• Clinical Psychopharmacology and Neuroscience
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• v.16 no.4
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• pp.449-460
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• 2018

Objective: Prior functional magnetic resonance imaging (fMRI) work has revealed that children/adolescents with disruptive behavior disorders (DBDs) show dysfunctional reward/non-reward processing of non-social reinforcements in the context of instrumental learning tasks. Neural responsiveness to social reinforcements during instrumental learning, despite the importance of this for socialization, has not yet been previously investigated. Methods: Twenty-nine healthy children/adolescents and 19 children/adolescents with DBDs performed the fMRI social/non-social reinforcement learning task. Participants responded to random fractal image stimuli and received social and non-social rewards/non-rewards according to their accuracy. Results: Children/adolescents with DBDs showed significantly reduced responses within the caudate and posterior cingulate cortex (PCC) to non-social (financial) rewards and social non-rewards (the distress of others). Connectivity analyses revealed that children/adolescents with DBDs have decreased positive functional connectivity between the ventral striatum (VST) and the ventromedial prefrontal cortex (vmPFC) seeds and the lateral frontal cortex in response to reward relative to non-reward, irrespective of its sociality. In addition, they showed decreased positive connectivity between the vmPFC seed and the amygdala in response to non-reward relative to reward. Conclusion: These data indicate compromised reinforcement processing of both non-social rewards and social non-rewards in children/adolescents with DBDs within core regions for instrumental learning and reinforcement-based decision-making (caudate and PCC). In addition, children/adolescents with DBDs show dysfunctional interactions between the VST, vmPFC, and lateral frontal cortex in response to rewarded instrumental actions potentially reflecting disruptions in attention to rewarded stimuli.

• Knowledge Management Research
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• v.20 no.3
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• pp.173-193
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• 2019

Reward-based crowdfunding is a funding platform that allows funds to be raised to early operators who have lack of funds, and is seen as an outstanding infrastructure that is going to lead the fourth industrial revolution in that it is a field of realization of new technologies and creative ideas by start-ups. Reward-based crowdfunding has grown in line with the trend of the fourth industrial revolution, and funding success cases are taking place in various industries that culture/art to technology/IT, including as a new means of knowledge management in a rapidly changing industrial environment. The study focused on the fact that consumer's donation purposes may also vary depending on the category of projects classified as reward-based crowdfunding. Because consumer payment decisions and motivation of consumer purchasing behavior are classified according to the purpose of purchase, the previous papers that the goal gradient effect that the main motivation of consumer donation for reward-based crowdfunding introduced vary depending on project category of utilitarian and hedonic. In this study, consumer's daily donation data is collected by Indiegogo which is a leading reward-based crowdfunding company using web-crawling and the model was defined as propensity score matching (PSM) and random effect model. The results showed that the goal gradient effect occurred in utilitarian project category, but no goal gradient effect for the hedonic project category. Furthermore, this paper developed the study of motivation of consumer donation and contributes theoretical foundation by the results consumer donation may vary depending on the project category; also, this paper has implications for an effective marketing strategy depending on the project category leaves real meaning to the projector.

• Journal of Industrial Convergence
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• v.21 no.2
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• pp.9-16
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• 2023

This study analyzed the effect of manager's ability on compensation policy (compensation level and performance-reward sensitivity). To this end, the final 14,150 company-year data were used for KOSPI and KOSDAQ listed companies excluding the financial industry from 2012 to 2019. As a result of the empirical analysis, the higher the manager's ability, the higher the next reward level (the manager's ability hypothesis), but the performance-reward sensitivity decreased. This confirms the manager ability hypothesis through a positive (+) relationship between manager ability and compensation, and means that high compensation for manager ability may be additional compensation for manager ability other than performance, rather than due to performance. This study differs from previous studies and has contributions in that it examines the more complex effects of managerial ability and compensation system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.110-113
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• 2022

Nowadays, breaking through the conventional traffic signal control method based on mathematical optimization, artificial intelligence began to be used in the area. In response to this trend, many studies are ongoing to figure out how to utilize AI technology properly for traffic signal optimization. They just simply focus on which method will work well besides lots of machine learning techniques and abandon the reward function engineering. In many cases, the reward function consists of the average delay of the vehicles in the intersection. However, this may lead to AI's misunderstanding about the traffic signal control: what AI regards as a good situation may not be realistic. Even the reward function itself may not meet the service level. Therefore, this study analyzes the problems of previous reward functions and will suggest how to reward function can be enhanced.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.6
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• pp.287-296
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• 2020

The study investigates the relationship and influence between religiosity, reward, and engagement in the public administration sector, both directly and causally via moderation. This study involved one hundred and twenty-three respondents in three local government organizations in Malang City, East Java Province, Indonesia, namely, the Malang City General Hospital, the Population and Civil Registry Office, and the Investment Office. The sampling method uses stratified random sampling from the total population of civil servants in Malang in the three institutions. The data analysis model of this study uses a quantitative approach with a unit of data analysis using the path analysis method. The analytical tools used are smart-PLS and SPSS. The results reveal that the direct combined effect of reward and religiosity has a positive and significant influence on the engagement. However, moderation between reward and commitment, which is bridged by religiosity, does not show positive and significant results. The non-positive relationship shown by testing moderation concludes that there is a separation of purpose between the portion of religiosity in the world of work, where religiosity in this study is only described as part of the concept of worship, and the relationship between the person and his God.

