Ansoff Strategic Posture and Firms’ Responses to Industry Norms under Environmental Turbulence
Description
This dataset contains the final firm–year analytic panel used to examine how firms adjust their strategic posture relative to industry norms under conditions of environmental turbulence, drawing on Igor Ansoff’s Strategic Success Hypothesis (SSH). The unit of analysis is the firm–year. The dataset covers publicly listed technology firms, primarily listed on NASDAQ, observed over the period 2006–2015. It reports firms’ strategic distance from contemporaneous industry benchmarks and the year-to-year change in that distance, which constitutes the primary dependent variable in the empirical analyses. Industry norms are constructed at the industry–year level as peer means excluding the focal firm. Firm-level strategic posture measures reflect the alignment among environmental turbulence, strategic aggressiveness, and capability responsiveness, coded from publicly available and auditable disclosures (e.g., annual reports, 10-K filings, investor materials). Environmental turbulence is treated as exogenous at the industry–year level. Key variables include firms’ distance from the industry norm, absolute distance, lagged distance measures, and the change in absolute distance between consecutive years, capturing convergence toward or divergence from industry benchmarks. The dataset also includes identifiers necessary for panel estimation and lag construction. This file represents the final merged dataset used in all reported regressions, figures, and hypothesis tests. Intermediate construction files (e.g., firm-level coding sheets and industry benchmark tables) are not included to ensure clarity and reproducibility. The dataset allows independent replication of the empirical results reported in the associated study.
Files
Steps to reproduce
Load the dataset Dataset_Benchmarking_Ansoff. Download and open the file .xlsx. The dataset is organized as a firm–year panel, where each row represents one firm observed in a given year. Identify key variables Locate the firm identifier and year variables, which define the panel structure. Identify the variables capturing firms’ strategic distance from the industry norm, absolute distance, lagged distance, and the change in absolute distance between consecutive years. The change in absolute distance is the primary dependent variable used in the empirical analyses. Prepare the panel structure In the statistical software of choice (e.g., Stata, R, or Python), declare the data as panel data using the firm identifier as the panel unit and year as the time dimension. Ensure that observations are correctly ordered by firm and year. Apply lag structure Use the lagged distance and lagged explanatory variables as specified in the Methods section of the associated paper. Observations without valid lagged values (e.g., the first year for each firm) should be excluded from regressions involving changes over time. Estimate the baseline model Estimate fixed-effects panel regressions with firm and year fixed effects. Cluster standard errors at the firm level. The dependent variable is the change in absolute distance from the industry norm. Independent variables include lagged environmental turbulence, lagged misalignment (or prior distance), and their interaction, along with control variables as specified in the paper. Compute marginal effects and figures After estimating the regression models, compute marginal effects of environmental turbulence at low and high levels of misalignment (e.g., 25th and 75th percentiles). Use these estimates to reproduce the marginal effects plot reported as Figure 1 in the paper. Verify results Confirm that coefficient signs, statistical significance, and marginal effect patterns match those reported in the Results section and tables of the associated study. These steps reproduce all reported regression results and figures using the deposited dataset alone. Intermediate data construction files are not required.
Institutions
- Ca' Foscari University of VeniceVeneto, Venice