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TEDARİK ZİNCİRİ GÖRÜNÜRLÜĞÜNÜN ÇEVİKLİK ÜZERİNDEKİ ETKİLERİ

Yıl 2020, Cilt: 3 Sayı: 1, 49 - 62, 22.06.2020

Sibel Yıldız Çankaya

Öz

İşletmeler karşılaştıkları belirsizliklerle başka çıkabilmek için daha
fazla bilgiye sahip olmayı istemektedirler. Fakat günümüzde çok fazla bilgiye
sahip olmakta yeterli olmamakta, aynı zamanda bilginin doğru, güncel, eksiksiz
ve kullanılabilir formda olması da gerekmektedir. Bu yüzden son yıllarda,
tedarik zinciri görünürlüğü konusu oldukça dikkat çekmektedir. Bu çalışmanın
iki temel amacı bulunmaktadır. Birincisi, tedarik zinciri görünürlüğünün
çevikliği nasıl etkilediğini ortaya çıkarmaktır. İkincisi ise, bu iki değişken
arasındaki ilişkide büyük veri analitiğinin düzenleyici (moderatör) etkisini
incelemektir. Söz konusu amaçlara ulaşmak adına geliştirilen hipotezleri test
etmek için kısmi en küçük kareler yapısal eşitlik modeli (PLS-YEM)
kullanılmıştır. Doksan dokuz firma üzerinde yapılan araştırma sonuçları,
görünürlüğün çeviklik üzerinde olumlu bir etkisi olduğunu göstermektedir. Fakat
bu iki değişken arasındaki ilişkide büyük veri analitiğinin düzenleyici etkisi
tespit edilememiştir.

