نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشجوی ارشد، رشته فناوری اطلاعات، دانشگاه صنعتی، ارومیه، ایران
2 گروه مهندسی فناوری اطلاعات، دانشکده فناوری های صنعتی، دانشگاه صنعتی ارومیه، ارومیه، ایران
3 گروه مهندسی برق، دانشگاه صنعتی ارومیه، ارومیه، ایران
چکیده
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Introduction: This study aims to propose suitable business and revenue models for businesses venturing into electronic commerce within the agricultural sector. The research methodology involves an in-depth examination and classification of top global businesses in various sectors, with a focus on identifying the challenges and revenue models they employ.
Materials and Methods: The study utilizes qualitative evaluation indices to assess the business models of top Iranian and foreign websites and applications. Data mining methods, clustering techniques, and MATLAB data mining tools are employed to classify businesses and compare them with global counterparts. The research identifies and categorizes the challenges and revenue models of top businesses across various sectors, with a specific focus on the agricultural domain.
Results and Discussion: The study yields valuable insights, presenting ten significant challenges within the agricultural sector, which are used for clustering businesses and proposing revenue models tailored to this specific industry. Additionally, the research introduces a model for businesses to identify suitable revenue models by recognizing the challenges they aim to address and their functional domain. The proposed model serves as a comprehensive guide for businesses seeking to establish a presence in electronic commerce within the agricultural sector.
Conclusion: In conclusion, this study highlights the potential for policymakers to update the identified challenges based on evolving environmental requirements. Moreover, by drawing insights from successful global businesses, the study suggests the possibility of updating clustering and revenue models to align with current industry trends and best practices.
Keywords: Electronic Commerce, Application, Agricultural Sector, Business Models, Revenue Models, Data Mining, Clustering, Qualitative Evaluation.
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