Showing papers in "International Journal of Business Intelligence Research in 2020"
TL;DR: In this article, the authors provide a literature review to systematize the research made in business analytics information systems in the hospitality industry, which can help identify different research attributes and the most relevant theories developed in the past two decades related to business analytics tools.
Abstract: With the growth of data generated by all systems involved in a hotel, terms like big data and business analytics (BA) gain strength within the hotel industry. Business analytics can be used in hospitality management to increase business knowledge and to improve the decision-making process. This study's main questions are: RQ1 – Which are the main research attributes studied in the past two decades related to analytics in the hospitality sector? RQ2 – What are the main differences between business intelligence and business analytics? RQ3 – What are the main trends in business analytics? RQ4 – Which are the main business intelligence perceptions and beliefs? To answer these research questions, this article provides a literature review to systematize the research made in business analytics information systems in the hospitality industry. The results can help identify different research attributes and the most relevant theories developed in the past two decades related to business analytics tools.
7 citations
TL;DR: This research presents a meta-analyses that shows that not only are data-driven organizations likely to be more successful than or better connected to their users, but also that they are more likely to have a positive impact on the quality of their users' lives.
Abstract: Becoming a data-driven organization is a vision for several organizations. It has been frequently mentioned in the literature that data-driven organizations are likely to be more successful than or ...
5 citations
TL;DR: This study uses a recommendation and sentiment classification model for analyzing the data of beer product based on online beer reviews and rating dataset of beer products and uses them to improve the recommendation performance of the recommendation model for different customer needs.
Abstract: Accurate analysis and recommendation on products based on online reviews and rating data play an important role in precisely targeting suitable consumer segmentations and therefore can promote merchandise sales. This study uses a recommendation and sentiment classification model for analyzing the data of beer product based on online beer reviews and rating dataset of beer products and uses them to improve the recommendation performance of the recommendation model for different customer needs. Among them, the beer recommendation is based on rating data; 10 classification models are compared in text sentiment analysis, including the conventional machine learning models and deep learning models. Combining the two analyses can increase the credibility of the recommended beer and help increase beer sales. The experiment proves that this method can filter the products with more negative reviews in the recommendation algorithm and improve user acceptance.
4 citations
TL;DR: A meta-algorithmic modelling framework for processing internal business policies based on three natural language processing techniques, namely information extraction, automatic summarization, and automatic keyword extraction is introduced.
Abstract: In a time when the employment of natural language processing techniques in domains such as biomedicine, national security, finance, and law is flourishing, this study takes a deep look at its application in policy documents. Besides providing an overview of the current state of the literature that treats these concepts, the authors implement a set of natural language processing techniques on internal bank policies. The implementation of these techniques, together with the results that derive from the experiments and expert evaluation, introduce a meta-algorithmic modelling framework for processing internal business policies. This framework relies on three natural language processing techniques, namely information extraction, automatic summarization, and automatic keyword extraction. For the reference extraction and keyword extraction tasks, the authors calculated precision, recall, and F-scores. For the former, the researchers obtained 0.99, 0.84, and 0.89; for the latter, this research obtained 0.79, 0.87, and 0.83, respectively. Finally, the summary extraction approach was positively evaluated using a qualitative assessment. KeywoRDS Applied Data Science, Automatic Summarization, Financial Industry, Information Extraction, Keyword Extraction, Natural Language Processing, Policy Documents
4 citations
TL;DR: The level of trust organizations have in their analytics tools and how these tools have changed their decision-making processes are examined to add to the broad understanding of how and where data analytics tools fit into the data-driven organization.
Abstract: The use of data analytics of all kinds is making inroads into almost all industries. There are many studies that explore the usefulness and organizational benefits of these tools. However, there ha...
4 citations
TL;DR: It is necessary to have a better understanding of customers’ needs and to predict their demands and reach out to reach their satisfaction.
Abstract: Companies can incur heavy losses when customers do not return; therefore, they need to have a better understanding of their customers’ behaviors in order to improve service and products. And nowadays, there are multiple resources of customers’ data especially with web services, such as websites, chatbots, emails, social media, PoS, ERP, CRM, SCM, therefore, it becomes difficult to collect all this huge data altogether and analyze it manually This paper highlights the role of business intelligence in improving the relationship with the customers, and explores the techniques used to analyze customers’ data in order to predict their demands and reach their satisfaction. KeYwoRdS Business Performance Management, CRM, Customer Retention, Data Mining, Data Warehouse, ETL, OLAM, OLAP
4 citations
TL;DR: A distributed data mining model using access-control architecture in a bid to realize the needs for an online CRM that intends to deliver web content to a specific group of customers is presented.
Abstract: The rising adoption of e-CRM strategies in marketing and customer relationship management has necessitated to more needs especially where a specific customer segment is targeted and the services are personalized. This paper presents a distributed data mining model using access-control architecture in a bid to realize the needs for an online CRM that intends to deliver web content to a specific group of customers. This hybrid model utilizes the integration of the mobile agent and client server technologies that could easily be updated from the already existing web platforms. The model allows the management team to derive insights from the operations of the system since it focuses on e-personalization and web intelligence hence presenting a better approach for decision support among organizations. To achieve this, a software approach made of access-control functions, data mining algorithms, customer-profiling capability, dynamic web page creation, and a rule-based system is utilized.
3 citations
TL;DR: This document presents the results of a study on human resource management and HRM/HRM/SPSS decision-making.
Abstract: This article presents the results of an exploratory study of the use of business intelligence (BI) tools to help to make decisions about human resources management in Portuguese organizations. The purpose of this article is to analyze the effective use of BI tools in integrating reports, analytics, dashboards, and metrics, which impacts on the decision making the process of human resource managers. The methodology approach was quantitative based on the results of a survey to 43 human resource managers and technicians. The data analysis technique was correlation coefficient and regression analysis performed by IBM SPSS software. It was also applied qualitative analysis based on a focus group to identify the impacts of business intelligence on the human resources strategies of Portuguese companies. The findings of this study are that: business intelligence is positively associated with HRM decision-making, and business intelligence will significantly predict HRM decision making. The research also examines the process of the information gathered with BI tools from the human resources information system on the decisions of the human resources managers and that impacts the performance of the organizations. The study also gives indications about the practices and gaps, both in terms of human resources management and in processes related to business intelligence (BI) tools. It points out the different factors that must work together to facilitate effective decision-making. The article is structured as follows: a literature review concerning the use of the business intelligence concept and tools and the link between BI and human resources management, methodology, and the main findings and conclusions. KeywoRdS Business Intelligence, Decision making, Human Resources Management, Human Resources Metrics
2 citations