Constructing a Causal Model for Logistics Management in Thailand’s Automotive Industry
Article Sidebar
Main Article Content
Abstract
This study conducts a comprehensive analysis of the significant transformations and challenges faced by the international automotive industry from 2019 to 2023, attributing these changes to advancements in production and management systems, globalization, digitalization, and increasing market competition. The research constructs a causal model of logistics management, tailored explicitly to Thailand’s automotive sector, using a sequential explanatory mixed-method approach. Data was initially gathered through stratified sampling from 683 companies, involving surveys administered to executives, managers, and logistics personnel across 360 companies, and was analyzed using descriptive statistics and structural equation modeling (SEM). This quantitative analysis was enriched with qualitative insights from focus group discussions with experts in automotive logistics, revealing a complex interplay of factors such as organizational structuring, knowledge management capabilities, and governmental policies. These factors exhibited direct and indirect effects on logistics management through supply chain practices. The empirical validation of the model demonstrated robust statistical indices (χ2 = 118.52, df = 98, χ2/df = 1.20, P-Value = 0.077, CFI = 0.996, GFI = 0.966, AGFI = 0.941, RMR = 0.00267, and RMSEA = 0.024), confirming the reliability of the identified causal relationships. The findings emphasize the practical implications for enhancing logistics management, advocating for restructuring logistics divisions to increase efficiency, and advanced technologies such as IoT, blockchain, and analytics to maintain competitiveness and adapt to market changes. The study highlights the necessity of leveraging data-driven strategies, including structural equation modeling, to refine supply chain practices and align closely with governmental policies, thereby significantly improving logistics operations and contributing to overall business success and customer satisfaction.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright: CC BY-NC-ND 4.0
References
Aalst, W. M. P. (2013). Business process management: A comprehensive survey. ISRN Software Engineering, 2013, 1-37.
Aharonovitz, M. C. S., Vidal Vieira, J. G., & Suyama, S. S. (2018). How logistics performance is affected by supply chain relationships. The International Journal of Logistics Management, 29(1), 284-307.
Aljawarneh, N., Taamneh, M., Alhndawi, N., Alomari, K., & Masad, F. (2021). Fog computing-based logistic supply chain management and organizational agility: The mediating role of user satisfaction. Uncertain Supply Chain Management, 9(3), 767-778.
Almalki, S. (2016). Integrating quantitative and qualitative data in mixed methods research—challenges and benefits. Journal of Education and Learning, 5(3), 288-296.
Badakhshan, P., Conboy, K., Grisold, T., & Vom Brocke, J. (2020). Agile business process management. Business Process Management Journal, 26(6), 1505-1523.
Barber, L., Gerke, T., Markt, S. C., Peisch, S. F., Wilson, K. M., Ahearn, T., Giovannucci, E.,
Parmigiani, G., & Mucci, L. A. (2018). Family History of Breast or Prostate Cancer and
Prostate Cancer Risk. Clinical Cancer Research, 24(23), 5910-5917.
Hazen, B. T., & Byrd, T. A. (2012). Toward creating competitive advantage with logistics information technology. International Journal of Physical Distribution & Logistics Management, 42(1), 8-35.
Bhattacharya, M., & Bhattacharya, S. N. (2014). Economic growth and energy consumption nexus in developing world: The case of China and India. Journal of Applied Economics & Business Research, 4(1), 150-167.
Byrne, B. M. (2010). Structural equation modeling with amos: Basic concepts, applications, and programming (2nd eds.). New York: Taylor and Francis Group.
Chalapathi, G. S. S., Chamola, V., Gurunarayanan, S., & Sikdar, B. (2019). E-SATS: An efficient and simple time synchronization protocol for cluster- based wireless sensor networks. IEEE Sensors Journal, 19(21), 10144-10156.
Chamaram, S. (2016). New ways to work with mobile technology: A new challenging issue of employee recruitment and selection in Thailand 4.0. Journal of Business, Economics and Communications, 12(2), 9-22.
