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The idea of value co-creation involves the benefit actors gain from integrating resources through activities and interactions within a service network, with the environment enabling high-quality collaboration. This paradigm highlights customers’ ability to co-create value with service providers and other customers. This idea is gaining traction in health care. These days, patients are no longer passive recipients of health-care services; rather they have started taking proactive roles in their self-health management. This study aims to understand the phenomenon of value co-creation among patients within online health communities (OHCs).

A systematic literature review of papers published from 2003 to 2024 in Web of Science-indexed journals was conducted. The review highlights theories, contexts, characteristics and methodologies in this area, synthesizing insights from previous research and presenting a future research agenda for underexplored and unexplored contexts using emerging theoretical perspectives and analytical methodologies.

The review illuminates theoretical and empirical studies on value co-creation among patients in OHCs. Previous research shows that value co-creation among patients leads to cognitive, affective and physical benefits such as reduced anxiety and stress, increased assurance and self-confidence, improved quality of life, enhanced patient empowerment, acceptance of disease and treatment effectiveness and a sense of self-worth and well-being.

This review synthesizes insights from previous works and outlines a research agenda for future studies in underexplored and unexplored contexts using new theoretical perspectives and methodologies. Considering the role social media plays in an individual’s life, this work will help in deep diving into the role of such online communities in the health-care sector.

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.

The Grey System Theory (GST) is an emerging area of research within artificial intelligence. Since its founding in 1982, it has seen a lot of multidisciplinary applications. In just a short period, it has garnered some considerable strengths. Based on the 1987–2021 data collected from the Web of Science (WoS), the current study reports the advancement of the GST.

Research papers utilizing the GST in the fields of economics and education were retrieved from the Web of Science (WoS) platform using a set of predetermined keywords. In the final stage of the process, the papers that underwent analysis were manually chosen, with selection criteria based on the information presented in the titles and abstracts.

The study identifies prominent authors, institutions, publications and journals closely associated with the subject. In terms of authors, two major clusters are identified around Liu SF and Wang ZX, while the institution with the highest number of publications is Nanjing University of Aeronautics and Astronautics. Moreover, significant keywords, trends and research directions have been extracted and analyzed. Additionally, the study highlights the regions where the theory holds substantial influence.

The study is subject to certain limitations stemming from factors such as the language employed in the chosen literature, the papers included within the Web of Science (WoS) database, the designation of works categorized as “articles” in the database, the specific selection of keywords and keyword combinations, and the meticulous manual process employed for paper selection. While the manual selection process itself is not inherently limiting, it demands a greater investment of time and meticulous attention, contributing to the overall limitations of the study.

The significance of the study extends not only to scholars and practitioners but also to readers who observe the development of emerging scientific disciplines.

The analysis of trends revealed a growing emphasis on the application of GST in diverse domains, including supply chain management, manufacturing and economic development. Notably, the emergence of COVID-19 as a new research focal point among GST scholars is evident. The heightened interest in COVID-19 can be attributed to its global impact across various academic disciplines. However, it is improbable that this interest will persist in the long term, as the pandemic is gradually brought under control.

Supply chains, as prototypical uncertain systems, are crucial for national security and socioeconomic development. Grey system theory (GST) is an effective tool for addressing uncertainties and has played a pivotal role in related research within the supply chain domain. This study aims to systematically summarize the research achievements and knowledge structures pertaining to GST in supply chain studies. Current and potential research hotspots are also analyzed.

CiteSpace is used to conduct a bibliometric analysis of 1,617 articles sourced from the Web of Science (WOS). The analysis aims to summarize the current state of research and the knowledge structure in the field. A strategic diagram incorporating two data indicators, namely, novelty and concern, is constructed based on keyword clustering to identify and analyze current and potential research hotspots.

Studies utilizing GST to guide supply chain practices have attracted the interest of scholars from 205 research institutions across 85 countries and regions globally, which earned recognition from 183 high-level academic journals. In this field, the School of Economics and Management at Nanjing University of Aeronautics and Astronautics stands out as a core research institution, with Professor Deng Julong, who is the founder of GST, being the most frequently cited scholar. Current research hotspots are complex equipment supply chains, drivers and challenges in supply chain management, supply chain risk management, closed-loop supply chain and supply chain operation in the big data era. In addition, emerging research hotspots include digital and intelligent logistics technology, sustainable supplier management, determinants and flexibility of supply chain contracts, supply chain strategy, purchase management, grey prediction of demand and consumption, grey forecasting and economy efficiency, China-specific issues and grey model construction.

