• Knowledge Management Research
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• v.19 no.4
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• pp.1-17
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• 2018

Hotel consumers tend to rely on online reviews to reduce the risk to hotel products when they book hotel rooms because hotel products are high-risk products due to their intangibility. However, the development of ICT has caused information load, and it is an important issue to be perceived as useful information to consumer because a large amount of information complicates the decision making process of consumers. Drawn from Heuristic-Systematic Model(HSM), the present study explored the role of heuristic and systematic cues composing an online review influencing consumers' perception of hotel online reviews. More specifically, this study identified reviewers' identity, level of the reviewer, review star ratings, and attached hotel photo as heuristic cue, while review length, cognitive level of review and negativity in review as systematic cues. The binary logistic regression was adopted for analysis. This study found that only systematic cues of online review were found to affect the usefulness of it. Moreover, we preceded further study examining the moderating effect of seasonality in the relationships between systematic cues and usefulness.

• Journal of Korea Port Economic Association
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• v.29 no.3
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• pp.159-174
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• 2013

This paper mainly deals with the appropriation of ship voyage allocation, using a heuristic regression model, in order to reduce total costs incurred in port, yard and at sea under the specific port condition. Because of different behavior of costs incurred in port, yard and at sea, an effort to minimize these costs by adjusting the number of voyages for three ship classes(50,000, 100,000, and 150,000-ton) should be made. For instance, if the port managers attempt to reduce the sea transport cost by increasing the annual allocated number of ship voyages classed 150,000-ton for economies of scale, they have no choice but to suffer a significant increase in queueing cost due to port congestion. To put it differently, there are trade-off relationships among the costs incurred in port, yard, and at sea. We utilized a computer simulation result to perform a couple of regression analyses in order to figure out the appropriate range of allocated number of voyages of each ship class using a heuristic approach. The detailed analytical results will be shown at the main paper. We also suggested a net present value(NPV) model to make a proper investment decision for an additional berth of 200,000-ton class that alleviates port congestion and reduces transport cost incurred both in port and at sea.

• Journal of Distribution Science
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• v.17 no.10
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• pp.73-81
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• 2019

Purpose - The purpose of this study was to examine the effect of the heuristic type of consumers affecting purchase decision making and the intention of shopping value in their relationship to derive mobile social commerce purchase promotion plans. Research design, data, and methodology - A research model was constructed to relate the mediating effect of shopping value between heuristic types and purchase intentions. A total of 233 valid questionnaires were used for analysis for users using mobile social commerce. The statistical program used SPSS 24.0 and AMOS 24.0, and correlation analysis, regression analysis, and 3-step parametric regression analysis were used for the analysis. Results - The results of the analysis showed that representativeness heuristics, availability heuristics, adjustment heuristics, and affect heuristics had a statistically significant effect on the utilitarian value and the hedonic value. On the other hand, affect heuristics among the heuristic types were found to have the greatest influence not only on the utilitarian value but also on the hedonic value. The two types of shopping value were found to be partially mediated between representativeness heuristics and purchase intentions, between adjustment heuristics and purchase intentions, and fully mediated between availability heuristics and purchase intentions, affect heuristics and purchase intentions. Conclusions - These findings suggest that mobile social commerce companies should check in advance how consumer heuristic types affect purchase intentions. In particular, affect heuristics are caused by consumers' emotional mood such as mood or external stimulus being more important to decision making than rational decision making. Therefore, the result of this study suggests that it can be an important factor to secure the competitiveness that the potential customers who access to use mobile social commerce can feel enough fun and enjoyment in the platform provided by the company. It is also worth paying attention to the utilitarian and hedonic values perceived by consumers. This is because the judgment regarding the economic, convenience and important information provided by the mobile social commerce users affects the purchase intention through the trust of the information, past use, and shopping experience displayed on the mobile social commerce platform.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.173-179
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• 2000

Genetic algorithm(GA) is applied for detecting multiple outliers. GA is a heuristic optimization tool solving for near optimal solution. We compare the performance of GA and the other diagnostic measures commonly used for detecting outliers in regression model. The results show that GA seems to have better performance than the others for the detection of multiple outliers.

• Asia pacific journal of information systems
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• v.30 no.2
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• pp.420-438
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• 2020

Recently, with the advance of the wireless Internet access via mobile devices, a myriad of game development companies have forayed into the mobile game market, leading to intense competition. To survive in this fierce competition, mobile game developers often try to get a grasp of the rapidly changing needs of their customers by operating their own official communities where game users freely leave their requests, suggestions, and ideas relevant to focal games. Based on the heuristic-systematic model (HSM) and the data quality (DQ) framework, this study derives key content, non-content, and hybrid cues that can be utilized when game developers accept suggested postings in these online communities. The results of hierarchical multiple regression analysis show that relevancy, timeliness, amount of writing, and the number of comments are positively associated with mobile game developers' knowledge adoption. In addition, title attractiveness mitigates the relationship between amount of writing/the number of comments and knowledge adoption.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.1
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• pp.66-78
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• 2012

