Salespeople act at the point of MOT with customers and deliver the enterprise's message to the customers. They build up relationships with customers as well as deliver the customer's message to the enterprise. The salespeople's activity at the point of MOT with the customers and the degree of satisfaction of the customers' needs will affect the customers' attitude toward the enterprise, brand loyalty, and retention intention. Ultimately, it will influence the enterprise's financial performance. The control of salespe1ople is one of the most interesting topics of marketing. This research investigates the relationships of the control types over salespeople(positive/negative outcome control, positive/negative behavior control) and job satisfaction and their mediating variables. The mediating variables in the relationships have been identified as outcome/behavior-related role clarity and self-efficacy. The purpose of this study is more specifically as follows: First, it investigate how the perception of salespeople control types affect role-clarity. Second, it examines how the perception of salespeople control types influence self-efficacy. Third, it investigate the mediating role of role-clarity between the perception of salespeople control types and self-efficacy. Fourth, it investigates how role-clarity affect self-efficacy and job satisfaction. Finally, it will investigates how self-efficacy influences job satisfaction. Data were collected from the pharmaceutical industry salespeople and analyzed by SPSS 12.0 and AMOS 6.0. The data were collected by 400 respondents and 377 valid questionnaires were analyzed. The results are summarized as follows: First, positive/negative outcome controls had a positive relationship with outcome-related role clarity. Also positive behavior control had a positive effect on behavior-related role clarity, but negative behavior control didn't influence behavior-related role clarity. Second, positive outcome control influenced self-efficacy positively, but positive behavior control didn't have a positive effect on self-efficacy. In addition negative outcome control and negative behavior control had a positive effect on self-efficacy due to the mediating role of outcome-related and behavior-related role clarity. Third, outcome-related role clarity and behavior-related role clarity influenced self-efficacy positively. Behavior-related role clarity had a positive effect on job satisfaction, but outcome-related role clarity didn't influence job satisfaction. Finally, self-efficacy didn't have any effect on job satisfaction. The contributions of this study are as follows: First, existing studies have investigated the direct causal relationship between salespeoples' control type and performance, but this study investigates the structural causality between salespeoples' control types, responses, and performances. Second, this study found the mediating role of outcome-related/behavior-related role-clarity between outcome/behavior control and self-efficacy. Finally, the findings of this study further insight to existing studies on the relationship between job satisfaction and self-efficacy. The confidence of salespeoples' task influenced job satisfaction positively in existing articles,field studies, but the relationship between these two variables was not significant in this study. This means that there can be a different relationship between confidence and job satisfaction according to salespeoples' business. That is, the business environment may not be satisfying, even if the salespeople say that they have ability and confidence about their business. This means that able salespeople who have ability and confidence about their business are not satisfied with their job advancement in the company. Therefore, enterprise need to provide training that can establish a business environment that can satisfy the salespeole's expectation level which will secure good salespeople. This study may have limitation when applied to future studies. First,in this study as with existing studies it investigates the control level that salespeople feel is being measured. Actuality, the control level that a manager enforces and the control level that salespeople perceive when one is late can be different. There is need to measure lateness from both the perspective of the manager and salespeople should be done to supplement this study in the future Second, this study used variables that were connected with action result but salespeople's job satisfaction is due to the result of control. But, focusing on result of control can provide a more important financial result than sales performance. This study is also limited in that it did not consider financial result by result of control. Further studies on this will need to be done in the future. Third, this study may have a further limitation,because the investigation was restricted to pharmaceutical salespeople selling to hospitals. It is necessary to execute investigations in various industries to increase the generalization of the study findings Fourth, in this study, role clarity and self-efficacy by response variable for control and considered job satisfaction by outcome variable of control was considered. But, can other variables be considered beside response variable and result variable for control? For example, can financial affairs and change of post by outcome variable along with business stress by response variable for control be considered? Therefore, future studies need to consider various control variables. Finally, there is limited supporting research in the field of marketing which restricts the generalization of the study finding along with collecting material through random sampling of a limited size. This research summarizes the research in this area, the difference from the previous research, and provides a discussion of its limitations and the need and direction for further future research.
