• Asia Marketing Journal
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• v.16 no.1
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• pp.71-100
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• 2014

While the role of emotional antecedents of effective selling behavior would be important, the issue has not been fully addressed in the sales literature. To fill this gap, we conceptualize and empirically examine the relationships among salesperson's emotional regulation processes such as emotional intelligence (EI) and emotional labor (EL), effective selling behavior, and sales performance on the basis of educational, occupational, social psychology literature and marketing literature (e.g., Henning-Thurau, Groth, Paul, and Gremler 2006; Kidwell et al. 2011; Liu et al. 2008; Mayer, Salovey, and Caruso 2008). First, salesperson's EI is defined as his or her capability that enables correct perceptions about emotional situations in sales interactions. The EI is expected to work as psychological resources for different types of EL (i.e., deep acting and surface acting) to be performed by salesperson as emotional expression strategies (e.g., Lie et al. 2008). It is, then, expected that the features of EL selected by the salesperson would lead to different levels of adaptive selling behavior (ASB) and thereby sales performance (Monaghan 2006). Further, given that salesperson's customer orientation (CO) is found to be an important correlate of ASB (Franke and Park 2006), it is expected that CO would moderate the relationship between EL and ASB (Rozell, Pettijohn, and Parker 2004). Hence, this research attempts to shed additional light on emotionally-driven (EL) as well as cognitively-driven (CO) antecedents of ASB (Frank and Park 2006). The findings of the survey research, done with 336 salespersons in insurance and financial companies, are summarized as follows. First, salespersons with a high level of EI are found to use both deep acting (regulating the emotions themselves) and surface acting (controlling only emotional expressions) in a versatile way, when implementing EL. Second, the more the salesperson performs deep acting, the more he or she shows ASB. It is, then, important for salespersons to use deep acting more frequently in the EL process in order to enhance the quality of interacting with customers through ASB. On the other hand, the salesperson's surface acting did not have a significant relationship with ASB. Moreover, CO was found to moderate the relationship between the salesperson's deep acting and ASB. That is, the context of high CO culture and individual salesperson's deep acting would synergistically make the selling efforts adaptive to customer preferences. Conceptualizing and empirically verifying the antecedent roles of important emotional constructs such as EI and EL in salesperson's effective selling behavior (ASB) and sales performance is a major theoretical contribution in the sales literature. Managerially, this research provides a deeper understanding on the nature of tasks performed by salespersons in service industries and a few guidelines for managing the sales force. First, sales organizations had better consciously assess EI capacity in the selection and nurturing processes of salespersons, given that EI can efficiently drive EL and the resulting effective selling behavior and performance. Further, the concept of EL could provide a framework to understand the salespersons' emotional experiences in depth. Especially, sales organizations may well think over how to develop deep acting capabilities of their sales representatives. In this direction, the training on deep acting strategies would be an essential task for improving effective selling behavior and performance of salespersons. This kind of training had better incorporate the perspectives of customers such that many customers can actually discern whether salespersons are doing either surface acting or deep acting. Finally, based on the synergistic effects of deep acting and CO culture, how to build and sustain CO is always an ever-important task in sales organizations. While the prior sales literature has emphasized the process and structure of highly customer-oriented sales organization, our research not only corroborates the important aspects of customer-oriented sales organization, but also adds the important dimension of competent sales representatives who can resonate with customers by deep acting for sales excellence.

• Information Systems Review
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• v.25 no.4
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• pp.131-159
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• 2023

