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Savior or Distraction for Survival: Examining the Applicability of Machine Learning for Rural Family Farms in the United Arab Emirates
Machine learning (ML) has seen a substantial increase in its role in improving operations for staff and customers in different industries. However, there appears to be a somewhat limited adoption of ML by farm businesses, highlighted by a review of the literature investigating innovative behaviors by rural businesses. A review of the literature identified a dearth of studies investigating ML adoption by farm businesses in rural regions of the United Arab Emirates (UAE), especially in the context of family-owned farms. Therefore, this paper aims to investigate the drivers and barriers to ML adoption by family/non-family-owned farms in rural UAE. The key research questions are (1) what are the drivers and barriers for rural UAE farms adopting ML? As well as (2) is there a difference in the drivers and barriers between family and non-family-owned farms? Twenty semi-structured interviews were conducted with farm businesses across several rural regions in the UAE. Then, through a Template Analysis (TA), drivers and barriers for rural UAE-based farm owners adopting ML were identified. Interview findings highlighted that farms could benefit from adopting ML in daily operations to save costs and improve efficiency. However, 16 of 20 farms were unaware of the benefits related to ML due to access issues (highlighted by 12 farms) in incorporating ML operations, where they felt that incorporating ML into their operations was costly (identified by 8 farms). It was also identified that non-family-owned farms were more likely to take up ML, which was attributed to local culture influencing family farms (11 farms identified culture as a barrier). This study makes a theoretical contribution by proposing the Machine Learning Adoption Framework (MLAF). In terms of practical implications, this study proposes an ML program specifically targeting the needs of farm owners in rural UAE. Policy-based implications are addressed by the findings aligning with the United Nations’ Sustainability Development Goals 9 (Industry, Innovation, and Infrastructure) and 11 (Sustainable Cities and Communities).