Reliability analysis

The reliability analysis of the questionnaire aims to measure the reliability of the sample data, that is, whether the respondents truthfully answer the items in the questionnaire. Cronbach's Alpha test is a commonly used method. If Cronbach's Alpha coefficient exceeds 0.7, the scale has good reliability, and the closer it is to 1, the better the reliability of the scale. The reliability analysis of the formal survey in this study is shown in Table 1:

Table 1 Reliability Test of the Scale

The scale of this study includes a total of 9 latent variables, as shown in Table 1. The Cronbach's alpha coefficient for job success is 0.931, for interpersonal success is 0.836, for financial success is 0.798, for hierarchical success is 0.867, for extraversion is 0.905, for conscientiousness is 0.907, for agreeableness is 0.775, for emotional stability is 0.824, and for openness is 0.750. It can be seen that the Cronbach's alpha coefficients for each latent variable exceed 0.7, indicating that the questionnaire scale of this study has good reliability.

The total correlation of the corrected items, namely CITC value, is used to describe the correlation between each item and other items. As shown in Table 1, the CITC values between each latent variable and each observed variable are all greater than 0.4, indicating a high correlation between the items of each latent variable, suggesting that the items of each latent variable are well set.

In addition, as can be seen from the "cloned Bach coefficient after item deletion" of each observed variable in Table 1, deleting any item in the questionnaire will not increase the overall cloned Bach coefficient, indicating that each item is reasonably set.

Validity Analysis

Validity refers to effectiveness, which measures whether the comprehensive evaluation system can accurately reflect the evaluation purposes and requirements. The use of KMO value expresses the magnitude of validity. The closer KMO is to 1, the stronger the correlation between variables, indicating the presence of interactions and influences among variables, making the original variables more suitable for factor analysis. Conversely, when it approaches 0, the correlation between variables gradually weakens, reducing the possibility of factor analysis, and further studying the relationship between variables becomes less meaningful. Bartlett's sphericity test is used to test the independence of variables, with a large value. When the p-value of Bartlett's sphericity test is less than 0.05, it indicates that the variables are correlated with each other, rejecting the null hypothesis, and the data is spherically distributed, allowing the research to continue with factor analysis.

Next, the validity of the nine factors of work success, interpersonal success, financial success, hierarchical success, extraversion, conscientiousness, agreeableness, emotional stability, and openness, as well as the overall scale, will be tested. The results of the single-factor validity test are shown in Table 2:

Table 2 KMO and Bartlett test table

As shown in Table 2, the KMO values of the 4 variables representing occupational success are all greater than 0.7, and Bartlett's sphericity test is less than 0.05. The variables represented by the Big Five personality traits have a KMO value of 0.5 due to only having two items, but Bartlett's sphericity test is still less than 0.05, making it suitable for factor analysis. The overall validity test results are shown in Table 3:

Table 3 Overall KMO and Bartlett test of the questionnaire

From Table 3, it can be seen that the KMO of the survey questionnaire is greater than 0.7, and it has passed the Bartlett sphericity test at a significance level of 0.05. Therefore, the data of the survey questionnaire has passed the validity test, indicating that the survey questionnaire data used in this study can represent the variables used in the subsequent research.

Exploratory Factor Analysis

Exploratory factor analysis refers to extracting the overlapping information in the original measurement items as factors in a condensed questionnaire, aiming to reduce the number of questionnaire items. Key indicators in exploratory factor analysis include factor loading coefficients, communality (common factor variance), eigenvalues (before and after rotation), variance explained % (before and after rotation), and cumulative variance explained % (before and after rotation). Factor loading coefficients measure the degree of correlation between variables and their latent factors, with coefficients typically greater than 0.6 considered to be meaningful. Communality reflects the proportion of variance in variables explained by all factors, with variables having communality greater than 0.4 suitable for inclusion in the analysis. Eigenvalues represent the amount of variance in variables that factors can explain, with factors having eigenvalues greater than 1 considered worth retaining. Variance explained % shows the percentage of total variance explained by each factor, aiming for a higher cumulative variance explained %, typically aiming for over 60% to ensure the model's explanatory power. In conducting exploratory factor analysis, this study used the principal component analysis method, considering eigenvalues greater than 1 as factors, extracting common factors. When rotating factors, orthogonal varimax rotation was used for factor analysis.

