• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.2
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• pp.565-577
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• 2020

This study empirically examined how a firm size affects the determinants of innovative activities using the data of the Korean Innovation Survey (KIS) 2016. With data from 2,003 firms in the manufacturing sector, we performed logistic regression analysis and zero-inflated negative binomial regression analysis. R&D expenditure and patent applications were used as proxies for innovative activity. The independent variables included the firm's characteristics variables such as the firm's age, tech-level industry, RDemp (R&D employee ratio), venture, export, and industrial characteristics variables such as networking, appropriability, and spillovers. The empirical findings are that there are some differences in firms' innovative activity determinants among the firms' size groups. Next, strategic appropriability has negative impacts on small firms' R&D expenditure and medium-firms' patents. Networking is an important determinant of innovative activity for all firms, except for large firms. Furthermore, in deciding R&D activities, small and medium-sized firms were significantly influenced by industrial characteristics as compared to that of large firms. Our findings suggest some R&D promotion policies. Policies fostering firms' technological interaction would allow firms to take advantage of technological spillovers and thus raise the probability of investing in R&D.

• Management & Information Systems Review
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• v.33 no.5
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• pp.155-169
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• 2014

We meet many difficult challenges from many factors such as crime, natural disasters, social unrest, financial problems, employment, and so on. It therefore essential to cope with these negative stressful events to attain a state of personal well-being. Lately in the field of psychology and psychiatry, a concept called 'resilience' has received increasing attention. Resilience embodies the personal qualities that enable one to thrive in the face of adversity. Also it refers to the process of overcoming the negative effect of risk exposure, coping successfully with adversity, and avoiding the negative trajectories associated with risks. Resilience people were expected to do their innovative work behavior well. And SUS(supervisor support), COS(coworker support), OEMC(organizational error management culture) influence the relationship of resilience between Innovative Behavior. This study focused on three question. First, how is resilience relate to individual performance in the organization? And second, are there any moderate factors between resilience and individual performance. As the result of the hierarchical regression analysis, resilience indicates positive effects on IB and IB was predicted by interaction of resilience by SUS and OEMC. Findings in this study indicated that the level of SUS and OEMC should be considered in interpreting the resilience and Innovative Behavior.

• Industry Promotion Research
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• v.4 no.1
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• pp.57-66
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• 2019

The extant research literature is scant in telling us how organizations actually implement lifelong learning practices and policies. Hence, the purpose of this paper is to describe how lifelong learning is grounded in practice. We do this by introducing a new conceptual framework that was developed on the basis of interviews with a number of leading edge corporations from Canada, the USA, India and Korea. At the heart of our model, and any effective lifelong learning system, is a performance management system. The performance management system allows for an ongoing interaction between managers and employees whereby challenging performance and learning goals are set, and concrete plans are made to achieve them. Those plans involve three types of learning activities. First, employees may be encouraged to engage in formal learning. This could be provided in-house, or the employee may take a leave of absence and return to school. Second, managers may deploy their subordinates to different departments or teams, so that they can take part in new work-based learning opportunities. Finally, employees may be encouraged to learn on their own time. By this we mean learning after organizational hours through firm-sponsored 5 programs, such as e-learning courses. Fueled by the performance management system, we posit that these three learning outlets lead to effective lifelong learning in organizations.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.3
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• pp.29-46
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• 2022

COVID-19 has radically changed the behavior of members of society for exchange. In particular, the strong contagiousness of the virus is increasing networking on online platforms while reducing people's networking in the real world. Recently, the metaverse, which strengthened the presence based on 3D technology, is attracting attention from members of society such as individuals and companies. We present a method to improve metaverse utilization from the perspective of organizations and employees who have introduced metaverse for work. In other words, we check the effect of metaverse social function and telepresence on the employee's intention to offer support by improving the trust of the metaverse participants. We obtained samples through questionnaires targeting employees of organizations that introduced metaverse to their work, and verified the research hypothesis by applying the structural equation model. As a result, social interactivity, reciprocal favor, and telepresence of metaverse partially affected metaverse trust (platform, peer, organization), and metaverse trust increased the intention to offer support. Our study suggests a strategic direction to improve the metaverse utilization and exchange level of employees of organizations who want to use the metaverse for business.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.5
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• pp.53-68
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• 2014

This study is aimed at analyzing the factors that affect the behaviors of employee's investment, such as a decision making process in a variety of views and proving the extent of how those factors influence on their investment. The basic assumption is that the preceding factors that can be determined by the personal investment propensity, a psychological factor asserted by Behavior Financial Theory and financial-economic and social environment. This study uses Hershey's Investment Behavior Model(2007) as the main analysis tool to explain the investment behavior of individuals and deals with personal investment inclination in the psychological perspective of overconfidence, self-control and the risk tolerance propensity and add the financial and economic factors in terms of financial literacy and economic distress. Also the new preceding social environmental factors like social interaction and the effect of reference group are added to make this research to be more precise. This study analyze the adjustment effect of professional invest-consulting service that affect the fluctuation influence between the individual variables(those factors) and subordination variable(the level of investment satisfaction). The study reveals that overconfidence and self-control in direct ways have a positive effect on the level of investment satisfaction in terms of investment behavior and economic distress has a negative effect on the level of investment satisfaction. The adjustment effect provided by financial experts in investment consulting service is affirmed as the critical factor that increase the influence between self-control and the level of investment satisfaction. To conclude, the research reveals that the psychological factors are the main criteria when the workers as employees have to make investment decisions. To make investors be reasonable, a systematic financial education system provided by experts is needed from the early adolescent stages and financial companies should develop the relevant services of consulting service department as a key financial sector and financial investment products and consulting program and marketing tool pertinent to investors ages, vocational traits and their inclinations.

