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Corrosion thinning reduces the effective cross-sectional areas of steel structures and degrades their mechanical properties. This study aims to investigate the relationship between the corrosion thinning of carbon steel for transmission towers and the degradation of its mechanical properties.

A macroscopic finite element model of a transmission tower was established and then combined with the corrosion thinning and mechanical properties of Q355 steel in different test periods measured in neutral salt spray, SO₂ atmosphere and wet heat environments to conduct a finite element simulation of a transmission tower with different corrosion thinning of Q355 steel.

When the residual thickness of the tower leg angle was reduced to 4.03 mm, the maximum stress solved in the simulation exceeded the yield strength, with the tower already at risk of collapse owing to corrosion failure under extreme conditions of basic wind speed.

This study innovatively utilises transmission tower finite element models and experimental data from mechanical degradation experiments to quantify the relationship between corrosion thinning and the mechanical properties of Q355 steel, ensuring the effective assessment of the mechanical properties of corroded transmission towers.

This study aims to introduce a novel machine learning feature vector (MLFV) method to bring machine learning to overcome the time-consuming computational fluid dynamics (CFD) simulations for rapidly predicting turbulent flow characteristics with acceptable accuracy.

In this method, CFD snapshots are encoded in a tensor as the input training data. Then, the MLFV learns the relationship between data with a rod filter, which is named feature vector, to learn features by defining functions on it. To demonstrate the accuracy of the MLFV, this method is used to predict the velocity, temperature and turbulent kinetic energy fields of turbulent flow passing over an innovative nature-inspired Dolphin turbulator based on only ten CFD data.

The results indicate that MLFV and CFD contours alongside scatter plots have a good agreement between predicted and solved data with R² ≃ 1. Also, the error percentage contours and histograms reveal the high precisions of predictions with MAPE = 7.90E-02, 1.45E-02, 7.32E-02 and NRMSE = 1.30E-04, 1.61E-03, 4.54E-05 for prediction velocity, temperature, turbulent kinetic energy fields at Re = 20,000, respectively.

The method can have state-of-the-art applications in a wide range of CFD simulations with the ability to train based on small data, which is practical and logical regarding the number of required tests.

The paper introduces a novel, innovative and super-fast method named MLFV to address the time-consuming challenges associated with the traditional CFD approach to predict the physics of turbulent heat and fluid flow in real time with the superiority of training based on small data with acceptable accuracy.

This research investigates the role of customer forgiveness as the result of online service recovery transparency in predicting customer engagement. It also examines the moderating roles of timeliness and personalization in this proposed model.

An online survey study using retrospective experience sampling and a scenario-based experimental study were conducted to test the proposed hypotheses.

Customer forgiveness positively influences customer engagement and plays a mediating role in the relationship between service recovery transparency and customer engagement. Additionally, timeliness and personalization moderate the positive influence of service recovery transparency on customer forgiveness. The positive influence of service recovery transparency on customer forgiveness is more apparent when levels of timeliness and personalization decrease.

To retain focal customers' engagement after a service failure, firms must obtain their forgiveness. One of the firm's online complaint handling strategies to increase the forgiveness level of focal customers is to provide a high level of service recovery transparency (i.e. responding to their complaints in a public channel), especially when the firm is unable to respond to online complaints quickly or provide highly personalized responses.

This research provides new insights into the underlying mechanism of customer engagement by applying the concept of customer forgiveness. It also contributes to the social influence theory by applying the essence of the theory to explain how other customers' virtual presence during the online complaint handling influences the forgiveness of focal customers in order to gain their engagement. Additionally, it provides insight into the conditions under which the role of service recovery transparency can be very effective in dealing with online complaints.

The aim of this study is to (1) construct a standard framework for assessing the capability of bioenergy enterprises' digital green innovation partners; (2) quantify the choice of partners for digital green innovation by bioenergy enterprises; (3) propose based on a dual combination empowerment niche digital green innovation field model.

Fuzzy set theory is combined into field theory to investigate resource complementarity. The successful application of the model to a real case illustrates how the model can be used to address the problem of digital green innovation partner selection. Finally, the standard framework and digital green innovation field model can be applied to the practical partner selection of bioenergy enterprises.

