R&D

Overconfident managers, who tend to overestimate their capabilities, underestimate the possibility and impact of side effects in projects. The purpose of this study is to review the effect of managerial overconfidence on research and development expenditures and the moderating effect of firm value and liquidity on this relationship. For this purpose 51 companies were chosen from oil/gas, and petrochemical Companies listed on the Tehran Stock Exchange over the period 2012-2017. This research, within three basic hypotheses, is analyzed by Eviews software and shows that managerial overconfidence has positive effect on research and development. Company liquidity has direct effect on relationship between managerial overconfidence and R&D, but firm value has no meaningful effect on the relationship between managerial overconfidence and R&D. <strong>Keywords</strong>: managerial overconfidence, R&D, liquidity, firm value

According to published reports on the level of internalization of commercial vehicles, it shows that most carmakers pursue a minimal domestic manufacturing policy and less have a policy of increasing domestic manufacturing on their agenda. The most important research gaps are need to study R&D activities in the form of a dynamic system in the automotive industry based on the three variables of supply network capabilities, product innovation and process. The purpose of this study is to provide a dynamic model to investigate the behavior of a commercial vehicle R&D system. The present study is applied in terms of purpose. The basis of the system analysis is previous research as well as information obtained from interviews with automotive industry experts. For this purpose, first the variables affecting the cycle of R&D activities are studied and modeling is presented using the dynamics approach. Also, various policies on the performance of the development research cycle based on the time of knowledge acquisition and product introduction have been simulated and analyzed. The results of system performance simulation show that the weak share of research and development budget along with low and limited market share and lack of proper investment makes the research and development capability at an unfavorable level during the next 10 years. Therefore, it is suggested that the necessary measures in this regard be taken in the fields of product sales marketing along with appropriate investment in research and development.

Thispaper first reviewed the relevant literature and several R&D evaluation models in various industries. Then, a suitable model for the evaluation of R&D capabilities was selected through a New Product Development (NPD) approach within the Iranian automotiveindustry in 9 main dimensions and 29 indicators, validated based on the structural equations proposed in another paper. In the first stage, the coefficient matrix of criteria was identified according to the pairwise comparison matrix. Then, the VIKOR values S, R, and Q were calculated. The ranking involved smaller Q values. The criteria were ranked as follows: 1) research and design capabilities, 2) customer and market, 3) technological capabilities,4) financial capabilities resources, 5) organizationalstructure, 6) intellectual capital capabilities,7) NPDprocess, 8) management capabilities and, 9) strategic capabilities. Finally, there were suggestions about the capabilities falling under the lowest ranks

Objective : This study examines how different research and development (R&D) funding sources—business enterprise R&D (BERD), higher education R&D (HERD), and government-financed R&D (GOVERD)—influence national innovation and competitiveness, measured by the Global Innovation Index (GII) and Global Competitiveness Index (GCI). This study addresses a critical gap by moving beyond aggregate R&D spending to examine how funding composition shapes innovation capacity.  Methodology: Ordinary least squares (OLS) regression analysis was conducted on a sample of 47 countries using data from OECD R&D Statistics, World Bank Development Indicators, and GII/GCI reports. Missing values (< 3%) were imputed using mean substitution. Diagnostic tests were applied to verify normality, minimal multicollinearity, and compliance with heteroscedasticity assumptions. Two models were subsequently estimated with GII and GCI as dependent variables. Results : For the GII model (R² = 0.601, F = 12.37, p < 0.001), overall GERD intensity was significantly positive (β = 10.54, p = 0.040), while disaggregated components (BERD, HERD, GOVERD) showed no individual significance due to multicollinearity. GDP per capita was robust (β = 8.78e-05, p = 0.019). For the GCI model (R² = 0.651, F = 15.31, p < 0.001), GERD was non-significant; GDP per capita remained the strongest predictor (β = 0.0003, p < 0.001). Regression assumptions were satisfied (Jarque-Bera p > 0.44; Durbin-Watson ≈ 2.1). Conclusion : Overall R&D intensity significantly influences innovation, but relationships with disaggregated sources are complex. The importance of GDP per capita shows that the quality of institutions and the ability to absorb new ideas are both important for turning R&D spending into innovation and competitiveness benefits. Policymakers should prioritize both R&D funding levels and the institutional environment enabling effective R&D utilization.