The Role of Predictive Analytics in Business Decision-Making: Intuition or Algorithms (#491)

Authors

Berlioz Pastor, José Guillermo

Bautista Barrios, Allison Cristabell

Oseguera Osorio, Ashly Maryann

Abstract

In highly competitive and dynamic business contexts, strategic decision-making constitutes a critical factor for organizational performance. Although the use of advanced analytical tools, such as predictive analytics, has strengthened the ability to anticipate market behavior, the systematic integration of algorithmic models with managerial intuition—conceptualized as the “Corporate Mind”—remains an underexplored gap in emerging economies. This study analyzes the implementation and effectiveness of predictive analytics in domestic and multinational companies operating in Honduras, with the aim of examining how the combination of algorithmic approaches and human judgment contributes to improving strategic decision-making. A quantitative, descriptive, and correlational research design was employed, using an online structured questionnaire administered to 46 companies. The data were analyzed using Spearman correlations, Cronbach’s alpha, nonparametric tests, and descriptive statistics. The results show that structured training in data analytics significantly increases the perceived impact of predictive models (ρ = 0.459, p = 0.014), while teams’ operational capacity moderates the translation of this perception into tangible organizational outcomes (p = 0.043). The findings confirm that technology alone does not guarantee performance improvements and that its effectiveness is maximized through an integrated approach that combines training, team capacity, and a data-driven organizational culture. This study provides empirical evidence to the discussion on hybrid analytical–intuitive strategies and offers a practical framework applicable to organizations operating in complex and rapidly transforming business environments.