New approaches to improve organisational performance in firms are evolving in this data-driven age. However, there is lack of studies in examining the relationship between revenue, net profit, cash flow per share, and earnings per share. The energy sector remains under-researched regarding the multi-dimensional drivers of profitability. Existing research shows inconclusive evidence with studies predominantly examining revenue—performance relationship limiting to a single factor and not guiding potential investors regarding future earnings per share in the energy industry. This paper aims to bridge the gap in literature by proposing a data-driven approach to analyse the relationships between revenue, net profit, cash flow per share, and earnings per share. We examine these relationships by conducting an empirical analysis using secondary data derived from published annual reports of the energy firms listed on the Australian Securities Exchange (ASX). Our empirical study uses Pearson correlations and regression techniques to test the hypotheses on the relationships between revenue, net profit, cash flow per share, and earnings per share. Also, we use market capitalisation as a control variable and predictor of earnings per share in the energy industry. The data analysis results in four findings: (i) revenue positively influences earnings per share because higher revenue expands the firm’s earnings capacity within the financial performance, (ii) net profit has a strong positive effect on earnings per share, consistent with profitability theory and the direct derivation of EPS from net income, (iii) cash flow per share influences earnings per share because liquidity supports operational stability, investment decisions, and earnings sustainability (e.g., heavy capital expenditure contexts), and (iv) the combined effects of revenue, net profit, and cash flow per share provide a stronger and more holistic prediction of earnings per share than any single variable, consistent with multidimensional organisational performance theory (a more holistic valuation model than looking at single factors). In addition, the results indicate that market capitalisation (control variable) has both strong prediction of earnings per share and strong association with earnings per share. The results of this study can offer practitioners and investors in Australia and other countries for a better understanding of the relationships between revenue, net profit, cash flow per share, and earnings per share from energy companies. The data will help investors to make good investment data-driven decisions in the energy industry or other industries. It also motivates researchers to conduct similar studies in different contexts. We further provide recommendations, including a closed-loop Artificial Intelligence (AI) data-driven approach integrated into energy accounting and operational processes to enhance profitability. This approach operationalises the revenue and earnings-per-share (EPS) strategies identified in our empirical analysis, offering practical value for industry practitioners and guiding future research in this direction.
Msimangira et al. (Mon,) studied this question.