Person-Environment Fit and Organizational Performance: Polynomial Regression and Response Surface Analysis

Person-Environment Fit and Organizational Performance: Polynomial Regression and Response Surface Analysis | 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 Person-Environment Fit and Organizational Performance: Polynomial Regression and Response Surface Analysis Daokui Jiang, Lei Ning, Yiting Zhang, Qian Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1941683/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 In the past, the linear effect of person–environment fit on the organizational process and results covers up its complex relationship. Behavioral Reciprocal Determinism Theory holds that the reasons for the changes of individual attitudes and behaviors cannot be simply attributed to individual or environmental factors, but rather to the effect of their interaction. Based on matching theory, the cross-time point method is used to collect data, and 274 valid questionnaires are obtained. The effects of person–environment fit on work satisfaction and organizational performance are analyzed by polynomial regression and response surface analysis. Bootstrapping is applied to confirm the mediating roles of work satisfaction in the above relationship. The results show that (1) Needs-Supplies (NS) fit and Demands-abilities (DA) fit and work satisfaction have an inverted U-shaped curve relationship; (2) work satisfaction has U-shaped curve relationships with task, relationship, and innovation performances; and (3) work satisfaction mediates the influence of person-environment fit and organizational performance. These findings contribute to person–environment fit research and to human resource management practices. person-environment fit work satisfaction organizational performance polynomial regression response surface analysis Figures Figure 1 Figure 2 Introduction With the continuous adjustment of economic structure, the industrial economy in China has gradually turned into a service environment (Jehanze and Mohanty, 2018). On the one hand, product development and technology updates are accelerating under the new economic form. In addition, employees’ needs and work concepts are constantly changing, which has a profound effect on enterprise human resource management. Organizational managers face problems such as high turnover rate as well as low productivity and loyalty, which affect the competitive advantage and organizational development (Zhang et al., 2012). On the other hand, the deep application of new generation information technologies, such as big data, cloud computing, and artificial intelligence, has brought many various new opportunities for enterprise management. Human resource management is more comprehensive and objective while being more humanized. The methods to improve employees’ positive work experience and well-being through effective talent management, to enhance employees’ sense of identity and loyalty to the organization, requires study as a significant issue in management innovation and improving organizational competitive advantage (Kauppila et al., 2021). The person–environment fit is a core aspect in organizational behavior, especially in the field of human resource management. However, many controversies remain regarding the content of person–environment fit and its effect mechanism on organizational performance (Van Vianen, 2018). Organizational scholars have long recognized person–environment (PE) fit (i.e., the degree of compatibility between the characteristics of employees and those of the work environment) as a dynamic process of adjustment between employees and their work environment. Nevertheless, extant studies largely treat person–environment fit as ‘static’ by assessing only one point in time and then connecting to employee outcomes ( Kim et al., 2018). As research methods improve and research perspectives change, the previous controversial views have gradually been discussed. For example, the content of person–environment fit was developed from the absorption-selection-assimilation model, then to demands–abilities (DA) fit, needs–supplies (NS) fit, next to complementary fit, consistent fit, and then to the current person–post fit, person–colleagues fit, and people–organization (Van Vianen, 2000). Specifically, process factors (leadership style, motivation, authorization, and communication) and results (such as turnover intention (Carless, 2011), employee satisfaction, organizational performance, organizational citizenship behavior) play a vital role between person–environment fit and organizational performance (Yu, 2014). This effect mechanism also extends from a linear to a curve relationship (Jiang et al., 2021). Employee satisfaction has a pivotal positive influence on employee loyalty, work engagement, and job performance in actual practice. The antecedent factors of employee satisfaction are largely included in the scope of person–environment fit (Li et al., 2021; Obrenovic et al., 2020). We find a critical clue in investigating the influence factors of work satisfaction to identify the linear or curvilinear relationship between person–environment fit and organizational performance. Previous research of this effect mechanism have produced contradictory conclusions (Li et al., 2021). Several studies show that the person–environment fit is positively correlated with organizational performance (Tesi, 2021; Tina et al., 2021; Zeijen et al., 2021) while others suggest no significant relationship between the two (Cable and DeRue, 2002; Astakhova et al., 2017). If employees’ needs are highly satisfied, then improving the person–environment fit by increasing organizational supply may not promote performance. In addition, previous measurements of person–environment fit have various insurmountable problems in theory and methods, such as reliability reduction and confusion between individuals and environmental effects (Edwards et al., 2006). The polynomial regression with response surface analysis is a matching measurement and statistical analysis strategy developed to overcome the above shortcomings (Edwards et al., 1994). Although this method is widely investigated and applied in Western contexts, local scholars rarely use this method for research in related fields. Based on person–environment fit theory, this study discusses how such fit predicts organizational performance through polynomial regression and response surface analysis. In summary, this study contributes to literature on person–environment fit theory in several important ways. First, we pay attention to NS and DA fits, which provide new perspectives to enrich the theory. In addition, this study further proves a nonlinear relationship between person–environment fit and work satisfaction, which enriches similar literature. Finally, work satisfaction is identified as a mediating role between person–environment fit and organizational performance. Furthermore, we emphasize the U-shape influence of employee satisfaction on organizational performance (task, relationship, innovation, and learning performance) to explore under which situation the work satisfaction positively predicts organizational performance. Figure 1 shows the conceptual model, which we discuss in the next sections. This paper is organized as follows. Theoretical background and hypotheses are presented in section two. Section three outlines the research methods, including the participants and procedures, measurement, and analytical strategy. Section four comprises the analysis and results, and finally presents the conclusions and implications. Theoretical Background And Hypotheses The concept of person–environment fit has long enjoyed high popularity in professional behavior and management literature (Chatman, 1989). For matching theory, relevant research includes the fit between person–job, person–subordinate, person–team, person–organization, and person–environment. Among these fields, the latter is a relatively broad concept, including that of NS, referring to the matching between environmental supply and employees’ psychological needs (e.g., desires, values, and goals), and DA fit, referring to the compatibility between environmental needs and individual personality, knowledge, skills, and abilities (Kristof-Brown, 2000; Kristof, 2006; Rounds et al., 1987; O’reilly et al., 1991; Vecchione et al., 2016). The person–environment fit is shown to have a positive effect on employees’ mental health (Caplan, 1987; Dec and Ryan, 2008; Hogg, 2000), such as personal will (Chatman, 1989; Vansteenkiste et al., 2007; Shen et al., 2018), job satisfaction (Gregory et al., 2010), organizational commitment (Milliman et al., 2017), job performance and organizational citizenship behavior (Nye et al., 2012; Marstand et al., 2017), and interpersonal relationships (Hogg, 2000; Edwards and Cable, 2009). By contrast, mismatched relationships have a negative effect on unproductive work behavior (Nye et al., 2017; Van Iddekinge et al., 2011). At the same time, the matching between people and environment has a significant effect on the basic results (work attitude, such as satisfaction) (Judge et al., 2002; Steel et al., 2008) has no significant effect on behavior results (e.g., performance, turnover rate, and job choice) (Judge and Bono, 2001; Bakker et al., 2014). Person-Environment Fit and Work Satisfaction Person–environment fit is essentially a matter of matching person characteristics with environment traits, including personality, job, colleagues, organization, and other factors. Previous literature focuses on consistency and complementary fit (Edwards, 1996; Arthur et al., 2006; Slocombe and Bluedorn, 1999; Jun and Gentry, 2005; Kristof-Brown, et al., 2005). Consistency fit refers to that of values and goals between individuals and organizations, while complementary fit refers to the consistency of needs from each other between individuals and the organization, mainly involving the complementarity between individual needs and organization supply, as well as individual capabilities and organizational demand (Lang et al., 2007). Other existing studies also focus on the person–colleague (leaders) fit, which is closely related to values and work style to a great extent. Limited to the content of this study, and following Shen et al. (2018), we use the complementary fit to represent person–environment fit, which includes NS and DA fit. Person–environment fit is the match between personal and organizational characteristics, specifically the extent to which individual traits satisfy the organization's demand. These characteristics mainly include employees’ knowledge, skills, and abilities. The person–environment fit contains two main aspects, namely, DA and NS fit. The former mainly refers to the consistency of employees’ knowledge, skills, and abilities with job responsibilities and work contents while the latter refers to whether employees’ material and spiritual needs are met by the organization through their work. Previous findings on how person–environment fit affects employee behavior remain inexact or even contradictory. Several scholars suggest that person-environment fit is positively related to work satisfaction (Rounds et al., 1987; Kristof-Brown et al., 2005; Edwards, 2008; Edwards and Shipp, 2007; Marstand et al., 2017), while others argue that such relationship is not always positive (Atitsogbui et al., 2018). If employees’ needs are highly satisfied, then improving the person–environment fit by increasing organizational supply does not necessarily benefit performance (Andela and Doef, 2018; Rauvola et al., 2020). Therefore, we believe that nonlinear models can better describe this relationship (Pee and Min, 2017). In the organizational environment, a high level of work satisfaction requires much personal work engagement. People who feel an imbalance between engagement and rewards may feel frustrated and stressed or even leave their jobs. This trend also shows the non-linear relationship between person–environment fit and work satisfaction. Generally, job and employees’ personal characteristics fall under the category of person–job fit, that is, the work environment is contained in the person–organization fit, while relationship with colleagues is contained in the person–colleague fit. Therefore, as the level of person–environment fit increases, the level of employee satisfaction also increases (Atitsogbui and Amponsah-Tawiah, 2019). In other words, better NS and DA fit bring higher level of work satisfaction. However, as the degree of matching increases, employees work hard to acquire new knowledge, master new skills, and even change old cognition. Thus, work satisfaction eventually suffers. On the basis of the above analysis, we propose the following hypotheses: Hypotheses H 1a: Needs-Supplies (NS) fit has a significant positive linear effect and a negative curvilinear (inverted U-shape) effect on work satisfaction. Specifically, work satisfaction tends to increase as the gap between needs and supplies decreases. Hypotheses H 1b : Demands-abilities (DA) fit has a significant positive linear effect and a negative curvilinear (inverted U-shape) effect on work satisfaction. Specifically, work satisfaction tends to increase as the gap between demand and ability decreases. The Mediating Role of Work Satisfaction in the Relationship Between Person-Environment Fit and Organizational Performance Work satisfaction is the overall status regarding employee perceptions and expectations regarding their work environment, referring to psychological gap between their desired and actual results, which ultimately affects job outcomes (Bertrais et al., 2021). Work satisfaction is considered closely related to job burnout, turnover intention, and organizational citizenship behavior. Generally, as the gap between employee perceptions and expectations of their environment decreases, their job satisfaction increases (Gerich and Weber, 2020). When perceptions are lower than expectations, employees develop dissatisfaction, which manifests as