Supply chain management practices and operational performance of fair price shops in India : an empirical study

Kumar, A.; Kushwaha, G. S.

doi:10.17270/J.LOG.2018.237

Artykuł - szczegóły

Tytuł artykułu

Supply chain management practices and operational performance of fair price shops in India : an empirical study

Autorzy

Kumar A. , Kushwaha G. S.

Treść / Zawartość

Pełne teksty:

Pobierz

Identyfikatory

DOI

10.17270/J.LOG.2018.237

Warianty tytułu

Praktyka zarządzania łańcuchem dostaw i działania operacyjne sklepów z fair price w Indiach : studium przypadku

Języki publikacji

Abstrakty

Background: In the current business environment, competition is no longer between the organisations but it is among the supply chains of the organisations. India's public distribution system is one of the biggest systems for food supply and distribution carried out through fair price shop (FPS). There is a wide gap concerning the empirical study on the fair price shops (FPS) and this is the rationale of the study. The paper aims to determine the relationship between different supply chain management practices and operational performance of the fair price shops in India. The authors propose the theoretical framework and empirically test the model. The study aims to expand the knowledge structure of supply chain management field. Methods: The paper opted for an exploratory cum descriptive study. The authors randomly selected 200 Fair price shops from a list available on the government website and identified the key persons from each shop as the respondent to get our questionnaire filled. The data were collected using structured questionnaire. Total 200 questionnaires were given to MBA students to collect the data from those fair price shops dealers who qualify the screening questions and situated in Bhopal Madhya Pradesh (India) in the month of March - April' 2017. Finally, 87 useful questionnaires were obtained, with a response rate of 43.5%, Authors employed the PLS-structural equation modelling (SEM) to test the theoretical model and hypothesis. Results: The study provides that three dimensions of SCM practices have a significant and positive relationship with the operational performance. The paper provides empirical insights about how change is brought about operational performance of the fair price shops. It suggests that supply chain management practices positively & significantly associated with the performance of fair price shops. Conclusion: This paper emphasizes the importance of supply chain management practices on the day to day operations of the fair price shops and provides an insight that these practices if employed properly they will give an added advantage. The study fulfils an identified need to study how supply chain management practices can impact the performance of fair price shops and the study directly measures the impact of supply chain management practices on operational performance of the fair price shops in India. The biggest limitation of this study is the size of the sample. Thus the research results may not be generalized. Therefore, researchers are encouraged to test the proposed framework at a broader level.

Wstęp: W obecnie istniejącym środowisku biznesowym, należy mówić raczej o rywalizacji nie między poszczególnymi organizacjami a sieciami organizacji. Indyjski publiczny system dystrybucji jest jednym z największych systemów dostaw i dystrybucji żywności realizowany poprzez sieć sklepów tzw. fair price (FPS). Pomimo tego faktu stosunkowo mało jest badań naukowych dotyczących tego typu sieci. Celem pracy jest określenie zależności pomiędzy różnymi metodami zarządzania łańcuchem dostaw i działaniami operacyjnymi tego typu sklepów w Indiach. Autorzy zaproponowali model teoretyczny oraz test empiryczny. Celem pracy jest poszerzenie wiedzy strukturalnej na temat zarządzania łańcuchem dostaw. Metody: Została wybrana próba losowa 200 sklepów typu fair price z listy dostępnej na stronie rządowej. Następnie zidentyfikowano osobę kluczową z każdego sklepu, jako ankietera uczestniczącego w badaniu. Dane zostały zebrane poprzez specjalnie stworzony kwestionariusz. Wywiady zostały przeprowadzone przez studentów MBA z wybranymi uprzednio osobami. Były one zbierane w Bhopal Madhya Pradesh w okresie marzec-kwiecień 2017. W rezultacie otrzymano 87 kompletnych kwestionariuszy (współczynnik udziału 43,5%). Model SEM strukturalnego równania PLS został użyty do testowania modelu teoretycznego i hipotez. Wyniki: Badania wykazały, że trzy wymiary metod zarządzania łańcuchem dostaw mają istotną i pozytywną zależność z działalnością operacyjną. Pokazały empirycznie jak zmiany wpływają na działalność operacyjną sklepów fair price. Wskazują, ze metody zarządzania łańcuchem dostaw pozytywnie i istotnie związane są z działalnością sklepów typu fair price. Wnioski: Praca wskazuje na istotny wpływ metod zarządzania łańcuchem dostaw na bieżąca działalność sklepów typu fair price oraz dowodzi pozytywny wpływ tych praktyk prawidłowo wdrożonych na uzyskanie wartości dodanej. Przeprowadzone badania uzupełniają lukę w badaniach nad wpływem metod zarządzania łańcuchem dostaw na działalność sklepów typu fair price w Indiach. Największym ograniczeniem tego badania była próba, na podstawie której uzyskano wyniki. W związku z tym nie jest możliwe uogólnienie tych wyników. Badania tego typu należałoby kontynuować w szerszym zakresie.

