Reasonable Incentive Research Articles

The impact of organizational incentives on the loyalty of studying abroad teachers in Hainan, China: The mediating role of organizational commitment

Competition in higher education is essentially a competition for talent, particularly manifested in the competition among high-level university teachers. Therefore, enhancing teachers' loyalty to their profession and universities, controlling talent turnover, and establishing a responsible and well-structured teaching staff are fundamental to the sustainable development of universities. This study, based on the motivation-hygiene theory, utilized convenience sampling to conduct a questionnaire survey on internationally studying teachers from 21 universities in Hainan Province, China. A total of 667 valid questionnaires were collected, and a structural equation model was employed to investigate the influence of organizational incentives in Hainan's universities on the loyalty of international teaching staff; as well as the mediating effect of organizational commitment of international teachers between organizational incentives and loyalty. The research results show that both organizational incentives and organizational commitment can positively predict teachers' loyalty, and organizational incentives can positively predict organizational commitment. Organizational commitment plays a partial mediating role between organizational incentives and teachers' loyalty. Based on this, this study suggests that university administrators should establish a reasonable incentive mechanism and cultivate the organizational commitment of overseas-study teachers, in order to comprehensively enhance the loyalty of overseas-study teachers and ensure the stability of the teaching staff.

Federated Learning Incentive Mechanism Setting in UAV-Assisted Space–Terrestrial Integration Networks

The UAV-assisted space–terrestrial integrated network provides extensive coverage and high flexibility in communication services. UAVs and ground terminals collaborate to train models and provide services. In order to protect data privacy, federated learning is widely used. However, the participation of UAVs and ground terminals is not gratuitous, and reasonable incentives for federated learning need to be set up to encourage their participation. To address the above issues, this paper proposes a federated reliable incentive mechanism based on hierarchical reinforcement learning. The mechanism allocates inter-round incentives at the upper level to ensure the maximisation of the server’s utility, and performs inter-client incentive allocation at the lower level to ensure the minimisation of each round’s latency. The reasonable incentive allocation enables the central server to achieve higher model training accuracy under the limited incentive budget, which reduces the cost of model training. At the same time, an attack detection mechanism is implemented to identify malicious clients participating in federated learning, preventing their involvement in aggregation and revoking their incentives. This better ensures the security of model training. Finally, we conducted experiments on Fmnist, and the results indicate that this method effectively improves the accuracy and security of model training.

A dynamic incentive mechanism for data sharing in manufacturing industry

Data sharing is a critical component in a blockchain traceability platform. Therefore, creating a reasonable incentive mechanism to ensure that all enterprises participate in data sharing is vital for blockchain platforms. Currently, many researchers employ evolutionary game theory to analyze problems related to data sharing. However, evolutionary game theory typically assumes that the population composed of enterprises is mixed uniformly. Enterprises in the manufacturing industry are not uniformly mixed, as they tend to have specific connections with each other due to the size of enterprises and volume of business. Therefore, a networked evolutionary game is introduced to solve this problem. Firstly, an incentive model for enterprises sharing data is established. Then, a scale-free network is employed to simulate the connections between enterprises. To comprehensively consider the individual and group benefits of enterprises in the game, this study designs a strategy update rule for networked evolutionary game based on Discrete Particle Swarm Optimization and Variable Neighborhood Descent algorithm. To tackle the challenge of determining reasonable incentive values in networked evolutionary games, this study proposes a dynamic incentive mechanism based on the Q-Learning algorithm. Finally, the experiments indicate that this method can successfully facilitate the stable involvement of enterprises in data sharing.

Incentive contract design considering quotas production: A principal-agent perspective

The quality of primary products in a supply chain is determined by the agent and the principal. Simultaneously, there are quota production constraints on the principals. Our discourse centers on the design of incentive contracts for agents within these supply chains. We derived the parameters describing the contract, level of effort, profits for both sides, and the minimum requirements of the principal for the proportion of high-quality primary products. This study compares the decision-making paradigms of agents and principals in various contexts. The results show that decision-making mechanisms are strongly influenced by individual effort costs, various internal and external organizational variables, and the interplay of efforts on both sides. Using numerical experiments, we investigate the effects of different situations between clients and contractors on contracting and effort strategies. The results show that when the agent exerts unilateral effort, reasonable incentive contracts can spur the agent to increase effort and thus increase the proportion of high-quality primary products. In the case of bilateral efforts, a well-calibrated contract design benefits the agent (bearing less risk). For the principal, the expected profit increases in most cases. When considering the quota production, it is necessary to set an appropriate limit on the proportion of high-quality primary products.

