Ali Saleh Alshebami’s research while affiliated with King Faisal University and other places

Micro- and small-sized enterprises (MSEs) play a key role in developing emerging countries’ economies. However, concerns remain about their resilience and continuity, especially during periods of conflict and crisis. To address this gap, this research explores key factors that enhance adaptive capacity (AC) and entrepreneurial resilience (ER) amongst MSEs. Data were collected from 301 micro- and small-sized entrepreneurs operating different business activities using an online questionnaire and on-site visits. Partial least squares–structural equation modeling was employed to analyze the data. Findings revealed that crisis management preparedness (CMP) and customer-centric adaptation (CCA) positively and significantly influence AC, which, in turn, positively affects ER. In addition, CMP and CCA directly influence ER. Moreover, AC partially mediates the relationship between CMP, CCA, and ER. This study offers significant practical and theoretical implications for policymakers in making strategic action plans.

Drawing from a sample of Chinese small-medium organizations (SMEs), this study explored the interplay between stakeholders' pressure (PSP), environmental commitment (EC), circular economy performance (CEP), and CSR for sustainability (PCS). Quantitative data from 137 managers of manufacturing SMEs was collected using convenience sampling and cross-sectional design. PLS-SEM processed the data. The findings revealed that the PSP directly affected EC and CEP but not PCS. Further, we deployed two mediators (EC and CEP) to explore the indirect mechanisms within the research framework. The mediation analysis revealed a significant association between PSP and PCS, as well as between EC and PCS. This study explored the possibility of integrating the stakeholder theory and resource-based views, which could enable policymakers to have an insight into utilizing the nuance liaison between the stakeholder and the reflection within the manufacturing sectors for sustainable development.

This study examined the influence of perceived ease of use, perceived usefulness, social influence, perceived trust, perceived convenience, and emotional experience on intention to use and adoption of cashless transactions in Bangladesh. The present study used convenience sampling to collect quantitative data from 324 individuals in Bangladesh using structured interviews. Data was analyzed using PLS-SEM. Results revealed that perceived usefulness, perceived trust, perceived convenience, and emotional experience significantly and positively affect the intention to use cashless transactions. Intentions to use cashless transactions have a significant positive effect on adoption of cashless transactions. Finally, intention to use cashless transactions mediated the effect of per-ceived usefulness, perceived trust, perceived convenience, and emotional experience on adoption of cashless transactions in Bangladesh. This study contributes to empirical literature on digital transactions, particularly adding the developing countries’ perspective. The findings further contribute by applying the extended TAM in an under-researched developing nations’ perspective. Our findings have greater significance for policy makers and management of financial institutions to pursue strategies that can expand the adoption of cashless transactions. Several other implications for diverse stakeholders have also been narrated.

Limited resources have always been critical for the sustainability, growth, and development of micro and small enterprises. Understanding how the available limited resources can be best utilized during adverse conditions seems essential. Accordingly, this study attempts to understand key factors enhancing entrepreneurial bricolage (EB) among micro and small entrepreneurs, leading to better growth aspirations (GA). The study gathered a sample of 280 micro and small entrepreneurs from December 2024 to January 2025 through online and in-person visits to their business locations. Data were analyzed with the help of partial least square-structural equation modeling (PLS-SEM). The study has reported that commitment (COM) and soft skills development (SSD) are positively and significantly correlated with EB, and EB positively and significantly influences GA. Furthermore, EB mediates the relationship between COM, SSD, and GA. However, COM reveals no direct connection with GA, but SSD reports a positive connection with GA. The study provides theoretical and practical implications for policymakers, stakeholders, and academic researchers. Acknowledgment This work was supported by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [Grant No. KFU251164].

Micro and small enterprises (MSMEs) play a positive and significant role in developing economies by creating job opportunities and mitigating poverty; this necessitates their attention and focus, mainly during challenging times. Accordingly, this study explored the key factors contributing to enhancing entrepreneurial competency (EC) and the intention to maintain business continuity among MSMEs in Yemen during adverse times. A sample of 280 responses was collected from MSMEs operating diverse types of businesses in the capital of Yemen, Sanaa. The collected data were analysed using partial least squares–structural equation modelling (PLS-SEM), which is considered suitable for this purpose. The data collection process included an online questionnaire and a physical visit to the business locations of the business owners. The findings of the study reported that entrepreneurs’ network ties (ENT), as well as recovering capability (RC), positively and significantly influence the EC of MSMEs in the context of the study. Additionally, EC positively and significantly influences business continuity intention (BCI). Finally, EC partially mediates the relationship between ENT, RC, and BCI. The study concludes by providing recommendations and implications for policymakers.