Słowa kluczowe

supply chain management fair price shops PLS structure modelling distribution system

zarządzanie łańcuchem dostaw sklepy typu fair price PLS modelowanie struktury system dystrybucji

Wydawca

Wyższa Szkoła Logistyki

Czasopismo

LogForum

Rocznik

2018

Tom

Vol. 14, no. 1

Strony

85--99

Opis fizyczny

Bibliogr. 63 poz., rys., tab.

Twórcy

autor

Kumar A.

anilkr.iimt@gmail.com

VNS Group of Institutions Bhopal MP, India

autor

Kushwaha G. S.

gskushwaha2015@yahoo.com

Department of Management studies, MANIT Bhopal MP, India

Bibliografia

1. Aibinu A.A., Ling F.Y.Y., Ofori G., 2011. Structural equation modelling of organizational justice and cooperative behaviour in the construction project claims process: contractors' perspectives. Construction Management and Economics, 29(5), 463-481. http://dx.doi.org/10.1080/01446193.2011.564195
2. Aramyan L.H., Oude Lansink A.G., Van Der Vorst J.G., Van Kooten O., 2007. Performance measurement in agri-food supply chains: a case study. Supply Chain Management: An International Journal, 12(4), 304-315. https://doi.org/10.1108/13598540710759826
3. Banerjee M., Mishra M., 2017. Retail supply chain management practices in India: A business intelligence perspective. Journal of Retailing and Consumer Services, 34, 248-259. https://doi.org/10.1016/j.jretconser.2015.09.009
4. Bharadwaj A.S., 2000. A resource-based perspective on information technology capability and firm performance: an empirical investigation. MIS quarterly, 169-196. http://dx.doi.org/10.2307/3250983
5. Brandyberry A., Rai A., White G.P., 1999. Intermediate performance impacts of advanced manufacturing technology systems: An empirical investigation. Decision Sciences 30 (4), 993-1020. http://dx.doi.org/10.1111/j.1540-5915.1999.tb00916.x
6. Baltacioglu T., Ada E., Kaplan M.D., Yurt And O., Cem Kaplan Y., 2007. A new framework for service supply chains. The Service Industries Journal, 27(2), 105-124. http://dx.doi.org/10.1080/02642060601122629
7. Chen I.J., Paulraj A., Lado A.A., 2004. Strategic purchasing, supply management, and firm performance. Journal of operations management, 22(5), 505-523. https://doi.org/10.1016/j.jom.2004.06.002
8. Cho D.W., Lee Y.H., Ahn S.H., Hwang M.K., 2012. A framework for measuring the performance of service supply chain management, Computers & Industrial Engineering, 62, 801-818. https://doi.org/10.1016/j.cie.2011.11.014
9. Chin W.W. 1998. The partial least squares approach to structural equation modelling. In: G. A. Marcoulides (Ed.), Modern methods for business research, 295-336. Mahwah, NJ' Lawrence Erlbaum Associate.
10. Chang W., Ellinger A.E., Kim K.K., Franke G.R., 2016. Supply chain integration and firm financial performance: A metaanalysis of positional advantage mediation and moderating factors. European Management Journal, 34(3), 282-295. https://doi.org/10.1016/j.emj.2015.11.008
11. Chin W.W., 2010. How to write up and report PLS analyses. In Handbook of partial least squares, 655-690. Springer Berlin Heidelberg.
12. Closs D.J., Jacobs M.A., Swink M., Webb G.S., 2008. Toward a theory of competencies for the management of product complexity: six case studies. Journal of Operations Management, 26(5), 590-610. https://doi.org/10.1016/j.jom.2007.10.003
13. Childerhouse P., Towill D.R., 2003. Simplified material flow holds the key to supply chain integration. Omega, 31(1), 17-27. https://doi.org/10.1016/S0305-0483(02)00062-2
