Search results

The objective of the research is to examine the impact of global governance and macroeconomic indicators on the lending capacity of banks in India.

Employing a comprehensive time series dataset spanning from 1996 to 2022, we utilize the Nonlinear Autoregressive Distributed Lag model approach to investigate the short-run and long-run impact of government policy (GP) effectiveness, lending interest rates and remittance inflows (RI) on the lending capacity of banks in India.

The findings of the study indicate that lending interest rates have a statistically insignificant impact on lending capacity in the short term. However, in the long run, an increase in the lending interest rate leads to a decrease in lending capacity, whereas a decrease in the lending interest rate has a non-significant impact. On the other hand, the effectiveness of GPs affects both short-term and long-term lending capacity. In the short run, positive or negative changes in GP effectiveness lead to a decline in lending capacity. Whereas in the long run, a positive shock in GP effectiveness increases lending capacity, while a negative shock decreases it. Lastly, RI indicated no significant short-term impact on the lending capacity of the banks. Conversely, in the long run, a positive change in RI enhances lending capacity, whereas a negative change in RI reduces it, with a more pronounced effect.

The novelty of the study lies in the fact that it is a pioneering study that utilizes global governance and macroeconomic indicators to examine the impact on the lending capacity of banks and financial institutions in India. Moreover, the study adopts a non-linear approach to examine the relationship between the chosen variables, which enables an understanding of the impact of both positive and negative shocks on the dependent variable both in the short and long run. Lastly, the examination sheds light on the achievement of Sustainable Development Goal 8.10, which is related to financial inclusion and it is a major concern for a large developing nation like India.

In smart cities striving for innovation, development, and prosperity, hydrogen offers a promising path for decarbonization. However, its effective integration into the evolving energy landscape requires understanding regional intricacies and identifying areas for improvement. This chapter examines hydrogen transport from production to utilization, evaluating technologies’ pros, cons, and process equations and using Analytic Hierarchy Process (AHP) as a Multi-Criteria Decision-Making (MCDM) tool to assess these technologies based on multiple criteria. It also explores barriers and opportunities in hydrogen transport within the 21st-century energy transition, providing insights for overcoming challenges. Evaluation criteria for hydrogen transport technologies were ranked by relative importance, with energy efficiency topping the list, followed by energy density, infrastructure requirements, cost, range, and flexibility. Safety, technological maturity, scalability, and compatibility with existing infrastructure received lower weights. Hydrogen transport technologies were categorized into three performance levels: low, medium, and high. Hydrogen tube trailers ranked lowest, while chemical hydrides, hydrail, liquid organic hydrogen carriers, hydrogen pipelines, and hydrogen blending exhibited moderate performance. Compressed hydrogen gas, liquid hydrogen, ammonia carriers, and hydrogen fueling stations demonstrated the highest performance. The proposed framework is crucial for next-gen smart cities, cutting emissions, boosting growth, and speeding up development with a strong hydrogen infrastructure. This makes the region a sustainable tech leader, improving air quality and well-being. Aligned with Gulf Region goals, it is key for smart cities. Policymakers, industries, and researchers can use these insights to overcome barriers and seize hydrogen transport tech opportunities.

This study aims to demonstrate the impact of Sharia-compliance (SC) on attracting various types of investment, including foreign, family, institutional, royal, government and large individual investments.

The sample comprises 168 nonfinancial companies listed in the financial markets of the Gulf Cooperation Council (GCC) countries from 2009 to 2019, totaling 1,848 observations. The researchers used the ordinary least squares panel data method, with additional tests conducted using the two-stage least squares method.

The results indicate a negative relationship between SC and both foreign and institutional investments. Conversely, there is a positive relationship between SC and both family investment and large individual investor investment. Furthermore, the study found no significant relationship between SC and both government and royal investments (RIs).

The study enhances understanding of the role of Sharia-compliant companies in attracting investment. For managers of such companies, there is a need to make their firms more appealing to diverse investor types. Current and potential investors in Sharia-compliant companies should be aware of the investor nature controlling these companies. This study is beneficial for policymakers and regulators to assess the impact of Islamic Sharia-imposed restrictions on financial decision-making in companies. Policymakers should develop and monitor indicators of companies’ adherence to SC law in the six GCC countries and should also issue rules to enhance Sharia-compliant companies’ commitment to governance and transparency.

To the best of the authors’ knowledge, this study is the first of its kind to address the impact of SC on attracting different investment types. It includes six distinct investment types, notably RI, a significant variable in GCC countries’ business environment due to the considerable wealth and influence of royal family members.

