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Systematic Review

Determinants of Women’s Well-Being in Sustainable Operations Management: A Human-Centric, Industry 5.0 Perspective on the Moroccan Automotive Industry

by
Amina Chandad
1,*,
Mohamed Amine Benchekroun
2 and
Mostafa Abakouy
1
1
ENCG Tangier, Abdelmalek Essaâdi University, Tangier 90000, Morocco
2
Laboratory of Systems, Control, and Decision (LSCD), New Science School of Engineering (ENSI), Tangier 90000, Morocco
*
Author to whom correspondence should be addressed.
Sustainability 2026, 18(16), 8055; https://doi.org/10.3390/su18168055
Submission received: 27 April 2026 / Revised: 21 June 2026 / Accepted: 10 July 2026 / Published: 7 August 2026
(This article belongs to the Special Issue From Industry 4.0 to Industry 5.0 in Sustainable Operations Management: AI-Driven, Human-Centric and Resilient Pathways)
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Abstract

The Industry 5.0 paradigm reframes sustainable operations management around human-centric, resilient and responsible production, yet the conditions under which digital and AI-enabled manufacturing translate into genuine worker well-being—particularly for women—remain under-investigated. This study analyses the determinants of women’s well-being at work in the Moroccan automotive industry, a sector that has become the country’s largest industrial exporter and a strategic laboratory for Industry 4.0-to-5.0 transitions. A systematic review was first conducted in Scopus (2015–2025) following PRISMA 2020 guidelines, yielding 54 eligible studies, of which 18 explicitly addressed automotive or Industry 4.0–5.0 contexts. Building on Job Demands–Resources theory and the human-centric tenets of Industry 5.0, a conceptual model articulated five antecedents—perceived supervisor support, job autonomy, work–life balance, technology-inclusive AI environment, and organisational justice—and a moderator, Industry 5.0 maturity. The model was tested via PLS-SEM (SmartPLS 4) on survey data from 412 women working in supplier and OEM plants across Tangier, Kénitra and Casablanca. Measurement quality was satisfactory (Cronbach’s α: 0.92–0.94; CR: 0.94–0.96; AVE: 0.81–0.85; HTMT < 0.63). All five antecedents significantly predicted well-being (β = 0.09–0.29; p ≤ 0.01), explaining 62% of its variance (Q2 = 0.571). Industry 5.0 maturity amplified the effect of a technology-inclusive AI environment on well-being (β interaction = 0.120; p < 0.001). The findings support a contingent, human-centric view of smart manufacturing and provide actionable levers for sustainable, gender-inclusive operations management.

1. Introduction

The reconfiguration of industrial production around digital platforms, artificial intelligence and collaborative robotics has progressively shifted the operations-management agenda from the efficiency-driven logic of Industry 4.0 toward the human-centric, resilient and sustainable ambition of Industry 5.0 [1,2]. Whereas Industry 4.0 foregrounded cyber-physical integration and autonomous optimisation, the 5.0 paradigm promoted by the European Commission places human well-being at the centre of industrial value creation, alongside environmental sustainability and systemic resilience [3]. This evolution has direct consequences for sustainable operations management, which now must reconcile productivity, ecological transition and the quality of the working life of heterogeneous workforces exposed to rapidly changing technologies [4,5].
Within this broader transformation, the situation of women in manufacturing deserves specific scrutiny. Despite the growing feminisation of automotive supply chains, women remain under-represented in qualified technical positions and over-exposed to repetitive, physically demanding tasks that can erode well-being, commitment and retention [6,7]. Evidence accumulated since the mid-2010s suggests that the relationship between smart manufacturing and employee well-being is not monotonic: technologies can relieve physical strain and open new skill trajectories but may equally intensify cognitive demands, heighten surveillance and deepen gender gaps when deployed in organisational environments that lack supportive supervision, procedural fairness and work–life accommodations [8,9,10,11]. The Moroccan automotive industry offers a particularly revealing empirical setting. With more than 250 plants, over 220,000 workers and exports exceeding USD 15 billion, it has become the country’s first industrial exporter [12]. Its rapid scaling, its integration into global value chains and the coexistence of traditional assembly lines with highly automated OEM facilities make it a critical site to study how the Industry 5.0 transition reshapes women’s work experiences.
Despite a sustained body of research on workplace well-being, three gaps motivate the present study. First, the literature on Industry 4.0/5.0 largely treats the workforce as a homogeneous category, leaving the gendered experience of technological transitions in manufacturing partially invisible [13,14]. Second, most empirical studies on women’s well-being focus on service sectors or Western contexts, with limited insight into emerging automotive hubs in North Africa [15]. Third, few studies jointly examine human-centred organisational resources (supervisor support, autonomy, work–life balance, and organisational justice) and technology-related resources (inclusive AI environments) within a single integrative model that reflects the ambition of Industry 5.0. The present research addresses these gaps by asking the following: Which organisational and technological determinants shape women’s well-being in the Moroccan automotive industry, and to what extent does the Industry 5.0 maturity of their employer amplify or attenuate these effects?
Three contributions are pursued. Conceptually, the paper proposes an integrative model grounded in the Job Demands–Resources (JD-R) framework and the human-centric premises of Industry 5.0, linking five antecedents—perceived supervisor support (PSUP), job autonomy (JA), work–life balance (WLB), technology-inclusive AI environment (TIA) and organisational justice (ORJ)—to women’s well-being at work (WB), with Industry 5.0 maturity (I5M) as a contextual moderator. Empirically, it provides PLS-SEM evidence on a large dataset of 412 women surveyed in three Moroccan industrial zones. Managerially, it offers evidence-based levers to support gender-inclusive and sustainable operations management. The remainder of the paper is organised as follows. Section 2 presents the theoretical background, the PRISMA-based systematic review and the hypotheses. Section 3 describes the Materials and Methods. Section 4 reports the Results. Section 5 discusses the findings, as well as their implications and limitations. Section 6 concludes the paper.

2. Theoretical Background and Hypotheses

2.1. Systematic Review Protocol (Scopus, 2015–2025)

To anchor the theoretical framework in recent and traceable evidence, a systematic review was conducted in strict accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA 2020) statement [16]. All 27 items of the PRISMA 2020 checklist were addressed, and the completed checklist is provided as Supplementary Material Table S2. The review protocol was developed a priori by the three authors and was not registered in PROSPERO because the study addresses a management-science topic outside the database’s health-research scope; this departure from registration is acknowledged as a limitation. The Scopus database was queried on 8 March 2025 with the following Boolean string applied to TITLE-ABS-KEY: (“well-being” OR “wellbeing” OR “job satisfaction” OR “quality of working life”) AND (“women” OR “female” OR “gender”) AND (“manufacturing” OR “automotive” OR “Industry 4.0” OR “Industry 5.0” OR “smart factory”). Scopus filters restricted the search to publication years spanning 2015–2025; document types limited to articles; journal source type; English or French language; and the subject areas of business/management, social sciences, engineering and decision sciences. Selection followed a two-stage procedure. First, the automated Scopus filters listed above reduced 13,085 identified records to 581 records eligible for manual screening. Second, the first author conducted a single title/abstract/keywords screening pass on the 581 remaining records, with structured verification by the two co-authors for doubtful cases; 527 records were excluded for lack of topical relevance to women’s well-being at work in industrial settings, and 54 studies were retained for qualitative synthesis. The two co-authors independently verified the coding of a random 20% of the retained studies, and all discrepancies were resolved by consensus. Figure 1 summarises the identification, screening and inclusion stages.
The Scopus search returned 13,085 records. Application of the automated Scopus filters (year window, target subject areas, and document type restricted to articles) removed 12,504 records upstream of manual screening (3173 outside the 2015–2025 window; 8867 outside the target subject areas; 464 non-article document types), leaving 581 records eligible for title, abstract and keywords screening. Of these, 527 were excluded for lack of topical relevance to women’s well-being at work in industrial settings, and 54 studies were retained for qualitative synthesis; 12 of them were explicitly set in the automotive industry or in Industry 4.0/5.0 environments. The corpus was analysed thematically around four clusters: organisational resources, technological environment, contextual inequalities, and outcomes related to well-being and retention. Methodological quality was appraised with the Mixed Methods Appraisal Tool (MMAT) 2018 [17]. Each study received a percentage score reflecting the proportion of applicable criteria met; the distribution across the 54 studies was 100% for 11 studies, 80% for 22 studies and 75% for 21 studies. Therefore, all included studies met the pre-specified inclusion threshold of ≥75%. The characteristics of the 54 included studies (identifier, author, year, journal, country/setting, sector, automotive/Industry 4.0–5.0 flag, design, sample, constructs of interest and MMAT score) are summarised in Table S1, provided as Supplementary Material Table S1. The corresponding bibliographic entries appear as references [18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71] in the References list of this article, ensuring full congruence between Figure 1 and the reference list, in line with PRISMA 2020 item 17. The review fully complies with the 27 reporting items of the PRISMA 2020 statement, including the title (item 1), structured abstract (item 2), rationale (item 3), objectives (item 4), eligibility criteria (item 5), information sources (item 6), search strategy (item 7), selection process (item 8), data collection process (item 9), data items (item 10), risk-of-bias assessment (item 11), and synthesis methods (items 13a–f). The completed PRISMA 2020 checklist mapping each item to its corresponding manuscript location is provided as Supplementary Material Table S2.

