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The circular economy (CE) represents an industry-wide transition from linear to circular processes. There has been a proliferation of literature on CE in the last decade. However, the existing studies on the adaption of CE in small and medium-sized enterprises (SMEs) are scarce. This study aims to develop a research agenda and the way forward for future researchers focusing on the adoption of CE.

This article analyses the CE concepts through a Systematic Literature Review (SLR). Coding and content analysis are performed to generate emergent themes with the help of “Atlas.ti” software.

The authors uncovered the contemporary significance of adopting CE and the state-of-the-art literature on CE. The study's findings fall into four broad themes: Technical know-how, resource and process optimization, reverse practices and technology and innovation. Ten thought-provoking questions were identified in the four themes that researchers can explore further in embracing CE to achieve sustainability in SMEs.

The study has highlighted the importance of CE adoption and CE's benefits to stakeholders across all three dimensions, i.e. social, economic and ecological. Practitioners can use the agenda in four themes to strengthen the practitioners' existing practices in SMEs to promote CE.

The study's uniqueness is the supply of current knowledge from diverse literature and practical consequences for SMEs. This study opens new lines of inquiry to adopt CE in SMEs, streamlining the existing literature into four themes to focus future research.

Accurately evaluating fluid flow behaviors and determining permeability for deforming porous media is time-consuming and remains challenging. This paper aims to propose a novel machine-learning method for the rapid estimation of permeability of porous media at different deformation stages constrained by hydro-mechanical coupling analysis.

A convolutional neural network (CNN) is proposed in this paper, which is guided by the results of finite element coupling analysis of equilibrium equation for mechanical deformation and Boltzmann equation for fluid dynamics during the hydro-mechanical coupling process [denoted as Finite element lattice Boltzmann model (FELBM) in this paper]. The FELBM ensures the Lattice Boltzmann analysis of coupled fluid flow with an unstructured mesh, which varies with the corresponding nodal displacement resulting from mechanical deformation. It provides reliable label data for permeability estimation at different stages using CNN.

The proposed CNN can rapidly and accurately estimate the permeability of deformable porous media, significantly reducing processing time. The application studies demonstrate high accuracy in predicting the permeability of deformable porous media for both the test and validation sets. The corresponding correlation coefficients (R²) is 0.93 for the validation set, and the R² for the test set A and test set B are 0.93 and 0.94, respectively.

This study proposes an innovative approach with the CNN to rapidly estimate permeability in porous media under dynamic deformations, guided by FELBM coupling analysis. The fast and accurate performance of CNN underscores its promising potential for future applications.

In this study, the authors explore the association between employee education level and the efficiency of corporate investment using data from a sample of Chinese listed firms during the period from 2011 to 2018. By examining the impact of education on investment efficiency, the authors' study provides valuable insights that contribute to a deeper understanding of the underlying economic mechanisms related to education.

The authors conduct multivariate regression analyses to examine the relationship between investment efficiency (following Richardson, 2006) and the level of employee education, along with a series of control variables. To ensure the reliability of the authors' findings, the authors subject the their results to a comprehensive set of robustness tests, such as a staggered difference-in-difference (DiD) regression approach, an instrumental variable (IV) method and the use of alternative employee education level and investment efficiency measurements.

The findings offer compelling evidence that higher levels of education have a positive impact on firms' investment efficiency, and this effect remains robust across various model specifications and endogeneity considerations. Moreover, the influence of education is more pronounced in firms that prioritize employee training, maintain effective internal communication and offer attractive financial rewards. Furthermore, the results suggest that the relationship between education and investment efficiency is influenced by the firms' business nature and competitive environment. Factors such as business complexity, labor intensity and business location also play a role in shaping the impact of education on investment outcomes.

The study emphasizes the crucial role of education in influencing investment decisions and performance within firms. By delving into this previously unexplored area, the authors' research contributes to the existing literature, establishing that the level of employee education is a significant determinant of corporate investment efficiency. This valuable insight has substantial implications for firms aiming to enhance their investment decision-making processes and overall performance. Understanding the positive impact of education on investment efficiency can empower organizations to leverage their human capital effectively and achieve better investment outcomes, ultimately contributing to long-term success and competitiveness in the market.

