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Benchmarking greenlogistics performance with
a composite indexKwok Hung Lau
School of Business Information Technology and Logistics, College of Business,Royal Melbourne Institute of Technology University, Melbourne, Australia
Abstract
Purpose – This paper aims to discuss the development and use of a green logistics performanceindex (GLPI) for easy comparison of performance among industries and countries. It uses the surveydata collected from the home electronic appliance industry in China and Japan as an example todemonstrate the index development process and compare the performance of green logistics (GL)practices between the two countries using the proposed index.
Design/methodology/approach – Two-sample t-test and one-way analysis of variance (ANOVA)were used to analyse the data collected from a questionnaire survey. Principal component analysis(PCA) was employed to derive the weights from the survey data for the GLPI.
Findings – The findings reveal that the GLPI derived using PCA is robust and gives similar resultsas obtained through two-sample t-test and ANOVA of the dataset in the comparison of performanceamong firms and between countries in the study.
Research limitations/implications – This study lends insight into the use of an objectivelyderived composite index to measure and compare GL performance. To serve mainly as a proof ofconcept and to enhance response rate in the questionnaire survey, the scope of the study is limited tothree major logistics functions in an industry in two countries.
Practical implications – Managers can use the GLPI to benchmark their performance in therespective logistics areas and revise their supply chain strategy accordingly. The proposed index mayalso assist governments in formulating policies on promoting their GL implementation.
Social implications – A comprehensive composite index to benchmark GL performance canfacilitate and encourage industries to invest in GL. This will help reduce negative impacts of logisticsactivities on the environment.
Originality/value – Research in GL to date has largely focused on theory and managementapproach. This paper fills the gap in the literature by empirically comparing GL performance amongfirms and countries through the use of a composite index. It also contributes to a better understandingof the association between GL performance and firm size as well as the driving factors behind it.
Keywords Benchmarking, Green logistics, Performance, Sustainable development,Extended producer responsibility, Resource-based view, China, Japan, Distribution management
Paper type Research paper
IntroductionEnvironmental impact of business activities has become an important issue in recentyears due to growing public awareness of environmental conservation, increasing needfor sustainable development, and introduction of environmental legislations
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1463-5771.htm
The author would like to sincerely thank the retailers for providing the information used in thisstudy. He also wishes to extend his gratitude to the two anonymous reviewers for providingvaluable comments and suggestions for improving the paper.
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Benchmarking: An InternationalJournal
Vol. 18 No. 6, 2011pp. 873-896
q Emerald Group Publishing Limited1463-5771
DOI 10.1108/14635771111180743
and regulations in developed countries. Companies are redesigning their logisticspractices to make the activities more energy efficient and environment friendly. Greensupply chain initiatives in procurement, manufacturing, distribution, and recycling arerapidly emerging as major trends (Mason, 2002). Consequently, green logistics (GL) havebecome an important consideration and a big challenge to supply chain managementaround the globe (Murphy and Poist, 2000; Rao and Holt, 2005; Vachon and Klassen, 2006).
The need to lessen the impact of business logistics activities on the environment isconstantly increasing. In a series of workshops organized by the University of Hullinvolving academics and practitioners in supply chain management to investigate theissues and challenges of the next generation supply chains, environmental issues withcost effectiveness is always the major and most imminent concern identified (EPSRC,2010). Generally speaking, GL refer to “attempts to measure and minimize the ecologicalimpact of logistics activities” (Reverse Logistics Executive Council, 2010). They includegreen purchasing, green material management and manufacturing, green distributionand marketing, as well as reverse logistics (Hervani et al., 2005). The overall objective isto reduce impact on the environment, lower production cost, and improve product value.GL can lead to lower inventory level, reduced logistics cost, increased revenue, improvedcustomer service, enriched information for reverse logistics, and enhanced corporateimage (Murphy et al., 1995). Effective management of GL activities not only affects anorganization’s operational and economic performance (Tooru, 2001; Alvarez et al., 2001)but also increases its competitiveness in the long run (Bacallan, 2000; Rao, 2004).
From a broader perspective, GL can be regarded as part of green supply chainmanagement (GSCM) that aims at integrating environmental thinking into closed-loopsupply chain management. The activities involved include product design, supplierselection and material sourcing, inbound transportation, manufacturing processes,waste reduction, product packaging, distribution and delivery to customers, andend-of-life product returns for recycling and reuse (Beamen, 1999; Linton et al., 2007;Srivastara, 2007). With the growing concern of the public about the environment, GSCMhas moved to the top of the research agenda. There have been studies investigating thevarious aspects of GSCM in recent years (Table I). For example, Zhu and Sarkis (2004)explore the relationship between GSCM practices and firm performance in themanufacturing industry of China. Hervani et al. (2005) develop a conceptual frameworkand proposed some metrics to measure environmental performance. Kainuma andTawara (2006) apply the multiple attribute utility theory to assess a supply chain withre-use and recycling throughout the life cycle of products and services. Simpson et al.(2007) study the role of supply chain relationship in GSCM and the conditions forpositive response from supplier to customer’s environmental requirements. Walker et al.(2008) investigate the drivers, such as regulations and customer preferences, and thebarriers, such as costs and poor commitment, that companies face in implementingGSCM practices. Zhu et al. (2008) test the validity of including factors such as internalenvironmental management, green purchasing, cooperation with customers, eco-designpractices, and investment recovery in the measurement models of GSCM practicesimplementation. More recently, Sundarakani et al. (2010) measure the carbon footprintsacross the supply chain using a mobile (logistics) emission diffusion model.
GL and GSCM are particularly important to developing countries such as China,which has now become a global manufacturing base for many developed countriesbecause of cheap labour supply and other incentives offered to foreign investors
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(Langley Jr et al., 2007). Nevertheless, comprehensive regulations in many developingcountries to protect the environment from heavy industrial and business activitieshave yet to be introduced. GL and GSCM practices are relatively uncommon andmostly initiated by large corporations with more resources to invest in these practices.While there are studies investigating the emergent GSCM practices in severalmanufacturing industries of China (Zhu and Sarkis, 2006; Zhu et al., 2007), research incomparing GL or GSCM performance among industries or countries is limited. Thepurpose of this study is to fill this gap by proposing the use of a Green LogisticPerformance Index?? (GLPI) to facilitate the comparison of GL performance acrossindustries or nations. The concept is similar to the logistics performance index (LPI)developed by the The World Bank (2010) which can be used to assess and benchmarkperformance of different countries using the same set of criteria. As an example toillustrate the development and the application of the proposed index, the current GLpractices and performance of the home electronic appliance (HEA) manufacturers inChina and Japan are investigated and compared.