14. Chatfield D.C., Kim J.G., Harrison T.P., Hayya J.C., 2004. The bullwhip effect-impact of stochastic lead time, information quality, and information sharing: a simulation study. Production and Operations Management, 13(4), 340-353. http://dx.doi.org/10.1111/j.1937-5956.2004.tb00222.x
15. Dehning B., Richardson V.J., Zmud R.W., 2007. The financial performance effects of IT-based supply chain management systems in manufacturing firms. Journal of Operations Management, 25(4), 806-824. https://doi.org/10.1016/j.jom.2006.09.001
16. Daugherty P.J., Richey R.G., Roath A.S., Min S., Chen H., Arndt A.D., Genchev S.E., 2006. Is collaboration paying off for firms?. Business Horizons, 49(1), 61-70. https://doi.org/10.1016/j.bushor.2005.06.002
17. Fynes B., De Burca S., Mangan J., 2008. The effect of relationship characteristics on relationship quality and performance. International Journal of Production Economics, 111(1), 56-69. https://doi.org/10.1016/j.ijpe.2006.11.019
18. Flynn B.B., Huo B., Zhao X., 2010. The impact of supply chain integration on performance: A contingency and configuration approach. Journal of operations management, 28(1), 58-71. https://doi.org/10.1016/j.jom.2009.06.001
19. Fornell C., Larcker D.F., 1981. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39-50. http://dx.doi.org/10.2307/3151312
20. Green Jr K.W., McGaughey R., Casey K.M., 2006. Does supply chain management strategy mediate the association between market orientation and organizational performance?. Supply Chain Management: An International Journal, 11(5), 407-414. https://doi.org/10.1108/13598540610682426
21. Gunasekaran A., Kobu B., 2007. Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995-2004) for research and applications. International journal of production research, 45(12), 2819-2840. http://dx.doi.org/10.1080/00207540600806513
22. Gunasekaran A., Lai K.H., Cheng T.E., 2008. Responsive supply chain: a competitive strategy in a networked economy. Omega, 36(4), 549-564. https://doi.org/10.1016/j.omega.2006.12.002
23. Gonzalez-Benito J., 2007. A theory of purchasing's contribution to business performance. Journal of Operations Management, 25(4), 901-917. https://doi.org/10.1016/j.jom.2007.02.001
24. Hui C.H., Triandis H.C., 1985. Measurement in cross-cultural psychology: A review and comparison of strategies. Journal of crosscultural psychology, 16(2), 131-152. https://doi.org/10.1177/0022002185016002001
25. Hendricks K.B., Singhal V.R., 2003. The effect of supply chain glitches on shareholder value. Journal of Operations Management 21, 501-522. https://doi.org/10.1016/j.jom.2003.02.003
26. Henseler J., Ringle C.M., Sinkovics R.R., 2009. The use of partial least squares path modelling in international marketing. Advances in international marketing, 20, 277-319.
27. Henseler J., 2010. On the convergence of the partial least squares path modelling algorithm. Computational statistics, 25(1), 107-120. http://dx.doi.org/10.1007/s00180-009-0164-x
28. Hamister J.W., 2012. Supply chain management practices in small retailers. International Journal of Retail & Distribution Management, 40(6), 427-450. https://doi.org/10.1108/09590551211230250
29. Hulland J., 1999. Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2), 195- 204.
30. Hair J.F., Ringle C.M., Sarstedt M., 2011. PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152. http://dx.doi.org/10.2753/MTP1069-6679190202
31. Jayachandran S., Sharma S., Kaufman P., Raman P., 2005. The role of relational information processes and technology use in customer relationship management. Journal of marketing, 69(4), 177-192. https://doi.org/10.1509/jmkg.2005.69.4.177
