Comparison of factors affecting turnover intention by job field in biopharmaceutical industry in Korea: A questionnaire-based study

Comparison of factors affecting turnover intention by job field in biopharmaceutical industry in Korea: A questionnaire-based study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comparison of factors affecting turnover intention by job field in biopharmaceutical industry in Korea: A questionnaire-based study Ae-Ryeo Cho, Jungtae Park, Sun-Hong Kwon, Jeong-min Choi, Wonsang Robert Yu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3608647/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Excessive turnover in the biopharmaceutical industry can negatively impact public health and corporate management. This study aims to determine the difference in turnover intention by job field and compare the factors affecting it. Methods An online self-report survey was administered to employees working in the production, sales/marketing, and clinical/regulatory affairs fields of biopharmaceutical companies in Korea from September 1 to October 31, 2020. The questionnaire addressed sociodemographic, constructs but also job, organization, and personal-related factors, as well as turnover intention. The difference in turnover intention by job field was confirmed by using the analysis of variance test. Multivariate regression analysis was performed to identify the factors affecting turnover intention by job field. Results A total of 529 employees responded to the questionnaire, and 500 cases were analyzed after discarding 29 cases with missing data. Turnover intention differed according to job field (p < 0.001), and production was the highest. In the production field, the higher both the satisfaction with the supervisor (β = -0.326, p-value = 0.005), the lower the turnover intention. Greater satisfaction with the work scope (β = -0.181, p-value = 0.01), salary (β = -0.169, p-value = 0.005) and corporate culture (β = -0.314, p < 0.001) factors showed low turnover intention for sales/marketing field, and the higher the satisfaction with the work scope (β = -0.350, p-value = 0.035), the lower the turnover intention for clinical/regulatory affairs field. Conclusions To reduce the turnover rate in the biopharmaceutical industry, it is necessary to develop policies that align with the unique needs of each job field. Companies should focus on increasing satisfaction with their supervisor for production field, and work scope for sales/marketing and clinical/regulatory affairs fields. Additionally, salary and corporate culture are important factors for sales/marketing field. Turnover Turnover intention Biopharmaceutical company Job category Factors analysis Background The pharmaceutical industry has been shifting its focus globally from synthetic drug-oriented structures to biopharmaceuticals that process drugs using biotechnology-applied and biological raw materials ( 1 , 2 ). As the biopharmaceutical industry has rapidly grown as a major part of the health field concerned with disease treatment and industrial aspects, the demand for human resources is also sharply increasing in Korea and worldwide ( 3 ). However, there is a severe shortage of manpower in the fields of development, production, licensing, and marketing for biologics because they require high-level expertise and experience compared to chemical drugs. Employee turnover is a major cause of shortages in the health sector ( 4 , 5 ). Regarding pharmaceutical companies, losing employees is tantamount to not only losing knowledge and experience but also dampening staff morale and health, while increasing organizational costs ( 6 ). In other words, the loss of good employees not only engenders a loss in productivity and revenue but also brings about high costs with regard to staff replacement ( 7 ). From a social perspective, the biopharmaceutical industry, which develops, produces, and sells drugs, is important for public health ( 8 ). In particular, supplying people with new drugs to quickly overcome the COVID-19 pandemic has been the most decisive strategy for humans ( 9 ). Therefore, securing and maintaining a professional workforce in the biopharmaceutical industry is becoming increasingly important. Many studies have been conducted on turnover intentions in the pharmaceutical industry. Among them, several studies ( 10 – 12 ) have identified the factors that affect employees’ turnover intention based on job or pharmaceutical company types. However, no study has targeted turnover intention in pharmaceutical companies only. Moreover, no study that subdivides turnover intention by job category has been conducted. The major fields in the biopharmaceutical industry include manufacturing, regulatory affairs, sales, and marketing. To strengthen the competitiveness of pharmaceutical companies, it is important to smoothly supply excellent human resources in each field. Manpower for clinical studies and regulatory affairs enables early market entry through faster drug approval ( 13 ), while those for production manufacture high-quality drugs ( 14 ) and those for sales and marketing maximize sales and reinvestment using marketing ( 15 ). However, there are differences in the turnover rate and turnover factors for each job field, as their respective tasks differ. Therefore, this study aims to investigate and compare the turnover rate and factors affecting turnover by job field in biopharmaceutical companies. Methods Study participants This study used a cross-sectional survey of employees working in the production, sales or marketing, and clinical or regulatory affairs departments of biopharmaceutical companies in South Korea. Study samples were selected using stratified sampling with two steps based on the type of biopharmaceutical company and the respondents’ sociodemographic characteristics. Notably, 14 biopharmaceutical companies that agreed to participate in this survey were randomly selected from among approximately 100 biopharmaceutical companies in Korea that produced and imported recombinant pharmaceuticals, vaccines, antitoxins, plasma derivatives, and advanced biopharmaceuticals. Regarding the respondents’ characteristics, the study samples were chosen from among the above-selected companies, considering their age distribution, working period, and educational careers. In this study, we set our target sample size to 500 with a precision of ± 5% based on Israel’s “Determining Sample Size (PEOD6)” ( 16 ). We also considered nonresponses and set the number of survey participants at 550. Survey questionnaire distribution and collection were conducted using an online link via e-mail for two months, from September 1 to October 31, 2020. The study protocol was approved by the Institutional Review Board of Sungkyunkwan University (IRB no. SKKU- 2019-10-032). Data collection instrument We developed a structured questionnaire to collect quantitative data on turnover intention. The survey questionnaire was set up with a focus on job-, organization-, and personal-related factors based on Cotton and Tuttle’s ( 17 ) study that classified factors that affected employee turnover intention into the three categories stated above. Turnover intention, a dependent variable, comprises items measuring how satisfied the respondents are with their current jobs, including six items on turnover intention that directly examine respondents’ intention to leave. The higher the score, the greater the turnover intention. To set the independent variables to be included in the turnover-related factors, we referenced previous studies based on the three categories, items whose statistical significances had been demonstrated in previous studies (Cotton and Tuttle’s study ( 17 ), Lee and Mowday’s study ( 18 ), Song’s study ( 19 ), Smith, Kandell, and Hulin’s study ( 20 ), and Cha, Ryu, and Lee’s study ( 21 ) were selected as variables (55 items). Among the three categories of turnover-related factors as independent variables, the “job-related factor category” comprised six variables, namely, salary (five items) ( 17 ), work scope (five items) ( 18 ), promotion (four items) ( 18 ), industry demand for the job (three items) ( 18 ), work environment (six items) ( 19 ), and social evaluation and perception of the job (three items). The latter, that is, social evaluation and perception of the job (three items), although omitted in previous studies, is expected to affect turnover factors by the job fields of biopharmaceutical companies through consultation with experts related to biopharmaceutical companies, and is thus included herein. Finally, the job-related factor category comprised 26 items corresponding to the 6 variables. Social evaluation and perception of the job refers to the respondents’ social perception of their current job. Specifically, items such as ( 1 ) I think that outside people evaluate my work well, ( 2 ) I think I work in a job that belongs to the upper class of society, and ( 3 ) I take pride in knowing what I do were included in the questionnaire. As for the “organization-related factor category,” corporate culture, supervisor ( 17 ), colleague ( 17 ), organization commitment ( 20 , 21 ), and social evaluation and perception of the company were included as independent variables (21 items). Organizational commitment (four items) ( 20 , 21 ) was shown to have a significant impact on turnover intention ( 21 ) and was included along with the supervisor (five items) and colleague (four items) variables ( 17 ). In addition, corporate culture (five items) and social evaluation and perception of the company (three items), which could significantly affect turnover intention, were added as variables (social evaluation and perception) of job-related factors after consultation with experts. Social evaluation and perception of the company, unlikely social evaluation and perception of the company of job-related factors, refers to the respondents’ social perception of their company of employment. Therefore, items such as ( 1 ) Our company is well known in society, ( 2 ) Outside people tend to evaluate the company I work for, and ( 3 ) I am proud that others know that I work for this company were included in the questionnaire. The “personal-related factor category” included three variables (eight items) as independent variables, namely, financial factors (two items), family responsibility (three items), and educational and residential environment (three items), based on Song’s ( 19 ) study. “Job-related,” “organization-related,” and “personal-related” are the theoretical classification scheme for each factor. These are not expressed as functions, and the factors that constitute the scheme are not weighted. Demographic factors, such as respondents' personal information (gender, age, marriage, dependents, and education) and their work conditions (company type, job category, position, and job change experience), were collected. All the questions, except for the demographic variables, were measured on a 5-point Likert scale (1 = strongly disagree, 2 = disagree, 3 = neither agree nor disagree, 4 = agree, and 5 = strongly agree), which asked respondents how much they agreed with each item. Statistical analysis The respondents’ characteristics were summarized in terms of the frequency and proportion of categorical data by job field. For the survey questionnaire, the internal consistency of the items used in this study was measured using Cronbach’s alpha which was judged to be reliable if it was 0.6 or more, and items with a value less than 0.6 were deleted ( 22 ). Confirmatory factor analysis (CFA) was used to evaluate the validity of the model and aggregate items from predefined factors into a factor score ( 23 ). It was considered that concept validity was secured when the standardized factor loading was 0.4 or more ( 24 , 25 ). We deleted the items with a Cronbach’s alpha of less than 0.6 or a standardized factor loading of less than 0.4. It is recommended to include at least three items per factor ( 26 , 27 ), so factors that did not meet the minimum number of items were excluded from the analysis. After confirming the concept validity, we calculated composite reliability (CR) for construct reliability and the average variance extracted (AVE) for convergent validity. According to Hair et al. ( 28 ), CR values above 0.6 are considered acceptable, and an AVE value of 0.5 higher indicates that each construct ensures convergent validity. Our study also assessed discriminant validity according to the Fornell–Larcker criterion and the heterotrait–monotrait (HTMT) ratio of correlations. Fornell and Larcker ( 29 ) suggest that discriminant validity is adequate when the square root of the AVE is larger than the correlation of the