• The Journal of Bigdata
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• v.9 no.1
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• pp.31-47
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• 2024

With the advancement of artificial intelligence, the travel and hospitality industry is also adopting AI and machine learning technologies for various purposes. In the tourism industry, demand forecasting is recognized as a very important factor, as it directly impacts service efficiency and revenue maximization. Demand forecasting requires the consideration of time-varying data flows, which is why statistical techniques and machine learning models are used. In recent years, variations and integration of existing models have been studied to account for the diversity of demand forecasting data and the complexity of the natural world, which have been reported to improve forecasting performance concerning uncertainty and variability. This study also proposes a new model that integrates various machine-learning approaches to improve the accuracy of hotel sales demand forecasting. Specifically, this study proposes a new time series forecasting model based on XGBoost that selectively utilizes a local model by clustering with DTW K-means and a global model using the entire data to improve forecasting performance. The hotel demand forecasting model that selectively utilizes global and regional models proposed in this study is expected to impact the growth of the hotel and travel industry positively and can be applied to forecasting in other business fields in the future.

• Journal of the Korean Institute of Landscape Architecture
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• v.44 no.1
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• pp.16-28
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• 2016

The study was conducted to compare and analyze the importance and performance of leisure destination selection attributes of persons who use recreational forests and rural tourism villages. This researcher investigated the use patterns of users to identify the ground for connection between recreational forest and rural tourism village, analyzed their recognition differences in physical selection attribute, program selection attribute, and service selection attribute in order for leisure destination selection, and conducted importance-performance analysis(IPA analysis) to draw a plan for connection. The main results and suggestions are presented as follows. First, recreational forests were visited by family users in order for rest and emotional cultivation and provided experience programs using simple public interest function of forest, whereas rural tourism villages were visited by family users, friends and co-workers, groups and club members to experience a variety of annual programs and understand regional cultures. It was found that it was necessary to connect natural forest with rural tourism village in order to meet the leisure needs of the people changed in diversified ways. Secondly, it was found that the connection between rural tourism village and recreational forest visited mainly for simple rest led to positive visit intention of users. It was expected that there will be various kinds of uses, including experience program participation, child education, and safe accommodations security. In other words, the connection between recreational forest and rural tourism village is an alternative to trigger actual demands and recreational forest activities with high quality. Thirdly, in the case of users of recreational forests, their performance of all selection attributes was lower than their importance of them. Therefore, overall improvements were needed. In particular, needed were the diversity, benefit, and promotion of programs, improvements in locality(themes), supply of lodges and convenient facilities, booking system, the purchase system of local special products, and professional skills of operators and managers. On contrary, the performance of program selection attribute of rural tourism village was high. Therefore, it was found that program attribute of rural tourism village was the main connection factor to activate recreational forest use. Fourthly, according to IPA analysis, the proper connections between loges, convenient facilities, and nearby touristattractions, which give high expectations and satisfaction to users, needed to remain. And it was required to make common efforts to accomplish the goal (income creation) of rural tourism village and improve booking system for visitors and performance of local special products sales opportunity. In addition, the essential factors to induce users' leisure destination selection were found to be maintenance of the use fee system of recreational forest, diversity of rural tourism village program, and retention of locality.

• Journal of Distribution Research
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• v.12 no.1
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• pp.1-31
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• 2007

While trust and commitment are core mediating variables for the purpose of maintaining the long-term relationship, in the context of the characteristics of company and the relationship performance of its members, there have been limited studies which explore as to how each of the dimensions has affects differently. The basic purpose of this study is to examine the relationship between an automobile manufacturer and its agencies. The main purpose of this study is to examine how each different dimension of trust and commitment on the automobile manufacturer has different mediating effects between the characteristics of company(organization characteristics, relationship characteristics) and relationship performance perceived by its agents. Another purpose is to investigate the mechanism by which the relationship performance of the agencies is improved. An empirical study surveying 115 sales office managers at a leading automobile manufacturer in Korea was conducted. An analysis of the collected data indicates that while the characteristics of company have a positive influence on the agencies' relational performance through the mediating role of both trust of benevolence and commitment, agencies' trust of their headquarter's benevolence has a different influence on the dimensions of commitment. Finally, the authors discussed some theoretical contributions and managerial implications. And then, they presented limitations of this study and the future research directions.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.29-56
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• 2021

