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This study aims to explore the determinants of digital technology adoption within micro, small and medium enterprises (MSMEs) operating in a developing economy. Grounded in the theoretical frameworks of fit-viability and task-technology fit models, this study investigates the impact of environmental fit, task-technology fit and viability on the intention to adopt digital technologies among MSMEs.

This study validates the theoretical model using structural equation modeling, using data collected from 280 experienced respondents employed in the MSMEs in India.

The results indicate that both functional and symbolic benefits positively impact managers’ intention to adopt digital technology. However, subgroup analysis reveals that in the case of service enterprises, only functional benefits have a positive influence on managers’ intention to adopt digital technology. In addition, the findings underscore the crucial role of viability in shaping the intention to adopt digital technologies among MSMEs. This study highlights how functional and symbolic benefits motivate digital technology adoption in MSMEs.

There is a dearth of empirical studies investigating the factors influencing the adoption of digital technology by MSME firms, especially within the context of developing economies and specifically within the MSME domain. This study contributes to the theoretical discussion surrounding digital technology adoption among MSMEs in India. Through empirical research, it expands on the fit-viability model and formulates a technology adoption model within the MSME context.

This study aims to implement a novel approach of using the Realized generalized autoregressive conditional heteroskedasticity (GARCH) model within the conditional extreme value theory (EVT) framework to generate quantile forecasts. The Realized GARCH-EVT models are estimated with different realized volatility measures. The forecasting ability of the Realized GARCH-EVT models is compared with that of the standard GARCH-EVT models.

One-step-ahead forecasts of Value-at-Risk (VaR) and expected shortfall (ES) for five European stock indices, using different two-stage GARCH-EVT models, are generated. The forecasting ability of the standard GARCH-EVT model and the asymmetric exponential GARCH (EGARCH)-EVT model is compared with that of the Realized GARCH-EVT model. Additionally, five realized volatility measures are used to test whether the choice of realized volatility measure affects the forecasting performance of the Realized GARCH-EVT model.

In terms of the out-of-sample comparisons, the Realized GARCH-EVT models generally outperform the standard GARCH-EVT and EGARCH-EVT models. However, the choice of the realized estimator does not affect the forecasting ability of the Realized GARCH-EVT model.

It is one of the earliest implementations of the two-stage Realized GARCH-EVT model for generating quantile forecasts. To the best of the authors’ knowledge, this is the first study that compares the performance of different realized estimators within Realized GARCH-EVT framework. In the context of high-frequency data-based forecasting studies, a sample period of around 11 years is reasonably large. More importantly, the data set has a cross-sectional dimension with multiple European stock indices, whereas most of the earlier studies are based on the US market.

This study aims to forecast daily value-at-risk (VaR) for international stock indices by using the conditional extreme value theory (EVT) with the Realized GARCH (RGARCH) model. The predictive ability of this Realized GARCH-EVT (RG-EVT) model is compared with those of the standalone GARCH models and the conditional EVT specifications with standard GARCH models.

The authors use daily data on returns and realized volatilities for 13 international stock indices for the period from 1 January 2003 to 8 October 2014. One-step-ahead VaR forecasts are generated using six forecasting models: GARCH, EGARCH, RGARCH, GARCH-EVT, EGARCH-EVT and RG-EVT. The EVT models are implemented using the two-stage conditional EVT framework of McNeil and Frey (2000). The forecasting performance is evaluated using multiple statistical tests to ensure the robustness of the results.

The authors find that regardless of the choice of the GARCH model, the two-stage conditional EVT approach provides significantly better out-of-sample performance than the standalone GARCH model. The standalone RGARCH model does not perform better than the GARCH and EGARCH models. However, using the RGARCH model in the first stage of the conditional EVT approach leads to a significant improvement in the VaR forecasting performance. Overall, among the six forecasting models, the RG-EVT model provides the best forecasts of daily VaR.

To the best of the authors’ knowledge, this is the earliest implementation of the RGARCH model within the conditional EVT framework. Additionally, the authors use a data set with a reasonably long sample period (around 11 years) in the context of high-frequency data-based forecasting studies. More significantly, the data set has a cross-sectional dimension that is rarely considered in the existing VaR forecasting literature. Therefore, the findings are likely to be widely applicable and are robust to the data snooping bias.

The purpose of this paper is to examine the incidence and determinants of educational mismatch in the Indian labour market. It also attempts to measure the wage effects of educational mismatch, and other individual and work-related characteristics.

Educational mismatch is modelled using multiple Mincerian equations. Wage effects are measured using the novel identification strategy of Lewbel (2012), which constructs internal instruments to obviate potential endogeneity problems.

The authors find that the returns to over-education are positive and significant, while the returns to under-education are negative and significant. However, over-educated would earn less than the workers who have the same educational level, but who are engaged in occupations for which they are adequately educated.

This study is one of the earliest attempts to identify the determinants of educational mismatch in the Indian labour market. Additionally, it measures the effect of educational mismatch on labour market earnings, as well as marginal wage effects of each surplus (or deficit) year of education. Methodological improvements ensure that the results are robust to the sample selection bias, as well as the endogeneity bias.

The purpose of this paper is to understand whether the adoption of environmental management practices and firm characteristics influence the environmental benchmarking in Indian firms. It further looks into the impact of environmental benchmarking practices on firms’ environmental performance.

The study conducts a research survey to obtain the practitioner’s responses on the different aspects of environmental benchmarking. The survey data of 104 firms provide an empirical basis to investigate different research hypotheses using statistical techniques.

The results indicate that the firms which implement environmental management practices are more likely to adopt environmental benchmarking in one or more areas of their operations. The findings signify that firms which benchmarks for environmental purposes are more likely to have better environmental performance. The study confirms that large firms have significant chances of having environmental benchmarking compared to small and medium sized firms. The firms in different sectors have different relative preference to eight different areas of environmental benchmarking. However, all these preferences are not significant at 95 per cent confidence level.

