Research progress on the evaluation mechanism of scientific research teams in the digital economy era

Abstract

Purpose

In today’s digital economy, it is very important to cultivate digital professionals with advanced interdisciplinary skills. The purpose of this paper is that universities play a vital role in this effort, and research teams need to use the synergistic effect of various educational methods to improve the quality and efficiency of personnel training. For these teams, a powerful evaluation mechanism is very important to improve their innovation ability and the overall level of talents they cultivate. The policy of “selecting the best through public bidding” not only meets the multi-dimensional evaluation needs of contemporary research, but also conforms to the current atmosphere of evaluating scientific and technological talents.

Design/methodology/approach

Nonetheless, since its adoption, several challenges have emerged, including flawed project management systems, a mismatch between listed needs and actual core technological needs and a low rate of conversion of scientific achievements into practical outcomes. These issues are often traced back to overly simplistic evaluation methods for research teams. This paper reviews the literature on the “Open Bidding for Selecting the Best Candidates” policy and related evaluation mechanisms for research teams, identifying methodological shortcomings, a gap in exploring team collaboration and an oversight in team selection criteria.

Findings

It proposes a theoretical framework for the evaluation and selection mechanisms of research teams under the “Open Bidding for Selecting the Best Candidates” model, offering a solid foundation for further in-depth studies in this area.

Originality/value

Research progress on the Evaluation Mechanism of Scientific Research Teams in the Digital Economy Era from the Perspective of “Open Bidding for Selecting the Best Candidates.”

Citation

He, Y., Li, F. and Liu, X. (2024), "Research progress on the evaluation mechanism of scientific research teams in the digital economy era", Journal of Internet and Digital Economics, Vol. 4 No. 3, pp. 218-241. https://doi.org/10.1108/JIDE-04-2024-0016

1. Introduction

The report to the 20th National Congress of the Communist Party of China emphasizes accelerating the development of the digital economy, promoting the deep integration of the digital economy and the real economy and creating digital industry clusters with international competitiveness. On February 21, 2024, the Cyberspace Administration of China, Ministry of Education, Ministry of Industry and Information Technology and Ministry of Human Resources and Social Security jointly issued the “2024 Key Points for Enhancing the National Digital Literacy and Skills,” proposing the cultivation of high-level composite digital talents, including comprehensively improving the digital literacy and skills of teachers and students, enhancing the digital capabilities of leaders and civil servants, fostering high-level digital craftsmen, nurturing rural digital talents and expanding the team of industry digital talents. Cultivating high-level composite digital talents requires fostering their innovation capabilities and teamwork spirit, continuously enhancing team cooperation and communication capabilities through collaborative efforts, making them talents with innovation spirit and teamwork capabilities. Therefore, strengthening the construction of digital domain scientific research teams and accelerating the formation of a digital talent team echelon is particularly important.

Universities are the cradles of talent cultivation and university research teams should fully leverage the synergistic advantages of diverse training bodies to cultivate high-level composite digital talents suitable for the digital economy era. In recent years, the construction of university research teams has faced issues such as a shortage of high-level teams, long-term teams, top-down teams and interdisciplinary and inter-departmental teams (Sun, 2023). How to effectively evaluate high-quality research teams is the core link to improve the innovation capability and talent training of university research teams. From a national perspective, the evaluation policy of research teams has gradually shifted from quantity-oriented to quality-oriented, focusing on the collaboration ability among research team members, the service efficiency of research team management and the social contribution of research outcomes transformation. On July 3, 2018, the General Office of the CCP and the General Office of the State Council issued “Opinions on Deepening the Reform of Project Review, Talent Evaluation and Institution Assessment,” proposing to further promote the “three evaluations” reform, further optimize the scientific research project review management mechanism, improve the evaluation methods for scientific and technological talents, perfect the evaluation system for research institutions and strengthen the supervision and evaluation and the construction of a scientific research integrity system. In November of the same year, the Office of the Ministry of Education issued “Notice on Initiating a Special Campaign to Address the Overemphasis on Publications-Only, Titles-Only, Professional Positions-Only, Degrees-Only, and Awards-Only,” deciding to conduct a cleanup in relevant universities. On May 21, 2021, the 19th meeting of the Central Committee for Comprehensive Deepening Reform reviewed and approved “Guiding Opinions on Improving the Evaluation Mechanism for Scientific and Technological Achievements” and other documents. The meeting emphasized solving the issues of “what to evaluate,” “who evaluates,” “how to evaluate” and “how to use the evaluation results.”

General Secretary Xi Jinping pointed out in the report to the 20th National Congress of the Communist Party of China that “we must adhere to the core position of innovation in our country's modernization drive,” “accelerate the achievement of high-level scientific and technological self-reliance and self-strength” and “strengthen the talent support for modernization construction.” On June 22, 2022, the Ministry of Science and Technology and eight other departments issued the “Work Plan for the Pilot Reform of Scientific and Technological Talent Evaluation,” targeting the prominent issues of insufficient establishment of new standards, innovation in evaluation methods, and resource allocation evaluation reform after “breaking the four onlys” and formulated the work plan for the pilot reform of scientific and technological talent evaluation. In fact, the “Open Bidding for Selecting the Best Candidates” policy reflects a problem-oriented scientific and technological innovation thought, oriented by major demands, allowing the talents who open the bids to focus their efforts, work hard on public relations and strive to break through the “neck-constraining” places of key core technologies, which aligns with the concept of breaking the five dimensions and evaluating scientific and technological talents. At the cybersecurity and informatization work symposium held on April 19, 2016, General Secretary Xi Jinping pointed out: “We should concentrate efforts on scientific research investment to do big things, actively promote the transformation of core technological achievements, promote strong cooperation, collaborate on tackling key problems and explore the use of alliances in component science. We can explore implementing ‘Open Bidding for Selecting the Best Candidates’, list the needed key core technology projects, heroes regardless of their origins, whoever is capable should open the bid.” The government work report in 2020 clearly proposed “implementing ‘Open Bidding for Selecting the Best Candidates’ for key project breakthroughs, letting those who can do it”. In 2021, “Open Bidding for Selecting the Best Candidates” was written into reports such as the National Science and Technology Work Conference and “the Recommendations of the Central Committee of the Communist Party of China for Formulating the 14th Five-Year Plan for Economic and Social Development and Long-Range Objectives” were approved by the Fifth Plenum of the 15th CPC Central Committee, which explicitly proposed to deepen the reform of the scientific and technological system, improve the organization and management of scientific and technological projects and implement the “Open Bidding for Selecting the Best Candidates” policy.

Since the implementation of the “Open Bidding for Selecting the Best Candidates” policy, a series of issues have arisen, such as the lack of standardization in project systems, inconsistency between the demand on the list and the actual core technological needs and the conversion rate of scientific and technological achievements of projects on the list (Sun et al., 2022; Song and Si, 2022), mainly reflected in the problem of a single method of research team evaluation. The comprehensive evaluation system for research teams overly emphasizes standardization and unification, with a single mode of evaluation, and the “leaders” selected for projects in universities are mostly those who have already held titles such as Yangtze River Scholars et al. (Han and Chen, 2023), the imperfect selection mechanism, leading to the reliance on existing evaluation standards often unable to accurately select the most suitable talent teams capable of solving key areas of core problems, will further affect the enthusiasm of research teams to participate in “Open Bidding for Selecting the Best Candidates” projects. In view of this, on the basis of reviewing the “public bidding to select the best” and the evaluation mechanism of scientific research teams, this paper focuses on the shortcomings of previous studies and explores the future research direction. Based on Bandura's “reciprocal determinism,” this paper constructs the theoretical framework of the evaluation mechanism of “public bidding to select the best” scientific research teams, which lays a theoretical foundation for the construction of the evaluation system and the exploration of the selection mechanism of “public bidding to select the best” scientific research teams.

