Search results

Entrepreneurship, along with its effect on economic growth, has been a major topic of research for quite some time now. However, none of these studies employs the use of entrepreneurial intention, a key indicator of latent entrepreneurs, as a measure of entrepreneurship. Till now, some small-scale studies have been done using survey data, with results indicating that external entrepreneurial environment affects entrepreneurial intention. A handful of studies have also looked at the linkages between economic freedom and entrepreneurial activities. The paper aims to discuss this issue.

Using a panel data setting, this paper investigates the effects of economic freedom, especially regulation, on entrepreneurial intention. The empirical analysis uses data for 79 countries from 2001 to 2012.

The findings suggest that stricter credit market regulation reduces entrepreneurial intention whereas more stringent labor regulations restricts job availability and thereby encourage more people to take up entrepreneurship as a career choice.

The entrepreneurial intention data available from GEM is a highly unbalanced data and the data also does not differentiate between latent entrepreneurship in agricultural and non-agricultural sectors.

Future research should focus more on latent entrepreneurship which is a rough estimate of future entrepreneurs.

Entrepreneurship acts as a channel to improve economic growth by creating more jobs and the institutional qualities might act as a barrier for aspiring entrepreneurs to take up entrepreneurship as their career choices in developing countries.

This study has a twofold contribution in the literature. First, it is the foremost large scale study that deals with entrepreneurial intention using secondary data from Global Economic Monitor (GEM) report. Second, this study explores the linkages between economic freedom index and entrepreneurial intention.

We explore how economic freedom measurements can be used to guide policy.

We propose a method for creating a growth-enhancing economic freedom index, which allows for nonlinearities and interaction effects between the components to economic freedom. We use this method to illustrate that US states differ in which policy area generates the greatest gains.

To validate the method presented, we apply our index to state bond markets. Financial market participants have the incentive to properly evaluate states’ policies. If our measurement is useful, then it should correlate with bond ratings. Consistent with this hypothesis, we present evidence that state bond ratings are strongly correlated with our growth-enhancing economic freedom index.

It has been well-established that economic freedom is associated with good economic outcomes. Economic freedom is comprised of numerous dimensions. Thus, the marginal benefit of improving policy in one area can be expected to depend on the amount of freedom in the other dimensions. Which policy improvement is most impactful depends on the entire menu of current policies and, therefore, differs between states. Our new method can then be used as a guide to determining for a particular state which policies can be expected to impact economic well-being the most.

This study aims to examine the combinations of internal and external knowledge flows between research and development (R&D) incumbents and start-ups in the context of open innovation. While there is a growing body of knowledge that has examined how, in a knowledge economy, a firm’s knowledge and innovation activities are closely linked, there is no systematic review available of the key antecedents, perspectives, phenomenon and outcomes of knowledge spillovers.

The authors have conducted dual-stage research. First, the authors conducted a systematic review of literature (97 research articles) by following the theories–contexts–methods framework and the antecedent-phenomenon-outcomes logic. The authors identified the key theories, contexts, methods, antecedents, phenomenon and outcomes of knowledge spillovers between R&D-driven incumbents and start-ups in the open innovation context. In the second stage, the findings of stage one were leveraged to advance a nomological network that depicts the strength of the relationship between the observable constructs that emerged from the review.

The findings demonstrate how knowledge spillovers can help incumbent organisations and start-ups to achieve improved innovation capabilities, R&D capacity, competitive advantage and the creation of knowledge ecosystems leading to improved firm performance. This study has important implications for practitioners and managers – it provides managers with important antecedents of knowledge spillover (knowledge capacities and knowledge types), which directly impact the R&D intensity and digitalisation driving open innovation. The emerging network showed that the antecedents of knowledge spillovers have a direct relationship with the creation of a knowledge ecosystem orchestrated by incumbents and that there is a very strong influence of knowledge capacities and knowledge types on the selection of external knowledge partners/sources.

This study has important implications for practitioners and managers. In particular, it provides managers with important antecedents of knowledge spillover (knowledge capacities and knowledge types), which directly impact the R&D intensity and digitalisation driving open innovation. This will enable managers to take important decisions about what knowledge capacities are required to achieve innovation outcomes. The findings suggest that managers of incumbent firms should be cautious when deciding to invest in knowledge sourcing from external partners. This choice may be driven by the absorptive capacity of the incumbent firm, market competition, protection of intellectual property and public policy supporting innovation and entrepreneurship.

Identification of the key antecedents, phenomenon and outcomes of knowledge spillovers between R&D-driven incumbents and start-ups in the open innovation context. The findings from Stage 1 helped us to advance a nomological network in Stage 2, which identifies the strength and influence of the various observable constructs (identified from the review) on each other. No prior study, to the best of the authors’ knowledge, has advanced a nomological network in the context of knowledge spillovers between R&D-driven incumbents and start-ups in the open innovation context.

The study aims to co-create a “priority action roadmap for women's economic empowerment” based on women's top priorities to charting recovery directions. Doing so contributes to the growing body of knowledge on COVID-19 literature in at least four areas: assessing COVID-19 impacts on women entrepreneurs; mapping these impacts with four interdependent women's entrepreneurial ecosystem components; innovating a co-creation methodology based on remote participatory research; and providing a replicable model to perform action-oriented research in the context of COVID-19 impacts.

A co-creation methodology is proposed, combining systems-thinking and remote participatory research to engage women entrepreneurs and institutional stakeholders to prioritize impact, response actions and recovery needs in the wake of COVID-19. A ranking exercise using the analytic hierarchy process was used to derive ranking and assess user inputs' consistency.

The study exemplifies the integration of participatory methods and mathematical tool to engage stakeholders in prioritizing recovery work. PARWEE action items ranked by entrepreneurs and vetted by institutional stakeholders cover: access to finances, capacity building, health care, public and private partnership, marketing opportunities and formation of active advocacy groups to voice out women entrepreneurs' needs to institutional stakeholders. Results show a slight difference in the ranking of priority actions between experience owners and fresh starters.

This study innovated a new co-creation methodology for remotely engaging stakeholders of the women's entrepreneurial ecosystem, which is grounded in evidence and provides a replicable model for performing action-oriented research.