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The complexity of projects has become a serious issue and obstacle in their successful completion. In order to overcome these complexities, it has become imperative to identify the relevant management competencies of project managers. The purpose of this paper is to address the problem of cost, time and scope in engineering infrastructure projects due to their complexities through management competencies.

In the first phase of the study, 32 experts were interviewed through semi-structured pre-tested questionnaire. In this phase, essential elements of complexities were identified initially. This was followed by finding required dimensions of competencies to counter these complexities and to acquire improved performance. In the final stage, required levels of competencies for specific elements of complexity were identified. In the second phase, 85 “project managers” were also approached to get feedback about their recently completed public sector engineering infrastructure projects in Pakistan.

The study identified additional dimensions, i.e. honesty, enthusiasm and dedication, in the case of competencies and adverse law and order situation, political instability, land issues, energy crisis and weak authorization of project managers in the case of complexities. Leadership, management skill, communication skill, effectiveness and result orientation were identified as top quality traits required. The study concluded that there is a significant impact of management competencies and complexities on project performance.

The study contributes to a better understanding of how to improve performance in complex engineering infrastructure projects through adopting management competencies. It also empirically illustrates the relations among project management competencies, complexities and project performance. Although the research is grounded on public sector infrastructure projects, its findings may also be helpful for practices in project management of other sectors.

Spatio-temporal variations in precipitation pattern of district Sargodha is one of the most significant researchable questions because of the massive reliance on rainfall for agricultural practice in the study area. The pattern of current rainfall in the study area is unexpectedly changed. The purpose of the present study is to examine the changing precipitation pattern and to link it with climate change.

The study was conducted by using rainfall data of the past 30 years collected from 8 meteorological stations around the study area. The averages of rainfall on monthly basis were temporally arranged, and the fluctuation trends were studied using GIS and statistics. The temporal data of rainfall were compared and contrasted with the precipitation normals of the study area from 1981to 2010. The rainfall deviation in the present study was calculated. The spatial pattern of rainfall was plotted by interpolating the eight points of Punjab around the study area for the first two decades, whereas the past decade was analysed by incorporating five more points of Tehsils in the existing eight. The spatial and statistical representation of data were examined by compare and contrast with the previous findings.

The rainfall in the study area showed remarkable changes in magnitude and spatiality. The rainfall in the district is on the rise, whereas the spatial pattern of rainfall is becoming more complex and anomalous in character. This paper provides convincing evidence about the impact of climate change on the magnitude and spatial patterns of precipitation in the study area.

It will be helpful for understanding the shifts in the rainfall pattern in future as well as for the preparation of response to the issue of climate change and its impacts.

The current manuscript, for the very first time, provided detailed insights about the precipitation pattern shifting during the last 30 years in district Sargodha, Punjab, Pakistan. Furthermore, agricultural sector would likely get severally affected because of seasonal changes in climatic factors like rainfall and have strong food security implications. The current findings will be useful to manage the climate change-related issues in Pakistan and helpful for the policy makers to design a coping strategy for climate change impacts.

Based on the arguments of conservation of resource theory (COR), this study aims to investigate the mechanism underlying the linkage of COVID fear, work-family conflict (W-FC) and family-work conflict (F-WC) while investigating the mediating role of COVID stress and social distancing. The study also tests the moderating role of religiosity in these relationships.

In total, 401 survey responses from doctors and university teachers are collected through online and personally administrated questionnaires. Partial least square-based structural equation modeling technique is applied using Smart PLS software.

This study finds that COVID fear has a positive and significant impact on COVID stress and social distancing while religiosity negatively impacts COVID fear. Further, COVID stress and social distancing mediates the relationships between COVID fear and W-FC/ F-WC. However, the study found that COVID stress is a more effective mechanism as compared to social distancing in explaining the mediation process. It is also found that religiosity significantly moderates the nexus between COVID stress and both W-FC and F-WC negatively.

Findings imply that the teachers, as well as doctors, must be immediately be provided with the proper facilitation to maintain their work from home operations with the best of the institutional facilities. Moreover, the recruitment policy in such professions can also consider religious practices to be the indicators of problem-solving and stress management in such a challenging context.

This study provides timely and novel insight into the interplay between the domains of work and family during the period of stressful COVID outbreak. By distinguishing W-FC and F-WC, it provides a detailed understanding of the process during the COVID period. The groundbreaking finding in the research is with the moderation of religiosity.

