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This paper aims to examine the performance of Islamic and conventional stocks listed at the Pakistan Stock Exchange by using both parametric and non-parametric approaches. The motivation is to do risk-return analysis of Islamic stock prices and conventional stock prices.

It uses various measures of performance, e.g. Sharpe ratio, Treynor ratio, Jensen's alpha, beta, generalized auto-regressive conditional heteroskedasticity and stochastic dominance. Using the Karachi Meezan Index-30 (KMI-30) and the Karachi Stock Exchange Index-30 (KSE-30) as proxies for Islamic and conventional stock prices, respectively, it examines the performance of Islamic and conventional stocks. The daily data of KMI-30 and KSE-30, covering period from June 9, 2009 to June 20, 2020 are used.

The results show that the overall KMI-30 outperforms the KSE-30. The returns of the KMI-30 are greater than the KSE-30. However, the risk and volatility of the KMI-30 and KSE-30 are similar. Further, the KMI-30 has higher excess returns per unit of total risk than the KSE-30. But both indexes have similar excess returns per unit of systematic risk. Moreover, the KMI-30 returns have stochastically dominance over the KSE-30 returns. These results reveal that the Islamic index performs better than the conventional index.

The findings provide several practical implications in financial and investment decisions making by investors, managers and policymakers such as strategies for asset allocation and investment. Further, in risk management, it provides guidance for allocating portfolios and managing risk. The investment in Islamic stocks may mitigate potential risk within asset portfolios.

This research is unique in its approach to the analysis of the performance comparison of conventional and Islamic stock by using comprehensive parametric and non-parametric estimation techniques. Such research has not been undertaken in the Pakistan's equity market since.

This study aims to conduct a geographical analysis of the distribution and type of activities developed by social enterprises in rural and urban areas of Ireland.

The study analyses data of more than 4,000 social enterprises against a six-tier rural/urban typology, using descriptive statistics and non-parametric tests to test six hypotheses.

The study shows a geographical rural–urban pattern in the distribution of social enterprises in Ireland, with a positive association between the remoteness of an area and the ratio of social enterprises, and a lack of capital-city effect related to the density of social enterprises. The analysis also shows a statistically significant geographical rural–urban pattern for the types of activities developed by social enterprises. The authors observe a positive association between the remoteness of the areas and the presence of social enterprises operating in the community and local development sector whereas the association is not significant for social enterprises developing welfare services.

The paper shows the potential of using recently developed rural–urban typologies and tools such as geographical information systems for conducting geographical research on social enterprises. The findings also have implications for informing spatially sensitive policymaking on social enterprises.

The merging of a large national data set of social enterprises with geographical tools and data at subregional level contributes to the methodological advancement of the field of social enterprises, providing tools and frameworks for a nuanced and spatially sensitive analysis of these organisations.

This study aims to examine the role of technological capabilities in the competitive advantage of supply chains for the “countryside” hub of São Paulo state.

An interdisciplinary approach was adopted by combining the strategic orientation of supply chain management as a link between market and operational activities, with technological capabilities as an essential factor for competitive advantage. This exploratory, mixed-method study was conducted in the Campinas technology hub, which has become important for its available skilled workforce, and broad and structured supply chain. The study sample included ten companies and dealt with the techniques of content analysis and non-parametric statistics – the Mann–Whitney U technique.

The results revealed that strategic supply chain management has an impact on technological capabilities and competitive advantage.

They also showed that technological capabilities have a partial mediating effect on competitive advantage.

The purpose of this research is to investigate the short-term capital markets' reactions to the public announcement first local detection of novel corona virus (COVID 19) cases in 12 major Asian capital markets.

Using the constant mean return model and the market model, an event study methodology has been implied to determine the cumulative abnormal returns (CARs) of 10 pre and post-event trading days. The statistical significance of the data was assessed using both parametric and nonparametric test statistics.

First discovery of local COVID 19 cases had a substantial impact on all 12 Asian markets on the event day, as shown by statistically significant negative average abnormal return (AAR) and cumulative average abnormal return (CAAR). The single factor ANOVA result has also demonstrated that there is no variability among 12 regional markets in terms of short-term market responses. Furthermore, there is little evidence that these major Asian stock market indices differ significantly from the FTSE All-World Index which might suggest possible spillover impact and co-integration among the major Asian capital markets. The study further discovers that market capitalization and liquidity did not have any significant impact on market reaction to announcement.

