Research on the Effect of R&D Investment Intensity and Sales Expense on the Performance of Biomedical Enterprises

Objective To explore the influence of new drug R&D investment and sales expense on the performance of biomedical enterprises.Methods The financial statements of 76 listed biomedical enterprises for 5 consecutive years were selected,and the data were modeled to study the effect of R&D investment and sales expense on the performance of biomedical enterprises by using financial indicators as tools and statistical methods of multiple linear regression.Results and Conclusion Under the premise that the weak related factors such as enterprise scale,life cycle and asset-liability ratio are set as unrelated variables,the R&D investment intensity of biomedical enterprises is negatively correlated with the current performance,which also shows that the R&D of biomedical enterprises has the characteristics of high risk.Besides,the influence of early R&D investment is delayed.However,the sales expense of leading biomedical enterprises with large scales have higher proportion.Meanwhile the greater sales expense of the same enterprise in different periods,the better the enterprise performance is.Biomedical enterprises should consider their own development stage to develop more patented drugs.Besides,they must formulate plans for allocating reasonable sales personnel and cost expense to ensure that enterprises can obtain better benefits.

Selecting suitable key supplier for core components during smart complex equipment central-private enterprises collaborative development process:from two different forms of evaluation information and matching perspective

With the development of central-private enterprises integration,selecting suitable key suppliers are able to provide core components for smart complex equipment.We consider selecting suitable key suppliers from matching perspective,for it not only satisfies natural development of smart complex equipment,it is also a good implementation of equipment project in central-private enterprises integration context.In in this paper,we carry out two parts of research,one is evaluation attributes based on comprehensive analysis,and the other is matching process between key suppliers and core components based on the matching attribute.In practical analysis process,we employ comprehensive evaluated analysis methods to acquire relevant attributes for the matching process that follows.In the analysis process,we adopt entropy-maximum deviation method(MDM)-decision-making trial and evaluation laboratory(DEMATEL)-technique for order preference by similarity to an ideal solution(TOPSIS)to obtain a comprehensive analysis.The entropy-MDM is applied to get weight value,DEMATEL is utilized to obtain internal relations,and TOPSIS is adopted to get ideal evaluated solution.We consider aggregating two types of evaluation information according to similarities of smart complex equipment based on the combination between geometric mean and arithmetic mean.Moreover,based on the aforementioned attributes and generalized power Heronian mean operator,we aggregate preference information to acquire relevant satisfaction degree,then combine the constructed matching model to get suitable key supplier.Through comprehensive analysis of selecting suitable suppliers,we know that two-sided matching and information aggregation can provide more research perspectives for smart complex equipment.Through analysis for relevant factors,we find that leading role and service level are also significant for the smart complex equipment development process.

Optimization of Chinese power enterprises R&D staff incentive mechanism

With the deepening of electric power market reform in China,the monopoly edge of the state-owned electric power enterprises will lose.On the basis of the existing post performance salary mechanism,Chinese power enterprises need to optimize the incentive mechanism of R&D staff,to arouse the R&D staff's enthusiasm and creativity,to adapt to the new market competition and further improve market value.Whilst the incentive mechanism optimizing processing needs to consider not only the changing market environment but also the personal and working characteristics of R&D staff.This paper summarizes the characteristics of the current Chinese power enterprises' R&D staff:staff's theory quality is high,but insensitive to the market;they are confronted with heavy workload and diversified job choices;managers can observe their behavior choices or not;besides,the process of R&D is complex and the market reactions of R&D achievements are uncertain.Based on the premise of the above features,two incentive models are established in this paper from the point of view of enterprise managers.One is for the situation when staff's behavior choices can be observed;the other is for the situation when staff's behavior choices cannot be observed.Through solving the model,we analyze the optimization path of electric power enterprises R&D staff incentive mechanism under these conditions:(1) when staff's behavior choices can be observed,managers can pay more to the R&D staff who develop products with higher output value,in order to encourage them to work harder.(2) when staff's behavior choices cannot be observed,managers should take reasonable strategies according to the different situations:a.when R&D staff incentive totally depend on the market value of the R&D achievements,managers should allocate workload rationally according to their different technical levels;b.when the market reactions of R&D results become more precarious,managers need to reduce the incentive intensity which based on the market value and raise their fixed salary level;c.when R&D staff become more risk averse,managers should reduce the incentive intensity which based on the market value and raise their fixed salary level;on the contrary,managers should improve the incentive intensity and reduce the fixed salary level.