2.2. Industry 5.0 and Human-Centric Operations Management

The notion of Industry 5.0 was formalised by the European Commission as an extension—rather than a substitution—of Industry 4.0, adding a triple normative lens: human-centricity, sustainability and resilience [3]. In operations-management research, this shift translates into growing attention to cobotics, explainable AI, socio-technical design, and the capability of manufacturing systems to support worker agency and learning rather than discipline [1,72]. Prior reviews converge on three propositions. First, 5.0-aligned technologies enhance productivity only when coupled with participatory work design and managerial practices that protect autonomy and skill development [73,74]. Second, the human-centricity ideal remains aspirational for most firms, which still operate under predominantly 4.0 logic of efficiency and control [75]. Third, the gendered consequences of this transition are rarely problematised, despite evidence that women are disproportionately exposed to routine tasks most likely to be automated and to job roles where algorithmic management is rolled out first [8,76].

2.3. Well-Being at Work: Conceptualisation

The literature has gradually moved from narrow conceptions of well-being centred on job satisfaction to multidimensional approaches capturing affective, cognitive and eudemonic facets [77,78]. Following Diener and colleagues, this study defines workplace well-being as a tripartite construct reflecting positive affect at work, cognitive evaluation of the job, and perceived meaningfulness and growth [79]. The JD-R model offers a widely replicated framework to explain variation in such a construct: job resources buffer the negative effects of job demands and predict engagement, while a lack of resources—particularly social and organisational ones—depletes well-being and fuels turnover intentions [80,81]. In technologically intensive environments, the JD-R framework has been enriched to account for digital demands and digital resources, including AI-enabled support systems and algorithmic management [82,83].

2.4. Hypotheses

Drawing on the synthesised literature, five direct hypotheses and a moderating hypothesis are formulated. Perceived supervisor support (PSUP) refers to the extent to which employees feel that their direct manager values their contributions and cares about their welfare [84]. Consistent evidence shows that supportive supervision buffers the effects of intense production pressure, which is particularly salient on automotive assembly lines, where cycle times are short and physical demands are high [85,86]. For women, PSUP also signals procedural acceptance in traditionally male environments, reinforcing psychological safety and organisational identification [87]. Hence:
Hypothesis 1 (H1).
Perceived supervisor support is positively associated with women’s well-being at work.
Job autonomy (JA)—the degree of discretion employees enjoy in choosing how and when to perform tasks—remains a core job resource in the JD-R tradition [80]. In digitally mediated workplaces, autonomy takes on an additional meaning: it captures the scope left to the worker in a context of algorithmic monitoring and standardised work instructions [88]. Empirically, autonomy has been repeatedly linked to higher engagement, lower emotional exhaustion and better mental health outcomes [89,90]. In automotive plants that layer lean management over digital controls, residual autonomy becomes a key boundary condition for well-being [91]. Therefore:
Hypothesis 2 (H2).
Job autonomy is positively associated with women’s well-being at work.
Work–life balance (WLB) refers to the perceived fit between occupational demands and family or personal responsibilities [92]. In manufacturing environments organised around shifts, overtime and weekend work, women often carry a disproportionate share of unpaid care work, which compounds occupational fatigue and depresses well-being [93]. Recent studies in emerging-market factories confirm that flexible scheduling, predictable rotations and transportation arrangements significantly moderate this double burden [94]. Hence:
Hypothesis 3 (H3).
Work–life balance is positively associated with women’s well-being at work.
A technology-inclusive AI environment (TIA) captures the extent to which algorithmic and AI-based tools are designed and deployed in ways that are transparent, accessible and supportive of worker agency rather than opaque and coercive [95,96]. A growing body of evidence shows that when AI systems augment human decision-making and include feedback loops for workers, they contribute to learning and perceived meaningfulness; when they enforce rigid targets or monitor micro-gestures, they erode well-being and trust [97,98]. Given the rapid adoption of AI-enabled quality control and predictive-maintenance tools in Moroccan automotive plants, we expect the following:
Hypothesis 4 (H4).
A technology-inclusive AI environment is positively associated with women’s well-being at work.
Organisational justice (ORJ) aggregates perceptions of distributive, procedural and interactional fairness, which consistently predict affective commitment, engagement and well-being [99,100]. In contexts where gender, ethnicity and seniority stratify career trajectories, procedural justice carries particular weight: it signals that women can expect equal access to training, promotion and voice [101,102]. The automotive sector in emerging markets has historically been criticised for opaque performance-appraisal systems, which reinforces the relevance of this variable [103]. Thus:
Hypothesis 5 (H5).
Organisational justice is positively associated with women’s well-being at work.
Beyond direct effects, the Industry 5.0 maturity (I5M) of the firm is expected to play both a direct and a moderating role. Directly, more 5.0-mature firms should show higher average well-being because their socio-technical architecture internalises human-centric design, as the reviewed literature suggests [1,72]. As a moderator, I5M is expected to amplify the positive effect of a technology-inclusive AI environment on well-being because 5.0 maturity signals that AI tools are embedded in a coherent value system of worker-centred design, participatory governance and continuous upskilling [3,75]. Accordingly:
Hypothesis 6 (H6).
Industry 5.0 maturity is positively associated with women’s well-being at work.
Hypothesis 7 (H7).
Industry 5.0 maturity positively moderates the relationship between a technology-inclusive AI environment and women’s well-being such that the effect is stronger in firms with higher 5.0 maturity.
The resulting conceptual model is depicted in Figure 2.

3. Materials and Methods

3.1. Research Design and Sampling

A cross-sectional, quantitative design was adopted, consistent with the confirmatory intent of the hypotheses and with the sensitivity of the industry to time-bounded production cycles. The target population comprised women employed in automotive assembly plants, cable manufacturing units and Tier-1/Tier-2 suppliers operating in three Moroccan industrial zones: Tangier Free Zone (TFZ), the Kénitra Atlantic Free Zone and the Casablanca cluster. These three zones concentrate more than 85% of Morocco’s automotive industrial footprint [12].
A multistage stratified sampling procedure was followed. Firms were stratified by zone, by tier (OEM vs. supplier) and by size (SME < 250 employees vs. large firms ≥ 250 employees). Within each stratum, firms were approached through a combination of industry associations (AMICA), human-resource directors and union representatives. Proportional quotas were applied to approximate the known gender composition of each stratum, based on AMICA 2024 internal figures. Data collection ran from October 2024 to February 2025 using a self-administered bilingual (French–Arabic) questionnaire distributed in paper and tablet formats during working hours. Prior to participation, every respondent received a written information sheet (in French and Arabic) describing the study’s objectives, the voluntary and anonymous nature of participation, the absence of any consequence for declining or withdrawing, the data-protection arrangements, and the contact details of the principal investigator and the institutional ethics committee. Each participant then signed a written informed-consent form before completing the questionnaire; no incentive was offered. The questionnaire was distributed and collected without managerial presence to protect respondents’ freedom of expression. Out of 510 distributed questionnaires, 438 were returned; after data cleaning for completeness and inattentive responding (straight-lining and bogus items), 412 valid responses were retained, yielding an effective response rate of 80.8%. All identifying information was removed at the data-entry stage and replaced by anonymous codes; the linkage table was destroyed after the cleaning phase, in line with the ethics committee’s requirements.

