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The purpose of this paper is to analyse 12 assessment tools of sustainability in universities and develop the structure and the contents of these tools to be more intelligible. The configuration of the tools reviewed highlight indicators that clearly communicate only the essential information. This paper explores how the theoretical concept of a sustainable university is translated into more measurable variables to support practitioners and academics in assessing sustainability in universities.

The main method for this paper was a desk study approach, which incorporated reviewing research papers, graduate theses, academic books, network platforms and websites.

The tools reviewed share similar traits in terms of criteria, sub-criteria and indicators. Five benchmarks are essential for a holistic framework: management; academia; environment; engagement and innovation.

This research can not only be used to improve existing assessment tools but also as a means to develop new tools tailored for universities that face a variety of challenges and lack the ability to measure their sustainability policies.

Making higher education more sustainable through all the criteria mentioned influences students, as well as staff, to maintain a culture of sustainability.

This study contributes to the literature by simplifying and detailing the structure and contents of the tools in a way which indicators are shown, giving a full picture of these tools to enable universities to be more aware of the sustainability issues that affect them.

This study investigated the effect of cooperative learning on the performance of female college students enrolled in “writing and research” courses at Prince Mohammad Bin Fahd University (Al Khobar, Saudi Arabia). Cooperative learning (CL) activities were employed in place of traditional lectures and group activities. Two populations were compared, one with the CL methodology and one with traditional methodology. The results were assessed by final exam scores, final course grades, the number of students passing, pre and post-tests and a self-report survey. We found that this sample of Arab college students performed at a higher level in the CL classes than in the traditional classes. Findings suggest that CL is an effective method for teaching Arab college students.

The integration of sustainability is more matured in the corporate sector. However, the topic received less attention in higher education institutions (HEIs). Therefore, this study aims to conceptualize an integration framework and proposes sustainability integration and implementation index in the HEIs.

The index was developed from previous literature. For the implementation of the index, three universities were selected. The required data collected from websites, internal documents and interviews with the high officials of these universities.

Sample universities have somehow inclination toward the broad agenda of sustainable development, but still, they lack integration, implementation and reporting of their sustainable practices. Moreover, the departments have positive and practical intent toward sustainability especially regarding the environment, designing curriculum, teaching, research activities and volunteerism. However, in assessment, the social and economic contributions of these universities revealed to be somewhat mild. The findings also evidenced heterogeneity among the sample institutions to follow the said agenda which reflects a lack of awareness and policy or a centralized or regulatory push from the top.

The study is limited to three universities in Khyber Pakhtunkhwa, Pakistan. Hence, in the future, the framework and index may be tested in other universities of the country or region. Nevertheless, the study carries theoretical significance in the literature of sustainability.

The study has practical implications for academia, regulatory bodies and universities to integrate, implement and report sustainability in HEIs.

The study proposes and validates the sustainability integration and implementation index in HEIs. The index is unique and has originality or value particularly for HEIs of the developing countries.

This paper aims to highlight a multidimensional model of sustainability practices that are considered meaningful and practical for implementation by higher education institutions (HEIs).

The Concept System® Global MAX© software facilitated the bottom-up approach of this study, where 32 staff and students of a private HEI were prompted with the question, “What design features or facilities should the university building have for you to consider the building sustainable?” Their opinions were collected as statements via online brainstorming and the process was followed through with the sorting and rating of reduced statements. The software was also used to perform multi-dimensional scaling and cluster analysis to generate the findings of this study.

An eight-cluster solution was proposed based on the themes within respective clusters. Respondents rated the importance of sustainability resource usage and sustainability and technology highly. Whilst sustainable campus facilities appeal to students, education for sustainable development were more important to staff.

The community-based participatory research recommends activities that enhance the adoption of sustainable development in making universities a “living lab” for sustainability.

With the proliferation of terrorist/extremist websites on the World Wide Web, it has become progressively more crucial to detect and analyze the content on these websites. Accordingly, the volume of previous research focused on identifying the techniques and activities of terrorist/extremist groups, as revealed by their sites on the so-called dark web, has also grown.

This study presents a review of the techniques used to detect and process the content of terrorist/extremist sites on the dark web. Forty of the most relevant data sources were examined, and various techniques were identified among them.

Based on this review, it was found that methods of feature selection and feature extraction can be used as topic modeling with content analysis and text clustering.