Research on the Relationship between the Profitability and R&D Investment of Enterprises of Different Scales in China’s Pharmaceutical Industry

Objective To analyze the relationship between the profitability of pharmaceutical enterprises of different scales and their R&D investment.Methods The data of China’s pharmaceutical manufacturing industry from 1995 to 2016 were selected to establish a vector auto-regression mode,and then an impulse response function and variance decomposition were used to analyze the data.Results and Conclusion For large-scale pharmaceutical enterprises,1%increase in R&D input will lead to 0.792%increase of their main business profit.For medium-sized enterprises,if their main business profit increases by 1%,their R&D input will increase by 0.965%.Therefore,if their R&D input increases by 1%,their main business profit will increase by 1.036%.The improvement of the profitability of large-scale pharmaceutical enterprises cannot promote the increase of R&D capital investment.But the increase of profitability from medium-sized enterprises can promote the increase of R&D capital investment.As a result,the increase of R&D capital investment of large and medium-sized enterprises can improve corporate profitability.

Research on the Influence of Government R&D Funding and Enterprise R&D Investment on the Innovation Efficiency of Pharmaceutical Industry

Objective To study the effect of government R&D funding and enterprise R&D input on innovation efficiency of China’s pharmaceutical industry.Methods Based on the inter-provincial panel data of China’s pharmaceutical industry from 2009 to 2019,the stochastic frontier analysis method was used to study the influence of government R&D funding and enterprise R&D input on the innovation efficiency.Results and Conclusion With 1%increase in government R&D funding,the innovation efficiency of enterprises will increase by 0.122%.When R&D investment increases by 1%,innovation efficiency will increase by 0.169%.Both government R&D funding and enterprise R&D investment promote the innovation efficiency of pharmaceutical enterprises,but the increase of enterprise R&D investment contributes more to the improvement of pharmaceutical innovation efficiency.

Evaluating R&D efficiency of China's listed lithium battery enterprises

Promoting the growth of the lithium battery sector has been a critical aspect of China's energy policy in terms of achieving carbon neutrality.However,despite significant support on research and development(R&D)investments that have resulted in increasing size,the sector seems to be falling behind in technological areas.To guide future policies and understand proper ways of promoting R&D efficiency,we looked into the lithium battery industry of China.Specifically,data envelopment analysis(DEA)was used as the primary approach based on evidence from 22 listed lithium battery enterprises.The performance of the five leading players was compared with that of the industry as a whole.Results revealed little indication of a meaningful improvement in R&D efficiency throughout our sample from 2010 to 2019.However,during this period,a significant increase in R&D expenditure was witnessed.This finding was supported,as the results showed that the average technical efficiency of the 22 enterprises was 0.442,whereas the average pure technical efficiency was at 0.503,thus suggesting that they were suffering from decreasing returns to scale(DRS).In contrast,the performance of the five leading players seemed superior because their average efficiency scores were higher than the industry's average.Moreover,they were experiencing increasing scale efficiency(IRS).We draw on these findings to suggest to policymakers that supporting technologically intensive sectors should be more than simply increasing investment scale;rather,it should also encompass assisting businesses in developing efficient managerial processes for R&D.