Table 4 Factor Variance Explained Rate

As shown in Table 4, based on the criterion of eigenvalues greater than 1, factor analysis extracted a total of 9 factors, which together explained 76.684% of the total variance, exceeding 60%, indicating that the selected factors are highly representative.

Next, let's look at the factor loading matrix

Table 5 Factor Loading Coefficient Table

From Table 5, it can be seen that the common factor variances corresponding to all measurement indicators are higher than 0.4, indicating a strong correlation between the measurement indicators and the factors, and the factors can effectively extract information. The factor loading values of each measurement indicator within the same factor in the scale are all greater than 0.6, and no case was found where an indicator had a cross-factor loading exceeding 0.4. Therefore, the 29 measurement indicators can be well distributed among the various factors.

The scree plot is a graphical representation of the results of exploratory factor analysis, which arranges the eigenvalues of each factor by factor number to help observe the decreasing trend of eigenvalues. Factors typically considered meaningful are those before the point where the eigenvalues start to decrease more slowly, as they contribute to a larger variance explanation. By identifying this turning point, the scree plot can intuitively show the number of factors that need to be retained.

Figure 1 Gravel Image

From the scatter plot, it can be seen that the characteristic value curve becomes smooth starting from the 10th point, indicating once again that taking the first 9 factors is appropriate.

Descriptive Analysis

Basic Information Description Statistics

In this study, if the respondent's answer time is less than 3 minutes or if the questionnaire filled out by the respondent has too many identical options, the questionnaire is considered invalid. After screening out all valid questionnaires, the basic information of the questionnaire fillers is shown in Table 6:

Table 6 Basic Information of Valid Samples

According to the above table, the following analysis can be made: Based on the statistics of the basic information of the samples, it can be seen that all the respondents participating in this survey are over 18 years old. The frequency data shows that out of the respondents who answered the question "Are you over 18 years old?", 127 people chose "Yes", accounting for 100%, while 0 people chose "No", accounting for 0%. This indicates that all the samples in this survey come from the adult population, with no minors participating. This result ensures the legality and reliability of the survey data, as many surveys and studies require participants to be adults to ensure they have sufficient judgment and independent opinions.

Descriptive statistics of the measurement project

Next, in-depth statistical analysis of the measurement indicators for each factor will be conducted.

Table 7 Basic Indicators for Work Success Statistics

Table 8 In-depth Indicators for Work Success Statistics

According to the above table, the following analysis can be conducted. From the "Basic Indicators of Work Success Statistics" table, it can be seen that the sample size for all eight indicators is 127, indicating that the data collection is somewhat representative and consistent. The minimum and maximum values for each indicator are all between 1 and 5, showing that the responses from survey participants cover the entire range of ratings from lowest to highest. Looking at the median values of these indicators, most of them are 3.00, indicating that for the majority of respondents, the perception is at a moderate level for these indicators. However, the median values for "Fully supported by management" and "Feeling happiest at work" are 2.00, implying some deficiencies in these aspects. Further analysis of the "In-depth Indicators of Work Success Statistics" table reveals that the mean values for each indicator are between 2.60 and 2.75, clearly indicating that respondents perceive different aspects of their work success to be at a medium to lower level. The standard deviations for these indicators range from 1.099 to 1.239, showing that the data distribution is relatively consistent but also reflects some differences among individuals. The skewness values for each indicator are close to zero, indicating a relatively symmetrical data distribution with slight negative skewness, especially in the "Committed to my work" item, showing a higher frequency of low ratings. Additionally, the kurtosis values are negative, particularly significant for certain indicators, indicating a relatively flat data distribution without distinct peaks, suggesting no concentration of extreme high or low ratings. The coefficient of variation (CV) ranges from 0.406 to 0.470, showing a relatively high degree of variability in the data for each indicator, but overall remaining within a reasonable range. The interquartile range (IQR) is 2.00 for all indicators, indicating that 50% of the data is concentrated within a relatively narrow range, supporting the consistency of the data. These results collectively indicate that for the respondents, their evaluations in aspects such as "Receiving positive feedback from all sources," "Having opportunities for further education," "Having sufficient work responsibilities," "Fully supported by management," "Having opportunities to learn new skills," "Feeling happiest at work," "Being committed to work," and "Doing work they truly enjoy" are generally consistent and perceived as average overall. However, there is significant room for improvement in the areas of "Fully supported by management" and "Feeling happiest at work," and related companies and organizations may focus on these aspects for effective enhancement and improvement. In summary, these analytical results can provide relevant departments with some data support and decision-making basis for enhancing employees' sense of work success and happiness.

