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This study aims to examine the influence of reward personalization on financing outcomes in the Industry 5.0 era, where reward-based crowdfunding meets the personalized needs of individuals.

The study utilizes a corpus of 218,822 crowdfunding projects and 1,276,786 reward options on Kickstarter to investigate the effect of reward personalization on investors’ willingness to participate in crowdfunding. The research draws on expectancy theory and employs quantitative and qualitative approaches to measure reward personalization. Quantitatively, the number of reward options is calculated by frequency; whereas text-mining techniques are implemented qualitatively to extract novelty, which serves as a proxy for innovation.

Findings indicate that reward personalization has an inverted U-shaped effect on investors’ willingness to participate, with investors in life-related projects having a stronger need for reward personalization than those interested in art-related projects. The pledge goal and reward text readability have an inverted U-shaped moderating effect on reward personalization from the perspective of reward expectations and reward instrumentality.

This study refines the application of expectancy theory to online financing, providing theoretical insight and practical guidance for crowdfunding platforms and financiers seeking to promote sustainable development through personalized innovation.

The performance of the first batch of listed companies since the restart of new initial public offerings (IPOs) in January 2014 and their accounting information face repeated and volatile questioning from different sides. This paper aims to take Guirenniao (China) Co. Ltd. (GRN for short), one of the first batch of listed companies in 2014 that suffered performance decline, as an example to analyze how it managed earnings before IPO.

This paper examines earnings management signs that exist in GRN through analysis of its financial statements compared to those of its industry peers. This paper then uses the modified Jones model to detect its accrual earnings management and build three models, which are abnormal levels of cash flows from operations, abnormal production costs and abnormal discretionary expenses, to detect real earnings management.

This paper finds that GRN managed earnings through accrual and real activities in 2012 and 2013. Finally, this paper provides evidence on the specific methods of earnings management, which are easing credit policy to recognize revenue in advance, abnormal expansion, decreasing costs and connected transactions.

This paper examines earnings management signs exist in GRN through analysis of its financial statements comparing to those of its industry peers. This paper then uses the modified Jones Model to detect its accrual earnings management and build three models which are abnormal levels of cash flows from operations, abnormal production costs and abnormal discretionary expenses to detect real earnings management.