• The Journal of Asian Finance, Economics and Business
• /
• 제8권2호
• /
• pp.143-150
• /
• 2021

This study investigates and analyzes the difference in Indonesian banks' earnings quality in the pre-audit and post-audit period. This study also investigates the difference in audit quality done by public accounting firms. This study employs time series data taken from the unaudited and audited financial statements of banks listed on the Indonesia Stock Exchange in 2012-2016. Sample selection is made by using a purposive sampling method. The population of this study is 43 banks, and after checking the data for validity and reliability, the final sample size was 26 banks. Audit quality is operationalized with the size of the auditor. Earnings quality is proxied by accruals calculated using the Beaver and Engel (1996) model. The data analysis method used in this study is the paired-sample t-test and chow test. This study shows that there is no difference in earnings quality in the pre-audit and post-audit period. This study also reveals no difference in audit quality between the big four and non-big four auditors. These findings mean that independent auditors do not play a useful role in increasing the reliability of accounting information presented by management to stakeholders. Besides, this study's results do not verify the agency theory regarding auditors' role to minimize opportunistic management behavior in preparing financial statements.

• 한국정보통신학회논문지
• /
• 제19권6호
• /
• pp.1341-1346
• /
• 2015

현재의 국내의 많은 자영업자들이 창업의 실패를 경험하고 있다. 이러한 점에서 무분별한 창업을 줄이고, 창업의 성공률을 높이기 위해 창업 준비과정에서 명확하고 통합된 정보의 제공이 요구된다. 본 연구는 다양한 공공기관들이 분산되어 보유하고 있는 다양한 데이터를 통합한 빅데이터를 제언하고자 한다. 이를 위해 창업에서 요구되는 데이터의 유형을 분류하고 통합적 창업지원 정보시스템 구축을 위한 데이터 통합, 분석 기술, 창업자를 위한 웹 또는 스마트 서비스의 유형을 제시하고자 한다.

• 동아시아경상학회지
• /
• 제10권1호
• /
• pp.109-121
• /
• 2022

Purpose - This study attempted to analyze news big data in order to examine the trend of change in housework due to technological innovation and family changes. Research design, data, and methodology - News big data was collected from Bigkinds for the purpose of trend analysis. A total of 8,270 articles containing 'housework' were extracted from news articles between January 1, 1990 and December 31, 2021. 11 general daily newspapers and 8 business newspapers were selected and were analyzed by dividing them into five-year units. Result - The change of trends in housework that appeared through news big data analysis can be summarized as below. First, the tendency to regard housework as work of women or housewives is gradually weakening. Instead, the centrality of connection with double income is increasing. Second, there is a tendency to strengthen the institutional approach to evaluation of the productivity of housework. Third, the possibility of market substitution for housework is expanding. Conclusion - In the era of the 4th industrial revolution, examining the impact of technological innovation and family change on housework not only enables the prospect of an industry, but also provides implications for policies related to housework. In addition, this study is differentiated in that it contributed to expand the field of housework research previously limited to analyzing survey data.

• 패션비즈니스
• /
• 제24권4호
• /
• pp.144-160
• /
• 2020

Today, the development of innovative technologies is accompanied by changes in the industrial structure and the Big Blur phenomenon, where the boundaries in various fields are blurred. The purpose of this study was to view the Big Blur phenomenon as a big paradigm shift in the 21st century and derive environmental changes and characteristics of the Korean fashion market. The research method included an analysis of the fashion brands after 2015. Through this study, we intended to establish a framework for understanding the changes in the fashion market from the perspective of Big Blur and discuss the direction of brand marketing. The research results showed the hyperlinks, connectivity, openness, homeostasis, synchronicity, mobility, interactivity, and brand experience of online and offline spaces beyond the boundaries of virtual space and offline physical spaces such as online physical and spatial viewpoints. It also showed the characteristics. The characteristics from the socio-cultural point of view were characteristic of diversity, mixture, coexistence, composability, and pluralism beyond the traditional socio-cultural and regulatory scopes. Hip hop fashion, street fashion, unisex, genderless, androgynous fashion, and kid fashion are the backbone of the Big Blur and are becoming important factors in fashion. The characteristics of the market and economic viewpoint are prosumers that play roles both as producers and consumers. It shows the extensibility of consumers as producers, the cohesiveness of producers and consumers, the cooperation, and the interconnectivity.