Table 9 Basic Indicators of Interpersonal Success Statistics

Table 10 In-depth Indicators of Interpersonal Success Statistics

According to the above table, the following analysis can be conducted. From the "Interpersonal Success Basic Indicators" table, it can be seen that the sample size for all four indicators ("Respected by colleagues", "Received good performance evaluations", "Accepted by colleagues", "Earned trust from superiors") is 127, indicating consistency and wide coverage of data. The minimum and maximum values for each indicator are all 1.00 and 5.00, indicating that the survey results cover the full range of ratings from lowest to highest. Additionally, the median for each indicator is 3.00, indicating that the ratings for these indicators are at a moderate level without significant bias, meaning that most respondents consider themselves to be at an average level of interpersonal success. Further observation of the "Interpersonal Success In-depth Indicators" table reveals that the mean for each indicator is slightly lower than the median of 3.00, but close to around 2.80, showing that respondents generally perceive themselves to be slightly below average in terms of being respected by colleagues, receiving good performance evaluations, being accepted by colleagues, and earning trust from superiors. The differences between these means are not significant, ranging from 2.724 to 2.819, indicating a relatively consistent evaluation of the indicators by respondents. In terms of standard deviation, the values for the four indicators range from 1.084 to 1.159, indicating a relatively consistent level of data dispersion, but also showing that there are some differences in respondents' abilities and perceptions of interpersonal success. The skewness values are close to zero, with slight positive or negative differences, namely 0.029, -0.016, 0.019, and -0.122, indicating that most data are symmetrically distributed, with a few showing slight left or right skew. The kurtosis values are all negative, especially for "Respected by colleagues" (-1.196) and "Earned trust from superiors" (-1.167), indicating a relatively flat data distribution without significant peaks, meaning that respondents' responses are relatively balanced and not concentrated on a single score point. The coefficient of variation (CV) ranges from 0.387 to 0.426, reflecting a moderate level of data dispersion within a reasonable range. The interquartile range (IQR) is 2.00 for all indicators, indicating that 50% of the data is concentrated within a relatively narrow range, reflecting high data consistency. In summary, these indicators suggest that in terms of interpersonal success being evaluated, respondents generally perceive themselves to have a certain level of average ability, although there are also some differences and breadth of distribution. The data analysis results support further exploration of how to enhance individuals' interpersonal success in the workplace, especially in terms of interactions with colleagues and superiors and receiving performance evaluations. These analytical results can provide a basis for relevant professional development training and organizational management measures.

