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Feedback is crucial in a learning process, particularly in an online interaction where both learners and instructors are distantly located. Thus, this paper aims to investigate the association between feedback strategies, embedded course syllabus and learning improvement in the Sakai Learning Management System.

This paper uses a survey design to collect cross-sectional data from adult distance learning students. The data were analysed using descriptive statistics and a standard multiple regression model in Stata.

The results show that feedback strategies (timing, mode, quality and quantity) and embedded course syllabus have a significant relationship with learning improvement. However, the feedback strategy – target – is not significantly related to learning improvement though it is the highest feedback strategy.

This paper has contributed to the extant literature by providing empirical evidence to support the constructivism theory of learning from a distance learning perspective in a developing country. The study has shown that if the feedback strategies are well managed and applied, they would make a considerable impact on distance education students' academic pursuits. Hence, the paper provides a pedagogical foundation for short and long-term distance learning policy.

Total risk score (TRS) is considered one of the main indicators for risk evaluation. Several studies attempted to employ different types of risk indices for the evaluation of cost overrun causes. Hence, this study aims at performing a comparative analysis to evaluate the efficiency of three different approaches for TRS calculation.

Thirty-eight unique causes of cost overrun in urban-related construction projects were identified and a survey was conducted among construction professionals in Iran. The TRS for each cost overrun cause is calculated using single-attribute (SA), double-attribute (DA), and multiple-attribute (MA) approaches, and eventually, causes were ranked. Furthermore, principal component analysis (PCA), logistic regression analysis (LRA), and K-means clustering are utilized to compare the differences in the generated TRS using different approaches.

The results revealed that the TRS generated through the MA approach demonstrated the highest efficiency in terms of generating correlation between causes and their identified latent constructs, prediction capability, and classification of the influential causes in the same group.

The originality of this study primarily stems from the adoption of statistical approaches in the evaluation of the recently introduced TRS calculation approach in comparison to traditional ones. Additionally, this study proposed a modified application of the relative importance index (RII) for risk prioritization. The results from this study are expected to fulfill the gap in previous literature toward exploring the most efficient TRS calculation approach for those researchers and practitioners who seek to utilize them as a measure to identify the influential cost overrun causes.

Developments regarding the use of artificial intelligence (AI) in transportation systems, one of the important stakeholders of tourism, are remarkable. However, no review thus far has provided a comprehensive overview of research on AI in transportation systems.

To fill this gap, this study uses the VOSviewer software to present a bibliometric review of the current scientific literature in the field of AI-related tourism research. The theme of AI in transportation systems was explored in the Web of Science database.

The original search yielded 642 documents, which were then filtered by parameters. For publications related to AI in transportation systems, the most cited documents, leading authors, productive countries, co-occurrence analysis of keywords and bibliographic matching of documents were examined. This report shows that there has been a recent increase in research on AI in transport systems. However, there is only one study on tourism. The country that contributed the most is China with 298 studies. The most used keyword in the documents was intelligent transportation system.

The bibliometric analysis of the existing work provided a valuable and seminal reference for researchers and practitioners in AI-related in transportation system.

The purpose of this study is to identify the impact of online learning on university librarians’ professional development and library services.

A mixed-methods study through an explanatory research design was applied to address the study’s objectives. Quantitative data were gathered from 341 librarians working in 221 universities, while qualitative data were gathered from 27 experts working in 21 different universities of Pakistan.

The findings of the study revealed that online learning has a significant positive impact on the professional development of university librarians. Results revealed that online learning assists in the provision of sustainable, innovative library services in university libraries.

The study has offered a model in light of the study's quantitative and qualitative findings. It contributes to theoretical understanding by expanding the existing knowledge base. It offers managerial insights, enabling the development of policies that foster the professional development of library personnel and the implementation of smart library services.

The construction industry and its activities harmfully affect the environment. Hence, adopting green building (GRB) practices can be helpful in achieving sustainable development goals. Therefore, this study aims to identify the factors affecting the intention to adopt GRB practices by extending theory of planned behavior (TPB).

Using non-probability purposive sampling technique, data was gathered from consultant and contractor engineers in the construction industry through a questionnaire. The analysis was done using partial least square-structural equation modeling technique on a useful sample of 290.

Findings revealed that the core constructs of TPB [i.e. attitude (AT), subjective norms (SUBN) and perceived behavioral control (PBC)] significantly affect the intention to adopt GRB practices. Moreover, government support and knowledge of green practices (KNGP) were found to be critical influencing factors on AT, SUBNs and PBC. Lastly, the findings confirmed that environmental concerns (ENC) play as a moderating between SUBN and intention to adopt GRB practices, as well as AT and intention to adopt GRB practices.

This study contributes to existing knowledge on GRB, offering evidence base for policy choices regarding climate change adaptation and mitigation in the construction industry.

This study provides insights from the perspective of a developing economy and confirms the applicability of TPB in the adoption of GRB practices. Moreover, this study confirms the moderation role of ENC in between TPB constructs and intention to GRB that is not tested earlier in the context of GRB. This study also confirms that government sustainable support positively affects PBC, and KNGP significantly affects SUBNs.