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Identifying the best predictors of environmental citizenship behavior (ECB) has been a major concern of both researchers and educators aimed at protecting environmental quality and sustain person-environment transactions. This study aims to examine the unique contribution of personality traits and self-efficacy beliefs to the ECB of university youth in Sri Lanka.

Quantitative research approach used. The internet-based survey method was used to collect data from undergraduates studying at six state universities and two nonstate universities. The measurement items of five personality traits, ECB and academic self-efficacy were adopted on established scales from the literature.

Multiple regression results revealed that the personality traits of extraversion, agreeableness and openness to experience, as well as academic self-efficacy, are significant predictors of ECB. Extraversion, agreeableness, conscientiousness and openness to experience significantly influenced academic self-efficacy. Model 6 of the PROCESS macro results indicated that academic self-efficacy partially mediated the contribution of extraversion, agreeableness and openness to experience traits to ECB.

These findings have broad implications for interventions aimed at enhancing youth environmental behavior. Whereas personality traits represent stable individual characteristics that mostly derive from individual hereditary endowment.

The study showed a holistic approach in explaining ECB that combined both personality traits and self-efficacy beliefs, indicating that they are interrelated and should not be treated in isolation.

The current study’s goal is to investigate how lean, agile, resilient and sustainable human resource management (LARS HRM) affects green innovation and environmental performance, both directly and indirectly.

Partial least squares-structural equation modeling (PLS-SEM) was used to analyze the data based on a sample of 273 Tunisian businesses in the industrial and service sectors that were certified ISO 9001.

With the exception of AHRM–GPdtI, the results show that the mainstream advanced theory on direct effects was verified. With regard to indirect effects, everything of the literature that was presented was accepted, with the exception of the relationship between AHRM–GPdtI–EP, AHRM–GPssI–EP and RHRM–GPdtI–EP.

This research is distinctive in that it aims to incorporate every LARGS paradigm within the HRM field. By taking green innovation into consideration, it closes the current gaps on the direct and indirect effects of LARS HRM on environmental performance. Our study is unique in that it incorporates large, industry-operating, certified ISO 9001 firms with those in the service sector, with the goal of achieving greater generalization of results.

This study aims to clarify the following research questions: to what extent do people consider natural disaster risks as important for residential selection? what personal demographics and attitudes toward natural disaster risks are associated with the relative importance of natural disasters for residential selection? and to what extent do the associated personal attributes influence the relative importance of natural disasters for residential selection?

An internet-based survey was performed to collect 2,000 responses from residents of Osaka Prefecture, Japan, to gauge people’s relative importance of safety against natural disasters regarding residential preference. The obtained results were analysed using two types of statistical analysis, specifically chi-square test and multivariable logistic regression analyses.

It was found that 37.3% of the respondents in Osaka Prefecture, Japan, considered the “safety against natural disasters” relatively important when selecting a residential location. The statistical analysis also demonstrated that those having a relatively higher level of disaster awareness and preparedness were 1.41 times more likely to prefer to live in a place that is safer from natural disasters. Thus, it was suggested that disaster education aimed at raising the level of people’s disaster awareness could be effective to increase the number of people who choose to live in a safer place from natural disasters.

Living in an area that is safer from natural disasters can effectively minimize human and property damage. Recently, several measures have been taken in Japan to guide people to live in a safer place. The clarification of the extent to which people consider natural disaster risks as important for residential selection and the understanding of the categories of the people who are likely to do so is important to develop more effective natural disaster measures; however, there has been less attention on such investigation. Therefore, this study conducted an internet-based survey and examined it.

This study aims to evaluate and summarize the effectiveness of cognitive behavioral therapy (CBT) and internet-based CBT (ICBT) interventions on relapse prevention and severity of symptoms among individuals with major depressive disorder (MDD). CBT is one of the most used and suggested interventions to manage MDD, whereas ICBT is a novel effective proposed approach.

The review was conducted following the preferred reporting items for systematic review and meta-analysis protocol. A comprehensive and extensive search was performed to identify and evaluate the relevant studies about the effectiveness of CBT and ICBT on relapse prevention and severity of symptoms among patients with MDD.

A total of eight research studies met the inclusion criteria and were included in this systematic review. RCT studies were conducted to assess and evaluate the effectiveness of CBT and ICBT on relapse prevention and severity of symptoms among patients with MDD. It has been found that CBT is a well-supported and evidently based effective psychotherapy for managing depressive symptoms and reducing the relapse and readmission rate among patients diagnosed with MDD. The ICBT demonstrated greater improvements in depressive symptoms during major depressive episodes among patients with MDDS. The ICBT program had good acceptability and satisfaction among participants in different countries.