Prediction models are essential tools for transportation agencies to forecast the condition of bridge decks based on available data, and artificial intelligence is paramount for this purpose. This study aims at proposing a bridge deck condition prediction model by assessing various classification and regression algorithms.

The 2019 National Bridge Inventory database is considered for model development. Eight different feature selection techniques, along with their mean and frequency, are used to identify the critical features influencing deck condition ratings. Thereafter, four regression and four classification algorithms are applied to predict condition ratings based on the selected features, and their performances are evaluated and compared with respect to the mean absolute error (MAE).

Classification algorithms outperform regression algorithms in predicting deck condition ratings. Due to its minimal MAE (0.369), the random forest classifier with eleven features is recommended as the preferred condition prediction model. The identified dominant features are superstructure condition, age, structural evaluation, substructure condition, inventory rating, maximum span length, deck area, average daily traffic, operating rating, deck width, and the number of spans.

The proposed bridge deck condition prediction model offers a valuable tool for transportation agencies to plan maintenance and resource allocation efficiently, ultimately improving bridge safety and serviceability.

This study provides a detailed framework for applying machine learning in bridge condition prediction that applies to any bridge inventory database. Moreover, it uses a comprehensive dataset encompassing an entire region, broadening the model’s applicability and representation.

The number of construction dispute cases has maintained a high growth trend in recent years. The effective exploration and management of construction contract risk can directly promote the overall performance of the project life cycle. The miss of clauses may result in a failure to match with standard contracts. If the contract, modified by the owner, omits key clauses, potential disputes may lead to contractors paying substantial compensation. Therefore, the identification of construction project contract missing clauses has heavily relied on the manual review technique, which is inefficient and highly restricted by personnel experience. The existing intelligent means only work for the contract query and storage. It is urgent to raise the level of intelligence for contract clause management. Therefore, this paper aims to propose an intelligent method to detect construction project contract missing clauses based on Natural Language Processing (NLP) and deep learning technology.

A complete classification scheme of contract clauses is designed based on NLP. First, construction contract texts are pre-processed and converted from unstructured natural language into structured digital vector form. Following the initial categorization, a multi-label classification of long text construction contract clauses is designed to preliminary identify whether the clause labels are missing. After the multi-label clause missing detection, the authors implement a clause similarity algorithm by creatively integrating the image detection thought, MatchPyramid model, with BERT to identify missing substantial content in the contract clauses.

1,322 construction project contracts were tested. Results showed that the accuracy of multi-label classification could reach 93%, the accuracy of similarity matching can reach 83%, and the recall rate and F1 mean of both can reach more than 0.7. The experimental results verify the feasibility of intelligently detecting contract risk through the NLP-based method to some extent.

NLP is adept at recognizing textual content and has shown promising results in some contract processing applications. However, the mostly used approaches of its utilization for risk detection in construction contract clauses predominantly are rule-based, which encounter challenges when handling intricate and lengthy engineering contracts. This paper introduces an NLP technique based on deep learning which reduces manual intervention and can autonomously identify and tag types of contractual deficiencies, aligning with the evolving complexities anticipated in future construction contracts. Moreover, this method achieves the recognition of extended contract clause texts. Ultimately, this approach boasts versatility; users simply need to adjust parameters such as segmentation based on language categories to detect omissions in contract clauses of diverse languages.

Megaproject's construction is essential for the development and economic growth of any country, especially in the developing world. In Kuwait, megaprojects are facing many restrictions that discourage their execution causing a significant delay in bidding, design, construction and operation phases with the execution quality being affected. The objective of this study is to develop a complexity measurement model using analytic hierarchy process (AHP) for megaprojects in Kuwait, with a focus on the New Kuwait University multi-billion campus Shadadiyah (College of Social Science, Sharia and Law (CSSL)) as a case study.

The study applies a hybrid fuzzy analytic hierarchy process (FAHP) method to compare the results with those obtained using the conventional AHP method. This can facilitate the project management activities during the different stages of construction. Data were collected based on the results of a two-round Delphi questionnaire completed by seniors and experts of the selected project.

It was found that project modeling methodology was responsible for complexity. It was grouped under several categories that include technological, goal, organizational, environmental and cultural complexities. The study compares complexity degrees assessed by AHP and FAHP methods. “Technological Complexity” scores highest in both methods, with FAHP reaching 7.46. “Goal Complexity” follows closely behind, with FAHP. “Cultural Complexity” ranks third, differing between methods, while “Organizational” and “Environmental Complexity” consistently score lower, with FAHP values slightly higher. These results show varying complexity levels across dimensions. Assessing and understanding such complexities were essential toward the completion of such megaprojects.

