The current issue and full text archive of this journal is available at www.emeraldinsight.com/1463-5771.htm Benchmarking Benchmarking green green logistics logistics performance with performance a composite index 873 Kwok Hung LauSchool of Business Information Technology and Logistics, College of Business, Royal Melbourne Institute of Technology University, Melbourne, AustraliaAbstractPurpose – 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 managementPaper type Research paperIntroductionEnvironmental 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 Benchmarking: An International Journal Vol. 18 No. 6, 2011The author would like to sincerely thank the retailers for providing the information used in this pp. 873-896 q Emerald Group Publishing Limitedstudy. He also wishes to extend his gratitude to the two anonymous reviewers for providing 1463-5771valuable comments and suggestions for improving the paper. DOI 10.1108/14635771111180743
BIJ and regulations in developed countries. Companies are redesigning their logistics18,6 practices to make the activities more energy efﬁcient and environment friendly. Green supply chain initiatives in procurement, manufacturing, distribution, and recycling are rapidly emerging as major trends (Mason, 2002). Consequently, green logistics (GL) have become an important consideration and a big challenge to supply chain management around the globe (Murphy and Poist, 2000; Rao and Holt, 2005; Vachon and Klassen, 2006).874 The need to lessen the impact of business logistics activities on the environment is constantly increasing. In a series of workshops organized by the University of Hull involving academics and practitioners in supply chain management to investigate the issues and challenges of the next generation supply chains, environmental issues with cost effectiveness is always the major and most imminent concern identiﬁed (EPSRC, 2010). Generally speaking, GL refer to “attempts to measure and minimize the ecological impact of logistics activities” (Reverse Logistics Executive Council, 2010). They include green purchasing, green material management and manufacturing, green distribution and marketing, as well as reverse logistics (Hervani et al., 2005). The overall objective is to reduce impact on the environment, lower production cost, and improve product value. GL can lead to lower inventory level, reduced logistics cost, increased revenue, improved customer service, enriched information for reverse logistics, and enhanced corporate image (Murphy et al., 1995). Effective management of GL activities not only affects an organization’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 chain management (GSCM) that aims at integrating environmental thinking into closed-loop supply chain management. The activities involved include product design, supplier selection and material sourcing, inbound transportation, manufacturing processes, waste reduction, product packaging, distribution and delivery to customers, and end-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, GSCM has moved to the top of the research agenda. There have been studies investigating the various aspects of GSCM in recent years (Table I). For example, Zhu and Sarkis (2004) explore the relationship between GSCM practices and ﬁrm performance in the manufacturing industry of China. Hervani et al. (2005) develop a conceptual framework and proposed some metrics to measure environmental performance. Kainuma and Tawara (2006) apply the multiple attribute utility theory to assess a supply chain with re-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 for positive response from supplier to customer’s environmental requirements. Walker et al. (2008) investigate the drivers, such as regulations and customer preferences, and the barriers, such as costs and poor commitment, that companies face in implementing GSCM practices. Zhu et al. (2008) test the validity of including factors such as internal environmental management, green purchasing, cooperation with customers, eco-design practices, and investment recovery in the measurement models of GSCM practices implementation. More recently, Sundarakani et al. (2010) measure the carbon footprints across 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 countries because of cheap labour supply and other incentives offered to foreign investors
BenchmarkingCategory Focus/theme Studies green logisticsTheoretical Concept, deﬁnition, and overview of GSCM Linton et al. (2007), Srivastara (2007), performance Van Hoek (1999) Theory and approach to assessing green Handﬁeld et al. (2002), Kainuma and supply chain Tawara (2006) GSCM strategies and decision framework Sarkis (2003), Sheu and Chen (2009) 875 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 and Hui et al. (2007), Sheu et al. (2005) simulation Carbon management and measurement of Butner et al. (2008), Sundarakani et al. carbon footprints in supply chain (2010)Empirical Performance measurement of green supply Hervani et al. (2005), Zhu and Sarkis chain (2004, 2007), Zhu et al. (2008) Table I. GSCM practices in manufacturing Ferretti et al. (2007), Shang et al. (2010), GSCM studies conducted industries Simpson et al. (2007), Zhu et al. (2007) in recent years(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,
