Abstract
Background
Workplace violence (WPV) represents a growing global health and occupational safety concern, with significant implications for employees’ well-being and job satisfaction as well as organizational performance. This study explores the prevalence of non-physical WPV, specifically perceived sexual harassment (PSH) and perceived gender discrimination (PGD), and examines their potential adverse outcomes such as job stress and intention to leave employment across the public and private sectors in Saudi Arabia.
Methods
A cross-sectional online survey using non-probability purposive sampling was conducted from November 2023 to March 2025, involving 558 employees from 20 public and private sector organizations in Saudi Arabia. Descriptive statistics and multivariable generalized linear models with gamma regression were used for the analysis.
Results
The mean PSH score was 18.5 (SD = 5.98) and the mean PGD score was 19.6 (SD = 7.97). The multivariable generalized linear models indicated that PGD was associated with higher PSH (adjusted rate ratio = 1.009, 95% CI [1.007, 1.012]) and that elevated job stress was associated with increased PSH (adjusted rate ratio = 1.005, 95% CI [1.002, 1.009]) and PGD (adjusted rate ratio = 1.015, 95% CI [1.010, 1.021]).
Conclusions
Non-physical forms of WPV, including PSH and PGD, are prevalent across workplace settings in Saudi Arabia and are associated with adverse psychological and occupational outcomes. The results underscore the necessity for organizational policies and psychologically informed workplace interventions to mitigate harassment and discrimination, enhance employee well-being, and foster safer workplace environments.
Keywords: Workplace violence, Perceived sexual harassment, Perceived gender discrimination, Job satisfaction, Job stress, Intention to leave employment
Background
Workplace violence (WPV) is a serious occupational concern that requires context-specific assessment to guide prevention [1]. The International Labour Organization defines WPV as “a range of unacceptable behaviours and practices, or threats thereof, whether a single occurrence or repeated, that aim at, result in, or are likely to result in physical, psychological, sexual, or economic harm” [2]. Following prior research, WPV is conceptualized in this study as a broad construct, with a focus on non-physical forms, operationalized through perceived sexual harassment (PSH) and perceived gender discrimination (PGD).
PSH and PGD, as key manifestations of WPV, disproportionately affect women [3]. Research shows that exposure to PSH and PGD is linked to increased job stress (JS), burnout, turnover intention, and reduced job satisfaction (JSAT) [4, 5]. These effects have consequences for employees’ psychological and emotional well-being, in both the short and the long term [6, 7]. The experience and impact of non-physical WPV may also vary across organizational and sociocultural contexts; similarly, workplace responses, perceptions, and reporting behaviors vary according to the cultural and institutional environment [8–10]. This highlights the need for a context-sensitive theoretical examination rather than purely descriptive documentation [8]. However, research has largely treated WPV as a context-independent phenomenon, with limited attention paid to how sociocultural and organizational conditions shape the strength and manifestation of these associations.
Non-physical violence, specifically verbal abuse, threats, and sexual harassment, is highly prevalent in healthcare settings [2–5, 7, 11–13]. A meta-analysis of 253 studies found that over 62% of hospital employees have experienced WPV [12]. In addition, a systematic review of 42 studies reported that verbal violence is the most frequently documented type of WPV, followed by threats, physical, and sexual violence [6]. The review emphasized the importance of training, preventive measures, and awareness campaigns [6].
Although this work has been instrumental in documenting WPV, limited empirical attention has been paid to PSH and PGD outside healthcare settings. Non-physical forms of workplace violence (WPV) may deplete individuals’ vital resources and result in psychological strain, including emotional exhaustion and stress [14, 15]. Therefore, in this study, we adopt the lens of conservation of resources (COR) theory. COR theory assumes that individuals strive to use all available means to protect their valued resources such as social status, self-esteem, dignity, and emotional well-being [16]. When one of these resources is threatened, individuals develop stress [15, 16]. These workplace stressors are thus associated with adverse outcomes among employees such as burnout, job dissatisfaction, and turnover intention [14, 17, 18]. However, the extent of such resource loss may vary depending on organizational and contextual conditions [15, 16]. Previous studies have primarily employed descriptive or correlational approaches to examine associations between WPV and employee outcomes [17–21], with limited application of resource-based theoretical frameworks. Therefore, this study conceptualizes WPV as a form of resource depletion and examines its associations with workplace outcomes across employees in both healthcare and non-healthcare settings.
Research in non-healthcare settings shows that PSH and PGD are significant occupational stressors across sectors. Studies conducted among journalists, industrial workers, and service employees have shown positive associations among PGD, PSH, JS, depressive symptoms, and intention to leave the job (INTL) [19–21]. Notably, one study found that exposure to WPV is linked to an eightfold increased risk of depression [20], while another reported a significant association between workplace gender discrimination and depressive symptoms [19]. Collectively, these findings suggest that non-physical types of WPV extend beyond healthcare settings and operate as a generalizable occupational stressor but one that may manifest differently depending on the organizational and contextual conditions. Thus, the importance of this study lies in investigating WPV through COR theory across both healthcare and non-healthcare settings.
