Purchasing & Supply Management | My Assignment Tutor

Contents lists available at ScienceDirectJournal of Purchasing & Supply Managementjournal homepage: www.elsevier.com/locate/pursupThe use of secondary data in purchasing and supply management (P/SM)research☆Lisa M. Ellrama, Wendy L. Tateba Farmer School of Business, Department of Management MSC1080, Miami University, Farmer Building 3030, Oxford, OH 45056, United Statesb Department of Marketing and Supply Chain Management, Haslam College of Business, University of Tennessee, United StatesA R T I C L E I N F OKeywords:Secondary dataPurchasing and supply management researchBig dataA B S T R A C TThe use of secondary data, data that has been gathered for another purpose but may be suitable for research, isgrowing in relevance and importance in purchasing and supply management research. This paper presentsissues associated with using secondary data as a principal source of data or as supplemental to other data ormethods. As editors increasingly call for multiple data sources in research publications, secondary data becomeeven more relevant. Secondary data have many benefits, and some secondary data sets are well established andhighly credible. The authors describe some of the ways that secondary data has been used in purchasing andsupply management research, as well as some of the sources of data. Secondary data does have its limitations,ranging from bias in collection and reporting to difficulty in identifying and accessing appropriate sources ofsecondary data to dealing with its predominantly unstructured nature. Suggestions for improving the reliabilityand validity of secondary data are provided, as well advice for dealing with big data.1. IntroductionSecondary data is defined as quantitative or qualitative data thathas been collected by someone other than the researcher(s) for adifferent purpose than its intended use in research. There are manydifferent types of secondary data available. Some of the more commonly used are existing literature, census data, governmental information, financial data, organizational reports and records (Lind et al.,2012). This data may be free to access, available by permission of theparty collecting the data, or may require payment of a fee.Researchers in P/SM often use secondary data to triangulatefindings from principal data collection such as interviews, case studies,surveys and experiments (Tatsis et al., 2006; Sancha et al., 2015). Onecommon use is as an objective source of performance outcomesassociated with various P/SM practices among a research sample.Researchers have shown that the use of secondary data as the principaldata source can reduce the bias that is sometimes introduced duringcase studies and the intrusiveness of data collection that is inherent inmore experiential methods such as action research, experiments orinterviews (Rabinovich and Cheon, 2011). Secondary data can also helpovercome issues of survey fatigue or the use of survey companies suchas QDAMiner or survey monkey that have questionable demographicsand a lack of control over survey respondents (Schoenherr et al., 2015).There are some important practices for using secondary data inorder to address the appropriate research questions, which arediscussed in this note. With the increased awareness of data availability, calls for the use of archival and secondary data as a principalsource of data in P/SM research are increasing, as there are numerousbenefits to its use (Calantone and Vickery, 2009).2. Why use secondary data in P/SM researchIncreasingly, it has been difficult to garner significant responserates using a survey method, and the litigious propensity of the currentsociety has made access to case study participants challenging. Manypopulations have been over-sampled, and suffer from survey fatigue,which creates an overall unwillingness to respond to surveys. In orderto gather survey data, researchers often rely on the expensive servicesof survey companies such as survey monkey and Qualtrics that havedeveloped databases of survey takers qualified to answer surveys on awide variety of topics.Secondary data is effective in P/SM research to address a number ofresearch topic areas such as sustainability, financial performance,literature analyses and more. Methodologically, secondary data