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An attempt was made to explore the use of nutrition labelling in the Indian context. The purpose of this paper is to study the determinants of healthier food choices, as well as the role of label use in this context.

Data were collected from 150 respondents using the survey method and field experiment. The respondents were enquired about various aspects of label use and were asked to make a choice, from two products in three food product categories, on the basis of real information. Multivariate probit models were built for product choice situations.

Moderate to low use of nutrition labelling was found. Significant differences in label use were found on the basis of gender (Sugar (p = 0.011), Additives (p = 0.014), Proteins (p = 0.03)) and education (Additives (p = 0.002), Colouring agents (p = 0.003), Transfats (p < 0.001)). Higher label use was leading (p = 0.031) to more likelihood of choosing healthier potato chips. Women reported higher label use (p = 0.004) but were choosing relatively unhealthier health supplement (p = 0.003). Effect of price was not unidirectional in terms of making healthier food choices. It was observed that label use is not solely responsible for aiding the choice of healthier food products. Individual characteristics were playing important role in choice of food products.

Findings indicate that merely provision of label information is not adequate for the choice of better food options. Provision of labelling information in simple format and equipping the consumers to make effective use of the same carry importance.

The paper is original and makes an attempt to study the effect of label use, along with the individual characteristics, on healthier food choices. Given the availability of few studies in the domain from the emerging markets, the paper adds to the existing body of knowledge.

This study highlights the trends of qualitative intellectual capital disclosures and patent statistics in the Indian manufacturing context by considering the numerous patent applications, patent grants, forward citations and backward citations. Furthermore, the study investigates the relation among qualitative disclosures, patent statistics and firm performance.

All manufacturing companies of CNX 500 Index of National Stock Exchange of India Limited are considered. Based on data availability, 243 manufacturing firms spanning across seven major manufacturing sectors are included. Secondary data were obtained from the annual report of companies and patent databases from 2004 to 2005 to 2013–2014, generating a sample of 2,430 firm years. Content analysis and citation analysis are used for collecting the relevant data.

Overall, the study results indicated increasing trends for all types of intellectual capital disclosures. Similar trends are observed for patent applications and patent grants, indicating a surge in patenting activities across the manufacturing sector. However, increasing trends in patenting activities are not reflected for forward and backward citations. In addition, significant differences in means and trend coefficients for qualitative disclosures and patent statistics indicated industry specificity within the Indian manufacturing sector. Furthermore, industry specificity is observed when translating intellectual capital to firm performance. The measure of firm performance, that is, Tobin's Q, is having a significant positive association with qualitative disclosures and patent statistics.

As the study is based on secondary data, its accuracy is limited by the accuracy of the data sources such as the annual reports of companies and patent databases.

The study findings imply that policymakers should devise and execute sector-specific policy interventions. Moreover, managers and policymakers should emphasize the qualitative aspect of patenting activities.

The study is an original work that highlights the trends in qualitative disclosures in the Indian manufacturing context. The value relevance of intellectual capital and patent statistics has been established.

The study examines herding in the Indian stock and commodity futures market including agricultural, metal and energy commodities. Herding is studied under various market conditions: rising and declining, high and low volatility. The study also examines spillover effects of herding.

The study adapts the cross-sectional absolute deviation model given by Chang et al. (2000) to examine herding in Indian stock and commodity futures markets.

The results of the study indicate absence of herding among commodity futures under all market conditions except for the declining market where herding is present among energy futures. The investors investing in agricultural and energy commodities have a higher tendency to herd during high volatility days as compared to low volatility days. Further, the study of herding spillover effects indicates that the price fluctuations in metal commodities affect herding in agricultural and energy commodities.

The results can help market participants to diversify the risk by investing in agricultural, metal and energy futures along with the stocks.

Majority of the previous studies explore herding among stocks and ignore commodities especially agricultural commodities. This study attempts to fill the gap by studying herding among various commodity futures. To the best of our knowledge this is the first study to explore herding spillover effects in the Indian stock and commodity futures market.

