We investigate the effects of heterogeneity, incomplete information and communication on aggregate contributions to a public good using the voluntary contribution mechanism in a nonlinear laboratory environment. One-dimensional heterogeneity (heterogeneity in income or preferences) and two-dimensional heterogeneity (heterogeneity in income and preferences) both increase voluntary contributions. The effect is greatest when information is incomplete in the sense that subjects do not know each other's payoffs. Incomplete information also reduces contributions in the homogeneous case. Communication reverses the relative importance of oneand two-dimensional heterogeneity in promoting cooperation.
Purpose
The purpose of this paper is to analyze social profit institutions (SPIs) in the context of the social economy. By drawing on case studies of existing businesses, this paper attempts to situate these businesses more broadly within the social economy.
Design/methodology/approach
Various case studies are investigated to illustrate the innovative features of each model through a Lean Canvas tool.
Findings
The findings of the paper provide academics and social entrepreneurs alike more clarity on some of the evolving defining attributes and design features of each of the models SPIs employ.
Research limitations/implications
One of the future challenges is to devise a framework or categorization system that encompasses all of the new forms of businesses, and hybrids, in a way which reflects their uniqueness and individual design.
Practical implications
It allows for entrepreneurs in search of a sustainable business model to develop innovative business models and it provides better understanding on how to meet dual objectives.
Originality/value
The paper proposes a definition for SPIs and establishes the importance of classifying SPIs.
The exact randomization technique is a distribution‐free, computationally‐intensive statistical method of dealing with small samples and uncertain distributions. The technique was first proposed by Fisher in 1935 as a method of dealing with small biological samples. Because of the computational demands, only the recent development of powerful desktop computers permits researchers to regularly use this technique. Suppose two sets of observations are recorded and labeled according to whether they come from a treatment or a control group. The concept of randomization is based on the observation that a conventional test statistic (e.g. a
t
statistic) can be interpreted as a typical draw from the distribution of test statistics that would be generated from randomly shuffling the treatment and control labels among the observed data. In fact, the exact randomization test is essentially a controlled example of bootstrapping.
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