Chandak, A., Kumar, N., & Dalpati, A. (2019). The relationship between supply chain strategy and supply chain performance: an empirical investigation using structural equation modeling.
The IUP Journal of Supply Chain Management, 16(4), 39-49.
Chen, C. J. (2018). Developing a model for supply chain agility and innovativeness to enhance firms’ competitive advantage. Management Decision, 57(7), 1511-1534.
Chimjiw, S. (2021). Logistics management of the auto industry for the duration of the COVID-19 outbreak. Ramkhamhaeng Journal of Public Administration, 5(1), 39-52.
Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research. (3rd eds.). Thousand Oaks, CA: SAGE.
Cronbach, L. J. (1990). Essentials of psychological testing (5th eds.). New York: Harper & Row.
Diamantopoulos, A., & Siguaw, J. A. (2000). Introducing LISREL. London: Sage Publications.
Dubey, R., Gunasekaran, A., & Singh, T. (2015). Building theory of sustainable manufacturing using total interpretive structural modelling. International Journal of Systems Science: Operations & Logistics, 2(4), 231-247.
Dumas, M., La Rosa, M., Mendling, J., & Reijers, H. A. (2013). Fundamentals of business process management. Berlin, Germany: Springer, Heidelberg.
Esper, T. L., Fugate, B. S., & Davis-Sramek, B. (2007). Logistics Learning Capability: Sustaining the Competitive Advantage Gained Through Logistics Leverage. Journal of Business Logistics, 28(2), 57-82.
Etikan, I. (2017). Sampling and sampling methods. Biometrics & Biostatistics International Journal, 5(1), 215-217.
Evangelista, P., & Durst, S. (2015). Knowledge management in environmental sustainability practices of third-party logistics service providers. Vine, 45(4), 509-529.
Global Trade Atlas. (2021). Members and associate member. Automotive Industries. Retrieved from http://www.acsaec.org/index.php
Gunasekaran, A., Subramanian, N., & Papadopoulos, T. (2017). Information technology for competitive advantage within logistics and supply chains: A review. Transportation Research Part E: Logistics and Transportation Review, 99(C), 14-33.
Hadrawi, H. (2019). The impact of firm supply performance and lean processes on the relationship between supply chain management practices and competitive performance. Uncertain Supply Chain Management, 7(2), 341-350.
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019) When to use and how to report the results of PLS-SEM. European Business Review, 31, 2-24.
Harn Engineering Solutions. (2021). Annual registration statement annual report 2021 (from 56-1 one report). Bangkok, Thailand: Harn Engineering Solutions Publishers.
Herden, T. T. (2020). Explaining the competitive advantage generated from Analytics with the knowledge-based view: the example of Logistics and Supply Chain Management. Business Research, 13(1), 163-214.
Hong, J., Zhang, Y., & Shi, M. (2017). The impact of supply chain quality management practices and knowledge transfer on organizational performance: An empirical investigation from China. International Journal of Logistics Research and Applications, 21(5), 1-20.
Howard, L., & Bruce, L. B. (2017). Qualitative research method for the social sciences. Vivar, Malaysia: Pearson.
International Labour Organization (2024). Decent work and responsible business practices in Thailand’s automotive manufacturing sector. Retrieved from https://creativecommons.org/licences/by/4.0/.
Iorga, M., Feldman, L., Barton, R., Martin, M. J., Goren, N., & Mahmoudi, C. (2018). Fog computing conceptual model: Recommendations of the national institute of standards and technology.
Maryland, United States: National Institute of Standards and Technology.
Jakhar, S., & Barua, M. K. (2013). An integrated model of supply chain performance evaluation and decision-making using structural equation modelling and fuzzy AHP. Production Planning & Control, 25(11), 938-957.
Jamfa, A. (2019). Automotive industry. Retrieved from https://www.gsbresearch.or.th/wpcontent/
uploads/2019/10/IN_car_10_62_detail1.pdf
Jaroenchai, K., Saorom, W., & Chotchui, N. (2017). Logistics Management Models for Competitive Advantage of the Automobile and Auto Parts Industry in Thailand. Journal of MCU Social Science, 6(2), 571-586.