• Journal of Korea Game Society
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• v.9 no.4
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• pp.57-66
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• 2009

A quest system is one of the important parts in the MMORPG (Massive Multiplayer Online Role Playing Game) contents. Because of its complexity in combining various content components, quest reward design belongs to a complicated work in estimating quest reward levels correctly in the initial development stage. In this paper, we suggest a new quest reward design technique by considering optimal quest play paths. We model a quest reward problem as the TSP (Traveling Salesman Problem) and solve that by adopting genetic algorithms. With our system, game designers easily estimate the optimal quest play path and it can be useful in reducing the trial-errors in the initial quest design process.

• Journal of Korean Academy of Nursing
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• v.39 no.3
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• pp.329-337
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• 2009

Purpose: The purpose of this study was to investigate the determinants of job satisfaction of hospital nurses. The focus was on work rewards. A causal model of job satisfaction of hospital nurses was constructed based on situational perspectives. Methods: The sample for this study consisted of 505 nurses from 2 general hospitals located in Seoul and Kyeonggi Province, Korea. Data were collected with self-administrated questionnaires and analyzed by hierarchical multiple regression. Results: All variables except workload were positively correlated with job satisfaction. It was found that three task reward variables(workload, meaning, and participation), two organizational reward variables(security and promotional chances) and one social reward variable(family support) had significant influence on nurses' job satisfaction. The explained variance for job satisfaction was 41.4%. The data further indicate that task rewards were the most significant determinants of nurse job satisfaction. Conclusion: Theses findings provide strong empirical evidence for importance of task, organizational and social reward variables in explaining job satisfaction of nurses. The model used for this study will be useful for predicting nurse job satisfaction.

• Journal of Distribution Science
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• v.18 no.3
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• pp.43-51
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• 2020

Purpose: This study demonstrated and analyzed the role of distributive justice and procedural justice in explaining the organizational effectiveness of flight attendant. In addition, analyzing the role of the airline type in the coordination between reward justice and organizational effectiveness. Research design, data and methodology: An abstract is the impact relationship between the reward justice and organizational effectiveness of flight attendant and the adjustment effect of the airline type was reviewed. To examine these research models, samples were collected from 281flight attendants during Nov, 2019. Results: Reward justice has a positive effect on organizational effectiveness, and the types of airlines have a meaningful adjustment effect in terms of the effect of reward justice on organizational effectiveness. Conclusions: Procedural justice and distributive justice have positive influence on two sub factors of organizational effectiveness of the flight attendant. It suggests that the standards, procedures and processes of compensation must be fair, the degree of effort, the stress or the tension of the flight attendant should be considering, and it is necessary for the airline to respect the personality of the flight attendant and provide them with accurate compensation information in a timely manner. This will increase the awareness of reward.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.9
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• pp.2202-2217
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• 2012

The transmission opportunities between nodes in Delay Tolerant Network (DTNs) are uncertain, and routing algorithms in DTNs often need nodes serving as relays for others to carry and forward messages. Due to selfishness, nodes may ask the source to pay a certain reward, and the reward may be varying with time. Moreover, the reward that the source obtains from the destination may also be varying with time. For example, the sooner the destination gets the message, the more rewards the source may obtain. The goal of this paper is to explore efficient ways for the source to maximize its total reward in such complex applications when it uses the probabilistic two-hop routing policy. We first propose a theoretical framework, which can be used to evaluate the total reward that the source can obtain. Then based on the model, we prove that the optimal forwarding policy confirms to the threshold form by the Pontryagin's Maximum Principle. Simulations based on both synthetic and real motion traces show the accuracy of our theoretical framework. Furthermore, we demonstrate that the performance of the optimal forwarding policy with threshold form is better through extensive numerical results, which conforms to the result obtained by the Maximum Principle.

• Journal of Korean Association for Spatial Structures
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• v.22 no.2
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• pp.39-46
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• 2022

Recently, machine learning is widely used to solve optimization problems in various engineering fields. In this study, machine learning is applied to development of a control algorithm for a smart control device for reduction of seismic responses. For this purpose, Deep Q-network (DQN) out of reinforcement learning algorithms was employed to develop control algorithm. A single degree of freedom (SDOF) structure with a smart tuned mass damper (TMD) was used as an example structure. A smart TMD system was composed of MR (magnetorheological) damper instead of passive damper. Reward design of reinforcement learning mainly affects the control performance of the smart TMD. Various hyper-parameters were investigated to optimize the control performance of DQN-based control algorithm. Usually, decrease of the time step for numerical simulation is desirable to increase the accuracy of simulation results. However, the numerical simulation results presented that decrease of the time step for reward calculation might decrease the control performance of DQN-based control algorithm. Therefore, a proper time step for reward calculation should be selected in a DQN training process.