2. Literature review

2.1 Review of literature on “Open Bidding for Selecting the Best Candidates Policy”

2.1.1 Concept definition, connotation and characteristics study of the “Open Bidding for Selecting the Best Candidates Policy”

The “Open Bidding for Selecting the Best Candidates Policy” represents an innovative mechanism for managing science and technology projects, with its core aimed at stimulating the potential for scientific and technological innovation. In most cases, this mechanism is led by government agencies at various levels, which publicly announce challenging issues and key technological problems in the field of science and technology through an open bidding process. This move mobilizes and attracts capable research teams to engage in innovative research on these projects, aiming to assemble the best resources through a competitive selection mechanism to overcome scientific and technological challenges. The “Open Bidding for Selecting the Best Candidates Policy” can effectively promote the optimal allocation of scientific and technological resources, accelerate the output of scientific and technological achievements and thereby drive scientific progress and social development (Zhu et al., 2023).

2.1.1.1 Definition and connotation of the “Open Bidding for Selecting the Best Candidates Policy”

Zhang and Sun (2021) provided a deep explanation of the concept of “Open Bidding for Selecting the Best Candidates.” They believe that “Open Bidding” originally refers to the act of unveiling a list, symbolizing a selection process, the transparency of information and the certainty of the final result, closely connected to the setting, announcement and review of the list. “Selecting the Best Candidates,” derived from ancient military terminology, refers to a general leading an expedition, but in a modern context, it represents taking on a leadership or responsible role, implying an individual's capability, authority and sense of responsibility. The integration of these two concepts, through a specific “Open Bidding” procedure, selects and assigns those talented individuals specific tasks. In the context of contemporary scientific and technological innovation, the essence of this policy can be summarized as driven by the demand for key technological breakthroughs and strategic scientific and technological goals, it promotes a new system, mechanism and model of open innovation and collaborative development through resource integration, talent selection and innovative management of scientific research funding. Chen et al. (2022) further proposed that “Open Bidding for Selecting the Best Candidates” is based on the orientation of key technologies and strategic scientific and technological tasks at the national strategic level, relying on a new type of national governance system, with the national strategic scientific and technological force as the core executor, constructing an open scientific research topic distribution mechanism oriented towards talent selection and capability. This mechanism forms a results-oriented, broad-access yet strictly managed and non-periodic scientific research funding system through the actual output of scientific research achievements as the basis for funding support.

2.1.1.2 Characteristics and categories of “Open Bidding for Selecting the Best Candidates”

Zeng et al. (2023) believe that analyzing the technical characteristics, regional characteristics and thematic features of the list is crucial for understanding the “Open Bidding for Selecting the Best Candidates Policy.” The technical focus of the list is concentrated in areas that promote economic and social development, thus showing significant regional differences. Most regions tend to issue lists around the frontier and common issues of technology, while a few regions issue technical lists based on local characteristics and immediate needs. In terms of the distribution in technical fields, whether in more concentrated areas such as biomedicine, high-end manufacturing, new materials, electronics and information or in other less involved technical fields, the difference in the proportion of lists is relatively small. According to current data analysis, the technical fields of the list have covered the needs of economic and social development and the health of the people, but the representation of international scientific and technological frontiers and national strategic needs is still insufficient. Based on regional characteristics, lists can be classified into regional scientific and technological frontier, common scientific and technological frontier, regional situational and common emergency types. Among them, regional scientific and technological frontier lists focus on frontier technologies in the characteristic industrial fields of the region; common scientific and technological frontier lists widely cover the frontier technology development in various fields; regional situational lists focus on the region's key development and characteristic industries, concentrating on immediate situational technologies; common emergency lists focus on common urgent technical needs of society. This classification helps to more accurately identify and respond to the scientific and technological innovation needs of different regions and fields, thus effectively promoting the implementation and development of the “Open Bidding for Selecting the Best Candidates Policy.”

When discussing the classification of “Open Bidding for Selecting the Best Candidates” lists, they can be finely divided based on different issuing entities, the nature of the projects and the ways of issuance. Sun et al. (2022) proposed that if classified by issuing entity, lists can be categorized into government, enterprise and other types. Government lists mainly focus on key technologies and solving “stranglehold” problems, funded by the government and posed as challenges, with enterprises responsible for bidding and participating in project implementation. Enterprise lists focus on the bidirectional integration of industry and technology, usually proposed by enterprises, with the government issuing the list and research institutions participating in the bidding, thus accelerating the industrialization process of scientific and technological achievements. Other types of lists involve public welfare issues in specific fields, possibly initiated by individuals, social organizations or their consortia, closely related to people's livelihood issues. Second, if classified by the type of project demand, lists can be divided into results transformation and technological breakthroughs. Results transformation projects are usually based on independent intellectual property or patents already mastered by universities, technology companies or research institutes, issued nationwide, with bidders usually limited to enterprises within the province. Technological breakthrough projects focus on scientific and technological problems that need to be solved in the short term, with recruitment open nationwide. Third, based on the different ways of issuance, project lists can be divided into targeted issuance and open issuance. Targeted issuance is suitable for projects with clear team advantages, high requirements for organizational implementation integration and projects unsuitable for public relations. In contrast, open issuance is suitable for projects with diverse technical routes, unclear advantage teams or multiple possibilities. This classification method helps to more accurately match project needs with potential bidders, thereby improving the efficiency and effectiveness of the “Open Bidding for Selecting the Best Candidates Policy.”

2.1.1.3 Analysis of characteristics of “Selecting the Best Candidates” Talents

In the context of the “Open Bidding for Selecting the Best Candidates Policy,” “Selecting the Best Candidates” talents or bidders, refer to individuals or teams that can successfully meet challenges and lead projects to success. Scholars like Zeng et al. (2023) suggest that companies demonstrate significant innovation vitality under this policy, while the innovation vitality of research institutions is relatively weaker. In scenarios where enterprises lead, companies act as entities closely integrating technological innovation with economic development. The study found that companies participating in bidding are often young and possess a high level of research and despite potential deficiencies in intellectual property and title recognition, these companies can still obtain bidding qualifications through competitive list results. Regarding universities “leading the charge,” universities are an important driving force in the “Open Bidding for Selecting the Best Candidates Policy,” and bidding activities are influenced by the differences in resource allocation, resource acquisition capabilities and social influence among different levels of universities, resulting in a higher proportion of top-level institutions in bidding activities. Moreover, universities participating in bidding generally have a high level of talent reserves and relatively complete scientific research facilities. Regarding the situation of research institutes “leading the charge,” research institutes play an indispensable role in technological breakthroughs. Although the deepening of marketization reforms may challenge research institutes in terms of R&D investment and output, leading to a relatively low proportion in bidding activities, the significant growth in technological income and a substantial increase in scientific output indicators after the reforms still indicate that these bidding research institutes possess deep historical roots and outstanding research strength.

2.1.2 Study on the operation mechanism of the “Open Bidding for Selecting the Best Candidates” system

2.1.2.1 Analysis of the operation process of the “Open Bidding for Selecting the Best Candidates” system

The “Open Bidding for Selecting the Best Candidates” mechanism, as a new way of project approval and organizational management in scientific research, perfecting the operation process is the fundamental link to exert the effectiveness of the system. The practical process of the “Open Bidding for Selecting the Best Candidates” system varies in detail across different regions, but can generally be divided into three main stages: “setting the list,” “posting the list,” and “revealing the list.”

