• 유통과학연구
• /
• 제22권8호
• /
• pp.65-76
• /
• 2024

Purpose: The study aims to recognize the relationship between big data analytics capabilities, big data analytics process, and perceived business performance from supply chain management to total customer satisfaction. Research design, data and methodology: The study followed a quantitative approach with a descriptive design. The data was collected from leading e-commerce companies in India using a structured questionnaire, and the data was coded and decoded using MS Excel, SPSS, and R language. It was further tested using Cronbach's alpha, KMO, and Bartlett's test for reliability and internal consistency. Results: The results showed that the big data analytics process acts as a robust mediator between big data analytics capabilities and perceived business performance. The 'direct, indirect and total effect of the model' and 'PLS-SEM model' showed that the big data analytics process directly impacts business performance. Conclusions: A complete indirect relationship exists between big data analytics capabilities and perceived business performance through the big data analytics process. The research contributesto e-commerce companies' understanding of the importance of big data analytics capabilities and processes.

• Educational Technology International
• /
• 제24권2호
• /
• pp.295-329
• /
• 2023

The recent development of metaverse-related technology has led to efforts to overcome the limitations of time and space in education by creating a virtual educational environment. To make use of this platform efficiently, applying learning analytics has been proposed as an optimal instructional and learning decision support approach to address these issues by identifying specific rules and patterns generated from learning data, and providing a systematic framework as a guideline to instructors. To achieve this, we employed an inductive, bottom-up approach for framework modeling. During the modeling process, based on the activity system model, we specifically derived the fundamental components of the learning analytics framework centered on learning activities and their contexts. We developed a prototype of the framework through deduplication, categorization, and proceduralization from the components, and refined the learning analytics framework into a 7-stage framework suitable for application in the metaverse through 3 steps of Delphi surveys. Lastly, through a framework model evaluation consisting of seven items, we validated the metaverse learning analytics framework, ensuring its validity.

• 정보관리학회지
• /
• 제34권4호
• /
• pp.33-57
• /
• 2017

본 연구는 새로운 분석법으로 떠오르는 처방적 분석 기법을 소개하고, 이를 분류 기반의 시스템에 효율적으로 적용하는 방안을 제시하는 것을 목적으로 한다. 처방적 분석 기법은 분석의 결과를 제시함과 동시에 최적화된 결과가 나오기까지의 과정 및 다른 선택지까지 제공한다. 새로운 개념의 분석 기법을 도입함으로써 문헌 분류를 기반으로 하는 응용 시스템을 더욱 쉽게 최적화하고 효율적으로 운영하는 방안을 제시하였다. 최적화의 과정을 시뮬레이션하기 위해, 대용량의 학술문헌을 수집하고 기준 분류 체계에 따라 자동 분류를 실시하였다. 처방적 분석 개념을 적용하는 과정에서 대용량의 문헌 분류를 위한 동적 자동 분류 기법과 학문 분야의 지적 구조 분석 기법을 동시에 활용하였다. 실험의 결과로 효과적으로 서비스 분류 체계를 수정하고 재적용할 수 있는 몇 가지 최적화 시나리오를 효율적으로 도출할 수 있음을 보여 주었다.

• 유통과학연구
• /
• 제21권1호
• /
• pp.53-63
• /
• 2023

Purpose: Big Data analytics (BDA) has been recognized to improve firm performance because it can efficiently manage and process large-scale, wide variety, and complex data structures. This study examines the determinants of Big Data analytics adoption toward marketing and financial performance of global logistic companies in Thailand. The research framework is adopted from the technology-organization-environment (TOE) model, including technological factors (relative advantages), organizational factors (technological infrastructure and absorptive capability), environmental factors (industry competition and government support), Big Data analytics adoption, marketing performance, and financial performance. Research design, data, and methodology: A quantitative method is applied by distributing the survey to 450 employees at the manager's level and above. The sampling methods include judgmental, stratified random, and convenience sampling. The data were analyzed by Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM). Results: The results showed that all factors significantly influence Big Data analytics adoption, except technological infrastructure. In addition, Big Data analytics adoption significantly influences marketing and financial performance. Conversely, marketing performance has no significant influence on financial performance. Conclusions: The findings of this study can contribute to the strategic improvement of firm performance through Big Data analytics adoption in the logistics, distribution, and supply chain industries.

• ETRI Journal
• /
• 제44권1호
• /
• pp.135-146
• /
• 2022

The importance of big data analytics has become apparent with the increasing volume of data on the Internet. The amount of data will increase even more with the widespread use of Internet of Things (IoT). One of the most important application areas of the IoT is healthcare. This study introduces new real-time data analytics architecture for an IoT-based smart healthcare system, which consists of a wireless sensor network and a radio-frequency identification technology in a vertical domain. The proposed platform also includes high-performance data analytics tools, such as Kafka, Spark, MongoDB, and NodeJS, in a horizontal domain. To investigate the performance of the system developed, a diagnosis of Wolff-Parkinson-White syndrome by logistic regression is discussed. The results show that the proposed IoT data analytics system can successfully process health data in real-time with an accuracy rate of 95% and it can handle large volumes of data. The developed system also communicates with a riverbed modeler using Transmission Control Protocol (TCP) to model any IoT-enabling technology. Therefore, the proposed architecture can be used as a time-saving experimental environment for any IoT-based system.