loss, complaints, intention to leave, and even tardiness, absenteeism, and anti-productivity. When perceptions are equal to expectations, employees develop general satisfaction, which manifests as work on time. When perceptions exceed expectations, employees develop a sense of superiority and happiness, which manifest in high organizational commitment, loyalty, and citizenship behaviors. Hauff et al. (2015) studied the effect of ethnic culture on work satisfaction according to Hofstede's cultural dimension. Significant differences were observed in the effects of job characteristics on work satisfaction across countries. Afsar (2015) investigated how person–environment fit affects employees’ innovative work behaviors and ultimately affects job performance, suggesting its positive relation on innovative work behavior and to job performance through innovation trust. Yu (2016) examined the above relationships by multiple regression analysis and found that dimensions of person–environment fit significantly and positively predicted employees’ work satisfaction. NS fit shows the strongest effect while DA fit shows the weakest effect. The types of fit can improve work satisfaction by promoting work–family balance. On the basis of the above analysis, we propose the following hypotheses: Hypotheses H 2: Work satisfaction has a positive curvilinear relationship (U-shaped) with organizational performance and its various dimensions (task, relationship, innovation, and learning). Specifically, the dimensions of organizational performance tend to increase as the work satisfaction increases. Hypotheses H 3: Work satisfaction plays a mediating role in the effect of person – environment fit on various dimensions of organizational performance (task, relationship, innovation, and learning). Research Methods Participants and Procedures This study collected data by means of key investigation on employees in the marine characteristic industrial park of the blue economic zone. The types of enterprises mainly include fishery, manufacturing, services, and transportation. The data were collected uniformly by the park management committee, and the corresponding questionnaire validity items were set as the deletion standard. Data collection was carried out twice to avoid the homology error caused by single data source (common method variance). The first round investigated the person-environment fit and work satisfaction. After an interval of one month, the second round investigated organizational performance. Subsequently, the data of the two surveys were matched. A total of 400 questionnaires were distributed. After the deletion of unqualified responses, the sample size still met the requirements. A total of 274 valid questionnaires were collected and the recovery effective rate was 68.5%. To evaluate the influence of homologous variance on the results, the exploratory factor analysis was used on the measurement items. The variables were subjected to principal component analysis and variance maximum orthogonal rotation method. Subsequently, confirmatory factor analysis was conducted by structural equation model to evaluate the consistency of each item. The results showed that no single factor explains most of the variations, and the homology error was small. Descriptive statistics show that, in terms of gender, male account for 54.4% and female for 45.6%; In terms of age, those under 25 accounted for 4.7%, 26-30 accounted for 17.2%, 31-40 accounted for 34.7%, 41-50 accounted for 27.7%, and more than 51 accounted for 15.7%; In terms of education level, 29.2% of them have college degree or below, 63.5% have bachelor degree, and 7.3% have graduate degree or above. In this study, gender, age and education were used as the control variables. Measurement All measures, adopted from previous research and examined appropriate properties, were translated from English to Chinese. Items were measured using Likert 5-point scale ranging from 1 to 5. For items of variable needs-supplies fit, demands-abilities fit and organizational performance, 1 means “strongly disagree” and 5 means “strongly agree”, and for items of work satisfaction, 1 means “strongly dissatisfied” and 5 points means “strongly satisfied” (Li and Lin, 2021; Briker et al., 2020; Naseer et al., 2021). Needs-supplies (NS) fit. Needs-supplies fit was measured with the 3-item measure (Cable and De Rue, 2002). An examples of items is “The job supply and what I pursue at work can match very well”. The Cronbach’s α of the scale was 0.843. Demands-abilities (DA) fit. Demands-abilities fit was measured with 3-item according to Cable et al. (2002) An example scale item was “My ability and training can be well matched with job requirements”. The Cronbach’s α of the scale was 0.826. Work satisfaction. Price et al. (1986) scale was used to measure target employees’ work satisfaction in their work places. Specifically, there are 5 items in this scale. For example, “How satisfied are you with your job.” “How satisfied are you with your colleagues”, etc. The Cronbach’s α of the scale was 0.854. Organizational performance. Organizational performance was divided into task performance, relationship performance, innovation performance, and learning performance (Janssen et al., 2004). Task performance was measured with 10 items (e.g., “You are competent for the tasks arranged by the organization”). The Cronbach’s α of the scale was 0.912. Relationship performance was measured with 14 items (e.g., “Even if the superiors are not present, you follow the instructions”). The Cronbach’s α of the scale was 0.938. Innovation performance was measured with 14 items (e.g., “You can propose new ideas to improve the current situation”). The Cronbach’s α of the scale was 0.921. Learning performance was measured with 14 items (e.g., “You pay much attention to gain experience through learning to improve work efficiency”). The Cronbach’s α of the scale was 0.913. Analytical Strategy The polynomial regression with response surface analysis were performed to test the relationship between person–environment fit and organizational performance. With its suitability to test the degree of association between mutual consistency or difference of two predictor and outcome variables, the polynomial regression has been widely valued and applied in recent years (Weidmann et al, 2017; Bar-Kalifa, 2017; Audenaert et al, 2018; Chen et al, 2019; Qiu et al, 2019; Paletta et al, 2021; Guo et al, 2021; Richard et al., 2021a; Richard et al., 2021b). The following equation was formulated to test the effects of person–environment fit on organizational performance: where X and Y represent NS and DA fit respectively, and Z represents organizational performance. As seen in Equation (1), the regression coefficients of X, Y, X 2 , XY, and Y 2 need to be obtained. Before analysis, we carried out a counter-check of the independent variables X and Y to reduce multicollinearity (Edwards and Parry, 1993; Edwards, 1994). The response surface technique has three key metrics: fixed point, principal axis, and slope and curvature. The principal axis describes the direction of the response surface on the X–Y axis. The first and the second principal axes are perpendicular to each other and intersect at the fixed point. Accordingly, the shape of the response surface can be assessed. the curvature along the first principal axis is the largest while that along the second principal axis is the smallest for a convex surface, and is opposite for a concave surface. Along the X=Y line, the slope is (b 1 +b 2 ) and the curvature is (b 3 +b 4 +b 5 ); along the X=-Y line, the slope is (b 1 -b 2 ) and the curvature is (b 3 -b 4 +b 5 ). When (b 3 +b 4 +b 5 ) and (b 3 -b 4 +b 5 ) are negative and statistically significant, a concave (U-shaped) surface forms along this line. Conversely, when they are positive and statistically significant, a convex (inverted U-shaped) surface is formed. Results And Analysis Reliability and Validity Analysis The internal consistency reliability and combination reliability were tested. The internal consistency reliability was based on Cronbach’s α coefficient measurement, and the results show that the Cronbach’s α is greater than 0.7, and the criterion of Composite Reliability (CR) is greater than 0.7. The results are shown in TABLE 1 . The validity analysis usually tests the construction validity of measurement factors, including convergence validity and discriminant validity. The convergence validity uses the CR value and Average Variance Extracted (AVE) discrimination. The results show that the criteria of CR is greater than 0.7 and AVE is greater than 0.5. The discriminant validity compares the individual AVE value of the two constructs with the correlation coefficient between the two constructs. If the AVE value of the two constructs is greater than the square of the correlation coefficient of the two construct variables, it indicates that there is good discriminant validity between the constructs. The results show that the discriminant validity meets the requirements, that is, the value of correlation coefficient matrix is less than the square root of diagonal AVE. TABLE 1. Correlation coefficient. Confirmatory Factor Analysis and Common Method Bias Testing Amos 22.0 structural equation was used to test the construction discrimination validity, as well as the fitting degree of the models was compared. As shown in TABLE 2 , the fitting degree of the seven factor model is better than that of the other models (Chin=2128.835, df=968, Chin/df=2.199<3, CFI=0.879, NFI=0.800, RMSEA=0.066<0.08). Referring to the treatment of Podsakoff et al. (2003) and Liang et al. (2007), this study used the ULMC (Unmeasured Latent Method Construct) method to test the effect of common bias, and the results showed that the mean of explanation level was 13.95, while the mean of ULMC was 0.29, and the ratio between the two was 47:1. Based on the above judgment, the effect of common method bias was not significant in this study. TABLE 2. Results of CFA Hypotheses Testing Effects of Person–Environment Fit on Work Satisfaction The variables of NS fit and DA must fit centralization to avoid multicollinearity. Then, the square and interactive terms must be calculated. Subsequently, the analysis method of quadratic polynomial regression equation is as follows. The first step is to place NS and DA fit into the regression equation to test their linear relationship with work satisfaction (model 1). Next, the square and interactive term of NS fit and DA fit are placed into Equation (1) to test the curve relationship and interaction effect (model 2). If the incremental meaning on statistical indicators R 2 is significant by comparing models 1 and 2, then further response surface analysis is needed. TABLE 3 presents the results for the polynomial and hierarchical regression analyses in relation to the effects of NS and DA fit on work satisfaction. The results are significant, with NS fit having a greater effect on work satisfaction (b 1 =0.653, p<0.001; b 2 =0.126, p<0.05) than DA. According to the results, changes between models 1 and 2 have significant incremental meaning (△R 2 = 0.021, p<0.001), and therefore further response surface analysis is required. TABLE 3. Results of polynomial regression analyses FIGURE 2 , shows a concave as the response surface. The function of the fixed point is to estimate the best or worst matching conditions of the predicted variables. The best combination coordinate is the fixed point (X 0 =22.97, Y 0 =13.10). The first principal axis equation is Y 1 =-0.33+0.58X and the second is Y 2 =52.41-1.71X. The following conclusions can be drawn from Table 2. First, the slope of the surface along the line of congruence (X=Y) is significantly positive with significant curvature (a 2 =-0.068, p<0.01), indicating that work satisfaction increases as the gap between NS and DA fit decreases. Thus, H 1a and H 1b are supported. Effects of Work Satisfaction on Organizational Performance We test the effects of work satisfaction on organizational performance through models 1–8 and regression analysis was performed on organizational performance. TABLE 4 shows the results . Models 1 and 2 show a significant positive relationship between work satisfaction and task performance, specifically, a U-shaped curve relationship. Models 3 to 6 show the same effects of work satisfaction on relationship and innovation performance. However, Models 7 and 8 show a significant positive relationship but no U-shaped curve between work satisfaction and learning performance. Combining models 1, 3, 5, 7 and the above analysis, we can determine that the effect of employee work satisfaction on task, relationship, innovation, and learning performance are significantly positive. The largest effects are found on innovation followed by task performance, indicating that the most apparent work satisfaction is in terms of innovation and then task completion. According to models 2, 4, 6, and 8, we can determine that work satisfaction also has a curvilinear influence relationship on task, relationship, and innovation performance. The most apparent influence is on task performance and no curvilinear influence relationship on learning, indicating that as the work satisfaction increases, the organizational performance in task, relationship, and innovation also increases. Thus, H 2 is supported. TABLE 4. Results of regression analysis The Mediating Effect of Work Satisfaction Bootstrapping (N=1000) was used to examine the mediating effect of work satisfaction. TABLE 5 shows the results. According to the criteria, if the confidence interval does not include 0, then the mediating effect is significant. Table 4 shows that, except for model 7, NS fit only has a direct effect on learning performance while work satisfaction has a mediating effect between person–environment fit on organizational performance in other models. H 3 is thus supported. TABLE 5. Results of the mediating effects Conclusion And Discussion Conclusion This study examines the effects of person–environment fit (NS and DA) on