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 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.
Data literacy is an important skill for students in studying physics. With data literacy, students have the ability to collect, analyze and interpret data as well as construct data-based scientific explanations and reasoning. However, students’ ability to data literacy is still not satisfactory. On the other hand, various learning strategies still provide opportunities to design learning models that are more directed at data literacy skills. For this reason, in this research a physics learning model was developed that is oriented towards physics objects represented in various modes and is called the Object-Oriented Physics Learning (OOPL) Model. The learning model was developed through several stages and based on the results of the validity analysis; it shows that the OOPL model is included in the valid category. The OOPL model fulfils the elements of content validity and construct validity. The validity of the OOPL model and its implications are discussed in detail in the discussion.
This research aims to investigate the factors shaping the investment choices of individuals in Saudi Arabia concerning cryptocurrencies, particularly focusing on the influence of the Fear of Missing Out (FOMO) psychological phenomenon. This study employs a mixed-methods approach to comprehend the factors influencing Saudi investors' decisions in the cryptocurrency realm. Quantitative surveys are conducted to gauge perceptions of risk, return, regulatory factors, and social influence. Additionally, qualitative interviews delve into the nuanced interplay of these elements and the impact of FOMO on decision-making. Integrating the Theory of Planned Behavior and Behavioral Finance theories, this research offers a holistic understanding of cryptocurrency investment determinants. The combined quantitative and qualitative methods provide a comprehensive view, enabling an in-depth analysis of the subject matter. The study reveals that Saudi Arabian investors' decisions regarding cryptocurrencies are significantly influenced by multiple factors, including perceived risk, potential return, regulatory environment, and social dynamics. FOMO emerges as a crucial psychological factor, interacting with these influences and driving decision-making. This research underscores the intricate interplay between these factors and FOMO, shedding light on the dynamics of cryptocurrency investment choices in the Saudi Arabian market. The findings hold implications for policymakers, financial institutions, and investors seeking deeper insights into this evolving landscape. Drawing from the Theory of Planned Behavior and Behavioral Finance, it examines perceived risk, return, regulatory factors, and social influence in influencing cryptocurrency investment choices among Saudi investors, focusing on the influence of Fear of Missing Out (FOMO). The research outcome provides insights for policymakers, financial institutions, and investors seeking to understand cryptocurrency investment dynamics in Saudi Arabia.
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