Incentive Schemes Research Articles

Incentive strategies of an e-tailer considering online reviews: Rebates or services

This paper mainly considers an e-tailer-Stackelberg supply chain and characterizes the formative processes and distinct impacts of positive reviews and negative reviews on consumer purchases. The e-tailer can adopt either a pre-sales strategy (i.e., service strategy) or an after-sales strategy (i.e., rebate strategy) to encourage consumer purchases and stimulate positive reviews. By studying the above two strategies, we show that: (1) positive reviews and negative ones will exert a synergistic adverse effect on price decisions. (2) Given the exogenous rebate and service levels, the service (rebate) strategy becomes optimal for the e-tailer if consumers are more (less) willing to leave positive reviews. Notably, the e-tailer should refrain from adopting any incentive strategy when consumers exhibit a moderate willingness to provide positive reviews. (3) When the e-tailer can determine rebate and service levels, it should adopt the service (rebate) strategy if the threshold for positive reviews is intermediate (very low or high).

Research on the Impact of User Behavior on Sustainable Design Strategies

This study aims to explore the influence of user behavior on sustainable design strategies and proposes a framework for optimizing design interventions. User behavior data regarding energy use, resource consumption, and waste management were collected through surveys, in-depth interviews, and virtual reality simulations. K-means clustering and Principal Component Analysis (PCA) were employed to systematically identify user behavior patterns. The findings reveal three distinct user groups: environmentally conscious, economically driven, and neutral/passive. Experimental results validated the effectiveness of various design intervention strategies—such as behavioral incentives, interactive feedback, and visual prompts—in increasing the frequency of energy-saving actions and reducing energy consumption. The behavioral incentive strategy showed the most significant impact, reducing energy consumption by 20% and achieving the highest user satisfaction. Through multiple regression analysis and ANOVA, the statistical significance of the behavioral incentive strategy was further confirmed. The innovation of this study lies in optimizing user behavior through customized strategies and proposing a combination of interventions to achieve greater energy savings. This provides new theoretical and practical support for sustainable design practices.

Can Servicification Benefit Manufacturing Exports? Empirical Results for India

To enhance manufacturing exports, India, a major player in the services segment, has recently created an enabling environment through the launch of several programmes, for example, the ‘Make-in-India’ and the production-linked incentive (PLI) schemes. As the firm-level evidence on the synergies between services input use (i.e., servicification) and manufacturing exports in the country is relatively scarce, the current analysis studies the relationship between them by using the CMIE-Prowess dataset over a period of 2000–2019, for both new and existing exporting firms. At the firm level, the article uses a two-step system generalized method of moments (GMM) estimator for studying the impact on export intensity, while a dynamic panel probit model is used to study the impacts on the decision to export. Apart from the analysis at the aggregate level, the influence of servicification is also judged for the low-tech and mid-to-hi-tech sectors separately and the disaggregated industry level. The observed positive relationship between servicification and manufacturing exports implies that the integration of competitiveness in the field of services with hi-tech manufacturing sectors can enhance India’s exports on the one hand and deepen firm-level global value chains participation on the other hand. JEL Codes: C23, F12, F14, F23, L25, L80

Regulation techniques and applications of distributed load resources in urban power grids based on internet of things

The power consumption of urban power systems is increasing rapidly with two typical trends: the first one is that the daily peak-valley difference of loads is more significant, and the power supply is tight during peak hours, which threatens the system’s safe and stable operation; the second one is that the load energy efficiency in urban power systems is not high, which is the primary source of carbon emission in the power industry. Therefore, reducing the peak power and improving the system’s energy efficiency are urgent tasks for enhancing the system’s security and achieving the carbon emission goals. The rapid development of the Internet of Things (IoTs) ushers new opportunities for regulating demand-side loads. By analyzing the technical characteristics of load control based on IoTs, this paper investigates the modeling methods of load resources. On this basis, different control and optimization methods of load resources are analyzed and compared thoroughly. Besides, considering that load control is not only related to technical methods but also impacted by incentive strategies, the load control mechanisms under the mature and immature market environments are analyzed. Finally, the research gap and prospect of load regulation are proposed.

