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This study aims to reveal the mechanism of CEO overconfidence in the digital transformation of specialized, refined, distinctive and innovative (SRDI) enterprises, thereby enriching research related to upper echelons theory and corporate digital transformation.

This study uses listed SRDI companies in China from 2017 to 2022 as a sample and adopts a fixed-effects regression model to analyze the direct, mediating, and moderating effects of CEO overconfidence on corporate digital transformation.

First, CEO overconfidence significantly promotes SRDI enterprises' digital transformation. Second, according to the “cognition-behavior-outcome” model, we found that entrepreneurial orientation plays a mediating role. Third, based on the principle of procedural rationality and the interaction perspective between the CEO and the executive team, we introduce the heterogeneity of the executive team as a moderating variable. Our findings indicate that age heterogeneity within the executive team has a negative moderating effect, whereas educational and occupational heterogeneities have positive moderating effects.

This study expands on earlier research that focuses primarily on CEO demographic characteristics. It enriches the analytical perspective of upper echelons theory on corporate digital transformation by analyzing the psychological characteristics of CEOs, that is, overconfidence and its mediating pathways. Moreover, this study goes beyond the previous literature that does not differentiate between CEOs and executive teams by introducing the concept of CEOs' interactions with the executive team and including the heterogeneity of the executive team as a moderating variable in the literature. Thus, continuing to deepen the application of upper echelons theory to corporate digital transformation. Additionally, this study contributes to the literature on the positive consequences of overconfidence.

Chinese internet literature (CIL) platforms afford freedom for creative expression and opportunities for direct interactions between writers and fans and among fans. Enabled by these platforms' technological and commercial arrangement, a new form of literary production and consumption has emerged, the most significant of which is the role of fans participation. A social network analysis of the interaction patterns in online fan communities was conducted to investigate fan communication activities at scale. Of particular interest is how the socio-technical system of the site influences its network topology.

Online forums for 10 popular fiction titles in Qidian, the leading CIL platform, were analyzed. Social networks were constructed based on a post–reply–reply threaded discussion structure. Various aspects of fan interactions were analyzed, including number of replies per post, post length and emerging network patterns.

Similarities in network topology shared by CIL fan forums and other online communities, such as small-world and scale properties, were discovered; however, distinct network dynamics were also identified. Consistent with previous findings, writers and moderators, along with a few highly ranked fans, occupied the central positions in the network. This was due to their social roles and the nature of their posts rather than, as the conventional explanation goes, preferential attachment.

The findings demonstrate how community-specific circumstances and norms influence interaction patterns and the resultant network structure. It was revealed that in the CIL sites, the users adopted the technologies in unexpected ways. And the resulting network topology can be attributed to the interplay between the sites' official arrangement and users' adaptive tactics.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-11-2021-0596.

Live-streaming platforms emphasize dynamic social interaction and fan engagement. Users integrate into the live-streaming community through continuous social learning activities, such as sending bullet comments, reviewing comments and interacting with celebrities. However, comprehensive research on the transactional intricacies of live-streaming e-commerce from the perspectives of community and learning is still lacking.

Focusing on the behavior characteristics of the reference group represented by online celebrities and fans in the live-streaming environment, this study utilized social learning as the theoretical basis to examine how reference groups affect consumer purchase intention through a series of intermediary effects. An empirical investigation and machine learning algorithms were utilized to explore and verify the hypothesized model.

The results show that: (1) reference groups’ behavior positively stimulated social presence and enhanced consumer purchase intention through the chain-mediating effect of social presence and trust in online celebrities; (2) celebrity characteristics (professionalism, attractiveness and interactivity) positively impacted consumer trust; (3) in addition, machine learning algorithms substantiated that reference groups’ behavior, social presence, trust and celebrity characteristics had a remarkably robust predictive effect on purchase intention.

These findings hold theoretical implications for understanding how the social community affects consumers’ purchase intention in the live-streaming context and practical significance for marketing strategies toward live-streaming e-commerce.

This study aims to establish an evaluation method for cross-regional major infrastructure project (CRMIP) supportability. The focus is to identify evaluation indicators from a complexity perspective and develop an evaluation model using qualitative and quantitative methods. Case studies are carried out to verify the reliability of the evaluation model, thereby providing theoretical and practical guidance for CRMIP operations and maintenance (O&M).

Guided by the idea of complexity management, the evaluation indicators of CRMIP supportability are determined through literature analysis, actual O&M experience and expert interviews. A combination of qualitative and quantitative methods, consisting of sequential relationship analysis, entropy weighting, game theory and cloud model, is developed to determine the indicator weights. Finally, the evaluation model is used to evaluate the supportability of the Hong Kong–Zhuhai–Macao Bridge (HZMB), which tests the rationality of the model and reveals its supportability level.

The results demonstrate that CRMIPs' supportability is influenced by 6 guideline-level and 18 indicator-level indicators, and the priority of the influencing factors includes “organization,” “technology,” “system,” “human resources,” “material system,” and “funding.” As for specific indicators, “organizational objectives,” “organizational structure and synergy mechanism,” and “technical systems and procedures” are critical to CRMIPs' O&M supportability. The results also indicate that the supportability level of the HZMB falls between good and excellent.

Under the guidance of complexity management thinking, this study proposes a supportability evaluation framework based on the combined weights of game theory and the cloud model. This study provides a valuable reference and scientific judgment for the health and safety of CRMIPs' O&M.

This paper seeks to discuss the genealogical sources for Chinese immigrants as well as the settlement of Chinese in the USA and the historical evolution of Chinese names, their origins, arrangement and development. It aims to cover the origins of various classes of Chinese surnames, followed by the content description of a traditional genealogical book for jiapu.