• 디지털융복합연구
• /
• 제13권3호
• /
• pp.55-63
• /
• 2015

정부는 능력중심사회 만들기를 핵심 국정과제로 확정하고, 국가직무능력표준(NCS, National Competency Standards) 개발 및 활용 계획을 발표하였다. 그 일환으로 정부는 2014년까지 833개 직종에 대해 국가직무능력표준을 개발했다. 그러나 빅데이터의 경우 새롭게 등장한 직무로서 아직 산업현장에서도 안정적인 직무 형태로 자리매김했다고 볼 수 없는 상황이며, 융합적이고 다학제적인 성격을 지니고 있다. 또한 주요 선진국이나 국내를 막론하고 빅데이터를 활용하기 위한 다양한 형태의 지식과 기술(skills)의 교육 및 직무역량 모형 등이 나오고 있지만, 확실히 정착된 것은 아니며 다소간의 혼선이 있는 상황이다. 이에 본 연구는 기업 및 조직이 효과적인 빅데이터 활용을 하도록 하기 위한 빅데이터 직무능력 참조 모형을 제시하고자 하는 목적 실현을 위해 네가지 직무 유형을 도출하고 이를 수행하기 위한 우리는 능력단위요소로 20개 지식과 15개 기술을 정리하였다.

• 한국컴퓨터정보학회:학술대회논문집
• /
• 한국컴퓨터정보학회 2015년도 제51차 동계학술대회논문집 23권1호
• /
• pp.277-278
• /
• 2015

The volume sub-challenge requires novel approaches, often referred to as Big Data technologies and methodologies. Data is generated constantly in an ever growing number of places and by an ever growing number of actors while a large proportion of potentially re-usable data resides within silos within institutions or companies. These are needed when conventional database technologies cannot be applied to storage and computing issues. The issue of big data has been referred to as the next frontier in computing. In this paper, we research on factors to design an organizational value chain for Big Data.

• 벤처창업연구
• /
• 제16권6호
• /
• pp.249-267
• /
• 2021

디지털화의 진전과 함께 축적된 빅데이터의 활용은 글로벌 농산업계에 파괴적 혁신을 가져오고 있다. 최근 정부는 농업 빅데이터 플랫폼 구축 및 지원조직 신설 등의 조치를 취하고 있으나 국내 농산업계는 재배생육 분야의 일부기업 외에는 빅데이터 활용이 미흡한 실정이다. 이러한 배경에서 본 연구는 빅데이터를 선도적으로 활용하여 혁신을 창출하는 주체가 되어야 할 농업벤처를 중심으로 기술, 조직, 환경의 맥락에서 빅데이터 활용의도에 영향을 미치는 요인을 규명하고 기술분야에 따른 조절효과를 확인하고자 하였다. 이에 농업기술실용화재단 A+센터의 지원을 받는 농업벤처 309개로부터 연구 데이터를 확보하여 SPSS 22.0을 이용하여 분석하였다. 연구결과, 기술적 요인 중에서는 상대적 이점과 호환성이 유의한 정(+)의 영향을 미치고, 조직적 요인 중에서는 경영층 지원이 정(+)의 영향을, 비용이 부(-)의 영향을 미치며, 환경적 요인 중에서는 정책적 지원이 정(+)의 영향을 미치는 것으로 나타났다. 기술분야의 조절효과 검증 결과, 재배생육 외 기업일수록 상대적 이점, 호환성, 경쟁자 압력 외의 모든 변수와 빅데이터 활용의도와의 관계를 완화하는 조절효과가 있는 것으로 나타났다. 이러한 결과를 통해 다음과 같은 시사점을 제시하였다. 첫째, 빅데이터 활용을 통해 농업벤처에 새로운 수익창출 및 운영효율성 제고 기회를 제공할 핵심사업을 선정하여 정책적으로 협업기회를 늘릴 필요가 있다. 둘째, 농산업 특성으로 인한 분석의 어려움을 극복할 수 있는 빅데이터 분석 솔루션 제공이 필요하다. 셋째, 농업벤처와 같은 소규모 조직에서는 최고경영층의 빅데이터 활용에 대한 높은 이해수준으로부터 출발한 조직문화 재편 의지가 선행되어야 한다. 넷째, 중소·벤처기업 수준에서 벤치마킹할 수 있는 성공사례를 발굴하고 홍보하는 것이 중요하다. 다섯째, 농업벤처 기술분야별로 핵심사업 추진과 지원사업의 우선순위를 나누어 추진하는 것이 보다 효과적일 것으로 판단된다. 마지막으로 본 연구의 한계점과 후속 연구과제를 제시하였다.