Recently, amidst the backdrop of the COVID-19 pandemic shifting towards an endemic phase, there has been a rise in discussions and debates about the future of the metaverse. Simultaneously, major metaverse platforms like Roblox have been launching services integrated with generative AI, and Apple's mixed reality hardware, Vision Pro, has been announced, creating new expectations for the metaverse. In this situation where the outlook for the metaverse is divided, it is crucial to diagnose the metaverse from an ecosystem perspective, examine its key ecological features, driving forces for development, and future possibilities for advancement. This study utilized Wang's (2021) Information Ecology Theory (IET) framework, which is representative of ecosystem research in the field of Information Systems (IS), to derive the Metaverse Industrial Ecosystem (MIE). The analysis revealed that the MIE consists of four main domains: Tech Landscape, Category Ecosystem, Metaverse Platform, and Product/Service Ecosystem. It was found that the MIE exhibits characteristics such as digital connectivity, the integration of real and virtual worlds, value creation capabilities, and value sharing (Web 3.0). Furthermore, the interactions among the domains within the MIE and the four characteristics of the ecosystem were identified as driving forces for the development of the MIE at an ecosystem level. Additionally, the development of the MIE at an ecosystem level was categorized into three distinct stages: Narrow Ecosystem, Expanded Ecosystem, and Everywhere Ecosystem. It is anticipated that future advancements in related technologies and industries, such as robotics, AI, and 6G, will promote the transition from the current Expanded Ecosystem level of the MIE to an Everywhere Ecosystem level, where the connection between the real and virtual worlds is pervasive. This study provides several implications. Firstly, it offers a foundational theory and analytical framework for ecosystem research, addressing a gap in previous metaverse studies. It also presents various research topics within the metaverse domain. Additionally, it establishes an academic foundation that integrates concept definition research and impact studies, which are key areas in metaverse research. Lastly, referring to the developmental stages and conditions proposed in this study, businesses and governments can explore future metaverse markets and related technologies. They can also consider diverse metaverse business strategies. These implications are expected to guide the exploration of the emerging metaverse market and facilitate the evaluation of various metaverse business strategies.

• Journal of Venture Innovation
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• v.5 no.3
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• pp.71-80
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• 2022

The real estate market is an important part of a country's economy and plays a major role in economic growth through the growth of many related industries. Changes in interest rates affect asset prices and have a significant impact on housing prices. This study analyzed housing prices by dividing them into nationwide, local, and Seoul housing prices in order to analyze whether the effect of changes in interest rates on housing prices shows regional differences. The analysis was conducted from the first quarter of 2011 to the fourth quarter of 2021, and was analyzed using the DOLS model. The main analysis results are as follows. First, interest rates were found to have a significant negative effect on national housing prices, and a drop in interest rates significantly increased national housing prices and an increase in interest rates significantly lowered national housing prices. The consumer price index and loan growth rate also had a positive effect on housing prices nationwide, but statistical significance was not high. Second, interest rates had a negative effect on local housing prices, unlike national housing prices, but were not statistically significant. On the other hand, it was found that the consumer price index and loan growth rate had a larger and significant positive effect on local housing prices compared to national housing prices. Finally, it was found that the interest rate had the only significant negative effect on housing prices in Seoul. And this effect was greater and more significant than the effect on national and local housing prices. In the end, it was found that the effect of interest rates on Korean housing prices differs locally. Interest rates have a significant negative effect on national housing prices, and local housing prices, but they are not statistically significant. In addition, the interest rate was found to have the largest and most significant negative effect on housing prices in Seoul. In addition, it was found that there was a difference in the effect of macroeconomic variables on housing prices. This means that there are differences between regions with different factors influencing local and Seoul housing prices, and this point should be considered when drafting and implementing real estate policies.

• Journal of Venture Innovation
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• v.6 no.4
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• pp.41-62
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• 2023

