Assessing the Effect of Perceived Diversity Practices and Psychological Safety on Contextual Performance for Sustainable Workplace

: Perceiving discrimination in workplace practices psychologically damages employees and affects their work performance. The current study aims to ﬁnd differences in perceived diversity practices (i.e., equal representation and developmental opportunities and gender diversity promotion) and the psychological safety of diverse groups in the workplace. Further, the study investigates the relationship of equal representation and developmental opportunities, gender diversity promotion, and psychological safety with employee contextual performance. A sample size of ( n = 536) respondents was collected from the private banking sector in India, and was further analyzed using statistical tools, such as factor analysis, correlations, analysis of variance, and regression analysis. The ﬁndings indicate differences in gender and tenure diversity regarding “equal representation and development opportunities”, and “gender diversity promotion.” Furthermore, “equal representation and development opportunities,” and “psychological safety” showed a signiﬁcant negative relationship with the employee contextual performance. Further, no association between perceived “gender diversity promotion” practices and “contextual performance” was found. The novelty of the current research is unlike previous research; the study highlights that excess psychological safety, equality in representation, and development opportunities to have “workforce diversity” make employees aggressively explicit and disengage them from additional work behavior. The ﬁndings of the study and application of SPSS software for the analysis of the employee’s perception, behavior, and performance will aid managers and leaders in better decision making for employees; business growth; effective human resource and business management; and for building a healthy, collaborative, and sustainable work environment in the private banks and similar organizations.

Introduction
The concept of diversity is highly subjective. Diverse individuals have diverse interpretations of diversity based on the social groups they belong. Majorly diverse visible traits comprise gender, age, region, caste, color, etc., whereas invisible attributes work experience, education, values, etc. Past research in diversity is primarily based on objective differences, and perceived differences were not considered, e.g., in [1][2][3]. However, perceived diversity is essential to study because different individuals have different perceptions about different compositions, and based on these perceptions, they behave in their immediate environment. Perceiving differences and equality based on different diversity traits affect employee performance, both positively and negatively. Previous research indicated the direct association between diversity and performance [4,5]. Contrary, few studies also showed no direct relationship between the two [6][7][8]. It has been speculated that, when employees perceive diversity and differential treatment among a diverse workforce, it affects them psychologically. On perceiving differences, an individual feels psychologically insecure, and, while sensing parity in the organization, they feel psychologically secure. It is a primary reason why diversity research started focusing on employee psychological safety. Psychological safety (PS) is the employee's perception about their confidence in expressing themselves freely in a given environment without any fear of repercussion in their career growth and deterioration in self-image [9]. Singh et al. [10] found the relationship between diversity environment, psychological safety, and organizational citizenship behavior (OCB). However, such studies are scarce and primarily carried out in western countries. Due to a scarcity of research on diversity that takes psychological safety as an essential factor in Asian countries, it is critical to undertake psychological safety in diversity research in India. Similarly, employee contextual performance (CP), directly and indirectly, impacts company performance since it aids the organization's smooth operation. It is the employee's performance in addition to the central task [11]. Several past research conducted in Western nations examined the link between workplace diversity and contextual performance (CP) (i.e., organizational citizenship behavior (OCB)) [6,10,12]. In contrast, several studies investigated the link between perceived diversity and firm performance [13,14]. When investigating the influence of diversity on performance in Asian counties, previous studies have neglected the relevance of contextual performance and were focused on organizational performance, e.g., [13]. As a result, we argue that contextual performance is critical in ensuring job performance and overall organizational success. Diversity programs in India are largely focused on gender. As a result, gender diversity is investigated to understand different gender perceptions of diversity approaches better. In addition, tenure diversity is taken into account in the current study due to a lack of research on employee work tenure in the Asian setting.
In the presence of a diverse workforce, analyzing the impact of perceived diversity on employee contextual performance with statistical methods and software can help technically understand cause-effect relationship between employee perception, behavior, and performance. Answers to underlying questions about "how diverse employees differ in their perceptions about organizational diversity practices and psychological safety?", and "whether perceived diversity practices and psychological safety affect employee additional work behavior?" will assist human resource (HR) policymakers and managers in implementing sustainable diversity policies and practices that will benefit the diverse workers and instill good attitudes and behaviors, thus improving employee performance and achieve the organization's common goal [15]. Thus, in the current study, using multiple data analysis techniques on SPSS software, we aim to assess the difference in the perception of tenure and gender diversity about existing diversity practices and psychological safety. Furthermore, we aim to analyze the relationship between perceived diversity practices and psychological safety with contextual performance. The conceptual model, based on the objectives of the current study, is shown in Figure 1.

Dimensions
The three main dimensions of the present study are perceived diversity, contextual performance, and psychological safety. These dimensions are discussed in the following sub-sections.

Dimensions
The three main dimensions of the present study are perceived diversity, contextual performance, and psychological safety. These dimensions are discussed in the following sub-sections.

Perceived Diversity
Huang and Iun [16] defined perceived diversity as the extent to which an individual perceives themselves as similar to others. Perceived diversity is also an employee's perception and experiences about specific groups, management policies, practices related to diversity, and organizational environment. It is essential to study perceived diversity because different team members perceive diverse team composition differently based on their beliefs related to diversity [17]. Such perceptual differences are also an outcome of individuals association with superior or inferior groups within the team [18], leading to different dynamics between employees [19]. In the current study, as shown in Figure 1, perceived diversity practices have been assessed with two variables. Firstly, "equal representation and developmental opportunities" (ERDO), i.e., equality, representation, and growth opportunities to all employees working in the organization irrespective of differences. Secondly, "gender diversity promotion" (GP), i.e., employee's perception of the importance of gender diversity about women employees.

Contextual Performance
Contextual performance (CP) is the employee's performance in addition to the central task, which they voluntarily take [11]. CP improves the overall organizational performance [20], but is not rewarded by the organization's official reward/compensation system [21]. Employees perform it at their discretion for a smooth workflow. Contextual performance is also called organizational citizenship behavior (OCB) as it includes additional supporting activities that aid task performance [22]. CP is a subset of OCB and encompasses various characteristics of it [23]. OCB is an extra work behavior [24], and an employee's voluntary work, which aids organizational effectiveness but is not officially compensated [21]. Both OCB and CP are additional performances, not measured in the official reward system yet are imperative to study because they indirectly aid the organization's success. Thus, based on previous research definitions, we regarded CP and OCB as synonyms.

Psychological Safety
Psychological safety is a psychological state in which individuals feel safe, secure, and at ease [25]. It is employee's perception about their confidence in expressing freely in a given environment without any fear of repercussion in their career growth and deterioration in self-image [9]. It is essential for diverse employees to feel psychologically safe in the workplace as psychologically safe employees are more engaged, have proactive behaviors, take the initiative, and voice opinions [26]. Similarly, psychological safety make employees proactive [27], further improving their performance at work [28].

Review of Literature
Based on the objectives, this section includes a literature review to develop various hypotheses.

Differences in Gender Diversity
Gender diversity is a different gender working together in organizations. Various research on diversity indicated different genders significantly differ in their responses. Gender substantially differs in the receptivity of a diverse workforce towards diversity management [29]. A study in Australia indicated that females are more receptive to diversity practices in the organization than males [30]. Similarly, female's attitude towards workforce diversity and practices is more favorable than males and give more importance to workforce diversity [31]. Another study in India indicated a significant difference in gender attitude towards diversity practices in the organization [32]. Various studies also indicated the difference of gender in the perception of equality in the workplace. Employee's perceptions about equality and diversity issues differ across gender. Kundu [33] indicated gender differs in their perception of gender diversity promotion practices in the IT workplaces, India. These differences in the perception of male and female are supported by the social identity theory (SIT), which explain differences based on gender and race in the work environment categorize individuals into social groups. These subgroups hold stereotypical perceptions about each other. A study based on hospitality employees from Canary Island (a tourist destination) indicated that women employees had a stronger positive relationship between perceived diversity management and job satisfaction than male employees [34]. Similarly, research on top management teams indicated that gender diversity has a positive effect on organizational performance; however, female employees feel less psychologically safe and seek more validation than male employees [35]. Past diversity research in various countries indicated the significant differences in both males and females in the workplace, necessitating studying differences in the perception of gender diversity. Thus, the current study aims to find out differences in the gender perception with regards to equality in representation and development opportunities in the organization (H1a); healthy and prosperous environment for females, i.e., gender diversity promotion (H2a); and psychological safety amongst male and female in the workplace (H3a).
Hypothesis 1a (H1a). Male and female employees have different perspectives on "Equal Representation and Development Opportunities" in the workplace.

Hypothesis 2a (H2a).
Male and Female employees have different perspectives on "Gender Diversity Promotion" in the workplace.
Hypothesis 3a (H3a). Male and Female employees differ regarding "Psychological Safety" in the workplace.

Differences inTenure Diversity
Tenure is the total length of years an employee has worked in a single organization. Employees with different tenure differ in their perception, views, attitudes, and approach towards a particular situation [36]. For example, longer tenure employees may have different perceptions about diversity issues and practices than newcomers, and may feel more psychologically safe. Few studies indicated that diversity (i.e., social categorization) decreases social integration and group cohesion, further increasing conflict [37,38]. Similarly, tenure diversity in the organization also categorizes groups based on different tenures which further affect the work environment and performance due to differences in their perceptions. Various past research indicated the relationship between diversity and performance. Tenure, race, and gender diversity have adverse effects on performance, e.g., [39,40]. Furthermore, longer-tenured employees are more committed and participative than less tenured employees [41]. Jokisaari and Nurmi [42] found that employee's perception about development opportunities evolves over their tenure, which foster newcomers to have different perceptions related to management practices than longer tenure employees. Thus, employees having different tenure in the organization may have different perceptions about diversity practices, i.e., equal representation and development opportunities (H1b), gender diversity promotion (H2b), and their psychological safety (H3b).

