UET–Strategic Posture Integrated Dataset (NASDAQ Firms, 2014–2024)

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.