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This study aims to explore the dimensional structure of hotel digital intelligence capability and develop a measurement scale.

This study adopts qualitative and quantitative approaches to conduct an exploratory inquiry into the structural dimensions of hotel digital intelligence capability with the help of grounded theory. Based on this, several questionnaires were developed to test the measurement scale and verify its validity.

The results reveal that hotel digital intelligence capability comprises four dimensions: data collection and processing capability, customer service personalization capability, digital intelligence decision support capability and sustainable development capability. The measurement scale consists of four factors and 13 items, with reliability and validity tests demonstrating ideal levels.

This study not only provides a new perspective to understand hotel digital intelligence capability but also develops a corresponding measurement scale, laying a solid theoretical basis for hotel managers to scientifically evaluate this capability to achieve sustainable competitive advantage.

本研究旨在探索酒店数智化能力的维度结构，并开发其测量量表

本研究采用定性和定量的方法，对酒店数智化能力的结构维度进行探索性研究。在此基础上，编制了调查问卷以检验量表并验证其有效性

研究结果表明，酒店数智化能力包括四个维度：数据收集与处理能力、客户服务个性化能力、数智化决策支持能力以及可持续发展能力。测量量表由四个因子和13个条目组成，信度和效度测试表明其达到了理想水平

本研究不仅为理解酒店数智化能力提供了新的视角，还开发了相应的测量量表，为酒店管理者科学评估该能力以实现可持续竞争优势奠定了坚实的理论基础

In the brain imaging based on electrical impedance tomography, it is sometimes not able to attach 16 electrodes due to space restriction caused by craniotomy. As a result of this, the number of boundary measurements decreases, and spatial resolution of reconstructed conductivity distribution is reduced. The purpose of this study is to enhance reconstruction quality in cases of limited measurement.

A new data expansion method based on the shallow convolutional neural network is proposed. An eight-electrode model is built from which fewer boundary measurements can be obtained. To improve the imaging quality, shallow convolutional neural network is constructed which maps limited voltage data of the 8-electrode model to expanded voltage data of a quasi-16-electrode model. The predicted data is compared with the quasi-16-electrode data. Besides, image reconstruction based on L1 regularization method is conducted.

The results show that the predicted data generally coincides with the quasi-16-electrode data. It is found that images reconstructed with the data of eight-electrode model are the poorest. Nevertheless, imaging results when the limited data is expanded by the proposed method show large improvement, and there is a minor difference with the images recovered with the quasi-16-electrode data. Also, the impact of noise is studied, which shows that the proposed method is robust to noise.

To enhance reconstruction quality in the case of limited measurement, a new data expansion method based on the shallow convolutional neural network is proposed. Both simulation work and phantom experiments have demonstrated that high-quality images of cerebral hemorrhage and cerebral ischemia can be obtained when the limited measurement is expanded by the proposed method.

This study aims to investigate the effect of high-performance HR practices (HPHRP) on the innovative work behaviour (IWB) of employees. Drawing on social exchange theory, when employees perceive their exchange relationship in terms of HPHRP and leadership support as fair, we hypothesize that employees will demonstrate greater IWB. However, drawing on social identity theory, we hypothesize that when the attitude of employees towards their employer with best employer practices is favourable, the impact of HPHRP mediated by organizational pride and organizational identification, has a greater impact on employee IWB.

Survey research was used to empirically validate the study involving employees (n = 370) who belong to the best employer brands in India. The data was analysed using Process Macro Models 7 for moderated mediation and Model 6 for serial mediation using bootstrapping procedures.

The results suggest that perceived leadership support moderated the indirect effect of HPHRP on IWB through organizational pride. Again, organizational pride and identification partially and serially mediated the impact of HPHRP on IWB.

Organizations must invest in HPHRP, with supportive leadership practices that can foster an emotional attitude of pride and a cognitive attitude of organizational identification to be an employer of choice resulting in employees’ IWB.

The study investigating the mediating impact of the emotional and cognitive attitudes of pride and organizational identification has not been previously explored, in the relationship between HPHRP and IWB, from a social identity perspective.

Linear addition is commonly used to generate ensemble forecasts for decomposition ensemble models but traditionally treats individual modes with equal weights for simplicity. Using Taiwan air passenger flow as an empirical case, this study examines whether incorporating weighting for individual single-mode forecasts assessed by grey relational analysis into linear addition can improve the accuracy of the decomposition ensemble models used to forecast air passenger demand.

