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1 – 3 of 3Md Shamim Hossain and Mst Farjana Rahman
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Practical implications
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.
Social implications
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.
Originality/value
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.
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Md Shamim Hossain, Md.Sobhan Ali, Md Zahidul Islam, Chui Ching Ling and Chorng Yuan Fung
This study examines the impact of profitability, firm size and leverage on corporate tax avoidance in Bangladesh, an emerging South Asian economy.
Abstract
Purpose
This study examines the impact of profitability, firm size and leverage on corporate tax avoidance in Bangladesh, an emerging South Asian economy.
Design/methodology/approach
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.
Findings
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.
Research limitations/implications
The use of cash effective tax rate (ETR) to investigate firms’ tax avoidance practices poses some limitations, and the results should be interpreted cautiously.
Practical implications
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.
Originality/value
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.
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Md Maruf Hossan Chowdhury, A.K.M. Shakil Mahmud, Shanta Banik, Fazlul K. Rabbanee, Mohammed Quaddus and Mohammed Alamgir
Drawing on the dynamic capability view (DCV), this research determines the suitable configurations of resilience strategies for sustainable tourism supply chain performance amidst…
Abstract
Purpose
Drawing on the dynamic capability view (DCV), this research determines the suitable configurations of resilience strategies for sustainable tourism supply chain performance amidst “extreme” disruptive events affecting the entire supply chain.
Design/methodology/approach
This research applies a multi-study and multi-method approach. Study 1 utilizes in-depth interviews to identify a list of tourism supply chain sustainability risks and resilience strategies. Study 2, using quality function deployment (QFD) technique, determines the most important resilience strategies corresponding to highly significant risks. Study 3, on the other hand, adopts a fuzzy set qualitative comparative analysis (fsQCA) to determine the best recipe of resilience strategies and risks to make the tourism supply chain performance sustainable.
Findings
The findings reveal that sustainable tourism performance during an extreme disruptive event (e.g. COVID-19 health crisis) depends on the combined effect of tourism resilience strategies and risks instead of their individual effect.
Practical implications
The research findings offer significant managerial implications. Managers may experiment with multiple causal conditions of risks and resilience strategies to engender the expected outcome.
Originality/value
This research extends current knowledge on tourism supply chain and offers insights for managers to mitigate the risks and ensures sustainable performance in the context of extreme disruptive events.
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