• Informatization Policy
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• v.28 no.1
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• pp.43-63
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• 2021

The purpose of this research is to examine the relationships among data literacy, organizational culture, and data analytics maturity and the moderating effects of organizational culture. Analysis of the relationship between data literacy and data analytics maturity shows that the higher the data literacy competency of employees, the higher the organization's data analytics maturity. In examining the relationship between organizational culture and data analytics maturity, it is found that relationship culture and innovation culture are positively related to data analytics maturity. In addition, relationship culture and hierarchy culture show significant moderating effects. Relationship culture shows a synergistic effect, whereas hierarchy culture has a buffer effect between data literacy and data analytics maturity.

• Asia pacific journal of information systems
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• v.32 no.1
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• pp.70-91
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• 2022

Organizations today depend and thrive on timely, accurate and strategically relevant information. Business analytics (BA) holds the key to many of these issues. This paper validates a model on how the usage of BA leads to operational efficiency. We identified the factors of basic analytical usage from the Business Capacity Maturity Model (BCMM). The scope of the study is restricted to the Information Technology Infrastructure and Application management domain. A survey was conducted among the managers of the IT companies in Bengaluru, India. The results showed a significant influence of data-oriented culture and BA tools and infrastructure on BA usage. We found a significant influence of BA usage and pervasive use on operational efficiency. The speed to insight is still not practised in organizations. The awareness level of analytical skills in organizations is very low.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.2
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• pp.54-63
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• 2016

As technology has developed and cost for data processing has reduced, big data market has grown bigger. Developed countries such as the United States have constantly invested in big data industry and achieved some remarkable results like improving advertisement effects and getting patents for customer service. Every company aims to achieve long-term survival and profit maximization, but it needs to establish a good strategy, considering current industrial conditions so that it can accomplish its goal in big data industry. However, since domestic big data industry is at its initial stage, local companies lack systematic method to establish competitive strategy. Therefore, this research aims to help local companies diagnose their big data capabilities through a reference model and big data capability assessment system. Big data reference model consists of five maturity levels such as Ad hoc, Repeatable, Defined, Managed and Optimizing and five key dimensions such as Organization, Resources, Infrastructure, People, and Analytics. Big data assessment system is planned based on the reference model's key factors. In the Organization area, there are 4 key diagnosis factors, big data leadership, big data strategy, analytical culture and data governance. In Resource area, there are 3 factors, data management, data integrity and data security/privacy. In Infrastructure area, there are 2 factors, big data platform and data management technology. In People area, there are 3 factors, training, big data skills and business-IT alignment. In Analytics area, there are 2 factors, data analysis and data visualization. These reference model and assessment system would be a useful guideline for local companies.

• Journal of Information Technology Applications and Management
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• v.31 no.3
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• pp.165-179
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• 2024

Although firms with advanced analytics and machine learning (which is often called AI) capabilities are considered to be highly successful in the market by making decisions and actions based on quantitative analysis using data, the scarcity of historical data and the lack of right data infrastructure are the problems for the organizations to perform such projects. The objective of this study, is to identify a road map for the organization to reach data capability maturity to become AI matured organizations. First, this study defines the terms, AI capability, data capability and AI matured organization. Then using content analyses, organizations' data practices performed for AI system development and operation are analyzed to infer a data capability roadmap to become an AI matured organization.

• Management & Information Systems Review
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• v.36 no.1
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• pp.273-290
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• 2017

Internet of Things (IoT) is considered to be the next wave of Information Technology transformation after the Internet has changed the process of doing business. Since the domain of IoT ranging from the sensor technology to service to the users is wide, the structure of the research domain is not delineated clearly. To do that we suggest to use the Technology Stack Model proposed by Porter et al.(2014) to measure the maturity level of IoT in organizations. Based on the Stack Model, for the general understandings of IoT, we do keyword analyses on the academic papers whose major research issue is IoT. It is found that the current status of IoT application from the perspectives of cloud and big data analytics is not active, meaning that the real value of IoT has not been realized. We also examine the cases which deal with the part of cloud process which is crucial for value accrual. Based on these findings, we suggest the future direction of IoT research. We also propose that IT is to value chain what IoT is to the Stack Model to derive value in organizations.

• Journal of Korean Society for Quality Management
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• v.49 no.4
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• pp.447-466
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• 2021

Purpose: This article aims 1) to propose Quality 4.0 concept through surveying related literature, 2) to suggest key elements of Quality 4.0 by arranging the elements of Quality 4.0 that appeared in the literature, 3) to determine the levels of Quality 4.0, and 4) to suggest ideas for effective deployment of Quality 4.0. Methods: Eleven papers or documents are reviewed for Quality 4.0 concept; two papers and one document are investigated for key element extraction of Quality 4.0; and smart factory roadmap and industry 4.0 maturity model are studied to determine the levels of Quality 4.0. Results: 1) Quality 4.0 definition is proposed. 2) Three key elements are determined: data acquisition and analytics, connection and integration, and leadership and culture. 3) Six Quality 4.0 levels are determined. 4) Some suggestions are addressed for effective deployment of Quality 4.0. Conclusion: 1) Definition, key elements, levels, and some suggestions on effective deployment of Quality 4.0 are addressed. 2) Specific contents of Quality 4.0 education and training courses should be provided in the future. 3) Two future research directions are proposed.