An assessment of operational efficiencies in the UK retail sector Wantao Yu and Ramakrishnan Ramanathan Jubilee Campus, Nottingham University Business School, Nottingham, UK Abstract Purpose – The paper’s aim is to assess performance of firms in the UK retail sector. Design/methodology/approach – Economic efficiencies of 41 retail companies working in the UK between 2000 and 2005 are examined in this study using three related methodologies: data envelopment analysis (DEA), Malmquist productivity index (MPI), a bootstrapped Tobit regression model. DEA is used to calculate technical and scale efficiencies of companies. Two outputs (turnover, profit before taxation) and three inputs (total assets, shareholders funds, and number of employees) are employed for the efficiency measurement. MPI is used to analyze the patterns of efficiency change over the six year period 2000-2005. DEA efficiencies are then used to test important hypotheses on the impact of environmental variables, namely head office location, type of ownership, years of incorporation, legal form and retail characteristic, on the functioning of the UK retail sector using bootstrapped Tobit regression. Findings – DEA analysis has shown that only ten retail companies are considered as efficient under CRS assumption, and 16 firms under VRS assumption in 2005. MPI results have indicated that about 50 percent of retail companies have registered progress in terms of MPI during 2000 and 2005. Twenty out of 41 retail companies have adopted advanced and efficient retailing technologies during this period. Three environmental variables, namely the type of ownership, legal form and retail characteristic, have been found to play significant roles influencing retail efficiency using bootstrapped Tobit regression. Research limitations/implications – Data availability has limited the level of analysis in some parts of this study, especially in the bootstrapped Tobit regression. Originality/value – This study seems to be the first in applying productivity analysis using DEA for the UK retail sector. Keywords Business performance, Retailing, Data analysis, United Kingdom Paper type Research paper Introduction Over the past few decades, efficiency and productivity have become an important issue for managers, both in the manufacturing and the service sector (Sellers-Rubio and Mas-Ruiz, 2007). In the retail industry, retail productivity plays an important role in the control and management of retail companies, and providing vital information for a number of tactical, strategic, and policy related decisions (Dubelaar et al., 2002; Sellers-Rubio and Mas-Ruiz, 2007). The analysis of productivity and efficiency has become an important activity in retailing (Oxford Institute of Retail Management, 2004; Barros and Alves, 2004; Lusch et al., 1995). However, it has been well-recognized that attempts to measure efficiencies and productivities of firms in the retail sector face a number of challenges owing to the difficulties in identifying the level of retail services. The current issue and full text archive of this journal is available at www.emeraldinsight.com/0959-0552.htm A previous version of this paper was presented at the Logistics Research Network Annual Conference 2007 held at Hull University, UK, 5-7 September 2007. An assessment of operational efficiencies 861 Received 14 November 2007 Revised 4 March 2008 Accepted 9 April 2008 International Journal of Retail & Distribution Management Vol. 36 No. 11, 2008 pp. 861-882 q Emerald Group Publishing Limited 0959-0552 DOI 10.1108/09590550810911656
Transcript
An assessment of operationalefficiencies in the UK retail sector
Wantao Yu and Ramakrishnan RamanathanJubilee Campus, Nottingham University Business School, Nottingham, UK
Abstract
Purpose – The paper’s aim is to assess performance of firms in the UK retail sector.
Design/methodology/approach – Economic efficiencies of 41 retail companies working in the UKbetween 2000 and 2005 are examined in this study using three related methodologies: dataenvelopment analysis (DEA), Malmquist productivity index (MPI), a bootstrapped Tobit regressionmodel. DEA is used to calculate technical and scale efficiencies of companies. Two outputs (turnover,profit before taxation) and three inputs (total assets, shareholders funds, and number of employees) areemployed for the efficiency measurement. MPI is used to analyze the patterns of efficiency change overthe six year period 2000-2005. DEA efficiencies are then used to test important hypotheses on theimpact of environmental variables, namely head office location, type of ownership, years ofincorporation, legal form and retail characteristic, on the functioning of the UK retail sector usingbootstrapped Tobit regression.
Findings – DEA analysis has shown that only ten retail companies are considered as efficient underCRS assumption, and 16 firms under VRS assumption in 2005. MPI results have indicated that about50 percent of retail companies have registered progress in terms of MPI during 2000 and 2005. Twentyout of 41 retail companies have adopted advanced and efficient retailing technologies during thisperiod. Three environmental variables, namely the type of ownership, legal form and retailcharacteristic, have been found to play significant roles influencing retail efficiency usingbootstrapped Tobit regression.
Research limitations/implications – Data availability has limited the level of analysis in someparts of this study, especially in the bootstrapped Tobit regression.
Originality/value – This study seems to be the first in applying productivity analysis using DEAfor the UK retail sector.
Keywords Business performance, Retailing, Data analysis, United Kingdom
Paper type Research paper
IntroductionOver the past few decades, efficiency and productivity have become an important issuefor managers, both in the manufacturing and the service sector (Sellers-Rubio andMas-Ruiz, 2007). In the retail industry, retail productivity plays an important role in thecontrol and management of retail companies, and providing vital information for anumber of tactical, strategic, and policy related decisions (Dubelaar et al., 2002;Sellers-Rubio and Mas-Ruiz, 2007). The analysis of productivity and efficiency hasbecome an important activity in retailing (Oxford Institute of Retail Management, 2004;Barros and Alves, 2004; Lusch et al., 1995). However, it has been well-recognized thatattempts to measure efficiencies and productivities of firms in the retail sector face anumber of challenges owing to the difficulties in identifying the level of retail services.
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0959-0552.htm
A previous version of this paper was presented at the Logistics Research Network AnnualConference 2007 held at Hull University, UK, 5-7 September 2007.