Optimal incentive strategy in blockchain-based mobile crowdsensing using game theory

Mobile crowdsensing (MCS) is a cutting-edge technology that leverages the computing power of mobile devices to gather environmental data, efficiently utilizing underutilized resources to complete sensing tasks. However, the lack of initiative and motivation among participants in existing MCS systems is a crucial factor limiting its development. As a result, examining the incentive mechanism in MCS becomes essential for attracting a greater number of participants to join. In addition, existing MCS systems typically employ a centralized platform, but this structure poses security issues, as the platform can manipulate MCS operations to gain inappropriate profits and is prone to single point of failures. In response to the above-mentioned issues, this paper proposes a novel blockchain-based MCS system, referred to as CrowdBS. Firstly, the system adopts a decentralized structure and distributed consensus methods to enhance the reliability and security of the system. Additionally, the use of smart contracts allows the system to automatically execute predefined operations without the need for human intervention. In MCS, this means that it can automatically trigger operations such as data collection, reward distribution, task assignment, etc., thereby improving efficiency and reducing the risk of human intervention. Secondly, the system introduces a two-stage Stackelberg game model, attracting more sensing users to participate in tasks through reasonable incentive mechanisms and ensuring fairness in reward distribution, thereby promoting efficient, reliable, and sustainable execution of crowdsensing tasks. Finally, the correctness of the model is verified through simulation experiments, and compared with the existing MCS system. The comparison results show that the system and its incentive mechanism have more comprehensive advantages in terms of reliability, security and performance.

The impact of executive compensation incentive on corporate innovation capability: Evidence from agro-based companies in China.

This paper aims to examine the impact of executive compensation incentive on corporate innovation capability by dividing executive compensation incentive into short-term monetary incentive and long-term equity incentive. We also investigate the interaction between the two types of executive compensation incentive. Data are collected from China's agro-based companies during 2012-2019, and multiple regression analysis is utilized. The empirical results show that short-term monetary incentive has no impact on innovation capability, while long-term equity incentive stimulates innovation capability. Regarding company ownership, the impact of long-term equity incentive in state-owned enterprises is greater than that in private-owned enterprises. In addition, the complementary effect between short-term and long-term compensation incentive has a positive impact on innovation capability regardless of company ownership. The findings of this paper could help agribusiness managers to design the reasonable incentive system to incentivize corporate executives and enhance the capability of independent innovation.

Research on human resource allocation management of enterprise information project construction

Abstract Strengthening the management of science and technology resources and improving the intensive management of science and technology resources is a shortcut for the development of traditional industries to informatization. This paper analyzes a series of problems in human resource allocation by studying the current situation of enterprise informationization project implementation and constructs a human resource management system in informationization project implementation by combining relevant theoretical systems. The system uses the key chain method to find capacity-constrained resources and takes resource balancing measures, which can solve the multi-project resource allocation problem where the types and quantities of available resources have been determined. To ensure the convergence and uniform distribution of the resource allocation solution space, an orthogonal design method is used for the initialized population, and the solution space is expanded without significant changes in production costs and benefits compared to the original reference solution. After the enterprise used the system improvement, in the enterprise recruitment sales class, technical class, and administrative class onboarding rate increased by 13.2%, 57.3%, and 19.7%, respectively, the overall onboarding rate increased by 46.9%, the enterprise overall staff turnover rate reduced from 47.2% to 27.3%, technical class employees both time incentive ratio exceeded 60%. It can be seen from the information technology project that technical talent introduction, as well as technical team building, strengthening personnel training efforts, and establishing a reasonable incentive assessment system, can effectively improve the project team’s human resources management level.

