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This study aims to present a model for detecting and predicting creative accounting in companies listed on the Tehran Stock Exchange (TSE).

The authors conduct this research in three stages. First, the authors review the literature to determine the dimensions, components, indicators and techniques of creative accounting. Second, the authors conduct semi-structured interviews with experts using the fuzzy Delphi technique to obtain screening and reach a consensus. Finally, the authors develop a model to predict creative accounting by classifying the financial statements of the sample companies into two groups based on the use or non-use of creative accounting techniques, measuring the indicators determined in the previous stage, running various machine learning algorithms and choosing the superior algorithm.

The results indicate the usefulness of accounting information for detecting and predicting creative accounting and the relevance of several financial attributes as important predictors. The results also indicate the superiority of extremely randomized trees over other algorithms in predicting creative accounting and suggest that the primary purpose of creative accounting in Iran is earnings management. Contrary to the political cost hypothesis, large Iranian companies use creative accounting to inflate profits.

The present research also has several limitations that must be considered, and caution must be exercised in interpreting and generalizing the findings as specified in the revised manuscript.

This study’s implications are significant for policymakers, standard-setters and practitioners. By recognizing the detrimental effects of creative accounting on financial transparency within companies, policymakers can address existing gaps in accounting standards to minimize the potential for earnings manipulation. Consequently, strengthening internal and external mechanisms related to a firm’s financial performance becomes achievable. The study provides evidence of the need for audit firms to recognize the importance of creative accounting and consider creative accounting in their audit plans to prevent insufficient or even misleading disclosure by companies that extensively use creative accounting practices in their financial reporting. Moreover, knowledge of creative accounting techniques can help auditors assess audit and detection risks and serve as a valuable guide for reducing audit costs and improving audit quality.

Given that creative accounting practices distort the true or real accounting results, curbing creative accounting practices reduces corporate failures and could lead to the reduction of job losses and other social consequences.

This study uses a unique database in Iran to determine a model for predicting creative accounting using a mixed-method methodology, qualitative and quantitative, to identify creative accounting techniques and run various machine learning algorithms.

This study aims to investigate the digital entrepreneurial intentions of Albanian youth, identify the obstacles they face in starting digital businesses and examine their preferences regarding the types of businesses they aspire to establish. The Theory of Planned Behavior (TPB) is used as a framework to analyze these factors.

Primary data were collected via questionnaires distributed in public and private universities. In a sample of 325 students, Structural Equation Modeling with Confirmatory Factor Analysis, path analysis and machine learning-based text analysis were used.

This study reveals significant impacts of innovativeness, attitude towards entrepreneurship, subjective norms, perceived behavioral control and self-efficacy on digital entrepreneurial intentions among Albanian students. Additionally, text mining highlights a strong preference for digital entrepreneurship.

The theoretical contributions of this study include applying Structural Equation Modeling to reveal insights into the impact of entrepreneurial factors and obstacles. The findings can inform policymakers and educators in designing targeted interventions to support student entrepreneurship. Meanwhile, the limitations of this study encompass a small sample size, lack of time series and panel data and the absence of an evaluation of the impact of education system practices, along with the need to investigate the effects of young population emigration from Albania to the EU.

This research contributes to the understanding of digital entrepreneurial intentions and behavior by using TPB in the Albanian context, offering access to a diverse dataset from Albanian universities, testing the direct impact of innovativeness on entrepreneurial behavior and pioneering the use of machine learning techniques for text analysis. Thus, it provides novel insights into the entrepreneurial landscape in Albania. In addition, this work can drive initiatives to support student entrepreneurship and bridge the gap between academia and industry in Albania.