바로가기메뉴

본문 바로가기 주메뉴 바로가기

logo

Key Drivers of Operational Performance of E-commerce Distribution Service Providers in Thailand

The Journal of Distribution Science / The Journal of Distribution Science, (P)1738-3110; (E)2093-7717
2022, v.20 no.12, pp.89-98
https://doi.org/https://doi.org/10.15722/jds.20.12.202212.89
VONGURAI, Rawin
  • Downloaded
  • Viewed

Abstract

Purpose: Due to the rapid growth of e-commerce in Thailand, the operational excellence of distribution service providers has been elevated. Thus, this research investigated the key drivers of operational performance of e-commerce distributors in Thailand. The research contains key variables: the analytics capabilities of an organization, supply chain disruption orientation, innovation capability, and operational performance. Research design, data, and methodology: An online survey is administered to top managers and key personnel (N=425) employed for at least one year in Thailand's top five e-commerce distributors. The sampling methods were conducted using purposive sampling, quota sampling, and convenience sampling. Confirmatory Factor Analysis and Structural Equation Model were applied to analyze and confirm the model's goodness-of-fit and hypothesis testing. Results: The findings reveal that an organization's analytics capabilities significantly affect supply chain disruption orientation and supply chain resilience. Furthermore, operational performance is affected by supply chain disruption, supplier quality management, and innovation capability. Nevertheless, supply chain resilience and digital supply chain have no significant effect on operational performance. Conclusions: The results imply that supply chain digitalization could drive higher operational performance. Distribution businesses are encountering transformation and disruption, which should address the high level of a digital supply chain, innovation, and quality management to maximize their profit margin and delivery service quality.

keywords
Operational Performance, Distribution, Digital Supply Chain, Innovation Capability, Quality Management

Reference

1.

Agarwal, R., & Dhar, V. (2014). Big data, data science, and analytics: The opportunity and challenge for IS research. Information Systems Research, 25(3), 443–448.

2.

Ambulkar, S., Blackhurst, J., & Grawe, S. (2015). Firm’s resilience to supply chain disruptions: Scale development and empirical examination. Journal of Operations Management, 33(1), 111–122.

3.

Arbuckle, J. J. (1995). AMOS user's guide. Small Waters.

4.

Baird, K., Hu, K. J., & Reeve, R. (2011). The relationships between organizational culture, total quality management practices and operational performance. International Journal of Operations and Production Management, 31(7), 789-814.

5.

Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136-162). Sage.

6.

Büyüközkan, G., & Göçer, F. (2018). Digital supply chain:Literature review and a proposed framework for future research. Computers in Industry, 97, 157–177.

7.

Camisón, C., & Villar-López, A. (2014). Organizational innovation as an enabler of technological innovation capabilities and firm performance. Journal of Business Research, 67(1), 2891–2902. https://doi.org/10.1016/j.jbusres.2012.06.004

8.

Chae, B. K., Yang, C., Olson, D., & Sheu, C. (2014). The impact of advanced analytics and data accuracy on operational performance: A contingent resource-based theory (RBT)perspective. Decision Support Systems, 59, 119–126.

9.

Crossan, M. M., & Apaydin, M. (2010). A multi-dimensional framework of organizational innovation: A systematic review of the literature. Journal of Management Studies, 47(6), 1154–1191. https://doi.org/10.1111/j.1467-6486.2009.00880.x

10.

Damanpour, F., Walker, R. M., & Avellaneda, C. N. (2009). Combinative effects of innovation types and organizational performance: A longitudinal study of service organizations. Journal of Management Studies, 46(4), 650–675. https://doi.org/10.1111/j.1467-6486.2008.00814.x

11.

Devaraj, S., Krajewski, L., & Wei, J. C. (2007). Impact of eBusiness technologies on operational performance: The role of production information integration in the supply chain. Journal of Operations Management, 25(6), 1199–1216.

12.

Farahani, P., Meier, C., & Wilke, J. (2017). Digital supply chain management agenda for the automotive supplier industry. In G. Oswald & M. Kleinemeier (Eds.), Shaping the Digital Enterprise (pp.157-172). Springer International Publishing.

13.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312

14.