other factors. Henseler et al. ( 30 ) proposed a new approach to verify discriminant validity by observing the HTMT ratio of correlations and suggested a threshold value of 0.9 or 0.85 (for a more conservative criterion). Second, we defined the factor score as the weighted sum of the items with standardized factor loadings. The standardized factor loading of each item is multiplied by its respective response (i.e., the Likert scale score). The factor score is determined by summing the weighted scale scores of each item on a related factor. For instance, turnover intention comprises five items, each of which has a standardized factor loading. Five responses from the respondents to each question about turnover intention were multiplied by the corresponding standardized factor loading and summed. Finally, we conducted the univariate and multivariate analyses described below using these scores. The difference in turnover intention by job field was compared using the analysis of variance (ANOVA) test, and Bonferroni’s post hoc test was used to determine whether there was a difference among the groups. In addition, the correlation between each factor and turnover intention was tested (significance level: 0.05) by job field. In this study, the lower the score of each factor, the higher the score for the turnover intention factor; the stronger the negative correlation, the better it is designed to fit the proposed turnover intention model. Multivariate regression analysis was performed to examine the effect of each factor on turnover intention. We fitted linear regression models and included all factors as variables. All statistical analyses were conducted using the R program, version 4.1.0, and statistical significance was tested at p < 0.05. Results Sociodemographic characteristics of the respondents A total of 529 participants responded to the questionnaire, and 500 cases were analyzed after discarding 29 cases with missing data. Regarding job field, 132 (26.4%) were in production, 280 (56.0%) were in sales or marketing, and 88 (17.8%) were in clinical or regulatory affairs. Most respondents were men (n = 380, 76.0%). More than half of the respondents were under the age of 40, married, and had dependent family members. Finally, the proportion of respondents who experienced at least one turnover was the highest in clinical or regulatory affairs, followed by sales, marketing, and production (Table 1 ). Table 1 Sociodemographic characteristics by job field. Variable All (n = 500) Job fields Production (n = 132) Sales / marketing (n = 280) Clinical / RA a (n = 88) Gender, n (%) Male 380 (76.0) 90 (68.2) 254 (90.7) 36 (40.9) Female 120 (24.0) 42 (31.8) 26 (9.3) 52 (59.1) Age, n (%) < 40 284 (56.8) 85 (64.4) 154 (55.0) 45 (51.1) ≥ 40 216 (43.2) 47 (35.6) 126 (45.0) 43 (48.9) Marital status, n (%) Unmarried 179 (35.8) 66 (50.0) 83 (29.6) 30 (34.1) Married 321 (64.2) 66 (50.0) 197 (70.4) 58 (65.9) Dependent family members, n (%) 0 158 (31.6) 57 (43.2) 72 (25.7) 29 (33.0) ≥ 1 342 (68.4) 75 (56.8) 208 (74.3) 59 (67.1) Education, n (%) Undergraduate school or lower 393 (78.6) 97 (73.5) 259 (92.5) 37 (42.1) Graduate school 107 (21.4) 35 (26.5) 21 (7.5) 51 (58.0) Company type, n (%) Medium-sized or large enterprises 271 (54.2) 29 (22.0) 211 (75.4) 31 (35.2) Others a 229 (45.8) 103 (78.0) 69 (24.6) 57 (64.8) Position, n (%) Individual contributor 228 (45.6) 65 (49.2) 136 (48.6) 27 (30.7) Any level of manager 272 (54.4) 67 (50.8) 144 (51.4) 61 (69.3) Turnover experience, n (%) 0 170 (34.0) 58 (43.9) 88 (31.4) 24 (27.3) 1 194 (38.8) 32 (24.2) 133 (47.5) 29 (33.0) ≥ 2 136 (27.2) 42 (31.8) 59 (21.1) 35 (39.8) a : RA, Regulatory Affairs, b : Small or venture companies Reliability analysis and confirmatory factor analysis of the survey questionnaire The Cronbach's alpha value for industry demand (0.16) among the job-related variables was very low. One item each in family responsibility and educational and residential environment factor had standardized factor loading value of less than 0.4. When these items were deleted, the minimum number of items could not be met, so the variables were excluded form analysis. After removing items with a Cronbach’s alpha value less than 0.6 or standardized factor loadings less than 0.4, as well as factors that did not meet the minimum number of items (i.e., personal-related financial factors), the measurement construct was redesigned from the previous 61 items to 42 items (Table 2 ). Table 2 Comparing Cronbach’s alphas for reliability when items were deleted. Variables Number of items Cronbach’s alpha Before removal After removal Before removal After removal Turnover intention 6 5 0.8 0.83 Job-related Work scope 5 5 0.78 0.78 Salary 5 4 0.67 0.78 Promotion 4 4 0.78 0.78 Social evaluation and perception of the job 3 3 0.69 0.69 Work environment 6 3 0.5 0.62 Industry demand 3 0 0.16 - Organization-related Corporate culture 5 4 0.74 0.82 Supervisor 5 4 0.78 0.81 Colleague 4 3 0.64 0.68 Organization commitment 4 4 0.79 0.79 Social evaluation and perception of the company 3 3 0.77 0.77 Personal-related Financial factors 2 0 0.48 - Family responsibility 3 0 0.65 - Educational and residential environment 3 0 0.49 - Table 3 shows that the standardized factor loading value is 0.4 or higher, thus confirming the validity of this study. The mean, standard deviation, skewness, and kurtosis of the 42 items are showed in S1 Table. The values of standard deviation range from 0.72 to 1.382. Skewness and kurtosis are less than 0.1 and 4.4, respectively. Table 3 Factor analysis (42 items [N = 500)) and reliability of factors. Variables Item number Factor loading Standardized factor loading Cronbach’s alpha Turnover intention 1 1.000 0.815 0.83 2 0.799 0.658 3 0.773 0.580 4 0.842 0.773 5 0.892 0.707 Job-related Work scope 1 1 0.820 0.78 2 0.643 0.617 3 1.005 0.847 4 0.831 0.603 5 0.714 0.488 Salary 1 1 0.776 0.78 2 0.912 0.633 3 0.67 0.525 4 0.994 0.836 Promotion 1 1 0.816 0.78 2 0.729 0.621 3 0.867 0.643 4 0.784 0.660 Social evaluation and perception of the job 1 1 0.610 0.69 2 1.196 0.657 3 1.107 0.704 Work environment 1 1 0.791 0.62 2 0.832 0.650 3 0.623 0.434 Organization-related Corporate culture 1 1 0.787 0.82 2 1.115 0.821 3 0.946 0.756 4 0.733 0.557 Supervisor 1 1 0.814 0.81 2 0.971 0.802 3 0.711 0.591 4 0.895 0.697 Colleague 1 1 0.741 0.68 2 0.915 0.710 3 0.786 0.502 Organization commitment 1 1 0.660 0.79 2 1.439 0.895 3 0.98 0.547 4 0.917 0.581 Social evaluation and perception of the company 1 1 0.561 0.77 2 1.168 0.719 3 1.497 0.862 The CR, AVE, and HTMT ratios are listed in Tables S2 and S3. The CR values of the factors ranged from 0.666 to 0.835 and met the acceptable level of 0.6. Some AVE values were below 0.5, but convergent validity was tolerable because the CR values exceeded 0.6 ( 29 , 31 ). Based on the Fornell–Larcker criterion, the square root of most AVEs were greater than the correlation, but there were a few violations (in bold in S2 Table). From the HTMT results (in bold) in S3 Table, the values were below the HTMT criterion of 0.85, except for two values. Only the HTMT ratio of the correlation between organization-related corporate culture and job-related work environment was above 0.9. These implied that as all but a few factors meet the criteria, partial evidence of discriminant validity was confirmed. Comparison of turnover intention and related job field factors Table 4 shows the mean score for each factor by job field for all related items found to be statistically significantly different (p < 0.05). The higher the turnover intention score, the higher the willingness to leave. Production jobs (12.56) among job fields had the highest turnover intention, followed by clinical or regulatory jobs (11.68) and sales or marketing jobs (11.16). Based on the Bonferroni's post hoc test, turnover intention shows a significant difference in response among the “sales/marketing – production” group (p-value < 0.001). As a result of the ANOVA to examine the differences among jobs in each factor score, there were differences in all factors, including turnover intention. Based on the results of Bonferroni’s post hoc test, the production group had lower job-related factor scores except promotion and social evaluation and perception of the job and all organization factors than other fields. Table 4 Mean factor score by job fields and comparing the values via ANOVA. Variables Pro-duction Sales/ marketing Clinical /RA ANOVA (p-value) Mean difference SM-P a CR-P b CR-SM c Turnover intention 12.56 11.16 11.68 < 0.001* -1.40* -0.88 0.52 Job-related Work scope 11.96 13.37 13.50 < 0.001* 1.41* 1.54* 0.13 Salary 7.47 8.78 8.49 < 0.001* 1.31* 1.02* -0.29 Promotion 7.73 8.21 8.90 < 0.01* 0.48 1.17* 0.69* Social evaluation and perception of the job 6.60 6.77 7.58 < 0.001* 0.17 0.98* 0.81* Work environment 5.79 6.47 7.23 < 0.001* 0.68* 1.44* 0.76* Organization-related Corporate culture 8.71 9.53 10.73 < 0.001* 0.82* 2.02* 1.20* Supervisor 9.98 11.01 11.60 < 0.001* 1.03* 1.62* 0.59 Colleague 7.21 7.75 7.74 < 0.001* 0.54* 0.53* -0.01 Organization commitment 9.85 10.70 10.54 < 0.001* 0.85* 0.69* -0.16 Social evaluation and perception of the company 7.52 7.95 8.20 < 0.01* 0.43* 0.68* 0.25 a SM-P, Mean value of Sales or Marketing - Mean value of Production. b CR-P, Mean value of Clinical or Regulatory Affairs - Mean value of Production. c CR-SM, Mean value of Clinical or Regulatory Affairs - Mean value of Sales or Marketing. P-value < 0.05, * Correlation of turnover intention and the related factors by job field When we examined the correlation coefficients between turnover intentions and each factor, we found that the correlation between supervisor and turnover intention in the production field was the highest at -0.61 (Table 5 ). The strongest correlation was found for corporate culture (r = -0.53) in the sales/marketing field and salary (r = -0.52) in the clinical/regulatory affairs field. When testing the hypothesis that the correlation coefficient is zero, all variables in production and the sales/marketing fields are statistically significant (p-values < 0.05). For the production and clinical/ regulatory affairs field, most factors, except organization-related colleague factor, were statistically significant. Table 5 Correlation coefficient and the result of correlation test between each variable and turnover intention for each job field. Variables Turnover intention of Production Turnover intention of Sales/marketing Turnover intention of Clinical/RA Job-related Work scope -0.55* -0.51* -0.39* Salary -0.49* -0.43* -0.52* Promotion -0.48* -0.38* -0.41* Social evaluation and perception of the job -0.41* -0.41* -0.24* Work environment -0.37* -0.43* -0.27* Organization-related Corporate culture -0.51* -0.53* -0.42* Supervisor -0.61* -0.38* -0.40* Colleague -0.22* -0.22* -0.09 Organization commitment -0.60* -0.51* -0.39* Social evaluation and perception of the company -0.42* -0.42* -0.22* P-value < 0.05, * Factors associated with turnover intention by job field In the multivariate regression analysis, the model for all job fields was statistically suitable based on the F-statistic (Table 6 ). In production jobs, the higher the satisfaction with the supervisor (β = -0.326, p = 0.005), the lower the turnover intention. For sales or marketing jobs, satisfaction with the work scope (β = -0.181, p = 0.010), salary (β = -0.169, p = 0.005) and corporate culture (β = -0.314, p < 0.001) factors showed low turnover intention. The turnover intention was lower when working at medium-sized or large enterprises (β = -0.106, p = 0.029). Men had lower turnover intentions than women (β = 0.101, p = 0.044). For clinical or regulatory affairs jobs, the work scope and company type appeared to significantly affect turnover intention. Higher satisfaction with the work scope (β = -0.350, p = 0.035) led to lower intention to turnover, and workers employed in the larger company (medium-sized or large enterprises) (β = -0.236, p-value = 0.021) had a lower intention to turnover (Table 6 ). Table 6 Factors associated with turnover intention by job fields. Variables Production Sales/marketing Clinical/RA a \(\beta\) b p-value \(\beta\) b p-value \(\beta\) b p-value Job-related Work scope -0.103 0.343 -0.181* 0.010 -0.350* 0.035 Salary -0.094 0.318 -0.169* 0.005 -0.230 0.151 Promotion -0.177 0.075 0.114 0.143 -0.094 0.500 Social evaluation and perception of the job 0.023 0.816 -0.024 0.739 0.091 0.594 Work environment 0.182 0.056 -0.025 0.717 0.178 0.256 Organization-related Corporate culture 0.006 0.959 -0.314* 0.000 -0.318 0.090 Supervisor -0.326* 0.005 -0.036 0.551 -0.197 0.197 Colleague 0.082 0.285 -0.024 0.636 0.237 0.102 Organization commitment -0.229 0.068 -0.117 0.082 -0.026 0.879 Social evaluation