Recently, various types of products have been launched with the rapid growth of the e-commerce market. As a result, many users face information overload problems, which is time-consuming in the purchasing decision-making process. Therefore, the importance of a personalized recommendation service that can provide customized products and services to users is emerging. For example, global companies such as Netflix, Amazon, and Google have introduced personalized recommendation services to support users' purchasing decisions. Accordingly, the user's information search cost can reduce which can positively affect the company's sales increase. The existing personalized recommendation service research applied Collaborative Filtering (CF) technique predicts user preference mainly use quantified information. However, the recommendation performance may have decreased if only use quantitative information. To improve the problems of such existing studies, many studies using reviews to enhance recommendation performance. However, reviews contain factors that hinder purchasing decisions, such as advertising content, false comments, meaningless or irrelevant content. When providing recommendation service uses a review that includes these factors can lead to decrease recommendation performance. Therefore, we proposed a novel recommendation methodology through CNN-based review usefulness score prediction to improve these problems. The results show that the proposed methodology has better prediction performance than the recommendation method considering all existing preference ratings. In addition, the results suggest that can enhance the performance of traditional CF when the information on review usefulness reflects in the personalized recommendation service.

• Asia Marketing Journal
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• v.14 no.1
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• pp.1-24
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• 2012

Nowadays, customer satisfaction has been one of company's major objectives, and the index to measure and communicate customer satisfaction has been generally accepted among business practices. The major issues of CSI(customer satisfaction index) are three questions, as follows: (a)what level of customer satisfaction is tolerable, (b)whether customer satisfaction and company performance has positive causality, and (c)what to do to improve customer satisfaction. Among these, the second issue is recently attracting academic research in several perspectives. On this study, the second issue will be addressed. Many researchers including Anderson have regarded customer satisfaction as core competencies, such as brand equity, customer equity. They want to verify following causality "customer satisfaction → market performance(market share, sales growth rate) → financial performance(operating margin, profitability) → corporate value performance(stock price, credit ratings)" based on the process model of marketing performance. On the other hand, Insoo Jeon and Aeju Jeong(2009) verified sequential causality based on the process model by the domestic data. According to the rejection of several hypotheses, they suggested the balance model of marketing performance as an alternative. The objective of this study, based on the existing process model, is to examine the causal relationship between customer satisfaction and corporate value performance. Anderson and Mansi(2009) proved the relationship between ACSI(American Customer Satisfaction Index) and credit ratings using 2,574 samples from 1994 to 2004 on the assumption that credit rating could be an indicator of a corporate value performance. The similar study(Sangwoon Yoon, 2010) was processed in Korean data, but it didn't confirm the relationship between KCSI(Korean CSI) and credit ratings, unlike the results of Anderson and Mansi(2009). The summary of these studies is in the Table 1. Two studies analyzing the relationship between customer satisfaction and credit ratings weren't consistent results. So, in this study we are to test the conflicting results of the relationship between customer satisfaction and credit ratings based on the research model considering Korean credit ratings. To prove the hypothesis, we suggest the research model as follows. Two important features of this model are the inclusion of important variables in the existing Korean credit rating system and government support. To control their influences on credit ratings, we included three important variables of Korean credit rating system and government support, in case of financial institutions including banks. ROA, ER, TA, these three variables are chosen among various kinds of financial indicators since they are the most frequent variables in many previous studies. The results of the research model are relatively favorable : R², F-value and p-value is .631, 233.15 and .000 respectively. Thus, the explanatory power of the research model as a whole is good and the model is statistically significant. The research model has good explanatory power, the regression coefficients of the KCSI is .096 as positive(+) and t-value and p-value is 2.220 and .0135 respectively. As a results, we can say the hypothesis is supported. Meanwhile, all other explanatory variables including ROA, ER, log(TA), GS_DV are identified as significant and each variables has a positive(+) relationship with CRS. In particular, the t-value of log(TA) is 23.557 and log(TA) as an explanatory variables of the corporate credit ratings shows very high level of statistical significance. Considering interrelationship between financial indicators such as ROA, ER which include total asset in their formula, we can expect multicollinearity problem. But indicators like VIF and tolerance limits that shows whether multicollinearity exists or not, say that there is no statistically significant multicollinearity in all the explanatory variables. KCSI, the main subject of this study, is a statistically significant level even though the standardized regression coefficients and t-value of KCSI is .055 and 2.220 respectively and a relatively low level among explanatory variables. Considering that we chose other explanatory variables based on the level of explanatory power out of many indicators in the previous studies, KCSI is validated as one of the most significant explanatory variables for credit rating score. And this result can provide new insights on the determinants of credit ratings. However, KCSI has relatively lower impact than main financial indicators like log(TA), ER. Therefore, KCSI is one of the determinants of credit ratings, but don't have an exceedingly significant influence. In addition, this study found that customer satisfaction had more meaningful impact on corporations of small asset size than those of big asset size, and on service companies than manufacturers. The findings of this study is consistent with Anderson and Mansi(2009), but different from Sangwoon Yoon(2010). Although research model of this study is a bit different from Anderson and Mansi(2009), we can conclude that customer satisfaction has a significant influence on company's credit ratings either Korea or the United State. In addition, this paper found that customer satisfaction had more meaningful impact on corporations of small asset size than those of big asset size and on service companies than manufacturers. Until now there are a few of researches about the relationship between customer satisfaction and various business performance, some of which were supported, some weren't. The contribution of this study is that credit rating is applied as a corporate value performance in addition to stock price. It is somewhat important, because credit ratings determine the cost of debt. But so far it doesn't get attention of marketing researches. Based on this study, we can say that customer satisfaction is partially related to all indicators of corporate business performances. Practical meanings for customer satisfaction department are that it needs to actively invest in the customer satisfaction, because active investment also contributes to higher credit ratings and other business performances. A suggestion for credit evaluators is that they need to design new credit rating model which reflect qualitative customer satisfaction as well as existing variables like ROA, ER, TA.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.159-172
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• 2010

