Search results

This study investigates the influence of corporate culture on financial reporting transparency within Iranian firms.

Leveraging a dataset of 1,480 firm-year observations from the Tehran Stock Exchange spanning from 2013 to 2022, the study employs text mining to quantify linguistic features of corporate culture and transparency, specifically readability and tone, within annual financial statements and Management Discussion and Analysis (MD&A) reports.

Our results confirm a positive and significant relationship between corporate culture and financial reporting transparency. The distinct dimensions of corporate culture — Creativity, Competition, Control, and Collaboration — each uniquely enhance financial transparency. Robustness tests including firm fixed-effects, entropy balancing, Generalized Method of Moments (GMM), and Propensity Score Matching (PSM) validate the profound influence of corporate culture on transparency. Additionally, our analysis shows that corporate culture significantly affects the disclosure of business, operational, and financial risks, with varying impacts across risk categories. Cross-sectional analysis further reveals how the impact of corporate culture on transparency varies significantly across different industries and firm sizes.

The study’s scope, while focused on Iran, opens avenues for comparative research in different cultural and regulatory environments. Its reliance on text mining could be complemented by qualitative methods to capture more nuanced linguistic subtleties.

Findings underscore the strategic importance of cultivating a transparent corporate culture for enhancing financial reporting practices and stakeholder trust, particularly in emerging economies with similar dynamics to Iran.

This research is pioneering in its quantitative analysis of the textual features of corporate culture and its impact on transparency within Iranian corporate reports, integrating foundational theoretical perspectives with empirical evidence.

Chat Generative Pretrained Transformer (ChatGPT), a chatbot with artificial intelligence (AI) technology, opens up new directions for innovation. However, the extent to which literature has not considered the trustworthiness and satisfaction of ChatGPT. Those are important elements leading to continuous use (CU). Particularly, this study investigates the use of the ChatGPT Translate function. Requirements for task-AI-technology fit, trust and satisfaction relevant to ChatGPT Translate are addressed in this study.

Task-technology fit (TTF) theory forms the theoretical lens to examine the influences of TTF, AI-tech trust and satisfaction on CU of AI technology. A questionnaire survey was used for data collection. Structural equation modeling was employed to test the research model.

The findings show task and technology characteristics have positive effects on task-AI-technology fit. Task-AI-technology fit has a positive effect on AI-tech trust, which in turn has a positive effect on the CU of AI technology. Finally, the level of CU of AI technology by users satisfied with its responses is higher than users dissatisfied with its responses.

The results have important theoretical and practical implications for academia and industry to devise strategies and policies on a free-to-use AI system.