Given that SME workers are the driving force of national competitiveness and the basis and cornerstone of the industry, it is meaningful to study workers' job satisfaction and the factors that affect job satisfaction. In addition to variables related to corporate competitiveness and organizational justice, this study introduced variables such as environmental(E) activities, social(S) activities, and governance(G) activities, which th national government uses as major management evaluation indicators. Therefore, a literature study and empirical analysis were conducted on how self-efficacy affects job satisfaction when workers are faced with a changed work environment. To conduct this study, 300 copies of data were collected from workers in small and medium-sized enterprises and used for analysis. For data analysis, the SPSS statistical program (Ver. 25.0) was used. The study finds, first, that product or service quality and employee competency among corporate competitiveness had a significant positive(+) effect on job satisfaction. Secondly, among ESG management activities, social(S) activities and governance(G) activities were found to have a significant positive(+) effect on job satisfaction. Third, among organizational justice, distribution justice and procedural justice were found to have a positive(+) effect on job satisfaction. Fourth, self-efficacy was found to mediate the effect of product or service quality, employee competency, social(S) and governance(G) activities among ESG management activities, and procedural justice among organizational justice on job satisfaction. The academic value of this study is that it empirically analyzed the factors that ESG management activities affect workers' jobs,. As a result, it was confirmed that workers were satisfied with their jobs by actively showing interest in social(S) activities and governance(G) activities among ESG management activities and participating in corporate management. In addition, workers sensitive to changes in the external environment can become satisfied with their jobs through self-efficacy when SMEs actively enhance corporate competitiveness, execute ESG management activities, and provide a fair organizational culture. Finally, this study suggests that there's a possibility of improving the competitiveness of SMEs through a virtuous cycle created by a change in perception of job conversion and a decrease in turnover.

• Economic and Environmental Geology
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• v.57 no.2
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• pp.233-241
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• 2024

This study was conducted to investigate the geological distribution characteristics of fluorine in rocks, which can be a major resource of forest aggregates in Korea. Samples of forest aggregates were collected from 224 sites in 22 cities and counties for this study. The national background concentration was 344 mg/kg, which was significantly lower than the average fluorine concentration of crustal, which was 625 mg/kg, and slightly higher than the average fluorine concentration of world soil, which was 321 mg/kg. In terms of region and tectonic structure, fluorine concentrations were investigated to be highest in Gyeonggi-do(394 mg/kg) and Gyeonggi massif(396 mg/kg), respectively. The concentration distribution by the origin of the parent rock was in the order of metamorphic rock(362 mg/kg) > sedimentary rock(354 mg/kg) > igneous rock(328 mg/kg), and the concentration distribution by geologic ages was the highest in the Paleozoic at 394 mg/kg. The concentration distribution by rock types was in the order of diorite(515 mg/kg) > gneisses(377 mg/kg) > schists(344 mg/kg) > phyllite(306 mg/kg) > granites(305 mg/kg) > quartz porphyry(298 mg/kg). Consequently, it is speculated that gneisses and schists, Precambrian metamorphic rocks in the Gyeonggi massif that forms the crust of Gyeonggi-do, contain high fluorine concentrations.

• Journal of the Korean Society of Food Science and Nutrition
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• v.37 no.6
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• pp.729-737
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• 2008

The purposes of this study were to investigate supporting status and subsidy for school food service and to analyze the perception of school food service officials at the educational board on using rice bread to the school food service menu. The questionnaire was developed by content analysis, situation analysis, in-depth interview and checked by the school food service officials at the educational board. The questionnaires were responded by 33 officials (respondent rate: 86.8%) during September 1 to October 26 in 2007. The major findings of this study were as follows: First, most of the respondents were women (93.9%), and worked an average of 104.36 months at school-related work. The metropolitan & provincial office of education had prevalently jurisdiction over 272.3 rural and self-operation type of elementary schools, 115.50 rural and self-operation type of middle schools and 73.0 rural and self-operation type of high schools. In the case of the district office of education, 23.3 urban and self-operation type of elementary schools, 11.6 urban and self-operation type of middle schools and 5.3 urban and contracted type of high schools were averagely managed. Second, all the respondents supported meal cost for low-income group and 50.5% provided reimbursement for organic environmental agricultural products. The highest subsidy was 16.8 billion won as meal cost for low-income group in metropolitan & provincial office and 1,050 million won as labor cost in district office. Third, the experience of performing policies for using rice was relatively lower than perception of rice bread application to school food service menu. Fourth, the advantages of using rice bread were acceleration of consuming rice (32.0%), excellence of nutrition (24.0%) and promotion of healthy image (22.7%). On the other hand, the difficulties of using rice bread were lack of facilities (72.7%), higher cost compared to wheat bread (54.5%), limitation of menu application and cooking method (15.7% each). Fifth, the opinion of utilizing rice and that of applying rice bread were significantly correlated (p<0.001). Desirability and willingness were correlated with reality for applying rice bread to the school food service menu (p<0.001). Also, comparative analysis between divided groups by perception of utilizing rice showed that willingness and experience were significantly different.