Notably, limited empirical attention has been paid to PSH and PGD outside healthcare settings in Saudi Arabia. Saudi workplace dynamics have recently transformed under Vision 2030, which promotes a vibrant society and diverse economy. This has led to a structural shift in workplace norms and expectations, potentially introducing ambiguity in behavioral standards and organizational responses to WPV. Women’s participation in mixed-gender work environments has expanded, with women occupying over 43% of leadership positions in 2024 [22]. Such rapid changes in employment patterns, including workforce composition and gender integration, may reshape interpersonal dynamics and influence how WPV is experienced, interpreted, and reported. This raises an important theoretical question about whether rapid structural changes in gender integration and workplace norms alter how WPV is experienced and how strongly it is associated with employee well-being and behavioral outcomes.
Similar to global trends, WPV has been identified in Saudi workplaces. One study in hospitals in the Eastern region found that 48% of emergency department workers have experienced at least one form of WPV over a year, with verbal abuse being the most prevalent [11]. However, recent empirical evidence from non-healthcare settings in Saudi Arabia remains limited, with most studies focusing on healthcare populations or single occupational groups. For example, a study of Saudi female journalists found a positive association between PGD and JS [21]. These contextual changes may have implications for how established WPV–outcome relationships manifest in this setting.
As a preventive measure, the Saudi Arabian Ministry of Human Resources and Social Development (HRSD) has established clear guidelines to identify and prevent WPV. The HRSD defines WPV as including abusive, threatening, and discriminatory behaviors, physical, verbal, or otherwise, which may cause physical, psychological, sexual, and economic harm to employees [23]. This definition both reflects the legal framework for workplace conduct in Saudi Arabia and provides the conceptual basis for this study. However, the presence of formal definitions does not necessarily ensure consistent implementation across organizations, particularly in rapidly evolving labor markets in which enforcement mechanisms may still be developing.
Despite the increased recognition of WPV, underreporting remains a major challenge globally, hindering the implementation of preventive measures [24, 25]. Several factors contribute to low reporting: a lack of training, limited knowledge, confidentiality concerns, and the normalization of WPV as part of the job [25–27]. These barriers may be further influenced by cultural expectations, power distance, and concerns about job security, which can suppress reporting behavior in certain contexts.
Accordingly, this study examines the prevalence and impact of non-physical WPV, specifically PSH and PGD, in organizations in Saudi Arabia. Although international evidence highlights consistent associations between WPV and adverse outcomes across sectors, evidence from non-healthcare and rapidly transforming contexts remains limited, particularly in Middle Eastern labor markets undergoing structural change, where most national studies have focused on exploring WPV in healthcare settings [28, 29, 30]. Specifically, this study examines how these non-physical forms of WPV are associated with adverse work-related outcomes, including JS, JSAT, and INTL. Grounded in COR theory, its findings aim to extend existing research by examining WPV as a resource-depleting stressor within a context undergoing rapid sociocultural and institutional transformation like Saudi Arabia, where evolving gender integration and workplace norms may shape the strength and nature of established relationships. Therefore, this study clarifies how macro-level contextual changes influence the psychological and behavioral effects of WPV in sectors beyond those traditionally examined. This is crucial given the limitations of prior research, including the overreliance on healthcare samples, cross-sectional designs, and limited examination of non-physical forms of WPV in non-Western organizational contexts.
Based on the above theoretical arguments and empirical evidence, the following hypotheses are formulated:
H1: PSH is significantly associated with higher JS.
H2: PGD is significantly associated with higher JS.
H3: PSH is significantly associated with lower JSAT.
H4: PGD is significantly associated with lower JSAT.
H5: PSH is significantly associated with higher INTL.
H6: PGD is significantly associated with higher INTL.
Methods
Study design, setting, and participants
A cross-sectional study was conducted using non-probability purposive sampling among employees working in the public and private sectors across Saudi Arabia. Organizations were approached based on their accessibility and willingness to participate. Specifically, a formal invitation letter, including the survey link, was sent to 20 governmental, semi-governmental, and private organizations in Saudi Arabia, requesting their participation and the distribution of the questionnaire to their employees. The survey link was then disseminated electronically through the organization’s communication channels (e.g., email and internal platforms).
The inclusion criterion was that individuals had to be aged 18 or above and working in the private or public sectors in Saudi Arabia, including banks, hospitals, universities, and ministries. The exclusion criteria were individuals younger than 18 years and those with communication difficulties. The sample size was determined using the Raosoft® online sample size calculator with a 95% confidence level, 5% margin of error, and a response distribution of 50%. The target sample size was 384 participants. The study was run between November 2023 and March 2025. Ethical approval was obtained from the Ethics Committee of the Deanship of Scientific Research at King Saud University (No. KSU-HE-23-884 on 19/9/2023). Participation in the study was voluntary, and informed consent was obtained electronically prior to the commencement of the survey.