The coronavirus disease 2019 (COVID-19) pandemic caused significant disruption to the global labor market, resulting in a rapid transition toward remote work, e-commerce and workforce automation. This shift has sparked a considerable amount of public discussion. This study aims to explore the online public's sentiment toward remote work amid the pandemic.

Based on justice theory, this paper examines user-generated content on social media platforms, particularly Twitter, to gain insight into public opinion and discourse surrounding remote work during the COVID-19 pandemic. Employing content analysis techniques such as sentiment analysis, text clustering and evolutionary analysis, this study aims to identify prevalent topics, temporal patterns and instances of sentiment polarization in tweets.

Results show that people with positive opinions focus mainly on personal interests, while others focus on the interests of the company and society; people's subjectivities are higher when they express extremely negative or extremely positive emotions. Distributive justice and interactional justice are distinguishable with a high degree of differentiation in the cluster map.

Previous research has inadequately addressed public apprehensions about remote work during emergencies, particularly from a justice-based perspective. This study seeks to fill this gap by examining how justice theory can shed light on the public's views regarding corporate policy-making during emergencies. The results of this study provide valuable insights and guidance for managing public opinion during such events.

Mianzi can powerfully influence tourists’ behavior. Compared with product brands, destination brands are more multidimensional, consisting of intangible and tangible elements. Therefore, this paper aims to explore the relationships among the Chinese culture-related variable mianzi, destination product quality, destination service quality, destination brand value, destination brand resonance, destination brand self-congruity and destination overall brand equity.

A questionnaire survey was conducted in 2021, yielding 475 valid responses from tourists who had traveled to Hainan, China. Data was analyzed using structural equation modeling.

The results showed that mianzi plays a significant role in shopping destination brand equity, and the mianzi effect shapes tourists’ positive perceptions of destination product and service quality. Meanwhile, destination product quality and service quality enhance shopping destinations’ overall brand equity through destination brand value, brand resonance and brand self-congruity.

This study, focusing on shopping destinations, presents a novel view of brand equity. The research also uncovers influencing factors (e.g. product quality and service quality) that further enrich the destination brand equity model. Finally, findings offer valuable insights for academic research and the practical development of shopping destinations.

面子能有力地影响游客的行为。与产品品牌相比, 目的地品牌更加多维, 由无形要素和有形要素构成。因此, 本文旨在探讨与中国文化相关的变量面子、目的地产品质量、目的地服务质量、目的地品牌价值、目的地品牌共鸣、目的地品牌自我一致和目的地整体品牌资产之间的关系。

2021年对去过中国海南旅游的游客进行问卷调查, 最终获取了 475 份有效问卷。

果表明, 结果表明面子在购物目的地品牌资产中发挥着重要作用, 面子效应塑造了游客对目的地产品和服务质量的积极认知。同时, 目的地产品质量和服务质量通过目的地品牌价值、品牌共鸣和品牌自我一致提升了购物目的地的整体品牌资产。

本研究以购物目的地为重点, 提出了一种新颖的品牌资产观点。研究还发现了一些影响因素（如产品质量和服务质量）, 进一步丰富了目的地品牌资产模型。最后, 研究结果为学术研究和购物目的地的实际发展提供了宝贵的见解。

Mianzi puede influir poderosamente en el comportamiento de los turistas. En comparación con las marcas de producto, las marcas de destino son más multidimensionales y constan de elementos intangibles y tangibles. Por lo tanto, este artículo pretende explorar las relaciones entre la variable mianzi relacionada con la cultura china, la calidad del producto del destino, la calidad del servicio del destino, el valor de la marca del destino, la resonancia de la marca del destino, la autocongruencia de la marca del destino y el valor general de la marca del destino.

