In the realm of evolving e-commerce sales channels, the e-commerce sale of agricultural products has become a vital avenue for cherry farmers. However, a notable discrepancy exists between the intentions and actual behaviors of cherry farmers regarding e-commerce participation. In this study, binary logistic regression and interpretive structural model were used, and the cherry producing area of Yantai City, Shandong Province, China, was taken as the study area, and a total of 501 actual valid questionnaires were returned, and the validity rate of the questionnaires was 95.1 per cent. The results of the study show that the deviation of cherry farmers’ willingness and behavior is mainly affected by age, frequency of online shopping, whether to participate in e-commerce training, and whether to join a cooperative in farmers’ individual characteristics, revenue expectations and profit expectations in behavioral attitudes, government publicity and neighborhood effects in subjective norms, e-commerce use in perceived behavioral attitudes, the number of agricultural population in household resource endowment and logistics costs and e-commerce training in external scenarios Impact. On this basis, the 11 influencing factors are analyzed in depth and three transmission paths are analyzed. The study further proposes recommendations to enhance the translation of cherry farmers’ e-commerce intentions into action, such as bolstering e-commerce promotion, increasing the frequency of training, improving supporting infrastructure, and reducing logistics costs.

This study aims to underscore the relevance of pre-existing resilience experiences within communities affected by socio-political violence in Colombia, particularly in the context of developing effective risk management practices and enriching the CBDM model. This research employs a qualitative design, incorporating a multiple case study approach, which integrates a comprehensive literature review, in-depth interviews, and focus groups conducted in two Colombian communities, namely Salgar and La Primavera. The community of La Primavera effectively harnessed community empowerment and social support practices to confront socio-political violence, which evolved into a form of social capital that could be leveraged to address disaster risks. Conversely, in Salgar, individual and familial coping strategies took precedence. It is concluded that bolstering citizen participation in disaster risk management in both communities and governmental support for community projects aimed at reducing vulnerability is imperative. This study reveals that capabilities developed through coping with the humanitarian consequences of armed conflict, such as community empowerment and practices of solidarity and social support, can enhance community resilience in the face of disasters.

The rapid shift to online learning during COVID-19 posed challenges for students. This investigation explored these hurdles and suggested effective solutions using mixed methods. By combining a literature review, interviews, surveys, and the analytic hierarchy process (AHP), the study identified five key challenges: lack of practical experience, disruptions in learning environments, condensed assessments, technology and financial constraints, and health and mental well-being concerns. Notably, it found differences in priorities among students across academic years. Freshmen struggled with the absence of hands-on courses, sophomores with workload demands, and upperclassmen with mental health challenges. The research also discussed preferred strategies for resolution, emphasizing independent learning methods, managing distractions, and adjusting assessments. By providing tailored insights, this study aimed to enhance online learning. Governments and universities should support practical work, prioritize student well-being, improve digital infrastructure, adapt assessments, foster innovation, and ensure resilience.

The primary objective of this research is to investigate how non-financial incentives impact employee motivation within the Small and Medium Enterprises (SMEs) operating in Saudi Arabia. Employing a positivist research approach, we employed a carefully crafted survey to collect data from 365 employees employed by SMEs situated in Jeddah. The study explores various aspects, including the most common non-monetary motivators, the interplay between non-monetary and monetary incentives, and the effects of non-financial incentives on employee engagement, job satisfaction, and commitment. The results of the study indicate that employees working in small and medium-sized enterprises (SMEs) in Saudi Arabia place a significant emphasis on a good work environment, recognition, possibilities for personal and professional development, and career growth as prevalent non-monetary motivators. Additionally, the research illustrates a notable difference in the perceived efficacy of non-financial and financial incentives, whereby non-financial incentives are seen to have an equal, if not greater, impact on both motivation and work satisfaction. Moreover, the study reveals robust positive correlations between non-financial incentives and employee outcomes, underscoring the significance of these incentives in augmenting work satisfaction, job engagement, and commitment. The consequences of employee motivation are influenced by control factors, which have diverse influences, highlighting the complex nature of this phenomenon.

The Consumer Price Index (CPI) is a vital gauge of economic performance, reflecting fluctuations in the costs of goods, services, and other commodities essential to consumers. It is a cornerstone measure used to evaluate inflationary trends within an economy. In Saudi Arabia, forecasting the Consumer Price Index (CPI) relies on analyzing CPI data from 2013 to 2020, structured as an annual time series. Through rigorous analysis, the SARMA (0,1,0) (12,0,12) model emerges as the most suitable approach for estimating this dataset. Notably, this model stands out for its ability to accurately capture seasonal variations and autocorrelation patterns inherent in the CPI data. An advantageous feature of the chosen SARMA model is its self-sufficiency, eliminating the need for supplementary models to address outliers or disruptions in the data. Moreover, the residuals produced by the model adhere closely to the fundamental assumptions of least squares principles, underscoring the precision of the estimation process. The fitted SARMA model demonstrates stability, exhibiting minimal deviations from expected trends. This stability enhances its utility in estimating the average prices of goods and services, thus providing valuable insights for policymakers and stakeholders. Utilizing the SARMA (0,1,0) (12,0,12) model enables the projection of future values of the Consumer Price Index (CPI) in Saudi Arabia for the period from June 2020 to June 2021. The model forecasts a consistent upward trajectory in monthly CPI values, reflecting ongoing economic inflationary pressures. In summary, the findings underscore the efficacy of the SARMA model in predicting CPI trends in Saudi Arabia. This model is a valuable tool for policymakers, enabling informed decision-making in response to evolving economic dynamics and facilitating effective policies to address inflationary challenges.