Understanding motivations for entrepreneurship:a review of recent research evidence

Our systematic search for empirical studies on entrepreneurial motivation published over the last five years (2008-2013) retrieved 51 relevant studies (filtered from over 1,200 search results), which form the basis of this review. Considering the type and quality of studies suggests that we can be relatively confident in our answer to the first review question below (typologies). The evidence-base for the second and third review questions (drivers and consequences of entrepreneurial motivation) is weaker and still developing. Beyond answering the three broad research questions below, we develop a framework for future research synthesising the review findings. 1) What typologies exist to describe entrepreneurial motivation? 2) What influences and shapes entrepreneurial motivation? 3) What consequences have different entrepreneurial motivations for entrepreneurial performance?

Understanding motivations for entrepreneurship

The main aims of the study were to explore the different factors motivating entrepreneurs to start a business; explore whether motivations for entrepreneurship change and the impact of the recession; identify any correlates of motivations for entrepreneurship; and to examine the consequences of the different motivations for the entrepreneurial process and performance. The study is based on a re-survey of 1,000 entrepreneurs first identified in GEM and supplemented by in-depth interviews carried out with 40 entrepreneurs.

The Long-Term impact of Business Support? - Exploring the Role of Evaluation Timing using Micro Data

The original contribution of this work is threefold. Firstly, this thesis develops a critical perspective on current evaluation practice of business support, with focus on the timing of evaluation. The general time frame applied for business support policy evaluation is limited to one to two, seldom three years post intervention. This is despite calls for long-term impact studies by various authors, concerned about time lags before effects are fully realised. This desire for long-term evaluation opposes the requirements by policy-makers and funders, seeking quick results. Also, current ‘best practice’ frameworks do not refer to timing or its implications, and data availability affects the ability to undertake long-term evaluation. Secondly, this thesis provides methodological value for follow-up and similar studies by using data linking of scheme-beneficiary data with official performance datasets. Thus data availability problems are avoided through the use of secondary data. Thirdly, this thesis builds the evidence, through the application of a longitudinal impact study of small business support in England, covering seven years of post intervention data. This illustrates the variability of results for different evaluation periods, and the value in using multiple years of data for a robust understanding of support impact. For survival, impact of assistance is found to be immediate, but limited. Concerning growth, significant impact centres on a two to three year period post intervention for the linear selection and quantile regression models – positive for employment and turnover, negative for productivity. Attribution of impact may present a problem for subsequent periods. The results clearly support the argument for the use of longitudinal data and analysis, and a greater appreciation by evaluators of the factor time. This analysis recommends a time frame of four to five years post intervention for soft business support evaluation.

The long-term impact of business support? - Exploring the role of evaluation timing using micro data

The original contribution of this work is threefold. Firstly, this thesis develops a critical perspective on current evaluation practice of business support, with focus on the timing of evaluation. The general time frame applied for business support policy evaluation is limited to one to two, seldom three years post intervention. This is despite calls for long-term impact studies by various authors, concerned about time lags before effects are fully realised. This desire for long-term evaluation opposes the requirements by policy-makers and funders, seeking quick results. Also, current ‘best practice’ frameworks do not refer to timing or its implications, and data availability affects the ability to undertake long-term evaluation. Secondly, this thesis provides methodological value for follow-up and similar studies by using data linking of scheme-beneficiary data with official performance datasets. Thus data availability problems are avoided through the use of secondary data. Thirdly, this thesis builds the evidence, through the application of a longitudinal impact study of small business support in England, covering seven years of post intervention data. This illustrates the variability of results for different evaluation periods, and the value in using multiple years of data for a robust understanding of support impact. For survival, impact of assistance is found to be immediate, but limited. Concerning growth, significant impact centres on a two to three year period post intervention for the linear selection and quantile regression models – positive for employment and turnover, negative for productivity. Attribution of impact may present a problem for subsequent periods. The results clearly support the argument for the use of longitudinal data and analysis, and a greater appreciation by evaluators of the factor time. This analysis recommends a time frame of four to five years post intervention for soft business support evaluation.