Economic dispatch of generation load aggregators based on two-stage robust optimization

Introduction: In recent years, with the rapid development of renewable energy generation, the stability of the power grid has been greatly reduced. In response to this problem, integrating the user side transferable load into the power market has become the key to the development of future power grid. At present, large transferable loads have entered the electricity market in some pilot areas of China, but the relevant research on small and medium-sized transferable users entering the electricity market is still few.Methods: This paper proposes the concept of generation load aggregators. A two-stage generation load aggregator robust optimization model is developed to obtain the scheduling scheme with the lowest operating cost under the worst scenario. The model consists of distributed renewable power, transferable load, self-provided power, energy storage, etc. Uncertainties of renewable energy and load are introduced in the model. By using the column constraint generation algorithm and strong pairwise theory, the original problem is decomposed into the main problem and sub-problems to be solved alternately, so as to obtain the scheduling scheme with the lowest operating cost in the worst scenario under different conservatism.Results: The solved results are compared with those without the generation load aggregator, illustrating the role of the generation load aggregator in relieving peak and valley pressure on the grid from the load side, reducing the cost of electricity for loads, and promoting the consumption of renewable energy. The comparison with the deterministic optimization algorithm shows a significant decrease in the total cost and validates the performance of the selected solution algorithm. The boundary conditions for the use of energy storage by generation load aggregators for peak and valley reduction under the time-sharing tariff mechanism are also derived.Discussion: This study can provide reference for the investors of generation load aggregators when planning whether to install energy storage or the scale of energy storage, and also help the power market management department to design a reasonable incentive mechanism.

Incentive Mechanism of Utility Tunnel PPP Projects with User Involvement

Users of utility tunnel public–private partnership (PPP) projects are productive corporate entities with a specific scope, a limited quantity, and strong bargaining power. They possess the ability and motivation to participate in PPP projects through equity investments. Tunnel PPP projects involving user participation are implemented jointly by consortia comprising construction contractors and users. Given that the government, construction contractors, and users have different interests, significant conflicts of interest arise among them. Furthermore, there exists dual information asymmetry between the government and construction contractors, as well as between the government and users. The government lacks a direct observation of the true ability endowments of construction contractors and users prior to signing PPP contracts, and it cannot directly observe the effort levels exerted by the construction contractors and users after contract signing. The combination of dual information asymmetry and conflicting interests may result in adverse selection and moral hazard on the part of construction contractors and users. Drawing on principal–agent theory, this study constructed incentive models with and without user involvement, examined the impact of user involvement on the effort level of construction contractors and the performance of PPP projects, and designed a reasonable incentive mechanism. The objective is to ensure that construction contractors and users truthfully report their ability endowments during the bidding stage and exert optimal efforts during the construction and operation stages. This research provides new insights for promoting the sustainable development of tunnel PPP projects. A case analysis of a PT tunnel PPP project and an SC tunnel PPP project demonstrates the reasonableness and feasibility of the research findings.

An Evolutionary Game Analysis of Stakeholders’ Decision-Making Behavior in Medical Data Sharing

In the era of big data, medical data sharing has become an inevitable requirement to improve the quality and efficiency of medical services. To advance the progress of medical data sharing and expedite the circulation of data value, it becomes crucial to examine the decision-making behavior of stakeholders involved in the medical-data-sharing process. To this end, we construct a three-way evolutionary game model applicable to the medical sharing scenario, analyzing the evolutionary trends in the selection strategies of data providers, the medical-data-sharing platform and data demanders. Furthermore, through theoretical analysis and simulation experiments, we explore the game equilibrium point of the system and analyze key factors that affect stakeholder strategy selection. The results of the experiment show that, in addition to data security, platforms and regulators should pay attention to the regulation and governance of the quality of data flows, which involves reasonable incentives–feedback–rewards and penalties. By strengthening the security technology and data governance system construction of sharing platforms, as well as promoting regulatory authorities to implement reward and punishment measures, etc., a stable state can be achieved in such systems. In addition, this article also proposes relevant management suggestions for medical data sharing in order to provide useful references for scientific decision making by stakeholders.

Research of Unbalanced Regional Economic Development and Entrepreneurial Happiness based on CFPS Data

Based on the perspective of happiness economics and using the data of China Family Panel Studies in 2020, this paper empirically analyzes the impact of regional economic development imbalance on entrepreneurial happiness. The results show that regional economic development has a significant negative impact on entrepreneurial happiness on the whole, and it changes with the regional difference and the difference of urban and rural structure. Further research shows that economic development can improve the happiness of urban entrepreneurial groups in economically underdeveloped areas, but it has a significant hindering effect on the happiness of rural entrepreneurial groups in economically developed areas. In addition, this paper studies the mechanism of economic development on entrepreneurial happiness, and finds that each component of entrepreneurial happiness responds differently to economic development. The research results of this paper will provide theoretical basis for the government to formulate more reasonable incentive policies for innovation and entrepreneurship.