Data series are decomposed into several single modes by empirical mode decomposition, and then different artificial intelligence methods are applied to individually forecast these decomposed modes. By incorporating the correlation between each forecasted mode series and the original time series into linear addition for ensemble learning, a genetic algorithm is applied to optimally synthesize individual single-mode forecasts to obtain the ensemble forecasts.

The empirical results in terms of level and directional forecasting accuracy showed that the proposed decomposition ensemble models with linear addition using grey relational analysis improved the forecasting accuracy of air passenger demand for different forecasting horizons.

Accurately forecasting air passenger demand is beneficial for both policymakers and practitioners in the aviation industry when making operational plans.

In light of the significance of improving the accuracy of decomposition ensemble models for forecasting air passenger demand, this research contributes to the development of a weighting scheme using grey relational analysis to generate ensemble forecasts.

This paper aims to solve the problem that multiscale dispersion entropy (MDE) is prone to information loss in the process of coarse-grain, which makes it difficult to extract bearing fault information comprehensively.

A new fault diagnosis method of rolling bearing using refined composite multiscale peak-to-peak normalized dispersion entropy (RCMPNDE) and sparrow search algorithm optimized probabilistic neural network (SSA-PNN) is proposed. First, coarse-graining employs the peak-to-peak value calculation instead of the segmented mean calculation in the RCMDE algorithm, which can overcome the shortcomings of traditional coarse-graining and highlight the fault characteristics. Then, the influence of the selection of different parameters is reduced through the normalization operation, and the RCMPNDE is formed. Finally, the extracted feature parameters are combined with SSA-PNN for diagnosis recognition to construct the RCMPNDE-SSA-PNN fault diagnosis method.

The proposed RCMPNDE-SSA-PNN fault diagnosis method is tested on actual data sets and its outcomes have been compared to those generated by methods built upon MDE, RCMDE and PNN. The comparison results showed that the proposed method can extract the fault feature information of rolling bearings more accurately and improve the accuracy of fault classification. The recognition accuracy reached 98.5% under the conditions of this experiment.

The RCMPNDE-SSA-PNN method can obtain more accurate fault diagnosis accuracy and provide a new reliable diagnosis method for rolling bearing fault diagnosis.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-09-2024-0332/

As a clean and renewable energy source, wind energy will become one of the main sources of new energy supply in the future. Relying on the stable and strong wind resources at sea, wind energy has great potential to become the primary energy. As a critical part of the wind turbine, the gearbox of a wind turbine often bears a large external load. Especially at sea, due to the effects of ocean corrosion, waves and wind, the burden of the wind turbine gearbox is greater, which brings great challenges to its reliability analysis. This study aims to systematically review the reliability research in wind turbine gearboxes and guide future research directions and challenges.

This study systematically reviews some design requirements and reliability analysis methods for wind turbine gearboxes. Then, it summarizes previous studies on wind load uncertainty modeling methods, including the processing of wind measurement data and the summary of three different classifications of random wind speed prediction models. Finally, existing reliability analysis studies on two major parts of the gearbox are described and summarized.

First, the basic knowledge of wind turbine gearboxes and their reliability analysis is introduced. The requirements and reliability analysis methods of wind turbine gearboxes are explained. Then, the processing methods of wind measurement data and three different random wind speed prediction models are described in detail. Furthermore, existing reliability analysis studies on two common parts of wind turbine gearboxes, gears and bearings, are summarized and classified, including a summary of bearing failure modes. Finally, three possible future research directions for wind turbine gearbox reliability analysis are discussed, namely, reliability research under the influence of multiple factors on gears, damage indicators of bearing failure modes and quantitative evaluation criteria for the overall dynamic characteristics of offshore wind turbine gearboxes and a summary is also given.

This paper aims to systematically introduce the relevant contents of wind turbine gearboxes and their reliability analysis. The contents of wind speed data processing, predictive modeling and reliability analysis of major components are also comprehensively reviewed, including the classification and principle introduction of these contents.

Transporting hydrogen through natural gas pipelines in blended compositions has been proven to be a highly feasible solution in the short term. However, under hydrogen-rich environments, steel structures are prone to hydrogen-induced damage (HID). Additionally, uncertainties in various parameters can significantly impact the performance evaluation of hydrogen pipelines. Efficient reliability and sensitivity analyses of medium- to high-strength steel pipelines considering HID have become a challenge. Therefore, the primary aim of this study is to address this issue.