An assessmentof operational
efficiencies
861
Received 14 November 2007Revised 4 March 2008Accepted 9 April 2008
International Journal of Retail &Distribution Management
Vol. 36 No. 11, 2008pp. 861-882
q Emerald Group Publishing Limited0959-0552
DOI 10.1108/09590550810911656
Previous studies in this area have presented a number of measures, models andmethods to assess retail productivity and efficiency, including regression, stochasticfrontier analysis and data envelopment analysis (DEA). Particularly, in this study weestimate the economic efficiency of selected companies in the UK retail sector usingDEA. DEA is an operations research based performance evaluation methodology thathas been used in assessing managerially useful measure of company/store level retailproductivity. DEA allows using multiple measures of inputs and outputs forevaluating the performance of decision-making units (DMUs) within a retail companyor among companies in the retail industry (Wen et al., 2003). Over the last few years,efficiency in the retail industry in several countries has been analyzed using DEA by anumber of studies (Thomas et al., 1998; Ratchford, 2003; Donthu and Yoo, 1998; Kehand Chu, 2003; Barros and Alves, 2003, 2004; Kamakura et al., 1996). However, to ourknowledge, there seems to be no study on UK retail sector using DEA, though there arestudies in productivity of UK retail sector (Oxford Institute of Retail Management,2004).
In this study, economic efficiency of 41 retail companies working in the UK between2000 and 2005 are examined, employing three related methodologies: DEA, Malmquistproductivity index (MPI), and a bootstrapped Tobit regression model. DEA is used tocalculate technical and scale efficiencies of firms. The use of DEA for the analysisof comparative retail companies’ efficiency can be of value in examining thecompetitiveness of the retail industry as a whole. Competitiveness should be based onbenchmarking the retail companies, which compose the sector. The retail companiesthat achieved the highest efficiency are considered as benchmark and the economyefficiency of the other companies are evaluated relative to this benchmark. Twooutputs (turnover, profit before taxation) and three inputs (total assets, shareholdersfunds, and number of employees) are introduced for the efficiency measurement. MPI isemployed to analyze the patterns of efficiency change over the period 2000-2005. DEAefficiencies are then used to test important hypotheses on the impact of environmentalvariables, including head office location, types of ownership, years of incorporation,legal form, and retail characteristic, on the functioning of the UK retail sector usingbootstrapped Tobit regression. To overcome the problem of the inherent dependency ofDEA efficiency scores when used in regression analysis, a bootstrapping technique isapplied. The aim of this regression is to seek out those best practices that will lead toimproved performance throughout the whole retail chain.
The remainder of this paper is organized as follows. We first provide thefoundations of DEA methodology. A brief literature review on the DEA applications inretail sector follows. We then discuss the input/output and environmental variables,the proposed DEA model, and data collection issues. Next, we describe an empiricalstudy in which DEA is employed to assess the efficiencies of 41 retail companies in theUK. Managerial implications, limitations and future research are also discussed.
Review of DEA and its applications in retail sectorThis section makes a literature review of DEA, MPI, and bootstrapped Tobitregression analysis. For the sake of brevity of this paper, detailed discussions of thesetools are not described here. Important references are provided to help the interestedreaders. Then applications of DEA in retail sector over the last few years are reviewed.
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Data envelopment analysis (DEA)DEA is a mathematical programming technique that calculates the relative efficienciesof organizations (usually refers to a DMUs) based on multiple inputs and outputs(Charnes et al., 1978). To calculate efficiency scores employing DEA, two differentassumptions can be made, i.e. constant return to scale (CRS) and variable returns toscale (VRS). The VRS efficiency score measures pure technical efficiency, i.e. a measureof efficiency without scale efficiency. On the other hand, the CRS efficiency scorerepresents technical efficiency, which measures inefficiencies due to the input/outputconfiguration and the size of operations (Cooper et al., 2007). Scale efficiency can becomputed by the ratio of CRS efficiency to VRS efficiency. Hence, scale efficiency of aDMU operating in its most productive scale size is one. More details on DEA can befound in Cooper et al. (2007) and Ramanathan (2003).
Malmquist productivity index (MPI)A special method of time series analysis in DEA is to use the results of DEA inconjunction with the MPI. The MPI was introduced as a theoretical index by Caves et al.(1982) and popularized as an empirical index by Fare et al. (1994). The MPI is definedas the product of the “catching-up” and the “frontier shift” terms. The “catching-up”term relates to the extent by which a company improves its efficiency, while the“frontier-shift” term reflects the change in the efficient frontier surrounding thecompany between the two periods of time (Sellers-Rubio and Mas-Ruiz, 2006). Thisindex allows changes in productivity to be broken down into changes in efficiency(deviations from the best practice frontier) and technology change (TC) (movements ofthe frontier), and is defined using distance functions.
Bootstrapped Tobit regressionTobit regression is often encountered in second stage DEA, i.e. when the relationshipbetween exogenous factors (non-physical inputs) and DEA efficiency scores isassessed (Hoff, 2007). However, the previous DEA studies have shown that the efficientscores obtained in the first stage are correlated with the explanatory variables used inthe second term, so that the second-stage estimates will be inconsistent and biased(Xue and Harker, 1999; Simar and Wilson, 2000). Therefore, Simar and Wilson (1999)suggested that a bootstrap procedure should be employed to overcome thisproblem. The bootstrap is a computer-based method for assigning measures ofaccuracy to statistical estimates. It was first introduced by Efron (1979) and since thenit has become a popular and powerful statistical tool (Casu and Molyneux, 2000).More details on truncated regression with bootstrap can be seen in Simar andWilson (2007).
