Business Data Analytics Research Articles

The Impact of Television Advertisements on Student Decision-Making Process for College Admission: An exploratory study in India

COVID-19 drove universities throughout the world forcing Higher Education Institutions (HEIs) to go online or virtual. Admissions advertising and student recruitment were two of the sectors that were severely hit. Internet-based advertising has to entirely replace the old advertising environment. Although certain institutions were still able to conduct virtual tours, forums, and information sessions, the amount of involvement differed among colleges and universities. An exploratory study was conducted to investigate how effective Internet advertisements were in influencing students' admission decisions. During the academic year, 2020-21 at several institutions in India's western region, data were collected using the snowball sampling approach on 930 freshly enrolled students. The findings show that the efficacy of Internet ads for university admission is negatively correlated with the student's age group. Surprisingly, the study discovered that both rural and urban students were equally interested in online ads, and that family background had no impact on receptiveness to internet advertisements. (*The paper was presented at the 2nd Conference on Business Data Analytics: Innovation in emerging trends in management data analytics. Apeejay School of Management, Dwarka, Delhi, India. November 2021)

Application of Disruptive Technologies on Environmental Health: An overview of artificial intelligence, blockchain and internet of things

Environmental monitoring technology is a critical component in assisting and ensuring environmental management success. The development patterns of disruptive technologies for environmental monitoring should be studied and anticipated in order to aid in the promotion of India's environmental monitoring and protection. The primary objective is to examine previous research on disruptive technology and its current environmental applications, as well as to determine the degree of connection between the chosen variables, using the Vos Viewer Software, which is being used in this study. A discussion of the existing environmental impacts associated with artificial intelligence (AI), blockchain, and the Internet of Things (IoT), as well as new advancements in these fields, is presented in this article. As a result of the research, the difficulties and possibilities presented by the stated technologies on the entire Indian environmental monitoring network have been highlighted that can be useful for researchers and government for future advancement in environmental health. (*The paper was presented at the 2nd Conference on Business Data Analytics: Innovation in emerging trends in management data analytics. Apeejay School of Management, Dwarka, Delhi, India. November 2021)

In This Issue

This issue is the fourth and final issue of the Journal for 2021. It reflects a widespread interest in the Asia Pacific with 30 articles from 11 countries. These countries include Australia, India, Iran, Indonesia, the Philippines, Saudi Arabia, Bangladesh. Vietnam, Thailand, Turkey and Hong Kong, China. This represents a significant collaboration of healthcare managers, leaders, academics, students, and health professionals from diverse health systems. This is a credit to them all and our reviewers. The topics addressed within the issue continue to be dominated by Covid_19 but are also more extensive than that topic. The topics include leadership, engagement, health literacy, employee performance, job satisfaction, mentoring, , artificial intelligence, big data to name many but not all. This is extensive collection of health management topics that should be of interest. Another continuing feature of this issue is the interest in publishing articles from international conferences. There are articles from four recent conferences. These included the 2021 Shape Symposium conducted from Australia and online, “Shaping the future for health management education and research in a time of flux and uncertainty”, held online in July and available at http://shape.org.au/. The Hong Kong Polytechnic University’s College of Professional and Continuing Education (CPCE) Conference “Post-pandemic health and long-term care: A new paradigm”. September 2021 with online and ‘in presence’ attendance. Two conferences highlighted in this issue are recognised from India with some eight articles published from these conferences. The conferences were the AICTE sponsored International Conference on Circular Economy, Management and Industry Leading towards Sustainability, October 2021 and the 2nd Conference on Business Data Analytics, November 2021 We wish to specifically mention these conferences and recognise the conference organisers and our guest co -editors on this occasion, who assisted us with the 8 selected articles. The guest co-editors are Mr. Anuj Kumar, Assistant Professor, Apeejay School of Management, Dwarka, Delhi, India https://www.apeejay.edu/asm/?utm_source=google&utm_medium=google+business+listing&utm_campaign=organic. (Top PGDM Colleges Delhi NCR) and Dr. Nimit Gupta, Professor, School of Management, The NorthCap University, Gurugram, India https://www.ncuindia.edu/educate-india- society/.