BIJ using environment-friendly materials in production or recycled parts in18,6 remanufacturing not only lessens the adverse effect on the environment but also reduces manufacturing cost (Karpak et al., 2001). Similarly, the use of green or recycled packaging materials, together with improved packaging designs and techniques, help manufacturers reduce packaging waste and cost (Crumrine et al., 2004). In transportation, consolidation of orders and optimisation of schedules and routes876 decrease distribution frequency and cut fuel consumption (Rao et al., 1991). The use of more fuel-efﬁcient vehicles or alternative energy sources directly reduces greenhouse gas emission (European Commission, 2001). Purchasing, packaging, and transportation also broadly represent the major upstream and downstream logistics functions in a supply chain. GL practices in these three functions can, to a certain extent, reﬂect the state of GSCM in an industry. Table II summarizes the beneﬁts of and challenges in implementing the three categories of GL activities as reported in the literature. Surveys also reveal an increasing awareness, interest, and emphasis in green purchasing, packaging, and transportation. A survey of 527 US enterprises by Min and Galle (2001) reveals that over 84 percent of the ﬁrms have participated in some form of green purchasing initiatives. Involvement in green purchasing is found to be related positively to ﬁrm size and attitude towards regulatory compliance. Similarly, a survey of 1,225 packaging personnel by the sustainable packaging coalition and packaging digest shows that 73 percent of the respondents report that their companies have increased an emphasis on packaging sustainability (Kalkowski, 2007). Sustainability innovators and early adopters of green packaging practices tend to be those who work for larger organizations that have a high level of commitment at the corporate level, and with staff dedicated to the sustainability function. This ﬁnding suggests that green packaging may be related to ﬁrm size. Another study reveals that 72 percent of the 235 transportation and logistics professionals surveyed are planning to improve energy efﬁciency and 42 percent are planning to use vehicle re-routing to reduce Activity Beneﬁt Challenge Studies Green Reduces waste and High set up cost Karpak et al. (2001), purchasing liability cost Requires management Min and Galle (2001), Builds a “green” image for commitment and Rao and Holt (2005) the company company-wide standards Green Reduces packaging cost High cost of using Crumrine et al. (2004), packaging and solid waste alternative packaging Delaney (1992), Maximizes environment materials and techniques Harrington (1994) friendliness through the use of alternative packaging materials and techniques Green Reduces fuel consumption High investment cost of Rao et al. (1991), transportation and cuts operating cost alternative fuel vehicles Vannieuwenhuyse et al.Table II. Generates less noise, air (2003), Wu and Dunn (1995)Beneﬁts and challenges pollution, and trafﬁcof green purchasing, congestionpackaging, and Improves customer andtransportation public relationships
mileage (O’Reilly, 2008). Relative importance of green issues to a company is found to Benchmarkingbe related positively to its annual revenue suggesting that larger ﬁrms accord higher green logisticspriority to green transportation and logistics. performanceGreen 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 assessment 877of 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:
BIJ RQ1. What is the current GL performance of the HEA manufacturing industry18,6 in China and Japan? RQ2. What are the differences in GL practices identiﬁed through the comparison of performance? RQ3. Can an overall GLPI be developed to simplify performance comparison with878 reliable result? Research methodology To answer the above research questions, this paper reports the ﬁndings of a questionnaire survey of 107 HEA manufacturing companies – 58 in China and 49 in Japan on their current GL adoption and performance. The data collected are used to develop a GLPI for comparison. Companies participated in the questionnaire survey were requested to evaluate their own performance in 15 GL activities with reference to the industry practices. The self-evaluation approach has been adopted in many studies on supply chain and logistics performance (Carter, 2005; Lee et al., 2007; Lin and Ho, 2009; McCormack et al., 2008; Zhu and Sarkis, 2004). Although there might be possibilities of under- or over-assessment of performance on certain activities by individual respondents, the aggregate ﬁndings should reﬂect more or less the current situation. The emphasis on relative rather than absolute performance using a ﬁve-point scale will further lessen the impact of any random assessment bias. In this survey, the focus is placed on three major logistics areas in the HEA supply chain, namely, purchasing, packaging, and transportation, where GL can bring signiﬁcant beneﬁts (Guide, 2000; Wu and Dunn, 1995). Sample selection and survey instrument design As successful GL implementation requires resources and experiences, it is more likely that companies practicing GL are relatively large and well-established organizations. Therefore, for the survey, only companies operating for at least ﬁve years in the industry with 200 or more employees and an average annual sales volume greater than US$30 million were selected. Based on these criteria, altogether 176 HEA manufacturers in China and 165 in Japan were identiﬁed from the industry member lists of the two countries compiled through internet search. These HEA manufacturers cover a wide range of industry segments producing products such as television, refrigerator, microwave oven, washing machine, air-conditioner, household audio and video 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 under investigation. Apart from providing information on company proﬁle as to years of establishment, number of employees, and annual sales, etc. respondents were also asked if their companies had implemented GL. If afﬁrmative, they were requested to evaluate the GL performance of their companies in various activities with reference to the industry practices. To encourage response, a relatively short questionnaire was designed involving only 15 GL activities (Table III). They include the use of environment-friendly raw materials, adoption of environment-friendly packaging design, and optimisation of distribution process to reduce transportation hence carbon emission, etc. To standardize replies so as to facilitate statistical analysis, closed-end questions with multiple-choice answers in a ﬁve-point scale, ranging from worst (1) to best (5), were asked.
