US Green Goods SMEs, 2008-2012
Description
Measuring dynamic capabilities in new ventures: Exploring strategic change in US green goods manufacturing using website data. Firm website and Dun and Bradstreet (DUNS) measures, US Green Goods SMEs, 2008-2012 (212 data variables; 298 total observations; 223 observations without missing variables). Anonymized. Stata dta format and Stata do file. The website data for the target firms is derived from archived website data from the Wayback Machine. We also use business data for these firms from Dun and Bradstreet. See associated paper for added details including definitions of "green goods" manufacturing and small and medium-sized enterprises (SMEs); enterprise sample selection; and methods applied to use website data combined with other available business data to gauge enterprise capabilities for market sensing and responding. In the analytic model, we use data variables for two time periods, 2008-09 and 2010-11, to explain sales growth for green goods enterprises in two later time periods, from 2010 to 2012.
Files
Steps to reproduce
The "firm_website_and_duns_measures.dta" file is a Stata .dta dataset comprising 212 data variables developed for the analysis reported and published in Arora et al (2019) "Measuring dynamic capabilities in new ventures: exploring strategic change in US green goods manufacturing using website data," Journal of Technology Transfer, DOI: 10.1007/s10961-019-09751-y. There are 298 firm observations in this data set. The "jtt-code.do" file is a Stata .do file that will reproduce key models and regression results reported in the paper for the 223 firm observations without missing variables. To undertake a new analysis with the same data set of firms, users may modify the Stata .do file. It is also possible to import the .dta data set into a file format useable by other statistical analysis programs. To undertake a new analysis with a different set of firms, it will be necessary to obtain the public website data for those firms, following the methods for web crawling, keyword variable extraction, and LDA topic modeling described in the paper, and to match with Dun and Bradstreet firm data. Additional references related to these methods are included in the paper.