3.2. Measurement Instruments

All constructs were measured with multi-item reflective scales adapted from established instruments and calibrated to the automotive context through two rounds of expert review with three academic scholars and two HR directors, followed by a pilot on 28 women workers. All items were rated on a 7-point Likert scale (1 = strongly disagree; 7 = strongly agree). Perceived supervisor support was measured with four items adapted from Eisenberger et al. [84]. Job autonomy relied on four items from the Work Design Questionnaire [88]. Work–life balance used four items based on Haar et al. [92]. Organisational justice was captured through a short four-item scale aggregating distributive and procedural dimensions following Colquitt [99]. The technology-inclusive AI environment construct (TIA) was developed from the recent literature on human-centred AI at work [95,96,97], with four items probing transparency, accessibility, worker voice in tool design and algorithmic fairness. Industry 5.0 maturity (I5M) was operationalised through three items derived from the European Commission’s 5.0 assessment framework [3,75], covering human-centric design, sustainability integration and resilience capability. Well-being at work (WB) was assessed through six items covering affective, cognitive and eudemonic dimensions, adapted from Diener et al. [79] and Bakker and Oerlemans [78]. Demographic controls included age, tenure, occupational status, education level, firm size, industrial zone, marital status and number of children.

3.3. Sample Characteristics

The effective sample (n = 412) combined operational and technical profiles representative of the industry’s gendered workforce. The mean age was 32.5 years (SD = 6.9); mean tenure was 4.6 years. Occupational status comprised 42.0% operators, 28.6% technicians, 20.4% mid-level managers and 9.0% senior managers. Education levels ranged from Baccalauréat (20.9%) to postgraduate degrees (11.9%), with 67.2% holding a two- to five-year post-secondary diploma. Marital status was distributed between single (38.1%), married (55.1%) and other (6.8%); 56.1% of respondents had at least one child. Geographical distribution mirrored the industrial footprint, with 48.1% of respondents based in Tangier Free Zone, 30.8% in Kénitra and 21.1% in Casablanca. A proportion of 71.8% worked in large firms (≥250 employees). Table 1 summarises these characteristics; Figure 3 (Section 4) displays the main distributions.

3.4. Analytical Strategy

Partial least-squares structural equation modelling (PLS-SEM) was selected for several reasons. PLS-SEM is particularly well suited to explanatory–predictive research with complex models, moderation effects and moderately normal data distributions [104,105]. It also performs well with samples of the present magnitude and is widely adopted in operations-management research. Estimation was conducted in SmartPLS 4 (version 4.1.0.9). The analytical procedure followed the two-step protocol of Hair and colleagues [104]: assessment of the measurement model (indicator reliability, internal consistency, convergent validity via AVE, and discriminant validity via HTMT and the Fornell–Larcker criterion), followed by assessment of the structural model (collinearity via VIF, path coefficients with bootstrapped t-values on 5000 resamples, effect sizes (f2), coefficient of determination (R2) and predictive relevance (Q2)). The moderation effect of Industry 5.0 maturity on the TIA–WB relationship was estimated through the two-stage interaction approach recommended by Henseler and Chin [106]. Common-method bias was appraised through Harman’s single-factor test, the full-collinearity VIF procedure of Kock [107], and by introducing a marker variable uncorrelated with the focal constructs. During data preparation and code commenting, the authors used ChatGPT (GPT-4) solely for grammar checking; all analytical choices, computations and interpretations were performed and reviewed by the authors, who take full responsibility for the content.

4. Results

4.1. Descriptive Statistics and Demographic Distribution

Mean scores across the seven constructs ranged from 3.96 (TIA, SD = 0.98) to 4.36 (WB, SD = 1.01), indicating moderate levels consistent with industrial environments in rapid transition (details in Appendix A, Table A1). Skewness and kurtosis values fell within the ±2 and ±7 thresholds recommended for PLS-SEM [104]. Figure 3 visualises the distribution of the three most structurally relevant demographic variables: occupational status, industrial zone and education level.

4.2. Assessment of the Measurement Model

All outer loadings exceeded the 0.70 threshold; Table 2 reports the range for each construct. Internal consistency was strong, with Cronbach’s α between 0.920 and 0.943 and composite reliability (CR) between 0.940 and 0.963, comfortably above the 0.70 benchmark. Convergent validity was supported: average variance extracted (AVE) ranged from 0.810 to 0.849, which is above the 0.50 threshold recommended by Fornell and Larcker [108]. Discriminant validity was examined via the HTMT ratio (Table 3): all values remained well below the 0.85 threshold, with the highest ratio at 0.626 (WB–PSUP). The Fornell–Larcker criterion was also satisfied, as the square root of each construct’s AVE exceeded its correlations with any other latent variable. Finally, full-collinearity VIF values ranged from 1.015 to 1.512, all below the strict 3.3 threshold [107], suggesting that common-method bias was unlikely to threaten the results.

4.3. Assessment of the Structural Model

Collinearity among the structural predictors was acceptable (VIF of 1.01–1.51). The model explained 62.0% of the variance in women’s well-being (R2 = 0.620)—a substantial value relative to Hair and colleagues’ benchmarks [104]—and predictive relevance was strong (Q2 = 0.571). All five direct hypotheses were supported at conventional thresholds (Table 4; Figure 4). The three strongest effects originated from human-centric organisational resources: perceived supervisor support (β = 0.290; t = 7.26; p < 0.001; f2 = 0.116), work–life balance (β = 0.247; t = 7.30; p < 0.001; f2 = 0.119) and job autonomy (β = 0.245; t = 7.14; p < 0.001; f2 = 0.101). Industry 5.0 maturity also exerted a direct effect on well-being (β = 0.214; t = 6.76; p < 0.001; f2 = 0.059). Organisational justice contributed significantly (β = 0.154; t = 4.02; p < 0.001; f2 = 0.042), while the effect of a technology-inclusive AI environment was smaller yet still significant (β = 0.089; t = 2.85; p = 0.004; f2 = 0.016). The I5M × TIA interaction term was positive and significant (β = 0.120; t = 4.09; p < 0.001), supporting H7 and indicating that the effect of AI-inclusive environments on well-being is substantially stronger in firms with higher Industry 5.0 maturity.

4.4. Moderation Analysis

The I5M × TIA significant interaction term warrants closer examination through a simple-slopes plot (Figure 5). At low Industry 5.0 maturity (−1 SD), the slope of TIA on WB is small and approximately flat (β_simple = −0.031), suggesting that in firms still operating under predominantly 4.0 logics, AI-inclusive features generate little additional well-being. At the mean level of I5M, the slope is positive and moderate (β_simple = 0.089). At high Industry 5.0 maturity (+1 SD), the slope becomes substantially larger (β_simple = 0.209), indicating that AI-inclusive work environments translate into tangible well-being gains primarily when firms have internalised the human-centric, sustainable and resilient tenets of Industry 5.0. The pattern is consistent with the theoretical premise that technology alone is insufficient and that the organisational envelope within which AI is deployed conditions its effect on workers.

5. Discussion

5.1. Theoretical Implications

The results extend the JD-R framework to the Industry 5.0 context along three lines. First, they reaffirm the explanatory weight of classical human-centric resources—supervisor support, autonomy, work–life balance and organisational justice—in a setting often described through techno-centric lenses [80,81,88]. Taken together, these four variables account for the bulk of the explained variance, echoing the results of Pansini et al. [8] and Bakker and colleagues [83] on European and Asian industrial samples. Second, the findings nuance the automation optimism that pervades some of the Industry 4.0–5.0 literature: a technology-inclusive AI environment contributes positively to women’s well-being but with a modest main effect (β = 0.089). This is consistent with recent cautionary evidence that AI tools translate into well-being benefits only when integrated into participatory, transparent and trustworthy work designs [96,97,98]. Third, the moderation pattern reinforces a contingent reading of Industry 5.0: AI-enabled environments deliver substantial well-being gains, primarily when embedded in firms with genuine 5.0 maturity, not merely digital equipment [3,75]. This contributes to the clarification of how the 5.0 paradigm differs operationally from its predecessor.