Digital green innovation technology of superposition of complementarity, mutual trust and resources makes the digital green innovation knowledge from partners to biofuels in the enterprise. The index rating system included eight target layers: digital technology innovation level, bioenergy technology innovation level, bioenergy green level, aggregated digital green innovation resource level, bioenergy technology market development ability, co-operation mutual trust and cooperation aggregation degree.

This study helps to (1) construct the evaluation standard framework of digital green innovation capability based on the dual combination empowerment theory; (2) develop a new digital green innovation domain model for bioenergy enterprises to select digital green innovation partners; (3) assist bioenergy enterprises in implementing digital green innovation practices.

This research examines how universities enhance the virality of their social media messages among students. Specifically, we explore whether and how positive affective content in universities’ social media posts can influence sharing behavior. We also investigate the mediating roles of perceived effort and positive emotional reaction, as well as the moderating effect of visual content (i.e. photos).

Drawing upon the emotions as social information model, we conducted (1) an online experiment (N = 222) and (2) text analysis of 1,269,798 Twitter posts extracted from the accounts of 94 UK universities over 11 years (2010–2020) to test our hypotheses.

The findings show that social media posts containing positive affective content encourage sharing behavior and the relationship is mediated by both perceived effort and positive emotional reaction. An additional finding suggests that the use of visual content (photos) strengthens the relationship between positive affective content and sharing behaviors through an interaction effect.

This study contributes to the scant research focusing on positive affective content in the higher education context. The findings shed light on how universities could create social media communications that engage current and prospective students.

Based on the ability, motivation and opportunity (AMO) theory, this study aims to investigate the role of digital human resources management (digital-HRM) practices in influencing hotel employee behaviors, especially their adoption of work-at-home (WAH).

The study was conducted in two stages in hotels in Vietnam. Stage 1 used a mixed method to develop an instrument to measure digital-HRM practices. In Stage 2, through a survey of 303 respondents, the research investigated digital-HRM practices’ additive and interactive effects on WAH.

The study shows that digital-HRM comprised five factors. Except for digital recruitment, the other digital-HRM practices significantly affected WAH. In addition, the research suggests that digital training and employee involvement should be combined to enhance employee willingness for WAH.

Drawing on the AMO theory, this study constructs a digital-HRM measurement scale to study the antecedents and consequences of these practices to improve employees’ digital work efficiency. In addition, through both additive and combinative (a two-way interaction) models, the study enhances the HRM and hotel management theory by understanding why digital-HRM practices are essential to boost employees’ digital competencies to adopt remote working.

By investigating the role of digital-HRM practices in improving employees’ adoption of WAH, this study provides empirical implications for hotels to manage digital-HRM practices better and thus makes remote working effective.

The existing literature reveals the lack of a deep understanding of how HRM practices can promote digital devices and services and their influence on employee behaviors, especially in the hotel sector. To the best of the authors’ knowledge, this study is unique in extending the AMO theory into the digital context to illuminate components of digital-HRM practices and clarify how digitalizing HRM practices can motivate hotel employees to accept WAH.

Despite an enormous body of literature on conflict management, intra-group conflicts vis-à-vis team performance, there is currently no study investigating the conflict prevention approach to handling innovation-induced conflicts that may hinder smooth implementation of big data technology in project teams.

This study uses constructs from conflict theory, and team power relations to develop an explanatory framework. The study proceeded to formulate theoretical hypotheses from task-conflict, process-conflict, relationship and team power conflict. The hypotheses were tested using Partial Least Square Structural Equation Model (PLS-SEM) to understand key preventive measures that can encourage conflict prevention in project teams when implementing big data technology.

Results from the structural model validated six out of seven theoretical hypotheses and identified Relationship Conflict Prevention as the most important factor for promoting smooth implementation of Big Data Analytics technology in project teams. This is followed by power-conflict prevention, prevention of task disputes and prevention of Process conflicts respectively. Results also show that relationship and power conflicts interact on the one hand, while task and relationship conflict prevention also interact on the other hand, thus, suggesting the prevention of one of the conflicts could minimise the outbreak of the other.

The study has been conducted within the context of big data adoption in a project-based work environment and the need to prevent innovation-induced conflicts in teams. Similarly, the research participants examined are stakeholders within UK projected-based organisations.

