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The purpose of this paper is to provide an overview of the evolution of service supply chain management from a behavioural operations perspective, pointing out future research directions for scholars.

This study searched five databases for relevant literature published between 2009 and 2018, selecting 64 papers for this review. The selected literature was categorised according to two dimensions: a service supply chain link perspective and a behavioural factor perspective. Comparative analysis was used to identify gaps in the literature, and five future research agendas were proposed.

In terms of the perspective of service supply chain link, extant literature primarily focuses on service supply and service co-ordination management, and less on service demand and integration management. In terms of the behavioural factor’s perspective, most focus on classic behaviour factors, with less attention paid to emerging behaviour factors. This paper thus proposes five research agendas: demand-oriented management and integrated supply chain-oriented behavioural research; broadening the understanding of the scope of behavioural operations; integrating the latest backgrounds and trends of service industry into the research; greater attention to behavioural operations in service sub-industries; and multimethod combination is encouraged to be used to dig into the interesting research problems.

This study constitutes the first systematic review of service supply chain research from a behavioural perspective. By categorising the literature into two dimensions, the state of existing research is evaluated with an eye towards future research avenues.

This paper aims to analyse the effects of events image from host communities’ perspective on the city’s overall image and the intention to recommend the events and the city as a tourism destination.

The research used a bivariate data analysis based on Spearman’s correlation and regression analysis to determine useful variables to predict the intention to recommend the city as a tourism destination. Data collection was face-to-face and online with a non-probabilistic sample of Viseu city residents, the second largest city in the central region of Portugal.

The findings had implications for researchers, governments and stakeholders. From the resident’s point of view, there is a high correlation between the overall city image and the intention to recommend it as a tourism destination. Event image and the intention to recommend the event participation affect the overall city image. Results point out the resident as natural promoters of events and their city if the local events have an appeal that generates their participation. Conclusions indicated that cities need to re-thinking tourism from the citizen’s perspective as staycation is a grown option.

Event image by host-city residents’ perceptions is an underdevelopment theme in the literature, although residents’ participation is essential to the success of most events. Local events can promote tourist citizenship and reinforce the positioning of tourism destinations, associating them with an image of desirable places to visit and live.

This study aims to explore the intention to quit and its associations among ambulance personnel and to compare the intention to quit and its associations between paramedic and non-paramedic staff.

A cross-sectional study was conducted on 492 Australian ambulance personnel. Participants were selected by stratified random sampling. Data were collected using phone interview-administered questionnaires. Descriptive analyses, bivariate associations and structural equation modelling were performed for data analysis.

The study found that 70% of ambulance personnel intended to quit their jobs. Intention to quit was similar between paramedics and non-paramedic staff. In both staff groups, supervisors' and colleagues' support was associated with mental health symptoms; job satisfaction was associated with the intention to quit. Supervisors' and colleagues' support was indirectly associated with the intention to quit via increasing job satisfaction and reducing the experience of mental health symptoms among paramedics only. Mental health symptoms were directly associated with the intention to quit and indirectly associated with the intention to quit via reducing job satisfaction among paramedics only.

The study findings provide evidence for resource allocation in human resource management. The findings suggest that interventions to increase job satisfaction may reduce the intention to quit for all ambulance personnel. Interventions to improve supervisors' and colleagues' support and to manage depression, anxiety and stress symptoms may help to reduce the intention to quit for paramedics only.

This is the first study to model and compare the direct and indirect associations of intention to quit between paramedics and non-paramedic staff in ambulance personnel.

This study aims to investigate the specific difficulties involved in implementing electronic recordkeeping for anti-corruption investigations in Nigeria. It recognises the importance of technological advancements in such investigations and the need for efficient, internationally recognised services, especially in a country where manual processes are prevalent.

This study uses a qualitative, exploratory case study approach. Data were gathered through interviews with 15 anti-corruption investigators in Nigeria in the year 2020 and analysed using thematic analysis.

This study identified two main challenges: resistance to adopting technological change and indifference towards information technology.

This study highlights the transformative potential of technology, specifically cloud computing and forensic technology, in an investigative context. By intentionally integrating technology, existing deficiencies can be addressed, investigative processes can be streamlined and a culture of accountability can be cultivated. It contributes to ongoing discussions and emphasises the capacity of technology to drive significant transformation in the pursuit of integrity and justice.

