Integrated UET–Strategic Posture Panel Dataset for NASDAQ Firms (2006–2015)
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 provides a structured firm-year panel for 25 NASDAQ-listed firms over the period 2006–2015. The dataset combines three components. First, strategic posture and performance variables are drawn from the author’s doctoral research (Bonelli, 2017), including a composite posture score (X1), internal structure (X2), an observed alignment differential (Diff), short-term operating growth, and analyst-based estimated growth. These variables are based on Ansoff’s Optimal Strategic Performance Positioning (OSPP) framework. Second, the dataset incorporates a hand-coded executive layer based on publicly available disclosures. Executive variables include CEO duality, founder status, a composite power index, insider versus outsider succession, CEO tenure, functional background, technical orientation, and industry familiarity. Coding is conducted using a structured protocol informed by Upper Echelons Theory and the CUP-W analytical framework, relying on annual reports, proxy statements, and investor relations materials. Third, the dataset includes derived subsets to support empirical analysis, including a main estimation sample, a balanced panel subset, and a transition subset capturing CEO changes. A separate validation component based on a 2024 intercoder dataset is included for measurement support but is not part of the main estimation panel. The purpose of this dataset is to enable multi-level analysis linking executive structure to strategic posture and firm performance. It supports models testing whether executive characteristics are associated with strategic calibration and whether posture acts as an intermediate mechanism connecting upper echelons to firm outcomes. The dataset is suitable for replication, extension, and methodological research in strategic management, corporate governance, and behavioral finance. It provides a transparent and reproducible framework for studying how executive attributes are reflected in firm-level strategic behavior and performance dynamics.
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Steps to reproduce
This dataset can be reproduced through a structured process combining archival data extraction, strategic posture construction, and executive-level coding. 1. Sample selection Identify the 25 NASDAQ-listed firms included in the study and construct a firm-year panel covering 2006–2015. Ensure that firm identifiers are consistent across all data sources. For firms with incomplete listing histories, include only valid firm-years and mark unavailable observations explicitly. 2. Strategic posture and performance variables Extract strategic and performance variables from the archival dataset developed in Bonelli (2017). These include X1 (composite strategic posture), X2 (internal structure), Diff (observed alignment differential), Growth (short-term operating performance), and Estimated Growth (forward-looking expectations). These variables are based on Ansoff’s OSPP framework and derived from publicly available financial and strategic information. 3. Executive variable coding (UET layer) For each firm-year, identify the CEO in office at fiscal year end using annual reports and proxy statements. Code executive variables from public disclosures, including CEO duality, founder status, insider versus outsider succession, CEO tenure, functional background, technical orientation, and industry familiarity. Use a standardized coding protocol informed by Upper Echelons Theory and the CUP-W framework. When CEO transitions occur within a year, assign the CEO at fiscal year end and document the transition. 4. Data integration Merge the strategic posture, performance, and executive datasets by firm and year. Verify alignment of identifiers and ensure that all variables are correctly matched across observations. 5. Data cleaning and validation Handle missing values by retaining only valid firm-year observations and clearly marking unavailable data. Compute derived variables such as Power_Index (CEO duality plus founder status). Check internal consistency, including tenure progression, stability of executive attributes within CEO regimes, and correct alignment of posture variables. 6. Analysis replication To reproduce the empirical results, compute descriptive statistics and correlations, estimate regression models linking strategic posture (X1) to performance outcomes, and extend models by including executive variables. Indirect effects can be evaluated by testing whether executive variables are associated with X1 and whether X1 is associated with performance. These steps reproduce the integrated panel linking executive characteristics, strategic posture, and firm outcomes.
Institutions
- Ca' Foscari University of VeniceVeneto, Venice