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Student attritions in tertiary educational institutes may play a significant role to achieve core values leading towards strategic mission and financial well-being. Analysis of data generated from student interaction with learning management systems (LMSs) in blended learning (BL) environments may assist with the identification of students at risk of failing, but to what extent this may be possible is unknown. However, existing studies are limited to address the issues at a significant scale.

This study develops a new approach harnessing applications of machine learning (ML) models on a dataset, that is publicly available, relevant to student attrition to identify potential students at risk. The dataset consists of the data generated by the interaction of students with LMS for their BL environment.

Identifying students at risk through an innovative approach will promote timely intervention in the learning process, such as for improving student academic progress. To evaluate the performance of the proposed approach, the accuracy is compared with other representational ML methods.

The best ML algorithm random forest with 85% is selected to support educators in implementing various pedagogical practices to improve students’ learning.

This study aims to contribute to the debate on the efficacy of softer regulations to prevent violations of workers’ rights in the global clothing supply chain.

This study draws on value trap and adverse incorporations as a theoretical lens to understand the reasons behind the continued violations of workers’ rights. The empirical findings are based on an analysis of 24 semi-structured interviews with workers and owners. Extensive documentary evidence to track the plight of workers in Bangladeshi clothing factories during the pandemic.

The study demonstrates how imbalances in supply chain relationships allow retailers to take advantage of the pandemic. The authors find that some retailers worsened the working conditions by cancelling orders, demanding discounts on old orders and forcing suppliers to agree to a lower price for new orders. Large brands and retailers’ responses to the COVID-19 pandemic remind us that softer regulations, such as third-party audits, are likely to be ineffective given the power imbalance at the heart of the supply chain.

The study presents a case for regulatory frameworks and intense stakeholder activism to encourage large retailers and brands to behave responsibly. This is especially important when a supply chain is value-trapped and workers are adversely incorporated and unprotected.

Drawing on studies on adverse incorporations, value-trapped supply chains and the plight of workers during the COVID-19 pandemic, the study offers a broader understanding of the continued violation of workers’ rights and the efficacy of softer regulations.

In the current study, two grey prediction models, Even GM (1, 1) and Non-homogeneous discrete grey model (NDGM), and ARIMA models are deployed to forecast cocoa bean production of the six major cocoa-producing countries. Furthermore, relying on Relative Growth Rate (RGR) and Doubling Time (D_t), production growth is analyzed.

The secondary data were extracted from the United Nations Food and Agricultural Organization (FAO) database. Grey forecasting models are applied using the data covering 2008 to 2017 as their performance on the small sample size is well-recognized. The models' performance was estimated through MAPE, MAE and RMSE.

Results show the two grey models fell below 10% of MAPE confirming their high accuracy and forecasting performance against that of the ARIMA. Therefore, the suitability of grey models for the cocoa production forecast is established. Findings also revealed that cocoa production in Côte d'Ivoire, Cameroon, Ghana and Brazil is likely to experience a rise with a growth rate of 2.52, 2.49, 2.45 and 2.72% by 2030, respectively. However, Nigeria and Indonesia are likely to experience a decrease with a growth rate of 2.25 and 2.21%, respectively.

For a sustainable cocoa industry, stakeholders should investigate the decline in production despite the implementation of advanced agricultural mechanization in cocoa farming, which goes further to put food security at risk.

The study presents a pioneering attempt of using grey forecasting models to predict cocoa production.

Budget hotels are becoming an emerging industry for convenience and affordability, where consumer sentiments are of paramount importance. Tourism has become increasingly dependent on social media and online platforms to gather travel-related information, purchase travel products, food, lodging, etc., and share views and experiences. The user-generated data helps companies make informed decisions through predictive and behavioural analytics.

Design/Methodology/Approach: This study uses text mining, deep learning, and machine learning techniques for data collection and sentiment analysis based on 117,151 online reviews of the customers posted on the TripAdvisor website from May 2004 to May 2019 from 197 hotels of five prominent budget hotel groups spread across India using Feedforward Neural Network along with Keras package and Softmax activation function.

Findings: The word-of-mouth turns into electronic word-of-mouth through social networking sites, with easy access to information that enables customers to pick a budget hotel. We identified 20 widely used words that most customers use in their reviews, which can help managers optimise operational efficiency by boosting consumer acceptability, satisfaction, positive experiences, and overcoming negative consumer perceptions.

Practical Implications: The analysis of the review patterns is based on real-time data, which is helpful to understand the customer’s requirements, particularly for budget hotels.

Originality/Value: We analysed TripAdvisor reviews posted over the last 16 years, excluding the Corona period due to industry crises. The findings reverberate in consonance with the performance improvement theory, which states feed-forward a neural network enhances organisational, process, and individual-level performance in the hospitality industry based on customer reviews.

Based on trait activation theory, the purpose of the present study is to explore the relationship between the antecedents and outcomes of informal learning with the moderating effect of supervisor feedback environment (SFE) in small- and medium-sized enterprises (SMEs).

