[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2644":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":28,"view_count":34,"doi":35,"paper":36,"created_at":49},2644,"Digital Competence as a Predictor of Smart Agricultural Technology Utilization among Agricultural Education Lecturers in Colleges of Education in South-South Nigeria","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22762486","Abstract This study determined digital competence as a predictor of smart agricultural technology utilization among Agricultural Education lecturers in Colleges of Education in South-South Nigeria. Specifically, the study determined the level of digital competence, identified the availability and extent of utilization of smart agricultural technologies, examined factors influencing technology utilization, and determined the relationship between digital competence and technology utilization. A descriptive survey research design was adopted for the study. The population comprised all Agricultural Education lecturers in Colleges of Education in South-South Nigeria, while an illustrative sample of 220 lecturers was used for demonstration purposes. Data were collected using the Digital Competence and Utilization of Smart Agricultural Technologies Questionnaire (DCUSATQ). The instrument was validated by experts in Agricultural Education, Educational Technology, and Measurement and Evaluation, while Cronbach's Alpha reliability coefficients of 0.87, 0.84, and 0.81 were obtained for the Digital Competence, Technology Utilization, and Influencing Factors scales, respectively, with an overall reliability coefficient of 0.86. Data were analyzed using frequency, percentage, mean, standard deviation, Pearson Product Moment Correlation, and Multiple Regression at the 0.05 level of significance. The findings revealed that Agricultural Education lecturers possessed a high level of digital competence. Smart agricultural technologies were moderately utilized, with online agricultural databases and mobile agricultural applications being the most frequently used, while drone technology and precision agriculture tools recorded relatively low utilization. Institutional support, digital infrastructure, internet connectivity, and training opportunities were identified as major factors influencing technology utilization. The study further revealed a significant positive relationship between digital competence and smart agricultural technology utilization. Multiple regression analysis showed that digital competence significantly predicted smart agricultural technology utilization, while the combined influence of digital competence, institutional support, ICT infrastructure, and training exposure explained the variance in technology utilization. Based on the findings, it was recommended that there should be continuous professional development in digital agriculture, improved institutional support, enhanced ICT infrastructure, and increased investment in smart agricultural technologies to strengthen technology integration in Agricultural Education programmes.","摘要 本研究确定了数字能力作为尼日利亚南南地区教育学院农业教育讲师使用智能农业技术的预测因素。具体而言，本研究确定了数字能力的水平，识别了智能农业技术的可用性及其使用程度，考察了影响技术使用的因素，并确定了数字能力与技术使用之间的关系。本研究采用描述性调查研究设计。研究总体包括尼日利亚南南地区教育学院的所有农业教育讲师，并使用220名讲师的示例样本进行演示。数据通过数字能力与智能农业技术使用问卷（DCUSATQ）收集。该工具由农业教育、教育技术以及测量与评价领域的专家进行了效度检验，数字能力、技术使用和影响因素量表的Cronbach's Alpha信度系数分别为0.87、0.84和0.81，总体信度系数为0.86。数据采用频数、百分比、均值、标准差、Pearson积矩相关和多元回归进行分析，显著性水平为0.05。研究结果显示，农业教育讲师具有较高水平的数字能力。智能农业技术的使用程度为中等，其中在线农业数据库和移动农业应用程序使用最为频繁，而无人机技术和精准农业工具的使用相对较低。机构支持、数字基础设施、互联网连接和培训机会被确定为影响技术使用的主要因素。研究进一步揭示，数字能力与智能农业技术使用之间存在显著正相关关系。多元回归分析表明，数字能力显著预测智能农业技术使用，而数字能力、机构支持、信息通信技术基础设施和培训经历的联合影响解释了技术使用的变异。基于研究结果，建议应持续开展数字农业专业发展，改善机构",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-09-15T00:00:00Z","论文",25,false,59,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,18,14,10,9,1,"尼日利亚农业教育讲师数字素养与智慧农业技术采纳的实证研究，样本220人、信度0.86，结论有参考价值但属区域性调查，国际借鉴意义有限。",[25,26],{"name":10,"url":6},{"name":10,"url":27},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22762487",[29,30,31,32,33],"智慧农业","数字素养","农业无人机","农业教育","尼日利亚",0,"10.5281\u002Fzenodo.22762486",{"doi":35,"openalex_id":37,"authors":38,"venue":10,"cited_by_count":34,"oa_url":6,"card":41,"direction":47,"ingested_from":48},"W7213296064",[39],{"name":40,"orcid":9},"Chijioke-Eke Joy Ndidi",{"tldr":42,"method":43,"finding":44,"direction":45,"opportunity":46},"研究尼日利亚农业教育讲师数字能力对智慧农业技术使用的影响。","描述性调查，220名讲师问卷，相关与多元回归分析。","数字能力高但智慧农业技术使用中等，数字能力显著正向预测技术使用。","数字乡村与农业信息化","可延伸至教师数字能力培训干预与智慧农业技术采纳的因果机制研究。","智慧农业 \u002F 农业物联网","openalex","2026-09-16T23:30:11.968810Z"]