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This paper aims to propose a novel model that addresses the limitations of current practices, through considering quantitative and qualitative criteria in the decision-making process for equipment replacement.

Literature review and consultation with professionals in the heavy construction industry was conducted to identify the criteria influencing the replacement of construction machines. A questionnaire survey using analytic hierarchy process and multi-attribute utility theory was used to rank these criteria and establish their utility scores. Sensitivity analysis was performed to assess how adjustments in the weights of main criteria would impact equipment replacement decisions.

The identified criteria were classified into three categories: economic, technical and socioenvironmental, encompassing a total of 15 criteria. The findings indicated that salvage value/meeting payback period/maximizing profitability held the highest importance in the replacement process, followed by considerations like high repair and maintenance cost; working condition and economic conditions. Safety and social benefits scored the least among all criteria and categories.

This study focuses on earth-moving equipment and involves experts from the Eastern Province of Saudi Arabia. The model introduces a novel methodology to aid decision-makers, particularly contractors and project managers, in determining when to replace heavy construction equipment, which results in resource efficiency and time saving.

The model integrates expertise and knowledge from experts to establish criteria for replacing construction equipment. This research aims to improve the functionality of the decision-making process regarding the acquisition or replacement of equipment throughout its lifespan.

The Metaverse and Internet of Things (IoT) have emerged like a tidal wave, and it is creating a transformative impact on society and industry. The metaverse and IoT changed the way companies were operating earlier and customers were living their lives. On the other hand, Metaverse enriches the customer experience by offering a matchless virtual experience using augmented reality and state-of-the-art technology. The metaverse and the IoT can be used in various sectors such as manufacturing, transportation, retailing, health care, banking, and automobiles to make cities smart. Metaverse and IoT provide real-time data, reduces operational cost and errors, improves efficiency, and helps industries to make intelligent decisions. Although the IoT and Metaverse offer significant benefits, it is not free from limitations. Ethical dilemmas, privacy issues, data breaches, and difficulty in extracting relevant data impose serious challenges that need to be addressed. There is an urgent and dire need to create a trade-off between the interest of the business and the privacy and security of customers. This chapter aims to discover the potential of Metaverse and IoT in various sectors (e.g., healthcare, transportation, and electronics). This study will bring significant insights to researchers and policymakers by exploring the likely benefits of IoT and metaverse in diverse sectors to develop smart cities. This chapter will also explain the challenges of metaverse and IoT, which can be addressed by integrating data analytics tools optimally and efficiently.

This paper aims to examine the extent to which board composition and ownership structure (OS) affect a firm’s dividend payout (DP) policy.

The sample comprises a total of 1,432 firm-year observations for Gulf Cooperation Council (GCC) nonfinancial firms. Total 179 firms were analyzed from 2009 to 2016. To test the research hypotheses, the paper used panel data analysis (i.e. fixed effects model) and instrumental variable method to ensure the robustness of results against endogeneity effects.

Corporate governance (CG) variables were found to significantly impact DP. Specifically, independent directors on board, institutional ownership and royal ownership were positively associated with DP. In contrast, board size, management, government and family ownership had a negative association with DP. The empirical evidence presented in this study supports that CG elements can be both an outcome and substitute of DP as a disciplinary mechanism.

This study excluded financial firms from the sample list. Future studies should carry out on financial firms to observe if the findings are different. Future research is suggested to incorporate more CG mechanisms. Future studies are suggested to use a dynamic panel regression due to its advantages.

Practitioners, such as investors and lenders operating in GCC markets, can derive valuable insights from this study regarding the influence of board components on DP policy. The study also provides information about the investment and business environment in GCC.

The contribution of this study lies in providing empirical evidence regarding the impact of CG mechanisms and OS on DP in GCC countries.

The locational disadvantage of a health-care centre often restricts adequate delivery of health-care services in an area. The purpose of this study is to examine the status of primary health-care services in such a geographically disadvantageous area which is confined by forests, tea gardens and undulating topography.

Necessary secondary data of 13 primary health centres and 236 sub-centres has been collected from the Office of the Chief Medical Officer of Health. Based on obtained data, Health-care Infrastructure Index has been prepared which has been validated by an expert panel, and subsequently, the Thiessen Polygon method has been applied through Arc GIS software to show spatial variation of health-care services delivered by different health-care centres.

In the study area, there is wide variation found in the case of physical facilities, caregivers and connectivity of road networks, which altogether affect the overall status of health-care services. Among all the indicators, some health-care centres experience staff shortages for prolonged non-recruitment, inaccessibility and inconsistent patient load in different health centres.

In spite of the unfavourable geographical landscape, health-care centres have to be set up wherever possible. There is a need to make new roads and simultaneously the existing road connectivity should be improved so that patients and caregivers can move quickly whenever required. Existing physical facilities need to be renewed or redeveloped along with increasing the number of doctors and other health-care providers as per the need of people with an adequate and optimum level of services.

This paper investigates how stockist brand equity is created in the unorganized pharma retail sector in emerging markets despite the absence of any proactive brand-building efforts by distribution firms (stockists). In addition, this study also aims to unravel the sources of stockist brand equity.

Drawing from grounded theory, a qualitative research design using semi-structured interviews was conducted, and a theoretical saturation approach was used for this purpose. The retailer–stockist (business-to-business [B2B]) relationship in the Indian pharmaceutical market context was used as the study context.

The findings show that in the absence of any brand-building activities, stockists’ sales representatives play an important role in creating stockist brand awareness. In addition, word of mouth from other retailers also plays a minor role. Rational and emotional brand associations which are strong, favorable and unique are created 1) directly by the services provided by stockists, which includes product availability, deliverability, accuracy in billing and batch numbers, credit facilities and discounts, handling issues such as product expiry, and 2) indirectly by the services provided by stockists’ sales representatives which includes their frequency of visits, proactive customer orientation rather than sales orientation, fulfilling commitments and relationship with retailers. The strength of the retailer–stockist (B2B) relationship also depends on retailer-driven factors and other external factors discussed in this study.

