MarketingAssociation pp 186ndash90


4 the results of the study including a description of the statistical treatmentand

5 a discussion of the contribution of this study to the retailing sector

Introduction

Over the years many conceptual frameworks and measurement classi cationschemes have attempted to match measurement with business strategy TheBoston Consulting Grouprsquos (BCG) portfolio planning model devised categoriesfor strategic business units (SBUs) based on market attractiveness and strengthof the rm in that market The GEMcKinsey Matrix and the TechnologyPortfolio Matrix followed with more theories (eg strategic gameboard andcontingency perspective) for expanding strategic measurement and managementof complex organizations (Bettis and Hall 1982 Coe 1981 Hapeslagh 1982Farguhar and Shapiro 1983 Hamermesh 1986) However they were limited bywhat they measured and how they evaluated the market environment thebusiness opportunity and competitive pressures (Abell 1980 Wind and Mahajan1981 Wernerfeldt and Montgomery 1986 Varadarajan 1989 Kerin et al1990)

Much has been written about factors that in uence the success of retail rmsBuzzell and Dew (1980) attempted to develop statistical relationships betweenstrength in the market and attractiveness of the market as CSFs in retailingManagement Horizons (1987) identi ed ve themes for success market respon-siveness and focused market dominance professional management and entrepre-neurial are innovative highly programmed resource relationships a leadershipposition in productivity and technology and a high value offer A Wall Streetreport summarizing other studies indicates that outstanding retailers holddominant market positions emphasize their merchandising uniqueness possessmanagement depth emphasize effective store location and invest in systems(Varadarajan 1991) Bell and Salmon (1996) enumerate six critical success factorsin retailing but they are addressed in a very general manner Lusch and Jaworski(1991) suggest that in retailing measurable output is not created until atransaction with a customer occurs and the customer exists in an uncontrollableexternal environment The ef ciency of service businesses such as retailing maybe more dif cult to evaluate than manufacturing business ef ciency in that it isdif cult to determine the (appropriate) amount of resources required to produceservice outputs (Sherman 1984) Schmenner (1986) suggests that concreteoperational measures are dif cult to put in place due to the variability ofemployeendashcustomer interaction related to services (such as in the retail trans-action process)

There is building consensus that traditionally de ned nancial performancemeasures such as return on investment models are not enough to evaluate acompanyrsquos competitive position and understand lsquohow we are doingrsquo in anincreasingly complex environment (Sherman 1984 Schmenner 1986 Eccles1991 Webster 1992 Stalk et al 1992 Kaplan and Norton 1992 1993 1996a)Financial measures alone are too simplistic in their interpretation of servicebusinessesrsquo performance (Bharadwaj and Menon 1993)









The empirical study





























076 089


739 067


completed31705 6619


on hand191878 29690




store1383 2671


102784 27723


2580 1134




0047 0095


29881 6762




















































Results






Tab

le 2

Int

erco

rrel

atio

n m

atri

x of

inp

ut v

aria

bles

(N

554

2)

