The social and economic outputs of SME-GSI research collaboration in an emerging economy: An ecosystem perspective

ABSTRACT

Extant literature on research collaboration between firms and government-sponsored institutions (GSIs) focuses predominantly on the formation of strategic alliances and its resulting economic performance. We take a new perspective and conceptualize the collaboration between small and medium enterprises (SMEs) and GSIs as social and economic pillars in emerging economies. We theorize how the collaborations with GSIs help SMEs increase their social and economic outputs and develop a sustainable ecosystem for business development through ongoing outward knowledge sharing. Our tests with data on Mexico’s industrial sectors evidence a greater positive effect of SME-GSI collaboration on social than economic output. R&D intensity mitigates more on SME-GSI collaboration’s impact on economic than that on social output. A decentralized management structure strengthens the effect of SME-GSI collaboration on only social output. Finally, a sustainable ecosystem is formed when SMEs engage in outward knowledge sharing as retribution to society. Social output exerts a stronger mediation for SME-GSI collaboration’s influence on outward knowledge sharing than economic output. Overall, our findings offer important insights into the theoretical, managerial, and policy implications of SME-GSI collaboration and business development ecosystems in emerging economies.

KEYWORDS:

• Social output
• government-sponsored institutions (GSIs)
• ecosystem
• inclusive business
• small and medium enterprises (SMEs)

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary material

Supplemental data for this article is available online at https://doi.org/10.1080/00472778.2022.2073362

Notes

¹ SMEs obtain knowledge from society through their collaborations with GSIs. GSIs funding comes from tax-payers’ (society’s) contributions, and therefore society plays a significant role in the SME-GSI collaboration process. When SMEs disseminate knowledge to other firms, this dissemination becomes a form of reciprocation to society because the SME-GSI collaboration then benefits third parties and can potentially trigger further knowledge spillovers.

² We suggest that initially, society (GSIs) unidirectionally invests in SMEs because when the SME-GSI collaboration begins, an SME receives information, resources, and knowledge from the GSI. In contrast, the GSI does not receive the same level of resources from the SME.

³ Our dependent variables, economic/social output and knowledge sharing, which are measured for the current year, reflect the lagging effect of GSI collaboration, such that GSI is measured using a time window of past five years.

⁴ Our construct items are designed to capture improvement, as SMEs were asked to report how they have innovated and improved with the help of different government initiatives. Therefore, these reported outcomes are implicitly the indicators for potential innovative and developmental improvement. Moreover, all construct items have words of positive tone such as “creation”, “new”, “better”, and so on, or/and the textual content of “improvement” and “positivity”.

⁵ Our survey questions well capture the multi-faceted nature, that is, the breadth of improvements of economic and social outputs. As to the magnitude side, we only dichotomously capture two levels: yes (as significant and salient) or no (as insignificant or negligible).

⁶ In our analysis, we used the Wald test to assess whether the size of two coefficients is statistically different, where H0: β1 = β2. If p < .10, we can strongly reject the null hypothesis that the size or magnitude of both coefficients is the same. Given that we are using SUR models with standardized coefficients, if the Wald test is p < .10, we can assume that the coefficient with the greater magnitude has a greater effect on the dependent variable.

⁷ We use the nomenclature “marginal effects tests” when we test for differences between the marginal effects of two different models. We followed this procedure: (1) we obtained the marginal effects for the corresponding models (for example, Model 4 and Model 8); and (2) we conducted a Wald test using the marginal effects of the models to evaluate the statistical significance of the difference between coefficient sizes. A p-value of less than 0.10 indicates that the coefficients’ sizes are statistically different.