This study investigates the supply chain efficiency of selected companies in the Indian food processing sector. Additionally, it explores the relationship between supply chain efficiency and firm performance.

To determine the supply chain efficiency, the study uses supply chain efficiency measures, such as supply chain length, inefficiency ratio and working capital productivity. Secondary data were collected from the Center for Monitoring Indian Economy (CMIE) Prowess database for the years 2011–2017. Various return measures, such as Return on Net Worth (RONW), Return on Total Assets (ROTA) and Return on Capital Employed (ROCE), were used to measure firm performance. Collected data were analyzed to investigate the relationship between supply chain efficiency and firm performance.

Findings of the study reveal the prevalence of inefficient supply chains in the context of the selected companies. There is a significant negative correlation between supply chain efficiency and firm performance. RONW has a significant negative correlation with the length of supply chain as well as supply chain inefficiency.

This study expands the limited existing research perspective; the study helps to understand the supply chain efficiency and firm performance.

This is an original piece of work and provides valuable insight into the relationship between supply chain efficiency and firm performance.

The stock market generates massive databases of various financial companies that are highly volatile and complex. To forecast daily stock values of these companies, investors frequently use technical analysis or fundamental analysis. Data mining techniques coupled with fundamental and technical analysis types have the potential to give satisfactory results for stock market prediction. In the current paper, an effort is made to investigate the accuracy of stock market predictions by using the combined approach of variables from technical and fundamental analysis for the creation of a data mining predictive model.

We chose 381 companies from the National Stock Exchange of India's CNX 500 index and conducted a two-stage data analysis. The first stage is identifying key fundamental variables and constructing a portfolio based on that study. Artificial neural network (ANN), support vector machines (SVM) and decision tree J48 were used to build the models. The second stage entails applying technical analysis to forecast price movements in the companies included in the portfolios. ANN and SVM techniques were used to create predictive models for all companies in the portfolios. We also estimated returns using trading decisions based on the model's output and then compared them to buy-and-hold returns and the return of the NIFTY 50 index, which served as a benchmark.

The results show that the returns of both the portfolios are higher than the benchmark buy-and-hold strategy return. It can be concluded that data mining techniques give better results, irrespective of the type of stock, and have the ability to make up for poor stocks. The comparison of returns of portfolios with the return of NIFTY as a benchmark also indicates that both the portfolios are generating higher returns as compared to the return generated by NIFTY.

As stock prices are influenced by both technical and fundamental indicators, the current paper explored the combined effect of technical analysis and fundamental analysis variables for Indian stock market prediction. Further, the results obtained by individual analysis have also been compared. The proposed method under study can also be utilized to determine whether to hold stocks for the long or short term using trend-based research.

The purpose of this paper is to study the status and trend of intellectual capital disclosures by selected companies in India. Three categories of intellectual capital disclosures across six industry groups were measured. The relation of the three categories of disclosures, i.e. human capital, relational capital and structural capital disclosures with the measures of organisational performance such as sales, R&D, R&D intensity, net profit and export intensity has also been studied.

Based on National Industrial Classification 2008, six sectors, namely pharmaceutical, basic metals, industrial manufacturing, energy, financial services and information technology were included in the study and 20 companies were selected from each sector based on the availability of data from 2004-2005 to 2013-2014, thus, making a sample of 1,200 firm-years. For collecting the data, a list of keywords related to various dimensions of intellectual capital was prepared and the count of keywords was searched in the annual reports of the companies.

Significant and positive trend coefficients were found in the majority of the sectors. Analysis revealed that trend coefficients differed across various sectors indicating the presence of sector specificity. Results of trend analysis reveal that structural capital-related disclosures have stagnated in case of pharmaceutical sector after hitting the peak. Significant variations were found across sectors in terms of all three types of intellectual capital disclosures. Results of study empirically support the fact that intellectual capital disclosures tend to increase with size of the organisation.

As data have been collected from annual reports of the companies, the accuracy of the findings is limited to the accuracy of the reported data.

The study is an original piece of work. This study provides an insight into the disclosure trend of intellectual capital in an emerging economy.