Journal of Business Economics and Management

Based on external uncertainty environment and R&D innovation wave background, this paper explores the impact of information noise caused by economic policy uncertainty and the peer effect on corporate R&D innovation activities, using the multiple regression method and the quarterly data of listed Chinese companies from 2010 to 2020, the influencing mechanism and boundary condition of economic policy uncertainty on the peer effect of corporate R&D were analyzed. Results show that there is a significant peer effect at the industry level in the R&D innovation behavior of corporates, with said effect and the uncertainty of economic policies both significantly stimulate the R&D innovation activities of corporates. The imitation learning path of peer effect is obviously targeted, and corporates in the same industry prioritize corporates with comparative advantages in the industry. Economic policy uncertainty and peer effect also present a certain selection effect on corporate R&D innovation, possibly further enabling corporates with better operating conditions to gain greater market share and gradually eliminate corporates with low R&D innovation ability. The conclusions help decision makers use the peer effect to implement incentive policies and optimize management.

This research highlights the extent to which fiscal policies, along with government effectiveness, trade, economic growth, and education, influence the entrepreneurial environment, starting from the assumption that the effectiveness of public policies and the public administration support for entrepreneurship has proven to be a trigger for development and a means of reducing social inequalities and increasing well-being. Our analysis is based on a panel threshold regression model, which returns parameters for the predictors, in which there is a changing point in fiscal policy as the threshold variable on entrepreneurship activity. The database consisted of annual data from 2002-2019, referring to the European Union’s member states, presented visually based on the data mapping process to evidence the hierarchy of the EU countries for each of the analysed phenomena. Our results evidenced tax policy, trade, and government effectiveness as significant influential factors in stimulating entrepreneurship in EU countries. Although GDP growth and education positively influenced entrepreneurial activity, the statistical tests did not confirm it. Therefore, Governments can encourage entrepreneurial opportunities through harmonised tax legislation with EU regulations and a lighter regulatory burden and policies that foster competition, lower taxes, increase transparency and provide open access for all businesses.

Firms can purchase innovation results to improve their technology. In this context, the key to transfer success is reasonably priced innovation results. Considering the match between innovation results and firm technology, this study analyzes the nonlinear improvement effect of innovation results on technology. The pricing decision is then assessed by a game model of the innovation results transfer and pricing that is based on the entire innovation process, including research and development (R&D) and transfer. Then the method for transfer pricing of innovation results is obtained from the equilibrium of game. The results show that firms tend to evaluate innovation results by matching them with their own technologies, and then make bids based on the R&D costs. Here, innovation results are obtained by firms with high-level matching. After considering the matching, the transfer pricing of innovative results will prosper the transfer market and improve the success rate of transfer. Several factors affect the possibility of transfer of innovation results and their price, including the R&D ability of the institution, the technology levels of firms, and the technological competition between firms. These conclusions were validated using a numerical example.

This study offers an in-depth analysis of labour productivity of manufacturing sector in Turkey and provides a comparison with EU27 and EA19 countries utilizing Eurostat time series data of 63 quarters covering 2005/first quarter-2020/third quarter time interval. Productivity trends are identified and interpreted by relating them with the key macroeconomic events and factors. Multiple linear and non-linear regression equations, and ARIMA model with different parameters are applied to the time series data considering the periods with and without covid effect. Future projections are made for the periods 2020–2023 for Turkey manufacturing sector based on the best fitting regression and ARIMA solutions and they are compared. Findings revealed that extreme covid conditions of even two quarters of data have significant impact on the forecasted values for Turkey, EU27 and EA19 countries. ARIMA analysis with 12 different parameter settings provided accurate results, supported by Thiel’s inequality coefficients and standard error measures. Analysis has shown consistent patterns between EA19 and EU27 countries. ARIMA results represent better compatibility with the regression results for Turkey. Study is valuable by providing comprehensive and comparative analysis, revealing future forecasts and covid effect and degree of recovery from the pandemic.

The unexpected pandemic eruption in March 2020, the European efforts to diminish the gas house emissions, prolonged drought, higher inflation and the war in Ukraine clearly have had a strong impact on the electricity price. In this paper, we analyze the electricity prices on the Romanian Day-Ahead Market (DAM) along with other variables (inflation, consumption and traded volume of gas on DAM) over the last three and a half years in an attempt to understand its evolution and future trend in the economic and geopolitical context. Autoregressive Distributed Lag models are proposed to analyze the causality among variables on short- and long-term perspective, whereas Quantile Regression (QR) is proposed to enhance the results of the Ordinary Least Squares (OLS) regression. Furthermore, using market concentration metrics – Herfindahl-Hirschman Index (HHI), C1 and C3 ratio, we analyze the competitiveness on the Romanian DAM and correlate it with the electricity price evolution. The concentration indicators on this market reflect the degree of competition manifested between sellers and buyers respectively, their dynamics being able to influence the price level. The higher concentration on the sellers’ side (HHI = 1500) indicates a potential speculative behavior on this market that led to higher prices on DAM.