DEA applications in retail sectorThe analysis of productivity and efficiency has become an important activity inretailing (Barros and Alves, 2004; Lusch et al., 1995; Kamakura et al., 1996). Whenreviewing contemporary researches in efficiency measurement that were publishedover the last few years (Thomas et al., 1998; Ratchford, 2003; Donthu and Yoo,1998; Keh and Chu, 2003; Barros and Alves, 2003, 2004), it is apparent that thereis an increase in the use of DEA to evaluate retail efficiency and productivity(Table I).
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IJRDM36,11
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An assessmentof operational
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Sellers-Rubio and Mas-Ruiz (2006) have used DEA to estimate the economic efficiencyof supermarket chains in the Spanish retailing industry. The empirical application hasbeen carried out on a sample of 100 supermarket chains between 1995 and 2001. Thestudy has revealed high levels of economic inefficiency in the Spanish retailing sector.Barros (2006) has analyzed a representative sample of 22 hypermarkets andsupermarkets working in the Portuguese market, adopting a two-stage procedure tobenchmark the retail companies. In the first stage DEA has been used and in thesecond stage a Tobit model has been employed to estimate the efficiency drivers. Thefollowing are important conclusions from this study:
. efficiency of hypermarket and supermarket retail companies is high compared toother sectors;
. larger retail groups are more efficient than the smaller retailers;
. national retailers are on average more efficient than regional retailers;
. scale plays an important role in this market;
. factors such as market share, number of outlets and location are importantefficiency drivers; and
. regulation has a negative effect on efficiency.
Donthu and Yoo (1998) have analyzed 24 outlets of a fast-food restaurant chainusing DEA.
These previous studies have evaluated cost efficiency (Ratchford, 2003), technicalefficiency (Thomas et al., 1998; Donthu and Yoo, 1998; Keh and Chu, 2003; Barros andAlves, 2003) and scale efficiency (Keh and Chu, 2003; Barros and Alves, 2003). Themajority of studies adopt a static perspective (Thomas et al., 1998; Ratchford, 2003;Donthu and Yoo, 1998; Keh and Chu, 2003; Barros and Alves, 2003), whereas onlyBarros and Alves (2004) and Sellers-Rubio and Mas-Ruiz (2007) adopted a dynamicperspective, examining the patterns of changes in efficiency using MPI. For example,Sellers-Rubio and Mas-Ruiz (2007) have used the MPI for a sample of 96 supermarketchains operating in Spain between 1995 and 2003 to estimate total productivity changein these retailing firms and to decompose it into efficiency change and technicalchange (i.e. the consequence of innovation and adoption of new technologies).They have concluded that information, communication and technology (ICT) have thecapacity to alter the productive structures of retail firms, favoring their productivity.Barros and Alves (2004) have estimated total productivity change and decomposed itinto technically efficient change and technological change for a Portuguese retail storechain with MPI. The authors have ranked the stores according to their totalproductivity change for the period 1999-2000, and conclude that some storesexperienced productivity growth while others experienced productivity decrease.
Selection of input and output variables. The choice of the input and output variablesis vital to the successful application of DEA. Donthu and Yoo (1998) stated that inputand output variables for DEA should exactly reflect the retail company’s objectivesand sales situation. Based on a review of the literature, main inputs and outputscriteria that have been used to examine retail efficiency and productivity aresummarised in Table I. Previous studies have proposed different measures of output,both in monetary units (such as sales revenue, profit volume and value added)(Thomas et al., 1998; Donthu and Yoo, 1998; Keh and Chu, 2003) and in non-monetary
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units (such as customer store loyalty and satisfaction, and service quality) (Donthu andYoo, 1998; Keh and Chu, 2003).
The literature on productivity assessment in retail sector generally differentiatestwo different kinds of input–controllable inputs and non-controllable inputs, accordingto whether the company considers them or not in its management action plans (Donthuand Yoo, 1998; Sellers-Rubio and Mas-Ruiz, 2006). Since controllable inputs can becontrolled by companies to gain competitive advantage, it is a common practice to usethem as part of efficiency assessment. Examples of controllable inputs considered inthe literature include company managerial factors and personnel factors, such as sizeof company (e.g. square feet of selling space) (Pilling et al., 1995; Lusch and Serpkenci,1990), the number of outlets in the supermarket chain (Sellers-Rubio and Mas-Ruiz,2006), current total assets (Doutt, 1984), and the number of employees (Thomas et al.,1998; Sellers-Rubio and Mas-Ruiz, 2006). In contrast, non-controllable inputs aregenerally considered as environmental variables since they could influence theefficiency of companies but are not directly controllable by the companies. Examples ofenvironmental variables considered in the literature include retail structure (Goldman,1992), retail sector conditions (Goldman, 1992), location (Donthu and Yoo, 1998),demographics of clientele in the area (Donthu and Yoo, 1998), and national economicdevelopment (Pilling et al., 1995). Normally, non-controllable inputs are ignored in theestimation of retail productivity (Donthu and Yoo, 1998). We follow a similar strategyin choosing the controllable and environmental variables for this study and the detailsare discussed in the next section.