5.2. Managerial and Policy Implications

Several actionable levers emerge for automotive firms and for the Moroccan policy ecosystem. For firms, the strongest effects originate from investments that are organisationally intensive rather than capital-intensive: training front-line supervisors to be supportive and fair, redesigning work to preserve residual autonomy under lean digital constraints, and formalising work–life accommodations for shift-based roles. Such investments do not require advanced 5.0 technologies to deliver well-being improvements. When firms deploy AI-enabled tools, they should treat transparency, explainability and worker voice in tool design as non-negotiable features, not ergonomic luxuries. For public authorities, the results underline the importance of gender-sensitive indicators in the national Industry 5.0 roadmap, especially in free-zone regimes where productivity metrics still dominate. The observed pattern also suggests that upskilling programmes for women in automotive plants should be coupled with organisational-design interventions—otherwise, technical upskilling without organisational maturity risks leaving the well-being effect flat, as the low-I5M slope indicates.

5.3. Limitations and Future Research

Three main limitations should temper the interpretation of these results. First, the cross-sectional design prevents strong causal claims; although common-method bias indicators were favourable, a longitudinal or panel design would strengthen inference. Second, the focus on Morocco and on three industrial zones, while internally coherent, limits the external validity of the findings. Comparable studies in Tunisia, Türkiye or Eastern European automotive hubs would help assess the generalisability of the moderation pattern. Third, the measurement of Industry 5.0 maturity remains nascent: the three-item scale used in this study captures broad dimensions but would benefit from richer multi-method operationalisation, combining self-reports with audit-based indicators. Future research could also investigate the mediating role of psychological safety, examine the differential effects of specific AI use cases (quality control vs. predictive maintenance vs. algorithmic scheduling), and explore intersectional analyses accounting for ethnicity, parenthood and disability.

6. Conclusions

This study investigated the determinants of women’s well-being at work in the Moroccan automotive industry from the perspective of a human-centric Industry 5.0, which reframes sustainable operations management around people, resilience and responsibility. Combining a PRISMA-based systematic review of the Scopus literature (2015–2025) with a survey-based PLS-SEM analysis of 412 respondents, the study shows that four classical human-centric resources—perceived supervisor support, work–life balance, job autonomy and organisational justice—remain the strongest predictors of women’s well-being and that a technology-inclusive AI environment contributes significantly but modestly. Industry 5.0 maturity exerts both a direct and a moderating effect: more mature firms enjoy higher baseline well-being, and the well-being payoff of AI-inclusive environments grows sharply with 5.0 maturity. The implication for sustainable operations management is clear: the transition to Industry 5.0 will only deliver its promised human-centric dividend if organisations invest as heavily in the social and organisational fabric of work as they do in their digital and AI stack and if they attend explicitly to the gendered experiences that shape how smart manufacturing is lived on the shop floor.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su18168055/s1, Table S1: Characteristics of the 54 studies included in the systematic review; Table S2: Completed PRISMA 2020 checklist mapping each review item to its location in the manuscript.

Author Contributions

Conceptualization, A.C. and M.A.; methodology, A.C. and M.A.B.; software, A.C. and M.A.B.; validation, A.C., M.A.B. and M.A.; formal analysis, A.C.; investigation, A.C.; resources, M.A.; data curation, A.C. and M.A.B.; writing—original draft preparation, A.C.; writing—review and editing, A.C., M.A.B. and M.A.; visualization, A.C.; supervision, M.A.; project administration, A.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Research Ethics Committee of Abdelmalek Essaâdi University, ENCG Tangier (approval code CER-ENCGT-2024-017; date of approval: 14 October 2024).

Informed Consent Statement

Written informed consent for participation in the study was obtained from all subjects individually before they completed the questionnaire. Each respondent received a bilingual (French–Arabic) information sheet detailing the study’s objectives, the voluntary and anonymous nature of participation, the right to withdraw at any time without consequence, the data-storage and protection arrangements, and the contact details of the principal investigator and the Research Ethics Committee. No incentives were offered. As the manuscript does not contain any individual person’s identifying information or images, no separate consent for publication was required. Original signed consent forms are retained by the corresponding author for five years in compliance with the institutional research-ethics policy and are available for inspection upon reasonable request.

Data Availability Statement

The anonymised dataset used in this study is available from the corresponding author upon reasonable request and in compliance with participant confidentiality commitments.

Acknowledgments

The authors gratefully acknowledge the contributions of participating plants, AMICA, and the HR managers who facilitated access to respondents. During the preparation of this manuscript, the authors used ChatGPT (OpenAI, GPT-4) for the sole purpose of grammar and spelling checking of the English text. The authors have reviewed and edited the output and take full responsibility for the content of this publication.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
AIArtificial Intelligence
AMICAMoroccan Association for the Automotive Industry and Trade
AVEAverage Variance Extracted
CRComposite Reliability
HTMTHeterotrait–Monotrait Ratio of Correlations
I5MIndustry 5.0 Maturity
JAJob Autonomy
JD-RJob Demands–Resources (model)
ORJOrganisational Justice
PLS-SEMPartial Least Squares Structural Equation Modelling
PSUPPerceived Supervisor Support
TIATechnology-Inclusive AI Environment
VIFVariance Inflation Factor
WBWomen’s Well-Being at Work
WLBWork–Life Balance

Appendix A

This appendix presents complementary descriptive statistics and the measurement items used in the survey.
Table A1. Descriptive statistics of construct scores (n = 412).
Table A1. Descriptive statistics of construct scores (n = 412).
ConstructItemsMeanSDSkewnessKurtosis
PSUP44.021.05−0.18−0.41
JA44.140.92−0.07−0.22
WLB44.191.08−0.24−0.37
TIA43.960.98+0.11−0.30
ORJ44.081.03−0.12−0.33
I5M34.031.01+0.05−0.28
WB64.361.01−0.21−0.19
Table A2. Selected measurement items (translated from the French–Arabic bilingual questionnaire).
Table A2. Selected measurement items (translated from the French–Arabic bilingual questionnaire).
Construct Sample Item
PSUPMy direct supervisor really cares about my well-being at work.
JAI can decide on my own how to organise my tasks during a shift.
WLBMy current work schedule allows me to fulfil my family responsibilities.
TIAThe AI-based tools I use at work are transparent about how they make recommendations.
ORJDecisions about promotions in my unit are made through fair and consistent procedures.
I5MMy company explicitly integrates worker well-being and sustainability into its digital transformation roadmap.
WBMost of the time, I feel engaged and enthusiastic while performing my job.

