• The Journal of Industrial Distribution & Business
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• v.13 no.11
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• pp.1-10
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• 2022

Purpose: This study measures the influence of facilitating conditions on employees' attitudes towards the adoption of big data analytics by selected medical aid organizations in Durban. In the health care sector, there are various sources of big data such as patients' medical records, medical examination results, and pharmacy prescriptions. Several organizations take the benefits of big data to improve their performance and productivity. Research design, data, and methodology: A survey research strategy was conducted on some selected medical aid organizations. A non-probability sampling and the purposive sampling technique were adopted in this study. The collected data was analysed using version 23 of Statistical Package for Social Science (SPSS) Results: the results show that the "facilitating conditions" have a positive influence on employees' attitudes in the adoption of big data analytics Conclusions: The findings of this study provide empirical and scientific contributions of the facilitating conditions issues regarding employee attitudes toward big data analytics adoption. The findings of this study will add to the body of knowledge in this field and raise awareness, which will spur further research, particularly in developing countries.

• Knowledge Management Research
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• v.19 no.4
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• pp.59-76
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• 2018

With the arrival of the big data era, customer data and data mining analysis have gradually dominated the process of Customer Relationship Management (CRM). This phenomenon indicates that customer data along with the use of information techniques (IT) have become the basis for building a successful CRM strategy. However, some companies can not discover valuable information through a large amount of customer data, which leads to the failure of making appropriate business strategy. Without suitable strategies, the companies may lose the competitive advantage or probably go bankrupt. The purpose of this study is to propose CRM strategies by segmenting customers into VIPs and Non-VIPs and identifying purchase patterns using the the VIPs' transaction data and data mining techniques (K-means clustering and association rules) of online shopping mall in Korea. The results of this paper indicate that 227 customers were segmented into VIPs among 1866 customers. And according to 51,080 transactions data of VIPs, home product and women wear are frequently associated with food, which means that the purchase of home product or women wears mainly affect the purchase of food. Therefore, marketing managers of shopping mall should consider these shopping patterns when they build CRM strategy.

• ETRI Journal
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• v.40 no.6
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• pp.745-758
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• 2018

A wide range of studies in various disciplines has focused on the Internet of Things (IoT) and cyber-physical systems (CPS). However, it is necessary to summarize the current status and to establish future directions because each study has its own individual goals independent of the completion of all IoT applications. The absence of a comprehensive understanding of IoT and CPS has disrupted an efficient resource allocation. To assess changes in the knowledge structure and emerging technologies, this study explores the dynamic research trends in IoT by analyzing bibliographic data. We retrieved 54,237 keywords in 12,600 IoT studies from the Scopus database, and conducted keyword frequency, co-occurrence, and growth-rate analyses. The analysis results reveal how IoT technologies have been developed and how they are connected to each other. We also show that such technologies have diverged and converged simultaneously, and that the emerging keywords of trust, smart home, cloud, authentication, context-aware, and big data have been extracted. We also unveil that the CPS is directly involved in network, security, management, cloud, big data, system, industry, architecture, and the Internet.

• The Journal of Economics, Marketing and Management
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• v.9 no.1
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• pp.1-14
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• 2021

Purpose: The article deals with the proposition that consumers' fashion consumption behavior will still follow the consumption behavior of fast fashion, despite recognizing the importance of slow fashion. Research design, data and methodology: The research model to verify this proposition is topic modelling with big data including unstructured textual data. we combined 5,506 news articles posted on Naver news search platform during the 2003-2019 period about fast fashion and slow fashion, high-frequency words have been derived, and topics have been found using LDA model. Based on these, we examined consumers' perception and consumption behavior on slow fashion through the analysis of Topic Network. Results: (1) Looking at the status of annual article collection, consumers' interest in slow fashion mainly began in 2005 and showed a steady increase up to 2019. (2) Term Frequency analysis showed that the keywords for slow fashion are the lowest, with consumers' consumption patterns continuing around 'brand.' (3) Each topic's weight in articles showed that 'social value' - which includes slow fashion - ranked sixth among the 9 topics, low linkage with other topics. (4) Lastly, 'brand' and 'fashion trend' were key topics, and the topic 'social value' accounted for a low proportion. Conclusion: Slow fashion was not a considerable factor of consumption behavior. Consumption patterns in fashion sector are still dominated by general consumption patterns centered on brands and fast fashion.

• The Journal of Society for e-Business Studies
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• v.22 no.1
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• pp.123-146
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• 2017

The information that consumers share in the SNS (Social Networking Service) has a great influence on the purchase of consumers. Therefore, it is necessary to pay attention to new research methodology and advertising strategy using Social Big Data. In this context, the purpose of this study is to quantitatively analyze customer value through Social Big Data. In this study, we analyzed the value structure of consumers for the three smartphone brands through text mining and positive/negative image analysis. Analysis result, it was possible to distinguish the emotional aspects (sensitivity) and rational aspects (rationality) for customer value per brand. In the case of the Galaxy S7 and iPhone 6S, emotional aspects were important before the launch, but the rational aspects was important after release date. On the other hand, in the case of the LG G5, emotional aspects were important before and after launch. We can propose two core advertising strategies based on analyzed consumer value. When developing advertising strategy in the case of the Galaxy S7, there is a need to emphasize the rational aspects of product attributes and differentiated functions. In the case of the LG G5, it is necessary to consider the emotional aspects of happiness, excitement, pleasure, and fun that are felt by using products in advertising strategy. As a result, this study will provide a good standard for actual advertising strategy through consumer value analysis. Advertising strategies are primarily driven by intuition or experience. Therefore, it is important to develop advertising strategies by analyzing consumer value through social big data analysis.