canhttp://dx.doi.org/10.1016/j.pursup.2016.08.005Received 28 July 2016; Accepted 24 August 2016☆ The purpose of this note is to discuss the use of secondary data in purchasing/supply management (P/SM) research. Secondary data is widely available and may be useful as aprincipal data source or as supplemental data for other research methodologies.E-mail addresses: [email protected] (L.M. Ellram), [email protected] (W.L. Tate).Journal of Purchasing & Supply Management 22 (2016) 250–2541478-4092/ © 2016 Elsevier Ltd. All rights reserved.Available online 22 September 2016crossmarkbe analyzed through econometric analysis of archival data, contentanalyses, simulation, event studies, meta-analysis and GIS (geographicinformation systems) (Rabinovich and Cheon, 2011).3. Benefits of using secondary dataThere are many potential benefits to using secondary data, asshown in Table 1 below. For example, there are many sources of suchdata. Some are available free through libraries and other sources, andothers may be purchased at a relatively low price versus what it wouldcost for a researcher to create the dataset. This may save a great deal oftime and human effort, although some formatting and cleaning ofsecondary data sets is often required.Secondary datasets often use well-established measures such assales revenue, inventory turnover, and so on, that add credibility whencombined with the results of another study. Further, using common,well-established measures can provide clarity. Some secondary datasuch as financial data are even audited, adding to the credibility of thedata and the associated research project.4. What type of data has been used in P/SM research?For illustrative purposes, a search on google scholar with thekeywords “secondary data” and “supply” was run for the years 2010until 2015. The first 200 hits were checked for potential applicabilitybased on the brief abstract, the key words and the journal where thearticle was published. Of these 200, there were 62 articles thatwarranted full paper reviews. Of these 62 articles, 21 used secondarydata as principal data sources and 20 used secondary data totriangulate findings from other data collection methods. A list ofarticles using secondary data for principal reasons are included inTable 2 (Altay and Ramirez, 2010; Beske et al., 2014; Busse, 2010;Crum et al., 2011; Ellinger et al., 2012; Ellinger et al., 2011; Ellramet al., 2013; Ghadge et al., 2013; Ghadge et al., 2012; Hora et al., 2011;Horn et al., 2013; Johnson and Templar, 2011; Kovács et al., 2010;Lanier et al., 2010; Min and Kim, 2012; Modi and Mabert, 2010;Narasimhan and Schoenherr, 2012; Stentoft Arlbjørn and Pazirandeh,2011; Tangpong, 2011; Tate et al., 2010; Yang et al., 2010).Based on the articles listed in Table 2, Table 3 provides a partial listof some of the types of data that have been used in secondary researchin supply chain management.5. Why does secondary data make sense for P/SM research?There are many reasons why secondary data is a good fit for P/SMresearch. If using a broad, well-established source of data, there arefewer chances to skew the data collection process based on researcherspreconception and bias. This is essential to generating meaningful,generalizable and publishable results.Further, secondary data sets are often already validated. This allowsresearchers to focus on validation of new constructs and measures thatare critical to move research forward. Established databases havehigher internal validity, and the clear descriptions surrounding thesedatasets presents greater opportunity for replication. Finally, in manycases consistent longitudinal data are available that provide the timeseries/temporal data needed for trend analysis. There are many ways inwhich to analyze the data including content analysis, running an eventstudy, simulation, or meta-analysis (Rabinovich and Cheon, 2011).6. General limitations of secondary dataWhen seeking secondary data to use in analysis, there can besignificant time spent searching for appropriate datasets and interpreting the data, trying to understand issues such as: where areappropriate data published, are there common reporting measuresused for this type of data and are there common time frames? Also, asignificant problem is that the a single database or source secondarydata may not be available