References

  1. Xu, X.; Lu, Y.; Vogel-Heuser, B.; Wang, L. Industry 4.0 and Industry 5.0—Inception, Conception and Perception. J. Manuf. Syst. 2021, 61, 530–535. [Google Scholar] [CrossRef]
  2. Leng, J.; Sha, W.; Wang, B.; Zheng, P.; Zhuang, C.; Liu, Q.; Wuest, T.; Mourtzis, D.; Wang, L. Industry 5.0: Prospect and Retrospect. J. Manuf. Syst. 2022, 65, 279–295. [Google Scholar] [CrossRef]
  3. Breque, M.; De Nul, L.; Petridis, A. Industry 5.0: Towards a Sustainable, Human-Centric and Resilient European Industry; European Commission, Directorate-General for Research and Innovation, Publications Office of the European Union: Luxembourg, 2021. [CrossRef] [PubMed]
  4. Nahavandi, S. Industry 5.0—A Human-Centric Solution. Sustainability 2019, 11, 4371. [Google Scholar] [CrossRef]
  5. Ivanov, D. The Industry 5.0 Framework: Viability-Based Integration of the Resilience, Sustainability and Human-Centricity Perspectives. Int. J. Prod. Res. 2023, 61, 1683–1695. [Google Scholar] [CrossRef]
  6. International Labour Organization. The Future of Work in the Automotive Industry: The Need to Invest in People’s Capabilities and Decent and Sustainable Work; ILO: Geneva, Switzerland, 2021. [Google Scholar]
  7. Ghislieri, C.; Molino, M.; Cortese, C.G. Work and Organizational Psychology Looks at the Fourth Industrial Revolution: How to Support Workers and Organizations? Front. Psychol. 2018, 9, 2365. [Google Scholar] [CrossRef] [PubMed]
  8. Pansini, M.; Buonomo, I.; De Vincenzi, C.; Ferrara, B.; Benevene, P. Positioning Technostress in the JD-R Model Perspective: A Systematic Literature Review. Healthcare 2023, 11, 446. [Google Scholar] [CrossRef] [PubMed]
  9. Kadir, B.A.; Broberg, O.; Souza da Conceição, C. Current Research and Future Perspectives on Human Factors and Ergonomics in Industry 4.0. Comput. Ind. Eng. 2019, 137, 106004. [Google Scholar] [CrossRef]
  10. Parent-Rocheleau, X.; Parker, S.K. Algorithms as Work Designers: How Algorithmic Management Influences the Design of Jobs. Hum. Resour. Manag. Rev. 2022, 32, 100838. [Google Scholar] [CrossRef]
  11. Howard, M.C.; Gutworth, M.B. A Meta-Analysis of Virtual Reality Training Programs for Social Skill Development. Comput. Educ. 2020, 144, 103707. [Google Scholar] [CrossRef]
  12. Moroccan Ministry of Industry and Trade. Automotive Sector in Morocco: Key Figures 2024; Ministry of Industry and Trade: Rabat, Morocco, 2024.
  13. Cotrino, A.; Sebastián, M.A.; González-Gaya, C. Industry 4.0 Roadmap: Implementation for Small and Medium-Sized Enterprises. Appl. Sci. 2020, 10, 8566. [Google Scholar] [CrossRef]
  14. Lu, Y.; Zheng, H.; Chand, S.; Xia, W.; Liu, Z.; Xu, X.; Wang, L.; Qin, Z.; Bao, J. Outlook on Human-Centric Manufacturing towards Industry 5.0. J. Manuf. Syst. 2022, 62, 612–627. [Google Scholar] [CrossRef]
  15. Alvesson, M.; Billing, Y.D. Understanding Gender and Organizations, 3rd ed.; Sage: London, UK, 2022. [Google Scholar]
  16. Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. BMJ 2021, 372, n71. [Google Scholar] [CrossRef] [PubMed]
  17. Hong, Q.N.; Fàbregues, S.; Bartlett, G.; Boardman, F.; Cargo, M.; Dagenais, P.; Gagnon, M.-P.; Griffiths, F.; Nicolau, B.; O’Cathain, A.; et al. The Mixed Methods Appraisal Tool (MMAT) Version 2018 for Information Professionals and Researchers. Educ. Inf. 2018, 34, 285–291. [Google Scholar] [CrossRef]
  18. Sharma, L.; Srivastava, M. A scale to measure organizational stress among women workers in the garment industry. Eur. J. Train. Dev. 2022, 46, 820–846. [Google Scholar] [CrossRef]
  19. Sasikumar, G.M.; Sujatha, S. Perceptions of Women Employees on the Quality of Work Life Practices in The Electronics Manufacturing Industry-an Analysis. Qubahan Acad. J. 2024, 4, 129–152. [Google Scholar] [CrossRef]
  20. Farrukh, A.; Mehddi, A.; Sajjad, A. Female social sustainability initiatives in the textile industry: An institutional theory and social resource-based view. Int. J. Logist. Manag. 2025. Epub ahead of printing. [Google Scholar] [CrossRef]
  21. D’souza, C.P.; Tapas, P. Stereotypes, struggles and SDG 5: Supporting “sheroes” of supply chain. Int. J. Logist. Manag. 2025. Epub ahead of printing. [Google Scholar] [CrossRef]
  22. Rastogi, M.; Rangnekar, S.; Rastogi, R. Enhancing quality of work life in India: The role of workplace flexibility. Ind. Commer. Train. 2018, 50, 234–249. [Google Scholar] [CrossRef]
  23. Petreski, M.; Petreski, B.; Tanevski, S. Soft skills, hard results: The productivity impact of employer-supported soft skills training, with focus on women. Eur. J. Train. Dev. 2025, 50, 313–339. [Google Scholar] [CrossRef]
  24. Kokubun, K.; Yasui, M. Gender differences in organizational commitment and rewards within Japanese manufacturing companies in China. Cross Cult. Strateg. Manag. 2020, 28, 501–529. [Google Scholar] [CrossRef]
  25. Hinge, P.; Salunkhe, H.A.; Boralkar, M.; Bang, S.; Thakur, A.; Adhegaonkar, V.R. Measuring employee attrition intention in an auto-component manufacturing organisation. SA J. Hum. Resour. Manag. 2025, 23, 11. [Google Scholar] [CrossRef]
  26. Dahiya, R.; Rangnekar, S. Validation of satisfaction with life scale in the Indian manufacturing sector. Asia-Pac. J. Bus. Adm. 2020, 12, 251–268. [Google Scholar] [CrossRef]
  27. Thaiduong, N. Navigating male-dominated industries: Factors in the career development of women leaders in Vietnamese manufacturing and technology. Humanit. Soc. Sci. Commun. 2025, 12, 848. [Google Scholar] [CrossRef]
  28. Ganiyu, I.O.; Derera, E.; Atiku, S.O.; Fields, Z. Measuring the effectiveness of work–life balance strategies in the manufacturing sector. SA J. Hum. Resour. Manag. 2020, 18, 10. [Google Scholar] [CrossRef]
  29. Siddika, A.; Lund-Thomsen, P.; Hammer, A.; Vahl, M.; Rayat. ‘Our Backs Are Against the Wall’: The Story of a Bangladeshi Woman Garment Worker in the COVID-19 ‘New Normal’. Work. Employ. Soc. 2024, 38, 1725–1735. [Google Scholar] [CrossRef]
  30. EL Aissoug, C.; Kim, T.Y.; Choi, H.; Choi, D.-H. Revisiting the concept of decent work: Exploring work values and job satisfaction in the era of Logistics 4.0. Int. J. Phys. Distrib. Logist. Manag. 2025, 56, 109–137. [Google Scholar] [CrossRef]
  31. Lin-Hi, N.; Gao-Urhahn, X.; Biemann, T.; Koch-Bayram, I.F. Internal CSR and blue-collar workers’ attitudes and behaviors in China: A combination of a cross-sectional study and a field experiment. Asian Bus. Manag. 2023, 22, 1185–1213. [Google Scholar] [CrossRef]
  32. Salvadorinho, J.; Hines, P.; Kumar, M.; Ferreira, C.; Teixeira, L. Empowering Generation Z in manufacturing organizations: A 6-factor self-determination extension. J. Work.-Appl. Manag. 2024, 18, 130–149. [Google Scholar] [CrossRef]
  33. Johnson, R.C.; Eatough, E.M.; Chang, C.-H.D.; Hammer, L.B.; Truxilllo, D. Home is where the mind is: Family interference with work and safety performance in two high risk industries. J. Vocat. Behav. 2019, 110, 117–130. [Google Scholar] [CrossRef]
  34. Chuang, Y.-T.; Chiang, H.-L.; Lin, A.-P. Insights from the Job Demands–Resources Model: AI’s dual impact on employees’ work and life well-being. Int. J. Inf. Manag. 2025, 83, 102887. [Google Scholar] [CrossRef]
