• Asia pacific journal of information systems
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• v.24 no.1
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• pp.31-50
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• 2014

The ubiquitous accessibility of information through mobile devices has led to an increased mobility of workers from their fixed workplaces. Market researchers estimate that by 2016, 350 million workers will be using their smartphones for business purposes, and the use of smartphones will offer new business benefits. Enterprises are now adopting mobile technologies for numerous applications to increase their operational efficiency, improve their responsiveness and competitiveness, and cultivate their innovativeness. For these reasons, various organizational aspects concerning "mobile work" have received a great deal of recent attention. Moreover, many CIOs plan to allocate a considerable amount of their budgets mobile work environments. In particular, with the consumerization of information technology, enterprise mobile applications (EMA) have played a significant role in the explosive growth of mobile computing in the workplace, and even in improving sales for firms in this field. EMA can be defined as mobile technologies and role-based applications, as companies design them for specific roles and functions in organizations. Technically, EMA can be defined as business enterprise systems, including critical business functions that enable users to access enterprise systems via wireless mobile devices, such as smartphones or tablets. Specifically, EMA enables employees to have greater access to real-time information, and provides them with simple features and functionalities that are easy for them to complete specific tasks. While the impact of EMA on organizational workers' productivity has been given considerable attention in various literatures, relatively little research effort has been made to examine how EMA actually lead to users' job performance. In particular, we have a limited understanding of what the key antecedents are of such an EMA usage outcome. In this paper, we focus on employees' perceived job performance as the outcome of EMA use, which indicates the successful role of EMA with regard to employees' tasks. Thus, to develop a deeper understanding of the relationship among EMA, its environment, and employees' perceived job performance, we develop a comprehensive model that considers the perceived-fit between EMA and employees' tasks, satisfaction on EMA, and the organizational environment. With this model, we try to examine EMA to explain how job performance through EMA is revealed from both the task-technology fit for EMA and satisfaction on EMA, while also considering the antecedent factors for these constructs. The objectives of this study are to address the following research questions: (1) How can employees successfully manage EMA in order to enhance their perceived job performance? (2) What internal and/or external factors are important antecedents in increasing EMA users' satisfaction on MES and task-technology fit for EMA? (3) What are the impacts of organizational (e.g. organizational agility), and task-related antecedents (e.g., task mobility) on task-technology fit for EMA? (4) What are the impacts of internal (e.g., self-efficacy) and external antecedents (e.g., system reputation) for the habitual use of EMA? Based on a survey from 254 actual employees who use EMA in their workplace across industries, our results indicate that task-technology fit for EMA and satisfaction on EMA are positively associated with job performance. We also identify task mobility, organizational agility, and system accessibility that are found to be positively associated with task-technology fit for EMA. Further, we find that external factor, such as the reputation of EMA, and internal factor, such as self-efficacy for EMA that are found to be positively associated with the satisfaction of EMA. The present findings enable researchers and practitioners to understand the role of EMA, which facilitates organizational workers' efficient work processes, as well as the importance of task-technology fit for EMA. Our model provides a new set of antecedents and consequence variables for a TAM involving mobile applications. The research model also provides empirical evidence that EMA are important mobile services that positively influence individuals' performance. Our findings suggest that perceived organizational agility and task mobility do have a significant influence on task-technology fit for EMA usage through positive beliefs about EMA, that self-efficacy and system reputation can also influence individuals' satisfaction on EMA, and that these factors are important contingent factors for the impact of system satisfaction and perceived job performance. Our findings can help managers gauge the impact of EMA in terms of its contribution to job performance. Our results provide an explanation as to why many firms have recently adopted EMA for efficient business processes and productivity support. Our findings additionally suggest that the cognitive fit between task and technology can be an important requirement for the productivity support of EMA. Further, our study findings can help managers in formulating their strategies and building organizational culture that can affect employees perceived job performance. Managers, thus, can tailor their dependence on EMA as high or low, depending on their task's characteristics, to maximize the job performance in the workplace. Overall, this study strengthens our knowledge regarding the impact of mobile applications in organizational contexts, technology acceptance and the role of task characteristics. To conclude, we hope that our research inspires future studies exploring digital productivity in the workplace and/or taking the role of EMA into account for employee job performance.