This study first establishes reliability analysis models for medium- to high-strength steels, represented by X65 and X80. In these models, the effect of HID is expressed by reduced stress, and its statistical parameters are calculated. Then, a highly efficient enhanced first order reliability method (FORM) is proposed for pipeline reliability analysis. This method overcomes the oscillation and convergence issues of traditional FORM when dealing with certain problems and can compute negative reliability indices. The proposed reliability analysis method is applied to solve the constructed reliability models. Finally, a reliability sensitivity analysis is conducted on the models to identify the key variables affecting the reliability of medium- to high-strength steel pipelines under HID.

First, two reliability analysis examples are used to validate the effectiveness of the proposed enhanced FORM. Then, using this method to solve the constructed reliability models for X65 and X80 steel pipelines under HID reveals that, for both types of steel, the reliability indices decrease significantly when considering HID compared to cases without HID. The decline is more pronounced for X80 steel than for X65 steel. As internal pressure increases, the reliability of both steels drops sharply, showing a concave parabolic trend. Moreover, the reliability sensitivity analysis shows that at a pressure of 10 MPa, for both X80 and X65, internal pressure, pipeline wall thickness and model error are the top three factors influencing reliability. As internal pressure increases, its influence becomes stronger, while the impact of other variables diminishes. Notably, for X80 steel, the presence of hydrogen amplifies the effect of internal pressure on pipeline reliability compared to when HID is not considered, but for X65, this trend is reversed.

Given the urgent need for safety evaluation studies on hydrogen transport through natural gas pipelines, this research provides new insights by constructing reliability models for X65 and X80 pipeline steels under HID and introducing an enhanced FORM method. The results of the reliability and sensitivity analyses of the models offer valuable insights and serve as a reference for engineering design.

Drawing from resource orchestration theory, this research proposed an integrative model that leverages insights into counter resource constraints and uncertainty in start-up business model innovation (BMI). It investigated the influences of entrepreneurial networks and effectuation on BMI through bricolage in uncertain environments.

The research surveyed 481 start-ups in China. LISREL 8.80 and SPSS 22.0 were employed to test the validity and reliability of key variables, respectively. Additionally, hypotheses were examined through multiple linear regression.

First, entrepreneurial networks and effectuation were positively related to BMI, and combining these two factors improved BMI for start-ups. Second, bricolage contributed to BMI and played mediating roles in translating entrepreneurial networks and effectuation into BMI. Third, environmental uncertainty weakened the linkage between bricolage and BMI.

Future research should replicate the results in other countries because only start-ups in China were investigated in the study, and it is necessary to extend this research by gathering longitudinal data. This research emphasized the mediating effects of bricolage and the moderating influence of environmental uncertainty, and new potential mediating and moderating factors should be explored between resources and BMI.

There are three significant theoretical contributions. First, the findings enrich the literature on the complex antecedents of BMI by combining the impacts of entrepreneurial networks and effectuation. Second, an overarching framework is proposed explaining how bricolage (resource management) links entrepreneurial networks and effectuation and BMI. Third, it demonstrates the significance of environmental uncertainty in the bricolage–BMI linkage, deepening the understanding of the bricolage boundary condition.

While prior studies predominantly focus on the overall impact of digital transformation on environmental, social and governance (ESG) performance, this study employs dynamic capability theory to examine two different dimensions of digital transformation, namely digital transformation quantity and digital transformation structure, and how they influence the ESG performance of enterprises. The mediating roles of social attention and green innovation are investigated to further explore the underlying mechanisms.

The authors apply fixed effects models and empirically test the hypotheses using samples of Chinese A-share listed companies from 2011 to 2020. In addition, difference-in-differences and instrumental variable methods are used in the robustness test.

When digital transformation is categorized into quantity and structure, the impact mechanisms are found to be distinct. Externally, digital transformation quantity attracts social attention, aiding enterprises in evolutionary adaptability and acquiring resources to support ESG practices. Internally, digital transformation structure fosters green innovation, enabling enterprises to overcome technical obstacles and harness technology’s potential to enhance their ESG performance.

This study contributes to the current knowledge by differentiating digital transformation into quantity and structure, which helps to further explore the mechanism of digital transformation on ESG and address the research gap. Meanwhile, the concept of adaptability in the dynamic capability theory is employed to construct the model, offering a deeper perspective and expanding the theory. This nuanced investigation of the mediating effects of social attention and green innovation elucidates how different dimensions of digital transformation contribute to the development and utilization of dynamic capabilities, thereby enhancing enterprises’ ESG performance.