• KDI Journal of Economic Policy
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• v.14 no.1
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• pp.3-36
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• 1992

"Corporate control by owners" characterizes the current structure of ownership, control and management of big business groups in Korea. It has become an ever more serious obstacle for the Korean economy to end its distinctive "personal capitalism" and to transform the current system into people's capitalism. The current issue, the deconcentration of ownership, through the course of heated debates should be treated from an integrated perspective. That is, the debate should center on the concentration of economic power and it effects on national economy, instead of sticking to the issue of ownership-control issue per se. This paper, by referring to the historical experiences and development paths which advanced countries have already traveled, analyzes the respective aspects of the concentration issue in a rather descriptive and taxonomist manner - market concentration, business diversification, ownership concentration, integrated management of conglomerates, i.e., managing in groups' unit, and the roles of financial institutions. The government policies against the concentration of economic power have so far focused on the size of big business groups and their diversification activities. The two major policy measures are restrictions on cross-ownership and excess capital investment by big business groups, and controls on their credit deals. This paper strongly suggests that the government should change its current priorities in targeting its policies against concentration. The government should reduce the regulations on size and diversification, and focus its policies on substantial dispersion of corporate ownership. The efficacy of government intervention in the management and control of business enterprises seems quite dubious and even anachronistic given the extent of maturity of Korean firms. Therefore, it should be noted that the current regulation-oriented stance taken by the government against the management style of big business groups should be suppressed, as it has assumed some a priori and typical pattern in advance in directing big business groups, such as independent and specialized management in respective firms' unit. This paper, also, raises the need for introducing new regulations on inter-sectoral diversifications between finance, industry and the press.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.7
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• pp.1-13
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• 2020

This study analyzes the key drivers (commitment, integration of big data, green supply chain management, and green human resource practices) of sustainable capabilities and the influence to which these sustainable capabilities impact the banks' environmental and financial performance. Additionally, this study analyzes the impact of green management practices on the integration of big data technology with operations. The theory of dynamic capability was deployed to propose and empirically test the conceptual model. Data was collected through a self-administrated survey questionnaire from 319 participants employed at 35 banks located in six ASEAN countries. The findings indicate that big data analytics strategies have an impact on internal processes and banks' sustainable and financial performance. This study indicates that banks committed towards proper data monitoring of its clients achieve operational efficiency and sustainability goals. Moreover, our results confirm that banks practising green innovation strategies experience better environmental and economic performance as the employees of these banks have received advance green human resource training. Finally, our study found that internal and external green supply chain management practices have a positive impact on banks' environmental and financial performance, which confirms that ASEAN banks contributing in reduction of environmental impact through its operations will ultimately experience increased financial performance.

• Journal of the Korea Convergence Society
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• v.10 no.6
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• pp.203-218
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• 2019

Using the sample of 104 mutual savings banks inspected by the Financial Supervisory Service (FSS) on June 2011, this study examines if mutual savings banks manage BIS capital adequacy ratio using allowance for bad debts through comparison of BIS capital adequacy ratio before and after the 2011 when mutual savings banks experienced a large-scale restructuring by financial supervisory authorities. We find that mutual savings banks mainly use the allowance for bad debts to manage BIS capital adequacy ratio. It also shows that mutual savings banks with a business suspension order by FSS manage BIS capital adequacy ratio more than the others. Lastly, we find that Non Big4 auditors as well as Big 4 auditors don't effectively audit the use of the allowance for bad debts for mutual savings banks to manage their BIS capital adequacy ratio.

• Advanced Industrial SCIence
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• v.3 no.2
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• pp.17-22
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• 2024

Recently, as the ICT field has been used in various environments, it has become possible to analyze pests by crops, use robots when harvesting crops, and predict by big data by utilizing ICT technologies in a sustainable agricultural environment. However, in a sustainable agricultural environment, efforts to solve resource depletion, agricultural population decline, poverty increase, and environmental destruction are constantly being demanded. This paper proposes an artificial intelligence-based big data processing analysis method to reduce the production cost and increase the efficiency of crops based on a sustainable agricultural environment. The proposed technique strengthens the security and reliability of data by processing big data of crops combined with AI, and enables better decision-making and business value extraction. It can lead to innovative changes in various industries and fields and promote the development of data-oriented business models. During the experiment, the proposed technique gave an accurate answer to only a small amount of data, and at a farm site where it is difficult to tag the correct answer one by one, the performance similar to that of learning with a large amount of correct answer data (with an error rate within 0.05) was found.

• Journal of Korean Society for Quality Management
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• v.51 no.4
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• pp.551-571
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• 2023

Purpose: The purpose of this study is to propose useful suggestions by analyzing the causal effect relationship between the failure rate of quality and the process variables in the C5ISR domain of the defense industry. Methods: The collected data through the in house Systems were analyzed using Big data analysis. Data analysis between quality data and A/S history data was conducted using the CRISP-DM(Cross-Industry Standard Process for Data Mining) analysis process. Results: The results of this study are as follows: After evaluating the performance of candidate models for the influence of inspection data and A/S history data, logistic regression was selected as the final model because it performed relatively well compared to the decision tree with an accuracy of 82%/67% and an AUC of 0.66/0.57. Based on this model, we estimated the coefficients using 'R', a data analysis tool, and found that a specific variable(continuous maximum discharge current time) had a statistically significant effect on the A/S quality failure rate and it was analysed that 82% of the failure rate could be predicted. Conclusion: As the first case of applying big data analysis to quality issues in the defense industry, this study confirms that it is possible to improve the market failure rates of defense products by focusing on the measured values of the main causes of failures derived through the big data analysis process, and identifies improvements, such as the number of data samples and data collection limitations, to be addressed in subsequent studies for a more reliable analysis model.