Methodology and variablesMethodologyIn this paper, three methodologies, namely DEA, MPI, and bootstrapped Tobitregression are used to study the operational efficiencies of retail sector in the UK. Theconceptual framework is proposed in Figure 1. We use the two-stage method in thisstudy (Coelli et al. (2005) for more details). In the first-stage analysis, DEA is used tocalculate technical and scale efficiencies of retail companies, which includes only theconventional inputs and outputs. Two outputs (turnover, profit before taxation) andthree inputs (total assets, shareholders funds, and number of employees) are employedfor the efficiency measurement. In general, any DEA study considers performance
Figure 1.Conceptual framework
Operational Efficiencyfor the Year 2005
ControllableInputs OutputsTransformation
EnvironmentalVariables
DEA
Bootstrapped Tobit Regression
Operational EfficiencyTrends over Time
MPI
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analysis at a given point time. However, extensions to the standard DEA proceduressuch as MPI approach have been reported to provide performance assessment over aperiod time (Ramanathan, 2003). Thus, MPI is used to examine the patterns ofefficiency change over the period 2000-2005. In the second-stage, the DEA efficiencyscores from the first-stage are used to test important hypotheses on the impact ofenvironmental variables, including head office location, types of ownership, years ofincorporation, legal form, and retail characteristic, on the functioning of the UK retailsector using bootstrapped Tobit regression (Figure 1). The regression aims toinvestigate those best practices that will generate improved performance throughoutthe whole retail chain. More details on the implementation of the conceptual frameworkin our analysis, such as the choice of inputs, outputs and environmental variables, arediscussed in the next subsection.
Selection of inputs, outputs, and environmental variablesInputs and outputs. As can be seen from Table I, different authors have employeddifferent measures of output, such as sales revenue, customer satisfaction, and servicequality. In our study, we use two monetary outputs. Sales revenue (Donthu and Yoo,1998; Barros and Alves, 2003, 2004; Zhu, 2000; Sellers-Rubio and Mas-Ruiz, 2006) is thefirst output. Justification for this selection is that retail companies work with a largerange of products and services, which hinders the collection of disintegratedinformation on outputs produced (Sellers-Rubio and Mas-Ruiz, 2006). Moreover, retailcompanies can achieve typical income apart from their main activity, which is notincluded in their sales volume figures; apart from sales volumes, retailers pay specialattention to results as they guarantee the viability of the company; and considering thevolume of profits allows for inclusion of the influence of other types of costs notconsidered as inputs (Sellers-Rubio and Mas-Ruiz, 2006). Therefore, the second outputused is the profit volume of the company (Barros and Alves, 2003, 2004; Zhu, 2000;Sellers-Rubio and Mas-Ruiz, 2006).
With regard to inputs, as above mentioned literature review, the factors used toproduce goods/services can be divided into controllable and non-controllable. In ourstudy, we apply three controllable productive factors, namely, the number ofemployees (Thomas et al., 1998; Barros and Alves, 2003, 2004; Sellers-Rubio andMas-Ruiz, 2006), total assets (Doutt, 1984), and shareholders’ funds (Sellers-Rubio andMas-Ruiz, 2006, 2007; Ratchford, 2003) as well. Here, shareholders’ funds are equal tothe total of share capital plus reserves. Some details of the outputs and controllableinputs used in this study are shown in Table II.
Environmental variables. In the second-stage, the technical efficiency variable returnto scale index of the retail companies is regressed using bootstrapped Tobit regressionmethodology to identify the impact of the environmental variables listed in Table II.The Tobit model used in our study is presented as follows:
u ¼ aþ bðvariableÞ:
Here, u is VRS efficiency scores of retail companies in 2005, we use five environmentalvariables (Table II) that could influence a retail company’s operational efficiency.These factors are not the conventional inputs and output in the DEA model and areassumed not under the control of business management (Boame, 2004; Casu andMolyneux, 2000). The explanatory factors can include legal form (public/private),
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6
Table II.Inputs, outputs, and
environmental variables(data in 2005)
An assessmentof operational
efficiencies
869
location characteristics, company size, age, industry group, and governmentregulations (Barros, 2006; Casu and Molyneux, 2000; Wincent, 2005; Temtime andPansiri, 2005; Pansiri, 2007; Glancey, 1998). In our study, the following environmentalvariables are considered: head office location, types of ownership, years of incorporation,legal form, and retail characteristic. Data on these variables were independently selectedand care was taken to separate controllable input factors that determine efficienciesand environmental variables that characterise the management practices of retailcompanies.
Head office location is a dummy variable. It is classified into eight different areas inaccordance with data from FAME (Table II). A head office is like a large laboratory inbusiness management, which accumulates knowledge on personnel management, newproduct development, quality control and operations strategy (Kono, 1999). Ownershipis also a dummy variable (one for national firms and two for foreign firms), which isused to measure the advantages achieved through knowledge and experience of thelocal market. Evidence for the variable has been found by a number of previousstudies. For example, as mentioned above, Barros (2006) has found that nationalretailers in Portugal are on average more efficient than local retailers. Years ofincorporation means the time of company forming a legal corporation. This variable isdesigned to evaluate operations experience, which a retail company has. Experience isa major factor shaping strategic directions and collective knowledge. The impact ofcompany’s experience on business performance has been widely discussed. A positiverelationship between company age and efficiency may be expected if older companiesbenefit from dynamic economies of scale by learning from experience. Older companiesmay also benefit from reputation effects, which allow them to achieve a higher marginon sales (Glancey, 1998). Legal form stands for the legal nature of a company, here onefor private limited, two for public quoted, and three for public not quoted. There areadvantages and disadvantages to both structures of private company and publiccompany. An advantage is that public companies are able to more easily raise fundsand capital through the sale of their securities. Because they are answerable toshareholders, publicly quoted companies face greater pressure to achieve acceptablereturns on investment over shorter periods of time than do private companies (White,1995). As such, private companies have the ability to focus more on the achievement oflong-term returns. Finally, retail characteristic is another dummy variable (one for foodretailing, two for home appliances retailing, three for DIY and home improvementretailing, four for fashion retailing, and five for others), which is carried out to assessmarket coverage of both food and non-food retail sectors. Grocery retailing is thelargest sector in the UK’s retail market economy. According to IGD Research (2007),the UK grocery retail market will continue to grow at an average rate of 2.9 percentover the next five years and will be worth £138.2bn (at current prices) by 2010. It canbe argued that distinguishing between retailers on the basis of sector groups (food andnon-food retailing) is critical, since different retail sector groups have experienceddifferent degrees of success. However, the relationships between retail sector groupsand organizational performance remain speculation only.