Strong stockist brand equity helps build trust and loyalty with pharmacies, ensuring a consistent and timely supply of medicinal products, which will benefit their end consumers or society.

This study identifies the antecedents determining the strength of the retailer–stockist (B2B) relationship, a precursor for establishing retailer-based stockist brand equity in the unorganized sector. This study also highlights the consequences of establishing strong retailer-based stockist brand equity.

Student open feedback is an essential element to improve the teaching service. Comprehending the feedback collected daily may not be possible especially in a large classroom. There is needed an automated system that processes feedback and helps to recommend focused, precise points to the teacher stating the positives and negatives of a class. Also, the feedback texts are neither going to be grammatically correct nor going to consist only of English. Hence, an automated feedback processing system is essential that processes the mixed-language language text that provides crisp clear insights to the teachers, thus making effective student–teacher interaction.

This research is designed to analyse daily feedback from the students in grammarless English-Tamil mixed feedback and creates a dashboard that displays concise keywords regarding positive and negative aspects of the class. An ML-based system architecture is proposed for processing English-Tamil mixed grammarless feedback texts and validates the same with an experimental prototype and compares the results with other state-of-the-art models. This prototype classifies the text into different categories and provides the concise view with topic modelling techniques. This system is useful in progressive improvement of teaching learning process, subsequently leading to better teaching learning environment.

The proposed web-based architecture is validated with a prototype by comparing the results with other state-of-the-art models. The accuracy of the results is higher (>90%) in the proposed architecture than other models (<60%). The created teacher dashboard is highly recommendable and provides day-to-day recommendation for finetuning teaching and learning process. The web-based dashboard created for teachers enables them to interpret the student feedback with much ease due to the Machine learning algorithms used in implementing the web-based solution.

This system is designed to help the teachers to improve themselves in the teaching learning process with the feedback. The proposed system is a prototype that is initially tested with sample feedback texts obtained in sessions in postgraduate classrooms. The implementation of the prototype and analysis of teacher and student experience are presented as the immediate scope of this research work. This helps the teachers to get an overall view on the best teaching practices and what to improve. This work currently uses Bidirectional Encoder Representations from Transformers (BERT) uncased and in the increase of native language text the system may work with BERT multilingual.

This prototype will be implemented as a web-mobile based application. Students can submit their daily feedback through a mobile app, while teachers will access a dashboard that presents a concise overview generated by the proposed system architecture. The dashboard will also provide trend analysis, highlighting positive and negative aspects of the sessions. The system's effectiveness will be evaluated through a qualitative study, incorporating feedback from teachers and insights from students. This evaluation will help teachers gain a comprehensive understanding of the most effective teaching practices and areas needing improvement, thereby enhancing the teaching-learning process. The web-mobile application aims to Streamline the feedback process, making it easy for students to share their thoughts and for teachers to receive actionable insights. This study offers a clear and concise summary of student feedback and trend analysis from which the teachers can quickly identify patterns and make necessary adjustments to their teaching methods. Ultimately, this approach will foster a more responsive and effective educational environment, supporting continuous improvement and better student–teacher interactions. Further, the proposed system requires lesser technical knowledge and can be used by anyone.

A literature review has identified a critical need for a feedback processing system that functions at short intervals. Such a system is essential for providing teachers with concise, periodic summaries of students' open-ended feedback, which is vital for fostering continuous improvement in the teaching-learning process. The immediate processing of feedback, particularly when it contains English-mixed texts, is crucial for making timely adjustments that enhance both student performance and experience. By swiftly addressing concerns and reinforcing positive feedback, the system will improve student–teacher interactions, provide meaningful insights that contribute to progressive educational growth. This will help implement a feedback system that operates in these short intervals and allows for real-time monitoring and response to students' needs and experiences. Additionally, by highlighting areas of success, teachers can build on effective strategies and practices.

This research paper proposes a system architecture PSFAS: Progressive Student Feedback Analysis System with Multi Level Classification and Clustering that enables effective interaction between the student and the teacher with the findings from feedback and presenting an experimental prototype that can be incorporated into the regular teaching learning process, whether online or offline. It has been found from the literature review, that feedback processing is mostly done in the English language. This work proposes a system architecture that gives higher accuracy than other state-of-the art models for feedback texts having English-mix grammarless sentences.

The variance between the winning bid and the owner's estimated cost (OEC) is one of the construction management risks in the pre-tendering phase. The study aims to enhance the quality of the owner's estimation for predicting precisely the contract cost at the pre-tendering phase and avoiding future issues that arise through the construction phase.

This paper integrated artificial neural networks (ANN), deep neural networks (DNN) and time series (TS) techniques to estimate the ratio of a low bid to the OEC (R) for different size contracts and three types of contracts (building, electric and mechanic) accurately based on 94 contracts from King Saud University. The ANN and DNN models were evaluated using mean absolute percentage error (MAPE), mean sum square error (MSSE) and root mean sums square error (RMSSE).

The main finding is that the ANN provides high accuracy with MAPE, MSSE and RMSSE a 2.94%, 0.0015 and 0.039, respectively. The DNN's precision was high, with an RMSSE of 0.15 on average.

The owner and consultant are expected to use the study's findings to create more accuracy of the owner's estimate and decrease the difference between the owner's estimate and the lowest submitted offer for better decision-making.

This study fills the knowledge gap by developing an ANN model to handle missing TS data and forecasting the difference between a low bid and an OEC at the pre-tendering phase.