Despite the significant findings from this systematic review, certain limitations should be acknowledged. First, it is important to note that all the studies included in this review were exclusively conducted in the English language, potentially limiting the generalizability of the findings to non-English speaking populations. Second, the number of research studies incorporated in this systematic review was relatively limited, which may have resulted in a narrower scope of analysis. Finally, a few studies within the selected research had small sample sizes, which could potentially impact the precision and reliability of the overall conclusions drawn from this review. The authors recommend that nurses working in psychiatric units should use CBT interventions with patients with MDD.

This paper, a review of the literature gives an overview of CBT and ICBT interventions to reduce the severity of depressive symptoms and prevent patients’ relapse and rehospitalization and shows that CBT interventions are effective on relapse prevention among patients with MDD. In addition, there is still no standardized protocol to apply the CBT intervention in the scope of reducing the severity of depressive symptoms and preventing depression relapse among patients with major depressive disorder. Further research is needed to confirm the findings of this review. Future research is also needed to find out the most effective form and contents of CBT and ICBT interventions for MDD.

CBT is a psychological intervention that has been recommended by the literature for the treatment of major depressive disorder (MDD). It is a widely recognized and accepted approach that combines cognitive and behavioral techniques to assist individuals overcome their depressive symptoms and improve their overall mental well-being. This would speculate that effectiveness associated with several aspects and combinations of different approaches in CBT interventions and the impact of different delivery models are essential for clinical practice and appropriate selection of the interventional combinations.

This systematic review focuses on the various studies that explore the effectiveness of face-to-face CBT and ICBT in reducing depressive symptoms among patients with major depressive disorder. These studies were conducted in different countries such as Iran, Australia, Pennsylvania and the USA.

Using role stress theory, this study examines how work connectivity behavior (WCB) blurs the lines between employees' work and personal lives, thereby encouraging procrastination at work (PAW). The study also investigates the importance of role stress and remote work self-efficacy (RWSE) as mediating and moderating factors, respectively.

The study examines the direct and indirect relationships between WCB and PAW using hierarchical regression and data from 415 Chinese teleworkers. RWSE is also estimated as a second-stage moderator.

The findings indicate that WCB has a direct and indirect (via role stress) positive influence on PAW; however, these effects are weaker among employees with higher (vs lower) RWSE.

This study assists managers and organizations in developing more efficient ways of maximizing employee and organizational performance while minimizing the counterproductive behaviors associated with excessive technology use.

By investigating the links between WCB and PAW in the post-pandemic context, this study adds a new perspective on how excessive technology use for work and non-work purposes can be counterproductive.

The purpose of this paper is to give an overview of artificial intelligence (AI) and other AI-enabled technologies and to describe how COVID-19 affects various industries such as health care, manufacturing, retail, food services, education, media and entertainment, banking and insurance, travel and tourism. Furthermore, the authors discuss the tactics in which information technology is used to implement business strategies to transform businesses and to incentivise the implementation of these technologies in current or future emergency situations.

The review provides the rapidly growing literature on the use of smart technology during the current COVID-19 pandemic.

The 127 empirical articles the authors have identified suggest that 39 forms of smart technologies have been used, ranging from artificial intelligence to computer vision technology. Eight different industries have been identified that are using these technologies, primarily food services and manufacturing. Further, the authors list 40 generalised types of activities that are involved including providing health services, data analysis and communication. To prevent the spread of illness, robots with artificial intelligence are being used to examine patients and give drugs to them. The online execution of teaching practices and simulators have replaced the classroom mode of teaching due to the epidemic. The AI-based Blue-dot algorithm aids in the detection of early warning indications. The AI model detects a patient in respiratory distress based on face detection, face recognition, facial action unit detection, expression recognition, posture, extremity movement analysis, visitation frequency detection, sound pressure detection and light level detection. The above and various other applications are listed throughout the paper.

Research is largely delimited to the area of COVID-19-related studies. Also, bias of selective assessment may be present. In Indian context, advanced technology is yet to be harnessed to its full extent. Also, educational system is yet to be upgraded to add these technologies potential benefits on wider basis.

First, leveraging of insights across various industry sectors to battle the global threat, and smart technology is one of the key takeaways in this field. Second, an integrated framework is recommended for policy making in this area. Lastly, the authors recommend that an internet-based repository should be developed, keeping all the ideas, databases, best practices, dashboard and real-time statistical data.

As the COVID-19 is a relatively recent phenomenon, such a comprehensive review does not exist in the extant literature to the best of the authors’ knowledge. The review is rapidly emerging literature on smart technology use during the current COVID-19 pandemic.

The tender documents, an essential data source for internet-based logistics tendering platforms, incorporate massive fine-grained data, ranging from information on tenderee, shipping location and shipping items. Automated information extraction in this area is, however, under-researched, making the extraction process a time- and effort-consuming one. For Chinese logistics tender entities, in particular, existing named entity recognition (NER) solutions are mostly unsuitable as they involve domain-specific terminologies and possess different semantic features.

