This article attempts to develop a model by integrating interpretive structural modeling (ISM) and quality function deployment (QFD) methodology by establishing the relationship between the Indian... Show More

This article attempts to develop a model by integrating interpretive structural modeling (ISM) and quality function deployment (QFD) methodology by establishing the relationship between the Indian retail service quality dimensions and service quality enablers. The integrated approach is employed to translate customers' requirements/needs into specific service design factors/requirements in the Indian retail context. The retail service quality dimensions are identified using factor analysis and are considered as the customer demands in QFD process. Thirteen retail enablers were identified through an extensive literature survey and expert opinions. The enablers identified for the study were treated as design requirement for employing quality function deployment (QFD) in order to prioritize the design requirements. The results found showed that retail enablers ‘Image of the Store' and ‘Value Conscious Consumers' can be emphasized more in a priority basis by the Indian retailers followed by retail enablers ‘Location of store' and ‘Globalization/Competition'.

Customer segmentation is the process of forming smaller groups of customers according to their characteristics. Now companies can develop proper marketing strategies for each group to get the... Show More

Customer segmentation is the process of forming smaller groups of customers according to their characteristics. Now companies can develop proper marketing strategies for each group to get the desired results. This type of direct marketing is practiced by most organizations from the size of smallest start-up to the Fortune 500 leaders. Clustering is the ideal data mining technique for customer segmentation. In this article, the authors have proposed a clustering algorithm based on the self-organizing map and minimum spanning tree for customer segmentation. The authors have used several synthetic and real-life datasets to evaluate the clustering performance of their approach. To demonstrate the effectiveness of the authors' proposed approach, they have trained few classifiers with the groups extracted from a direct marketing campaign of a Portuguese banking institution and show that the classification accuracy is better compared to the results obtained in some previous work where the full dataset has been used to train the same classifiers.

Behavioural finance has gained research interest among researchers because of investor behavior and market anomalies. Investor behaviour varies with demographics and geographic characteristics.... Show More

Behavioural finance has gained research interest among researchers because of investor behavior and market anomalies. Investor behaviour varies with demographics and geographic characteristics. Further, investor behavior towards a gold exchange trade fund is gaining research interest due to various factors. Not much research has been carried out in this direction, with the exception of some comparisons. Therefore, the performance of a gold exchange traded fund needs to be assessed from the investor behavior perspective. Additionally, the investors behavior contains uncertainties. Thus, there is a need for intelligent techniques for identifying the investors behavior despite the presence of uncertain behavioral characteristics. Therefore, to study uncertain behavior characteristic in gold exchange traded fund, in this article the authors employ a fuzzy rough set. They employ fuzzy rough quick reduct algorithm to find the superfluous attributes. Further decision rules are generated to identify the chief feature of investors' behavior towards gold exchange traded fund.

Sentiment analysis manages the computational treatment of conclusion, notion, and content subjectivity. In this article, three sentiment classes such as positive, negative and neutral emotions have... Show More

Sentiment analysis manages the computational treatment of conclusion, notion, and content subjectivity. In this article, three sentiment classes such as positive, negative and neutral emotions have been demonstrated by appropriate features from raw unstructured data followed by data preprocessing steps. Applying best in class social analytics methodology to examine the sentiments embedded with purchaser remarks, encourages both producer and individual customers. Machine learning methods such as Naïve Bayes, maximum entropy classification, Deep Neural Networks were used upon the data, extracted from some websites such as Samsung and Apple for sentiment classification. In the online business arena, the application of sentiment classification explores a great opportunity. The subsidy of such an investigation is that associations can apply the proposed social examination framework to exploit the entire social information on the web and therefore improve their proper blueprint promoting strategies corresponding business.

Currency counterfeiting is a serious matter of concern for government and national finance organizations. The policy makers have formed a zero tolerance policy for counterfeiting, but still the... Show More

Currency counterfeiting is a serious matter of concern for government and national finance organizations. The policy makers have formed a zero tolerance policy for counterfeiting, but still the incidence of counterfeit notes is becoming an alarming situation. The counterfeited banknotes reduce the value of real money and an increase in the money supply is not just a solution, because it leads to inflation. Counterfeiting poses tangible as well as intangible impacts and affects business organizations and thereby a nation's economy. Therefore, the research study aims to identify the factors that affects the counterfeiting in Indian banknotes. Questionnaire as a data collection instrument was used to collect data from the north-west region of Delhi. The factors were then identified through exploratory factor analysis and standardized using a confirmatory factor analysis. At last, the relative effect of each factor was measured using multiple regression analysis as a part of confirmatory factor analysis. IBM SPSS 20 and IBM AMOS is used for factor analysis.