• Asia Marketing Journal
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• v.14 no.1
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• pp.83-98
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• 2012

In a market where new and used cars are competing with each other, we would run the risk of obtaining biased estimates of cross elasticity between them if we focus on only new cars or on only used cars. Unfortunately, most of previous studies on the automobile industry have focused on only new car models without taking into account the effect of used cars' pricing policy on new cars' market shares and vice versa, resulting in inadequate prediction of reactive pricing in response to competitors' rebate or price discount. However, there are some exceptions. Purohit (1992) and Sullivan (1990) looked into both new and used car markets at the same time to examine the effect of new car model launching on the used car prices. But their studies have some limitations in that they employed the average used car prices reported in NADA Used Car Guide instead of actual transaction prices. Some of the conflicting results may be due to this problem in the data. Park (1998) recognized this problem and used the actual prices in his study. His work is notable in that he investigated the qualitative effect of new car model launching on the pricing policy of the used car in terms of reinforcement of brand equity. The current work also used the actual price like Park (1998) but the quantitative aspect of competitive price promotion between new and used cars of the same model was explored. In this study, I develop a model that assumes that the cross elasticity between new and used cars of the same model is higher than those amongst new cars and used cars of the different model. Specifically, I apply the nested logit model that assumes the car model choice at the first stage and the choice between new and used cars at the second stage. This proposed model is compared to the IIA (Independence of Irrelevant Alternatives) model that assumes that there is no decision hierarchy but that new and used cars of the different model are all substitutable at the first stage. The data for this study are drawn from Power Information Network (PIN), an affiliate of J.D. Power and Associates. PIN collects sales transaction data from a sample of dealerships in the major metropolitan areas in the U.S. These are retail transactions, i.e., sales or leases to final consumers, excluding fleet sales and including both new car and used car sales. Each observation in the PIN database contains the transaction date, the manufacturer, model year, make, model, trim and other car information, the transaction price, consumer rebates, the interest rate, term, amount financed (when the vehicle is financed or leased), etc. I used data for the compact cars sold during the period January 2009- June 2009. The new and used cars of the top nine selling models are included in the study: Mazda 3, Honda Civic, Chevrolet Cobalt, Toyota Corolla, Hyundai Elantra, Ford Focus, Volkswagen Jetta, Nissan Sentra, and Kia Spectra. These models in the study accounted for 87% of category unit sales. Empirical application of the nested logit model showed that the proposed model outperformed the IIA (Independence of Irrelevant Alternatives) model in both calibration and holdout samples. The other comparison model that assumes choice between new and used cars at the first stage and car model choice at the second stage turned out to be mis-specfied since the dissimilarity parameter (i.e., inclusive or categroy value parameter) was estimated to be greater than 1. Post hoc analysis based on estimated parameters was conducted employing the modified Lanczo's iterative method. This method is intuitively appealing. For example, suppose a new car offers a certain amount of rebate and gains market share at first. In response to this rebate, a used car of the same model keeps decreasing price until it regains the lost market share to maintain the status quo. The new car settle down to a lowered market share due to the used car's reaction. The method enables us to find the amount of price discount to main the status quo and equilibrium market shares of the new and used cars. In the first simulation, I used Jetta as a focal brand to see how its new and used cars set prices, rebates or APR interactively assuming that reactive cars respond to price promotion to maintain the status quo. The simulation results showed that the IIA model underestimates cross elasticities, resulting in suggesting less aggressive used car price discount in response to new cars' rebate than the proposed nested logit model. In the second simulation, I used Elantra to reconfirm the result for Jetta and came to the same conclusion. In the third simulation, I had Corolla offer $1,000 rebate to see what could be the best response for Elantra's new and used cars. Interestingly, Elantra's used car could maintain the status quo by offering lower price discount ($160) than the new car ($205). In the future research, we might want to explore the plausibility of the alternative nested logit model. For example, the NUB model that assumes choice between new and used cars at the first stage and brand choice at the second stage could be a possibility even though it was rejected in the current study because of mis-specification (A dissimilarity parameter turned out to be higher than 1). The NUB model may have been rejected due to true mis-specification or data structure transmitted from a typical car dealership. In a typical car dealership, both new and used cars of the same model are displayed. Because of this fact, the BNU model that assumes brand choice at the first stage and choice between new and used cars at the second stage may have been favored in the current study since customers first choose a dealership (brand) then choose between new and used cars given this market environment. However, suppose there are dealerships that carry both new and used cars of various models, then the NUB model might fit the data as well as the BNU model. Which model is a better description of the data is an empirical question. In addition, it would be interesting to test a probabilistic mixture model of the BNU and NUB on a new data set.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.53-77
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• 2015

