• Journal of Distribution Science
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• v.15 no.10
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• pp.41-50
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• 2017

Purpose - Online product review has become a crucial part of the online retailer's market performance for a wide range of products. This research aims to investigate how an individual reviewer's review frequency and timing affect her/his average attitude toward products. Research design, data, and methodology - To conduct reviewer-level analysis, this study uses 42,172 posted online review messages generated by 6,941 identified reviewers for 59 movies released in the South Korea from July 2015 to December 2015. This study adopts Tobit model specification to take into account the censored nature and the selection bias arising from the nature of J-shaped distribution of movie rating. Results - Our estimation results support that the negative impact of review frequency and timing on valence. Furthermore, review timing has an inverted-U relationship with the user's average valence and enhance the negative effect of review frequency. Conclusions - This study contributes to the growing literature on the understanding how eWOM is generated at the individual consumer level. On the basis of the main empirical findings, this study provides insights into building a recommendation system in online retail store based on the consumer's review history data - frequency, timing, and valence.

• Bulletin of the Korean Chemical Society
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• v.12 no.5
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• pp.574-582
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• 1991

The CO molecules adsorbed on Ni(111) surface is studied in the cluster approximation employing EH method with self-consistent charge iteration. The effect of CO coverage is simulated by allowing the variation of valence state ionization potentials of each Ni atom in model cluster according to the self-consistent charge iteration method. The CO coverage dependent C-O stretching frequency shift, adsorption site conversion, and metal work function change are attributed to the charge transfer between metal surface and adsorbate. For CO/Ni(111) system, net charge transfer from Ni surface to chemisorbed CO molecules makes surface Ni atoms be more positive with increasing coverage, and lowers Ni surface valence band. This leads to a weaker interaction between metal surface valence band and Co $2{\pi}^{\ast}$ MO, less charge transfer to a single CO molecule, and the bule shift of C-O stretching frequency. Further increase of coverage induces the conversion of 3-fold site CO to lower coordination site CO as well as the blue shift of C-O stretching frequency. This whole process is accompanied by the continuous increase of metal work function.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.79-82
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• 2020

얼굴 영상으로부터 사람의 감정을 예측하는 연구는 최근 딥러닝의 발전과 함께 주목받고 있다. 본 연구에서 우리는 연속적인 변수를 사용하여 감정을 표현하는 dimensional model에 기반하여 얼굴 영상으로부터 감정 상태를 나타내는 지표인 valance/arousal(V/A)을 예측하는 딥러닝 네트워크를 제안한다. 그러나 V/A 예측 모델의 학습에 사용되는 기존의 데이터셋들은 데이터 불균형(data imbalance) 문제를 가진다. 이를 해소하기 위해, 우리는 오토인코더 구조를 가지는 얼굴 영상 생성 네트워크를 학습하고, 이로부터 얻어지는 균일한 분포의 데이터로부터 V/A 예측 네트워크를 학습한다. 실험을 통해 우리는 제안하는 얼굴 생성 오토인코더가 in-the-wild 환경의 데이터셋으로부터 임의의 valence, arousal에 대응하는 얼굴 영상을 성공적으로 생생함을 보인다. 그리고, 이를 통해 학습된 V/A 예측 네트워크가 기존의 under-sampling, over-sampling 방영들과 비교하여 더 높은 인식 성능을 달성함을 보인다. 마지막으로 기존의 방법들과 제안하는 V/A 예측 네트워크의 성능을 정량적으로 비교한다.

• Knowledge Management Research
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• v.16 no.2
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• pp.213-226
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• 2015

This study investigates the effects of the volume and valence of the movie reviews on the weekly box-office revenues. Existing literature shows that only the volume of movie reviews influences the box office results, but not valence. However, it has limitations in that it includes only the positivity or negativity ratio of the reviews, not the strength of the valence. In order to overcome such limitations, this study includes the degree of valence. This study used approximately 1.3 million reviews about 300 movies as the sample which was collected from a movie review site in an online portal, that is, movie.naver.com. SPSS was used to test the proposed model. The results of this study show different findings compared to those of the previous studies. First, the volume of movie reviews has been a consistent predictor of the box office success throughout the study periods. Second, the ratio of positive reviews has no impact on the first week's results, but shows significant effects on the box office results during the second week. Third, regarding the degree of positivity or negativity in reviews, the degree of positivity has a significant impact on the box office results only during the first week, while the degree of negativity does not have any significant effects on the results. However, from the second week, the situation is reversed; that is, only the degree of negativity has a significant impact on the box office results, but not the positivity.

• Journal of Distribution Science
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• v.22 no.9
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• pp.119-128
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• 2024

Purpose: his study examines the effect of message valence on consumer perceptions of sales messages and salesperson evaluations in retail contexts. In contrast to previous studies on the negativity effect, it examines the positivity effect, which implies that the effect of positive information may outweigh that of negative information in certain situations. In addition, the current research examines how the content of the sales message influences consumers' perceptions of salespeople. Research design and methodology: The study presents an analytical model in which a potentially altruistic salesperson transmits quality information as a form of cheap talk. Several predictions were derived from the model and then empirically tested in two experiments. Results: When the sales message is about relatively less expensive products, positive information can be more credible and diagnostic than negative information. In addition, positive sales messages about the less expensive products signal the salesperson's benevolence. Conclusion: This paper is one of the few studies to predict and empirically test the positivity effect. It also contributes to the literature on trust in salespeople by showing that message valence influences buyers' perceptions of salespeople.

