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This study aims to measure impact of several firm-specific factors on alternative measures of leverage. The authors also aim to study impact of the subprime crisis on such associations.

The authors utilized an unbalanced panel data of 973 firm-year observations on 47 UK listed non-financial firms for the years 1990–2019. Book-based and market-based long-term and total leverage measures have been used as explained variables. The explanatory variables are profitability, size, two measures of growth, asset tangibility, non-debt tax shields, firm age and product uniqueness. Fixed effect and random effect models with clustered robust standard errors have been utilized for data analysis. To find the effect of subprime crisis, original dataset was split to create pre-crisis and post-crisis datasets.

The authors find that profitability significantly reduces leverage while firms having more tangible assets use significantly more debt in capital structure. Firm size and non-debt tax shield have statistically insignificant positive impact on leverage. Having more unique products reduces use of external debt, albeit insignificantly. Growth, when measured as market-to-book ratio, has inconsistent impact, whereas capital expenditure insignificantly reduces leverage. Age is found to be an insignificant predictor of leverage. After the subprime crisis, firms started relying more on internal fund instead of external debt, more particularly short-term debt. Having more collateral is gradually becoming more important for availing external debt.

Data limitations restrict generalization of the findings.

This is one of the pioneering attempts to show how subprime crisis altered the theoretical domain of capital structure research in the UK.

The purpose of this paper is to investigate the organizational readiness perspective of adopting artificial intelligence and big data analytics in the textile and garment industry in Bangladesh along with identifying the associated factors.

The research uses a qualitative method using semi-structured interviews with representatives of business organizations and stakeholders of Bangladesh’s textile and garment industry.

The research reveals that the textile and garment industry in Bangladesh currently has low organizational readiness to adopt artificial intelligence and big data analytics. This is due to moderate knowledge- and leadership-readiness along with low human-, finance- and engagement-readiness of most of the business organizations. The readiness aspects interplay with each other and need to be improved holistically.

Considering the significant global and national importance of Bangladesh’s textile and garment industry, gaining insights into the industry’s current state of readiness for adopting artificial intelligence and big data analytics would offer valuable assistance to both national and global economies and may enhance economic outcomes.

Since no exploratory study was conducted to understand the organizational readiness aspects of adopting artificial intelligence and big data analytics of the globally significant textile and garment industry in Bangladesh, the paper analyzes five key aspects of such readiness and offers a basis for conducting similar studies in other emerging economies.

Purpose: This chapter provides a thorough explanation of the growth of community-based tourism in Bangladesh, encompassing its historical background, challenges faced, and potential strategies for future advancement.

Methodology: We employed a descriptive analysis based on the literature review of the development and expansion of community-based tourism in Bangladesh. This study looks at the development of community-based tourism (CBT) throughout history, as well as the innovative contributions made by Non-Governmental Organizations (NGOs) and local groups in CBT initiatives, government policies, international recognition, challenges encountered (such as environmental and economic concerns), and potential strategies for future expansion.

Findings: The chapter suggests various methods for future growth, including developing policies, involvement of the private sector, execution of marketing strategies, and empowerment of the community through training and enhancing their abilities.

Value: The study provides insightful information regarding the distinctive characteristics of community-based tourism in Bangladesh, drawing attention to the country's long-standing tradition of extended hospitality and cultural heritage. Moreover, the study analyzes the difficulties and opportunities that CBT efforts encounter in the region and the proposition of individualized solutions for sustainable growth.

Implications: This study not only improves the quality of experiences that tourists have, but it also gives a voice to underrepresented groups by prioritizing local communities. In addition, it instills a sense of ownership and pride in the community's residents, motivating them to make a long-term commitment to preserving the legacy of developing CBT in Bangladesh.