This study aims to develop a classification model for predicting the occurrence of hyperlipidemia, one of the chronic diseases. Prior studies applying data mining techniques for predicting disease can be classified into a model design study for predicting cardiovascular disease and a study comparing disease prediction research results. In the case of foreign literatures, studies predicting cardiovascular disease were predominant in predicting disease using data mining techniques. Although domestic studies were not much different from those of foreign countries, studies focusing on hypertension and diabetes were mainly conducted. Since hypertension and diabetes as well as chronic diseases, hyperlipidemia, are also of high importance, this study selected hyperlipidemia as the disease to be analyzed. We also developed a model for predicting hyperlipidemia using SVM and meta learning algorithms, which are already known to have excellent predictive power. In order to achieve the purpose of this study, we used data set from Korea Health Panel 2012. The Korean Health Panel produces basic data on the level of health expenditure, health level and health behavior, and has conducted an annual survey since 2008. In this study, 1,088 patients with hyperlipidemia were randomly selected from the hospitalized, outpatient, emergency, and chronic disease data of the Korean Health Panel in 2012, and 1,088 nonpatients were also randomly extracted. A total of 2,176 people were selected for the study. Three methods were used to select input variables for predicting hyperlipidemia. First, stepwise method was performed using logistic regression. Among the 17 variables, the categorical variables(except for length of smoking) are expressed as dummy variables, which are assumed to be separate variables on the basis of the reference group, and these variables were analyzed. Six variables (age, BMI, education level, marital status, smoking status, gender) excluding income level and smoking period were selected based on significance level 0.1. Second, C4.5 as a decision tree algorithm is used. The significant input variables were age, smoking status, and education level. Finally, C4.5 as a decision tree algorithm is used. In SVM, the input variables selected by genetic algorithms consisted of 6 variables such as age, marital status, education level, economic activity, smoking period, and physical activity status, and the input variables selected by genetic algorithms in artificial neural network consist of 3 variables such as age, marital status, and education level. Based on the selected parameters, we compared SVM, meta learning algorithm and other prediction models for hyperlipidemia patients, and compared the classification performances using TP rate and precision. The main results of the analysis are as follows. First, the accuracy of the SVM was 88.4% and the accuracy of the artificial neural network was 86.7%. Second, the accuracy of classification models using the selected input variables through stepwise method was slightly higher than that of classification models using the whole variables. Third, the precision of artificial neural network was higher than that of SVM when only three variables as input variables were selected by decision trees. As a result of classification models based on the input variables selected through the genetic algorithm, classification accuracy of SVM was 88.5% and that of artificial neural network was 87.9%. Finally, this study indicated that stacking as the meta learning algorithm proposed in this study, has the best performance when it uses the predicted outputs of SVM and MLP as input variables of SVM, which is a meta classifier. The purpose of this study was to predict hyperlipidemia, one of the representative chronic diseases. To do this, we used SVM and meta-learning algorithms, which is known to have high accuracy. As a result, the accuracy of classification of hyperlipidemia in the stacking as a meta learner was higher than other meta-learning algorithms. However, the predictive performance of the meta-learning algorithm proposed in this study is the same as that of SVM with the best performance (88.6%) among the single models. The limitations of this study are as follows. First, various variable selection methods were tried, but most variables used in the study were categorical dummy variables. In the case with a large number of categorical variables, the results may be different if continuous variables are used because the model can be better suited to categorical variables such as decision trees than general models such as neural networks. Despite these limitations, this study has significance in predicting hyperlipidemia with hybrid models such as met learning algorithms which have not been studied previously. It can be said that the result of improving the model accuracy by applying various variable selection techniques is meaningful. In addition, it is expected that our proposed model will be effective for the prevention and management of hyperlipidemia.