work satisfaction and organizational performance using quadratic polynomial and response surface analysis method. The mediating role of work satisfaction on the relationship between person-environment fit and organizational performance is tested. Thus, this study extends the theoretical model of person-environment fit and reveals its inverted U-shaped relationship with work satisfaction. Meanwhile, a relationship model between work satisfaction and organizational performance is established and the U-shaped curve effects of work satisfaction on task, relationship, and innovation performance are further examined. Theoretical and Practical Implications This study presents several theoretical contributions to the person–environment fit literature. First, the effect of person–environment fit on work satisfaction is examined, thereby expanding the research on factors affecting work satisfaction. Second, the effect of work satisfaction on organizational performance is confirmed, adding research on the latter’s influencing factors. Third, the mediating role of work satisfaction in the relationship of person–environment fit with organizational performance is investigated. Thus, the understanding of the relationship between person–environment fit and organizational performance and the theory of person-environment are likewise extended. A diverse workforce is a very important foundation for ensuring the vitality of the organization. This study emphasizes the importance of person–environment fit to the management of employee work satisfaction and organizational performance, and provides practical management implications. First, the person in charge of the organization must coordinate the overall effect of matching employees with the environment. The results indicate that the effects of NS and DA fit on work satisfaction and organizational performance are not simply linear, but affect each other or even have curve-influence relationships. Organization managers must grasp the person–environment fit as a whole rather than consider single elements. The ultimate goals are to achieve the sustainable competitive advantage of employees and the organization, and to maximize the long-term value of both parties. Second, scientific training and learning are necessary to improve the comprehensive ability of employees. Employees who feel that their organization exerts attempts to improve the management, are more satisfied with their work. Thus, the organizational performance also improves. Employees’ learning, which can improve their knowledge, skills, and attributes, is based on organizational learning ability. Organizations must establish a list of employee abilities to better help the latter understand the relationship between role changes and career development or other new management initiatives. In this way, the organization can also assign appropriate roles to employees based on their abilities and skills. At the same time, encouraging employees to share knowledge with their peers can generate a flow of ideas, thereby forming a consensus library for development. Third, managers must pay attention to enrich the content and methods of motivation to meet the diverse employee needs. Employees wish to perform their duties without concerns of damaging their personality, self-worth, and self-esteem; and thus tend to perform better in a work environment that is non-threatening and high in motivation, mutual assistance, and enjoyment. Therefore, to promote high work engagement, enterprises need to provide employees with opportunities to freely express opinions, emotions, and attitudes. A trust mechanism is necessary. In addition, organizations that allow participation in decision-making, grant work freedom, and provide a certain degree of independent decision-making power can arouse employee enthusiasm to contribute their talents, and increase the opportunities for employees to realize their self-worth. Limitations and Future Research Despite its contributions, this study has several limitations. First, the measures were all self-reported, which raises the possibility for common method bias. However, the evaluation of the person–organization fit and employee satisfaction may be subjective, and self-reports may be the best method to capture these feelings. Future research may solve this problem by classifying supervisors and colleagues according to their positions or departments to measure work input and innovation performance. Grouping regression can be carried out to provide more targeted meaning. Second, there are inconsistencies in the measurement of variables, such as person-environment fit, organizational performance and work satisfaction. Future research need to unify this measurement. Third, the response surface regression method is an indirect measurement strategy. Comparison of individuals and organizational attributes is the result of the cognitive evaluation of individual and environmental perceptions, which are subjective and ignores the self-evaluation of the matching between people and environment. In the future, more exploration and improvement are necessary in considering these factors and their effects on different industries. Declarations Ethics approval and consent to participate The experimental protocol was established, according to the ethical guidelines of the Helsinki Declaration and was approved by the Human Ethics Committee of Shandong Normal University. Informed consent was obtained from individual participants. Consent for publication Not applicable Availability of data and materials All data generated or analyzed during this study are included in this published article [and its supplementary information files]. Competing interests The authors declare that they have no competing interests. Funding This research was funded by Guangdong Youth Innovative Talents Project, grant number 2021WQNCX157. 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(2000). Person-organization fit: The match between newcomers’ and recruiters’ preferences for organizational cultures. Personnel Psychology, 53 , 113-149. doi: 10.1111/j.1744-6570.2000.tb00196.x Van Vianen, A. E. M. (2018). Person-environment fit: A review of its basic tenets. Annual Review of Organizational Psychology and Organizational Behavior, 5, 75-101. doi: 10.1146/annurev-orgpsych-032117-104702 Vansteenkiste, M., Bart, N., Christopher, N., Soenens, B., De Witte, H., and Brock, A. (2007). On the relations among work value orientations, psychological need satisfaction and job outcomes: A self-determination theory approach. Journal of Occupational & Organizational Psychology. 80, 251-277. doi: 10.1348/096317906X111024 Vecchione, M., Schwartz, S., Alessandri, G., Doring, A. K., Castellani, V., and Caprara, M. G. (2016). Stability and change of basic personal values in early adulthood: An 8-year longitudinal study. 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Academy of Management Journal. 55 (1), 111-130. doi: 10.5465/amj.2009.0865 Tables TABLE 1. Correlation coefficient. Variables Mean SD 1 2 3 4 5 6 7 8 9 10 1 Gender 1.456 0.499 1 2 Age 3.325 1.079 -.045 1 3 Education 1.781 0.564 .109 -.057 1 4 Needs-supplies 3.572 0.714 -.012 .094 .188 ** .872 5 Demands-abilities 3.697 0.681 -.045 .179 ** .084 .694 ** .862 6 Work Satisfaction 3.452 0.704 -.068 .075 .160 ** .653 ** .586 ** .797 7 Task Performance 3.758 0.576 .022 .213 ** .116 .694 ** .577 ** .613 ** .748 8 Relationship Performance 3.797 0.569 .071 .251 ** .108 .691 ** .524 ** .605 ** .674 ** .748 9 Innovation Performance 3.697 0.637 .043 .126 * .110 .615 ** .509 ** .642 ** .622 ** .672 ** .804 10 Learning Performance 3.855 0.617 .049 .236 ** .056 .653 ** .514 ** .505 ** .674 ** .676 ** .627 ** .812 Pearson two tailed test. * p<0.05, ** p<0.01, The diagonal is the square root of the variable AVE. TABLE 2. Results of CFA. Model fit CMIN/DF CFI NFI RMSEA a One-factor 2.879 0.807 0.733 0.083 b Two-factor 2.677 0.762 0.669 0.078 c Three-factor 2.485 0.789 0.693 0.074 d Four-factor 2.462 0.793 0.696 0.073 e Five-factor 2.449 0.795 0.698 0.073 f Six-factor 2.198 0.831 0.730 0.068 g Seven-factor 2.199 0.879 0.800 0.066 h ULMC 2.034 0.901 0.824 0.062 N = 274. RMSEA = Root Mean Square Error of Approximation, NS = Needs-supplies, DA = Demands-abilities, WS = Work Satisfaction, TP = Task Performance, RP = Relationship Performance, IP = Innovation Performance, LP = Learning Performance, ULMC = Unmeasured Latent Method Construct. a One-factor = all variables merged. b Two-factor = NS+DA, WS+TP+RP+IP+LP. c Three-factor = NS+DA, WS, TP+RP+IP+LP. d Four-factor = NS+DA, WS, TP, RP+IP+LP. e Five-factor = NS+DA, WS, TP, RP, IP+LP. f Six-factor = NS+DA, WS, TP, RP, IP, LP. g Seven-factor = hypothesized model. h ULMC = Seven-factor+CMB. TABLE 3. Results of polynomial regression analyses. B SE B SE Constant 3.524 *** .153 3.558 *** .152 Gender -.082 .056 -.081 .056 Age -.006 .026 -.001 .026 Education .038 .051 .050 .050 Needs-supplies(NS) fit, (b1) .653 *** .055 .620 *** .057 Demands-abilities(DA) fit, (b2) .126 ** .058 .124 ** .059 (NS fit) 2 , (b3) -.101 .067 NS fit × NS fit, (b4) .307 *** .101 (DA fit) 2 , (b5) -.274 *** .079 △R 2 .539 *** .021 *** a 1 =b 1 +b 2 .744 *** .290 a 2 =b 3 +b 4 +b 5 -0.068 * .269 a 3 =b 1 -b 2 .486 *** .367 a 4 =b 3 -b 4 +b 5 -.682 .348 N=274. Unstandardized regression coefficients are reported. * p<0.001, ** p<0.05, *** p<0.01 TABLE 4. Results of regression analysis. Task performance Relationship performance Innovation performance Learning performance Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Constant 1.603 *** 3.105 *** 1.544 *** 2.490 *** 1.360 *** 2.349 *** 1.833 *** 2.474 Gender .078 .073 .136 * .133 * .115 .111 .117 .115 Age .092 *** .088 *** .112 *** .109 *** .048 .046 .115 *** .113 *** Education .023 .018 .012 .009 .003 .000 -.025 -.027 Work satisfaction .491 *** -.427 .481 *** -.097 .581 *** -.024 .439 *** .047 Work satisfaction 2 .136 *** .086 * .090 * .058 Adjusted R 2 .400 *** .426 *** .415 *** .424 * .418 *** .426 * .293 *** .295 F 46.500 41.487 49.341 41.161 50.064 41.520 29.316 23.849 N=274. Unstandardized regression coefficients are reported. * p<0.001, ** p<0.05, *** p<0.01 TABLE 5. Results of the mediating effects. Task performance Relationship performance Innovation performance Learning performance Model 1’ Model 2’ Model 3’ Model 4’ Model 5’ Model 6’ Model 7’ Model 8’ Constant 2.826 2.759 2.754 2.530 2.732 2.403 3.387 3.059 Gender .058 .078 .117 .136 .093 .114 .092 .116 Age .080 .047 .100 .073 .035 .008 .100 .067 Education -.014 .023 -.024 .012 -.038 .003 -.071 -.025 Needs-supplies (NS) fit .420 [.318, .523] .416 [.068, .270] .472 [.361, .582] .534 [.417, .650] Demands-abilities (DA) fit .524 [.449, .599] .447 [.368, .526] .473 [.382, .563] .555 [.463, .648] Work satisfaction .176 [.318, .523] .200 [.127, .272] .169 [.316, .515] .233 [.157, .309] .227 [.115, .339] .318 [.231, .405] .038 [-.080, .155] .129 [.040, .218] Additional Declarations No competing interests reported. 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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-1941683","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":128042230,"identity":"37ed9278-cb79-4fc3-a285-1ce6db6dc72e","order_by":0,"name":"Daokui Jiang","email":"","orcid":"","institution":"Shandong Normal University","correspondingAuthor":false,"prefix":"","firstName":"Daokui","middleName":"","lastName":"Jiang","suffix":""},{"id":128042231,"identity":"474a5e8a-5237-4903-808f-a5beb07916b0","order_by":1,"name":"Lei Ning","email":"","orcid":"","institution":"Shandong Normal University","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Ning","suffix":""},{"id":128042232,"identity":"19df7d8d-6cfc-47ea-b7e3-e9c188c12e16","order_by":2,"name":"Yiting Zhang","email":"","orcid":"","institution":"Shandong Normal University","correspondingAuthor":false,"prefix":"","firstName":"Yiting","middleName":"","lastName":"Zhang","suffix":""},{"id":128042233,"identity":"331b6d62-5775-4653-ac79-f4a28ae4d9f7","order_by":3,"name":"Qian Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIie3QsWrDMBCA4QsCZRH1qkCw8wgKAS/11hc5LcriQEcPhlok1ENt+gh9hY4dAwJPSuaM8iNky1TazC2Ws2XQN9/PnQQQBHeIRlvjkGVxMtXaYVH6kwfeKeHmarVsjBHOdv4khnwxc5mR1UmpWb8jIw4DCwJzM9FVnhayohDVbzickHbv0K7JFGx6kl9z4Pbw6dlyRCGbRzrRzW9iKQi+8SW54PKbMDAsfZavZGSC7IlDRxWMS66fjEyJZUMMR9sx71uS963pLyx7+Uh6fb4UZRzV7XDyB7ttPAiCIPjXDwfOS5JdIZXUAAAAAElFTkSuQmCC","orcid":"","institution":"Guangdong Construct Polytech","correspondingAuthor":true,"prefix":"","firstName":"Qian","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2022-08-08 14:29:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1941683/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1941683/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":25163139,"identity":"13528891-cfd3-4beb-b3be-2e3b7ef06bf4","added_by":"auto","created_at":"2022-08-12 21:25:28","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":54996,"visible":true,"origin":"","legend":"\u003cp\u003eThe research model\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1941683/v1/aedd6bd21493b19f5983c378.jpg"},{"id":25163381,"identity":"6ef98cc9-649f-4424-aa11-29036de974e9","added_by":"auto","created_at":"2022-08-12 21:30:28","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":51233,"visible":true,"origin":"","legend":"\u003cp\u003eResponse surface of the effect of person-environment fit on work satisfaction\u003c/p\u003e","description":"","filename":"figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1941683/v1/cb0fc2078cfd807500a04a8b.jpg"},{"id":26051274,"identity":"6f4e8b33-cc75-467d-93b9-8d465a2a6b01","added_by":"auto","created_at":"2022-09-05 06:59:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":739679,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1941683/v1/b000d228-f372-4dcb-9607-65bf8e6f9c58.pdf"},{"id":25163138,"identity":"a374831e-d21e-4bc7-9374-0194f9cffa87","added_by":"auto","created_at":"2022-08-12 21:25:28","extension":"sav","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":40369,"visible":true,"origin":"","legend":"","description":"","filename":"supplement.sav","url":"https://assets-eu.researchsquare.com/files/rs-1941683/v1/5c6e69f1f50c0ee79287cf42.sav"}],"financialInterests":"No competing interests reported.","formattedTitle":"Person-Environment Fit and Organizational Performance: Polynomial Regression and Response Surface Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWith the continuous adjustment of economic structure, the industrial economy in China has gradually turned into a service environment (Jehanze and Mohanty, 2018). On the one hand, product development and technology updates are accelerating under the new economic form. In addition, employees\u0026rsquo; needs and work concepts are constantly changing, which has a profound effect on enterprise human resource management. Organizational managers face problems such as high turnover rate as well as low productivity and loyalty, which affect the competitive advantage and organizational development (Zhang et al., 2012). On the other hand, the deep application of new generation information technologies, such as big data, cloud computing, and artificial intelligence, has brought many various new opportunities for enterprise management. Human resource management is more comprehensive and objective while being more humanized. The methods to improve employees\u0026rsquo; positive work experience and well-being through effective talent management, to enhance employees\u0026rsquo; sense of identity and loyalty to the organization, requires study as a significant issue in management innovation and improving organizational competitive advantage (Kauppila et al., 2021).