The purpose of this study is to investigate the ability of EFL learners’ cohesion after the implementation of small group flipped instruction model through WhatsApp with small group writing activities compared with individual flipped instruction model through WhatsApp with individual writing activities.

A quasi-experimental study with a nonequivalent control group and a pre-test/post-test design was implemented to find any significant difference between the two combinations. The small group was treated using small group flipped instruction model through WhatsApp with small group writing activities, and an individual class was exposed to individual group flipped instruction model through WhatsApp with individual writing activities as well. The instrument of this study was a writing test.

The findings revealed that the mean score from the small group flipped instruction model through WhatsApp with small group writing activities at 66.17 was higher than the mean score individual flipped model via WhatsApp with individual writing activities at50.19 with a level of significance < 0.05. He meant that the small group flipped classroom instruction model through WhatsApp with small group writing activities performed better than teaching cohesion with individual flipped instruction through WhatsApp with individual writing activities. The results suggested small group flipped teaching–learning cohesion with WhatsApp in writing served as one of the alternatives flipped group discussion to improve learners’ cohesion in writing.

Flipped classroom innovation has attracted English language teaching researchers’ attention to scrutinize its effectiveness. This inquiry, therefore, elaborated the effect off-lipping individual and small group classroom instruction with WhatsApp on EFL learners’ cohesion as part of EFL writing skills.

This study aims to investigate the effects of board characteristics on the cost of debt for non-financial companies in the Turkish capital markets.

Using a sample of 211 non-financial companies listed on Borsa Istanbul, this study examines how chairperson gender and board characteristics affect the cost of debt by using panel data analysis over the period of 2016–2020. A system generalized method of moments model is also applied to test the endogeneity issue.

The findings show that the presence of female chairperson and female directors on board reduces the cost of debt and the perceptions of default risk by fund providers, while board independence and board size do not have a significant impact on the cost of debt. The results provide insightful information for companies and policymakers. Companies can alter board composition through gender diversity, while policymakers can introduce new policies in encouraging the presence of female directors on boards.

This study primarily enriches the literature on the effect of board diversity on debt financing cost in a leading emerging market, enabling companies in emerging markets to better mitigate agency costs and finance their investment through effective board composition. Second, it provides evidence that financial institutions consider companies with chairwomen and women directors on the boards less risky and charge them less for debt financing than they do for companies with man chairperson. Finally, the results support policymakers to take actions to increase female presence on board.

Much work about health reform and systems improvement in healthcare looks at shortcomings and universal problems facing health systems, but rarely are accomplishments dissected and analyzed internationally. The purpose of this paper is to address this knowledge gap by examining the lessons learned from health system reform and improvement efforts in 60 countries.

In total, 60 low-, middle- and high-income countries provided a case study of successful health reform, which was gathered into a compendium as a recently published book. Here, the extensive source material was re-examined through inductive content analysis to derive broad themes of systems change internationally.

Nine themes were identified: improving policy, coverage and governance; enhancing the quality of care; keeping patients safe; regulating standards and accreditation; organizing care at the macro-level; organizing care at the meso- and micro-level; developing workforces and resources; harnessing technology and IT; and making collaboratives and partnerships work.

These themes provide a model of what constitutes successful systems change across a wide sample of health systems, offering a store of knowledge about how reformers and improvement initiators achieve their goals.

Few comparative international studies of health systems include a sufficiently wide selection of low-, middle- and high-income countries in their analysis. This paper provides a more balanced approach to consider where achievements are being made across healthcare, and what we can do to replicate and spread successful examples of systems change internationally.

The purpose of this paper is to investigate the Malaysian homeowners’ intention to participate in the Cash Waqf-Financial Cooperative-Musharakah Mutanaqisah (CWFCMM) home financing model using rigorous scale validation procedures.

An adapted questionnaire with 26 items was administered to 382 academic and supporting staffs, postgraduate and undergraduate students in three states in Malaysia. The data were analyzed using exploratory factor analysis and confirmatory factor analysis with SPSS 21 and AMOS 21, respectively.

The results further support the parsimonious nature theory of planned behavior (TPB) with its three original construct of attitude, subjective norm, and perceived behavioral control and an additional construct of perceived cost advantages of the CWFCMM Model. A validated TPB scale with 24 items measured can be proposed to be used as an evaluative tool to assess the level to which the homeowners are interested to participate in the CWFCMM Model.

The modified TPB scale would also facilitate the identifications of factors that influence homeowners’ intention to opt for Islamic home financing (IHF) by non-bank Islamic financial institutions such as financial cooperative.