The bibliometric analysis reveals the current state and knowledge structure of research in this field. Previous studies on research hotspots have primarily relied on subjective judgments, which lacked empirical data support. This study constructs a strategic diagram incorporating two data indicators, namely, novelty and concern, to provide a more objective and reliable analysis of research hotspots.

The proposed model aims to tackle the data quality issues in multivariate time series caused by missing values. It preserves data set integrity by accurately imputing missing data, ensuring reliable analysis outcomes.

The Conv-DMSA model employs a combination of self-attention mechanisms and convolutional networks to handle the complexities of multivariate time series data. The convolutional network is adept at learning features across uneven time intervals through an imputation feature map, while the Diagonal Mask Self-Attention (DMSA) block is specifically designed to capture time dependencies and feature correlations. This dual approach allows the model to effectively address the temporal imbalance, feature correlation and time dependency challenges that are often overlooked in traditional imputation models.

Extensive experiments conducted on two public data sets and a real project data set have demonstrated the adaptability and effectiveness of the Conv-DMSA model for imputing missing data. The model outperforms baseline methods by significantly reducing the Root Mean Square Error (RMSE) metric, showcasing its superior performance. Specifically, Conv-DMSA has been found to reduce RMSE by 37.2% to 63.87% compared to other models, indicating its enhanced accuracy and efficiency in handling missing data in multivariate time series.

The Conv-DMSA model introduces a unique combination of convolutional networks and self-attention mechanisms to the field of missing data imputation. Its innovative use of a diagonal mask within the self-attention block allows for a more nuanced understanding of the data’s temporal and relational aspects. This novel approach not only addresses the existing shortcomings of conventional imputation methods but also sets a new standard for handling missing data in complex, multivariate time series data sets. The model’s superior performance and its capacity to adapt to varying levels of missing data make it a significant contribution to the field.

Several chaotic system-based encryption techniques have been presented in recent years to protect digital images using cryptography. The challenges of key distribution and administration make symmetric encryption difficult. The purpose of this paper is to address these concerns, the novel hybrid partial differential elliptical Rubik’s cube algorithm is developed in this study as an asymmetric image encryption approach. This novel algorithm generates a random weighted matrix, and uses the masking method on image pixels with Rubik’s cube principle. Security analysis has been conducted, it enhances and increases the reliability of the proposed algorithm against a variety of attacks including statistical and differential attacks.

In this light, a differential elliptical model is designed with two phases for image encryption and decryption. A modified image is achieved by rotating and mixing intensities of rows and columns with a masking matrix derived from the key generation technique using a unique approach based on the elliptic curve and Rubik’s cube principle.

To evaluate the security level, the proposed algorithm is tested with statistical and differential attacks on a different set of test images with peak signal-to-noise ratio, unified average changed intensity and number of pixel change rate performance metrics. These results proved that the proposed image encryption method is completely reliable and enhances image security during transmission.

The elliptic curve–based encryption is hard to break by hackers and adding a Rubik’s cube principle makes it even more complex and nearly impossible to decode. The proposed method provides reduced key size.

Despite the ongoing scholarly interest in greenwashing, it is not well known the impact of multiple institutional pressures on greenwashing in corporate social responsibility (CSR). Following the institutional logics perspective, this study investigates how three distinct logics – commercial, public, and social welfare – drive greenwashing and whether organizational capability for blending diverse CSR expectations reverses this link.

The current study conceptualized and tested an original model on how three institutional logics influence greenwashing in CSR, with the mediation effect of hybridization capability as a response to logic plurality. Partial least squares structural equation modeling was performed on a survey data, which was collected from 150 middle managers in Turkey.

The results show that while commercial logic has no direct or indirect impact on greenwashing, public and social welfare logics drive greenwashing in CSR. However, these effects are reversed when the CSR hybridization capability increases.

This study contributes to the understanding of what predicts CSR greenwashing by integrating a comprehensive theoretical framework involving multiple institutional logics, conflicting stakeholder demands, and organizational hybridity.

To the best of our knowledge, this is the first study that theoretically and empirically analyzed how the exposure of multiple external pressures affects the CSR greenwashing and how it can be reversed by CSR hybridization capability. This capability mitigates the threats and challenges of multiple logics and turns them into an opportunity to gain legitimacy in the eyes of stakeholders by preventing greenwashing.

The purpose of this paper is to examine the potential relationship between green finance and greenwashing to rationalize resource allocation better. Additionally, it explores the interconnections among various subgroups of green finance products (GFPs) and identifies any overlooked or underrepresented subgroups.

This paper uses a mixed-method exploratory sequential design. Initially, the authors collected a sample of 313 relevant documents. Thematic analysis and hierarchical coding were then performed using NVivo software to uncover correlations between various nodes and address our research questions. Additionally, a word cloud analysis was conducted to assess the potential research value of stakeholders as moderating variables. Following this, the role of stakeholders was reevaluated, leading to the selection of 58 samples for separate content analysis.

