• Smart Media Journal
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• v.11 no.10
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• pp.9-21
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• 2022

The recent expansion of the use of social media has served as an opportunity to express users' opinions in real time in various fields such as society, economy, politics, and culture, and brought many platforms that provide various information about companies. Among them, Glassdoor.com which started 2008 in US provides users with evaluations of the current and the former employees of their companies and also provides a outlooks for the company's growth Such a platform has the utility of providing necessary information to whom want to find a job or change jobs. In addition to this, variable studies have shown that the company information provided through these platforms is useful for investors as well. In this study, it was tested whether the corporate growth prospects of employees provided by Jobplanet, a platform with a typical function similar to Glassdoor.com in Korea, have predictive power to predict actual corporate growth. The forecast provided by Jobplanet and the company's financial indicator data received from FnGuide were collected and composed of panel data and analyzed using fixed effect model regression analysis. As a result, it was found that companies with positive prospects had higher employment growth than companies with negative prospects. When the outlook was neutral, the employment growth rate was higher than that of companies with a negative outlook.

• Journal of Internet Computing and Services
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• v.22 no.5
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• pp.79-86
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• 2021

As the usefulness of big data analysis has been drawing attention, many studies in the business research area begin to use big data to predict firm performance. Previous studies mainly rely on data outside of the firm through news articles and social media platforms. The voices within the firm in the form of employee satisfaction or evaluation of the strength and weakness of the firm can potentially affect firm value. However, there is insufficient evidence that online employee reviews are valid to predict firm value because the data is relatively difficult to obtain. To fill this gap, from 2014 to 2019, we employed 97,216 reviews collected by JobPlanet, an online firm review website in Korea, and developed a machine learning-based predictive model. Among the proposed models, the LSTM-based model showed the highest accuracy at 73.2%, and the MAE showed the lowest error at 0.359. We expect that this study can be a useful case in the field of firm value prediction on domestic companies.

• Journal of Information Technology Services
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• v.15 no.3
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• pp.157-171
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• 2016

As unemployment rates and concerns about turnover keep growing, the need for information is also increasing. In these situations, the job reviews which share information about the company catch people's attention because they are usually created by people who worked at the company. The development of SNS and mobile environments has led to an increase in the web services that provide job reviews. For example, Jobplanet is a job review service in Korea, and Glassdoor.com offers a similar service in the US. Despite this attention, however, research utilizing job reviews is insufficient. This paper asks whether there are differences in ratios of job satisfaction factors by industry, using LDA topic modeling and co-occurrence analysis to explore the differences. Through the results of LDA, we find that the ratios of job satisfaction factors are similar by industry. At the same time, the results of co-occurrence analysis show that the co-occurrence frequency of some job satisfaction factors appears high: pay and welfare, balance of work and life, company culture. We expect that the result of this research will be helpful in comparative analysis of job satisfaction factors by industry. Furthermore, in this paper we suggest how to use the job review data in organizational behavior research.

• Journal of Information Technology Services
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• v.19 no.3
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• pp.35-56
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• 2020

The purpose of this study is to measure corporate personality by analyzing the internal employees' corporate reviews and to identify the impact of the representative corporate personality on the relationship between job satisfaction of internal employees and the turnover rate of the company. To this end, we first created a dictionary of words representing the corporate personality with a Word2vec method based on words explaining five corporate personalities, such as reliability, initiative, practicality, activism, and femininity, obtained from the preceding study. Next, we analyzed reviews which were written by internal employees on their companies to measure the score of corporate personality at a review level, aggregated the review level scores for each company to calculate the company level score of corporate personality, and assigned to each company the corporate personality with the maximum score among the five such scores. Also, job satisfaction and turnover rate were measured from internal employees' corporate evaluation scores and the percentage of former employees of each company who left a review on the company, respectively. This study collected datasets of corporate reviews, employee information, and corporate information from Job-Planet from 2014 to 2017, conducted a technical statistic check and correlation analysis to confirm the suitability of the datasets, and performed linear regression analysis to evaluate the research model and verify hypotheses. As a result of the analysis, the job satisfaction of the internal staff has a significant negative impact on the corporate's turnover rate. In addition, companies having a personality of reliability, initiative and femininity also showed a significant cause-and-effect relationship between job satisfaction and turnover rate and among them, job satisfaction of companies having a personality, initiative, showed a greater impact on turnover rate. In sum, we not only proposed a novel method of measuring corporate personality, but also showed that corporates need to identify its corporate personality and to utilize a different strategy to reduce their employee's turnover rate depending on the corporate personality.

• Information Systems Review
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• v.20 no.2
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• pp.39-62
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• 2018

Thanks to the growth of computing power and the recent development of data analytics, researchers have started to work on the data produced by users through the Internet or social media. This study is in line with these recent research trends and attempts to adopt data analytical techniques. We focus on the impact of "internal marketing" factors on firm performance, which is typically studied through survey methodologies. We looked into the job review platform Jobplanet (www.jobplanet.co.kr), which is a website where employees and former employees anonymously review companies and their management. With web crawling processes, we collected over 40K data points and performed morphological analysis to classify employees' reviews for internal marketing data. We then implemented econometric analysis to see the relationship between internal marketing and market capitalization. Contrary to the findings of extant survey studies, internal marketing is positively related to a firm's market capitalization only within a limited area. In most of the areas, the relationships are negative. Particularly, female-friendly environment and human resource development (HRD) are the areas exhibiting positive relations with market capitalization in the manufacturing industry. In the service industry, most of the areas, such as employ welfare and work-life balance, are negatively related with market capitalization. When firm size is small (or the history is short), female-friendly environment positively affect firm performance. On the contrary, when firm size is big (or the history is long), most of the internal marketing factors are either negative or insignificant. We explain the theoretical contributions and managerial implications with these results.