An exponential growth in the Indian e-commerce sector has attracted many national and international online retailers. Besides, price and promotional schemes, these e-retailers use website features-informativeness, entertainment and effectiveness of content, to differentiate from other players. This paper attempts to decipher the influence of hedonic and utilitarian shopping values and web atmospheric cues on e-satisfaction. From the survey findings, it is observed that web cues have greater influence in shaping e-satisfaction. Further, within the input variables effectiveness of information content has the largest influence on e-satisfaction. The study also highlights the importance of mediating role of e-satisfaction between shopping values, website features and e-loyalty. The paper suggests select marketing strategies for e-retailers.

In this paper we examine the stock market co-movement and volatility spillover dynamics in the Pacific developed markets for a period spanning over January 05, 2001 to January 09, 2018. We employ wavelet-based techniques to study the multiscale co-movement dynamics of stock returns. Additionally, we also study the subtleties of volatility spillover of returns among the sample countries. We find that: (a) diversification benefits in these markets are limited due to higher degrees of integration, (b) Pacific developed markets co-move strongly during the periods of financial crisis (i.e. the contagion hypothesis) and (c) higher degree of volatility spills during financial crisis. We believe our study holds significance in the perspective of international portfolio diversification.

The paper presents a modified visual clustering method for patients visiting diagnostic units (DUs) using self-organizing map approach. The clustering of patients in homogenous groups helps healthcare managers in efficient scheduling of patients in each homogenous group such that their waiting time can be minimized. The grouping of patients would also help in enhancing coordination among diagnostic units (DUs). Modified Kohonen's self-organizing map (SOM) was used to solve the visual clustering problem. Two distinct cases for patients visiting diagnostic units of clustering problems were solved in this paper. In the first case, patients are allowed to visit DU's in any order of sequence. In the second case, patients are allowed to visit DUs based on a predefined sequence. Numerical experiments were conducted using randomly generated data sets. Finally, performance of modified visual SOM approach was measured using grouping efficiency for the first case and group technology efficiency for the second case.

We attempt to establish the relationship between Economic Policy Uncertainty (EPU) and international tourist footfalls in the USA. In the first stage, we investigate the influence of country-specific EPU and global EPU on tourist footfalls. Since, these two are overlapping in nature, in the second stage, we study the isolated influence of country-specific EPU on footfalls by eliminating the influence of global EPU and vice versa. We consider a study period spanning over January 1997 to April 2017. To capture the variations in the relationship at different time dimensions, we apply wavelet-based techniques. We observe the following: (a) the impact of policy uncertainty shock has a little immediate impact on tourist footfalls, (b) medium to long-run shocks persist due to occurrence of major undesirable economic events, and (c) the influence of domestic (country-specific) EPU is dominant in comparison to global EPU for the USA.

This study assesses the cross-country co-movements of gold spot returns among the major gold consuming countries in Asia using wavelet-based analysis for a dataset spanning over 26 years. Wavelet-based analysis is used since it allows measuring co-movements in a time-frequency space. The results suggest intense and positive co-movements in Asia after the Asian financial crisis of 1997 at all frequencies. In addition, the Asian gold spot markets depict a state of impending perfect market integration. Finally, Thailand emerges as the potential market leader in all wavelet scales except one, which is led by India. The study has important implications for international diversification of a single-asset (gold) portfolio.

Development of predictive modeling framework for observational data, exhibiting nonlinear and random characteristics, is a challenging task. In this study, a neoteric framework comprising tools of nonlinear dynamics and machine learning has been presented to carry out predictive modeling and assessing causal interrelationships of financial markets. Fractal analysis and recurrent quantification analysis are two components of nonlinear dynamics that have been applied to comprehend the evolutional dynamics of the markets in order to distinguish between a perfect random series and a biased one. Subsequently, three machine learning algorithms, namely random forest, boosting and group method of data handling, have been adopted for forecasting the future figures. Apart from proper identification of nature of the pattern and performing predictive modeling, effort has been made to discover long-rung interactions or co-movements among the said markets through Bayesian belief network as well. We have considered daily data of price of crude oil and natural gas, NIFTY energy index, and US dollar-Rupee rate for empirical analyses. Results justify the usage of presented research framework in effective forecasting and modeling causal influence.

