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Given the regional heterogeneity of economic development, electricity consumption in various regions exhibits a discrepant growth pattern. The purpose of this study is to construct a grey system forecasting model with intelligent parameters for predicting provincial electricity consumption in China.

First, parameter optimization and structural expansion are simultaneously integrated into a unified grey system prediction framework, enhancing its adaptive capabilities. Second, by setting the minimum simulation percentage error as the optimization goal, the authors apply the particle swarm optimization (PSO) algorithm to search for the optimal grey generation order and background value coefficient. Third, to assess the performance across diverse power consumption systems, the authors use two electricity consumption cases and select eight other benchmark models to analyze the simulation and prediction errors. Further, the authors conduct simulations and trend predictions using data from all 31 provinces in China, analyzing and predicting the development trends in electricity consumption for each province from 2021 to 2026.

The study identifies significant heterogeneity in the development trends of electricity consumption systems among diverse provinces in China. The grey prediction model, optimized with multiple intelligent parameters, demonstrates superior adaptability and dynamic adjustment capabilities compared to traditional fixed-parameter models. Outperforming benchmark models across various evaluation indicators such as root mean square error (RMSE), average percentage error and Theil’s index, the new model establishes its robustness in predicting electricity system behavior.

Acknowledging the limitations of traditional grey prediction models in capturing diverse growth patterns under fixed-generation orders, single structures and unadjustable background values, this study proposes a fractional grey intelligent prediction model with multiple parameter optimization. By incorporating multiple parameter optimizations and structure expansion, it substantiates the model’s superiority in forecasting provincial electricity consumption.

The booming social media attracts construction professionals (CPs) to express emotions caused by work pressure (WP) through online behaviors. Previous works focus on the analysis of WP and emotions but do not adequately consider how WP can be reflected through online emotions. Thus, this study aims to attempt to explore the quantitative relationship between online emotional intensity and WP.

This study developed a linguistic-sticker (LS) model to quantitatively evaluate the sentiment intensity of posts published on social media. Moreover, the authors designed two econometric models of ordinary least squares regression and negative binomial regression to test the hypothesis.

The research found that posts with stronger negative sentiment (or positive sentiment) indicate that CPs face higher (or lower) WP. Besides, there is a negative bias between the sentiment intensity of posts and the comment quantity.

The positive correlation between sentiment intensity of posts and WP has been confirmed, which indicates that construction managers should pay more attention to CPs' behavior on social media, and take a more direct way to analyze work-related online behavior (e.g. posting, commenting). The dynamic monitoring of emotion-related posts also provides a direct basis for the management team to learn about CP's pressure status and propose measures to reduce their negative emotions. Furthermore, the emotional posts published by CPs on social media provide a direct basis for team managers to obtain their psychological state.

The research contributes to incorporating CPs' emotions into the LS model and to providing information systems artifacts and new findings on the analysis of WP and online emotions.

The explosion of data due to the sophistication of information and communication technology makes it simple for prospective tourists to learn about previous hotel guests' experiences. They prioritize the rating score when selecting a hotel. However, rating scores are less reliable for suggesting a personalized preference for each aspect, especially when they are in a limited number. This study aims to recommend ratings and personalized preference hotels using cross-domain and aspect-based features.

We propose an aspect-based cross-domain personalized recommendation (AsCDPR), a novel framework for rating prediction and personalized customer preference recommendations. We incorporate a cross-domain personalized approach and aspect-based features of items from the review text. We extracted aspect-based feature vectors from two domains using bidirectional long short-term memory and then mapped them by a multilayer perceptron (MLP). The cross-domain recommendation module trains MLP to analyze sentiment and predict item ratings and the polarities of the aspect based on user preferences.

Expanded by its synonyms, aspect-based features significantly improve the performance of sentiment analysis on accuracy and the F1-score matrix. With relatively low mean absolute error and root mean square error values, AsCDPR outperforms matrix factorization, collaborative matrix factorization, EMCDPR and Personalized transfer of user preferences for cross-domain recommendation. These values are 1.3657 and 1.6682, respectively.