The data collection process was lengthy, as it required acquiring approval from all participating organizations to coordinate and distribute the survey. Only three organizations, however, declined to participate without providing a specific explanation. The total number of employees who received the survey link could not be precisely determined; therefore, a response rate was not calculated. A total of 898 participants initiated the survey, of whom 558 completed the questionnaire and were included in the final analysis. Responses were collected anonymously, and no personally identifiable information was obtained. Data were stored securely and accessed only by the research team.
Study instruments
This study adapted widely used, reliable, and validated scales to measure PGD and PSH, which were treated as the operational indicators of non-physical WPV, as well as the three work-related outcomes (JS, JSAT, and INTL). All items were rated using five-point Likert scales, with higher scores indicating stronger agreement on each scale. We performed construct validity and reliability tests for all the scales using exploratory factor analysis and Cronbach’s α coefficient tests (Table 1).
Table 1.
Internal consistency and dimensionality of the measured questionnaires
| Scale | No. of items | Cronbach’s alpha | UniCo | ECV | MI-REAL | Closeness to unidimensionality | Suggested number of factors from the exploratory factor analysis & parallel analysis test | Explained variance (%) |
|---|---|---|---|---|---|---|---|---|
| Mean PSH score | 16 | 0.945 | 0.919 | 0.908 | 0.229 | Yes | Four | 74.8 |
| Factor 1: Perceived sexist behavior score | 4 | 0.787 | 0.911 | 0.874 | 0.203 | Yes | One | 61 |
| Factor 2: Perceived crude sexually offensive behavior score | 4 | 0.907 | 0.99 | 0.985 | 0.09 | Yes | One | 72.5 |
| Factor 3: Perceived unwanted sexual attention-seeking behavior score | 4 | 0.87 | 0.977 | 0.855 | 0.274 | Yes | One | 72 |
| Factor 4: Perceived sexually coercive behavior score | 4 | 0.964 | 0.997 | 0.946 | 0.18 | Yes | One | 90.3 |
| PGD score | 8 | 0.933 | 0.995 | 0.931 | 0.194 | Yes | One | 66.8 |
| JS score | 6 | 0.92 | 0.988 | 0.908 | 0.229 | Yes | One | 71.5 |
| JSAT score | 6 | 0.931 | 0.993 | 0.919 | 0.217 | Yes | One | 74.6 |
| INTL score | 5 | 0.896 | 0.996 | 0.962 | 0.144 | Yes | One | 70 |
Note. A UniCo (Unidimensional Congruence) or I-UniCo (Item Unidimensional Congruence) value greater than 0.95 indicates that the data are essentially unidimensional. Similarly, ECV (Explained Common Variance) or I-ECV (Item Explained Common Variance) values above 0.85 support essential unidimensionality. MIREAL (Mean of Item Residual Absolute Loadings) or I-REAL (Item Residual Absolute Loadings) values below 0.300 also indicate that the data can be considered essentially unidimensional
The instrument consisted of six sections: a self-developed questionnaire and questionnaires for the five main variables. First, the self-developed questionnaire assessed the participants’ sociodemographic and employment-related information. It included questions on their age, sex, marital status, level of education, employment type, years in current employment, and sex of the current supervisor. To ensure data quality, responses were screened for completeness, and incomplete questionnaires were excluded from the final analysis.
Second, the PGD questionnaire was composed of eight items adapted from Sanchez and Brock’s scale to assess PGD in the workplace [10, 24]. The participants were asked to rate the extent to which they agreed with statements reflecting discriminatory attitudes, behaviors, and institutional practices related to gender in their workplace. Responses were recorded on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), with higher scores indicating greater PGD. The PGD questionnaire demonstrated a Cronbach’s α of 0.93.
Third, the PSH questionnaire was composed of 16 items from the widely used Sexual Experiences Questionnaire for measuring sexual harassment in the workplace. The scale comprises three factors: unwanted sexual attention, gender harassment, and sexual coercion at work. The participants indicated how often they experienced each type of harassment from employees, coworkers, and supervisors using a Likert scale from 1 (never) to 5 (very often) [31]. The Cronbach’s α of the scale was 0.94.
Fourth, the JS questionnaire, reflecting employees’ perceived psychological strain at work, was composed of six items measuring JS on a five-point Likert scale (1 = strongly disagree, 5 = strongly agree) [32]. Higher scores indicate higher levels of JS. The JS questionnaire demonstrated a Cronbach’s α of 0.92.
Fifth, five items from Brayfield and Rothe’s JSAT scale were used to assess JSAT (1 = strongly disagree, 5 = strongly agree), with higher scores meaning greater agreement with the statements [33]. The JSAT instrument showed a Cronbach’s α of 0.93.
Finally, the INTL instrument was composed of four items used to assess turnover intention (1 = strongly disagree, 5 = strongly agree), adapted from Mobley et al.’s theory of turnover intention among employees, which has been widely applied in organizational research [34]. Higher scores indicate employees’ higher intention to leave their job. The INTL instrument showed a Cronbach’s α of 0.89.
Exploratory factor analyses with UniCo, ECV, MI-REAL, closeness to unidimensionality, and the Cronbach’s alphas of all the scales are reported in Table 1.
