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This paper investigates the spillover effect of firms’ social media engagement with investors on consumption market performance and examines the impact of balanced/imbalanced social media stakeholder engagement strategies on firms’ consumption market performance.

The study employs multi-source secondary data covering 3,856 quarterly observations of 188 firms in the Chinese retail industry over six years (2015–2020). Polynomial regression analysis and response surface methodology are used to test the hypotheses.

The study reveals that firms’ social media engagement with investors has a positive spillover effect on consumption market performance. Additionally, the authors find that a balanced social media engagement strategy, which allocates resources evenly between consumers and investors, is more likely to optimize firm performance than an imbalanced strategy.

The research reveals cross-stakeholder spillover effects of social media engagement, introduces balanced/imbalanced engagement strategy concepts and extends the balanced marketing perspective to the social media context, providing guidance for firms to optimize their social media strategies.

With the accumulation of theoretical research and practical experience in the field of garment production research, it is imperative to methodically analyze and reflect on the achievements that have been made. This review aims to systematically map the academic landscape of research articles on garment production, elucidate the evolutionary trajectory of this discipline, identify emerging research frontiers and provide insights into its prospects.

Based on the Web of Science core database, 307 research articles were systematically analyzed by CiteSpace software. The study employed bibliometric and thematic analyses to offer in-depth insights into the dynamics and evolution of research on garment production.

Results reveal that keyword analysis emphasizes the significance of topics such as apparel assembly line, lean production, circular economy, fuzzy logic, global production networks, social sustainability and supply chain management in garment production research. Citation analysis demonstrates that articles related to environmental impact, supply chain management, production process and production technology constitute the knowledge base and core of garment production research. Eight principal research themes emerge: customized garment production, production technology, quality assurance, equipment, production lines, supply chain management, environmental impact and social and human impact. Future research hotspots will focus more on sustainable, intelligent and digital clothing production.

The findings systematically sort out the hotspots and trends in garment production, establish knowledge structures and display them through intuitive representations. The rich insights set the stage for the development of garment production and provide future guidance for theoretical research.

This study aims to improve the stability and obstacle surmounting ability of the traditional wall-climbing robot on the surface of the ship, a wheel-track composite magnetic adsorption wall-climbing robot is proposed in this paper.

The robot adopts a front and rear obstacle-crossing mechanism to achieve a smooth crossover. The robot is composed of two passive obstacle-crossing mechanisms and a frame, which is composed of two obstacle-crossing magnetic wheels and a set of tracks. The obstacle-crossing is realized by the telescopic expansion of the obstacle-crossing mechanism. Three static failure models are established to determine the minimum adsorption force for the robot to achieve stable motion. The Halbach array is used to construct the track magnetic circuit, and the influence of gap, contact area and magnet thickness on the adsorption force is analyzed by parameter simulation.

The prototype was designed and manufactured by the authors for static failure and obstacle crossing tests. The prototype test results show that the robot can cross the obstacle of 10 mm height under the condition of 20 kg load.

A new structure of wall-climbing robot is proposed and verified. According to the test results, the wall-climbing robot can stably climb over the obstacle of 10 mm height under the condition of 20 kg load, which provides a new idea for future robot design.

This study aims to understand how Gen Z consumers perceive fashion brands’ corporate social responsibility (CSR) communication through emotional appeals on Instagram and how such perception affects their overall behaviour towards the brand.

The study adopts a qualitative research approach through photo-elicitation and 14 semi-structured in-depth interviews with members of Gen Z, using one of the world’s largest fast fashion brands, H&M, as the study context.

It is increasingly difficult to capture the attention of Gen Z as they have become immune to the typical CSR messages despite attempting to appeal to their emotions. This makes CSR communication alone challenging in influencing brand perception. However, behaviour towards the brand, such as purchase intention, is not necessarily dependent on whether consumers are convinced of the brand’s CSR activities, as greater value is placed on fast fashion’s price and availability. For Gen Z, such elements surpass their concern for sustainable fashion. Since more emphasis is placed on neutral endorsers due to their trustworthiness, CSR efforts may be disseminated through such third parties to achieve desired outcomes.

Understanding consumers’ perceptions of the current CSR effort allows brand managers to reevaluate their CSR communication strategies to appeal to Gen Z and encourage positive brand behaviour.

Contrary to previous studies, which have focused on organisational outcomes, the study has in-depth explored consumers’ perception of CSR efforts on Instagram and the implications of such perceptions for long-term brand building.