\u003c/p\u003e\n\u003cp\u003eThe person\u0026ndash;environment fit is a core aspect in organizational behavior, especially in the field of human resource management. However, many controversies remain regarding the content of person\u0026ndash;environment fit and its effect mechanism on organizational performance (Van Vianen, 2018).\u0026nbsp;Organizational scholars have long recognized person\u0026ndash;environment (PE) fit (i.e., the degree of compatibility between the characteristics of employees and those of the work environment) as a dynamic process of adjustment between employees and their work environment. Nevertheless, extant studies largely treat person\u0026ndash;environment\u0026nbsp;fit as \u0026lsquo;static\u0026rsquo; by assessing only one point in time and then connecting to employee outcomes\u0026nbsp;(\u003ca href=\"https://onlinelibrary.wiley.com/action/doSearch?ContribAuthorRaw=Kim,+Tae-Yeol\"\u003eKim\u003c/a\u003e et al., 2018).\u0026nbsp;As research methods improve and research perspectives change, the previous controversial views have gradually been discussed. For example, the content of person\u0026ndash;environment fit was developed from the absorption-selection-assimilation model, then to demands\u0026ndash;abilities (DA) fit, needs\u0026ndash;supplies (NS) fit, next to complementary fit, consistent fit, and then to the current person\u0026ndash;post fit, person\u0026ndash;colleagues fit, and people\u0026ndash;organization (Van Vianen, 2000). Specifically, process factors (leadership style, motivation, authorization, and communication) and results (such as turnover intention (Carless, 2011), employee satisfaction, organizational performance, organizational citizenship behavior) play a vital role between person\u0026ndash;environment fit and organizational performance (Yu, 2014). This\u0026nbsp;effect mechanism also extends from a linear to a curve relationship (Jiang et al., 2021).\u003c/p\u003e\n\u003cp\u003eEmployee satisfaction has a pivotal positive influence on employee loyalty, work engagement, and job performance in actual practice. The antecedent factors of employee satisfaction are largely included in the scope of person\u0026ndash;environment fit (Li et al., 2021; Obrenovic et al., 2020). We find a critical clue in investigating the influence factors of work satisfaction to identify the linear or curvilinear relationship between person\u0026ndash;environment fit and organizational performance. Previous research of this effect mechanism have produced contradictory conclusions (Li et al., 2021). Several studies show that the person\u0026ndash;environment fit is positively correlated with organizational performance (Tesi, 2021; Tina et al., 2021; Zeijen et al., 2021) while others suggest no significant relationship between the two (Cable and DeRue, 2002; Astakhova et al., 2017). If employees\u0026rsquo; needs are highly satisfied, then improving the person\u0026ndash;environment fit by increasing organizational supply may not promote performance. In addition, previous measurements of person\u0026ndash;environment fit have various insurmountable problems in theory and methods, such as reliability reduction and confusion between individuals and environmental effects (Edwards et al., 2006). The polynomial regression with response surface analysis is a matching measurement and statistical analysis strategy developed to overcome the above shortcomings (Edwards et al., 1994). Although this method is widely investigated and applied in Western contexts, local scholars rarely use this method for research in related fields.\u003c/p\u003e\n\u003cp\u003eBased on person\u0026ndash;environment fit theory, this study discusses how such fit predicts organizational performance through polynomial regression and response surface analysis.\u0026nbsp;In summary, this study contributes to literature on\u0026nbsp;person\u0026ndash;environment fit theory\u0026nbsp;in several important ways. First, we pay attention to NS and DA fits, which provide new perspectives to enrich the theory.\u0026nbsp;In addition,\u0026nbsp;this study further proves a nonlinear relationship between person\u0026ndash;environment fit and work satisfaction, which enriches similar literature.\u0026nbsp;Finally,\u0026nbsp;work satisfaction is identified as a mediating role between person\u0026ndash;environment fit and organizational performance. Furthermore, we emphasize the U-shape influence of employee satisfaction on organizational performance (task, relationship, innovation, and learning performance) to explore under which situation the work satisfaction positively predicts organizational performance.\u0026nbsp;\u003cstrong\u003eFigure\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003ca href=\"https://onlinelibrary.wiley.com/doi/full/10.1111/joms.12433#joms12433-fig-0001\"\u003e1\u003c/a\u003e\u003c/strong\u003e shows\u0026nbsp;the\u0026nbsp;conceptual model, which we discuss in the next sections.\u003c/p\u003e\n\u003cp\u003eThis paper is organized as follows. Theoretical background and hypotheses are presented in section two. Section three outlines the research methods, including the participants and procedures, measurement, and analytical strategy. Section four comprises the analysis and results, and finally presents the conclusions and implications.\u003c/p\u003e"},{"header":"Theoretical Background And Hypotheses","content":"\u003cp\u003eThe concept of person\u0026ndash;environment fit has long enjoyed high popularity in professional behavior and management literature (Chatman, 1989). For matching theory, relevant research includes the fit between person\u0026ndash;job, person\u0026ndash;subordinate, person\u0026ndash;team, person\u0026ndash;organization, and person\u0026ndash;environment. Among these fields, the latter is a relatively broad concept, including that of NS, referring to the matching between environmental supply and employees\u0026rsquo; psychological needs (e.g., desires, values, and goals), and DA fit, referring to the compatibility between environmental needs and individual personality, knowledge, skills, and abilities (Kristof-Brown, 2000; Kristof, 2006; Rounds et al., 1987; O\u0026rsquo;reilly et al., 1991; Vecchione et al., 2016).\u003c/p\u003e\n\u003cp\u003eThe person\u0026ndash;environment fit is shown to have a positive effect on employees\u0026rsquo; mental health (Caplan, 1987; Dec and Ryan, 2008; Hogg, 2000), such as personal will (Chatman, 1989; Vansteenkiste et al., 2007; Shen et al., 2018), job satisfaction (Gregory et al., 2010), organizational commitment (Milliman et al., 2017), job performance and organizational citizenship behavior (Nye et al., 2012; Marstand et al., 2017), and interpersonal relationships (Hogg, 2000; Edwards and Cable, 2009). By contrast, mismatched relationships have a negative effect on unproductive work behavior (Nye et al., 2017; Van Iddekinge et al., 2011). At the same time, the matching between people and environment has a significant effect on the basic results (work attitude, such as satisfaction) (Judge et al., 2002; Steel et al., 2008) has no significant effect on behavior results (e.g., performance, turnover rate, and job choice) (Judge and Bono, 2001; Bakker et al., 2014).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePerson-Environment Fit and Work Satisfaction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePerson\u0026ndash;environment fit is essentially a matter of matching person characteristics with environment traits, including personality, job, colleagues, organization, and other factors. Previous literature focuses on consistency and complementary fit (Edwards, 1996; Arthur et al., 2006; Slocombe and\u0026nbsp;Bluedorn, 1999;\u0026nbsp;Jun\u0026nbsp;and\u0026nbsp;Gentry, 2005;\u0026nbsp;Kristof-Brown, et al., 2005).\u0026nbsp;Consistency fit refers to that of values and goals between individuals and organizations, while complementary fit refers to the consistency of needs from each other between individuals and the organization, mainly involving the complementarity between individual needs and organization supply, as well as individual capabilities and organizational demand (Lang\u0026nbsp;et al., 2007).\u0026nbsp;Other existing studies also focus on the person\u0026ndash;colleague (leaders) fit, which is closely related to values and work style to a great extent. Limited to the content of this study, and following Shen et al. (2018), we use the complementary fit to represent person\u0026ndash;environment fit, which includes NS and DA fit.\u003c/p\u003e\n\u003cp\u003ePerson\u0026ndash;environment fit is the match between personal and organizational characteristics, specifically the extent to which individual traits satisfy the organization\u0026apos;s demand. These characteristics mainly include employees\u0026rsquo; knowledge, skills, and abilities. The person\u0026ndash;environment fit contains two main aspects, namely, DA and NS fit.\u0026nbsp;The former mainly refers to the consistency of employees\u0026rsquo; knowledge, skills, and abilities with job responsibilities and work contents while the latter refers to whether employees\u0026rsquo; material and spiritual needs are met by the organization through their work.\u003c/p\u003e\n\u003cp\u003ePrevious findings on how person\u0026ndash;environment fit affects employee behavior remain inexact or even contradictory. Several scholars suggest that person-environment fit is positively related to work satisfaction (Rounds et al., 1987; Kristof-Brown et al., 2005; Edwards, 2008; Edwards and Shipp, 2007; Marstand et al., 2017), while others argue that such relationship is not always positive (Atitsogbui et al., 2018).\u0026nbsp;If employees\u0026rsquo; needs are highly satisfied, then improving the person\u0026ndash;environment fit by increasing organizational supply does not necessarily benefit performance (Andela and Doef, 2018; Rauvola et al., 2020).\u0026nbsp;Therefore, we believe that nonlinear models can better describe this relationship (Pee and Min, 2017).\u0026nbsp;In the organizational environment, a high level of work satisfaction requires much personal work engagement. People who feel an imbalance between engagement and rewards may feel frustrated and stressed or even leave their jobs. This trend also shows the non-linear relationship between person\u0026ndash;environment fit and work satisfaction. Generally, job and employees\u0026rsquo; personal characteristics fall under the category of person\u0026ndash;job fit, that is, the work environment is contained in the person\u0026ndash;organization fit, while relationship with colleagues is contained in the person\u0026ndash;colleague fit. Therefore, as the level of person\u0026ndash;environment fit increases, the level of employee satisfaction also increases (Atitsogbui and Amponsah-Tawiah, 2019). In other words, better NS and DA fit bring higher level of work satisfaction. However, as the degree of matching increases, employees work hard to acquire new knowledge, master new skills, and even change old cognition. Thus, work satisfaction eventually suffers. On the basis of the above analysis, we propose the following hypotheses:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHypotheses H\u003csub\u003e1a:\u003c/sub\u003e\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eNeeds-Supplies (NS) fit has a significant positive linear effect and a negative curvilinear (inverted U-shape) effect on work satisfaction. Specifically, work satisfaction tends to increase as the gap between needs and supplies decreases.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHypotheses H\u003csub\u003e1b\u003c/sub\u003e\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e:\u0026nbsp;\u003c/em\u003e\u003cem\u003eDemands-abilities (DA) fit has a significant positive linear effect and a negative curvilinear (inverted U-shape) effect on work satisfaction. Specifically,\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003ework satisfaction tends to increase as the gap between demand and ability decreases.