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This study aims to examine the relationship between managerial ability and innovation efficiency, the mediating effect of digital transformation and the moderating effect of internal control.

This study collected A-share manufacturing listed companies in China from 2008 to 2019 and analyzed the data by means of multiple regression analysis, mediating effect test, moderating effect test and heterogeneity test. Finally, the authors conducted robustness test by remeasuring key variables and adding control variables.

The empirical results show that the higher managerial ability can improve innovation efficiency, internal control has a positive moderating effect and digital transformation plays a partial mediating effect on the relationship between managerial ability and innovation efficiency. Specially, it is found that the mediating effect of digital transformation is not significant in non-state-owned firms.

This study suggests that it is necessary to focus on the managerial ability in terms of both cultivation and supervision, to further deepen the digital transformation from the aspects of firms, government and society, especially to support the digital transformation of non-state-owned firms, and to make efforts to improve the corporate governance mechanism and internal control system, so as to better comprehensively realize the improvement of enterprise innovation efficiency.

Based on the mediating effect analysis of digital transformation and the moderating effect analysis of internal control, this study explores the role of managerial ability on innovation efficiency from a new perspective, expanding the related theoretical framework and research boundaries.

This study aims to fabricate the reduced graphene oxide (rGO)/ethylene vinyl acetate copolymer (EVA) composite films with electric-driven two-way shape memory properties for deployable structures application. The effect of dicumyl peroxide (DCP) and rGO on the structure and properties of the rGO/EVA composite films were systematically investigated.

The rGO/EVA composite films were fabricated by melting blend and swelling-ultrasonication method, DCP and rGO were used the crosslinking agent and conductive filler, respectively.

The research results indicate that the two-way shape memory properties of rGO/EVA composite films were significantly improved with the increase of DCP content. The rGO endowed rGO/EVA composite films with excellent electric-driven reversible two-way shape memory and anti-ultraviolet aging properties. The sample rGO/EVA-9 can be heated above Tm within 8 s at a voltage of 35 V and can be heated above the Tm temperature within 12 s under near-infrared light (NIR). Under a constant stress of 0.07 MPa, the reversible strain of the sample rGO/EVA-9 was 8.96% and its electric-driven shape memory behavior maintained great regularity and stability.

The rGO/EVA composite films have potential application value in the field of deployable structures.

With the increase of DCP content, the two-way shape memory properties of rGO/EVA composite films were significantly improved, which effectively solved the problem that the shape memory properties of EVA matrix decreased caused by swelling. The rGO endowed rGO/EVA composite films with excellent electric/NIR driven reversible two-way shape memory properties.

The purpose of this article is to investigate the influence that firm-specific characteristics, such as organisational capabilities, risk management methods and stakeholder relationships, have on political risks (PRs) that are associated with multinational construction projects in Pakistan.

The methodology employed in this investigation involved the acquisition of data through the use of questionnaires administered to experts in the construction industry. The research applied a quantitative method, and the sources of the data are from the Pakistani stakeholders. One hundred questionnaires were used for the data collection during field visits. Based on the data, it has been ensured that the valid questionnaires were utilised, and the data were tested for validity and reliability. The analysis tool utilised was SPSS software. For the questionnaire, a total of 15 firm-specific factors were considered in order to design the survey, which specifically targeted the identified features. The factors identified as risks were investigated using quantitative method to determine firm-specific risks.

It was found that when stakeholders have a better grasp of these dynamics, they are better able to strengthen their resilience and efficacy in managing PRs, which ultimately increases the likelihood that the project will be successful.

International construction projects (ICPs) in emerging countries are substantially impacted by PRs, which can have a considerable impact on their success and sustainability. The study is localised and not generic as it is limited to Pakistan, and the risk factors considered are firm-specific but related to PRs.

By identifying key risk factors, these firms can develop targeted risk management strategies, leading to enhanced decision-making and more efficient resource allocation. Effective strategies include diversification, local partnerships and comprehensive risk assessments tailored to the unique challenges faced by international contracting firms in Pakistan.