32. Kutsikos K., Sakas D., 2014. A framework for enabling service configuration decisions: the case of IT outsourcing providers. Procedia-Social and Behavioral Sciences, 148, 604-610. https://doi.org/10.1016/j.sbspro.2014.07.086
33. Korhonen P., Huttunen K., Eloranta E., 1998. Demand chain management in a global enterprise-information management view. Production Planning & Control, 9(6), 526-531. http://dx.doi.org/10.1080/095372898233777
34. Lambert D.M., Cooper M.C., Pagh J.D., 1998. Supply chain management: implementation issues and research opportunities. The International Journal of Logistics Management 9 (2), 1-19. https://doi.org/10.1108/09574099810805807
35. Li G., Yang H., Sun L., Sohal A.S., 2009. The impact of IT implementation on supply chain integration and performance. International Journal of Production Economics, 120(1), 125-138. https://doi.org/10.1016/j.ijpe.2008.07.017
36. Li S., Ragu-Nathan B., Ragu-Nathan T.S., Rao S.S., 2006. The impact of supply chain management practices on competitive advantage and organizational performance, Omega 34, 107-124. https://doi.org/10.1016/j.omega.2004.08.002
37. Liu H., Wei S., Ke W., Wei K.K., Hua Z., 2016. The configuration between supply chain integration and information technology competency: A resource orchestration perspective, Journal of Operations Management,44,13-29. https://doi.org/10.1016/j.jom.2016.03.009
38. Marinagi C., Trivellas P., Reklitis P., 2015. Information quality and supply chain performance: The mediating role of information sharing. Procedia-Social and Behavioral Sciences, 175, 473-479. https://doi.org/10.1016/j.sbspro.2015.01.1225
39. McCorriston S., MacLaren D., 2016. Parastatals as instruments of government policy: The Food Corporation of India. Food Policy, 65, 53-62. https://doi.org/10.1016/j.foodpol.2016.10.005
40. Mentzer J.T., Min S., & Zacharia Z.G. (2000). The nature of inter firm partnering in supply chain management. Journal of Retailing, 76(4), 549-568. https://doi.org/10.1016/S0022-4359(00)00040-3
41. Marcoulides G.A., Saunders C., 2006. Editor's Comments - PLS: A Silver Bullet? MIS Quarterly, 30(2), iii-ix.
42. Oke A., Kach A., 2012. Linking sourcing and collaborative strategies to financial performance: The role of operational innovation. Journal of Purchasing and Supply Management, 18(1), 46-59. https://doi.org/10.1016/j.pursup.2012.01.001
43. Paulraj A., Lado A.A., Chen I.J., 2008. Interorganizational communication as a relational competency: Antecedents and performance outcomes in collaborative buyer-supplier relationships, Journal of Operations Management 26, 45-64. https://doi.org/10.1016/j.jom.2007.04.001
44. Peng D.X., Lai F., 2012. Using partial least squares in operations management research: A practical guideline and summary of past research. Journal of Operations Management, 30(6), 467-480. https://doi.org/10.1016/j.jom.2012.06.002
45. Power D., 2005. Supply chain management integration and implementation: a literature review. Supply chain management: an International journal, 10(4), 252-263. https://doi.org/10.1108/13598540510612721
46. Randall W.S., Gibson B.J., Clifford Defee C., Williams B.D., 2011. Retail supply chain management: key priorities and practices. The International Journal of Logistics Management, 22(3), 390-402. https://doi.org/10.1108/09574091111181381
47. Ross A., 2002. A multi-dimensional empirical exploration of technology investment, coordination, and firm performance. International Journal of Physical Distribution and Logistics Management 32 (7), 591-609. https://doi.org/10.1108/09600030210442603
