International Journal of Business Intelligence and Systems Engineering
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International Journal of Business Intelligence and Systems Engineering (4 papers in press)
EVALUATING THE OPERATING EFFICIENCY OF AIRLINES IN THE UNITED STATES by D.K. Malhotra Abstract: This study benchmarks the six largest airline companies in the United States against one another using linear programming technique of data envelopment analysis (DEA). We evaluate their operating efficiencies for the period 2013 to 2017. The DEA model uses well-performing airlines (efficiency of 1 or 100%) that are closest to the under-performing airline on the efficiency frontier as a role model (peer units) for the under-performing airline. We find that only one of the six airlines, Jet Blue Airways Corporation, was 100% efficient throughout the sample period of 2013 to 2017. All six major U.S. airlines showed improvement in efficiency from 2013 to 2017. Keywords: operating efficiency; data envelopment analysis; and airline industry.
Public Services Data Analytics using Artificial Intelligence Solutions derived from Telecommunications Systems by Christophe Gaie, Markus Mueck Abstract: AbstractIn the present article, we propose to bridge twornworlds as we suggest to repurpose artificial intelligence solutionsrnoriginally developed for telecommunications systems to the fieldrnof tax fraud detection by government administrations. ThernEuropean Telecommunications Standards Institute (ETSI) hasrnindeed recently published a related architecture GrouprnSpecification which introduces a number of building blocks and arngeneral high level approach enabling automated data analysisrnand related decision making in the context of large scalerncommunication systems. Keywords: Data Analytics; Fraud Detection; Artificial Intelligence; Tax Recovery; Optimization; Telecommunications.
Predicting the acceptance of UPI based mobile payment system by using logistic regression and artificial neural networks-A Study of Indian Engineering Students by Ajay Kumar, Gaurav Agrawal, Amandeep Singh Abstract: The article develops a predictive model of acceptance of UPI based payment system by utilizing primary datasets collected from the young engineering students of India. Initially, some prominent factors affecting mobile payment have been identified from the literature, and then by using the chi-squared test, some relevant factors are extracted. As the output variable is binary in nature, so the logistic regression model has been used to predict the acceptance of UPI based mobile payment system. Finally, the Artificial Neural network (ANN) has been employed to overcome the limitations of logistic regression as ANN works better when the data are non-linear. The final model consists of nine factors, and the final predictive ability was 82.7%. The factors are also ranked as per their usefulness. The study may help the service providers to make a convenient system that may be trustworthy and easy to use for the end-users. Keywords: Mobile Payment System; Arti?cial Neural network (ANN); Logistics Regression;rnMultilayer perceptron (MLP); electronic commerce.
Factors Influencing Adoption of Business Intelligence in South African National Government Departments by Fhatuwani Netshifhefhe, Tope Samuel Adeyelure, Osden Jokonya Abstract: Business Intelligence (BI) solutions offer enormous varying benefits such as decision support, business performance monitoring, and an enterprise-wide central point of data for both the private and public sector organizations. However, with data growing rapidly, BI has become more of a necessity for organizations to draw insights, gain competitive advantage, and to make fact-based decisions timeously. Although the public sector domain deals with voluminous data, which is expanding rapidly yearly, the uptake or adoption of business intelligence solutions has not taken off. This study investigated the factors that influence the adoption of business intelligence in the national government departments of South Africa. The outcome of this study provides insights into which factors and their correlations influence the adoption of BI in the public sector. Keywords: National Government Departments; Business Intelligence; Adoption.