Statistical analysis
The frequency, percentage, mean, and standard deviation were used to describe the continuous variables, whereas the median and interquartile range were used for variables with statistical skewness. Histograms, skewness and kurtosis indices, and Kolmogorov–Smirnov normality tests were applied to assess the normality assumption for the continuous variables. Box-and-whisker plots were used to assess outliers in the distributions of the continuous variables. Cronbach’s alpha was used to assess the internal consistency reliability of the questionnaires.
Additionally, exploratory factor analysis using principal axis factoring with parallel analysis and closeness-to-unidimensionality indices (MI-REAL, ECV, UniCo) were applied to the measured questionnaires to assess their dimensionality, internal structure, and validity. Principal axis factoring was preferred for questionnaires with an ordinal and/or non-normal distribution. Spearman’s rank correlation coefficient (rho) was used to assess the correlations among the measured variables. Friedman’s non-parametric two-way ANOVA was used to assess statistically significant differences in the continuous variables measured within individuals (e.g., harassment types from different offender sources). Because of the skewness of the analyzed outcomes and non-normality of the multivariable residual distributions, multivariable generalized linear models with gamma regression and the log-link function were applied to assess the significance of relevant associated factors for individuals’ perceptions of PSH, PGD, and work-related outcomes (JS, JSAT, and INTL). PSH and PGD were included as correlated independent variables representing non-physical WPV.
Model selection was conducted iteratively to identify parsimonious models based on COR theory and prior empirical literature examining workplace violence, discrimination, job stress, job satisfaction, and turnover intention [15, 17–19, 21].The associations between the independent variables and outcome variables were estimated using multivariable regression models and the results were reported as adjusted rate ratios (RRs) with 95% confidence intervals (CIs). IBM SPSS Statistics was used for the data analysis. The exploratory factor analysis, parallel analysis, and dimensionality tests were carried out using the stand-alone FACTOR program [35]. The significance level was considered to be alpha = 0.05.
Results
Participants’ characteristics
The initial sample comprised 898 respondents, of whom 558 completed the survey (completion rate = 62%). Approximately 39% were men (n = 216) and 61% were women (n = 342). The other sociodemographic characteristics of the participants, including their education level and marital status, are shown in Table 2.
Table 2.
Sociodemographic characteristics of the participants (n = 558)
| Variable | N (%) |
|---|---|
| Age | |
| 18–24 years | 19 (03.4%) |
| 25–34 years | 210 (37.6%) |
| 35–44 years | 208 (37.3%) |
| 45–54 years | 86 (15.4%) |
| ≥ 55 years | 35 (06.3%) |
| Gender | |
| Male | 216 (38.7%) |
| Female | 342 (61.3%) |
| Education level | |
| High school | 20 (03.6%) |
| Diploma | 27 (04.8%) |
| Bachelor’s degree | 294 (52.7%) |
| Master’s degree | 117 (21.0%) |
| Doctorate | 100 (17.9%) |
| Marital status | |
| Single | 207 (37.1%) |
| Married | 308 (55.2%) |
| Divorced or widowed | 43 (07.7%) |
| Employment type | |
| Full-time | 539 (96.6%) |
| Part-time | 11 (02.0%) |
| Other | 08 (01.4%) |
| How long have you worked in your current organization? | |
| < 1 year | 40 (07.2%) |
| 1–3 years | 119 (21.3%) |
| 4–6 years | 86 (15.4%) |
| 7–10 years | 70 (12.5%) |
| > 10 years | 243 (43.5%) |
| My current supervisor is a | |
| Man | 202 (36.2%) |
| Woman | 356 (63.8%) |
Table 3 shows the bivariate Friedman’s ANOVA analysis by the workplace offender sources. The participants identified “Treated you differently because of your gender” as the highest rated item. This behavior was attributed to supervisors (M = 1.57, SD = 1.06), coworkers (M = 1.53, SD = 0.99), and other employees (M = 1.30, SD = 0.79).
Table 3.
Bivariate Friedman’s ANOVA analysis of the participants’ PSH by the workplace offender sources
| Variable | Mean PSH score | ||||
|---|---|---|---|---|---|
| By supervisor | By coworkers | By employees | Test statistic | p-value | |
| Perceived sexist behavior score | 2.06 | 2.12 | 1.82 | χ²(2) = 77.36 | < 0.001 |
| Perceived crude sexually offensive behavior score | 1.96 | 2.18 | 1.89 | χ²(2) = 96.54 | < 0.001 |
| Perceived unwanted sexual attention-seeking behavior score | 1.97 | 2.07 | 1.98 | χ²(2) = 42.80 | < 0.001 |
| Perceived sexually coercive behavior score | 2 | 2.01 | 1.99 | χ²(2) = 5.34 | 0.069 |
Table 4 presents the descriptive statistics of PSH, PGD, JSAT, JS, and INTL. The mean PSH score was 18.5 (SD = 5.70), the mean PGD score was 19.63 (SD = 7.97), the mean JSAT score was 19.20 (SD = 6.46), the mean JS score was 19.16 (SD = 6.45), and the mean INTL score was 13.63 (SD = 5.68). The Spearman’s rank correlation tests indicated the significant associations among PGD, PSH, JS, JSAT, and INTL (p < 0.01; Table 5).