DataTo estimate the production frontier, we used panel data on retail companies for theyears 2000-2005. All the data required for this study are obtained from the FAME
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database (Fame database, Copyright q 2007 Bureau van Dijk Electronic Publishinghttps://fame.bvdep.com/). Since, we use profits and shareholders funds as two of threeinputs, 13 retail companies made negative profits and shareholders funds areeliminated in this study, for example, Sainsbury group, Somerfield group, B&Q Plc,Comet group Plc, Phones4u Ltd, Iceland foods Ltd, etc. In addition, due tonon-availability of appropriate archival data, some more retail companies could not beanalyzed in this analysis, for example, Next Plc, First queen retailing Ltd, WH SmithPlc, and Burberry group Plc, etc. The final sample consists of 41 different retailcompanies (Table III) operating continually between 2000 and 2005 in the UK retailmarket, and these retail firms operate their business in several different sectors, suchas food retailing, home appliances, DIY and home improvement, and fashion retailing.
ResultsThe results of data analysis are presented in the following three sections. The firstsubsection analyzes the results of DEA analysis. Second, the trends in efficiency andproductivity over time are discussed in the second subsection. Finally, test importanthypotheses on the impact of variables on the functioning of the UK retail sectorthrough regression analysis.
Efficiency of retail companies in 2005 using DEAThe DEA index can be computed in several ways. In this section, we evaluate theeconomic efficiency of retail companies and divide it into CRS efficiency, VRSefficiency, and scale efficiency. Table III shows that the efficiency scores of the 41 retailcompanies in the year of 2005. It can be seen that under the CRS assumption the mostefficient firms are Asda Group Ltd (1.00); BHS Group Ltd (1.00); Boots Company Plc(The) (1.00); Farmfoods Ltd (1.00); HMV Music Ltd (1.00); Jewson Ltd (1.00); Lidl Ltd(1.00); Netto Foodstores Ltd (1.00); River Island Clothing Co. Ltd (1.00); and RobertWiseman & Sons Ltd (1.00). These ten retail firms’ CRS efficiencies are all 1.00, andlocated on the efficient frontier. It implies that these companies have produced themaximum possible outputs (turnover, profit before the taxation) for the given level ofinputs (total assets, shareholders funds, and the number of employees). We can see thatall these ten companies are also efficient when VRS is assumed. The average efficiencyscore under CRS assumption is equal to 0.73. If we include all source of inefficiency,retail companies could operation on average at 73 percent of their current outputs levelusing their given input value. The other several companies, such as Allied Domecq Ltd(0.963); Boots The Chemists Ltd (0.919); DSG Retail Ltd (0.969); Tesco Stores Ltd(0.988); and The Carphone Warehouse Ltd (0.978) also have good relative CRSefficiency.
With regard to VRS efficiency that measures pure technical efficiencies are largerthan CRS efficiencies. The CRS efficiencies of Allied Domecq Ltd (0.963); DSG RetailLtd (0.969); Kellogg UK Holding Company Ltd (0.706); Marks and Spencer Plc (0.506);Tesco Plc (0.734); and Tesco Stores Ltd (0.988) are all less than 1.00, but their VRSefficiencies are 1.00. This could mean that these firms could not achieve 100 percentCRS efficiencies since they do not operate at their most productive scale size. Scaleefficiency can be calculated as the ratio of CRS and VRS efficiency. Beside the ten CRSefficient retail companies, some firms have higher scale efficiency near to 1.00,including Allied Domecq Ltd (0.963); Argos Ltd (0.99); Boots The Chemists Ltd (0.942);
An assessmentof operational
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DSG Retail Ltd (0.969); House of Fraser (0.995); IKEA Ltd (0.98); JJB Sports Plc (0.992);John Lewis Partnership Plc (0.938); John Lewis (0.938); Kingfisher Plc (0.939); New LookRetailers Ltd (0.998); Primark Stores Ltd (0.976); Sports World International Ltd (0.998);Tesco Stores Ltd (0.988); The Carphone Warehouse Ltd (0.998); The Game Group Plc(0.999); TK Maxx Group Ltd (0.922); and Wal-Mart Stores (UK) Ltd (0.955).
Retail company CRS efficiency VRS efficiency Scale efficiency
Notes: drs, decreasing returns to scale; irs, increasing returns to scale; crs, constant returns to scale
Table III.DEA scores for 41 retailcompanies in 2005
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Trends of changes in efficiency scores over time using MPIIn this section, patterns of changes in efficiency of the retail companies during theperiod of 2000-2005 employing the MPI approach are presented.
Based on the above analysis of efficiency scores of 41 retail companies in 2005, weselected top ten retail companies whose CRS efficiency was 1.00 in 2005 and thencalculated their CRS efficiency during the previous six years, from 2000 to 2005. TheCRS efficiency scores are presented in Figure 2. The figure demonstrates the CRSefficiency of Lidl Ltd kept the same scores of 1.00 in the period considered. Asda GroupLtd, Boots Company Plc (The), Netto Foodstores Ltd, River Island Clothing Co. Ltd,and Robert Wiseman & Sons Ltd experienced a significant progress over the years.Particularly, the CRS efficiency of Netto Foodstores Ltd was 0.049 in 2000, it increase to1.00 in 2004, the next year of 2005 maintained the same score of 1.00. Boots CompanyPlc (The) also improved from 0.546 in 2000 to 1.00 in 2002; the next three years (2003,2004, and 2005) kept the score of 1.00. However, some companies experienced afluctuation during the period of 2000-2005. For example, The CRS efficiency ofFarmfoods Ltd was 1.00 in 2000, but it decreased to 0.565 in 2002, and then increased to1.00 in 2003, it retained the same score in 2004 and 2005. BHS Group Ltd improvedfrom 0.541 in 2000 to 1.00 in 2001, and then dropped to 0.854 in 2003, over the nextthree years maintain the higher scores of 1.00.
