Control Variables

Firm Size: Prior studies have shown that larger firms are more adept at mobilizing resources than smaller firms (Aldrich 2003). Banks and funding agencies are more likely to provide financing to larger organizations with an established track record.

Further, the likelihood of disbanding for small businesses is strongly associated with their initial size (Burderl et al 1992). I control for firm size, by measuring the number of employees involved in the parent organization. I obtain firm size information, a continuous variable, from the venture websites or from Guidestar, Hoovers, and ReferenceUSA.

Venture age: Population ecologists have empirically demonstrated that older firms are more likely to survive and obtain resources than younger firms (Aldrich & von Glinow 1992). Theoretically, the liability of newness (Stinchcombe 1965) increases the mortality rates for new firms, making it harder for them to mobilize resources. I control for venture age by adding a continuous variable for the number of years since founding. Depending on the type of venture, I obtain venture age information from the venture website, from Guidestar, Hoovers or ReferenceUSA.

158

Product age: Product age may be associated with the level of collective agency.

For example, Shah (2006) demonstrated that early-stage products are more likely to have user participation in the development process than later stage products. In contrast, the longer a product is available, the more likely it may be that an ecosystem of technological partners influences the product development process (Shapiro & Varian 1998). I include a control variable for the age of the product measured in years from the date of introduction. I obtain product age information from the venture website and the Tech Awards application form.

Social Sector: I control for the social sector associated with the venture by including a dummy variable for each sector: Health, Education, Environment, Equality, and Economic Development. I obtain data for this variable from the Tech Awards application forms. Institutions may provide greater support mobilizing resources in some sectors. For example, the health sector has traditionally gained more support from the World Bank, Government agencies and the United Nations than the environmental sector (UNDP 2001). The Tech Awards application form lists the sector associated with each venture.

Venture type: Non-profit and for-profit ventures may have different sources of funding and support. For example, non-profits are more likely to seek grants while for-profit ventures are more likely to seek small business loans or venture funding

159 (Lasprogata & Cotton 2003, Gair 2006). I include a control dummy variable for venture type (Non-profit, For-profit, Government). This information is provided on the Tech Awards application forms and from Guidestar, Hoovers and ReferenceUSA.

160 Table 5.2: Template to categorize Bricolage and Resource-seeking activity

Bricolage Template

This template is used to check for evidence of “bricolage” when assembling resources for the social venture. Bricolage is defined as: “Making do with current resources, and creating new products or services from tools and materials at hand.” Please list evidence of bricolage (if any) along each of the following 3 resource dimensions: Materials, Labor and Skills.

Materials: Forgotten, discarded, worn, or presumed "single-application" materials with new use.

Labor: Involving customers, suppliers and hangers-on in providing free work on projects.

Skills: Permitting and encouraging the use of self-taught skills on-the-job.

For each activity, please include a description, or copy the text that illustrates why the resource activity qualifies as bricolage. (cite source: awards form, venture website, other).

Resource-seeking Template

This template is used to check for evidence of “resource-seeking” when assembling resources for the social venture. Resource-seeking is defined as:

“Procuring standard external resources and assembling them to create a new product.” Please list resource-seeking activities (if any) along each of the following 3 resource dimensions: Materials, Labor and Skills.

Material: Buys standard components for the project. The components fit together readily.

Labor: Employs workers with skills suited to the project. Uses paid employees, contractors or specialists to complete parts of the project.

Skills: Formal education and prior professional experience are employed to develop the project.

For each activity, please include a description, or copy the text that illustrates why the resource activity qualifies as resource-seeking. (cite source:

awards form, venture website, other).

161 5.5 Model Specification and Estimation

I use two regression models to test my hypotheses advanced in Chapter 4.

The first model is the Resource Mobilization model, and the second model is the Market Scalability model. The resource mobilization model will test for the effect of the seed funding source and the institutional environment (regulatory, technology, political stability, human development) on the venture’s use of bricolage or resource-seeking to mobilize resources. The Market Scalability model tests for the relationship between bricolage, collective agency and market scalability.

Resource-Mobilization Model:

I test three dependent variables -- bricolage, seeking, and resource-mobilizationcomposite. The linear form of the regression equations are:

Bricolage = β0 + β1 Seed Funding Source +β2 Institutional Support + β3

Venture Age+β4 Parent age + β5 Venture Type +β6 Sector

Resource-seeking = β0 + β1 Seed Funding Source +β2 Institutional Support + β3 Venture Age+β4 Parent age + β5 Venture Type +β6 Sector

Resource-Mobilizationcomposite = β0 + β1 Seed Funding Source +β2 Institutional Support + β3 Venture Age+β4 Parent age + β5 Venture Type +β6 Sector

162 Market Scalability Model:

I test market scalability as my dependent variable, with bricolage and collective agency as independent variables. The linear form of the regression equation is:

Market Scalability = β0 + β1 Bricolage+β2 Collective Agency +β3 Collective Agency x Bricolage +β4 Seed Funding Source +β5 Institutional Support + β6

Venture Age+β7 Parent age + β8 Venture Type +β9 Sector

The dependent variables bricolage, resource-seeking, and market scalability are count variables that take on non-negative integer values. Having a count variable as the DV may violate assumptions of homoskedasticity and normal distribution of errors (Hausman, Hall & Griliches, 1984). Hence using Ordinary Least Squares (OLS) regression can lead to biases in the estimated parameters. To avoid this, I use the Generalized Least Squares regression approach and estimate robust standard errors using a Huber-White sandwich estimator. Such robust standard errors can deal with a collection of minor concerns about failure to meet assumptions, such as minor problems about normality, heteroscedasticity, or some observations that exhibit large residuals, leverage or influence. The point estimates of the coefficients are exactly the same as in ordinary OLS, but the standard errors take into account issues concerning heterogeneity and lack of normality.

163 Chapter 6: Results

6.1 Introduction

In this chapter I describe the results of the empirical study designed to test the Resource Mobilization model and the Market Scalability model discussed in Chapter 5. Using the resource mobilization model, I tested the effects of seed funding and institutional support on bricolage and resource-seeking. In the growth model I tested for the effects of bricolage and collective agency on market scalability. In the sections that follow, I will first describe the sample and the descriptive statistics, then report the regression results for each model. Finally, I discuss and summarize key findings.