UET–Strategic Posture Integrated Dataset (NASDAQ Firms, 2014–2024)
Description
This dataset integrates Upper Echelons Theory (UET) variables with Ansoff’s Strategic Posture framework to examine the relationship between executive characteristics, strategic calibration, and firm outcomes. It combines archival firm-level data with hand-coded executive attributes to create a multi-level research dataset suitable for empirical analysis in strategic management and corporate finance. The dataset includes firm-year observations for NASDAQ-listed companies across three periods (2014, 2015, and 2024). It merges three components: (1) strategic posture indicators derived from Ansoff’s Optimal Strategic Performance Positioning (OSPP) framework, including Environmental Turbulence (ETL), Strategic Aggressiveness (SA), Capability Responsiveness (CR), and a composite posture score (X1); (2) performance measures, including short-term operating growth and analyst-based estimated growth; and (3) executive-level variables constructed from publicly available disclosures. Executive variables are hand-coded using a structured protocol informed by Upper Echelons Theory and the CUP-W analytical framework. These include CEO duality, founder status, power concentration (composite index), industry familiarity, CEO tenure, and functional background. Data sources include annual reports, proxy statements, and investor relations disclosures. Coding is based exclusively on publicly available information. The dataset also incorporates an intercoder-validated subset of firms (2024) to support measurement reliability of the strategic posture variables. This validation component provides independent ratings of ETL, SA, and CR across multiple coders using a standardized item-based framework. The purpose of this dataset is to enable analysis of how executive characteristics are associated with strategic posture and how posture, in turn, relates to firm performance. It supports both direct-effect and mediation-style empirical designs linking executive structure to organizational outcomes through strategic calibration. This dataset is suitable for replication, extension, and sensitivity analysis in research on strategic management, corporate governance, and behavioral finance.
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Steps to reproduce
This dataset can be reproduced in four stages using archival sources, strategic posture coding, and executive-level coding. Sample selection Identify the NASDAQ firms included in the study. The dataset combines a historical panel for 2014–2015, originally assembled for the author’s doctoral research, with a 2024 validation subset used for intercoder reliability. Firms are matched by ticker and year. Strategic posture construction Construct the core Ansoff OSPP variables: Environmental Turbulence (ETL), Strategic Aggressiveness (SA), and Capability Responsiveness (CR). For the 2024 subset, use the 65-item coding instrument rated on a 1–5 scale by multiple coders and compute firm-level means. For 2014–2015, derive posture variables from archival strategic and financial indicators following the same conceptual framework. Compute X1 as the composite posture score and derive alignment gaps where available. Performance and executive variables Match each firm-year to performance variables, including Growth and Estimated Growth, using financial databases and analyst-based sources. Then hand-code executive variables from annual reports, proxy statements, and investor relations disclosures. These include CEO duality, founder status, power index, industry familiarity, CEO tenure, and functional background. Coding follows a standardized protocol informed by Upper Echelons Theory and the CUP-W framework. Integration and analysis Merge posture, performance, and executive data by firm and year. Clean the dataset by handling missing values, trimming extreme observations where appropriate, and constructing derived variables such as composite indices and gap measures. To replicate the empirical analysis, generate descriptives and correlations, estimate baseline regressions of performance on X1, add gap and executive variables, and test indirect effects using regression or bootstrap mediation models. These steps reproduce the integrated design linking executive characteristics, strategic posture, and firm outcomes.
Institutions
- Ca' Foscari University of VeniceVeneto, Venice