“Setting the list” is divided into two parts: solicitation of topics and selection of topics. Scholars such as Sha et al. (2022) conducted an in-depth analysis of the formation path of the “Open Bidding for Selecting the Best Candidates” system, revealing that this path can mainly be divided into two categories: government-led formulation and extensive civilian solicitation, with a relatively small proportion of government formulation. When the government formulates the list, it usually does so based on a profound understanding of the needs of economic and social development and field investigations of technological bottlenecks for industrial development, especially in times of special circumstances, focusing on technological needs, thus autonomously formulating the list. In contrast, civilian solicitation is more common, including not only openly soliciting technological needs from society and incorporating them into the local demand project library but also involving regular argumentation and release of projects, as well as tasks for project solicitation at various government levels and selection of projects suitable for adoption. In consideration of both the necessity of the project and risk prevention, the selection of list topics needs to be carried out during the “setting the list” process. Generally speaking, the importance of regional industrial common technology research is higher than that of key technology research for individual enterprises and technological research with greater influence and scope has a more obvious advantage in competing for financial support. The selection process of list topics mainly includes two key steps: project qualification argumentation and demand-side qualification argumentation. In order to select projects with wide-ranging influence and application, project qualification argumentation assesses factors such as the technical level, feasibility and urgency of the project. At the same time, to prevent potential problems on the demand side, collusion with the bidders to obtain financial support and to ensure the qualification of the project and the effective application of its later results, demand-side qualification argumentation is also indispensable. Zou and Hao (2020) believe that “Open Bidding for Selecting the Best Candidates” is a mechanism for selecting talents for concentrated research on key core technologies. This mechanism organically integrates various elements such as scientific research leaders, research teams, government, market, funds, resources, etc. takes the country's major needs as the traction force, evaluates the practical effectiveness of problem-solving as the evaluation criteria, stimulates innovation vitality through market competition mechanisms and mobilizes the synergy of resources and elements from all parties with a common goal. In addition, they propose that “Open Bidding for Selecting the Best Candidates” should be guided by target demands, focus on major and fundamental issues and strengthen macro-management and coordination of achieving the goals of the list.

“Posting the list” refers to the release of the list. The release of the list in the “Open Bidding for Selecting the Best Candidates” system mainly relies on online channels, but there are problems with the lack of targeting. The list publishers usually publish information through government websites and actively promote it through newspapers and media. Some regions publish project lists by establishing professional information platforms or using platforms for regional major scientific and technological activities. Because this method of posting the list directly targets the entire society, it may not be conducive to accurately attracting specific innovative entities. Therefore, the government can consider directly issuing demands to institutions with significant research capabilities in relevant technical fields to more effectively attract high-quality bidders with the ability to tackle key issues. Zhang (2022) proposes that the list publishers in the “Open Bidding for Selecting the Best Candidates” organization should establish specific work goals and plans, clarify the categories of list-publishing companies, select appropriate cooperation partners and adopt various methods to stimulate enthusiasm of list-publishing participation to ensure the collection of high-quality lists. The types of technical demand of list-publishing companies can be divided into clear-demand type, vague-demand type, conceptual-demand type and directionless-demand type. For companies with vague demands, further clarification and refinement of demands should be made before publication; whereas for directionless demands, long-term tracking and feedback are needed to ensure the accuracy and feasibility of demands.

“Revealing the list” includes three key steps: applying for revealing the list, selecting the best candidate and announcing the project. In the stage of applying for revealing the list, applicants can reveal the list in various forms, including formulating project plans or submitting relevant materials based on project needs and their own conditions or jointly applying with the demand side. In the process of selecting the best candidate, it involves the evaluation of the bidders and the final determination of the bidders. In the evaluation of bidders, due to differences in the formation path of projects, there may be slight differences in the process argumentation; especially for “government-led” projects, they are usually recommended by designated units first, and then experts organized by the government conduct argumentation. As for determining the bidders, for “government-led” projects and projects jointly applied by bidders and demand sides, the winning list is usually determined by the government, whereas for projects applied separately, the government proposes suggestions and organizes both sides for communication. The project announcement stage reflects the inherent requirements of openness and transparency. Regarding the key elements of “Open Bidding for Selecting the Best Candidates,” Zhou and Hao (2020) believe that selecting appropriate scientific research leaders is the key to selecting the leader, cultivating efficient scientific research teams is the core of selecting the leader and encouraging non-consensual innovation is the foundation of selecting the leader. The tackling of key core technologies is not achieved overnight; it requires collaborative teams with strong technological innovation capabilities, breaking conventional thinking through joint tackling and collective intelligence tackling. At the same time, establishing a mechanism led by the market to provide living space for non-consensual innovation and optimizing the development environment are of great significance for promoting technological progress.

2.1.2.2 Matching of list leaders in “Open Bidding for Selecting the Best Candidates”

Zeng et al. (2023) deeply explored the current situation of list leader matching in “Open Bidding for Selecting the Best Candidates” from three dimensions: regional matching, thematic matching and technical matching. The research reveals that although there are certain limitations in regional matching, both thematic matching and technical matching show a high level. The “Open Bidding for Selecting the Best Candidates” system breaks through the regional constraints of scientific and technological innovation subjects. However, since the list of subjects are mainly composed of governments, local governments are often influenced by various factors in the process of selecting talents and tend to choose local subjects. In addition, the fiscal scientific research funds of local governments cannot currently flow across provinces and borders, which leads to a higher risk burden for innovative subjects outside the region in the process of “Open Bidding for Selecting the Best Candidates,” thus making the system mainly dominated by list publishers and list revealers within the region.

In the “Open Bidding for Selecting the Best Candidates” system, the types of entities involved mainly include government agencies, business entities, higher education institutions and research institutes. It is worth noting that the current “Open Bidding for Selecting the Best Candidates” policy documents do not specify the types of themes for list leaders, allowing the system to produce diverse matching results. In related academic research, it has been found that for technology tackling lists, thematic matching shows high diversity, while for outcome transformation and platform construction lists, thematic matching shows the opposite trend. For different types of lists, the matching degree of list leaders is generally high. Based on the names and technical fields of the lists, Zeng et al. (2023) conducted detailed investigations into technical matching. The research results show that the technical matching of technology tackling lists is consistent with the overall distribution trend; the number of technical matching in the low- and high-scoring groups of outcome transformation lists is balanced; and the technical matching of platform construction lists is generally in the middle to high scoring group range. This finding provides valuable insights for understanding the main subject matching characteristics under different list types.

2.1.2.3 Problems in the operation of the “Open Bidding for Selecting the Best Candidates” policy

The “Open Bidding for Selecting the Best Candidates” Policy involves many subjects and complex processes, with multiple risk factors in terms of time, funds, etc. The problems and challenges encountered in the implementation process of this policy mainly focus on the following aspects: First, the participation enthusiasm of some subjects is not high, lacking initiative. Second, the work processes and methods need to be standardized. Third, sometimes project selection fails to closely integrate with actual needs. Fourth, it's difficult to balance between the “single bidding” and “parallel racing” models. Fifth, the fault-tolerant mechanism is not yet perfect (Han, 2023). Han and Chen (2023) further analyzed under the GCE framework, pointing out that the “Open Bidding for Selecting the Best Candidates” projects face a lack of strategic goals, arbitrariness in list setting, alienation of subject behavior including the appearance of questioning behavior and the imperfection of incentive-constraint mechanisms, resulting in the problem of old wine in new bottles. In pursuit of trends, some regions generalize the application areas of lists and companies lack effective guidance from the government in setting lists, resulting in arbitrariness in the lists and failure to closely align with national major needs. Few companies actively propose technical demands through list publishing and the overall participation enthusiasm is not high. In the process of selecting bidders, the review group may not have a comprehensive understanding of the applicant's information, and information asymmetry may lead to opportunistic behavior. The lag in supporting reforms in fund management greatly restricts the enthusiasm of researchers, limits the autonomy of bidders and lacks openness in unit selection. In addition, the regulatory mechanism supporting the new organizational model has not been established and unclear review mechanisms constrain the further development of the “Open Bidding for Selecting the Best Candidates” Policy.