INV

TU

RN

OV

ER

TR

AN

SS

TO

RE

AG

EH

RLY

EX

PM

GR

EX

PF

TSE

RV

CF

TP

TC

OS

TH

OU

RS

UR

RH

OU

SP

OP

ST

OR

PR

OX

IMO

TH

ER

OP

FAC

ICO

ST

INV

100

01

74

15

01

26

33

06

20

12

07

06

74

28

TU

RN

OV

ER

100

05

04

ndash41

ndash

13

07

04

ndash23

0

30

10

40

12

7T

RA

NS

100

13

02

29

54

11

18

34

17

09

84

44

STO

RE

AG

E1

002

11

71

10

61

41

40

3ndash

080

6ndash

23

HR

LYE

XP

100

27

02

04

38

04

03

ndash07

01

ndash05

MG

RE

XP

100

16

08

27

ndash07

09

ndash02

24

01

FT

SERV

C1

004

61

63

21

7ndash

045

13

7F

TPT

100

03

06

ndash02

ndash01

10

ndash04

CO

STH

OU

R1

001

81

2ndash

27

22

34

SUR

RH

OU

S1

000

6ndash

15

30

45

POPS

TO

R1

000

32

52

4PR

OX

IM1

000

71

6O

TH

ER

OP

100

07

FAC

ICO

ST1

00

Si

gni

cant

at

05

or l

ower














Inputvariable




coef cient






Lowperformers

Highperformers


Lowperformers

Highperformers







Inputvariable




coef cient






Lowperformers

Highperformers


Lowperformers

Highperformers







Inputvariable




coef cient






Lowperformers

Highperformers


Lowperformers

Highperformers



Tab

le 7

A s

umm

ary

of a

naly

ses

for

OP

PRO

FIT

S

AL

ES

QF

T a

nd G

MR

OI

Inpu

tva

riab

leA

spec

t of

scor

ecar

dO

PPR

OF

ITC

oef

cien

tR

anki

ngSA

LE

SQF

TC

oef

cien

tR

anki

ngG

MR

OI

Coe

fci

ent

Ran

king

TU

RN

OV

ER

Inte

rnal

054

6mdash

mdashmdash

mdashT

RA

NS

Inte

rnal

106

31

114

81

118

11

ST

OR

EA

GE

Inno

vati

on1

395

096

70

297

HR

LY

EX

PIn

nova

tion

mdashmdash

mdashmdash

mdashmdash

MG

RE

XP

Inno

vati

on0

407

220

41

253

FT

SE

RVC

Cus

tom

er2

513

394

21

114

FT

PT

Cus

tom

erndash

035

8mdash

mdashmdash

mdashC

OST

HO

UR

Cus

tom

erndash

172

41

445

066

6S

UR

RH

OU

SF

inan

cial

mdashmdash

ndash26

43

mdashmdash

PO

PS

TO

RF

inan

cial

mdashmdash

080

81

972

PR

OX

IMF

inan

cial

033

9mdash

mdashmdash

mdashFA

CIC

OST

Fin

anci

al-1

055

2ndash

105

6ndash

073

5

St

anda

rdiz

ed c

oef

cien

ts f

rom

the

res

ampl

ing

mod

el (

data

bei

ng r

un 1

000

tim

es)

are

bein

g us

ed



Discussion



















A perspective




Summary


References






















































































































The empirical study





























076 089


739 067


completed31705 6619


on hand191878 29690




store1383 2671


102784 27723


2580 1134




0047 0095


29881 6762




















































Results






Tab

le 2

Int

erco

rrel

atio

n m

atri

x of

inp

ut v

aria

bles

(N

554

2)

INV

TU

RN

OV

ER

TR

AN

SS

TO

RE

AG

EH

RLY

EX

PM

GR

EX

PF

TSE

RV

CF

TP

TC

OS

TH

OU

RS

UR

RH

OU

SP

OP

ST

OR

PR

OX

IMO

TH

ER

OP

FAC

ICO

ST

INV

100

01

74

15

01

26

33

06

20

12

07

06

74

28

TU

RN

OV

ER

100

05

04

ndash41

ndash

13

07

04

ndash23

0

30

10

40

12

7T

RA

NS

100

13

02

29

54

11

18

34

17

09

84

44

STO

RE

AG

E1

002

11

71

10

61

41

40

3ndash

080

6ndash

23

HR

LYE

XP

100

27

02

04

38

04

03

ndash07

01

ndash05

MG

RE

XP

100

16

08

27

ndash07

09

ndash02

24

01

FT

SERV

C1

004

61

63

21

7ndash

045

13

7F

TPT

100

03

06

ndash02

ndash01

10

ndash04

CO

STH

OU

R1

001

81

2ndash

27

22

34

SUR

RH

OU

S1

000

6ndash

15

30

45

POPS

TO

R1

000

32

52

4PR

OX

IM1

000

71

6O

TH

ER

OP

100

07

FAC

ICO

ST1

00

Si

gni

cant

at

05

or l

ower














Inputvariable




coef cient






Lowperformers

Highperformers


Lowperformers

Highperformers







Inputvariable




coef cient






Lowperformers

Highperformers


Lowperformers

Highperformers







Inputvariable




coef cient






Lowperformers

Highperformers


Lowperformers

Highperformers



Tab

le 7

A s

umm

ary

of a

naly

ses

for

OP

PRO

FIT

S

AL

ES

QF

T a

nd G

MR

OI

Inpu

tva

riab

leA

spec

t of

scor

ecar

dO

PPR

OF

ITC

oef

cien

tR

anki

ngSA

LE

SQF

TC

oef

cien

tR

anki

ngG

MR

OI

Coe

fci

ent

Ran

king

TU

RN

OV

ER

Inte

rnal

054

6mdash

mdashmdash

mdashT

RA

NS

Inte

rnal

106

31

114

81

118

11

ST

OR

EA

GE

Inno

vati

on1

395

096

70

297

HR

LY

EX

PIn

nova

tion

mdashmdash

mdashmdash

mdashmdash

MG

RE

XP

Inno

vati

on0

407

220

41

253

FT

SE

RVC

Cus

tom

er2

513

394

21

114

FT

PT

Cus

tom

erndash

035

8mdash

mdashmdash

mdashC

OST

HO

UR

Cus

tom

erndash

172

41

445

066

6S

UR

RH

OU

SF

inan

cial

mdashmdash

ndash26

43

mdashmdash

PO

PS

TO

RF

inan

cial

mdashmdash

080

81

972

PR

OX

IMF

inan

cial

033

9mdash

mdashmdash

mdashFA

CIC

OST

Fin

anci

al-1

055

2ndash

105

6ndash

073

5

St

anda

rdiz

ed c

oef

cien

ts f

rom

the

res

ampl

ing

mod

el (

data

bei

ng r

un 1

000

tim

es)