Table 11 Financial Success Statistics Basic Indicators

Table 12 Financial Success Statistics Detailed Indicators

According to the above table, the following analysis can be conducted. From the "Financial Success Basic Statistics Index" table, it can be seen that there are three indicators, each with a sample size of 127 people, demonstrating the breadth and consistency of data collection. The minimum and maximum values for each indicator are both 1 and 5, indicating that the survey results cover the entire range of ratings from lowest to highest, with median values of 3.00 for each indicator. This suggests that when evaluating their financial success, respondents tend to rate themselves at a moderate level, without significant skew. This indicates that most people rate their financial success, such as fair compensation compared to peers, high income, and the match between income and job value, as relatively average. Further observation of the "Financial Success Detailed Statistics Index" table shows that the mean values for the three indicators are 2.724, 2.677, and 2.646, indicating that most respondents consider themselves to be at a slightly below average level on the above financial success indicators. The standard deviations are 1.125, 1.090, and 1.151, indicating that there is some variation in how different respondents evaluate these indicators, with a moderate level of data dispersion. In terms of skewness, the first two indicators are -0.185 and 0.039, slightly close to zero, indicating a relatively balanced data distribution, while the skewness of the third indicator is 0.128, also close to zero, but showing that a very small number of people rate slightly higher than the mean. The kurtosis values are negative, -1.263, -0.998, and -1.001, indicating a relatively flat data distribution without significant peaks, suggesting that the ratings of different respondents are dispersed and not concentrated in a specific range. The coefficients of variation (CV) are 0.413, 0.407, and 0.435, reflecting a relatively low level of data dispersion and variation within a reasonable range. The interquartile ranges (IQR) are all 2.000, indicating that the middle 50% of the data is concentrated in a narrow range, showing a certain degree of data consistency. Overall, these indicators suggest that respondents generally have varying degrees of distribution in various aspects of self-assessed financial success, but most people consider their financial success to be at a moderate or slightly below average level. These results indicate that in terms of financial compensation, income, and self-worth matching, respondents' evaluations are relatively balanced, but there is also a certain breadth of distribution. Therefore, these analytical results can provide important basis for further exploration on how to improve employee compensation and benefits, and increase job satisfaction.

Table 13 Basic Statistics of Successful Leveling

Table 14 In-depth Index of Successful Statistics by Level

According to the above table, the following analysis can be conducted. From the "Hierarchy Success Statistics Basic Indicators" table, it can be seen that the sample size for all four indicators is 127, showing consistency in data collection. The minimum value for each indicator is 1, and the maximum value is 5, indicating that the survey results cover the entire range of ratings from lowest to highest. However, there are some differences in the median values. For the first two indicators, "Satisfaction with current promotions received" and "Achieving career goals within self-set time," the median is 3.000, indicating that most respondents rate these aspects at a moderate level. In contrast, the median for the indicators "Ability to achieve all career goals" and "Being in a job that provides promotion opportunities" is 2.000, indicating lower ratings from most respondents in these two aspects. Further observation of the "Hierarchy Success Statistics In-depth Indicators" table shows slight differences in the mean values of the four indicators. The mean values for "Satisfaction with current promotions received" and "Achieving career goals within self-set time" are 2.669 and 2.661, respectively, close to the median level. The mean values for "Ability to achieve all career goals" and "Being in a job that provides promotion opportunities" are 2.661 and 2.559, both below 3.000, indicating lower satisfaction from respondents in the latter two indicators. The standard deviations are close for each indicator, ranging from 1.135 to 1.206, indicating a consistent level of data distribution dispersion and reflecting some differences in respondents' opinions in these aspects. The skewness values for each indicator are close to zero, with both positive and negative values, indicating a generally symmetric data distribution with slight deviations. For example, the skewness for "Satisfaction with current promotions received" is -0.003, indicating an almost symmetric data distribution, while the skewness for "Ability to achieve all career goals" and "Being in a job that provides promotion opportunities" are 0.203 and 0.218, showing a slight right skew, meaning more people self-rate above the mean. The kurtosis values are all negative, ranging from -1.149 to -1.001, indicating a relatively flat data distribution without significant peaks. The coefficient of variation (CV) ranges from 0.427 to 0.471, reflecting a relatively low level of data dispersion within a reasonable range. The interquartile range (IQR) is 2.000 for all, indicating that the middle 50% of the data is concentrated within a narrow range, reflecting a certain degree of data consistency. Overall, these indicators suggest that respondents have varying evaluations of their career development, especially in terms of the ability to achieve all career goals and being in a job that provides promotion opportunities, where satisfaction levels are relatively low. The data analysis results support further exploration of how to improve employee satisfaction with career development, particularly focusing on strategies to enhance promotion opportunities and help employees achieve all career goals. These analytical results can provide decision-making basis for enterprise human resource management, aiming to enhance employee satisfaction with career development.