This study aims to determine the antecedents that influence attitudes towards the use of environmentally friendly household appliance products and consumers' green purchase intention among consumers in Harare, Zimbabwe.

Data were collected from 329 consumers in Harare, Zimbabwe's commercial capital who were served from five using a structured questionnaire via an online web-based cross-sectional survey. Hypothesised relationships were tested through structural equation modelling with the aid of Smart PLS software.

Green product awareness, social influence, perceived benefit and attitude towards green appliances were found to have a significant positive effect on green purchase intention.

The study's findings may not be generalised to other contexts as sample data was only collected in Zimbabwe. Complementary cross-sectional research studies can be done in other parts of the world to enable cross-cultural comparisons and methodological validations.

The green appliance and energy saving practices are vastly growing, with many multinational appliance companies introducing green products within their product lines and adopting the concept of sustainability through modifications in production, design and consumption of household appliance products that encompass fewer harmful consequences on the environment in response to their concerns about the scarcity of natural resources, environmental well-being and the potential detriment of future generations.

Notwithstanding the limitations of the current study, the results have the potential to contribute to an improved understanding of influence attitudes towards the use of environmentally friendly household appliance products.

Surface roughness has a serious impact on the fatigue strength, wear resistance and life of mechanical products. Realizing the evolution of surface quality through theoretical modeling takes a lot of effort. To predict the surface roughness of milling processing, this paper aims to construct a neural network based on deep learning and data augmentation.

This study proposes a method consisting of three steps. Firstly, the machine tool multisource data acquisition platform is established, which combines sensor monitoring with machine tool communication to collect processing signals. Secondly, the feature parameters are extracted to reduce the interference and improve the model generalization ability. Thirdly, for different expectations, the parameters of the deep belief network (DBN) model are optimized by the tent-SSA algorithm to achieve more accurate roughness classification and regression prediction.

The adaptive synthetic sampling (ADASYN) algorithm can improve the classification prediction accuracy of DBN from 80.67% to 94.23%. After the DBN parameters were optimized by Tent-SSA, the roughness prediction accuracy was significantly improved. For the classification model, the prediction accuracy is improved by 5.77% based on ADASYN optimization. For regression models, different objective functions can be set according to production requirements, such as root-mean-square error (RMSE) or MaxAE, and the error is reduced by more than 40% compared to the original model.

A roughness prediction model based on multiple monitoring signals is proposed, which reduces the dependence on the acquisition of environmental variables and enhances the model's applicability. Furthermore, with the ADASYN algorithm, the Tent-SSA intelligent optimization algorithm is introduced to optimize the hyperparameters of the DBN model and improve the optimization performance.

This study aims to analyze consumer motivations to share information about corporate social responsibility (CSR) activities through electronic word of mouth. It examines the roles of self-enhancement, identity signaling and social bonding as antecedents of consumers’ CSR engagement on social media.

A quantitative approach is used with a single-factor between-subjects experimental design in which the presence vs absence of CSR information on a company website is manipulated. The hypotheses are tested through structural equation modeling.

Results show that after viewing the company’s CSR message on its website, consumers who generated more CSR associations were more motivated to engage with the CSR information to satisfy fundamental personality traits (need for self-enhancement) and social relationship motivations (social bonding), which increased their intention to share the information.

This study is restricted to CSR information on websites. Further research should consider what happens if such information is shared on social media, as consumers are more likely to spread CSR messages when they are shared by other public social network sites.

The study highlights the relevance of including CSR information on websites and offers insights into the importance of considering consumers in disseminating CSR information. Consumers share information when they have personal motivation for doing so.

This study put the focus on the role of consumers in the diffusion of corporate information.

The results show the importance of personal motivations such as self-enhancement and social bonding in sharing CSR information on social media.

El estudio analiza las motivaciones que tiene el consumidor para compartir información sobre acciones de responsabilidad social corporativa (RSC) a través de boca oído electrónico (eWOM). En particular, las motivaciones de mejora del auto-concepto, necesidad de mostrar una identidad deseada y la vinculación social.

Se utiliza un diseño experimental entre sujetos donde se manipuló la presencia vs ausencia de información sobre la RSC de la empresa. Las hipótesis se contrastaron mediante un modelo de ecuaciones estructurales.

Los resultados muestran que los consumidores con más asociaciones de RSC comparten más la información de RSC motivados por satisfacer la mejora del auto-concepto y vinculación social.