BUSINESS INTELLIGENCE AS A TOOL FOR SUPPORTING DECISION-MAKING IN THE RESTAURANT INDUSTRY

The article considers the concept of «business analytics» in the context of modern tools of the decision-making system of food services, noting the importance of such at the present stage of development of the restaurant industry in the general context of changes in the technological infrastructure of restaurants and catering, and during the coronavirus crisis; have been identified areas of application of business intelligence in improving the efficiency of business processes, strategic management of the restaurant industry and ensuring sustainable competitive advantage in the era of digital transformation. The subject of research – economic and managerial relations that arise in the restaurant industry in the process of implementing business intelligence solutions. The methodology was based on general scientific and special research methods. The importance of business intelligence has been a solution in providing business opportunities in terms of real-time information («real-time information flows») in accordance with modern socio-economic challenges and the permanent complexity of the technological infrastructure of restaurants and the Internet space. It was found that better awareness of consumer and customer inquiries, desires and preferences, as well as knowledge of the potential of partners and contractors, can improve corporate business processes by implementing the latest design and services using the capabilities of business intelligence. Analyzed and determined that strategic solutions developed using business analytics and data analysis transform the restaurant industry, providing managers with valuable solutions to optimize customer service, measures to increase customer engagement and improve menu performance. The business intelligence in the modern restaurant business has been determined the key given the high competition in this area, rapid changes in consumer behavior and customer expectations from restaurants. Further developments on this topical issue will be implemented, in particular, primarily in the preparation of new concepts of restaurants and reformatting the business processes in the recovery of the restaurant industry after the coronavirus crisis.

Multiple Herbicide Use in Cropland: A Discrete-Continuous Model for Stated Choice Data

Andrea Pellegrini, Visiting Research Fellow Centre for Business Intelligence and Data Analytics, Business School, University of Technology, Sydney; Andrea.Pellegrini{at}uts.edu.au

ANALYSIS OF RESPONSIBILITY CENTERS PERFORMANCE IN BUSINESSES BY SYSTEM DYNAMICS METHOD

Today, the high dynamism of the business sector makes it difficult for managers to make decisions about the future of their businesses. Business managers need to keep their businesses and all units of them under control in all aspects in order to make effective decisions. In order to achieve this, responsibility centers and the data produced in these centers are used. Today, analyzing the data produced in the responsibility centers dynamically has become a necessity. System dynamics models that enable dynamic analysis of business data have been used successfully in recent years. System dynamics approach, which is also used in different business sectors, is a simulation method that can be used in solving dynamic problems and developing business strategies that traditional management accounting techniques have difficulty in responding. In this study, a model of a manufacturing enterprise has been created to measure the performance of responsibility centers within the enterprise. The data obtained from this model is evaluated with the help of the model and the future performance of the enterprise is tried to be predicted. Using this model, different scenarios were tried out and the results were compared, and it was revealed that the system dynamics could be used effectively in the evaluation of responsibility centers and business performance.

Transformation in Data Storing Technique- Big Data: A Literature Review

Abstract: Loss of data implies loss of valuable information. An appropriate gathering of data and finding hidden patterns out of it is key for any business organization to thrive fiscally. With exponential rise in the internet users from the early 2000’s, traditional databases fell short to collect the enormous amount of unstructured data/ semi-structured data, which contained extremely insightful information. Today, data accumulated is not only enormous, but also collected with high speed, having variety, which necessitated special database/software for data gathering and making key decisions based on that. These gigantic amounts of data generated can advocate companies to examine the market trends, market demands, and customer expectation, which endorses them to make relevant foremost decisions. This study discusses the stymie in conventional databases to process the immense data and entailment of advanced databases/software. Furthermore, a case study is presented later to understand the role of big data analytics in business and technical firms. Keywords: Big Data, structured data, unstructured data, NoSQL, Hadoop.