Mobile commerce provides a convenient shopping experience in which users can buy products without the constraints of time and space. Mobile commerce has already set off a mega trend in Korea. The market size is estimated at approximately 15 trillion won (KRW) for 2015, thus far. In the Korean market, social commerce and open market are key components. Social commerce has an overwhelming open market in terms of the number of users in the Korean mobile commerce market. From the point of view of the industry, quick market entry, and content curation are considered to be the major success factors, reflecting the rapid growth of social commerce in the market. However, academics' empirical research and analysis to prove the success rate of social commerce is still insufficient. Henceforward, it is to be expected that social commerce and the open market in the Korean mobile commerce will compete intensively. So it is important to conduct an empirical analysis to prove the differences in user experience between social commerce and open market. This paper is an exploratory study that shows a comparative analysis of social commerce and the open market regarding user experience, which is based on the mobile users' reviews. Firstly, this study includes a collection of approximately 10,000 user reviews of social commerce and open market listed Google play. A collection of mobile user reviews were classified into topics, such as perceived usefulness and perceived ease of use through LDA topic modeling. Then, a sentimental analysis and co-occurrence analysis on the topics of perceived usefulness and perceived ease of use was conducted. The study's results demonstrated that social commerce users have a more positive experience in terms of service usefulness and convenience versus open market in the mobile commerce market. Social commerce has provided positive user experiences to mobile users in terms of service areas, like 'delivery,' 'coupon,' and 'discount,' while open market has been faced with user complaints in terms of technical problems and inconveniences like 'login error,' 'view details,' and 'stoppage.' This result has shown that social commerce has a good performance in terms of user service experience, since the aggressive marketing campaign conducted and there have been investments in building logistics infrastructure. However, the open market still has mobile optimization problems, since the open market in mobile commerce still has not resolved user complaints and inconveniences from technical problems. This study presents an exploratory research method used to analyze user experience by utilizing an empirical approach to user reviews. In contrast to previous studies, which conducted surveys to analyze user experience, this study was conducted by using empirical analysis that incorporates user reviews for reflecting users' vivid and actual experiences. Specifically, by using an LDA topic model and TAM this study presents its methodology, which shows an analysis of user reviews that are effective due to the method of dividing user reviews into service areas and technical areas from a new perspective. The methodology of this study has not only proven the differences in user experience between social commerce and open market, but also has provided a deep understanding of user experience in Korean mobile commerce. In addition, the results of this study have important implications on social commerce and open market by proving that user insights can be utilized in establishing competitive and groundbreaking strategies in the market. The limitations and research direction for follow-up studies are as follows. In a follow-up study, it will be required to design a more elaborate technique of the text analysis. This study could not clearly refine the user reviews, even though the ones online have inherent typos and mistakes. This study has proven that the user reviews are an invaluable source to analyze user experience. The methodology of this study can be expected to further expand comparative research of services using user reviews. Even at this moment, users around the world are posting their reviews about service experiences after using the mobile game, commerce, and messenger applications.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.59-77
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• 2012

