Shah Muhammad Kamran

The Defined Benefit (DB) pension system operates under a Pay-As-You-Go (PAYG) framework, providing guaranteed post-retirement income and family pension benefits to Government Employees worldwide. However, rising pension liabilities, demographic shifts, and limited fiscal space have rendered the system financially unsustainable. Pakistan is not out of this problem, and Higher Education Institutions (HEIs) are particularly affected, as an increasing proportion of their recurrent grants is diverted toward pension obligations, constraining academic and developmental activities. While global pension reforms are well documented, the perspectives of operational pension administrators within Pakistan's HEIs remain largely unexplored. This study investigates sustainable reforms to optimise the DB pension payment system in HEIs. A qualitative multiple-case study design was used, including in-depth interviews with directors of finance, pension officers, auditors, and members of the HEC Board of Governors (BoG), supported by an extensive literature review. Using thematic analysis, both pre-empirical (literature-driven) and post-empirical (field-driven) frameworks were developed, identifying 25 additional mechanisms for controlling pension payments. The study proposes a comprehensive, evidence-based framework to assist policymakers, HEIs, and governing authorities in strengthening the financial sustainability of the DB pension system while ensuring the welfare of current and future employees.

The innovative knowledgebase system enables the healthcare system to address patient’s customized requirements. Developing and implementing this system with context-aware modifications is a challenge for developing countries, where it can change the healthcare delivery landscape from diagnosis to treatment. Although many studies have been conducted on the development of a knowledge-based system for early breast cancer detection using a rule-based approach, they lack contextual factors such as cultural aspects and socioeconomic features. This paper proposes a contextually aware machine learning (ML)-based knowledge base system for early breast cancer detection in the Pakistani context. Established risk and preventive breast cancer factors were identified from an extensive literature review and validated by academic and industry experts. Contextual factors were investigated with the assistance of field experts, and predictions from the ML algorithm were duly validated. The nested if-then rule, based on established risk and preventive factors, was combined with ML prediction to form the foundation of the proposed knowledgebase system. This research will improve the performance of healthcare systems in terms of accuracy and cost-effectiveness.

In this study, the focus is on investigating comovement patterns between eight sectors of companies listed on the Pakistan Stock Exchange. This research aims to use frequency-time bands and multiscale wavelet analysis to enhance portfolio diversification and asset allocation techniques. The results indicate that comovement patterns between pairs of two sectors can be utilized for short-, medium-, and long-term investments, allowing investors to construct portfolios based on their preferred investment horizons. Additionally, the study identifies sectors that are strongly correlated, such as banks-cement, cement-auto, and chemical-auto, where investment is not recommended. The results of the study can be used by policymakers, investors, and fund managers to develop effective portfolio diversification and risk management strategies. It is expected that the findings will contribute to the literature on international finance, providing useful insights into the dynamics of comovements in the Pakistani stock market. By applying the results of this research, investors may be better equipped to navigate the complexities of the stock market, thereby increasing their chances of achieving their investment goals.

Throughout history, Pakistan and the United States have experienced a complex and evolving relationship. This relationship has witnessed periods of cooperation, strain, and scrutiny, particularly concerning formal engagement, financial collaboration, military ties, and regional security matters. This study is critical because it illustrates the evolving geopolitical landscape by revealing how military aid and nuclear concerns have affected South Asian security. By highlighting strategic interactions between global powers and regional entities, this research contributes to the international relations discourse. To assess the state of US-Pakistan relations post-9/11, this study investigates counterterrorism, nonproliferation, and regional geopolitics. It examines how Pakistan's counterterrorism operations and regional security are influenced by US military support, as well as the impact of regional geopolitics on the partnership. Employing mixed methods research, the study reveals diverse perspectives on the impact of aid, including both cooperation and concerns regarding conditional assistance. Regional dynamics, such as Afghanistan's stability and India-Pakistan tensions, emerge as critical factors influencing the partnership's evolution. Challenges stemming from divergent interests and perceptions are also highlighted. Furthermore, the quantitative aspect indicates that gender influences the outcome variable, suggesting potential behavioral or attitudinal differences between men and women regarding the studied environment.

COVID-19 established the need for even more social entrepreneurship globally. It is important for keeping society together in times of crises because it creates an environment that improves the quality of life during hard times and public health emergencies such as COVID-19. Even though it plays a unique role in returning things back to normal after a crisis, it faces opposition from many parts of society, especially the government. Still, there are not many studies that look at what the government should do to help or stop social enterprise during public health emergencies. That is why the goal of this study was to find out how the government has helped or hindered social entrepreneurs. Content analysis was conducted on the carefully mined data from the internet. The research found that regulations for social enterprises should be loosened, especially during and after pandemics and disasters. This could also make it easier to accomplish things in the government. It was also found that, in addition to financial help, capacity building through training can help social enterprises do more and make a bigger difference. This research provides broader guidelines for policymakers and new entrants in the field.

The scale of entrepreneurial ecosystems (EE) assesses the perceptions about entrepreneurial ecosystem domains, finances, capital finances, support, support professions, policies, markets, human resources, and culture. The scales are always error-prone—these scales must possess properties that enable them it to provide maximum information and validity reliability. Convenient sampling data from (n = 474) founders, co-founders, and entrepreneurs were collected. The IRT-GRM model is used to validate and test the instrument-based on polytomous scales. IRT yields discriminating power—the level of difficulty of the items of the scale. The scale consists of 48 items. The item Pol5 (4.13) was found to have the highest discriminating value (4.13), the item mar5 had the lowest discriminating value (1.57), and all items had discriminating values greater than the threshold value of 0.60. The EE Scale showed good reliability based on McDonald’s omega and Cronbach’s alpha (0.80 and 0.88). The parallel and factor analysis showed good agreement of the one-dimesnionality of the scale. The model goodness of fit statistics based on the comparative fit index (CFI) and the Tucker–Lewis index, (TLI) and the standardized root mean square residual (SRMR) showed a satisfactory level of fit; however, the root mean square error of approximation (RMSE) showed a poor fit. The item characteristic curves showed that the all item responses were properly ordered. The items of the scale showed a satisfactory level of discrimination power and level of difficulty, and it was found to have three levels of agreement about entrepreneurial ecosystem scale. It is concluded that the EE scale possesses good psychometric properties and that it is reliable and valid instrument to measure the entrepreneurial ecosystem of the given region.

Social entrepreneurs are catalysts for social change on account of social value creation and opportunity identification, thereby improving the quality of life. Their contribution to society is particularly significant in times of crises and pandemics. Hence, the world health crisis caused by the COVID-19 pandemic has increased the need for social entrepreneurship across the globe. Despite social entrepreneurship’s relevance in social value creation, the studies regarding opportunity identification in times of social distancing are particularly rare. This constructivist-qualitative research fills the gap by employing the EDraw Mind Map tool to explore how the COVID-19 pandemic in general, and social distancing in particular, have shaped entrepreneurial opportunities for social innovation. The findings from content analysis reveal that ICT-based services and industry 4.0 hold a promising future during and post-COVID-19 scenario. They can facilitate a transformation of the threats of social distancing into distant socialization benefits and demand fulfillment. Furthermore, the study contributes to providing a comprehensive depiction of the myriad of opportunities created by social entrepreneurs worldwide. It also guides aspiring social entrepreneurs to adopt such technologies and aim for social integration to ensure quality mental health, education, employment, and manufacturing services in low-resource and developing countries’ contexts, as they are severely impacted by the pandemic.