Jimenez-Jimenez, D., Martínez-Costa, M., & Rodriguez, C. S. (2019). The mediating role of supply chain collaboration on the relationship between information technology and innovation. Journal of Knowledge Management, 23(3), 548-567.
Jindal, S. K., Dhaliwal, M. S., & Chawla, N. 2015. Comparative Performance of Different Tomato Hybrids under Naturally Ventilated Polyhouse. International Journal of Horticulture, 5(14), 112.
Kafetzopoulos, D., Gotzamani, K., & Gkana, V. (2015). Relationship between quality management, innovation and competitiveness. Evidence from Greek companies. Journal of Manufacturing Technology Management, 26(8), 1177-1200.
Karia, N. (2018). Knowledge resources, technology resources and competitive advantage of logistics service providers. Knowledge Management Research & Practice, 16(4), 451-463.
Kanagavalli, G., & Azeez, R. (2019). Logistics and E- Logistics Management: Benefits and Challenges. International Journal of Recent Technology and Engineering, 8(4), 12804-12809.
Khan, S. A. R., Zhang, Y., Kumar, A., Zavadskas, E., & Streimikiene, D. (2020). Measuring the impact of renewable energy, public health expenditure, logistics, and environmental performance on sustainable economic growth. Sustainable Development, 28(4), 833-843.
Khomchunsri, B., & Dokthaisong, B. (2019). Knowledge Management. Journal of MCU Humanities Review, 5(1), 89-102.
Kim, E. J., & Park, S. (2020). Top management support for talent and culture on career changers’ organizational commitment and job satisfaction. Journal of Career Development, 47(6), 686-700.
Klangphahon, K. (2020). Mixed methods research. Journal of Graduate Studies Valaya Alongkorn Rajabhat University, 14(1), 235-256.
Kohlbacher, M., & Reijers, H. A. (2013). The effects of process-oriented organizational design on firm performance. Business Process Management Journal, 19(2), 245-262.
Kohn, J. W., McGinnis, M. A., & Kara, A. (2011). A structural equation model assessment of logistics strategy. The International Journal of Logistics Management, 22(3), 284-305.
Peirchyi, L., & Fang-I, K. (2016). Innovation-oriented supply chain integration for combined competitiveness and firm performance. International Journal of Production Economics, 174(C), 142-155.
Likert, R. (1972). Likert technique for attitude measurement Social Psychology: Experimentation, Theory, Research, Sahakian. Scranton, USA: Intext Educational Publishers.
Malinova, M., & Mendling, J. (2018). Identifying do’s and don’t using the integrated business process management framework. Business Process Management Journal, 24(4), 882-899.
Mangla, S. K., Govindan, K., & Luthra, S. (2016). Critical success factors for reverse logistics in Indian industries: a structural model. Journal of Cleaner Production, 129, 608-621.
Martins, V. W. B., Rampasso, I. S., Anholon, R., Quelhas, O. L. G., & Leal Filho, W. (2019). Knowledge management in the context of sustainability: Literature review and opportunities for future research. Journal of Cleaner Production, 2(29), 489-500.
McCoy, D. (2011). Business process management (BPM) key initiative overview. USA: Gartner Group.
Kucukoglu, M. T., & Pinar, R. I. (2015). Positive Influences of Green Innovation on Company Performance. Procedia - Social and Behavioral Sciences, 195(2015), 1232-1237.
Munim, Z. H., & Schramm, H. J. (2018). The impacts of port infrastructure and logistics performance on economic growth: the mediating role of seaborne trade. Journal of Shipping and Trade, 3(1), 1-19.
Muntaka, A. S., Haruna, A., & Kofi Mensah, H. (2017). supply chain integration and flexibility and its impact on business performance. International Journal of Business and Management, 12(4), 130-143.
Niekurzak, M., Lewicki, W., Coban, H. H., & Bera, M. (2023). A model to reduce machine changeover time and improve production efficiency in an automotive manufacturing organisation. Sustainability, 15(13), 10558.