Leveraging Unmanned Aerial Vehicle Technologies to Facilitate Precision Water Management in Smallholder Farms: A Scoping Review and Bibliometric Analysis

While there is immense potential in using unmanned aerial vehicles (UAVs) to facilitate precision water management, there is currently no consensus on practical strategies to operationally implement these technologies to guide water resources management decisions, particularly within smallholder farming contexts. To address this gap, this study employs bibliometric techniques to assess the current state of UAV applications for evapotranspiration (ET) estimation in agricultural settings. The analysis of 49 peer-reviewed papers from Scopus was conducted using Biblioshiny and VOSviewer to enhance comprehension of this expanding research field. The study highlights a significant increase in scholarly research on utilising UAVs for precision water management over the past decade. The investigations indicate that UAVs in agriculture are gaining prominence and exhibit substantial potential for various precision agriculture (PA) applications. Significant cost reductions for UAV technology and remote sensing (RS) are anticipated soon, primarily driven by the availability of open-source platforms for processing tasks, such as Google Earth Engine. This research aims to inform smallholder farmers about the benefits of integrating UAVs into their farming practices, enhancing operational efficiency and productivity. Policymakers can use these findings to develop regulatory frameworks and incentive schemes that facilitate UAV adoption among smallholder farmers. Additionally, technology developers can leverage insights from this study to identify areas needing innovation and optimisation tailored to small-scale agriculture. Hence, this study seeks to bridge the gap between technological advancements and practical agricultural applications, promoting sustainable farming practices and enhancing the socioeconomic welfare of smallholder farmers.

Evolving nature-based solutions for Australia’s Indigenous estate in 2024 – opportunities and challenges

Globally, there is growing interest and potential for investment in Nature-based Solutions (NbS) to protect, manage or restore ecosystems through incentive schemes including Payment for Ecosystem Services (PES), Nature Repair, Carbon Markets, Common Asset Trusts. Collectively, these NbS markets establish interchangeable mechanisms to help address biodiversity and climate crises, as well as socio-economic issues concerning many Indigenous Peoples and Local Communities (IPLCs). IPLCs manage and/or own almost 32% of the world’s land area through customary and community-based tenure arrangements. Since 2000 several PES schemes have been implemented in Australia. These schemes have been overwhelmingly implemented as public-good expenditure, with governments providing ~90% of total funding. Indigenous people have either the legal right to run or veto a carbon market or nature repair project over 57% of the Australian land mass, increasing to 63% in savanna and 85% in desert regions. Here we critically assess opportunities and challenges for Australia’s Indigenous estate associated with existing and emerging NbS market approaches, especially the Commonwealth’s current Savanna Fire Management (SFM) and Human Induced Regeneration methods, evolving Integrated Farm & Land Management (IFLM) and Blue Carbon methods, and proposed Nature Repair (NR) market. To date, Indigenous NbS interests have focused especially on SFM across northern Australia, with prospective opportunities especially for IFLM and NR markets. Most available schemes focus on remediation of degraded lands and seas, ignoring cost-effective investment opportunities to maintain habitats and ecosystems in less-degraded condition. Government-supported Common Asset Trusts can provide effective models for governance of stewardship schemes relevant to on-going care for relatively intact ecosystems. In Discussion we summarise key methodological, institutional, and policy opportunities and challenges for constructive Indigenous engagement with developing NbS markets. Our purpose is to provide an Indigenous land and sea management context to inform development of rapidly evolving NbS markets in Australia.

Hybrid reward-punishment in feedback-evolving game for common resource governance.

How to maintain the sustainability of common resources is a persistent challenge, as overexploiters often undermine collective efforts by prioritizing personal gain. To mitigate the overexploitation of resources by violators, previous theoretical studies have revealed that the introduction of additional incentives, whether to reward rule-abiding cooperators or to punish those who overexploit, can be beneficial for the sustainability of common resources when the resource growth rate is not particularly low. However, these studies have typically considered rewarding and punishing in isolation, thus overlooking the role of their combination in common resource governance. Here, we introduce a hybrid incentive strategy based on reward and punishment within a feedback-evolving game, in which there is a complex interaction between human decision making and resource quantity. Our coevolutionary dynamics reveal that resources will be depleted entirely, even with cooperative strategies for prudent exploitation, when resource growth is slow. When the rate of resource growth is not particularly low, we find that the coupled system can generate a state where resource sustainability and cooperation can be maintained. Furthermore, when the rate of resource growth is moderate, we find that achieving this state cannot simply allocate all incentive budgets to reward. In addition, the increase in per capita incentives significantly promotes the stability of this state.