An efficient user demand response framework based on load sensing in smart grid

The current residential electricity demand is increasing. The demand side response of smart grid power users aims to enable users to reasonably plan their own power consumption through price incentives, so as to solve the problems of unreasonable power energy structure and low utilization rate. It is prominent to mine the rules of user response behaviors and design a reasonable incentive mechanism to maximize the enthusiasm of all participants. The traditional demand response is to ensure the stability of the power system from the macro-control load of the grid, which cannot meet the personalized requirements of power users. The existing incentive mechanism also does not comprehensively consider the profits of grid companies, low-voltage users, aggregators and other parties. In this paper, we propose a user demand response framework based on load awareness. Firstly, we devise a user demand response behaviour model based on short-term memory network. Secondly, we propose a demand response incentive scheme based on electric power scores. We also construct a deviation optimization integration adjustment model based on game theory to achieve the balance of profits among grid, aggregators and low-voltage users. The extensive experimental results show the effectiveness of our proposed framework.

Distributed collaborative optimization for coupled transportation and power systems operation considering carbon emission and elastic travel demand

With the continuous expansion of the scale of electric vehicles (EV), the coupling between the distribution system and the urban transportation system is deepening. In the context of global decarbonization, it is necessary to formulate reasonable incentives to optimize the operation of coupled transportation and power systems towards an economical and low-carbon system. To achieve this goal, we propose a collaborative optimization model that considers the overall carbon emission cost of the coupled system. To respect the actual living habits of users, elastic travel needs are also considered. First, on the transportation system side, considering two main participants, electric vehicles and gasoline vehicles (GV), a game-theoretic based traffic assignment model using user equilibrium criteria is built. To reduce the computational complexity of the sub-problem in achieving the user equilibrium, an efficient path generation model is designed, which considers the charging decision and range anxiety problems of electric vehicles and the cost of carbon emissions of gasoline vehicles. Second, on the power grid side, the optimal power flow using second-order cone programming (SOCP) for power distribution systems is built, considering the cost and carbon emission of various energy sources. Third, considering the incomplete information between the coupled system, the entire model is decoupled and solved by a distributed collaborative optimization model using the alternating direction method of multipliers (ADMM) algorithm. Finally, the proposed model and solution methods are verified by comprehensive case studies. Simulation results show that the distributed collaborative optimization model can effectively reduce the emissions of the coupled system, and provide a scientific basis for the formulation of charging prices.

Optimal dispatch of hydrogen/electric vehicle charging station based on charging decision prediction

The hydrogen/electric vehicle charging station (HEVCS) is widely regarded as a highly attractive system for facilitating the popularity of hydrogen and electric vehicles in the future. However, conventional optimal dispatch of HEVCS could lead to poor performance due to the lack of adequate consideration of vehicle charging decision behaviours and neglection of the impacts of different information sources on it. This paper investigates a charging demand prediction method that considers multi-source information and proposes a multi-objective optimal dispatching strategy of HEVCS. First, an information interaction framework of integrated road network, vehicles and HEVCS is introduced. Road network model and HEVCS model are established based on the proposed framework. To improve the flexibility of dispatch, two charging modes are designed, which are intended to guide drivers to adjust their consumption behaviour by electricity price incentives. Furthermore, psychologically based hybrid utility-regret decision model and Weber-Fechner (W–F) stimulus model are developed to reasonably predict drivers' choice of charging stations and charging modes. The daily revenue of HEVCS and the total queuing time of drivers are the objective functions considered in this paper simultaneously. The above multi-objective optimization results that the proposed strategy can effectively improve the benefits of HEVCS and reduce energy waste. Additionally, this paper discusses the results of a sensitivity analysis conducted by varying incentive discount, which reveals the combined benefits of the HEVCS and the vehicles are effectively increased by setting reasonable incentive discounts.