BenchmarkingCategory Activity green logisticsGreen purchasing A1 – purchase of environment-friendly raw materials performance A2 – substitution of environment harmful raw materials with friendly ones A3 – purchase of recycled raw materials A4 – use of suppliers that meet stipulated environmental criteria A5 – compliance with international environmental regulations in purchasing 879Green packaging A6 – use of environment-friendly materials in packaging A7 – use of environment-friendly design in packaging A8 – use of cleaner technology in packaging A9 – use of recycled packaging materials purchased externally A10 – taking back waste packaging materials from customers for recyclingGreen transportation A11 – optimisation of efﬁciency through the use of energy efﬁcient vehicles A12 – optimisation of distribution process through better routing and scheduling Table III. A13 – use of integrated delivery to reduce transportation Green logistics activities A14 – use of environment-friendly technology in transportation investigated in the A15 – managing reverse material ﬂows to reduce transportation questionnaire surveyThe 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.
BIJ Data collection and tools of analysis18,6 The questionnaires were e-mailed directly to the logistics managers of the companies selected 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 was dispatched. The mailing of survey questionnaires and reminders and collection of returns were completed in October 2007. A total of 341 questionnaires – 176 to China880 and 165 to Japan were sent using the e-mail addresses provided in the industry member lists. A total of 107 valid returns – 58 from the Chinese and 49 from the Japanese manufacturers were received (Table IV). Of the 107 companies, 69 reported that they had implemented GL to various extents (36 in China and 33 in Japan). As shown in Table V, the 107 responding HEA manufacturing companies were divided 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 (European Commission, 2003). Pearson’s x 2-test (Pearson, 1900) was used to investigate if there is association 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 and Japan in the performance of various GL activities among the surveyed HEA manufacturers. one-way analysis of variance (ANOVA) (Fisher, 1925) and Scheffe’s ´ (1953) test were used to test if there are signiﬁcant differences in GL performance among 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 of GL performance between the two countries. China Japan Total Questionnaires sent 176 165 341 Questionnaires successfully delivered 172 159 331 Questionnaires returned 59 51 110 Valid returns 58 49 107Table IV. Response rate (%) 33.7 30.8 32.3Response rate of Manufacturers with GL adoption 36 33 69questionnaire survey Manufacturers with no GL adoption 22 16 38 Group of ﬁrms Number of employees Count %Table V.Classiﬁcation of 1. Medium sized ,250 38 35.5responding companies 2. Large sized $250 and ,1,000 49 45.8based on number 3. Very large sized $1,000 20 18.7of employees Total 107 100
Results and discussions BenchmarkingAdoption of GL practices and ﬁrm size green logisticsReturns from the survey reveal that adoption of GL practices in the HEAmanufacturing industry is not particularly widespread. Only about 65 per cent of the performanceresponding 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. 881 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 TotalAdoption 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 [ Reject H0: GL adoption is independentp-value 0.004 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 signiﬁcant Table VI.Correlation coefﬁcient Pearson’s x 2-testContingency coefﬁcient 0.308 for independency ofC adoption of GL practices ´Cramer’s V 0.323 from ﬁrm size