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Mediating Role of Work Satisfaction in the Relationship Between Person-Environment Fit and Organizational Performance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWork satisfaction is the overall status regarding employee perceptions and expectations regarding their work environment, referring to psychological gap between their desired and actual results, which ultimately affects job outcomes (Bertrais et al., 2021). Work satisfaction is considered closely related to job burnout, turnover intention, and organizational citizenship behavior. Generally, as the gap between employee perceptions and expectations of their environment decreases, their job satisfaction increases (Gerich and Weber, 2020). When perceptions are lower than expectations, employees develop dissatisfaction, which manifests as loss, complaints, intention to leave, and even tardiness, absenteeism, and anti-productivity. When perceptions are equal to expectations, employees develop general satisfaction, which manifests as work on time. When perceptions exceed expectations, employees develop a sense of superiority and happiness, which manifest in high organizational commitment, loyalty, and citizenship behaviors.\u0026nbsp;Hauff et al. (2015) studied the effect of ethnic culture on work satisfaction according to Hofstede\u0026apos;s cultural dimension. Significant differences were observed in the effects of job characteristics on work satisfaction across countries.\u0026nbsp;Afsar (2015) investigated how person\u0026ndash;environment fit affects employees\u0026rsquo; innovative work behaviors and ultimately affects job performance, suggesting its positive relation on innovative work behavior and to job performance through innovation trust.\u0026nbsp;Yu (2016)\u0026nbsp;examined the above relationships by multiple regression analysis and found that dimensions of person\u0026ndash;environment fit significantly and positively predicted employees\u0026rsquo; work satisfaction. NS fit shows the strongest effect while DA fit shows the weakest effect. The types of fit can improve work satisfaction by promoting work\u0026ndash;family balance.\u0026nbsp;On the basis of the above analysis, we propose the following hypotheses:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHypotheses H\u003csub\u003e2:\u003c/sub\u003e\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eWork satisfaction has a positive curvilinear relationship (U-shaped) with organizational performance and its various dimensions (task, relationship, innovation, and learning). Specifically, the dimensions of organizational performance tend to increase as the work satisfaction increases.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHypotheses H\u003csub\u003e3:\u003c/sub\u003e\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eWork satisfaction plays a mediating role in the effect of person\u003c/em\u003e\u0026ndash;\u003cem\u003eenvironment fit on various dimensions of organizational performance (task, relationship, innovation, and learning).\u003c/em\u003e\u003c/p\u003e"},{"header":"Research Methods","content":"\u003cp\u003e\u003cstrong\u003eParticipants and Procedures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study collected data by means of key investigation on employees in the marine characteristic industrial park of the blue economic zone. The types of enterprises mainly include fishery, manufacturing, services, and transportation. The data were collected uniformly by the park management committee, and the corresponding questionnaire validity items were set as the deletion standard. Data collection was carried out twice to avoid the homology error caused by single data source (common method variance). The first round investigated the person-environment fit and work satisfaction. After an interval of one month, the second round investigated organizational performance. Subsequently, the data of the two surveys were matched. A total of 400 questionnaires were distributed. After the deletion of unqualified responses, the sample size still met the requirements. A total of 274 valid questionnaires were collected and the recovery effective rate was 68.5%. To evaluate the influence of homologous variance on the results, the exploratory factor analysis was used on the measurement items. The variables were subjected to principal component analysis and variance maximum orthogonal rotation method. Subsequently, confirmatory factor analysis was conducted by structural equation model to evaluate the consistency of each item. The results showed that no single factor explains most of the variations, and the homology error was small.\u003c/p\u003e\n\u003cp\u003eDescriptive statistics show that, in terms of gender, male account for 54.4% and female for 45.6%; In terms of age, those under\u0026nbsp;25\u0026nbsp;accounted for 4.7%, 26-30\u0026nbsp;accounted for 17.2%, 31-40 accounted for 34.7%, 41-50 accounted for 27.7%, and more than 51 accounted for 15.7%; In terms of education level, 29.2% of them have college degree or below, 63.5% have bachelor degree, and 7.3% have graduate degree or above. In this study, gender, age and education were used as the control variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll measures, adopted from previous research and examined appropriate properties, were translated from English to Chinese. Items were measured using Likert 5-point scale ranging from 1 to 5. For items of variable needs-supplies fit, demands-abilities fit and organizational performance, 1 means \u0026ldquo;strongly disagree\u0026rdquo; and 5 means \u0026ldquo;strongly agree\u0026rdquo;, and for items of work satisfaction, 1 means \u0026ldquo;strongly dissatisfied\u0026rdquo; and 5 points means \u0026ldquo;strongly satisfied\u0026rdquo; (Li and Lin, 2021; Briker et al., 2020; Naseer et al., 2021).\u003c/p\u003e\n\u003cp\u003eNeeds-supplies (NS) fit.\u0026nbsp;Needs-supplies fit was measured with the 3-item measure\u0026nbsp;(Cable and De Rue, 2002). An examples of items is \u0026ldquo;The job supply and what I pursue at work can match very well\u0026rdquo;. The Cronbach\u0026rsquo;s \u0026alpha; of the\u0026nbsp;scale\u0026nbsp;was 0.843.\u003c/p\u003e\n\u003cp\u003eDemands-abilities (DA) fit.\u0026nbsp;Demands-abilities fit was measured with 3-item according to Cable et al. (2002) An example scale item was \u0026ldquo;My ability and training can be well matched with job requirements\u0026rdquo;. The Cronbach\u0026rsquo;s \u0026alpha; of\u0026nbsp;the\u0026nbsp;scale was 0.826.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWork satisfaction.\u0026nbsp;Price et al. (1986) scale was used to measure target employees\u0026rsquo; work satisfaction in their work places.\u0026nbsp;Specifically, there are 5 items in this scale. For example, \u0026ldquo;How satisfied are you with your job.\u0026rdquo; \u0026ldquo;How satisfied are you with your colleagues\u0026rdquo;, etc. The Cronbach\u0026rsquo;s \u0026alpha; of the scale was 0.854.\u003c/p\u003e\n\u003cp\u003eOrganizational performance. Organizational performance was divided into task performance, relationship performance, innovation performance, and learning performance (Janssen et al., 2004). Task performance was measured with 10 items (e.g., \u0026ldquo;You are competent for the tasks arranged by the organization\u0026rdquo;). The Cronbach\u0026rsquo;s \u0026alpha; of the scale was 0.912. Relationship performance was measured with 14 items (e.g., \u0026ldquo;Even if the superiors are not present, you follow the instructions\u0026rdquo;). The Cronbach\u0026rsquo;s \u0026alpha; of the\u0026nbsp;scale\u0026nbsp;was 0.938. Innovation performance was measured with 14 items (e.g., \u0026ldquo;You can propose new ideas to improve the current situation\u0026rdquo;). The Cronbach\u0026rsquo;s \u0026alpha; of the scale was 0.921. Learning performance was measured with 14 items (e.g., \u0026ldquo;You pay much attention to gain experience through learning to improve work efficiency\u0026rdquo;). The Cronbach\u0026rsquo;s \u0026alpha; of the scale was 0.913.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalytical Strategy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe polynomial regression with response surface analysis were performed to test the relationship between person\u0026ndash;environment fit and organizational performance. With its suitability to test the degree of association between mutual consistency or difference of two predictor and outcome variables, the polynomial regression has been widely valued and applied in recent years (Weidmann et al, 2017; Bar-Kalifa, 2017; Audenaert et al, 2018; Chen et al, 2019; Qiu et al, 2019; Paletta et al, 2021; Guo et al, 2021;\u0026nbsp;Richard\u0026nbsp;et al., 2021a; Richard et al., 2021b).\u003c/p\u003e\n\u003cp\u003eThe following equation was formulated to test the effects of person\u0026ndash;environment fit on organizational performance:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003ewhere X and Y represent NS and DA fit respectively, and Z represents organizational performance.\u003c/p\u003e\n\u003cp\u003eAs seen in Equation (1), the regression coefficients of X, Y, X\u003csup\u003e2\u003c/sup\u003e, XY, and Y\u003csup\u003e2\u003c/sup\u003e need to be obtained. Before analysis, we carried out a counter-check of the independent variables X and Y to reduce multicollinearity (Edwards and Parry, 1993; Edwards, 1994).\u003c/p\u003e\n\u003cp\u003eThe response surface technique has three key metrics: fixed point, principal axis, and slope and curvature. The principal axis describes the direction of the response surface on the X\u0026ndash;Y axis. The first and the second principal axes are perpendicular to each other and intersect at the fixed point. Accordingly, the shape of the response surface can be assessed. the curvature along the first principal axis is the largest while that along the second principal axis is the smallest for a convex surface, and is opposite for a concave surface.\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003eAlong the X=Y line, the slope is (b\u003csub\u003e1\u003c/sub\u003e+b\u003csub\u003e2\u003c/sub\u003e) and the curvature is (b\u003csub\u003e3\u003c/sub\u003e+b\u003csub\u003e4\u003c/sub\u003e +b\u003csub\u003e5\u003c/sub\u003e); along the X=-Y line, the slope is (b\u003csub\u003e1\u003c/sub\u003e-b\u003csub\u003e2\u003c/sub\u003e) and the curvature is (b\u003csub\u003e3\u003c/sub\u003e-b\u003csub\u003e4\u003c/sub\u003e+b\u003csub\u003e5\u003c/sub\u003e). When (b\u003csub\u003e3\u003c/sub\u003e+b\u003csub\u003e4\u003c/sub\u003e+b\u003csub\u003e5\u003c/sub\u003e) and (b\u003csub\u003e3\u003c/sub\u003e-b\u003csub\u003e4\u003c/sub\u003e+b\u003csub\u003e5\u003c/sub\u003e) are negative and statistically significant, a concave (U-shaped) surface forms along this line. Conversely, when they are positive and statistically significant, a convex (inverted U-shaped) surface is formed.\u003c/p\u003e"},{"header":"Results And Analysis","content":"\u003cp\u003e\u003cstrong\u003eReliability and Validity Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe internal consistency reliability and combination reliability were tested. The internal consistency reliability was based on Cronbach\u0026rsquo;s \u0026alpha; coefficient measurement, and the results show that the Cronbach\u0026rsquo;s \u0026alpha; is greater than 0.7, and the criterion of Composite Reliability (CR) is greater than 0.7. The results are shown in \u003cstrong\u003eTABLE 1\u003c/strong\u003e. The validity analysis usually tests the construction validity of measurement factors, including convergence validity and discriminant validity. The convergence validity uses the CR value and\u0026nbsp;Average Variance Extracted\u0026nbsp;(AVE) discrimination. The results show that the criteria of\u0026nbsp;CR\u0026nbsp;is greater than 0.7 and\u0026nbsp;AVE is\u0026nbsp;greater than 0.5. The discriminant validity compares the individual\u0026nbsp;AVE\u0026nbsp;value of the two constructs with the correlation coefficient between the two constructs. If the\u0026nbsp;AVE\u0026nbsp;value of the two constructs is greater than the square of the correlation coefficient of the two construct variables, it indicates that there is good discriminant validity between the constructs. The results show that the discriminant validity meets the requirements, that is, the value of correlation coefficient matrix is less than the square root of diagonal AVE.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 1.\u003c/strong\u003e Correlation coefficient.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConfirmatory Factor Analysis and Common Method Bias Testing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmos 22.0 structural equation was used to test the construction discrimination validity, as well as the fitting degree of the models was compared. As shown in \u003cstrong\u003eTABLE 2\u003c/strong\u003e, the fitting degree of the seven factor model is better than that of the other models (Chin=2128.835, df=968, Chin/df=2.199\u0026lt;3, CFI=0.879, NFI=0.800, RMSEA=0.066\u0026lt;0.08).\u003c/p\u003e\n\u003cp\u003eReferring to the\u0026nbsp;treatment\u0026nbsp;of\u0026nbsp;Podsakoff et al. (2003) and Liang et al. (2007), this study used the ULMC (Unmeasured Latent Method Construct) method to test the effect of common bias, and the results showed that the mean of explanation level was 13.95, while the mean of ULMC was 0.29, and the ratio between the two was 47:1. Based on the above judgment, the effect of common method bias was not significant in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 2.\u003c/strong\u003e Results of CFA\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypotheses Testing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEffects of Person\u0026ndash;Environment Fit on Work Satisfaction\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe variables of NS fit and DA must fit centralization to avoid multicollinearity. Then, the square and interactive terms must be calculated. Subsequently, the analysis method of quadratic polynomial regression equation is as follows. The first step is to place NS and DA fit into the regression equation to test their linear relationship with work satisfaction (model 1). Next, the square and interactive term of NS fit and DA fit are placed into Equation (1) to test the curve relationship and interaction effect (model 2). If the incremental meaning on statistical indicators R\u003csup\u003e2\u003c/sup\u003e is significant by comparing models 1 and 2, then further response surface analysis is needed. \u003cstrong\u003eTABLE 3\u003c/strong\u003e presents the results for the polynomial and hierarchical regression analyses in relation to the effects of NS and DA fit on work satisfaction. The results are significant, with NS fit having a greater effect on work satisfaction (b\u003csub\u003e1\u003c/sub\u003e=0.653, p\u0026lt;0.001; b\u003csub\u003e2\u003c/sub\u003e=0.126, p\u0026lt;0.05) than DA. According to the results, changes between models 1 and 2 have significant incremental meaning (△R\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e= 0.021, p\u0026lt;0.001), and therefore further response surface analysis is required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 3.