and perception of the company 0.043 0.664 0.000 0.997 0.134 0.439 Sociodemographic c Gender (female) -0.062 0.427 0.101* 0.044 -0.058 0.601 Age (≥ 40) -0.186 0.076 -0.114 0.140 -0.078 0.551 Marital status (Unmarried) 0.081 0.516 0.018 0.831 0.170 0.339 Dependent family members (≥ 1) -0.080 0.499 0.002 0.978 0.009 0.953 Education (Graduate school) 0.020 0.790 0.075 0.131 -0.028 0.794 Company type (Medium-sized or large enterprises) -0.113 0.146 -0.106* 0.029 -0.236* 0.021 Position (any level of manage) 0.121 0.222 -0.077 0.328 0.215 0.130 Turnover experience (1) 0.024 0.752 0.058 0.278 -0.132 0.271 Turnover experience (≥ 2) -0.047 0.557 0.078 0.154 -0.181 0.161 Constant < 2e-16 < 2e-16 1.29E-10 R square (Adjusted R square) 0.5549 (0.4794) 0.4721 (0.4335) 0.472(0.3245) F-statistic (p-value) 7.35 (< 0.001) 12.24 (< 0.001) 3.2 (< 0.001) P-value < 0.05, * a RA, Regulatory affairs. b Standardized beta coefficient. c Sociodemographic variables are dummies. Reference group (variables): male (gender), < 40 (age), married (marital status), 0 (dependent family members), undergraduate school or lower (education), except medium-sized or large enterprises (company size), individual contributor (position), and 0 (turnover experience). Discussion The factors affecting turnover differed among the three fields. The turnover intention in production positions was mainly organization-related, whereas job-related factors affected the turnover intention in sales, marketing, and clinical or regulatory affairs positions. In production positions, the higher the satisfaction with the supervisor, the lower the turnover intention. As production workers who handle dangerous machinery and chemicals often have a vertical organizational culture for creating a safe working environment, it can be seen that the worse the relationship with the supervisor in the organization, the higher the intention to leave ( 32 ). However, job-related factors did not affect turnover intentions. A fundamental reason is the characteristics of production workers, in which standardized work is repeatedly performed based on protocols and standards of procedure ( 33 ). Therefore, the job competencies required for production workers include accurately an accurate understanding of the order and content of work and carrying out the assigned work. The sales or marketing job group generally establishes not only sales goals by analyzing the market environment, competitors, and customers but also strategies to achieve such goals. Job-related factors, such as work stress for achieving the goals, influence the intention to leave, as job performance is focused on achieving the set goal, which is consistent with Kim’s ( 10 ) study. Moreover, unstable wages and incentive systems based on performance-based achievements influence turnover intention ( 34 ). Clinical or regulatory affairs positions in charge of drug clinical trials and approvals have a higher level of job difficulty than other pharmaceutical jobs because this job category requires in-depth expertise. According to Myung’s ( 11 ) study, employees who do not have sufficient professional knowledge may experience decreased job satisfaction because of difficulties in performing their duties ( 11 ). Thus, satisfaction with work scope as a job-related factor can be considered to affect turnover intention ( 11 , 35 ). Regarding sociodemographic factors, employees in small-sized or venture companies show higher turnover intention rates because social evaluation is relatively lower than that of large companies, and the welfare of such companies is also poor ( 36 ). This finding is similar to that of a previous study ( 12 ) that found a negative relationship between company performance and size and employee turnover. Women were more likely to leave their jobs than men in sales or marketing positions. This could be seen as a phenomenon created by the top-down working atmosphere in which the proportion of men was high in the past corporate culture. However, because there are conflicting research results ( 37 ) showing that women's turnover rates are generally lower than that of men, it would be difficult to generalize the results of this study that was analyzed with a limited number of samples. To the best of our knowledge, this study is the first to compare and analyze workers’ turnover intention by job field in biopharmaceutical companies and derive recommendations for how biopharmaceutical companies should manage human resources by job field. Notwithstanding its strengths, this study had some limitations. First, the findings of this study might be limited in generalizability to other countries, as the case study of Korea was investigated. However, this study compared the factors related to turnover intention among the three job categories, and these differences could have implications for other countries. Second, the results of this study may differ from the actual turnover situation because it investigates turnover intention. Therefore, it would be desirable to interpret this on the premise that the intention to leave led to change. Conclusions This study confirms the differences in turnover intention in biopharmaceutical companies by job field. Despite these limitations, the findings of this study can help biopharmaceutical companies to manage their workforce by job field. The factors affecting the turnover of production workers were organization-related factors, such as supervisors and organization commitment. Job-related factors affect turnover intention in sales or marketing (work scope and salary), and clinical or regulatory affairs (work scope). Therefore, measures such as creating a collaborative culture to improve organizational engagement, providing leadership training for supervisors, availing job training to improve job satisfaction, establishing an appropriate wage compensation system for sales or marketing fields, and ensuring job training to enhance job satisfaction for clinical or regulatory affairs employees are needed to reduce turnover intention in the biopharmaceutical industry. Ultimately, it will be possible to reduce workers’ turnover rates and contribute to stabilizing biopharmaceutical companies’ organizations and businesses. List Of Abbreviations CFA Confirmatory factor analysis CR Composite reliability AVE Average variance extracted HTMT Heterotrait–monotrait ANOVA Analysis of variance Declarations Acknowledgements No sources of funding were used in the preparation of this study. Availability of data and material The data that support the findings of this study are available. Ethics approval and consent to participate The approval of the Institutional Review Board for this study was approved by the Institutional Review Board of Sungkyunkwan University (IRB no. SKKU- 2019-10-032), and all methods applied in this study were performed in accordance with Article 16 of the Rule of the Bioethics and Safety Act in Korea. Also, informed consent was obtained from all participants who responded to the questionnaire. Competing interests The authors declare that they have no competing interests. Authors' contributions JP, DH, and EKL designed the study and research concept. ARC, SHK, JMC, and WRY acquired and arranged the data. Analysis was performed by ARC and JP, and interpretation of analysis results was conducted by DH and EKL. SHK, JMC, and WRY undertook the literature review, ARC and JP drafted the initial manuscript, and DH and EKL confirmed the manuscript. All authors read and approved the final manuscript. The authors of this manuscript take responsibility for the integrity of the data and the accuracy of the data analysis. Consent for publication Not applicable Funding Not applicable References Ralf Otto AS, and Ulf Schrader. Biopharmaceuticals could become the core of the pharmaceutical industry, but not without significant transformation in the laboratory and in strategy, technology, and operations 2014 [Available from: https://www.mckinsey.com/industries/life-sciences/our-insights/rapid-growth-in-biopharma. Robert A, Benoit-Vical F, Liu Y, Meunier B. Small Molecules: The Past or the Future in Drug Innovation? Met Ions Life Sci. 2019;19. Dukart H, Patel P, Telpis V, Yngve J. Reskilling employees to address talent gaps can help a company retain the bulk of its operations workers and empower them to take advantage of a new world 2020 [Available from: https://www.mckinsey.com/industries/life-sciences/our-insights/pharma-operations-creating-the-workforce-of-the-future#/. Poon YR, Lin YP, Griffiths P, Yong KK, Seah B, and Liaw SY. A global overview of healthcare workers' turnover intention amid COVID-19 pandemic: a systematic review with future directions. Hum Resour Health. 2022;20(1):70. Dawson AJ, Stasa H, Roche MA, Homer CS, and Duffield C. Nursing churn and turnover in Australian hospitals: nurses perceptions and suggestions for supportive strategies. BMC Nurs. 2014;13(1):11. Perreira TA, Berta W, Herbert M. The employee retention triad in health care: Exploring relationships amongst organisational justice, affective commitment and turnover intention. J Clin Nurs. 2018;27(7-8):e1451-e61. Marufu TC, Collins A, Vargas L, Gillespie L, Almghairbi D. Factors influencing retention among hospital nurses: systematic review. Br J Nurs. 2021;30(5):302-8. Kovner CT, Brewer CS, Fatehi F, Jun J. What does nurse turnover rate mean and what is the rate? Policy Polit Nurs Pract. 2014;15(3-4):64-71. Robinson PC, Liew DFL, Tanner HL, Grainger JR, Dwek RA, Reisler RB, et al. COVID-19 therapeutics: Challenges and directions for the future. Proc Natl Acad Sci U S A. 2022;119(15):e2119893119. Kim J-H, A Study on factors affecting voluntary turnover intention [master's thesis]. The graduate school of business administration Yeungnam University; 2006. Myung M-K, Factors affecting on turnover intention of clinical nurses [master's thesis]. The graduate school of Kwangju Women's University; 2013. Awwad MS and Heyari HI, Predicting employee turnover using financial indicators in the pharmaceutical industry, Industrial and Commercial Training, 2022;54(3): 476-496, https://doi.org/10.1108/ICT-01-2022-0004. Kepplinger EE. FDA's Expedited Approval Mechanisms for New Drug Products. Biotechnol Law Rep. 2015;34(1):15-37. UNCTAD Secretariat. The role of competition in the pharmaceutical sector and its benefits for consumers: note/by the UNCTAD secretariat. 2015. Calfee JE. The role of marketing in pharmaceutical research and development. Pharmacoeconomics. 2002;20 Suppl 3:77-85. Israel GD. Determining sample size. University of Florida Cooperative Extension Service. 1992. Cotton JL, Tuttle JM. Employee turnover: A meta-analysis and review with implications for research. Academy of management Review. 1986;11(1):55-70. Lee TW, Mowday RT. Voluntarily leaving an organization: An empirical investigation of Steers and Mowday's model of turnover. Academy of Management journal. 1987;30(4):721-43. Song K. A Study on factors affecting voluntary turnover intention [master's thesis]. Seoul: The graduate school of education Hanyang University; 2000. Smith PC, Kandell LM, Hulin CL. The measurement of satisfaction in work and retirement: A strategy for the study of attitudes. Chicago, Illinois: Rand McNally and Company; 1969. Cha J-B, Ryu G-Y, Lee H-Y. An empirical study on the relationship effect of pharmaceutical sales representative’s personal-job fit/person-organization fit and job satisfaction, organizational commitment, and turnover intentions. Korean Journal of Business Administration. 2013;26(3):567-88. Griethuijsen RALF, Eijck MW, Haste H, Brok PJ, Skinner NC, Mansour N, et al. Global Patterns in Students’ Views of Science and Interest in Science. Research in Science Education. 2015;45(4):581-603. Crede M, Harms P. Questionable research practices when using confirmatory factor analysis. Journal of Managerial Psychology. 2019;34:18-30. Guadagnoli, E., & Velicer, W. F. (1988). Relation of sample size to the stability of component patterns. Psychological bulletin, 103(2), 265. Pituch, K. A., & Stevens, J. P. (2015). Applied multivariate statistics for the social sciences: Analyses with SAS and IBM’s SPSS. Routledge. Costello AB, Osborne J. Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical assessment, research, and evaluation. 2005;10. Raubenheimer J. An item selection procedure to maximize scale reliability and validity. SA Journal of Industrial Psychology. 2004;30(4):59–64. Hair JF Jr, Hult GTM, Ringle CM, Sarstedt M, Danks NP, Ray S. Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. 1st ed. Cham, Switzerland: Springer Nature; 2021. Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. J Mark Res [Internet]. 1981;18(1):39. Henseler J, Ringle CM, Sarstedt M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J Acad Mark Sci [Internet]. 