• The Journal of Information Systems
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• v.30 no.1
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• pp.21-44
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• 2021

Purpose The opportunities and approaches to big data have grown in various ways in the digital era. Business analytics is nowadays an inevitable strategy for organizations to earn a competitive advantage in order to survive in the challenged environments. The purpose of this study is to analyze the intellectual structure of business analytics literature to have a better insight for the organizations to the field. Design/methodology/approach This research analyzed with the data extracted from the database Web of Science. Total of 427 documents and 23,760 references are inserted into the analysis program CiteSpace. Author co-citation analysis is used to analyze the intellectual structure of the business analytics. We performed clustering analysis, burst detection and timeline analysis with the data. Findings We identified seven sub- areas of business analytics field. The top four sub-areas are "Big Data Analytics Infrastructure", "Performance Management System", "Interactive Exploration", and "Supply Chain Management". We also identified the top 5 references with the strongest citation bursts including Trkman et al.(2010) and Davenport(2006). Through timeline analysis we interpret the clusters that are expected to be the trend subjects in the future. Lastly, limitation and further research suggestion are discussed as concluding remarks.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.10
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• pp.129-135
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• 2022

This study aims to identify the factors supporting and inhibiting the digitalization of blue economy MSMEs in Bitung, Indonesia. The literature shows little research on digitalization related to the blue economy in Southeast Asia, especially in Indonesia. This indicates that there is a large research gap related to digitalization and the blue economy in the Indonesian context. Data was collected through the distribution of questionnaires with open-ended questions to blue economy MSMEs. Data was also obtained from in-depth interviews with representatives of Aruna, an Indonesian company that focuses on simplifying the supply chain of fishery products by connecting small-scale fishers to the global market through technology. According to the study's findings, two primary factors-motivation to develop their business and efforts to maintain seller-buyer interaction-support SMEs' use of technology in the blue economy. However, digital literacy and technological infrastructure, such as the internet network, are the two main factors that become obstacles in the effort to digitize MSMEs in the blue economy. The role of the government is also a contingent factor that can strengthen the relationship between factors that support digitization and weaken the relationship between factors that hinder digitalization.

• Journal of Distribution Science
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• v.21 no.2
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• pp.35-43
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• 2023

Purpose: As Thailand seeks to become a regional startup hub, Thai startups have been acquiring growth and scalability in the last ten years. Hence, this paper examines influential factors in Thailand's growth of logistics tech startups. The conceptual framework incorporates sensing user needs, sensing technological options, conceptualizing, scaling, and stretching, co-producing, and orchestrating, business strategy, strategic flexibility, and startup growth. Research design, data, and methodology: The quantitative method was applied to distribute the questionnaire to 500 managers and above in logistics tech startups in Thailand. The sampling techniques involve judgmental, convenience, and snowball samplings. Before the data collection, The Item Objective Congruence (IOC) Index and pilot test (n=45) were employed for content validity and reliability. The data were mainly analyzed by Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM). Results: The findings revealed that sensing technological options, scaling, and stretching, co-producing, and orchestrating, and business strategy significantly influence the growth of startups in Thailand. Nevertheless, sensing user needs, conceptualizing, and strategic flexibility have no significant relationship with startup growth. Conclusions: For Thailand to accelerate its digital economy driven by tech startups, firms must emphasize influential factors to accelerate growth by providing the right tech solutions for people's lives.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.91-100
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• 2022

Major developed countries are seriously considering smart factories to increase their manufacturing competitiveness. Smart factory is a customized factory that incorporates ICT in the entire process from product planning to design, distribution and sales. This can reduce production costs and respond flexibly to the consumer market. The smart factory converts physical signals into digital signals, connects machines, parts, factories, manufacturing processes, people, and supply chain partners in the factory to each other, and uses the collected data to enable the smart factory platform to operate intelligently. Enhancing personalized value is the key. Therefore, it can be said that the success or failure of a smart factory depends on whether big data is secured and utilized. Standardized communication and collaboration are required to smoothly acquire big data inside and outside the factory in the smart factory, and the use of big data can be maximized through big data analysis. This study examines big data analysis and standardization in smart factory. Manufacturing innovation by country, smart factory construction framework, smart factory implementation key elements, big data analysis and visualization, etc. will be reviewed first. Through this, we propose services such as big data infrastructure construction process, big data platform components, big data modeling, big data quality management components, big data standardization, and big data implementation consulting that can be suggested when building big data infrastructure in smart factories. It is expected that this proposal can be a guide for building big data infrastructure for companies that want to introduce a smart factory.