The recommender system is one of the possible solutions to assist customers in finding the items they would like to purchase. To date, a variety of recommendation techniques have been developed. One of the most successful recommendation techniques is Collaborative Filtering (CF) that has been used in a number of different applications such as recommending Web pages, movies, music, articles and products. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. Broadly, there are memory-based CF algorithms, model-based CF algorithms, and hybrid CF algorithms which combine CF with content-based techniques or other recommender systems. While many researchers have focused their efforts in improving CF performance, the theoretical justification of CF algorithms is lacking. That is, we do not know many things about how CF is done. Furthermore, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting the performances of CF algorithms in advance is practically important and needed. In this study, we propose an efficient approach to predict the performance of CF. Social Network Analysis (SNA) and Artificial Neural Network (ANN) are applied to develop our prediction model. CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. SNA facilitates an exploration of the topological properties of the network structure that are implicit in data for CF recommendations. An ANN model is developed through an analysis of network topology, such as network density, inclusiveness, clustering coefficient, network centralization, and Krackhardt's efficiency. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Inclusiveness refers to the number of nodes which are included within the various connected parts of the social network. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. Krackhardt's efficiency characterizes how dense the social network is beyond that barely needed to keep the social group even indirectly connected to one another. We use these social network measures as input variables of the ANN model. As an output variable, we use the recommendation accuracy measured by F1-measure. In order to evaluate the effectiveness of the ANN model, sales transaction data from H department store, one of the well-known department stores in Korea, was used. Total 396 experimental samples were gathered, and we used 40%, 40%, and 20% of them, for training, test, and validation, respectively. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. The input variable measuring process consists of following three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used Net Miner 3 and UCINET 6.0 for SNA, and Clementine 11.1 for ANN modeling. The experiments reported that the ANN model has 92.61% estimated accuracy and 0.0049 RMSE. Thus, we can know that our prediction model helps decide whether CF is useful for a given application with certain data characteristics.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.2
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• pp.121-134
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• 2016

