Competitive Intelligence–Driven Agricultural Digitalization and Green Productivity Transformation: Evidence from China's Provincial AGTFP Dynamics
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Keywords

Agricultural Total Factor Productivity
Digital Agriculture
Green Productivity
Financial Inclusion
Panel Data
China

How to Cite

Wang, M., Supanut, A., Romprasert, S., & Liu, J. (2026). Competitive Intelligence–Driven Agricultural Digitalization and Green Productivity Transformation: Evidence from China’s Provincial AGTFP Dynamics . Journal of Sustainable Competitive Intelligence , 16, e0693. https://doi.org/10.37497/eagleSustainable.v16i.693

Abstract

Purpose: This study investigates whether competitive intelligence (CI) driven agricultural digitalization enhances Agricultural Green Total Factor Productivity (AGTFP) across China's 30 provinces. Three research questions guide the inquiry: (1) does digitalization significantly improve AGTFP? (2) through which institutional mechanisms digital financial inclusion and land transfer—does this impact operate? and (3) do the effects exhibit regional heterogeneity and nonlinear dynamics that generate paradoxical outcomes under certain conditions?

Methodology/approach: A composite agricultural digitalization index is constructed via the entropy-weighting method. AGTFP is measured using an input-oriented Slack-Based Measure (SBM) model with undesirable outputs. Within a two-way fixed-effects panel framework, this study applies mediation analysis, moderation and threshold tests, quantile regression, and regional subgroup regressions.

Originality/Relevance: By integrating competitive intelligence theory with green productivity analysis, this paper develops a unified 'mechanism–context' framework to explain how identical digital investments produce divergent efficiency outcomes across regions. The study extends digital agriculture theory beyond technology adoption narratives toward ecosystem-level structural transformation. 

Key findings: Digitalization exerts a significant positive effect on AGTFP (β = 0.495, p < 0.05) under the preferred two-way fixed-effects specification. Digital financial inclusion mediates this relationship more effectively (indirect effect = 0.051) than land transfer (indirect effect = 0.002). Moderate fiscal support amplifies digitalization effectiveness while excessive intervention weakens it. Regional analysis reveals strong positive effects in eastern China but adverse outcomes in the central region, suggesting transitional inefficiency. Quantile regression confirms that the productivity-enhancing effect is strongest among lower-performing provinces.

Theoretical/methodological contributions: The study contributes a multidimensional digitalization index, an SBM-based green productivity measure, a staged nonlinear modernization trajectory, and a conditioned policy-effectiveness framework to digital agriculture and green productivity literature.

https://doi.org/10.37497/eagleSustainable.v16i.693
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References

Bao, H., Liu, X., Xu, X., Shan, L., Ma, Y., Qu, X., & He, X. (2023). Spatial-temporal evolution and convergence analysis of agricultural green total factor productivity in the Yangtze River Delta, China. PLOS ONE, 18, e0271642. https://doi.org/10.1371/journal.pone.0271642

Cai, Q., & Han, X. (2024). Impacts and mechanisms of digital village construction on agricultural green total factor productivity. Frontiers in Sustainable Food Systems. https://doi.org/10.3389/fsufs.2024.1431294

Chen, C., & Jiang, W. (2024). The impact and mechanisms of rural digitization on agricultural green total factor productivity. Polish Journal of Environmental Studies. https://doi.org/10.15244/pjoes/192568

Chen, Y., Hu, S., & Wu, H. (2023). The digital economy, green technology innovation, and agricultural green total factor productivity. Agriculture, 13(10), 1961. https://doi.org/10.3390/agriculture13101961

Créti, A. (2001). Network technologies, communication externalities and total factor productivity. Structural Change and Economic Dynamics, 12(1), 1–28. https://doi.org/10.1016/S0954-349X(99)00029-6

Calof, J. L., & Wright, S. (2008). Competitive intelligence: A practitioner, academic and inter-disciplinary perspective. European Journal of Marketing, 42(7/8), 717–730. https://doi.org/10.1108/03090560810877114

