Empirical Analysis Techniques in Strategic Management

Empirical analysis in strategic management encompasses a spectrum of quantitative and computational methods designed to uncover patterns, test hypotheses and inform decision-making in competitive environments. Traditional econometric approaches, such as ordinary least squares and panel-data regression, remain foundational for modelling the effects of strategic choices on firm performance over time. Extensions include Tobit and hurdle models to handle censored or bounded outcome variables and logit frameworks adapted for small samples to mitigate bias. Quasi-experimental designs and event‐study methods allow scholars to infer causal impacts of strategic events—mergers, regulatory shifts or technological launches—by comparing treated and control groups. More recently, the field has embraced machine-learning algorithms and Bayesian techniques to accommodate high-dimensional data, explore non-linear relationships and integrate prior knowledge into estimation. Natural‐language processing and text‐mining tools have unlocked unstructured sources—annual reports, news articles and social media—to quantify managerial tone, stakeholder sentiment and strategic narratives. Network analysis further elucidates inter-firm alliances, supply-chain linkages and innovation ecosystems by mapping relational data. Across these techniques, careful attention to endogeneity, model specification and robustness checks ensures that inferences remain credible. Collectively, these empirical advances enable practitioners and policymakers to forecast market dynamics, optimise resource allocation and design strategies tailored to global and local conditions.

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Empirical Analysis Techniques in Strategic Management publication trend

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Technical terms

Marginal effects: Quantities summarising how changes in an explanatory variable alter predicted outcomes in non-linear or interactive models.

Tobit model: An econometric estimator designed for dependent variables that are censored or truncated, accommodating excess zeros or upper/lower bounds.

Penalised maximum likelihood estimator: A bias-reduction technique that adds a penalty term to the likelihood function, improving coefficient estimates in small or sparse samples.

References

1. How to Interpret Statistical Models Using marginaleffects for R and Python. Journal of Statistical Software (2024).
2. Tobit models in strategy research: Critical issues and applications. Global Strategy Journal (2019).
3. Estimating logit models with small samples. Political Science Research and Methods (2021).