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Though public sector organisations have continuously borrowed human resource management practices from the private sector, there seems to be sparse evidence on the effectiveness of financial rewards for public sector employees, especially in developing countries where pay remains low. Therefore, the objective of this research is to test the effectiveness of financial rewards on the job performance of those working in the Maldives civil service from the perspective of a developing country where public sector pay, especially civil pay, remains comparatively low. Additionally, this study tested the mediating effect of organisational commitment on the relationship between financial rewards and job performance.

A cross-sectional study was conducted using quantitative design methodology, whereby data were collected from 341 employees working in the Maldives civil service and analysed using structural equation modelling.

The findings indicate that financial rewards negatively affect civil service employees’ job performance. However, financial rewards improve organisational commitment, which reduces the negative effects, although the effect sizes of the mediator are not very significant.

The results of this study present critical theoretical and practical contributions to public administration researchers on using financial incentives as a mechanism to boost job performance, particularly in developing countries, where salaries and other benefits remain low. Furthermore, it presents practical recommendations for managing employees in the Maldives and other countries, where the public sector is less developed and budget constraints remain a challenge.

This paper aims to explain and determine the effect of rewards on employees’ autonomy by investigating the mediating effect of enabling controls on their relationship.

A three-wave survey strategy has been used to collect data from the alumni of a French business school. Structural equation modelling has been used for measures validating and hypotheses testing.

The study reveals a positive relationship between rewards and autonomy, mediated by enabling controls.

The study guides the process of administrating rewards to employees in a way that maximizes their autonomy, highlighting the crucial role of supervisors through enabling controls.

The study strives to create consensus regarding the long-existing debate on the effect of rewards on employees’ autonomy with the help of organizational theory literature. By considering the role of enabling controls, it provides a unique, cohesive framework to illustrate the intertwined relationship between the constructs.

In current era, retaining the best-performing employees has become essential for businesses to compete in the dynamic technological landscape. Consequently, organizations must ensure that their star performers believe that company’s reward and recognition (R&R) system is fair and equal. This study aims to use an explainable machine learning (eXML) model to develop a prediction algorithm for employee satisfaction with the fairness of R&R systems.

The current study uses state-of-the-art machine learning models such as Naive Bayes, Decision Tree C5.0, Random Forest and support vector machine-RBF to predict employee satisfaction towards fairness in R&R. The primary data used in the study has been collected from the employees of a large public sector undertaking from an emerging economy. This study also proposes a novel improved Naïve Bayes (INB) algorithm, the efficiency of which is compared with the state-of-the-art algorithms.

It is seen that the proposed INB model outperforms the state-of-the-art algorithms in many scenarios. Further, the proposed model and feature interaction are explained using the explainable machine learning (XML) concept. In addition, this study incorporates text mining techniques to corroborate the results from XML and suggests that “Transparency”, “Recognition”, “Unbiasedness”, “Appreciation” and “Timeliness in reward” are the most important features that impact employee satisfaction.

To the best of the authors’ knowledge, this is one of the first studies to use INB algorithm and mixed method research (text mining along with machine learning algorithms) for the prediction of employee satisfaction with respect to the R&R system.

The relationship between perceived overqualification and knowledge sharing has always been a hot topic, but scholars have come to different conclusions on this issue. The purpose of this study is to integrate conflicting conclusions by considering the moderating role of rewards for knowledge sharing and the mediating role of intrinsic motivation in the relationship between perceived overqualification and knowledge sharing based on self-determination theory.

The authors collected three-wave survey data from 246 research and development employees in four companies in China.

The results showed that when rewards for knowledge sharing was higher, employees with perceived overqualification would have higher intrinsic motivation, which could promote their knowledge-sharing behavior. However, when rewards for knowledge sharing was lower, employees with perceived overqualification would have lower intrinsic motivation, thus inhibiting their knowledge-sharing behavior. This result supported the informational function rather than the controlling function of rewards for knowledge sharing.

By considering the important boundary condition of rewards for knowledge sharing, this study reconciles the contradictory conclusions on the relationship between perceived overqualification and knowledge-sharing behavior. At the same time, the authors tell organizations that they can increase the knowledge-sharing behavior of overqualified employees through rewards for knowledge sharing.