  35. Kamboj, J.; A., E. Mapping the complex interactions between work-family conflict, AI technostress, work-life balance and employees’ well-being: Moderated mediation analysis. Int. J. Confl. Manag. 2025, 37, 713–741. [Google Scholar] [CrossRef]
  36. Kong, H.; Yin, Z.; Baruch, Y.; Yuan, Y. The impact of trust in AI on career sustainability: The role of employee–AI collaboration and protean career orientation. J. Vocat. Behav. 2023, 146, 103928. [Google Scholar] [CrossRef]
  37. Kokot, K.; Kokotec, I.Đ.; Čalopa, M.K. Digital Leadership and Maturity as a Key to Successful Digital Transformation: Country Case Study of Croatia. TEM J. 2023, 12, 192–199. [Google Scholar] [CrossRef]
  38. Tucker, G. Sustainable product lifecycle management, industrial big data, and internet of things sensing networks in cyber-physical system-based smart factories. J. Self-Gov. Manag. Econ. 2021, 6, 9. [Google Scholar] [CrossRef]
  39. Samadhiya, A.; Agrawal, R.; Luthra, S.; Kumar, A.; Garza-Reyes, J.A.; Srivastava, D.K. Total productive maintenance and Industry 4.0 in a sustainability context: Exploring the mediating effect of circular economy. Int. J. Logist. Manag. 2023, 34, 818–846. [Google Scholar] [CrossRef]
  40. Shen, Y.; Zhang, X. The impact of artificial intelligence on employment: The role of virtual agglomeration. Humanit. Soc. Sci. Commun. 2024, 11, 122. [Google Scholar] [CrossRef]
  41. Begum, G.T. Achieving sustainable development goals for mental health and gender equality through work-life balance and emotional intelligence. Gend. Manag. 2025, 40, 134–150. [Google Scholar] [CrossRef]
  42. Stankevičienė, A.; Šimelytė, A.; Tamaševičius, V.; Petkinytė, E. The Impact of Sociodemographic Factors and Work-Family Conflict on the Relationship Between Organizational Support and Women’s Burnout. Probl. Perspect. Manag. 2023, 21, 497–510. [Google Scholar] [CrossRef]
  43. Agbanyo, G.K.; Jiang, J. Two-instance moderating dilemma between responsible leadership and family-supportive supervisor behavior on women’s career sustainability. Humanit. Soc. Sci. Commun. 2025, 12, 688. [Google Scholar] [CrossRef]
  44. Rasheed, M.; Iqbal, S.; Mustafa, F. Work-family conflict and female employees’ turnover intentions. Gend. Manag. 2018, 33, 636–653. [Google Scholar] [CrossRef]
  45. Takawira, N. Predictors of career satisfaction among working women: Mediating role of supportive resources. SA J. Hum. Resour. Manag. 2025, 23, 9. [Google Scholar] [CrossRef]
  46. Jamunarani, H.S.; Syed, R. Flexible work arrangements and their impact on women’s job satisfaction and quality of life: The mediating role of work-life balance. Probl. Perspect. Manag. 2025, 23, 130–144. [Google Scholar] [CrossRef] [PubMed]
  47. Steffan, B.; Loretto, W. Menopause, work and mid-life: Challenging the ideal worker stereotype. Gend. Work. Organ. 2025, 32, 116–131. [Google Scholar] [CrossRef]
  48. El-Kot, G.; Burke, R.J.; Fiksenbaum, L.M. Egyptian women supervisory empowerment behaviors on well-being outcomes. Gend. Manag. 2019, 34, 350–365. [Google Scholar] [CrossRef]
  49. Snipes, R.L.; Pitts, J.P.; Bryant, P.C.; Huning, T.M.; Snipes, A. Job Satisfaction and Politics in the Modern Workplace: An Empirical Examination of the Moderating Effects of Gender and Age on the Perception of Organizational Politics-Job Satisfaction Relationship. Empl. Responsib. Rights J. 2024, 36, 337–365. [Google Scholar] [CrossRef]
  50. Olckers, C.; Koekemoer, E. Decent work and work fulfilment: The mediating role of work engagement and the moderating role of psychological ownership. Aust. J. Career Dev. 2024, 33, 242–253. [Google Scholar] [CrossRef]
  51. Máynez-Guaderrama, A.I.; Cavazos-Arroyo, J. Background and Effects of Emotional Exhaustion: Structural Model and Multigroup Analysis; [Antecedentes y efectos del agotamiento emocional: Modelo estructural y análisis multigrupo]. Estud. Gerenciales 2025, 41, 156–168. [Google Scholar] [CrossRef]
  52. Alzadjali, B.; Ahmad, S.Z. The impacts of a high commitment work system on well-being: The mediating role of organization support and employee work-life balance. Ind. Commer. Train. 2024, 56, 53–77. [Google Scholar] [CrossRef]
  53. Nabawanuka, H.; Ekmekcioglu, E.B. Millennials in the workplace: Perceived supervisor support, work–life balance and employee well–being. Ind. Commer. Train. 2021, 54, 123–144. [Google Scholar] [CrossRef]
  54. Rueda-López, R.; Aja-Valle, J.; García-García, L.; Vázquez-García, M.J. The Influence of Inclusive Leadership on the Well-Being of Employees; [La influencia del liderazgo inclusivo sobre el bienestar laboral de las personas trabajadoras]. Rev. Galega De Econ. 2024, 33, 1–25. [Google Scholar] [CrossRef]
  55. Atarah, B.A.; Frimpong, N.; Ahwireng, P.O. Occupational health and safety management policies and employee quality of life in developing countries: Insights from Ghana. Bus. Strategy Dev. 2023, 6, 859–872. [Google Scholar] [CrossRef]
  56. Sorribes, J.; Celma, D.; Martínez-Garcia, E. Sustainable human resources management in crisis contexts: Interaction of socially responsible labour practices for the wellbeing of employees. Corp. Soc. Responsib. Environ. Manag. 2021, 28, 936–952. [Google Scholar] [CrossRef]
  57. Noermijati, N.; Farhiya, D.; Kurniawati, D.T.; Rosita, F.; Masyhuri, M. Utilizing Flexible Working Arrangement into Organizational Citizenship Behavior: A Double Mediation Model. Bus. Manag. Econ. Eng. 2025, 23, 280–301. [Google Scholar] [CrossRef]
  58. Sareminia, S.; Sajedi Haji, F. Enhancing employees’ quality of work life and engagement to foster corporate social responsibility: A data mining approach. Ind. Commer. Train. 2024, 56, 213–239. [Google Scholar] [CrossRef]
  59. Pathak, A.; Singh, N.; Singh, S.; Prasad, K.D.V.; Dave, K.K.; Kothari, H. Relationship between Emotional Maturity and Employer Satisfaction: An Empirical Study Concerning Telecom Industry Employees. Empl. Responsib. Rights J. 2024. [Google Scholar] [CrossRef]
  60. Al-Fayez, D.; Goodman, D. Health-Related Assistance Programs, Leadership Support, and Organizational Performance: Evidence From the U.S. Federal Agencies. Public Pers. Manag. 2024, 53, 6–35. [Google Scholar] [CrossRef]
  61. Ngqeza, K.; Dhanpat, N. Investigating the effects of employee empowerment on turnover intention in a mining organisation. SA J. Hum. Resour. Manag. 2021, 19, 12. [Google Scholar] [CrossRef]
  62. Sims, R.L.; Ruppel, C.P.; Zeidler, P. Work strain, job satisfaction, and intention to quit: The Moderating Effect of Long-Term Orientation. Int. J. Stress Manag. 2016, 23, 23–43. [Google Scholar] [CrossRef]
  63. Potnuru, R.K.G.; Sahoo, C.K.; Sharma, R. Team building, employee empowerment and employee competencies: Moderating role of organizational learning culture. Eur. J. Train. Dev. 2019, 43, 39–60. [Google Scholar] [CrossRef]
  64. Boyatzis, R.; Rochford, K.; Cavanagh, K.V. Emotional intelligence competencies in engineer’s effectiveness and engagement. Career Dev. Int. 2017, 22, 70–86. [Google Scholar] [CrossRef]
  65. Islam, T.; Abdullah, H.; Samreen, U.; Arfat, Y. Walking on thin ice: A moderated mediation path from distributive injustice to organizational deviant behavior. Ind. Commer. Train. 2025, 57, 388–404. [Google Scholar] [CrossRef]