The study's contribution might have been compromised by the absence of socio-demographic, technical, financial and other significant policy factors from the analysis.

These findings will be considerably helpful in tackling this unprecedented epidemic issue for personal and institutional investors, industrial and economic experts, government and policymakers in assessing the market in special circumstances, diversifying risk and developing financial and monetary policy proposals.

This paper is the first to examine the effects of local COVID 19 detection announcement on major Asian capital markets. This study will add to the literature by investigating unusual market returns generated by infectious illness outbreaks and the overall market efficiency and investors' behavioral pattern of major Asian capital markets.

This study aims to perform a benefit segmentation and then a classification of visitors that travel to the Rocha Department in Uruguay from the capital city of Montevideo during the summer months.

A convenience sample was obtained with an online survey. A total of 290 cases were usable for subsequent data analysis. The following statistical techniques were used: hierarchical cluster analysis, K-means cluster analysis, machine learning, support vector machines, random forest and logistic regression.

Visitors that travel to the Rocha Department from Montevideo can be classified into four distinct clusters. Clusters are labelled as “entertainment seekers”, “Rocha followers”, “relax and activities seekers” and “active tourists”. The support vector machine model achieved the best classification results.

Implications for destination marketers who cater to young visitors are discussed. Destination marketers should determine an optimal level of resource allocation and destination management activities that compare both present costs and discounted potential future income of the different target markets. Surveying non-residents was not possible. Future work should sample tourists from abroad.

The combination of market segmentation of Rocha Department’s visitors from the city of Montevideo and classification of sampled individuals training various machine learning classifiers would allow Rocha’s destination marketers determine the belonging of an unsampled individual into one of the already obtained four clusters, enhancing marketing promotion for targeted offers.

The link between confidence and economic decisions has been widely covered in the economic literature, yet it is still an unexplored field in tourism. The purpose of this paper is to address this gap, and investigate benefits in forecast accuracy that can be achieved by combining the UNWTO Tourism Confidence Index (TCI) with statistical forecasts.

Research is conducted in a real-life setting, using UNWTO unique data sets of tourism indicators. UNWTO TCI is pooled with statistical forecasts using three distinct approaches. Forecasts efficiency is assessed in terms of accuracy gains and capability to predict turning points in alternative scenarios, including one of the hardest crises the tourism sector ever experienced.

Results suggest that the TCI provides meaningful indications about the sign of future growth in international tourist arrivals, and point to an improvement of forecast accuracy, when the index is used in combination with statistical forecasts. Still, accuracy gains vary greatly across regions and can hardly be generalised. Findings provide meaningful directions to tourism practitioners on the use opportunity cost to produce short-term forecasts using both approaches.

Empirical evidence suggests that a confidence index should not be collected as input to improve their forecasts. It remains a valuable instrument to supplement official statistics, over which it has the advantage of being more frequently compiled and more rapidly accessible. It is also of particular importance to predict changes in the business climate and capture turning points in a timely fashion, which makes it an extremely valuable input for operational and strategic decisions.

The use of sentiment indexes as input to forecasting is an unexplored field in the tourism literature.

The purpose of this study is to present a training system on smart phones to develop fashion and textile concepts among the female students with severe hearing impairment and then to determine the effectiveness of the training system in developing fashion and textile concepts among the female students with severe hearing impairment.

The analytical descriptive methodology and experimental methodology were used to examine the training system in developing fashion and textile concepts among the female students with severe hearing impairment.

The findings of this study indicate that the training system on smart phones was effective in developing fashion and textile concepts among the female students with severe hearing impairment.

This study has some limitations such as limited sample size, lack of control group and lack of long-term.

This study provides practical implications for educators, researchers and practitioners in terms of using smart phone applications in educational processes for people with disabilities.

This study has social implications for people with disabilities as it provides them with access to education through smart phone applications which can help them develop their skills in fashion and textile design.

