FROM DATA TO DECISIONS: THE ROLE OF BI&A IN SUPPORTING SUSTAINABLE AGRICULTURAL DEVELOPMENT

Florin Daniel Militaru

Abstract


This study examines the role of Business Intelligence and Analytics (BI&A) in advancing sustainable agricultural development. We propose a conceptual framework linking Data-Driven Culture, BI&A Adoption, Decision-Making Effectiveness, and Sustainable Agricultural Performance. A structured survey will be administered to managers across forty agribusiness firms, and data will be analyzed using Partial Least Squares . Anticipated findings are expected to reveal how an analytics-oriented culture and BI&A tools jointly enhance decision quality, optimize resource use, and improve environmental and economic outcomes on the farm. The paper offers both theoretical insights into BI&A’s mechanisms and practical guidance for agribusiness leaders seeking to leverage digital technologies for resilient, sustainable farming.


Keywords


Business Intelligence & Analytics; sustainable agriculture; decision support; data-driven culture

Full Text:

PDF

References


ALARY V., MESSAD S., ABOUL-NAGA A., OSMAN M.A. H. ABDELSABOUR T., SALAH A.-A.E., JUANES X., 2020. Multi-criteria assessment of the sustainability of farming systems in the reclaimed desert lands of Egypt. Agricultural Systems, [online] 183, p.102863. https://doi.org/10.1016/j.agsy.2020.102863

ALI A., HUSSAIN T., ZAHID A., 2025, Smart Irrigation Technologies and Prospects for Enhancing Water Use Efficiency for Sustainable Agriculture. AgriEngineering 7, 106. https://doi.org/10.3390/agriengineering7040106

ALI H., DADZIE S., 2021, Assessing Global Fit in PLS-SEM: SRMR and Goodness-of-Fit Metrics, Information Systems Journal, vol. 31, no. 3, pp. 50–70, doi:10.1111/isj.12220

ALI Z., MUHAMMAD A., LEE N., WAQAR M., LEE, S.W., 2025. Artificial Intelligence for Sustainable Agriculture: A Comprehensive Review of AI-Driven Technologies in Crop Production. Sustainability, [online] 17(5), p.2281. https://doi.org/10.3390/su17052281

AHMED N., SHAKOOR N., 2025. Advancing agriculture through IoT, Big Data, and AI: A review of smart technologies enabling sustainability. Smart Agricultural Technology, [online] 10, p.100848. https://doi.org/10.1016/j.atech.2025.100848.

ANJUM M., KRAIEM N., MIN H., DUTTA A.K., DARADKEH Y.I., SHAHAB, S., 2025. Big data-driven agriculture: a novel framework for resource management and sustainability. Cogent Food & Agriculture, [online] 11(1). https://doi.org/10.1080/23311932.2025.2470249

ANTON E., SMOLNIK T., 2023, Beyond digital data and information technology: Conceptualizing data-driven culture, Pacific Asia Journal of the Association for Information Systems, 36 pages, DOI:10.17705/1pais.15301

ARNOTT D., G. PERVAN, 2016, A critical analysis of decision support systems research, Journal of Information Technology, vol. 31, no. 3, pp. 187–213. https://doi.org/10.1007/978-3-319-29272-4_3

CAO G., DUAN Y. AND LI G., 2015, Linking Business Analytics to Decision Making Effectiveness: A Path Model Analysis. IEEE Transactions on Engineering Management, [online] 62(3), pp.384–395. https://doi.org/10.1109/tem.2015.2441875.

