Big data analytics-driven agribusiness: assessing the impact on the competitiveness of Moroccan private companies through dynamic capabilities

Purpose In a digital world, with the problems that resource management faces most and under pressure from international competitiveness, integrating Big Data goes from being merely advantageous to becoming necessary and strategic. While existing literature highlights the general value of Big Data Analytics Capabilities (BDAC) in developed markets, few studies have contextualized this relationship for agro-industry in the North Africa region, where structural transformations are rapidly reshaping competitive dynamics. The present research aims to assess the association between BDAC and competitive advantage among private firms in the Moroccan agro-industry and to empirically test the mediating role of dynamic capabilities. Design/methodology/approach Drawing on the resource-based view and dynamic capabilities framework, a quantitative study conducted among 51 Moroccan agri-food companies enabled the results to be statistically modeled by developing a conceptual model that decomposes BDAC into three interrelated dimensions. Findings The research was able to demonstrate that Big Data has a positive and statistically significant influence on competitive advantage. More specifically, managerial skills and a data-driven culture appear to be the most important aspects of Big Data. Furthermore, the study discloses that this influence is mostly determined by the company's innovation capability and the evolution of its internal processes, where the findings offer valuable managerial insights to the executives seeking to turn their investments in technology into a sustainable competitive advantage. Research limitations/implications This study provides important information; however, it does contain limitations which would allow for further research in regards to the subject. Firstly, while the sample size (n = 51) only had a modest number of respondents, it was acceptable from a partial least squares structural equation modeling standpoint; however, this lack of power limits both the degree of generalizability of our findings and the power associated with our analysis of the data. Future research utilizing a model with a greater number of respondents and a more diverse sample, and/or conducting research with respondents from additional sectors than those in Maghreb; significantly enhances the external validity of the findings. Further, the majority of data used was collected at a single point in time; therefore, we can only make inferences about the relationship at that moment. Values associated with the establishment of a BDAC and their impact on agility within the organization are evolving, dynamic processes that require extensive longitudinal analysis to identify how these values will develop and how the outcomes associated with performance will vary over time. Third, the use of perceptual measures reported by respondents to assess competitive advantage, although common in this type of study, may introduce subjectivity bias. While perceptual measures are frequently used to assess competitive advantage in this type of research, we recommend that future research include objective measures of performance (return on assets, revenue growth, actual market share) to triangulate the data and enhance the confidence of the conclusions made. Fourth, although our study focuses on firms located in different regions of Morocco, we did not explicitly account for the geographic/spatial dimension in our modeling. Agro-industrial dynamics often exhibit significant regional heterogeneity linked to factors such as access to infrastructure, proximity to markets or innovation clusters, and ignoring potential spatial autocorrelation could introduce bias into our estimates. Social implications This research draws attention to the societal advantages of utilizing BDAC in Morocco's agri-food sector. BDAC, by encouraging decisions based on evidence, make the production of food more transparent and accountable, which is a direct advantage for the health and safety of consumers. Besides, the focus on human talent upgrading helps to improve digital skills, generate jobs with high added value, and close the digital gap. Lastly, the BDAC supported dynamic capabilities that drive supply chain resilience and the reduction of food waste, which align with environmental sustainability. In summary, these results indicate that responsible use of data analytics can lead to a food system that is not only resilient, equitable and sustainable but also one that has positive societal impacts. Originality/value Through main contributions, the present work broadens the scope of BDAC studies by considering the lesser-known case of the Moroccan agri-food industry and the RBV of the firm is shown to be applicable to emerging economies as well. A validated measurement model is given, serving as a tool for future research.