ICPs in emerging countries like Pakistan face critical problems, which include the presence of PRs. Although the larger political environment plays a significant part, the manner in which businesses navigate and mitigate PRs is also influenced by firm-specific elements.

The study is novel in terms of the factors looked at, the data, the conceptual framework and the findings of the study. The dynamic political scene, which is characterised by instability, policy changes, corruption and geopolitical conflicts, poses significant dangers to the timeliness of projects, the expenses of such projects and the investments that are made in those projects.

This study proposes a deep learning algorithm-based model to predict the repair and maintenance costs of apartment buildings, by collecting repair and maintenance cost data that were incurred in an actual apartment complex. More specifically, a long short-term memory (LSTM) algorithm was adopted to develop the prediction model, while the robustness of the model was verified by recurrent neural networks (RNN) and gated recurrent units (GRU) models.

Repair and maintenance cost data incurred in actual apartment complexes is collected, along with various input variables, such as repair and maintenance timing (calendar year), usage types, building ages, temperature, precipitation, wind speed, humidity and solar radiation. Then, the LSTM algorithm is employed to predict the costs, while two other learning models (RNN and GRU) are taught to validate the robustness of the LSTM model based on R-squared values, mean absolute errors and root mean square errors.

The LSTM model’s learning is more accurate and reliable to predict repair and maintenance costs of apartment complex, compared to the RNN and GRU models’ learning performance. The proposed model provides a valuable tool that can contribute to mitigating financial management risks and reducing losses in forthcoming apartment construction projects.

Gathering a real-world high-quality data set of apartment’s repair and maintenance costs, this study provides a highly reliable prediction model that can respond to various scenarios to help apartment complex managers plan resources more efficiently, and manage the budget required for repair and maintenance more effectively.

The purpose of this paper is to examine the relationship between Chinese female executives and corporate risk-taking the contingencies that affect this relationship.

A integrated theoretical framework was established, on the basis of which theoretical hypotheses were developed and tested using 20,315 firm-year observations collected from China’s publicly listed companies during the period 2005–2020. Data were collected from China's Shanghai and Shenzhen A-share Stock Exchanges and analyzed using a moderated regression analysis, PSM, 2SLS-IV and PSM-DID model.

The empirical results indicate a negative effect of the ratio of female executives in top management team on corporate risk-taking, and this negative effect can be weakened by the social capital of board directors and the regional marketization.

The paper contributes to research on the relationship between female executives and risk-taking by considering the effect of eastern culture on female executives’ business decision-making and examining the moderating factors inside and outside the firm.

The paper illustrates the active steps that corporations can take to enhance female executives' willingness and capacity to take firm-related risks so as to improve the firm value in the long run.

The paper explores how Chinese culture and Chinese traditional value affect female executives’ decision-making on risky projects or uncertain investments. In addition, our study for the first time examines the moderating effect of board social capital as an internal factor and marketization as an external one on the relationship between Chinese female executives and corporate risk taking. The research examines the gender inequality in the work and competitive environment facing female executives in the areas of different marketization level, which would affect female executives’ cognition and motivation in corporate risk taking.

Digital technologies have transformed business management practices and adapted them to shorter product lifecycles. As a result, firms are shifting their approach to building new competitive advantage from cost-oriented to entrepreneurial orientation (EO). This study aims to analyze the innovation performance (IP) in the context of EO from a knowledge management perspective. It constructs the functional path of the relationships among EO, knowledge coupling (KC) and IP of Chinese manufacturing firms to achieve business success.

Using data from 157 listed Chinese manufacturing firms from 2012 to 2021, the authors construct a panel data model to test the effect of EO on IP. This study classifies KC into existing knowledge coupling (EKC) and new and existing knowledge coupling (NKC) and analyzes their mediating effects in the above relationships.

This study finds that EO has an insignificant, inverted U-shaped relationship with IP. Both EKC and NKC have a significant, inverted U-shaped relationship with IP; in other words, if EKC and NKC increase, the IP of Chinese manufacturing firms first increases and then decreases, and EKC and NKC have a complete mediating effect on the relationship between EO and IP.