At the end of the review, present the current state-of-the- art and certain open issues associated with Arabic dark Web content analysis.

The purpose of this study is to examine the state of research into adoption of machine learning systems within the health sector, to identify themes that have been studied and observe the important gaps in the literature that can inform a research agenda going forward.

A systematic literature strategy was utilized to identify and analyze scientific papers between 2012 and 2022. A total of 28 articles were identified and reviewed.

The outcomes reveal that while advances in machine learning have the potential to improve service access and delivery, there have been sporadic growth of literature in this area which is perhaps surprising given the immense potential of machine learning within the health sector. The findings further reveal that themes such as recordkeeping, drugs development and streamlining of treatment have primarily been focused on by the majority of authors in this area.

The search was limited to journal articles published in English, resulting in the exclusion of studies disseminated through alternative channels, such as conferences, and those published in languages other than English. Considering that scholars in developing nations may encounter less difficulty in disseminating their work through alternative channels and that numerous emerging nations employ languages other than English, it is plausible that certain research has been overlooked in the present investigation.

This review provides insights into future research avenues for theory, content and context on adoption of machine learning within the health sector.

This paper aims to conduct an interdisciplinary systematic literature review (SLR) of fake news research and to advance the socio-technical understanding of digital information practices and platforms in business and management studies.

The paper applies a focused, SLR method to analyze articles on fake news in business and management journals from 2010 to 2020.

The paper analyzes the definition, theoretical frameworks, methods and research gaps of fake news in the business and management domains. It also identifies some promising research opportunities for future scholars.

The paper offers practical implications for various stakeholders who are affected by or involved in fake news dissemination, such as brands, consumers and policymakers. It provides recommendations to cope with the challenges and risks of fake news.

The paper discusses the social consequences and future threats of fake news, especially in relation to social networking and social media. It calls for more awareness and responsibility from online communities to prevent and combat fake news.

The paper contributes to the literature on information management by showing the importance and consequences of fake news sharing for societies. It is among the frontier systematic reviews in the field that covers studies from different disciplines and focuses on business and management studies.

Topic model has been widely applied to discover important information from a vast amount of unstructured data. Traditional long-text topic models such as Latent Dirichlet Allocation may suffer from the sparsity problem when dealing with short texts, which mostly come from the Web. These models also exist the readability problem when displaying the discovered topics. The purpose of this paper is to propose a novel model called the Sense Unit based Phrase Topic Model (SenU-PTM) for both the sparsity and readability problems.

SenU-PTM is a novel phrase-based short-text topic model under a two-phase framework. The first phase introduces a phrase-generation algorithm by exploiting word embeddings, which aims to generate phrases with the original corpus. The second phase introduces a new concept of sense unit, which consists of a set of semantically similar tokens for modeling topics with token vectors generated in the first phase. Finally, SenU-PTM infers topics based on the above two phases.

Experimental results on two real-world and publicly available datasets show the effectiveness of SenU-PTM from the perspectives of topical quality and document characterization. It reveals that modeling topics on sense units can solve the sparsity of short texts and improve the readability of topics at the same time.

The originality of SenU-PTM lies in the new procedure of modeling topics on the proposed sense units with word embeddings for short-text topic discovery.

This paper aims to develop a simple model for estimating sustainable development goals index using the capabilities of artificial neural networks.

Sustainable development has three pillars, including social, economic and environmental pillars. Three clusters corresponding to the three pillars were created by extracting sub-indices of three 2018 global reports and performing cluster analysis on the correlation matrix of sub-indices. By setting the sustainable development goals index as the target variable and selecting one indicator from each cluster as input variables, 20 artificial neural networks were run 30 times.

Artificial neural networks with seven nodes in one hidden layer can estimate sustainable development goals index by using just three inputs, including ecosystem vitality, human capital and gross national income per capita. There is an excellent similarity (>95%) between the results of the artificial neural network and the sustainable development goals index.

Instead of calculating 232 indicators for determining the value of sustainable development goals index, it is possible to use only three sub-indices, but missing 5% of precision, by using the proposed artificial neural network model.

The study provides additional information on the estimating of sustainable development and proposes a new simple method for estimating the sustainable development goals index. It just uses three sub-indices, which can be retrieved from three global reports.