The MPI over the time of six years from 2000 to 2005 is presented in Table IV, thevalues are geometric means of MPI for the five period 2000-2001, 2001-2002, 2002-2003,2003-2004, and 2004-2005. It shows that 22 out of 41 retail companies have progressedin terms of MPI during the period. Here, we select Netto Foodstores Ltd as a sample
Figure 2.The pattern of CRS
efficiency scoresof ten efficient companies
during 2000-2005
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Asda G
roup
Ltd.
BHS Gro
up L
td.
Boots
Compa
ny P
lc (T
he)
Farmfo
ods L
td.
HMV M
usic
Ltd.
Jewso
n Ltd
Lidl L
td.
Netto F
oods
tores
Ltd
River I
sland
Clot
hing C
o. Ltd.
Robert
Wise
man &
Son
s Ltd.
CR
S E
ffic
ienc
y Sc
ores
2000 2001 2002 2003 2004 2005
An assessmentof operational
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that has registered the highest improvement in MPI (1.712) and discuss it in moredetail. The MPI for the company is 1.712, which means there is an increase inimproving the performance in terms of “Turnover” and “Profit before taxation” for thegiven level of “Total assets”, “Shareholders funds”, and “the number of employees” in
Note: All Malmquist index averages are geometric means
Table IV.MPI analysis of 41 retailcompanies during2000-2005
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the year of 2005 compared to the year of 2000. It can be seen that the progress of MPIfor Netto Foodstores Ltd was contributed by a significant increase in technicalefficiency change (1.829) rather than a decrease in technology change (0.936) over thetime. The change in technical efficiency is the diffusion of best-practice technology inthe management of the activity and is attributed to investment planning, technicalexperience, and management and organization in the companies. Hence, it can beconcluded that the diffusion of best-practice technology in Netto Foodstores Ltdimproved in the period, and the improvement of MPI is contributed by better efficiencyprogress rather than technology changes. Moreover, for the period under analysis, weverify that there is an increase in the VRS technical efficiency change (1.026) and scaleefficiency change (1.783). The improvement in pure technical efficiency implies thatNetto Foodstores Ltd has conducted investment in organizational factors in accordancewith the company management, for example better balance between inputs andoutputs, and efficient quality management. Another sample of Allied Domecq Ltd alsoanalyzed, its MPI is 1.008, indicating more outputs (turnover, and profit beforetaxation) was produced using a given level of inputs (total assets, shareholders funds,and the number of employees). However, the progress of MPI for Allied Domecq Ltd iscontrary to Netto Foodstores Ltd, it was contributed by technology changes (1.016)rather than technical efficiency change (0.993). In addition, there are no change in VRStechnical efficiency change (1.000), and a reduction in scale efficiency change (0.993)over the time.
As above-mentioned analysis, Table IV demonstrates that 22 retail firms haveprogressed in terms of MPI between 2000 and 2005. Among these 22 companies, NettoFoodstores Ltd was ranked first for the highest improvement in MPI (1.712), andfollowed by Asda Group Ltd (1.218); HMV Music Ltd (1.194); Boots Company Plc(The) (1.174); River Island Clothing Co. Ltd (1.161); and Jewson Ltd (1.131). Among those19 retail companies who have suffered regress in MPI, IKEA Ltd has revealed thehighest regress (0.859) during the period, the next one is Matalan Retail Ltd (0.873).The MPI for Farmfoods Ltd is 1.00, which indicates that no change in averageproductivity of the region in the period considered. Technological change is the result ofinnovation, i.e. the adoption of new technologies, and communication system, by thebest-practice companies. Approximately, half of retail companies (about 50 percent,20 out of 41) have introduced the advanced and efficient retailing technologies over thelast six years. Let us undertake a further analysis, Jewson Ltd has revealed thehighest progress (1.131) in terms of technology change, it can be argued that Jewson Ltdhas obtained better achievements than other retail companies in the UK in adoptingefficient technologies for retailing operations. It is followed by Asda Group Ltd (1.124),the company also seems to achieve better than others. In addition, the Table IV showsthat the 41 retail companies in UK have obtained various levels of improvement orregress in the terms of VRS technical efficiency change and scale efficiency change.
Based on the above-mentioned analysis, it can be concluded that these 41 retailcompanies in the UK experienced progress, regress and no change in MPI over thelast six years 2000-2005. 22 out of 41 (about 54 percent) retail companieshave revealed progress, but only one firm, i.e. Farmfoods Ltd has expressed nochange in the productivity. Also, approximately 50 percent of retail companieshave shown good achievements on introducing efficient technologies for retailingoperations.
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875
It may be noted that the company with the highest progress (Netto Foodstores Ltd)is a Danish company. Other foreign companies operation in the UK, namely AsdaGroup Ltd and Wal-Mart Stores (UK) Ltd have also shown progress in MPI during theperiod of study. Hence, there seems to be a linkage between the ownership (foreign orUK) with the efficiencies. Moreover, there seems to be a reasonable linkage between thelegal form and efficiencies. There are many private companies (e.g. Allied Domecq Ltdand HMV Music Ltd) that have registered progress while several public companies(e.g. JJB sports) do not seem to have registered good progress. The next section dealswith these linkages quantitatively by employing statistical concepts. We use DEAefficiencies as indicators of performance instead of MPI in the next section.