This study provides an in-depth analysis of IP from an EO perspective. The study’s findings enrich and extend the theoretical relationship between EO and IP. The authors also propose a knowledge management perspective for entrepreneurship research. These findings improve the current understanding of the role and function of KC in EO.

High-speed turnouts are more complex in structure and thus may cause abnormal vibration of high-speed train car body, affecting driving safety and passenger riding experience. Therefore, it is necessary to analyze the data characteristics of continuous hunting of high-speed trains passing through turnouts and propose a diagnostic method for engineering applications.

First, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is performed to determine the first characteristic component of the car body’s lateral acceleration. Then, the Short-Time Fourier Transform (STFT) is performed to calculate the marginal spectra. Finally, the presence of a continuous hunting problem is determined based on the results of the comparison calculations and diagnostic thresholds. To improve computational efficiency, permutation entropy (PE) is used as a fast indicator to identify turnouts with potential problems.

Under continuous hunting conditions, the PE is less than 0.90; the ratio of the maximum peak value of the signal component to the original signal peak value exceeded 0.7, and there is an energy band in the STFT time-frequency map, which corresponds to a frequency distribution range of 1–2 Hz.

The research results have revealed the lateral vibration characteristics of the high-speed train’s car body during continuous hunting when passing through turnouts. On this basis, an effective diagnostic method has been proposed. With a focus on practical engineering applications, a rapid screening index for identifying potential issues has been proposed, significantly enhancing the efficiency of diagnostic processes.

The purpose of this research paper is to explore the determinants of patients' service choices between telephone consultation and text consultation in online health communities (OHCs).

This study utilized an empirical model based on the elaboration likelihood model and examined the effect of information, regarding service quality (the central route) and service price (the peripheral route), using online health consultation data from one of the largest OHCs in China.

The logistic regression results indicated that both physician- and patient-generated information can influence the patients' service choices; service price signals will lead patients to cheaper options. However, individual motivations, disease risk and consulting experience change a patients' information processing regarding central and peripheral cues.

Previous researchers have investigated the mechanism of patient behavior in OHCs; however, the researchers have not focused on the patients' choices regarding the multiple health services provided in OHCs. The findings of this study have theoretical and practical implications for future researchers, OHC designers and physicians.

Although artificial intelligence (AI) is an essential component of hospitality in the technological empowerment era, AI’s effectiveness as an attraction in this context remains unclear. Grounded in Herzberg’s motivation theory and complexity theory, this study aims to explore configurational paths whereby combinations of qualities lead to success for different types of AI-themed hotels.

This study innovatively blends topic modeling and fuzzy-set qualitative comparative analysis (fsQCA) to investigate configurational paths whereby combined qualities produce positive guest evaluations of 12 AI-themed hotels as evidenced by 7,431 customer reviews.

The results indicate that AI could serve as a “theme” to attract customers under certain circumstances. First, “attractive” and “must-be” qualities are first identified for different types of AI-themed hotels. Furthermore, 6, 15 and 15 configurational paths inspiring favorable guest evaluations of luxury-independent, budget-independent and chain AI-themed hotels, respectively. Technology-related qualities are found to be especially attractive for luxury-independent AI-themed hotels, whereas the role of technology is minimal for budget AI-themed hotels. The impact of technology is salient for chain AI-themed hotels when combined with other factors. In addition, the effect of price differs among the configurational paths for the three hotel types.

This study expands the understanding of AI applications within the hospitality context by exploring the role of AI in AI-themed hotels and comparing its effectiveness in attracting customers across various hotel types. It also provides operational strategies for adopting AI for different types of hotels and for other hospitality and tourism sectors.

This study represents an early attempt to integrate topic modeling and fsQCA to clarify customers’ perceptions of AI-themed hotels and the combined impacts of various qualities. The findings expand on Kano’s model by classifying technology-related qualities into attractive qualities within AI-themed hotels.