• Journal of the Korean Society for Library and Information Science
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• v.56 no.4
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• pp.491-514
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• 2022

Public reforms based on New Public Management have made the public sector specialized, and accordingly the role of public administration has expanded as well as the demand on professional jobs has increased. On the other hand, with the rapid development of information and communication technology, the data produced by public sector organizations has also significantly increased. This environmental changes made data management and a data management job in the public sector critical. However, there have been very few studies of conceptualizations and systematic investigations on data management jobs. Moreover, specific definitions, types or qualifications of/for a data management job or a person who do this job are rarely reflected in relevant laws and regulations. Based on the systematic literature review, this study conceptualized professionalism, identified its multiple dimensions, and draw a conceptual research framework. Focusing on the professional control on personnel management which is one of the dimensions of professionalism, relevant laws, work guidelines and job descriptions included in job openings were analyzed with regard to a data management job in the public sector. The findings are as follows. First, an assigned role and responsibility associated with a data management job have vague boundaries. Second, work guidelines and manuals only focus on the post quality control stage rather than equally addressing all the eight stages of the data lifecycle. Third, neither a data management job in the public sector nor a person who take care of this job is not appropriately defined. Therefore, a role and responsibility of/for the job and a person in charge should be reflected in the relevant laws and guidelines in a tailored way. More importantly, job analyses and evaluations should be thoroughly conducted to enhance professionalism on data management jobs in the long term.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.3
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• pp.157-172
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• 2023

The recent Fourth Industrial Revolution is accelerating changes due to digital transformation. According to this trend, the existing start-up paradigm is changing, and new business models based on new technologies and creative ideas are emerging. In addition, the diversity of mentoring relationships and environments such as online mentoring, reverse mentoring, group mentoring, and multiple mentoring is also increasing. However, most mentors in their 50s and 60s, who are mainly active in the start-up field, have been able to help mentees a lot based on their own experience and expertise, but they are having difficulty responding to the changing environment due to a lack of understanding and experience of new technologies and environments. To cope with these changes well, mentors must constantly study, acquire and apply the latest technologies to improve their understanding of new technologies and the environment. In addition, it is necessary to have an understanding and respect for the diversity of mentoring relationships and environments, and to maximize the effectiveness of mentoring by actively utilizing them. Therefore, mentors should recognize that they directly affect the growth and development of mentees, constantly acquire new knowledge and skills to maintain and develop expertise, and actively deliver their knowledge and experiences to mentees. Therefore, in this study, was tried to empirically analyze the relationship between mentoring's influence on mentor's job satisfaction through mentor's personal learning and self-efficacy. The results of the empirical analysis were as follows. Among the functions of mentoring, career function and role modeling were found to have a positive effect on both personal learning and self-efficacy, which are parameters, and job satisfaction, which is a dependent variable. On the other hand, psychological and social functions have a positive effect on personal learning, but they do not have an effect on self-efficacy and job satisfaction. In addition, as a result of analyzing the mediating effect, all mediating effects were confirmed for career functions, and only the mediating effect of self-efficacy was confirmed for role modeling. Through this study, mentoring is an important factor in promoting job satisfaction, personal learning and self-efficacy, and this study can be said to be academically and practically meaningful in that it confirmed personal learning and self-efficacy as factors that increase mentor's job satisfaction, and the focus of mentoring research was shifted from mentee to mentor to study the impact of mentoring on mentors.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.1
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• pp.157-175
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• 2022

At this moment, a paradigm shift is taking place across all sectors of society for the transition movements to the digital economy. Various movements are taking place in the global agricultural industry to achieve innovative growth using big data which is a key resource of the 4th industrial revolution. Although the government is making various attempts to promote the use of big data, the movement of the agricultural industry as a key player in the use of big data, is still insufficient. Therefore, in this study, effects of performance expectations, effort expectations, social impact, facilitation conditions, based on the Unified Theory of Acceptance and Use of Technology(UTAUT), and innovation tendencies on the acceptance intention of big data were analyzed using the economic and practical benefits that can be obtained from the use of big data for agricultural-related companies as moderating variables. 333 questionnaires collected from agricultural-related companies were used for empirical analysis. The analysis results using SPSS v22.0 and Process macro v3.4 were found to have a significant positive (+) effect on the intention to accept big data by effort expectations, social impact, facilitation conditions, and innovation tendencies. However, it was found that the effect of performance expectations on acceptance intention was insignificant, with social impact having the greatest influence on acceptance intention and innovation tendency the least. Moderating effects of economic benefit and practical benefit between effort expectation and acceptance intention, moderating effect of practical benefit between social impact and acceptance intention, and moderating effect of economic benefit and practical benefit between facilitation condition and acceptance intention were found to be significant. On the other hand, it was found that economic benefits and practical benefits did not moderate the magnitude of the influence of performance expectations and innovation tendency on acceptance intention. These results suggest the following implications. First, in order to promote the use of big data by companies, the government needs to establish a policy to support the use of big data tailored to companies. Significant results can only be achieved when corporate members form a correct understanding and consensus on the use of big data. Second, it is necessary to establish and implement a platform specialized for agricultural data which can support standardized linkage between diverse agricultural big data, and support for a unified path for data access. Building such a platform will be able to advance the industry by forming an independent cooperative relationship between companies. Finally, the limitations of this study and follow-up tasks are presented.