In the construction of enterprise talent teams, Guo (2023) believes that the “Open Bidding for Selecting the Best Candidates” Policy has not yet formed a systematic application, the scale is still insufficient, and the effectiveness has not been fully demonstrated. Specifically, first, in the selection of scientific and technical talents and the construction of engineering and technical talent teams, the potential of this policy has not been fully utilized; the evaluation system of engineering and technical talent projects needs to be improved; and technical personnel face a lack of opportunities and platforms. Second, this policy has not been widely used in the construction of high-skilled talent teams and the selection of leading craftsmen, lacking effective ways and means of “bidding.” Third, an internal mature evaluation system has not been established and most rely on competitive activities organized by external consulting agencies, which is not conducive to the deepening and internalization of the policy within enterprises. Zhang (2022) pointed out from the perspective of enterprise technical demands that there are several problems in the “posting” process, including insufficient authenticity of enterprise technical demands, lack of clarity in demand descriptions, urgent need to strengthen privacy protection measures, and the need to improve the integrity of management work. These problems pose challenges to the effective implementation and optimization of the “Open Bidding for Selecting the Best Candidates” Policy.

There are a series of problems and deficiencies in the practice of universities participating in “Open Bidding for Selecting the Best Candidates” projects. The main problems lie in the need to optimize the management and organizational models. Many researchers reflect that the current “Open Bidding for Selecting the Best Candidates” projects are not much different from traditional research projects in terms of organization and management mode, the maturity of the policy is insufficient, the scope of application is not clear, the combination of list content and actual needs is not close enough, and the fairness of list revealer selection has not been fully resolved. The enthusiasm of researchers needs to be improved. The current funding support for “Open Bidding for Selecting the Best Candidates” projects is limited, the projects are difficult, there is insufficient matching between supply and demand; the assessment and evaluation mechanism is not perfect; and the fault-tolerant mechanism lacks clarity. In addition, the funding support policies of some projects are unclear or adopt post-subsidy methods, which require researchers to raise funds on their own in the early stage of the project, increasing the threshold for participating in the project (Li and Gao, 2023).

Most research focuses on the assessment and evaluation of phased achievements and final results of “Open Bidding for Selecting the Best Candidates” projects, while there is still insufficient depth and breadth in how to create a more relaxed research environment during project implementation (Li et al., 2023). Based on the risk system theory, Yi (2023) conducted a systematic analysis of the risks and their formation mechanisms of “Open Bidding for Selecting the Best Candidates” projects. The study shows that some regions and institutions blindly carry out “Open Bidding for Selecting the Best Candidates” process, simply transforming traditional research mechanisms superficially. Under the joint action of various risk factors such as information asymmetry, the uncertainty of technological innovation and selective behavior, various sources of risk may trigger a series of “chain aggregation reactions,” ultimately affecting the risk receptors and leading to the emergence and spread of various risks.

2.1.2.4 Measures to improve the “Open Bidding for Selecting the Best Candidates” policy

Enhancing the maturity and effectiveness of the “Open Bidding for Selecting the Best Candidates” policy crucially depends on breakthroughs in key aspects, ensuring sufficient investment and clarifying funding sources and insisting on diversified channels for funding. Introducing a multi-party collaborative research model lowers the participation threshold, rigorously defines the results acceptance process, establishes a diversified mechanism for project sourcing and requires efforts from multiple dimensions to deeply explore high-quality projects. Selecting a batch of innovative projects aimed at grassroots implementation for different levels of talent (teams) should possess wide applicability, a high success rate and closely align with actual needs. Improving talent service and security mechanisms ensures the service guarantee work at critical junctures, continuously strengthening policy support, summarizing advanced experiences, promoting local practices based on local conditions and enhancing the institutionalization and scale of the “Open Bidding for Selecting the Best Candidates” model. Stimulating corporate innovation vitality, encouraging innovative entities such as new research and development institutions and enterprises to undertake the responsibility of results transformation, fully leveraging the advantages of talent stations in various places and simplifying research processes through online platforms to ensure practical results are achieved (Han, 2023). Additionally, under the GCE framework, for the effective implementation of the “Open Bidding for Selecting the Best Candidates” mechanism, further unification of goals and clarification of subject positioning are necessary. Based on national strategic and security needs, industrial competitiveness enhancement and major livelihood needs, scientific research tasks should be determined. Focusing on the weaknesses and strategic contested areas of industrial development, refining research tasks, introducing long-term high-level talents, unleashing their innovative potential, avoiding interest risks, emphasizing the role of enterprises, encouraging collaborative innovation among technology research and development supply and demand parties, jointly tackling research and development challenges and activating corporate innovation vitality. Promoting the establishment of supply-demand matching platforms to improve efficiency, constructing an “Open Bidding for Selecting the Best Candidates” cloud platform to achieve precise docking between listers and bidders and strengthening research on the technology supply market. Improving and perfecting incentive-constraint mechanisms, including funding system support, talent reward mechanisms, innovation in project service mechanisms, new project fault-tolerant mechanisms and improvement of supervision and evaluation mechanisms (Han and Chen, 2023).

In terms of project management, scholars like Li et al. (2023) propose four key focuses for promoting the “Open Bidding for Selecting the Best Candidates” mechanism: First, scientifically defining the scope of application of this mechanism, clarifying the research and development cycle and expected results, formulating the application principles of “Open Bidding for Selecting the Best Candidates” for major national scientific research tasks and establishing a list evaluation and argumentation mechanism. Second, during the list compilation stage, the end-users should be clearly defined to prevent the selection of weakly representative and limitedly influential unit users, ensuring clear demands and precise strategies. Third, it is necessary to further clarify the management responsibilities of relevant parties, strengthen project supervision through multi-party management, define responsibility boundaries, form management synergy and enhance management efficiency. Fourth, exploring the establishment of a comprehensive incentive mechanism for the “best,” namely, project leaders and studying and issuing corresponding incentive policies, granting more autonomy to the listers, establishing a scientific and reasonable fault-tolerant mechanism, leveraging institutional advantages, allowing project leaders to manage funding subsidies independently, with product performance as the standard for measuring project success or failure. From the perspective of project risk management, Yi (2023) believes in establishing an “Open Bidding for Selecting the Best Candidates” risk response mechanism to reduce information asymmetry through smooth information channels, relying on market forces to construct a national or regional big data platform for “Open Bidding for Selecting the Best Candidates” for scientific and technological projects and forming a service big data ecological alliance. Utilizing cutting-edge technologies such as big data, the Internet and blockchain, developing corresponding data platforms or application software to achieve linkage with technology trading markets and science and technology information markets. Enhancing the technological innovation capability of listers, coping with the uncertainty of technological innovation, improving the scientific research mechanism, promoting the aggregation of high-quality resources in core links and strengthening collaborative innovation. Improving the operational mechanism and dynamic supervision mechanism of “Open Bidding for Selecting the Best Candidates,” avoiding participants’ “selective behavior,” and protecting the innovation enthusiasm of listers. Regarding the arrangement of scientific research funds, Zou and Hao (2020) suggest controlling the total investment amount, emphasizing the improvement of the utilization efficiency of scientific research funds; establishing a mechanism tolerant of failure, acknowledging the sunk cost of scientific research failure; and constructing a trust mechanism; and forming competitive backups to ensure the effective allocation of scientific research resources and the healthy development of scientific research activities.

To address the problems existing in the construction of enterprise talent teams under the “Open Bidding for Selecting the Best Candidates” mechanism, Guo (2023) proposed a series of improvement suggestions: First, deeply summarize past “open bidding” cases to form replicable results, and based on this, compile and solidify internal processes, construct a systematic management mechanism, adhere to the principle of the Party's management of cadres and ensure the implementation of political standards and principles centered on enterprise tasks. Second, the application scope of the “open bidding” mechanism should be expanded to ensure solid talent support for various business lines of the enterprise. Third, fully utilize various organizational forms such as projects, topics and task forces to decompose large projects into small lists, thereby encouraging scientific and engineering talents to actively participate in open bidding. Fourth, identify and summarize the shortcomings of the existing mechanism, establish a probation period for recruited talents, conduct regular follow-up visits to talent-using departments, assess performance and thereby establish a scientific and reasonable talent selection mechanism. Fifth, establish a dynamic talent pool, strengthen the circulation of talents, standardize the procedures of “Open Bidding for Selecting the Best Candidates,” and establish a reserve talent pool to promote continuous exchange and development of talents. Through these measures, the construction of enterprise talent teams is optimized and the effectiveness and adaptability of the “Open Bidding for Selecting the Best Candidates” mechanism are enhanced.

