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We propose and test a theoretically grounded structural model of our postulation, providing insights into how students’ COVID experiences affect their general learning experiences, specific learning experiences and specific class performance post-COVID.

Numerous studies have reported how COVID-19 has impacted student learning in higher education during the pandemic from Spring 2020 to Fall 2021. Students were found to be disengaged, unmotivated, isolated, anxious, having difficulty with emergency remote teaching (ERT) and dealing with financial, physical and/or mental health issues. Against this backdrop, we conduct our own investigation to ascertain what the lasting impacts of COVID-19 are on student learning using a survey instrument. Specifically, we postulate that the academic and social disruptions of COVID-19 affected students’ social connectedness and mental well-being, which in turn, affected their affective and cognitive learning outcomes. We used structural equation modeling to validate a structural model grounded in self-determination theory that capatures the complex relationships between genaral and specfiic COVID-19 impact factors on student learning as seven hypotheses.

All seven of our hypotheses are supported suggesting that both class-specific factors and broader general factors beyond the classroom affect student's satisfaction with and learning in the class, as postulated in our structural model.

We advance the work of self-determination theory by conceptualizing and modeling the roles that all three self-determination needs play in investigating COVID-19's impact on learning. Overall, our study confirms the value of considering affective factors such as anxiety and satisfaction in learning research.

This survey explores the application of real options theory to the field of health economics. The integration of options theory offers a valuable framework to address these challenges, providing insights into healthcare investments, policy analysis and patient care pathways.

This research employs the real options theory, a financial concept, to delve into health economics challenges. Through a systematic approach, three distinct models rooted in this theory are crafted and analyzed. Firstly, the study examines the value of investing in emerging health technology, factoring in future advantages, associated costs and unpredictability. The second model is patient-centric, evaluating the choice between immediate treatment switch and waiting for more clarity, while also weighing the associated risks. Lastly, the research assesses pandemic-related government policies, emphasizing the importance of delaying decisions in the face of uncertainties, thereby promoting data-driven policymaking.

Three different real options models are presented in this study to illustrate their applicability and value in aiding decision-makers. (1) The first evaluates investments in new technology, analyzing future benefits, discount rates and benefit volatility to determine investment value. (2) In the second model, a patient has the option of switching treatments now or waiting for more information before optimally switching treatments. However, waiting has its risks, such as disease progression. By modeling the potential benefits and risks of both options, and factoring in the time value, this model aids doctors and patients in making informed decisions based on a quantified assessment of potential outcomes. (3) The third model concerns pandemic policy: governments can end or prolong lockdowns. While awaiting more data on the virus might lead to economic and societal strain, the model emphasizes the economic value of deferring decisions under uncertainty.

This research provides a quantified perspective on various decisions in healthcare, from investments in new technology to treatment choices for patients to government decisions regarding pandemics. By applying real options theory, stakeholders can make more evidence-driven decisions.

Decisions about patient care pathways and pandemic policies have direct societal implications. For instance, choices regarding the prolongation or ending of lockdowns can lead to economic and societal strain.

The originality of this study lies in its application of real options theory, a concept from finance, to the realm of health economics, offering novel insights and analytical tools for decision-makers in the healthcare sector.

The paper examines the recent trends in information technology for combating corruption and its impact on the Indian economy. It further explores how technology is being used to tackle corruption in India and the resulting economic benefits.

The methodology encompasses qualitative analysis to investigate corruption comprehensively. It involves content analysis of corruption-related documents, case studies, and expert interviews. Recent information technological advancements are explored, including blockchain and AI, for their anti-corruption potential.

This study reveals that the negative impacts of corruption on society include reduced economic growth, weakened institutions, and decreased public trust in government. Various technological advancements such as e-governance, blockchain, AI, and big data analytics have been implemented to enhance transparency and accountability in government processes. Special cases and examples of application of such technology tools and techniques adopted by the organizations to control corruption are discussed.

This paper highlights the need for legal reforms, institutional strengthening, and awareness-raising campaigns to complement technological advancements in the fight against corruption.