This study assists users in recommending hotels based on their priority preferences. Users do not need to read other people's reviews to capture the key aspects of items. This model could enhance system reliability in the hospitality industry by providing personalized recommendations.

This study introduces a new approach that embeds aspect-based features of items in a cross-domain personalized recommendation. AsCDPR predicts ratings and provides recommendations based on priority aspects of each user's preferences.

This study aims to provide a systematic and complete knowledge map for use by researchers working in the field of research data. Additionally, the aim is to help them quickly understand the authors' collaboration characteristics, institutional collaboration characteristics, trending research topics, evolutionary trends and research frontiers of scholars from the perspective of library informatics.

The authors adopt the bibliometric method, and with the help of bibliometric analysis software CiteSpace and VOSviewer, quantitatively analyze the retrieved literature data. The analysis results are presented in the form of tables and visualization maps in this paper.

The research results from this study show that collaboration between scholars and institutions is weak. It also identified the current hotspots in the field of research data, these being: data literacy education, research data sharing, data integration management and joint library cataloguing and data research support services, among others. The important dimensions to consider for future research are the library's participation in a trans-organizational and trans-stage integration of research data, functional improvement of a research data sharing platform, practice of data literacy education methods and models, and improvement of research data service quality.

Previous literature reviews on research data are qualitative studies, while few are quantitative studies. Therefore, this paper uses quantitative research methods, such as bibliometrics, data mining and knowledge map, to reveal the research progress and trend systematically and intuitively on the research data topic based on published literature, and to provide a reference for the further study of this topic in the future.

In recent years, railway systems worldwide have faced challenges such as the modernization of engineering projects, efficient management of intelligent digital railway equipment, rapid growth in passenger and freight transport demands, customized transport services and ubiquitous transport safety. The transformation toward intelligent digital transformation in railways has emerged as an effective response to the formidable challenges confronting the railway industry, thereby becoming an inevitable global trend in railway development.

This paper, therefore, conducts a comprehensive analysis of the current state of global railway intelligent digital transformation, focusing on the characteristics and applications of intelligent digital transformation technology. It summarizes and analyzes relevant technologies and applicable scenarios in the realm of railway intelligent digital transformation, theoretically elucidating the development process of global railway intelligent digital transformation and, in practice, providing guidance and empirical examples for railway intelligence and digital transformation.

Digital and intelligent technologies follow a wave-like pattern of continuous iterative evolution, progressing from the early stages, to a period of increasing attention and popularity, then to a phase of declining interest, followed by a resurgence and ultimately reaching a mature stage.

The results offer reference and guidance to fully leverage the opportunities presented by the latest wave of the digitalization revolution, accelerate the overall upgrade of the railway industry and promote global collaborative development in railway intelligent digital transformation.

This article aims to comprehensively review the measurement and management of supply chain performance (SCP) and strategic performance (SP). It strives to identify integrable features regarding frameworks, measurement approaches, practices and emerging research issues in these areas to integrate SCP and SP for measuring and managing performance. It intends to develop a dynamic-integrated-performance-system by incorporating integrable aspects of SCP and SP to link these domains for organizational performance improvement.

Using systematic-literature-review, this study analyzes 154 articles published in selected peer-reviewed international journals from 2000 to 2023 regarding SCP and SP. It assesses existing knowledge regarding research-design followed, challenging areas and imperatives in these critical business domains to investigate the prior conceptual, empirical, case study-based and literature-review-based articles.

The study identifies integrable features regarding key theoretical and measurement frameworks, critical objectives, significant measures, effective practices for measuring and managing SCP and SP and emerging research issues common to these areas. The findings help develop a dynamic-integrated-performance-system that uses the theoretical lenses of resource-based-view/dynamic-capability-theory and adopts a comprehensive framework like DBSC (system-dynamic-model with BSC perspectives). It incorporates identified integrable measures and best practices to monitor, measure, manage and improve organizational performance for sustainable competitive advantage. The article reveals that earlier studies have overlooked analyzing SCP and SP integration aspects.