Drivers of efficiency using bootstrapped Tobit regressionWe conducted both Tobit regression and bootstrapped Tobit regression analysis.Recent DEA literature supports using bootstrapped Tobit regression in order toovercome the problem of inherent dependency of efficiency scores when used inregression analysis (Xue and Harker, 1999; Simar and Wilson, 2007). Hence, we reportthe results of bootstrapped Tobit regression in this paper. The Stata software program(version 10.0) was used to carry out the bootstrapped Tobit analysis. In order to bemore objective and reliable, we undertook bootstrapped Tobit analysis by consideringthe impact of one variable at a time instead of using a multiple regression equation.The result of analysis is presented in the Table V. The observed coefficients, t-ratios,Wald x 2, Prob . x 2, Pseudo R 2, Sigma, and Log likelihood are reported in this table.It reveals that efficiency scores are positively and statistically significant with threevariables (type of ownership, legal form and retail characteristic). Thus, thebootstrapped Tobit regression analysis shows that ownership, legal form and retailsector can be considered as the driving forces influencing efficiency of retailers. Theother two variables, namely head office location and years of incorporation, do nothave significant relationship with efficiency.
Results of bootstrapped Tobit regression analysis in Table V show that type ofownership has a significant positive coefficient. This means that the VRS efficiency offoreign retailers is on an average 11.31 percent higher than that of local retailcompanies. This result agrees with the observation on the impact of foreign and localretailers using the MPI analysis in the previous section. In addition, the legal form(private limited, public quoted, and public not quoted) of a company seems to havesignificant relationship with its VRS efficiency. This suggests that private retailcompanies seemed to be more efficient than the other two legal forms. Based on the
Environmental variablesObserved
coefficients Wald x 2 Prob. . x 2 Pseudo R 2 SigmaLog
Notes: Dependent variable is VRS efficiency scores in 2005; *significant are at ten percent level; a thenumbers in parentheses are t-ratios
Table V.Results of bootstrappedTobit regressionwith C ¼ 500 samplesof size 41
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regression results shown in Table V, it may be safe to think that the efficiencies ofprivate companies could be, on an average, 8.8 percent more than their publiccounterparts. Table V also shows that retail characteristic of companies is alsostatistically significant with their efficiencies. Looking at the results of Table V, foodretail companies seem to be more efficient than other retail sectors in the UK. Theefficiency, on an average, increases by about 3.4 percent when we move from fashionretailing, to DIY and home improvement retailing, to home appliances retailing, and, tofood retailing. The DEA analysis also obtained the same results (Table III), four out often (40 percent) CRS efficient companies are food retailer, i.e. Asda Group Ltd,Farmfoods Ltd, Lidl Ltd, and Netto Foodstores Ltd The next section provides adiscussion on this and additional results.
Discussion and managerial implicationsIn this section, we critically analyze the DEA, MPI and bootstrapped Tobit regressionresults described in the previous sections, and discuss their managerial implicationsas well.
The results derived from DEA analysis indicate that only ten retail companies havethe highest CRS efficiency of 1.00. And among these ten CRS efficient companies, thereare four food retailers, namely, Asda Group Ltd, Farmfoods Ltd, Lidl Ltd, and NettoFoodstores Ltd Three efficient companies, i.e. Asda Group Ltd (USA), Lidl Ltd(Germay), and Netto Foodstores Ltd (Denmark), are foreign-owned companies in UK.This presents that these three firms have generated the maximum possible outputs(turnover, profit before the taxation) for the given level of inputs (total assets,shareholders funds, and the number of employees). According to Retail KnowledgeBank (2001), there were totally four foreign-controlled food retailers in 2001 in the UK,namely, Asda Group Ltd (USA), Lidl Ltd (Germany), Netto Foodstores Ltd (Denmark),and Aldi Ltd (Germany). To our knowledge, the number has not changed until today.Food retailing is the largest sector within the UK’s retail market. There are 102,511grocery stores in 2006 (IGD Research, 2007). Tesco and Sainsbury are the two majorUK based firms in the food sector, but CRS efficiency of Tesco Plc was 0.734 that isclose to the average CRS efficiency of 0.73 in 2005, while that of Sainsbury Plc (0.693)was below the average level. Therefore, it can be argued that the efficiency offoreign-owned food retail companies seems to be higher than that of local retailers.Under the VRS assumption, there are 16 retail companies that have achievedefficiencies. But Argos Ltd, JJB Sports Plc, New Look Retailers Ltd, and WhitbreadGroup Ltd seem to be performing much worse VRS efficiency. Inefficiency reflects thefailure of these firms to obtain the maximum feasible output given the amount ofinputs used.
The advances in the information, communication, and technology coupled withglobal and regional competitions have changed the landscape of various serviceproviders. Technological change is one of the most important elements of futurecompetition in retailing, and correct adaptation of new technologies gives significantcompetitive advantages to firms achieving the innovation process. The results of MPIconsidered in our study verify that about 50 percent (22 out of 41) of retail companieshave expressed progress in terms of MPI during the period 2000 and 2005. Especially,Netto Foodstores Ltd achieved the highest improvement in MPI (1.712), which wascontributed by a significant increase in technical efficiency change rather than
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877
a technology change. And, approximately half of retail companies (20 out of 41) haveintroduced the advanced and efficient retailing technologies during the periodconsidered. However, there were 19 firms have suffered regression in MPI, for example,IKEA Ltd and Matalan Ltd and, about 50 percent of companies have registered regressin terms of technology change in the period, which means that these 21 retailcompanies have not adopted or implemented effectively new technologies in theiroperations procedures. Information technology is a tool to enhance the overall strategyof the organization as well as being used to promote competitive advantage in themarket, consequently to improve organizational performance. Technical efficiency is aconsequence of various factors such as managerial policies, company financialcondition, and scale. Therefore, it can be suggested that theses companies shouldreview the operational procedures that would improve the efficiency of operations, andadaptation of new technology in retailing operations.
