Purpose <p>When considering the influence of recognition-based heuristics on entrepreneurs’ strategic decision-making (ESDM), especially in emerging markets, conventional theories and literature on entrepreneurs’ management approach are notably sparse. This study investigates how recognition-based heuristics influence ESDM, particularly in an emerging Asian economy.</p> Design/methodology/approach <p>Data was collected through a survey completed by 237 owners and senior managers of small and medium-sized enterprises (SMEs) in the service, trade, and manufacturing sectors located in the Pakistani cities of Rawalpindi and Islamabad (twin cities). Data was collected using a convenient purposive sampling technique and snowball sampling method. A structural equation modeling-artificial neural network (SEM-ANN) based approach was applied to evaluate the role of recognition-based heuristic predictors. The results were authenticated using regression analysis.</p> Findings <p>The results indicate that recognition-based heuristics—such as alphabetical order, name fluency, and name memorability—have a positive impact on ESDM. This means that recognition-based heuristics are useful tools for entrepreneurs in strategic decision-making. Entrepreneurs who use recognition-based heuristics are more likely to make effective strategic decisions. The ANN results reveal that name memorability has the highest predictive power in positively influencing ESDM, suggesting that memorability plays a crucial role in facilitating more efficient and informed strategic choices.</p> Originality/value <p>This study pioneers research examining the connection between recognition-based heuristics—alphabetical order, name fluency and name memorability—and ESDM in an emerging Asian market. This study contributes to the entrepreneurial management field, particularly regarding the role of recognition-based heuristics in strategic decision-making. This research area is still in its early stages, even in developed economies, and very little work has been conducted in emerging economies. This study makes a significant contribution to the literature in this field. We employed a novel SEM-ANN based evaluation approach that combines the strengths of SEM and ANN. This integration allows for a comprehensive analysis of both linear and nonlinear relationships between variables, providing a nuanced understanding of the complex dynamics involved in ESDM, and differentiating this study from other studies in the field.</p>