With the increasing importance of sentiment analysis to grasp the needs of customers and the public, various types of deep learning models have been actively applied to English texts. In the sentiment analysis of English texts by deep learning, natural language sentences included in training and test datasets are usually converted into sequences of word vectors before being entered into the deep learning models. In this case, word vectors generally refer to vector representations of words obtained through splitting a sentence by space characters. There are several ways to derive word vectors, one of which is Word2Vec used for producing the 300 dimensional Google word vectors from about 100 billion words of Google News data. They have been widely used in the studies of sentiment analysis of reviews from various fields such as restaurants, movies, laptops, cameras, etc. Unlike English, morpheme plays an essential role in sentiment analysis and sentence structure analysis in Korean, which is a typical agglutinative language with developed postpositions and endings. A morpheme can be defined as the smallest meaningful unit of a language, and a word consists of one or more morphemes. For example, for a word '예쁘고', the morphemes are '예쁘(= adjective)' and '고(=connective ending)'. Reflecting the significance of Korean morphemes, it seems reasonable to adopt the morphemes as a basic unit in Korean sentiment analysis. Therefore, in this study, we use 'morpheme vector' as an input to a deep learning model rather than 'word vector' which is mainly used in English text. The morpheme vector refers to a vector representation for the morpheme and can be derived by applying an existent word vector derivation mechanism to the sentences divided into constituent morphemes. By the way, here come some questions as follows. What is the desirable range of POS(Part-Of-Speech) tags when deriving morpheme vectors for improving the classification accuracy of a deep learning model? Is it proper to apply a typical word vector model which primarily relies on the form of words to Korean with a high homonym ratio? Will the text preprocessing such as correcting spelling or spacing errors affect the classification accuracy, especially when drawing morpheme vectors from Korean product reviews with a lot of grammatical mistakes and variations? We seek to find empirical answers to these fundamental issues, which may be encountered first when applying various deep learning models to Korean texts. As a starting point, we summarized these issues as three central research questions as follows. First, which is better effective, to use morpheme vectors from grammatically correct texts of other domain than the analysis target, or to use morpheme vectors from considerably ungrammatical texts of the same domain, as the initial input of a deep learning model? Second, what is an appropriate morpheme vector derivation method for Korean regarding the range of POS tags, homonym, text preprocessing, minimum frequency? Third, can we get a satisfactory level of classification accuracy when applying deep learning to Korean sentiment analysis? As an approach to these research questions, we generate various types of morpheme vectors reflecting the research questions and then compare the classification accuracy through a non-static CNN(Convolutional Neural Network) model taking in the morpheme vectors. As for training and test datasets, Naver Shopping's 17,260 cosmetics product reviews are used. To derive morpheme vectors, we use data from the same domain as the target one and data from other domain; Naver shopping's about 2 million cosmetics product reviews and 520,000 Naver News data arguably corresponding to Google's News data. The six primary sets of morpheme vectors constructed in this study differ in terms of the following three criteria. First, they come from two types of data source; Naver news of high grammatical correctness and Naver shopping's cosmetics product reviews of low grammatical correctness. Second, they are distinguished in the degree of data preprocessing, namely, only splitting sentences or up to additional spelling and spacing corrections after sentence separation. Third, they vary concerning the form of input fed into a word vector model; whether the morphemes themselves are entered into a word vector model or with their POS tags attached. The morpheme vectors further vary depending on the consideration range of POS tags, the minimum frequency of morphemes included, and the random initialization range. All morpheme vectors are derived through CBOW(Continuous Bag-Of-Words) model with the context window 5 and the vector dimension 300. It seems that utilizing the same domain text even with a lower degree of grammatical correctness, performing spelling and spacing corrections as well as sentence splitting, and incorporating morphemes of any POS tags including incomprehensible category lead to the better classification accuracy. The POS tag attachment, which is devised for the high proportion of homonyms in Korean, and the minimum frequency standard for the morpheme to be included seem not to have any definite influence on the classification accuracy.