CAO G., DUAN Y., 2014, A path model linking business analytics, data-driven culture, and competitive advantage, ECIS Proceedings, pp. 1–16. https://aisel.aisnet.org/ecis2014/proceedings/track04/1/

CASTELO S.L., GOMES C.F., 2023. The role of performance measurement and management systems in changing public organizations: An exploratory study. Public Money & Management, [online] 44(5), pp.399–406. https://doi.org/10.1080/09540962.2023.2204400

CHATTERJEE S., SAXENA A., 2021, Survey Design and Sampling Techniques in Agritech Research, Journal of Agricultural Studies, vol. 9, no. 2, pp. 45–62, doi:10.5539/jas.v9n2p45

CHAMORRO-PADIAL J., VIRGILI-GOMÁ J., VIRGILI J., GIL R. M., TEIXIDÓ M., GARCÍA R., 2025, “Agriculture Data Sharing Review,” Heliyon, vol. 11, no. 1, e41109, 10 pages, doi:10.1016/j.heliyon.2024.e41109

CHEN H., CHIANG R. H. L., STOREY V. C., 2012, Business Intelligence and Analytics: From Big Data to Big Impact, MIS Quarterly, vol. 36, no. 4, pp. 1165–1188, https://doi.org/10.2307/41703503

CHIDERA VICTORIA IBEH, ONYEKA FRANCA ASUZU, TEMIDAYO OLORUNSOGO, OLUWAFUNMI ADIJAT ELUFIOYE, NDUBUISI LEONARD NDUUBUISI AND ANDREW IFESINACHI DARAOJIMBA, 2024. Business analytics and decision science: A review of techniques in strategic business decision making. World Journal of Advanced Research and Reviews, [online] 21(2), pp.1761–1769. https://doi.org/10.30574/wjarr.2024.21.2.0247

CHOUDHURI R., MITRA D., 2024, Adoption of robust business analytics for product innovation and organizational performance: Mediating role of organizational data-driven culture, Annals of Operations Research, 24 pages, DOI: 10.1007/s10479-021-04407-3

DEBNATH J., KUMAR K., ROY K., CHOUDHURY R.D. U, A.K.P., 2024. Precision Agriculture: A Review of AI Vision and Machine Learning in Soil, Water, and Conservation Practice. International Journal for Research in Applied Science and Engineering Technology, [online] 12(12), pp.2130–2141. https://doi.org/10.22214/ijraset.2024.66166.

DEL-COCO M., LEO M., CARCAGNÌ P., 2024, Machine Learning for Smart Irrigation in Agriculture: How Far along Are We? Information, 15, 306. https://doi.org/10.3390/info15060306

DIBBERN T., ROMANI L. A. S., MASSRUHÁ S. M. F. S., 2024, Main drivers and barriers to the adoption of digital agriculture technologies, Smart Agricultural Technology, vol. 8, article 100459, https://doi.org/10.1016/j.atech.2024.100459

DIYA V. A., NANDAN P., DHOTE R. R., 2022, “IoT-based Precision Agriculture: A Review,” in Proceedings of Emerging Trends and Technologies on Intelligent Systems, Advances in Intelligent Systems and Computing, vol. (PP.CC), pp. 373–386, doi:10.1007/978-981-19-4182-5_30

EASTWOOD C., KLERKX L., AYRE M. ET AL., 2019, “Managing Socio-Ethical Challenges in the Development of Smart Farming: From a Fragmented to a Comprehensive Approach for Responsible Research and Innovation,” Journal of Agricultural and Environmental Ethics, vol. 32, pp. 741–768, doi:10.1007/s10806-017-9704-5

EASTWOOD C., KLERKX L., NETTLE E., 2019, “Adoption of precision agriculture in sustainable systems,” Agricultural Systems, vol. 153, pp. 165–173, doi:10.1007/s10676-020-09543-1

EASTWOOD C., KLERKX L., NETTLE R., 2017. Dynamics and distribution of public and private research and extension roles for technological innovation and diffusion: Case studies of the implementation and adaptation of precision farming technologies. Journal of Rural Studies, [online] 49, pp.1–12. https://doi.org/10.1016/j.jrurstud.2016.11.008

ELGENDY N., ELRAGAL A., 2016. Big Data Analytics in Support of the Decision Making Process. Procedia Computer Science, [online] 100, pp.1071–1084. https://doi.org/10.1016/j.procs.2016.09.251