The paper researches the various ways that a Chinese person can find out about their ancestry.

The paper reveals the roles of libraries, including serving the needs of patrons interested in genealogical research, preserving and interpreting information through oral and family history projects and collaborating with other libraries through interlibrary loan, document delivery, library consortia, collection management and international resource‐sharing.

The study provides information on where and how to locate the genealogical resources for researching the genealogy of a Chinese family.

The paper analyzes the value of genealogical research as a documentary source for population history, life expectancy in a clan, marriages and family connections, as well as lineage organizations and inter‐lineage relations.

The purpose of this paper is to establish the damage alarming indexes for ancient wood structures and study the damage sensitivity and noise robustness of these indexes under random excitation.

Xi’an Bell Tower is taken as a case in this paper to simulate the damage of ancient wood structures through finite element (FE) simulation and determine the satisfactory damage alarming indexes with wavelet packet energy spectrum.

The results of this paper show that: 1) the damage alarming indexes can effectively identify the damage of ancient wood structures, each index with a different damage sensitivity; 2) the energy ratio deviation is greater than the energy ratio variance and is close to the maximum variation of energy ratio; 3) the energy ratio deviation has a better alarming effect than the energy ratio variance during the initial period of the damage. With the accumulation of the damage, the energy ratio variance outperforms the energy ratio deviation; 4) the sensitivity of the energy ratio deviation and variance varies from positions, changing from the highest to lowest at the mortise-and-tenon joints, the beam mid-span and the plinth; 5) if signal to noise ratio (SNR) is 40db or larger, the indexes can accurately identify the damage of ancient wood structures. As SNR increases, the indexes will have an increasingly higher sensitivity and certain ability to resist noise.

The FE model is simpiy, it does not completely reflect Xi’an Bell Tower.

It will provide a theoretical basis for the damage alarming of Xi’an Bell Tower.

It makes structural health monitoring through structural vibration response under ambient excitation a new research field in damage detection as well as a positive way of ancient architecture protection.

This paper studies the damage alarming effect on ancient wood structures from different wavelet functions and wavelet packet decomposition levels. To study the effect under white noise environment, this paper adds Gaussian white noise with a SNR of 10, 20, 30, 40 and 50 db to the acceleration response signal of intact structure and damaged structure.

The purpose of this paper is to investigate the effects of political connections on the association between firms' business strategy and their tax aggressiveness in an emerging economy such as China.

The authors study a large sample of Chinese public firms from 2011 to 2017 using a panel regression model. In addition, a change analysis, an instrument variable test and alternative measures/samples are implemented as robustness tests.

Firms adopting innovative business strategy are more tax aggressive overall. However, innovative firms with political connections are less tax aggressive compared to those without political connections.

This paper contributes to the understanding of firms' tax behaviors in an emerging economy setting. It suggests that there are costs associated with political connections, such as foregone tax saving opportunities, which are understudies in the prior literature.

The purpose of this paper is to investigate the impact of mechanization services on farm productivity in Northern China from an empirical perspective, with the aim to identify the underlying market and institutional barriers.

The authors apply the regression method with the control of village fixed effects to examining the relationship between capital–labor ratio, mechanization service ratio and farm productivity, using the panel data collected in 2013 and 2015 by CCAP.

Mechanization services improve farm productivity through substituting labor, but it may generate a less positive impact on farms who do not have self-owned capital equipment.

It is the first study to investigate how mechanization services affect farm productivity for grain producers in Northern China.

This study aims to enhance the classification and processing of online appeals by employing a deep-learning-based method. This method is designed to meet the requirements for precise information categorization and decision support across various management departments.

This study leverages the ALBERT–TextCNN algorithm to determine the appropriate department for managing online appeals. ALBERT is selected for its advanced dynamic word representation capabilities, rooted in a multi-layer bidirectional transformer architecture and enriched text vector representation. TextCNN is integrated to facilitate the development of multi-label classification models.

Comparative experiments demonstrate the effectiveness of the proposed approach and its significant superiority over traditional classification methods in terms of accuracy.

The original contribution of this study lies in its utilization of the ALBERT–TextCNN algorithm for the classification of online appeals, resulting in a substantial improvement in accuracy. This research offers valuable insights for management departments, enabling enhanced understanding of public appeals and fostering more scientifically grounded and effective decision-making processes.

To deepen the structural reform of the financial system on the supply side and mitigate associated risks in the economic and financial fields, with significant practical implications for FinTech development.

Based on microdata of listed companies, this paper constructs multi-level indicators of FinTech development. Robustness tests include alternative measures of the degree of long-term use of short-term debts, sample adjustments and heterogeneity in firm characteristics and regional differences.

FinTech can significantly alleviate the long-term use of short-term corporate debt, although there are heterogeneous effects. The alleviation effect is more pronounced for state-owned enterprises, non-technology-intensive enterprises and other companies with lower levels of short-term debt maturity. Additionally, in regions with high capital mismatch and high levels of financial development, FinTech exhibits a significant suppressive effect on the long-term use of short-term corporate debt.

The paper suggests promoting the diversification of FinTech products, emphasizing the importance of inclusive finance through FinTech, and driving China’s economic transformation and high-quality development.

By constructing a theoretical analysis framework of “FinTech—corporate investment and financing term mismatch,” this paper provides a multi-level estimation of the factors influencing FinTech’s impact on the long-term use of short-term corporate debt. This framework aids in developing a more dialectical and objective understanding of the economic effects of FinTech’s development.