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The aim of this study is to present an overview of sustainable practices in the development of waterproof breathable fabrics for garments. It aims to provide insights into the current state of academic research in this domain and identify and analyze major sustainable trends in the field.

This study conducts a thorough examination of research publications sourced from the Scopus database spanning the years 2013–2023 by employing a systematic approach. The research utilizes both descriptive analysis and content analysis to identify trends, notable journals and leading countries in sustainable waterproof breathable fabric development.

The study reveals a notable increase in studies focusing on sustainable approaches in the development of waterproof breathable fabrics for garments. Descriptive analysis highlights the most prominent journal and leading country in terms of research volume. Content analysis identifies four key trends: minimizing chemical usage, developing easily degradable materials, creating fabrics promoting health and well-being and initiatives to reduce energy consumption.

The main limitation of this research lies in its exclusive reliance on the Scopus database.

The insights derived from this study offer practical guidance for prospective researchers interested in investigating sustainable approaches to developing waterproof breathable fabric for garments. The identified trends provide a foundation for aligning research endeavors with contemporary global perspectives, facilitating the integration of sustainable methodologies into the garment industry.

This systematic literature review contributes original insights by synthesizing current research trends and outlining evolving sustainable practices in the development of waterproof breathable fabrics. The identification of key focus areas adds a novel perspective to existing knowledge.

This paper aims to examine whether chief executive officer’s (CEO) information technology (IT) background can affect enterprises’ continuous green innovation (CGI).

This study uses the data of China’s listed enterprises from 2011 to 2019.

The statistical results reveal that when a company hires a CEO with an IT background, its CGI can be higher. Firm ownership, firm digitization and industry bias alter the impact of CEO’s IT background on firms’ CGI. This effect is most pronounced in non-state-owned enterprises (non-SOEs), high-digitalized enterprises and skill-biased industries, while not in SOEs, low-digitalized enterprises and labor-biased industries.

This study has practical implications, as it measures CGI of enterprises. It also points to the necessity for a CEO’s IT background to enhance CGI.

The findings provide new strategies for incentivizing sustainable development and green innovation.

To the best of the authors’ knowledge, this study is the first to discuss the association between CEO’s IT background and enterprises’ CGI. The conclusions enrich both upper echelons theory and enterprise green innovation literature.

Project-based organizations (PBOs) face challenges to enhance employee work engagement because of dynamic and constant role configuration. Accordingly, this study aims to explore how ethical leadership enhances employee work engagement from a sensemaking perspective.

This study used a questionnaire-based quantitative research design to collect data from 194 full-time employees in PBOs. The data were analyzed via partial least squares-structural equation modeling (PLS-SEM) technique to test hypotheses.

The findings show a positive relationship between ethical leadership and work engagement. Additionally, the relationship between ethical leadership and work engagement is mediated by two sensemaking mechanisms, i.e. goal commitment and prosocial.

This study deepens the understanding of how ethical leadership enhances work engagement in PBOs by providing two sensemaking mechanisms. By exploring the sensemaking process through which ethical leaders help employees construct identity, the findings contribute to the current literature on how ethical leadership enhances work engagement in PBOs.

In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network.

We use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms.

The results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms.

To the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.

With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.

In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.

On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.

In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.

Despite the knowledge transfer between projects has received increasing attention from scholars, few scholars still conduct comprehensive research on inter-project knowledge transfer from both horizontal and vertical perspectives. Besides, knowledge transfer is affected by multiple antecedent conditions, and these factors should be combined for analysis. Therefore, this paper aims to explore the key factors influencing knowledge transfer between projects using the fuzzy-set qualitative comparative analysis (fsQCA) method from both horizontal and vertical perspectives and how these factors combine to improve the effectiveness of knowledge transfer (EKT) between projects.

First, nine factors affecting knowledge transfer between projects were identified, which were from the four dimensions of subject, relationship, channel, and context, namely temporary nature (TN), time urgency (TU), transmit willingness (TW), receive willingness (RW), trust (TR), project-project transfer channels (PPC), project-enterprise transfer channels (PEC), organizational atmosphere (OA), and motivation system (MS). Then, the source of the samples was determined and the data from the respondents was collected for analysis. Following the operation steps of the fsQCA method, variable calibration, single condition necessity analysis, and configuration analysis were carried out. After that, the configurations of influencing factors were obtained and the robustness test was conducted.

The results of the fsQCA method show that there are five configurations that can obtain better EKT between projects. Configuration 3 (∼TN * ∼TU * TW * RW * TR * ∼PPC * PEC * MS) has the highest consistency, indicating that it has the highest degree of the explanatory variable subset. Configuration 1 (∼TN * ∼TU * TW * RW * PEC * OA * MS) has the highest coverage, meaning that this configuration can explain most cases. Also, the five configurations were divided into three types: vertical transfer, horizontal-vertical transfer, and channel-free transfer category.