En 2021 se llevó a cabo una encuesta por cuestionario, que arrojó 475 respuestas válidas de turistas que habían viajado a Hainan, China. Los datos se analizaron mediante un modelo de ecuaciones estructurales.

Los resultados mostraron que el mianzi desempeña un papel significativo en el valor de marca de los destinos de compras, y que el efecto mianzi determina las percepciones positivas de los turistas sobre la calidad de los productos y servicios del destino. Por su parte, la calidad de los productos y servicios del destino mejora el valor de marca global de los destinos de compras a través del valor de marca del destino, la resonancia de la marca y la autocongruencia de la marca.

Este estudio, centrado en los destinos de compras, presenta una visión novedosa del valor de marca. La investigación también descubre factores influyentes (por ejemplo, la calidad del producto y la calidad del servicio) que enriquecen aún más el modelo de valor de marca del destino. Por último, los resultados ofrecen valiosas perspectivas para la investigación académica y el desarrollo práctico de los destinos de compras.

By adopting learning theory and a guanxi perspective, this study aims to investigate the effects of interpersonal guanxi (interpersonal networks or connections) and relationship learning on companies’ business performance when operating in a large emerging market.

Using a sample of 294 sales managers and salespeople in the Chinese hotel sector, the authors empirically test the authors' arguments through a structural equation modeling (SEM) approach.

The authors' findings indicate that strong interpersonal guanxi tends to generate more positive business performance. Furthermore, the authors find that relationship learning plays a mediating role in the association between interpersonal guanxi and hotel companies’ business performance in a Chinese context. Finally, the authors empirically explore the moderating effect of inter-firm dependence on the contribution of interpersonal guanxi to relationship learning. Findings demonstrate that this effect varies significantly based on inter-firm dependence, with interpersonal guanxi exhibiting a greater positive impact if such dependence is high.

This study enriches our understanding of interpersonal guanxi and of how companies can enhance the companies' business performance in an emerging market context.

This review paper seeks to enhance knowledge of how pre-loading affects reinforced concrete (RC) beams under fire. It investigates key factors like deflection and load capacity to understand pre-loading's role in replicating RC beams' actual responses to fire, aiming to improve fire testing protocols and structural fire engineering design.

This review systematically aggregates data from existing literature on the fire response of RC beams, comparing scenarios with (WP) and without pre-loading (WOP). Through statistical tools like the two-tailed t-test and Mann–Whitney U-test, it assesses deflection extremes. The study further examines structural responses, including flexural and shear behavior, ultimate load capacity, post-yield behavior, stiffness degradation and failure modes. The approach concludes with a statistical forecast of ideal pre-load levels to elevate experimental precision and enhance fire safety standards.

The review concludes that pre-loading profoundly affects the fire response of RC beams, suggesting a 35%–65% structural capacity range for realistic simulations. The review also recommended the initial crack load as an alternative metric for determining the pre-loading impact. Crucially, it highlights that pre-loading not only influences the fire response but also significantly alters the overall structural behavior of the RC beams.

The review advances structural fire engineering with an in-depth analysis of pre-loading's impact on RC beams during fire exposure, establishing a validated pre-load range through thorough statistical analysis and examination of previous research. It refines experimental methodologies and structural design accuracy, ultimately bolstering fire safety protocols.

The ongoing paradigm shift in the energy sector holds paramount implications for the realization of the sustainable development goals, encompassing critical domains such as resource optimization, environmental stewardship and workforce opportunities. Concurrently, this transformative trajectory within the power sector possesses a dual-edged nature; it may ameliorate certain challenges while accentuating others. In light of the burgeoning research stream on open innovation, this study aims to examine the intricate dynamics of knowledge-based industry-university-research networking, with an overarching objective to elucidate and calibrate the equilibrium of ambidextrous innovation within power systems.

The authors scrutinize the role of different innovation organizations in three innovation models: ambidextrous, exploitative and exploratory, and use a multiobjective decision analysis method-entropy weight TOPSIS. The research was conducted within the sphere of the power industry, and the authors mined data from the widely used PatSnap database.