The purpose of this study is to explore the complex interplay between technology, personal norms and emotional factors in shaping the sustainable housing choices of millennials in emerging economies. It integrates the model of goal-directed behavior, technology acceptance model and norm activation model, incorporating both self-interest and prosocial motivations. Key adaptations involve replacing perceived behavioral control with financial self-efficacy and substituting hedonic motivation for anticipated positive emotions. Moreover, it introduces location as a practical anchor.

A quantitative, cross-sectional and descriptive research design was used in this study. Data were gathered from a sample of 610 millennial residential real estate investors across Indian smart cities. A multistage stratified sampling technique was used to ensure a representative sample. For data analysis, partial least squares structural equation modeling was used. The analysis focused on hypothesis testing to examine the relationships between the constructs of interest. Bootstrap t-values and effect sizes were used to assess the significance and magnitude of these relationships, respectively.

One of the key findings of this study was the establishment of significant positive relationships between awareness of consequences, ascription of responsibility and personal norms with behavioral intentions. This underscored the importance of personal ethical considerations in shaping intentions. Perceived usefulness and ease of use were found to significantly influence attitudes positively, highlighting the relevance of these factors in forming favorable attitudes toward behaviors. Attitude, subjective norms, financial self-efficacy and location played significant positive roles. However, negative anticipated emotions decreased desire. This illustrated the complex role emotions play in motivational processes. The study also revealed that subjective norms did not significantly contribute to shaping personal norms. This indicated a potential decoupling of societal expectations from personal ethical obligations in the decision-making process.

This study offers actionable insights for both policymakers and real estate developers. For policymakers, the findings highlight the need to craft initiatives that go beyond mere awareness, instead fostering a deep sense of personal responsibility and environmental stewardship among potential homebuyers. For real estate developers, the emphasis on financial self-efficacy and location suggests a strategy shift toward designing sustainable homes that not only meet environmental standards but also align with buyers’ financial confidence and geographic preferences. Together, these strategies can drive a more widespread adoption of sustainable housing, making sustainability a tangible and appealing choice for millennials.

To the best of the authors’ knowledge, this empirical research study was one of the first studies that contributed to the literature by integrating the model of goal-directed behavior, technology acceptance model and norm activation model. This study thus offered a nuanced understanding of the interplay between normative influences, usability perceptions, ethical considerations and emotions in the context of behavioral intentions.

This paper examines the post-COVID-19 travel intentions to Kenya among Hong Kong outbound travelers using the theory of planned behavior (TPB) over three different time horizons of 1, 5, and 10 years.

An extension was made by including two new constructs of perceived destination image and travel constraints. A cross-sectional sample of Hongkongers was surveyed. Data were collected using a self-administrated bilingual (English and Chinese) online survey. Exploratory factor analysis, linear regression and mediation analysis were conducted to test the research model.

The findings from 216 Hongkongers reveal that different combinations of the four constructs, namely, perceived behavioral control, attitude, subjective norms, and destination image, share a positive effect on individuals' travel intention to Kenya over the three different time horizons. Travel constraints act as a significant negative mediator on the four constructs in predicting travel intention to Kenya among Hongkongers.

The results provide useful insight to Kenya's destination marketing organization (DMO) and Hong Kong outbound travel agencies to integrate prominent elements into marketing strategies to arouse travel intention and expand their business prospects, which will also accelerate tourism recovery in the post-pandemic era.

By integrating two extended variables into the TPB model, this study makes a contribution by overcoming the deficiency of the original theory.

Health technologies are advancing rapidly and becoming more expensive, posing a challenge for financing healthcare systems. Health technology assessment (HTA) improves the efficiency of resource allocation by facilitating evidence-informed decisions on the value of health technologies. Our study aims to create a customized HTA roadmap for Oman based on a gap analysis between the current and future status of HTA implementation.

We surveyed participants of an advanced HTA training program to assess the current state of HTA implementation in Oman and explore long-term goals. A list of draft recommendations was developed in areas with room for improvement. The list was then validated for its feasibility in a round table discussion with senior health policy experts to conclude on specific actions for HTA implementation.