The contribution of this study is on providing the empirical evidential knowledge for the priority over construction complexities in a developing country (Kuwait) in the Middle East.

The primary objective of this investigation is to explore the factors that lead to entrepreneurial re-entry. The study examines the influence of social support and resilience on re-entry intention through the lens of the theory of planned behaviour (TPB).

To test the study model, data were collected from 255 failed Omani entrepreneurs accessed during a rehabilitation and incubation programme.

Key findings indicate that social support boosts the confidence of failed entrepreneurs to start anew and enhances their resilience, ultimately leading to the development of re-entry intentions.

The field of entrepreneurship research has expanded significantly in recent years. Nevertheless, there remains a dearth of studies focusing on entrepreneurial re-entry. This research provides a unique perspective on the cognitive processes that influence re-entry entrepreneurial behaviour, highlighting the roles of social support and resilience among entrepreneurs in this process.

Nurse turnover in critical care units (CCU) significantly affects patient outcomes and health systems worldwide. To safeguard patient care quality, hospitals must address the underlying reasons for turnover and strategize to retain their skilled nursing workforce. The study proposes a prescriptive framework to reduce nurse turnover in CCUs.

In this study, the integrated methodology of Delphi-AHP-Entropy was used for the comparative prioritization of factors and subfactors that influence nursing staff turnover in CCUs.

Study findings reveal that “Organizational factors” and “Individual factors” dictate critical care nurse attrition rate. At the subfactor level, staffing policy, chronic fatigue, and perceived career are the leading concerns for the decision of nurses whether to work or leave.

This study is valuable for both researchers and healthcare professionals. It examines whether actions related to nurse retention align with existing theory and identifies areas requiring further theoretical or applied studies to enhance understanding in this area. This insight can bolster the field’s knowledge base and integrate theoretical and applied knowledge effectively. Additionally, for healthcare professionals, the study provides an overview of key factors conducive to retaining nursing staff in the CCU, offering valuable guidance for implementing effective strategies.

This study uniquely positions itself by presenting a comprehensive and prescriptive framework for critical care nurse retention in the UAE.

The authors’ examination aims to offer a quantitative perspective on the interrelationships between tourist harassment, the destination image, emotions and destination revisit intent. Furthermore, it explores the moderating role of travelers' experiences and tolerance in the link between tourist harassment, the destination image and revisit intentions.

The authors’ examination seeks to fill this research gap by utilizing a combination of qualitative and quantitative methods to test eight hypotheses using AMOS 23 and PROCESS MARCO.

The findings showed that tourist harassment negatively impacts the destination image and revisit intentions. Moreover, it indicated that tourists' experiences and tolerance moderate the link between harassment, the destination image and revisit intentions for travelers with high levels of experience and tolerance compared to those with low levels.

This article contributes to travel research and service failure recovery research on tourist harassment and its consequences. To this end, it developed and validated a new tourist harassment scale. Moreover, it is the first study that examines the moderating role of visitors' experiences and tolerance on the link between tourist harassment, the destination image and revisit intentions. Finally, this article is the first to empirically offer destination harassment reduction techniques.

This case study is built from secondary data such as news articles, regulations and videos. Several drafts of the case study with a teaching note were tested in the classroom setting and shared in a case writing conference. The case was revised based on feedback from students and roundtable discussions from the conference.

Mrs Hsu, the Deputy Director of the National Taxation Bureau’s Nantou County Branch in Taiwan, faced a dilemma in June 2021. One of her employees, Mrs Chiang, had requested to return to work after taking several years of parental leave since August 2017. This long absence had put a strain on colleagues, who either had to cover for her or work with temporary replacements. While Mrs Chiang’s actions were legal and protected by her government employee role, her decision to take another leave immediately after receiving a COVID-19 vaccine raised eyebrows. Her peers accused her of using her frontline worker status to gain early vaccine access and other work benefits. Mrs Hsu, upon reviewing Mrs Chiang’s employment history, pondered her next steps concerning Mrs Chiang’s new leave request.

This case would be appropriate for a course in Human Resource Management, Organizational Behavior or Gender, Family and Work, especially with the topic of Employment Rights/Legal Protections (in HR), and/or Justice and Ethics (in OB).