are

bein

g us

ed



Discussion



















A perspective




Summary


References





































































































































076 089


739 067


completed31705 6619


on hand191878 29690




store1383 2671


102784 27723


2580 1134




0047 0095


29881 6762




















































Results






Tab

le 2

Int

erco

rrel

atio

n m

atri

x of

inp

ut v

aria

bles

(N

554

2)

INV

TU

RN

OV

ER

TR

AN

SS

TO

RE

AG

EH

RLY

EX

PM

GR

EX

PF

TSE

RV

CF

TP

TC

OS

TH

OU

RS

UR

RH

OU

SP

OP

ST

OR

PR

OX

IMO

TH

ER

OP

FAC

ICO

ST

INV

100

01

74

15

01

26

33

06

20

12

07

06

74

28

TU

RN

OV

ER

100

05

04

ndash41

ndash

13

07

04

ndash23

0

30

10

40

12

7T

RA

NS

100

13

02

29

54

11

18

34

17

09

84

44

STO

RE

AG

E1

002

11

71

10

61

41

40

3ndash

080

6ndash

23

HR

LYE

XP

100

27

02

04

38

04

03

ndash07

01

ndash05

MG

RE

XP

100

16

08

27

ndash07

09

ndash02

24

01

FT

SERV

C1

004

61

63

21

7ndash

045

13

7F

TPT

100

03

06

ndash02

ndash01

10

ndash04

CO

STH

OU

R1

001

81

2ndash

27

22

34

SUR

RH

OU

S1

000

6ndash

15

30

45

POPS

TO

R1

000

32

52

4PR

OX

IM1

000

71

6O

TH

ER

OP

100

07

FAC

ICO

ST1

00

Si

gni

cant

at

05

or l

ower














Inputvariable




coef cient






Lowperformers

Highperformers


Lowperformers

Highperformers







Inputvariable




coef cient






Lowperformers

Highperformers


Lowperformers

Highperformers







Inputvariable




coef cient






Lowperformers

Highperformers


Lowperformers

Highperformers



Tab

le 7

A s

umm

ary

of a

naly

ses

for

OP

PRO

FIT

S

AL

ES

QF

T a

nd G

MR

OI

Inpu

tva

riab

leA

spec

t of

scor

ecar

dO

PPR

OF

ITC

oef

cien

tR

anki

ngSA

LE

SQF

TC

oef

cien

tR

anki

ngG

MR

OI

Coe

fci

ent

Ran

king

TU

RN

OV

ER

Inte

rnal

054

6mdash

mdashmdash

mdashT

RA

NS

Inte

rnal

106

31

114

81

118

11

ST

OR

EA

GE

Inno

vati

on1

395

096

70

297

HR

LY

EX

PIn

nova

tion

mdashmdash

mdashmdash

mdashmdash

MG

RE

XP

Inno

vati

on0

407

220

41

253

FT

SE

RVC

Cus

tom

er2

513

394

21

114

FT

PT

Cus

tom

erndash

035

8mdash

mdashmdash

mdashC

OST

HO

UR

Cus

tom

erndash

172

41

445

066

6S

UR

RH

OU

SF

inan

cial

mdashmdash

ndash26

43

mdashmdash

PO

PS

TO

RF

inan

cial

mdashmdash

080

81

972

PR

OX

IMF

inan

cial

033

9mdash

mdashmdash

mdashFA

CIC

OST

Fin

anci

al-1

055

2ndash

105

6ndash

073

5

St

anda

rdiz

ed c

oef

cien

ts f

rom

the

res

ampl

ing

mod

el (

data

bei

ng r

un 1

000

tim

es)

are

bein

g us

ed



Discussion



















A perspective




Summary


References

































































































































































Results






Tab

le 2

Int

erco

rrel

atio

n m

atri

x of

inp

ut v

aria

bles

(N

554

2)