Anahtar Kelimeler

Tedarik zinciri görünürlüğü Büyük veri analitiği Çevik tedarik zinciri PLS-YEM

Kaynakça

  • Akter, S., Bandara, R., Hani, U., Fosso, S., & Foropon, C. (2019). Analytics-based decisionmaking for service systems: A qualitative study and agenda for future research. International Journal of Information Management, 48(1), 85-95.
  • Alzoubi, H. M., & Yanamandra, R. (2020). Investigating the mediating role of information sharing strategy on agile supply chain. Uncertain Supply Chain Management, 8, 273-284.
  • Armstrong, J. S., & Overton, T. S. (1977). Estimating nonresponse bias in mail surveys. Journal of Marketing Research, 14(3), 396-402.
  • Barratt, M., & Oke, A. (2007). Antecedents of supply chain visibility in retail supply chains: A resource-based theory perspective. Journal of Operations Management, 25(6), 1217–1233.
  • Bass, B., Avolio, B., Jung, D., & Berson Y. (2003). Predicting unit performance by assessing transformational and transactional leadership. Journal of Applied Psychology, 88(2), 207–218.
  • Brandon-Jones, E., Squire, B., Autry, C. W., & Petersen, K. J. (2014). A contingent resource-based perspective of supply chain resilience and robustness. Journal of Supply Chain Management, 50(3), 55-73.
  • Braunscheidel, M. J., & Suresh, N. C. (2009). The organizational antecedents of a firm’s supply chain agility for risk mitigation and response. Journal of Operations Management, 27, 119-140.
  • Caridi, M., Moretto, A., Perego, A., & Tumino, A. (2014). The benefits of supply chain visibility: A value assessment model. International Journal Production Economics, 15, 1-19.
  • Chin, W. (1998). The Partial Least Squares Approach to Structural Equation Modeling. G. A Marcoulides (Ed.). Modern Methods for Business Research, (s.295-336), New York: Lawrence Erlbaum Associates.
  • Chin, W. W., Marcolin, B. L., & Newsted, P. R (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a monte carlo simulation study and electronic mail emotion/adoption study. Information Systems Research, 14(2), 189-217.
  • Christopher, M. (2000). The agile supply chain: competing in volatile markets. Industrial Marketing Management, 29(1), 37-44.
  • Clos, D. J., Goldsby, T. J., & Clinton, S. R. (1997). Information technology influences on world class logistics capability. International Journal of Physical Distribution & Logistics Management, 27(1), 4-17.
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Mahwah, NJ: Lawrence Erlbaum.
  • DeGroote, S. E., & Marx, T. G. (2013). The impact of it on supply chain agility and firm performance: An empirical investigation. International Journal of Information Management, 33(6), 909-916.
  • Dejonckheere, J., Disney, S. M., Lambrecht, M. R., & Towill, D. R. (2004). The impact of information enrichment on the bullwhip effect in supply chains: A control engineering perspective. European Journal of Operational Research, 153,727-750.
  • Dubey, R., Altay, N., Gunasekaran, A., Blome, C., Papadopoulos, T., & Childe, S. J. (2018). Supply chain agility, adaptability and alignment empirical evidence from the indian auto components industry. International Journal of Operations & Production Management, 38(1), 129-148.
  • Dubey, R., Gunasekaran, A., Childe S. J., Roubaud, D., Wamba, S. M., Giannakis, M., & Foropon, C. (2019). Big data analytics and organizational culture as complements to swift trust and collaborative performance in the humanitarian supply chain. International Journal of Production Economics, 210, 120–136.
  • Eckstein, D., Goellner, M., Blome, C., & Henke, M. (2015). The performance impact of supply chain agility and supply chain adaptability: The moderating effect of product complexity. International Journal of Production Research, 53(10), 3028-3046.
  • Eltantawy, R. A., & Giunipero, L. (2013). An empirical examination of strategic sourcing dominant logic: Strategic sourcing centricity. Journal of Purchasing and Supply Management, 19(49), 215-226.
  • Gligor, D. M., & Holcomb, M. C., (2012). Antecedents and consequences of supply chain agility: establishing the link to firm performance. Journal of Business Logistics, 33(4), 295–308.
  • Goh, M., De Souza, R., Zhang, A. N., He, W., & Tan, P. S (2009). Supply chain visibility: A decision making perspective. IEEE, 2546-2551.
  • Hair, J. F., Black, W., Babin, B., & Anderson, R. (2009). Multivariate data analysis. N.J: Prentice Hall.
  • Henseler, J., & Chin, W.W. (2010). A comparison of approaches for the analysis of interaction effects between latent variables using partial least squares path modeling. Structural Equation Modeling: A Multidisciplinary Journal, 17(1), 82-109.
  • Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems,116(1), 2-20.
  • Henseler, J., Ringle, C., & Sinkovics, R. (2009). The use of partial least squares path modeling in international marketing. Advances in International Marketing, 20(2009), 277–320.
  • Jermsittiparsert, K., & Srisawat, S. (2019). The role of supply chain visibility in enhancing supply chain agility. International Journal of Innovation, Creativity and Change, 5(2), 485-501.
  • Kaipia, R., & Hartiala, H. (2006). Information-sharing in supply chains: five proposals on how to proceed. The International Journal of Logistics Management, 17(3), 377-393.
  • Kaufmann, L., & Gaeckler, J. (2015). A structured review of partial least squares in supply chain management research. Journal of Purchasing & Supply Management, 21(2015), 259–272.
  • Kim, H., & Kankanhalli, A. (2009). Investigating user resistance to information sys-tems implementation: A status quo bias perspective. MIS Quarterly, 33(3), 567–582.
  • Kim, M., & Chai, S. (2017). The impact of supplier innovativeness, information sharing and strategic sourcing on improving supply chain agility: Global supply chain perspective. International Journal of Production Economics, 187, 42-52.
  • Kisperska-Moron, D., & de Haan, J. (2011). Improving supply chain performance to satisfy final customers: "Leagile" experiences of a polish distributor. International Journal of Production Economics, 133(1), 127-134.
  • Lai, F., Zhang M., Lee, D. M. S., & Zhao, X. (2012). The impact of supply chain integration on mass customization capability: An Extended Resource-Based View. IEEE Transactions on Engineering Management, 59(3), 443-456.
  • Lummus, R. R., Vokurka, R. J., & Duclos, L. K.(2005). Delphi study on supply chain flexibility. International Journal of Production Research, 43(13), 2687-2708.
  • Merschmann, U., & Thonemann, U. W. (2011). Supply chain flexibility, uncertainty and firm performance: An empirical analysis of German manufacturing firms. International Journal of Production Economics, 130(1), 43-53.
  • Mohr, J. J., & Sohi, R. (1995). Communication flows in distribution channels: impact on assessments of cımmunication quality and satisfaction. Journal Retailing, 71(4), 393-416.
  • Podsakoff, P. M., & Organ, D. (1986). Self-Reports in organizational research: Problems and prospects. Journal of Management, 12(Winter), 531-43.
  • Qrunfleh, S., & Tarafdar, M. (2013). Lean and agile supply chain strategies and supply chain responsiveness: The role of strategic supplier partnership and postponement. Supply Chain Management: An International Journal, 18(6), 571-582.
  • Russell, D. W., & Swanson, D. (2019). Transforming information into supply chain agility: an agility adaptation typology. The International Journal of Logistics Management, 30(1), 329-355.
  • Srinivasan, R., & Swink, M. (2018). An investigation of visibility and flexibility as complements to supply chain analytics: An organizational information processing theory perspective. Production and Operations Management, 27(10), 1849-1867.
  • Swafford, P. M., Ghosh, S., & Murthy, N. (2006). The antecedents of supply chain agility of a firm: scale development and model testing. Journal of Operations Management, 24, 170-188.
  • Swafford, P. M., Ghosh, S., & Murthy, N. (2008). Achieving supply chain agility through IT integration and flexibility. International Journal of Production Economics, 116(2008), 288–297.
  • Tarafdar, M., & Qrunfleh, S. (2017). Agile supply chain strategy and supply chain performance: complementary roles of supply chain practices and information systems capability for agility. International Journal of Production Research, 55(4), 925-938.
  • Wamba, S. F., Dubey, R., Gunasekaran, A., & Akter, S. (2019). The performance effects of big data analytics and supply chain ambidexterity: The moderating effect of environmental dynamism. International Journalof Production Economics, 222, 1-14.
  • Wang, E. T. G., & Wei, H. L.(2007). Interorganizational governance value creation: coordinating for information visibility and flexibility in supply chains. Decision Sciences, 38(4), 647-674.
  • Wei, H. L., & Wang, E. T. G. (2007). Creating strategic value from supply chain visibility- the dynamic capabilities view. Proceedings of the 40th Hawaii International Conference on System Sciences, 1-10.
  • Williams, B. D., Roh, J., Tokar, T., & Swink M. (2013). Leveraging supply chain visibility for responsiveness: The moderating role of internal integration. Journal of Operations Management, 31, 543-554.
  • Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2017). Intelligent manufacturing in the context of industry 4.0: A review. Engineering, 3, 616-630.