Data (n = 388) were gathered from middle managers of Pakistan’s SME sector by following the purposive sampling technique. The proposed model was examined by means of structural equation modeling analysis with AMOS 24.

The study results delineate that middle managers with high-level self-efficacy and motivation to learn more actively participate in informal learning activities. The study also found that the SFE is a strong contextual moderator between the nexus of personal factors and informal learning. In addition, informal learning also has significant and positive effect on work engagement.

Human resource practitioners and organizational leaders of small businesses can use informal learning to improve employees’ skills and knowledge with less cost. For instance, management should use different strategies to enhance the effectiveness of informal learning by providing SFE.

There are numerous recent calls for research to ascertain the interaction of individual, contextual factors and outcomes of informal learning. The study addresses these voids by empirically examining antecedents and outcomes of informal learning simultaneously through the person–situation interaction perspective. It demonstrates that informal learning at the workplace becomes more effective in SFE.

Community-based tourism (CBT) is known as a strong strategy and tool to promote community development and conservation of natural resources through its various virtues in both developing and developed economies. Local people's active and functional participation is considered as the focal point in CBT practice and development. However, their functional participation doesn't always come instinctively; rather, it requires proper extrinsic and intrinsic motivation in the form of both tangible and intangible, which ultimately help them to influence their behavior and pursuit of goals that may ensure their participation in CBT and to receive various benefits. These benefits are linked to the sustainability of CBT development including community development. Therefore, strategic CBT planning and its implementation are essential to ensure sustainable CBT which can also safeguard the link between the local community people and their guests as well as other stakeholders including internationals to facilitate local community development. This chapter focuses on various theories and concepts of motivation from various fields of research, and efforts have been taken to apply those in the field of CBT development to explore its optimum potential for the sake of human welfare. Moreover, attempts have been taken to use various CBT initiatives in Bangladesh to relate these theories and concepts to evaluate these initiatives as well as to provide some suggestive measures to improve the performance of CBT and to facilitate more community development as a whole.

Despite continuing under-representation of women in science, technology, engineering and mathematics (STEM) fields, the literature still falls short on identifying and explaining the factors that could contribute to women's persistence and commitment. The purpose of this research is to identify cognitive and behavioral factors that will support the occupational commitment of women in STEM.

Quantitative analysis is based on a questionnaire survey of 375 women working in STEM in the Middle East region. Multiple regression and bootstrapping methods were employed in the analysis of the data.

The results support the following hypotheses: personal skills development has a positive impact on affective occupational commitment and coping self-efficacy, and coping self-efficacy mediates the relationship between personal skills development and affective occupational commitment.

This study adds insights on the dynamic approaches adopted by women in STEM fields to overcome occupational career challenges by testing several internal drivers, coping self-efficacy and personal learning.

Despite the increasing demand for renewable energy (RE) as a low-carbon energy source, the transition to RE is very slow in many regions, including Oman, the case country for this study. It is critical to offer strategic insights to energy supply chain participants towards the sustainable transition to renewable energy (STRE). The purpose of this study is to identify viable RE sources in Oman as a case study of a GCC member country, develop a comprehensive framework of STRE, and suggest future research opportunities.

The paper addressed this problem through a country/regional study of Oman by conducting a systematic literature review (SLR) of RE-related peer-reviewed publications spanning over 21 years from January 2000 to February 2021. The qualifying articles are evaluated using template analysis qualitatively to identify viable renewable energy sources, build a holistic framework of STRE and recommend future research opportunities.

Findings confirm the potential of solar, wind, biomass and geothermal energies driven by environmental, economic and social sustainability concerns. However, results suggest that to fast-track the STRE, more emphasis should be accorded to solar and wind energies owing to the geographical composition of Oman. Findings reveal that policies and regulations, advanced and cost-effective technologies, subsidy regimes, grid connectivity and capacity, storage capacity and land availability influence the STRE. Gaps in the literature are identified from the results to clarify and suggest future research opportunities.

To the best of the authors’ knowledge, this is the first study that conducted an SLR that was evaluated using the template analysis technique to build a novel and updated framework that facilitates a crystalline understanding of STRE to guide policymakers and professionals in strategic decision-making.

The purpose of this study is to summarize the available pool of literature on service quality to identify different dimensions of service quality in the healthcare industry and understand how it is measured. The study attempts to explore the research gaps in the literature about different service quality dimensions and patient satisfaction.

A systematic literature review process was followed to achieve the objectives of the study. Various inclusion and exclusion criteria were used to select relevant research articles from 2000–2020 for the study, and a total of 100 research articles were selected.

The study identified 41 different dimensions of healthcare service quality measurement and classified these dimensions into four categories, namely servicescape, personnel, hospital administration and patients. It can be concluded that SERVQUAL is the most widely used service quality measurement tool.

The study identified that a majority of the researchers deduced a positive relationship between SERVQUAL dimensions and the quality of healthcare services. The findings of study will assist hospital executives in formulating effective strategies to ensure that patients receive superior quality healthcare services.