While a comprehensive GLPI should cover all the GL and GSCM practices in itsformulation, collection of data on all GL activities from companies in a pilot study tohelp develop the index as proof of concept will be too ambitious and hence affect theresponse rate. This is particularly so when GSCM practices are not fully adopted bymany firms especially the small- and medium-sized manufacturers. To serve as ademonstration of feasibility and to simplify data collection, this study has focusedmainly on three categories of GL activities, namely, purchasing, packaging, andtransportation in the data collection. The rationale of choosing these three activities forinvestigation is given in the next section.
GL activitiesWhile all logistics activities affect the environment in one way or the other, activities incertain areas tend to generate larger impacts and the adoption of GL would bringrelatively greater benefits (Guide, 2000; Wu and Dunn, 1995). For example,
Category Focus/theme Studies
Theoretical Concept, definition, and overview of GSCM Linton et al. (2007), Srivastara (2007),Van Hoek (1999)
Theory and approach to assessing greensupply chain
Handfield et al. (2002), Kainuma andTawara (2006)
GSCM strategies and decision framework Sarkis (2003), Sheu and Chen (2009)GSCM drivers and barriers Testa and Iraldo (2010), Walker et al. (2008),
Zhu and Sarkis (2006)Green supply chain design Beamen (1999)Green supply chain modelling andsimulation
Hui et al. (2007), Sheu et al. (2005)
Carbon management and measurement ofcarbon footprints in supply chain
Butner et al. (2008), Sundarakani et al.(2010)
Empirical Performance measurement of green supplychain
Hervani et al. (2005), Zhu and Sarkis(2004, 2007), Zhu et al. (2008)
GSCM practices in manufacturingindustries
Ferretti et al. (2007), Shang et al. (2010),Simpson et al. (2007), Zhu et al. (2007)
Table I.GSCM studies conducted
in recent years
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using environment-friendly materials in production or recycled parts inremanufacturing not only lessens the adverse effect on the environment but alsoreduces manufacturing cost (Karpak et al., 2001). Similarly, the use of green or recycledpackaging materials, together with improved packaging designs and techniques,help manufacturers reduce packaging waste and cost (Crumrine et al., 2004). Intransportation, consolidation of orders and optimisation of schedules and routesdecrease distribution frequency and cut fuel consumption (Rao et al., 1991). The use ofmore fuel-efficient vehicles or alternative energy sources directly reduces greenhousegas emission (European Commission, 2001). Purchasing, packaging, and transportationalso broadly represent the major upstream and downstream logistics functions in asupply chain. GL practices in these three functions can, to a certain extent, reflect thestate of GSCM in an industry. Table II summarizes the benefits of and challenges inimplementing the three categories of GL activities as reported in the literature.
Surveys also reveal an increasing awareness, interest, and emphasis in greenpurchasing, packaging, and transportation. A survey of 527 US enterprises by Min andGalle (2001) reveals that over 84 percent of the firms have participated in some form ofgreen purchasing initiatives. Involvement in green purchasing is found to be relatedpositively to firm size and attitude towards regulatory compliance. Similarly, a surveyof 1,225 packaging personnel by the sustainable packaging coalition and packagingdigest shows that 73 percent of the respondents report that their companies haveincreased an emphasis on packaging sustainability (Kalkowski, 2007). Sustainabilityinnovators and early adopters of green packaging practices tend to be those who workfor larger organizations that have a high level of commitment at the corporate level,and with staff dedicated to the sustainability function. This finding suggests thatgreen packaging may be related to firm size. Another study reveals that 72 percentof the 235 transportation and logistics professionals surveyed are planning to improveenergy efficiency and 42 percent are planning to use vehicle re-routing to reduce
Activity Benefit Challenge Studies
Greenpurchasing
Reduces waste andliability costBuilds a “green” image forthe company
High set up costRequires managementcommitment andcompany-wide standards
Karpak et al. (2001),Min and Galle (2001),Rao and Holt (2005)
Greenpackaging
Reduces packaging costand solid waste
High cost of usingalternative packagingmaterials and techniques
Crumrine et al. (2004),Delaney (1992),Harrington (1994)Maximizes environment
friendliness through theuse of alternativepackaging materials andtechniques
Greentransportation
Reduces fuel consumptionand cuts operating cost
High investment cost ofalternative fuel vehicles
Rao et al. (1991),Vannieuwenhuyse et al.(2003), Wu and Dunn (1995)Generates less noise, air
pollution, and trafficcongestionImproves customer andpublic relationships
Table II.Benefits and challengesof green purchasing,packaging, andtransportation
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mileage (O’Reilly, 2008). Relative importance of green issues to a company is found tobe related positively to its annual revenue suggesting that larger firms accord higherpriority to green transportation and logistics.
Green logistic performance indexBased on the same concept of the LPI developed by the The World Bank (2010), the GLPIproposed in this study is designed to facilitate cross-industry or cross-country assessmentof GL performance and identification of gaps in GL practices. Similar to the LPI, the GLPIand its underlying indicator variables constitute a dataset to measure GL performanceamong industries or countries across several major categories of GL activities. The richerthe dataset is in terms of categories of GL activities investigated and the number ofindustries or countries surveyed, the more robust the comparison and benchmarkingwill be. While the LPI considers various attributes affecting the logistics performance of acountry such as infrastructure, information technology, service quality, governmentregulations and policies, etc. the GLPI looks at investment of resources, adoption of latesttechnology, and compliance with environmental regulations, etc. to determine the overallperformance of the industry or nation in GL activities.
The approach adopted in developing the GLPI is also similar to that of the LPI.A five-point scale is used to gauge the performance of a surveyed firm in various GLactivities. These numeric outcomes, from 1 (worst) to 5 (best), serve as indicators toindicate how bad or good a firm in the industry performs in the surveyed activities incomparison with others. The GLPI is then aggregated as a weighted average of thevarious performance scores using the principal component analysis (PCA) method toderive the weights for the indicator variables thereby improving the statisticalconfidence of the composite index.
Unlike the LPI which surveys the logistics companies and professionals tradingwith the countries under study on the various dimensions of logistics performance, theGLPI relies on the self-assessment of firms to report their performance in the surveyedGL activities. There are reasons for taking this approach. First, unlike logisticsoutsourcing, GL practices are still mainly in-sourced since the scale and the scope ofactivities on many occasions are still relatively small. Second, as a pilot study to collectdata to prove the concept of the GLPI, limitation in resources has restricted theopportunity of hiring an expert panel to perform the evaluation.