Technological Innovation and Circular Economy Practices: Business Strategies to Mitigate the Effects of COVID-19

The Corona Virus Disease 2019 (COVID-19) has been a tough test on companies in the global food sector supply chain, exacerbating the realities and deficiencies it presents in developing economies. This situation has been reflected in the firm’s performance (F.P.) due to the lack of business conditions to respond to the current pandemic. However, in some companies, the adverse effects of COVID-19 have been counteracted due to endowment and technological capabilities. Thus, this study examined the role of technological innovation (T.I.) and business data analytics (B.D.A.) in the F.P. of foods in Ecuador during COVID-19. A questionnaire collected the information from the food firms. Then, Covariance-Based Structural Equation Modeling processed the collected information. We found that (B.D.A.) mechanisms and different levels of T.I. within the developing market significantly shape the F.P. The results showed that the B.D.A. enables circular economy (C.E.) practices and the improvement of product delivery services, which constitutes an improvement of the F.P. The COVID-19 outbreak did not significantly affect T.I., unlike what happened with B.D.A. This study concluded that firms with the most extraordinary technological production processes have been the least affected during COVID-19. This study suggested that policy measures should boost food firms’ technological endowment to improve their resilience in uncertainty and risk scenarios.

Practical Research on Business Data Analysis Course Based on Project Curriculum Reform

The teaching of professional courses in domestic colleges and universities mostly focuses on the teaching of theoretical knowledge, and there is still a lot of room to improve students' practical ability, ability to analyze and solve practical problems and innovation ability. Most of the professional courses in many colleges and universities are cases formed by manual sorting or over processing, which are quite different from the actual project situation. Aiming at the problem that the traditional professional course teaching is out of touch with the practical application ability of enterprises, this paper explores the curriculum reform method of immersing real projects in the teaching process, which is oriented to the needs of enterprises. This study analyzes the teaching background of data analysis courses, describes the content, process and problems of project immersion teaching.

Business Data Analytics Applications to Online Product Reviews and Nationalism

This paper investigates the data analytics between consumer purchase decisions relative to the on-line reviews. The multi-attributes associated with purchase decisions are comprised of nationalism and consumer preference to be correlated with online reviews using big data analytics. By far, a small fraction of meaningful studies have sought to correlate nationalism and ethnocentrism with big data analytics to date. Globally accepted generic products are selected to expedite the process of data engineering. Two sets were arranged: passenger automobiles for transportation with an estimated $9 trillion global market and the smart phone, boosting its market size of approximately $5 billion. Both products provide minimized cultural, linguistic, gender, age, and/or custom barriers of entry for prospective digital consumers, thereby allowing relatively unrestricted engagement with online reviews and purchases. A series of hypothesis tests indicate that there is a positive correlation between nationalism and automobiles. As to smart cell phones, however, nationalism had nominal control factors. Multi-variate analytics were performed by using R and Tableau Public.

Data Mining Technology in Business Data Analysis

With the vigorous development of e-commerce, people pay more and more attention to data mining technology. This research mainly discusses the application of data mining technology in business data analysis. This study uses the clustering algorithm of buyers’ purchasing behavior as an example to illustrate the definition of VMML in the clustering analysis of purchasing behavior. In this piece of XML, through the integration of data, an e-commerce research data warehouse is constructed, which is also used as the source data of EBizDAS. A certain amount of research has been done on buyers, sellers, commodities and transaction data, and the results obtained by EBizDAS analysis are displayed on the C2C e-commerce data analysis web platform for C2C users to browse and reference online. In this study, the 10 queries in CoHadoop took a total of 849 seconds, while the distributed strategy took a total of 748 seconds, with a performance improvement of about 12%, and the overall performance improvement was still considerable. This research has positive significance for the development of e-commerce.

Measuring the Level of Voluntary Disclosure of Information in the Published Annual Financial Reports of Iraqi Banks