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.75-95
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• 2020

With the growth of social networks, various forms of SNS have emerged. Based on various motivations for use such as interactivity, information exchange, and entertainment, SNS users are also on the fast-growing trend. Facebook is the main SNS channel, and companies have started using Facebook pages as a public relations channel. To this end, in the early stages of operation, companies began to secure a number of fans, and as a result, the number of corporate Facebook fans has recently increased to as many as millions. from a corporate perspective, Facebook is attracting attention because it makes it easier for you to meet the customers you want. Facebook provides an efficient advertising platform based on the numerous data it has. Advertising targeting can be conducted using their demographic characteristics, behavior, or contact information. It is optimized for advertisements that can expose information to a desired target, so that results can be obtained more effectively. it rethink and communicate corporate brand image to customers through contents. The study was conducted through Facebook advertising data, and could be of great help to business people working in the online advertising industry. For this reason, the independent variables used in the research were selected based on the characteristics of the content that the actual business is concerned with. Recently, the company's Facebook page operation goal is to go beyond securing the number of fan pages, branding to promote its brand, and further aiming to communicate with major customers. the main figures for this assessment are Facebook's 'OK', 'Attachment', 'Share', and 'Number of Click' which are the dependent variables of this study. in order to measure the outcome of the target, the consumer's response is set as a key measurable key performance indicator (KPI), and a strategy is set and executed to achieve this. Here, KPI uses Facebook's ad numbers 'reach', 'exposure', 'like', 'share', 'comment', 'clicks', and 'CPC' depending on the situation. in order to achieve the corresponding figures, the consideration of content production must be prior, and in this study, the independent variables were organized by dividing into three considerations for content production into three. The effects of content material, content structure, and message styles on Facebook's user behavior were analyzed using regression analysis. Content materials are related to the content's difficulty, company relevance, and daily involvement. According to existing research, it was very important how the content would attract users' interest. Content could be divided into informative content and interesting content. Informational content is content related to the brand, and information exchange with users is important. Interesting content is defined as posts that are not related to brands related to interesting movies or anecdotes. Based on this, this study started with the assumption that the difficulty, company relevance, and daily involvement have an effect on the dependent variable. In addition, previous studies have found that content types affect Facebook user activity. I think it depends on the combination of photos and text used in the content. Based on this study, the actual photos were used and the hashtag and independent variables were also examined. Finally, we focused on the advertising message. In the previous studies, the effect of advertising messages on users was different depending on whether they were narrative or non-narrative, and furthermore, the influence on message intimacy was different. In this study, we conducted research on the behavior that Facebook users' behavior would be different depending on the language and formality. For dependent variables, 'OK' and 'Full Click Count' are set by every user's action on the content. In this study, we defined each independent variable in the existing study literature and analyzed the effect on the dependent variable, and found that 'good' factors such as 'self association', 'actual use', and 'hidden' are important. Could. Material difficulties', 'actual participation' and 'large scale * difficulties'. In addition, variables such as 'Self Connect', 'Actual Engagement' and 'Sexual Sexual Attention' have been shown to have a significant impact on 'Full Click'. It is expected that through research results, it is possible to contribute to the operation and production strategy of company Facebook operators and content creators by presenting a content strategy optimized for the purpose of the content. In this study, we defined each independent variable in the existing research literature and analyzed its effect on the dependent variable, and we could see that factors on 'good' were significant such as 'self-association', 'reality use', 'concernal material difficulty', 'real-life involvement' and 'massive*difficulty'. In addition, variables such as 'self-connection', 'real-life involvement' and 'formative*attention' were shown to have significant effects for 'full-click'. Through the research results, it is expected that by presenting an optimized content strategy for content purposes, it can contribute to the operation and production strategy of corporate Facebook operators and content producers.