Research objectiveThis study attempts to use China, a developing country, and Japan, a developed country,as case studies to illustrate how a GLPI can be developed and used to compare the overallGL performance of the two nations. As a rapidly developing country, China has becomethe world’s biggest manufacturing base for many developed nations (Langley et al.,2007). Consequently, there is an urgent need to implement GL and GSCM in variousindustry sectors to help reduce negative impact on the environment. In contrast, Japan asa developed country has widely implemented GL and GSCM in many industries. Formany years, it has been the world’s leading country in the number of ISO 14001 certifiedfirms (ISO World, 2007). Using the HEA manufacturing industry as an example, thisstudy aims at developing a GLPI and revealing the differences in GSCM practicesbetween the two countries. The objective of this study is to answer the followingresearch questions:
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RQ1. What is the current GL performance of the HEA manufacturing industryin China and Japan?
RQ2. What are the differences in GL practices identified through the comparisonof performance?
RQ3. Can an overall GLPI be developed to simplify performance comparison withreliable result?
Research methodologyTo answer the above research questions, this paper reports the findings of a questionnairesurvey of 107 HEA manufacturing companies – 58 in China and 49 in Japan on theircurrent GL adoption and performance. The data collected are used to develop a GLPI forcomparison. Companies participated in the questionnaire survey were requested toevaluate their own performance in 15 GL activities with reference to the industrypractices. The self-evaluation approach has been adopted in many studies on supply chainand logistics performance (Carter, 2005; Leeetal., 2007; Lin and Ho, 2009; McCormack etal.,2008; Zhu and Sarkis, 2004). Although there might be possibilities of under- orover-assessment of performance on certain activities by individual respondents, theaggregate findings should reflect more or less the current situation. The emphasis onrelative rather than absolute performance using a five-point scale will further lessen theimpact of any random assessment bias. In this survey, the focus is placed on three majorlogistics areas in the HEA supply chain, namely, purchasing, packaging, andtransportation, where GL can bring significant benefits (Guide, 2000; Wu and Dunn, 1995).
Sample selection and survey instrument designAs successful GL implementation requires resources and experiences, it is more likelythat companies practicing GL are relatively large and well-established organizations.Therefore, for the survey, only companies operating for at least five years in theindustry with 200 or more employees and an average annual sales volume greater thanUS$30 million were selected. Based on these criteria, altogether 176 HEAmanufacturers in China and 165 in Japan were identified from the industry memberlists of the two countries compiled through internet search. These HEA manufacturerscover a wide range of industry segments producing products such as television,refrigerator, microwave oven, washing machine, air-conditioner, household audio andvideo entertainment equipment, and communication devices.
A self-administered questionnaire was employed to collect data for analysis.It focused on evaluating the performance of GL activities in the three areas underinvestigation. Apart from providing information on company profile as to years ofestablishment, number of employees, and annual sales, etc. respondents were also askedif their companies had implemented GL. If affirmative, they were requested to evaluatethe GL performance of their companies in various activities with reference to theindustry practices. To encourage response, a relatively short questionnaire was designedinvolving only 15 GL activities (Table III). They include the use of environment-friendlyraw materials, adoption of environment-friendly packaging design, and optimisation ofdistribution process to reduce transportation hence carbon emission, etc. To standardizereplies so as to facilitate statistical analysis, closed-end questions with multiple-choiceanswers in a five-point scale, ranging from worst (1) to best (5), were asked.
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The survey questions are developed from the literature of GL practices reviewed in theprevious sections. For example, the use of recycled packaging materials (A9) andenvironment-friendly packaging design (A7) to reduce waste are based on the studyof Crumrine et al. (2004). The purchase of environment-friendly raw materials forproduction (A1) and recycled parts for remanufacturing (A3) come from the findingsof Karpak et al. (2001). Also, the use of consolidation of orders (A13) and optimizationof schedules (A12) to reduce distribution frequency and to cut fuel consumption arederived from the studies of Rao et al. (1991) and Wu and Dunn (1995). Many of theactivities investigated in this study also align with the actual practices of the industriesas well as the recommendations made by major logistics consulting companies. Forexample, activities A6, A7, and A11-A14 are in agreement with the GL principlesadopted by the Italian automobile manufacturer Fiat. These principles include:
. increased use of low-emission vehicles;
. use of intermodal solutions to reduce road transportation;
. optimisation of transport capacity through consolidation and scheduling; and
. reduced use of packaging and protective materials through lightweight design(Fiat Group, 2010).
Similarly, the activities match well with some of the major GL opportunitiesrecommended by the global management consulting firm (Accenture, 2008) whichinclude:
. network optimisation;
. improvement inventory management;
. improved vehicle fuel consumption;
. reduced warehouse energy consumption; and
. packaging reduction.
Category Activity
Green purchasing A1 – purchase of environment-friendly raw materialsA2 – substitution of environment harmful raw materials with friendly onesA3 – purchase of recycled raw materialsA4 – use of suppliers that meet stipulated environmental criteriaA5 – compliance with international environmental regulations in purchasing
Green packaging A6 – use of environment-friendly materials in packagingA7 – use of environment-friendly design in packagingA8 – use of cleaner technology in packagingA9 – use of recycled packaging materials purchased externallyA10 – taking back waste packaging materials from customers for recycling
Green transportation A11 – optimisation of efficiency through the use of energy efficient vehiclesA12 – optimisation of distribution process through better routing and
schedulingA13 – use of integrated delivery to reduce transportationA14 – use of environment-friendly technology in transportationA15 – managing reverse material flows to reduce transportation
Table III.Green logistics activities
investigated in thequestionnaire survey
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Data collection and tools of analysisThe questionnaires were e-mailed directly to the logistics managers of the companiesselected for the survey with a covering letter explaining the purpose of the study.A reminder was sent to encourage response two weeks after the questionnaire wasdispatched. The mailing of survey questionnaires and reminders and collection ofreturns were completed in October 2007. A total of 341 questionnaires – 176 to Chinaand 165 to Japan were sent using the e-mail addresses provided in the industry memberlists. A total of 107 valid returns – 58 from the Chinese and 49 from the Japanesemanufacturers were received (Table IV). Of the 107 companies, 69 reported that theyhad implemented GL to various extents (36 in China and 33 in Japan).
As shown in Table V, the 107 responding HEA manufacturing companies weredivided into three groups:
(1) medium-sized firms;
(2) large-sized firms; and
(3) very large-sized firms
based on their number of employees following the European practice (EuropeanCommission, 2003). Pearson’s x 2-test (Pearson, 1900) was used to investigate if there isassociation between adoption of GL practices and firm size. Two-sample t-test(Student, 1908) was used to test if there are significant differences between China andJapan in the performance of various GL activities among the surveyed HEAmanufacturers. one-way analysis of variance (ANOVA) (Fisher, 1925) and Scheffe’s(1953) test were used to test if there are significant differences in GL performanceamong the surveyed HEA manufacturers of different firm size. PCA (Hotelling, 1933)was used to obtain the weights to develop the GLPI used for an overall comparison ofGL performance between the two countries.
