Incentive Design Research Articles

A Comprehensive Physical Layer Security Mechanism for Mobile Edge Computing

Mobile edge computing (MEC) is becoming popular in many civilian applications. However, due to the broadcast nature of wireless connections, traditional MEC is open to eavesdropping attacks that endanger mobile users’ information confidentiality. Friendly jamming (FJ), as one of the physical layer security techniques, can efficiently protect users from such threats by degrading attackers’ wiretap channel capacities. In this paper, we propose an FJ-based physical layer security (PLS) mechanism to safeguard the confidentiality of data uploading in MEC services. We design a comprehensive security scheme that uses FJ from both base station (BS) and nearby mobile users to cover upload transmission. Accordingly, the eavesdropping risk zone (ERZ) is formulated as an area under high secrecy outage probability (SOP). To promote FJ from users, we further introduce Device-to-device (D2D) communication-based FJ, which is inspired by non-orthogonal multiple access (NOMA) techniques. Then, we formulate, simplify, and solve the problem by optimizing the MEC secrecy performance under the required risk, with efficiencies in data uploading, energy consumption, and incentive delivery. Furthermore, we tackle the challenge of efficient incentive design under information asymmetry by introducing Contract Theory and providing differentiated rewards. By simulations, we evaluate the mechanism in terms of secrecy performance and incentive efficiency under information asymmetry, and the results accordingly show the effectiveness of the proposed security mechanism.

The Role of Tax Incentives in Encouraging Innovation and Technology Adoption in Industrial Management

In the context of globalization and increasingly fierce industrial competition, technological innovation is the key to increasing the efficiency and competitiveness of an industry. This research will explore the relationship between tax incentives provided by the government and the level of innovation and adoption that occurs in industry, as well as its impact on industrial management. This research uses a qualitative approach with descriptive methods. The research results show that tax incentives play a crucial role in encouraging innovation and technology adoption in the industrial sector. Clear and flexible incentive designs have a positive impact, while factors such as complex provisions, policy uncertainty, and the availability of skilled labor can limit their effectiveness. Involvement in industrial collaboration, level of education, and positive perceptions of tax incentives also contribute to increasing the use of these incentives. In conclusion, the importance of designing tax policies that take into account the dynamics of the industry and business environment can create effective tax incentives in supporting innovative growth at the industry level.

Integrating Fiscal Policy Mix and Environmental Policy in East Java's Circular Economy Development: A Multidimensional Approach Assessing Economic, Social, and Legal Factors

This study aims to provide a more holistic understanding of how environmental and fiscal policies can complement each other to promote a circular economy in East Java. The method used in this study is panel data regression analysis using secondary data covering the period 2001-2020. Data were collected from government agencies such as the Ministry of Finance, the East Java Central Bureau of Statistics, the Ministry of Environment and Forestry, and the Supreme Audit Agency. The results show that environmental and fiscal policies significantly impact the circular economy in East Java. Environmental regulations, which include central and local government policies, promote the growth of the circular economy. The Regional Original Revenue (PAD) also plays an essential role in influencing the circular economy in the region. The proposed policy recommendations include the design of incentives to encourage the adoption of circular economy principles, the optimal allocation of special environmental and circular economy funds, the adaptation of education and training to the needs of the circular economy, the promotion of research and innovation in the circular economy, and the joint implementation of environmental regulations.

On the Interplay of Production Flexibility, Capital Structure, and Investment Timing

Problem definition: Modern technologies have made it viable for firms to lower the costs of switching on and off production in response to market changes. In this paper, we explore how production start–stop flexibility impacts joint operating policies, financing, and investment timing decisions. Methodology/results: We develop a continuous-time, optimal stopping model in which equity holders of the firm make operational decisions regarding pausing and restarting production as well as when to default. The degree of production flexibility is measured by switching costs. On the one hand, production flexibility influences the trade-off between tax shields and default costs for the capital structure decision; on the other hand, debt levels impact the equity holders’ incentive to use flexibility by pausing and restarting operations. We find that optimal debt usage is not monotone in production flexibility. Specifically, when switching costs are in a low region, the optimal debt level decreases slowly as switching costs increase. As switching costs increase into an intermediate region, the optimal debt level decreases sharply because the firm needs to reduce its debt to ensure the equity holders maintain flexible operating policies. However, when switching costs exceed a threshold, the cost of compromising the use of debt becomes excessive, and the firm substantially increases its debt to gain the full benefit of the tax shield; in so doing, the equity holders forgo flexibility and maintain production continuously until default. This financing strategy affects the firm’s investment timing decision, which also exhibits a nonmonotone pattern. Managerial implications: When a firm optimizes the debt usage and investment timing, the incentive of utilizing flexibility embedded in the production technology by the equity holders needs to be taken into account. Our findings also reveal new benefits and guidance for the potential design of incentive contracts to mitigate agency costs. Funding: Q. Wu was supported by Weatherhead School of Management Intramural Grant [Grant IG121420-05-QXW132] for this research. Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2022.0213 .

