An algorithm for the anchor points of the PPS of the BCC model

Akbarian, Dariush; Bani, Ali Akbar; Rostamy-Malkhalifeh, Mohsen; Lotfı, Farhad Hosseinzadeh

doi:10.1186/s40854-022-00392-z

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…In 1978, operations researcher Charnes et al [22] assumed that the returns from the production scale remained constant and proposed a data envelopment analysis model called CCR for the first time to evaluate the technological efficiency of decision-making units. Subsequently, other scholars further proposed different models to meet the actual needs of their research, such as BCC [23], Malmquist [24], SBM [25], EBM [26], and three-stage DEA [27]. DEA is still the main research model adopted in academia.…”

Section: Technological Innovation Efficiencymentioning

confidence: 99%

Does Technological Innovation Efficiency Improve the Growth of New Energy Enterprises? Evidence from Listed Companies in China

Chen,

Li,

Zhang

et al. 2024

Sustainability

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With the implementation of “carbon peaking and carbon neutrality” in China, new energy enterprises, as the vanguard in this strategy, have entered a new era of innovation-driven development. However, enterprises at different lifecycle stages will face different internal and external conditions, and there are differences in their internal mechanisms and business performance. In this case, whether technological innovation efficiency can have an obviously positive effect on their growth and what different effects it can have for enterprises at different lifecycle stages have become issues of great concern to company management, investors, governments, and other stakeholders. This research takes 81 new Chinese energy enterprises as the research objects. First, they are divided into growing, mature, and declining enterprises based on the cash flow combination method. Then, their technological innovation efficiencies from 2016 to 2021 are calculated based on the stochastic frontier method and their growth evaluations are performed by taking both financial and non-financial indicators into consideration. Finally, by taking mediating effects into consideration, the heterogeneity effects of technological innovation efficiency on their growth are studied from the perspective of enterprise lifecycles based on the fixed-effect model. The research results indicate that the technological innovation efficiency of new Chinese energy enterprises has fluctuated around 0.90 in recent years, and is generally at a high level. The efficiency ranking of enterprises at different lifecycle stages is mature period > growing period > declining period. Technological innovation efficiency has a positive impact on their growth, and market share plays a mediating role in this process. The effects of technological innovation efficiency on enterprises at different stages are different, with growing and mature enterprises showing a positive impact. Growing enterprises are more affected by technological innovation efficiency due to their demand for innovation-driven development, while declining enterprises often face difficulties such as unstable operating conditions and outdated equipment, and unreasonable technological innovations may actually accelerate their decline.

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