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The purpose of this paper is to explore the mechanisms by which high-commitment organization is associated with employees’ job performance through the perspective of taking charge and the perceived strength of the human resource management (HRM) system.

Based on conservation of resources (COR) theory, the authors used two-wave survey data from 200 supervisor-subordinates pairs in China. This study uses hierarchical linear regression and bootstrapping method to analyze the mediated moderation effect.

The authors found that perceived high-commitment organization and perceived strength of the HRM system interact in predicting employees’ taking charge behavior, such that perceived high-commitment organization is more positively associated with taking charge when the perceived strength of the HRM system is high rather than low. In addition, taking charge mediates the relationship between interactive effects of perceived high-commitment organization and the perceived strength of the HRM system on employees’ job performance.

This study extended the high-commitment organization from the perspective of individual perception based on the COR theory and regard perceived high-commitment organization as an organizational resource.

Green product development is a pivotal way to achieve environmental sustainability. The purpose of this paper is to theorize and empirically test how environmentally specific leadership enhances the green product development performance from the perspective of the HRM system. In this regard, the authors investigate the mediating role of the strength of the HRM system to change with regard to the relationship between environmentally specific leadership and green product development performance. For a substantial explanation of the boundary condition, the authors investigate the moderating role of the green HRM on the relationship between environmentally specific transformational leadership and the strength of the HRM system.

Based on climate strength theory, the authors use the three-wave survey data from 362 top or middle managers in the new energy industry of China. This study uses hierarchical linear regression and bootstrapping method to analyze the mediated moderation effect.

Results confirm a positive effect of environmentally specific leadership and the strength of the HRM system on green product development performance. The authors also found the mediation effect of the strength of the HRM system and the moderation effect of green HRM are all significant.

This study integrates the perspectives of both content-focused HRM and process-focused HRM and demonstrates why leadership and the HRM system could jointly enhance green product development performance in Asia.

Based on the JD-R model and process-focused HRM perspective, this research paper aims to investigate the processes underlying the relationship between AI-enabled HR analytics and employee well-being outcomes (resilience) that received less attention in the AI-driven HRM literature. Specifically, this study aims to examine the indirect effect between AI-enabled HR analytics and employee resilience via job crafting, moderated by HRM system strength to highlight the contextual stimulus of AI-enabled HR analytics.

The authors adopted a time-lagged research design (one-month interval) to test the proposed hypotheses. The authors used two-wave surveys to collect data from 175 full-time hotel employees in China.

The findings indicated that employees' perceptions of AI-enabled HR analytics enhance their resilience. This study also found the mediation role of job crafting in the mentioned relationship. Moreover, the positive effects of AI-enabled HR analytics on employee resilience amplify in the presence of a strong HRM system.

Organizations that aim to utilize AI-enabled HR analytics to achieve organizational missions should also dedicate attention to its associated employee well-being outcomes.

This study enriched the literature with regard to AI-driven HRM in that it identifies the mediating role of job crafting and the moderating role of HRM system strength in the relationship between AI-enabled HR analytics and employee resilience.

This study/paper aims to understand the public perceptions of AI through mass media discourse. In the past few years, significant progress has been made in the field of artificial intelligence (AI). The benefits of AI are obvious, but there is still huge uncertainty and controversy over the public perception of AI. How does the mass media conceptualize AI?

In this paper, the authors analyze the evolution of AI covered by five major news media outlets in the past 30 years from 7 dimensions: scientific subject, keyword, country, institution, people, topic and opinion polarity.

First of all, different subjects are competing for and dividing up the right to speak of AI, leading to the gradual fragmentation of the concept of AI. Second, reporting on AI often includes reference to commercial institutions and scientists, showing a successful integration of science and business. Moreover, the result of topic modeling shows that news media mainly defines AI from three perspectives: an imagination, a commercial product and a field of scientific research. Finally, negative reports have focused on various issues relating to AI ethics.

The results can help bridge various conversations surrounding AI and promote richer discussions, increase the participation of scientists, businesses, governments and the public and provide more perspectives on the functions, prospects and pitfalls of AI.

This paper aims to examine whether managerial sentiment, extracted from annual reports, is associated with corporate risk-taking in the context of Chinese companies. This study expands the vein of literature on overconfidence theory.

By leveraging textual analysis on Chinese listed companies’ annual reports, the authors construct firm-level managerial sentiment during 2007 and 2021 to examine how managerial sentiment influences corporate risk-taking after control for firm characteristics. Corporate risk-taking is denoted by corporate investment engagements: capital expenditures and net fixed asset investment.

Results show that incentives for corporate risk-taking are likely to increase with the positive managerial sentiment and decrease with the negative sentiment in companies’ annual reports. Positive managerial sentiment is associated with over-/under-investment and low/high investment efficiency. Further additional tests show that the managerial sentiment effect only holds during low economic uncertain years and samples of private-owned firms. Furthermore, the robust tests indicate that there is no endogenous issue between managerial sentiment and corporate risk-taking.