Regarding the practices of universities undertaking “Open Bidding for Selecting the Best Candidates” projects, Li and Gao (2023) proposed five suggestions: First, improve the organizational and management mechanism of “Open Bidding for Selecting the Best Candidates,” focus on core issues and strictly screen list contents. Local governments should extensively solicit opinions and suggestions from diverse entities including universities, enterprises and research institutes to create a fair competition environment and establish a unified national online information platform. Second, strengthen incentive and constraint mechanisms, fully authorize and empower team leaders of open bidding in universities to have autonomy in funding use, talent introduction, title evaluation and performance assessment. Promote the implementation of systems such as the fund endowment system, horse race system and negative list of scientific research funds in universities and improve the assessment and evaluation mechanism of projects. Universities that undertake multiple national key projects, key core technology research and development and significant achievements in frontier scientific and technological innovation tasks should be given policy preferences, increase the distribution ratio and rewards of project teams in intellectual property rights and investment transfer income and establish a scientific and reasonable fault-tolerant mechanism. Third, expand funding financing channels, build a diversified scientific research investment system, actively seek investment from enterprises and society in university research and development, encourage and support universities to increase funding sources through government-industry-university-research cooperation and joint establishment of scientific funds, etc. Fourth, strengthen top-level design, accelerate the formulation and promotion of university research plans and schemes, focus on national strategic needs, optimize resource allocation and strengthen project tracking evaluation and dynamic adjustment. Fifth, strengthen the connection between scientific and technological innovation and achievement transformation, promote collaborative open bidding tackling industry key common technology problems between universities and enterprises. Guide universities to construct a full-chain scientific and technological innovation ecosystem covering “basic research + technology tackling + achievement industrialization + science and technology finance,” strengthen the construction of cross-regional research institutes, university-enterprise joint research and development centers and technology transfer subcenters, promote the connection between technology development and market demand and form a market-oriented science and technology achievement transfer and transformation operation system. Through these measures, the efficiency of universities in “Open Bidding for Selecting the Best Candidates” projects is enhanced and the transformation and application of scientific and technological innovation achievements are accelerated.

Wang et al. (2023) conducted an in-depth analysis of the implementation process of “Open Bidding for Selecting the Best Candidates” using the triple helix model and proposed corresponding suggestions. The research indicates that from the government's perspective, attention should be paid to the transformation of government functions from a traditional directive government model to a service-oriented government model to effectively mobilize the participation of various innovation subjects. Emphasizing the importance of coordinated practices among local governments in the implementation of the “Open Bidding for Selecting the Best Candidates” mechanism, advocating for enhanced information exchange and resource sharing between regions to promote regional integration development. At the enterprise level, it is suggested that companies should pay attention to research and development investment and achieve the transformation from commercial companies to knowledge-intensive companies. Enterprises should explore open innovation paths, build cross-disciplinary and cross-domain joint bidding teams, integrate innovative resources and industrial chains effectively and attach importance to talent introduction and cultivation to select excellent leaders and teams for projects. For universities, the study suggests that universities should focus on the connection between academic achievements and the industry, play a key role in socio-economic development and attach importance to technology transfer work, enhance the capability transformation from applied research to commercialization. Universities, enterprises and governments should break organizational boundaries, fully utilize social resources to improve the efficiency of technological innovation and industrial application through cross-border cooperation and resource integration. Through such cross-border cooperation and resource integration, the triple helix model is expected to promote knowledge creation and technology transfer and accelerate the industrialization process of scientific and technological achievements.

2.2 Literature review of research team evaluation mechanism

2.2.1 Concept definition and feature analysis of research team evaluation

2.2.1.1 Definition of research teams

In discussing the origin and development of teams, combined with the commonality and characteristics of team definitions, Huang (2018) proposed that a team is an organizational entity composed of two or more members who, based on mutual influence, jointly achieve established goals through the complementarity of skills. In such organizational structures, members collaborate around common goals and visions, share responsibilities and stimulate team synergy through communication, trust and cooperation. Research teams, as a special type of team, mainly focus on research projects to achieve dual objectives of economic benefits and talent cultivation. In such teams, members complement each other's skills, share responsibilities and respect each other. Cao (2018) further elaborated on the composition of formal organizations, suggesting that these organizations consist of different individuals who collaborate to achieve common goals. In such organizations, each member is responsible for achieving common performance goals and the skills of team members complement each other. The decision-making process of the team allows each member to participate in discussions and jointly make decisions. In contrast, the definition of a group differs from that of a team, as a group may have common goals but lacks collaboration, appearing as an aggregation lacking cohesion. In a group, members have different skills and independently complete assigned tasks and group performance can be seen as a simple aggregation of individual performance. In the decision-making process, decisions are often made by the leaders of the group, who then authorize other members to execute those decisions.

Chen and Yang (2002) proposed that research teams consist of a group of technologically complementary researchers who are responsible for achieving common research goals and adopting research methods. These team members are relatively few in number and focus on scientific research and development activities. Similarly, Katzenbach and Smith (2015) interpreted the concept of research teams, stating that research teams consist of fewer members who engage in relevant research activities for common academic research goals. From the perspective of the number of research teams, Rey-Rocha et al. (2006) defined research teams as a single entity belonging to the same research unit or consisting of two or more personnel. Such teams have the same scientific interests and academic goals, conduct academic research in the same professional field, share resources, jointly publish research results and have a certain degree of autonomy in economic and decision-making. These definitions and viewpoints provide multidimensional perspectives for understanding the structure and function of research teams.

2.2.1.2 Characteristics and classification of research teams

Huang (2018) elaborated on the characteristics of research teams, including the following aspects: first, research teams with strong research capabilities must have clear research directions and feasible research goals. Second, team members should complement each other in terms of knowledge structure, thinking mode, research experience, humanistic literacy and work ability and effectively communicate and influence each other on this basis. Third, research teams should strengthen mutual respect and trust among members. Fourth, team leaders should have a strategic vision and excellent coordination skills. Fifth, research teams should continuously produce innovative research results. To achieve efficient operation of the team, the goals of team members should be consistent with the overall goals of the team to ensure that members can make sufficient and effective contributions to the development of the team. Wang (2013) defined university research teams as formal groups led by academic leaders, where team members complement each other in professional skills and age structure, are willing to take responsibility for common research tasks and form voluntarily. Such a definition illustrates the structural characteristics of university research teams and the spirit of cooperation among members.

According to differences in main characteristics, research teams can be divided into three main types: university research teams, government research institutions and enterprise research teams (Huang, 2018). University research teams mainly focus on basic research and applied research, relying on key laboratories to strengthen scientific innovation capabilities. Team members are usually composed of outstanding young and middle-aged scientists within universities, who can achieve interdisciplinary cooperation and collaboration across multiple departments and disciplines. Government research institutions mainly exist in the form of research institutes, with research fields mainly focusing on applied basic research and standard development. The government, as the property rights subject of research institutions, establishes these institutions based on national policies such as social, economic, scientific and technological and national defense policies, with the significant public welfare. Enterprise research teams focus on applied research, with research results mainly reflected in technological innovation and product innovation. Enterprises aim to maximize profits and enterprise research teams rely on high-value-added new technologies or products, showing characteristics of high income, high risk and high return. In addition, Lu et al. (2008) proposed that research teams, in addition to having the characteristics of general teams, also have the unique attribute of high member knowledge levels. In the corporate environment, research and development team members belong to a group with high levels of knowledge. Lack of solid professional knowledge will lead to inadequate technology and an inability to create profits for the enterprise. Therefore, mastery of professional knowledge is fundamental to engaging in research and development work and completing team research tasks.

