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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.

This study aims to investigate the situational factors that intensify the impact of leader bottom-line mentality (BLM) on employee pro-self-unethical behavior. In particular, the moderating role of contingent rewards and punishments is evaluated under the lens of situational strength theory.

Data were collected from 218 full-time employees working in the USA in a time-lagged study and analyzed using SPSS Process Macro.

Statistical analysis reveal contingent rewards and punishments significantly moderate the positive relationship between BLM and pro-self-unethical behavior.

This paper highlights the need for more balanced reward systems that incorporate moral conduct into work performance. It also emphasizes the role of robust accountability and monitoring systems in minimizing employees’ unethical behavior.

To the best of the authors’ knowledge, this is the first study to investigate the moderating role of contingent rewards and punishments on the relationship between leader BLM and subordinate pro-self-unethical behavior. Moreover, it provides significant empirical support to situational strength theory.

Addressing the significant differences between referral programs and traditional promotional marketing, this paper aims to investigate and examine the impact of how reward-related information is presented within referral programs and how it interacts with reward size and reward allocation.

This study adopts framing effect and equity theory to build the relationship between reward presentation, reward size and reward allocation. Then, two scenario-based experimental studies are designed and conducted on Amazon Mechanical Turk.

The results show that there is no direct impact of reward presentation on referral likelihood, while the effect relies on reward size. As the levels of reward size increase, the referral likelihood gradually shifts from percentage form to dollar form as perceived size mediates the interaction effect on referral likelihood. Further, adding information about reward allocation also indicate the different impacts of equity and inequity on influencing the above findings.

The study contributes to the literature by introducing reward presentation and emphasizes its impact on individual’s behavior decisions in the context of referral programs. This study extends and broadens the scope and effectiveness of the framing effect on traditional promotional marketing strategies, while also bridging the gap in the literature by examining the combined role of information about rewards.

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.

This study aims to investigate the relationship between extrinsic rewards and employee creativity through the intervening mechanism of perceived organisational support (POS) and work engagement. The moderating role of intrinsic motivation on the relationship between work engagement and employee creativity is also examined.

The authors report the results of a survey completed by 372 respondents employed in the United Arab Emirates. Structural equation modelling was applied to test the hypothesised relationships.

The main findings are that extrinsic rewards influence employee creativity through POS and work engagement. Moreover, the effect of work engagement on employee creativity is moderated by intrinsic motivation. This model effect is stronger for employees with high intrinsic motivation.

Convenience sampling was used, which limits its generalisability. Also, the data were collected through a cross-sectional survey at one point in time.

Managers should consider provision of extrinsic rewards and support to increase employee motivation and engagement in creative work.

This study contributes to the limited amount of available literature on creativity and rewards adding to our knowledge about the influence of extrinsic rewards on creativity considered in the presence of intrinsic motivation. Theoretical and practical recommendations are discussed.

Online retailers are pioneering the incentivization of customers to generate more product reviews by rewarding them. However, little is known about the impact of reward types on customers' review behavior, including review frequency and sentiment. To address this gap, we investigated the effects of different reward types on customers' review behavior and how these rewards influence customers' review behavior.

We collected secondary data and empirically tested the hypothesis by analyzing the change in reward policy. Regression and two-stage Heckman models were applied to investigate the effects, with the latter used to control potential selection issues.

The results revealed that monetary rewards can stimulate customers to generate more positive product reviews. Furthermore, the reward amount has a negative moderating effect on the aforementioned relationship. Additionally, customer tenure negatively moderates the relationship between monetary rewards and review behavior.

This study contributes to the understanding of user-generated content motivation and provides managerial implications for reward programs.

Nucleic acid testing is an effective method of accurate prevention and control and a key measure to block the spread of the epidemic. However, the fraud in nucleic acid testing occurred frequently during epidemics. This paper aims to provide a viable scheme for the government to strengthen the supervision of nucleic acid testing and to provide a new condition for the punishment for the negative act of the government and the upper limit of the reward for nucleic acid testing institution of no data fraud.

This paper formulates an evolutionary game model between the government and nucleic acid testing institution under four different mechanisms of reward and punishment to solve the issue of nucleic acid testing supervision. The authors discuss the stability of equilibrium points under the four distinct strategies and conduct simulation experiments.

The authors find that the strategy of dynamic reward and static penalty outperforms the strategies of static reward and static penalty, dynamic reward and static penalty, static reward and dynamic penalty, dynamic reward and dynamic penalty. The results reveal the appropriate punishment for the negative act of the government can enhance the positivity of the government's supervision in the strategy of dynamic reward and static penalty, while the upper limit of the reward for nucleic acid testing institution of no data fraud cannot be too high. Otherwise, it will backfire. Another interesting and counterintuitive result is that in the strategy of dynamic reward and dynamic penalty, the upper limit of the penalty for data fraud of nucleic acid testing institution cannot be augmented recklessly. Otherwise, it will diminish the government's positivity for supervision.

Most of the existing evolutionary game researches related to the reward and punishment mechanism and data fraud merely highlight that increasing the intensity of reward and punishment can help improve the government's supervision initiative and can minimize data fraud of nucleic acid institution, but they fall short of the boundary conditions for the punishment and reward mechanism. Previous literature only study the supervision of nucleic acid testing qualitatively and lacks quantitative research. Moreover, they do not depict the problem scenario of testing data fraud of nucleic acid institution regulated by the government via the evolutionary game model. Thus, this study effectively bridges these gaps. This research is universal and can be extended to other industries.

This study aims to investigate the factors that lead to the failure of some rewards to induce knowledge sharing behavior among employees, with much focus on employees’ attitudes and leadership’s knowledge of employees’ preferences, and presents a model that depicts the linkages.

To investigate why the provision of some rewards fails to induce knowledge sharing behavior among employees, this study uses the preferred reporting items for systematic reviews and meta-analyses framework to identify and analyze 56 articles published from 2000 to 2021.

Knowledge sharing is positively linked to organizational performance. Further, employees’ negative attitudes toward a reward system negatively relate to knowledge sharing behavior. Furthermore, management’s lack of knowledge of employees’ preferences regarding rewards leads to the provision of incorrect rewards that do not enhance knowledge sharing behavior. Finally, a conceptual model depicting the linkages among the variables under consideration has been presented.

Through the present study, employees’ attitudes toward rewards and leadership’s knowledge of employees’ preferences have been presented as critical factors that can lead to the failure of some rewards to induce knowledge sharing behavior. Further, the conceptual framework that can guide managers and leaders in strategizing on how best to develop and implement correct reward systems has been presented.

The present study is a significant contribution to the literature by focusing on the negative side of rewards toward knowledge sharing behavior with a focus on employees’ attitudes and leadership’s awareness of employees’ preferences regarding rewards.