From the theoretical viewpoint, the present SLR is unique in three ways: first, in investigating both the measurement and management of SCP and SP holistically; second, in identifying integrative features of these two; and third, in proposing a DIPS to link SCP and SP for performance improvement. The study reveals that existing literature has focused on measuring and managing SCP and SP in isolation without attempting a comprehensive and unified approach to integrate the respective domains. The present SLR adopts a holistic approach to link SCP and SP from SCM and strategic-management perspectives. The study proposes a dynamic-integrated-performance-system to measure, manage and improve performance in a unified method.

This study provides SC and strategy practitioners with an understanding of strategy-performance pathways for achieving strategic objectives and executing risk mitigation initiatives to counter disruptions. It enables SC managers to comprehend SC practices and SCP leading to dynamic SC capabilities development. Operationalizing the proposed DIPS will help firms link SCP and SP, align operational SC practices with strategic sustainability and circularity objectives and meet sustainable development goals while benefiting social and environmental stakeholders.

Assessing relationships and identifying a unified approach integrating SCP with SP have not been addressed earlier. This study's uniqueness is finding integrable features of SCP and SP and constructing a dynamic-integrated-performance-system to link these domains for achieving strategic competitiveness.

From a psychosocial perspective, this study aims to understand the impact of psychosocial safety climate (PSC) on the intent to stay of construction workers and provides practical recommendations for construction enterprises to retain construction workers.

This study proposes the conceptual framework explained by the conservation of resources (COR) theory and develops a mediation model of “PSC – job satisfaction – intent to stay” within the framework supported by the stimulus–organism–response (SOR) model. Then, a questionnaire survey of 489 construction workers in Guangzhou was conducted and structural equation modeling (SEM) analysis was performed on the data collected.

Results show that PSC has a significant and positive effect on job satisfaction and intent to stay. In addition, job satisfaction partially mediates the effect of PSC on intent to stay. Hence, the theoretical model of “PSC – job satisfaction – intent to stay” has been empirically tested and supported.

This study is the first to investigate the effect of PSC on intent to stay and enriches the research on the retention of construction workers. The COR theory explains well the mechanism of PSC influence on intent to stay, thus expanding its application to the construction field. Moreover, this study provides practical recommendations for construction enterprises to retain workers so as to build a stable and productive workforce.

For a thermal protection system (TPS) of long endurance hypersonic flight vehicle (HFV), its thermal insulation property not only determines by the manufactured morphology but also changes along time. A thermal conductivity prediction model for aerogel considering heat treatment effect is carried out and applied to solve the heat conduction problem of a TPS. The aim of this study is to provide theoretical and numerical references for further development of aerogels applying to TPSs.

A thermal conductivity prediction model for aerogel is established considering treatment effect. The heat conduction problem of a TPS is derived and solved by combining the differential quadrature method and the Runge–Kutta method. The prediction results of aerogel thermal conductivities are verified by comparing with those in literature, while the calculated temperature field of TPS is verified by comparing with that by ABAQUS.

Numerical results show that when applying the current prediction model, the calculated high temperature area in the aerogel layer is narrowed due to the decrease of the thermal conductivity during heat treatment process.

This study will be beneficial to carry out the precise design of TPS for long endurance HFVs.

The purpose of this paper is to understand the factors that enable and inhibit WhatsApp users' discontinuance intention (DI) following the change in WhatsApp's privacy policy.

Using the enabler-inhibitor model as a framework, a research model consisting of discontinuation enabler distrust (DT) and the DT's antecedents [(negative electronic word of mouth (NEWOM), negative offline word of mouth (NOWOM) and privacy invasion (PI)], discontinuation inhibitor inertia (INR) and INR's antecedents (affective commitment, switching cost and use habit) and moderator structural assurance was proposed and tested with data from 624 WhatsApp users using partial least square structure equational modeling (PLS-SEM).

The results show that DT created due to NEWOM and a sense of PI significantly impact DI. However, INR has no significant impact on DI. Structural assurance significantly moderates the relationship between DT and DI.

The paper collected data when many WhatsApp users switched to other platforms due to the change in WhatsApp's terms of service. The timing of data collection allowed for collecting the real impact of the sense of PI compared to other studies where the effect is hypothetically induced. Further, the authors acknowledge social media providers' efforts to address privacy criticism and regain users’ trust, an area that has received little attention in prior literature.