2.2.2 Study on factors influencing research team evaluation

2.2.2.1 Analysis of influencing factors

Regarding individual factors, Amabile et al. (2004) conducted in-depth research on how leadership behavior influences the creativity of research teams. The results showed a positive correlation between leadership support and team members' creativity assessment in projects. By combining qualitative and quantitative analysis methods, the study identified specific leadership behaviors related to perceived leadership support and explored how these behaviors affect team members' perceptions, emotional responses and creativity. The research demonstrated that through effective behaviors such as monitoring progress, providing consultative decisions, offering emotional support and acknowledging excellent work, leaders can motivate research team members to work more diligently. Liu (2016) believed that leadership level is one of the key factors influencing team members' job satisfaction and has a direct impact on team performance. In innovative research teams in universities, academic leaders should possess transactional and transformational leadership abilities. Academic leaders with transactional leadership characteristics can clarify team roles, work requirements and goals and improve the fairness of benefit distribution, thereby enhancing team performance to some extent. In knowledge-intensive and innovative research teams, team members with high innovation potential demonstrate higher potential for work innovation in open thinking, work dedication and challenging traditions, which contributes to outstanding work performance.

Regarding team factors, Levi and Slem (1995) discussed how internal relationships within teams affect team efficacy. The research revealed a significant correlation between social relationship factors among team members and team success. In the process of problem-solving within the team, the team performs well as a whole, although occasionally disturbed by individual member behavior. The study observed that team members generally perceived the inadequacy of reward mechanisms, which influenced their assessment of the team's self-management capabilities. These findings demonstrate the importance of considering social relationship factors in team management and the necessity of establishing effective reward mechanisms to enhance team self-management capabilities and overall efficacy.

Regarding team interaction process factors, Cao (2018) constructed a research cooperation network by analyzing author information in journal articles published by research teams and explored the impact of co-authorship relationships on research team performance. This study delved into key performance indicators that may affect team output in research cooperation networks and revealed the significant impact of cooperation network structure on team research performance. Through this method, the study provided a new perspective for understanding the internal cooperation dynamics of research teams and their role in research output mechanisms.

2.2.2.2 Path evolution process

Chen and Feng (2002) systematically studied the life cycle of research teams, dividing it into four stages: incubation, formation, operation and dissolution. In the incubation stage, at the beginning of the establishment of the research team, it is mainly evaluated whether it is appropriate to conduct research projects in a team mode. This stage requires careful identification based on project characteristics. When the required resources for the project exceed individual capabilities, it may be considered to advance the work through the formation of a research team. In the formation stage, core team members begin to build the team, including project analysis, team member selection, task allocation, establishment of benefit distribution mechanisms and planning of research team organizational structure. The operation stage marks the formal start and operation of the research team. During this stage, the team needs to supervise and control the completion of tasks by members, motivate members to efficiently complete research tasks and manage team knowledge assets properly. Key tasks in this stage include member motivation, supervision management and knowledge management. To promote effective cooperation among team members, it is necessary to establish reasonable incentive mechanisms and ensure that knowledge is fully shared within the team to ensure that the team can complete the established tasks on time. The dissolution stage is when the research team moves towards dissolution. The main tasks include distributing member benefits and comprehensive performance evaluation based on predetermined benefit distribution mechanisms.

As a crucial organizational form, the construction and development of university research teams rely on a series of basic elements. Liang (2015) categorized these elements into three main categories: human elements, material elements and organizational system elements. Human elements are the cornerstone of building research teams, covering various levels such as academic leaders, academic backbone members, general researchers, research assistants and management personnel. The professional skills, knowledge level and collaborative work ability of these personnel are the core driving forces for promoting the development of research teams. Material elements refer to the material basis required for the operation of research teams, including but not limited to laboratory facilities, equipment and research materials. These material conditions are the basic prerequisites for ensuring that research teams can carry out work smoothly. Organizational system elements involve various aspects such as the organizational structure of research teams, operation mechanisms, team goals, team culture, team spirit and management systems. These institutional arrangements provide effective organizational guarantees for research teams, ensuring that teams can operate efficiently and coordinate to achieve established research goals. Through the organic combination of these elements, university research teams can unleash their maximum potential in research activities, promoting the output of academic achievements and the enhancement of innovation capabilities.

2.2.3 Research on the performance evaluation of research teams

2.2.3.1 Performance evaluation mechanism

Scientific and reasonable performance evaluation of university research teams is a crucial aspect of research team management. Constructing a quality-oriented performance evaluation mechanism for research teams is of significant importance in promoting collaborative innovation between universities and various innovation entities in society, enhancing overall innovation capabilities and competitiveness. Although quantitative evaluation of research outcomes has a certain degree of rationality, its limitations cannot be ignored. The academic quality of research outcomes cannot be comprehensively evaluated solely through quantitative indicators. Therefore, shifting the evaluation mechanism from the traditional core of paper publication and award acquisition to a quality-oriented approach, focusing on discipline construction, deepening institutional reforms, emphasizing the improvement of innovation capabilities, using innovation quality and contribution as evaluation criteria and emphasizing the actual effects of original innovation and addressing major national needs. Establishing a comprehensive evaluation mechanism and an exit mechanism, encouraging competition and dynamic development, fully leveraging the unique role of higher education as an important nexus of science and technology and talent in national development (Xin, 2014). To prevent the simple piecing together of research teams, individual assessments of team members are required. The study proposed a system of overall assessment of research team and individual performance assessment indicators, establishing performance evaluations based on the characteristics of research teams, defining task performance and peripheral performance as two dimensions of performance evaluation. Task performance involves performance output directly related to the work responsibilities of team members, reflecting the direct results of individual members' research work. Peripheral performance covers factors such as team members' sense of responsibility to the team, initiative and enthusiasm for research work, innovative spirit and spirit of cooperation. Although these factors indirectly affect the overall performance of the team, they are equally important. In addition, in practice, team assessment is divided into annual assessment and term assessment, in order to better stimulate the positive motivation for team construction and development (Yu and Hu, 2008).

Performance evaluation of research teams is a cognitive activity, the core of which lies in using established research goals as benchmarks, employing a combination of qualitative and quantitative methods to comprehensively assess various aspects of research team behavior, resource input and output, member attitudes and growth. This process effectively manages, supervises, predicts and controls research activities and provides scientific basis for relevant decisions (Wang, 2013). Liu (2016), starting from a systemic perspective, comprehensively analyzed the multiple factors influencing the performance of innovation teams in universities. The study proposed that team performance is the result of the joint action of multiple factors in the three stages of system input, process and output, including individual factors, team characteristics, environmental conditions and team interaction processes. These influencing factors mainly include leadership factors, team atmosphere factors and group potential factors, which, respectively, reflect the leadership ability of team leaders, members' perception of the work environment and the team's belief in its work ability. The study deeply explored the influencing mechanism of the performance of innovation teams in universities and constructed a performance evaluation index system based on leadership and group potential. Zhang (2009) believes that the goal of forming university research teams should be positioned to pursue high standards, high levels and high performance, which is not only the value pursuit of research teams but also the core of evaluating team construction effectiveness. Therefore, the evaluation of research teams should focus on performance evaluation, evaluation indicators should be comprehensively quantified and operationalized, to ensure the accuracy and practicality of evaluation results.

