International Journal of Business and Data Analytics (7 papers in press)
3 years - 3 moves Government verdicts to renovate Customary Bharat to Contemporary India: Evaluation of opinions from citizens
by Riktesh Srivastava
Abstract: Data is an important asset for the government. Enormous data from numerous sources are collected by the government every day. An assortment of data is one trait, deriving meaningful insights from these data is also important. The emergence of various analytics algorithms can turn these data into actionable information. In other words, Big Data Analytics can create a link between effective governance and improved service delivery. The two types of data collected by the government are Structured and Unstructured. Structured data are collected through operational work carried by government departments and gets stored in traditional row/column databases. Governments are already using these data for decision making and is thus beyond the choice of study. Other forms of data collected are unstructured data, where data is not stored in traditional databases, but now forms an integral form of decision making for governments, are the ones collected from social media, websites and other online media. With the number of online media mounting, Government of India (GOI) too identified the need to integrate it for direct engagement with citizens. The study is initiated on usage of 8 such online media, where, the opinions concerning 3 historic moves - Digital India, Demonetization, and GST are collected and analyzed. These 8 online media include 6 Hindi News channels (with the news published in YouTube) and also news published on 2 English Newspaper. The texts collected are evaluated based on Profile Of Mood States (POMS) tagging and captures the opinion analysis on through attitudes users, based on the news posted.
Keywords: Digital India; Demonetization; GST; Profile of Mode States; opinion analysis; Bayes algorithm.
Analysis of Mediation Effect of Country-Of-Origin Image on Brand Equity
by Vishal Jain, Shalini Bariar
Abstract: Marketers are required to study consumer psyche for the development of sustainable competitive advantage over other brands. One of the most significant challenges for marketing professionals is the ability to create, maintain and enhance brands equity, as it is the most critical indicator of the product performance. The purpose of this research is to investigate a theoretical framework in which brands country-of-origin image is suggested to influence the level of brand equity. In addition, the relationships between brand equity and its dimensions are varying in nature for different products and markets. The present study attempts to explore the impact of brands country-of-origin image on the development of brand equity of haircare products. It also studies the mediation effects of various dimensions of brand equity. The dimensions of brand equity studied in the current research are brand loyalty, brand awareness, brand association and perceived quality. The brand equity and its dimensions of hair care products are examined to accomplish the appropriate results. The study shows that, in the absence of mediators, the country-of-origin image has a positive and significant influence on brand equity. However, in the presence of mediators, the relationship between country-of-origin image and brand equity becomes insignificant. It shows mediation effect of country-of-origin image on brand equity. The present study will be useful to create the right message for the target customers and enjoy the customer preferences.
Keywords: Country-of-origin Image; Brand Equity; Brand Loyalty; Brand Awareness; Brand Association; Perceived Quality.
Sustained Business Competitive Advantage with Data Analytics
by Adarsh Garg
Abstract: In todays highly networked and digital business, data is the only gold element with extensive capability to create business value which we have never seen in the traditionally executed business. This new business world is stimulated by analytics and its ability to make the most of data for sustained competitive advantage. The organizations need to adapt new technologies to prevent the wearing out of the existing knowledge and sustain competitively for a long period according to the increasing customer demands. Analytics has the power to forecast future happenings based on some data which is available to the organization, so making use of the data with analytics. However, new technology, here Data Analytics, alone will be of little use if the strategic managers lack to understand the business context in which the forecasted knowledge be useful and help in sustained competitive advantage. A strong framework needs to be in place to integrate analytics with the knowledge base of the organization along with skilled managers. This research paper is an attempt to provide a theoretical framework and its empirical analysis to apply analytics effectively so as to remove uncertainties in business for its sustainability.
Keywords: Data analytics; Competitive advantage; Sustainability; Technology.