to answer the research question, soresearchers must be able to appropriately synthesize the differentdatabases to address their needs.In addition, all secondary data and empirical data in general,whether voluntarily provided or mandated and standardized, is asnapshot at a point in time (Snow and Thomas, 1994). Archival datafocus on what has already occurred (Snow and Thomas, 1994). Inaddition, the way that the data were gathered and the information thatis included is all subject to interpretation unless a very precise directivefor reporting is provided. Even then, data can be distorted by companydifferences in accounting policies or other internal practices(Venkatraman and Vasudevan, 1986). The biases and data accessibilityof those responsible for collecting and reporting the data are alsoconsiderations, even with data that appear to be very quantitative innature, such as financial reports.Further, data are not always available for all time periods, for allphenomena, and for all organizations of interest (Venkatraman andVasudevan, 1986). Such bias is present even in data that we assume tobe very standardized and subject to external auditing, such as financialreports of publicly held corporations (Snow and Thomas, 1994). Thus,green-washing might be a threat even if the CSR reports were audited.Another complication is that is that 80% of the data available todayare unstructured (Zikopoulos et al., 2012). Unstructured data includesdata that is not managed by a standard database management system,information that does not have a predefined data model or is notorganized in a predefined manner. While some would argue that suchdata is not designed as a data set because of the extensive transformation that needs to be performed to make it useable as research data, weinclude such data in our definition of unstructured data.7. Specific limitations of voluntarily reported dataSpecific to voluntarily reported data such as CSR data and nonfinancial data from company websites, there is no auditing of the data.While it is unlikely that companies would wildly distort such data dueto the potential impact that this could have on reputation, self-reporteddata tend to be presented in a favorable light (Jose and Lee, 2007). Inaddition, because there is no requirement that companies discloseeverything, reports will likely emphasize what is perceived as important, and areas where they excel, rather than disclose all relevantactivities.Along similar lines, there is a lack of common language andmeasurement (Fratocchi, et al., 2014) for many types of data such assustainability initiatives and supplier performance. Terminology maydiffer somewhat by industry and by country. Thus, there may bedifferential interpretation of what companies are actually doing. Inaddition to these limitations, data cleansing can be an arduous task.Depending on the format of the data, there may be a tremendousamount of time required to transform the data into a useable form,then cleanse the data for errors and extraneous characters andstandardize the format of the data just to prepare it for analysis.Table 1Benefits of using secondary data.• Relatively large amounts of data available.• Less money• Less time• May be viewed as more objective than primary data such as cases or surveydata• Fewer personnel needed to collect resources• Combine with other types of data to investigate phenomenon morethoroughly• Corporate data available – don’t have to rely on perceptions of history• Common, well-defined and established measures may be usedL.M. Ellram, W.L. Tate Journal of Purchasing & Supply Management 22 (2016) 250–2542518. What are some techniques to ensure reliability andvalidity?Reliability assesses whether the data is consistently reported overtime. With secondary data, it is thus essential to understand how thedata have been collected. Were consistent measures used over time,and across different data sets purporting to measure the same thing?The researcher should be able to explain what was being measured,how, and in some cases by whom (self-reporting versus laypersonreporting, versus professional researcher). This helps ensure thatdifferences in the data are meaningful, not simply an aberration inreporting.If the researcher cannot be reasonably certain that the data arereliable, any results found in