ELRAGAL A., ELGENDY N., 2017, Big Data Analytics: A Literature Review Paper, in Proceedings of the 2017 International Conference on Cloud Computing and Big Data Analysis, pp. 1–7. DOI:10.1007/978-3-319-08976-8_16

FORNELL W., LARCKER D. F., 1981, Evaluating Structural Equation Models with Unobservable Variables and Measurement Error, Journal of Marketing Research, vol. 18, no. 1, pp. 39–50, doi:10.1177/002224378101800104

HAIR JR. 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, 378 pages

HAIR JR. J. F., BLACK W. C., BABIN B. J., ANDERSON R. E., 2019, Multivariate Data Analysis, 7th ed., Cengage Learning, 816 pages.

HENSELER J., RINGLE C. M., SARSTEDT R., 2015, A New Criterion for Assessing Discriminant Validity in Variance‐Based Structural Equation Modeling, Journal of the Academy of Marketing Science, vol. 43, no. 1, pp. 115–135, doi:10.1007/s11747-014-0403-8

HENSELER J., RINGLE M., 2017, Partial Least Squares Path Modeling, in Advanced Methods for Modeling Product Complexity, Springer, pp. 93–120, doi:10.1007/978-3-319-90868-4_4

HURBEAN L., MILITARU F., MUNTEAN M., DANAIATA D., 2023. The Impact of Business Intelligence and Analytics Adoption on Decision Making Effectiveness and Managerial Work Performance. Scientific Annals of Economics and Business, [online] 70(SI), pp.43–54. https://doi.org/10.47743/saeb-2023-0012

JIWAT R., ZHANG Z. (LEO), 2022. Adopting big data analytics (BDA) in business-to-business (B2B) organizations – Development of a model of needs. Journal of Engineering and Technology Management, [online] 63, p.101676. https://doi.org/10.1016/j.jengtecman.2022.101676

JOSHI S., SHARMA M., LUTHRA S., AGARWAL R., RATHI R., 2024. Role of industry 4.0 in augmenting endurability of agri-food supply chains amidst pandemic: organisation flexibility as a moderator. Operations Management Research. [online] https://doi.org/10.1007/s12063-023-00436-2

KAMBLE S., GUNASEKARAN R., SHARMA A., 2020, Achieving sustainable performance in a data-driven agriculture supply chain: Review for research and applications, International Journal of Production Economics, pp. 179–194, doi:10.1016/j.ijpe.2020.107948 https://doi.org/10.1016/j.ijpe.2019.05.022

KAMILARIS A., KARTAKOULLIS A., PRENAFETA-BOLDÚ F. X., 2017, A review on the practice of big data analysis in agriculture, Computers and Electronics in Agriculture, vol. 143, pp. 23–37, https://doi.org/10.1016/j.compag.2017.09.037

KAMILARIS A., PRENAFETA-BOLDÚ F. X., 2018, Deep learning in agriculture: A survey, Computers and Electronics in Agriculture, vol. 147, pp. 70–90, https://doi.org/10.1016/j.compag.2018.02.016

KLERKX L., JAKKU E., LABARTHE P., 2019, A review of social science on digital agriculture, smart farming and agri-tech, NJAS - Wageningen Journal of Life Sciences, vol. 90–91, p. 100315, https://doi.org/10.1016/j.njas.2019.100315

KOUR V.P., ARORA S., 2020. Recent Developments of the Internet of Things in Agriculture: A Survey. IEEE Access, [online] 8, pp.129924–129957. https://doi.org/10.1109/access.2020.3009298.

LEE S., KIM Y., 2023, “A deep learning framework for crop disease detection in real-time,” Research Square, preprint, 12 pages, doi:10.21203/rs.3.rs-6589718/v1

LEONELLI S., WILLIAMSON H.F., 2022. Introduction: Towards Responsible Plant Data Linkage. Towards Responsible Plant Data Linkage: Data Challenges for Agricultural Research and Development, https://doi.org/10.1007/978-3-031-13276-6_1.