• 패션비즈니스
• /
• 제22권6호
• /
• pp.83-93
• /
• 2018

The purpose of this research is to investigate the characteristics of big data-based fashion shopping (BDFS) application, perceived usefulness, and expectation confirmation that influence the continuous usage intention of BDFS application users based on the expectation-confirmation model. A survey was conducted with female consumers in their 20s, who are living in Seoul and Incheon area and have used BDFS applications, A total of 182 responses were used for the data analysis. Five hypotheses were proposed, and regression analyses were conducted to test those hypotheses. The results indicated that the users' perceived usefulness increased with the increase of accuracy and personalization characteristics of the app and the expectation confirmation. The result suggested that it is essential to provide accurate information for users to feel useful and to develop the personalized offerings and services which can be the biggest strength of the big-data based mobile fashion store. It was also found that continuous usage intention increases with increased perceived usefulness and expectation confirmation. This result suggests that expectations can play a critical role in perceiving the usefulness of BDFS applications and the user's expectation confirmation also significantly affected the users' continuous usage intention.

• 지식경영연구
• /
• 제16권4호
• /
• pp.69-87
• /
• 2015

Given the huge number of data produced by its users, SNS is a great source of customer insights. Since viral trends in SNS reflect customers' direct feedback, companies can draw out highly meaningful business insights when such data is effectively analyzed and managed. However, while the importance of understanding SNS big data keeps growing, the methods for analyzing atypical data such as SNS postings for business insights over product has not been well studied. This study aims to demonstrate the way to exploit topic modeling method to support marketing strategy generation and therefore leverage business process. First, we conducted topic modeling analysis for twitter data of Apple and Samsung smartphones. Then we comparatively examined the analysis results to draw meaningful market insights about each smartphone product. Finally, we draw out a strategic marketing recommendation for each smartphone brand based on the findings.

• Asia pacific journal of information systems
• /
• 제26권1호
• /
• pp.66-79
• /
• 2016

The availability of detailed data on customers' online behaviors and advances in big data analysis techniques enable us to predict consumer behaviors. In the past, researchers have built purchase prediction models by analyzing clickstream data; however, these clickstream-based prediction models have had several limitations. In this study, we propose a new method for purchase prediction that combines information theory with machine learning techniques. Clickstreams from 5,000 panel members and data on their purchases of electronics, fashion, and cosmetics products were analyzed. Clickstreams were summarized using the 'entropy' concept from information theory, while 'random forests' method was applied to build prediction models. The results show that prediction accuracy of this new method ranges from 0.56 to 0.83, which is a significant improvement over values for clickstream-based prediction models presented in the past. The results indicate further that consumers' information search behaviors differ significantly across product categories.