In addition, input–output is one of the important performance indicators. Huang (2018) used the Analytic Hierarchy Process to construct a performance evaluation system for research teams with input and output as primary indicators. In this system, input is seen as the basis for the successful implementation of projects and also as a key variable for evaluating project outcomes. The study further subdivided input into three subdimensions: human resource input, financial input and fixed asset input. Regarding output, as the core of research team work, it directly reflects the team's performance and is crucial for evaluating research team performance. Output is subdivided into three specific aspects: output formation, economic benefits and talent cultivation. He and Chen (2012) in defining the R&D performance of research teams, defined it as the economic performance, technical performance and extension performance generated based on certain inputs. Wang (2013) proposed that in research evaluation standards, reliance on quantity indicators should be reduced and emphasis should be placed on evaluating the quality of research results and the performance of dissemination and application. Performance evaluation of research activities based on input–output perspective is not only intuitive but also conducive to stimulating the innovation vitality of teams, improving innovation quality and thus building research teams that operate scientifically, openly, cooperatively and efficiently.

2.2.3.2 Performance evaluation methods

Starting from the perspective of strategic construction evaluation mechanisms for research teams, Huang Huang et al. (2018) determined a strategic value weight structure with strategic orientation and regulatory effects. Starting from the perspective of giving full play to the subjective initiative of team members, they selected leading evaluation methods and optimized competitive evaluation methods, constructing a comprehensive evaluation mechanism that includes evaluation methods, implementation processes and specific application methods of evaluation results, providing beneficial supplements to relevant evaluation work. Liu et al. (2011) proposed in the study that research team evaluation mainly adopts two methods: peer review and bibliometrics. Peer review relies on the knowledge and wisdom of experts to evaluate the scientific contributions of teams and the value of their achievements based on certain criteria. Bibliometric methods are widely used due to their high objectivity and have become the main method for evaluating team performance. Bibliometric indicators mainly include the total number of publications, average number of publications, total number of citations, average number of citations, impact factor of papers and number of highly cited papers.

In recent years, the Data Envelopment Analysis model has been applied to evaluate the performance of research teams, confirming its effectiveness in research team evaluation. Han (2013) believes that the performance evaluation of university research teams should be based on a performance appraisal system, using a combined quantitative and qualitative method to analyze the factors influencing team performance and fully considering the research cycle to improve the research and innovation performance evaluation system. This system should shift from quantity evaluation to quality evaluation, from process management to goal management, enhance the initiative and creativity of team members and build a performance evaluation mechanism oriented toward innovation quality and contribution. At the same time, a dedicated research team management mechanism should be established to grasp the research dynamics and needs of the team, stimulate enthusiasm for cooperative innovation, improve the incentive mechanism for scientific and technological evaluation, strengthen scientific and technological performance evaluation and management, effectively incentivize teams to conduct in-depth research on new topics and create new achievements.

Liu et al. (2011) using social network analysis, proposed a series of strategies to improve the performance of research teams: first, strengthen scientific cooperation within the team, promote knowledge exchange and communication, increase the frequency of cooperation among team members to fully leverage the synergistic effects of team cooperation. Second, actively organize domestic and international academic conferences to create opportunities for collaboration among scholars and establish diverse cooperation platforms. Third, strengthen the cultivation of core leaders to fully leverage their leadership roles within the team. Liu (2019) proposed that research is an activity that requires long-term accumulation. Evaluation should follow the inherent laws of disciplines and the research outcomes of certain disciplines should be evaluated over a longer time span to prevent researchers from overly responding to short-term assessments. Tailored evaluation schemes should be formulated based on the differences in research rules. The current evaluation mechanism for research outcomes has not fully considered the research process and the quality of outcomes and lacks supervision from the public. To address these issues, it is suggested to establish a fair evaluation system, promote academic supervision mechanisms involving multiple parties, remove barriers to the participation of social supervision forces in research evaluation and ensure the quality of research outcomes. Building on the improvement of the peer review system, explore ways to broaden public participation in academic evaluation supervision. In the process of research evaluation, introduce a third-party evaluation system, involving multiple parties such as industries, enterprises and social institutions, to ensure the independence of experts and reduce administrative intervention. In addition, it is suggested to improve the leadership mechanism, comprehensively consider the positions and career development of team members during the evaluation of research outcomes and ensure the equivalence between research risks and evaluation rewards. Zhang (2023) found that the evaluation of research teams faces problems such as incomplete institutional norms, lack of qualitative evaluation indicators, insufficient attention to the transformation and application effects of achievements and optimization needs in organization implementation. To address these issues, she proposed strengthening top-level design, improving institutional construction, combining quantitative and qualitative evaluations, optimizing the evaluation indicator system, improving performance evaluation mechanisms, enhancing the targeting of evaluations and emphasizing the application of evaluation results to promote the healthy development of research teams.

Liang (2015) when discussing strategies to enhance the innovation capabilities of university research teams, proposed a series of strategies: first, introduce and cultivate academic leaders and backbone personnel, clarify research directions and integrate them with the needs of local socio-economic development. Second, strengthen organizational institutional construction, optimize management mechanisms and select appropriate research team organizational models to ensure clarity and optimization of institutions. Third, strengthen the construction of scientific and technological innovation platforms, especially focusing on specific measures for resource integration. Chen (2023) based on the correlation analysis between team structure characteristics and research performance, proposed suggestions for enhancing the diversity of high-level research team members and institution distribution based on the developmental characteristics and shortcomings of research teams. It is believed that strengthening the construction of high-level teams in different regions, universities and disciplines, promoting coordinated development between institutions and disciplines. In the process of team construction, attention should be paid to enhancing internal cooperation and mutual learning among team members, gradually building an efficient team collaboration mechanism. Maintaining and enhancing the stability of research team talent and key talent, enhancing communication and cooperation among members, thereby forming more core research teams.

Xu and Sun (2023) address the problems existing in China's talent system mechanism, such as the irrationality of talent cultivation support systems, the homogenization of evaluation mechanisms, the unsmooth channels for talent mobility, the insufficient activation of scientific and technological innovation capabilities, the imperfections in the talent introduction system and the deficiencies in the policy environment and guarantee mechanism construction conducive to the growth of scientific and technological talents, proposed a series of improvement suggestions. First, improve the support mechanism for talent cultivation, establish a relationship of mutual cooperation and constraint between the government and the market and form a new talent governance system and a benign interaction mechanism, to break through the systemic barriers to talent mobility and market segmentation. Second, innovate the talent evaluation mechanism and establish an evaluation system oriented toward scientific and technological development goals, with innovation capability, quality and contribution as the core. Third, improve the talent flow allocation mechanism, eliminate barriers to talent mobility, promote efficient and free movement of scientific and technological talents based on market rules and establish bidirectional talent flow mechanisms and compensation mechanisms for high-level talent movement, ensuring the flexibility and bidirectionality of talent mobility. Fourth, strengthen incentives for talent innovation and entrepreneurship, increase incentives for innovative talents, improve incentives for intellectual property rights of scientific and technological achievements and market evaluation factors and distribution mechanisms based on contributions, strengthening the protection of intellectual property rights of innovative achievements. Fifth, build an internationally competitive talent introduction and utilization mechanism, further improve the mechanism for open cooperation in scientific and technological innovation and the strategy for introducing overseas talents. Finally, establish a guarantee mechanism prioritizing talent development, improve the scientific and technological talent service system, increase investment in the construction of public service systems for talents, to promote the comprehensive optimization and development of scientific and technological talent system mechanisms.

2.3 Literature review

2.3.1 Previous research insufficiencies

Single Research Methods: Many scholars have explored the “Open Bidding for Selecting the Best Candidates” policy from diverse perspectives, mainly focusing on defining the concept, elucidating its intrinsic attributes and characteristics and analyzing its operational mechanisms. These studies generally employ comparative analysis methods to identify problems encountered during the implementation of the policy and propose corresponding improvement suggestions. However, existing literature lacks in-depth analysis using empirical research methods. Given the emergence of a series of issues and rich implementation cases nationwide since the promotion and implementation of this policy, empirical analysis is essential to comprehensively evaluate the effectiveness of the “Open Bidding for Selecting the Best Candidates” policy. Through field investigations and research, utilizing empirical analysis methods to collect first-hand data will provide compelling evidence for assessing the performance of the policy implementation. Moreover, empirical research results will play a crucial role in accurately optimizing the content of bid announcements, effectively screening and identifying leading teams, thereby promoting the efficient execution of this policy.