The research use only qualitative responses on environment management aspects and could be further extended by incorporating the quantitative (emission) data of different industries.

The study provides an insight into the environmental benchmarking practices of Indian firms for better management of environmental performance of the firms.

The study investigates the experience and attitude of Indian firms to environmental benchmarking based on an empirical research. It adds to the knowledge in the field of environmental benchmarking in developing countries with specific focus on India.

The purpose of this paper is to compare the daily conditional variance forecasts of seven GARCH-family models. This paper investigates whether the advanced GARCH models outperform the standard GARCH model in forecasting the variance of stock indices.

Using the daily price observations of 21 stock indices of the world, this paper forecasts one-step-ahead conditional variance with each forecasting model, for the period 1 January 2000 to 30 November 2013. The forecasts are then compared using multiple statistical tests.

It is found that the standard GARCH model outperforms the more advanced GARCH models, and provides the best one-step-ahead forecasts of the daily conditional variance. The results are robust to the choice of performance evaluation criteria, different market conditions and the data-snooping bias.

This study addresses the data-snooping problem by using an extensive cross-sectional data set and the superior predictive ability test (Hansen, 2005). Moreover, it covers a sample period of 13 years, which is relatively long for the volatility forecasting studies. It is one of the earliest attempts to examine the impact of market conditions on the forecasting performance of GARCH models. This study allows for a rich choice of parameterization in the GARCH models, and it uses a wide range of performance evaluation criteria, including statistical loss functions and the Mince-Zarnowitz regressions (Mincer and Zarnowitz 1969). Therefore, the results are more robust and widely applicable as compared to the earlier studies.

The purpose of this paper is to utilize a constrained random portfolio-based framework for measuring the skill of a cross-section of Indian mutual fund managers. Specifically, the authors test whether the observed performance implies superior investment skill on the part of mutual fund managers. Additionally, the authors investigate the suitability of mutual fund investments under diverse investor expectations.

The authors use a new skill measurement methodology based on a cross-section of constrained random portfolios (Burns, 2007).

The authors find no evidence of superior investment skill in the sample of Indian equity mutual funds. Using a series of statistical tests, the authors conclude that the mutual funds fail to outperform the random portfolios. Furthermore, mutual funds show no persistence in their performance over time. These results are robust to choice of performance measure and the investment horizon. However, mutual funds provide lower downside risks and may be suitable for investors with high degree of risk aversion.

The authors extend Burns’ (2007) methodology in several aspects, especially by using a much wider range of performance and downside risk measures to address diverse investor expectations. To the best of the authors’ knowledge, this is first study to apply the constrained random portfolios-based skill tests in an emerging market.

The purpose of this paper is to develop chicken cutlets enrobed with bread crumbs vis-à-vis dried carrot pomace and to assess its effect on physico-chemical properties, sensory attributes and texture profile analysis.

Three experimental groups were made: control group chicken cutlets (C), chicken cutlets enrobed with bread crumbs group (T_b) and chicken cutlets enrobed with dried carrot pomace group (T_c). All the procedures used in the study for estimation of various physico-chemical properties, sensory evaluation and texture profile analysis were standard protocols.

There was a significant (p < 0.05) increase in water holding capacity, crude fibre content and ash content of enrobed chicken cutlets, whereas moisture, fat content and shrinkage of product were significantly (p < 0.05) decreased. The results for sensory evaluation and texture profile analysis of enrobed chicken cutlets were better than control group. Overall acceptability score of chicken cutlets enrobed with dried carrot pomace was revealed to be highest (7.5 ± 0.29) and that of control group was found to be lowest (6.4 ± 0.22). Hardness (N/cm²) value found for control group chicken cutlets, chicken cutlets enrobed with bread crumbs group and chicken cutlets enrobed with dried carrot pomace group were 2.2 ± 0.17, 3.1 ± 0.29 and 4.3 ± 0.27, respectively.

Future research may benefit to assess the effect of enrobing with bread crumbs and dried carrot pomace on mineral and vitamin content and lipid profile of meat products.

Enrobing of chicken cutlets with bread crumbs and dried carrot pomace improved the sensory attributes along with texture profile analysis. Hence, enrobing with bread crumbs and dried carrot pomace could be used as processing technology to improve sensory appeal, especially crispiness of meat products.

The purpose of this paper is to determine the role of service quality (SQ), trust and commitment to customer loyalty (CL) for telecom service users. Further, the moderating role of gender, marital status and connection type within the model was tested.

A measurement model was created based on valid 615 responses from Indian TSUs for SQ, trust, commitment and loyalty with the help of partial least squares structural equation modeling (PLS-SEM). Multi-group analysis (MGA) was conducted to understand the moderating effect of marital status, gender and connection type within the model.

The results suggest that, out of five dimensions of SQ, only responsiveness, assurance and empathy have a significant positive relationship with both commitment and trust. Tangibility has a significant positive relationship with trust only. Both commitment and trust have a significant impact on loyalty. It was noticed that both commitment and trust act as mediators between three SQ dimensions (assurance, empathy and responsiveness) and CL. MGA revealed that empathy and responsiveness positively induce trust in telecom users who are single. Whereas, assurance increases commitment toward telecom service providers in married users. Assurance and empathy significantly contribute toward commitment and trust, respectively, in male users as compared to females. Empathy was found important for postpaid users for trust-building, whereas trust was found to be more important for prepaid users to stay loyal to the service provider.

This article contributes toward understanding the role of SQ, trust and commitment to CL moderated by marital status, gender and connection type in an integrated model concerning telecom service.