EU Real-World Evidence Supports New Drug Research and Development Decisions and Its Implications for China

Objective To analyze the application of EU real-world evidence in the decision-making of new drug research and development(R&D),and to provide policy recommendations for China’s government to make new drug R&D decisions.Methods The relevant policy documents of the EU on the development of new drugs and other domestic and foreign literature on the real-world evidence were analyzed to obtain the role and application of the current EU real-world evidence in the implementation of new drug development policies.Results and Conclusion At present,the EU is carrying out the national synchronous scientific advisory policy,urging the formation of a European innovation framework,and providing decision-making for new drug R&D selection and program design based on real-world evidence.It is recommended that China build a real-world medical database and design a new drug screening platform to help companies,scientific research institutions assess target drugs.In addition,a national scientific advisory platform should be set up to integrate scientific research strength and provide technical support for new drug R&D institutions.

A hesitant fuzzy linguistic terms set-based AHP-TOPSIS approach to evaluate ERP software packages

Purpose–In this paper,two popular multiple-criteria decision-making(MCDM)methods with hesitant fuzzy logic approach;hesitant fuzzy analytic hierarchy process(hesitant F-AHP)and hesitant fuzzy the technique for order preference by similarity to ideal solution(HF-TOPSIS)are integrated as HF-AHP-TOPSIS to evaluating a set of enterprise resource planning(ERP)alternatives and rank them by weight to reach to the ultimate one that satisfies the needs and expectations of a company.Design/methodology/approach–Selecting the best ERP software package among the rising number of the options in market has been a critical problem for most companies for a long time because of the reason that an improper ERP software package might lead to many issues(i.e.time loss,increased costs and a loss of market share).On the other hand,finding the best ERP alternative is a comprehensive MCDM problem in the presence of a set of alternatives and several potentially competing quantitative and qualitative criteria.Findings–In this integrated approach,the hesitant F-AHP is used to determine the criteria weights,as the hesitant F-TOPSIS is utilized to rank ERP package alternatives.The proposed approach was also validated in a numerical example that has five ERP package alternatives and 12 criteria by three decision-makers in order to show its applicability to potential readers and practitioners.Research limitations/implications–If the number of the alternatives and criteria are dramatically increased beyond reasonable numbers,the reaching to final solution will be so difficult because of the great deal of fuzzy based calculations.Therefore,the number of criteria and alternatives should be at reasonable numbers.Practical implications–The proposed approach was also validated in a illustrated example with the five ERP package options and 12 criteria by the three decision-makers in order to show its applicability to potential readers and practitioners.Originality/value–Furthermore,in literature,to the best of our knowledge,the authors did not come cross any work that integrates the HF-AHP with the HF-TOPSIS for ERP software package selection problem.

Exploring the Current Practices,Costs and Benefits of FAIR Implementation in Pharmaceutical Research and Development:A Qualitative Interview Study

The findable,accessible,interoperable,reusable(FAIR)principles for scientific data management and stewardship aim to facilitate data reuse at scale by both humans and machines.Research and development(R&D)in the pharmaceutical industry is becoming increasingly data driven,but managing its data assets according to FAIR principles remains costly and challenging.To date,little scientific evidence exists about how FAIR is currently implemented in practice,what its associated costs and benefits are,and how decisions are made about the retrospective FAIRification of data sets in pharmaceutical R&D.This paper reports the results of semi-structured interviews with 14 pharmaceutical professionals who participate in various stages of drug R&D in seven pharmaceutical businesses.Inductive thematic analysis identified three primary themes of the benefits and costs of FAIRification,and the elements that influence the decision-making process for FAIRifying legacy data sets.Participants collectively acknowledged the potential contribution of FAIRification to data reusability in diverse research domains and the subsequent potential for cost-savings.Implementation costs,however,were still considered a barrier by participants,with the need for considerable expenditure in terms of resources,and cultural change.How decisions were made about FAIRification was influenced by legal and ethical considerations,management commitment,and data prioritisation.The findings have significant implications for those in the pharmaceutical R&D industry who are engaged in driving FAIR implementation,and for external parties who seek to better understand existing practices and challenges.