Hypothesis 1b (H1b).
Employees with different work tenure have different perspectives on "Equal Representation and Development Opportunities" in the workplace.

Hypothesis 2b (H2b).
Employees with different work tenure have different perspectives on "Gender Diversity Promotion" in the workplace.

Hypothesis 3b (H3b).
Employees with different work tenure differ regarding "Psychological Safety" in the workplace.

Workforce Diversity and Contextual Performance
Contextual performance is an employee's additional work behavior that is not stated in the job description. Various studies analyzed the relationship between workforce diversity and contextual performance. Chattopadhyay [12] studied 401 respondents from manufacturing organizations in the Midwest, USA, and found that demographic workforce diversity has a significant relationship with OCB/CP, mediated by employee self-esteem. Similarly, Bizri [7] analyzed responses of 300 employees working in the banking sector in Lebanon, and found a positive indirect relationship between diversity practices and OCB. In comparison, Choi [21], in a Korean-based study, indicated that employee OCB/CP deflates when they experience education and gender diversity in the workplace. Contrary, employee OCB/CP increases when they perceive tenure diversity.
Employee organizational commitment is associated with diversity practices based on the SIT (i.e., reciprocity principle), and employees are more responsible and act for organizational benefits when they perceive that organizational diversity practices benefit them emotionally, socially, and economically [43]. Few other authors also had similar findings. A conceptual model related to diversity management and contextual performance indicated that, when employees perceive equality, their engagement increases [44]. Similarly, a Malaysia-based study indicated that employee OCB increases when they experience justice and equality in the organization's diversity management policies and practices. Various SIT-based research found that HR practices affect employee's supportive behavior to attain organizational goals [45][46][47]. Ng et al. [48] demonstrated that, when employees perceive discrimination in the workplace, it affects their work output adversely. Few other studies indicated a negative association between diversity-based discrimination and contextual performance. The USA-based study found that employee's self-esteem decreases when they perceive inequality in the workplace, which adversely affects their contextual performance, e.g., behavior/initiatives towards other employees and their organization [6]. Similarly, a study based on Chinese employees indicated that abusive supervision, i.e., any verbal, non-verbal, and unfair treatment from supervisor adversely affects employee's OCB [49]. Discrimination is the result of stereotypes, and stereotypes are an outcome of differences. When employees experience such biases, their OCB gets affected adversely [50]. Likewise, workforce diversity invokes distrust and low interaction among employees, decreasing their contextual performance [51], such inverse relationship is also mediated by trust [52]. Thus, we argue that employee's perception of diversity practices, such as ERDO and GP, has a significant relationship with contextual performance (H4 and H5).

Hypothesis 4 (H4). Perceived "Equal Representation and Development
Opportunities" is significantly related to Contextual Performance.

Psychological Safety and Contextual Performance
Psychological safety is a state in which an individual feels included, safe to learn, contribute, and challenge the status quo without any fear of embarrassment, marginalization, and any form of punishment [53].Various studies demonstrated that discrimination adversely affects psychological well-being. Perceiving discrimination enhances psychological distress [54], and is strongly correlated with mental well-being, depression, and satisfaction [55]. Groups that perceive discrimination feel psychologically unsafe, which adversely affect their mental health, e.g., enhanced anxiety, depression symptoms, and post-traumatic stress disorder [56], further affecting their work performance [57]. Conversely, psychological safety enhances employee physical, emotional, and cognitive engagement [58,59]. It is vital for the smooth function of an organization as psychologically safe employees adapt easily in a turbulent work environment [60]. In the dynamic environment, psychological safety subjugates individual barriers (e.g., defensive attitude towards change, reluctance for better learning, and overwhelming feeling), thus psychologically safe employees are ready to take the initiative for the organizational goals rather than being defensive [61]. Several studies indicated the relevance of psychological safety in employee performance. Liu and Keller [62] studied 400 employees of R&D teams from Taiwan and found that psychological safety enhances employee knowledge sharing and OCB. Likewise, a study on 46 HR managers and 374 employees from various companies in China indicated that psychological safety enhances employee's promotive voice and additional innovative behavior [63]. Psychological safety plays a mediating role between the perceived subgroups and group learning behavior [64]. A Midwest, USA-based study found that psychological safety plays a mediating role in perceived diverse environment and employee performance [10]. A France-based study indicated a negative association between expertise diversity and team performance when team members have low psychological safety [65]. Likewise, a Netherlands-based study indicated a positive effect of psychological safety between expertise diversity and team performance [66]. Similarly, a Germany-based study indicated organizations have outperformed when their employees feel psychologically safe [67]. The benefits of diversity could not be reaped in the absence of psychological safety amongst diverse group members [68,69]. Thus, based on the above studies, we hypothesize that employee psychological safety affects their contextual performance (H6).

Hypothesis 6 (H6).
Psychological Safety is significantly associated with Contextual Performance.

Methodology
This section includes various sub-sections pertaining to the research methodology employed in the current study.

Sample
The present examination utilized a survey overview approach to deal with the information to assess the research speculations. Data were collected from respondents working in the private banking sector, India. Unlike public organizations in India, the private banking sector doesn't have caste-based job reservations (affirmative action) and employee's career growth primarily depends on their performance. The private banking sector employs a diverse workforce. However, it remains untapped in diversity research because of its strict privacy policy, making it both challenging and appealing. A study of workforce diversity in the banking industry would fill a vacuum in the research. As a result, the current study targeted employees working in the private banking sector. The questionnaires were distributed physically and a snowball sampling method was used to collect data since covering all the private banks in India was not practical. Data were collected from January 2019 to February 2020 from employees working in multiple private banks across various states in India. Questionnaires were distributed to 650 targeted respondents, 612 of which were obtained with a return rate of 94.15%. From the obtained responses, 536 (87.5%) completely filled responses were used for further data analysis. Figure 2 shows the demographic distribution of the responses. Out of sample (n = 536), 236 were female and 300 were male. Age distribution indicates that 148 respondents were in the age group of less than or equal to 25, 173 respondents were in the age group of 26-35, 98 respondents were in the age group of 36-45, 69 respondents were from age group 46-55, and 48 respondents belonged to the age group of 56 and above. The work tenure distribution indicated that 204 respondents work tenure of 1-2 years, 195 respondents work tenure of 2-4 years, and 137 respondents work tenure of 4-5 years of work in their respective organizations. Since the minimum requirement to work in the private banks in India is graduation, all the respondents were at least graduates from various disciplines, such as commerce, science, engineering, etc., and were working at diverse white-collar job positions. 35, 98 respondents were in the age group of 36-45, 69 respondents were from age group 46-55, and 48 respondents belonged to the age group of 56 and above. The work tenure distribution indicated that 204 respondents work tenure of 1-2 years, 195 respondents work tenure of 2-4 years, and 137 respondents work tenure of 4-5 years of work in their respective organizations. Since the minimum requirement to work in the private banks in India is graduation, all the respondents were at least graduates from various disciplines, such as commerce, science, engineering, etc., and were working at diverse white-collar job positions.

Measures
Overall, the survey included 20 items questionnaire using a 5-point Likert scale to assess the response regarding various constructs used in the study. Nine items scale was used to measure employee perceived diversity practices, including "equal representation and development opportunities" with seven items and "gender diversity promotion" with two items [13,33], which was developed by Kossek and Zonia, 1993. Further, including a six-item scale for "psychological safety" [25]. Lastly, "contextual performance" was measured using five items scale [70], which was originally developed by Koopmans, 2012.

Independent and Dependent Variables
In the analysis of variance (ANOVA), "equal representation and development opportunities," "psychological safety," and "gender diversity promotion" were considered as dependent variables. In contrast, work tenure and gender diversity were considered as

Measures
Overall, the survey included 20 items questionnaire using a 5-point Likert scale to assess the response regarding various constructs used in the study. Nine items scale was used to measure employee perceived diversity practices, including "equal representation and development opportunities" with seven items and "gender diversity promotion" with two items [13,33], which was developed by Kossek and Zonia, 1993. Further, including a six-item scale for "psychological safety" [25]. Lastly, "contextual performance" was measured using five items scale [70], which was originally developed by Koopmans, 2012.

Independent and Dependent Variables
In the analysis of variance (ANOVA), "equal representation and development opportunities," "psychological safety," and "gender diversity promotion" were considered as dependent variables. In contrast, work tenure and gender diversity were considered as independent variables. Whereas, in regression analysis, "equal representation and development opportunities" "psychological safety," and "gender diversity promotion" were taken as independent variables and "contextual performance" as the dependent variables.

Statistical Tools
Various statistical tools were used to empirically analyze obtained data, including correlations, factor analysis, analysis of variance (ANOVA), Cronbach's alfa, and regression analysis. To assess the reliability, Cronbach's alfa value was calculated. Correlations were utilized to see the link between distinct variables. To reduce complex dimensions and to reduce data, factor analysis was used. The two-way ANOVA analysis highlights critical differences in the responses of gender and work tenure. The direction and magnitude of the differences were clarified by mean and grand mean scores. The effect of perceived diversity practices and psychological safety on contextual performance was assessed via regression analysis.

