Research on World Agricultural Economy

From Smart Adoption to Export Competitiveness: Innovation Diffusion and Value-Chain Upgrading in Agrifood Markets in the UAE and Bulgaria

Haitham M. Alzoubi

Faculty of Management, Technical University of Soϔia, 1000 Soϔia, Bulgaria; School of Business, Horizon University College, Ajman P.O. Box 5700, United Arab Emirates; Applied Science Research Center, Applied Science Private University, Amman 11931, Jordan

Yordanka S. Angelova

Faculty of Management, Technical University of Soϔia, 1000 Soϔia, Bulgaria

Mounir El Khatib

School of Business and Quality Management, Hamdan Bin Mohamad Smart University, Dubai P.O. Box 71400, United Arab Emirates

Cheng Ling Tan

Graduate School of Business, Universiti Sains Malaysia, Penang 11800, Malaysia

Gouher Ahmed

School of Business, Horizon University College, Ajman P.O. Box 5700, United Arab Emirates

DOI: https://doi.org/10.36956/rwae.v7i3.2875

Received: 30 October 2025 | Revised: 5 December 2025 | Accepted: 9 December 2025 | Published Online: 2 July 2026

---

Abstract

This study examines how smart and agri-digital technologies contribute to export competitiveness in agrifood markets through innovation diffusion and value-chain upgrading, while considering the conditioning role of the climate risk and policy support. Cross-sectional survey data were collected from processed-food and dairy firms in the United Arab Emirates and Bulgaria, and analyzed using Partial Least Squares Structural Equation Modeling with latent-moderation and serial mediation and multi-group analysis (MGA). Robustness is validated with 2-Stage Least Squares/Instrumental Variables (2SLS-IV) and Covariance-Based Structural Equation Modeling (CB-SEM) comparisons. The findings indicate that digital adoption stimulates both process and product innovation, which subsequently enhances value-chain upgrading and export competitiveness. Climate risk weakens the positive effect of innovation diffusion and upgrading, whereas policy support strengthens the conversion of upgrading outcomes into export performance. Comparative analysis shows stronger adoption-to-upgrading and upgrading-to-export pathways in the UAE than in Bulgaria, reflecting the institutional and logistical differences. The study advances the integration of the Diffusion of Innovations and the Global Value Chain theories by explaining when and how digital transformation yields export gains under varying risk and policy environments. These insights provide practical direction for the agrifood firms and policymakers seeking to strengthen digitalization strategies, innovation capacity, and climate-resilient export performance.

Keywords: Digital Agriculture; Smart Adoption; Innovation Diffusion; Value‑Chain Upgrading; Export Competitive‑ ness; Climate Risk; Policy Support

---

References

[1] Finger, R., 2023. Digital Innovations for Sustainable and Resilient Agricultural Systems. European Review of Agricultural Economics. 50(4), 1277–1309. DOI: https://doi.org/10.1093/erae/jbad021

[2] Ali, I., Govindan, K., 2021. Extenuating Operational Risks through Digital Transformation of Agri-Food Supply Chains. Production Planning and Control. 34(12), 1165–1177. DOI: https://doi.org/10.1080/09537287.2021.1988177

[3] Zhao, P., Zhang, W., Cai, W., et al., 2022. The Impact of Digital Finance Use on Sustainable Agricultural Practices Adoption Among Smallholder Farmers: Evidence from Rural China. Environmental Science and Pollution Research. 29, 39281–39294. DOI: https://doi.org/10.1007/s11356-022-18939-z

[4] Gereffi, G., Humphrey, J., Sturgeon, T., 2005. The Governance of Global Value Chains. Review of International Political Economy. 12(1), 78–104. DOI: https://doi.org/10.1080/09692290500049805

[5] Bozzola, M., Lamonaca, E., Santeramo, F.G., 2023. Impacts of Climate Change on Global Agri-Food Trade. Ecological Indicators. 154, 110680. DOI: https://doi.org/10.1016/j.ecolind.2023.110680

[6] Campiglio, E., Daumas, L., Monnin, P., et al., 2023. Climate-Related Risks in Financial Assets. Journal of Economic Surveys. 37(3), 950–992. DOI: https://doi.org/10.1111/joes.12525

[7] Su, Z., Deng, C., Feng, Y., et al., 2025. Climate Risks and Corporate Supply Chain Configuration: Evidence from China. Research in International Business and Finance. 76, 102852. DOI: https://doi.org/10.1016/j.ribaf.2025.102852

[8] Peres, R., Muller, E., Mahajan, V., 2010. Innovation Diffusion and New Product Growth Models: A Critical Review and Research Directions. International Journal of Research in Marketing. 27(2), 91–106. DOI: https://doi.org/10.1016/j.ijresmar.2009.12.012

[9] Pasquali, G., Krishnan, A., Alford, M., 2021. Multichain Strategies and Economic Upgrading in Global Value Chains: Evidence from Kenyan Horticulture. World Development. 146, 105567.