Threshold-based incentives for ride-sourcing drivers: Implications on supply management and welfare effects

Ride-sourcing companies have been widely using threshold-based incentive programs to encourage drivers to extend their work hours. In such programs, a driver receives a certain amount of monetary reward if she completes a given supply task within a predetermined time window. However, despite the popularity of these incentives, little is known about how drivers respond to them in practice, and currently, there are no means to comprehensively evaluate and optimize their designs. To fill the void, we develop a dynamic discrete choice model that formulates ride-sourcing drivers’ working decisions influenced by threshold-based incentives and then calibrate it using real-world data from a ride-sourcing company. Our results provide fresh insights into the market and welfare effects of the threshold-based incentive and its various designs. It is found that the threshold-based incentive could increase welfare significantly for full-time drivers but marginally for part-time drivers. In contrast, involving part-time drivers in the incentive programs generally can yield higher profits for the platform, while incentivizing full-time drivers is mostly unprofitable. On incentive design, the incentive threshold and reward must be closely paired for different driver groups to avoid inferior consequences for the platform and drivers. In addition, switching from threshold-based incentives to direct wage increments may benefit both full-time drivers and the ride-sourcing company.

Design and Incentives of Equity Incentives in Chinese SOEs in Response to Environmental Uncertainty -- Case study of Hikvision based on grounded theory

High-tech companies are the most affected by restrictions on their ability to innovate. As the epitome of high-tech enterprise development in China, Hikvision actively explores and boldly innovates the equity incentive method and is always adapting to cope with internal and external environmental instability. This paper analyzes and compares the first four equity incentives based on the uncertainty factors and motivations of the internal and ex-ternal environment of the enterprise, takes this as the starting point, starts from the environmental motivations, and according to the constructively-rooted theory proposed by Glaser and Strauss, carries out programmed coding for the three dimensions of environmental factors, equity incentive plan selection and overall enterprise value enhancement. In this paper, we investigate the rationality of Hikvision's equity incentive policy setting, evaluate the effectiveness of the equity incentive policy, and predict the likelihood of equity lock-up under future environmental trends through tools such as excess return and return on equity. Based on the advantages and disadvantages, this paper puts forward the improvement of the equity incentive policy already implemented, and puts forward the universal suggestions on the application of equity incentive to deal with the environmental uncertainty, and establishes the equity incentive model for enterprises to deal with the uncertain environment to help enterprises make better management decisions.

Insights into agroecological farming practice implementation by conservation-minded farmers in North America

IntroductionThe transformation of our food system towards a more resilient agroecological framework is one of the most pressing needs faced by our global community. Understanding the use of multiple conservation practices is important in the development of research, education, and policy to accelerate their more widespread integration of into farming systems.MethodsThe aim of this study was to conduct a preliminary investigation of the trends regarding multiple practice adoption of conservation farming practices by conventional and organic farmers engaging with sustainable farming methods. Forty farmers were interviewed regarding their use of conservation practices, as well as their motivations, barriers, and future plans for new implementation and expansion of current practices.ResultsFarmers spontaneously identified cover crops and vegetation strips as the most frequently used conservation practices; however, upon more specific inquiry, we found that more than 50% of farmers used additional agroecological conservation practices including local crop varieties, intercropping, managed grazing, crop rotations, and no-till, with many farmers using multiple practices. Overall, we found no correlation of organic certification with the number of conservation practices implemented by farmers. The major motivations towards the adoption of practices included improved soil quality and profitability. Main identified barriers included financial means and risk, lack of knowledge, and access to resources. Farmers showed interest in further implementation of additional conservation practices, including expanded use of cover crops, tree plantings, and no-till practices.DiscussionFurther understanding complementarities, as well as differences in barriers and motivations, can contribute to the design of effective education strategies and financial incentives to promote the simultaneous implementation of agroecological conservation practices.