Results
Principal component factor analysis with varimax rotation was used to reduce a few dimensions called "factors." Items with a factor loading > 0.50 clearly describe the factors and, consequently, three clear factors with more than 1.00 eigenvalues were found. The commonalities of these variables ranged from 0.380 to 0.748. The factors, factor loadings, eigenvalues, and percentage of the variance of all factors are shown in Table 1. The obtained factors were bifurcated into sub-scales for further analysis. The factor "equal representation and development opportunities" loaded seven variables, suggesting that diverse workforce perception about equal representation and growth avenues. The factor "gender diversity promotion" loaded two variables and assessed employee's perception of the relevance of gender diversity. The factor "psychological safety" is loaded with six variables, evaluating the degree of employee psychological safety in their respective organizations. The Cronbach's alpha values for the three sub-scales ranged from 0.672 to 0.853 (as shown in Table 1), with an overall weight of 0.858, more than the adequate value of 0.6, and preferred value of 0.7 indicated sufficient internal consistency [71,72].
The single extracted factor "contextual performance" results, factor loadings, eigenvalues, and percentage of variance are shown in Table 2. All five items of contextual performance were significantly associated with the factor "contextual performance". The derived factor explained 54.994% of the variance with a 2.750 eigenvalue. The Cronbach's α value was 0.795, indicating adequate reliability of the scale [72].  Table 3. It was observed that two variables, "ERDO" and "GP" were significantly correlated with contextual performance. Variance inflation factors (VIF), average VIF, and tolerance statistics were measured to test the degree of multicollinearity in the regression equation. All the VIF values were 1, which is below the cut-off value of 10, indicating no multicollinearity issue in the variables [73]. The obtained average VIF was 1 and not higher. Thus, the regression has no bias. On the interaction between two components, the model of observations includes the interaction term and is referred to as a "non-additive model," making the terms "interaction" and "non-additivity" interchangeable. When multiple observations are made at each level of the combination of two variables, the standard two-way ANOVA approach can simply be used to evaluate non-additivity. Thus, hypotheses H1a, H1b, H2a, H2b, H3a, and H3b were analyzed using ANOVA. Table 4 indicates the ANOVA's result for sub-scale and respective significance levels of the gender effect, work tenure effect, and the interaction effect. F-values demonstrate the differences in responses of different gender (i.e., between male and female) and different work tenures (i.e., 1-2 years, 2-4 years, and 4-5 years) as well as the interaction between the two independent variables. Hypotheses H1a and H1b were verified for "equal representation and development opportunities" (ERDO) as significant differences in the gender effect with p-value = 0.037 * and the interaction effect between gender and work tenure with p-value = 0.025 *. The gender (male and female) groups with p-value = 0.022 * and employees belonging to various work tenure groups with p-value= 0.030 * differed significantly for "gender diversity promotion." Consequently, the result indicates that hypotheses H2a and H2b were verified for gender and work tenure. Further, employees belonging to various gender groups with p-value = 0.802 * and work tenure groups with p-value = 0.642 * indicated no differences in the perception of psychological safety. The results obtained for hypotheses H3a and H3b were not significant for psychological safety, and thus, hypotheses H3a and H3b were not verified. The effects of employee-perceived diversity practices and psychological safety on contextual performance (i.e., hypotheses H4, H5, and H6) were used using regression analysis as it is a mathematical technique for estimating the cause-effect relationship. The results are shown in Table 5. In terms of R 2 values of diverse groups, the crosssectional research of Model 1 is (0.002) and Model 2 is (0.015) [74]. Significance of the relationships was tested using p-value and β value. Model 1 was considered as the base model with the control variable "work tenure." The result indicated work tenure had no significant association with contextual performance (β = 0.058, p-value = 0.288). Thus, the control variable work tenure was not substantial according to F-statistics (p ≥ 0.05). Whereas, Model 2 examined the direct effects of employee perceived diversity practice and psychological safety on contextual performance. The factor "equal representation and development opportunities" was significant (p-value ≤ 0.10, p-value = 0.068, β = −0.079) considering the F-statistics. Similarly, the coefficient of the factor "psychological safety" was significant for contextual performance (p-value ≤ 0.05, p-value = 0.033, β = −0.092). In contrast, the coefficients of the factor "gender diversity promotion" was insignificant (p-value < 0.10, p-value = 0.940, β = −0.003,). Therefore, based on the results, hypotheses H4 and H6 were verified, whereas H5 was not verified. Notes: * p ≤ 0.10: ** p ≤ 0.05.

Discussion
The current study aims to discover differences in perceptions of diverse groups, based on gender and tenure, regarding their psychological safety and HR diversity practices, such as ERDO and GP. Further, the study aimed to explore the effect of employee perception of diversity practices and PS on CP. The sample size of n = 536 respondents working in the private banking sector was analyzed using statistical tools, such as factor analysis, correlations, analysis of variance, and regression analysis in SPSS software to fulfill research objectives.
Firstly, the findings indicated that different genders, i.e., male and female, have different perceptions of equal representation and developmental opportunities in the organization. Tenure diversity, i.e., employees of diverse tenure groups, also indicated different perceptions of equal representation and developmental opportunities. Similarly, both gender diversity (male and female) and diverse tenure employees have different perceptions about gender diversity promotion. Thus, demonstrating that the diverse groups have different perceptions about the relevance of gender diversity, hiring, and retaining women employees. Findings of gender and tenure diversity with equal representation and development opportunities and gender promotion are consistent with the various studies. Employees belonging to different tenure groups have different perspectives, approaches, and opinions of a given situation, e.g., newcomers may perceive diversity issues differently from that of longer-tenured employees [36,43]. Other studies also supported that different groups of employees differ in their perception of diversity practices [32,36], gender promotion [75], and equality and diversity-related issues [33]. Further, no significant difference is found for both (gender and tenure diversity) when assessing the difference in perspectives about psychological safety. Contrary to prior studies [37,41], different gender and tenured employees indicated an identical perception of psychological safety. No difference in gender diversity and tenure diversity indicates that, irrespective of different categories of employees, all the employees have a similar notion about psychological safety, i.e., all the employees have a similar opinion about taking risks, making mistakes, feeling of acceptance, and asking for help in the workplace.
Most importantly, "ERDO" indicated a significant negative relationship with "CP". The result demonstrated that, when an employee feels equality in terms of growth opportunities and representation in the workplace, irrespective of the gender difference, they are more likely to disengage from contextual performance. Previous research indicated that, when employees perceive equal development, representation, and growth opportunities in the organization, they will go out of their way for the collective organizational success, i.e., helping their colleagues, taking the initiative, updating their skills, etc. [6,7,44,48,49]. However, the current investigation found that, when employees are treated equally regarding the availability of development opportunities, they disengage from contextual performance because they have an excess sense of security at the workplace.
The result further indicated no significant relationship between employee's perception about gender diversity promotion and contextual performance, i.e., perceived gender promotion practices pertaining to females in the organization do not strongly affect employee additional efforts (CP). The result contradicts prior research that indicates a significant relationship between gender diversity and OCB [12] and experiencing gender diversity significantly decreases OCB [21].
Interestingly, the current analysis lastly indicated that psychological safety has a significant negative relationship with contextual performance. Prior research demonstrated that, when employees feel psychologically safe to take risks, make mistakes, ask for help, and feel accepted in different groups of people working in the organization, they are more likely to engage in additional tasks voluntarily, for the organization's benefit [10,60,[62][63][64]. However, the study found that when employees feel psychologically safe, their contextual performance decreases. The high sense of security in workplaces makes employees aggressively expressive, making small issues big, taking risks, making mistakes, further avoiding extra tasks at the workplace [76,77]. The significant negative relationship between psychological safety and contextual performance is contrasting to various studies, e.g., [10,60,[62][63][64].

Implications
An employee's behavior at work depends on how psychologically safe one feels. If employees feel psychologically unsafe, their behaviors at work get adversely affected. However, the study indicated contrasting results. Theoretically, the study builds on past research and supports the idea that development opportunities and gender diversity promotion must be carried out based on merit and not just to showcase a diverse workforce pool. The novelty of the present study is unlike prior research that indicated a positive relationship between psychological safety and employee performance the present research indicates that excessive psychological safety makes employees indulge in anti-organizational behavior, reducing the fear of repercussion, and decreasing contextual performance. Furthermore, a similar study has not been conducted previously in the private banking sector in India. Practically, the study will further aid managers by understanding the vitality of psychological safety and contextual performance for improving and sustaining employee performance and attaining organizational success. The study will aid private banking organizations in India and other private organizations with no caste-based job reservations (affirmative action) around the world, where employee growth is purely dependent on meritocracy. Furthermore, the findings may apply to other culturally similar countries to India. The recommendations can be extended to various private corporations that have a significant portion of their business involving interaction with customers. Telecom and insurance are other sectors where the current study is applicable. Private organizations in Asian countries, such as Singapore, Thailand, Japan, China, and other countries that are making policies related to inclusion and opportunities of diverse workforce in workplaces, can also use the results to improve employee additional work behavior. Results obtained via data analysis using SPSS software provide conclusive results to help top management of private organizations to formulate policies; implement diversity practices; and build a healthy, collaborative, and sustainable work environment.