Perubahan Skema Insentif pada PT Grab dan Gojek: Dampak terhadap Kepuasan Drive

Incentive changes can have a negative impact on driver satisfaction, leading to feelings of unfair compensation and income uncertainty. These negative consequences can result in a decline in service quality and increased driver turnover. While incentive changes tend to have negative effects, there are also some positive impacts, such as increased earnings for drivers in high-demand areas. Driver satisfaction is crucial for improving driver availability, service quality, platform reputation, and profitability. Therefore, companies need to reconsider their incentive schemes and make appropriate adjustments to improve driver satisfaction. Efforts to enhance driver satisfaction include implementing fair and transparent policies, increasing market demand, educating passengers, developing platform technology, and enhancing driver support. This study employs qualitative methods and secondary data to support the research findings, ensuring the effectiveness and accuracy of the data used.

Enterprise Management Strategy Based on Incentive Theory

With the vigorous development of modern society, incentive theory plays an important role in enterprise management, which can provide effective means and concepts for enterprise management, pay attention to the realization of personal value and emotional support of employees, and help improve the enthusiasm of employees, strengthen the internal management level of enterprises, and promote the highquality development of enterprises. Therefore, the implementation of incentive strategy has strong necessity and importance. By analyzing the enterprise management strategy based on incentive theory, this paper puts forward optimization countermeasures such as strengthening the construction of professional management personnel, giving full play to the role of information technology, and establishing and perfecting the enterprise management system, so as to realize the effective improvement of the enterprise management level.

Comment on “Salesforce Compensation with Inventory Considerations”

This note critically analyzes the conditions for equilibrium existence in a model involving incentive schemes and inventory management. It identifies a technical flaw where equilibrium is asserted to exist under certain parameter values when, in fact, it does not. However, by introducing a reasonable assumption that inventory can only be ordered in sufficiently large increments, the qualitative outcomes and key insights remain intact. This paper was accepted by Jeannette Song, operations management. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.03914 .

Fair Incentives for Repeated Engagement

We study a decision-maker’s problem of finding optimal monetary incentive schemes for retention when faced with agents whose participation decisions (stochastically) depend on the incentive they receive. Our focus is on policies constrained to fulfill two fairness properties that preclude outcomes wherein different groups of agents experience different treatment on average. We formulate the problem as a high-dimensional stochastic optimization problem and study it through the use of a closely related deterministic variant. We show that the optimal static solution to this deterministic variant is asymptotically optimal for the dynamic problem under fairness constraints. Though solving for the optimal static solution gives rise to a nonconvex optimization problem, we uncover a structural property that allows us to design a tractable, fast-converging heuristic policy. Traditional schemes for retention ignore fairness constraints; indeed, the goal in these is to use differentiation to incentivize repeated engagement with the system. Our work (i) shows that even in the absence of explicit discrimination, dynamic policies may unintentionally discriminate between agents of different types by varying the type composition of the system, and (ii) presents an asymptotically optimal policy to avoid such discriminatory outcomes.

Lying in competitive environments: Identifying behavioral impacts

Incentive schemes based on relative performance provide high effort incentives, but may backfire by increasing incentives for misconduct. Previous literature supports this view by demonstrating that, as compared to individual incentive schemes based on absolute performance only, highly competitive environments are associated with higher degrees of lying and cheating. However, it is not clear if this is (mainly) driven by stronger financial incentives or by competition per se and its behavioral effects. We conduct an online experiment with competitive and individual incentive schemes in which the financial incentives to lie are held constant. From a behavioral perspective, a competitive environment may increase the willingness for misconduct via a desire-to-win, but may also decrease it via the negative payoff externality on competitors. Our results provide evidence of a significant lying-enhancing desire-to-win-effect and an insignificant lying-reducing negative externality effect.