BIJ be readily available. The organization structure of smaller companies may also not be18,6 able to provide proper management to support GL. Last but not least, economies of scale can also play an important role. Larger ﬁrms tend to invest more in GL and are more likely to beneﬁt from economies of scale than their smaller counterparts (Min and Galle, 2001). This in turn can provide additional incentive for larger companies to further invest in GL practices.882 GL performance between HEA manufacturers in China and Japan For each sample, one-sample t-test was ﬁrst used to determine if the mean performance score of each GL activity surveyed is signiﬁcantly different from the conjectured value of three (i.e. average performance). Two-sample t-test was then used to determine if there is any signiﬁcant difference in average performance in the various GL activities of the two countries. Results of Levene’s (1960) test for equality of variance show that equal variance can be assumed in the analysis. Therefore, the pooled-t method can be used to increase the power of the test if necessary. To be prudent, however, the two-sample method with no pooling of variances was used as recommended in many recently published statistics textbooks (Sharpe et al., 2010, p. 358). The results are summarized 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 of the mean scores below 3). The two-sample t-test results show that, for more than half of the surveyed activities, the differences in performance between the two samples are signiﬁcant at a ¼ 0.05 suggesting that there is room for improvement for the Chinese manufacturers. Among the 15 activities investigated, the Chinese manufacturers perform 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 the cost reduction aspect of GL implementation. The use of recycled raw materials and taking back waste packaging materials from customers for recycling can help reduce purchasing 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 manufacturers clearly excel in performance. This ﬁnding suggests that to the Japanese manufacturers GL may be adopted for reasons other than sheer cost reduction. Considerations such as extended producer responsibility (EPR), sustainable development, and long-term competitive advantage, etc. may be equally important. In other words, the Chinese manufacturers seem to focus more on the short-term cost beneﬁt of GL and may not appreciate the greater long-term beneﬁt arising from environmental consideration as the Japanese manufacturers do. GL performance among different groups of HEA manufacturers ANOVA was used to determine if the mean performance scores of the three groups of ´ manufacturers in the 15 GL activities surveyed are different. Scheffe’s test was then employed for post hoc multiple comparisons to detect pairwise differences among the groups. The analysis and test were applied to both the samples from China and Japan for comparison and the results are given in Tables VIII and IX. The mean performance scores of the different groups of HEA manufacturers in China and Japan align with the earlier ﬁnding of the aggregate analysis using Chi-square test
Benchmarking One-sample t-test Two-sample t-test China Japan green logistics (n ¼ 36) (n ¼ 33) Reject performanceActivity Mean p Mean p t-value p H0?A1 – purchase of environment-friendly raw materials 2.44 * 0.010 3.67 * 0.003 24.20 0.000 U 883A2 – substitution of environment harmful raw materials with friendly ones 2.81 0.352 3.39 0.062 22.03 0.047 UA3 – 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 UA5 – compliance with international environmental regulations in purchasing 2.86 0.492 3.48 * 0.021 22.21 0.031 UA6 – use of environment-friendly materials in packaging 2.67 0.103 3.48 * 0.024 22.87 0.006 UA7 – use of environment-friendly design in packaging 2.69 0.155 3.55 * 0.010 22.93 0.005 UA8 – use of cleaner technology in packaging 2.72 0.185 3.48 * 0.011 22.79 0.007 UA9 – use of recycled packaging materials purchased externally 3.00 1.000 3.45 * 0.030 21.60 0.116 XA10 – taking back waste packaging materials from customers for recycling 3.31 0.110 3.12 0.488 20.73 0.473 XA11 – optimization of efﬁciency through the use of energy efﬁcient vehicles 2.56 0.051 3.52 * 0.030 23.04 0.003 UA12 – optimization of distribution process through better routing and scheduling 2.89 0.606 3.39 0.062 21.71 0.093 XA13 – use of integrated delivery to reduce transportation 3.47 * 0.042 3.21 0.344 0.83 0.412 XA14 – use of environment-friendly technology in transportation 2.64 0.074 3.06 0.786 21.43 0.157 XA15 – managing reverse material ﬂows to reduce transportation 3.06 0.793 3.36 0.076 21.06 0.293 X Table VII. Comparison ofNotes: *Signiﬁcant at: a ¼ 0.05; H0: there is no difference in average performance in the GL activity differences in GLconcerned between China and Japan; performance score: 1 (worst)-5 (best), X – do not reject H0, performance betweenU – reject H0 China and Japanthat 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.
