Purpose – The purpose of this paper is to seek the determinants of service quality and evaluate the impact of service quality and consumers’ characteristics on channel selection in the context of beverage industry in Bangladesh. Design/methodology/approach – Data were collected using convenience sampling method. Initially exploratory factor analysis was performed to extract the key dimensions of service quality and then structural equation modeling was employed to verify the causal relationships between the dimensions of service quality and service quality itself. χ2 test was used to determine whether any association exists between service quality or demographic variables and choice of channel types. Cramer’s V was performed to measure the strength of association. One-way ANOVA was carried out to identify significant impact of demographic variables on perception of service quality. Findings – The research findings indicated that personal interaction, appearance, reliability, policy, and problem solving are the key determinants of service quality in terms of beverage industry in Bangladesh. It is observed that customers preferring factors like personal interaction and problem solving intend to purchase beverage items from super shop. Customers who are deemed socioeconomically high taking profession and monthly income into account prefer shopping at super shop. Customers who are female or married with no kid or service holders showed better satisfaction with service quality. Research limitations/implications – Due to money and time constraints the study could not cover up the whole country. Conclusions and predictions to be applied to consumers in general may not be appropriate entirely since specific age group was deemed, given that the subject was beverage products. Practical implications – The model proposed in this study will help managers and suppliers understand how consumers assess the quality of services. In order to strengthen brand and create brand loyalty, marketing planners, managers, and suppliers must be aware of the dimensions of service quality, and consumers’ characteristics. Selecting an optimal sales channel is imperative for suppliers in order to make their products reach to target consumers. Originality/value – Service quality plays crucial role in ameliorating customer satisfaction and creating competitive advantage whereas sales channels have impact on sustaining the long-term profitability of the company. This paper has contributed significantly to these issues and sought consumers’ characteristics and preference as well.

Background Malnutrition imposes enormous costs resulting from lost investments in human capital and increased healthcare expenditures. There is a dearth of research focusing on the prediction of women’s body mass index (BMI), and the malnutrition outcomes (underweight, overweight and obesity) in developing countries. This paper attempts to fill out this knowledge gap by predicting the BMI and the risks of malnutrition outcomes for Bangladeshi women of childbearing age from their economic, health, and demographic features. Methods Data from the 2017-18 Bangladesh Demographic and Health Survey and a series of supervised machine learning (SML) techniques are used. Additionally, this study circumvents the imbalanced distribution problem in obesity classification by utilizing an oversampling approach. Results Study findings demonstrate that support vector machine and k-nearest neighbor are the two best-performing methods in BMI prediction based on coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The combined predictor algorithms consistently yield top specificity, Cohen’s kappa, F1-score, and AUC in classifying the malnutrition status, and their performance is robust to alternative standards. The feature importance ranking based on several nonparametric and combined predictors indicates that socioeconomic status, women’s age, and breastfeeding status are the most important features in predicting women’s nutritional outcomes. Furthermore, the conditional inference trees corroborate that those three features along with the partner’s educational attainment and employment significantly predict malnutrition risks. Conclusion To the best of our knowledge, this is the first study that predicts BMI and one of the pioneer studies to classify all three malnutrition outcomes for women of childbearing age in Bangladesh, let alone in any lower-middle income country, using SML techniques. Moreover, in the context of Bangladesh, this paper is the first to identify and rank features that are critical in predicting nutritional outcomes using several feature selection algorithms. The estimators from this study predict the outcomes of interest most accurately and efficiently compared to other existing studies in the relevant literature. Therefore, study findings can aid policymakers in designing policy and programmatic approaches to address the double burden of malnutrition among Bangladeshi women, thereby reducing the country’s economic burden.

Objectives: This paper examines the associations of socioeconomic and demographic correlates with malnutrition among women and investigates education and wealth-related inequalities in malnutrition among women by region. Design: We utilize a two-level mixed-effects logistic regression model to evaluate the associations and employ the concentration, Wagstaff, and Erreygers’s correction indices to measure socioeconomic inequalities in malnutrition among women. Setting: Bangladesh Demographic and Health Survey data. Participants: Non-pregnant women aged 15-49. Results: We find evidence of a significant cluster effect in the data. Women’s age, marital status, total children ever born, education level, husband’s/partner’s education level, residence, and wealth index appear to be significantly associated with women underweight and overweight/obesity status. Underweight status is higher among less-educated women and women from poor households, whereas overweight/obesity is more concentrated among higher educated women and women from wealthy households. The southwestern region of the country demonstrates lower education and wealth-related inequalities in malnutrition among women. In contrast, the central and the northeastern areas apparently experience the highest education and wealth-related inequalities in malnutrition among women. The regional differences in predicted probabilities of being underweight shrink at higher education level and the richest quintile, whereas the differences in overweight/obese diminish at the primary education level and lower quintile households. Conclusions: Our findings strengthen the evidence base for effective regional policy interventions to mitigate education and wealth-related inequalities in malnutrition among women. There is a need for developing regional awareness programs and establishing regional monitoring cells to ensure proper health and nutrition facilities in underprivileged regions.

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Purpose The transformation of coronavirus disease (COVID-19) from a regional health crisis in a Chinese city to a global pandemic has caused severe damage not only to the natural and economic lives of human beings but also to the financial markets. The rapidly pervading and daunting consequences of COVID-19 spread have plummeted the stock markets to their lowest levels in many decades especially in South Asia. This concern motivates us to investigate the stock markets’ response to the COVID-19 pandemic in four South Asian countries: Bangladesh, India, Pakistan and Sri Lanka. This study aims to investigate the causal impact of the number of confirmed COVID-19 cases on stock market returns using panel data of the countries stated above. Design/methodology/approach This study collects and analyzes the daily data on COVID-19 spread and stock market return over the period May 28, 2020 to October 01, 2020. Using Dumitrescu and Hurlin panel Granger non-causality test, the empirical results demonstrate that the COVID-19 spread measured through its daily confirmed cases in a country significantly induces stock market return. This paper cross-validates the results using the pairwise Granger causality test. Findings The empirical results suggest unidirectional causality from COVID-19 to stock market returns, indicating that the spread of COVID-19 has a dominant short-term influence on the stock movements. To the best of the knowledge, this study provides the first empirical insights into the impact of COVID-19 on the stock markets of selected South Asian countries taking the cross-sectional dependence into account. The results are also in line with the findings of other existing literature on COVID-19. Moreover, the results are robust across the two tests used in this study. Originality/value The findings are equally insightful to the fund managers and investors in South Asian countries. Taking into account the possible impact of COVID-19 on stock markets’ returns, investors can design their optimal portfolios more effectively. This study has another important implication in the sense that the impact of COVID-19 on the stock markets of South Asian countries may have spillover effects on other developing or even developed countries.