Computational intelligence (CI) and artificial intelligence (AI) have incredible roles to play in the development of smart and sustainable healthcare systems by facilitating the integration of smart technologies with conventional medical procedures. The Internet of Things (IoT) and CI healthcare systems rely heavily on data collection and machine learning since miniature devices represent the foundation and paradigm shift to sustainable healthcare. With these advancements in AI techniques, we can reduce our environmental impact, while simultaneously enhancing the quality of our services. Widespread use of these devices for innovative IoT applications, however, generates massive amounts of data, which can significantly strain processing power. There is still a need for an efficient and sustainable model in the area of disease predictions, such as lung cancer, blood cancer, and breast cancer. The fundamental purpose of this research is to prove the efficacy of a secure Internet of Medical Things (IoMT) in the detection and management of breast cancer via the use of gated recurrent units (GRUs), which are a more recent version of recurrent neural networks (RNNs). The blockchain has been employed to achieve the secure IoMT. Unlike long short-term memory units, they do not have a cell state of their own. Therefore, we have combined GRU with RNN to achieve the best results. When training a GRU-RNN classifier, it is typically necessary to collect tagged IoT data from many sources, which raises significant concerns about the confidentiality of the data. To verify the model, the experiment is performed on Wisconsin Diagnostic Breast Cancer (WDBC). The experimental result shows that the GRU-RNN has been archived 95% in terms of the accuracy metric, and the efficacy of the proposed IoMT model is superior to the existing approach in terms of accuracy, precision, and recall.

While entrepreneurship is believed to play a crucial role in economic growth and job creation in various parts of the world, particularly in developed countries, the key factors enhancing entrepreneurship behavior and intention in developing countries still need to be discovered. Therefore, this study examines the influence of personality traits and environmental and situational factors on the development of entrepreneurial intention among young students in Yemen. Data were collected through a survey responded to by 487 final-year university students from two universities (public and private) in Yemen. The study’s hypotheses were tested using structural equation modeling (SEM). The study reveals that personality traits of the need for achievement (nAch) and locus of control (LoC) positively correlate with entrepreneurial selfefficacy (ESE) and entrepreneurial intention. Instrumental readiness positively correlates with ESE but not with entrepreneurial intent. The situational factors show a positive association with entrepreneurial intention but not ESE and a positive relationship between ESE and entrepreneurial intention. Furthermore, the study’s findings show that ESE partially mediates the relationship between the nAch, LoC, instrumental readiness, and entrepreneurial intention. However, ESE did not mediate the relationship between situational factors and entrepreneurial intention. The study suggests that situational factors can influence entrepreneurial intention among Yemeni students and provide several recommendations to academicians and policymakers.

The housing market is a crucial economic indicator to which the government must pay special attention because of its impact on the lives of freshly minted city inhabitants. As a guide for government regulation, individual property purchases, third-party evaluation, and understanding how housing prices are distributed geographically may be of great practical use. Therefore, much research has been conducted on how to arrive at a more accurate and efficient way of calculating housing prices in the current market. The goal of this study was to use the artificial neural network (ANN) technique to correctly identify real estate prices. The novelty of the proposed research is to build a prediction model based on ANN for predicting future house prices in Saudi Arabia. The dataset was collected from Aqar in four main Saudi Arabian cities: Riyadh, Jeddah, Dammam, and Al-Khobar. The results showed that the experimental and predicted values were very close. The results of the proposed system were compared with different existing prediction systems, and the developed model achieved high performance. This forecasting system can also help increase investment in the real estate sector. The ANN model could appropriately estimate the housing prices currently available on the market, according to the findings of the assessments of the model. Thus, this study provides a suitable decision support or adaptive suggestion approach for estimating the ideal sales prices of residential properties. This solution is urgently required by both investors and the general population as a whole.