This study presents an original model of a training system on smart phones for developing fashion and textile concepts among the female students with severe hearing impairment, which can be used as a reference for other studies related to this field.

The aims of the paper are to investigate IT software and service export function for India. First, cointegration tests have been used to investigate the long-run equilibrium relationship of the given variables. Second, long-run coefficients and associated error correction mechanism are estimated.

Annual time series data on IT software and service exports, human capital, exchange rate, investment in IT, external demand and openness index have been used for the present study during the period 1980–2017. The data are collected from the National Association of Software and Service Companies (NASSCOM), Planning Commission of India, University Grants Commission (UGC) of India, real effective exchange rate (REER) database and World Bank development indicators. Auto regressive distributed lag (ARDL) model is used to analyze both short-run and long-run dynamic behaviour of economic variables with appropriate asymptotic inferences.

Results of the analysis show the stable long-run equilibrium relationship among the given variables. It is found that external demand, exchange rate, human capital and openness index have a substantial long-run impact on the IT software and service exports. We also found that the coefficient of error correction term is negative and significant at 1% of the level of significance, which confirms the existence of stable long-run relationship which means adjustment will take place when there is a short-run deviation to its long-run equilibrium after a shock.

There may be other determinants of software and service exports apart from those considered by the present study. Due to the non-availability of data, the study considers only important determinants that determine the software and service exports in India. The IT exports are an emerging and dynamic field of economic activity and the rate of change is so rapid that the relevance of individual factors may change over time. The study period is also limited to available data.

The paper has implications for achieving sustainability in IT software and service exports growth. It is recommended that policies directed at improving the performance of IT software and service exports should largely consider the long-run behaviour of these variables.

This paper focuses on originality in the analysis of the relationship among the given variables including IT software and service exports, human capital, exchange rate, investment in IT, external demand and openness index in India. All the work has been done in original by the authors, and the work used has been acknowledged properly.

Bank mobilizes savings and transforms it into credit for investments in various sectors, which helps the economy running. The purpose of this paper is to examine the efficiency of three bank groups in India with data spanning from 2009–2010 to 2018–2019.

The study uses data envelopment analysis for measuring the efficiency of the selected banks. It measures the efficiency both from the revenue dimension and from the supply-side dimension of financial inclusion.

The study finds that foreign banks on average are working efficiently far better than the public-sector and private-sector banks. It indicates that foreign banks in India are operating at 92.53% efficiency level, whereas private- and public-sector banks are operating at 90.20 and 86.04% efficiency levels, respectively. Further, the result of the Friedman test reveals that there is no significant difference in efficiency scores amongst these three bank groups. As major challenges, non-performing assets of the banking industry to be reduced by 15% as radial and 53.18% as slack.

One of the notable innovativeness of this study is that, unlike most of the previous studies that are mostly selected few banks and specific group, the present study may place itself as a unique inquiry in the domain of technical efficiency in macro concept by considering three major bank groups operating in India. An important contribution of the study is the classification of reasons behind the inefficiency, i.e. managerial or inappropriate scale size and further projections of input factors for the same level of output.

Understanding humans’ food intake practices is helpful in reducing health problems. The purpose of this study was to investigate the relationships between eating behaviours and binge eating and to examine the influence of sex and weight status on these relationships.

A total of seventy-eight participants (39 with overweight; mean age: 38.1 ± 6.3 years; body mass index [BMI]: 25 ± 5.7 kg/m²) underwent measurements of binge eating, eating styles and body compositions. A hierarchical multiple regression analysis was performed to predict binge eating as a function of eating styles and demographic variables.

Women presented higher levels of binge eating symptomatology and emotional eating than men. The analysis showed that age and gender did not emerge as important predictors of binge eating. In contrast, emotional and external eating and BMI were found to be important predictors of binge eating. The results indicate that higher emotional and external eating behaviour with higher BMI are important risk factors for binge eating in a non-clinical sample.

In this study, BMI was used as a causal factor rather than a consequence of deregulation of eating behaviour. An individual’s tendency to binge eat may be determined by BMI, emotional eating and sensitivity to environmental food stimuli. Understanding sex differences and causal relationships between eating behaviours is critical for the prevention and treatment of obesity and related health problems and for proper dietary management.