2015;43(1):115–35. Lam LW. Impact of competitiveness on salespeople’s commitment and performance. J Bus Res [Internet]. 2012;65(9):1328–34. Jang J, Yoo T. The effect of perception of organizational politics on turnover intention: The mediating effect of stress and organizational commitment and moderating effect of honesty. Korean Journal of Industrial and Organizational Psychology. 2013;26(3):413-36. Ahn K-Y, Chang KS. The relationship between knowledge management and knowledge management performance, and the moderating effect of organizational culture in small business. Korean Business Education Review. 2012;27((1)71):88-106. Zhou S, Lee JE. The effects of pay satisfaction on job satisfaction and turnover intention. The Journal of the Korea Contents Association. 2016;16(10):693-700. Lee SY, Lee GS. A Study on Gyeonggi Province on Turnover Intentions of IT Industry. Korean Management Consulting Review. 2012;12(2):133-53. Moon Y-M, Hong J-P. Youth Employees Turnover Determinants by Business Scale and Wage Effects. Korean Journal of Labor Studies. 2017;23(2):195-230. Sung JM, Ahn JY. Job Satisfaction, Intention of Job Separation and Quit Behavior. Korean Journal of Labor Studies. 2016;22(2):135-79. Additional Declarations No competing interests reported. Supplementary Files SupportingInfromation.zip Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3608647","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":249199486,"identity":"56e553b6-f766-423f-add3-a8d78af34c5c","order_by":0,"name":"Ae-Ryeo Cho","email":"","orcid":"","institution":"Sungkyunkwan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ae-Ryeo","middleName":"","lastName":"Cho","suffix":""},{"id":249199487,"identity":"493011c9-6ea2-4d5f-82f4-7b6f3b563eab","order_by":1,"name":"Jungtae Park","email":"","orcid":"","institution":"Sungkyunkwan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jungtae","middleName":"","lastName":"Park","suffix":""},{"id":249199489,"identity":"27b375a9-8975-40e3-ba09-d5d77459a0b1","order_by":2,"name":"Sun-Hong Kwon","email":"","orcid":"","institution":"Sungkyunkwan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sun-Hong","middleName":"","lastName":"Kwon","suffix":""},{"id":249199490,"identity":"46c5c0e9-be4c-4a42-8319-4145090c3c0c","order_by":3,"name":"Jeong-min Choi","email":"","orcid":"","institution":"Sungkyunkwan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jeong-min","middleName":"","lastName":"Choi","suffix":""},{"id":249199491,"identity":"9f00ce1c-2fde-4239-9f45-086644f361de","order_by":4,"name":"Wonsang Robert Yu","email":"","orcid":"","institution":"Sungkyunkwan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wonsang","middleName":"Robert","lastName":"Yu","suffix":""},{"id":249199492,"identity":"b9a839f0-e1f3-4d2a-a18c-dd87c9afa0e3","order_by":5,"name":"Dongmun Ha","email":"","orcid":"","institution":"Mokpo National University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dongmun","middleName":"","lastName":"Ha","suffix":""},{"id":249199493,"identity":"88b33adb-1dea-400b-a8af-578960c62909","order_by":6,"name":"Eui-Kyung Lee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYDACHjBpwyABZrARryWNdC2HSdDCz3PGTLrg13l7yZ4zBgwfyg4T1iLZ22MmPbPvduJs3h4DxhnniNBicJ7HTJq353aCHD+PATNvGxFa7CFaztmDtfwlRosBL9BhPD8OMIIcxsxIjBaJM8eKrXkbkhNn9hwrONhzLp2wFv6e5I23ef7Y2UucSd744EeZNWEtDAwcBgyMbRDmAWLUAwH7AwaGP0SqHQWjYBSMgpEJACoeNbPC4VNAAAAAAElFTkSuQmCC","orcid":"","institution":"Sungkyunkwan University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Eui-Kyung","middleName":"","lastName":"Lee","suffix":""}],"badges":[],"createdAt":"2023-11-14 06:44:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3608647/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3608647/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67114556,"identity":"331d0fe1-6ca2-45de-a6dd-99cdc59353e8","added_by":"auto","created_at":"2024-10-21 10:16:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1212568,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3608647/v1/ccce456a-b4fb-40ec-a4ed-483c2757fa27.pdf"},{"id":46546699,"identity":"51d0149c-44ba-439d-9eb5-b2845cc45d00","added_by":"auto","created_at":"2023-11-16 09:54:44","extension":"zip","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":161682,"visible":true,"origin":"","legend":"","description":"","filename":"SupportingInfromation.zip","url":"https://assets-eu.researchsquare.com/files/rs-3608647/v1/bfda8d6b30ff7a00a01c4d00.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparison of factors affecting turnover intention by job field in biopharmaceutical industry in Korea: A questionnaire-based study","fulltext":[{"header":"Background","content":"\u003cp\u003eThe pharmaceutical industry has been shifting its focus globally from synthetic drug-oriented structures to biopharmaceuticals that process drugs using biotechnology-applied and biological raw materials (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). As the biopharmaceutical industry has rapidly grown as a major part of the health field concerned with disease treatment and industrial aspects, the demand for human resources is also sharply increasing in Korea and worldwide (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). However, there is a severe shortage of manpower in the fields of development, production, licensing, and marketing for biologics because they require high-level expertise and experience compared to chemical drugs. Employee turnover is a major cause of shortages in the health sector (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding pharmaceutical companies, losing employees is tantamount to not only losing knowledge and experience but also dampening staff morale and health, while increasing organizational costs (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). In other words, the loss of good employees not only engenders a loss in productivity and revenue but also brings about high costs with regard to staff replacement (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). From a social perspective, the biopharmaceutical industry, which develops, produces, and sells drugs, is important for public health (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In particular, supplying people with new drugs to quickly overcome the COVID-19 pandemic has been the most decisive strategy for humans (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Therefore, securing and maintaining a professional workforce in the biopharmaceutical industry is becoming increasingly important.\u003c/p\u003e \u003cp\u003eMany studies have been conducted on turnover intentions in the pharmaceutical industry. Among them, several studies (\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) have identified the factors that affect employees\u0026rsquo; turnover intention based on job or pharmaceutical company types. However, no study has targeted turnover intention in pharmaceutical companies only. Moreover, no study that subdivides turnover intention by job category has been conducted. The major fields in the biopharmaceutical industry include manufacturing, regulatory affairs, sales, and marketing. To strengthen the competitiveness of pharmaceutical companies, it is important to smoothly supply excellent human resources in each field. Manpower for clinical studies and regulatory affairs enables early market entry through faster drug approval (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), while those for production manufacture high-quality drugs (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) and those for sales and marketing maximize sales and reinvestment using marketing (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). However, there are differences in the turnover rate and turnover factors for each job field, as their respective tasks differ. Therefore, this study aims to investigate and compare the turnover rate and factors affecting turnover by job field in biopharmaceutical companies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy participants\u003c/h2\u003e \u003cp\u003eThis study used a cross-sectional survey of employees working in the production, sales or marketing, and clinical or regulatory affairs departments of biopharmaceutical companies in South Korea. Study samples were selected using stratified sampling with two steps based on the type of biopharmaceutical company and the respondents\u0026rsquo; sociodemographic characteristics. Notably, 14 biopharmaceutical companies that agreed to participate in this survey were randomly selected from among approximately 100 biopharmaceutical companies in Korea that produced and imported recombinant pharmaceuticals, vaccines, antitoxins, plasma derivatives, and advanced biopharmaceuticals. Regarding the respondents\u0026rsquo; characteristics, the study samples were chosen from among the above-selected companies, considering their age distribution, working period, and educational careers. In this study, we set our target sample size to 500 with a precision of \u0026plusmn;\u0026thinsp;5% based on Israel\u0026rsquo;s \u0026ldquo;Determining Sample Size (PEOD6)\u0026rdquo; (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). We also considered nonresponses and set the number of survey participants at 550.\u003c/p\u003e \u003cp\u003eSurvey questionnaire distribution and collection were conducted using an online link via e-mail for two months, from September 1 to October 31, 2020. The study protocol was approved by the Institutional Review Board of Sungkyunkwan University (IRB no. SKKU- 2019-10-032).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData collection instrument\u003c/h2\u003e \u003cp\u003eWe developed a structured questionnaire to collect quantitative data on turnover intention. The survey questionnaire was set up with a focus on job-, organization-, and personal-related factors based on Cotton and Tuttle\u0026rsquo;s (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) study that classified factors that affected employee turnover intention into the three categories stated above. Turnover intention, a dependent variable, comprises items measuring how satisfied the respondents are with their current jobs, including six items on turnover intention that directly examine respondents\u0026rsquo; intention to leave. The higher the score, the greater the turnover intention. To set the independent variables to be included in the turnover-related factors, we referenced previous studies based on the three categories, items whose statistical significances had been demonstrated in previous studies (Cotton and Tuttle\u0026rsquo;s study (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), Lee and Mowday\u0026rsquo;s study (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), Song\u0026rsquo;s study (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), Smith, Kandell, and Hulin\u0026rsquo;s study (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), and Cha, Ryu, and Lee\u0026rsquo;s study (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) were selected as variables (55 items).\u003c/p\u003e \u003cp\u003eAmong the three categories of turnover-related factors as independent variables, the \u0026ldquo;job-related factor category\u0026rdquo; comprised six variables, namely, salary (five items) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), work scope (five items) (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), promotion (four items) (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), industry demand for the job (three items) (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), work environment (six items) (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), and social evaluation and perception of the job (three items). The latter, that is, social evaluation and perception of the job (three items), although omitted in previous studies, is expected to affect turnover factors by the job fields of biopharmaceutical companies through consultation with experts related to biopharmaceutical companies, and is thus included herein. Finally, the job-related factor category comprised 26 items corresponding to the 6 variables. Social evaluation and perception of the job refers to the respondents\u0026rsquo; social perception of their current job. Specifically, items such as (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) I think that outside people evaluate my work well, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) I think I work in a job that belongs to the upper class of society, and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) I take pride in knowing what I do were included in the questionnaire.