The CWFCMM Model aims to harness the potential synergy between third-sector economy players such as waqf and financial cooperatives in offering affordable IHF solution to potential homeowners.

The CWFCMM Model may enable the Malaysian homeowners to enjoy a more affordable IHF solution, thereby, among others, reducing the purchase and monthly repayment affordability of Malaysians.

There is a dearth of Islamic financial services selection studies, especially within the non-banking, Islamic financial services milieu. The CWFCMM Model, which has been developed by the authors, enriches the limited literature on this subject matter. It will also add value to understand how the customers would react to an alternative IHF provider.

This study aims to investigate the long-run relationship between financial inclusion and agricultural growth in Pakistan for the period of 1960–2018.

The autoregressive distributed lag (ARDL) approach, the Johansen co-integration test and the dynamic ordinary least squared (DOLS) method are used for the evaluation.

The results show that in both short- and long run, domestic credit has a significantly negative impact on the agricultural growth, while broad money and cropped area positively affected the agricultural growth in Pakistan in both cases.

The government and policymakers need to develop strategies that bring together agriculturalists on a single platform so that the government can clearly distinguish the interests of these farmers and can obtain precise information for allocating agricultural expenditure and easing access to credit for small-scale agriculturalists.

This is the first study to evaluate the impact of financial inclusion on the agricultural growth in Pakistan by using different econometric techniques, including the ARDL-bound approach, Johansen co-integration test and DOLS method.

Purpose: This chapter aims to examine machine learning (ML) models for predicting credit card fraud (CCF).

Need for the study: With the advance of technology, the world is increasingly relying on credit cards rather than cash in daily life. This creates a slew of new opportunities for fraudulent individuals to abuse these cards. As of December 2020, global card losses reached $28.65billion, up 2.9% from $27.85 billion in 2018, according to the Nilson 2019 research. To safeguard the safety of credit card users, the credit card issuer should include a service that protects customers from potential risks. CCF has become a severe threat as internet buying has grown. To this goal, various studies in the field of automatic and real-time fraud detection are required. Due to their advantageous properties, the most recent ones employ a variety of ML algorithms and techniques to construct a well-fitting model to detect fraudulent transactions. When it comes to recognising credit card risk is huge and high-dimensional data, feature selection (FS) is critical for improving classification accuracy and fraud detection.

Methodology/design/approach: The objectives of this chapter are to construct a new model for credit card fraud detection (CCFD) based on principal component analysis (PCA) for FS and using supervised ML techniques such as K-nearest neighbour (KNN), ridge classifier, gradient boosting, quadratic discriminant analysis, AdaBoost, and random forest for classification of fraudulent and legitimate transactions. When compared to earlier experiments, the suggested approach demonstrates a high capacity for detecting fraudulent transactions. To be more precise, our model’s resilience is constructed by integrating the power of PCA for determining the most useful predictive features. The experimental analysis was performed on German credit card and Taiwan credit card data sets.

Findings: The experimental findings revealed that the KNN achieved an accuracy of 96.29%, recall of 100%, and precision of 96.29%, which is the best performing model on the German data set. While the ridge classifier was the best performing model on Taiwan Credit data with an accuracy of 81.75%, recall of 34.89, and precision of 66.61%.

Practical implications: The poor performance of the models on the Taiwan data revealed that it is an imbalanced credit card data set. The comparison of our proposed models with state-of-the-art credit card ML models showed that our results were competitive.

Purpose: In this study, an attempt has been made to examine the compliance unit’s role in mediating the electronic government’s role in money laundering. E-government is clarified as the application of Information technology to encourage access and transfer of all aspects of government amenities and operations that impact transparency and accountability for the benefit of the people, trades, workforces and other stakeholders. The current study aims to assess whether the e-government can lessen or counterbalance the risks related to money laundering in the country and the mediating role of the compliance unit in reducing money laundering.

Methodology: This study practices structural modelling to assess the direct linkage between e-government and anti-money laundering and the indirect path between e-government and anti-money laundering that passes through the compliance unit as a mediator.

Findings: The findings prove that the compliance unit fully mediates the relationship between E-government and anti-money laundering. The direct path shows an insignificant relationship between e-government and money laundering, but this association becomes significant when the compliance unit is brought as a mediator.

Originality: This study directs that e-government runs on a sustainable ICT platform to improve transparency and accountability of all aspects of government facilities and actions for sustainable economic goals and help to diminish money laundering by enhancing transparency and accountability of government administration.