International Journal of Business and Data Analytics
These articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.
Forthcoming articles must be purchased for the purposes of research, teaching and private study only. These articles can be cited using the expression "in press". For example: Smith, J. (in press). Article Title. Journal Title.
Articles marked with this shopping trolley icon are available for purchase - click on the icon to send an email request to purchase.
International Journal of Business and Data Analytics (5 papers in press)
A New Method for Predicting Stock Market Crashes Using Classification and Artificial Neural Networks by Saeed Tabar, Sushil Sharma, David Volkman Abstract: The stock market prediction is an interesting topic, especially for traders and investors. One important aspect of predicting the stock market is identifying price patterns which may result in a market crash. With the advancement of computer technology, particularly in the area of artificial intelligence, a large number of new models have been proposed. The proposed method in this article is based on identifying the normal behaviour of a crowd in the stock market using exponential moving average and then classifying the price fluctuations into three categories BUY, SELL, and STOP. An artificial neural network (ANN) with five input neurons, ten hidden neurons, and three output neurons is then used to learn from the price fluctuations and predict one day ahead. The final results show that the algorithm is capable of identifying the market crashes in advance by issuing STOP labels. Keywords: Artificial Neural Networks; Classification; Stock Market Prediction; Market Crash; Crowd Behavior. DOI: 10.1504/IJBDA.2019.10023276
A Study on Data Mining tools directed towards modern day Automobile Industries by Anirudh Ganesh Sriraam Abstract: The genesis of Industry 4.0 has brought with it a plethora of opportunities to use Big Data Analytics in the manufacturing sector. The customers increasing demand for customisation has led to increasingly complex manufacturing layouts. As most of the work in major manufacturing plant is done using robots, there is a gamut of sources of data. This data has never been utilized to its full potential. It has been used to monitor the statuss of production mostly and has helped in ad-hoc maintenance scenarios. The purpose of this paper is to elucidate upon certain ways to increase efficiency of a big manufacturing plant using methods like Data Mining Association Rules and Multiple regression. In addition, this paper can be referred as a detailed tutorial on how to tackle huge data sets incoming from a large automobile manufacturing organization and all the factors that need to be taken in consideration. Keywords: Apriori Algorithm; Multiple regression; Process Optimisation; Business Intelligence; Data Analytics; Suspected Operational Causes; Quality Improvement; Downtime reduction. DOI: 10.1504/IJBDA.2019.10028534
Application of an extended CBBE model with particular reference to Private Car Insurance Providers in India by Joyee Chatterjee Abstract: This paper attempts to evaluate a new CBBE model, an extension of Netmeyers CBBE model in the context of private car insurance providers in India. Erstwhile researches with regards to measuring CBBE do not consider certain aspects such as attitude towards the brand i.e. both hedonic and utilitarian in nature. This research evaluates the relationship between various variables such as brand attitude, perceived quality, perceived value, uniqueness of the brand, satisfaction and loyalty intention along with CBBE. The paper validates Taylors model with reference to private car insurance companies in India. The research also establishes that Hedonic brand attitude wields a significant impact on building CBBE as opposed to utilitarian attitude and brand uniqueness. Keywords: CBBE; Brand Attitude; Perceived Quality; Perceived Brand Value; Customer Perception; Loyalty; Customer Satisfaction. DOI: 10.1504/IJBDA.2019.10028549
Supply chain inventory stockout prediction using machine learning classifiers by Dony S. Kurian, C.R. Maneesh, V. Madhusudanan Pillai Abstract: Inventory stockouts occurring in supply chain systems are expensive and it is common in distribution systems. Nowadays, organisations are interested in making use of predictive inventory analytics to reduce stockouts and thereby achieving competitive advantage. Predicting the periods where stockout occurs will help the organisations to take preventive measures and to improve the overall supply chain performance. In this paper, machine learning classifiers are used to predict the occurrence of stockout in a period, and are proposed for the members of a four-stage serial supply chain that operates on order-up-to (OUT) inventory policy. Initially, the classifier models are trained and tested using the data collected through a spreadsheet-based simulation experiment, and performance of the classifiers are then evaluated. Application of the prediction model shows that the supply chain operated using OUT policy and stockout prediction outperforms the same supply chain operated using OUT policy alone. Keywords: supply chain; inventory stockout prediction; classification; machine learning; ensemble methods. DOI: 10.1504/IJBDA.2019.10028793
Impact of business student satisfaction on their
intention to stay in higher education by Rajani Kumari Sarangal, Meenakshi Nargotra, Rabinder Singh Abstract: Students are considered as the primary customers for the educational market. It has, therefore, become essential for the higher education market to satisfy and retain their students. Thus, the purpose of this study is to explore the key factors of student satisfaction in the higher education sector. It also studies the impact of student satisfaction on student intention to stay within the select institutes. The results of the study show that student satisfaction has a positive and significant impact on their intention to stay. Further, the impact of an individual dimension of student satisfaction, i.e., faculty ability and support, and faculty expertise have a positive and significant impact on intention to stay but faculty-student relationship dimension has a positively insignificant impact on student intention to stay. The present study will be useful for management to develop strategies for student satisfaction and
retention. Keywords: business student satisfaction; faculty ability and support; faculty expertise; faculty-student relationship; intention to stay; higher education. DOI: 10.1504/IJBDA.2020.10030378