\u003c/strong\u003e Results of polynomial regression analyses\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFIGURE 2\u003c/strong\u003e, shows a concave as the response surface. The function of the fixed point is to estimate the best or worst matching conditions of the predicted variables. The best combination coordinate is the fixed point (X\u003csub\u003e0\u003c/sub\u003e=22.97, Y\u003csub\u003e0\u003c/sub\u003e=13.10). The first principal axis equation is Y\u003csub\u003e1\u003c/sub\u003e=-0.33+0.58X and the second is Y\u003csub\u003e2\u003c/sub\u003e=52.41-1.71X. The following conclusions can be drawn from Table 2. First, the slope of the surface along the line of congruence (X=Y) is significantly positive with significant curvature (a\u003csub\u003e2\u003c/sub\u003e=-0.068, p\u0026lt;0.01), indicating that work satisfaction increases as the gap between NS and DA fit decreases. Thus,\u0026nbsp;H\u003csub\u003e1a\u003c/sub\u003e and H\u003csub\u003e1b\u003c/sub\u003e are supported.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEffects of Work Satisfaction on Organizational Performance\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe test the effects of work satisfaction on organizational performance through models 1\u0026ndash;8 and regression analysis was performed on organizational performance. \u003cstrong\u003eTABLE 4\u003c/strong\u003e shows the results\u003cstrong\u003e.\u003c/strong\u003e Models 1 and 2 show a significant positive relationship between work satisfaction and task performance, specifically, a U-shaped curve relationship. Models 3 to 6 show the same effects of work satisfaction on relationship and innovation performance. However, Models 7 and 8 show a significant positive relationship but no U-shaped curve between work satisfaction and learning performance. Combining models 1, 3, 5, 7 and the above analysis, we can determine that the effect of employee work satisfaction on task, relationship, innovation, and learning performance are significantly positive. The largest effects are found on innovation followed by task performance, indicating that the most apparent work satisfaction is in terms of innovation and then task completion. According to models 2, 4, 6, and 8, we can determine that work satisfaction also has a curvilinear influence relationship on task, relationship, and innovation performance. The most apparent influence is on task performance and no curvilinear influence relationship on learning, indicating that as the work satisfaction increases, the organizational performance in task, relationship, and innovation also increases. Thus, H\u003csub\u003e2\u003c/sub\u003e is supported.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 4.\u0026nbsp;\u003c/strong\u003eResults of regression analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eThe Mediating Effect of Work Satisfaction\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBootstrapping (N=1000) was used to examine the mediating effect of work satisfaction. \u003cstrong\u003eTABLE 5\u0026nbsp;\u003c/strong\u003eshows the results. According to the criteria, if the confidence interval does not include 0, then the mediating effect is significant. Table 4 shows that, except for model 7, NS fit only has a direct effect on learning performance while work satisfaction has a mediating effect between person\u0026ndash;environment fit on organizational performance in other models.\u0026nbsp;H\u003csub\u003e3\u003c/sub\u003e is thus supported.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 5.\u003c/strong\u003e Results of the mediating effects\u003c/p\u003e"},{"header":"Conclusion And Discussion","content":"\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study examines the effects of person\u0026ndash;environment fit (NS and DA) on work satisfaction and organizational performance using quadratic polynomial and response surface analysis method. The mediating role of work satisfaction on the relationship between person-environment fit and organizational performance is tested. Thus, this study extends the theoretical model of person-environment fit and reveals its inverted U-shaped relationship with work satisfaction. Meanwhile, a relationship model between work satisfaction and organizational performance is established and the U-shaped curve effects of work satisfaction on task, relationship, and innovation performance are further examined.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTheoretical and Practical Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study presents several theoretical contributions to the person\u0026ndash;environment fit literature. First, the effect of person\u0026ndash;environment fit on work satisfaction is examined, thereby expanding the research on factors affecting work satisfaction. Second, the effect of work satisfaction on organizational performance is confirmed, adding research on the latter\u0026rsquo;s influencing factors. Third, the mediating role of work satisfaction in the relationship of person\u0026ndash;environment fit with organizational performance is investigated. Thus, the understanding of the relationship between person\u0026ndash;environment fit and organizational performance and the theory of\u0026nbsp;person-environment are likewise extended.\u003c/p\u003e\n\u003cp\u003eA diverse workforce is a very important foundation for ensuring the vitality of the organization. This study\u0026nbsp;emphasizes\u0026nbsp;the importance of person\u0026ndash;environment fit to the management of employee work satisfaction and organizational performance, and provides practical management implications.\u003c/p\u003e\n\u003cp\u003eFirst, the person in charge of the organization must coordinate the overall effect of matching employees with the environment. The results indicate that the effects of NS and DA fit on work satisfaction and organizational performance are not simply linear, but affect each other or even have curve-influence relationships.\u0026nbsp;Organization managers must grasp the person\u0026ndash;environment fit as a whole rather than consider single elements.\u0026nbsp;The ultimate goals are to achieve the sustainable competitive advantage of employees and the organization, and to maximize the long-term value of both parties.\u003c/p\u003e\n\u003cp\u003eSecond, scientific training and learning are necessary to improve the comprehensive ability of employees. Employees who feel that their organization exerts attempts to improve the management, are more satisfied with their work. Thus, the organizational performance also improves.\u0026nbsp;Employees\u0026rsquo; learning, which can improve their knowledge, skills, and attributes, is based on organizational learning ability.\u0026nbsp;Organizations must establish a list of employee abilities to better help the latter understand the relationship between role changes and career development or other new management initiatives.\u0026nbsp;In this way, the organization can also assign appropriate roles to employees based on their abilities and skills. At the same time, encouraging employees to share knowledge with their peers can generate a flow of ideas, thereby forming a consensus library for development.\u003c/p\u003e\n\u003cp\u003eThird, managers must pay attention to enrich the content and methods of motivation to meet the diverse employee needs.\u0026nbsp;Employees wish to perform their duties without concerns of damaging their personality, self-worth, and self-esteem; and thus tend to perform better in a work environment that is non-threatening and high in motivation, mutual assistance, and enjoyment.\u0026nbsp;Therefore, to promote high work engagement, enterprises need to provide employees with opportunities to freely express opinions, emotions, and attitudes. A trust mechanism is necessary.\u0026nbsp;In addition, organizations that allow participation in decision-making, grant work freedom, and provide a certain degree of independent decision-making power can arouse employee enthusiasm to contribute their talents, and increase the opportunities for employees to realize their self-worth.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations and Future Research\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDespite its contributions, this study has several limitations. First, the measures were all self-reported, which raises the possibility for common method bias. However, the evaluation of the person\u0026ndash;organization fit and employee satisfaction may be subjective, and self-reports may be the best method to capture these feelings. Future research may solve this problem by classifying supervisors and colleagues according to their positions or departments to measure work input and innovation performance. Grouping regression can be carried out to provide more targeted meaning. Second, there are inconsistencies in the measurement of variables, such as person-environment fit, organizational performance and work satisfaction. Future research need to unify this measurement. Third, the response surface regression method is an indirect measurement strategy. Comparison of individuals and organizational attributes is the result of the cognitive evaluation of individual and environmental perceptions, which are subjective and ignores the self-evaluation of the matching between people and environment. In the future, more exploration and improvement are necessary in considering these factors and their effects on different industries.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe experimental protocol was established, according to the ethical guidelines of the Helsinki Declaration and was approved by the Human Ethics Committee of Shandong Normal University. Informed consent was obtained from individual participants.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNot applicable\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAll data generated or analyzed during this study are included in this published article [and its supplementary information files].\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe authors declare that they have no competing interests.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis research was funded by Guangdong Youth Innovative Talents Project, grant number 2021WQNCX157.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDJ designed the research, and collected the data, LN and YZ analyzed the data, and QL\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eexamined and critically contributed to and finally approved the manuscript.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWe thank the reviewers’ and editors’ work.\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAndela, M., and Doef, M. 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Person-environment fit and work satisfaction: Exploring the conditional effects of age. \u003cem\u003eWork, Aging and Retirement.\u003c/em\u003e 6, 101-117. doi: 10.1093/workar/waz011\u003c/li\u003e\n \u003cli\u003eRichard, O. C., Triana, M. C., and Li, M. (2021a). The Effects of Racial Diversity Congruence between Upper Management and Lower Management on Firm Productivity. \u003cem\u003eAcademy of Management Journal\u003c/em\u003e, 64, 1355-1382. doi: 10.5465/amj.2019.0468\u003c/li\u003e\n \u003cli\u003eRichard, O. C., Triana, M. C., Yucel, I., Li, M., and Pinkham, B. (2021b). The Impact of Supervisor-Subordinate Incongruence in Power Distance Orientation on Subordinate Job Strain and Subsequent Job Performance. \u003cem\u003eJ Bus Psychol\u003c/em\u003e. doi: 10.1007/s10869-021-09738-3\u003c/li\u003e\n \u003cli\u003eRounds, J. B., Ren\u0026eacute;, V. D., and Lofquist, L. H. (1987). Measurement of person-environment fit and prediction of satisfaction in the theory of work adjustment. \u003cem\u003eJournal of Vocational Behavior.\u0026nbsp;\u003c/em\u003e31, 297-318. doi: 10.1016/0001-8791(87)90045-5\u003c/li\u003e\n \u003cli\u003eShen, X. L., Li, Y. J., Sun, Y., and Zhou, Y. (2018). Person-environment fit, commitment, and customer contribution in online brand community: A nonlinear model. \u003cem\u003eJ. Bus. Res.\u003c/em\u003e\u003cem\u003e85\u003c/em\u003e, 117-126. doi: 10.1016/j.jbusres.2017.12.007\u003c/li\u003e\n \u003cli\u003eSlocombe, T., and Bluedorn, A. C. (1999). Organizational behavior implications of the congruence between preferred polychronicity and experienced work-unit polychronicity. \u003cem\u003eJournal of Organizational Behavior.\u003c/em\u003e\u003cem\u003e20\u003c/em\u003e, 75-99. doi: 10.1002/(SICI)1099-1379(199901)20:1\u0026lt;75::AID-JOB872\u0026gt;3.0.CO;2-F\u003c/li\u003e\n \u003cli\u003eSteel, P., Schmidt, J., and Shultz, J. (2008). Refining the relationship between personality and subjective well-being. \u003cem\u003ePsychological Bulletin. 134\u003c/em\u003e, 138-161. doi: 10.1037/0033-2909.134.1.138\u003c/li\u003e\n \u003cli\u003eTesi, A. (2021). A dual path model of work-related well-being in healthcare and social work settings: The interweaving between trait emotional intelligence, end-user job demands, coworkers related job resources, burnout, and work engagement. \u003cem\u003eFrontiers in Psychology. 12\u003c/em\u003e, 660035. doi: 10.3389/fpsyg.2021.660035\u003c/li\u003e\n \u003cli\u003eTina, P., Van de Karina, V., and Jaap, P. (2021). Exploring the nature and antecedents of employee energetic well-being at work and job performance profiles. \u003cem\u003eSustainability\u003c/em\u003e.