analyzing the data may be spurious. If thisis the case, the researcher should consider looking for other sources ofsecondary data. The researcher may be able to triangulate that similardata from various sources show similar results, thereby increasing thecredibility that the data are reliable. Data from well-establishedsources, such as the US Census, Figshare, UK data sources andCOMPUSTAT have a high level of credibility for internal consistency,so if the researcher can stick with established sources, that can increasereliability.However these well-respected sources may not supply the type ofdata that the researcher requires. There are initiatives from leadinginstitutions such as MIT to create and make available databases for usein P/SM research. Parties who provide data (especially publicly fundedTable 2List of articles using secondary data as a principal data source 2010–2015.Altay, N., & Ramirez, A. (2010). Impact of disasters on firms in different sectors:implications for supply chains. Journal of Supply Chain Management, 46(4), 59–80.Horn, P., Schiele, H., & Werner, W. (2013). The “ugly twins”: Failed low-wagecountry sourcing projects and their expensive replacements. Journal of Purchasingand Supply Management, 19(1), 27–38.Beske, P., Land, A., & Seuring, S. (2014). Sustainable supply chain managementpractices and dynamic capabilities in the food industry: A critical analysis of theliterature. International Journal of Production Economics, 152, 131–143.Johnson, M., & Templar, S. (2011). The relationships between supply chain and firmperformance: The development and testing of a unified proxy. International Journal ofPhysical Distribution & Logistics Management, 41(2), 88–103.Busse, C. (2010). A procedure for secondary data analysis: innovation by logisticsservice providers. Journal of Supply Chain Management, 46(4), 44–58.Kovács, G., Matopoulos, A., & Hayes, O. (2010). A community-based approach tosupply chain design. International Journal of Logistics: Research and Applications, 13(5), 411–422.Crum, M., Poist, R., Carter, C.R., & Liane Easton, P. (2011). Sustainable supply chainmanagement: evolution and future directions. International Journal of PhysicalDistribution & Logistics Management, 41(1), 46–62.Lanier, D., Wempe, W.F., & Zacharia, Z.G. (2010). Concentrated supply chainmembership and financial performance: Chain-and firm-level perspectives. Journal ofOperations Management, 28(1), 1–16.Ellinger, A., Shin, H., Magnus Northington, W., Adams, F. G., Hofman, D., & O’Marah,K. (2012). The influence of supply chain management competency on customersatisfaction and shareholder value. Supply Chain Management: An InternationalJournal, 17(3), 249–262.Min, H., & Kim, I. (2012). Green supply chain research: past, present, and future.Logistics Research, 4(1–2), 39–47.Ellinger, A.E., Natarajarathinam, M., Adams, F.G., Gray, J.B., Hofman, D., & O’Marah,K. (2011). Supply chain management competency and firm financial success.Journal of Business Logistics, 32(3), 214–226.Modi, S.B., & Mabert, V.A. (2010). Exploring the relationship between efficientsupply chain management and firm innovation: an archival search and analysis.Journal of Supply Chain Management, 46(4), 81–94.Ellram, L.M., Tate, W.L., & Feitzinger, E.G. (2013). Factor-Market Rivalry andCompetition for Supply Chain Resources. Journal of Supply Chain Management,49(1), 29–46.Narasimhan, R., & Schoenherr, T. (2012). The effects of integrated supplymanagement practices and environmental management practices on relativecompetitive quality advantage. International Journal of Production Research, 50(4),1185–1201.Ghadge, A., Dani, S., Chester, M., & Kalawsky, R. (2013). A systems approach formodelling supply chain risks. Supply Chain Management: An International Journal,18(5), 523–538.Stentoft Arlbjørn, J., & Pazirandeh, A. (2011). Sourcing in global health supply chainsfor developing countries: Literature review and a decision making framework.International Journal of Physical Distribution & Logistics Management, 41(4), 364–384.Ghadge, A., Dani, S., & Kalawsky, R. (2012). Supply chain risk management: presentand future scope. The International Journal of Logistics Management, 23(3), 313–339.Tangpong, C. (2011). Content analytic approach to measuring constructs in operationsand supply chain management. Journal of Operations Management, 29(6), 627–638.Hora, M., Bapuji, H., & Roth, A.V. (2011). Safety hazard and time to recall: The role ofrecall strategy, product defect type, and supply chain player in the US toy industry.Journal of Operations Management, 29(7), 766–777.Tate, W.L., Ellram, L.M., & Kirchoff, J.F. (2010). Corporate social responsibilityreports: a thematic analysis related to supply chain management. Journal of SupplyChain Management, 46(1), 19–44.Yang, C.