Mohammed Wamique Hisam

This study investigates the effectiveness of predictive performance of multiple modeling techniques in forecasting Nigeria’s Food Price Index (FPI) using monthly data from the Food and Agriculture Organization (FAO) Food Price Index spanning January 1990 to January 2025. Five major food subsector indices—Meat, Dairy, Cereals, Oils,and Sugar—serve as predictors. Initial regression on the raw series produced inflated R2 values, prompting diag‑nostic tests that revealed severe non‑stationarity and multicollinearity. These issues were addressed through a two‑step transformation: natural logarithmic conversion to stabilize variance, followed by first‑order differencing to achieve stationarity and eliminate false trends. Post‑transformation diagnostics confirmed full correction, with all variables stationary and multicollinearity reduced to acceptable levels. Using standardized log‑differenced data, five models were evaluated: Multiple Linear Regression, Artificial Neural Network, Random Forest, Support Vector Machine, and Long Short‑Term Memory (LSTM). On the transformed scale, the Support Vector Machine was the strongest performer; however, after back‑transforming predictions to the original scale through recursive exponential reconstruction, Random Forest achieved the highest accuracy (R2 = 0.9342, MAPE = 0.65%). LSTM models performed poorly, indicating a structural mismatch with differenced series lacking persistent trends. The study provides three methodological insights: (1) top performance on transformed data does not guarantee accuracy after inverse conversion; (2) log‑differencing is highly effective for resolving non‑stationarity, heteroscedasticity, and multicollinearity while preserving economic interpretability; and (3) ensemble averaging enhances recursive multi‑step forecasting stability. For practical policy applications requiring multi‑month food price projections, Random Forest is recommended, with Multiple Linear Regression serving as a transparent alternative for coefficient interpretation. These findings support the design of more informed food security interventions and market stabilization strategies in Nigeria.

The purpose of the present study is to investigate the effect of community-based tourism factors on sustainable tourism development, investigate the consequences of community-based tourism on local communities, including the distribution of benefits and any potential negative repercussions, and explore the long-term viability of tourism ventures and their influence on local resources. The emphasis on sustainable tourism development in Oman arose as the nation sought to reduce its reliance on oil according to Oman Vision 2040. The contribution of CBT to this vision necessitates a shared understanding among policymakers, tourism practitioners, and local communities. Preliminary data was collected through online questionnaires from 402 respondents from local community, representatives of tourism sector and tourists in Dhofar region of Oman. Structural equation modeling (SEM) was used to analyse the data by WarpPLS software. The findings indicate that the three factors of community-based tourism (i.e., Economic Factor, Social Factor, and Environmental Factor) have significantly affect Sustainable Tourism Development (STD). This work is among the first empirical studies in this field, and its results can be useful for tourism sector to increase a better awareness of the potential advantages and difficulties of CBT as well as how to apply it in their particular domains.

Purpose: The aim of this research was to evaluate the external factors and core elements of the technology acceptance model (TAM) that influence the adoption of distance learning platforms (DLP) and to examine the impact of DLP adoption on students’ academic performance. Design/methodology/approach: To achieve the research objectives, an online survey was conducted with 783 students from Pakistani higher education institutions. The collected data were analyzed using structural equation modeling (SEM) in AMOS v26. Findings: Results indicate that perceived usefulness and perceived ease of use positively influenced the adoption of DLP among students during the COVID-19 pandemic. Furthermore, the adoption of DLP is associated with improved academic performance of students in the COVID-19 pandemic. The study also reveals that social isolation positively influences the perceived usefulness and adoption of DLP. Moreover, COVID-19 fear moderates the relationship between subjective norms and the adoption of DLP, but not between social isolation and the adoption of DLP among students. Research limitations/implications: This study draws guidelines to assist researchers, students and relevant educational institutions in identifying the factors that influence DLP adoption and finding effective ways to promote its use. Social implications: This study highlights the importance of adopting DLP to mitigate the impact of pandemics like COVID-19 on education. By embracing DLP, societies can better control the spread of such disasters and ensure the provision of education in society, even in challenging circumstances. Originality/value: This study contributes to TAM by emphasizing the importance of adopting DLP during pandemics such as COVID-19 and providing insights into the determining factors of DLP adoption.