BIJ 18,6 884 Table VIII. Comparison of performance in GL of HEA manufacturers activities among groups between China and Japan One-sample t-test on signiﬁcance of mean performance score China Japan Medium Very large Medium Very large sized (M) Large sized sized (VL) sized (M) Large sized sized (VL) n ¼ 13 (L) n ¼ 16 n¼7 n¼4 (L) n ¼ 19 n ¼ 10Activity Mean p Mean p Mean p Mean p Mean p Mean pA1 – purchase of environment-friendly raw materials 1.85 * 0.000 2.75 0.483 2.86 0.788 2.25 0.319 3.37 0.110 4.80 * 0.000A2 – substitution of environment harmful raw materials withfriendly ones 2.38 0.055 2.44 * 0.034 4.43 * 0.003 2.75 0.761 3.16 0.546 4.10 * 0.003A3 – purchase of recycled raw materials 2.85 0.711 3.75 * 0.023 3.14 0.818 2.25 0.215 3.21 0.508 3.80 0.070A4 – use of suppliers that meet stipulated environmental criteria 2.69 0.455 2.13 * 0.011 3.29 0.457 3.50 0.495 3.32 0.316 3.90 * 0.029A5 – compliance with international environmental regulations inpurchasing 2.62 0.096 2.50 0.150 4.14 * 0.005 3.00 1.000 3.47 0.120 3.70 0.066A6 – use of environment-friendly materials in packaging 1.85 * 0.000 3.25 0.388 2.86 0.788 2.25 0.319 3.21 0.331 4.50 * 0.000A7 – use of environment-friendly design in packaging 2.08 * 0.004 2.44 * 0.034 4.43 * 0.003 2.75 0.761 3.42 0.119 4.10 * 0.003A8 – use of cleaner technology in packaging 2.77 0.534 2.56 0.186 3.00 1.000 4.25 0.080 3.26 0.310 3.60 0.051A9 – use of recycled packaging materials purchased externally 2.15 * 0.005 3.44 0.130 3.57 0.280 3.25 0.628 3.53 0.096 3.40 0.223A10 – taking back waste packaging materials from customers forrecycling 2.92 0.673 3.38 0.252 3.86 0.143 3.00 1.000 2.95 0.826 3.50 0.138A11 – optimisation of efﬁciency through the use of energy efﬁcientvehicles 1.85 * 0.001 2.75 0.483 3.43 0.407 2.25 0.319 3.26 0.331 4.50 * 0.001A12 – optimisation of distribution process through better routingand scheduling 2.54 0.165 2.50 0.088 4.43 * 0.003 2.75 0.761 3.16 0.546 4.10 * 0.003A13 – use of integrated delivery to reduce transportation 3.08 0.861 3.75 * 0.023 3.57 0.280 2.25 0.215 3.16 0.578 3.70 0.132A14 – use of environment-friendly technology in transportation 2.23 * 0.018 2.88 0.697 2.86 0.766 3.25 0.761 3.16 0.615 2.80 0.591A15 – managing reverse material ﬂows to reduce transportation 2.62 0.096 2.94 0.872 4.14 * 0.005 3.00 1.000 3.26 0.367 3.70 0.066Notes: *Signiﬁcant at: a ¼ 0.05; performance score: 1 (worst)-5 (best)
´ ANOVA and Scheffe’s test on differences in mean performance scores China Japan Between Between Between Between Between BetweenActivity F p Diff. M and L M and VL L and VL F p Diff. M and L M and VL L and VLA1 – purchase of environment-friendly rawmaterials 2.66 0.085 X X X X 14.86 0.000 U X U UA2 – substitution of environment harmfulraw materials with friendly ones 12.30 0.000 U X U U 3.19 0.055 X X X XA3 – purchase of recycled raw materials 1.63 0.211 X X X X 2.13 0.136 X X X XA4 – use of suppliers that meet stipulatedenvironmental criteria 2.21 0.126 X X X X 0.70 0.505 X X X XA5 – compliance with internationalenvironmental regulations in purchasing 6.60 0.004 U X U U 0.52 0.602 X X X XA6 – use of environment-friendly materialsin packaging 6.71 0.004 U U X X 10.13 0.000 U X U UA7 – use of environment-friendly design inpackaging 15.63 0.000 U X U U 2.44 0.104 X X X XA8 – use of cleaner technology in packaging 0.31 0.737 X X X X 1.66 0.208 X X X XA9 – use of recycled packaging materialspurchased externally 6.48 0.004 U U X X 0.11 0.900 X X X XA10 – taking back waste packagingmaterials from customers for recycling 1.72 0.196 X X X X 1.05 0.361 X X X XA11 – optimisation of efﬁciency through theuse of energy efﬁcient vehicles 4.26 0.023 U X U X 7.03 0.003 U X U UA12 – optimisation of distribution processthrough better routing and scheduling 9.21 0.001 U X U U 3.19 0.055 X X X XA13 – use of integrated delivery to reducetransportation 0.92 0.407 X X X X 2.03 0.149 X X X XA14 – use of environment-friendlytechnology in transportation 1.25 0.301 X X X X 0.30 0.746 X X X XA15 – managing reverse material ﬂows toreduce transportation 4.05 0.027 U X U X 0.70 0.505 X X X XNotes: X – No difference; U- – difference exists; a – 0.05 green logistics in GL performance performance Benchmarking manufacturers between Comparison of difference China and Japan Table IX. 885 among groups of HEA