The fourth industrial revolution represents a revolutionary change in the business environment and its ecosystem, which is a fusion of Information Technology (IT) and other industries. In line with these recent changes, the Ministry of Employment and Labor of South Korea announced 'the Fourth Industrial Revolution Leader Training Program,' which includes five key support areas such as (1) smart manufacturing, (2) Internet of Things (IoT), (3) big data including Artificial Intelligence (AI), (4) information security, and (5) bio innovation. Based on this program, we can get a glimpse of the South Korean government's efforts and willingness to emit leading human resource with advanced IT knowledge in various fusion technology-related and newly emerging industries. On the other hand, in order to nurture excellent IT manpower in preparation for the fourth industrial revolution, the role of educational institutions capable of providing high quality IT education services is most of importance. However, these days, most IT educational institutions have had difficulties in providing customized IT education services that meet the needs of consumers (i.e., learners), without breaking away from the traditional framework of providing supplier-oriented education services. From previous studies, it has been found that the provision of customized education services centered on learners leads to high satisfaction of learners, and that higher satisfaction increases not only task performance and the possibility of business application but also learners' recommendation intention. However, since research has not yet been conducted in a comprehensive way that consider both antecedent and consequent factors of the learner's satisfaction, more empirical research on this is highly desirable. With the advent of the fourth industrial revolution, a rising interest in various convergence technologies utilizing information technology (IT) has brought with the growing realization of the important role played by IT-related education services. However, research on the role of IT education service quality in the context of IT education is relatively scarce in spite of the fact that research on general education service quality and satisfaction has been actively conducted in various contexts. In this study, therefore, the five dimensions of IT education service quality (i.e., tangibles, reliability, responsiveness, assurance, and empathy) are derived from the context of IT education, based on the SERVPERF model and related previous studies. In addition, the effects of these detailed IT education service quality factors on learners' educational satisfaction and their work application/recommendation intentions are examined. Furthermore, the moderating roles of learner position (i.e., practitioner group vs. manager group) and participation motivation (i.e., voluntary participation vs. involuntary participation) in relationships between IT education service quality factors and learners' educational satisfaction, work application intention, and recommendation intention are also investigated. In an analysis using the structural equation model (SEM) technique based on a questionnaire given to 203 participants of IT education programs in an 'M' IT educational institution in Seoul, South Korea, tangibles, reliability, and assurance were found to have a significant effect on educational satisfaction. This educational satisfaction was found to have a significant effect on both work application intention and recommendation intention. Moreover, it was discovered that learner position and participation motivation have a partial moderating impact on the relationship between IT education service quality factors and educational satisfaction. This study holds academic implications in that it is one of the first studies to apply the SERVPERF model (rather than the SERVQUAL model, which has been widely adopted by prior studies) is to demonstrate the influence of IT education service quality on learners' educational satisfaction, work application intention, and recommendation intention in an IT education environment. The results of this study are expected to provide practical guidance for IT education service providers who wish to enhance learners' educational satisfaction and service management efficiency.
Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.
Shim, Jae Eok;Byeon, Moo Jang;Moon, Hyo Gon;Oh, Jay In
Asia pacific journal of information systems
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v.23
no.3
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pp.25-53
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2013
This study analyzes the effect of Computer Aided Innovation (CAI) to improve R&D Capabilities empirically. Survey was distributed by e-mail and Google Docs, targeting CTO of 235 SMEs. 142 surveys were returned back (rate of return 60.4%) from companies. Survey results from 119 companies (83.8%) which are effective samples except no-response, insincere response, estimated value, etc. were used for statistics analysis. Companies with less than 50billion KRW sales of entire researched companies occupy 76.5% in terms of sample traits. Companies with less than 300 employees occupy 83.2%. In terms of the type of company business Partners (called 'partners with big companies' hereunder) who work with big companies for business occupy 68.1%. SMEs based on their own business (called 'independent small companies') appear to occupy 31.9%. The present status of holding IT system according to traits of company business was classified into partners with big companies versus independent SMEs. The present status of ERP is 18.5% to 34.5%. QMS is 11.8% to 9.2%. And PLM (Product Life-cycle Management) is 6.7% to 2.5%. The holding of 3D CAD is 47.1% to 21%. IT system-holding and its application of independent SMEs seemed very vulnerable, compared with partner companies of big companies. This study is comprised of IT infra and IT Utilization as CAI capacity factors which are independent variables. factors of R&D capabilities which are independent variables are organization capability, process capability, HR capability, technology-accumulating capability, and internal/external collaboration capability. The highest average value of variables was 4.24 in organization capability 2. The lowest average value was 3.01 in IT infra which makes users access to data and information in other areas and use them with ease when required during new product development. It seems that the inferior environment of IT infra of general SMEs is reflected in CAI itself. In order to review the validity used to measure variables, Factors have been analyzed. 