\u003c/p\u003e \u003cp\u003eAs for the \u0026ldquo;organization-related factor category,\u0026rdquo; corporate culture, supervisor (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), colleague (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), organization commitment (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), and social evaluation and perception of the company were included as independent variables (21 items). Organizational commitment (four items) (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) was shown to have a significant impact on turnover intention (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) and was included along with the supervisor (five items) and colleague (four items) variables (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). In addition, corporate culture (five items) and social evaluation and perception of the company (three items), which could significantly affect turnover intention, were added as variables (social evaluation and perception) of job-related factors after consultation with experts. Social evaluation and perception of the company, unlikely social evaluation and perception of the company of job-related factors, refers to the respondents\u0026rsquo; social perception of their company of employment. Therefore, items such as (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Our company is well known in society, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Outside people tend to evaluate the company I work for, and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) I am proud that others know that I work for this company were included in the questionnaire.\u003c/p\u003e \u003cp\u003eThe \u0026ldquo;personal-related factor category\u0026rdquo; included three variables (eight items) as independent variables, namely, financial factors (two items), family responsibility (three items), and educational and residential environment (three items), based on Song\u0026rsquo;s (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) study. \u0026ldquo;Job-related,\u0026rdquo; \u0026ldquo;organization-related,\u0026rdquo; and \u0026ldquo;personal-related\u0026rdquo; are the theoretical classification scheme for each factor. These are not expressed as functions, and the factors that constitute the scheme are not weighted. Demographic factors, such as respondents' personal information (gender, age, marriage, dependents, and education) and their work conditions (company type, job category, position, and job change experience), were collected.\u003c/p\u003e \u003cp\u003eAll the questions, except for the demographic variables, were measured on a 5-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree, 2\u0026thinsp;=\u0026thinsp;disagree, 3\u0026thinsp;=\u0026thinsp;neither agree nor disagree, 4\u0026thinsp;=\u0026thinsp;agree, and 5\u0026thinsp;=\u0026thinsp;strongly agree), which asked respondents how much they agreed with each item.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe respondents\u0026rsquo; characteristics were summarized in terms of the frequency and proportion of categorical data by job field. For the survey questionnaire, the internal consistency of the items used in this study was measured using Cronbach\u0026rsquo;s alpha which was judged to be reliable if it was 0.6 or more, and items with a value less than 0.6 were deleted (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Confirmatory factor analysis (CFA) was used to evaluate the validity of the model and aggregate items from predefined factors into a factor score (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). It was considered that concept validity was secured when the standardized factor loading was 0.4 or more (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). We deleted the items with a Cronbach\u0026rsquo;s alpha of less than 0.6 or a standardized factor loading of less than 0.4. It is recommended to include at least three items per factor (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), so factors that did not meet the minimum number of items were excluded from the analysis. After confirming the concept validity, we calculated composite reliability (CR) for construct reliability and the average variance extracted (AVE) for convergent validity. According to Hair et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), CR values above 0.6 are considered acceptable, and an AVE value of 0.5 higher indicates that each construct ensures convergent validity. Our study also assessed discriminant validity according to the Fornell\u0026ndash;Larcker criterion and the heterotrait\u0026ndash;monotrait (HTMT) ratio of correlations. Fornell and Larcker (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) suggest that discriminant validity is adequate when the square root of the AVE is larger than the correlation of the other factors. Henseler et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) proposed a new approach to verify discriminant validity by observing the HTMT ratio of correlations and suggested a threshold value of 0.9 or 0.85 (for a more conservative criterion). Second, we defined the factor score as the weighted sum of the items with standardized factor loadings. The standardized factor loading of each item is multiplied by its respective response (i.e., the Likert scale score). The factor score is determined by summing the weighted scale scores of each item on a related factor. For instance, turnover intention comprises five items, each of which has a standardized factor loading. Five responses from the respondents to each question about turnover intention were multiplied by the corresponding standardized factor loading and summed. Finally, we conducted the univariate and multivariate analyses described below using these scores. The difference in turnover intention by job field was compared using the analysis of variance (ANOVA) test, and Bonferroni\u0026rsquo;s post hoc test was used to determine whether there was a difference among the groups. In addition, the correlation between each factor and turnover intention was tested (significance level: 0.05) by job field. In this study, the lower the score of each factor, the higher the score for the turnover intention factor; the stronger the negative correlation, the better it is designed to fit the proposed turnover intention model. Multivariate regression analysis was performed to examine the effect of each factor on turnover intention. We fitted linear regression models and included all factors as variables. All statistical analyses were conducted using the R program, version 4.1.0, and statistical significance was tested at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSociodemographic characteristics of the respondents\u003c/h2\u003e \u003cp\u003eA total of 529 participants responded to the questionnaire, and 500 cases were analyzed after discarding 29 cases with missing data. Regarding job field, 132 (26.4%) were in production, 280 (56.0%) were in sales or marketing, and 88 (17.8%) were in clinical or regulatory affairs. Most respondents were men (n\u0026thinsp;=\u0026thinsp;380, 76.0%). More than half of the respondents were under the age of 40, married, and had dependent family members. Finally, the proportion of respondents who experienced at least one turnover was the highest in clinical or regulatory affairs, followed by sales, marketing, and production (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSociodemographic characteristics by job field.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;500)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eJob fields\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProduction\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;132)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSales / marketing\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;280)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eClinical / RA\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;88)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e380 (76.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (68.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e254 (90.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36 (40.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120 (24.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (31.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52 (59.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e284 (56.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (64.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e154 (55.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45 (51.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e216 (43.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (35.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126 (45.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43 (48.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e179 (35.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83 (29.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30 (34.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e321 (64.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e197 (70.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58 (65.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDependent family members, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e158 (31.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (43.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72 (25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29 (33.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e342 (68.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75 (56.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e208 (74.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59 (67.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUndergraduate school or lower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e393 (78.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97 (73.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e259 (92.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37 (42.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGraduate school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107 (21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51 (58.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCompany type, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium-sized or large enterprises\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e271 (54.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e211 (75.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31 (35.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e229 (45.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103 (78.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69 (24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57 (64.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePosition, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual contributor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e228 (45.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 (49.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136 (48.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27 (30.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny level of manager\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e272 (54.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67 (50.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e144 (51.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61 (69.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTurnover experience, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e170 (34.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (43.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88 (31.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (27.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e194 (38.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e133 (47.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29 (33.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136 (27.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (31.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 (21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35 (39.