First, there is a negative relationship between green finance and greenwashing. Second, a negative relationship is observed between GFPs and greenwashing. The authors’ correlation coefficient analysis suggests that environmental, social and governance funds, as a non-mainstream research focus within GFPs, deserve further in-depth investigation.

While a significant portion of the existing literature focuses on the relationship between green bonds and greenwashing, a noticeable gap exists regarding the broader spectrum of GFPs and their potential association with greenwashing. The lack of a direct connection between broader GFPs and greenwashing suggests that this area is underexplored in literature. This paper fills this gap by investigating the role of broader GFPs in either promoting or mitigating greenwashing.

Within the labor-intensive construction industry characterized by distinctly structural shortages in the labor force worldwide, efficient and effective migration of construction workers across regions is critical for the smooth operation of construction activities. This study aims to investigate how the interregional migration patterns of construction workers are impacted by the disparities in both employment opportunities and environment amenities between the origin and destination provinces.

Drawing on the push and pull theory and the archival data on 13,728 migrant construction workers in China, descriptive analyses are first performed to characterize the interregional migration patterns of the investigated construction workers. Combining regional data in the National Bureau of Statistics of China, this study uses hierarchical regression modeling techniques to empirically test the relative importance of the employment-related and environment-related factors in driving the interregional migration of construction workers after controlling for the effects of related economic and geographic factors.

The results provide evidence that the interregional migration of construction workers is principally driven by the disparities in employment opportunities while disparities in environment amenities (including climate comfort disparity, medical service disparity and educational service disparity) generally play much fewer substantive roles. With regard to the impacts of employment opportunities, the results provide evidence that compared with the disparity in job market size, the disparities in job income and industry development level are more significantly relevant factors, which positively pull and adversely push the interregional migration flows, respectively.

This study contributes to a deepened understanding of how workers specifically balance their employment and amenity needs to make temporary migration decisions in the “laggard” labor-intensive construction industry. This study also adds to the literature on population migration by characterizing the specific characteristics of construction workers and the temporary nature of the workers' migration activities. The findings hold important practical implications for construction organizations and policymakers for effectively managing the mobility of migrant construction workers.

The extant literature on migrant construction workers has primarily focused on the consequences of international migration and the generalization of empirical findings on population migration mechanisms in other domains to the construction industry is substantially limited by the specific characteristics of construction workers and the temporary nature of their migration activities. In addressing this gap, this study represents an exploratory effort to quantitatively characterize the interregional migration patterns of construction workers in the labor-intensive construction industry and examines the roles of employment opportunity and environmental amenity in driving interregional migration.

The spatial–temporal conflicts in the construction process of concrete arch dams are related to the construction quality and duration, especially for pouring blocks with a continuous high-strength and high-density construction process. Furthermore, the complicated construction technology and limited space resources aggravate the spatial–temporal conflicts in the process of space resource allocation and utilization, directly affecting the pouring quality and progress of concrete. To promote the high-strength, quality-preserving and rapid construction of dams and to clarify the explosion moment and influence degree of the spatial–temporal conflicts of construction machinery during the pouring process, a quantification method and algorithm for a “Conflict Bubble” (CB) between construction machines is proposed based on the “Time–Space Microelement” (TSM).

First, the concept of a CB is proposed, which is defined as the spatial overlap of different entities in the movement process. The subsidiary space of the entity is divided into three layered spaces: the physical space, safe space and efficiency space from the inside to the outside. Second, the processes of “creation,” “transition” and “disappearance” of the CB at different levels with the movement of the entity are defined as the evolution of the spatial–temporal state of the entity. The mapping relationship between the spatial variation and the running time of the layered space during the movement process is defined as “Time–Space” (TS), which is intended to be processed by a microelement.

The quantification method and algorithm of the CB between construction machinery are proposed based on the TSM, which realizes the quantification of the physical collision accident rate, security risk rate and efficiency loss rate of the construction machinery at any time point or time period. The risk rate of spatial–temporal conflicts in the construction process was calculated, and the outbreak condition of spatial–temporal conflict in the pouring process was simulated and rehearsed. The quantitative calculation results show that the physical collision accident rate, security risk rate and efficiency loss rate of construction machinery at any time point or time period can be quantified.

This study provides theoretical support for the quantitative evaluation and analysis of the spatial–temporal conflict risk in the pouring construction process. It also serves as a reference for the rational organization and scientific decision-making for pouring blocks and provides new ideas and methods for the safe and efficient construction and the scientific and refined management of dams.