BIJ This ﬁnding suggests that very large-sized ﬁrms are embracing GL to a greater18,6 extent than their smaller competitors. Like their Japanese counterparts, very large manufacturers in China (many are multinational corporations) may have greater 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 investment886 in resources, can also be seen as a long-term strategy to sharpen competitiveness of the company (Bacallan, 2000; Chan and Chan, 2008; Deshmukh et al., 2006). The mean performance scores in Table VIII also indicate that medium-sized ﬁrms in China are performing signiﬁcantly below average in A1, A6, A7, and A9. This ﬁnding again suggests 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 very large-sized ﬁrms in A11 and A14. The use of latest technology in green transportation requires signiﬁcant capital investment and is usually only affordable to larger manufacturers. Although for the Japanese manufacturers the differences in performance among groups are not as big as that of their Chinese counterparts, the ﬁnding also supports the view that a ﬁrm’s ability to invest in GL affects its performance. As shown in Tables VIII and IX, very large-sized ﬁrms in Japan are performing better than the other two groups of manufacturers in A1, A6, and A11. All these activities incur higher cost or require signiﬁcant capital investment that is more affordable to very large corporations than smaller companies. The differences in GL performance between ﬁrms of different sizes in China and Japan revealed in the survey data suggest that there are basically two approaches to GL implementation. As shown in Figure 1, GL practices can be just a reactive response of smaller ﬁrms with limited resources to comply with environmental regulations and to reduce production cost (as reﬂected in the case of China). In contrast, larger ﬁrms may take a proactive approach in which GL is seen not only as sheer compliance with laws and regulations or a mere cost saving measure but also unique capability that adds value to product. Large ﬁrms tend to embrace GL in a fuller scale and invest extensively to develop GL as a unique capability to enable the company to attain long-term competitive advantage over their competitors (as reﬂected in both the cases of China and Japan). In this regard, the RBV theory can be used to account for the incorporation of GL as part of long-term business strategy by some large corporations (Clendenin, 1997; Wells and Seitz, 2005). PCA to generate GLPI To generate a GLPI for overall comparison combining all the indicator variables investigated in the survey, PCA is adopted to help determine the weights for the variables that constitute the index. PCA as a multivariate statistical weighting approach Firm Size Approach to GL Implementation GL Performance - Amount of resources available affects 1. Reactive approach affects 1. Reactive approach - Strength of corporate social responsibility - Law compliance and cost saving - Focuses mainly on low-cost activitiesFigure 1. - Significance of company image 2. Proactive approach 2. Proactive approachDifferent approaches - Level of pressure from stakeholders - Unique capability building - Invests in technologies and infrastructureto GL implementation Underpinned by the RBV theory
is often used in the development of composite index. Examples include Jollands et al. Benchmarking(2004), Ali (2009), and Primpas et al. (2010). PCA weighs data by combining the indicator green logisticsvariables into linear combinations that explain as much variation in the dataset aspossible. It provides a relatively objective approach to setting weights that is less biased performancethan 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, PCA 887is 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 PC 2 – PC 3 – PC 4 – PC 1 – awareness of compliance cost PC 5 – availabilityof environmental with reduction willingness PC 6 –Variable (or activity) alternatives conservation regulations measures to invest EPRA2 0.979 0.058 0.103 20.031 20.039 0.021A12 0.974 0.053 0.079 20.049 20.036 2 0.025A7 0.922 0.117 0.154 20.061 0.111 0.079A1 0.145 0.924 20.015 0.021 0.057 2 0.005A6 0.090 0.912 20.125 0.025 0.060 2 0.020A11 2 0.021 0.777 0.240 20.065 20.034 0.185A15 0.177 0.079 0.880 0.065 0.207 2 0.090A5 0.147 0.144 0.862 0.021 0.250 0.031A10 0.029 2 0.138 0.649 0.047 20.166 0.226A3 2 0.043 0.016 0.055 0.951 20.017 2 0.017A13 2 0.080 2 0.029 0.053 0.949 20.103 0.052A14 2 0.036 2 0.036 0.010 20.005 0.864 0.198A9 0.044 0.101 0.189 20.128 0.765 2 0.079A8 2 0.007 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 Table X. Principal componentNotes: KMO measure of sampling adequacy ¼ 0.592; Bartlett’s test of sphericity (approx. analysis of the surveyx 2 ¼ 690.74, df ¼ 105, p ¼ 0.000) dataset
