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The purpose of this study is to determine the influence of technology visibility and subsequent perceptions of VFRs on consumers' intention to adopt VFRs in the online shopping context. A cross-cultural comparison was conducted to examine the different relationships among technology visibility, consumer perceptions and adoption intentions between the Chinese and Korean consumers.

Data were collected from 306 Chinese and 324 Korean consumers. The data were empirically analysed using structural equation modelling as well as multi-group comparisons.

Empirical results suggest significant influence of technology visibility on consumers' experiential and functional perceptions towards VFRs and accordingly on their adoption intention towards VFRs. Significant differences were also revealed between the Chinese and Korean consumers in their adoption behaviours towards VFRs.

The comparison was only conducted between the Chinese and Korean consumers. If two countries from two dramatically different cultures were compared, the results might be more significant.

An important implication is that enhancement of visibility is crucial for technology adoption considering its importance in shaping consumers' perceptions towards the technology.

The paper empirically tested the importance of technology visibility in consumers' new technology adoption in the VFR context from a cross-cultural perspective.

The year 2022 marks the 40th anniversary of the establishment of the grey system theory (GST), which has been widely applied in the engineering field. This paper aims to systematically identify the achievements, hotspots, knowledge structure and emerging trends in this field.

A bibliometrics analysis was conducted on relevant publications retrieved from Web of Science (WoS) using CiteSpace and MapEquation. A statistical analysis of the collected 3,384 papers was completed. Three networks, including a co-occurrence network, cooperation network and co-citation network, were obtained to draw knowledge structure, hotspots and research frontiers.

The top four applied engineering fields are engineering electrical electronics, computer science artificial intelligence, engineering multi-disciplinary and automation control system. In total, 65 countries have engaged in this field, and China has occupied a leading position, with the largest number of articles published and the widest cooperation with other countries. The USA, United Kingdom (UK) and China Taiwan also contribute a lot. The Nanjing University of Aeronautics and Astronautics and Professor Liu Sifeng have a core position in the cooperation network. More hotspots appear in the last ten years. Regarding the emerging trends, the combination of theoretical models and practical engineering problems has attracted more attention. Besides, the application of GST in environment protection and the integration of the GST and intelligent algorithm became more popular.

The comprehensive bibliometrics analysis and visualization demonstration were conducted, presenting the interdisciplinary characteristics, major research topics and research frontiers in this field.

This study investigates the impact of AI finance on financing constraints of non-SOE firms in an emerging market.

Using a sample of non-SOE listed companies in China from 2011 to 2018, this research employs the cash–cash flow sensitivity model to examine the effect of AI finance on financing constraints of non-SOE firms.

We find that the development of AI finance can alleviate the financing constraints of non-SOE firms. Further, we document that such effect is more pronounced for smaller firms, more innovative firms and firms in developing areas.

This study suggests that emerging market countries can ease the financing constraints of non-SOE firms by promoting AI finance development.

This study, to the best of our knowledge, is the first one to explore the relationship between AI finance development and financing constraints of non-SOE firms in emerging markets.

In the rapidly evolving digital economy, businesses face formidable pressures to maintain their competitive standing, prompting a surge of interest in the intersection of artificial intelligence (AI) and digital transformation (DT). This study aims to assess the impact of AI technologies on corporate DT by scrutinizing 3,602 firm-year observations listed on the Shanghai and Shenzhen stock exchanges. The research delves into the extent to which investments in AI drive DT, while also investigating how this relationship varies based on firms' ownership structure.

To explore the influence of AI technologies on corporate DT, the research employs robust quantitative methodologies. Notably, the study employs multiple validation techniques, including two-stage least squares (2SLS), propensity score matching and an instrumental variable approach, to ensure the credibility of its primary findings.

The investigation provides clear evidence that AI technologies can accelerate the pace of corporate DT. Firms strategically investing in AI technologies experience faster DT enabled by the automation of operational processes and enhanced data-driven decision-making abilities conferred by AI. Our findings confirm that AI integration has a significant positive impact in propelling DT across the firms studied. Interestingly, the study uncovers a significant divergence in the impact of AI on DT, contingent upon firms' ownership structure. State-owned enterprises (SOEs) exhibit a lesser degree of DT following AI integration compared to privately owned non-SOEs.

This study contributes to the burgeoning literature at the nexus of AI and DT by offering empirical evidence of the nexus between AI technologies and corporate DT. The investigation’s examination of the nuanced relationship between AI implementation, ownership structure and DT outcomes provides novel insights into the implications of AI in the diverse business contexts. Moreover, the research underscores the policy significance of supporting SOEs in their DT endeavors to prevent their potential lag in the digital economy. Overall, this study accentuates the imperative for businesses to strategically embrace AI technologies as a means to bolster their competitive edge in the contemporary digital landscape.