China Japan Total
Questionnaires sent 176 165 341Questionnaires successfully delivered 172 159 331Questionnaires returned 59 51 110Valid returns 58 49 107Response rate (%) 33.7 30.8 32.3Manufacturers with GL adoption 36 33 69Manufacturers with no GL adoption 22 16 38
Table IV.Response rate ofquestionnaire survey
Group of firms Number of employees Count %
1. Medium sized ,250 38 35.52. Large sized $250 and ,1,000 49 45.83. Very large sized $1,000 20 18.7Total 107 100
Table V.Classification ofresponding companiesbased on numberof employees
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Results and discussionsAdoption of GL practices and firm sizeReturns from the survey reveal that adoption of GL practices in the HEAmanufacturing industry is not particularly widespread. Only about 65 per cent of theresponding companies have reported GL adoption. Pearson’s x 2-test was applied todetermine if there is any association between GL adoption and firm size. The result isshown in Table VI.
The x 2-test result suggests that there is a positive association between adoption ofGL practices and firm size. In other words, larger firm has a higher propensity to adoptGL. The correlation coefficients C and V are both around 0.3 indicating that theassociation is only a moderate one. Results of the Marascuilo (1966) procedure, whichallows a simultaneous testing of differences of all pairs of proportions when there areseveral populations under investigation, indicate that the level of GL implementation ofmedium-sized firms is significantly lower than that of the other two groups. On theother hand, there is not enough evidence to suggest that large- and very large-sizedfirms are different in the likelihood of adoption. The observed difference may be relatedto the ability to invest in GL, the management support available, and the organizationstructure of the companies. As GL requires additional resources for planning andimplementation, larger firms are more capable to invest in the area and use GL as acompetitive edge. This finding aligns with the literature that many big companies andorganizations are incorporating GL or GSCM as part of their corporate strategies(Murray, 2000; Olson, 2008). The observation can be explained by the resource-basedview (RBV) theory, which advocates that to gain sustainable competitive advantagelarge firms tend to use their resources to develop unique capability that is difficultfor their competitors to imitate or substitute (Barney, 1991; Conner, 1991; Grant, 1991;Wernerfelt, 1984). In contrast, investment in environmental program may be a heavyeconomic burden to smaller firms. Therefore, support from top management may not
Group of firms(1) Medium sized (2) Large sized (3) Very large sized Total
Adoption of GL practicesGL practices adopted 17 35 17 69GL practices not adopted 21 14 3 38Total 38 49 20 107Pearson’s x2-testCalculated x 2-value 11.178Degree of freedom 2Critical x 2-value at a ¼ 0.05 5.992
p-value 0.004[ Reject H0: GL adoption is independent
of firm sizeMarascuilo procedureProportions Absolute difference Critical rangej Group 1-Group 2 j 0.267 0.253 [ Significantj Group 1-Group 3 j 0.403 0.278 [ Significantj Group 2-Group 3 j 0.136 0.251 [ Not significantCorrelation coefficientContingency coefficient 0.308CCramer’s V 0.323
Table VI.Pearson’s x 2-test
for independency ofadoption of GL practices
from firm size
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be readily available. The organization structure of smaller companies may also not beable to provide proper management to support GL. Last but not least, economies ofscale can also play an important role. Larger firms tend to invest more in GL and aremore likely to benefit from economies of scale than their smaller counterparts (Min andGalle, 2001). This in turn can provide additional incentive for larger companies tofurther invest in GL practices.
GL performance between HEA manufacturers in China and JapanFor each sample, one-sample t-test was first used to determine if the mean performancescore of each GL activity surveyed is significantly different from the conjectured valueof three (i.e. average performance). Two-sample t-test was then used to determine ifthere is any significant difference in average performance in the various GL activitiesof the two countries. Results of Levene’s (1960) test for equality of variance show thatequal variance can be assumed in the analysis. Therefore, the pooled-t method can beused to increase the power of the test if necessary. To be prudent, however, thetwo-sample method with no pooling of variances was used as recommended in manyrecently published statistics textbooks (Sharpe et al., 2010, p. 358). The results aresummarized in Table VII.
The findings reveal that in general HEA manufacturers in Japan perform better in GL(with all of the mean scores above 3) than their counterparts in China (with majority ofthe mean scores below 3). The two-sample t-test results show that, for more than halfof the surveyed activities, the differences in performance between the two samplesare significant ata ¼ 0.05 suggesting that there is room for improvement for the Chinesemanufacturers. Among the 15 activities investigated, the Chinese manufacturersperform best (and on par with the Japanese manufacturers) in A3, A10, and A13.This finding suggests that the Chinese manufacturers may be more concerned with thecost reduction aspect of GL implementation. The use of recycled raw materials andtaking back waste packaging materials from customers for recycling can help reducepurchasing and packaging costs. The use of integrated delivery to reduce transportation,which requires little capital investment to implement, also lowers distribution cost.
For the more costly activities such as A1, A7, A8, and A11, the Japanese manufacturersclearly excel in performance. This finding suggests that to the Japanese manufacturersGL may be adopted for reasons other than sheer cost reduction. Considerations suchas extended producer responsibility (EPR), sustainable development, and long-termcompetitive advantage, etc. may be equally important. In other words, the Chinesemanufacturers seem to focus more on the short-term cost benefit of GL and may notappreciate the greater long-term benefit arising from environmental consideration as theJapanese manufacturers do.
GL performance among different groups of HEA manufacturersANOVA was used to determine if the mean performance scores of the three groups ofmanufacturers in the 15 GL activities surveyed are different. Scheffe’s test was thenemployed for post hoc multiple comparisons to detect pairwise differences amongthe groups. The analysis and test were applied to both the samples from China andJapan for comparison and the results are given in Tables VIII and IX.
The mean performance scores of the different groups of HEA manufacturers in Chinaand Japan align with the earlier finding of the aggregate analysis using Chi-square test
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that GL adoption is related to firm size. In both cases, it can be seen that very large-sizedfirms are performing better than large- and medium-sized firms in most of the GLactivities. The ANOVA results shown in Table IX indicate that there is significantdifference in performance among the three groups of HEA manufacturers in China ineight activities, namely, A2, A5, A6, A7, A9, A11, A12, and A15. In contrast, the differenceamong the three groups of Japanese manufacturers only exists in three activities,namely, A1, A6, and A11. This suggests that the performance of different groups ofmanufacturers in China is more diverse than that of the Japanese manufacturers. Therelative consistency in performance of the Japanese manufacturers may be due to greaterawareness of environmental protection, more stringent environmental regulations,as well as longer history of GL adoption in developed countries.
