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This study aims to investigate the motivations behind the issuance of financial instruments with characteristics of equity (FICE), economic consequences associated with their issuance and accounting classifications based on a value-relevance approach.

Using a sample of 169 financial and nonfinancial firms from 10 jurisdictions that adopted International Financial Reporting Standards, the authors use a difference-in-differences econometric approach.

The findings reveal that FICE issuers are more leveraged companies with higher costs of equity and, in some cases, lower effective tax rates. This evidence corroborates the hypothesis that issuers of FICEs seek to increase their book values of equity (accounting treatment as equity) and, simultaneously, generate deductible expenses for tax purposes (tax treatment as liability).

This finding suggests that market participants do not treat these instruments as regular equity but rather as quasi-equity. The findings suggest that a binary classification of FICE as debt or equity may not be the accounting treatment that best represents the underlying economic substance of these contracts. Furthermore, this study reinforces the IASB indication regarding to increase the FICE disclosure to allow stakeholders to better understand the economic essence of these bonds.

This study assesses the economic outcomes and market evaluation of a specific type of FICE that has not been previously studied, which is similar to the examples provided by the IASB in their materials on the subject.

Digital technology's integration into education has transformed learning frameworks, necessitating the exploration of factors influencing students’ engagement in digital informal settings. This study, grounded in self-determination theory (SDT), proposes a model comprising artificial intelligence (AI) competence, chatbot usage, perceived autonomy (PA), digital informal learning (DIL) and students’ engagement.

The study collected survey data from 409 participants at Saudi Arabian universities, ultimately using 387 valid responses for analysis. This dataset was subjected to a thorough examination to confirm the validity of our proposed model. To decipher the complex interactions within our model, we utilized partial least squares structural equation modeling (PLS-SEM). The study adopted a disjoint two-stage method to formulate a reflective-formative higher-order construct (HOC).

The study's findings showed that cognitive learning (CL), metacognitive learning (MCL) and social and motivational learning (SML) are the essential components of DIL. Significantly, the study determined that AI competence, chatbot usage, PA and DIL markedly affect students’ engagement. Moreover, the R² value of 0.592 for student engagement indicates the model's robustness in explaining 59.2% of the variance, highlighting its effectiveness in identifying key drivers of student engagement in DIL contexts.

This research enhances understanding by detailing the intricate relationships among AI competence, chatbot usage, and students’ engagement in informal digital learning. It extends SDT to emphasize intrinsic motivations and AI capabilities, introducing reflective-formative HOCs for comprehending educational intricacies. It provides practical strategies for enhancing AI abilities and chatbot use in education, promoting personalized, engaging and autonomous digital learning spaces, thereby advancing educational theory and practice.

The integration of digital technologies into education has brought about a profound transformation, fundamentally reshaping the learning landscape. The purpose of this study is to underscore the importance of investigating the factors influencing students’ engagement (SE) in this evolving digital era, particularly within formal digital learning environments. To address this need, the study is grounded in self-determination theory (SDT) and presents a comprehensive model comprising interconnected elements: digital competence (DC), smartphone use (SPU), perceived autonomy (PA), digital formal learning (DFL) and SE.

The research conducted an investigation within Saudi Arabian universities, collecting a robust data set of 392 cases. This data set underwent rigorous analysis to validate the proposed model. To untangle the intricate relationships within the framework, the study used partial least squares structural equation modelling. Given the distinct dimensions of the two constructs under study, the researcher used a disjoint two-stage approach to establish reflective-formative higher-order constructs (HOC).

The findings revealed that digital literacy and digital skills (DS) constitute the foundational constituents of DC. Simultaneously, the study identified facilitation, distraction and connectedness as integral components of SPU. Importantly, the study established that DC, SPU, PA and DFL significantly influence SE. Furthermore, the research illuminated the mediating roles played by SPU, PA and DFL in the complex relationship between DC and SE.

This study advances the literature by delineating the dynamic interplay between DC, SPU and SE in digital learning. It extends SDT within educational contexts, emphasizing the role of internal motivations and DS. Methodologically, it innovates through reflective-formative HOCs, deepening the analysis of complex educational constructs. Managerially, it guides institutions in enhancing DC and integrating smartphones effectively into learning, advocating for tailored strategies to foster engaging and autonomous digital learning environments, thereby enriching both theoretical understanding and practical application in education.