The study urges organisations wishing to embrace big data innovation to evolve a multipronged approach for facilitating smooth implementation through prevention of conflicts among project frontlines. This study urges organisations to anticipate both subtle and overt frictions that can undermine relationships and team dynamics, effective task performance, derail processes and create unhealthy rivalry that undermines cooperation and collaboration in the team.

The study also addresses the uncertainty and disruption that big data technology presents to employees in teams and explore conflict prevention measure which can be used to mitigate such in project teams.

The study proposes a Structural Model for establishing conflict prevention strategies in project teams through a multidimensional framework that combines constructs like team power conflict, process, relationship and task conflicts; to encourage Big Data implementation.

This study investigates the impact of personal, organisational and innovation capability on technological innovation and further impacts on small and medium enterprises’ (SMEs') performance during uncertainty. Moreover, the moderator role of social sustainability orientation on the relationships of technological innovation, innovation performance and organisational resilience is also examined.

A quantitative approach using 524 SME responses and Smart PLS 4.0 are adopted in this research.

The results indicate a correlation between three types of capabilities with technological innovation and further conversion to organisational resilience. Additionally, social sustainability shows a negative moderating effect between innovation performance and organisational resilience. The research findings advanced the resources-based-view (RBV) by proposing three capability dimensions as platforms for SMEs' innovation success, which later generate resilience possibilities. Specifically highlighted in this study are the personal capabilities of managers, organisational capabilities and innovation capabilities in setting business objectives and resource allocation towards economic and sustainable goals during turbulence and uncertainty.

This study investigates the role of technological innovation and innovation on SME resilience. Notably, we deploy the social sustainability orientation as moderators towards the relationship between technological innovation, innovation performance and SMEs’ resilience. SMEs employing social sustainability orientation might negatively inhibit the translation from innovation performance to SME resilience, providing novel insights into navigating uncertainty in modern business. It has no effect on the relationship between technological innovation and its consequences.

Nanotechnology (NT) advancements in personal protective textiles (PPT) or personal protective equipment (PPE) have alleviated spread and transmission of this highly contagious viral disease, and enabled enhancement of PPE, thereby fortifying antiviral behavior.

Review of a series of state of the art research papers on the subject matter.

This paper expounds on novel nanotechnological advancements in polymeric textile composites, emerging applications and fight against COVID-19 pandemic.

As a panacea to “public droplet prevention,” textiles have proven to be potentially effective as environmental droplet barriers (EDBs).

PPT in form of healthcare materials including surgical face masks (SFMs), gloves, goggles, respirators, gowns, uniforms, scrub-suits and other apparels play critical role in hindering the spreading of COVID-19 and other “oral-respiratory droplet contamination” both within and outside hospitals.

When used as double-layers, textiles display effectiveness as SFMs or surgical-fabrics, which reduces droplet transmission to <10 cm, within circumference of ∼0.3%.

NT advancements in textiles through nanoparticles, and sensor integration within textile materials have enhanced versatile sensory capabilities, robotics, flame retardancy, self-cleaning, electrical conductivity, flexibility and comfort, thereby availing it for health, medical, sporting, advanced engineering, pharmaceuticals, aerospace, military, automobile, food and agricultural applications, and more. Therefore, this paper expounds on recently emerging trends in nanotechnological influence in textiles for engineering and fight against COVID-19 pandemic.

With the advent of transformational and digital technologies, companies are striving to integrate digital transformation strategy (DTS) into their business models to stay competitive. However, in many cases, the implementation of DTS fails because managers lack knowledge about the opportunities and challenges associated with this strategy.

First, a bibliometric analysis was conducted with data related to DTS from the Scopus database for the period 2011–2023 to identify scientific trends in the field of DTS. Subsequently, 2,363 documents were collected and analyzed using Vosviewer software. Finally, the opportunities and challenges within the field of DTS were identified as one of the latest trends through a meta-synthesis method.

The findings of this research highlight the current scientific trends in the field of DTS. Also, the challenges of implementing the DTS are lack of instructions and rules, financial weakness, lack of alignment, inappropriate organizational structure, lack of digital organizational culture, challenges related to employees, lack of a digital leader and cyber threats. In addition, DTS implementation opportunities can be divided into three groups: improving customer experience, improving business processes and strategic renewal and creation of new business models.

The present research introduces two key innovations. The first is to offer a summary of prior research in the field of DTS. The second is identifying the opportunities and challenges associated with adopting this strategy, which is one of the latest trends.