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Plant Science Today (PST)

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Assessing the digital transformation efficiency of agribusiness firms: A case study in the South-Western zone of Tamil Nadu

DOI
https://doi.org/10.14719/pst.5649
Submitted
9 October 2024
Published
30-12-2024

Abstract

The rapid pace of digitalization has transformed industries and consumer behaviour worldwide, prompting businesses to accelerate their digital transformation efforts. This study aimed to assess the efficiency of agribusiness firms in achieving digital transformation within the South-Western Zone of Tamil Nadu. Data were collected from 41 agribusiness firms and analysed using Data Envelopment analysis (DEA), a method for evaluating the efficiency of decision-making units (DMUs). The results showed that ten DMUs involved in the processing and value addition of millets and firms selling organic products, were efficient, accounting for 24% of the total agribusiness firms. The major cause of inefficiency was the decline in pure technical efficiency and the market's steadily declining returns to scale. Hence, firms must closely track their resource and scale allocations, manage their internal operations and modify their output in response to market conditions to avoid a decline in their technical efficiency. The export potential of processed foods is projected to increase by 35% during 2027-28. The export potential of processed foods is projected to increase by 35% during 2027-28. Therefore, the growth of agribusiness enterprises, especially food processing firms, provides increased value and employment opportunities. Firms should prioritize investments in recruiting a digitally skilled workforce and dedicate training hours to ensure the effective use of advanced digital technologies, thereby enhancing efficiency. Firms should prioritize investments in recruiting a digitally skilled workforce and dedicate training hours to ensure the effective use of advanced digital technologies, thereby enhancing efficiency.

