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Recently, digital competence has become one of the most important work competencies of employees. This study investigated students' digital competence in the context of Chinese higher education and examined digital competence's relationship with students' career adaptability.

The participants were 298 Chinese higher education students from both universities and colleges. MANOVA (Multivariate Analysis of Variance) was carried out through SPSS (Statistical Package for the Social Sciences) 26.0 to examine whether digital competence varied with the students' personal characteristics, i.e. gender, grade level and school type. In addition, the structural equation model was employed with Mplus 8.3 to analyse the relationship between digital competence and career adaptability as well as the mediation role of digital informal learning and academic performance.

The descriptive statistics revealed that students performed best in the safety construct, whilst worst in the digital content creation construct. The result of MANOVA showed that the digital competence of the participants significantly varied with participants' gender, school type and grade level. Furthermore, the structural equation model results demonstrated that higher education students' digital competence was positively associated with the students' career adaptability. And the mediation role of digital informal learning and academic performance was also confirmed.

To the authors' knowledge, this is the first study focussing on the relationship between digital competence and career adaptability. In particular, both working and learning perspectives were considered to explore this relationship. Besides, the authors also displayed Chinese higher education students' digital competence with consideration of different districts and school types.

This study aims to develop an architectural prototype of a Cyber-Physical System (CPS), as well as lay a technological foundation for future smart housing with improved health and well-being outcomes for its occupants.

This study deploys smart sensors to monitor the key environmental parameters of a house. Using Internet of Things technology, a prototype of a CPS has been developed for capturing the environmental conditions over time. A case study involving a property in New Zealand was undertaken to validate the prototype.

The study proposes a monitoring platform, enabled by the CPS and smart sensing devices, that collects, shares, stores, analyses and visualises indoor environment data. The reliability and accuracy of the monitoring system were enhanced by comparing the activity of house occupants with sensor data.

Due to limited time, the prototype was tested in one house for a period of one month. Air quality was not considered in this study. However, the work suggests that such an approach provides an effective solution for government organisations and housing agencies to collect information for the purpose of assessing building thermal performance.

This research proposes a new lens consisting of a home environment monitoring application with health and well-being implications. It could also be used to inform the future design of healthy homes and buildings, both in New Zealand and internationally.

This study aims to prepare a low-formaldehyde and environmentally friendly glucose-lignin-based phenolic resin.

The authors directly used lignin to substitute formaldehyde to prepare lignin-based phenolic resin (LPF) with urea as formaldehyde absorbent. To improve the performance of the adhesive, the biobased glucose was introduced and the modified glucose-LPF (GLPF) was obtained.

The results showed that when the replacing amount of lignin to formaldehyde reached 15 Wt.%, the physical properties of the prepared LPF met the Chinese national standard, and the bonding strength increased by 21.9%, from 0.75 to 0.96 MPa, compared with PF. The addition of glucose boost the performance of wood adhesive, for example, the free phenol content of the obtained GLPF was significantly reduced by 79.11%, from 5.60% to 1.17%, the bonding strength (1.19 MPa) of GLPF increased by 19.3% in comparison to LPF and the curing temperature of GLPF decreased by 13.08%.

The low-formaldehyde and environmentally friendly GLPF has higher bonding strength and lower curing temperature, which is profitable to industrial application.

The prepared GLPF has lower free formaldehyde and formaldehyde emission, which is cost-effective and beneficial to human health.

The joint work of lignin and glucose provides the wood adhesive with increased bonding strength, decreased free phenol content and reduced curing temperature.

Bismaleimide (BMI) is a kind of thermosetting resin and its application is usually limited by low toughness. In this paper, two kinds of reinforcement intercalator amino-terminated polyoxypropylene (POP) and octadecyl trimethyl ammonium chloride (OTAC) were designed and synthesized to toughen BMI resin and the toughening effect was compared and analyzed. The purpose of this paper is to toughen BMI resin and analyze the toughening effect of two reinforcements intercalator amino-terminated polyoxypropylene (POP) and octadecyl trimethyl ammonium chloride (OTAC).

Sodium-based montmorillonite (Na-MMT) was modified by POP and OTAC, and the ion-exchange reaction obtained organic montmorillonite (POP-MMT and OTAC-MMT). The polymer matrix (MBAE) was synthesized, in which 4,4’-diamino diphenyl methane BMI was used as the monomer and 3,3’-diallyl bisphenol A and bisphenol A diallyl ether were used as active diluents. And then, POP-MMT/MBAE and OTAC-MMT/MBAE composites were prepared using MBAE as matrix and POP-MMT or OTAC-MMT as reinforcement. The Fourier-transform infrared, X-ray diffraction and scanning electron microscope (SEM) of the filler and microstructure and mechanical properties of the composite were characterized to the better reinforcement.