Table 4.
Descriptive statistics of the participants’ PSH, PGD, JSAT, JS, and INTL scores
| Variable | Maximum possible score | Mean (SD) | Median (Q1, Q3) | Skewness | Kurtosis | |
|---|---|---|---|---|---|---|
| PSH score | 16–80 points | 18.48 (5.70) | 16.33 (16, 18.67) | 4.79 | 28.75 | |
| Perceived sexist behavior score | 4–20 points | 5.30 (2.31) | 4 (4, 5.67) | 2.68 | 8.65 | |
| Perceived crude sexually offensive behavior score | 4–20 points | 4.72 (1.83) | 4 (4, 4.33) | 3.81 | 17.78 | |
| Perceived unwanted sexual attention-seeking behavior score | 4–20 points | 4.28 (1.28) | 4 (4, 4) | 6.64 | 47.76 | |
| Perceived sexually coercive behavior score | 4–20 points | 4.20 (1.21) | 4 (4, 4) | 7.93 | 70.4 | |
| PGD score | 8–40 points | 19.63 (7.97) | 18 (14, 25) | 0.465 | -0.445 | |
| JS score | 6–30 points | 19.16 (6.45) | 19 (15, 24) | -0.203 | -0.744 | |
| JSAT score | 6–30 points | 19.20 (6.46) | 20 (15, 24) | -0.372 | -0.567 | |
| INTL score | 5–25 points | 13.63 (5.68) | 14 (9, 18) | 0.158 | -0.904 | |
Table 5.
Spearman’s rank correlation coefficient among the variables
| Score | PSH | SB | CSO | USAS-B | SCB | PGD | JS | JSAT |
|---|---|---|---|---|---|---|---|---|
| PSH score | 1.000 | |||||||
| Perceived sexist behavior score (SB) | 0.854** | |||||||
| Perceived crude sexually offensive (CSO) behavior score | 0.878** | 0.668** | ||||||
| Perceived unwanted sexual attention-seeking behavior score (USAS-B) | 0.870** | 0.550** | 0.695** | |||||
| Perceived sexually coercive behavior score (SCB) | 0.829** | 0.516** | 0.613** | 0.939** | ||||
| PGD score | 0.357** | 0.545** | 0.256** | 0.139** | 0.106* | |||
| JS score | 0.288** | 0.408** | 0.232** | 0.124** | 0.095* | 0.507** | ||
| JSAT score | -0.156** | -0.229** | -0.110** | -00.082 | -00.043 | -0.372** | -0.532** | |
| INTL score | 0.196** | 0.280** | 0.135** | 0.091* | 0.084* | 0.462** | 0.592** | -0.704** |
** Significant at the 1% level (two-tailed) (n = 558)
Multivariable analysis of the factors associated with PSH
The multivariable generalized linear models with gamma regression examining the factors associated with the mean PSH score (Table 6) showed that age was not significantly associated (RR = 0.988, 95% CI [0.970, 1.006], p = 0.192) and nor was sex (male vs. female; RR = 1.020, 95% CI [0.986, 1.055], p = 0.258). The JS score was positively associated with the PSH score (RR = 1.005, 95% CI [1.002, 1.009], p = 0.003), whereas JSAT (RR = 1.001, 95% CI [0.997, 1.005], p = 0.536) and INTL (RR = 0.997, 95% CI [0.993, 1.001], p = 0.162) were not significantly related to PSH. Finally, the PGD score was correlated with the PSH score (RR = 1.009, 95% CI [1.007, 1.012], p < 0.001), indicating that greater PGD was associated with higher PSH.
Table 6.
Multivariable generalized linear model analysis of the participants’ mean PSH score
| Variable | 95% CI | |||
|---|---|---|---|---|
| RR | Lower | Upper | p-value | |
| (Intercept) | 14.735 | 12.414 | 17.489 | < 0.001 |
| Age group | 0.988 | 0.970 | 1.006 | 0.192 |
| Sex=Male | 1.020 | 0.986 | 1.055 | 0.258 |
| Mean JS score | 1.005 | 1.002 | 1.009 | 0.003 |
| Mean JSAT score | 1.001 | 0.997 | 1.005 | 0.536 |
| Mean INTL score | 0.997 | 0.993 | 1.001 | 0.162 |
| Mean PGD score | 1.009 | 1.007 | 1.012 | < 0.001 |
Note. Table 6 shows the results of the multivariable generalized linear models with gamma regression examining the factors associated with the mean PSH score. Dependent variable: Mean PSH score. Estimation method: Maximum likelihood with log-link. Model fit indices: AIC = 3010.3, BIC = 3239.5
Multivariable analysis of the factors associated with PGD
The multivariable generalized linear models with gamma regression examining the factors associated with the mean PGD score (Table 7) showed that neither age (RR = 0.989, 95% CI [0.962, 1.018], p = 0.451) nor sex (male vs. female; RR = 0.956, 95% CI [0.906, 1.009], p = 0.099) had a statistically significant association with PGD. In addition, the JS score was significantly associated with higher PGD (RR = 1.015, 95% CI [1.010, 1.021], p < 0.001). Similarly, higher JSAT (RR = 1.007, 95% CI [1.001, 1.012], p = 0.031) and greater INTL (RR = 1.009, 95% CI [1.002, 1.016], p = 0.011) were both associated with modest increases in the PGD score. In addition, the PSH score was strongly correlated with PGD (RR = 1.010, 95% CI [1.005, 1.016], p < 0.001), indicating that PSH tended to accompany higher PGD.
