• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.1
• /
• pp.391-406
• /
• 2015

Social media have emerged as new communication channels between consumers and companies that generate a large volume of unstructured text data. This social media content, which contains consumers' opinions and interests, is recognized as valuable material from which businesses can mine useful information; consequently, many researchers have reported on opinion-mining frameworks, methods, techniques, and tools for business intelligence over various industries. These studies sometimes focused on how to use opinion mining in business fields or emphasized methods of analyzing content to achieve results that are more accurate. They also considered how to visualize the results to ensure easier understanding. However, we found that such approaches are often technically complex and insufficiently user-friendly to help with business decisions and planning. Therefore, in this study we attempt to formulate a more comprehensive and practical methodology to conduct social media opinion mining and apply our methodology to a case study of the oldest instant noodle product in Korea. We also present graphical tools and visualized outputs that include volume and sentiment graphs, time-series graphs, a topic word cloud, a heat map, and a valence tree map with a classification. Our resources are from public-domain social media content such as blogs, forum messages, and news articles that we analyze with natural language processing, statistics, and graphics packages in the freeware R project environment. We believe our methodology and visualization outputs can provide a practical and reliable guide for immediate use, not just in the food industry but other industries as well.

• Journal of Distribution Science
• /
• v.18 no.10
• /
• pp.65-75
• /
• 2020

Purpose: This study aims to examine how to review contents of experiential and utilitarian products (e.g., skincare products) and how to affect review helpfulness by applying natural language processing techniques. Research design, data, and methodology: This study uses 69,633 online reviews generated for the products registered at Amazon.com by 13 Korean cosmetic firms. The authors identify key topics that emerge about consumers' use of skincare products such as skin type and skin trouble, by applying bigram analysis. The review content variables are included in the review helpfulness model, including other important determinants. Results: The estimation results support the positive effect of review extremity and content on the helpfulness. In particular, the reviewer's skin type information was recognized as highly useful when presented together as a basis for high-rated reviews. Moreover, the content related to skin issues positively affects review helpfulness. Conclusions: The positive relationship between extreme reviews and helpfulness of reviews challenges the findings from prior literature. This result implies that an in-depth study of the effect of product types on review helpfulness is needed. Furthermore, a positive effect of review content on helpfulness suggests that applying big data analytics can provide meaningful customer insights in the online retail industry.

• Journal of Information Technology and Architecture
• /
• v.10 no.3
• /
• pp.387-397
• /
• 2013

This paper proposes an approach to big data analytics for phon call data. The analytical models for phon call data is composed of the PVPF (Parallel Variable-length Phrase Finding) algorithm for identifying verbal phrases of natural language and the word count algorithm for measuring the usage frequency of keywords. In the proposed model, we identify words using the PVPF algorithm, and measure the usage frequency of the identified words using word count algorithm in MapReduce. The results can be interpreted from various viewpoints. We design and implement the model based HDFS (Hadoop Distributed File System), verify the proposed approach through a case study of phon call data. So we extract useful results through analysis of keyword correlation and usage frequency.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2019.05a
• /
• pp.83-86
• /
• 2019

This study proposes a methodology for predicting co-authorship of contributors to a highly anticipated paper through link analysis and learning, taking into account the result of collaborative research. Previous studies predict the co-authorship with high accuracy, but this shows limitations in that the quality of the predicted relationship is not considered. Therefore, to solve the above problem, we propose three steps to predict the co-authorship that will help with the expected performance: (1) Construct a heterogeneous graph to measure results of collaborative research. (2) Analyze and learn links based on results of collaborative research. (3) Predict links that are anticipated to have high expectation. It is expected to be useful for increasing confidence in the predicted co-authorship.

• Journal of Distribution Science
• /
• v.20 no.12
• /
• pp.89-98
• /
• 2022

Purpose: Due to the rapid growth of e-commerce in Thailand, the operational excellence of distribution service providers has been elevated. Thus, this research investigated the key drivers of operational performance of e-commerce distributors in Thailand. The research contains key variables: the analytics capabilities of an organization, supply chain disruption orientation, innovation capability, and operational performance. Research design, data, and methodology: An online survey is administered to top managers and key personnel (N=425) employed for at least one year in Thailand's top five e-commerce distributors. The sampling methods were conducted using purposive sampling, quota sampling, and convenience sampling. Confirmatory Factor Analysis and Structural Equation Model were applied to analyze and confirm the model's goodness-of-fit and hypothesis testing. Results: The findings reveal that an organization's analytics capabilities significantly affect supply chain disruption orientation and supply chain resilience. Furthermore, operational performance is affected by supply chain disruption, supplier quality management, and innovation capability. Nevertheless, supply chain resilience and digital supply chain have no significant effect on operational performance. Conclusions: The results imply that supply chain digitalization could drive higher operational performance. Distribution businesses are encountering transformation and disruption, which should address the high level of a digital supply chain, innovation, and quality management to maximize their profit margin and delivery service quality.