INV

TU

RN

OV

ER

TR

AN

SS

TO

RE

AG

EH

RLY

EX

PM

GR

EX

PF

TSE

RV

CF

TP

TC

OS

TH

OU

RS

UR

RH

OU

SP

OP

ST

OR

PR

OX

IMO

TH

ER

OP

FAC

ICO

ST

INV

100

01

74

15

01

26

33

06

20

12

07

06

74

28

TU

RN

OV

ER

100

05

04

ndash41

ndash

13

07

04

ndash23

0

30

10

40

12

7T

RA

NS

100

13

02

29

54

11

18

34

17

09

84

44

STO

RE

AG

E1

002

11

71

10

61

41

40

3ndash

080

6ndash

23

HR

LYE

XP

100

27

02

04

38

04

03

ndash07

01

ndash05

MG

RE

XP

100

16

08

27

ndash07

09

ndash02

24

01

FT

SERV

C1

004

61

63

21

7ndash

045

13

7F

TPT

100

03

06

ndash02

ndash01

10

ndash04

CO

STH

OU

R1

001

81

2ndash

27

22

34

SUR

RH

OU

S1

000

6ndash

15

30

45

POPS

TO

R1

000

32

52

4PR

OX

IM1

000

71

6O

TH

ER

OP

100

07

FAC

ICO

ST1

00

Si

gni

cant

at

05

or l

ower














Inputvariable




coef cient






Lowperformers

Highperformers


Lowperformers

Highperformers







Inputvariable




coef cient






Lowperformers

Highperformers


Lowperformers

Highperformers







Inputvariable




coef cient






Lowperformers

Highperformers


Lowperformers

Highperformers



Tab

le 7

A s

umm

ary

of a

naly

ses

for

OP

PRO

FIT

S

AL

ES

QF

T a

nd G

MR

OI

Inpu

tva

riab

leA

spec

t of

scor

ecar

dO

PPR

OF

ITC

oef

cien

tR

anki

ngSA

LE

SQF

TC

oef

cien

tR

anki

ngG

MR

OI

Coe

fci

ent

Ran

king

TU

RN

OV

ER

Inte

rnal

054

6mdash

mdashmdash

mdashT

RA

NS

Inte

rnal

106

31

114

81

118

11

ST

OR

EA

GE

Inno

vati

on1

395

096

70

297

HR

LY

EX

PIn

nova

tion

mdashmdash

mdashmdash

mdashmdash

MG

RE

XP

Inno

vati

on0

407

220

41

253

FT

SE

RVC

Cus

tom

er2

513

394

21

114

FT

PT

Cus

tom

erndash

035

8mdash

mdashmdash

mdashC

OST

HO

UR

Cus

tom

erndash

172

41

445

066

6S

UR

RH

OU

SF

inan

cial

mdashmdash

ndash26

43

mdashmdash

PO

PS

TO

RF

inan

cial

mdashmdash

080

81

972

PR

OX

IMF

inan

cial

033

9mdash

mdashmdash

mdashFA

CIC

OST

Fin

anci

al-1

055

2ndash

105

6ndash

073

5

St

anda

rdiz

ed c

oef

cien

ts f

rom

the

res

ampl

ing

mod

el (

data

bei

ng r

un 1

000

tim

es)

are

bein

g us

ed



Discussion



















A perspective




Summary


References















































































































Tab

le 2

Int

erco

rrel

atio

n m

atri

x of

inp

ut v

aria

bles

(N

554

2)

INV

TU

RN

OV

ER

TR

AN

SS

TO

RE

AG

EH

RLY

EX

PM

GR

EX

PF

TSE

RV

CF

TP

TC

OS

TH

OU

RS

UR

RH

OU

SP

OP

ST

OR

PR

OX

IMO

TH

ER

OP

FAC

ICO

ST

INV

100

01

74

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Inputvariable




coef cient






Lowperformers

Highperformers


Lowperformers

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Inputvariable




coef cient






Lowperformers

Highperformers


Lowperformers

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Inputvariable




coef cient






Lowperformers

Highperformers


Lowperformers

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Tab

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Summary


References



























































































































Inputvariable




coef cient






Lowperformers

Highperformers


Lowperformers

Highperformers







Inputvariable




coef cient






Lowperformers

Highperformers


Lowperformers

Highperformers







Inputvariable




coef cient






Lowperformers

Highperformers


Lowperformers

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Tab

le 7

A s

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Summary


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Inputvariable




coef cient






Lowperformers

Highperformers


Lowperformers

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Inputvariable




coef cient






Lowperformers

Highperformers


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Tab

le 7

A s

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of a

naly

ses

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Discussion



















A perspective




Summary


References



















































































































Inputvariable




coef cient






Lowperformers

Highperformers


Lowperformers

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Tab

le 7

A s

umm

ary

of a

naly

ses

for

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PRO

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Discussion



















A perspective




Summary


References















































































































Tab

le 7

A s

umm

ary

of a

naly

ses

for

OP

PRO

FIT

S

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ES

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Discussion



















A perspective




Summary


References
















































































































Discussion



















A perspective




Summary


References





























































































































A perspective




Summary


References
















































































































Summary


References















































































































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