Table 7.
Multivariable generalized linear model analysis of the participants’ mean PGD score
| Variable | 95% CI | |||
|---|---|---|---|---|
| RR | Lower | Upper | p-value | |
| (Intercept) | 12.586 | 8.016 | 19.760 | < 0.001 |
| Age group | 0.989 | 0.962 | 1.018 | 0.451 |
| Sex=Male | 0.956 | 0.906 | 1.009 | 0.099 |
| Mean JS score | 1.015 | 1.010 | 1.021 | < 0.001 |
| Mean JSAT score | 1.007 | 1.001 | 1.012 | 0.031 |
| Mean INTL score | 1.009 | 1.002 | 1.016 | 0.011 |
| Mean PSH score | 1.010 | 1.005 | 1.016 | < 0.001 |
Note. Table 7 presents the results of the multivariable generalized linear models with gamma regression examining the factors associated with the mean PGD score. Dependent variable: Mean PGD score. Estimation method: Maximum likelihood with log-link. Model fit indices: AIC = 3524.5, BIC = 3593.7
Multivariable analysis of the factors associated with JS
The multivariable generalized linear models with gamma regression examining the factors associated with the mean JS scores (Table 8) showed that age was significantly associated with JS, where those aged ≤ 34 years reported higher perceived JS than those aged ≥ 35 years (RR = 1.061, 95% CI [1.004, 1.120], p = 0.034). Education level was also significantly associated with JS, with those holding a PhD reporting higher perceived JS than the others (RR = 1.175, 95% CI [1.019, 1.355], p = 0.026). However, holding a master’s degree, bachelor’s degree, or diploma was not significantly associated with JS (p > 0.05). The findings also showed that JSAT was negatively associated with JS, indicating that higher JSAT was correlated with lower JS (RR = 0.990, 95% CI [0.984, 0.995], p < 0.001). Higher INTL was positively associated with increased JS (RR = 1.019, 95% CI [1.012, 1.025], p < 0.001) and PGD was also a significantly positively associated with JS (RR = 1.010, 95% CI [1.005, 1.014], p < 0.001). PSH showed a marginal association with JS (RR = 1.004, 95% CI [1.00, 1.009], p = 0.075). Overall, the findings indicate that younger age, higher educational attainment, greater INTL, lower JSAT, and higher PGD are independently associated with increased JS.
Table 8.
Multivariable generalized linear model analysis of the participants’ mean JS score
| Variable | 95% CI | |||
|---|---|---|---|---|
| RR | Lower | Upper | p-value | |
| (Intercept) | 11.209 | 8.439 | 14.888 | < 0.001 |
| Sex=Male | 1.015 | 0.965 | 1.068 | 0.558 |
| Age < = 34 years vs. Age > = 35 | 1.061 | 1.004 | 1.120 | 0.034 |
| Education level = PhD | 1.175 | 1.019 | 1.355 | 0.026 |
| Education level=Master’s Degree | 1.135 | 0.987 | 1.305 | 0.075 |
| Education level=Bachelor’s degree | 1.113 | 0.974 | 1.271 | 0.116 |
| Education level=Diploma | 1.119 | 0.946 | 1.324 | 0.189 |
| Mean JSAT score | 0.990 | 0.984 | 0.995 | < 0.001 |
| Mean INTL score | 1.019 | 1.012 | 1.025 | < 0.001 |
| Mean PSH score | 1.004 | 1.000 | 1.009 | 0.075 |
| Mean PGD score | 1.010 | 1.005 | 1.014 | < 0.001 |
Note. Table 8 presents the results of the multivariable generalized linear models with gamma regression examining the factors associated with the mean JS score. Dependent variable: Perceived JS score. Estimation method: Maximum likelihood with log-link. Model fit indices: AIC = 3495.7, BIC = 3563.6
Discussion
Our study explored the prevalence of non-physical WPV, focusing on PSH and PGD and their associations with JS, JSAT, and INTL in organizations across Saudi Arabia. It contributes to the literature by examining these relationships within a rapidly transforming sociocultural and organizational context, which has been largely underexplored. The findings indicate that in Saudi Arabia, the ongoing gender integration and evolving workplace norms under Vision 2030 may not only provide a setting for WPV but also potentially shape how PSH and PGD are experienced, interpreted, and translated into stress and turnover-related outcomes. This extends existing research by suggesting that the effects of WPV are context-dependent and influenced by macro-level social change. The results suggest that transitional organizational conditions, including changing gender compositions, evolving reporting norms, and developing institutional responses to WPV, influence the extent to which such experiences lead to resource loss, as outlined by COR theory. Therefore, the study refines COR theory by highlighting transitional workplace environments as a potential boundary condition influencing resource loss processes.