• International Journal of Computer Science & Network Security
• /
• v.22 no.11
• /
• pp.204-212
• /
• 2022

In this paper, a contribution is presented covering the data set in improving and developing electronic services provided to citizens through e-government services based on business intelligence in government agencies in the Republic of Sudan. The Business Intelligence Concept Survey was conducted from the perceptions of information department employees in government agencies. The survey was conducted from April to June 2021 using questionnaires. The dataset contains responses about the factors that influence the use of business intelligence and the barriers and limitations to the use of business intelligence. A five-point Likert scale was used to analyze the quantitative data. The opportunities and challenges associated with it were also discussed and explored. As evidenced by the results, the information department employees agree that business intelligence improves the government decision-making process, which helps decision makers and decision-makers to find alternatives and opportunities that contribute to making more accurate and timely decisions. The results also indicate that creating the infrastructure for applying business intelligence in the e-government work model contributes to the successful implementation of business intelligence in Sudan.

• International Journal of Computer Science & Network Security
• /
• v.23 no.7
• /
• pp.71-78
• /
• 2023

Employee turnover is one of the most important challenges facing modern organizations. It causes job experiences and skills such as distinguished faculty members in universities, rare-specialized doctors, innovative engineers, and senior administrators. HR analytics has enhanced the area of data analytics to an extent that institutions can figure out their employees' characteristics; where inaccuracy leads to incorrect decision making. This paper aims to develop a novel model that can help decision-makers to classify the problem of Employee Turnover. By using feature selection methods: Information Gain and Chi-Square, the most important four features have been extracted from the dataset. These features are over time, job level, salary, and years in the organization. As one of the important results of this research, these features should be planned carefully to keep organizations their employees as valuable assets. The proposed model based on machine learning algorithms. Classification algorithms were used to implement the model such as Decision Tree, SVM, Random Frost, Neuronal Network, and Naive Bayes. The model was trained and tested by using a dataset that consists of 1470 records and 25 features. To develop the research model, many experiments had been conducted to find the best one. Based on implementation results, the Neural Network algorithm is selected as the best one with an Accuracy of 84 percents and AUC (ROC) 74 percents. By validation mechanism, the model is acceptable and reliable to help origination decision-makers to manage their employees in a good manner.

• Asia pacific journal of information systems
• /
• v.33 no.3
• /
• pp.677-701
• /
• 2023

With the rapid advancement of social media analytics and artificial intelligence, many studies have used online customer reviews as an important source to measure service quality in many industries, including the hotel industry. However, these studies have failed to identify the relative importance of different dimensions of service quality and their role in customer satisfaction. To fill this research gap, this study aims to identify the effects of service quality on hotel customer satisfaction from the multidimensional perspectives using sentiment analysis with self-training on online reviews. Additionally, the moderating role of the brand of origin for each service quality dimension is also investigated. Drawing on the SERVQUAL model and brand of origin concept, this study develops 12 hypotheses and empirically tests them using 30,070 online customer hotel reviews collected from TripAdvisor.com. The results indicated that overall service quality and each dimension of SERVQUAL significantly influenced customer satisfaction of hotels. The results also confirmed the moderating effects of brand of origin on overall service quality. However, the moderating effects of brand of origin for the tangible, reliability, and empathy dimensions of service quality were significant, whereas the effects for responsiveness and assurance were not. This study sheds new light on service quality measurement by analyzing the multidimensional features of service quality and the role of brand of origin in the hotel service context.

• Journal of Advanced Navigation Technology
• /
• v.18 no.5
• /
• pp.524-531
• /
• 2014

In this paper, we have proposed the processing and analyzing the linked open data (LOD), a kind of big-data, using resources of cloud computing. The LOD is web-based open data in order to share and recycle of public data. Specially, we defined the InfograaS (Info-graphic as a service), new business area of SaaS (software as a service), to support visualization technique for BA (business analytics) and Info-graphic. The goal of this study is easily to use it by the non-specialist and beginner without experts of visualization and business analysis. Data visualization is the process to represent visually and understand the data analysis easily. The purpose of data visualization is to deliver information clearly and effectively by chart and figure. The big data of public data are shared and presented in the charts and the graphics understood easily by various processing results using Hadoop, R, machine learning, and data mining of open source and resources of cloud computing.

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