This study explores the determinants of public willingness to comply with COVID-19 regulations to address the research gap at the intersection of public health and law enforcement within the unique sociocultural context of Taiwan.

Utilizing survey data from New Taipei City in 2021, the analysis involved multiple linear regression models to assess the influences of psychological conditions (i.e. distress and self-efficacy), community compliance and perceptions of government (i.e. general trust in government and specific perceptions of police procedural justice) on compliance tendencies while controlling for individual demographics.

The results indicated that self-efficacy, perceived community compliance, trust in government, and police procedural justice are positively associated with public compliance with COVID-19 regulations. Among these variables, trust in government and police procedural justice were identified as the most prominent factors, followed by self-efficacy and perceived community compliance. As demographic factors such as age, gender and education did not significantly affect willingness to comply, psychological, social and governmental influences are more powerful determinants of compliance than static demographic characteristics.

This study provides empirical evidence from Taiwan on the factors shaping public compliance during an unprecedented global pandemic. It highlights the importance of fostering governmental trust and enhancing police procedural justice during periods of stability to secure compliance with public health directives in times of crisis.

Implementing a green strategy to enhance the competitiveness of enterprises is a hot topic in current research. Although most enterprises have formed a green strategy orientation (GSO), it has not been transformed into green competitiveness (GC). Prior studies have not thoroughly studied the effect and mechanism of GSO on GC. To fill this research gap, based on optimal distinctiveness theory, this paper discusses the mediating role of two kinds of green innovation (GI) in the GSO–GC relationship and the moderating role of big data capability (BDC).

This study adopts the quantitative research methods of multiple linear regression, Bootstrap and structural equation modeling (SEM). Data were collected through a questionnaire and a random sampling method was used to survey middle and senior managers and professionals in manufacturing enterprises. About 400 questionnaires were distributed, and 342 valid questionnaires were collected.

The conclusions show that GSO significantly positively affects GI and GC. Still, it turns out that only strategic green innovation (SGI) mediates the GSO–GC relationship. BDC can positively moderate the mediation effect of SGI between GSO and GC, thus supporting the moderated mediation model.

This study used a survey questionnaire from Chinese manufacturing enterprises to collect data, but the sample size was limited. Furthermore, the mediating mechanism by which GSO affects GC requires further exploration. This study directly establishes the GSO–GC relationship based on the optimal distinctiveness theory, making an essential contribution to the literature on GSO and GC. At the same time, this paper uses GI as a bridge to connect the relationship between GSO and GC, enriching the literature on GI. In addition, we consider BDC to be a moderator, expanding the boundaries of the GSO–GC relationship.

This study provides new knowledge and insights for manufacturing enterprises to construct and implement green strategies to achieve GC. More importantly, managers should attach great importance to the critical role of SGI and BDC.

This study understands the importance of GSO, SGI and BDC to GC in theory and practice.

Accurate and rapid tracking and counting of building materials are crucial in managing on-site construction processes and evaluating their progress. Such processes are typically conducted by visual inspection, making them time-consuming and error prone. This paper aims to propose a video-based deep-learning approach to the automated detection and counting of building materials.

A framework for accurately counting building materials at indoor construction sites with low light levels was developed using state-of-the-art deep learning methods. An existing object-detection model, the You Only Look Once version 4 (YOLO v4) algorithm, was adapted to achieve rapid convergence and accurate detection of materials and site operatives. Then, DenseNet was deployed to recognise these objects. Finally, a material-counting module based on morphology operations and the Hough transform was applied to automatically count stacks of building materials.

The proposed approach was tested by counting site operatives and stacks of elevated floor tiles in video footage from a real indoor construction site. The proposed YOLO v4 object-detection system provided higher average accuracy within a shorter time than the traditional YOLO v4 approach.

The proposed framework makes it feasible to separately monitor stockpiled, installed and waste materials in low-light construction environments. The improved YOLO v4 detection method is superior to the current YOLO v4 approach and advances the existing object detection algorithm. This framework can potentially reduce the time required to track construction progress and count materials, thereby increasing the efficiency of work-in-progress evaluation. It also exhibits great potential for developing a more reliable system for monitoring construction materials and activities.

The purpose of this paper is to examine the influence of intelligent manufacturing on audit quality and its underlying mechanism as well as the variation in this influence across different types of organizations.

