Analyzing farming analytics adoption challenges in developing economies: a fuzzy Delphi–Neutrosophic DEMATEL approach
Isha Sharma & Jose Arturo Garza-Reyes
What the paper says
Purpose Exponential population growth coupled with dwindling resources has invariably led to the adoption of smart and sustainable agriculture methods. Data-driven predictive analytics are force multipliers in mitigating these challenges; however, critical challenges exist in their adoption in developing economies. This study provides insights into the challenges of adopting advanced analytics in the agricultural operations of developing economies. Design/methodology/approach The preliminary challenges were systematically identified through an extensive literature review and refined with expert validation using the fuzzy-Delphi Method (FDM). The opinions of nine experts were then applied in the Neutrosophic DEMATEL method to justify and construct the contextual interrelationships among the challenges, ensuring both rigor and methodological novelty. This integrated methodology led to precision in selecting the challenges and novelty in the causal mapping of their interplay. Findings The findings reveal that network outages and a lack of supporting infrastructure are significant challenges impeding the implementation of analytics solutions in farming operations. The considerable effect group challenges were data heterogeneity and lack of user expertise and skill, reflecting their dependency on other challenges. Research limitations/implications The research limitation lies in the small sample of experts and methodological dependency; however, it contributes to the literature by providing frameworks for understanding adoption challenges. Originality/value The originality of this work lies in its twofold contribution: first, systematically identifying and validating the critical challenges hindering the integration of analytics into farming operations in developing economies; second, offering actionable insights that support managers and policymakers in advancing data-driven and sustainable agricultural practices.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.