7 factors which have over 1.0 pure value of their dependent and independent variables were extracted. These factors appear to explain 71.167% in total of total variances. From the result of factor analysis about measurable variables in this study, reliability of each item was checked by Cronbach's Alpha coefficient. All measurable factors at least over 0.611 seemed to acquire reliability. Next, correlation has been done to explain certain phenomenon by correlation analysis between variables. As R&D capabilities factors which are arranged as dependent variables, organization capability, process capability, HR capability, technology-accumulating capability, and internal/external collaboration capability turned out that they acquire significant correlation at 99% reliability level in all variables of IT infra and IT Utilization which are independent variables. In addition, correlation coefficient between each factor is less than 0.8, which proves that the validity of this study judgement has been acquired. The pair with the highest coefficient had 0.628 for IT utilization and technology-accumulating capability. Regression model which can estimate independent variables was used in this study under the hypothesis that there is linear relation between independent variables and dependent variables so as to identify CAI capability's impact factors on R&D. The total explanations of IT infra among CAI capability for independent variables such as organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability are 10.3%, 7%, 11.9%, 30.9%, and 10.5% respectively. IT Utilization exposes comprehensively low explanatory capability with 12.4%, 5.9%, 11.1%, 38.9%, and 13.4% for organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability respectively. However, both factors of independent variables expose very high explanatory capability relatively for technology-accumulating capability among independent variable. Regression formula which is comprised of independent variables and dependent variables are all significant (P<0.005). The suitability of regression model seems high. When the results of test for dependent variables and independent variables are estimated, the hypothesis of 10 different factors appeared all significant in regression analysis model coefficient (P<0.01) which is estimated to affect in the hypothesis. As a result of liner regression analysis between two independent variables drawn by influence factor analysis for R&D capability and R&D capability. IT infra and IT Utilization which are CAI capability factors has positive correlation to organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability with inside and outside which are dependent variables, R&D capability factors. It was identified as a significant factor which affects R&D capability. However, considering adjustable variables, a big gap is found, compared to entire company. First of all, in case of partner companies with big companies, in IT infra as CAI capability, organization capability, process capability, human resources capability, and technology capability out of R&D capacities seems to have positive correlation. However, collaboration capability appeared insignificance. IT utilization which is a CAI capability factor seemed to have positive relation to organization capability, process capability, human resources capability, and internal/external collaboration capability just as those of entire companies. Next, by analyzing independent types of SMEs as an adjustable variable, very different results were found from those of entire companies or partner companies with big companies. First of all, all factors in IT infra except technology-accumulating capability were rejected. IT utilization was rejected except technology-accumulating capability and collaboration capability. Comprehending the above adjustable variables, the following results were drawn in this study. First, in case of big companies or partner companies with big companies, IT infra and IT utilization affect improving R&D Capabilities positively. It was because most of big companies encourage innovation by using IT utilization and IT infra building over certain level to their partner companies. Second, in all companies, IT infra and IT utilization as CAI capability affect improving technology-accumulating capability positively at least as R&D capability factor. The most of factor explanation is low at around 10%. However, technology-accumulating capability is rather high around 25.6% to 38.4%. It was found that CAI capability contributes to technology-accumulating capability highly. Companies shouldn't consider IT infra and IT utilization as a simple product developing tool in R&D section. However, they have to consider to use them as a management innovating strategy tool which proceeds entire-company management innovation centered in new product development. Not only the improvement of technology-accumulating capability in department of R&D. Centered in new product development, it has to be used as original management innovative strategy which proceeds entire company management innovation. It suggests that it can be a method to improve technology-accumulating capability in R&D section and Dynamic capability to acquire sustainable competitive advantage.