[10] Santeramo, F.G., Jeffliffe, J., Hoekman, B., 2024. Agri-Food Value Chains and the Global Food Dollar: The Role of Trade and Services. SSRN. DOI: https://dx.doi.org/10.2139/ssrn.4940299

[11] Zhang, D., Sun, Z., 2023. The Impact of Agricultural Global Value Chain Participation on Agricultural Total Factor Productivity. Agriculture. 13(11), 2151. DOI: https://doi.org/10.3390/agriculture13112151

[12] Beverelli, C., Gourevich, I., Heiland, I., et al., 2023. Trade and Welfare Effects of the WTO Trade Facilitation Agreement. World Trade Organization: Geneva, Switzerland. DOI: https://doi.org/10.30875/25189808-2023-4

[13] Barrett, C.B., Reardon, T., Swinnen, J., et al., 2022. Agri-Food Value Chain Revolutions in Low- and Middle-Income Countries. Journal of Economic Literature. 60(4), 1316–1377. DOI: https://doi.org/10.1257/jel.20201539

[14] Taglioni, D., Winkler, D., 2016. Making Global Value Chains Work for Development. The World Bank Group: Washington, DC, USA. DOI: https://doi.org/10.1596/978-1-4648-0157-0

[15] Hair Jr., J.F., Hult, G.T.M., Ringle, C.M., et al., 2022. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 3rd ed. SAGE: Thousand Oaks, CA, USA.

[16] Sarstedt, M., Liu, Y., 2024. Advanced Marketing Analytics Using Partial Least Squares Structural Equation Modeling (PLS-SEM). Journal of Marketing Analytics. 12, 1–5. DOI: https://doi.org/10.1057/s41270-023-00279-7

[17] Spoto, A., Nucci, M., Prunetti, E., et al., 2025. Improving Content Validity Evaluation of Assessment Instruments through Formal Content Validity Analysis. Psychological Methods. 30(2), 203–222.

[18] Kock, N., 2015. Common Method Bias in PLS-SEM: A Full Collinearity Assessment Approach. International Journal of e-Collaboration. 11(4), 1–10.

[19] Podsakoff, P.M., Podsakoff, N.P., Williams, L.J., et al., 2024. Common Method Bias: It’s Bad, It’s Complex, It’s Widespread, and It’s Not Easy to Fix. Annual Review of Organizational Psychology and Organizational Behavior. 11, 17–61. DOI: https://doi.org/10.1146/annurev-orgpsych-110721-040030

[20] Shmueli, G., Sarstedt, M., Hair, J.F., et al., 2019. Predictive Model Assessment in PLS-SEM: Guidelines for Using PLSpredict. European Journal of Marketing. 53(11), 2322–2347. DOI: https://doi.org/10.1108/EJM-02-2019-0189

[21] Henseler, J., Ringle, C.M., Sarstedt, M., 2016. Testing Measurement Invariance of Composites Using Partial Least Squares. International Marketing Review. 33(3), 405–431. DOI: https://doi.org/10.1108/IMR-09-2014-0304

[22] Chin, W.W., Dibbern, J., 2010. An Introduction to a Permutation Based Procedure for Multi-Group PLS Analysis: Results of Tests of Differences on Simulated Data and a Cross Cultural Analysis of the Sourcing of Information System Services Between Germany and the USA. In: Esposito Vinzi, V., Chin, W., Henseler, J., et al. (Eds.). Handbook of Partial Least Squares. Springer: Berlin/Heidelberg, Germany. pp. 171–193. DOI: https://doi.org/10.1007/978-3-540-32827-8_8

[23] Nitzl, C., Roldán, J.L., Cepeda, G., et al., 2016. Mediation Analysis in Partial Least Squares Path Modeling: Helping Researchers Discuss More Sophisticated Models. Industrial Management and Data Systems. 116(9), 1849–1864. DOI: https://doi.org/10.1108/IMDS-07-2015-0302

[24] Edeh, E., Lo, W.J., Khojasteh, J., 2023. Review of Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook: By Joseph F. Hair Jr., G. Tomas M. Hult, Christian M. Ringle, Marko Sarstedt, Nicholas P. Danks, Soumya Ray. Cham, Switzerland: Springer, (2021). 197 pp. $0, Open Access; $59.99, Hardcover Book. Structural Equation Modeling: A Multidisciplinary Journal. 30(1), 165–167. DOI: https://doi.org/10.1080/10705511.2022.2108813

[25] Li, J., Shi, J., Cao, R., et al., 2024. How E-Commerce Can Boost China’s High-Quality Agricultural Exports. Frontiers in Sustainable Food Systems. 8, 1372129. DOI: https://doi.org/10.3389/fsufs.2024.1372129

[26] Mi, J., 2023. The Impact of Food Standards on Export Quality in China: The Mediating Effect of TFP. Journal of the Faculty of Agriculture, Kyushu University. 68(1), 67–77. DOI: https://doi.org/10.5109/6770290