评估因素与模拟创新:中国数据科学专业人才创新能力研究

Yongfeng Zhang
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引用次数: 1

摘要

本研究旨在分析影响中国数据科学专业人员创新能力的多方面因素,并评估模拟对其创新技能的影响。样本由17位专家组成,他们积极参与讨论,并就影响其创新能力的因素提供了36个观点。研究方法采用德尔菲法,对363名数据科学专业人员进行四轮问卷调查,评估影响其创新能力的因素。使用数理统计和SPSS对数据进行了严格的分析,并强调了问卷的效度和信度。在信度分析中,Cronbach's α为0.98,表明内部一致性较高。研究结果的平均得分为4.79,SD = 0.39, IQR = 1,反映了专家对研究结果的强烈共识。采用探索性因子分析进行效度评估,发现第12个因子的累积方差解释率为76.54%,超过60%的阈值,表明问卷数据具有稳健的结构效度。本研究还利用AMOS软件模拟样本数据,评估个体、组织和家庭特征对创新能力的影响系数,结果分别为0.53、0.39和0.22,均大于0,表明影响关系良好。在此基础上,提出了中国数据科学专业人员创新能力的综合模型。本研究批判性地考察了中国学术界数据科学专业人员的创新潜力,其总体目标是提高他们在数据科学领域的创新技能和竞争力。此外,它还为促进大学内部的创新奠定了理论基础。
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Assessing Factors and Simulating Innovation: A Study of Innovative Capacities Among Data Science Professionals in China
This study aims to analyze the multifaceted factors influencing the innovative capabilities of data science professionals in China and assess the impact of simulations on their innovative skills. The sample comprises seventeen experts who actively participated in discussions and provided 36 perspectives on the factors affecting their innovation abilities. The research methodology utilized the Delphi method, involving four rounds of questionnaires distributed to 363 data science professionals to evaluate the factors affecting their innovation capacity. The data was rigorously analyzed using mathematical statistics and SPSS, with a strong emphasis on questionnaire validity and reliability. In the reliability analysis, Cronbach's α was found to be 0.98, indicating a high level of internal consistency. The research results yielded an average score of 4.79, SD = 0.39, IQR = 1, reflecting a strong consensus among experts in agreement with the research findings. Exploratory factor analysis was employed for validity assessment, revealing that the 12th factor accounted for a cumulative variance explanation rate of 76.54%, exceeding the threshold of 60%, signifying the robust structural validity of the questionnaire data. The study also utilized AMOS software to simulate sample data and assess the influence coefficients of individual, organizational, and family characteristics on innovation capacity, resulting in values of 0.53, 0.39, and 0.22, respectively, all greater than 0, indicating favorable influence relationships. Building upon these findings, a comprehensive model of creativity abilities among Chinese data science professionals is proposed. This research critically examines the innovation potential of data science professionals in Chinese academia, with the overarching goal of enhancing their creative skills and competitiveness within the data science field. Additionally, it lays the theoretical groundwork for fostering innovation within the university setting.
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