Conclusions, Limitations, and Future Directions
Unlike previous research that indicated a positive effect of perceived equality and psychological safety on employee's initiatives and additional work behavior [6,7,44,48,49], the current study highlights a significant negative relationship between psychological safety and contextual performance. Furthermore, ERDO had a significant negative relationship with CP irrespective of gender differences. The findings show that, when employees feel equal in terms of career advancement and representation in the workplace, regardless of gender, they are more likely to disengage from contextual performance. In addition, excessive psychological safety makes employees aggressively explicit and disengages them from additional work behavior. Thus, managers and leaders of the private banks and similar organizations must be careful and provide employees with sufficient psychological safety rather than excessive psychological safety in workplaces for sustainable growth of employees and workplace. Recommendations might also be applicable to culturally similar countries to India. It is pertinent to note that no significant relationship was found between perceived gender diversity promotion practices and contextual performance which is another area where managers and leaders can leverage this study to improve their workplace. The findings indicated differences in gender and tenure diversity concerning ERDO and GP, and no significant difference with regards to PS. The current study is unique as it indicates adverse effect of excessive ERDO and psychological on employees CP (i.e., additional work behavior). SPSS was a massive help in the study where a large amount of data was being analyzed. We can extrapolate this and confidently claim that data analysis via SPSS software can be conclusively used in large organizations.
The present study is based on ERDO and GP. In the future, variables such as compensation, appraisal, promotion, etc., can be studied to assess diversity practices. Additionally, since the sample survey included employees in the private banking sector of India, future research should focus on the public banking sector in India (including ERDO and GP) and compare the results. The study is based on gender diversity and tenure diversity. Different traits of diversity (e.g., regional diversity) can be studied further. The study was limited to find the difference in perception of gender and tenure diversity. Further, studying differences in sub-groups under gender and tenure diversity would clarify diverse employee's perceptions.
Author Contributions: Conceptualization, methodology, software, validation, formal analysis, R.D. and V.R.; investigation, R.D.; resources, R.D.; data curation, R.D.; writing-original draft preparation, R.D.; writing-review and editing, R.D. and V.R.; supervision, V.R. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.

Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.

Data Availability Statement:
The datasets used and analyzed in the current study are available from the corresponding author upon reasonable request.


Transition of chemical management in Japan – Shift to self-regulation and measures for small businesses –

Increased variety and use of chemicals and the number of chemical disasters have changed chemical management. Europe and the United States have adopted self-regulation in chemical management; furthermore, countries worldwide must comply with the relevant United Nations recommendations and international standards for chemical management. Japan has experienced numerous pollution incidents and occupational disasters, resulting in the development of laws and regulations on chemical management; however, these policies are inconsistent with international trends. In particular, the shift from a compliance approach to self-regulation and measures for small businesses remain as challenges. This paper discusses the current situation and issues in chemical management in Japan, focusing on international trends.

toward chemical management has made significant progress since the conference was held. Section 19 of Agenda 21 (Environmentally Sound Management of Toxic Chemicals, Including Prevention of Illegal International Traffic in Toxic and Dangerous Products) consists of six programs including Harmonization of Classification and Labelling of Chemicals, which resulted in the current UN Globally Harmonized System of Classification and Labelling of Chemicals (GHS) 4) . This system has become the driving force for a major shift in chemical management in Japan.
The current trends in chemical management are primarily characterized by self-regulation and implementation of recommendations in an international framework. Examples of the former include the occupational health and safety management system (e.g., International Organization for Standardization 45001), and Control Banding 5) specifically developed for chemical management in small and medium-sized businesses. Example application of the latter facet include the Montreal Protocol on Substances that Deplete the Ozone Layer 6) , the Basel Convention on the Control of Transboundary Movements of Hazardous Wastes and Their Disposal 7) , and the Stockholm Convention 8) . Other examples include action plans, such as the UN Sustainable Development Goals 9) and the GHS.

History and Current Situation of Chemical Management in Japan
Chemical management laws and regulations (preventive 299 CHEMICAL MANAGEMENT IN JAPAN report presented reform proposals, such as establishing independent administrative organizations, the shift to self-regulation, and the simplification of laws (such that only principles are described). In response, the Government of the United Kingdom enacted the Health and Safety at Work, etc. Act 2) in 1974; under the proposed reforms, the law only discusses principles, with regulations, guidelines, and approved implementation rules, among others, provided as supplemental materials. The act advises employers to adopt the "so far as is reasonably practicable" principle in dealing with safety and health, stating "if the employer could not prove that sufficient preventive measures had been taken when lawsuits were filed, the penalty would be applied". This basic principle entitled shift chemical management from a compliance approach to self-regulation. The policy was linked to occupational health and safety measures based on subsequent risk assessment and risk management decisions.
In recent years, due to the necessity of environmental protection measures, chemical management has been comprehensively carried out according to the life cycle of chemical products and has been progressing toward international unification. This trend was largely due to the adoption of Agenda 21 3) at the United Nations (UN) Conference on Environment and Development in Rio de Janeiro in 1992. Agenda 21 was prepared over two years by approximately 180 countries. It specifically presents 40 chapters on environmental issues that governments and other groups must address in the 21st century. The ongoing global movement 1  These regulations are based on the obligation to inform on the part of the supplier, the right to know on the part of the user (workers or consumers), and the recognition that hazard communication cannot occur without any law. Oddly, although Japan has encountered many pollution incidents and occupational diseases during its high economic growth period, it does not have even the most basic preventive laws and regulations concerning the collection and communication of hazard information in place. Moreover, even though informing users (workers and consumers) of chemical hazards is the first step in chemical management, a system that comprehensively informs users of chemical hazards (physical, health and environmental) easily and understandably is not available. Hazard communication was only incorporated in established laws and regulations as an additional stipulation, not as a system, for risk management concerning the specific hazard of a given chemical, thereby limiting the number of chemicals for which information should be transmitted and at the same time, the available information per se.
When the GHS was introduced in Japan in 2006, only 99 chemicals were required by the Industrial Safety and Health Act (ISHA) 13) to contain hazard information on labels. Penalties were imposed for failure to provide labels or for false labelling, and imprisonment with work for not more than six months or a fine of not more than 0.5 million yen was imposed for violations (Article 119 No.3 of the ISHA 13). The penalty provision effectively promoted compliance with the law, but it was ineffective in communicating the hazards of as many chemicals as possible. On the contrary, it led to the misunderstanding that nonpunitive chemicals were not hazardous, intentionally or otherwise. In addition, no other law stipulated that labels should include comprehensive hazard information.
In Japan, hazard communication generally refers to safety data sheets (SDSs), not labels, maybe due to the difference of the number of regulated target chemicals for each. Notwithstanding, only approximately 1,500 chemicals must have SDSs under the ISHA, the Act on Confirmation, etc. of Release Amounts of Specified Chemical Substances in the Environment and Promotion of Improvements to the Management Thereof (Pollution Release and Transfer Register [PRTRL] Law governing the PRTR-SDS System) 14) ; and the Poisonous and Deleterious Substances Control Act (PDSCA) 15) . Moreover, SDSs are rarely read by workers or consumers in general and are thus not a useful means of communicating information to them. In contrast to the case with labels, penalties are not imposed for failure to provide SDSs or false contents of SDSs. and/or counter-measures for health impairment) in Japan have been relatively well developed. They reflect pollution incidents and occupational diseases caused by chemicals that occurred during the country's high economic growth period, which began in the 1950s. The Chemical Examination and Regulation Law was established as a result of the Kanemi Oil Accident, involved polychlorinated biphenyl. The Ordinance on Prevention of Hazards due to Specified Chemicals was passed after a disaster involving vinyl chloride monomer occurred. Their creation demonstrates how many Japanese chemical management laws and regulations were formulated in response to major accidents and illnesses. These policies have been enacted to reduce the risk of accidents or for the early detection of diseases associated with the use of chemicals through the establishment of a management system in workplaces, assessment of hazards, facility requirements, handling methods, storage methods, installation of local exhaust ventilation, use of personal protective equipment, and medical examinations. By complying with these laws and regulations, employers attempt to prevent accidents and diseases caused by chemicals. Simply put, chemical management in Japan is based on and still widely observes the compliance approach.
For example, workplaces handling organic solvents have undertaken such measures as listing regulated organic solvents and implementing specific interventions, including hazard communication, education at the employment, the establishment of a safety and health committee, appointment of an operation chief, installation of local exhaust ventilation, special medical examinations, working environment measurements, biological monitoring, and protective equipment, among others (Ordinance on Prevention of Organic Solvent Poisoning) 10) . These measures and other distinct and overlapping obligations from other regulations are imposed on a limited number of listed chemicals. However, since the number of hazardous chemicals increased, the current scope of measures prevents the proper resources allocation to other nonregulated chemicals. That is, few specific legal obligations have been imposed on many other chemicals that are not listed as hazardous, such that insufficient action has been taken to fully comply with the relevant acts, laws, and regulations.
Japan also differs from Europe and the United States because it lacks a system for providing workers with chemical hazard information. For example, one provision (a council directive) 11) in Europe states that chemicals should not be placed on the market unless labelled in the 1970s. In the United States, the Hazard Communication Standard 12) was established in the early 1980s to alert workers on hazards.
H JONAI et al. 300 urge the government and industrial societies to take preventive measures should be reviewed 17) " In 2008, because of the damage caused by asbestos, the government revised Article 16 of the ISHA Enforcement Order 18) and completely prohibited the manufacture, import, supply, and use of materials containing more than 0.1% of asbestos.

