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区域协同视角下旅游业数字人才供需匹配研究——基于皮尔逊相关系数的培养模型分析
Research on the Supply-Demand Matching of Digital Talent in Tourism Industry from the Perspective of Regional Coordination—A Training Model Analysis Based on Pearson Correlation Coefficient

DOI: 10.12677/sd.2025.154116, PP. 317-324

Keywords: 长三角旅游业,数字经济人才培养,满意度分析,皮尔逊相关系数
Yangtze River Delta Tourism
, Digital Economy Talent Training, Satisfaction Analysis, Pearson Correlation Coefficient

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Abstract:

本研究基于长三角1052份问卷(有效回收率92.3%),经皮尔逊相关分析发现:高校培养目标清晰度与课程实践性(r = 0.37)、师资复合能力(r = 0.33)及培养路径多样性(r = 0.38)显著正相关。企业培训体系合理性对员工职业认同(r = 0.44)与考核系统效度(r = 0.46)有正向影响。数据揭示了结构性矛盾:高校仅27%的课程融入了前沿技术,仅15%的中小企业建立了系统培训机制。为此,研究构建六维协同模型,涵盖文旅场景导向目标、真实项目课程等,要素间相关系数均>0.3 (P < 0.01),可形成网络增效。此外,企业战略延续性关联职业发展契合度(r = 0.34)与专家适配性(r = 0.42)相关;员工满意度与考核合理性(r = 0.38)及指标科学性(r = 0.46)强相关。因此,亟需搭建“目标校准–资源融通–教学迭代–数据反馈”的动态育人生态。
Based on 1,052 questionnaires from the Yangtze River Delta region (with an effective recovery rate of 92.3%), this study found through Pearson correlation analysis that the clarity of university training objectives is significantly positively correlated with the practicality of courses (r = 0.37), the compound ability of teachers (r = 0.33), and the diversity of training paths (r = 0.38). The rationality of the enterprise training system has a positive impact on employees’ professional identity (r = 0.44) and the validity of the assessment system (r = 0.46). The data reveals structural contradictions: only 27% of university courses incorporate cutting-edge technologies, and only 15% of small and medium-sized enterprises have established systematic training mechanisms. Therefore, this study constructs a six-dimensional collaborative model covering cultural tourism scene-oriented goals, real project courses, etc. The correlation coefficients between the elements are all >0.3 (P < 0.01), which can form a network synergy effect. In addition, the continuity of enterprise strategy is related to the fit of career development (r = 0.34) and the adaptability of experts (r = 0.42). Employee satisfaction is strongly correlated with the rationality of assessment (r = 0.38) and the scientific nature of indicators (r = 0.46). Therefore, it is urgent to build a dynamic education ecosystem of “goal calibration-resource integration-teaching iteration-data feedback”.

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