Incentive mechanism design for Federated Learning with Stackelberg game perspective in the industrial scenario

Federated Learning (FL) is a typical decentralized Machine Learning framework in which clients invest resources to train their local models without sharing their data and then transmit the model parameters to the server for parameter aggregation. Therefore, Incentive Mechanism Design (IMD) is a basic and important research direction in FL that stimulates clients to invest more resources in model training. In this paper, we designed the incentive mechanism as a Stackelberg game in which the server acts as the leader and the clients act as followers. We leveraged modified NSGA-II to find the Nash equilibrium. A real industrial scenario of pre-baked carbon anode quality prediction is applied to verify the high performance of the proposed method.

Data-driven analysis and modeling of individual longitudinal behavior response to fare incentives in public transport

AbstractIncentive-based public transport demand management (PTDM) can effectively mitigate overcrowding issues in crowded urban rail systems. Analyzing passengers’ behavioral responses to the incentive can guide the design, implementation, and update of PTDM strategies. Though several studies reported passengers’ responses to fare incentives, they focused on passengers’ short-term behavioral responses. Limited studies explore passengers’ longitudinal behavioral responses for different types of adopters, which is important for policy assessment and adjustment. This paper explores and models passengers’ longitudinal behavior response to a pre-peak fare discount incentive using 18 months of smartcard data in public transport in Hong Kong. We classified adopters into six types based on their temporal travel pattern changes before and after the promotion. The longitudinal analysis reveals that among all adopters, 19% of users change their departure times to take advantage of fare discounts but do not contribute to the goal of reducing peak-hour travel. However, these adopters are more likely to sustain their changed behavior in a long term which is not desired by the incentive program. The spatial analysis shows that the origin station distribution of late adopters is relatively more diverse than the early adopters with more trips starting from distant areas. The diffusion modeling shows that the majority adopters are innovators and the word-of-mouth diffusion effect (imitators) is marginal. The discrete choice model results highlight the heterogeneous impact of factors on different types of adopters and their values of time changes. The significant factors common to adopters are: departure time flexibility, the expected money savings, the required departure time changes, and work locations. The findings are useful for public transport planners and policymakers for informed incentive design and management.

Goal-directed versus outcome-based financial incentives for smoking cessation among low-income, hospitalised patients: rationale and design of the Financial Incentives for Smoking Treatment II (FIESTA II) randomised controlled trial

IntroductionSmoking remains the leading preventable cause of death in the USA. Low utilisation of treatments for smoking cessation remains a major barrier for reducing smoking rates. Financial incentives represent an...

Pay for performance at a crossroads: lessons from taking a global perspective

PurposeThe use of pay for performance (P4P) as an instrument to incentivise quality improvements in health care is at a crossroads in high-income countries but has remained a commonly used tool in low- and middle-income countries. The authors aimed to take stock of the evidence on effectiveness and design from across income settings to reveal insights for the future design of performance payment across income contexts.Design/methodology/approachThe authors identified Cochrane literature reviews of the use of P4P in health care in any income setting, tracked the development in the quantity and quality of evidence over time, and compared the incentive design features used across high-income countries compared to low- and middle-income countries.FindingsThe quantity and quality of the evidence base have grown over time but can still be improved. Scheme design varies across income settings, and although some design choices may reflect differences in context, the authors find that incentive designers in both income settings can learn from practices used in the other setting.Originality/valueThe research and literature on P4P in high-, low- and middle-income countries largely operate in silos. By taking stock of the evidence on P4P from across income settings, the authors are able to draw out key insights between these settings, which remain underexplored in the literature.

Incentive contract design and selection for inhibiting unethical collusion in construction projects

