Dynamic impact of uncertainty on R&amp;D cooperation formation and research performance: Evidence from the bio-pharmaceutical industry

Banerjee, Tannista; Siebert, Ralph

doi:10.1016/j.respol.2017.05.009

Cited by 53 publications

(30 citation statements)

References 54 publications

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“…As a form of cooperation in which several independent economic entities agree to share certain R&D activities and results, R&D partnership has become a cooperative innovation mode among enterprises [3]. Partnerships in R&D are common in most industries, especially those with rapid technological development, and have led to a marked increase in interenterprise technological cooperation across a wide range of industries [4][5][6][7][8][9][10]. is type of R&D partnership among enterprises operating in the same or related industries forms an enterprise R&D network [11], which promotes the significant growth of enterprises' innovation ability and level [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

The Intertemporal Evolution Model of Enterprise R&D Cooperative Network

Qiu

Wang

Chen

2019

Discrete Dynamics in Nature and Society

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Cooperation plays an irreplaceable role in knowledge creation and innovation. Innovation cooperation among enterprises forms a complex network of enterprise R&D. Given the intertemporal R&D network evolution and the complex influence between stock knowledge, this study constructs a discrete indefinitely intertemporal evolution model of an enterprise R&D cooperation network. The model consists of two main parts, that is, first is how technological innovation depends on the structure of enterprise R&D cooperation network and the second is how the enterprise R&D cooperation network evolves according to the level of technological innovation. This work uses calculation experiment and simulation method to study the evolution characteristics of enterprise R&D network in different initial R&D network topology structures, such as Erdos–Renyi random graph, WS small-world network, and BA scale-free network, and determine how previous history, attractiveness, and reputation for enterprise influence the steady-state characteristics of R&D network evolution. Results show that (1) when the R&D network evolution reaches a stable state, the joint distribution of stock knowledge and the number of cooperative enterprises do not affect the initial R&D network topology. However, the evolution path of the enterprise R&D network is complicated by the initial R&D network topology. (2) Among the three factors through which enterprises make decisions, if the enterprise values previous history highly, then the stock knowledge in a steady state will dissipate; if the enterprise values reputation highly, then the stock knowledge in a steady state will decrease but always above a threshold; if the enterprise values attractiveness highly, then the stock knowledge in a steady state can rise to a high level. These conclusions have important theoretical values and practical significance for the promotion of enterprise scientific and technological innovation and cooperative research.

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Section: Introductionmentioning

confidence: 99%

The Intertemporal Evolution Model of Enterprise R&D Cooperative Network

Qiu

Wang

Chen

2019

Discrete Dynamics in Nature and Society

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“…The randomness of these parameters is generally linked to the poor predictability of fluctuations in stock markets, societal and economic crises, and consumer habits and demand (e.g. Banerjee and Siebert 2017;Caballero and Pindyck 1992;Gilli and Këllezi 2006;Wernerfelt and Karnani 1987;Zachary 2014). When the performance of industrial activities depends directly on the variability in environmental conditions, long-term economic investments must also embed other sources of uncertainty that are linked to the endemic variability of natural phenomena.…”

Section: Introductionmentioning

confidence: 99%

Incorporating Hydrologic Uncertainty in Industrial Economic Models: Implications of Extreme Rainfall Variability on Metal Mining Investments

2019

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Water balance uncertainties have long been known to lead to potential environmental hazards, but their effect on economic profitability of mines is an under-studied field of research. Historical rainfall data are analyzed using the extreme value theory (EVT) and the peak over threshold method (POT). The resulting distributions are used as inputs into a system dynamics techno-economic metal mining investment profitability model, and simulation analysis is performed. The proposed methodology incorporates rainfall extremes and uncertainty into techno-economic modeling of metal mining operations. A case study with real-life historical rainfall data was used to illustrate the relationship between hydrologic uncertainty and the economic value of a metal mining investment.

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“…Table (1) :Drug development phasesSource:(Banerjee & Siebert, 2017; p. 1257) Overall, four relevant strategic R&D decisions can identify the relation between R&D and performance; absorptive capacity, type of R&D, internal organization of R&D activities, and degree of openness in the R&D strategy.Absorptive capacity deals with employees' ability and proficiency to acquire new knowledge and transfer it to the company's experience in R&D field. The second decision related to hierarchical structure of R&D department in theStage DescriptionDiscoveryDetermining targets and used biochemistry technique.Formulation Assessing medicine stability levelLead molecule Defining lead molecule for improvement.…”

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confidence: 99%

أثر کثافة البحث والتطویر على الأداء المالی وقیمة الشرکة: دراسة تطبیقیة على شرکات الأدویة المدرجة فی البورصة المصریة (باللغة الانجلیزیة)

الدواياتي¹

2020

مجلة الاسکندریة للبحوث المحاسبیة

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This paper examines the relation between Research and Development (R&D) intensity and both of the financial performance and firm value for pharmaceutical companies listed on Egyptian Stock Exchange. Because of the future benefits related to R&D, the researcher examines the relationship between R&D intensity and both of the current and future performance. R&D intensity is measured as the ratio of R&D expenditures to total revenues. The financial performance as a first dependent variable is measured using three accounting-based operating performance measures; Return on asset (ROA), Return on Equity (ROE), and return on sales (ROS). ROA is computed as the ratio of Earnings Before Interest and Tax (EBIT) to total assets; ROE is the ratio of Net income after tax to stockholder equity, and ROS is the ratio of Net income after tax to net sales. With regard to the second dependent variable, firm value, Tobin's Q is used, which is measured by (market value of equity+ book value of liabilities) divided by book value of total assets. Using data of listed pharmaceutical companies on Egyptian stock market for the period between 2000 to 2019, empirical results showed a significant negative relationship between R&D intensity and current performance measured by ROE and ROS, in addition to Tobin's Q. However, the results showed an insignificant negative relation with ROA. On the contrary, Findings indicated a significant positive relationship between R&D intensity and future firm performance measured by ROA only and Tobin's Q, and an insignificant positive relation with ROE and ROS.

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Dynamic impact of uncertainty on R&D cooperation formation and research performance: Evidence from the bio-pharmaceutical industry

Cited by 53 publications

References 54 publications

The Intertemporal Evolution Model of Enterprise R&D Cooperative Network

The Intertemporal Evolution Model of Enterprise R&D Cooperative Network

Incorporating Hydrologic Uncertainty in Industrial Economic Models: Implications of Extreme Rainfall Variability on Metal Mining Investments

أثر کثافة البحث والتطویر على الأداء المالی وقیمة الشرکة: دراسة تطبیقیة على شرکات الأدویة المدرجة فی البورصة المصریة (باللغة الانجلیزیة)

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