LIAKOS C., BUSATO P., MOSHOU D., PEARSON S., BOCHTIS D., 2018, “Machine learning in agriculture: A review,” Sensors, vol. 18, no. 8, 2674–2700, https://doi.org/10.3390/s18082674

LUQUE-REYES J. R., ZIDI A., PEÑA-ACEVEDO A., GALLARDO-COBOS R., 2025, “Assessing Agri-Food Digitalization: Insights from Bibliometric and Survey Analysis in Andalusia,” World, vol. 6, no. 2, art. 57, 9 pages, doi:10.3390/world6020057

MAHLEIN S., 2016, Plant disease detection by imaging sensors – Parallels and specific demands for precision agriculture and plant phenotyping, Plant Disease, vol. 100, no. 2, pp. 241–251, https://doi.org/10.1094/PDIS-03-15-0340-FE

MEHRABI Z., MCDOWELL M. J., RICCIARDI V., LEVERS C., MARTÍNEZ J. D., ET AL., 2021, The global divide in data-driven farming, Nature Sustainability, vol. 4, pp. 154–160, https://doi.org/10.1038/s41893-020-00631-0

MIKALEF S., PAPPAS I.O., KROGSTIE J., GIANNAKOS M., 2018, Big data analytics capabilities: a systematic literature review and research agenda, Information Systems and e-Business Management, vol. 16, pp. 547–578. DOI: 10.1007/s10257-017-0362-y

NAQVI R.Z., NAEEM A., 2021, “Smart agriculture systems,” in Handbook of Smart Materials, Technologies, and Devices, Springer, pp. 1–27

OBAIDEEN K., YOUSEF B.A.A., ALMALLAHI M.N., TAN Y.C., MAHMOUD M., JABER H., RAMADAN M., 2022. An overview of smart irrigation systems using IoT. Energy Nexus, [online] 7, p.100124. https://doi.org/10.1016/j.nexus.2022.100124.

OJHA V.K., GOYAL S., CHAND M., KUMAR, A., 2024. A framework for data-driven decision making in advanced manufacturing systems: Development and implementation. Concurrent Engineering, [online] 32(1–4), pp.58–77. https://doi.org/10.1177/1063293x241297528

OLIVER M., BISHOP T., MARCHANT B. eds., 2013. Precision Agriculture for Sustainability and Environmental Protection. [online] Routledge. https://doi.org/10.4324/9780203128329

OLSZAK M. C., ZIEMBA E., 2006. Business Intelligence Systems in the Holistic Infrastructure Development Supporting Decision Making in Organisations. Interdisciplinary Journal of Information, Knowledge, and Management, [online] 1, pp.047–058. https://doi.org/10.28945/113

PAUDEL B., RIAZ S., TENG S.W., KOLLURI R.R. AND SANDHU, H., 2025. The digital future of farming: A bibliometric analysis of big data in smart farming research. Cleaner and Circular Bioeconomy, [online] 10, p.100132. https://doi.org/10.1016/j.clcb.2024.100132

PHILLIPS-WREN G., DALY M., BURSTEIN F., 2021. Reconciling business intelligence, analytics and decision support systems: More data, deeper insight. Decision Support Systems, [online] 146, p.113560. https://doi.org/10.1016/j.dss.2021.113560

POPOVIČ A., HACKNEY R., COELHO P.S., JAKLIČ J., 2016, Towards business intelligence systems success: Effects of maturity and culture on analytical decision making, Decision Support Systems, vol. 54, no. 1, pp. 729–739. https://doi.org/10.1016/j.dss.2012.08.017

POPOVIČ A., TURK T., 2010, Conceptual model of business value of business intelligence systems, Information Systems Management, Vol. 15, pp. 5-30

REISSIG L., 2024. From the Attitude Towards Digitalisation in Agriculture to the Acceptance of Future Agricultural Technologies. https://doi.org/10.2139/ssrn.4853426

