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The primary purpose of this study is to investigate the influence of tourists' perceived value, satisfaction and behavioral intention on the development of Lipu Yi costume culture to promote the development of intangible cultural tourism and better construct a model of the influencing factors of Lipu Yi costumes in the development of intangible cultural heritage tourism.

The study site is the intangible cultural district of Panzhihua, Sichuan Province, China. This study examines the interrelationships between tourists' perceived value of experience, behavioral intention and satisfaction as the tourists relate to Lipu Yi costume and intangible cultural heritage tourism. A sample of 225 tourists who had visited Panzhihua at least once was selected for the study.

All seven of the survey's hypotheses were supported. Therefore, this study concludes that tourists' perceived value, satisfaction and behavioral intention directly affect the development of intangible cultural tourism and significantly positively impact the growth of Lipu Yi costumes culture. Descriptive analysis, confirmatory factor analysis (CFA) and structural equation modeling (SEM) investigation methods were used.

This paper analyzes tourists' perceived value of Lipu costume culture and tourists' satisfaction and behavioral intention during the tourism process. This study provides a more in-depth understanding of the relationship between Lipu Yi costume and non-heritage tourism factors. Practical methods and approaches are sought to further develop Lipu Yi costume non-heritage tourism.

This study aims to contribute to the body of scientific knowledge about teaching and promoting social entrepreneurship in higher education institutions (HEIs) based on a measurement before and after concluding an educational experience.

It tests hypotheses to draw conclusions from analyzing the pre- and post-test results of three study cases with different training experiences, to know the characteristics of the 304 participants.

The study indicated that incorporating transversal social entrepreneurship projects in various courses resulted in students feeling more capable regarding their social entrepreneurship potential.

The study presents the analysis of social entrepreneur training in three different curricular study cases. The information obtained adds value to social entrepreneurship education research that takes social entrepreneurship beyond business schools.

With the help of “self-learning” algorithms and high computing power, companies are transforming Big Data into artificial intelligence (AI)-powered information and gaining economic benefits. AI-powered information and Big Data (simply data henceforth) have quickly become some of the most important strategic resources in the global economy. However, their value is not (yet) formally recognized in financial statements, which leads to a growing gap between book and market values and thus limited decision usefulness of the underlying financial statements. The objective of this paper is to identify ways in which the value of data can be reported to improve decision usefulness.

Based on the authors' experience as both long-term practitioners and theoretical accounting scholars, the authors conceptualize and draw up a potential data value chain and show the transformation from raw Big Data to business-relevant AI-powered information during its process.

Analyzing current International Financial Reporting Standards (IFRS) regulations and their applicability, the authors show that current regulations are insufficient to provide useful information on the value of data. Following this, the authors propose a Framework for AI-powered Information and Big Data (FAIIBD) Reporting. This framework also provides insights on the (good) governance of data with the purpose of increasing decision usefulness and connecting to existing frameworks even further. In the conclusion, the authors raise questions concerning this framework that may be worthy of discussion in the scholarly community.

Scholars and practitioners alike are invited to follow up on the conceptual framework from many perspectives.

The framework can serve as a guide towards a better understanding of how to recognize and report AI-powered information and by that (a) limit the valuation gap between book and market value and (b) enhance decision usefulness of financial reporting.

This article proposes a conceptual framework in IFRS to regulators to better deal with the value of AI-powered information and improve the good governance of (Big)data.