Reinforcement learning model for incentive-based integrated demand response considering demand-side coupling

Incentive-based integrated demand response has great potential in addressing supply-demand imbalance in integrated energy system. This study first proposes an incentive-based integrated demand response model based on multi-agent reinforcement learning, to deal with the multi energy coupling on the demand-side and enhance the applicability of the incentive-based demand response program in urban integrated energy system. Then, a self-adaptive method is proposed based on consumer energy consumption behavior, which utilizes nonlinear incentive strategy to encourage consumers to provide more demand response resources. Finally, the experimental results show that considering demand-side coupling can enhance the overall benefits of incentive-based integrated demand response, achieving a win-win situation between integrated energy service provider and consumers. After implementing incentive strategy that considers demand-side coupling, profit of integrated energy service provider increased by 16.27% and the discomfort costs borne by integrated energy consumers reduced by 11.5%. This study offers a promising approach to managing the complex and variable energy consumption behaviors in urban integrated energy system, contributing to more reliable and efficient operation of urban integrated energy system.

Robust Contracts: A Revealed Preference Approach

Abstract We study an agency model in which the principal knows the agent-optimal actions in response to K “known” contracts but is unaware of other actions available or their costs, and seeks a contract to maximize worst-case profits. The optimal contract is a mixture of the known contracts and output. Moreover, when K = 1, the single known contract maximizes the principal's profit guarantee, whereas with two known contracts, the optimal mixture puts positive weight on one of the known contracts. Our methodology is straightforward to implement, a point that we demonstrate using data from an experimental study of different incentive schemes.

Incentive Regulation: Expectations, Surprises, and the Road Forward

Forty years have passed since an inflation-adjusted price cap, widely called RPI-X, was proposed as a way of controlling prices in the UK’s newly privatised monopoly telecommunications company by a form of incentive regulation. The paper traces developments since then in several jurisdictions, within the context of a wider field of changing regulatory governance involving legislatures and governments as well as regulatory agencies. The focus is on, first, the experience of increasing complexity of the incentive schemes adopted, and second on the growing political salience of regulatory decisions which adds goals such as net zero, with more direct quantitative targets, to maximising consumer welfare. The implications of these changes for regulatory interventions are considered.

After Online Innovators Receive Performance-Contingent Material Rewards: A Study Based on an Open Innovation Platform.

In recent years, enterprises have increasingly recognized the pivotal role of external users in driving product innovation. Open innovation platforms (OIPs), which facilitate interactions between companies and external innovators, have emerged as critical conduits in this regard. However, OIP managers face the challenge of motivating innovators to sustain their contributions. While some OIPs have implemented material incentives, the impact of such rewards on users' ongoing innovation efforts remains uncertain. This study utilized a large-scale dataset from an OIP to examine how performance-contingent material rewards influence the subsequent behaviors of online innovators. Employing a quasi-experimental design involving propensity score matching (PSM) and difference-in-differences (DID) analysis, we found that receiving performance-contingent material rewards led to a decrease in the quantity of subsequent ideas generated by innovators. However, these rewarded innovators produced ideas of higher quality. Interestingly, the novelty of ideas submitted by innovators declined following their receiving of rewards. Moreover, newly enlisted innovators exhibited a more positive response to these incentives. Our findings provide valuable insights for platform managers seeking to optimize incentive mechanisms. We suggest adopting diversified incentive approaches and refining incentive strategies to effectively motivate continuous innovation among users on OIPs.

Uplift modeling and its implications for appointment date prediction in attended home delivery

Successful attended home delivery (AHD) is the most important aspect of e-commerce order fulfillment. Prior literature focuses on incentive scheme development for customers' choices of delivery windows and predictive analytics for delivery results, but it is not clear whether the effect of AHD on the appointment date set by customers increases the success rate of AHD. Therefore, we developed an uplift modeling method, PSM-NDML, as a relevant prescriptive analytic tool for AHD on an appointment date, which aims to estimate the causal effect of the by-appointment delivery on the delivery result. PSM-NDML integrates propensity score matching and double machine learning, effectively addressing sample selection bias, low predictive performance, and poor interpretability. Applied to a real-world product delivery dataset of a Chinese logistics company, PSM-NDML achieves superior performance relative to ten other state-of-the-art uplift models in terms of cumulative gain and the Qini coefficient. The predicted responses gained from PSM-NDML are also visually interpreted at the global and local levels, which reveals various managerial insights. In practice, the findings expand managers' understanding of the heterogeneous effects of AHD on appointment dates and provide decision support for logistics companies in the development of home delivery plans.