The study explored the relationship of the climatic predictor variables such as seasonal temperature and rainfall pattern and non-climatic variable such as area under cultivation with the predictand per capita food grain production. We applied a linear method "Generalized Linear Model" and two non-linear methods "Multivariate Adaptive Regression Spline" and "Generalized Additive Model" to Indian data and assessed the data on basis of their performance in predicting food grain production. It was found that an adaptive version of generalized additive model has yielded the lowest predictive error in terms of lower root mean squared error. Better predictability of food grain production based on climatic factors may necessarily help to anticipate the nation's food grain availability. The forecasts would facilitate scientists, farmers, policy makers, business organizations and the government to formulate appropriate adaptable strategies to cope with the climatic variability influence on food availability.

This study used a quantile autoregressive distributed lag (QARDL) model to capture asymmetric impact of rainfall on food production in India. It was found that the coefficient corresponding to the rainfall in the QARDL increased till the 75th quantile and started decreasing thereafter, though it remained in the positive territory. Another interesting finding is that at the 90th quantile and above the coefficients of rainfall though remained positive was not statistically significant and therefore, the benefit of high rainfall on crop production was not conclusive. However, the impact of other determinants, such as fertilizer and pesticide consumption, is quite uniform over the whole range of the distribution of food grain production.

This paper investigates the relationship between the United Kingdom's (hereafter referred as UK) bilateral intra-industry trade (IIT) and foreign intellectual property rights (IPRs) protections. The empirical investigation is based on pooled UK data and benefits from the theoretical distinction between horizontal and vertical IIT. It also estimates a gravity equation for international trade using both fixed and random effects models. We then extend the analysis by employing the GMM system for dynamic panel models. The principal findings suggest that the UK's IIT is stimulated when the level of a trading partner's IPRs and its imitative ability are considered jointly. However, when IPRs and imitation abilities are considered separately, their disparate effects are not an important factor in determining UK IIT flows.

In this paper, we analyzed changing comovement relationship between prices of crude oil and food crops/food index using a detrended cross-correlation analysis (DCCA) approach proposed by Podobnik and Stanley (2008). In order to detect the changing nature of cross-correlation coefficient over time, we estimated the detrended cross-correlation coefficient on a rolling window and found that cross-correlations were negative before 2004 for most of the items but they increased considerably after the surge in crude oil price in 2007 that caused diversion of food grains for production of biofuels.

This paper attempts to predict food shortages in advance from the analysis of rainfall during the monsoon months along with other inputs used for crop production, such as land used for cereal production, percentage of area covered under irrigation and fertiliser use. We used six binary classification data mining models viz., logistic regression, Multilayer Perceptron, kernel lab-Support Vector Machines, linear discriminant analysis, quadratic discriminant analysis and k-Nearest Neighbors Network, and found that linear discriminant analysis and kernel lab-Support Vector Machines are equally suitable for predicting per capita food shortage with 89.69 % accuracy in overall prediction and 92.06 % accuracy in predicting food shortage (true negative rate). Advance information of food shortage can help policy makers to take remedial measures in order to prevent devastating consequences arising out of food non-availability.

This paper focuses on the product-harm oriented brand crisis situation from the perspective of food safety scandal. Food safety scandals cause the deterioration of consumer confidence and thereby affect consumer food choices. However, there exists a lack of studies that examine the consumer response to a brand involved in a food safety scandal. The main research questions addressed are (a) to explicate a detailed mechanism of the consumer response towards a brand involved in food safety scandal and (b) what prompts a consumer to trust the faulty brand again. Further, this study presents empirical contextualization from a real brand-specific food safety scandal, the 2015 monosodium glutamate scandal of Nestle Maggi noodles in India. The study is based on 308 Indian consumers and employs structural equation modeling to analyze the data. The results show that the emotional attachment with the brand forms the basis of the restoration of brand trust by enhancing the authenticity perception of the brand among consumers. This, in turn, forms the basis for the enhancement of healthiness perception of ingredients in order to restore the purchase intention and mitigate blame attribution among consumers. The results are further analyzed from the brand perspective to outline critical implications toward food brand crisis management. This study is among the initial studies to focus on the aftermath of food safety scandals from the brand and consumer perspective.