Choo, C. W. (2001). Environmental scanning as information seeking and organizational learning. Information Research, 7(1), 1–14. https://doi.org/10.47989/ir070101

Dishman, P. L., & Calof, J. L. (2008). Competitive intelligence: A multiphasic precedent to marketing strategy. European Journal of Marketing, 42(7/8), 766–785. https://doi.org/10.1108/03090560810877141

Du Toit, A. S. A. (2015). Competitive intelligence research: An investigation of trends in the literature. Journal of Intelligence Studies in Business, 5(2), 14–21. https://doi.org/10.37380/jisib.v5i2.106

David, P. A. (2005). Understanding digital technology’s evolution and the path of measured productivity growth. In E. Brynjolfsson & B. Kahin (Eds.), Understanding the digital economy (pp. xx–xx). MIT Press. https://doi.org/10.7551/mitpress/6986.003.0005

Falki, N. (2023). Impact of ICT access on total factor productivity in Asian economies. Journal of Development and Social Sciences. https://doi.org/10.47205/jdss.2023(4-ii)55

Herring, J. P. (1992). The role of intelligence in formulating strategy. Journal of Business Strategy, 13(5), 54–60. https://doi.org/10.1108/eb039495

Hu, J. (2023). Green productivity growth and convergence in Chinese agriculture. Journal of Environmental Planning and Management, 67(10), 1775–1804. https://doi.org/10.1080/09640568.2023.2180350

Javaid, M., Haleem, A., Singh, R., & Suman, R. (2022). Enhancing smart farming through the applications of Agriculture 4.0 technologies. International Journal of Intelligent Networks, 3, 150–164. https://doi.org/10.1016/j.ijin.2022.09.004

Kahaner, L. (1996). Competitive intelligence: How to gather, analyze, and use information to move your business to the top. Simon & Schuster.

Li, H., Lin, Q., Wang, Y., & Mao, S. (2023). Can digital finance improve China’s agricultural green total factor productivity? Agriculture, 13(7), 1429. https://doi.org/10.3390/agriculture13071429

Li, S., & Jiang, Z. (2019). Study on total factor productivity of agricultural leading enterprises listed in Hunan Province. DEStech Transactions on Environment, Energy and Earth Sciences. https://doi.org/10.12783/DTEEES/ICNER2018/28478

Li, X. (2025). Research on the coordinated development of digital inclusive finance, new quality productivity and labor mobility. Frontiers in Humanities and Social Sciences. https://doi.org/10.54691/rhw49712

Liu, W., Liu, C., Luo, J., & Liu, F. (2024). How does digital transformation promote total factor productivity? Managerial and Decision Economics. https://doi.org/10.1002/mde.4155

Micheni, E., Machii, J., & Murumba, J. (2022). Internet of things, big data analytics, and deep learning for sustainable precision agriculture. In IST-Africa Conference Proceedings. https://doi.org/10.23919/IST-Africa56635.2022.9845510

Mi, J., Chen, T., Nanseki, T., & Chomei, Y. (2021). Does the productivity paradox exist in the Chinese food industry? Japanese Journal of Agricultural Economics. https://doi.org/10.18480/JJAE.23.0_95

Nasri, W. (2011). Competitive intelligence in Tunisian companies. Journal of Enterprise Information Management, 24(1), 53–67. https://doi.org/10.1108/17410391111097429

Peng, Y., Chen, Z., & Lee, J. (2024). Agricultural green total factor productivity in Shandong Province of China. German Journal of Agricultural Economics. https://doi.org/10.52825/gjae.v73i2.1351

Pellissier, R., & Nenzhelele, T. E. (2013). Towards a universal competitive intelligence process model. South African Journal of Information Management, 15(2), 1–7. https://doi.org/10.4102/sajim.v15i2.567