• Management & Information Systems Review
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• v.36 no.4
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• pp.117-127
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• 2017

In recent times the size of the world IPO in general has skyrocketed. Specifically, China's financial market development is becoming important as both the size of China's capital market and the number of companies going public are gradually increasing. This has led to a rapid development of venture vapital(VC) institutions in China for the past couple of decades. This study focuses on one of the three markets of China's Shenzhen Stock Exchange-the Growth Enterprise Board((GEB) hereafter, ChiNext). The ChiNext is established in October, 2009 to enable hi-tech or high growth potential technology companies that find it relatively difficult to fulfil the listing requirements of either the Shenzhen Main Board or Small and Medium Size Enterprise Board(SMEB) to go public. This study covers a three-year period(2012/01/-2015/01) and analyze first day initial return of 83 venture capital-backed companies and 53 non-venture capital-backed companies using T-test. Regression analysis is used as to examine the variables affecting IPO's first-day return. The empirical results are four-fold. First, the level of first day return of venture-backed is significantly lower than non venture capital backed support in the Chinese venture capital market. Second, the level of first-day return of listed companies supported by foreign venture capital is significantly higher than that of companies receiving domestic venture capital support. Third, the firms that have a large number of venture capital firms showed a low level of first-day return. Fourth, regression result for the IPO first-day return which is as dependent variable indicates that the venture capital support(VCAP), number of venture capital(VCNum), offering size(Lnsize) and PER all affect have negative effect on the first day initial return. Also, the venture capital type(VCType), turnover ratio and the the firm type(Tech-firms) statistically affect IPO first day return positively. Finally, by shedding more light on the IPO first-day return, this paper provides meaningful information to investors about the Chinese IPO market.

• Journal of Technology Innovation
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• v.28 no.3
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• pp.1-28
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• 2020

This study examines whether the firm with high innovation efficiency realizes high operating performance. We measured innovation efficiency by the ratio of patent applications for R&D expenditure or R&D stock and measured operating performance by the ratio of operating income or operating cash flow to total assets for the following year. The sample consists of 1,880 manufacturing firm-years, which listed on the Korean Exchange between 2014 and 2017. We analyze the effect of innovation efficiency on operating performance using a model of Hirshleifer et al. (2013) results show that both innovation efficiency variables have a significantly positive relationship with the total asset operating margin. Besides, the following year's performance, measured by the total asset operating cash flow ratio, also shows a positive relationship with the two innovation efficiency variables at the 5% and 1% significance levels, respectively. The results indicate that high innovation efficiency firms that link the outcomes of R&D to more patent applications realize higher operating performance. Also, we divided the R&D-intensive and non-R&D-intensive industries and performed the same analysis. As a result, the innovation efficiency has a significant positive effect on operating margin in both industries. However, the effect of innovation efficiency on the operating cash flow is only significant in R&D-intensive industries. This study suggests that the effects of innovation efficiency are more consistent in the R&D-intensive industry. Additionally, we divided the high patent application and low patent applications industries and performed the same analysis. As a result, the innovation efficiency has a significant positive effect on operating margin in both industries. This study suggests that the effects of innovation efficiency are more consistent in the high patent application industry. We show that a firm's innovation efficiency is a critical factor for a firm's performance, while prior studies on the R&D performance have not considered the innovation efficiency of each firm. The evidence suggests that firms not only consider R&D expenditures but also improve the performance of companies by increasing innovation efficiency. Investors need to consider their innovation efficiency when evaluating the value of firms.

• The Journal of the Korea Contents Association
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• v.8 no.7
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• pp.208-215
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• 2008

Sport industry creates value-added by production and distribution of product or service related with sports or sports. Because this industry shows promise in becoming a major industry area, the government is making an effort to bring up or develop it. The financing to firms of sport industry area(sporting goods industry, sport facility industry, sport service industry) with low interest rate and long-term repayment period on the basis of sports promotion fund is excellent scheme to promote sport industry. Nevertheless, Because many people indicate operational inefficiency of it, it is necessary to check operational efficiency through investigation for loan system of sports promotion fund. The purposes of this paper are to present some directions for the improvement of efficiency by the analysis to public loan system using funds of other government organization and to the current status of loan system of sports promotion fund. The result of this study are as follows. First, we propose enlargement of loan size and range. Second, it is necessary to use mortgages loan to technology in case of sporting goods industry and sport service industry. Third, we suggest the mitigation of loan disproportion and flexible application among loam amount of sport industry areas. Fourth, we suggest the construction of loan practical committee. Finally we propose the establishment of selection criteria to firms and ex-post evaluation system related with loan system of sports promotion fund.

• Information Systems Review
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• v.9 no.1
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• pp.105-120
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• 2007

With the fast development of the Internet and the increasing dependence on information infrastructures, companies are faced with various information security threats such as information leakages, modifications, and information breaches. South Korea is one of the leading countries in the Internet usage, but is ranked relatively low when it comes to information security. In fact, many Korean firms have suffered financial losses and damaged corporate images from the information security breaches. However, because of the difficulties in quantifying the costs of the information security breaches, Korean companies tend to delay their investment decisions on information security. The purpose of this study is to measure the cost of information security breach and the economic value of security investment using the event study methodology. Our results show that the announcement of an information security breach negatively influenced the market value of the corresponding company. The effect was statistically significant at the significance level of p=0.05. The breached companies lose, on average, 0.86% of their market values on the day of the announcement - an average loss in market capitalization of $55 million. On the other hand, the investment on information security had no effect on the stock price or the market value of the firm.

