Do recognition-based heuristics matter in entrepreneurial strategic decision-making: evidence from an emerging Asian economy

Purpose When considering the influence of recognition-based heuristics on entrepreneurs’ strategic decisionmaking (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.
Design/methodology/approach 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.
Findings 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.
Originality/value 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.