El estudio destaca la importancia de generar contenido de RSC en el sitio web de la empresa, y la importancia de los consumidores en la difusión de información de dicha información.

El estudio está restringido a la presencia de información de RSC en el sitio web de la empresa. Sería interesante evaluar lo que sucede si dicha información se comparte en redes sociales, en la medida en que los consumidores tienen mayor tendencia a compartir la información procedente de redes sociales.

Los resultados muestran la importancia de las motivaciones personales como la mejora del auto-concepto y la vinculación social a la hora compartir información de RSC en las redes sociales.

本研究分析了消费者通过电子口碑分享企业社会责任（CSR）活动信息的动机。它研究了自我提升、身份信号和社会联系作为消费者在社交媒体上参与企业社会责任的前因的作用。

采用单因素主体间实验设计的定量方法, 对公司网站上企业社会责任信息的存在与否进行操纵。假设通过使用R软件包lavaan的结构方程模型进行检验。

结果显示, 在观看公司网站上的企业社会责任信息后, 产生更多企业社会责任联想的消费者更有动力参与到企业社会责任信息中, 以满足基本的人格特征（自我提升的需要）和社会关系动机（社会纽带）, 这增加了他们分享信息的意向。

该研究强调了将企业社会责任信息纳入网站的相关性, 并对在传播企业社会责任信息时考虑消费者的重要性提出了见解。消费者在有个人动机的情况下会分享信息。

本研究仅限于网站上的企业社会责任信息。进一步的研究应该考虑到社交媒体, 因为当消费者在其他公共社交网站上分享企业社会责任信息时, 他们更有可能进行传播。

研究结果表明, 在社交媒体上分享企业社会责任信息时, 自我提升和社会联系等个人动机的重要性。

The influence of road surface temperature (RST) on vehicles is becoming more and more obvious. Accurate predication of RST is distinctly meaningful. At present, however, the prediction accuracy of RST is not satisfied with physical methods or statistical learning methods. To find an effective prediction method, this paper selects five representative algorithms to predict the road surface temperature separately.

Multiple linear regressions, least absolute shrinkage and selection operator, random forest and gradient boosting regression tree (GBRT) and neural network are chosen to be representative predictors.

The experimental results show that for temperature data set of this experiment, the prediction effect of GBRT in the ensemble algorithm is the best compared with the other four algorithms.

This paper compares different kinds of machine learning algorithms, observes the road surface temperature data from different angles, and finds the most suitable prediction method.

Although proactive fault handling plans are widely spread, many unexpected data center outages still occurred. To rescue the jobs from faulty data centers, the authors propose a novel independent job rescheduling strategy for cloud resilience to reschedule the task from the faulty data center to other working-proper cloud data centers, by jointly considering job nature, timeline scenario and overall cloud performance.

A job parsing system and a priority assignment system are developed to identify the eligible time slots for the jobs and prioritize the jobs, respectively. A dynamic job rescheduling algorithm is proposed.

The simulation results show that our proposed approach has better cloud resiliency and load balancing performance than the HEFT series approaches.

This paper contributes to the cloud resilience by developing a novel job prioritizing, task rescheduling and timeline allocation method when facing faults.

This study aims to provide a systematic and complete knowledge map for use by researchers working in the field of research data. Additionally, the aim is to help them quickly understand the authors' collaboration characteristics, institutional collaboration characteristics, trending research topics, evolutionary trends and research frontiers of scholars from the perspective of library informatics.

The authors adopt the bibliometric method, and with the help of bibliometric analysis software CiteSpace and VOSviewer, quantitatively analyze the retrieved literature data. The analysis results are presented in the form of tables and visualization maps in this paper.

The research results from this study show that collaboration between scholars and institutions is weak. It also identified the current hotspots in the field of research data, these being: data literacy education, research data sharing, data integration management and joint library cataloguing and data research support services, among others. The important dimensions to consider for future research are the library's participation in a trans-organizational and trans-stage integration of research data, functional improvement of a research data sharing platform, practice of data literacy education methods and models, and improvement of research data service quality.

Previous literature reviews on research data are qualitative studies, while few are quantitative studies. Therefore, this paper uses quantitative research methods, such as bibliometrics, data mining and knowledge map, to reveal the research progress and trend systematically and intuitively on the research data topic based on published literature, and to provide a reference for the further study of this topic in the future.