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This study examines the moderating role of female directors on the relationship between the firms’ characteristics and tax avoidance in an emerging economy.

This study employs the second-generation unit root test and the generalised method of moments (GMM) techniques. The Kao residual cointegration test corroborates a long-run cointegration among variables.

Female directors demonstrate mixed and unusual findings. No significant impact of female directors on tax avoidance is found. In addition, the presence of female directors does not show any negative or significant moderating impacts on the relationship between leverage, firm age, board size and tax avoidance. However, having more female directors can negatively and significantly moderate the relationship between more profitable firms, larger firms and tax avoidance. These findings show that the board of directors could use the presence of female directors to maximise their opportunistic behaviour, such as to avoid tax.

Research limitations – The study is limited by considering only 62 listed firms. The scope could be extended to include non-listed firms.

Research implications – There is increasing pressure for female directors on boards from diverse stakeholders, such as the European Commission, national governments, politicians, employer lobby groups, shareholders, and Fortune and Financial Times Stock Exchange (FTSE) rankings. This study provides input to decision-makers putting gender quota laws into practice. Our findings can help policy-makers adopt regulatory reforms to control tax avoidance practices and enhance organisational legitimacy. Policymakers can change their policy to include female directors up to the threshold suggested by the critical mass theory.

This is the first attempt in Bangladesh to explore the role of female directors in the relationship between the firms' characteristics and tax avoidance. The current study has significant ramifications for bringing gender diversity into practice as a component of good corporate governance.

The main goal of this study is to employ unsupervised (lexicon-based) learning approaches to identify readers' emotional dimensions and thumbs-up empathy reactions to reviews of online travel agency apps based on appraisal and stimulus–organism–response (SOR) theories.

Using the Google Play Scraper, we gathered a total of 402,431 reviews from the Google Play Store for two travel agency apps, Tripadvisor and Booking.com. Following the filtering and cleaning of user reviews, we used lexicon-based unsupervised machine learning algorithms to investigate the associations between various emotional dimensions of reviews and review readers' thumbs-up reactions.

The study's findings reveal that the sentiment of different sorts of reviews has a substantial influence on review readers' emotional experiences, causing them to give the app a thumbs up review. Furthermore, readers' thumbs-up responses to the text reviews differed depending on the eight emotional aspects of the reviews.

The results of this research can be applied in the development of online travel agency apps. The findings suggest that app developers can enhance users' emotional experiences by considering the sentiment and emotional aspects of reviews in their design and implementation. Additionally, the results can be used by travel agencies to improve their online reputation and attract more customers by providing a positive user experience.

The findings of this research have the potential to have a significant impact on society by providing insights into the emotional experiences of users when they engage with online travel agency apps. The study highlights the importance of considering the emotional aspect of user reviews, which can help app developers to create more user-friendly and empathetic products.

The current study is the first to evaluate the impact of users' thumbs-up empathetic reactions on user evaluations of online travel agency applications using unsupervised (lexicon-based) learning methodologies.

The goal of the current research is to use different machine learning (ML) approaches to examine and predict customer reviews of food delivery apps (FDAs).

Using Google Play Scraper, data from five food delivery service providers were collected from the Google Play store. Following cleaning the reviews, the filtered texts were classified as having negative, positive, or neutral sentiments, which were then scored using two unsupervised sentiment algorithms (AFINN and Valence Aware Dictionary for sentiment Reasoning (VADER)). Furthermore, the authors employed four ML approaches to categorize each review of FDAs into the respective sentiment class.

According to the study's findings, the majority of customer reviews of FDAs were positive. This research also revealed that, while all of the methods (decision tree, linear support vector machine, random forest classifier and logistic regression) can appropriately classify the reviews into a sentiment category, support vector machines (SVM) beats the others in terms of model accuracy. The authors' study also showed that logistic regression provided the highest recall, F1 score and lowest Root Mean Square Error (RMSE) among the four ML models.

The findings aid FDAs in determining customer review behavior. The study's findings could help food apps developers better understand how customers feel about the developers' products and services. The food apps developer can learn how to use ML techniques to better understand the users' behavior.