A two-stage robust optimization model for a virtual power plant considering responsiveness-based electric vehicle aggregation

With the goal of pursuing carbon neutrality, this study is aimed to investigate effectively managing distributed renewable energy. Considering the uncertainty of wind power (WP), photovoltaic power (PV), and load, a two-stage robust optimization model for virtual power plant (VPP) is proposed, with a focus on calculating the available capacity of electric vehicle (EV) aggregated virtual energy storage (VES). Time–space distribution characteristics of EVs and the responsiveness of EV users are analyzed and the available capacity calculation model is constructed. In the first stage, the bidding strategy and unit combination of the VPP are determined based on the predicted values of WP, PV, and load. Combining with the pre-decided decision from the first stage, the output of each unit is determined by considering an uncertainty set in the second stage, for achieving the best operational benefit under worst-case scenarios. Robust control parameters are introduced into the model and used to adjust conservativeness. The model is solved by applying the C&CG algorithm. Additionally, a carbon trading mechanism is leveraged to balance the system economy and environmental friendliness. The results show that the optimal scheduling solution helps to reduce fluctuations caused by uncertainties, balancing the economic benefits and operational risks of the VPP. Besides, guidance is provided for VPP operators to formulate reasonable incentives for EV users and the construction of energy storage systems. When the carbon trading price of the VPP proposed in the paper is set at 160 ¥/ton, economic and environmental sustainability can be effectively balanced.

Incentive Mechanism for Improving Task Completion Quality in Mobile Crowdsensing

Due to the randomness of participants’ movement and the selfishness and dishonesty of individuals in crowdsensing, the quality of the sensing data collected by the server platform is uncertain. Therefore, it is necessary to design a reasonable incentive mechanism in crowdsensing to ensure the stability of the sensing data quality. Most of the existing incentive mechanisms for data quality in crowdsensing are based on traditional economics, which believe that the decision of participants to complete a task depends on whether the benefit of the task is greater than the cost of completing the task. However, behavioral economics shows that people will be affected by the cost of investment in the past, resulting in decision-making bias. Therefore, different from the existing incentive mechanism researches, this paper considers the impact of sunk cost on user decision-making. An incentive mechanism based on sunk cost called IMBSC is proposed to motivate participants to improve data quality. The IMBSC mechanism stimulates the sunk cost effect of participants by designing effort sensing reference factor and withhold factor to improve their own data quality. The effectiveness of the IMBSC mechanism is verified from three aspects of platform utility, participant utility and the number of tasks completed through simulation experiments. The simulation results show that compared with the system without IMBSC mechanism, the platform utility is increased by more than 100%, the average utility of participants is increased by about 6%, and the task completion is increased by more than 50%.

Evolutionary Game of Digital-Driven Photovoltaic–Storage–Use Value Chain Collaboration: A Value Intelligence Creation Perspective

In the context of “carbon neutral”, distributed energy, including photovoltaic power generation and energy storage systems, is developing rapidly. Meanwhile, the new generation of information technology, such as “Cloud computing, Big data, the Internet of things, Mobile Internet, AI, Blockchain”, is driving the digital transformation of the energy industry. Under digital drive, how the agents in the photovoltaic–storage–use value chain collaborate and create value intelligently is a question worthy of deep consideration. Firstly, the value creation mechanism and collaborative process of the digital-driven photovoltaic–storage–use value chain are analyzed from a value intelligence creation perspective. Secondly, the tripartite evolutionary game model of photovoltaic power generator, energy storage provider and user is established. Finally, the influencing factors of digital- driven photovoltaic–storage–use value chain collaboration are explored through a numerical simulation, and management suggestions are put forward. The study finds the following: (1) The behavior choice of each agent in the value chain will affect the decision of other agents. In particular, the photovoltaic power generator has a great influence on the cooperative willingness of other agents. To promote value chain collaboration, the guiding role of the photovoltaic power generator should be fully realized. (2) Agents on the value chain can use a variety of digital technologies to improve enabling benefits, which is conducive to promoting value chain collaboration. (3) The driving costs and potential risks are obstacles for value chain collaboration. Cost reduction and risk prevention are effective ways to improve the willingness of collaboration. (4) Reasonable incentive compensation mechanisms and information asymmetry punishment measures are the keys to enhancing collective willingness. This research provides theoretical support for photovoltaic–storage–use value chain collaboration from a value intelligence creation perspective.