Table 15 Basic Indicators of Export Orientation

Table 16 In-depth Indicators of Export Orientation

According to the above table, the following analysis can be conducted. From the "Extraversion Statistical Basic Indicators" table, it can be seen that the sample sizes for both "I see myself as Extraverted, enthusiastic" and "I see myself as Reserved, quiet" indicators are 127, showing the breadth and consistency of data collection. The minimum and maximum values for these two indicators are 1 and 7 respectively, indicating that the survey results cover the entire range of scores from lowest to highest, with median values of 2.000 for both, suggesting that respondents rated themselves lower on extraversion traits with no extreme skew. Further observation of the "Extraversion Statistical In-depth Indicators" table shows that the mean values for "I see myself as Extraverted, enthusiastic" and "I see myself as Reserved, quiet" are 2.992 and 2.945 respectively, indicating that respondents generally rate themselves slightly below the median on extraversion traits. The standard deviations for both indicators are close, at 1.771 and 1.805 respectively, indicating a relatively consistent level of data dispersion, and the higher standard deviations also suggest significant differences in self-ratings on these traits among respondents. The skewness values are 0.831 and 0.906 respectively, indicating a right-skewed data distribution, with most people rating themselves below the average but with some high self-raters. The kurtosis values are -0.446 and -0.366, indicating a relatively flat data distribution without clear peaks. The coefficients of variation (CV) are 0.592 and 0.613 respectively, reflecting a moderate level of data dispersion within a reasonable range. The interquartile ranges (IQR) are both 2.000, indicating that the middle 50% of the data is concentrated within a narrow range, reflecting a certain degree of data consistency. In summary, these indicators suggest that most respondents generally rate themselves lower on extraversion traits, despite the presence of individual differences and a broad data distribution. The above data analysis can provide a deeper understanding of individual extraversion traits and provide data support for related psychological research and personality analysis.

Table 17 Basic Indicators of Accountability

Table 18 In-depth Indicators of Accountability Statistics

According to the above table, the following analysis can be conducted. From the "Responsibility Statistical Basic Indicators" table, it can be seen that both self-assessment questions describing responsibility have a sample size of 127, indicating that the data collection is sufficiently representative and consistent. The scoring range for the two questions is from 1 to 7, covering all ratings from completely disagree to completely agree. The median scores for these two questions are 3.00 and 2.00 respectively, showing that respondents rate themselves slightly higher in "self-discipline, reliability" compared to "disorganized, careless", implying the overall self-assessment of respondents in these two aspects. Further observation of the "Responsibility Statistical In-depth Indicators" table reveals that the mean for "I see myself as Dependable, self-disciplined" is 3.142, while the mean for "I see myself as Disorganized, careless" is 2.992. Although the difference is small, it still indicates that respondents slightly tend to perceive themselves as disciplined and reliable in these two aspects. The standard deviations for these two indicators are 1.798 and 1.739 respectively, indicating a certain level of variance in respondents' self-assessment of their responsibility traits. The skewness values are 0.699 and 0.776, these positive values suggest a slight right skew in the self-assessment in both aspects, meaning that most respondents rate their responsibility traits slightly higher than the average. The kurtosis values are -0.625 and -0.516, showing a relatively flat data distribution without clear peaks. The coefficients of variation (cv) are 0.572 and 0.581, these values are small, reflecting a low degree of variability in the data and relative stability in the self-assessment of responsibility traits. The interquartile ranges (IQR) are both 2.000, indicating that the middle 50% of the data is concentrated in a narrower rating range, further supporting the trend of data concentration. Overall, respondents' self-assessment of responsibility traits shows a certain level of consistency and breadth, but individual differences still exist. These analytical results can provide a basis for further exploration on how to enhance individual self-discipline and reliability, and develop relevant educational and intervention measures.