-L., Lin, S.-P., Chan, Y.-h., & Sheu, C. (2010). Mediated effect ofenvironmental management on manufacturing competitiveness: an empirical study.International Journal of Production Economics, 123(1), 210–220.Table 3Sampling of types of secondary data used in SCM research.Types of data Examples sourcesFinancial data Annual reports, financial databases such as Compustat, Dun & Bradstreet and BloombergSupply Chain Management Rankings – Gartner top 25, forexampleData on disasters: EM-DAT databaseSystematic Literature Review Subject matter of interest; content analysisSurvey data gathered for other purposes but publicallyavailable.Useful in longitudinal research. Example: Annual Mannheim Innovation Panel on innovation activity withinthe German economy Product recall dataLarge survey shared among researchersNewspaper article searchesVarious types of Company DataCompany sales and financial performancePress ReleasesConsumer Product Safety data on toy recall, Automotive recall dataGlobal Manufacturing Research Group (GMRG data)Factiva data base; specific newspaper archives/ websites such as Wall Street JournalAnnual reports, CSR reports and websites;Wharton Research Data ServicesFocus on specific companies or sectorsTranscripts from meetingsCustomer satisfaction/reviews on products and servicesInternal and external company sustainability dataWorkshop transcripts on a specific topicPublically available on websitesCorporate social responsibility reports, codes of conduct, copies of presentations, websites L.M. Ellram, W.L. Tate Journal of Purchasing & Supply Management 22 (2016) 250–254252data) may require that the authors publish as open source (available toall for free), which is common in less prestigious journals or only afterpaying the journal a hefty fee. This may limit the use of such datasources by researchers.Thus, a real challenge in using secondary data is validity. Thequestion here is whether the secondary data available measures theconstructs that the researcher is interested in, or provides a closeenough approximation to be used in a meaningful way. For example, ifa researcher is exploring how the number of suppliers a company usesvaries by industry, firm size, or something similar, he or she may findsecondary data on cost of goods sold. The researcher may make theleap to say that cost of goods sold is a surrogate for the number ofsuppliers. Of course, any student of purchasing literature would knowthat these are not causally related across companies, and that thiswould not be a valid measure for number of suppliers. In order to getsecondary data that measures what the researcher wants to measure(or to adapt to a particular research question), the researcher may haveto:1. Combine multiple data sources that measure some aspect of whatthe researcher is looking for; or2. Modify the construct of interest to better fit the available data if theresults will still be meaningful.9. Where does big data fit in?Big data is a term used to describe the massive amounts of data thatorganizations are collecting that cannot be processed, analyzed andmanaged, using traditional tools and approaches (Zikopoulos et al.,2012). Big data is formally defined as data that has volume, velocity,variety, and veracity (IBM, 2016). To the organization collecting it, it isprimary data. When we access it as researchers who did not collect thedata or direct the collection of the data for our purposes, it becomessecondary data.The vast majority of such data are unstructured, creating specialchallenges for both the organization collecting the data and theresearcher who wants to use the data. The data needs to have intrinsicvalidity, in other words, it needs to measure what it says it ismeasuring: be accurate, complete and consistent, as well as timely(Hazen et al., 2014).For a researcher to meaningfully use the data it must also