ROZENSTEIN O., COHEN Y., ALCHANATIS V., BEHRENDT K., BONFIL D.J., ESHEL G., HARARI A., HARRIS W.E., KLAPP I., LAOR Y., LINKER R., PAZ-KAGAN T., PEETS S., RUTTER S.M., SALZER Y., LOWENBERG-DEBOER J., 2023. Data-driven agriculture and sustainable farming: friends or foes? Precision Agriculture, [online] 25(1), pp.520–531. https://doi.org/10.1007/s11119-023-10061-5

SHARMA A., MITHAS M., KANKANHALLI A., 2017, Transforming decision-making processes: a research agenda for understanding the impact of business analytics on organisations, European Journal of Information Systems, vol. 23, no. 4, pp. 433–441. https://doi.org/10.1057/ejis.2014.17

SHOLLO A., 2013, The Role of Business Intelligence in Organizational Decision-making. Copenhagen Business School [Phd]. PhD series No. 10.2013

SON N., CHEN C.-R., SYU C.-H., 2024, Towards Artificial Intelligence Applications in Precision and Sustainable Agriculture. Agronomy, [online] 14(2), p.239. https://doi.org/10.3390/agronomy14020239

TANTALAKI N., SOURAVLAS S., ROUMELIOTIS, M., 2019, Data-Driven Decision Making in Precision Agriculture: The Rise of Big Data in Agricultural Systems. Journal of Agricultural & Food Information, [online] 20(4), pp.344–380. https://doi.org/10.1080/10496505.2019.1638264

TARAMUEL-TARAMUEL J.P., MONTOYA-RESTREPO I.A., BARRIOS D., 2023, Drivers linking farmers’ decision-making with farm performance: A systematic review and future research agenda. Heliyon, [online] 9(10), p.e20820. https://doi.org/10.1016/j.heliyon.2023.e20820

VAN DER BURG S., BOGAARDT M.-J., WOLFERT S., 2019, Ethics of smart farming: Current questions and directions for responsible innovation towards the future. NJAS: Wageningen Journal of Life Sciences, [online] 90–91(1), pp.1–10. https://doi.org/10.1016/j.njas.2019.01.001

WATSON H.J., WIXOM B.H., 2007. The Current State of Business Intelligence. Computer, [online] 40(9), pp.96–99. https://doi.org/10.1109/mc.2007.331

WEN R., LI, S., 2022. Spatial Decision Support Systems with Automated Machine Learning: A Review. ISPRS International Journal of Geo-Information, [online] 12(1), p.12. https://doi.org/10.3390/ijgi12010012

WERAIKAT D., ALSMIRAT M., 2024, Data Analytics in Agriculture: Enhancing Decision-Making for Crop Yield Optimization and Sustainable Practices, Sustainability, vol. 16, no. 17, 7331, pp. 1–17, https://doi.org/10.3390/su16177331

WOLFERT J., GE L., VERDOUW C., BOGAARDT M.J., 2017, “Big Data in Smart Farming – A review,” Agricultural Systems, vol. 153, pp. 69–80, https://doi.org/10.1016/j.agsy.2017.01.023


Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Florin Daniel Militaru

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.


LUCRĂRI ȘTIINȚIFICE MANAGEMENT AGRICOL

ISSN print 1453-1410
ISSN online 2069-2307
(former ISSN 1453-1410, E-ISSN 2069-2307)

PUBLISHER: AGROPRINT Timisoara, Romania
PAPER ACCESS: Full text articles available for free
FREQUENCY: Annual
PUBLICATION LANGUAGE: English

______________________________________________________________________________________________

Banat`s University of Agricultural Sciences and Veterinary Medicine “King Michael I of Romania” from Timisoara
Faculty of Management and Rural Tourism
300645, Timisoara, Calea Aradului 119, Romania

E-mail: tabitaadamov2003 [at] yahoo.com
Phone: +40-256-277439, Fax.: +40-256-277031