Participation in demand side response. Are individual energy users interested in this?

DSR is crucial for leveraging user flexibility to integrate renewable energy efficiently, also for smaller users like households, and for utilizing surplus energy from prosumers, highlighting its potential. Public perceptions, habits and responsiveness are crucial to its success. A method to determine the initial state of interest in DSR is a survey. This paper presents the results, obtained on the basis of statistical analysis of responses and averaging of scores according to the Likert scale. The survey was conducted among 400 Polish small electricity users. Poland is an exemplary country where external and internal signals can potentially accelerate building a new customer-supported energy sector. The research found low awareness of DSR among individual users, but a strong interest in the concept. It suggested that incentive schemes are preferred over dynamic tariff systems. In general, energy users show rational behaviour regarding specific DSR options, with financial benefits indicated as the primary motivation and social pressure and fulfilling energy policy objectives were deemed least important. This public perception, however, allows for a wider implementation of DSR. Transparent participation rules are crucial. The results research results can inspire policymakers, energy service providers, and aggregators to tailor DSR solutions for diverse end-users.

Who can predict farmers’ choices in risky gambles?

Abstract Risk is a pervasive factor in agriculture and a subject of great interest to agricultural economists. However, there is a lack of comprehensive understanding of the knowledge held by farm advisors, students, and economists with regards to farmers’ risk preferences. Misconceptions about farmers’ willingness to take risks could lead to misguided advice. This study builds upon a recent multinational endeavor that employed a multiple price list to assess risk preferences among European farmers. We expand this research by gathering predictions for farmers’ risk preferences from 561 farm advisors, students, and economists. Our objectives are threefold: firstly, we explore variations as to how accurately participants can predict risk preferences in different specializations; secondly, we compare the predictive accuracy of different groups of forecasters; and thirdly, we assess whether modifying incentive mechanisms can improve the accuracy of predictions. Whereas our findings reveal substantial variation in individual predictions, the averages closely align with the observed responses of farmers. Notably, the most accurate predictions were provided by a sample of experimental economics researchers. Furthermore,predictions for different production systems exhibit minimal disparities. Introducing incentive schemes, such as a tournament structure, where the best prediction receives a reward, or a high-accuracy system, where randomly selected participants are compensated for the accuracy of their predictions, does not significantly impact accuracy. Further research and exploration are needed to identify the most reliable sources of advice for farmers.

Rolling optimal scheduling for urban parcel crowdsourced delivery with new order insertion

The rapid development of mobile information technology has introduced numerous new solutions for delivery companies to enhance profits. One such solution employed by some companies is crowdsourced delivery. In this paper, we focus on rolling optimal scheduling for urban parcel crowdsourced delivery by utilizing private cars that will be in passing with the incorporation of new order insertion. The bonus incentive strategy is introduced to enhance the delivery probability of private car drivers. A static model and a rolling optimization model to maximize profits and the number of parcels delivered by private cars are established. To address the NP-hard problem, a hybrid genetic algorithm and insertion algorithm are designed. Numerical experiments are carried out to verify the proposed method in different scenarios, including the scattered network, clustered network, Dalian network, and Foursquare network. The computational results demonstrate that the method enhances the matching ratio and increases profits. Utilizing private cars that will be in passing for urban parcel delivery reduces the need for dedicated vehicles, mitigating traffic growth and alleviating traffic congestion. Increasing the private car-parcel ratio improves profits and the matching ratio while reducing traffic growth. Raising the incentive coefficient for bonuses increases the matching ratio and detour distance, but profits first increase and then decrease, and delivery distance by dedicated vehicles decreases. Our research findings offer a more rational basis for green urban parcel delivery decision-making by companies.