Tracking Survey Dataset on the Mechanism of Inclusive Leadership's Impact on Employee Innovation in Chinese Workplaces (2024)

This dataset is derived from a "three-stage tracking+national random sampling" questionnaire survey completed in June 2024, with the research subjects being on-the-job employees of various industries and enterprises in China. All three questionnaires were targeted and pushed through the "Wenjuanxing" enterprise version, with a four week interval between each stage: T1 collected inclusive leadership and demographic control variables (620 distributed and 598 collected); T2 collected a sense of professional mission and work reflection (562 responses were collected); T3 collected innovative behaviors from employees (519 were collected). After three rounds of matching, excluding questionnaires with a filling time of less than 120 seconds, 10 consecutive questions with the same score, and logical contradictions, 519 valid tracking samples were finally obtained, with an effective retention rate of 83.7%. The investigation did not use specialized experimental equipment and relied solely on self-assessment by the subjects. The questionnaire includes 6 demographic variables (gender, age, education, work experience, nature of the organization, industry), 9 inclusive leadership items (x1 – x9), 4 career mission items (m11 – m14), 12 work reflection items (m21 – m212), and 6 employee innovation behavior items (y1 – y6), all using a Likert 5-point scale (1=strongly disagree, 5=strongly agree), without measurement units. It should be noted that this data still belongs to the cross-sectional merging form and cannot dynamically reflect the evolution of variables over time; All items are employee self-assessment and may be influenced by social expectations and personality traits; The sample has a relatively high proportion among young employees, flat and open culture enterprises, and there are limitations in industry and group coverage. Subsequent research can further enhance the robustness and external validity of the model by combining empirical sampling, leader employee pairing data, and moderating variables such as Big Five personality traits. This document is the final data table after cleaning, which can be directly used for analysis by mainstream statistical software such as SPSS and Mplus.