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003ea\u003c/sup\u003e: RA, Regulatory Affairs,\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003eb\u003c/sup\u003e: Small or venture companies\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eReliability analysis and confirmatory factor analysis of the survey questionnaire\u003c/h2\u003e \u003cp\u003eThe Cronbach's alpha value for industry demand (0.16) among the job-related variables was very low. One item each in family responsibility and educational and residential environment factor had standardized factor loading value of less than 0.4. When these items were deleted, the minimum number of items could not be met, so the variables were excluded form analysis. After removing items with a Cronbach\u0026rsquo;s alpha value less than 0.6 or standardized factor loadings less than 0.4, as well as factors that did not meet the minimum number of items (i.e., personal-related financial factors), the measurement construct was redesigned from the previous 61 items to 42 items (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparing Cronbach\u0026rsquo;s alphas for reliability when items were deleted.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eNumber of items\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eCronbach\u0026rsquo;s alpha\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBefore removal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAfter removal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBefore removal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAfter removal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTurnover intention\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eJob-related\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWork scope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSalary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePromotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial evaluation and perception of the job\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWork environment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndustry demand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOrganization-related\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorporate culture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSupervisor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eColleague\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrganization commitment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial evaluation and perception of the company\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePersonal-related\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinancial factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFamily responsibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducational and residential environment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that the standardized factor loading value is 0.4 or higher, thus confirming the validity of this study. The mean, standard deviation, skewness, and kurtosis of the 42 items are showed in S1 Table. The values of standard deviation range from 0.72 to 1.382. Skewness and kurtosis are less than 0.1 and 4.4, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactor analysis (42 items [N\u0026thinsp;=\u0026thinsp;500)) and reliability of factors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItem number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFactor loading\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStandardized factor loading\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCronbach\u0026rsquo;s alpha\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"4\" nameend=\"c2\" namest=\"c1\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eTurnover intention\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.658\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.580\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eJob-related\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"18\" rowspan=\"19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eWork scope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSalary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.633\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.836\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePromotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.621\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSocial evaluation and perception of the job\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eWork environment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOrganization-related\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"17\" rowspan=\"18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eCorporate culture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSupervisor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.591\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.697\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eColleague\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.710\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eOrganization commitment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.581\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSocial evaluation and perception of the company\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.862\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe CR, AVE, and HTMT ratios are listed in Tables S2 and S3. The CR values of the factors ranged from 0.666 to 0.835 and met the acceptable level of 0.6. Some AVE values were below 0.5, but convergent validity was tolerable because the CR values exceeded 0.6 (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Based on the Fornell\u0026ndash;Larcker criterion, the square root of most AVEs were greater than the correlation, but there were a few violations (in bold in S2 Table). From the HTMT results (in bold) in S3 Table, the values were below the HTMT criterion of 0.85, except for two values. Only the HTMT ratio of the correlation between organization-related corporate culture and job-related work environment was above 0.9. These implied that as all but a few factors meet the criteria, partial evidence of discriminant validity was confirmed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eComparison of turnover intention and related job field factors\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the mean score for each factor by job field for all related items found to be statistically significantly different (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The higher the turnover intention score, the higher the willingness to leave. Production jobs (12.56) among job fields had the highest turnover intention, followed by clinical or regulatory jobs (11.68) and sales or marketing jobs (11.16). Based on the Bonferroni's post hoc test, turnover intention shows a significant difference in response among the \u0026ldquo;sales/marketing \u0026ndash; production\u0026rdquo; group (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). As a result of the ANOVA to examine the differences among jobs in each factor score, there were differences in all factors, including turnover intention. Based on the results of Bonferroni\u0026rsquo;s post hoc test, the production group had lower job-related factor scores except promotion and social evaluation and perception of the job and all organization factors than other fields.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean factor score by job fields and comparing the values via ANOVA.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePro-duction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSales/\u003c/p\u003e \u003cp\u003emarketing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eClinical\u003c/p\u003e \u003cp\u003e/RA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eANOVA\u003c/p\u003e \u003cp\u003e(p-value)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c12\" namest=\"c7\"\u003e \u003cp\u003eMean difference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eSM-P \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eCR-P \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eCR-SM \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTurnover intention\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e-1.40*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e-0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eJob-related\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWork scope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.41*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e1.54*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSalary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.31*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e1.02*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePromotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e1.17*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.69*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial evaluation and perception of the job\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.98*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.81*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWork environment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.68*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e1.44*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.76*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOrganization-related\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorporate culture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.82*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e2.02*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e1.20*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSupervisor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.03*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e1.62*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eColleague\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.54*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.53*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrganization commitment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.85*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.69*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial evaluation and perception of the company\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.43*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.68*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e\u003csup\u003ea\u003c/sup\u003e SM-P, Mean value of Sales or Marketing - Mean value of Production.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e\u003csup\u003eb\u003c/sup\u003e CR-P, Mean value of Clinical or Regulatory Affairs - Mean value of Production.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e\u003csup\u003ec\u003c/sup\u003e CR-SM, Mean value of Clinical or Regulatory Affairs - Mean value of Sales or Marketing.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eP-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation of turnover intention and the related factors by job field\u003c/h2\u003e \u003cp\u003eWhen we examined the correlation coefficients between turnover intentions and each factor, we found that the correlation between supervisor and turnover intention in the production field was the highest at -0.61 (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The strongest correlation was found for corporate culture (r = -0.53) in the sales/marketing field and salary (r = -0.52) in the clinical/regulatory affairs field. When testing the hypothesis that the correlation coefficient is zero, all variables in production and the sales/marketing fields are statistically significant (p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For the production and clinical/ regulatory affairs field, most factors, except organization-related colleague factor, were statistically significant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation coefficient and the result of correlation test between each variable and turnover intention for each job field.