The purpose of this paper is to investigate the effect of reward redemption programs on donation amount, donation percentage and donation intention in the context of a bank credit card.

A 2 × 2 × 3 experiment is implemented with 1,070 consumers accessing a national US-based sample with a small compensation. The authors use general linear model to test the proposed hypotheses.

The findings show the main effects of reward types, limited-time message and value of reward redemptions on the percentage of donations and overall donation intention to charity. The type of reward (cash/points) is found to interact with the limited-time message and with the value of reward redemptions.

No prior studies have addressed the relationship between credit card redemption rewards and scarcity messages in the donation context. The study contributes to the understanding of the effectiveness of credit card redemption rewards with scarcity message in improving a consumer’s donation intention.

This paper seeks to understand users' perceptions of their experiences in mobile applications (apps) with gamified loyalty programs (GLPs) that use rewards as the primary engagement vehicle. The research focuses particularly on the motivations to further interact with GLPs and the motivational changes occurring after successive interactions.

The authors conducted weekly open qualitative interviews over a month (four rounds of interviews) with five Portuguese active users of the mobile app Yorn Shake It, from Vodafone, which is a relevant case study to illustrate GLPs in mobile apps.

Participants' motivations to interact with the mentioned GLP are shaped by the reward incentive and users' perceptions of the gamified interactive experience. Motivational changes occur regardless of the presence of external contingencies and depend on contextual changes or perceived results of the gamified experience. This means that rewards also satisfy intrinsic needs, but users may remain connected to the system as long as fun experiences are provided without exhausting perceptions. Also, motivation may turn to reward contingencies when the challenge becomes boring.

This is the first qualitative study explaining the perceptions of gamified experiences after continued participation, extending knowledge about the importance of a fair balance between the value and achievement of rewards and the entertainment of the challenge provided after continued exposure. The findings provide insights to GLP marketing managers and developers to better engage target audiences according to their needs and past experience, creating levels of challenges and fair rewards to maintain motivation and prevent abandonment after continued exposure.

This study aims to examine the influence of reward personalization on financing outcomes in the Industry 5.0 era, where reward-based crowdfunding meets the personalized needs of individuals.

The study utilizes a corpus of 218,822 crowdfunding projects and 1,276,786 reward options on Kickstarter to investigate the effect of reward personalization on investors’ willingness to participate in crowdfunding. The research draws on expectancy theory and employs quantitative and qualitative approaches to measure reward personalization. Quantitatively, the number of reward options is calculated by frequency; whereas text-mining techniques are implemented qualitatively to extract novelty, which serves as a proxy for innovation.

Findings indicate that reward personalization has an inverted U-shaped effect on investors’ willingness to participate, with investors in life-related projects having a stronger need for reward personalization than those interested in art-related projects. The pledge goal and reward text readability have an inverted U-shaped moderating effect on reward personalization from the perspective of reward expectations and reward instrumentality.

This study refines the application of expectancy theory to online financing, providing theoretical insight and practical guidance for crowdfunding platforms and financiers seeking to promote sustainable development through personalized innovation.

The purpose of the paper is to propose and demonstrate a novel approach for addressing the challenges of path planning and obstacle avoidance in the context of mobile robots (MR). The specific objectives and purposes outlined in the paper include: introducing a new methodology that combines Q-learning with dynamic reward to improve the efficiency of path planning and obstacle avoidance. Enhancing the navigation of MR through unfamiliar environments by reducing blind exploration and accelerating the convergence to optimal solutions and demonstrating through simulation results that the proposed method, dynamic reward-enhanced Q-learning (DRQL), outperforms existing approaches in terms of achieving convergence to an optimal action strategy more efficiently, requiring less time and improving path exploration with fewer steps and higher average rewards.