• Korean Journal of Health Education and Promotion
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• v.2 no.1
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• pp.31-61
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• 2000

Introduction. Despite the fact that half of premature deaths are caused by unhealthy lifestyles such as smoking tobacco, sedentary lifestyle, alcohol and drug abuse and poor nutrition, there are no theoretical models which accurately explain these health promotion related behaviors. This study tests a new model of health behavior called the Model of Health Promotion Behavior. This model draws on elements and frameworks suggested by the Health Belief Model, Social Cognitive Theory, the Theory of Planned Action and the Health Promotion Model. This model is intended as a general model of behavior but this first test of the model uses amount of exercise as the outcome behavior. Design. This study utilized a cross sectional mail-out, mail-back survey design to determine the elements within the model that best explained intentions to exercise and those that best explained amount of exercise. A follow-up questionnaire was mailed to all respondents to the first questionnaire about 10 months after the initial survey. A pretest was conducted to refine the questionnaire and a pilot study to test the protocols and assumptions used to calculate the required sample size. Sample. The sample was drawn from 2000 eligible participants at two blue collar (utility company and part of a hospital) and two white collar (bank and pharmaceutical) companies located in Southeastern Michigan. Both white collar site had employee fitness centers and all four sites offered health promotion programs. In the first survey, 982 responses were received (49.1％) after two mailings to non-respondents and one additional mailing to secure answers to missing data, with 845 usable cases for the analyzing current intentions and 918 usable cases for the explaining of amount of current exercise analysis. In the follow-up survey, questionnaires were mailed to the 982 employees who responded to the initial survey. After one follow-up mailing to non-respondents, and one mailing to secure answers to missing data, 697 (71.0％) responses were received, with 627 (63.8％) usable cases to predict intentions and 673 (68.5％) usable cases to predict amount of exercise. Measures. The questionnaire in the initial survey had 15 scales and 134 items; these scales measured each of the variables in the model. Thirteen of the scales were drawn from the literature, all had Cronbach's alpha scores above .74 and all but three had scores above .80. The questionnaire in the second mailing had only 10 items, and measured only outcome variables. Analysis. The analysis included calculation of scale scores, Cronbach's alpha, zero order correlations, and factor analysis, ordinary least square analysis, hierarchical tests of interaction terms and path analysis, and comparisons of results based on a random split of the data and splits based on gender and employer site. The power of the regression analysis was .99 at the .01 significance level for the model as a whole. Results. Self efficacy and Non-Health Benefits emerged as the most powerful predictors of Intentions to exercise, together explaining approximately 19％ of the variance in future Intentions. Intentions, and the interaction of Intentions with Barriers, with Support of Friends, and with Self Efficacy were the most consistent predictors of amount of future exercise, together explaining 38％ of the variance. With the inclusion of Prior Exercise History the model explained 52％ of the variance in amount of exercise 10 months later. There were very few differences in the variables that emerged as important predictors of intentions or exercise in the different employer sites or between males and females. Discussion. This new model is viable in predicting intentions to exercise and amount of exercise, both in absolute terms and when compared to existing models.

• Journal of the Korean Society of Food Science and Nutrition
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• v.39 no.12
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• pp.1880-1892
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• 2010

The purposes of this study were to: a) provide evidence concerning the effects of emotional intelligence on job outcomes, b) examine the impacts of emotional intelligence on employee-related variables such as 'job satisfaction', 'organizational commitment', 'organizational performance', and 'turnover intention' c) identify the conceptual framework underlying emotional intelligence. A survey was conducted to collect data from foodservice managers (N=231). Statistical analyses were completed using SPSS Win (16.0) for descriptive analysis, reliability analysis, factor analysis, t-test, correlation analysis, cluster analysis and AMOS (16.0) for confirmatory factor analysis and structural equation modeling. The concept of emotional intelligence (EI) has been on the radar screens of many leaders and managers over the last several decades. The emotional intelligence is generally accepted to be a combination of emotional and interpersonal competencies that influence behavior, thinking and interaction with others. The main results of this study were as follows. The four EI (Emotional Intelligence) dimensions correlated significantly with age. The means of job satisfaction score were above the midpoint (3.04 point) scale. The organizational commitment score was above the midpoint (3.41 point) scale and was higher at 'loyalty' factor than 'commitment' factor. The means of organizational performance score were above the midpoint (3.34) scale. The correlations among the four EI (emotional intelligence) factors were significant with job satisfaction; organizational commitment, organizational performance and turnover intention. The test of hypothesis using structural equation modeling found that emotional intelligence produced positive effects on job attitude and job performance. Emotional intelligence enhanced organizational commitment, and in turn, managers' attitude produced positive effects on organizational performance; emotional intelligence also had a direct impact on organizational performance. This study has identified the effect of emotional intelligence on organizational performance and attitudes toward one's job.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.231-252
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• 2021

Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.