Scheffe’s test results in Table IX indicate that very large-sized firms in China areperforming better than large- and medium-sized firms in A2, A5, A7, and A12.
One-sample t-test Two-sample t-testChina
(n ¼ 36)Japan
(n ¼ 33) RejectH0?Activity Mean p Mean p t-value p
A1 – purchase of environment-friendly rawmaterials 2.44 * 0.010 3.67 * 0.003 24.20 0.000 U
A2 – substitution of environment harmful rawmaterials with friendly ones 2.81 0.352 3.39 0.062 22.03 0.047 U
A3 – purchase of recycled raw materials 3.31 0.196 3.27 0.247 0.10 0.921 XA4 – use of suppliers that meet stipulated
environmental criteria 2.56 * 0.047 3.52 * 0.024 23.13 0.003 U
A5 – compliance with internationalenvironmental regulations in purchasing 2.86 0.492 3.48 * 0.021 22.21 0.031 U
A6 – use of environment-friendly materials inpackaging 2.67 0.103 3.48 * 0.024 22.87 0.006 U
A7 – use of environment-friendly design inpackaging 2.69 0.155 3.55 * 0.010 22.93 0.005 U
A8 – use of cleaner technology in packaging 2.72 0.185 3.48 * 0.011 22.79 0.007 U
A9 – use of recycled packaging materialspurchased externally 3.00 1.000 3.45 * 0.030 21.60 0.116 X
A10 – taking back waste packaging materialsfrom customers for recycling 3.31 0.110 3.12 0.488 20.73 0.473 X
A11 – optimization of efficiency through the useof energy efficient vehicles 2.56 0.051 3.52 * 0.030 23.04 0.003 U
A12 – optimization of distribution processthrough better routing and scheduling 2.89 0.606 3.39 0.062 21.71 0.093 X
A13 – use of integrated delivery to reducetransportation 3.47 * 0.042 3.21 0.344 0.83 0.412 X
A14 – use of environment-friendly technology intransportation 2.64 0.074 3.06 0.786 21.43 0.157 X
A15 – managing reverse material flows to reducetransportation 3.06 0.793 3.36 0.076 21.06 0.293 X
Notes: *Significant at: a ¼ 0.05; H0: there is no difference in average performance in the GL activityconcerned between China and Japan; performance score: 1 (worst)-5 (best), X – do not reject H0,U – reject H0
Table VII.Comparison of
differences in GLperformance between
China and Japan
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A4
–u
seof
sup
pli
ers
that
mee
tst
ipu
late
den
vir
onm
enta
lcr
iter
ia2.
690.
455
2.13
*0.
011
3.29
0.45
73.
500.
495
3.32
0.31
63.
90*
0.02
9A
5–
com
pli
ance
wit
hin
tern
atio
nal
env
iron
men
tal
reg
ula
tion
sin
pu
rch
asin
g2.
620.
096
2.50
0.15
04.
14*
0.00
53.
001.
000
3.47
0.12
03.
700.
066
A6
–u
seof
env
iron
men
t-fr
ien
dly
mat
eria
lsin
pac
kag
ing
1.85
*0.
000
3.25
0.38
82.
860.
788
2.25
0.31
93.
210.
331
4.50
*0.
000
A7
–u
seof
env
iron
men
t-fr
ien
dly
des
ign
inp
ack
agin
g2.
08*
0.00
42.
44*
0.03
44.
43*
0.00
32.
750.
761
3.42
0.11
94.
10*
0.00
3A
8–
use
ofcl
ean
erte
chn
olog
yin
pac
kag
ing
2.77
0.53
42.
560.
186
3.00
1.00
04.
250.
080
3.26
0.31
03.
600.
051
A9
–u
seof
recy
cled
pac
kag
ing
mat
eria
lsp
urc
has
edex
tern
ally
2.15
*0.
005
3.44
0.13
03.
570.
280
3.25
0.62
83.
530.
096
3.40
0.22
3A
10–
tak
ing
bac
kw
aste
pac
kag
ing
mat
eria
lsfr
omcu
stom
ers
for
recy
clin
g2.
920.
673
3.38
0.25
23.
860.
143
3.00
1.00
02.
950.
826
3.50
0.13
8A
11–
opti
mis
atio
nof
effi
cien
cyth
rou
gh
the
use
ofen
erg
yef
fici
ent
veh
icle
s1.
85*
0.00
12.
750.
483
3.43
0.40
72.
250.
319
3.26
0.33
14.
50*
0.00
1A
12–
opti
mis
atio
nof
dis
trib
uti
onp
roce
ssth
rou
gh
bet
ter
rou
tin
gan
dsc
hed
uli
ng
2.54
0.16
52.
500.
088
4.43
*0.
003
2.75
0.76
13.
160.
546
4.10
*0.
003
A13
–u
seof
inte
gra
ted
del
iver
yto
red
uce
tran
spor
tati
on3.
080.
861
3.75
*0.
023
3.57
0.28
02.
250.
215
3.16
0.57
83.
700.
132
A14
–u
seof
env
iron
men
t-fr
ien
dly
tech
nol
ogy
intr
ansp
orta
tion
2.23
*0.
018
2.88
0.69
72.
860.
766
3.25
0.76
13.
160.
615
2.80
0.59
1A
15–
man
agin
gre
ver
sem
ater
ial
flow
sto
red
uce
tran
spor
tati
on2.
620.
096
2.94
0.87
24.
14*
0.00
53.
001.
000
3.26
0.36
73.
700.
066
Notes:
* Sig
nifi
can
tat
:a¼
0.05
;p
erfo
rman
cesc
ore:
1(w
orst
)-5
(bes
t)
Table VIII.Comparison ofperformance in GLactivities among groupsof HEA manufacturersbetween China and Japan
BIJ18,6
884
AN
OV
Aan
dS
chef
fe’s
test
ond
iffe
ren
ces
inm
ean
per
form
ance
scor
esC
hin
aJa
pan
Act
ivit
yF
pD
iff.
Bet
wee
nM
and
LB
etw
een
Man
dV
LB
etw
een
Lan
dV
LF
pD
iff.
Bet
wee
nM
and
LB
etw
een
Man
dV
LB
etw
een
Lan
dV
L
A1
–p
urc
has
eof
env
iron
men
t-fr
ien
dly
raw
mat
eria
ls2.