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTurnover intention of Production\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTurnover intention of Sales/marketing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTurnover intention of Clinical/RA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eJob-related\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWork scope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.55*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.51*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.39*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSalary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.49*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.43*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.52*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePromotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.48*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.38*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.41*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial evaluation and perception of the job\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.41*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.41*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.24*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWork environment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.37*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.43*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.27*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOrganization-related\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorporate culture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.51*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.53*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.42*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSupervisor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.61*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.38*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.40*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eColleague\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.22*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.22*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrganization commitment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.60*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.51*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.39*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial evaluation and perception of the company\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.42*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.42*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.22*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eP-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFactors associated with turnover intention by job field\u003c/h2\u003e \u003cp\u003eIn the multivariate regression analysis, the model for all job fields was statistically suitable based on the F-statistic (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). In production jobs, the higher the satisfaction with the supervisor (β = -0.326, p\u0026thinsp;=\u0026thinsp;0.005), the lower the turnover intention. For sales or marketing jobs, satisfaction with the work scope (β = -0.181, p\u0026thinsp;=\u0026thinsp;0.010), salary (β = -0.169, p\u0026thinsp;=\u0026thinsp;0.005) and corporate culture (β = -0.314, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) factors showed low turnover intention. The turnover intention was lower when working at medium-sized or large enterprises (β = -0.106, p\u0026thinsp;=\u0026thinsp;0.029). Men had lower turnover intentions than women (β\u0026thinsp;=\u0026thinsp;0.101, p\u0026thinsp;=\u0026thinsp;0.044). For clinical or regulatory affairs jobs, the work scope and company type appeared to significantly affect turnover intention. Higher satisfaction with the work scope (β = -0.350, p\u0026thinsp;=\u0026thinsp;0.035) led to lower intention to turnover, and workers employed in the larger company (medium-sized or large enterprises) (β = -0.236, p-value\u0026thinsp;=\u0026thinsp;0.021) had a lower intention to turnover (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactors associated with turnover intention by job fields.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eProduction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eSales/marketing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eClinical/RA \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eJob-related\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWork scope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.181*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.350*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSalary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.169*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePromotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial evaluation and perception of the job\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.594\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWork environment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOrganization-related\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorporate culture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.314*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSupervisor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.326*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eColleague\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrganization commitment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial evaluation and perception of the company\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.439\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSociodemographic\u003c/b\u003e \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender (female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.101*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge (\u0026ge;\u0026thinsp;40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarital status (Unmarried)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDependent family members (\u0026ge;\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducation (Graduate school)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCompany type (Medium-sized or large enterprises)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.106*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.236*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePosition (any level of manage)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTurnover experience (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTurnover experience (\u0026ge;\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2e-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2e-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.29E-10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eR square (Adjusted R square)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.5549 (0.4794)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.4721 (0.4335)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.472(0.3245)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eF-statistic (p-value)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e7.35 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e12.24 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e3.2 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eP-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003ea\u003c/sup\u003e RA, Regulatory affairs.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003eb\u003c/sup\u003e Standardized beta coefficient.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003ec\u003c/sup\u003e Sociodemographic variables are dummies.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eReference group (variables): male (gender), \u0026lt;\u0026thinsp;40 (age), married (marital status), 0 (dependent family members), undergraduate school or lower (education), except medium-sized or large enterprises (company size), individual contributor (position), and 0 (turnover experience).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe factors affecting turnover differed among the three fields. The turnover intention in production positions was mainly organization-related, whereas job-related factors affected the turnover intention in sales, marketing, and clinical or regulatory affairs positions. In production positions, the higher the satisfaction with the supervisor, the lower the turnover intention. As production workers who handle dangerous machinery and chemicals often have a vertical organizational culture for creating a safe working environment, it can be seen that the worse the relationship with the supervisor in the organization, the higher the intention to leave (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). However, job-related factors did not affect turnover intentions. A fundamental reason is the characteristics of production workers, in which standardized work is repeatedly performed based on protocols and standards of procedure (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Therefore, the job competencies required for production workers include accurately an accurate understanding of the order and content of work and carrying out the assigned work. The sales or marketing job group generally establishes not only sales goals by analyzing the market environment, competitors, and customers but also strategies to achieve such goals. Job-related factors, such as work stress for achieving the goals, influence the intention to leave, as job performance is focused on achieving the set goal, which is consistent with Kim\u0026rsquo;s (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) study. Moreover, unstable wages and incentive systems based on performance-based achievements influence turnover intention (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Clinical or regulatory affairs positions in charge of drug clinical trials and approvals have a higher level of job difficulty than other pharmaceutical jobs because this job category requires in-depth expertise. According to Myung\u0026rsquo;s (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) study, employees who do not have sufficient professional knowledge may experience decreased job satisfaction because of difficulties in performing their duties (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Thus, satisfaction with work scope as a job-related factor can be considered to affect turnover intention (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding sociodemographic factors, employees in small-sized or venture companies show higher turnover intention rates because social evaluation is relatively lower than that of large companies, and the welfare of such companies is also poor (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). This finding is similar to that of a previous study (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) that found a negative relationship between company performance and size and employee turnover. Women were more likely to leave their jobs than men in sales or marketing positions. This could be seen as a phenomenon created by the top-down working atmosphere in which the proportion of men was high in the past corporate culture. However, because there are conflicting research results (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) showing that women's turnover rates are generally lower than that of men, it would be difficult to generalize the results of this study that was analyzed with a limited number of samples.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, this study is the first to compare and analyze workers\u0026rsquo; turnover intention by job field in biopharmaceutical companies and derive recommendations for how biopharmaceutical companies should manage human resources by job field. Notwithstanding its strengths, this study had some limitations. First, the findings of this study might be limited in generalizability to other countries, as the case study of Korea was investigated. However, this study compared the factors related to turnover intention among the three job categories, and these differences could have implications for other countries. Second, the results of this study may differ from the actual turnover situation because it investigates turnover intention. Therefore, it would be desirable to interpret this on the premise that the intention to leave led to change.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study confirms the differences in turnover intention in biopharmaceutical companies by job field. Despite these limitations, the findings of this study can help biopharmaceutical companies to manage their workforce by job field. The factors affecting the turnover of production workers were organization-related factors, such as supervisors and organization commitment. Job-related factors affect turnover intention in sales or marketing (work scope and salary), and clinical or regulatory affairs (work scope). Therefore, measures such as creating a collaborative culture to improve organizational engagement, providing leadership training for supervisors, availing job training to improve job satisfaction, establishing an appropriate wage compensation system for sales or marketing fields, and ensuring job training to enhance job satisfaction for clinical or regulatory affairs employees are needed to reduce turnover intention in the biopharmaceutical industry. Ultimately, it will be possible to reduce workers\u0026rsquo; turnover rates and contribute to stabilizing biopharmaceutical companies\u0026rsquo; organizations and businesses.