havecontextual validity, or meaning within a given setting. This includesdimensions such as relevance, credibility, quantity and believability(Hazen et al., 2014). The challenge with using big data as a resource isthe complexity in understanding, manipulating, properly structuringand validating these data sets. These tasks may be so enormous that itconsumes the researcher. By the time they get a useable data set, it maybe old, or it may be so simplified that it is no longer very interesting. Orthe researcher may want to write an article about how challenging it isto make such data sets useable. When taking on the challenge of usingbig data, the researcher must ask him/herself:• Does the data have intrinsic and contextual validity?• How long might it take to clean and structure this data into a useableform?• Is the nature of this data different and somehow more interestingand valuable than other data sets available?• Does the “bigger” aspect of this data set really add value, or does itjust add computational complexity?10. Future research opportunities using secondary dataWith the growth of “big” data, the number of opportunities forusing secondary data is increasing. The important consideration isensuring that the data is used in a way that is meaningful and addsvalue in addressing real problems and issues, not simply in a way thatis convenient. Using traditional secondary data sources such as theCOMPUSTAT databases, census databases and others will continue tobe important. More companies have massive quantities of data thatthey capture on their performance, such as customer order fill rates byitem, by region, by customer, etc. Analysis of such data could providefodder for analysis of particular supply chain practices, provided thatthe results can be generalized to a wider population, or analyzed inconjunction with data from other companies to provide greater insights.11. ConclusionsWhile there is a wealth of potentially valuable and interestingsecondary data that can be mined by researchers, including a growingstore of mainly unstructured big data, the acquisition, cleansing,interpretation and publication of that data is not without extremechallenges. Reviewers may not be familiar with such data. This createsan extra responsibility for the authors to take care in the handling ofthe data, its interpretation and its presentation to the public. As in alldata analysis, care must be taken to strive for validity and generalizability while clearly acknowledging both the limitations and contributions of secondary data analysis.ReferencesAltay, N., Ramirez, A., 2010. Impact of disasters on firms in different sectors:implications for supply chains. J. Supply Chain Manag. 46 (4), 59–80.Beske, P., Land, A., Seuring, S., 2014. Sustainable supply chain management practicesand dynamic capabilities in the food industry: a critical analysis of the literature. Int.J. Prod. Econ. 152, 131–143.Busse, C., 2010. A procedure for secondary data analysis: innovation by logistics serviceproviders. J. Supply Chain Manag. 46 (4), 44–58.Calantone, R., Vickery, S.K., 2009. Special topic forum on using archival and secondarydata sources in supply chain management research. J. Supply Chain Manag. 45 (2),94–95.Crum, M., Poist, R., Carter, C.R., Liane Easton, P., 2011. Sustainable supply chainmanagement: evolution and future directions. Int. J. Phys. Distrib. Logist. Manag. 41(1), 46–62.Ellinger, A., Shin, H., Magnus Northington, W., Adams, F.G., Hofman, D., O’Marah, K.,2012. The influence of supply chain management competency on customersatisfaction and shareholder value. Supply Chain Manag.: Int. J. 17 (3), 249–262.Ellinger, A.E., Natarajarathinam, M., Adams, F.G., Gray, J.B., Hofman, D., O’Marah, K.,2011. Supply chain management competency and firm financial success. J. Bus.Logist. 32 (3), 214–226.Ellram, L.M., Tate, W.L., Feitzinger, E.G., 2013. Factor-market rivalry and competitionfor supply chain resources. J. Supply Chain Manag. 49 (1), 29–46.Fratocchi, L., Di Mauro, C., Barbieri, P., Nassimbeni, G., Zanoni, A., 2014. Whenmanufacturing moves back: concepts and questions. J. Purch. Supply Manag. 20 (1),54–59.Ghadge, A., Dani, S., Chester, M., Kalawsky, R., 2013. A systems approach for modellingsupply chain risks. Supply Chain Manag.: Int. J. 18 (5), 523–538.Ghadge, A., Dani, S., Kalawsky, R., 2012. Supply chain risk management: present andfuture scope. Int. J. Logist. Manag. 