Results show that the breadth of knowledge search and the strength of an organization’s direct relationships are crucial for ambidextrous innovation, with research institutions having the highest impact. In contrast, for exploitative innovation, depth of knowledge search, the number of R&D patents and the number of innovative products are paramount, with universities playing the most significant role. For exploratory innovation, the depth of knowledge search and the quality of two-mode network relations are vital, with research institutions yielding the best effect. Regional analysis reveals Beijing as the primary hub for ambidextrous and exploratory innovation organizations, while Jiangsu leads for exploitative innovation.

The study offers valuable implications to cope with the dynamic state of ambidextrous innovation performance of the entire power system. In light of the findings, the dynamic state of ambidextrous innovation performance within the power system can be adeptly managed. By emphasizing a balance between exploratory and exploitative strategies, stakeholders are better positioned to respond to evolving challenges and opportunities. Thus, the study offers pivotal guidance to ensure sustained adaptability and growth in the power sector’s innovation landscape.

The primary originality is to extend and refine the theoretical understanding of ambidextrous innovation within power systems. By integrating several theoretical frameworks, including social network theory, knowledge-based theory and resource-based theory, the authors enrich the theoretical landscape of power system ambidextrous innovation. Also, this inclusive examination of two-mode network structures, including the interplay between knowledge and cooperation networks, unveils the intricate interdependencies between these networks and the ambidextrous innovation of power systems. This approach significantly widens the theoretical parameters of innovation network research.

The purpose of this study is to explore how digital construction policy (DCP) drives enterprise green innovation (EGI) from an information processing theory (IPT) perspective, including the mediating mechanisms of market information accessibility and operational risk, the moderating role of intellectual property protection (IPP) and product market competition (PMC) and the heterogeneous effects of ownership, Internet development and managerial ability.

Based on the matched panel data of A-share listed enterprises from 2011 to 2019 and the Broadband China policy as a quasinatural experiment, this study investigates the impact of DCP on EGI by constructing a multi-time point difference-indifferences (DID) model.

Digital construction policies can significantly promote EGI. DCP works in two fundamental ways, namely by increasing market information accessibility and reducing operational risk. IPP and PMC significantly increased the contribution of digital construction policies to EGI. Heterogeneity analysis found that digital technology has a stronger promotion effect for SOEs, high-managerial-ability enterprises and enterprises in regions with low Internet development levels.

The study provides new insights about the antecedents of EGI from a DCP perspective. It also enlightens emerging economies to actualize green innovation under the digital wave.

From the perspective of IPT, this study explains the mechanism of DCP-driven EGI. It enhances understanding of the relationship between DCP and EGI.

This paper aims to clarify the impact of information asymmetry on users' payment rates and examine the role of perceived uncertainty (PU) and acceptable price (AP) in the relationship between information asymmetry and users' payment rates.

To test the influences of information asymmetry on users' payment rates, this paper collects 18,489 transaction data from the Chinese knowledge payment platform Zhihu with a Python crawler. This paper constructs a mediation model to define the relationship between information asymmetry and users' payment rates by introducing PU and AP as the mediators.

Information asymmetry negatively affects users' payment rates. In addition, PU and AP mediate the information asymmetry in users' payment rates bond.

This study only explores the mediators of the information asymmetry users’ payment rates bond, ignoring the effect of potential moderators, which would be an important direction for future research.

The findings of this paper suggest that information communication is essential in knowledge market transactions. Knowledge providers, as well as knowledge platforms, should enhance information exchange with consumers in order to increase product sales.

This paper provides a new perspective for understanding how information asymmetry affects users' payment rates and helps to guide suppliers to improve product quality. The research framework of this paper is universal to a certain extent.

This paper is one of the first to propose using PU and AP to construct a mediation model to study the information asymmetry between users' payment rates relationship. It provides a new perspective for understanding the channel of information asymmetry in customer behavior.