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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 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.

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.

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.

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 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.

Recently, electric vehicles have been widely used in the cold chain logistics sector to reduce the effects of excessive energy consumption and to support environmental friendliness. Considering the limited battery capacity of electric vehicles, it is vital to optimize battery charging during the distribution process.

This study establishes an electric vehicle routing model for cold chain logistics with charging stations, which will integrate multiple distribution centers to achieve sustainable logistics. The suggested optimization model aimed at minimizing the overall cost of cold chain logistics, which incorporates fixed, damage, refrigeration, penalty, queuing, energy and carbon emission costs. In addition, the proposed model takes into accounts factors such as time-varying speed, time-varying electricity price, energy consumption and queuing at the charging station. In the proposed model, a hybrid crow search algorithm (CSA), which combines opposition-based learning (OBL) and taboo search (TS), is developed for optimization purposes. To evaluate the model, algorithms and model experiments are conducted based on a real case in Chongqing, China.

The result of algorithm experiments illustrate that hybrid CSA is effective in terms of both solution quality and speed compared to genetic algorithm (GA) and particle swarm optimization (PSO). In addition, the model experiments highlight the benefits of joint distribution over individual distribution in reducing costs and carbon emissions.

The optimization model of cold chain logistics routes based on electric vehicles provides a reference for managers to develop distribution plans, which contributes to the development of sustainable logistics.

In prior studies, many scholars have conducted related research on the subject of cold chain logistics vehicle routing problems and electric vehicle routing problems separately, but few have merged the above two subjects. In response, this study innovatively designs an electric vehicle routing model for cold chain logistics with consideration of time-varying speeds, time-varying electricity prices, energy consumption and queues at charging stations to make it consistent with the real world.

China's universities have decoupled academic staff rewards and returnee status (scholars with a higher degree or substantial work experience gained outside China). This development possibly poses a threat to returnees' psychological contract fulfilment (PCF), i.e. the extent to which employees perceive their employer has fulfilled their promises or obligations regarding the employment relationship. Drawing on the efficiency–flexibility balance theory, the authors predict Chinese universities would institutionalise human resource management (HRM) practices intended to countervail the decoupling's potentially negative influence. Furthermore, the positive effect of returnee status on PCF would subsequently manifest as higher job satisfaction and lower turnover intention.

Utilising a mixed-method approach, the authors first undertook a large-scale multi-time field survey of Chinese business school academics from a group of non-elite universities located in Southern China. The authors then conducted a series of in-depth interviews with a subsample of the surveyed cohort, which was then analysed using multivariate regression analyses and machine-aided qualitative content analysis (i.e. NVivo 10).

The authors find that, despite the decoupling of returnee status and faculty rewards, returnee status is positively associated with PCF. This positive association further manifests as an indirect effect on job satisfaction and a negative indirect effect on turnover intention. The authors also determine that returnees experience higher PCF because universities have revised HRM practices to reward evidenced job activities. Returnees can gain a competitive advantage by using their skills gained overseas.

This study makes four original contributions. First, the authors investigate a neglected yet essential issue, namely, how returnee status relates to PCF in China's universities. Second, the authors enrich the theoretical understanding by introducing the efficiency–flexibility balance theory into the employee PCF literature. Third, the authors provide new insights on how China's universities maximise the effectiveness of academic returnees' talents and skills. Finally, by focusing on non-elite universities, the authors provide insights relevant to a broader faculty population than is available in the existing literature.