Ngamvichaikit, A. (2015). Mixed methods approach: qualitative and quantitative research design. Modern Management Journal, 13(1), 1-12.
Niponpittaya, T. (2018). The principle of human resource research. The Journal of Research and Academics, 1(2), 121-136.
Office of the Science Promotion Commission for Research and Innovation (2020). thailand logistics report. Retrieved from https://www.nesdb.go.th/ewt_dl_link.php?nid=3390.
Office of the National Economic and Social Development Board (2016). Thailand’s logistics report 2015. Retrieved from https://www.nesdc.go.th/download/logistics/report/2558/LogisticsReportTH.pdf
Şule, Ö. E., Özgür; K., & Füsun, Ü. (2019). Improving logistics performance by reforming the pillars of Global Competitiveness Index. Transport Policy, 81(C), 197-207.
Permpoon, T. (2015). Business process management. Retrieved from http://www.cioworldmagazine.com.
Pradabwong, J., Braziotis, C., Pawar, K. S., & Tannock, J. (2015). Business process management and supply chain collaboration: A critical comparison. Logistics Research, 8(1), 6.
Pradabwong, J., Braziotis, C., Tannock, J. D. T., & Pawar, K. S. (2017). Business process management and supply chain collaboration: effects on performance and competitiveness. Supply Chain Management: An International Journal, 22(2), 107-121.
Praipaisarnkit, K. (2021). Electric vehicles: Trends, motives and investment opportunities. Retrieved from https://www.setinvestnow.com/th/knowledge/article/198-investment -opportunity-from-automotive-industry.
Prajogo, D., Oke, P., Sameiro, A., & Olhager, J. (2016). Supply chain processes linking supply logistics integration, supply performance, lean process and competitive performance. International Journal of Operations & Production Management, 36(2), 220-238.
Promngam, P., & Aysanond, C. (2021). Structural model relationships between factors affecting automotive parts and parts producer’s operations in the eastern economic corridor (EEC), Thailand. NRRU Community Research Journal, 15(4), 196-209.
Puška, A., Kozarević, S., & Okičić, J. (2019). Investigating and analyzing the supply chain practices and performance in agro-food industry. International Journal of Management Science and Engineering Management, 15(1), 1-8.
Rajaguru, R., & Matanda, M. J. (2019). Role of compatibility and supply chain process integration in facilitating supply chain capabilities and organizational performance. Supply Chain Management: An International Journal, 24(1), 301-316.
Recker, J., & Mendling, J. (2016). The state of the art of business process management research as published in the BPM conference. Business & Information System Engineering, 58(1), 55-72.
Sakchutchawan, S., Hong, P. C., Callaway, S. K., & Kunnathur, A. (2011). Innovation and Competitive Advantage: Model and Implementation for Global Logistics. International Business Research, 4(3), 10-21.
Salam, M. A., Ali, M., & Seny Kan, K. A. (2017). Analyzing supply chain uncertainty to deliver sustainable operational performance: Symmetrical and asymmetrical modeling approaches. Sustainability, 9(12), 2217.
Saragih, J., Tarigan, A., Pratama, I., Wardati, J., & Silalahi, E. F. (2020). The impact of total quality management, supply chain management practices and operations capability on firm performance. Polish Journal of Management Studies, 21(2), 384-397.
Sareekham, W., Phromket, C., & Mettathamrong, J. (2020). Competency abilitygreen supply chain management to affect the performance of sakon nakhon’sruralcommunity enterprisesin the agricultural sector. Humanities, Social Sciences and arts, 12(5), 1482-1500.
Schniederjans, D. G., Curado, C., & Khalajhedayati, M. (2020). Supply chain digitisation trends: An integration of knowledge management. International Journal of Production Economics, 220, 107439.
Schumacker, R. E., & Lomax, R. G. (2010). A beginner’s guide to structural equation modeling. (3rd ed.). New Jersey: Lawrence Erlbaum Associates.
Sekaran, Uma. (2003). Research methods for business. (4th eds.). United Stated of America: John Wiley & Sons.