Table 19 Basic Indicators of Pleasantness Statistics

Table 20 In-depth Indicators of Pleasantness Statistics

According to the above table, the following analysis can be conducted. From the "agreeableness statistical basic indicators" table, it can be seen that the sample size for both indicators is 127, showing strong consistency in data collection. The minimum and maximum values for each indicator are 1 and 7, respectively, indicating that the survey results cover the complete range of scores from lowest to highest without any scoring limitations. The median for both indicators is 3.00, indicating that respondents' self-assessment in the emotions of "critical, argumentative" and "sympathetic, warm" tends towards a moderate level, without any clear bias. Further observation of the "agreeableness statistical in-depth indicators" table shows that the mean for "critical, argumentative" is 3.323, and for "sympathetic, warm" is 3.378, indicating that respondents' self-assessment in these two aspects is at a relatively neutral level, slightly leaning towards the positive end. The standard deviations for these two indicators are 1.628 and 1.790, respectively, indicating a relatively dispersed data distribution, suggesting significant differences among respondents in these two self-assessment aspects. The skewness values for both indicators are positive, at 0.811 and 0.755, indicating a slight right skew, meaning more people rate themselves higher than the mean. Meanwhile, the kurtosis values are -0.101 and -0.492, with negative values indicating a relatively flat data distribution without clear peaks. The coefficients of variation (CV) are 0.490 and 0.530, reflecting a moderate level of data dispersion, within a reasonable range. The interquartile ranges (IQR) are both 2.000, indicating that the middle 50% of the data is concentrated within a narrow range, reflecting a certain degree of consistency and concentration. Overall, in terms of agreeableness, the majority of respondents rate their traits at a slightly above average level, but there are also some differences and breadth in distribution. The data analysis results support further exploration of how to improve consistency and accuracy in individual trait assessments. These analytical results can provide a scientific basis for psychological assessment, personality research, and related intervention measures.

Table 21 Basic Statistics of Emotional Stability

Table 22 In-depth Statistics of Emotional Stability Indicators

According to the above table, the following analysis can be conducted. In terms of the "Emotional Stability Statistical Basic Indicators," the sample sizes for the two indicators "I see myself as Anxious, easily upset." and "I see myself as Calm, emotionally stable." are both 127, indicating that the data collection is extensive and consistent. The minimum and maximum values for both of these indicators are 1 to 7, indicating that the survey results cover the entire range of scores from lowest to highest. The median for both is 3.000, indicating that the respondents tend towards a moderate level in terms of emotional instability and stability, without extreme skewness. Further observation of the "Emotional Stability Statistical In-depth Indicators" table shows that the mean for "I see myself as Anxious, easily upset." is 3.260, while the mean for "I see myself as Calm, emotionally stable." is 3.205, indicating that respondents' average self-assessment in these two emotional states is almost equal, slightly leaning towards the upper middle range. This suggests that overall, respondents are neither overly anxious nor overly emotionally stable. The standard deviations are 1.639 and 1.687 respectively, indicating a relatively consistent level of data dispersion, but the standard deviation for "Calm, emotionally stable" is slightly higher, showing a slightly greater variation in emotional stability. The skewness values are 0.793 and 0.659 respectively, both positive, indicating a slight right skew in the distribution of self-assessment data for these two emotional states, meaning more people self-assess below the mean. The kurtosis values are -0.152 and -0.534 respectively, both negative, indicating a relatively flat data distribution without clear peaks. The coefficients of variation (cv) are 0.503 and 0.526 respectively, reflecting that the relative dispersion of the two indicators is not high and is within a reasonable range. The interquartile ranges (IQR) are both 2.000, indicating that the data is concentrated within a narrow range, reflecting data consistency. In summary, these indicators suggest that in terms of emotional stability, most respondents consider themselves to be neither overly anxious nor overly stable, but there are also some differences. Based on the data analysis results, these findings can provide a basis for further psychological health intervention measures. For example, specialized psychological counseling can be provided for individuals who lean towards the right, to help them better regulate their emotions.