To tackle this problem, a novel lattice long short-term memory (LSTM) model, combining a variant contextual feature representation and a conditional random field (CRF) layer, is proposed in this paper for identifying valuable entities from logistic tender documents. Instead of traditional word embedding, the proposed model uses the pretrained Bidirectional Encoder Representations from Transformers (BERT) model as input to augment the contextual feature representation. Subsequently, with the Lattice-LSTM model, the information of characters and words is effectively utilized to avoid error segmentation.

The proposed model is then verified by the Chinese logistic tender named entity corpus. Moreover, the results suggest that the proposed model excels in the logistics tender corpus over other mainstream NER models. The proposed model underpins the automatic extraction of logistics tender information, enabling logistic companies to perceive the ever-changing market trends and make far-sighted logistic decisions.

(1) A practical model for logistic tender NER is proposed in the manuscript. By employing and fine-tuning BERT into the downstream task with a small amount of data, the experiment results show that the model has a better performance than other existing models. This is the first study, to the best of the authors' knowledge, to extract named entities from Chinese logistic tender documents. (2) A real logistic tender corpus for practical use is constructed and a program of the model for online-processing real logistic tender documents is developed in this work. The authors believe that the model will facilitate logistic companies in converting unstructured documents to structured data and further perceive the ever-changing market trends to make far-sighted logistic decisions.

Open educational resources (OERs) are internet-based digital content that is used for academic purposes by instructors, students and researchers in the era of the information economy. Hence, this study aims to systematically review the literature, focusing on OERs’ benefits and challenges in the academic world.

The relevant literature systematically reviewed following the preferred reporting items for systematic literature reviews and meta-analyses (PRISMA) guidelines. The pertinent literature was obtained from four main scholarly databases, and finally, 21 papers that met the inclusion criteria were included in this study.

The findings revealed that the key benefits of OERs include expanded access to knowledge, supporting lifelong learning, pedagogical benefits and enhancing students’ learning outcomes. However, the key challenges include lack of time to find appropriate resources, lack of awareness about the usage and copyrights, quality assurance and technological limitations and lack of organizational support.

The practical and policy implications highlight the joint venture of academia and library professionals to help the students evaluating OERs, quality assurance, copyright issues and lifelong learning.

Earlier studies missed few significant insights of OERs, such as they did not address the quality assurance of OERs; the issue of understanding of copy right (creative common license) challenges related with OERs; and lack of time for finding suitable resources. Hence, this study identified significance insights related with OERs.

The purpose of this paper is to discuss the issues in studying hard-to-reach or dispersed populations, with particular focus on methodologies used to collect data and to investigate dispersed migrant entrepreneurs, illustrating shortcomings, pitfalls and potentials of accessing and disseminating research to hard-to-reach populations of migrant entrepreneurs.

A mixed methodology is proposed to access hard-to-reach or dispersed populations, and this paper explores these using a sample of Brazilian migrants settled in different countries of the world.

This paper explores empirical challenges, illustrating shortcomings, pitfalls and potentials of accessing and disseminating research to hard-to-reach populations of migrant entrepreneurs. It provides insights by reporting research experiences developed over time by this group of researchers, reflecting a “mixing” of methods for accessing respondents, contrasting to a more rigid, a-priori, mixed methods approach.

The main contribution of this paper is to showcase experiences from, and suitability of, remote data collection, especially for projects that cannot accommodate the physical participation of researchers, either because of time or cost constraints. It reports on researching migrant entrepreneurship overseas. Remote digital tools and online data collection are highly relevant due to time- and cost-efficiency, but also represent solutions for researching dispersed populations. These approaches presented allow for overcoming several barriers to data collection and present instrumental characteristics for migrant research.

The purpose of this study is to investigate the current research data management practices among researchers in Ghana and their impact on data reuse and collaborative research. The study aims to identify the methods used by researchers to store and preserve their research data, as well as to determine the extent to which researchers share their data with others.

The study uses a mixed-method research strategy to blend qualitative and quantitative data and is conducted at two public and two private universities in Ghana.

The study revealed that researchers in Ghana currently store and preserve their research data using personal devices, such as laptops, CDs and external flash drives, rather than keeping the data in university data repositories. They also do not share their research data with others, which negatively affects collaborative research. The current practice of storing data on personal devices and not sharing data with others hinders collaborative research. The study recommends that universities in Ghana revise their research policy documents to address RDM-related issues such as data storage, data preservation, data sharing and data reuse.

The study was conducted at two public and two private universities in Ghana, but the findings were placed in a wider context through appropriate references.

This study emphasises the need for sound research data management procedures to support research collaboration and data reuse in Ghana. Universities should provide incentives to academics to disclose their data to encourage data sharing and collaboration.

The government and management of universities should consciously invest in the needed technologies and equipment to implement research data management in their universities.

This study looks at how researchers in Ghana manage their research data and how it affects data reuse and collaborative research.