Sermsai, N., & Vongmahasetha, T. (2021). Competitive advantage of logistics service providers, Knowledge Management Research & Practice, 16(4), 451-463.
Shakya, R. K. (2012). Procurement governance framework: Success to e-government procurement
(E-GP) system implementation introduction. International Public Procurement Conference, Seattle, United States, 2, 562-575.
Shevchenko, I. (2017). Automotive industry and Ukraine − Oxymoron? Not Yet! Retrieved from https://achievementscenter.com/
Sorat, T. (2007). Application of logistics and supply chain. Bangkok, Thailand: Meeting thong printing group.
Suksawang, P. (2013). Structural equation model (1st ed.). Bangkok, Thailand: Wattana Panich.
Sumah, B., Masudin, I., Zulfikarijah, F., & Restuputri, D. P. (2020). Logistics management and electronic data interchange effects on logistics service providers’ competitive advantage. Journal of Business & Economic Analysis (JBEA), 3(2), 171-194.
Tellez, A., Garcia Cadena, C., & Corral-Verdugo, V. (2015). Effect size, confidence intervals and statistical power in psychological research. Psychology in Russia: State of the Art, 8(1), 27-46.
Teplická, K., Khouri, S., Mudarri, T., & Freňáková, M. (2023). Improving the quality of automotive components through the effective management of complaints in industry 4.0. Applied Sciences, 13(14), 8402.
Thai Federation on Logistics. (2019). Performance indicators of 5business types. Retrieved from http://www.thailog.org/index.php.
Thailand Automotive Institute. (2023). A study on the production structure of parts of the Thai automotive industry. Automotive Information Project. Retrieved from https://data.thaiauto.or.th/iu3/images/stories/PDF/Research/RD_Supply_Chain.pdf.
Utami, C. W., Sumaji, Y. M. P., Susanto, H., Septina, F., & Pratama, I. (2019). Effect of supply chain management practices on financial and economic sustainable performance of Indonesian SMEs. International Journal of Supply Chain Management, 8(5), 523-535.
Van Der Aalst, W. M. P., ter Hofstede, A. H. M., & Weske, M. (2003). Business process management: A survey (pp. 1-12). In Proceedings of the International Conference on Business Process Management. Berlin, Germany: Springer-Verlag Berlin .
Vanichbuncha, K. (2011). Statistics for research. (6th eds.). Bangkok, Thailand: Thammasan.
Vanichbuncha, K. (2013). Structural equation analysis (SEM). Bangkok, Thailand: Samlada.
Vom Brocke, J., & Rosemann, M. (2015). Handbook on business process management. (2nd eds.). Berlin & Heidelberg: Springer.
Watanaputi, A., Sansook, J., Boonannwong, C., & Chotworakan, A. (2020). Tourism logistics management influencing logistics performance and loyalty of tourisms in Phra Nakhon Si Ayutthaya Province. Modern Management Journal. 18(2), 15-29.
Wattanasungsuth, N., & Meethom, W. (2021). Improvement of pressing process in automotive industry. The Journal of Industrial Technology, 17(3), 79-98.
Weske, M. (2012). Business process management: Concepts, languages, architectures (2nd eds.). Berlin & Heidelberg: Springer.
Wiratchai, N. (2005). Lisrail model: Analytical statistics for research. (Ed.). Bangkok, Thailand: Chulalongkorn Educational Publishers.
Wong, W. P., & Tang, C. F. (2018). The major determinants of logistic performance in a global perspective: Evidence from panel data analysis. International Journal of Logistics Research and Applications, 21(4), 431-443.
Yongpisanphob, W. (2017). Automobile. Retrieved from https://www.krungsri.com/bank/
getmedia/cf250dec-3bbe-4629-8a6b-40f7937eee4b/IO_Automobile_2017_TH.aspx.
Zhang, Z., Liu, M., & Yang, Q. (2021). Examining the external antecedents of innovative work behavior: the role of government support for talent policy. International Journal of Environmental Research and Public Health, 18(3), 12.