Table 23 Basic Openness Statistics Indicators

Table 24 In-depth Indicators of Openness Statistics

According to the above table, the following analysis can be conducted. From the "Openness Statistical Basic Indicators" table, it can be seen that the sample sizes for the indicators "I see myself as Open to new experiences, complex" and "I see myself as Conventional, uncreative" are both 127, indicating that the data collection has a certain sample size guarantee, providing reliable data support for analysis. The minimum and maximum values for both indicators are 1 and 7, indicating that the questionnaire results cover the full range of the evaluation scale, with respondents' self-descriptions distributed from the lowest to the highest. The median for both indicators is 3.000, showing that on the dimensions of openness and self-assessment, respondents' median values are at a moderate level, indicating that most people's self-assessment is concentrated at a moderate level. Further observation of the "Openness Statistical In-depth Indicators" table shows that the mean values for "I see myself as Open to new experiences, complex" and "I see myself as Conventional, uncreative" are 3.551 and 3.575 respectively, indicating that in the sample, respondents generally perceive themselves as slightly more open to new experiences and slightly more conventional and uncreative than the average. The standard deviations are 1.418 and 1.551 respectively, indicating that there is a certain difference among respondents in these two self-assessments, especially in the self-assessment of "Conventional, uncreative". The skewness values are 0.615 and 0.649 respectively, with positive skewness indicating a slight rightward skew in the data distribution, meaning that more people rate themselves below the median. The kurtosis values are -0.236 and -0.189 respectively, with negative kurtosis indicating a relatively flat data distribution without clear peaks. The coefficients of variation (CV) are 0.399 and 0.434 respectively, indicating that the relative dispersion of the data is similar and within a reasonable range. The interquartile ranges (IQR) are 1.000 and 2.000 respectively, indicating that the distribution range of the middle 50% of the data on openness and innovation self-assessment differs, with the data on openness to new experiences being more concentrated and the distribution range for traditional, uncreative being wider. Overall, the data suggests that there is a certain diversity in respondents' self-awareness in terms of openness, with most people considering themselves to have moderate to slightly high levels of openness and conventional attributes. In addition, there are significant differences among respondents on the dimensions of self-assessment of openness to new experiences and traditional, uncreative, providing a reference for further research on individual openness traits and how to enhance innovation capabilities. These analytical results can also provide empirical evidence for related psychological and behavioral science research and intervention measures.

Relevance Analysis

Relevance analysis refers to a statistical method that studies the close relationship and degree of correlation between variables in research. It is mainly used to verify whether there is a correlated relationship among several variable factors in the research hypothesis and the degree of this relationship. This analysis adopts the Pearson correlation coefficient. When the correlation coefficient r is 0, it means there is no correlation between the two; when |r| is in the range of 0-0.3, it indicates a weak correlation between the two; when |r| is between 0.3-0.5, it means there is a low correlation between the two; when |r| is between 0.5-0.8, it indicates a significant correlation between the two; when |r| is between 0.8-1, it means there is a very high correlation between the two; when |r|=2, it means the two are in a completely correlated relationship. For details of the relevance analysis in this article, please refer to Table 25.

Table 25 Correlation Analysis Table

According to the analysis of the relevance matrix, there is a significant positive correlation (p < 0.01) among the dimensions of occupational success (work, interpersonal, financial, hierarchical), with the strongest correlation between financial success and interpersonal success (r = 0.387, p < 0.001). In terms of personality traits, extraversion and conscientiousness show significant positive correlations with all dimensions of occupational success. Extraversion has the highest correlation with work success (r = 0.360, p < 0.001), while conscientiousness has the strongest correlation with interpersonal success (r = 0.398, p < 0.001). Agreeableness is weakly correlated only with financial success (r = 0.193, p < 0.05), while emotional stability and openness show no significant correlations with any dimensions of occupational success. There are also some significant correlations between personality traits, such as the positive correlation between extraversion and conscientiousness (r = 0.251, p < 0.01) and openness (r = 0.246, p < 0.01), as well as the positive correlation between agreeableness and emotional stability (r = 0.232, p < 0.01). These findings highlight the potential importance of extraversion and conscientiousness in predicting occupational success, while also indicating that the interrelationships between personality traits may influence their predictive role in occupational success.