• 디지털콘텐츠학회 논문지
• /
• 제13권2호
• /
• pp.205-211
• /
• 2012

본 논문은 기업의 마케팅 캠페인 실행 성과를 향상시키기 위한 디지털 마케팅 모델에 대한 연구 결과를 제시하고 있다. 최근 ERP, CRM, SCM 등 비즈니스 가치 사슬 프로세스를 개선하기 위한 프로젝트를 마친 기업들은 마케팅 프로세스를 전사적으로 개선하기 위한 작업을 진행하고 있다. 기존 마케팅 기법의 한계를 극복하기 위해서 디지털 마케팅 기법을 활용하기 위한 시도가 많다. 특히 마케팅 프로세스의 실행 단계인 캠페인 수행성과를 향상시키기 위해 디지털 마케팅 기법들을 적용하고 있다. 이에 본 논문에서는 디지털 마케팅 연구 모델과 연구 가설을 수립하고, 마케팅 전문가 설문 조사를 통한 통계적 분석 방법을 통해 검증하고자 하였다. 연구를 통해서 디지털 마케팅 모델 중에서 웹 분석, 소셜 분석, 개인 맞춤형 고객 관계 분석, 캠페인 실행 자동화, 실시간 캠페인 관리 등의 기법이 기업의 마케팅 캠페인 실행 성과에 영향을 미치는 것을 실증적으로 검증하였다.

• Asia pacific journal of information systems
• /
• 제30권4호
• /
• pp.879-918
• /
• 2020

One of the challenges faced by Talent Acquisition teams today pertains to the acquisition of human resources by matching job descriptions and skillsets desired. It is more so in the case of competitive sectors like the Indian IT sector. There can be various channels for Talent Acquisition and accordingly, the cost and benefits might vary. However, the consequences of a mismatch have an impact on the quality of deliverables, high recruitment expenses and loss of revenue for the organization. With increased and diverse sources of data that are available to organizations today, there is ample opportunity to apply analytics for informed decision making in this field. This paper reveals useful insights that help streamline the Talent Acquisition process in the Indian IT Industry. The paper adopts a data-centric approach to examine the critical determinants for efficient and effective Talent Acquisition process in IT organizations. Selected supervised machine learning algorithms are applied for the analysis of the dataset. The study is likely to help organizations in reassessing their talent acquisition strategy with respect to key parameters like expected cost to company (CTC), candidate sourcing channels and optimal joining period.

• Journal of Information Technology Applications and Management
• /
• 제22권1호
• /
• pp.37-60
• /
• 2015

As the dependence on information systems in enterprises has grown dramatically, the importance of implementing information systems in audit has been increased as well. However, there is a lact of about utilization of information system for audit process. Thus, this study is to investigate the factors that effect auditor's adopting Data Analytics to audit work. Through literature research and focus group interview, we added two factors that affect the behavioral intention to UTAUT model. We have selected performance expectancy, effort expectancy, social influence, facilitating conditions, anxiety, task fit, behavioral intention as variables and verified hypotheses based on survey questionnaires from auditors. As a result, it was found that performance expectations, social influence, task fit influenced the behavior intention. In Addition, we analyzed adding two variables, IT-related work experience and type of auditor as moderate variable. This study has an implication for companies to motivate implementation as well as activation of Data Analytics technique.

• Journal of Information Processing Systems
• /
• 제15권6호
• /
• pp.1259-1264
• /
• 2019

Nowadays, cloud computing and big data analytics are at the center of many industries' concerns to take advantage of the potential benefits of building future smart cities. The integration of cloud computing and big data analytics is the main reason for massive adoption in many organizations, avoiding the potential complexities of on-premise big data systems. With these two technologies, the manufacturing industry, healthcare system, education, academe, etc. are developing rapidly, and they will offer various benefits to expand their domains. In this issue, we present a summary of 18 high-quality accepted articles following a rigorous review process in the field of cloud computing and big data analytics.

• 산업경영시스템학회지
• /
• 제42권4호
• /
• pp.84-90
• /
• 2019

Lately, there have been tremendous shifts in the business technology landscape. Advances in cloud technology and mobile applications have enabled businesses and IT users to interact in entirely new ways. One of the most rapidly growing technologies in this sphere is business intelligence, and associated concepts such as big data and data mining. BI is the collection of systems and products that have been implemented in various business practices, but not the information derived from the systems and products. On the other hand, big data has come to mean various things to different people. When comparing big data vs business intelligence, some people use the term big data when referring to the size of data, while others use the term in reference to specific approaches to analytics. As the volume of data grows, businesses will also ask more questions to better understand the data analytics process. As a result, the analysis team will have to keep up with the rising demands on the infrastructure that supports analytics applications brought by these additional requirements. It's also a good way to ascertain if we have built a valuable analysis system. Thus, Business Intelligence and Big Data technology can be adapted to the business' changing requirements, if they prove to be highly valuable to business environment.