POP-MMT and OTAC-MMT enhanced BMI-cured products’ toughness by generating microcracks in the polymer to absorb more fracture energy. Meanwhile, POP-MMT and OTAC-MMT were the main stress components and the enhancement of the interface interaction was beneficial to transfer the external force from the matrix to the reinforcement and improved the mechanical properties of the composite. Furthermore, with the intercalation rate increasing, the compatibility of the two phases was increased and the performance of MBAE was also elevated.

BMI is generally used as aerospace structural materials, functional materials, impregnating paint and other fields. However, high crosslinking density leads to moulding material’s brittleness and limits a wider range of applications. Therefore, it has become an urgent priority to explore and improve the mechanical properties of BMI resin.

POP and OTAC have successfully intercalated Na-MMT layers to get POP-MMT and OTAC-MMT, and the interplanar crystal spacing and the intercalation rate were calculated, respectively. The results were corresponding with the SEM images of POP-MMT and OTAC-MMT. After that, the morphology of composites illustrated the compatibility was related to the intercalation rate. According to the mechanism of modified MMT toughening epoxy resin, when they were dispersed uniformly in the matrix, the composite’s mechanical properties had been significantly improved. Additionally, OTAC-MMT with a higher intercalation rate had better compatibility and interfacial force with the matrix, so that the mechanical properties of OTAC-MMT/MBAE were the best.

Multiple infectious control measures, e.g. social distancing, city lockdown and mask-wearing, have been implemented since the coronavirus disease 2019 (COVID-19) outbreak. Given the bidirectional relationship between foundational movement skills (FoMS) and physical activity (PA), and inadequate PA in Chinese children and adolescents, FoMS tends to decrease during and after the COVID-19 pandemic. Therefore, the purpose of this paper is to systematically review the literature about the impact of the COVID-19 pandemic on FoMS of individuals aged 5–17 years in Chinese societies.

Preferred Reporting Items for Systematic Reviews and Meta-Analyses was followed. Peer-reviewed articles on four electronic databases (Scopus, Web of Science, EBSCOhost and PubMed) were searched on 8 May 2024. The quality of each study was evaluated by the Mixed Methods Appraisal Tool (MMAT) version 2018. Two independent reviewers were involved in all study selection and appraising procedures.

Among 18,450 records identified, 10 quantitative studies analysing student participants were included. The overall quality of these studies was high, with an average score of 86% in MMAT. The variations among these studies led to inconclusive evaluations. So as to advance the quality of future research and assessments, investigating more aspects of FoMS, standardising physical test protocols and report styles and adopting multiple research designs should be achieved.

To the best of the authors’ knowledge, this is the first review synthesising evidence about FoMS for Chinese children and adolescents. A definitive conclusion cannot be provided due to certain methodological issues. The current situation of FoMS and future research directions were illustrated.

Considers the effects on the mass media of China’s transition to a more open economy and the strategies which advertisers might use to overcome the problems it presents. Describes the media available (including the unofficial “black route”, the official but restricted private sector “white route”, and the state “red route”) and lists the challenges which advertisers face in using them. Suggests some strategies and practices which can help to overcome them and some hypotheses on future media use. Calls for further research in this area.

The television (TV) content ecosystem has shifted from traditional broadcasting systems to dedicated content producers and over-the-top (OTT) services. However, less empirical effort has been paid to the actual behaviors of the mobile users who watch TV content when explaining the impact of OTT service and mobile network profiles in watching TV content. This study aims to investigate the impact of gratifications and attitude formed by mobile TV users on actual mobile TV watching behaviors, as well as the moderating impacts of paid OTT service subscriptions and mobile network profiles, based on gratification theory, cognition–affect–behavioral (CAB) framework, sunk cost effect and walled-garden effect.

This study employs the generalized linear model (GLM) with generalized estimating equations (GEE) to test hypothesized relationships. A total of 338 mobile phone users who have been watching TV content using a mobile phone participated in the survey. The moderating variables, 4 types of paid streaming platform subscriptions, were classified based on the walled gardens formed by mobile telecom services.

The study’s results revealed that obtained gratifications and opportunity constructs substantially influenced a mobile phone user’s attitude and behaviors. Additionally, mobile network profiles and the degree of access to paid platform services played significant moderating roles in the relationship between users’ attitudes and behavior.