The current study uses ML methodologies to investigate and predict consumer attitude regarding FDAs.

There is a strong prerequisite for organizations to analyze customer review behavior to evaluate the competitive business environment. The purpose of this study is to analyze and predict customer reviews of halal restaurants using machine learning (ML) approaches.

The authors collected customer review data from the Yelp website. The authors filtered the reviews of only halal restaurants from the original data set. Following cleaning, the filtered review texts were classified as positive, neutral or negative sentiments, and those sentiments were scored using the AFINN and VADER sentiment algorithms. Also, the current study applies four machine learning methods to classify each review toward halal restaurants into its sentiment class.

The experiment showed that most of the customer reviews toward halal restaurants were positive. The authors also discovered that all of the methods (decision tree, linear support vector machine, logistic regression and random forest classifier) can correctly classify the review text into sentiment class, but logistic regression outperforms the others in terms of accuracy.

The results facilitate halal restaurateurs in identifying customer review behavior.

Sentiment and emotions, according to appraisal theory, form the basis for all interactions, facilitating cognitive functions and supporting prospective customers in making sense of experiences. Emotion theory also describes human affective states that determine motives and actions. The study looks at how potential customers might react to a halal restaurant’s consensus on social media based on reviewers’ opinions of halal restaurants because emotions can be conveyed through reviews.

This study applies machine learning approaches to analyze and predict customer sentiment based on the review texts toward halal restaurants.

Although research on patient satisfaction and loyalty has grown rapidly, the literature on corporate social responsibility (CSR) health care and patient satisfaction and loyalty is scarce. This paper aims to examine the impact of CSR health care on patient satisfaction and loyalty in Bangladesh.

A quantitative study was performed, and data were collected using purposive sampling among 195 patients who used CSR health-care services from six public and private hospitals in Bangladesh. The data were analysed using structural equation modelling through the partial least square approach.

The study found a significant positive relationship between CSR health-care services and patient satisfaction and between patient satisfaction and loyalty at p < 0.01.

The study provides insights into policymakers in the development of Bangladesh health sectors and CSR health-care activities. However, the results might not be generalisable due to the unavailability of a sample frame.

The study addresses the lacuna in the literature on CSR health-care practices of hospitals in Bangladesh from the perspective of patient satisfaction and loyalty.

The purpose of this study is to examine the link between research and practice within the context of Islamic marketing (IM), an issue which is controversial in the literature. It offers reasonable answers that bridge the gap between research and practice, as well as the way to mitigate it.

This study uses a critical approach to analytically review the literature on IM, and relates it to research and practice.

The study finds that the advancement of knowledge on IM necessitates research and practice. There is a gap between research and practice which evolved from decades of objectivity between researchers and practitioners in the field of IM. It is necessary to search for some practicable solutions that can narrow the gap between theory and practice.

The basic limitation of this study is that IM has not yet emerged as a distinct discipline. Hence, there is limited study on IM issues in the context of research and practice.

This study makes essential contributions to the chastisement by research and practice, a theoretically new field of IM subject.

The influence of website quality on online compulsive buying behavior (OCBB) in the context of online shopping based on the usage of a credit card (UCC) and online impulsive buying behavior (OIBB) was investigated in this study.

The authors used a research model to examine the relationships between the study components as per the prescription. For this investigation, the authors used an online survey form to obtain primary data from 350 respondents on social media. A covariance-based structural equation modeling approach was used to evaluate the structural research model and data.

The findings reveal that the quality of online shopping websites positively affects consumers' UCC and OIBB, and these in turn positively influence their OCBB.

The study emphasized impacting elements on consumer behavior and gave advice for future research based on the results. Using several dimensions of website quality, this study bridges the knowledge gap between UCC, OIBB and OCBB.

Based on UCC and OIBB, the authors developed a new model to investigate the link between website quality and OCBB. To the best of the authors' knowledge, it is the first experimental result that assesses the impact of website quality on OCBB.