This paper proposes a computation model of the quantity supplied to optimize inventory costs for the fast fashion. The model is based on a forecasting, a store and production capacity, an assortment planning and quick response model for fast fashion retailers, respectively. It is critical to develop a standardized business process and mathematical model to respond market trends and customer requirements in the fast fashion industry. Thus, we define a product supply model that consists of forecasting, assortment plan, store capacity plan based on the visual merchandising, and production capacity plan considering quick response of the fast fashion retailers. For the forecasting, the decomposition method and multiple regression model are applied. In order to optimize inventory costs. A heuristic algorithm for the quantity supplied is designed based on the assortment plan, store capacity plan and production capacity plan. It is shown that the heuristic algorithm produces a feasible solution which outperforms the average inventory cost of a global fast fashion company.

• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.203-210
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• 2014

This study proposes adaptive decision making method having feed-back structure of regression and simulation models to support the quick decision making of production managers by managing and integrating the mutual relationship among historical data. For that, from historical data that have extracted and accumulated from each process, we first selected major constraint resources that are used as independent variables in regression model. The regression model is designed by using the dependent variables (objectives) that defined above by managers and independent variables selected in previous step and simulation model that are composed of constraint resources is designed. In process of simulation run, we obtain the multiple feasible solutions (alternatives) by using meta-heuristic method. Each solution is substituted by regression equation and we found the optimal solution that is minimum of difference between values obtained by regression model and simulation results. The optimal solution is delivered and incorporated to production site and current operation results from production site is used to generate new regression model after that time.

• International Journal of Control, Automation, and Systems
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• v.6 no.5
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• pp.639-650
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• 2008

Electricity price forecasting has become an integral part of power system operation and control. In this paper, a wavelet transform (WT) based neural network (NN) model to forecast price profile in a deregulated electricity market has been presented. The historical price data has been decomposed into wavelet domain constitutive sub series using WT and then combined with the other time domain variables to form the set of input variables for the proposed forecasting model. The behavior of the wavelet domain constitutive series has been studied based on statistical analysis. It has been observed that forecasting accuracy can be improved by the use of WT in a forecasting model. Multi-scale analysis from one to seven levels of decomposition has been performed and the empirical evidence suggests that accuracy improvement is highest at third level of decomposition. Forecasting performance of the proposed model has been compared with (i) a heuristic technique, (ii) a simulation model used by Ontario's Independent Electricity System Operator (IESO), (iii) a Multiple Linear Regression (MLR) model, (iv) NN model, (v) Auto Regressive Integrated Moving Average (ARIMA) model, (vi) Dynamic Regression (DR) model, and (vii) Transfer Function (TF) model. Forecasting results show that the performance of the proposed WT based NN model is satisfactory and it can be used by the participants to respond properly as it predicts price before closing of window for submission of initial bids.

• 한국공간정보시스템학회:학술대회논문집
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• 2005.05a
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• pp.345-349
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• 2005

Massive amount of satellite image processing such asglobal/continental-level analysis and monitoring requires automated and speedy georegistration. There could be two major automated approaches: (1) rigid mathematical modeling using sensor model and ephemeris data; (2) heuristic co-registration approach with respect to existing reference image. In case of ETM+, the accuracy of the first approach is known as RMSE 250m, which is far below requested accuracy level for most of satellite image processing. On the other hands, the second approach is to find identical points between new image and reference image and use heuristic regression model for registration. The latter shows better accuracy but has problems with expensive computation. To improve efficiency of the coregistration approach, the author proposed a pre-qualified matching algorithm which is composed of feature extraction with canny operator and area matching algorithm with correlation coefficient. Throughout the pre-qualification approach, the computation time was significantly improved and make the registration accuracy is improved. A prototype was implemented and tested with the proposed algorithm. The performance test of 14 TM/ETM+ images in the U.S. showed: (1) average RMSE error of the approach was 0.47 dependent upon terrain and features; (2) the number average matching points were over 15,000; (3) the time complexity was 12 min per image with 3.2GHz Intel Pentium 4 and 1G Ram.

• Journal of Korean Society of Transportation
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• v.25 no.4
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• pp.109-121
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• 2007

In this study, an imputation model using circular probability distribution was developed in order to overcome problems of missing data from a traffic survey. The existing ad-hoc or heuristic, model-based and algorithm-based imputation techniques were reviewed through previous studies, and then their limitations for imputing missing traffic volume data were revealed. The statistical computing language 'R' was employed for model construction, and a mixture of von Mises probability distribution, which is classified as symmetric, and unimodal circular probability were finally fitted on the basis of traffic volume data at survey stations in urban and rural areas, respectively. The circular probability distribution model largely proved to outperform a dummy variable regression model in regards to various evaluation conditions. It turned out that circular probability distribution models depict circularity of hourly volumes well and are very cost-effective and robust to changes in missing mechanisms.