Both PSH and PGD were prevalent in the sample and linked to negative work outcomes. PGD was associated with higher JS, JSAT, and INTL, whereas PSH was mainly associated with higher JS. These results suggest that persistent gender discrimination may affect Saudi employees more than isolated harassment incidents. This distinction between chronic and episodic WPV advances our theoretical understanding of their different effects on employees. The literature supports this, showing that gender discrimination, rather than sexual harassment, is associated with JS and turnover intention [4, 5]. Thus, our findings not only support prior research but also extend it within a non-Western context.
The negative associations of PGD may be attributed to its subtle and chronic nature, which, consistent with COR theory, can contribute to gradual psychological resource depletion over time. This cumulative resource loss mechanism suggests that repeated exposure to PGD intensifies negative outcomes. Gender discrimination frequently manifests in routine workplace practices such as exclusion, stereotyping, and gender-based treatment. These behaviors can significantly undermine employees’ sense of belonging, perceptions of fairness, and professional legitimacy. Such mechanisms may account for the stronger and more persistent effects of PGD than PSH. By contrast, sexual harassment may occur less frequently or appear less actionable, particularly in environments in which social norms discourage or stigmatize reporting. Discriminatory experiences may thus accumulate over time and more directly affect stress levels and employment decisions. Indeed, repeated exposure to discrimination is linked to ongoing psychological strain and reduced JSAT [7, 19].
The study’s varied findings on PSH and PGD align with those in the literature. Our results show the relatively low levels of PSH. The most common experiences involved differential treatment and sexist remarks rather than explicit harassment. This aligns with the findings of Debnath et al., who reported that approximately one in four women face sexual harassment in various sectors [36]. Another review among Saudi healthcare workers found a prevalence of 1.9% to 76.5%, likely due to varying definitions and measurement tools [6]. Direct comparisons are difficult because of these methodological differences. Still, these findings highlight that sexual harassment rates vary and suggest that low reports may reflect underreporting, not a lack of harassment. This reinforces the importance of considering measurement and contextual factors when interpreting estimated prevalence.
Underreporting is a key consideration, particularly regarding PSH and its relationship with JS. Many WPV incidents go unreported in Saudi Arabia and elsewhere [26, 27]. Barriers such as the normalization of WPV, insufficient training, and privacy concerns can discourage employees from reporting, even when systems exist [25, 27]. Therefore, observed prevalence rates likely underestimate the actual extent of WPV, which is a significant measurement limitation. Subtle and normalized discrimination is difficult to identify and address, yet it affects employees’ well-being and job retention. These factors may also influence reporting behaviors in Saudi workplaces.
The multivariable generalized linear models indicated that neither age nor sex was significantly associated with PGD or PSH. However, this finding contrasts with those of previous studies that report gender discrimination [12, 37–39]. This inconsistency suggests that contextual or cultural moderators could influence these associations. The observed difference may be attributed to cultural factors and the implementation of Saudi laws and policies that strictly prohibit gender discrimination and sexual harassment in the workplace. These regulations impose legal sanctions, including imprisonment and fines [40]. Consequently, formal regulatory frameworks may influence both the prevalence and the reporting of WPV, representing critical boundary conditions for interpreting such phenomena.
This results further highlighted the different associations of PGD and PSH. PSH was associated with JS but not with JSAT or INTL. By contrast, PGD was associated with both JS and INTL. These results are consistent with research showing that gender discrimination affects job attitudes and turnover intention more persistently than harassment [4, 5, 19, 38]. This finding strengthens the argument that chronic workplace stressors have broader organizational consequences than isolated incidents. Exposure to gender discrimination also relates to depressive symptoms, reinforcing the psychological impact seen here [6, 7, 19, 20].
Surprisingly, this study found that PGD was associated with higher JSAT scores, which does not align with prior research. The literature points to the rejection identification model, which claims that while discrimination is negative, it can trigger a coping response, leading people to identify more strongly with a group, which then creates a psychological buffer that improves well-being and satisfaction [41–43]. This suggests a compensatory psychological mechanism that may buffer the negative effects under certain conditions. Some high-performing individuals may also develop resilience after discrimination, leading to higher JSAT [44]. However, this interpretation should be approached cautiously, as alternative explanations such as selection effects and reporting bias cannot be ruled out. The data show that higher JS was associated with lower JSAT and higher INTL. This aligns with earlier studies showing that JS reduces JSAT and increases turnover intention [4, 45].
Organizational policies often promote reporting of clear harassment but everyday subtle discriminatory practices can go unnoticed. As mentioned earlier, these repeated practices can have a greater impact on well-being and retention, especially for women in new mixed-gender roles. From a practical standpoint, organizations should implement structured interventions such as anonymous reporting systems, regular workplace climate assessments, and targeted bias awareness training to identify and address subtle forms of discrimination.