The results of DEA reveal that the primary cause of efficiency is the scaleeconomies, but it does not identify the other more driving factors influencing efficiency.A bootstrapped Tobit regression allows us to investigate other efficiency driversbeyond the scale economies. The bootstrapped Tobit analysis in our study presentsthat legal form is one of the determinants to retailers’ efficiency. As far as we are notaware of any DEA studies in retail sector that has used legal form in regressionanalysis. Our study shows that private retail companies in the UK seem to be moreefficient than public operators. The private limited company is the most commonbusiness structure in the UK retail sector, in our study 28 out of 41 retailers are privatelimited companies. The regression result also indicates that retail characteristic isanother driver of efficiency, and food retail companies are more efficient than non-foodretailers in the UK. According to IGD Research (2007), food retailing is one of the mostdynamic sectors in the UK’s retail market economy, and the grocery market was worth£123.9bn in 2006, an increase of 3.4 percent on 2005. It will continue to grow at anaverage rate of 2.9 percent over the next five years. Moreover, over the last few yearssupermarkets have expanded into the less-traditional non-food categories which nowaccount for more than 10 percent of sales through supermarkets (IGD Research, 2007).In addition, the study identifies that the national or foreign ownership is anotherdriving force influencing efficiency in the British retail sector. Foreign retail companiesin the UK seem to be more efficient local retailers. Although cultural and businessesdifference might create obstacles to successful market entry and adaptation, thesesobstacles can be overcome over time through learning. Moreover, theinternationalization literature ( Johanson and Vahlne, 1990,1992) has implied thatpsychically close countries are more similar and, because similarities are easier tomanage than differences, it is expected that businesses will achieve greater success insimilar markets. There is, to some extent, fewer and lower psychic distance, such ascultural, regulatory, legal, and financial, for foreign retailers from EU and USA (e.g.Asda Group Ltd (USA), Lidl Ltd (Germay), and Netto Foodstores Ltd (Denmark)) tooperate in the UK market. This result is contrary to Barros’s (2006) study, the authorhas found that national retailers in Portuguese market operated better than foreigncompanies. On the other hand, the factor of head office location does not havesignificant relationship with retail efficiency, 20 out of 41 retail companies’ head officesare located in London. London remains a powerful magnet for business, while othercities such as Glasgow, Edinburgh, Manchester, and Leeds have become increasingly
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attractive for subsequent roll-outs. Moreover, our study verifies that years ofincorporation is not an efficiency driver. The reasons for this result may be that thenew retail companies could be easier to adapt the new technology and businessmanagement which normally generate improved performance. These results contrastwith the significant positive relationship found by Dobson and Gerrard (1989) andhave suggested that older companies have not derived advantages from reputationeffects or accumulated experience. Glancey (1998) has also conclude that youngercompanies display significantly higher growth rates than older companies.
ConclusionThis study aims to investigate economic efficiency of 41 retail companies working inthe UK between 2000 and 2005. To our knowledge, this seems to be the first study toanalyze the performance efficiency of retail companies in the UK, and consequently thewhole UK’s retail sector. The measurements have been undertaken using a popularbenchmarking tool of the DEA, as well as MPI and a bootstrapped Tobit regressionmodel.
We first use the DEA methodology to estimate efficiency on a sample of 41 retailfirms during the period considered. The results using data for the year 2005 haveshown that only ten retail companies are considered as efficient under CRSassumption, and 16 firms under VRS assumption. The general conclusion is that theaverage efficiency of retail companies in the UK was less than 75 percent over the time.Benchmarks are provided for improving the operations of poorly performing retailers.Then, the MPI has been computed to estimate productivity change. The results haveshown that about 50 percent (22 out of 41) of retail companies have expressed progressin terms of MPI during 2000 and 2005, especially Netto Foodstores Ltd has achievedthe highest improvement in MPI, while IKEA Ltd has suffer the highest regress inproductivity during the period considered. And there are 20 out of 41 retail companieshave adopted the advanced and efficient retailing technologies during this period. Theanalysis also has shown that there has been no change in productivity of the regionduring the six years of 2000 to 2005, such as Farmfood Ltd Finally, we carried out abootstrapped Tobit analysis, under this the determinants of economic efficiency areinvestigated. The analysis has verified that legal form and ownership of company arethe possible driving forces influencing efficiency. It can be argued that private retailcompanies seemed to be more efficient than the other two legal forms, and foreignretailers in the UK seem to operate better than local companies. In addition, it is foundthat retail characteristic is another driver of efficiency. The other two factors, i.e. headoffice location and years of incorporation are not the efficiency drivers.
This study has some limitations. DEA is a model that evaluates the relativeefficiency of different homogeneous DMUs, based on linear programming techniques.However, although all the included firms are retail companies, the future study shouldtake into account that the market, services and business strategy are very differentamong food and non-food retailing (e.g. Tesco vs IKEA). Moreover, although we triedto collect data through a number of ways, some information about retail companies inthe special years is still not available. For example, during the bootstrapped Tobitregression analysis, just several environmental factors such as head office location,types of ownership, years of incorporation, legal form, and retail characteristic arewithin FAME. Therefore, the data set is short, and some factors have their own
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difficulties in the statistics analysis. The conclusions are limited. Reducing the numberof observations in the DEA variables may increase the possibility that a givenobservation will be considered relatively efficient. Therefore, further research is neededto do in this area and to also confirm the results obtained in our study.
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Further reading
Retail Knowledge Bank (2001), International Retailers in the UK, The Future of UK Retailing?Emap Retail.
Corresponding authorRamakrishnan Ramanathan can be contacted at: [email protected]
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