660.
085
XX
XX
14.8
60.
000
UX
UU
A2
–su
bst
itu
tion
ofen
vir
onm
ent
har
mfu
lra
wm
ater
ials
wit
hfr
ien
dly
ones
12.3
00.
000
UX
UU
3.19
0.05
5X
XX
XA
3–
pu
rch
ase
ofre
cycl
edra
wm
ater
ials
1.63
0.21
1X
XX
X2.
130.
136
XX
XX
A4
–u
seof
sup
pli
ers
that
mee
tst
ipu
late
den
vir
onm
enta
lcr
iter
ia2.
210.
126
XX
XX
0.70
0.50
5X
XX
XA
5–
com
pli
ance
wit
hin
tern
atio
nal
env
iron
men
tal
reg
ula
tion
sin
pu
rch
asin
g6.
600.
004
UX
UU
0.52
0.60
2X
XX
XA
6–
use
ofen
vir
onm
ent-
frie
nd
lym
ater
ials
inp
ack
agin
g6.
710.
004
UU
XX
10.1
30.
000
UX
UU
A7
–u
seof
env
iron
men
t-fr
ien
dly
des
ign
inp
ack
agin
g15
.63
0.00
0U
XU
U2.
440.
104
XX
XX
A8
–u
seof
clea
ner
tech
nol
ogy
inp
ack
agin
g0.
310.
737
XX
XX
1.66
0.20
8X
XX
XA
9–
use
ofre
cycl
edp
ack
agin
gm
ater
ials
pu
rch
ased
exte
rnal
ly6.
480.
004
UU
XX
0.11
0.90
0X
XX
XA
10–
tak
ing
bac
kw
aste
pac
kag
ing
mat
eria
lsfr
omcu
stom
ers
for
recy
clin
g1.
720.
196
XX
XX
1.05
0.36
1X
XX
XA
11–
opti
mis
atio
nof
effi
cien
cyth
rou
gh
the
use
ofen
erg
yef
fici
ent
veh
icle
s4.
260.
023
UX
UX
7.03
0.00
3U
XU
U
A12
–op
tim
isat
ion
ofd
istr
ibu
tion
pro
cess
thro
ug
hb
ette
rro
uti
ng
and
sch
edu
lin
g9.
210.
001
UX
UU
3.19
0.05
5X
XX
XA
13–
use
ofin
teg
rate
dd
eliv
ery
tore
du
cetr
ansp
orta
tion
0.92
0.40
7X
XX
X2.
030.
149
XX
XX
A14
–u
seof
env
iron
men
t-fr
ien
dly
tech
nol
ogy
intr
ansp
orta
tion
1.25
0.30
1X
XX
X0.
300.
746
XX
XX
A15
–m
anag
ing
rev
erse
mat
eria
lfl
ows
tore
du
cetr
ansp
orta
tion
4.05
0.02
7U
XU
X0.
700.
505
XX
XX
Notes:X
–N
od
iffe
ren
ce;U
-–
dif
fere
nce
exis
ts;a
–0.
05
Table IX.Comparison of difference
in GL performanceamong groups of HEAmanufacturers between
China and Japan
Benchmarkinggreen logistics
performance
885
This finding suggests that very large-sized firms are embracing GL to a greaterextent than their smaller competitors. Like their Japanese counterparts, verylarge manufacturers in China (many are multinational corporations) may havegreater awareness of environmental protection, rigorous compliance with regulations,and stronger sense of social responsibility (or EPR) as reported in the literature(Khetriwal et al., 2009; Lee et al., 2000). The practice, which requires higher investmentin resources, can also be seen as a long-term strategy to sharpen competitiveness of thecompany (Bacallan, 2000; Chan and Chan, 2008; Deshmukh et al., 2006). The meanperformance scores in Table VIII also indicate that medium-sized firms in Chinaare performing significantly below average in A1, A6, A7, and A9. This finding againsuggests that small firms may be more cost conscious as the use of environment-friendly materials incurs higher cost (Thomas, 2008). Probably for the same reason,medium-sized firms in China are also performing poorer than large- and verylarge-sized firms in A11 and A14. The use of latest technology in green transportationrequires significant capital investment and is usually only affordable to largermanufacturers.
Although for the Japanese manufacturers the differences in performance amonggroups are not as big as that of their Chinese counterparts, the finding also supportsthe view that a firm’s ability to invest in GL affects its performance. As shown inTables VIII and IX, very large-sized firms in Japan are performing better than the othertwo groups of manufacturers in A1, A6, and A11. All these activities incur higher costor require significant capital investment that is more affordable to very largecorporations than smaller companies.
The differences in GL performance between firms of different sizes in China andJapan revealed in the survey data suggest that there are basically two approaches toGL implementation. As shown in Figure 1, GL practices can be just a reactive responseof smaller firms with limited resources to comply with environmental regulations andto reduce production cost (as reflected in the case of China). In contrast, larger firmsmay take a proactive approach in which GL is seen not only as sheer compliance withlaws and regulations or a mere cost saving measure but also unique capability thatadds value to product. Large firms tend to embrace GL in a fuller scale and investextensively to develop GL as a unique capability to enable the company to attainlong-term competitive advantage over their competitors (as reflected in both the casesof China and Japan). In this regard, the RBV theory can be used to account for theincorporation of GL as part of long-term business strategy by some large corporations(Clendenin, 1997; Wells and Seitz, 2005).
PCA to generate GLPITo generate a GLPI for overall comparison combining all the indicator variablesinvestigated in the survey, PCA is adopted to help determine the weights for thevariables that constitute the index. PCA as a multivariate statistical weighting approach
Figure 1.Different approachesto GL implementation
- Amount of resources available 1. Reactive approach 1. Reactive approach- Strength of corporate social responsibility - Law compliance and cost saving - Focuses mainly on low-cost activities- Significance of company image 2. Proactive approach 2. Proactive approach- Level of pressure from stakeholders - Unique capability building - Invests in technologies and infrastructure
Firm Size Approach to GL Implementation GL Performance
Underpinned by the RBV theory
affects affects
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is often used in the development of composite index. Examples include Jollands et al.(2004), Ali (2009), and Primpas et al. (2010). PCA weighs data by combining the indicatorvariables into linear combinations that explain as much variation in the dataset aspossible. It provides a relatively objective approach to setting weights that is less biasedthan other subjective weighting methods such as opinion polls. Another advantage ofPCA is that it reports the amount of variance in the data that is explained by theresulting composite index indicating how representative the index is. Furthermore, PCAis a data reduction method and may help reduce the dimensionality of the dataset if someof the indicator variables are highly correlated. In this analysis, six components withEigenvalue greater than 1 are extracted and orthogonal rotation (varimax with Kaisernormalization) is used to improve interpretability (Costello and Osborne, 2005).Category labels are given to the components based on the indicator variables involved.Table X shows the component loadings after rotation with the largest values in eachcategory highlighted for easy interpretation.