Regression Analysis

The correlation analysis describes whether there is a relationship between the analysis items, while regression analysis explores the impact of independent variables on dependent variables. Through regression analysis, further validation of the research hypotheses and theoretical models of this article can be conducted. The regression model comprehensively considers multiple statistical indicators including unstandardized coefficients, standardized coefficients (Beta), t-values, p-values, and collinearity diagnostic indicators (such as variance inflation factor VIF and tolerance), making it possible to comprehensively evaluate the model's fit and the influence of each explanatory variable. Unstandardized coefficients and their standard errors reveal the direct relationship between independent and dependent variables and the accuracy of the estimates, while standardized coefficients provide dimensionless measures of influence, facilitating comparisons between different studies. The t-value and p-value jointly determine the statistical significance of the coefficients of the independent variables, thereby establishing the significant impact of variables on the dependent variable.

Collinearity diagnosis reflects the strength of the linear relationship between independent variables in the model through VIF and tolerance, assisting in identifying and addressing collinearity issues. The constant term gives the expected value of the dependent variable when all independent variables are zero. R² and adjusted R² values measure the overall explanatory power of the model, while the F-value tests the statistical significance of the entire model.

Next, a regression analysis of extraversion and other variables on career success will be conducted.

Return analysis of career success

Using extraversion, conscientiousness, agreeableness, emotional stability, and openness as independent variables, and career success as the dependent variable, a regression model is constructed as shown in Table 26.

Table 26 Regression Analysis of Variables such as Extraversion and Conscientiousness on Career Success

According to the above table, the model's F value is 10.770, significant at the 95% level, meaning that extraversion, conscientiousness, agreeableness, emotional stability, and openness have at least one independent variable that significantly influences career success, indicating that the model has research significance. The adjusted R-squared value of the model is 0.308, and the adjusted R-squared value is 0.279, indicating a good model fit. The regression coefficient for extraversion is 2.236, for conscientiousness is 2.955, for agreeableness is 0.377, for emotional stability is 0.372, and for openness is 0.540. The formula for this model is: Career Success = 2.236 * Extraversion + 2.955 * Conscientiousness + 0.377 * Agreeableness + 0.372 * Emotional Stability + 0.540 * Openness + 31.194. A test for multicollinearity in the model found that all VIF values are less than 5, indicating no collinearity issues in the model. Therefore, it can be concluded that extraversion and conscientiousness will have a significant positive impact on career success.

Hypothesis testing

Based on the above analysis, the given hypothesis can be verified:

H10: Accepted. The return coefficient of openness to career success did not reach the 95% significance level.

H20: Refusal. The coefficient of conscientiousness is positive (2.955) and reaches a significance level of 95%, indicating that conscientiousness has a significant impact on career success when controlling for other variables.

H30: Refusal. The regression coefficient of extraversion is positive (2.236), and reaches a significance level of 95%, indicating that extraversion has a significant impact on career success when controlling for other variables.

H40: Acceptance. The coefficient of agreeableness on the return to career success did not reach the 95% significance level.

H50: Accepted. The regression coefficient of emotional stability on career success did not reach the 95% significance level.

In summary, under the control of other variables, conscientiousness and extraversion significantly predict the professional success of automotive industry project managers, while the predictive roles of openness, agreeableness, and emotional stability are uncertain. Therefore, hypotheses H10, H40, and H50 are accepted, while hypotheses H20 and H30 are rejected.