References

  1. Census. Number of agricultural workers in India. Ministry of Agriculture and Farmers Welfare. 2011. https://pib.gov.in/
  2. Chand R, Singh J. From green revolution to Amrit kaal. National Institution for Transforming India. GoI. 2023.
  3. Lamberton C, Stephen AT. A thematic exploration of digital, social media and mobile marketing: Research evolution from 2000 to 2015 and an agenda for future inquiry. Journal of Marketing. 2016;80(6):146-72. https://doi.org/10.1509/jm.15.0415
  4. Kannan PK. Digital marketing: A framework, review and research agenda. International Journal of Research in Marketing. 2017;34(1):22-45. https://doi.org/10.1016/j.ijresmar.2016.11.006
  5. Statista. Digital transformation - statistics and facts. 2024. https://www.statista.com/
  6. Startup India. Number of agri-startups in India. Ministry of Commerce and Industry. 2021. https://www.startupindia.gov.in/
  7. APEDA. Export statistics of processed food products from India. Agricultural and Processed Food Products Export Development Authority. Ministry of Commerce and Industry. 2023. https://apeda.gov.in/
  8. Vial G. Understanding digital transformation: A review and a research agenda. Managing Digital Transformation. 2021;13-66. https://doi.org/10.4324/9781003008637-4
  9. Bowen R, Morris W. The digital divide: Implications for agribusiness and entrepreneurship. Lessons from wales. Journal of Rural Studies. 2019;72:75-84. https://doi.org/10.1016/j.jrurstud.2019.10.031
  10. Grimblatt V. IoT for agribusiness: An overview. In: 2020 IEEE 11th Latin American Symposium on Circuits and Systems (LASCAS); 1-4. https://doi.org/10.1109/LASCAS45839.2020.9068986
  11. Statista. Spending on digital transformation technologies and services worldwide from 2017 to 2027. https://www.statista.com
  12. Digital 2023: India. Number of internet and social media users in India. https://datareportal.com/reports/digital-2023-india
  13. Kamal MM. The triple-edged sword of COVID-19: understanding the use of digital technologies and the impact of productive, disruptive and destructive nature of the pandemic. Information Systems Management. 2020;37(4):310-17. https://doi.org/10.1080/10580530.2020.1820634
  14. Usai A, Fiano F, Petruzzelli AM, Paoloni P, Briamonte MF, Orlando B. Unveiling the impact of the adoption of digital technologies on firms’ innovation performance. Journal of Business Research. 2021;133:327-36. https://doi.org/10.1016/j.jbusres.2021.04.035
  15. Himesh S, Rao EP, Gouda KC, Ramesh KV, Rakesh V, Mohapatra GN, Ajilesh P. Digital revolution and big data: a new revolution in agriculture. CABI Reviews. 2018;1-7. https://doi.org/10.1079/PAVSNNR201813021
  16. Sridhar S, Fang E. New vistas for marketing strategy: digital, data-rich and developing market (D3) environments. Journal of the Academy of Marketing Science. 2019;47:977-85. https://doi.org/10.1007/s11747-019-00698-y
  17. Papagiannidis S, Harris J, Mor, Ton D. WHO led the digital transformation of your company? A reflection of IT related challenges during the pandemic. International Journal of Information Management. 2020;55(102166). https://doi.org/10.1016/j.ijinfomgt.2020.102166
  18. Kao LJ, Chiu CC, Lin HT, Hung YW, Lu CC. Evaluating the digital transformation performance of retail by the DEA approach. Axioms. 2022;11(6):284. https://doi.org/10.3390/axioms11060284
  19. Cooper WW, Seiford LM, Tone K. Data envelopment analysis: a comprehensive text with models, applications, references and DEA-solver software. Springer, 2007;2:489. https://doi.org/10.1007/978-0-387-45283-8
  20. Avkiran NK, Tone K, Tsutsui M. Bridging radial and non-radial measures of efficiency in DEA. Annals of Operations Research. 2008;164(1):27-138. https://doi.org/10.1007/s10479-008-0356-8
  21. Mitrovic D. Measuring the efficiency of digital convergence. Economics Letters. 2020;88(108982). https://doi.org/10.1016/j.econlet.2020.108982
  22. Zheng S, Khan R. Performance evaluation of e-commerce firms in China: Using three-stage data envelopment analysis and the Malmquist productivity index. Plos One. 2021;16(8):0255851. https://doi.org/10.1371/journal.pone.0255851
  23. Samoilenko S, Osei-Bryson KM. Using data envelopment analysis (DEA) for monitoring efficiency-based performance of productivity-driven organizations: Design and implementation of a decision support system. Omega. 2013;41(1):131-42. https://doi.org/10.1016/j.omega.2011.02.010
  24. Dube L, Guveya E. Technical efficiency of smallholder out-grower tea (Camellia sinensis) farming in Chipinge district of Zimbabwe. Greener Journal of Agricultural Sciences. 2014;4(8):368-77.
  25. Yasmin A, Tasneem S, Fatema K. Effectiveness of digital marketing in the challenging age: An empirical study. International Journal of Management Science and Business Administration. 2015;1(5):69-80.
  26. Toloo M, Keshavarz E, Hatami-Marbini A. Selecting data envelopment analysis models: A data-driven application to EU countries. Omega. 2021;101(102248). https://doi.org/10.1016/j.omega.2020.102248
  27. Coelli T. A guide to DEAP version 2.1: a data envelopment analysis (computer) program. Centre for Efficiency and Productivity Analysis, University of New England, Australia. 1996;96(8):1-49.
  28. Sharmaa KR, Leunga P, Zaleskib HM. Technical, allocative and economic efficiencies in swine production in Hawaii: a comparison of parametric and nonparametric approaches. Agricultural Economics. 1999;20:23-35. https://doi.org/10.1111/j.1574-0862.1999.tb00548.x
  29. Kumar S, Gulati R. An examination of technical, pure technical and scale efficiencies in Indian public sector banks using data envelopment analysis. Eurasian Journal of Business and Economics. 2008;1(2): 33-69.
  30. Ali OG, Yaman K. Selecting rows and columns for training support vector regression models with large retail datasets. European Journal of Operational Research. 2013;226(3): 471-80. https://doi.org/10.1016/j.ejor.2012.11.013
  31. Agustina SD, Bella C, Ramadhan MA. Support vector regression algorithm modelling to predict the availability of foodstuff in Indonesia to face the demographic bonus. In Journal of Physics: Conference Series; 2015. 1028(1):12240. https://doi.org/10.1088/1742-6596/1028/1/012240
  32. Alida M, Mustikasari M. Rupiah exchange prediction of US Dollar using linear, polynomial and radial basis function kernel in support vector regression. Journal Online Informatika. 2020;5(1):53-60. https://doi.org/10.15575/join.v5i1.537
  33. Barbour W, Mori JCM, Kuppa S, Work DB. Prediction of arrival times of freight traffic on US railroads using support vector regression. Transportation Research Part C: Emerging Technologies. 2018;93:211-27. https://doi.org/10.1016/j.trc.2018.05.019
  34. Mustakim AB, Hermadi I. Performance comparison between support vector regression and artificial neural network for prediction of oil palm production. Journal of Computer Science and Information. 2016;1:8.
  35. Scholkopf B, Smola AJ. Learning with kernels: support vector machines, regularization, optimization and beyond. MIT Press. 2018.
  36. Bharati M, Ramageri M. Data mining techniques and applications. Machine learning methods: An overview. Computer Modelling and New Technologies. 2010;19:14-29.
  37. Kyrgiakos LS, Kleftodimos G, Vlontzos G, Pardalos PM. A systematic literature review of data envelopment analysis implementation in agriculture under the prism of sustainability. Operational Research. 2023;23(1):7. https://doi.org/10.1007/s12351-023-00741-5
  38. Kouriati A, Tafidou A, Lialia E, Prentzas A, Moulogianni C, Dimitriadou E, Bournaris T. The impact of data envelopment analysis on effective management of inputs: The case of farms located in the regional unit of Pieria. Agronomy. 2023;13(8):2109. https://doi.org/10.3390/agronomy13082109
  39. Fazlollahi A, Franke U. Measuring the impact of enterprise integration on firm performance using data envelopment analysis. International Journal of Production Economics. 2018;200:119-29. https://doi.org/10.1016/j.ijpe.2018.02.011

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