• Asia pacific journal of information systems
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• v.25 no.4
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• pp.746-762
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• 2015

This study developed a conceptual model that integrated psychological and physical reactions resulting from online tourism reviews through a longitudinal trust-satisfaction model (LSTM) developed based on the extended valence framework and expectation-confirmation theory. Online reviews are essential factor of consumer's purchase decision. This phenomenon is well applied in a tourism context. However, investigations on online reviews in a longitudinal approach in a tourism context are quite limited. Therefore, this study suggests a conceptual model based on LTSM and several propositions about how online tourism reviews, which are divided into factual and experiential reviews, influence the future travelers' perceptions and attitudes, such as expectation, confirmation, and destination loyalty, in a longitudinal format by examining previous related studies. Finally, expected results were discussed and several implications were described theoretically and practically.

• Journal of Information Technology Applications and Management
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• v.31 no.1
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• pp.97-112
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• 2024

In order to verify the factors affecting the acceptance of Serious Games for Healthcare (SGHs), this study developed a hierarchical model of general and specific benefit and risk factors affecting the intention to use SGHs based on the valence framework. As a result based on 199 samples, it was revealed that perceived customization and perceived schedule flexibility had a positive effect on the perceived benefits, which, in turn, had a positive effect on the intention to use SGHs. However, among the specific risk factors, only privacy risk had a positive effect on perceived risk, but it did not have a effect on SGHs usage intention. The results related to the fact that the survey respondents were potential users of SGHs and the bias that may overestimate the benefits provided by SGHs called optimistic bias. Based on these findings, some implications were presented such as the spread and distribution of SGHs to the ordinary persons, improvement of negative perceptions of games, and the need for data-based services to refine customized services for SGHs.

• Asia Marketing Journal
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• v.15 no.1
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• pp.155-168
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• 2013

This study describes consumers' movie choices in conjunction with other group members and attempts to reassess the effect of the online word of mouth (WOM) source in a joint decision context. The tendency of many people to go to movies in groups has been mentioned in previous literature but there is no modeling research that studies movie choice from the group decision perspective. We found that ignoring the group movie-going perspective can result in a misunderstanding, especially underestimation of genre preference and the impact of the WOM variables. Most of the studies to measure online WOM effects were done at the aggregate level, and the role of online WOM variables(volume vs valence) is mixed in the literature. We postulate that group-level analysis might offer insight to resolve these mixed understanding of WOM effects in the literature. We implemented the study via a random effect model with group-level heterogeneity. Romance, drama, and action were selected as genre variables; valence and volume were selected as online WOM variables. A choice-based conjoint survey was used for data collection and the models was estimated via Bayesian MCMC method. The empirical results show that (i) both genre and online WOM are important variables when consumers choose movies, especially as group, and (ii) the WOM valence effect are amplified more than the volume effect does as individuals are engaged in group decision. This research contributes to the literature in several ways. First, we investigate movie choice from a group movie-going perspective that is more realistic and consistent with the market behavior. Secondly, the study sheds new light on the WOM effect. At group-level, both valence and volume significantly affect movie choices, which adds to the understanding of the role of online WOM in consumers' movie choice.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.6
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• pp.2056-2069
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• 2014

In order to implement interactive and personalized Web services properly, it is necessary to understand the tangible and intangible responses of the users and to recognize their emotional states. Recently, some studies have attempted to build emotional state estimation models based on facial expressions. Most of these studies have applied multiple regression analysis (MRA), artificial neural network (ANN), and support vector regression (SVR) as the prediction algorithm, but the prediction accuracies have been relatively low. In order to improve the prediction performance of the emotion prediction model, we propose a novel SVR model that is optimized using a genetic algorithm (GA). Our proposed algorithm-GASVR-is designed to optimize the kernel parameters and the feature subsets of SVRs in order to predict the levels of two aspects-valence and arousal-of the emotions of the users. In order to validate the usefulness of GASVR, we collected a real-world data set of facial responses and emotional states via a survey. We applied GASVR and other algorithms including MRA, ANN, and conventional SVR to the data set. Finally, we found that GASVR outperformed all of the comparative algorithms in the prediction of the valence and arousal levels.

• Journal of the Korea Convergence Society
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• v.9 no.5
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• pp.33-41
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• 2018

The purpose of this study is to develop more accurate and robust emotion prediction neural network (EPNN) model by combining heart rate variability (HRV) and neural network. For the sake of improving the prediction performance more reliably, the proposed EPNN model is based on various types of activation functions like hyperbolic tangent, linear, and Gaussian functions, all of which are embedded in hidden nodes to improve its performance. In order to verify the validity of the proposed EPNN model, a number of HRV metrics were calculated from 20 valid and qualified participants whose emotions were induced by using money game. To add more rigor to the experiment, the participants' valence and arousal were checked and used as output node of the EPNN. The experiment results reveal that the F-Measure for Valence and Arousal is 80% and 95%, respectively, proving that the EPNN yields very robust and well-balanced performance. The EPNN performance was compared with competing models like neural network, logistic regression, support vector machine, and random forest. The EPNN was more accurate and reliable than those of the competing models. The results of this study can be effectively applied to many types of wearable computing devices when ubiquitous digital health environment becomes feasible and permeating into our everyday lives.