Purpose The construction industry has long been criticized for unethical conduct. The owner usually manages the contractor's opportunistic behaviors by employing a professional supervisor, but there is a risk of covert collusion between the supervisor and contractor. Based on the principal–agent theory and collusion theory, this paper aims to investigate optimal collusion-proof incentive contracts.Design/methodology/approachThis paper presents a game-theoretic framework comprising an owner, supervisor and contractor, who interact and pursue maximized self-profits. Built upon the fixed-price incentive contract, cost-reimbursement contract, and revenue-sharing contract, different collusion-proof incentive contracts are investigated. A real project case is used to validate the developed model and derived results.FindingsThis paper shows that the presence of unethical collusion undermines the owner's interests. Especially, the possibility of agent collusion may induce the owner to abandon extracting quality information from the supervisor. Furthermore, information asymmetry significantly affects the construction contract selection, and the application conditions for different incentive contracts are provided.Research limitations/implicationsThis study still has some limitations that deserve further exploration. First, this study explores contractor–supervisor collusion but ignores the possibility of the supervisor abusing authority to extort the contractor. Second, to focus on collusion, this paper ignores the supervision costs. What's the optimal supervision effort that the owner should induce the supervisor to exert? Finally, this paper assumes that the colluders involved always keep their promises. However, what if the colluders may break their promises?Practical implicationsSeveral collusion-proof incentive contracts are explored in a project management setting. The proposed incentive contracts can provide the project owner with effective and practical tools to inhibit covert collusion in construction management and thus safeguard construction project quality.Originality/valueThis study expands the organization collusion theory to the field of construction management and investigates the optimal collusion-proof incentive contracts. In addition, this study is the first to investigate the effects of information asymmetry on contract selection.

Goals for Adherence with Low-cost Incentives (GOALS): a protocol for a randomized controlled trial evaluating the impact of small airtime incentives on ART adherence among young people living with HIV in Kampala, Uganda

BackgroundTreatment outcomes of HIV-positive individuals are threatened by low antiretroviral therapy (ART) adherence, a problem that is particularly acute among youth. Incentives are a promising tool to support ART adherence, but traditional incentive designs rewarding uniformly high levels of the desired health behavior may demotivate those with low levels of the behavior. In this study, we investigate the effectiveness of alternative approaches to target-setting for incentive eligibility using subgoals (i.e., individual-specific, interim targets leading up to the optimal target).Methods / designWe will enroll 628 HIV-positive youth between ages 15 and 30 into a 3-year randomized controlled trial. Participants will be randomized 1:1:1:1 to a control arm or one of three intervention arms (n = 157 each) that allow them to enter a prize drawing for small incentives if their ART adherence meets the given goal. In the first arm (T1, assigned subgoal), goals will be externally assigned and adapted to their initial adherence level. In the second arm (T2, participatory subgoal), participants can set their own interim goals. In the third arm (T3, fixed goal), all participants must reach the same target goal of 90% adherence. T1 and T2 participants are required to reach 90% adherence by month 12 to participate in a larger prize drawing. The control group receives the usual standard of care. All four groups will receive weekly motivational messages; the three treatment groups will additionally receive reminders of their upcoming prize drawing. Adherence will be measured continuously throughout the intervention period using electronic devices and for 12 months post-intervention. Surveys will be conducted at baseline and every 6 months. Viral loads will be measured annually. The primary outcome is Wisepill-measured adherence and a binary measure for whether the person took at least 90% of their pills. The secondary outcome is the log-transformed viral load as a continuous measure.DiscussionOur study is one of the first to apply insights about the psychology and behavioral economics of goal-setting to the design of incentives, by testing whether conditioning the eligibility threshold for incentives on subgoals (interim goals leading up to the ultimate, high goal) improves motivation and adherence more than setting a uniformly highly goal, and a comparison group.Trial registrationClinicalTrials.gov NCT05378607. Date of registration: May 18, 2022.

Transitioning from illegal rooftop dwellings to solar PV: Market-based incentive design and techno-economic analysis

Renewable energy plays a crucial role in achieving net-zero emissions. As an island with a high population density, Taiwan has increasingly favored rooftop solar photovoltaic (PV) systems over ground-mounted installations due to land-use competition. However, illegal rooftop dwellings in urban areas pose significant obstacles to renewable energy deployment. This study proposes a market-based incentive scheme for rooftop solar adoption to encourage property owners to shift away from illegal rooftop dwellings. We construct market supply curves for illegal rooftop rental housing based on real-world rental data and conduct a techno-economic analysis to examine the required government subsidy. The results provide empirical evidence for an important nonlinearity in the supply curves, implying that linearity-based policy frameworks may not be effective in achieving optimal results. We also identify that opportunity costs linked to rental loss constitute a substantial portion of total costs, deterring the owners from adopting the shift. The findings indicate that the existing feed-in-tariff policy is inadequate to incentivize property owners to deploy rooftop solar panels. The proposed scheme embraces the three dimensions of sustainable development—economic, social, and environmental, which could benefit cities worldwide with illegal rooftop structures.

