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This study aims to investigate the impact of earnings predictability and truthfulness on nonprofessional investors’ investment willingness.

Earnings predictability is captured by quarterly earnings autocorrelation, and earnings truthfulness is indicated by real earnings management (REM). The average of investment attractiveness and willingness measures investment willingness. The authors use experiments to isolate the impact of quarterly earnings autocorrelation and REM on investors’ investment behaviors.

From the 2 × 2 design, the authors observe that investors weight more on earnings predictability than earnings truthfulness.

The generalization of the findings may be constrained for the following reasons. First, the authors use only one proxy, REM, to measure earnings truthfulness. In addition, the authors provide the participants, Amazon Mechanical Turk, with earnings predictability. Results may no longer hold if each participant has different understanding and analysis of earnings predictability.

In periods of unprecedented and severe financial uncertainty (i.e. the COVID-19 pandemic), investors rely more on earnings predictability than on earnings truthfulness. The study assists managers to strategically emphasize the predictability of earnings to attract investors, especially when firms face financial challenges or uncertainty.

This study contributes to understanding investor behavior and the critical role of earnings predictability and truthfulness in shaping investment decisions.

This paper contributes to the literature of earnings properties in financial reporting, particularly by shedding light on the nuanced interplay between earnings predictability and earnings truthfulness. The research also demonstrates that elevated earnings autocorrelation indirectly stimulates investment willingness by enhancing the investors’ perception of earnings persistence of targeted firms.

Fabric defects detection is vital in the automation of textile industry. The purpose of this paper is to develop and implement a new fabric defects detection method based on adaptive wavelet.

Fabric defects can be regarded as the abrupt features of textile images with uniform background textures. Wavelets have compact support and can represent these textures. When there is an abrupt feature existed, the response is totally different with the response of the background textures, so wavelets can detect these abrupt features. This method designs the appropriate wavelet bases for different fabric images adaptively. The defects can be detected accurately.

The proposed method achieves accurate detection of fabric defects. The experimental results suggest that the approach is effective.

This paper develops an appropriate method to design wavelet filter coefficients for detecting fabric defects, which is called adaptive wavelet. And it is helpful to realize the automation of textile industry.

The purpose of this article is to emphasise the development, importance and pressures on the under‐researched area of Chinese small and medium‐sized enterprises (SMEs) and human resource management (HRM) and provide a review and research agenda, especially relating to the debate on convergence vis‐à‐vis divergence.

Recent research papers are discussed and possible convergence and the pressures on HRM and its practice in Chinese SMEs are examined. Key elements in Chinese HRM are highlighted. Four main dimensions of HRM are compared in terms of western and Chinese characteristics and their cultural impacts and problems. A conceptual framework to locate influences on Chinese HRM is outlined as well as a set of propositions for future research and examination of key HRM areas.

The continuing influences on HRM suggest that a distinctly Chinese HRM approach combining western practice with Chinese characteristics may be one way for SMEs to develop.

It is central to a better understanding of Chinese SMEs and helps fill some of the gap in the analysis of HRM in Chinese SMEs. It also offers insights which managers, policy makers and Chinese government can use to construct and reform the supporting system for SMEs.