  66. Waqas, M.; Fatima, T.; Anjum, Z.U.Z. Does basic need satisfaction foster engagement by serving as a personal demand? A mediation model based on a self-determination perspective. Asia-Pac. J. Bus. Adm. 2024, 16, 1015–1036. [Google Scholar] [CrossRef]
  67. Badri, M.; Al Khaili, M.; Al Dhaheri, H.; Yang, G.; Albahar, M. The job security well-being nexus: A path analysis of economic, social, and mental health outcomes. Humanit. Soc. Sci. Lett. 2025, 13, 1256–1268. [Google Scholar] [CrossRef]
  68. Hamouche, S.; Marchand, A. Occupational identity, work, psychological distress and gender in management: Results from SALVEO study. Gend. Manag. 2022, 37, 638–658. [Google Scholar] [CrossRef]
  69. Hammond, L.-S.; Coetzee, M. An exploration of factors influencing the retention of senior female employees in a financial services organisation. SA J. Hum. Resour. Manag. 2022, 20, 10. [Google Scholar] [CrossRef]
  70. Echebiri, C.; Mathibe, M.; Echebiri, C.M.; Osuigwe, U.U.S. Work From Home Model: An Exploration Into the Experiences of Working Mothers in the Service Sector: The Case of Nigeria and South Africa. Gend. Work. Organ. 2025, 32, 1758–1770. [Google Scholar] [CrossRef]
  71. Latif, Z.; Choudhary, M.A.; Sarwar, S.Z. The impact of work-life connectivity on professional women: A case study of telecom industry. Knowl. Manag. E-Learn. Int. J. 2016, 8, 271–291. [Google Scholar] [CrossRef]
  72. Maddikunta, P.K.R.; Pham, Q.-V.; Prabadevi, B.; Deepa, N.; Dev, K.; Gadekallu, T.R.; Ruby, R.; Liyanage, M. Industry 5.0: A Survey on Enabling Technologies and Potential Applications. J. Ind. Inf. Integr. 2022, 26, 100257. [Google Scholar] [CrossRef]
  73. Romero, D.; Stahre, J.; Taisch, M. The Operator 4.0: Towards Socially Sustainable Factories of the Future. Comput. Ind. Eng. 2020, 139, 106128. [Google Scholar] [CrossRef]
  74. Cimini, C.; Lagorio, A.; Romero, D.; Cavalieri, S.; Stahre, J. Smart Logistics and the Logistics Operator 4.0. IFAC-PapersOnLine 2020, 53, 10615–10620. [Google Scholar] [CrossRef]
  75. Fraga-Lamas, P.; Lopes, S.I.; Fernández-Caramés, T.M. Green IoT and Edge AI as Key Technological Enablers for a Sustainable Digital Transition towards a Smart Circular Economy: An Industry 5.0 Use Case. Sensors 2021, 21, 5745. [Google Scholar] [CrossRef] [PubMed]
  76. Hilbert, M. Digital Gender Divide or Technologically Empowered Women in Developing Countries? A Typical Case of Lies, Damned Lies, and Statistics. Women’s Stud. Int. Forum 2011, 34, 479–489. [Google Scholar] [CrossRef]
  77. Warr, P. The Measurement of Well-Being and Other Aspects of Mental Health. J. Occup. Psychol. 1990, 63, 193–210. [Google Scholar] [CrossRef]
  78. Bakker, A.B.; Oerlemans, W.G.M. Subjective Well-Being in Organizations. In The Oxford Handbook of Positive Organizational Scholarship; Cameron, K.S., Spreitzer, G.M., Eds.; Oxford University Press: Oxford, UK, 2011; pp. 178–189. [Google Scholar]
  79. Diener, E.; Wirtz, D.; Tov, W.; Kim-Prieto, C.; Choi, D.; Oishi, S.; Biswas-Diener, R. New Well-Being Measures: Short Scales to Assess Flourishing and Positive and Negative Feelings. Soc. Indic. Res. 2010, 97, 143–156. [Google Scholar] [CrossRef]
  80. Bakker, A.B.; Demerouti, E. Job Demands–Resources Theory: Taking Stock and Looking Forward. J. Occup. Health Psychol. 2017, 22, 273–285. [Google Scholar] [CrossRef] [PubMed]
  81. Schaufeli, W.B.; Taris, T.W. A Critical Review of the Job Demands–Resources Model: Implications for Improving Work and Health. In Bridging Occupational, Organizational and Public Health; Bauer, G.F., Hämmig, O., Eds.; Springer: Dordrecht, The Netherlands, 2014; pp. 43–68. [Google Scholar]
  82. Tarafdar, M.; Cooper, C.L.; Stich, J.F. The Technostress Trifecta—Techno Eustress, Techno Distress and Design: Theoretical Directions and an Agenda for Research. Inf. Syst. J. 2019, 29, 6–42. [Google Scholar] [CrossRef]
  83. Bakker, A.B.; Demerouti, E.; Sanz-Vergel, A. Job Demands–Resources Theory: Ten Years Later. Annu. Rev. Organ. Psychol. Organ. Behav. 2023, 10, 25–53. [Google Scholar] [CrossRef]
  84. Eisenberger, R.; Stinglhamber, F.; Vandenberghe, C.; Sucharski, I.L.; Rhoades, L. Perceived Supervisor Support: Contributions to Perceived Organizational Support and Employee Retention. J. Appl. Psychol. 2002, 87, 565–573. [Google Scholar] [CrossRef] [PubMed]
  85. Kurtessis, J.N.; Eisenberger, R.; Ford, M.T.; Buffardi, L.C.; Stewart, K.A.; Adis, C.S. Perceived Organizational Support: A Meta-Analytic Evaluation of Organizational Support Theory. J. Manag. 2017, 43, 1854–1884. [Google Scholar] [CrossRef]
  86. Fischer, T.; Riedl, R. Technostress Research: A Nurturing Ground for Measurement Pluralism? Commun. Assoc. Inf. Syst. 2017, 40, 375–401. [Google Scholar] [CrossRef]
  87. Edmondson, A.C.; Lei, Z. Psychological Safety: The History, Renaissance, and Future of an Interpersonal Construct. Annu. Rev. Organ. Psychol. Organ. Behav. 2014, 1, 23–43. [Google Scholar] [CrossRef]
  88. Morgeson, F.P.; Humphrey, S.E. The Work Design Questionnaire (WDQ): Developing and Validating a Comprehensive Measure for Assessing Job Design and the Nature of Work. J. Appl. Psychol. 2006, 91, 1321–1339. [Google Scholar] [CrossRef] [PubMed]
  89. Deci, E.L.; Olafsen, A.H.; Ryan, R.M. Self-Determination Theory in Work Organizations: The State of a Science. Annu. Rev. Organ. Psychol. Organ. Behav. 2017, 4, 19–43. [Google Scholar] [CrossRef]
  90. Humphrey, S.E.; Nahrgang, J.D.; Morgeson, F.P. Integrating Motivational, Social, and Contextual Work Design Features: A Meta-Analytic Summary. J. Appl. Psychol. 2007, 92, 1332–1356. [Google Scholar] [CrossRef] [PubMed]
  91. Lorente, L.; Salanova, M.; Martínez, I.M.; Vera, M. How Personal Resources Predict Work Engagement and Self-Rated Performance among Construction Workers: A Social Cognitive Perspective. Int. J. Psychol. 2014, 49, 200–207. [Google Scholar] [CrossRef] [PubMed]
  92. Haar, J.M.; Russo, M.; Suñe, A.; Ollier-Malaterre, A. Outcomes of Work–Life Balance on Job Satisfaction, Life Satisfaction and Mental Health: A Study across Seven Cultures. J. Vocat. Behav. 2014, 85, 361–373. [Google Scholar] [CrossRef]
  93. Greenhaus, J.H.; Allen, T.D. Work–Family Balance: A Review and Extension of the Literature. In Handbook of Occupational Health Psychology, 2nd ed.; Tetrick, L.E., Quick, J.C., Eds.; American Psychological Association: Washington, DC, USA, 2011; pp. 165–183. [Google Scholar]
  94. Allen, T.D.; French, K.A.; Dumani, S.; Shockley, K.M. A Cross-National Meta-Analytic Examination of Predictors and Outcomes Associated with Work–Family Conflict. J. Appl. Psychol. 2020, 105, 539–576. [Google Scholar] [CrossRef] [PubMed]
  95. Shneiderman, B. Human-Centered Artificial Intelligence: Three Fresh Ideas. AIS Trans. Hum. Comput. Interact. 2020, 12, 109–124. [Google Scholar] [CrossRef]
  96. Raisch, S.; Krakowski, S. Artificial Intelligence and Management: The Automation–Augmentation Paradox. Acad. Manag. Rev. 2021, 46, 192–210. [Google Scholar] [CrossRef]
  97. Kellogg, K.C.; Valentine, M.A.; Christin, A. Algorithms at Work: The New Contested Terrain of Control. Acad. Manag. Ann. 2020, 14, 366–410. [Google Scholar] [CrossRef]