BIJ of using PCA to obtain the weights for the variables, the ﬁgure has to be interpreted with caution. While the natural randomness in the dataset may actually be low in this case, the18,6 use of a coarse ﬁve-point measurement scale and a relatively small number of indicator variables may also result in lower variability hence the relatively high percentage of variance explained (Møller and Jennions, 2002). Based on the indicator variables or activities included in each category, the components are labelled as availability of888 alternatives, awareness of environmental conservation, compliance with regulations, cost reduction measures, willingness to invest, and EPR. They indicate the distinct dimensions in the measurement of GL performance of the ﬁrms in the dataset. Using the dominant statistical weights (with values greater than 0.6) obtained from the PCA and the performance scores A1-A15 of the 15 GL activities reported, the total performance score 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 ð1Þ þ 0:864A14 þ 0:880A15 As the scale used for all the indicator variables are from one to ﬁve, the absolute minimum and maximum values of S obtained using Equation (1) are Smin ¼ 12.94 and Smax ¼ 64.69. Using these values, the total performance score S of each ﬁrm in the survey 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: ðS 2 S min Þ100 I¼ ð2Þ S max 2 S min Comparison of performance using the GLPI By 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-based comparison among ﬁrms can also be made at a ﬁner level in the areas of green purchasing, packaging, and transportation by using the weights generated in the PCA but including only a subset of the indicator variables. Also, focusing on the six components identiﬁed, performance of ﬁrms based on the various drivers such as cost reduction and regulation compliance can also be easily compared. Table XI gives a summary of the comparison among ﬁrms of different size in China and Japan in different logistics functions based on their GL performance indices. It can be seen from Table XI that on the whole ﬁrms in Japan are performing better than their counterparts in China regardless of ﬁrm size. The average GLPI for China Green Green Green Overall purchasing packaging transportation performance China Japan China Japan China Japan China JapanTable XI. Medium-sized ﬁrms 37 43 33 52 37 43 36 46Average GLPI of ﬁrms Large-sized ﬁrms 44 57 50 57 49 55 47 57in different Very large-sized ﬁrms 65 77 63 72 68 69 65 72logistics functions All ﬁrms 45 62 46 61 48 58 47 60
and Japan for all ﬁrms are 47 and 60, respectively, indicting a big difference in Benchmarkingperformance. Nevertheless, the performance gap is larger for medium- and large-sized green logisticsﬁrms but relatively smaller for very large-sized companies. Looking at performance indifferent logistics functions, the gap is largest in green packaging between the performancemedium-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 of 889previous 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 EPR C J C J C J C J C J C JMedium-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 57 Table XII.Very large-sized Average GLPIﬁrms 86 78 51 90 77 66 59 69 35 52 53 68 of ﬁrms in differentAll ﬁrms 45 61 39 64 51 59 60 56 45 55 41 62 components or factors