Equity incentive contract characteristics and company operational performance-An empirical study of Chinese listed companies.

Equity incentive, as an institutional arrangement for the coordination of the interests of shareholders and managers, has been widely implemented by public companies in developed capital markets throughout Europe and America. However, does it work and/or when might it be more effective in emerging market economies such as China? We aimed to understand the effects of equity incentive plans implemented by listed companies in China and the potential influence of the general characteristics of contracts on the effectiveness of equity incentive plans. Based on behavioral decision theory, this paper adopts a multivariate linear regression model to analyze the 1695 equity incentive plans implemented in Chinese listed companies between 2010 and 2018 with their two-year lagged performance data. The empirical results show that the operational performance of companies after implementing equity incentive plans shows a trend of polarization. In the 95% confidence interval, the effect of restrictive stock incentive and exercise-constrained variables is not significant, while the validity period has a significant positive correlation and incentive intensity has a significantly negative correlation with the company's operational performance. Furthermore, the negative effects mentioned above become more obvious with a longer plan implementation period. Based on these conclusions, we suggest that companies could adopt equity incentive plans with a relatively longer validity period and more reasonable incentive intensity. Additionally, it would be better for companies to select non-restricted stocks as incentive tools if there is no obvious preference.

A novel deep learning-based forecasting model optimized by heuristic algorithm for energy management of microgrid

Recently, the integration of renewable energy sources (RESs) in microgrids (MGs) has risen significantly owing to extensive promotion of decarbonization and green energy. However, despite the environmental–economic benefits, RESs are intermittent, and increasing penetration of RESs into MG poses operation challenges in handling uncertainties. In this paper, a novel deep learning-based forecasting model is proposed for MG operation considering uncertainties of RESs, load, and day-ahead price (DAP). To handle the intrinsic uncertainties of MGs, a long short-term memory (LSTM) network is employed and a method for increasing prediction accuracy of the LSTM model based on a genetic algorithm–adaptive weight particle swarm optimization (GA-AWPSO) combination along with a global attention mechanism (GAM) is proposed. Herein, the hyperparameters of the LSTM model are optimized by the GA-AWPSO algorithm, and GAM is added to mine important features from input datasets to improve forecasting performance. To handle uncertainties through the demand side, A DM-CIDR program is developed for providing optimal incentive rate strategies to participants, as different customers exhibit diverse attitudes toward remunerated incentives. In this program, ordering points to identify the clustering structure (OPTICS) and k-nearest neighbor algorithms (k-NN) are used for clustering and classification, respectively, to determine reasonable incentive rates for customers according to their bid/offer data. A simulation was implemented on historical PJM datasets, the results of which revealed the performance and superiority of the proposed approach in handling uncertainties.

Marine Construction Waste Recycling Mechanism Considering Public Participation and Carbon Trading: A Study on Dynamic Modeling and Simulation Based on Sustainability Policy

The classification and recycling of construction waste is important for reducing waste emissions, preventing marine pollution, and protecting the natural environment, which can promote carbon trading and carbon sink cycles. Based on the evolutionary game theory, this paper investigated the evolutionary decision-making process and stable strategies of three stakeholders in the construction waste recycling system, namely, the Department of Environment Regulation (DER), the Construction Waste Recycler (CWR), and the Construction Project Contractor (CPC), and analyzed the main factors affecting the stakeholders’ strategies, the evolutionary stable strategies and stable conditions from the perspective of public participation and carbon trading. Then, a DER-CWR-CPC benefit matrix and a replicator dynamics equation representing strategy selection were constructed, in which parameters represent the interest relationship of the three parties, and evolutionary stable strategy (ESS) points were obtained by solving the Jacobian matrix. Finally, the validity of the model was verified by taking the actual values into the simulation. The results showed that DER needs to actively participate in the early stage of the development of the construction waste classification and recycling system, but with the increase of enterprises choosing to recycle construction waste, DER can gradually reduce its intervention in these enterprises. Setting reasonable incentives and penalties, mobilizing public participation, and developing cleaner construction waste sorting equipment to obtain more carbon emission trading targets can facilitate the development of construction waste recycling systems.