Implementation of the GHS in Laws and Regulations
When the GHS was issued in 2003, among many laws and regulations concerning chemical management in Japan, only the ISHA (Article 57) required the communication of health hazards in a comprehensive but easy, understandable way on labels. In April 2006, the Ministry of Health, Labor and Welfare (MHLW) enforced a revised version of the ISHA that required labelling 99 chemicals, according to Article 57, and the SDSs of 640 chemicals, according to Article 57 No.2, to be prepared according to the GHS. The revised ISHA also required information on physical hazards to be added to labels and SDSs. Moreover, it recommended that SDSs under the PRTR-SDS system and the PDSCA be prepared according to the GHS.
The implementation of the GHS in the ISHA in Japan, which has not ratified the ILO chemical convention No. 170 1) , was an epoch-making issue for occupational safety Data on occupational accidents in Japan reveal deficiencies in laws and regulations concerning hazard communication. Table 2 presents a summary of chemical-induced health disorders caused four or more days off according to specific regulations and type of disorder as of 2018. Eye and skin disorders accounted 85% of the cases 16) . Moreover, approximately 19%, 27%, 15% and 39% of the cases were caused by chemicals subject to specific regulations, chemicals subject to SDSs provision obligation, chemicals not subject to SDSs, and unidentified chemicals respectively. These figures indicate that unregulated chemicals caused more than half of the accidents. Many cases could have been prevented if hazard information were provided through labels and/or SDSs.
Severe cases of asbestos-related lung cancers and malignant mesotheliomas are increasing in Japan, suggesting that the obligation to inform and the right to know have not developed as basic social concepts. Although the hazards of asbestos were identified in the 1980s, no organization (e.g., government, companies, trade unions, consumer organizations, media, or academic societies) initiated a major campaign against asbestos disasters. In 2006, the Japan Society for Occupational Health published "Opinion of the Society on Asbestos Problems" on its website: "Although there has been considerable accumulation of scientific knowledge, the activities of the Society that did not function so far as to  sions, businesses handling designated chemicals must provide labels and SDSs according to JISs Z 7252 and Z 7253. This revision was incredibly significant for the implementation of the GHS in Japan. The PRTR Law encompasses environmental hazards that the ISHA does not cover, such that labels and SDSs included all hazards covered by the GHS in Japan. However, the number of chemicals was limited.
In 2006, Article 28 No.2 of the ISHA was established and enforced, with the recommendation (i.e., not an obligation) that hazardous chemicals based on the GHS be subjected to risk assessment. This provision also applied to chemicals for which labelling and SDS provision are not obligatory (with endeavor) under the OISH. In June 2014, Article 57 No.3 of the ISHA was revised to require a risk assessment of chemicals for those required labelling and SDS in Article 57. This amendment was a step forward in the shift of chemical management toward self-regulation based on risk assessment. Table 3 summarizes enforcement year for labelling, SDS provision and risk assessment under the ISHA and the OISH. Table 4 provides the updated list of chemicals covered by these regulations. The chemicals that would be considered in the short and long terms are cited from the work of the Investigative Committee on Management of Chemicals in the Workplace (see also

Issues for Small Businesses
The previous section discussed the characteristics and deficiencies of the chemical management laws and regulations in Japan. This section further examines the issues as they relate to small businesses with less than 50 workers.
Several diseases caused by serious chemical disasters have occurred in Japan in recent years, including 1,2-dichloropropane and dichloromethane induced bile duct can-and health administration. This revision of the ISHA positioned the GHS as the basis for chemical hazard communication, which paved the way for the construction of hazard communication systems and the shift to self-regulation in chemical management. On the other hand, although Japan was one of the first countries that implemented the GHS, the laws and standards in which it was incorporated had limited coverage.
On April 1, 2012, revised version of Article 24 No.14 (Labels) and No.15 (SDSs) of the Ordinance on Industrial Safety and Health (OIHS) 19) were enforced. The revisions required labels and SDSs (not obligatory but with endeavor) for all hazardous chemicals excluding chemicals stipulated in Article 57 of the ISHA. Simply put, the ISHA and the OIHS stipulated that information should be conveyed on labels or SDSs for all hazardous chemicals, avoiding duplication. To accomplish this, the MHLW issued the  21) . This document states that classification and labels as well as SDSs should be provided under Japanese Industrial Standards (JISs) Z 7252 and Z 7253, respectively. Essentially, the GHS was implemented in the ISHA and the OIHS with reference to JISs.
On June 1, 2012, the Ministerial Order Specifying Methods for Providing Information on the Properties and Handling of Designated Chemicals 22) and the Guidelines for Measures Related to the Management of Class 1 Designated Chemical Substances, etc. and Class 2 Designated Chemical Substances, etc. that should be taken by businesses handling designated chemical substances, etc., 23) concerning the PRTR Law, were revised by the Ministry of Economy, Trade, and Industry. According to these provi-     Table 3. Enforcement year of the ISHA concerning labelling, SDS provision and risk assessment mostly in small businesses. Table 5 presents data on the fatalities and disorders (caused four or more days off) in Japan as of 2017, by business size. Approximately 90% and 60% of the disasters occurred in businesses with less than 300 workers and in those with less than 50 workers respectively. Thirty percent of the accidents occurred in small businesses and due to unidentified chemicals 25) .
The smaller the size of the business, the lower the per-cer, indium induced lung disease, ortho-toluidine induced bladder cancer, suspected bladder cancer due to 4,4'-methylenebis-(2-chloroaniline), respiratory disease due to inhalable acrylic acid-based polymers, and acute toxicity due to a benzyl alcohol containing paint remover. Some of these chemicals were not covered by the measures stipulated under ISHAthat is, they were not listed as hazardous, and their hazard information was probably not relayed to most affected workers in effect. Notably, the disasters occurred      approximately half that of large businesses (Table 7) 27) . A recent online survey targeting small businesses handling chemicals conducted by National Institute of Occupational Safety and Health Japan revealed interesting findings. The survey covered 500 businesses (with employers or safety and health officers as respondents) and 1,000 workers with different profiles and backgrounds, respectively. Approximately 10% of the businesses reported that workers complained of symptoms caused by chemicals, whereas approximately 30% of the workers experienced symptoms thought to be caused by chemicals at work. Approximately 50% of the businesses were aware of the need centage of workers with awareness, education and knowledge on chemical hazards (Table 6). Workers' awareness of labels and SDSs, which serve as modes of direct communication of hazards, decreases with decreasing business size. The proportion of workers with an adequate level of awareness in small businesses is less than half of that in businesses with more than 1,000 workers 26) . In addition, the implementation rate of special medical examinations and working environment measurements, which are obligatory for workplaces handling specified chemicals, also decreases with decreasing business size. In particular, the risk assessment rate of businesses with less than 30 workers is H JONAI et al. 304 Industrial Health 2021, 59, 298 -307 3 (Report on worker's death, injuries, and diseases in 2017)    Table 8 Plan for the number of chemicals subject to labelling, SDS provision and risk assessment Table 7. Implementation rate of special medical examinations, working environment measurement, and risk assessment by business size pected to be reflected in the relevant acts and/or the ordinances eventually.

Revision of regulations (shift to self-regulation)
Based on the recognition of the current situation regarding chemical management in the workplace, it is appropriate to review the existing system, wherein the government conducts risk assessment of chemicals with high hazards (particularly carcinogenicity), adds them to the list of the Ordinance on Prevention of Hazards due to Specified Substances 28) and stipulates specific measures to be undertaken according to the regulations. From now employers should select and implement measures to prevent exposure based on the available hazard information; in addition, the government should create a system based on the principle of setting achievable targets.

Obligation to provide hazard communication and undertake risk assessment of GHS-classified chemicals
The government will collect hazard information on chemicals and continuously perform GHS-based classification and updating. Building on this classification, all chemicals classified shall be subject to labelling, SDS provision, and risk assessment, and applicable measures based on the results will be implemented.
To this end, the government will not only revise the enforcement order to include all GHS-classified chemicals in the scope of labelling under Article 57 and SDS provision under Article 57 No.2 of the ISHA but also continue to prepare and update model labels and SDSs according to the GHS. The following chemicals shall be prioritized for the expansion of labelling and SDS provision.
(1) Chemicals with high hazards (prioritized according to IARC groups 1, 2A and 2B) 29) (2) Chemicals that have caused occupational disasters (3) Chemicals that are imported and manufactured in large quantities in Japan (4) Chemicals with high exposure risk (e.g., those with high vapor pressure). The prospective chemicals to be added as subject to labelling, SDS provision, and risk assessment are shown in Table 8 30) . The chemicals classified before 2021 will be added between 2021 and 2023, those to be classified between 2021 and 2023 (150 -300 chemicals in total) will be added in 2024, and those to be classified in 2004 (50 -100 chemicals per year) will be added in 2025. Chemicals that will be classified after 2025 will be added in a manner and time frame similar to those just described.
to adhere to laws and regulations. Approximately 80% of the workers were aware of the hazards of the chemicals they handled; among them, approximately 80% and 45% obtained hazard information from labels and through workplace education, respectively. However only 7% of the workers knew what all nine GHS pictograms meant, and approximately 75% were familiar with only four or fewer pictograms. Approximately 20% of the workers did not know the hazards of the chemicals they handled, and approximately half of them wanted to know the hazards. These findings indicate that the hazards of the chemicals handled are not well recognized in the workplace, and employers do not sufficiently communicate that hazard information to their workers.
In the United States, after the GHS was implemented in the Hazard Communication Standard, the right to know and the right to understand were set as the slogans for hazard information. It is said that this will change the conventional inspector's question to employers as "Is SDS provided?" into the new question to workers as "What is the meaning of this pictogram?". This shift has substantial implications for how hazard information should be transmitted to workers in Japan.
The laws and regulations concerning industrial health in Japan seem relatively well developed. However, the obligation to establish a safety and health committee, appoint industrial physicians, and notify the relevant labor standard office of a health officer and the results of periodic health checkups is not stipulated for small businesses, as opposed to businesses with more than 50 workers. Measures that would make these systems more effective are necessary to raise health awareness among small business owners and workers.
Although small business may employ occupational health consultants and working environment measurement experts as occupational health professionals, doing so may not be economically easy or sound.