\u003cem\u003e\u0026nbsp;13\u003c/em\u003e, 7424. doi: 10.3390/su13137424\u003c/li\u003e\n \u003cli\u003eVan Iddekinge, C. H., Putka, D. J., and Campbell, J. P. (2011). Reconsidering vocational interests for personnel selection: The validity of an interest-based selection test in relation to job knowledge, job performance, and continuance intentions. \u003cem\u003eJournal of Applied Psychology. 96\u003c/em\u003e, 13-33. doi: 10.1037/a0021193\u003c/li\u003e\n \u003cli\u003eVan Vianen, A. E. M. (2000). Person-organization fit: The match between newcomers\u0026rsquo; and recruiters\u0026rsquo; preferences for organizational cultures. \u003cem\u003ePersonnel Psychology, 53\u003c/em\u003e, 113-149. doi: 10.1111/j.1744-6570.2000.tb00196.x\u003c/li\u003e\n \u003cli\u003eVan Vianen, A. E. M. (2018). Person-environment fit: A review of its basic tenets. \u003cem\u003eAnnual Review of Organizational Psychology and Organizational Behavior,\u0026nbsp;\u003c/em\u003e5, 75-101. doi: 10.1146/annurev-orgpsych-032117-104702\u003c/li\u003e\n \u003cli\u003eVansteenkiste, M., Bart, N., Christopher, N., Soenens, B., De Witte, H., and Brock, A. (2007). On the relations among work value orientations, psychological need satisfaction and job outcomes: A self-determination theory approach. \u003cem\u003eJournal of Occupational \u0026amp; Organizational Psychology.\u0026nbsp;\u003c/em\u003e80, 251-277. doi: 10.1348/096317906X111024\u003c/li\u003e\n \u003cli\u003eVecchione, M., Schwartz, S., Alessandri, G., Doring, A. K., Castellani, V., and Caprara, M. G. (2016). Stability and change of basic personal values in early adulthood: An 8-year longitudinal study. \u003cem\u003eJournal of Research in Personality.\u0026nbsp;\u003c/em\u003e63, 111-122. doi: 10.1016/j.jrp.2016.06.002\u003c/li\u003e\n \u003cli\u003eWeidmann, R., Schonbrodt, F. D., Ledermann, T., and Grob, A. (2017). Concurrent and longitudinal dyadic polynomial regression analyses of big five traits and relationship satisfaction: Does similarity matter?. \u003cem\u003eJ. Res. Pers.\u003c/em\u003e 70, 6-15. doi:10.1016/j.jrp.2017.04.003\u003c/li\u003e\n \u003cli\u003eYu, K. (2014). Inter-Relationships among different types of person-environment fit and job satisfaction. \u003cem\u003eApplied Psychology.\u003c/em\u003e 65, 38-65. doi: 10.1111/apps.12035\u003c/li\u003e\n \u003cli\u003eZeijen, M., Brenninkmeijer, V., Peeters, M., and Mastenbroek, N. (2021). Exploring the role of personal demands in the health-impairment process of the job demands-resources model: A study among master students. \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e. 18, 632. doi: 10.3390/ijerph18020632\u003c/li\u003e\n \u003cli\u003eZhang, Z., Wang, M., and Shi, J. (2012). Leader-follower congruence in proactive personality and work outcomes: The mediating role of leader-member exchange. \u003cem\u003eAcademy of Management Journal. 55\u003c/em\u003e(1), 111-130. doi: 10.5465/amj.2009.0865\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTABLE 1.\u003c/strong\u003e Correlation coefficient.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"110%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.73913043478261%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.73913043478261%\"\u003e\n \u003cp\u003e1 Gender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e1.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.73913043478261%\"\u003e\n \u003cp\u003e2 Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e3.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e1.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e-.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"4.784688995215311%\"\u003e\n \u003cp\u003e3 Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.4354066985645932%\"\u003e\n \u003cp\u003e1.781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.4354066985645932%\"\u003e\n \u003cp\u003e0.564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.4354066985645932%\"\u003e\n \u003cp\u003e.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.4354066985645932%\"\u003e\n \u003cp\u003e-.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.4354066985645932%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.440191387559809%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.440191387559809%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.440191387559809%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.679425837320574%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.679425837320574%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.679425837320574%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.679425837320574%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.73913043478261%\"\u003e\n \u003cp\u003e4 Needs-supplies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e3.572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e-.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.188\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e.872\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.73913043478261%\"\u003e\n \u003cp\u003e5 Demands-abilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e3.697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0.681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e-.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.179\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.694\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e.862\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.73913043478261%\"\u003e\n \u003cp\u003e6 Work Satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e3.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0.704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e-.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.160\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.653\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.586\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e.797\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.73913043478261%\"\u003e\n \u003cp\u003e7 Task Performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e3.758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0.576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.213\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.694\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.577\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.613\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e.748\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.73913043478261%\"\u003e\n \u003cp\u003e8 Relationship Performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e3.797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.251\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.691\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.524\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.605\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.674\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e.748\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.73913043478261%\"\u003e\n \u003cp\u003e9 Innovation Performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e3.697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0.637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.126\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.615\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.509\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.642\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.622\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.672\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e.804\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.73913043478261%\"\u003e\n \u003cp\u003e10 Learning Performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e3.855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.236\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.653\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.514\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.505\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.674\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.676\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e.627\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.521739130434782%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e.812\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003ePearson two tailed test. \u003csup\u003e*\u0026nbsp;\u003c/sup\u003ep\u0026lt;0.05, \u003csup\u003e**\u003c/sup\u003e p\u0026lt;0.01, The diagonal is the square root of the variable AVE.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 2.\u003c/strong\u003e Results of CFA.\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.53061224489796%\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel fit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCMIN/DF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCFI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNFI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRMSEA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.53061224489796%\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e One-factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e2.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e0.807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.53061224489796%\"\u003e\n \u003cp\u003e\u003csup\u003eb\u003c/sup\u003e Two-factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e2.677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e0.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e0.669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.53061224489796%\"\u003e\n \u003cp\u003e\u003csup\u003ec\u003c/sup\u003e Three-factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e2.485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e0.789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e0.693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.53061224489796%\"\u003e\n \u003cp\u003e\u003csup\u003ed\u003c/sup\u003e Four-factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e2.462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e0.793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.53061224489796%\"\u003e\n \u003cp\u003e\u003csup\u003ee\u003c/sup\u003e Five-factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e2.449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e0.795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.53061224489796%\"\u003e\n \u003cp\u003e\u003csup\u003ef\u003c/sup\u003e Six-factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e2.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e0.831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e0.730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.53061224489796%\"\u003e\n \u003cp\u003e\u003csup\u003eg\u003c/sup\u003e Seven-factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e2.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e0.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.53061224489796%\"\u003e\n \u003cp\u003e\u003csup\u003eh\u003c/sup\u003e ULMC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e2.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e0.901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e0.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eN = 274. RMSEA = Root Mean Square Error of Approximation, NS = Needs-supplies, DA = Demands-abilities, WS = Work Satisfaction, TP = Task Performance, RP = Relationship Performance, IP = Innovation Performance, LP = Learning Performance, ULMC = Unmeasured Latent Method Construct.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eOne-factor = all variables merged.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/em\u003e\u003cem\u003e\u0026nbsp;Two-factor = NS+DA, WS+TP+RP+IP+LP.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003csup\u003ec\u003c/sup\u003e\u003c/em\u003e\u003cem\u003e\u0026nbsp;Three-factor = NS+DA, WS, TP+RP+IP+LP.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003csup\u003ed\u003c/sup\u003e\u003c/em\u003e\u003cem\u003e\u0026nbsp;Four-factor = NS+DA, WS, TP, RP+IP+LP.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003csup\u003ee\u003c/sup\u003e\u003c/em\u003e\u003cem\u003e\u0026nbsp;Five-factor = NS+DA, WS, TP, RP, IP+LP.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003csup\u003ef\u003c/sup\u003e\u003c/em\u003e\u003cem\u003e\u0026nbsp;Six-factor = NS+DA, WS, TP, RP, IP, LP.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003csup\u003eg\u003c/sup\u003e\u003c/em\u003e\u003cem\u003e\u0026nbsp;Seven-factor = hypothesized model.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003csup\u003eh\u0026nbsp;\u003c/sup\u003e\u003c/em\u003e\u003cem\u003eULMC = Seven-factor+CMB.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 3.\u0026nbsp;\u003c/strong\u003eResults of polynomial regression analyses.\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.5%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.5%\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e3.524\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e3.558\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e.152\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.5%\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e-.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e-.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.5%\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e-.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e-.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.5%\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.5%\"\u003e\n \u003cp\u003eNeeds-supplies(NS) fit,\u0026nbsp;(b1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e.653\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e.620\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e.057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.5%\"\u003e\n \u003cp\u003eDemands-abilities(DA) fit,\u0026nbsp;(b2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e.126\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e.124\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.5%\"\u003e\n \u003cp\u003e(NS fit)\u003csup\u003e2\u003c/sup\u003e, (b3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e-.