Rohrbeck, R. (2012). Exploring value creation from corporate foresight activities. Futures, 44(5), 440–452. https://doi.org/10.1016/j.futures.2012.03.006

Rohrbeck, R., & Kum, M. E. (2018). Corporate foresight and its impact on firm performance: A longitudinal analysis. Technological Forecasting and Social Change, 129, 105–116. https://doi.org/10.1016/j.techfore.2017.12.013

Prakasha, N., Singh, S., & Sharma, S. (2024). Revisiting the productivity paradox: What is next for the BRICS and European banking systems? American Business Review. https://doi.org/10.37625/abr.27.2.401-438

Prescott, J. E., & Miller, S. H. (2001). Proven strategies in competitive intelligence: Lessons from the trenches. Wiley.

Singh, S., Suman, K., Kour, P., & Kumar, S. (2024). Digitalization and technology in agribusiness: Trends, impacts, and future directions. International Journal of Advanced Biochemistry Research, 8(11). https://doi.org/10.33545/26174693.2024.v8.i11a.2805

Taha, M., & Mao, C. (2025). Emerging technologies for precision crop management towards Agriculture 5.0. Agriculture, 15(6). https://doi.org/10.3390/agriculture15060582

Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of sustainable enterprise performance. Strategic Management Journal, 28(13), 1319–1350. https://doi.org/10.1002/smj.640

Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z

Wright, S., Pickton, D. W., & Callow, J. (2002). Competitive intelligence in UK firms: A typology. Marketing Intelligence & Planning, 20(6), 349–360. https://doi.org/10.1108/02634500210445346

Wan, B., & Zhou, E. (2021). Research of total factor productivity and agricultural management based on Malmquist-DEA modeling. Mathematical Problems in Engineering. https://doi.org/10.1155/2021/2828061

Wei, L., & Baharudin, M. (2025). Mapping the research of agricultural green total factor productivity: A bibliometric analysis using VOSviewer. Ciência Rural. https://doi.org/10.1590/0103-8478cr20240369

Xu, Q., Zhu, P., & Tang, L. (2022). Agricultural services and its effect on agricultural green total factor productivity in China. Land, 11(8), 1170. https://doi.org/10.3390/land11081170

Xu, Z., Niu, H., Wei, Y., Wu, Y., & Yu, Y. (2024). Impact and mechanisms of digital inclusive finance in relation to farmland transfer. Sustainability, 16(1), 408. https://doi.org/10.3390/su16010408

Yang, J., & Meseretchanie, A. (2024). The intermediate role of farmland transfer in the impact of digital financial inclusion on agricultural total factor productivity in China. Frontiers in Sustainable Food Systems. https://doi.org/10.3389/fsufs.2024.1345549

Zeng, F., Zhou, Y., & Wei, B. (2024). Empowering sustainable development: Revolutionizing agricultural green total factor productivity through rural digitalization. Frontiers in Sustainable Food Systems. https://doi.org/10.3389/fsufs.2024.1455732

Zeng, Y., et al. (2025). The impact of new digital infrastructure on agricultural green development. Frontiers in Environmental Economics. https://doi.org/10.3389/frevc.2025.1525531

Zhao, J., Al Anwar, R., Lou, Z., Liu, C., & Hou, Y. (2024). DEA Malmquist research on efficiency of agricultural infrastructure in Bangladesh. In 2024 IEEE International Conference on Systems, Man, and Cybernetics (pp. 521–526). IEEE. https://doi.org/10.1109/SMC54092.2024.10830987

Zhou, M., Zhang, H., Zhang,Z., & Sun, H. (2023). Digital financial inclusion, cultivated land transfer and cultivated land green utilization efficiency: An empirical study from China. Sustainability, 15(2), 1569. https://doi.org/10.3390/su15021569

Zhou, X., Chen, T., & Zhang, B. (2023). Research on the impact of digital agriculture development on agricultural green total factor productivity. Land, 12(1), 195. https://doi.org/10.3390/land12010195

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