Outlook of Measures
At present, the MHLW is considering updating chemical management protocols based on the work of the Investigative Committee on the Chemical Management in the Workplace. The main proposition of this committee is to adopt self-regulation in chemical management. This section introduces the results of the ministry's deliberation on the committee's interim 24) on the risk assessment and small business countermeasures, which are indispensable elements for carrying out self-regulation. These results are ex-305 CHEMICAL MANAGEMENT IN JAPAN chemical management in Japan for many years. This is very different from self-regulation which is a basic principle in Europe and the United States. Currently, Japan is trying to overcome the delay of several decades compared to Europe and the United States. In the near future, it would be sure for all workers to be informed of chemical hazards based on the GHS and for businesses to do chemical management according to their responsible priorities.

Support for small businesses
The measures listed below will enable small and medium-sized businesses with insufficient knowledge of and human resources on chemical management to handle chemicals properly.
(1) Establishment of guidelines for chemical management For chemicals that are particularly difficult to manage and chemicals that have high hazards, the government will cooperate with research institutes and industry associations to provide guidelines on standard management methods and related constructs.
(2) Establishment of a support system by experts With the cooperation of industry associations, the government will develop and deploy human resources with high expertise and abundant experience in chemical management and explore the establishment of a system that provides free consultation services and advice to small and medium-sized businesses in each regional area of Japan.
(3) Development of infrastructure to support chemical management The government will develop a simple management support system that can be easily implemented without specialized knowledge using smartphones and tablets. It will also consider the establishment of a web portal that aggregates information on chemical management. Furthermore, the government will develop tools that enable small and medium-sized businesses to prepare SDSs for mixtures easily.

Conclusion
The compliance approach has been the mainstream of   (The chemicals classified before 2021 will be added between 2021 and 2023, those to be classified between 2021 and 2023 (150 -300 chemicals in total) will be added in 2024, and those to be classified in 2004 (50 -100 chemicals per year) will be added in 2025. Chemicals that will be classified after 2025 will be added in a manner and time frame similar to those described.)


A deep reinforcement learning model for predictive maintenance planning of road assets: Integrating LCA and LCCA

Road maintenance planning is an integral part of road asset management. One of the main challenges in Maintenance and Rehabilitation (M&R) practices is to determine maintenance type and timing. This research proposes a framework using Reinforcement Learning (RL) based on the Long Term Pavement Performance (LTPP) database to determine the type and timing of M&R practices. A predictive DNN model is first developed in the proposed algorithm, which serves as the Environment for the RL algorithm. For the Policy estimation of the RL model, both DQN and PPO models are developed. However, PPO has been selected in the end due to better convergence and higher sample efficiency. Indicators used in this study are International Roughness Index (IRI) and Rutting Depth (RD). Initially, we considered Cracking Metric (CM) as the third indicator, but it was then excluded due to the much fewer data compared to other indicators, which resulted in lower accuracy of the results. Furthermore, in cost-effectiveness calculation (reward), we considered both the economic and environmental impacts of M&R treatments. Costs and environmental impacts have been evaluated with paLATE 2.0 software. Our method is tested on a hypothetical case study of a six-lane highway with 23 kilometers length located in Texas, which has a warm and wet climate. The results propose a 20-year M&R plan in which road condition remains in an excellent condition range. Because the early state of the road is at a good level of service, there is no need for heavy maintenance practices in the first years. Later, after heavy M&R actions, there are several 1-2 years of no need for treatments. All of these show that the proposed plan has a logical result. Decision-makers and transportation agencies can use this scheme to conduct better maintenance practices that can prevent budget waste and, at the same time, minimize the environmental impacts.

Introduction:
The performance of all national economies is dependent on road infrastructure, which delivers a wide range of economic and social advantages. At the same time, it has a significant environmental impact throughout its construction, maintenance, and use (Santero and Horvath 2009). The necessity of sustainable development of infrastructure systems, such as transportation networks, is now widely acknowledged, especially after the UN's International panel on climate change (IPCC) reported for "code red" in its latest report (Cartwright 2021). Maintaining and repairing the continually deteriorating pavement networks sustainably with minimal financial expenditure, greenhouse gas emissions, and non-renewable materials consumption has steadily attracted attention (Wang and Gangaram 2014).
Pellicer et al. (Pellicer, Sierra et al. 2016) found that conventional evaluations of pavement maintenance alternatives concentrated on economic and technical parameters, ignoring the relevance of environmental impacts. Air pollution and climate change are two of the most pressing environmental issues facing the globe today. If temperature levels continue to rise owing to anthropogenic greenhouse gas (GHG) emissions into the atmosphere, global warming might have severe environmental, social, and economic consequences throughout the world (Santos, Torres-Machi et al. 2020). With that being said, special attention must be paid to the transportation sector since it is responsible for spewing considerable amounts of pollutants up into the air (Watts, Amann et al. 2018). Moreover, in the presence of global warming, it is anticipated to have more Maintenance and Rehabilitation (M&R) practices because of increased natural disasters, and thus more pollutants will be released into the atmosphere (Santos, Torres-Machi et al. 2020).
Developing a practical long-term maintenance framework will not only help policymakers to achieve an acceptable level of service but enable them to avoid significant capital waste since there are limitations on the budget (France-Mensah and O'Brien 2018). There is a need for prediction models for developing an effective maintenance plan. Many budget allocation models have been proposed to address this issue for various purposes, both on the project and network level (Yu, Gu et al. 2015). There are lots of studies that have been conducted in the field of pavement performance forecast in recent years using the Long-Term Pavement Performance (LTPP) data (Piryonesi andEl-Diraby 2020, Yamany, Saeed et al. 2020) or Local data sets (Yao, Dong et al. 2019). The cost-effectiveness of methods, whether in data collection (Piryonesi and El-Diraby 2020) or maintenance framework, is essential because, in the case of data gathering, it should be practical for small firms to be cost-effective and in case of maintenance framework, it can prevent budget waste.
Engineers have been attempting to determine which maintenance techniques yield the best result. Many researchers used the notion of "Cost-effectiveness" to evaluate the efficiency of their methods. Torres-Machi et al (Torres-Machi, Pellicer et al. 2017) and Osorio et al (Osorio-Lird, Chamorro et al. 2018) employed cost-effectiveness or cost-benefit techniques extensively to examine both performance and economy. In order to make better decisions that are cost-effective and more environmentally friendly, pavement repair must be planned over a lengthy period (Alam, Hossain et al. 2020, Huang, Dong et al. 2021. Combining treatments and developing more 2 predictable schedules would considerably increase pavement service life and save a significant amount of capital (Irfan, Khurshid et al. 2012).
Another important parameter that must be considered is treatment timing. Determining a maintenance schedule aims to keep the pavement in good working order while saving money on maintenance. Much research has focused on estimating maintenance timing based on long-term pavement performance deterioration (Donev and Hoffmann 2020, Li, Feng et al. 2021, Qiao, Du et al. 2021, Renard, Corbett et al. 2021.
Numerous research has attempted to combine machine learning and civil engineering to tackle various engineering challenges, motivated by machine learning's incredible capacity to employ models built on data in order to replicate human intelligence activities (Neves, González et al. 2017, Chopra, Sharma et al. 2018, Deka 2019, Naranjo-Pérez, Infantes et al. 2020. Deep learning is the most prevalent among them and is commonly used in damage recognition, crack detection, and prediction , Athanasiou, Ebrahimkhanlou et al. 2020, Azimi and Pekcan 2020, Jiang and Zhang 2020, Piryonesi and El-Diraby 2020 . A Recent approach for finding the best treatment timing is through maximizing cost-effectiveness with reinforcement learning (Yao, Dong et al. 2020, Renard, Corbett et al. 2021. In table below, a summary of previous research on AI application in infrastructure management is presented in table 1. A machine learning approach known as reinforcement learning (RL) might be used to develop better maintenance techniques to solve the problems, that is, to deal with the long-term maintenance decision-making (Yao, Dong et al. 2020). Pavement authorities are now focusing more on the long-term cost-effectiveness of maintenance treatments, making maintenance planning a sequential decision-making issue. However, whether employing either professional expertise or mathematical programming approaches, long-term maintenance planning is problematic due to the complex process of pavement deterioration, multiple alternative treatments, and various pavement performance indicators. RL is an excellent tool for simulating sequential decision-making situations on a big scale. It discovers the best approach by maximizing the agent's cumulative rewards from the environment. RL-based pavement management decision-making techniques can include sophisticated pavement performance prediction, more specific treatment techniques and materials comparison, and numerous pavement treatment applications in a longterm optimization through agent-action environment interactions, as opposed to traditional pavement management decision-making techniques (Liu, Chen et al. 2020, Han, Ma et al. 2021, Renard, Corbett et al. 2021 RL has been widely employed in civil engineering to solve transportation issues. Its use in traffic signal control to reduce vehicle delays is a good example (Zolfpour-Arokhlo, Selamat et al. 2014, Zhang, Mao et al. 2015, Mannion, Duggan et al. 2016, Aslani, Seipel et al. 2018, Müller, Rangras et al. 2021. Moreover, RL has been used in route planning, traffic congestion alleviation and the traffic flow optimization problem (Walraven, Spaan et al. 2016, Peng, Xi et al. 2021, Zhu, Lu et al. 2021. To balance the distribution of passenger demand along a metro line, Jiang et al executed a coordinated passenger inflow management during peak hours. However, there has been less research that has applied RL in pavement management decision-making. This study's significant contribution may be summarized as follows: (a) for the first time, this paper is developing an RL framework that considers costs and environmental effects at the same time, and (b) developing an RL framework based on a public database (LTPP) is the first one in its kind. We used the LTPP database, as it is the most comprehensive database for road maintenance practices by now. Although there are many deficiencies in data, like missing data and irrational changes, we used some methods to tackle the problems, which will be discussed later in the data preparation section.
Following this introduction, section 2 introduces the approach we took in this paper. Section 3 discusses the proposed approach's result, including future maintenance planning, costs, and behavior of the approach under different conditions. Section 4 concludes this study and guides further research.