Survey results aligned well with expert discussions. The round table discussion concluded with a phasic action plan for HTA implementation. In the short term (1–2 years), efforts will focus on building capacity through training programs. For medium-term actions (3–5 years), plans include expanding the HTA unit and introducing multiple cost-effectiveness thresholds while from 6–10 years, publishing of HTA recommendations, critical appraisal reports, and timelines is recommended.

Although the HTA system in Oman is still in its early stages, strong initiatives are being taken for its advancement. This structured approach ensures a comprehensive integration of HTA into the healthcare system, enhancing decision-making and promoting a sustainable, evidence-based system addressing the population’s needs.

This study aims to explore how social support has reshaped the care of older adults living alone in northeast Thailand during the COVID-19 pandemic.

This study used a qualitative method using a narrative gerontological perspective with a descriptive design. Purposive sampling was conducted from 20 in-depth narrative interviews between November 2021 and February 2022 in Maha Sarakham province, northeast Thailand. Data transcriptions were performed using a narrative analytical process.

The in-depth narrative interviews indicated that older adults living alone during the coronavirus pandemic valued a comfortable life, a sense of place, favourable living arrangements, self-reliance, life goals and meaningful life. The gerontological analysis showed that social support for older adults living alone included a social safety net, networking, supporting needs, physical care and mental health responses to the COVID-19 pandemic.

To the best of the authors’ knowledge, this is the first qualitative narrative gerontological study to explore how social support reshaped the care of older adults living alone during the COVID-19 pandemic in northeast Thailand.

The objective of this study is to reevaluate the correlation among pharmaceutical consumption, per capita income, and life expectancy across different age groups (at birth, middle age, and advanced age) within the OECD countries between 1998 and 2018.

We employ a two-step methodology, utilizing two independent approaches. Firstly, we con-duct the Dumitrescu-Hurlin pairwise panel causality test, followed by Machine Learning (ML) experiments employing the Causal Direction from Dependency (D2C) Prediction algorithm and a DeepNet process, thought to deliver robust inferences with respect to the nature, sign, direction, and significance of the causal relationships revealed in the econometric procedure.

Our findings reveal a two-way positive bidirectional causal relationship between GDP and total pharmaceutical sales per capita. This contradicts the conventional notion that health expenditures decrease with economic development due to general health improvements. Furthermore, we observe that GDP per capita positively correlates with life expectancy at birth, 40, and 60, consistently generating positive and statistically significant predictive values. Nonetheless, the value generated by the input life expectancy at 60 on the target income per capita is negative (−61.89%), shedding light on the asymmetric and nonlinear nature of this nexus. Finally, pharmaceutical sales per capita improve life expectancy at birth, 40, and 60, with higher magnitudes compared to those generated by the income input.

These results offer valuable insights into the intricate dynamics between economic development, pharmaceutical consumption, and life expectancy, providing important implications for health policy formulation.

Very few studies shed light on the nature and the direction of the causal relationships that operate among these indicators. Exiting from the standard procedures of cross-country regressions and panel estimations, the present manuscript strives to promote the relevance of using causality tests and Machine Learning (ML) methods on this topic. Therefore, this paper seeks to contribute to the literature in three important ways. First, this is the first study analyzing the long-run interactions among pharmaceutical consumption, per capita income, and life expectancy for the Organization for Economic Co-operation and Development (OECD) area. Second, this research contrasts with previous ones as it employs a complete causality testing framework able to depict causality flows among multiple variables (Dumitrescu-Hurlin causality tests). Third, this study displays a last competitive edge as the panel data procedures are complemented with an advanced data testing method derived from AI. Indeed, using an ML experiment (i.e. Causal Direction from Dependency, D2C and algorithm) it is believed to deliver robust inferences regarding the nature and the direction of the causality. All in all, the present paper is believed to represent a fruitful methodological research orientation. Coupled with accurate data, this seeks to complement the literature with novel evidence and inclusive knowledge on this topic. Finally, to bring accurate results, data cover the most recent and available period for 22 OECD countries: from 1998 to 2018.