Eliciting Joint Truthful Answers and Profiles From Strategic Workers in Mobile Crowdsourcing Systems

Due to the openness of mobile crowdsourcing, workers may yield low-quality task answers. To alleviate this problem, substantial efforts have been devoted to eliciting truthful data from workers. On the other hand, to facilitate task assignment, workers are required to upload to the platform their profiles, such as locations and expertise. Therefore, task assignment outcomes and thus mobile crowdsourcing service accuracy are subject to the quality of worker's self-reported profiles. In this paper, we leverage incentive design to motivate workers to honestly reveal both task answers and their profiles. The challenge is to design one incentive payment for truth elicitation in two kinds of submissions. For this, we first derive the sufficient and necessary conditions for answer truthfulness and profile truthfulness separately. We then construct an incentive optimization problem that incorporates these conditions as constraints. Its optimal solution lists the payment to each worker that elicits answers and profiles jointly. Our proposed mechanism, with formally proved bounded approximation ratio, ensures that truth-telling is a Bayesian Nash equilibrium. We prototype the mechanism and conduct a series of experiments that involve 30 volunteers to validate the efficacy and efficiency of the proposed mechanism

Ecological and economic trade-offs between amount and spatial aggregation of conservation and the cost-effective design of coordination incentives

To counter not only the continuous loss but also the fragmentation of species habitats, coordination incentives (CI) have been proposed to incentivise the spatial aggregation of conservation efforts. An important issue is the cost-effective design of these instruments. Two main types of CI, the agglomeration bonus and the agglomeration payment, are analysed with stylised models. Their ecological effects are assessed through a metapopulation simulation model. Rather than choosing the usual approach and analysing the joint ecological-economic model, I analyse the ecological and economic sub-models separately and join the results within an economic production theory framework in which production factors (here, the proportion and aggregation of conserved land parcels) are financed under a budget- or cost constraint and generate an output (here, metapopulation viability). This decomposition of the ecological-economic analyses allows highlighting the ecological and economic trade-offs between proportion and spatial aggregation of conservation and generating a more general understanding of the budget- and cost-effectiveness of CI. Results include, among others, that the agglomeration payment is never more budget- and cost-effective than the agglomeration bonus and that in the agglomeration bonus the budget-effective level of spatial aggregation is lower than the cost-effective level.

Securing containerized supply chain through public and private partnership

Global trade is seafaring commerce; 90% of traded goods are carried by maritime transport, which has become vulnerable to security risks. This has led governments to initiate security programs serving tens of thousands of members worldwide. This paper studies the government's incentive design and the interaction between Customs inspection capacity and the incentives offered in security programs. Using the theory of incentives, we investigate the value of a partnership in improving the security of containerized supply chain. We developed a sequential game featuring the government, firms, and an adversary. The government selects the inspection capacity and incentives to foster the partnership, namely, an operational benefit in the form of a reduced inspection rate, and a security benefit obtained through reductions in the risks of adversarial infiltration. Firms subsequently decide on a collaboration level, followed by a strategic adversary's decision to infiltrate. Using the adversary's best response, we show that, in equilibrium, the government ranks all the firms and induces collaboration with only a subset of them. We demonstrate that, in equilibrium, while security incentives may benefit all participants, tailored operational incentives should be offered strictly to foster collaboration. The required condition to implement the inspection‐free lane for members is also characterized. Our results also inform practice to help security policymakers understand the underlying interaction between Customs inspection capacity and incentive design in forging collaboration with private firms. In particular, as firms opting for collaboration experience lower inspection rates, this further reduces overall congestion, which, in turn, creates a positive externality for nonmember firms. Therefore, having an excess inspection capacity may result in shorter wait times that could dissuade firms from collaborating.