The objective of this study is to investigate the dynamic relationship between crude oil and precious metals by employing DCC-GARCH model. The correlation structure obtained from the DCC-GARCH model is used to construct hedging ratios and portfolio weights. The analysis is repeated with ADCC-GARCH to check the robustness of the findings. The results of this study can be summarized as follows: First, there exists a dynamic conditional correlation between crude oil and precious metals. Second, on an average, the dynamic correlation between crude oil and precious metals increased with the advent of recent financial crisis till the effect of the crises subsided. Third, DCC results between crude oil and precious metals suggest that, precious metals seem to behave as a single asset class during the periods of uncertain outcome. Fourth, silver and palladium exhibit better hedging capability than gold.

This paper presents a crowding distance particle swarm optimization technique to optimize the design parameters of deep groove ball bearings. The design optimization problem is multi-objective in nature. The considered objectives are maximizing dynamic and static load bearing capacities and minimizing elasto-hydrodynamic film thickness. The technique is applied to bearings used in transmission system of a tractor for the purpose of farming. Pareto optimal solutions are obtained using the proposed technique. The results obtained from the technique are found to be superior compared with NSGA-II and catalogue values.

Understanding the behavior of corporate credit spread is of paramount importance to understand and manage risk in fixed income securities. This study examines the determinants of corporate credit spread in Indian bond market. By adopting a panel data approach, the study estimates a fixed-effects regression equation to analyze the determinants of corporate credit spread in Indian bond market. The variables used in the study are motivated from structural models and macroeconomic variables and include interest rate variables, stock market variables, inflation and economic growth. The study found that the interest rate variables, stock market variables and inflation are the key variables in explaining the level of corporate credit spread in Indian bond market. The results are robust to the traditional approach of using aggregate yield spread for the whole sample and also when the sample was divided using different industrial classification. Overall, the study highlights the importance of stable interest rate and inflationary environment for the development of Indian corporate bond market.

This paper examines the effect of bank-appointed directors on corporate cash holdings for Indian firms. We build on, and extend, the role of bank-appointed directors as a better monitor to mitigate agency problems, thereby alleviating the financial constraints of the firm. Our results indicate that firms with bank-appointed directors hold less cash compared to firms without bank-appointed directors. Nevertheless, we find greater reduction in cash holdings, because of bank-appointed directors, in firms that are not dependent on bank financing. We show that the above relationship is driven by bankers' ability to alleviate the financial constraints of the firm by improving access to bank and non-banking financial institution loans. We next document that these benefits of a banker's presence on a corporate board are true only for standalone firms, not for firms affiliated with business groups.

This study aims to investigate the manipulation of accounting numbers of Indian firms by deploying the innovative method of Financial Statement Deviation (FSD) score based on the larger distributional properties of natural numbers. We have selected five variables (Revenue, Operating Income, PAT, EPS, DPS) covering all the firms from National Stock Exchange (NSE) and Bombay Stock Exchange (BSE) for the financial years 1990-2017 and conducted the test for aggregated of all years and individual year-wise. FSD scores confirm the manipulation in DPS figures for all firms, for all year's aggregated and individual year wise. For negative firms, all variables have shown a higher level of manipulations and thus confirming the findings of earlier studies.

This paper studies the impact of the nonfinancial disclosure on firm value for a large sample of Indian firms, arguably emerging market with poor investor protections and legal enforcement. We proxy the nonfinancial disclosure by the Bloomberg score on the extent of a firm's Environmental, Social, and Governance disclosures, and find positive valuation effects associated with the nonfinancial disclosure. We find the nonfinancial disclosure is more valuable to standalone firms compared to business group firms. This paper shows that the positive valuation effects associated with the nonfinancial disclosure are attributable to lower cost of funds and higher operating cash flows.

Using a quantile causality approach, we examine the causal relationship among the spot prices of precious metals (gold, silver, platinum and palladium) through mean and variance. This methodology also allows investigation of the causality among precious metals during recessions, booms and normal market states. Employing daily spot price data from April 2000 to July 2016 we found evidence of bi-directional causality in mean and variance among the prices of precious metals. Results indicate a strong causality for the middle quantiles (normal time periods). Robustness of results is also examined by employing weekly spot price data. Overall our results have significant implications for policy makers, portfolio managers and investors.