• Journal of Technology Innovation
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• v.27 no.3
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• pp.1-26
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• 2019

The Korean economy has undergone growth based on manufacturing end products. The Korean government aims to advance industry by strengthening the materials and components industry, which is attracting more attention in terms of maintaining the competitiveness of existing key industries, and fostering new industries required in the era of the Fourth Industrial Revolution. Despite this importance, there is insufficient related research on the efficient R&D of the materials and components industry. This study analyzed the R&D efficiency. In addition, exploratory research was conducted on the impact of corporate size and type of cooperation on R&D efficiency. Output variables were set to reflect economic performance and the empirical analysis revealed that overall R&D efficiency is low. Small firms were found to perform better than large firms in terms of firm-size, and the efficiency of business-university-research cooperation is worse than other types of cooperation. This study is exploratory research considering the materials and components industry, and the results provide implications for research institutions and regarding types of cooperation. This is expected to help develop polices for qualitative growth and R&D strategies for investment and allocation.

• 한국벤처창업학회:학술대회논문집
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• 2008.04a
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• pp.33-59
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• 2008

Since the late 20th century, due to the globalization of markets and the rapid developments of information and communication technologies, the world has entered into the age of fierce competition. With their abundant low-cost labor, former communist countries such as China, Russia and East Europe and low-developed countries such as India and Brazil have received investments from major industrial companies. There are no other alternatives for Korea and OECD countries to move the industrial economy to the knowledge based innovation system. In knowledge based innovation system, economic development and employment of a region and nation depend solely on the number of its companies and their competitiveness. In this research, after reviewing the current state of technological innovation small companies in korea, we provide a theoretical framework to promote the level of technological innovative entrepreneurship. The framework is based on a value system model that consist of supply factors, demand factors, and supporting institutions. In order to create more innovative small businesses, we insist on the followings. First, more R&D activities related with promising new technologies should be performed. Second, more R&D funds for technological innovation small companies should be provided from governments. Third, more M&A transactions and technology transactions should be marketed freely and competitively. Fourth, more business incubating services and venture capital services should be provided. Fifth, entrepreneurship and innovation culture should be popular and institutionized among people.

• Journal of Information Technology Services
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• v.17 no.2
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• pp.1-16
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• 2018

Large corporations have the work and roles of employees clearly defined in accordance with their established internal structure whereas small and mid-sized firms may lack an official job specification or a written job description, causing ambiguity in the role of employees. In IT service companies, especially, there is a relatively large number of tasks requiring one person to play multiple roles and they are often highly dependent on few key players. In this reality where about 80% of the job seekers end up in SMEs, it is therefore necessary to look into how the turnover factors such as job strain and occupational burnout caused by the role ambiguity from extra work affect the turnover intentions of small and medium sized IT service firm employees. The outcome of this study is as follows. First, the relationship between job strain and turnover intention has been examined to find that a weak inverted U shaped relationship exists between role overload, which is a subset of job strain, and turnover intention. Second, the hypothesis based on the competing values framework has been tested that the fitness with organizational leadership and culture would moderate the relationship between job strain and turnover intention and it has been confirmed that the degree of relationship between role conflict and role ambiguity, the subsets of job strain, and turnover intention has changed. In conclusion, in order to discourage turnover intention, it is necessary to increase the fitness with the organization and leadership or generate a sufficient level of strain in case of an organization with low fitness.

• Asia pacific journal of information systems
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• v.19 no.4
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• pp.29-45
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• 2009

In the information age, cheap price of information processing and advances in personalization technology have allowed companies to enhance the relationships with their existing customers and to expand their customer base by effectively attracting new customers. However, most customers are reluctant to provide their personal information to companies. This study explores the tension between companies' desire to collect personal information to offer personalized services and their customers' privacy concerns. The psychological reactance theory suggests that when individuals feel that their behavioral choice is threatened or restricted, they are motivated to restore their freedom. Therefore, despite the expected benefits from personalized services, customers may perceive the services to be restrictive of their freedom to choose. This adverse effect may undermine the relationships between companies and their customers. We conducted experiments to explore the dynamic roles of transactional and environmental factors in motivating customers to provide personal information. We revisited online privacy issues from the perspective of psychological reactance. For the experiments, we created an online shop and randomly assigned the participants to one of the two experimental conditions-high and low levels of information requirements. The results of the experiment indicate that threatening the free choice serves as a transactional cost in online profiling. On the other hand, the expected benefits of personalization services have positive correlations with customers' willingness to provide personal information. This study explains privacy based on transactional and environmental factors. Our findings also indicate that the environmental factors such as the Internet privacy risk and trust propensity do not significantly affect the willingness to provide personal information when firms required much personal information. Implications and contributions are discussed.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.