Firstly, this study explores the key factors influencing knowledge transfer between projects from four dimensions, which presents the logical chain of influencing factors more clearly. Then, this study divided the five configurations obtained into three categories according to the transfer direction: vertical, horizontal-vertical, and channel-free transfer, which gives implications to focus on both horizontal knowledge transfer (HKT) and (VKT) when studying knowledge transfer between projects. Lastly, this study helps to realize the exploration of combined improvement strategies for EKT, thereby providing meaningful recommendations for enterprises and project teams to facilitate knowledge transfer between projects.

Despite an emerging interest in constructive deviance, the exploration of its antecedents is still limited, particularly from an ethical perspective. This study aims to uses moral disengagement theory to investigate how team identification, moral justification and team environmental instability interact to affect employee constructive deviance.

With survey data collected in two waves from 315 employees of 49 work teams in five service companies in China, this study develops four hypotheses and tests them through hierarchical linear model.

The survey results support the complete mediating effect of moral justification on the positive impact of team identification on constructive deviance. They also confirm the moderating effect of environmental instability on the relationship between team identification, moral justification and constructive deviance.

This study explores the sources of constructive deviance at team level from the ethical decision-making perspective and reveals the mechanism and contingency factors in the relationship between identity and constructive deviance. In practice, the study findings imply that managers should encourage their employees to cultivate their identification with their team and align their moral justification with the team’s norms especially when the team faces turbulent environment.

Although researchers have long recognized the importance of participating organizations’ (POs) relational behavior for mega construction projects (MCPs) performance, relational behavior may not be executed by POs without effective leadership from project owners. However, little is known about the mechanisms linking owners’ leadership styles to POs’ relational behavior. This study draws on full range leadership theory and role theory to examine the relationships between owners’ leadership styles (i.e. transformational and transactional) and relational behavior. POs’ role orientations (i.e. normative and economic) are considered as potential mediators.

Data were collected from 175 managers deeply involved in MCPs. Hierarchical regression model and bootstrapping methods were performed on the data to examine the direct effects of owners’ leadership on POs’ relational behavior and the mediating effects of POs’ role orientations.

The results revealed that both owners’ transformational and transactional leadership positively affect POs’ relational behavior, despite the former being higher than the latter, and indirectly influence relational behavior via POs’ normative and economic role orientation, respectively.

This study provides a clear picture of how owners’ leadership can motivate POs’ relational behavior to achieve high-quality inter-organizational relationships in MCPs. The findings can guide owners’ top manager selection by prioritizing those with transformational leadership, which is beneficial to achieving high-level relational behavior of POs. The results also imply that owners should pay greater attention to cultivating POs’ normative role orientation by encouraging teamwork and open communication to enhance their implementation of relational behavior.

Unlike previous research focusing more on intra-organizational leader–follower relationship within one PO, this study is one of the first to empirically confirm owners’ leadership as a critical antecedent of POs’ relational behavior, thus enhancing the theoretical understanding of inter-organizational relationship management in MCPs. Based on role theory, this study considers a novel organizational psychology mechanism, i.e. POs’ role orientations, as the mediator to unravel how owners’ leadership affects POs’ relational behavior, which was rarely invoked in MCP leadership literature.

Drawing upon work-home resources model, this study aims to investigate how and when humble leadership influences followers’ work–family enrichment. Specifically, this study focuses on the mediating role of thriving at work and moderating role of promotion focus.

Data were collected from 292 employees of science and technology enterprises in China through a three-wave questionnaire survey. Hierarchical regression analysis and bootstrapping approach were employed to test hypotheses.

This study found that thriving at work significantly mediated the relationship between humble leadership and work–family enrichment. Promotion focus strengthened the positive relationship between humble leadership and thriving at work and the indirect effect of humble leadership on work–family enrichment through thriving at work.

The findings of this study offer guidance for managers to enhance thriving at work and improve employees’ work–family experiences.

First, this study explores the work-to family spillover effects of humble leadership by examining the family outcomes of humble leadership. Second, this study further uncovers the underlying mechanism between humble leadership and work–family enrichment by demonstrating the mediating role of thriving at work. Third, by exploring the moderating role of promotion focus, this study provides insight into the boundary conditions of the impact of humble leadership.

In the era of digitalization, digital technology has transformed businesses and created enormous opportunities for organizations worldwide. Unsurprisingly, research on digital transformation has garnered significant interest among academics in recent decades. However, this study aims to recognize the key and holistic antecedents influencing digital transformation in manufacturing firms. This study also investigates the indirect relationships of antecedents with firm performance.

The hypothesis was investigated using the partial least squares structural equation modeling (PLS-SEM) approach. The data was collected from 279 employees through a self-administered survey of manufacturing firms.

The results described a significant and positive impact of competitive pressure, leadership role, organization culture, organization mindfulness, government regulation, and IT readiness on digital transformation and firm performance. Furthermore, digital transformation partially mediates the relationship between antecedents and firm performance.

The study finds a holistic perspective of the critical antecedents of digital transformation using the mediation role of digital transformation and moderating effects of firm agility. Additionally, all antecedents have a significant association with Firm Performance.