Deep reinforcement learning
In this paper, we aim to find a cost and environmentally-effective sequence of maintenance actions to provide the most budget-efficient set of maintenance actions and contribute to a more sustainable maintenance management system.
Initially, some basic principles of Deep reinforcement learning are briefly introduced. Reinforcement learning is a branch of machine learning algorithms specialized in solving sequential decision-making problems to allow software-defined agents to learn the optimum behaviors in a virtual environment to achieve their objectives (Andrew Barto 2014).
Reinforcement Learning differs from other categories in Machine Learning, considering the kind of feedback (reward) that the agent (algorithm) receives after making a decision. In supervised learning, the agent will simply understand how good it was in predicting (doing an action) by immediately comparing its result with the label (Cunningham, Cord et al. 2008), and the agent uses this method to improve its next prediction (action). For unsupervised learning, there is no feedback as the data is unlabeled (Ghahramani 2003). Reinforcement learning is something in between, which works with the term delayed reward, and agents are learning through a trial-anderror procedure of interacting with the environment. It starts with an initial state, and every time the agent makes a decision, it receives a reward and changes the state to another one. The goal is for the agent to find a Policy that leads to the maximum reward. As RL works on the Markov Decision Process (MDP) principles, we introduce our sustainable road maintenance decisionmaking system based on the MDP system (Van Otterlo and Wiering 2012). is a scalar discount factor. In a model-free prediction, T is always unknown and is thus omitted afterwards in this paper.
The best (optimal) policy for RL is one that maximizes the cumulative reward. At each step in policy π the agent at one specific state t s takes an action t a to observe the next resulting state 1 t s + and obtains a reward 1 t r + . The final cumulative reward in step t is defined by t G and can be defined as follow: n is the number of steps. As mentioned in Defenition2.1,  is called discount factor, and it plays a key role in "delayed reward" phenomenon. The more  is closed to 0, the less important are future rewards, and the agent focuses on immediate rewards (Sutton 1992). Achieving an optimal policy *  means that our agent has succeeded in finding the optimal sequence of actions in each time step which maximizes t G .

5
Before introducing different ways of finding *  , we first define our sustainable maintenance planning system based on MDP, thus defining Environment, states, actions and rewards in our proposed system ( Figure 2).

Environment
The agent's Environment is the world it moves and interacts with. The Environment receives the agent's current state and action as inputs and delivers the reward and the agent's future state as output.
In our sustainable maintenance planning system, the Environment should be a place that simulates the initial road condition and then determines the following condition of the road based on the chosen action. In a perfect world, the agent can perform its action on an actual road segment and quickly evaluate the post-action condition of the road to evaluate the effectiveness of the action. However, these real-world solutions and even their laboratory simulations in civil engineering situations are not practical; they are complex and time-consuming.
To overcome this problem, Machine Learning models are used to simulate the Environment and estimate future road conditions. This paper uses a Deep Artificial Neural Network (DANN) to estimate the following condition of a road section based on some initial (input) features. This ANN model is used further in RL to play the role of the Environment.
There are 40 factors used as the inputs for the ANN model, which are related to road structure features (24 factors), traffic conditions (6 factors), weather conditions (4 factors), initial performance indicator (1 factor), the interval between two indicator evaluations (1 factor), 6 maintenance type (3 factors) and maintenance date (1 factor). As we have considered two indicators in our Sustainable Maintenance Planning Model, two ANNs are used to predict each indicator separately. The Flowchart of these ANN models and their performance are illustrated in Figure 3 and 4, respectively.

Data
Long-term pavement performance (LTPP) data were employed in this paper. The following indicators for flexible pavements are presented in the dataset: 1) International Roughness Index (IRI), 2) Rutting Depth (RD), 3) Cracking Metric (CM) and 4) Faulting (F). However, several studies developed methods to create indicators that are not initially in the database (Piryonesi and El-Diraby 2020). Although the LTPP database is probably the most comprehensive dataset of road assets, there are deficiencies, including 1) missing data and 2) conflicting data. In order to deal with these shortcomings, several approaches have been employed to improve the quality of the data set, which will be discussed in the data preparation part. In addition, Based on the research conducted by (Lupia and Alter 2014), the transparency of the data is important; thus, it is worth 7 mentioning that the research in this paper is conducted based on an open-source dataset (as opposed to previous similar work), that may enhance the validity of the proposed methods.

Indicator Selection
Selecting a single indicator will result in inaccuracies since it cannot offer a comprehensive picture of road conditions. As a result, a collection of indicators is required to depict the whole situation accurately. As we previously noted, choosing the right indicator has a significant impact on the outcomes and may turn a credible, extensive, and complete maintenance system into a meaningless one. Different research has been considered in the process of selection (Domitrović, Dragovan et al. 2018, Marcelino, Lurdes Antunes et al. 2018, Visintine, Simpson et al. 2018, Fakhri and Dezfoulian 2019, Nabipour, Karballaeezadeh et al. 2019, Li, Liu et al. 2020, Cruz, Mendoza et al. 2021, Fakhri, Dezfoulian et al. 2021, Song, Thatcher et al. 2021. Based on previous research, the performance indicators should have the following characteristics:

Appropriateness
Is suitable as a measure at the national level (including accuracy, timeliness, consistency, and precision) Based on these considerations, three indicators had been selected initially, International Roughness Index (IRI), Cracking Metric (CM), Rutting Depth (RD). Later in the modeling for RL, the authors of this paper realized that incorporating the Cracking Metric as an indicator, due to much fewer data, will result in irrational final results (maintenance and rehabilitation practices). Thus, it has been omitted from consideration for the RL model.

Data Preparation
A major and time-consuming task for academic research papers dealing with big databases is the data preparation part (García, Luengo et al. 2015). As the required dataset for training the nextcondition predictor model described in the above section is based on some separate data sources (LTPP database and climate module MERRA provided by NASA), these data need to be first merged into a single one. Data preprocessing is also a must-do for our neural network prediction model (Environment). Without preprocessing of raw data, the neural network will not produce accurate forecasts.

8
The main tasks of the preprocessing part are as follows: First, the categorical data (e.g., road function class, Surface type, etc.) are converted into numerical ones, using the one-hot encoding method. Second, a Min-Max Normalizer is applied to the data for faster convergence and higher proficiency (Patro and Sahu 2015). Furthermore, considering traffic and weather data, there are some missing data, which can be handled by interpolation, fillna methods and deletion. In addition, because of some illogical cases in the dataset, IRI and RD might have decreased over time without any actions taken. To overcome this problem, a calibration method has been applied on those data points that have decreased between two action-performed dates (if two maintenance actions have been done on a single road segment, its indicator performance must increase gradually in this interval. Otherwise, it is erroneous and must be calibrated). The calibration process is illustrated in Figure 5, in which The blue squares are actual data. The red vertical lines show the time when a maintenance action has been done. In a rational situation, the performance indicator like RD decreases right after a maintenance action but then increases. Thus, those data points that decrease without performing maintenance action are wrong and must be calibrated. In this figure, the calibrated data points are indicated with green cross-shapes.

State (S)
A state is a situation that the agent knows about itself in the environment. In this paper, 19 features combined with the performance indicators build up the state space. These features are presented in Table 3. This set of features should include those that will change during the maintenance horizon. They are separated into three categories: 1) Structure, 2) Traffic, and 3) Climate.

Action
All motions that the agent can take are known as Action (A). Although an action is relatively selfexplanatory, agents typically pick from a list of discrete, viable acts. Our actions are the maintenance and rehabilitation that are practiced on the road and are also used as the inputs in the neural network prediction model. In order to make the developed models more realistic, we chose the actions based on their frequency in the data set (these sets of actions represent more than 80% of the actions practiced in the data set). "Do-nothing" is also considered to be an action. Different actions have been made through combinations of types, thicknesses and materials. The reason behind selecting these features to make different combinations is to reach the highest accuracy with the least computation. Table 4 represents actions that the agent can take. With the combination of different types, thicknesses and materials, the Action space would be built from which the agent can perform its action.