The determinant of the correlation matrix of all the indicator variables has a value of0.000015, which is larger than the necessary value of 0.00001 suggesting thatmulticollinearity is not a problem in this case. The Kaiser-Meyer-Olkin (KMO) measureof sampling adequacy is 0.592 which exceeds the recommended acceptance value of0.5 (Kaiser, 1974) suggesting that PCA can be applied. Bartlett’s test of sphericity(Bartlett, 1950) is significant ( p , 0.001) suggesting that there are relationships betweenvariables. The six components obtained from the dataset together account for81.3 per cent of the total variance. Albeit a good sign indicating the appropriateness
Principal component loading
Variable (or activity)
PC 1 –availabilityofalternatives
PC 2 –awareness of
environmentalconservation
PC 3 –compliance
withregulations
PC 4 –cost
reductionmeasures
PC 5 –willingness
to investPC 6 –
EPR
A2 0.979 0.058 0.103 20.031 20.039 0.021A12 0.974 0.053 0.079 20.049 20.036 20.025A7 0.922 0.117 0.154 20.061 0.111 0.079A1 0.145 0.924 20.015 0.021 0.057 20.005A6 0.090 0.912 20.125 0.025 0.060 20.020A11 20.021 0.777 0.240 20.065 20.034 0.185A15 0.177 0.079 0.880 0.065 0.207 20.090A5 0.147 0.144 0.862 0.021 0.250 0.031A10 0.029 20.138 0.649 0.047 20.166 0.226A3 20.043 0.016 0.055 0.951 20.017 20.017A13 20.080 20.029 0.053 0.949 20.103 0.052A14 20.036 20.036 0.010 20.005 0.864 0.198A9 0.044 0.101 0.189 20.128 0.765 20.079A8 20.007 0.089 0.041 20.098 20.079 0.842A4 0.089 0.042 0.104 0.200 0.349 0.688Total percentage ofvariance explained 19.1 15.9 14.1 12.6 10.9 8.9Cumulative (%) 19.1 34.9 49.0 61.6 72.4 81.3
Notes: KMO measure of sampling adequacy ¼ 0.592; Bartlett’s test of sphericity (approx.x 2 ¼ 690.74, df ¼ 105, p ¼ 0.000)
Table X.Principal component
analysis of the surveydataset
Benchmarkinggreen logistics
performance
887
of using PCA to obtain the weights for the variables, the figure has to be interpreted withcaution. While the natural randomness in the dataset may actually be low in this case, theuse of a coarse five-point measurement scale and a relatively small number of indicatorvariables may also result in lower variability hence the relatively high percentageof variance explained (Møller and Jennions, 2002). Based on the indicator variables oractivities included in each category, the components are labelled as availability ofalternatives, awareness of environmental conservation, compliance with regulations,cost reduction measures, willingness to invest, and EPR. They indicate the distinctdimensions in the measurement of GL performance of the firms in the dataset. Using thedominant statistical weights (with values greater than 0.6) obtained from the PCA andthe performance scores A1-A15 of the 15 GL activities reported, the total performancescore S across the six components can be calculated using Equation (1) as follows:
S ¼ 0:924A1 þ 0:979A2 þ 0:951A3 þ 0:688A4 þ 0:862A5 þ 0:912A6 þ 0:922A7
þ 0:842A8 þ 0:765A9 þ 0:649A10 þ 0:777A11 þ 0:974A12 þ 0:949A13
þ 0:864A14 þ 0:880A15
ð1Þ
As the scale used for all the indicator variables are from one to five, the absoluteminimum and maximum values of S obtained using Equation (1) are Smin ¼ 12.94 andSmax ¼ 64.69. Using these values, the total performance score S of each firm in thesurvey can be converted to a composite index I between 0 and 100 using Equation (2).Greater value of I implies a better performance on average across all measures:
I ¼ðS 2 Smin Þ100
Smax 2 Sminð2Þ
Comparison of performance using the GLPIBy calculating a GLPI for each firm and an average value for China and Japan,an objective comparison between the two countries can be made. The index-basedcomparison among firms can also be made at a finer level in the areas of greenpurchasing, packaging, and transportation by using the weights generated in the PCAbut including only a subset of the indicator variables. Also, focusing on the sixcomponents identified, performance of firms based on the various drivers such as costreduction and regulation compliance can also be easily compared. Table XI gives asummary of the comparison among firms of different size in China and Japan in differentlogistics functions based on their GL performance indices.
It can be seen from Table XI that on the whole firms in Japan are performing betterthan their counterparts in China regardless of firm size. The average GLPI for China
Greenpurchasing
Greenpackaging
Greentransportation
Overallperformance
China Japan China Japan China Japan China Japan
Medium-sized firms 37 43 33 52 37 43 36 46Large-sized firms 44 57 50 57 49 55 47 57Very large-sized firms 65 77 63 72 68 69 65 72All firms 45 62 46 61 48 58 47 60
Table XI.Average GLPI of firmsin differentlogistics functions
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and Japan for all firms are 47 and 60, respectively, indicting a big difference inperformance. Nevertheless, the performance gap is larger for medium- and large-sizedfirms but relatively smaller for very large-sized companies. Looking at performance indifferent logistics functions, the gap is largest in green packaging between themedium-sized firms (33 against 52 – a difference of 19 points in the GLPI) and smallestin green transportation between the very large-sized firms (68 against 69 – a differenceof only one point in GLPI) of the two countries. These results align with the outcome ofprevious comparison using two-sample t-test as shown in Table VII that medium-sizedfirms in China are performing poorly in costly activities such as the use ofenvironment-friendly materials and design in packaging. The alignment suggests thatthe GLPI developed in this case is robust and the use of it for comparison is relativelyconvenient. The outcome is also easier to interpret as the performance in variousactivities of a GL function is now measured using a single index.
Applying the same approach but looking at performance in the six dimensionsidentified in the PCA, another table of indices comparing the performance of firms ofdifference size in China and Japan can be generated. It can be seen from Table XII that,when all firms are considered, Japanese companies are having higher GLPI than theirChinese counterparts in all components except cost saving. The exception is attributedmainly to the high scores of the medium- and the large-sized Chinese firms in thisaspect. This suggests that many firms in China, particularly the medium- and large-sized ones, are implementing GL for cost reduction purposes. This finding also alignswith that of the previous analysis using ANOVA in Table IX. Again, it shows therobustness of the index and hence the merit of using it as a simple and objective meanto compare performance.