48. Ringle C.M., Sarstedt M., Mooi E.A., 2010. Response-based segmentation using finite mixture partial least squares. In: Data Mining (pp. 19-49). Springer US.
49. Stewart G., 1995. Supply chain performance benchmarking study reveals keys to supply chain excellence. Logistics Information Management, 8(2), 38-44. https://doi.org/10.1108/09576059510085000
50. Stein T., Sweat J., 1998. Killer supply chains. Information week, 708(9), 36-46.
51. Scott C., Westbrook R., 1991. New strategic tools for supply chain management. International Journal of Physical Distribution & Logistics Management, 21(1), 23-33. https://doi.org/10.1108/09600039110002225
52. Singh R., Sandhu H.S., Metri B.A., Kaur R., 2010. Relating organised retail supply chain management practices, competitive advantage and organisational performance. Vision, 14(3), 173-190. https://doi.org/10.1177/097226291001400303
53. Sambasivan M., Siew-Phaik L., Mohamed Z.A., Leong Y.C., 2013. Factors influencing strategic alliance outcomes in a manufacturing supply chain: role of alliance motives, interdependence, asset specificity and relational capital. International Journal of Production Economics, 141(1), 339-351. https://doi.org/10.1016/j.ijpe.2012.08.016
54. Srivastava S.K., 2006. Logistics and supply chain practices in India. Vision: The Journal of Business Perspective, 10(3), 69-79. https://doi.org/10.1177/097226290601000307
55. Tompkins J., Ang D., 1999, What are your greatest challenges related to supply chain performance measurement?, IIE Solutions, 31, 6, 66.
56. Tan K.C., Kannan V.R., Handfield R.B., 1998. Supply chain management: supplier performance and firm performance. Journal of Supply Chain Management, 34(3), 2-9.
57. Tan K.C., Kannan V.R., Handfield R.B., Ghosh S., 1999. Supply chain management: an empirical study of its impact on performance. International journal of operations & production Management, 19(10), 1034-1052. https://doi.org/10.1108/01443579910287064
58. Tanriverdi H., Venkatraman N., 2005. Knowledge relatedness and the performance of multibusiness firms. Strategic Management Journal, 26(2), 97-119. http://dx.doi.org/10.1002/smj.435
59. Vinzi V.E., Trinchera L., Amato S., 2010. PLS Path Modeling: From Foundations to Recent Developments and Open Issues for Model Assessment and Improvement. In: V. Esposito Vinzi et al. (eds.), Handbook of Partial Least Squares, Springer Handbooks of Computational Statistics, 47-82.
60. Wang M., Liu J., Wang H., Cheung W.K., Xie X., 2008. On-demand e-supply chain integration: A multi-agent constraint-based approach. Expert Systems with applications, 34(4), 2683-2692. https://doi.org/10.1016/j.eswa.2007.05.041
61. Wetzels M., Odekerken-Schroder C., Van Oppen, C., 2009. Using PLS path modelling for assessing hierarchical construct models: guidelines and empirical illustration. MIS Q. 33 (1), 177-195. https://doi.org/10.2307/20650284.
62. Zhou H., Shou Y., Zhai X., Li L., Wood C., Wu X., 2014. Supply chain practice and information quality: A supply chain strategy study. International Journal of Production Economics, 147, 624-633. https://doi.org/10.1016/j.ijpe.2013.08.025
63. Zailani S., Rajagopal P., 2006. The effects of Information Quality on Supply Chain Performance: New Evidence from Malaysia. In: Latif AlHakim (Ed.), Information Quality Management: Theory and Applications, Chapter XII, 275- 29, IGI Global, ISBN:1599040247. http://dx.doi.org/10.4018/978-1-59904-024-0.ch012

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-7400611e-02aa-45e6-879a-13de4721c7f9