Reward
A reward is feedback that we use to determine if an agent's activities in a particular state were successful or unsuccessful. The purpose of incentives is to offer feedback on an RL model's actions' performance. As a result, it is critical to define the reward accurately to direct the learning process properly. We proposed the reward in this paper based on the idea in . However, our reward function differs from the previous work in several terms, including the environmental cost. Figure  6 represents the calculation of actions' performance on an indicator, in which the initial point is the first year of planning. The purple curve shows the indicator performance after taking an action. The blue dotted area is the area that is used in the reward calculation formula, and the black curve is a hypothetical curve of not doing an action at all during the planning horizon. When the planning starts, the indicator is in an initial state. However, each year it decreases if no maintenance action is performed on the road, and it follows the black curve. The first time an action is performed (purple curve), it increases and separates from the Non_action curve. Generally, the performance will decrease between two maintenance actions (time t and 1 t + ). It is worth noting that for the sake of simplicity, we illustrate a decreasing performance indicator in Figure 6, while the proposed ones (IRI and RD) are inherently increasing.

Figure 6. The mechanism of calculating the effectiveness of actions for an indicator
The proposed reward function is as follows: In each time step, firstly, the cumulative area bounded with Non-action curve, Action curve, current time t and the next time 1 t + is calculated for each indicator (eq 1). In eq1, i indicates the indicator. In addition, the total cumulative cost is calculated as in eq 2. In this paper, as we have considered our maintenance system to be even environmentally effective as opposed to similar research, the total cost is the sum of both an action's cost and its environmental cost. In eq2, 1.04 is for 4% discount rate as it has been largely used in the previous research. Furthermore, the environmental cost of each action is calculated based on eq 3, in which In the next step, the effective cost is calculated based on eq 4, which is the effectiveness of applying an action (including non-action) so far in the system.
The effective cost for each indicator is then combined into a single term, as shown in eq 5.  (Mnih, Kavukcuoglu et al. 2013) is an off-policy method that learns the value of the optimal policy independently of the agent's actions. It uses an ε-greedy approach to choose an action in every time step. In DQN, samples are selected randomly from a replay memory as the inputs of a DNN to produce less correlated results than selected directly from consecutive samples. According to the loss function, in each step, the q-value (the output of the proposed action) together with the obtained reward will be combined with the max q of the same network ( * q ) to form the loss as mentioned earlier, and then update the weights through a back-propagation and gradientdescent approach.
One problem that arises by having only one DNN to approximate q values is that both q -values and target * q will update in the same direction, and the correlation between the q-values and target q -values can be high. To tackle this issue, a more efficient version of DQN, Double DQN uses two separate neural networks, one as policy network with parameter  , and another as target network with parameter  . The separated network uses the same architecture as the original one and updates the weights with regard to it every t-timesteps (  θ). Thus, the loss function is modified to be:

Proximal Policy Optimization (PPO)
Despite DQN, PPO is an on-policy method that uses policy gradient approaches to estimate the optimum policy. PPO algorithm makes the use of importance sampling similar to TRPO (Schulman, Wolski et al. 2017) method and KL divergence constraints on the basis of traditional policy gradient method. In PPO, the network tends to increase the probability of sampled actions through a gradient ascent approach. The gradient estimator in TRPO is as follows: is the updated policy that derives from the old policy old   and β is some coefficient.
However, due to the complexity of TRPO in policy estimation, PPO has been introduced, which is compatible with SGD and simplifies the algorithm by removing the KL penalty and needs to make adaptive updates with its clipped surrogate objective. It also ensures that the deviation from the previous policy is relatively small. The objective is then as follows:

PPO VS DQN
It can be observed that PPO provides a better convergence and performance rate than other techniques but is sensitive to changes. On the other hand, DQN alone is unstable and gives poor convergence, hence requiring several add-ons.
In our sustainable road maintenance decision-making system, we experimented with both DQN and PPO and compared the results in order to choose the best one. Both methods were implemented, and their parameters were tuned with several trials. However, our results showed a more stable convergence of PPO compared to DQN, and PPO gained a better cumulative reward with fewer samples. Thus, the final implementation is obtained using the PPO algorithm.
The obtained hyperparameters are shown in Table 5.

Cost Calculations: Integrating LCA and LCCA
To calculate the cost-effectiveness of the actions, first, we need to calculate the cost and environmental impact of treatments. The environmental damage cost (EDC) has generally been overlooked when calculating pavement costs. To consider EDC alongside economic cost, there should be an integration between LCA and LCCA. A model provided by Tol (Tol 2005) has been utilized for the integration. After this step, we integrated the costs and environmental impacts (as marginal costs) of the M&R actions. In the following paragraphs, a discussion has been made about our approaches.

LCA
For the LCA, a tool developed by a consortium on green design and manufacturing in the University of California, Berkeley (paLATE 2.0) (Figure 7) has been used with some modifications. The tool takes user input for the design, initial construction, maintenance, equipment use, and costs for a roadway, and provides outputs for the life-cycle environmental effects and costs. In this research, only production of material, transportation and construction have been considered. Other parts are simply beyond the scope of this research. Environmental effects investigated include CO2, CH4, N2O, PM2.5, SO2, CO and NOx. All of these pollutants have been considered in LCA as an equal amount of CO2, based on the global warming potential (GWP) ( Table 6) (Jing, Bai et al. 2012). In the following parts, more details will be discussed:

LCCA
The LCCA method has been established as a standard method for assessing the monetary competitiveness of various designs. A median value for road project item costs in Texas has been considered for cost calculations.

Case study
Our case study is a hypothetical 6 lane (3 lanes in each direction) highway located in Texas. The highway is set to be in a warm and wet climate. Daily traffic conditions (AADT, ESAL, different axle counts, Truck ration) are compatible with the road structure. The highway is composed of 46 sections and a total length of 23 kilometers For ease of calculation, all road segments are considered to be 500 meters in length, and the width of all lanes is 3.66 meters (46 segments in total). The reason for selecting a hypothetical case study is to implement a comprehensive set of characteristics in the case study to compare the performance of the model under different conditions. Different conditions for the case study are defined within the limits of similar highways in the same region. Road sections were classified as "wet" because the average site precipitation was greater than 20 inches/yr. Moreover, they are classified as "freeze" since the average site freeze index was greater than 46 °Cdegree days. Figure 8 denotes the learning process for the agent, which takes thousands of iterations. Each iteration or round is a set of 20 years of performing action on a road segment. As it can be seen the model converges after iteration 7000.  Figure 9 shows the projected maintenance and rehabilitation costs of all segments between 2022 and 2041. It is important to note that more budget is needed in some years. This is because the road is in an excellent overall condition in the early years, and thus, there is no need for heavy M&R practices. After several years that the road has been subjected to heavy traffics, the need for heavy M&R practices increased. Since after the heavy M&R, the condition of the road improves, there is a sharp decline in budget allocation. In Figure 10, which is only for a single road segment, it is obvious that in some years, there is no M&R, which happens after heavy rehabilitation practices. This means that the model can decide that there is no need for more M&R practices after recent rehabilitation.  Table 7 shows the percent of actions for each year. Since the road is in good condition at the start of 2022 (the beginning of the 20-year period), 60 percent of the first year's actions are non-actions. Moreover, the other 40 percent is a slight maintenance practice (aggregate seal coat). After the first year, the road will be subjected to heavy traffic, and the need for heavy M&R practices is necessary.

Result
In order to see if the model responds logically to different conditions, a sensitivity analysis has been conducted. Figure 11 shows that as the traffic volume increases (by 50, 100, 150, 200, and 250 percent), the need for maintenance and rehabilitation increases, which results in a drop in the cost-effectiveness of the model. It can be concluded that the developed model responds logically.

CONCLUSION
This study introduced a new framework for long-term rehabilitation and road maintenance strategies. A PPO model has been utilized to maximize the cost-effectiveness of actions. This tool can provide a basis to help decision-makers. The result shows that the proposed framework can successfully produce promising results. We also tested a DQN model, but PPO results were more accurate and logical. After each intervention, all lanes must reach the same elevation. Since the real-time practice for measuring the impact of traffic and M&R practices is not achievable, we used an ANN model to simulate the real-world condition. The environment comprises 40 parameters divided into four subcategories of 1) Structure, 2) Traffic, 3) PIs and, 4) Climate. The action space is made up of 32 actions (including do-nothing) which only 16 of them are taken by the agent. The main features of the study are as follows: 1) The proposed framework solves the problem of conflicting data by correcting the false data. 2) A neural network model specifically created to simulate the environment. 3) A sensitivity analysis was conducted to evaluate the proposed model's behavior. 4) The model integrates the environmental and economic impact of M&R practices to produce a more comprehensive basis for decision-makers. 5) the outcome of the research is a decision support tool for organizations to go towards M&R strategies that align with sustainable development goals.
Future studies could address the impact of climate change and evaluate how different climate change scenarios can affect the M&R strategies. Another direction can be pushing models toward a network of roads instead of considering one segment of road. Incorporating budget restrictions into the model will lead to more realistic results since there are always funding restrictions on the side of organizations responsible for M&R practices in the real condition. Finally, implementing the performance indicators in the reward calculation, so that different service levels have different values in the reward will lead to a more logical behavior of the model.