1. Introduction: Contrast to the offline purchasing environment, online store cannot offer the sense of touch or direct visual information of its product to the consumers. So the builder of the online shopping mall should provide more concrete and detailed product information(Kim 2008), and Alba (1997) also predicted that the quality of the offered information is determined by the post-purchase consumer satisfaction. In practice, many fashion and apparel online shopping malls offer the picture information with the product on the real person model to enhance the usefulness of product information. On the other virtual product experience has been suggested to the ways of overcoming the online consumers' limited perceptual capability (Jiang & Benbasat 2005). However, the adoption and the facilitation of the virtual reality tools requires high investment and technical specialty compared to the text/picture product information offerings (Shaffer 2006). This could make the entry barrier to the online shopping to the small retailers and sometimes it could be demanding high level of consumers' perceptual efforts. So the expensive technological solution could affects negatively to the consumer decision making processes. Nevertheless, most of the previous research on the online product information provision suggests the VR be the more effective tools. 2. Research Model and Hypothesis: Presented in
, research model suggests VR effect could be moderated by the product types by the usage situations. Product types could be defined as the portable product and installed product, and the information offering type as still picture of the product, picture of the product with the real-person model and VR. 3. Methods and Results: 3.1. Experimental design and measured variables We designed the 2(product types) X 3(product information types) experimental setting and measured dependent variables such as information usefulness, attitude toward the shopping mall, overall product quality, purchase intention and the revisiting intention. In the case of information usefulness and attitude toward the shopping mall were measured by multi-item scale. As a result of reliability test, Cronbach's Alpha value of each variable shows more than 0.6. Thus, we ensured that the internal consistency of items. 3.2. Manipulation check The main concern of this study is to verify the moderate effect by the product type of usage situation.
indicates that our experimental manipulation of the moderate effect of the product type was successful. 3.3. Results As
indicates, there was a significant main effect on the only one dependent variable(attitude toward the shopping mall) by the information types. As predicted, VR has highest mean value compared to other information types. Thus, H1 was partially supported. However, main effect by the product types was not found. To evaluate H2 and H3, a two-way ANOVA was conducted. As
indicates, there exist the interaction effects on the three dependent variables(information usefulness, overall product quality and purchase intention) by the information types and the product types. As predicted, picture of the product with the real-person model has highest mean among the information types in the case of portable product. On the other hand, VR has highest mean among the information types in the case of installed product. Thus, H2 and H3 was supported. 4. Implications: The present study found the moderate effect by the product type of usage situation. Based on the findings the following managerial implications are asserted. First, it was found that information types are affect only the attitude toward the shopping mall. The meaning of this finding is that VR effects are not enough to understand the product itself. Therefore, we must consider when and how to use this VR tools. Second, it was found that there exist the interaction effects on the information usefulness, overall product quality and purchase intention. This finding suggests that consideration of usage situation helps consumer's understanding of product and promotes their purchase intention. In conclusion, not only product attributes but also product usage situations must be fully considered by the online retailers when they want to meet the needs of consumers.
Oh, Mi Hyun;Choe, Jeong-Sook;Kim, Young;Lee, Sang Eun;Paik, Hee Young;Jang, Mi Jin
Journal of Nutrition and Health
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v.47
no.6
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pp.463-474
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2014
Purpose: The aim of this study was to assess the food environment, particularly focusing on restaurants in three areas (Suwon city, Hwaseong Byeongieom-dong, and Bibong-myun). Methods: A total of 662 persons were surveyed on customers' perceptions of the food environment in restaurants. A structured questionnaire composed of 30 questions on 7 factors, sanitation (4 items), displaying information (5), food quality (12), information on nutritional and healthy food choice (6), restaurant's accessibility (1), availability (1), and affordability (1) was used. In addition, an expert assessment of restaurant sanitation, and information on nutritional healthy food choice was conducted through visiting 126 restaurants. Results: Scores (range of score : 1~7) for each factors assessing the restaurant food environment were 5.06 for sanitation factors, 5.05 for displaying information factors, 5.13 for taste appearance factors, and 4.35 for healthy menu factors. Informations on nutritional healthy food choice showed a low rate: only 16.24% of the subjects answered that there is a message encouraging choice of healthy foods and 27.4% answered that menus contain nutritional information. Significant differences in food environment were observed by region (city, town, rural). The restaurants food environment in the rural area turned out to be poorer than that of the other two areas. In comparison of customer perception and expert assessment, significant differences were observed for 'Employee appearances and uniforms are clean and tidy' (p < .05), and 'There is a message encouraging the choice of healthy foods' (p < .05). Conclusion: This study provided evidence for differences of restaurant food environment by regions. In the rural area, there is a problem in restaurant's accessibility, availability, and affordability because of a lack of variety in menu items and restaurants. This results suggest that there is a need for more healthy food restaurants in the rural area.