Noncooperative and Cooperative Urban Intelligent Systems: Joint Logistic and Charging Incentive Mechanisms

Autonomous vehicles (AVs) have become an emerging crucial component of the intelligent transportation system (ITS) in modern smart cities. In particular, coordinated operations of AVs can potentially enhance the quality of public services, e.g. logistic and AV charging services. However, the joint logistic and AV charging scenario involves the sophisticated interactions between a large number of complicated agents, dynamic logistic, and electricity prices in real-world systems. Since AVs are individuals owned by different parties, the design of attractive incentive to motivate them to provide multiple public services becomes a fundamental issue. In this paper, we develop an urban intelligent system (UIS) by exploiting the efficient incentive mechanisms, e.g. non-cooperative and cooperative game theoretic approaches, to motivate the AVs to provide logistic and charging services in UIS. For the non-cooperative game approach, we formulate the interaction between the selfish AVs and the aggregator as a Stackelberg game. Meanwhile, the aggregator, known as the leader in the game, aims to decide the logistic and electricity trading prices, and then the AVs, executed as the followers, determine their service schedules. Furthermore, considering that all the players are willing to cooperate, we develop a cooperative potential game for the selfless AVs to maximize the social welfare of the UIS. These case studies demonstrate the effectiveness and practicability of proposed incentive mechanisms that can motivate EVs to provide high quality logistic and charging services by maximizing their utilities. Also, both the proposed schemes provide significant system revenues than that of conventional system optimization-based approaches.

RedPacketBike: A Graph-Based Demand Modeling and Crowd-Driven Station Rebalancing Framework for Bike Sharing Systems

Bike-sharing systems have been deployed globally. One of the key issues for high-quality bike-sharing systems is to rebalance city-wide stations to maintain bike availability. Traditional strategies usually operate in fixed paths and limited capacities, lacking the flexibility to cope with the highly dynamic and context dependent riding demands, and usually suffer from high costs and long delays. In this work, we propose RedPacketBike, an incentive-driven, crowd-based station rebalancing framework to effectively recruit participants from hybrid fleets based on the accurate forecast of bike demand leveraging deep learning techniques. First, we propose a spatiotemporal clustering method to extract bike demand hotspots from fluctuating bike usage data. Then, we build a context-aware deep neural network named BikeNet to forecast the trends of bike demand hotspots, simultaneously modeling the spatial correlations. Finally, we propose a reinforcement-learning-based method to find optimal station rebalancing schemes by generating station rebalancing tasks with an integer linear programming (ILP) algorithm and allocating tasks to participants from hybrid fleets with dynamic incentive designs and reward expectations. Experiments using real-world bike-sharing system data validate the performance of our framework, achieving a demand forecast error below 4.171 measured in MAE, and a 17.2% improvement of station availability, outperforming the state-of-the-art baselines.

Blood Donors' Preferences Toward Incentives for Donation in China

Blood donation is critical for health care systems, but donor retention remains challenging. Understanding donors' preferences can inform incentive design and improve retention rates. To identify donor preferences for incentive attributes and their relative importance in promoting blood donation among Chinese donors in Shandong. This survey study fielded a discrete choice experiment (DCE) with a dual response design among blood donors, analyzing the responses under forced and unforced choice settings. The study took place from January 1 to April 30, 2022, in 3 cities (Yantai, Jinan, and Heze) representing diverse socioeconomic strata in Shandong, China. Eligible participants were blood donors aged 18 to 60 years who had donated within the preceding 12 months. Participants were recruited using convenience sampling. Data were analyzed from May to June 2022. Respondents were presented with different blood donation incentive profiles, varying in health examination, blood recipient, honor recognition, travel time, and gift value. Respondent preferences for nonmonetary incentive attributes, attribute relative importance, willingness-to-discard values for attribute improvement, and estimated uptake of new incentive profiles. A total of 650 donors were invited, of which 477 were included for analysis. The respondents were predominately male (308 respondents [64.6%]), aged 18 to 34 years (291 respondents [61.0%]), and had undergraduate degrees or higher (286 respondents [59.9%]). Among the 477 valid respondents, the mean (SD) age was 31.9 (11.2) years. Respondents preferred comprehensive health examination, family members as recipients, central government recognition, 30-minute travel time, and a gift valued at 60 Renminbi (RMB). No significant differences were found between the model results of forced and unforced choice setting. Blood recipient was the most important attribute, followed by health examination and gifts, and then honor and travel time. Respondents were willing to discard RMB 32 (95% CI, 18-46) for an improved health examination and RMB 69 (95% CI, 47-92) for changing the recipient from themselves to family members. Scenario analysis estimated 80.3% (SE, 0.024) of donors would endorse the new incentive profile if the recipient was changed from themselves to family members. In this survey study, blood recipient, health examination, and gift value were perceived more important as nonmonetary incentives than travel time and honor recognition. Tailoring incentives according to these preferences may improve donor retention. Further research could help refine and optimize incentive schemes for blood donation promotion.