  98. Glikson, E.; Woolley, A.W. Human Trust in Artificial Intelligence: Review of Empirical Research. Acad. Manag. Ann. 2020, 14, 627–660. [Google Scholar] [CrossRef]
  99. Colquitt, J.A. On the Dimensionality of Organizational Justice: A Construct Validation of a Measure. J. Appl. Psychol. 2001, 86, 386–400. [Google Scholar] [CrossRef] [PubMed]
  100. Colquitt, J.A.; Scott, B.A.; Rodell, J.B.; Long, D.M.; Zapata, C.P.; Conlon, D.E.; Wesson, M.J. Justice at the Millennium, a Decade Later: A Meta-Analytic Test of Social Exchange and Affect-Based Perspectives. J. Appl. Psychol. 2013, 98, 199–236. [Google Scholar] [CrossRef] [PubMed]
  101. Ely, R.J.; Meyerson, D.E. An Organizational Approach to Undoing Gender: The Unlikely Case of Offshore Oil Platforms. Res. Organ. Behav. 2010, 30, 3–34. [Google Scholar] [CrossRef]
  102. Stamarski, C.S.; Son Hing, L.S. Gender Inequalities in the Workplace: The Effects of Organizational Structures, Processes, Practices, and Decision Makers’ Sexism. Front. Psychol. 2015, 6, 1400. [Google Scholar] [CrossRef] [PubMed]
  103. Barrientos, S.; Bianchi, L.; Berman, C. Gender and Governance of Global Value Chains: Promoting the Rights of Women Workers. Int. Labour Rev. 2019, 158, 729–752. [Google Scholar] [CrossRef]
  104. Hair, J.F.; Hult, G.T.M.; Ringle, C.M.; Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 3rd ed.; Sage: Thousand Oaks, CA, USA, 2022. [Google Scholar]
  105. Sarstedt, M.; Ringle, C.M.; Hair, J.F. Partial Least Squares Structural Equation Modeling. In Handbook of Market Research; Homburg, C., Klarmann, M., Vomberg, A., Eds.; Springer: Cham, Switzerland, 2022; pp. 587–632. [Google Scholar]
  106. Henseler, J.; Chin, W.W. A Comparison of Approaches for the Analysis of Interaction Effects between Latent Variables Using Partial Least Squares Path Modeling. Struct. Equ. Model. 2010, 17, 82–109. [Google Scholar] [CrossRef]
  107. Kock, N. Common Method Bias in PLS-SEM: A Full Collinearity Assessment Approach. Int. J. e-Collab. 2015, 11, 1–10. [Google Scholar] [CrossRef]
  108. Fornell, C.; Larcker, D.F. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
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Figure 1. PRISMA 2020 flow diagram of the systematic review on women’s well-being in manufacturing and automotive settings (Scopus, 2015–2025).
Figure 1. PRISMA 2020 flow diagram of the systematic review on women’s well-being in manufacturing and automotive settings (Scopus, 2015–2025).
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Figure 2. Conceptual model of the determinants of women’s well-being at work in the Moroccan automotive industry, with Industry 5.0 maturity as a contextual moderator.
Figure 2. Conceptual model of the determinants of women’s well-being at work in the Moroccan automotive industry, with Industry 5.0 maturity as a contextual moderator.
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Figure 3. Demographic distribution of the sample across occupational status, industrial zone and education level (n = 412).
Figure 3. Demographic distribution of the sample across occupational status, industrial zone and education level (n = 412).
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Figure 4. Structural model with standardised path coefficients (SmartPLS 4, 5000 bootstrap resamples).
Figure 4. Structural model with standardised path coefficients (SmartPLS 4, 5000 bootstrap resamples).
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Figure 5. Simple-slopes plot of the moderating effect of Industry 5.0 maturity (I5M) on the relationship between technology-inclusive AI environment (TIA) and women’s well-being (WB).
Figure 5. Simple-slopes plot of the moderating effect of Industry 5.0 maturity (I5M) on the relationship between technology-inclusive AI environment (TIA) and women’s well-being (WB).
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Table 1. Sample characteristics (n = 412).
Table 1. Sample characteristics (n = 412).
Variable Category n %
Occupational statusOperator17342.0
Technician11828.6
Mid-level manager8420.4
Senior manager379.0
EducationBaccalauréat8620.9
Bac + 214635.4
Bac + 3 to Bac + 513131.8
>Bac + 54911.9
Industrial zoneTangier Free Zone19848.1
Kénitra12730.8
Casablanca8721.1
Firm sizeSME (<250)11628.2
Large (≥250)29671.8
Marital statusSingle15738.1
Married22755.1
Other286.8
Children018143.9
19823.8
29924.0
≥3348.3
Table 2. Measurement model: reliability and convergent validity.
Table 2. Measurement model: reliability and convergent validity.
Construct Items Loadings Range Cronbach’s α CR AVE
PSUP40.903–0.9090.9280.9480.819
JA40.897–0.9060.9260.9460.813
WLB40.918–0.9250.9370.9570.849
TIA40.919–0.9260.9380.9580.849
ORJ40.901–0.9190.9300.9500.827
I5M30.904–0.9290.9200.9400.839
WB60.897–0.9100.9430.9630.810
Table 3. Heterotrait–monotrait ratio of correlations (HTMT).
Table 3. Heterotrait–monotrait ratio of correlations (HTMT).
PSUP JA WLB TIA ORJ I5M
JA0.422—————
WLB0.3230.282————
TIA0.1960.2360.101———
ORJ0.5310.3160.1990.195——
I5M0.1800.1360.1280.2870.280—
WB0.6260.5490.4910.3330.5230.426
Table 4. Structural paths, bootstrap results and effect sizes (5000 resamples).
Table 4. Structural paths, bootstrap results and effect sizes (5000 resamples).
Hypothesis/PathβSEtp95% CIf2Decision
H1. PSUP → WB+0.2900.0407.26<0.001[0.211; 0.368]0.116Supported
H2. JA → WB+0.2450.0347.14<0.001[0.179; 0.313]0.101Supported
H3. WLB → WB+0.2470.0347.30<0.001[0.180; 0.313]0.119Supported
H4. TIA → WB+0.0890.0312.850.004[0.030; 0.152]0.016Supported
H5. ORJ → WB+0.1540.0384.02<0.001[0.077; 0.229]0.042Supported
H6. I5M → WB+0.2140.0326.76<0.001[0.152; 0.277]0.059Supported
H7. I5M × TIA → WB+0.1200.0294.09<0.001[0.063; 0.179]0.023Supported
Note. β = standardised path coefficient; SE = bootstrap standard error; CI = 95% bias-corrected accelerated confidence interval; f2 = effect size; all coefficients based on 5000 bootstrap resamples; R2 (WB) = 0.620; Q2_predict (WB) = 0.571.
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Chandad, A.; Benchekroun, M.A.; Abakouy, M. Determinants of Women’s Well-Being in Sustainable Operations Management: A Human-Centric, Industry 5.0 Perspective on the Moroccan Automotive Industry. Sustainability 2026, 18, 8055. https://doi.org/10.3390/su18168055

AMA Style

Chandad A, Benchekroun MA, Abakouy M. Determinants of Women’s Well-Being in Sustainable Operations Management: A Human-Centric, Industry 5.0 Perspective on the Moroccan Automotive Industry. Sustainability. 2026; 18(16):8055. https://doi.org/10.3390/su18168055

Chicago/Turabian Style

Chandad, Amina, Mohamed Amine Benchekroun, and Mostafa Abakouy. 2026. "Determinants of Women’s Well-Being in Sustainable Operations Management: A Human-Centric, Industry 5.0 Perspective on the Moroccan Automotive Industry" Sustainability 18, no. 16: 8055. https://doi.org/10.3390/su18168055

APA Style

Chandad, A., Benchekroun, M. A., & Abakouy, M. (2026). Determinants of Women’s Well-Being in Sustainable Operations Management: A Human-Centric, Industry 5.0 Perspective on the Moroccan Automotive Industry. Sustainability, 18(16), 8055. https://doi.org/10.3390/su18168055

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