BIJ performance between the two countries. The ﬁndings reveal that China – a developing18,6 country – is still a distance behind Japan – a developed country – in GL implementation particularly in the upstream of the supply chain, i.e. purchasing. While the HEA industry of Japan has implemented GL throughout the whole supply chain with relatively good performance in almost all activities surveyed, the Chinese HEA manufacturers, particularly the small ones, are focusing mainly in certain downstream activities such890 as packaging with recycled material and consolidation to reduce transportation. These activities require relatively little investment in technology but the cost saving from GL is readily achievable. The ﬁndings also suggest that the main drivers for GL implementation in the HEA industry of China are still regulatory compliance and cost saving at this stage. The Japanese manufacturers are implementing GL more for reasons of stronger awareness, availability of alternative green materials and technologies, development of unique capability for long-term competition, and EPR. The different approaches to GL implementation by the small and the large ﬁrms can be accounted for using the RVB theory. With these ﬁndings, the ﬁrst two research questions are fully answered. Although this study was not designed to investigate the barriers to GL practices and GSCM, the ﬁndings have shed light on the challenges of GL implementation in developing countries such as China. These challenges include: . relatively low public awareness of sustainability and environmental protection hence weaker pressure on manufacturers to go green; . lack of comprehensive environmental policies, regulations, and directives such as the restriction of hazardous substance and the Waste Electrical and Electronic Equipment directives of the European Community (EU) (European Parliament and Council, 2003a, b) to force compliance; . limited investment in green technology, research and development to enhance efﬁciency and achieve economies of scale; . over-emphasis on low-cost production and short-term beneﬁts than long-term gains in order to maintain competitiveness in the global market; and . lack of resources, expertises, and management experiences in GSCM particularly for the small manufacturers. These observations align with the comments made by some researchers in China that both the country’s hardware and software for GL are lagging behind that of developed countries (Liu, 2009; Zhou, 2009). To promote GL practices and GSCM in developing countries, government can play a critical role in enhancing awareness through public education and industrial workshops, encouraging implementation through tax incentives and subsidies, enforcing compliance through legislations and regulations, sponsoring academic research for long-term sustainable development, and investing in infrastructure and technology to beneﬁt the entire industry. Manufacturers, particularly large corporations with more resources, can also take greater initiatives to invest in green technology, environment-friendly product design, cleaner manufacturing and distribution processes, and recycling. Strong collaboration among business partners across the supply chain will put pressure on smaller manufacturers to follow suit and help them develop their GL capabilities (Lau and Wang, 2009). The paper has also demonstrated the development of a GLPI using PCA to obtain the weights for the indicator variables involved in the equation. Results of comparison
among the surveyed ﬁrms in China and Japan using the GLPI align with the outcomes Benchmarkingobtained through other statistical analyses. The feasibility of using a single index for green logisticsGL 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 provide performancea 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 strategy 891accordingly. 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.ReferencesAccenture (2008), Driving Green Logistics: Practical Actions When Opex is Tight, available at: www.logisticsit.com/downloads/Accenture-Green-Logistics.pdf (accessed 6 October 2010).Ali, H.M.M. (2009), “Development of Arab water sustainability index using principal component analysis”, Proceedings of the 13th International Water Technology Conference, IWTC13 2009, Hurghada, Egypt, available at: www.iwtc.info/2009_pdf/19-1.pdf (accessed 6 October 2010).Alvarez, M., Jimenez, J. and Lorente, J. (2001), “An analysis of environmental management, organization context and performance of Spanish hotels”, Omega, Vol. 29 No. 6, pp. 457-71.Bacallan, J.J. (2000), “Greening the supply chain”, Business & Environment, Vol. 6 No. 5, pp. 13-15.Barney, J.B. (1991), “Firm resources and sustained competitive advantage”, Journal of Management, Vol. 17 No. 1, pp. 99-120.Bartlett, M.S. (1950), “Test of signiﬁcance in factor analysis”, British Journal of Psychology, Statistical Section, Vol. 3, pp. 77-85.
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