Annual report textual-based managerial sentiment may not perfectly reflect managers’ lower frequency sentiment (e.g. weekly, monthly and quarterly sentiment). Future studies could attempt to capture managers’ on-time sentiment by using media sources and corporate disclosures.

To the best of the authors’ knowledge, this paper is the first research to provide insights into supervising managers’ corporate decisions by observing their textual information usage in corporate disclosure. Moreover, the approach of measuring managerial sentiment might be a solution to monitoring managerial class.

This paper contributes to the literature on accounting and finance studies, adding another piece of empirical evidence on content analysis by examining a unique language and institutional context (i.e. China). Besides, the paper notes that in line with the English version disclosure, based on Chinese semantic words, managerial sentiment in the Chinese-speaking world has magnitude on corporate decisions. The research provides insights into supervising managers’ corporate decisions by observing their textual information usage in corporate disclosure. Moreover, the approach to measuring managerial sentiment may be a practical solution to monitoring managerial class.

The type 120 emergency valve is an essential braking component of railway freight trains, but corresponding diaphragms consisting of natural rubber (NR) and chloroprene rubber (CR) exhibit insufficient aging resistance and low-temperature resistance, respectively. In order to develop type 120 emergency valve rubber diaphragms with long-life and high-performance, low-temperatureresistant CR and NR were processed.

The physical properties of the low-temperature-resistant CR and NR were tested by low-temperature stretching, dynamic mechanical analysis, differential scanning calorimetry and thermogravimetric analysis. Single-valve and single-vehicle tests of type 120 emergency valves were carried out for emergency diaphragms consisting of NR and CR.

The low-temperature-resistant CR and NR exhibited excellent physical properties. The elasticity and low-temperature resistance of NR were superior to those of CR, whereas the mechanical properties of the two rubbers were similar in the temperature range of 0 °C–150 °C. The NR and CR emergency diaphragms met the requirements of the single-valve test. In the low-temperature single-vehicle test, only the low-temperature sensitivity test of the NR emergency diaphragm met the requirements.

The innovation of this study is that it provides valuable data and experience for future development of type 120 valve rubber diaphragms.

Light field images (LFIs) have gained popularity as a technology to increase the field of view (FoV) of plenoptic cameras since they can capture information about light rays with a large FoV. Wide FoV causes light field (LF) data to increase rapidly, which restricts the use of LF imaging in image processing, visual analysis and user interface. Effective LFI coding methods become of paramount importance. This paper aims to eliminate more redundancy by exploring sparsity and correlation in the angular domain of LFIs, as well as mitigate the loss of perceptual quality of LFIs caused by encoding.

This work proposes a new efficient LF coding framework. On the coding side, a new sampling scheme and a hierarchical prediction structure are used to eliminate redundancy in the LFI's angular and spatial domains. At the decoding side, high-quality dense LF is reconstructed using a view synthesis method based on the residual channel attention network (RCAN).

In three different LF datasets, our proposed coding framework not only reduces the transmitted bit rate but also maintains a higher view quality than the current more advanced methods.

(1) A new sampling scheme is designed to synthesize high-quality LFIs while better ensuring LF angular domain sparsity. (2) To further eliminate redundancy in the spatial domain, new ranking schemes and hierarchical prediction structures are designed. (3) A synthetic network based on RCAN and a novel loss function is designed to mitigate the perceptual quality loss due to the coding process.

The purpose of this paper is to provide a comprehensive review on electronic word-of-mouth (eWOM) credibility. Further, the authors propose a comprehensive and integrated model on eWOM credibility.

The authors conducted a systematic review of the extant literature on marketing, sociology and psychology to identify the factors that affect eWOM credibility. Further, the authors developed themes and identified factors which lead to eWOM credibility.

Four factors were identified, i.e. content, communicator, context and consumer, which affect eWOM credibility. Several variables associated with these four factors were identified, which result in eWOM credibility. Further, the authors developed 22 propositions to explain the causal relationship between these variables and eWOM credibility.

The conceptual model needs empirical validation across various eWOM platforms, i.e. social networking websites, e-commerce websites, etc.

Managers and e-commerce vendors can use these inputs to develop specific design elements and assessment tools which can help consumers to identify credible eWOM messages. Credible eWOM messages, in turn, will increase the “trust” and “loyalty” of the customers on e-commerce vendors.

This paper provides a conclusive takeaway of eWOM credibility literature by integrating multiple perspectives and arguments from the extant literature. This study also presents an integrated model, which provides a theoretical framework for researchers to further examine the interaction effect of various variables, which results in eWOM credibility.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-06-2020-0263