Title: Model Based Forecasting and Business Performance of Agricultural firms in Rivers State, Nigeria.
Authors: Enamuotor Russell and B. Chima Onuoha
Volume: 9
Issue: 10
Pages: 339-347
Publication Date: 2025/10/28
Abstract:
In today's highly dynamic and competitive agricultural environment, accurate forecasting plays a pivotal role in driving business success. This study investigated the relationship between model-based forecasting and business performance of agricultural firms in Rivers State, Nigeria. Specifically, the study examined the impact of two forecasting approaches-time-series forecasting and simulation-based forecasting-on operational performance and market performance, the two key dimensions of business performance. A quantitative research design was adopted, utilizing structured questionnaires administered to 136 respondents drawn from registered agricultural firms across Rivers State. The data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) through SmartPLS 4.0 from 126 valid questionnaires. Findings revealed that time-series forecasting significantly and positively impacts both operational and market performance. Similarly, simulation-based forecasting also demonstrated a strong, positive effect on both performance measures. These results highlight the critical role of predictive and scenario-based forecasting in achieving enhanced productivity, resource optimization, market competitiveness, and profitability. The study underscores the need for agricultural firms to invest in data analytics tools and capacity-building initiatives to improve their forecasting capabilities. It concludes that model-based forecasting should be strategically integrated into core business decision-making processes to bolster firm performance in the sector. Hence, it is recommended that agricultural firms should institutionalize the use of time-series forecasting by investing in data analytics tools and staff training