This research enriches the existing OTT service literature and is one of the pioneering studies investigating the walled-garden effect’s role in mobile phone users’ actual watching behaviors, offering valuable practical implications for the OTT platform providers.

The emergence of risk in today's business environment is affecting every managerial decision, majorly due to globalization, disruptions, poor infrastructure, forecasting errors and different uncertainties. The impact of such disruptive events is significantly high for perishable items due to their susceptibility toward economic loss. This paper aims to design and address an operational planning problem of a perishable food supply chain (SC).

The proposed model considers the simultaneous effect of disruption, random demand and deterioration of food items on business objectives under constrained conditions. The study describes this situation using a mixed-integer nonlinear program with a piecewise approximation algorithm. The proposed algorithm is easy to implement and competitive to handle stationary as well as nonstationary random variables in place of scenario techniques. The mathematical model includes a real-life case study from a kiwi fruit distribution industry.

The study quantifies the performance of SC in terms of SC cost and fill rate. Additionally, it investigates the effects of disruption due to suppliers, transport losses, product perishability and demand stochasticity. The model incorporates an incentive-based strategy to provide cost-cutting in the existing business plan considering the effect of deterioration. The study performs sensitivity analysis to show various “what-if” situations and derives implications for managerial insights.

The study contributes to the scant literature of quantitative modeling of food SC. The research work is original as it integrates a stochastic (uncertain) nature of SC simultaneously coupled with the effect of disruption, transport losses and product perishability. It incorporates proactive planning strategies to minimize the disruption impact and the concept of incremental quantity discounts on lot sizes at a destination node.

The purpose of this paper is to investigate the use of problem-solving approaches in maintenance cost management (MCM). In particular, the paper aims to examine characteristics of MCM models and to identify patterns for classification of problem-solving approaches.

This paper reflects an extensive and detailed literature survey of 68 (quantitative or qualitative) cost models within the scope of MCM published in the period from 1969 to 2013. The reviewed papers have been critically examined and classified based on implementing a morphological analysis which employs eight criteria and associated expressions. In addition, the survey identified two main perspectives of problem solving: first, synoptic/incremental and second, heuristics/meta-heuristics.

The literature survey revealed the patterns for classification of the MCM models, especially the characteristics of the models for problem-solving in association with the type of modeling, focus of purpose, extent and scope of application, and reaction and dynamics of parameters. Majority of the surveyed approaches is mathematical, respectively, synoptic. Incremental approaches are much less and only few are combined (i.e. synoptic and incremental). A set of features is identified for proper classification, selection, and coexistence of the two approaches.

This paper provides a basis for further study of heuristic and meta-heuristic approaches to problem-solving. Especially the coexistence of heuristic, synoptic, and incremental approaches needs to be further investigated.

The detected dominance of synoptic approaches in literature – especially in the case of specific application areas – contrasts to some extent to the needs of maintenance managers in practice. Hence the findings of this paper particularly address the need for further investigation on combining problem-solving approaches for improving planning, monitoring, and controlling phases of MCM. Continuous improvement of MCM, especially problem-solving and decision-making activities, is tailored to the use of maintenance knowledge assets. In particular, maintenance management systems and processes are knowledge driven. Thus, combining problem-solving approaches with knowledge management methods is of interest, especially for continuous learning from past experiences in MCM.

This paper provides a unique study of 68 problem-solving approaches in MCM, based on a morphological analysis. Hence suitable criteria and their expressions are provided. The paper reveals the opportunities for further interdisciplinary research in the maintenance cost life cycle.

The study aimed to examine how different communities concerned with dementia engage and interact on Twitter.

A dataset was sampled from 8,400 user profile descriptions, which was labelled into five categories and subjected to multiple machine learning (ML) classification experiments based on text features to classify user categories. Social network analysis (SNA) was used to identify influential communities via graph-based metrics on user categories. The relationship between bot score and network metrics in these groups was also explored.

Classification accuracy values were achieved at 82% using support vector machine (SVM). The SNA revealed influential behaviour on both the category and node levels. About 2.19% suspected social bots contributed to the coronavirus disease 2019 (COVID-19) dementia discussions in different communities.

The study is a unique attempt to apply SNA to examine the most influential groups of Twitter users in the dementia community. The findings also highlight the capability of ML methods for efficient multi-category classification in a crisis, considering the fast-paced generation of data.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2021-0208.