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.5%\"\u003e\n \u003cp\u003eNS fit\u0026nbsp;\u0026times;\u0026nbsp;NS fit,\u0026nbsp;(b4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e.307\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e.101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.5%\"\u003e\n \u003cp\u003e(DA fit)\u003csup\u003e2\u003c/sup\u003e, (b5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e-.274\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e.079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.5%\"\u003e\n \u003cp\u003e△R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e.539\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003e.021\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.5%\"\u003e\n \u003cp\u003ea\u003csub\u003e1\u003c/sub\u003e=b\u003csub\u003e1\u003c/sub\u003e+b\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003e.744\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003e.290\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.5%\"\u003e\n \u003cp\u003ea\u003csub\u003e2\u003c/sub\u003e=b\u003csub\u003e3\u003c/sub\u003e+b\u003csub\u003e4\u003c/sub\u003e+b\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003e-0.068\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003e.269\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.5%\"\u003e\n \u003cp\u003ea\u003csub\u003e3\u003c/sub\u003e=b\u003csub\u003e1\u003c/sub\u003e-b\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003e.486\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003e.367\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.5%\"\u003e\n \u003cp\u003ea\u003csub\u003e4\u003c/sub\u003e=b\u003csub\u003e3\u003c/sub\u003e-b\u003csub\u003e4\u003c/sub\u003e+b\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003e-.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003e.348\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eN=274. Unstandardized regression coefficients are reported. \u003csup\u003e*\u0026nbsp;\u003c/sup\u003ep\u0026lt;0.001, \u003csup\u003e**\u003c/sup\u003e p\u0026lt;0.05, \u003csup\u003e***\u003c/sup\u003e p\u0026lt;0.01\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 4.\u003c/strong\u003e Results of regression analysis.\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.262830482115085%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"20.684292379471227%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTask performance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"20.684292379471227%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelationship performance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"20.684292379471227%\"\u003e\n \u003cp\u003e\u003cstrong\u003eInnovation performance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"20.684292379471227%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLearning performance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.370892018779344%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.370892018779344%\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e1.603\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e3.105\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e1.544\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e2.490\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e1.360\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e2.349\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e1.833\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e2.474\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.370892018779344%\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.136\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.133\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.115\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.370892018779344%\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.092\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.088\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.112\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.109\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.115\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.113\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.370892018779344%\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e-.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e-.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.370892018779344%\"\u003e\n \u003cp\u003eWork satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.491\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e-.427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.481\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e-.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.581\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e-.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.439\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.370892018779344%\"\u003e\n \u003cp\u003eWork satisfaction\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.136\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.086\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.090\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.370892018779344%\"\u003e\n \u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.400\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.426\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.415\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.424\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.418\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.426\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.293\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e.295\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.370892018779344%\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e46.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e41.487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e49.341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e41.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e50.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e41.520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e29.316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.328638497652582%\"\u003e\n \u003cp\u003e23.849\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eN=274. Unstandardized regression coefficients are reported. \u003csup\u003e*\u0026nbsp;\u003c/sup\u003ep\u0026lt;0.001, \u003csup\u003e**\u003c/sup\u003e p\u0026lt;0.05, \u003csup\u003e***\u003c/sup\u003e p\u0026lt;0.01\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 5.\u0026nbsp;\u003c/strong\u003eResults of the mediating effects.\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.78103207810321%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"19.665271966527197%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTask performance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"19.665271966527197%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelationship performance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"19.665271966527197%\"\u003e\n \u003cp\u003e\u003cstrong\u003eInnovation performance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"20.2231520223152%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLearning performance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.897615708274895%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u0026rsquo;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u0026rsquo;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3\u0026rsquo;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 4\u0026rsquo;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 5\u0026rsquo;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 6\u0026rsquo;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.378681626928472%\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 7\u0026rsquo;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 8\u0026rsquo;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.897615708274895%\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e2.826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e2.759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e2.754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e2.530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e2.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e2.403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.378681626928472%\"\u003e\n \u003cp\u003e3.387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e3.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.897615708274895%\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e.136\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.378681626928472%\"\u003e\n \u003cp\u003e.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e.116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.897615708274895%\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.378681626928472%\"\u003e\n \u003cp\u003e.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.897615708274895%\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e-.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e-.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e-.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.378681626928472%\"\u003e\n \u003cp\u003e-.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e-.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.897615708274895%\"\u003e\n \u003cp\u003eNeeds-supplies (NS) fit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e.420\u003c/p\u003e\n \u003cp\u003e[.318, .523]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e.416\u003c/p\u003e\n \u003cp\u003e[.068,\u0026nbsp;.270]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e.472\u003c/p\u003e\n \u003cp\u003e[.361, .582]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.378681626928472%\"\u003e\n \u003cp\u003e.534\u003c/p\u003e\n \u003cp\u003e[.417, .650]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.897615708274895%\"\u003e\n \u003cp\u003eDemands-abilities (DA) fit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e.524\u003c/p\u003e\n \u003cp\u003e[.449,\u0026nbsp;.599]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e.447\u003c/p\u003e\n \u003cp\u003e[.368,\u0026nbsp;.526]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e.473\u003c/p\u003e\n \u003cp\u003e[.382, .563]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.378681626928472%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e.555\u003c/p\u003e\n \u003cp\u003e[.463, .648]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.897615708274895%\"\u003e\n \u003cp\u003eWork satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e.176\u003c/p\u003e\n \u003cp\u003e[.318, .523]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e.200\u003c/p\u003e\n \u003cp\u003e[.127,\u0026nbsp;.272]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e.169\u003c/p\u003e\n \u003cp\u003e[.316,\u0026nbsp;.515]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e.233\u003c/p\u003e\n \u003cp\u003e[.157,\u0026nbsp;.309]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e.227\u003c/p\u003e\n \u003cp\u003e[.115, .339]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e.318\u003c/p\u003e\n \u003cp\u003e[.231, .405]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.378681626928472%\"\u003e\n \u003cp\u003e.038\u003c/p\u003e\n \u003cp\u003e[-.080, .155]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.817671809256662%\"\u003e\n \u003cp\u003e.129\u003c/p\u003e\n \u003cp\u003e[.040, .218]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n"}],"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":"person-environment fit, work satisfaction, organizational performance, polynomial regression, response surface analysis","lastPublishedDoi":"10.21203/rs.3.rs-1941683/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1941683/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the past, the linear effect of person–environment fit on the organizational process and results covers up its complex relationship. Behavioral Reciprocal Determinism Theory holds that the reasons for the changes of individual attitudes and behaviors cannot be simply attributed to individual or environmental factors, but rather to the effect of their interaction. Based on matching theory, the cross-time point method is used to collect data, and 274 valid questionnaires are obtained. The effects of person–environment fit on work satisfaction and organizational performance are analyzed by polynomial regression and response surface analysis. Bootstrapping is applied to confirm the mediating roles of work satisfaction in the above relationship. The results show that (1) Needs-Supplies (NS) fit and Demands-abilities (DA) fit and work satisfaction have an inverted U-shaped curve relationship; (2) work satisfaction has U-shaped curve relationships with task, relationship, and innovation performances; and (3) work satisfaction mediates the influence of person-environment fit and organizational performance. These findings contribute to person–environment fit research and to human resource management practices.\u003c/p\u003e","manuscriptTitle":"Person-Environment Fit and Organizational Performance: Polynomial Regression and Response Surface Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-08-12 21:25:26","doi":"10.21203/rs.3.rs-1941683/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":"df23efe1-b045-43ab-940f-269140f39973","owner":[],"postedDate":"August 12th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-09-05T06:59:17+00:00","versionOfRecord":[],"versionCreatedAt":"2022-08-12 21:25:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1941683","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1941683","identity":"rs-1941683","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}