The accounting profession has witnessed a qualitative transformation in the last three decades in light of scientific and technical developments, So that it has become an integrated and comprehensive information system that serves all activities and businesses in various sectors, This is where the accounting disclosure has become an essential and effective means in the media and reporting of the outputs of the accounting system and their delivery to all users in the administration, investors and stakeholders, In a way that enhances their ability to make appropriate decisions, to the qualitative transformation that has occurred in the accounting profession, In exchange for the mandatory disclosure concerned with providing the minimum amount of accounting information as determined by the legal frameworks and professional standards, The voluntary disclosure has emerged as a trend that some banks take to disclose the largest amount of information that goes beyond the limits of what they are obligated to do in order to serve the users of financial statements, This, in turn, drew the researchers' attention to conduct many studies and researches concerned with measuring the level of voluntary disclosure in banks. And its role in serving and achieving its strategic objectives, And its role in serving and achieving its strategic objectives, and the problem of the current research is to measure the level of voluntary disclosure of information in the published annual financial reports of Iraqi banks listed on the Iraq Stock Exchange, This is done by developing a tool to measure the level of voluntary disclosure of information in the published annual financial reports In addition, the results of the study showed that the voluntary disclosure of public information about banks comes in first place with a rate of (84.6%). This is followed by management discussions and analysis of business data (83.8%), And then internal control and audits (81.9%). The study recommended the necessity of continuing to raise the level of voluntary disclosure in Iraqi banks, The necessity of increasing attention to the optional disclosure by the Iraqi Securities Commission and the scientific, professional and academic organizations and societies, This is to ensure that users of financial reports have access to additional financial and non-financial information, which helps them to make rational investment decisions.

Leveraging Business Data Analytics and Machine Learning Techniques for Competitive Advantage: Case Study Evidence from Small Businesses

Data analytics is the essential component in deriving insights from data obtained from multiple sources. It represents the technology, methods and techniques used to obtain insights from massive datasets. As data increases, companies are looking for ways to gain relevant business insights underneath layers of data and information, to help them better understand new business ventures, opportunities, business trends and complex challenges. However, to date, while the extensive benefits of business data analytics to large organizations are widely published, micro, small, and medium sized organisations have not fully grasped the potential benefits to be gained from data analytics using machine learning techniques. This study is guided by the research question of how data analytics using machine learning techniques can benefit small businesses. Using the case study method, this paper outlines how small businesses in two different industries i.e. healthcare and retail can leverage data analytics and machine learning techniques to gain competitive advantage from the data. Details on the respective benefits gained by the small business owners featured in the two case studies provide important answers to the research question.

Business Data Analysis Based on Hierarchical Clustering Algorithm in the Context of Big Data

With the rapid development of computer technology, computer technology has been applied to all aspects of the daily field, we are now in an era of data explosion. With the passage of time, the data on the network will become more and more, consumers will not be able to meet their consumption needs in the face of so much data. How to deal with these data is an urgent problem to be solved. Based on this, this paper uses a similar analytic hierarchy process algorithm, hierarchical clustering algorithm, to process the data. According to the browsing and consumption records of consumers, the algorithm can recommend the goods that consumers want to buy and the price of goods they want to buy. In the research, this paper analyzes the business data of consumers by hierarchical clustering analysis, and classifies people with different consumption levels into a large group, analyzes the purchasing tendency of consumers and the price of goods they want to buy, and then recommends products to consumers. The experimental results show that the analysis results of this paper using hierarchical clustering algorithm are consistent with the results of selling goods on the online platform. Therefore, according to the experimental results of this paper, businesses can promote more suitable products for consumers.

The Importance of Human Domain Knowledge and Business Data Analytics to Support Modern Financial Decisions

The purpose of this study is to identify how importance is human domain knowledge and business data analytics to support modern financial decision. Understanding whether social media narratives could provide a value-add to current customer relationship management practices could be quite valuable. Design/methodology/approach-An analysis of the literature was undertaken and based on an assessment of the literature, conceptual states and pragmatic approaches as well as existing theoretical understandings and frameworks. An explorative case study approach based on Yin’s design will be utilized as a framework as well as a demographic survey to distill even further the characteristics of the sampling from a customer, management and social media user perspective. Furthermore, a customer relationship management framework which would include the adjoining of data analytics and social media narratives will be discussed in context of the research findings. This will help researchers and practitioners more readily explore the shared value framework which the study will be based and contribute to a more fulsome consideration of customer relationship management practice shifts within a technological and social media-oriented age. The contributions of this research will also help reiterate the importance of context in data management as well as the importance of the paradigmatic power shifts reflected in consumer usage of social media, product or service offerings, social consciousness and ethical practice as it relates to the influence of consumer intentions and subsequent purchasing intentions.The purpose of this qualitative exploratory case study was to gain common understandings of how importance is human domain knowledge and business data analytics to support modern financial decision. In order to support reliable and valid research, a purposive sample of customer relations managers, business analysts who have customer relations management (CRM) roles, and customers who utilize social media for the purposes of product or service development was attained.