• The Journal of Industrial Distribution & Business
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• v.15 no.6
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• pp.25-32
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• 2024

Purpose: This research explores to (1) identify the leading challenges that the beauty care industry faces currently, which includes sustainable development, ethics, and industry laws, (2) describe how these challenges impact industries' practices and customer experience, and (3) propose plausible strategies to address these issues in an effort to enhance sustainability, ethical business practices, and compliance with legal norms in the beauty care industry. Research design, data and methodology: The research approach used is the systematic literature review approach to identify the relevant literature that addresses the current challenges in the beauty care industry and to assess the results of prior studies. Results: The finding indicated the following solutions to handle the current issues in the beauty industry: Solution to (1) Environmental Impact: Sustainable Production and Packaging, (2) Ethical Concerns: Enhancing Supply Chain Transparency, (3) Regulatory Challenges: Proactive Compliance and International Standardization, and (4) Technological Challenges: Personalization and Digital Engagement. Conclusion: Based on the conclusions made in the findings' section, this research examines the implications of the solutions to provide an insight into how the strategies can guide future practices in the beauty care industry. It also points out how these insights can be applied by industry practitioners to improve sector operational and strategic performance.

• Information Systems Review
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• v.6 no.2
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• pp.227-242
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• 2004

With the advent of the digital economy, the business eco-system has been changing from the competition system across individual companies to that of supply chains. Under the rapidly changing business environment, it becomes true that the competitive power of the steel maker depends on the shipbuilding company, an important customer of the steel maker. Accordingly, e-partnering between a steel maker and shipbuilding companies becomes important. Schemes of developing e-partnering are presented as follows: implementation of the inter-communication system, day/sequence order and supply, improvement of infrastructure such as transport, quay etc.

• Journal of Digital Convergence
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• v.12 no.4
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• pp.145-155
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• 2014

POSCO has been chosen as the most competitive steelmaker in the world, for the 4th year in a row. It's potentials and key success points lay at technology innovation as well as effective partnerships with suppliers and customers. Partnership with suppliers is based upon the effective supply chain management. While ERP system supports the standard business work flows, the biggest impact on business performance is created by exceptions and variability. A SCM Advanced Planning System reduces the amount of exceptional situations, helping to keep business in a standard mode of operation. A case summary on SCM Advanced Planning System of POSCO as well as its impact on firm performances is presented. As a supplement to this case study, we also investigate the employees' perceived level of SCM-related factors, including information sharing, collaboration, incentive system for suppliers and their impacts on SCM performances. As a conclusion, the practical implications of these findings are discussed.

• Journal of Digital Convergence
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• v.11 no.2
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• pp.91-99
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• 2013

The purpose of this study is to examine the value relevance of SCM using a regression model and we analyze the differences in the impact of industry type on corporate value. First, SCM key performance variables(asset utilization, cash flow, corporate growth, profitability) increases, the corporate value increase. Second, Asset utilization, cash flow, corporate growth in the high-tech industry showed a significant impact on the corporate value and corporate growth and profitability have an impact on the firm value in the non high-tech industry. This study are expected to be able to provide policy implications in the development of government policy to enable support for win-win cooperation, and ensuring the justification demonstrated by analyzing the impact of SCM enterprise value of the companies that want to maximize the effectiveness of SCM introduced.

• Journal of Korea Society of Digital Industry and Information Management
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• v.12 no.1
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• pp.1-12
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• 2016

The current economic crisis is making new demands on manufacturing industry, in particular, in terms of the flexibility and efficiency of production processes. This requires production and administrative processes to be meshed with each other by means of IT systems to optimise the use and capacity utilisation of machines and lines but also to be able to respond rapidly to wrong developments in production and thus to minimise adverse impacts on the business. The future scenario of the "smart factory" represents the zenith of this development. The factory can be modified and expanded at will, combines all components from different manufacturers and enables them to take on context-related tasks autonomously. Integrated user interfaces will still be required at most for basic functionalities. The complex control operations will run wirelessly and ad hoc via mobile terminals such as PDAs or smartphones. The comnination of IoT, and Big Data optimisation is bringing about huge opportunities. these processes are not just limited to manufacturing, anywhere a supply chain environment exists can benefit from information provided by linked devices and access to big data to inform their decision support. Building a smart factory with smart assets at its core means reaching those desired new levels of productivity and efficiency. It means smart products that leverage advanced traceability, connectivity and intelligence. For businesses, it means being able to address the talent crunch through more autonomous. In a Smart Factory, machinery and equipment will have the ability to improve processes through self-optimization and autonomous decision-making.

• Journal of Digital Convergence
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• v.15 no.9
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• pp.167-178
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• 2017

Big Data is a key asset for the company and a key factor in boosting its competitiveness in the logistics sector. However, there is still a lack of research on how to collect, analyze and utilize Big Data in logistics. In this context, this study has developed a value model applicable to logistics companies based on the results of analysis and application of Big Data in the logistics of previous studies and DHL. The purpose of this study is to improve the operational efficiency and customer experience maximization level of logistics companies through utilization of big data analysis in logistics, to improve competitiveness of big data utilization and to develop new business opportunities. This study has a significance to newly create a value model for utilization of big data analysis in logistics sector and can provide implications for other industries as well as logistics sector in the future.