Online game researchers focus on the flow and factors influencing flow. Flow is conceptualized as an optimal experience state and useful explaining game experience in online. Many game studies focused on the customer loyalty and flow in playing online game, In showing specific game experience, however, it doesn't examine multidimensional experience process. Flow is not construct which show absorbing process, but construct which show absorbing result. Hence, Flow is not adequate to examine multidimensional experience of games. Online game is included in hedonic consumption. Hedonic consumption is a relatively new field of study in consumer research and it explores the consumption experience as a experiential view(Hirschman and Holbrook 1982). Hedonic consumption explores the consumption experience not as an information processing event but from a phenomenological of experiential view, which is a primarily subjective state. It includes various playful leisure activities, sensory pleasures, daydreams, esthetic enjoyment, and emotional responses. In online game experience, therefore, it is right to access through a experiential view of hedonic consumption. The objective of this paper was to make up for lacks in our understanding of online game experience by developing a framework for better insight into the hedonic experience of online game. We developed this framework by integrating and extending existing research in marketing, online game and hedonic responses. We then discussed several expectations for this framework. We concluded by discussing the results of this study, providing general recommendation and directions for future research. In hedonic response research, Lacher's research(1994)and Jongho lee and Yunhee Jung' research (2005;2006) has served as a fundamental starting point of our research. A common element in this extended research is the repeated identification of the four hedonic responses: sensory response, imaginal response, emotional response, analytic response. The validity of these four constructs finds in research of music(Lacher 1994) and movie(Jongho lee and Yunhee Jung' research 2005;2006). But, previous research on hedonic response didn't show that constructs of hedonic response have cause-effect relation. Also, although hedonic response enable to different by stimulus properties. effects of stimulus properties is not showed. To fill this gap, while largely based on Lacher(1994)' research and Jongho Lee and Yunhee Jung(2005, 2006)' research, we made several important adaptation with the primary goal of bringing the model into online game and compensating lacks of previous research. We maintained the same construct proposed by Lacher et al.(1994), with four constructs of hedonic response:sensory response, imaginal response, emotional response, analytical response. In this study, the sensory response is typified by some physical movement(Yingling 1962), the imaginal response is typified by images, memories, or situations that game evokes(Myers 1914), and the emotional response represents the feelings one experiences when playing game, such as pleasure, arousal, dominance, finally, the analytical response is that game player engaged in cognition seeking while playing game(Myers 1912). However, this paper has several important differences. We attempted to suggest multi-dimensional experience process in online game and cause-effect relation among hedonic responses. Also, We investigated moderate effects of perceived complexity. Previous studies about hedonic responses didn't show influences of stimulus properties. According to Berlyne's theory(1960, 1974) of aesthetic response, perceived complexity is a important construct because it effects pleasure. Pleasure in response to an object will increase with increased complexity, to an optimal level. After that, with increased complexity, pleasure begins with a linearly increasing line for complexity. Therefore, We expected this perceived complexity will influence hedonic response in game experience. We discussed the rationale for these suggested changes, the assumptions of the resulting framework, and developed some expectations based on its application in Online game context. In the first stage of methodology, questions were developed to measure the constructs. We constructed a survey measuring our theoretical constructs based on a combination of sources, including Yingling(1962), Hargreaves(1962), Lacher (1994), Jongho Lee and Yunhee Jung(2005, 2006), Mehrabian and Russell(1974), Pucely et al(1987). Based on comments received in the pretest, we made several revisions to arrive at our final survey. We investigated the proposed framework through a convenience sample, where participation in a self-report survey was solicited from various respondents having different knowledges. All respondents participated to different degrees, in these habitually practiced activities and received no compensation for their participation. Questionnaires were distributed to graduates and we used 381 completed questionnaires to analysis. The sample consisted of more men(n=225) than women(n=156). In measure, the study used multi-item scales based previous study. We analyze the data using structural equation modeling(LISREL-VIII; Joreskog and Sorbom 1993). First, we used the entire sample(n=381) to refine the measures and test their convergent and discriminant validity. The evidence from both the factor analysis and the analysis of reliability provides support that the scales exhibit internal consistency and construct validity. Second, we test the hypothesized structural model. And, we divided the sample into two different complexity group and analyze the hypothesized structural model of each group. The analysis suggest that hedonic response plays different roles from hypothesized in our study. The results indicate that hedonic response-sensory response, imaginal response, emotional response, analytical response- are related positively to respondents' level of game satisfaction. And game satisfaction is related to higher levels of game loyalty. Additionally, we found that perceived complexity is important to online game experience. Our results suggest that importance of each hedonic response different by perceived game complexity. Understanding the role of perceived complexity in hedonic response enables to have a better understanding of underlying mechanisms at game experience. If game has high complexity, analytical response become important response. So game producers or marketers have to consider more cognitive stimulus. Controversy, if game has low complexity, sensorial response respectively become important. Finally, we discussed several limitations of our study and suggested directions for future research. we concluded with a discussion of managerial implications. Our study provides managers with a basis for game strategies.