\u003c/p\u003e"},{"header":"List Of Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfirmatory factor analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eComposite reliability\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAVE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAverage variance extracted\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHTMT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHeterotrait\u0026ndash;monotrait\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eANOVA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnalysis of variance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNo sources of funding were used in the preparation of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe approval of the Institutional Review Board for this study was approved by the Institutional Review Board of Sungkyunkwan University (IRB no. SKKU- 2019-10-032), and all methods applied in this study were performed in accordance with Article 16 of the Rule of the Bioethics and Safety Act in Korea. Also, informed consent was obtained from all participants who responded to the questionnaire.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJP, DH, and EKL designed the study and research concept. ARC, SHK, JMC, and WRY acquired and arranged the data. Analysis was performed by ARC and JP, and interpretation of analysis results was conducted by DH and EKL. SHK, JMC, and WRY undertook the literature review, ARC and JP drafted the initial manuscript, and DH and EKL confirmed the manuscript. All authors read and approved the final manuscript. The authors of this manuscript take responsibility for the integrity of the data and the accuracy of the data analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRalf Otto AS, and Ulf Schrader. Biopharmaceuticals could become the core of the pharmaceutical industry, but not without significant transformation in the laboratory and in strategy, technology, and operations 2014 [Available from: https://www.mckinsey.com/industries/life-sciences/our-insights/rapid-growth-in-biopharma.\u003c/li\u003e\n\u003cli\u003eRobert A, Benoit-Vical F, Liu Y, Meunier B. Small Molecules: The Past or the Future in Drug Innovation? Met Ions Life Sci. 2019;19.\u003c/li\u003e\n\u003cli\u003eDukart H, Patel P, Telpis V, Yngve J. Reskilling employees to address talent gaps can help a company retain the bulk of its operations workers and empower them to take advantage of a new world 2020 [Available from: https://www.mckinsey.com/industries/life-sciences/our-insights/pharma-operations-creating-the-workforce-of-the-future#/.\u003c/li\u003e\n\u003cli\u003ePoon YR, Lin YP, Griffiths P, Yong KK, Seah B, and Liaw SY. A global overview of healthcare workers\u0026apos; turnover intention amid COVID-19 pandemic: a systematic review with future directions. Hum Resour Health. 2022;20(1):70.\u003c/li\u003e\n\u003cli\u003eDawson AJ, Stasa H, Roche MA, Homer CS, and Duffield C. Nursing churn and turnover in Australian hospitals: nurses perceptions and suggestions for supportive strategies. BMC Nurs. 2014;13(1):11.\u003c/li\u003e\n\u003cli\u003ePerreira TA, Berta W, Herbert M. The employee retention triad in health care: Exploring relationships amongst organisational justice, affective commitment and turnover intention. J Clin Nurs. 2018;27(7-8):e1451-e61.\u003c/li\u003e\n\u003cli\u003eMarufu TC, Collins A, Vargas L, Gillespie L, Almghairbi D. Factors influencing retention among hospital nurses: systematic review. Br J Nurs. 2021;30(5):302-8.\u003c/li\u003e\n\u003cli\u003eKovner CT, Brewer CS, Fatehi F, Jun J. What does nurse turnover rate mean and what is the rate? Policy Polit Nurs Pract. 2014;15(3-4):64-71.\u003c/li\u003e\n\u003cli\u003eRobinson PC, Liew DFL, Tanner HL, Grainger JR, Dwek RA, Reisler RB, et al. COVID-19 therapeutics: Challenges and directions for the future. Proc Natl Acad Sci U S A. 2022;119(15):e2119893119.\u003c/li\u003e\n\u003cli\u003eKim J-H, A Study on factors affecting voluntary turnover intention [master\u0026apos;s thesis]. The graduate school of business administration Yeungnam University; 2006.\u003c/li\u003e\n\u003cli\u003eMyung M-K, Factors affecting on turnover intention of clinical nurses [master\u0026apos;s thesis]. The graduate school of Kwangju Women\u0026apos;s University; 2013.\u003c/li\u003e\n\u003cli\u003eAwwad MS and Heyari HI, Predicting employee turnover using financial indicators in the pharmaceutical industry, Industrial and Commercial Training, 2022;54(3): 476-496, https://doi.org/10.1108/ICT-01-2022-0004.\u003c/li\u003e\n\u003cli\u003eKepplinger EE. FDA\u0026apos;s Expedited Approval Mechanisms for New Drug Products. Biotechnol Law Rep. 2015;34(1):15-37.\u003c/li\u003e\n\u003cli\u003eUNCTAD Secretariat. The role of competition in the pharmaceutical sector and its benefits for consumers: note/by the UNCTAD secretariat. 2015.\u003c/li\u003e\n\u003cli\u003eCalfee JE. The role of marketing in pharmaceutical research and development. Pharmacoeconomics. 2002;20 Suppl 3:77-85.\u003c/li\u003e\n\u003cli\u003eIsrael GD. Determining sample size. University of Florida Cooperative Extension Service. 1992.\u003c/li\u003e\n\u003cli\u003eCotton JL, Tuttle JM. Employee turnover: A meta-analysis and review with implications for research. Academy of management Review. 1986;11(1):55-70.\u003c/li\u003e\n\u003cli\u003eLee TW, Mowday RT. Voluntarily leaving an organization: An empirical investigation of Steers and Mowday\u0026apos;s model of turnover. Academy of Management journal. 1987;30(4):721-43.\u003c/li\u003e\n\u003cli\u003eSong K. A Study on factors affecting voluntary turnover intention [master\u0026apos;s thesis]. Seoul: The graduate school of education Hanyang University; 2000.\u003c/li\u003e\n\u003cli\u003eSmith PC, Kandell LM, Hulin CL. The measurement of satisfaction in work and retirement: A strategy for the study of attitudes. Chicago, Illinois: Rand McNally and Company; 1969.\u003c/li\u003e\n\u003cli\u003eCha J-B, Ryu G-Y, Lee H-Y. An empirical study on the relationship effect of pharmaceutical sales representative\u0026rsquo;s personal-job fit/person-organization fit and job satisfaction, organizational commitment, and turnover intentions. Korean Journal of Business Administration. 2013;26(3):567-88.\u003c/li\u003e\n\u003cli\u003eGriethuijsen RALF, Eijck MW, Haste H, Brok PJ, Skinner NC, Mansour N, et al. Global Patterns in Students\u0026rsquo; Views of Science and Interest in Science. Research in Science Education. 2015;45(4):581-603.\u003c/li\u003e\n\u003cli\u003eCrede M, Harms P. Questionable research practices when using confirmatory factor analysis. Journal of Managerial Psychology. 2019;34:18-30.\u003c/li\u003e\n\u003cli\u003eGuadagnoli, E., \u0026amp; Velicer, W. F. (1988). Relation of sample size to the stability of component patterns. Psychological bulletin, 103(2), 265.\u003c/li\u003e\n\u003cli\u003ePituch, K. A., \u0026amp; Stevens, J. P. (2015). Applied multivariate statistics for the social sciences: Analyses with SAS and IBM\u0026rsquo;s SPSS. Routledge.\u003c/li\u003e\n\u003cli\u003eCostello AB, Osborne J. Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical assessment, research, and evaluation. 2005;10.\u003c/li\u003e\n\u003cli\u003eRaubenheimer J. An item selection procedure to maximize scale reliability and validity. SA Journal of Industrial Psychology. 2004;30(4):59\u0026ndash;64.\u003c/li\u003e\n\u003cli\u003eHair JF Jr, Hult GTM, Ringle CM, Sarstedt M, Danks NP, Ray S. Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. 1st ed. Cham, Switzerland: Springer Nature; 2021.\u003c/li\u003e\n\u003cli\u003eFornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. J Mark Res [Internet]. 1981;18(1):39.\u003c/li\u003e\n\u003cli\u003eHenseler J, Ringle CM, Sarstedt M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J Acad Mark Sci [Internet]. 2015;43(1):115\u0026ndash;35.\u003c/li\u003e\n\u003cli\u003eLam LW. Impact of competitiveness on salespeople\u0026rsquo;s commitment and performance. J Bus Res [Internet]. 2012;65(9):1328\u0026ndash;34.\u003c/li\u003e\n\u003cli\u003eJang J, Yoo T. The effect of perception of organizational politics on turnover intention: The mediating effect of stress and organizational commitment and moderating effect of honesty. Korean Journal of Industrial and Organizational Psychology. 2013;26(3):413-36.\u003c/li\u003e\n\u003cli\u003eAhn K-Y, Chang KS. The relationship between knowledge management and knowledge management performance, and the moderating effect of organizational culture in small business. Korean Business Education Review. 2012;27((1)71):88-106.\u003c/li\u003e\n\u003cli\u003eZhou S, Lee JE. The effects of pay satisfaction on job satisfaction and turnover intention. The Journal of the Korea Contents Association. 2016;16(10):693-700.\u003c/li\u003e\n\u003cli\u003eLee SY, Lee GS. A Study on Gyeonggi Province on Turnover Intentions of IT Industry. Korean Management Consulting Review. 2012;12(2):133-53.\u003c/li\u003e\n\u003cli\u003eMoon Y-M, Hong J-P. Youth Employees Turnover Determinants by Business Scale and Wage Effects. Korean Journal of Labor Studies. 2017;23(2):195-230.\u003c/li\u003e\n\u003cli\u003eSung JM, Ahn JY. Job Satisfaction, Intention of Job Separation and Quit Behavior. Korean Journal of Labor Studies. 2016;22(2):135-79.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Turnover, Turnover intention, Biopharmaceutical company, Job category, Factors analysis","lastPublishedDoi":"10.21203/rs.3.rs-3608647/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3608647/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eExcessive turnover in the biopharmaceutical industry can negatively impact public health and corporate management. This study aims to determine the difference in turnover intention by job field and compare the factors affecting it.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eAn online self-report survey was administered to employees working in the production, sales/marketing, and clinical/regulatory affairs fields of biopharmaceutical companies in Korea from September 1 to October 31, 2020. The questionnaire addressed sociodemographic, constructs but also job, organization, and personal-related factors, as well as turnover intention. The difference in turnover intention by job field was confirmed by using the analysis of variance test. Multivariate regression analysis was performed to identify the factors affecting turnover intention by job field.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 529 employees responded to the questionnaire, and 500 cases were analyzed after discarding 29 cases with missing data. Turnover intention differed according to job field (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and production was the highest. In the production field, the higher both the satisfaction with the supervisor (β = -0.326, p-value\u0026thinsp;=\u0026thinsp;0.005), the lower the turnover intention. Greater satisfaction with the work scope (β = -0.181, p-value\u0026thinsp;=\u0026thinsp;0.01), salary (β = -0.169, p-value\u0026thinsp;=\u0026thinsp;0.005) and corporate culture (β = -0.314, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) factors showed low turnover intention for sales/marketing field, and the higher the satisfaction with the work scope (β = -0.350, p-value\u0026thinsp;=\u0026thinsp;0.035), the lower the turnover intention for clinical/regulatory affairs field.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eTo reduce the turnover rate in the biopharmaceutical industry, it is necessary to develop policies that align with the unique needs of each job field. Companies should focus on increasing satisfaction with their supervisor for production field, and work scope for sales/marketing and clinical/regulatory affairs fields. Additionally, salary and corporate culture are important factors for sales/marketing field.\u003c/p\u003e","manuscriptTitle":"Comparison of factors affecting turnover intention by job field in biopharmaceutical industry in Korea: A questionnaire-based study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-11-16 09:54:39","doi":"10.21203/rs.3.rs-3608647/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"788f0edc-af98-48e5-afe1-ca0f4818e87e","owner":[],"postedDate":"November 16th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-21T10:08:51+00:00","versionOfRecord":[],"versionCreatedAt":"2023-11-16 09:54:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3608647","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3608647","identity":"rs-3608647","version":["v1"]},"buildId":"ehx78VzkSd0WSzXnipQa-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}