THE EFFECTS OF SUPPLY CHAIN VISIBILITY ON AGILITY

Yıl 2020, Cilt: 3 Sayı: 1, 49 - 62, 22.06.2020

Sibel Yıldız Çankaya

Öz

Firms aim
to obtain more information to be able to cope with uncertainties that they
encounter. However, having more information is not sufficient today, and the
information also needs to be in an accurate, up-to-date, complete and usable
form. For this reason, in recent years, the issue of supply chain visibility
has attracted much attention. This study has two main objectives. The first one
is to reveal how supply chain visibility affects agility. The second one is to
investigate the moderator effect of the capability of big data analytics in the
relationship between these two variables. To test the hypotheses that were
developed to reach these objectives, partial least squares - structural
equation modelling (PLS-SEM) was utilized. The results of the study conducted
on ninety-nine firms showed that visibility has a positive effect on agility.
However, a moderator effect of big data analytics could not be determined in
the relationship between these two variables.

Anahtar Kelimeler

Supply chain visibility Big data analytics Agile supply chain PLS-SEM

Kaynakça

  • Akter, S., Bandara, R., Hani, U., Fosso, S., & Foropon, C. (2019). Analytics-based decisionmaking for service systems: A qualitative study and agenda for future research. International Journal of Information Management, 48(1), 85-95.
  • Alzoubi, H. M., & Yanamandra, R. (2020). Investigating the mediating role of information sharing strategy on agile supply chain. Uncertain Supply Chain Management, 8, 273-284.
  • Armstrong, J. S., & Overton, T. S. (1977). Estimating nonresponse bias in mail surveys. Journal of Marketing Research, 14(3), 396-402.
  • Barratt, M., & Oke, A. (2007). Antecedents of supply chain visibility in retail supply chains: A resource-based theory perspective. Journal of Operations Management, 25(6), 1217–1233.
  • Bass, B., Avolio, B., Jung, D., & Berson Y. (2003). Predicting unit performance by assessing transformational and transactional leadership. Journal of Applied Psychology, 88(2), 207–218.
  • Brandon-Jones, E., Squire, B., Autry, C. W., & Petersen, K. J. (2014). A contingent resource-based perspective of supply chain resilience and robustness. Journal of Supply Chain Management, 50(3), 55-73.
  • Braunscheidel, M. J., & Suresh, N. C. (2009). The organizational antecedents of a firm’s supply chain agility for risk mitigation and response. Journal of Operations Management, 27, 119-140.
  • Caridi, M., Moretto, A., Perego, A., & Tumino, A. (2014). The benefits of supply chain visibility: A value assessment model. International Journal Production Economics, 15, 1-19.
  • Chin, W. (1998). The Partial Least Squares Approach to Structural Equation Modeling. G. A Marcoulides (Ed.). Modern Methods for Business Research, (s.295-336), New York: Lawrence Erlbaum Associates.
  • Chin, W. W., Marcolin, B. L., & Newsted, P. R (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a monte carlo simulation study and electronic mail emotion/adoption study. Information Systems Research, 14(2), 189-217.
  • Christopher, M. (2000). The agile supply chain: competing in volatile markets. Industrial Marketing Management, 29(1), 37-44.
  • Clos, D. J., Goldsby, T. J., & Clinton, S. R. (1997). Information technology influences on world class logistics capability. International Journal of Physical Distribution & Logistics Management, 27(1), 4-17.
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Mahwah, NJ: Lawrence Erlbaum.
  • DeGroote, S. E., & Marx, T. G. (2013). The impact of it on supply chain agility and firm performance: An empirical investigation. International Journal of Information Management, 33(6), 909-916.
  • Dejonckheere, J., Disney, S. M., Lambrecht, M. R., & Towill, D. R. (2004). The impact of information enrichment on the bullwhip effect in supply chains: A control engineering perspective. European Journal of Operational Research, 153,727-750.
  • Dubey, R., Altay, N., Gunasekaran, A., Blome, C., Papadopoulos, T., & Childe, S. J. (2018). Supply chain agility, adaptability and alignment empirical evidence from the indian auto components industry. International Journal of Operations & Production Management, 38(1), 129-148.
  • Dubey, R., Gunasekaran, A., Childe S. J., Roubaud, D., Wamba, S. M., Giannakis, M., & Foropon, C. (2019). Big data analytics and organizational culture as complements to swift trust and collaborative performance in the humanitarian supply chain. International Journal of Production Economics, 210, 120–136.
  • Eckstein, D., Goellner, M., Blome, C., & Henke, M. (2015). The performance impact of supply chain agility and supply chain adaptability: The moderating effect of product complexity. International Journal of Production Research, 53(10), 3028-3046.
  • Eltantawy, R. A., & Giunipero, L. (2013). An empirical examination of strategic sourcing dominant logic: Strategic sourcing centricity. Journal of Purchasing and Supply Management, 19(49), 215-226.
  • Gligor, D. M., & Holcomb, M. C., (2012). Antecedents and consequences of supply chain agility: establishing the link to firm performance. Journal of Business Logistics, 33(4), 295–308.
  • Goh, M., De Souza, R., Zhang, A. N., He, W., & Tan, P. S (2009). Supply chain visibility: A decision making perspective. IEEE, 2546-2551.
  • Hair, J. F., Black, W., Babin, B., & Anderson, R. (2009). Multivariate data analysis. N.J: Prentice Hall.