In this study, we attempt to propose a choice-based diffusion model with switching cost, which can be used to forecast the dynamic substitution and competition among previous and new products at both individual-level and aggregate level, especially when market data for new products is insufficient. Additionally, we apply the proposed model to the empirical case of substitution and competition among Analog Cable TV that represents previous fixed charged broadcasting service and Digital Cable TV and Internet Protocol TV (IPTV) that are new ones, verify the validities of our proposed model, and finally derive related empirical implications. For empirical application, we obtained data from survey conducted as follows. Survey was administered by Dongseo Research to 1,000 adults aging from 20 to 60 living in Seoul, Korea, in May of 2007, under the title of 'Demand analysis of next generation fixed interactive broadcasting services'. Conjoint survey modified as follows, was used. First, as the traditional approach in conjoint analysis, we extracted 16 hypothetical alternative cards from the orthogonal design using important attributes and levels of next generation interactive broadcasting services which were determined by previous literature review and experts' comments. Again, we divided 16 conjoint cards into 4 groups, and thus composed 4 choice sets with 4 alternatives each. Therefore, each respondent faces 4 different hypothetical choice situations. In addition to this, we added two ways of modification. First, we asked the respondents to include the status-quo broadcasting services they subscribe to, as another alternative in each choice set. As a result, respondents choose the most preferred alternative among 5 alternatives consisting of 1 alternative with current subscription and 4 hypothetical alternatives in 4 choice sets. Modification of traditional conjoint survey in this way enabled us to estimate the factors related to switching cost or switching threshold in addition to the effects of attributes. Also, by using both revealed preference data(1 alternative with current subscription) and stated preference data (4 hypothetical alternatives), additional advantages in terms of the estimation properties and more conservative and realistic forecast, can be achieved. Second, we asked the respondents to choose the most preferred alternative while considering their expected adoption timing or switching timing. Respondents are asked to report their expected adoption or switching timing among 14 half-year points after the introduction of next generation broadcasting services. As a result, for each respondent, 14 observations with 5 alternatives for each period, are obtained, which results in panel-type data. Finally, this panel-type data consisting of $4{\ast}14{\ast}1000=56000$observations is used for estimation of the individual-level consumer adoption model. From the results obtained by empirical application, in case of forecasting the demand of new products without considering existence of previous product(s) and(or) switching cost factors, it is found that overestimated speed of diffusion at introductory stage or distorted predictions can be obtained, and as such, validities of our proposed model in which both existence of previous products and switching cost factors are properly considered, are verified. Also, it is found that proposed model can produce flexible patterns of market evolution depending on the degree of the effects of consumer preferences for the attributes of the alternatives on individual-level state transition, rather than following S-shaped curve assumed a priori. Empirically, it is found that in various scenarios with diverse combinations of prices, IPTV is more likely to take advantageous positions over Digital Cable TV in obtaining subscribers. Meanwhile, despite inferiorities in many technological attributes, Analog Cable TV, which is regarded as previous product in our analysis, is likely to be substituted by new services gradually rather than abruptly thanks to the advantage in low service charge and existence of high switching cost in fixed charged broadcasting service market.
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