Network Structure Innovation and Demand Forecasting Performance of Oil Marketing Companies in Kenya

Abstract

Purpose: The study focused on the influence of network structure innovation on the demand forecasting performance of oil marketing companies in Kenya. The research used adaptive transport models, collaborative network platforms, scenario planning and testing as indicators for measuring business process innovation. Methodology: The study was based on a positivist research philosophy and employed a mixed-methods research design. The population consisted of 143 registered oil marketing companies, from which a purposive sample of 81 was selected. Primary data were collected through open-ended questionnaires. Secondary data were obtained from EPRA reports on demand performance and analysed using the moving-average method. 10% of the sample size, or 10 oil marketing companies, was used for a pilot study. A total of 51 valid responses were analysed using descriptive statistics, Pearson's correlation, and regression, while qualitative data were analysed thematically. Findings: The results indicated that oil marketing companies have moderately to highly adopted network structure innovations. There was a positive and statistically significant correlation between network structure innovation and demand forecasting performance. Regression analysis further confirmed that network structure innovation had a positive and statistically significant effect on demand forecasting performance. Unique Contribution to Theory, Practice, and Policy: The findings demonstrate how innovative network structures enhance information flows and inter-firm connectivity to improve demand forecasting performance, while extending Network Theory by quantifying the effects of network agility on forecasting outcomes. In practice, managers could integrate network structure innovations into operational and strategic practices to strengthen collaborative partnerships with logistics providers and suppliers. Policy makers can promote agile operations, collaboration, and technology adoption to support the implementation of advanced forecasting technologies.