Social commerce is a subpart of electronic commerce (e-commerce), where social media is forced to support user contributions. The purpose of this study is to measure the impact of customers' interpersonal interactions in social commerce on customer relationship management (CRM) performance, based on the flow, commitment-trust and stimulus–organism–response (SOR) theories.

On the basis of the SOR framework, the authors developed a study model to determine the impact on CRM performance of customers' interpersonal interactions in social commerce. The primary data of the study were collected from 640 users of social commerce through a web questionnaire during the COVID-19 (coronavirus disease 2019) pandemic situation, and the authors tested the study model using the approach of covariance-based structural equation modeling (SEM).

Results of the current study reveal that customers' interpersonal interactions in social commerce optimistically influence their perceived flow. Moreover, perceived flow absolutely controls users' trust and CRM performance. In turn, collective users' trust positively influences users' commitment and CRM performance. Finally, collective users' commitment absolutely influences the performance of CRM.

The authors provide a valuable contribution to the theoretical field of online marketing and CRM. Besides, the findings of this study are relevant for marketers to know the issues for increasing customer trust, commitment and performance of CRM.

The current study develops a model based on the flow, commitment-trust and stimulus–organism–response (SOR) theories. The authors' research is the first to estimate the effect of customers' interpersonal interactions in social commerce on CRM performance.

This study examines the impact of profitability, firm size and leverage on corporate tax avoidance in Bangladesh, an emerging South Asian economy.

A balanced panel data of 62 firms from Dhaka and Chittagong stock exchanges in Bangladesh from 2009 to 2020 were used to run the regression. This study employed the fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) to examine the hypotheses.

The findings show that large firms positively impact corporate tax avoidance. Similarly, profitability and leverage are positively associated with tax avoidance, and the results are significant. Furthermore, the study conducts robustness tests that confirm the findings.

The use of cash effective tax rate (ETR) to investigate firms’ tax avoidance practices poses some limitations, and the results should be interpreted cautiously.

The current study may help policymakers better enhance tax collection from business firms. The findings could serve as a valuable input for effectively monitoring tax collection from large profit-earning firms.

To the authors' best knowledge, this is the first historical attempt in Bangladesh to use panel data to examine the relationship between the firm’s level characteristics and corporate tax avoidance. Panel data often provides greater flexibility with large data, simplifying calculation and statistical analysis.

Consumers' perceptions of different aspects pertaining to servicescape and social servicescape at restaurants have received little consideration in the hospitality literature. To fill this gap, the authors develop a model that conceptualizes and empirically examines the impact of dissimilar attributes in restaurants on the development of negative emotions and the influence of negative emotions on consumers' dissatisfaction, which in turn determines consumers' behavioral intentions.

The authors used the moderating impact of restaurant attribute performance to support the link between negative emotions and dissimilar attributes. To achieve the study's goals, the authors conducted two investigations, Study 1 and Study 2, in Bangladesh and China, respectively. For study 1, 600 data were obtained from local Bangladeshi consumers, while for study 2, 396 foreign customers in China were surveyed. The collected data were examined by using Structural Equation Modeling (SEM) approach. The authors utilized IBM Analysis of Moment Structure (AMOS), version 24.0.

Both studies 1 and 2 found that dissimilar restaurant attributes had significant positive effects on the development of negative emotions, positive effects of negative emotions on consumer dissatisfaction and a positive influence of consumer dissatisfaction on consumers' behavioral intentions. Results of both studies 1 and 2 also showed that restaurant attributes performance positively moderate the relationships between dissimilar attributes and negative emotions.

The study's empirical results contribute to the body of knowledge in the domains of tourism, consumer psychology and consumer behavior. The study's findings can assist restaurant managers in better understanding how different features related to the servicescape and social servicescape dimensions cause unpleasant emotions and, as a result, influence consumer behavioral intentions.

No preceding research has looked at the link between dissimilar features and negative emotions in the restaurant setting to the authors' knowledge. Also, no previous research has looked at the moderating consequence of restaurant attributes in the association between dissimilar attributes and negative emotions. This research aims to fill those knowledge gap.