  • Henseler, J., & Chin, W.W. (2010). A comparison of approaches for the analysis of interaction effects between latent variables using partial least squares path modeling. Structural Equation Modeling: A Multidisciplinary Journal, 17(1), 82-109.
  • Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems,116(1), 2-20.
  • Henseler, J., Ringle, C., & Sinkovics, R. (2009). The use of partial least squares path modeling in international marketing. Advances in International Marketing, 20(2009), 277–320.
  • Jermsittiparsert, K., & Srisawat, S. (2019). The role of supply chain visibility in enhancing supply chain agility. International Journal of Innovation, Creativity and Change, 5(2), 485-501.
  • Kaipia, R., & Hartiala, H. (2006). Information-sharing in supply chains: five proposals on how to proceed. The International Journal of Logistics Management, 17(3), 377-393.
  • Kaufmann, L., & Gaeckler, J. (2015). A structured review of partial least squares in supply chain management research. Journal of Purchasing & Supply Management, 21(2015), 259–272.
  • Kim, H., & Kankanhalli, A. (2009). Investigating user resistance to information sys-tems implementation: A status quo bias perspective. MIS Quarterly, 33(3), 567–582.
  • Kim, M., & Chai, S. (2017). The impact of supplier innovativeness, information sharing and strategic sourcing on improving supply chain agility: Global supply chain perspective. International Journal of Production Economics, 187, 42-52.
  • Kisperska-Moron, D., & de Haan, J. (2011). Improving supply chain performance to satisfy final customers: "Leagile" experiences of a polish distributor. International Journal of Production Economics, 133(1), 127-134.
  • Lai, F., Zhang M., Lee, D. M. S., & Zhao, X. (2012). The impact of supply chain integration on mass customization capability: An Extended Resource-Based View. IEEE Transactions on Engineering Management, 59(3), 443-456.
  • Lummus, R. R., Vokurka, R. J., & Duclos, L. K.(2005). Delphi study on supply chain flexibility. International Journal of Production Research, 43(13), 2687-2708.
  • Merschmann, U., & Thonemann, U. W. (2011). Supply chain flexibility, uncertainty and firm performance: An empirical analysis of German manufacturing firms. International Journal of Production Economics, 130(1), 43-53.
  • Mohr, J. J., & Sohi, R. (1995). Communication flows in distribution channels: impact on assessments of cımmunication quality and satisfaction. Journal Retailing, 71(4), 393-416.
  • Podsakoff, P. M., & Organ, D. (1986). Self-Reports in organizational research: Problems and prospects. Journal of Management, 12(Winter), 531-43.
  • Qrunfleh, S., & Tarafdar, M. (2013). Lean and agile supply chain strategies and supply chain responsiveness: The role of strategic supplier partnership and postponement. Supply Chain Management: An International Journal, 18(6), 571-582.
  • Russell, D. W., & Swanson, D. (2019). Transforming information into supply chain agility: an agility adaptation typology. The International Journal of Logistics Management, 30(1), 329-355.
  • Srinivasan, R., & Swink, M. (2018). An investigation of visibility and flexibility as complements to supply chain analytics: An organizational information processing theory perspective. Production and Operations Management, 27(10), 1849-1867.
  • Swafford, P. M., Ghosh, S., & Murthy, N. (2006). The antecedents of supply chain agility of a firm: scale development and model testing. Journal of Operations Management, 24, 170-188.
  • Swafford, P. M., Ghosh, S., & Murthy, N. (2008). Achieving supply chain agility through IT integration and flexibility. International Journal of Production Economics, 116(2008), 288–297.
  • Tarafdar, M., & Qrunfleh, S. (2017). Agile supply chain strategy and supply chain performance: complementary roles of supply chain practices and information systems capability for agility. International Journal of Production Research, 55(4), 925-938.
  • Wamba, S. F., Dubey, R., Gunasekaran, A., & Akter, S. (2019). The performance effects of big data analytics and supply chain ambidexterity: The moderating effect of environmental dynamism. International Journalof Production Economics, 222, 1-14.
  • Wang, E. T. G., & Wei, H. L.(2007). Interorganizational governance value creation: coordinating for information visibility and flexibility in supply chains. Decision Sciences, 38(4), 647-674.
  • Wei, H. L., & Wang, E. T. G. (2007). Creating strategic value from supply chain visibility- the dynamic capabilities view. Proceedings of the 40th Hawaii International Conference on System Sciences, 1-10.
  • Williams, B. D., Roh, J., Tokar, T., & Swink M. (2013). Leveraging supply chain visibility for responsiveness: The moderating role of internal integration. Journal of Operations Management, 31, 543-554.
  • Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2017). Intelligent manufacturing in the context of industry 4.0: A review. Engineering, 3, 616-630.
Toplam 47 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular İşletme
Bölüm Araştırma Makalesi
Yazarlar

Sibel Yıldız Çankaya 0000-0003-4942-1415

Yayımlanma Tarihi 22 Haziran 2020
Kabul Tarihi 8 Haziran 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 3 Sayı: 1

Kaynak Göster

APA Yıldız Çankaya, S. (2020). TEDARİK ZİNCİRİ GÖRÜNÜRLÜĞÜNÜN ÇEVİKLİK ÜZERİNDEKİ ETKİLERİ. Business Economics and Management Research Journal, 3(1), 49-62.
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