By applying the same technique in a larger survey covering more firms in differentcountries, a list of indices can be produced similar to the one developed by The TheWorld Bank (2010) for comparison of logistics performance across developing anddeveloped nations. If deemed necessary, the survey can cover GL activities in areasother than the three major GL functions investigated in this study. Repeated surveys,similar to the annual third-party logistics study (Langley et al., 2007) can also beconducted to reveal the trend of development in GL performance of the differentcountries based on their respective indices.
Conclusions and implicationsSummary of findings and implicationsThis paper has presented and compared the GL performance of some of the HEAmanufacturers in China and Japan in purchasing, packaging, and transportation. It hasalso demonstrated the development and application of a GLPI for easy comparison of GL
Availability Awareness Compliance Cost saving Investment EPRC J C J C J C J C J C J
Medium-sized firms 33 44 21 31 42 50 49 31 30 56 43 73Large-sized firms 36 56 48 57 47 56 69 55 53 58 34 57Very large-sizedfirms 86 78 51 90 77 66 59 69 35 52 53 68All firms 45 61 39 64 51 59 60 56 45 55 41 62
Table XII.Average GLPI
of firms in differentcomponents or factors
Benchmarkinggreen logistics
performance
889
performance between the two countries. The findings reveal that China – a developingcountry – is still a distance behind Japan – a developed country – in GL implementationparticularly in the upstream of the supply chain, i.e. purchasing. While the HEA industryof Japan has implemented GL throughout the whole supply chain with relativelygood performance in almost all activities surveyed, the Chinese HEA manufacturers,particularly the small ones, are focusing mainly in certain downstream activities suchas packaging with recycled material and consolidation to reduce transportation. Theseactivities require relatively little investment in technology but the cost saving from GL isreadily achievable. The findings also suggest that the main drivers for GL implementationin the HEA industry of China are still regulatory compliance and cost saving at thisstage. The Japanese manufacturers are implementing GL more for reasons of strongerawareness, availability of alternative green materials and technologies, development ofunique capability for long-term competition, and EPR. The different approaches to GLimplementation by the small and the large firms can be accounted for using the RVBtheory. With these findings, the first two research questions are fully answered.
Although this study was not designed to investigate the barriers to GL practicesand GSCM, the findings have shed light on the challenges of GL implementation indeveloping countries such as China. These challenges include:
. relatively low public awareness of sustainability and environmental protectionhence weaker pressure on manufacturers to go green;
. lack of comprehensive environmental policies, regulations, and directives suchas the restriction of hazardous substance and the Waste Electrical and ElectronicEquipment directives of the European Community (EU) (European Parliamentand Council, 2003a, b) to force compliance;
. limited investment in green technology, research and development to enhanceefficiency and achieve economies of scale;
. over-emphasis on low-cost production and short-term benefits than long-termgains in order to maintain competitiveness in the global market; and
. lack of resources, expertises, and management experiences in GSCM particularlyfor the small manufacturers.
These observations align with the comments made by some researchers in China thatboth the country’s hardware and software for GL are lagging behind that of developedcountries (Liu, 2009; Zhou, 2009). To promote GL practices and GSCM in developingcountries, government can play a critical role in enhancing awareness throughpublic education and industrial workshops, encouraging implementation through taxincentives and subsidies, enforcing compliance through legislations and regulations,sponsoring academic research for long-term sustainable development, and investingin infrastructure and technology to benefit the entire industry. Manufacturers,particularly large corporations with more resources, can also take greater initiatives toinvest in green technology, environment-friendly product design, cleaner manufacturingand distribution processes, and recycling. Strong collaboration among business partnersacross the supply chain will put pressure on smaller manufacturers to follow suit andhelp them develop their GL capabilities (Lau and Wang, 2009).
The paper has also demonstrated the development of a GLPI using PCA to obtainthe weights for the indicator variables involved in the equation. Results of comparison
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among the surveyed firms in China and Japan using the GLPI align with the outcomesobtained through other statistical analyses. The feasibility of using a single index forGL performance evaluation is proved and the robustness of the index is established.The use of the GLPI can simplify the GL performance comparison process and providea simple and objective mean to compare among industries and countries. Managers canuse the GLPI to benchmark the performance of their firms in the respective logisticsareas against those adopting best practices and revise their supply chain strategyaccordingly. The proposed index may also assist governments in formulating policieson promoting GL implementation in various industry sectors. With the findings andconclusions, the RQ3 is also satisfactorily answered.
Limitations and future researchThis study has only covered three major GL functions involving 15 activities to helpdevelop a GLPI for easy comparison of performance in GL practices. While the study isadequate as a pilot to prove the feasibility of the concept, the index developed mayneed to include other GL activities in order to be comprehensive. A larger surveycovering more GL activities and industries would be needed for further investigation.Further, a seven- or ten-point scale can be used in gauging performance of GL activitiesin the survey so as to give a finer measurement. Also, self-appraisal of performancemay not be entirely objective. An expert panel or a study approach similar to the oneadopted by The World Bank in developing the LPI can be used. Restricted by the scopeof the study, findings from this research are also not able to disclose further details ofthe GL implementation such as the various drivers and obstacles of GL implementationand their correlations. To obtain a fuller picture of the situation, future researchmay further investigate the drivers and the obstacles of GL implementation faced bythe industry in comparison with other industry sectors. In this regard, a moresophisticated questionnaire survey design focusing on the relationships amongvariables or the use of in-depth exploratory case studies may be appropriate. Tofacilitate standardization of practices in the industry for higher efficiency, a study tocompare in detail the actual practices of firms of different size in adopting andimplementing GL is also recommended.
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About the authorKwok Hung Lau is a Senior Lecturer in the School of Business Information Technology andLogistics at the Royal Melbourne Institute of Technology (RMIT) University in Australia.He holds a Bachelor’s degree in geography, Master’s degrees in business administration,information systems, urban planning, and a PhD in geocomputation. He has papers published injournals and conference proceedings such as Environment and Planning (Part B), Transactionsin GIS, Supply Chain Management: An International Journal, International Journal of PhysicalDistribution & Logistics Management, International Journal of Information Systems & SupplyChain Management, Australasian Transport Reform Forum, International Conference on CityLogistics, and Australian and New Zealand Academy of Management Conference. His researchinterests include modelling and simulation in supply chain, e-supply chain management,outsourcing, benchmarking, reverse logistics, and green logistics. Kwok Hung Lau can becontacted at: [email protected]
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