Purpose The purpose of this paper is to investigate how business analytics, statistical learning and multiple regression analysis can streamline time equations within the Time-Driven Activity-Based Costing (TD-ABC) model. This study contributes to the TD-ABC literature by identifying statistical relationships between resource usage, time equations and costs. Design/methodology/approach This study uses a range of statistical techniques, culminating in multiple regression analysis, to determine which time equations are most relevant for decision-making within a TD-ABC framework. Findings Using a combination of synthetic and experimental data, the author demonstrate how statistical learning and regression analysis can identify the most impactful time variables for decision-making. This approach enables a perceptible reduction in the number of time equations by focusing only on those variables that are statistically significant and relevant for future decisions. Research limitations/implications As this study is based on a hypothetical TD-ABC layout and a data management approach, future research could explore the application of this statistical model in collaboration with real-world companies. Practical implications Limiting the number of time equations through regression analysis can reduce the number of time equations, complexity and cost of implementing TD-ABC. Moreover, the statistical approach supports a “cause-and-effect” assumption that underpins the TD-ABC methodology. Originality/value Beyond introducing a statistical approach to time equation modeling, this paper documents and discusses the process of identifying relevant time variables in a TD-ABC environment, offering a novel perspective on cost modeling and decision support.
Steen Nielsen (Mon,) studied this question.