Golgeci, I., & Ponomarov, S. Y. (2013). Does firm innovativeness enable effective responses to supply chain disruptions? An empirical study. Supply Chain Management: An International Journal, 18(6), 604–617.

15.

Grover, V., Chiang, R. H., Liang, T. P., & Zhang, D. (2018). Creating strategic business value from big data analytics: A research framework. Journal of Management Information Systems, 35(2), 388–423.

16.

Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate Data Analysis (6th ed.). Pearson Prentice Hall.

17.

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2nd ed.). SAGE.

18.

Hallgren, M., & Olhager, J. (2009). Lean and agile manufacturing:external and internal drivers and performance outcomes. International Journal of Operations & Production Management, 29(10), 976-999.

19.

Heij, C. V. (2015). Innovating beyond technology: Studies on how management innovation, co-creation and business model innovation contribute to firms. Erasmus Research Institute of Management.

20.

Heizer, J. H., Render, B., & Weiss, H. J. (2008). Principles of Operations Management (7th ed.). Pearson Prentice Hall.

21.

Hobbs, J. E. (2020). Food supply chains during the COVID-19pandemic. Canadian Journal of Agricultural Economics, 68(1), 171-176.

22.

Ivanov, D. (2020). Predicting the impacts of epidemic outbreaks on global supply chains: a simulation-based analysis on the coronavirus outbreak (COVID-19/SARS/Cov-2) case. Logistics and Transportation Review, 136(1), 1-14.

23.

Jaruwanakul, T. (2021). Key Influencers of Innovative Work Behavior in Leading Thai Property Developers. AU-GSB EJOURNAL, 14(1), 61-70. https://doi.org/10.14456/augsbejr.2021.7

24.

Kaynak, H. (2003). The relationship between total quality management practices and their effects on firm performance. Journal of Operations Management, 21(4), 405-435.

25.

Kebede Adem, M., & Virdi, S. S. (2021). The effect of TQM practices on operational performance: an empirical analysis of ISO 9001: 2008 certified manufacturing organizations in Ethiopia. The TQM Journal, 33(2), 407-440. https://doi.org/10.1108/TQM-03-2019-0076

26.

Kline, R. B. (2011). Principles and Practice of Structural Equation Modeling (3rd ed.). Guilford Press,

27.

Laguir, I., Modgil, S., Bose, I., Gupta, S., & Stekelorum, R. (2022). Performance effects of analytics capability, disruption orientation, and resilience in the supply chain under environmental uncertainty. Annals of Operations Research. Advance Online Publication. https://doi.org/ 10.1007/s10479-021-04484-4

28.

Lee, H. L. (2004). The triple-A supply chain. Harvard Business Review, 82(10), 102–113.

29.

Liu, Y., Lee, Y., & Chen, A. N. (2020). How IT wisdom affects firm performance: An empirical investigation of 15-year US panel data. Decision Support Systems, 133, 22-35. https://doi.org/10.1016/j.dss.2020.113300

30.

Lu, D., Ding, Y., Asian, S., & Paul, S. K. (2017). From supply chain integration to operational performance: The moderating effect of market uncertainty. Global Journal of Flexible Systems Management, 19(1), 3–20.

31.

Maldonado-Guzmán, G., Garza-Reyes, J. A., Pinzón-Castro, S. Y., & Kumar, V. (2019). Innovation capabilities and performance:Are they truly linked in SMEs? International Journal of Innovation Science, 11(1), 48–62. https://doi.org/10.1108/IJIS12-2017-0139

32.

Min, H. (2019). Blockchain technology for enhancing supply chain resilience. Business Horizons, 62(1), 35–45. https://doi.org/10.13106/JAFEB.2019.VOL6.NO2.213

33.

Nguyen, T. T., Le-Anh, T., & Nguyen, T. X. H. (2022). Factors Influencing Innovation Capability and Operational Performance: A Case Study of Power Generation Fields in Vietnam. The Journal of Asian Finance, Economics and Business, 9(5), 541–552. https://doi.org/10.13106/JAFEB.2022.VOL9.NO5.0541

34.