SEARCHING EFFICIENT ESTIMATOR OF POPULATION VARIANCE USING TRI-MEAN AND THIRD QUARTILE OF AUXILIARY VARIABLE
by S.K. Yadav, Dinesh Sharma, S.S. Mishra
Abstract: This paper concerns with the estimation of population variance of study variable using tri-mean and third quartile of the auxiliary variable. In this study, the sampling properties, bias and mean squared error of the proposed estimator are demonstrated. The justification of the performance of the proposed estimator under SRSWOR has been made with reference to the competing estimators of population variance, the sample variance, Isaki (1983) estimator, the estimator due to Upadhyaya and Singh (1999), Kadilar and Cingi (2006) estimators, Subramani and Kumarpandiyan (2012) estimators, Khan and Shabbir (2013) estimator and Maqbool and Javaid (2017) estimator of population variance. Based on data provided by Murthy (1967), it has been demonstrated that the proposed estimator has shown a significant improvement over all competing estimators of population variance.
Keywords: Study variable; Auxiliary variable; Bias; Mean squared error; Efficiency; Tri-mean; Third quartile.
Multiple Testing in the World of Business When and How?
by Nicolle Clements
Abstract: Multiplicity of data and compounding errors is often overlooked in data analysis for applied business scenarios. Statistical theory around multiple testing provides a framework for describing appropriate error rates and offers methods to control them in order to protect against wrong conclusions. However, these multiple testing procedures are often misunderstood and underutilized in applied business problems. In this article, existing multiple testing methodologies are reviewed and summarized. Specific numeric examples are shown to illustrate the techniques and demonstrate the statistical power of each. Finally, three cases are given of business-related situations when multiple testing can be overlooked in data analysis.
Keywords: Multiple Testing; Familywise Error Rate; False Discovery Rate; Type I Error; Business Analytics.
A Study to Enhance Candidate Screening Process Using Similarity Analysis
by Anshul Ujlayan, Manisha Sharma
Abstract: Recruitment process is always continuous and open positions mostly have short time to get filled thereby for any such open position, organizations generally have to deal with higher incremental costs. Many advanced recruitment processes/methodologies are directly associated with the risk of failure, loss of effort, loss of time and money. Every organization in any industry always aims to hire the best suitable candidate to the open position with right skillsets in the shortest possible time and with the lowest costs. Therefore, there is a need to experiment and explore the existing approaches, which can help in reducing the time and the screening effort in initially stage of recruitment practices. Consequently this paper attempts to implement the similarity analysis approach for a random sample of resumes from IT sector in NCR (National Capital Region), India to provide the best match of candidates' profile as per the required job description. The Latent Dirichlet allocation (LDA) is used to find the similarity between job description and candidates profile. The study will help the IT Industry in identifying and selecting the best matched candidates profile based on the key features in job description.
Keywords: Recruitment Process; Similarity Analysis; Latent Dirichlet Allocation; Candidates’ Profile; IT Sector.
ANALYZING THE RELATIONSHIP BETWEEN IDIOSYNCRATIC RISK & STRATEGIC CAPABILITIES USING PENALTY BASED SELECTION AND SHRINKAGE METHODS
by Sudhakar Raju, Wenbin Sun
Abstract: Recent research has documented the dramatic increase in idiosyncratic risk and the under-diversification of portfolios. We provide a unique marketing perspective to the financial risk management literature by suggesting that idiosyncratic volatility can be reduced by enhancing marketing, operational and R&D capabilities. We investigate the relationship between idiosyncratic risk, firm capabilities and financial control variables using the LASSO (Least Absolute Selection and Shrinkage Operator) - a penalty based, variable selection and shrinkage technique developed in the context of "machine learning" and "big data" that has not been much used in the empirical marketing literature. Our results differ from those reported in the literature. Using the more stringent criterion imposed by the LASSO, we find that whereas R&D, marketing and operational capabilities have no statistically significant individual effects, the interactive effects between marketing capability and R&D intensity have a significant effect on reducing idiosyncratic risk.
Keywords: Idiosyncratic Risk; Marketing Capability; R&D Intensity,Operational Capability; LASSO (Least Absolute Selection and Shrinkage Operator); Machine Learning; Big Data.