Inadequate Discussion on Team Collaboration: In the academic community both domestically and internationally, the evaluation system for university research teams generally focuses on assessing the existing achievements of the teams, while insufficiently considering the contributions of team collaboration and overall synergistic effects. This oversight fails to adequately reflect and incentivize the collaborative spirit of team members and the efficiency of team collaboration. Therefore, existing evaluation systems often face problems of inaccurate and incomplete evaluations when assessing the comprehensive strength of university research teams.

Neglect of Research on Team Selection: Existing literature lacks sufficient exploration of the team selection mechanism, characterized by the absence of a comprehensive theoretical framework and in-depth empirical analysis. This deficiency results in a lack of solid academic support for the design and implementation of the selection mechanism. The importance of the team selection mechanism cannot be underestimated, as its effectiveness directly affects the quality and efficiency of research teams, optimizes the allocation of research resources, stimulates interdisciplinary collaboration and innovation potential, fosters the continuity of research talent, enhances competitiveness in the research field, adapts to the dynamic research environment and accelerates the transformation of research results into practical applications.

2.3.2 Future research directions

2.3.2.1 Research on the evaluation system of “Open Bidding for Selecting the Best Candidates” research teams

Enhancing the effectiveness of the evaluation system for “Open Bidding for Selecting the Best Candidates” research teams is crucial for building an efficient, transparent and scientifically sound evaluation framework and organizational model that incentivizes the innovative potential of research teams. Future research can focus on developing a set of scientific and reasonable evaluation criteria and processes to comprehensively assess the overall performance of “Open Bidding for Selecting the Best Candidates” research teams. This assessment should not only cover the research effectiveness of the teams as a whole but also meticulously examine the individual contributions of academic leaders and team members. Specifically regarding the participation roles of higher education institutions' research teams in the “Open Bidding for Selecting the Best Candidates” mechanism, research should delve into how to enhance the application value and transformation efficiency of research results and whether these results can effectively address technical bottlenecks encountered in key core technology projects and major emergency research projects.

2.3.2.2 Research on the selection mechanism of “Open Bidding for Selecting the Best Candidates” research teams

An effective selection mechanism for “Open Bidding for Selecting the Best Candidates” research teams is crucial for ensuring the identification of teams with outstanding research capabilities and significant innovative potential. This, in turn, significantly enhances the quality of research outcomes and academic influence. Research on how to utilize the “Open Bidding for Selecting the Best Candidates” mechanism to conduct in-depth evaluations of the academic strength of university research teams and their contributions to society involves multiple dimensions such as academic reputation, collaboration networks, patent quality, technology transfer rate and the social application of scientific achievements. Constructing a key evaluation index system is essential in this regard. Additionally, the selection index system should encompass non-quantitative indicators such as academic communication skills and team cooperation atmosphere to identify and select research teams with significant advantages in technical capabilities. This approach can effectively enhance the operational efficiency and practical application value of the “Open Bidding for Selecting the Best Candidates” policy.

3. Building a theoretical framework for the evaluation mechanism of “Open Bidding for Selecting the Best Candidates” research teams

Based on Bandura's “reciprocal determinism,” this paper thinks that there is a close relationship between team leader evaluation, team member evaluation and scientific research team selection strategy and thus constructs a theoretical analysis framework of the evaluation mechanism of “selecting the best by open bidding,” which is mainly divided into three parts: team leader evaluation, team member evaluation and scientific research team selection strategy, as shown in Figure 1. The research conclusion will provide theoretical guidance for strengthening the precise connection between the tender announcement and the winning bid, thus promoting the practical application of the policy of “selecting the best through open tender” in China.

In the evaluation field of the team leader and its members, it can be observed from the practical cases of the “Open Bidding for Selecting the Best Candidates” project that a large proportion of team leaders in universities hold honorary titles such as Changjiang Scholars. This phenomenon has raised doubts about the validity of talent titles in assessing research innovation capabilities and team leadership. Furthermore, the question arises whether these talent titles should be a necessary condition for selecting team leaders in the “Open Bidding for Selecting the Best Candidates” project. Therefore, based on the concept of collective intelligence, this paper collects and organizes a series of data on open bidders from universities and then conducts empirical analysis for the first time to explore the correlation between the possession of talent titles and the research innovation capabilities and team leadership of scholars. This research provides a more scientific and objective evaluation basis for the selection mechanism of research team leaders in the “Open Bidding for Selecting the Best Candidates” project.

As for the selection strategy of research teams, the “Open Bidding for Selecting the Best Candidates” research team selection mechanism stimulates the innovation potential of research teams through open competition and breaks through the bottleneck of key core technologies. The core idea of this mechanism is to go beyond the traditional framework of selecting scientific researchers, abandon inherent biases towards the background, qualifications and titles of scientific researchers and focus on actual research capabilities and contributions to society, thereby promoting the optimization of research resource allocation and innovative reform of the research system and mechanism. Therefore, by collecting and analyzing the data of research teams participating in the “Open Bidding for Selecting the Best Candidates” project in recent years, this paper empirically explores the innovation achievements of research team leaders and members in academic papers, monographs, research topics, etc., as well as the social transformation effects of research achievements. This paper is committed to building and improving a diversified research team selection mechanism with the goal of solving “bottleneck” technical problems.

4. Conclusion

Against the background of rapid technological development and continuous globalization, the digital economy has increasingly become the key driving force for China's economic development. In the era of the digital economy, cultivating high-level compound digital talents is particularly important, which requires universities to focus on cultivating the innovation capabilities and teamwork spirit of talents in the education process. Through continuous collaborative cooperation, strengthen team cooperation ability and communication skills, cultivate high-quality talents with both innovation spirit and teamwork ability. As important bases for talent training, higher education institutions should fully utilize the collaborative advantages of diversified training subjects and focus on cultivating high-level compound digital talents adaptable to the era of the digital economy.

The evaluation of research teams has become a core link to improve the innovation capabilities of university research teams and the quality of talent training. In fact, the “Open Bidding for Selecting the Best Candidates” system embodies a problem-oriented scientific and technological innovation strategy, which aims to solve major needs, encourages open bidders to concentrate their efforts, work hard to overcome bottlenecks in key core technology areas and solve “bottleneck” problems. This system not only meets the requirements of multidimensional evaluation in current scientific research evaluation but also conforms to modern concepts of talent evaluation. Therefore, building a scientific and reasonable evaluation system is of great significance for stimulating the innovation vitality of research teams and cultivating compound talents adaptable to the development of the digital economy.

Since the implementation of the “Open Bidding for Selecting the Best Candidates” system, it has encountered a series of challenges in practice, including the imperfect project management system, the disconnection between the list demand and the actual core technology demand and the low conversion rate of technological achievements of open bidding projects. These problems are largely due to the singularity of research team evaluation methods. Therefore, after systematically reviewing the existing literature on the “Open Bidding for Selecting the Best Candidates” and research team evaluation mechanisms, this paper identifies the shortcomings in methodological singularity, insufficient discussion on the dimension of team cooperation and neglect of the selection of research team issues in previous studies. This paper further explores the research direction of the evaluation system and selection mechanism of research teams in the “Open Bidding for Selecting the Best Candidates” project and constructs a theoretical analysis framework to provide solid theoretical support for future in-depth research.

Figures

Figure 1

Theoretical analysis framework of the “Open Bidding for Selecting the Best Candidates” research team evaluation mechanism