This research utilizes a difference-in-differences (DID) method to examine how enterprises that apply intelligent manufacturing choose auditors and impact their audit work. The study is based on 15,228 observations of Chinese-listed A-shares from 2011 to 2020.

(1) There is a strong correlation between intelligent manufacturing and audit quality. (2) This positive correlation is statistically significant only in state-owned enterprises (SOEs), those that have steady institutional investors and where the roles of the CEO and chairman are distinct. (3) Enterprises that have implemented intelligent manufacturing are more inclined to employ auditors who possess extensive industry expertise. The auditor's industry expertise plays a crucial role in ensuring audit quality. (4) The adoption of intelligent manufacturing also leads to higher audit fees and longer audit delay periods.

This paper validates the beneficial impact of intelligent manufacturing on improving corporate governance. In addition, it is recommended that managers prioritize the involvement of skilled auditors with specialized knowledge in the industry to ensure the high audit quality and the transparency of information in intelligent manufacturing enterprises.

This study builds upon previous research that has shown the importance of artificial intelligence in enhancing audit procedures. It contributes to the existing body of knowledge by examining how enterprise intelligent manufacturing systems (IMS) enhance audit quality. Additionally, this study provides valuable information on how to improve audit quality in the field of intelligent manufacturing by strategically selecting auditors based on resource dependency theory.

This paper aims to compare the differences between single- and multi-person interactions in virtual tourism, underpinned by the stimulus-organism-response (S-O-R) framework and media richness theory (MRT).

In this study, quantitative data gathered from questionnaires applied to 558 individuals was analyzed by using partial least squares structural equation modeling. The moderating role of interaction type was tested through multigroup analysis (MGA).

The results showed that vividness positively influenced telepresence, perceived attractiveness and authentic experiences; telepresence positively affected authentic experiences and perceived attractiveness; and authentic experiences and perceived attractiveness positively impacted willingness to visit in both interaction groups. A difference was detected between the two groups in that perceptions of media vividness were more easily transformed into a willingness to visit through telepresence in the multi-person interaction group. Interaction type moderated the effect of vividness on telepresence. The vividness of the media had a more significant effect on telepresence among those who participated in virtual tourism together.

In this study, a model was developed to explain how media vividness affected willingness to visit by considering the relationships between telepresence, authentic experiences and perceived attractiveness in virtual reality, as well as the social interaction aspect.

本研究旨在比较虚拟旅游中单人和多人互动的差异, 基于刺激-有机体-反应（S-O-R）框架和媒介丰富度理论（MRT）。

本研究对 558 名受试者的问卷调查数据进行了定量分析, 采用 PLS-SEM 模型分析, 并通过多群组分分析（MGA）测试了互动类型的调节作用。

研究结果显示, 生动性对临场感、感知吸引力和真实体验有正向影响; 临场感对真实体验和感知吸引力有正向影响; 真实体验和感知吸引力对参观意愿有正向影响。两组之间的差异在于, 在多人互动组中, 媒介生动性更容易通过临场感转化为参观意愿。互动类型调节了生动性对临场感的影响, 参与虚拟旅游的多人群体中, 媒介的生动性对临场感的影响更为显著。

本研究构建了一个模型, 解释了在虚拟现实中, 媒介生动性如何通过临场感、真实体验和感知吸引力的关系影响参观意愿, 同时考虑了社会互动的因素。

This study aims to tackle the primary challenges in human–robot–environment interaction (HREI) within unknown environments. The key issues include recognizing human motion intention and managing force impacts during transitions from free space to constrained space. Addressing these challenges is critical for improving compliance, enhancing force control accuracy, and ensuring the safety and performance of HREI systems.

First, the energy equation of the second-order system is presented, and variable admittance control laws are designed for both free space and constraint space based on the energy equation. Then, a smooth switching method based on selection matrix is developed. Subsequently, the admittance-based overall control system is discussed. Finally, comparative simulations and experiments are conducted to verify the efficacy of the variable admittance control method using the 7-degree-of-freedom (7-DOF) manipulator Panda arm.

The simulation and experiment results demonstrate that the proposed variable admittance control method outperforms the traditional method in terms of force overshoot and accuracy.

This study does not account for the shape of unknown surfaces in the formulation of the variable admittance control law.

This paper proposes an energy-based variable admittance control method that uses energy considerations and uses a smooth switching technique to deduce human intentions and mitigate the effects of impact force.