The design adopted in this paper to achieve its purposes involves the following key components: (1) Combination of Q-learning and dynamic reward: the paper’s design integrates Q-learning, a popular reinforcement learning technique, with dynamic reward mechanisms. This combination forms the foundation of the approach. Q-learning is used to learn and update the robot’s action-value function, while dynamic rewards are introduced to guide the robot’s actions effectively. (2) Data accumulation during navigation: when a MR navigates through an unfamiliar environment, it accumulates experience data. This data collection is a crucial part of the design, as it enables the robot to learn from its interactions with the environment. (3) Dynamic reward integration: dynamic reward mechanisms are integrated into the Q-learning process. These mechanisms provide feedback to the robot based on its actions, guiding it to make decisions that lead to better outcomes. Dynamic rewards help reduce blind exploration, which can be time-consuming and inefficient and promote faster convergence to optimal solutions. (4) Simulation-based evaluation: to assess the effectiveness of the proposed approach, the design includes a simulation-based evaluation. This evaluation uses simulated environments and scenarios to test the performance of the DRQL method. (5) Performance metrics: the design incorporates performance metrics to measure the success of the approach. These metrics likely include measures of convergence speed, exploration efficiency, the number of steps taken and the average rewards obtained during the robot’s navigation.

The findings of the paper can be summarized as follows: (1) Efficient path planning and obstacle avoidance: the paper’s proposed approach, DRQL, leads to more efficient path planning and obstacle avoidance for MR. This is achieved through the combination of Q-learning and dynamic reward mechanisms, which guide the robot’s actions effectively. (2) Faster convergence to optimal solutions: DRQL accelerates the convergence of the MR to optimal action strategies. Dynamic rewards help reduce the need for blind exploration, which typically consumes time and this results in a quicker attainment of optimal solutions. (3) Reduced exploration time: the integration of dynamic reward mechanisms significantly reduces the time required for exploration during navigation. This reduction in exploration time contributes to more efficient and quicker path planning. (4) Improved path exploration: the results from the simulations indicate that the DRQL method leads to improved path exploration in unknown environments. The robot takes fewer steps to reach its destination, which is a crucial indicator of efficiency. (5) Higher average rewards: the paper’s findings reveal that MR using DRQL receive higher average rewards during their navigation. This suggests that the proposed approach results in better decision-making and more successful navigation.

The paper’s originality stems from its unique combination of Q-learning and dynamic rewards, its focus on efficiency and speed in MR navigation and its ability to enhance path exploration and average rewards. These original contributions have the potential to advance the field of mobile robotics by addressing critical challenges in path planning and obstacle avoidance.

The goal of this study is to investigate the mediating effect of referral rewards on consumer willingness to recommend poverty-alleviating products and to identify the most effective referral rewards for incentivizing consumers to recommend poverty-alleviating products.

Tournament rewards and piece-rate rewards are designed based on the theory of indebtedness, the related literature and the actual background. SPSS 26.0 and AMOS 17.0 are used to analyze the structural equation model.

According to the structural equation analysis, the following findings were found: under the tournament reward condition, social image, feelings of indebtedness and perceived reward value negatively affect consumer willingness to recommend. Under the piece-rate reward condition, social image and feelings of indebtedness significantly negatively affect consumer recommendation willingness, while perceived reward value significantly positively affects consumer recommendation willingness. The mean recommendation willingness of the tournament reward group is significantly lower than that of the control group. In contrast, the mean recommendation willingness of the piece-rate rewards group is significantly higher than that of the control group.

Based on the study findings, the authors propose that enterprises apply piece-rate rewards to incentivize consumers to recommend poverty-alleviating products when designing such rewards. In this way, the sale of poverty-alleviating products can be improved.

The purpose of this paper is to investigate both the impact of different reward types and the adoption of knowledge-sharing practice on a crowdsourcing-based open innovation contest. Despite the benefit of knowledge sharing, contestants could struggle to find a balance between knowledge sharing and knowledge protection in open innovation.

The authors' approach follows a stylised contest model in a game-theoretical setting in which contestants first decide on their efforts and then the contest sponsor chooses the winner. Moreover, the outcome of an open innovation contest is delineated as either intermediate goods that require further refinement and risk-taking versus a market-ready end product for the contest sponsor. The authors also investigate how knowledge sharing among contestants would be influenced by reward types such as fixed-monetary prizes vs performance-contingent awards.

The contest sponsor will lower the prize level after adopting knowledge sharing. Therefore, the total effort will decline regardless of the reward type. Moreover, the choice of reward types depends on the contest sponsor's characteristics because the performance-contingent award is suitable for a large market size but the fixed-monetary prize can more efficiently raise the quantity of contestant inputs.

Prior studies have tested the connection between contest performance and knowledge sharing in crowdsourcing-based contests; however, there is not an integrated framework to best design the operation of a contest when considering different reward types and knowledge-sharing practices.