• Journal of Distribution Science
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• v.21 no.6
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• pp.39-49
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• 2023

Purpose: This paper aims to investigate the influence of different methods of distributing sweepstakes (i.e., whether consumers choose to enter into the sweepstakes themselves or they are given the sweepstake ticket by default) on the effectiveness of the sweepstakes promotion (i.e., interest in the sweepstakes and intention to participate in the sweepstakes). Research design, data and methodology: The paper verifies this effect through three experimental studies: an online experiment using a sweepstakes promotion scenario at a department store, an online SNS sweepstakes promotion event, and a face-to-face card lottery game. Results: Participants belonging the group that chose sweepstakes tickets by themselves showed higher interest and intention to participate in the sweepstakes than those who were given the sweepstakes ticket by default. Furthermore, the group that chose the sweepstakes card thought it had a higher probability of winning than the group given the sweepstakes card. Conclusions: This paper shows a way to enhance the promotional effect of sweepstakes in the retail stores, without incurring additional costs, by approaching from sweepstakes design from the psychological perspective of the consumer. The study also sheds new light on the effect of sense of control manipulation using choice behavior in the promotional context.

• Korean Journal of Culture and Social Issue
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• v.10 no.3
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• pp.19-36
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• 2004

An experimental research investigated the components of lottery games affecting lottery-purchasing behaviors and the psychological consequences of the behaviors. In the experiment, participants were given a chance to purchase a lottery tickets during a series of computer games and their decision of purchasing the lottery ticket was measured. Also, the size and probability of the lottery games were manipulated and the perceived difficulty, satisfaction of the mid-outcome, and perceived probability of success in the computer game were measured in order to examine their impacts on participants' lottery-purchasing decisions. In addition, the behavioral tendency, satisfaction of the final outcome, and perceived self-capability in the computer game were measured at the end of the computer games in order to examine the effects of lottery-purchasing experiences on the variables. Participants who perceived the games as easier and estimated the probability of their success highly were more likely to buy the lottery tickets. However, the winning prize and odd of lottery tickets, perceived satisfaction of their own performance, and the performance itself did not influence the purchasing decisions. The common beliefs on the negative effects of lottery-purchasing experiences on human motivation and behaviors were not supported. The implications of the present research findings and limitations of the experimental research on lottery were discussed.

• Proceedings of the Korea Multimedia Society Conference
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• 2001.06a
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• pp.496-499
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• 2001

정보화 사회가 발전되고 전자 상거래가 새로운 상거래 시스템으로 부각되면서 전자상거래에 전자 복권에 대한 수요의 증가와 함께 연구가 활발히 진행되고 있다. 이중 전자 복권 시스템은 인터넷과 같이 개방된 네트워크 환경 하에서 사용하기 위한 목표로 기존의 복권을 대신하여 사용될 것이다. 복권번호 중복문제, 사용자·사업자간의 결탁문제 등 여러 가지의 문제점이 지적되고 있다. 본 고에서는 기존의 전자복권 시스템에 대한 고찰과 그에 상응하는 요구사항을 살펴본 뒤 실제로 제안된 기법을 검토하여 안전한 프로토콜에 대해 설명한다.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.115-128
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• 2023

Deep learning has been incorporating various optimization techniques motivated by new pragmatic optimizing algorithm advancements and their usage has a central role in Machine learning. In recent past, new avatars of various optimizers are being put into practice and their suitability and applicability has been reported on various domains. The resurgence of novelty starts from Stochastic Gradient Descent to convex and non-convex and derivative-free approaches. In the contemporary of these horizons of optimizers, choosing a best-fit or appropriate optimizer is an important consideration in deep learning theme as these working-horse engines determines the final performance predicted by the model. Moreover with increasing number of deep layers tantamount higher complexity with hyper-parameter tuning and consequently need to delve for a befitting optimizer. We empirically examine most popular and widely used optimizers on various data sets and networks-like MNIST and GAN plus others. The pragmatic comparison focuses on their similarities, differences and possibilities of their suitability for a given application. Additionally, the recent optimizer variants are highlighted with their subtlety. The article emphasizes on their critical role and pinpoints buttress options while choosing among them.