Could more innovation output bring better financial performance?The role of financial constraints

Innovation scholars highlight the economic benefits to firms,while research findings on the relationship between innovation output and economic returns remain mixed.In this study,we develop the profiting from innovation(PFI)framework and address the crucial role of financial constraints in the relationship between innovation output and financial performance.We argue that the liability of newness differentiates firms’financial performance during the commercialization of innovation,leading to a U-shaped relationship between firms’innovation output and financial performance.We further document the moderating impact of individual financial constraints(IFC)and market-based financial constraints(MFC)on this curvilinear relationship.Empirical tests based on the 142,972 firm-year observations of the multi-source dataset of Chinese manufacturing firms from 1999–2009 support our hypotheses.The additional analysis shows that non-state-owned enterprises and small and medium enterprises benefit more from the synergistic effect of reductions of IFC and MFC than state-owned enterprises and large firms.Our study enriches the literature of the PFI framework by uncovering the mechanism between innovation output and economic returns where financial constraints play an essential role.To the best of our knowledge,we are among the first to investigate the processes and mechanisms between innovation output and financial performance,generating novel insights for business practitioners and policymakers.

Influence of organization learning on innovation output in manufacturing firms in Kenya

Knowledge entrepreneurship is increasingly becoming important in driving innovation for high levels of competitiveness.The purpose of this study was to investigate the relationship between Organization Learning(OL)and Innovation Output(IO)for improved performance in manufacturing firms in Kenya.The theoretical underpinnings on this study are the Schumpeter’s(1934)innovation theory of and the Gleick(1987)complexity theory.The methodology used was mixed method research because it provides a more holistic understanding of a thematic area.The research design that was used is cross-sectional design because it allows for making observations on different characteristics that exist within a group at a particular time.The target population was manufacturing firms across the country.Multi-stage sampling strategy was used to sample 303 respondents from 101 firms.Primary and secondary data were used to collect both qualitative and quantitative data.The questionnaire,interview schedule and a checklist of key informants were used to collect data.Content validity was used to ascertain the credibility of the research procedure and internal consistency technique was used to test for reliability.Correlation and linear regression were used to determine the relationship between OL and IO.Work disruptions were avoided by making prior arrangements and appointments.The findings indicate that OL has a significant influence on IO.It is recommended that lifelong learning,management support and risk tolerance should be encouraged to improve creativity.High creativity is important in raising the capacity to integrate internal and external knowledge for greater levels of IO.Further research should be carried out to find how customers and suppliers information can be utilized to enriched OL.

Spatio-temporal evolution of urban innovation structure based on zip code geodatabase: An empirical study from Shanghai and Beijing

In today's world, the innovation of science and technology has become the key support for improving comprehensive national strength and changing the mode of social production and lifestyle. The country that possesses world-class scientific and technological innovation cities maximizes the attraction of global innovation factors and wins a strategic initiative in international competition. Based on the urban zip code geodatabase, an evaluation system of urban innovation with the perspective of innovation outputs, and the spatial evolutionary mode, concerning the structure of innovation space of Shanghai and Beijing from 1991 to 2014, was developed. The results of the research indicated that the zip code geodatabase provided a new perspective for studying the evolving spatial structure of urban innovation. The resulting evaluation of the spatial structure of urban innovation using the urban zip code geodatabase established by connecting random edge points, was relatively effective. The study illustrates the value of this methodology. During the study period, the spatial structure of innovation of Shanghai and Beijing demonstrated many common features: with the increase in urban space units participating in innovation year by year, the overall gap of regional innovation outputs has narrowed, and the trend towards spatial agglomeration has strengthened. The evolving spatial structure of innovation of Shanghai and Beijing demonstrated differences between the common features during the 25 years as well: in the trend towards the suburbanization of innovation resources, the spatial structure of innovation of Shanghai evolved from a single-core to a multi-core structure. A radiation effect related to traffic arteries as spatial diffusion corridors was prominent. Accordingly, a spatial correlation effect of its innovation outputs also indicated a hollowness in the city center; the spatial structure of innovation of Beijing had a single-core oriented structure all the way. Together with the tendency for innovation resources to be agglomerated in the city center, the spatial correlation effect of innovation outputs reflected the characteristics of the evolutionary feature where "rural area encircles cities". The innovation spatial structure of Shanghai and Beijing have intrinsic consistency with the spatial structure of their respective regions(Yangtze River Delta urban agglomeration and Beijing-Tianjin-Hebei metropolitan region), which suggested that the principle of proportional and disproportional distribution of a city-scale pattern of technological and innovational activities is closely related to its regional innovation pattern.