23 (3), 313–339.Hazen, B.T., Boone, C.A., Ezell, J.D., Jones-Farmer, L.A., 2014. Data quality for datascience, predictive analytics, and big data in supply chain management: anintroduction to the problem and suggestions for research and applications. Int. J.Prod. Econ. 154, 72–80.Hora, M., Bapuji, H., Roth, A.V., 2011. Safety hazard and time to recall: the role of recallstrategy, product defect type, and supply chain player in the US toy industry. J. Oper.Manag. 29 (7), 766–777.Horn, P., Schiele, H., Werner, W., 2013. The “ugly twins”: failed low-wage-countrysourcing projects and their expensive replacements. J. Purch. Supply Manag. 19 (1),127–138.IBM, 2016. What is Big Data? Retrieved from: 〈https://www.ibm.com/big-data/us/en/〉on June 25, 2016.Johnson, M., Templar, S., 2011. The relationships between supply chain and firmperformance: the development and testing of a unified proxy. Int. J. Phys. Distrib.Logist. Manag. 41 (2), 88–103.Jose, A., Lee, S.M., 2007. Environmental reporting of global corporations: a contentanalysis based on website disclosures. J. Bus. Ethics 72 (4), 307–321.Kovács, G., Matopoulos, A., Hayes, O., 2010. A community-based approach to supplychain design. Int. J. Logist.: Res. Appl. 13 (5), 411–422.Lanier, D., Wempe, W.F., Zacharia, Z.G., 2010. Concentrated supply chain membershipand financial performance: Chain-and firm-level perspectives. J. Oper. Manag. 28L.M. Ellram, W.L. Tate Journal of Purchasing & Supply Management 22 (2016) 250–254253(1), 1–16.Lind, L., Pirttilä, M., Viskari, S., Schupp, F., Kärri, T., 2012. Working capitalmanagement in the automotive industry: financial value chain analysis. J. Purch.Supply Manag. 18 (2), 92–100.Min, H., Kim, I., 2012. Green supply chain research: past, present, and future. Logist.Res. 4 (1–2), 39–47.Modi, S.B., Mabert, V.A., 2010. Exploring the relationship between efficient supply chainmanagement and firm innovation: an archival search and analysis. J. Supply ChainManag. 46 (4), 81–94.Narasimhan, R., Schoenherr, T., 2012. The effects of integrated supply managementpractices and environmental management practices on relative competitive qualityadvantage. Int. J. Prod. Res. 50 (4), 1185–1201.Rabinovich, E., Cheon, S., 2011. Expanding horizons and deepening understanding viathe use of secondary data sources. J. Bus. Logist. 32 (4), 303–316.Sancha, C., Longoni, A., Giménez, C., 2015. Sustainable supplier development practices:drivers and enablers in a global context. J. Purch. Supply Manag. 21 (2), 95–102.Schoenherr, T., Ellram, L.M., Tate, W.L., 2015. A note on the use of survey research firmsto enable empirical data collection. J. Bus. Logist. 36 (3), 288–300.Snow, C.C., Thomas, J.B., 1994. Field research methods in strategic management:contributions to theory building and testing. J. Manag. Stud. 31 (4), 457–480.Stentoft Arlbjørn, J., Pazirandeh, A., 2011. Sourcing in global health supply chains fordeveloping countries: literature review and a decision making framework. Int. J.Phys. Distrib. Logist. Manag. 41 (4), 364–384.Tangpong, C., 2011. Content analytic approach to measuring constructs in operationsand supply chain management. J. Oper. Manag. 29 (6), 627–638.Tate, W.L., Ellram, L.M., Kirchoff, J.F., 2010. Corporate social responsibility reports: athematic analysis related to supply chain management. J. Supply Chain Manag. 46(1), 19–44.Tatsis, V., Mena, C., Van Wassenhove, L.N., Whicker, L., 2006. E-procurement in theGreek food and drink industry: drivers and impediments. J. Purch. Supply Manag.12 (2), 63–74.Venkatraman, N., Ramanujam, V., 1986. Measurement of business performance instrategy research: a comparison of approaches. Acad. Manag. Rev. 11 (4), 801–814.Yang, C.-L., Lin, S.-P., Chan, Y.-h, Sheu, C., 2010. Mediated effect of environmentalmanagement on manufacturing competitiveness: an empirical study. Int. J. Prod.Econ. 123 (1), 210–220.Zikopoulos, P.C., Eaton, C., DeRoos, D., Deutsch, T. and Lapis, G., 2012. UnderstandingBig Data. McGraw-Hill, New York et al.L.M. Ellram, W.L. Tate Journal of Purchasing & Supply Management 22 (2016) 250–254254

QUALITY: 100% ORIGINAL PAPER – NO PLAGIARISM – CUSTOM PAPER

Leave a Reply

Your email address will not be published. Required fields are marked *