K-means clustering for the analysis of incomplete business data

Missing values can significantly reduce the accuracy and availability of business data. Usually, when clustering incomplete data, the data with missing values are deleted, and only the complete data are analyzed. However, this often leads to significant loss or deviation of information. This paper mainly studies how to use unsupervised machine learning techniques to deal with missing values. The combination of imputation method and clustering technology forms a new method to deal with missing values, which is helpful to overcome the problem of missing data. We propose a strategy based on the combination of K-means, big data K-means, p-k-means, and mean imputation method, singular value decomposition imputation method, k-nearest neighbor imputation method. By comparing the performance of nine methods in different business data sets. The experimental analysis was carried out on four benchmark data sets. The effectiveness of K-means clustering and imputation methods is verified on different data sets, and the results also have a certain application prospect.

Enhancing change mining from a collection of event logs: Merging and Filtering approaches

An event log is the key element of all change mining and process mining approaches. Those approaches bridge the gap between conventional business process management and data analysis techniques such as machine learning and data mining. In this day, companies and business organizations usually use a family of business processes that may face different variations and adjustments. Still, those processes are widely identical, with a slight difference in specific points. Consequently, performing a process mining or a change mining for each process will be a redundant task. The use of a configurable process model is a practical solution for redundancy problem. Thus, the process mining areas such as discovering verifying the conformity of a business process and enhancing processes, are reduced considerably. However, the configurable process models and the variability concept are rarely introduced in change mining approaches. The existing methods that analyse and manage event logs do not then consider the variability issue. Therefore, the fact of using a collection of event log becomes a challenging task. Our proposed approach is to merge and filter a collection of event logs from the same family with respect to variability. Our goal is to enhance change mining from a collection of event logs and detect changes in variable fragments of the obtained event log.

Application of Laboratory Data Decision Management System

With the rapid development of big data information technology and the in-depth application of network technology, information management has been widely used in the management of colleges and universities. The experimental teaching mode of colleges and universities has been transformed from traditional experience teaching and computer-aided teaching to data-driven teaching mode. On the basis of the original computer laboratory management system, it extends the personalized service functions such as intelligent access control, computer management, laboratory opening and booking, experimental arrangement management, experimental equipment management, experimental data management, attendance management, system management and basic data management. By collecting, storing and analyzing relevant data, a data decision subsystem is established. Data contains a lot of valuable information, which can provide guidance for experimental teaching and teacher teaching, improve the efficiency of experimental teaching, and better serve teachers and students. The purpose of the research and analysis of this system is to solve the problems existing in the traditional management mode, through information management can effectively improve the utilization rate of experimental instruments and equipment; It can reduce the working intensity of the experimentalists and improve the management efficiency. Through research and analysis, determine the system structure including business analysis, functional analysis and data analysis. In the process of business analysis, the original business process analysis and business process optimization analysis are adopted to provide powerful data support for the management of computer experiment teaching in colleges and universities.

A Method for Choosing Suitable Data Visualization in Business Data Analysis Projects

A Method for Choosing Suitable Data Visualization in Business Data Analysis Projects

Problems and Prospects of Accounting Management in the Digital Economy

There is an active digitalization of the collecting processes, consolidating and analyzing business data in the modern economy. Against this background, many classical problems of managerial accounting still remain unresolved, among which the selection of cost objects, the search for optimal methods for calculating costs, and the integration of various types and levels of accounting among themselves. The goal of the paper is to reveal key features of management accounting development in modern enterprises. The methodological basis of the work is a systematic approach, reflected in the idea of management accounting as a multi-level system that combines methodological, organizational and infrastructural aspect. The result of the study is the disclosure of the imbalance problems associated with increased attention to higher levels of management accounting without deepening the study of lower levels of business data. There are proposed the following directions of management accounting development: further division of accounting objects; expanding the possibilities of solving the problems of calculating costs due to modern technologies; integration of cost accounting with process models, which maintained in companies today outside the framework of accounting management. The results may be of interest to researchers, practitioners in the financial management and IT fields