• Title/Summary/Keyword: Data-driven Persona

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Data-driven Persona Analysis for Understanding Web Novel Users: Focusing on Quantitative Behavioral Pattern Data (웹소설 사용자 이해를 위한 데이터 기반 페르소나 분석: 정량적 행동 패턴 데이터 중심으로)

  • Ha, Sangjip;Park, Do-Hyung
    • Knowledge Management Research
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    • v.23 no.3
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    • pp.259-284
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    • 2022
  • In order to help the understanding of web novel users, this study was intended to quantitatively verify the user's behavioral types according to the characteristics of web novels. For this purpose, the direction of the study proceeded as follows. First, the motives of web novel users were investigated by referring to the motives of other digital content users. In addition, specific behavioral types of users were also collected. As a result, the motivation for using web novels was found to be 'interpersonal relationships and information acquisition with others', 'leisure activities', and 'escape from reality/relieve tension'. After that, the groups were classified as to whether there was a difference between groups according to the motives of use. As a result, the 'hobbies' type, a group with a particularly high motivation for using leisure activities, the 'stress relief' type, a group with very high escapism and tension relief characteristics, and a group with high interpersonal relationships and information acquisition with others The 'communication' type was classified as a 'multipurpose' type with high overall motivation characteristics. Then, in order to find out the specific characteristics between the types, personas were constructed based on the different behavior type data. Through this, the theoretical contribution of this study is meaningful in that it revealed the motives of web novel users. As a practical contribution, the persona was formed by combining the users' motives and behavioral patterns and visualized to be close to the actual representative users. These results are expected to help improve the web novel service by providing useful indicators for actual writers, platform managers, and users.

ICT Company Profiling Analysis and the Mechanism for Performance Creation Depending on the Type of Government Start-up Support Program (정부창업지원 프로그램 참여에 따른 ICT 기업 프로파일링과 성과창출 메커니즘)

  • Ha, Sangjip;Park, Do-Hyung
    • Journal of Intelligence and Information Systems
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    • v.28 no.3
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    • pp.237-258
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    • 2022
  • As the global market environment changes, the domestic ICT industry has a growing influence on the world economy. This industry is regarded as an important driving force in the national economy from a technological and social point of view. In particular, small and medium-sized enterprises (SMEs) in the ICT industry are regarded as essential actors of domestic economic development in terms of company diversity, technology development and job creation. However, since it is small compared to large-sized enterprises, it is difficult for SMEs to survive with a differentiated strategy in an incomplete and rapidly changing environment. Therefore, SMEs must make a lot of efforts to improve their own capabilities, and the government needs to provide the desirable help suitable for corporate internal resources so that they can continue to be competitive. This study classifies the types of ICT SMEs participating in government support programs, and analyzes the relationship between resources and performance creation of each type. The data from the "ICT Small and Medium Enterprises Survey" conducted annually by the Ministry of Science and ICT was used. In the first stage, ICT SMEs were clustered based on common factors according to their experiences with government support programs. Three clusters were meaningfully classified, and each cluster was named "active participation type," "initial support type," and "soloist type." As a second step, this study compared the characteristics of each cluster through profiling analysis for each cluster. The third step carried out in this study was to find out the mechanism of R&D performance creation for each cluster through regression analysis. Different factors affected performance creation for each cluster, and the magnitude of the influence was also different. Specifically, for "active participation type", "current manpower", "technology competitiveness", and "R&D investment in the previous year" were found to be important factors in creating R&D performance. "Initial support type" was identified as "whether or not a dedicated R&D organization exists", "R&D investment amount in the previous year", "Ratio of sales to large companies", and "Ratio of vendors supplied to large companies" contributed to the performance. Lastly, in the case of "soloist type", "current workforce" and "future recruitment plan", "technological competitiveness", "R&D investment", "large company sales ratio", and "overseas sales ratio" showed a significant relationship with the performance. This study has practical implications of showing what strategy should be established when supporting SMEs in the future according to the government's participation in the startup program and providing a guide on what kind of support should be provided.