Nguyen, W. P., & Nof, S. Y. (2019). Collaborative response to disruption propagation (CRDP) in cyber-physical systems and complex networks. Decision Support Systems, 117, 1–13.

35.

Nong, N.-M. T., & Ho, P. T. (2019). Criteria for Supplier Selection in Textile and Apparel Industry : A Case Study in Vietnam. The Journal of Asian Finance, Economics and Business, 6(2), 213–221. https://doi.org/10.13106/JAFEB.2019.VOL6.NO2.213

36.

Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.

37.

O’Reilly, C. A., III., & Tushman, M. L. (2013). Organizational ambidexterity: Past, present, and future. Academy of Management Perspectives, 27(4), 324–338.

38.

Sadikoglu, E., & Zehir, C. (2010). Investigating the effects of innovation and employee performance on the relationship between total quality management practices and firm performance: an empirical study of Turkish firms. International Journal Production Economics, 12(7), 13-26.

39.

Saggi, M. K., & Jain, S. (2018). A survey towards an integration of big data analytics to big insights for value-creation. Information Processing and Management, 54(5), 758–790.

40.

Salaheldin, S. I. (2009). Critical success factors for TQM implementation and their impact on Performance of SMEs. International Journal of Productivity and Performance Management, 58(3), 215-237.

41.

Saryatmo, M. A., & Sukhotu, V. (2021). The Influence of the Digital Supply Chain on Operational Performance: A Study of the Food and Beverage Industry in Indonesia. Sustainability, 13, 1-18. https://doi.org/10.3390/su13095109

42.

Shao, B. B., Shi, Z. M., Choi, T. Y., & Chae, S. (2018). A dataanalytics approach to identifying hidden critical suppliers in supply networks: Development of nexus supplier index. Decision Support Systems, 114, 37–48.

43.

Singhdong, P., Suthiwartnarueput, K., & Pornchaiwiseskul, P. (2021). Factors Influencing Digital Transformation of Logistics Service Providers: A Case Study in Thailand. The Journal of Asian Finance, Economics and Business, 8(5), 241–251. https://doi.org/10.13106/JAFEB.2021.VOL8.NO5.0241

44.

Studenmund, A. H. (1992). Using Econometrics: A Practical Guide. Harper Collins.

45.

Suangsub, P., Chemsripong, S., & Srisermpoke, K. (2022). High Performance Organization: A Case Study of the Logistics Industry in Thailand. Journal of Community Development Research (Humanities and Social Sciences), 15(1), 98-112.

46.

Sun, L., Wang, Y., Hua, G., Cheng, T. C. E., & Dong, J. (2020). Virgin or recycled? Optimal pricing of 3D printing platform and material suppliers in a closed-loop competitive circular supply chain. Resources, Conservation and Recycling, 162, 10-35. https://doi.org/10.1016/j.resconrec.2020.105035

47.

Syed, T. A., Blome, C., & Papadopoulos, T. (2020). Resolving paradoxes in IT success through IT ambidexterity: The moderating role of uncertain environments. Information &Management, 57(6), 13-45. https://doi.org/10.1016/j.im.2020.103345

48.

Tirkolaee, E. B., Hadian, S., Weber, G. W., & Mahdavi, I. (2020). A robust green traffic-based routing problem for perishable products distribution. Computational Intelligence, 36(1), 80-101.

49.

Tiwari, S., Wee, H. M., & Daryanto, Y. (2018). Big data analytics in supply chain management between 2010 and 2016: Insights to industries. Computers and Industrial Engineering, 115, 319–330.

50.

Tönnissen, S., & Teuteberg, F. (2020). Analysing the impact of blockchain-technology for operations and supply chain management: An explanatory model drawn from multiple case studies. International Journal of Information Management, 52, 101-109. https://doi.org/10.1016/j.ijinfomgt.2019.05.009

51.

Xue, K., Li, Y., Zhen, X., & Wang, W. (2018). Managing the supply disruption risk: Option contract or order commitment contract? Annals of Operations Research, 291, 985–1026.

52.

Zu, X., & Kaynak, H. (2012). An agency theory perspective on supply chain quality management. International Journal of Operations & Production Management, 32(4), 423-446. https://doi.org/10.1108/01443571211223086

The Journal of Distribution Science