The current insurance market is facing a real problem that the high cost of insurance spent in maintaining a non- face-to-face sales channels face of the channel facing growing contribution to the reduction of side. As a result, the productivity issue facing designers of representative organizations in the organization channel will be referred to an urgent problem. As a result of improved organizational productivity architect that is the goal of this study to demonstrate what a performance improvement factor of insurance agents. Personality factors and individual insurance agents individual-environmental suitability and job satisfaction, consider the impact on turnover intention year of the results architects extroversion, sincerity, openness, it won a chronic, emotional gender, personality representing the honesty factor is organizational commitment and job satisfaction It has had a significant impact on. In other words, this is a lively and extroverted nature of the actuary, the more harmonious interpersonal relationships and higher emotional empathy with others can raise the extent that has a strong sense of belonging and attachment to their company's commitment. Whereas personality factors were not significant influence turnover intention has. This can be made to represent the need for screening of agents introduced from the introduction stage. Depending on the personality factors of organizational commitment, personal planners also occurs because of the differences and job satisfaction. Whereas turnover of agents is the result of empirical factors that are affected by other agents than to individual character generated by the character of the individual agents. Compliance boss, job suitability, individuals representing a fellow fitness, tissue compatibility environmental compliance is having a significant impact on both the degree of organizational commitment, job satisfaction and turnover intention. In other words, the boss or colleague, values and personality, working method, as fits well the concerns and pursuing goals are similar, and their job aptitude higher the suitability of the organization is about to have a sense of belonging and attachment to the company commitment can do. This is the result of a demonstration that the work environment of the actuary agents productivity gains and loyalty depends on the insurance company, which currently belongs.

• Journal of Distribution Research
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• v.6 no.2
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• pp.1-17
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• 2002

The recent dot-com craze has been one of the main causes that accelerated the growth of internet-related companies in diversity as well as in size. Meanwhile, the domestic market of supplies and equipment for internet businesses has been dominated by major foreign companies. To regain their market positions, the domestic manufacturers had to find the way to build up their competitive advantages, such as meeting their customers needs and reducing overall costs. In this study, one domestic PC server manufacturer, which competes fiercely with foreign manufacturers for the top place, has been chosen as a model to evaluate its current supply chain and to find an area that can be improved for a better performance. System Dynamics is used throughout the study. The central concept to system dynamics is understanding how all the objects in a system interact with one another. It focuses on feedback and secondary effects to think through how a strategy might or might not work, depending on how organizational changes are received, and what kinds of consequences emerge. Then, computerized models were built for simulations, each with different conditions, and, finally, the results were evaluated based on some criteria which are considered to be important and meaningful. The inefficiency that exists in the supply chain was proved to be a thirty-day long purchasing order leadtime, and it was expected that more effective supply chain could be formed if the leadtme were reduced to 14 days or 7 days. The results of simulations showed that the overall expected costs in supply chain was the least with the purchasing leadtime being 7 days. The lower average number of parts held as inventory, along with the reduced lost sales, acted as the factor reducing the expected overall costs. Although there was a slight increase in the average number of final products held as inventory and the total ordering cost, the benefits from lower parts inventory and reduced lost sales were large enough to justify the overall cost reduction.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.

• Journal of the Korean Society of International Agriculture
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• v.30 no.4
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• pp.292-299
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• 2018

In a survey in which farmers were asked about their levels of satisfaction with agricultural machines, Japanese products scored higher than local products by 1.2, 1.3, and 1.4 times for tractors, combine harvesters, and rice transplanter, respectively. Japanese products corresponded to generally high satisfaction levels in terms of operating performance, operability, frequency of breakdowns, and durability, excluding sales price and after-sales services. Effective countermeasures through quality improvement are therefore necessary for Korean products. Furthermore, a survey of dealers showed that the components and consumables for core agricultural machines had high frequencies of breakdowns and repairs. Four major components of tractors represented 85.3% of all breakdowns and repairs, five components of combine harvesters represented 89.6%, and three components of rice transplanters represented 80.5%. Moreover, a comparison of the technological levels between local and imported machines showed that the local machines' levels were at 60-100% for tractors, 70-100% for combine harvesters, and 70-95% for rice transplanters. Small and mid-sized tractors, 4 interrow combine harvesters, and 6 interrow rice transplanters showed similar levels of technology. The results of the analysis suggest that action is urgently needed at a policy level to establish an agricultural machinery component research center for the development, production, and supply of commonly-used components, with the participation of manufacturers of agricultural machines and components, in order to enhance the competitiveness of local manufacturers and to revitalize the agricultural machine market.