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The purpose of this paper is to investigate whether a firm’s undertaking of a bond IPO influences the monitoring of the private loans granted to the firm by private lenders. If it does, in which direction the monitoring changes?

The author uses both univariate and multivariate analyses to test the hypothesis. For the purposes of this research, the author’s primary data sources are LPC Dealscan, which provides data on private loans; Mergent FISD, which provides data on public bond issues; and the Compustat Industrial Annual Database, which provides the required financial data for the sample firms. The author’s sample covers non-financial US firms for the period of 1991-2010. The author’s final sample consists of nearly 23,000 private loans granted to about 5,500 non-financial US firms.

The major finding of this research is that private lenders increase their degree of monitoring of loans that they extend to a firm after it issues a bond IPO. The results of the two-stage bond IPO anticipation model further strengthen the findings. The evidence suggests that as the firm issues public debt for the first time, private lenders get concerned about the potential increase of agency problems and leverage, and consequently, find it valuable to increase the degree of monitoring of loans. Also, the magnitude of change in monitoring is strongly influenced by the degree of information asymmetry, leverage, profitability, and potential to waste free cash flow.

This paper enhances one’s understanding of the contracting dynamics between private lenders and the firm as it issues in the public debt market. The findings can aid firms anticipate the borrowing conditions they will face if they undertake a bond IPO. Further, the cross-sectional analysis on covenant changes from pre- to post-bond IPO period identifies specific firm characteristics that impact the magnitude of change of covenant intensity and comprehensiveness. As a result, uncertainty regarding post-bond IPO outcomes is reduced for borrowing firms.

The purpose of this paper is to investigate the financing behavior of firms in Pakistan. Previous studies have investigated corporate leverage determinants within any particular industry, such as manufacturing industry, textiles industry, etc., with varying results. This is one of the few studies that examine the determinants of leveraging attitude of firms across industrial sectors for textiles, large industries, and small industries. Thus, the study provides an insight into the general debt financing behavior in Pakistan and allows a basis for comparison of the leveraging decisions across industries.

The study employs the structural equations methodology which captures the endogenous relationship between profitability and leverage. Thereby, eliminating bias and providing more accurate results.

The findings suggest that the leveraging decisions differ across sectors and that each industry has its own distinctive debt requirements/characteristics. The authors conclude that a singular approach taken by investors and analysts would provide inaccurate assessment of firms’ debt financing policies and strategies.

There is a limitation on data availability in emerging countries, and a larger sample would have provided more robust results. Therefore, the study has only taken three sector sub-divisions, and more industry categories would have provided in-depth insights into the industry-wise leveraging behavior.

This is the first study to suggest that the borrowing attitude of firms differ across industries and vary due to their specific needs. This has implications for government regulators, investors, and creditors in providing a more customized approach to analyzing and meeting the external financing needs of firms.

This study is the first to use simultaneous equations model to eliminate bias that is prevalent in similar studies in Pakistan. The SEM captures the endogenous relationship between profitability and leverage. The research provides important information about the underlying financing behavior across industries, which has largely been ignored.

Artificial intelligence (AI) is a powerful and promising technology that can foster the performance, and competitiveness of micro, small and medium enterprises (MSMEs). However, the adoption of AI among MSMEs is still low and slow, especially in developing countries like Jordan. This study aims to explore the elements that influence the intention to adopt AI among MSMEs in Jordan and examines the roles of firm innovativeness and government support within the context.

The study develops a conceptual framework based on the integration of the technology acceptance model, the resource-based view, the uncertainty reduction theory and the communication privacy management. Using partial least squares structural equation modeling – through AMOS and R studio – and the importance–performance map analysis techniques, the responses of 471 MSME founders were analyzed.

The findings reveal that perceived usefulness, perceived ease of use and facilitating conditions are significant drivers of AI adoption, while perceived risks act as a barrier. AI autonomy positively influences both firm innovativeness and AI adoption intention. Firm innovativeness mediates the relationship between AI autonomy and AI adoption intention, and government support moderates the relationship between facilitating conditions and AI adoption intention.

The findings provide valuable insights for policy formulation and strategy development aimed at promoting AI adoption among MSMEs. They highlight the need to address perceived risks and enhance facilitating conditions and underscore the potential of AI autonomy and firm innovativeness as drivers of AI adoption. The study also emphasizes the role of government support in fostering a conducive environment for AI adoption.

As in many emerging nations, the AI adoption research for MSMEs in Jordan (which constitute 99.5% of businesses), is under-researched. In addition, the study adds value to the entrepreneurship literature and integrates four theories to explore other significant factors such as firm innovativeness and AI autonomy.

Motivated by lack of empirical research on sukuk (Islamic bonds) defaults and factors influencing the credit risk in sukuk industry, the study investigates the impact of corporate governance (CG) practices and corporate social sustainability (CS) disclosures on default risk of Islamic bonds in an emerging market.

In the Malaysian context the authors use generalized method of moments (GMM) to examine the mitigating effect of CG structure and CS disclosures on distance to default (DD) of sukuk issuers.

The results show that although both CG and CS have a significant and positive relationship with distance to default, the contribution of CS to augment DD is higher. Moreover, different CG variables have a varied relationship with distance to default, while the association is positive for all three pillars of CS, videlicet economic, social and environmental sustainability.

The findings of the study hold important implications for issuers, subscribers and regulators in the sukuk industry.

Limited research investigates the relationship between CG, CS and default risk of Islamic bonds. In light of this, the study attempts to fill the theoretical void in literature by examining the relationship among the underlying variables.