These findings show that stopping WPV demands more than formal reporting systems. The Saudi HRSD framework emphasizes education and protection against retaliation [23]. This highlights a national commitment to safe and ethical workplaces and to moving forward against embedded discrimination. Organizations should operationalize these principles by integrating anti-discrimination policies into daily practices, ensuring accountability mechanisms, and routinely evaluating reporting system effectiveness.
Our study illustrates that to tackle WPV in Saudi Arabia, organizations must address both harassment and daily, subtle discriminatory acts. Leaders are required to build inclusive cultures, increase accountability, ensure transparency, and foster trust in reporting systems to recognize all forms of WPV [24, 46]. These actions are expected to reduce JS, improve JSAT, and decrease turnover intention, although implementation may face challenges such as cultural resistance and underreporting. Future research could identify the most effective reporting channels, remove obstacles, test trust across systems, and examine the long-term effects of WPV. Future studies could also evaluate the effectiveness of certain organizational interventions and explore sector-specific differences.
This study has its limitations. The cross-sectional design precluded us from making causal inferences; thus, the observed relationships should be interpreted as associations rather than causal effects. This limits the ability to determine the directionality between WPV and employee outcomes.
Second, the use of a convenience sample may limit the generalizability of the findings to other occupational or cultural contexts. Additionally, the sample was skewed toward highly educated individuals, with a limited representation of workers from lower educational and occupational backgrounds. This imbalance may restrict the extent to which the findings reflect the experiences of the broader workforce.
Third, the reliance on self-reported data may introduce social desirability or recall bias, particularly for sensitive experiences such as sexual harassment. This may lead to underreporting or misclassification.
Fourth, the potential for non-response bias should be considered, as the experiences of WPV and related outcomes among individuals who declined participation may differ systematically from those of the participants, which may affect the representativeness of the findings.
Finally, although the study included various organizations, the sampling approach did not allow the findings to be generalized to the entire Saudi workforce. Future research could employ longitudinal and mixed-methods research to address these limitations by examining changes over time and capturing temporal dynamics as well as gaining a deeper understanding of employees’ lived experiences. Longitudinal designs would be particularly valuable for testing causal pathways and resource loss mechanisms.
Conclusions
This study shows that non-physical WPV, particularly through gender discrimination, extends beyond healthcare settings in Saudi Arabia and is associated with adverse occupational outcomes. The findings contribute to the literature by highlighting the distinct impacts of two forms of non-physical WPV in a non-Western, rapidly evolving context. These findings are consistent with the Conservation of Resources Theory, suggesting that persistent exposure to workplace discrimination may contribute to cumulative psychological resource depletion and adverse work-related outcomes. The study finds that PGD, more than PSH, is more strongly associated with job stress and intention to leave employment. The absence of gender differences in PSH, along with higher PGD, underscores the importance of addressing structural and culturally embedded forms of inequality rather than focusing solely on overt harassment behaviors. As women’s participation in the Saudi workforce continues to rise, addressing gender discrimination is vital to employees’ well-being and retention as well as organizational integrity. These findings should be interpreted in light of the study’s methodological and sampling limitations, including its cross-sectional design and non-probability sampling. Nevertheless, the findings emphasize that addressing WPV requires more than formal policies. Organizations must build inclusive practices, ensure transparent accountability, and create environments in which employees feel safe reporting and challenging discrimination. Such efforts are likely to improve employee well-being and reduce turnover-related outcomes.
Acknowledgements
We would like to thank Editage (www.editage.com) for English language editing.
Abbreviations
- WPV
Workplace violence
- PSH
Perceived sexual harassment
- PGD
Perceived gender discrimination
- JS
Job stress
- JSAT
Job satisfaction
- INTL
Intention to leave employment
- HRSD
Ministry of Human Resources and Social Development
- SD
Standard deviation
- CI
Confidence interval
- SE
Standard error
- Mdn
Median
Authors’ contributions
Conceptualization, F.A., S.A. and A.A.M.; supervision, F.A.; data curation, F.A., F.A., A.S.A., A.A., F.M.A., G.K.A., A.I.A., R.A.M., A.A.M. and S.A.; writing—original draft preparation, F.A.; writing—review and editing, F.A., F.A., A.S.A., A.A., F.M.A., G.K.A., A.I.A., R.A.M., A.A.M. and S.A. All authors have read and agreed to the published version of the manuscript.
Funding
This study received approval for a grant number CMRC-DHG-4/020 through the Dallah Grant for Medical Research. However, no financial support has yet been received. The funding source was not involved in the study.
Data availability
Data from this study are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
The study was conducted in accordance with the principles of the Declaration of Helsinki and written informed consent was obtained from all the participants. The study was approved by the Ethics Committee of the Deanship of Scientific Research at King Saud University (No. KSU-HE-23-884 on September 19, 2023).
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data from this study are available from the corresponding author upon reasonable request.