Mediating Role Of Sustainable Representative Participation In The Relationship Between AI Behavioural Intention And Employee Satisfaction: Evidence From Nigerian Financial Institutions

The integration of artificial intelligence into financial services has accelerated dramatically across global markets, with applications ranging from algorithmic lending and fraud detection to personalized banking and operational automation (Rahman et al., 2023). While the operational and strategic benefits of AI adoption have received substantial research attention, the human implications of these technologies remain comparatively underexplored, particularly within emerging economy contexts (Obeng et al., 2026). This gap is significant because employees are not passive recipients of technological change; their psychological and behavioural responses to AI adoption fundamentally shape organizational outcomes.

The concept of behavioural intention to use AI defined as an employee's conscious plan to employ AI-based tools in daily work represents a critical early psychological response to digital transformation. Drawing on the Technology Acceptance Model, intention is shaped by perceptions of usefulness and ease of use, yet the relationship between intention and subsequent job satisfaction is far from straightforward (Rahman et al., 2023). Early encounters with AI may generate disruption, learning burdens, and uncertainties about job relevance, potentially diminishing work fulfillment. However, when organizations establish systematic channels through which employees can influence AI implementation, these adverse effects may be substantially mitigated.

This study introduces the construct of sustainable representative participation, defined as the degree to which employees experience permanent, meaningful voice in decisions concerning AI adoption, governance, and refinement. In contrast to ad hoc consultations or episodic feedback exercises, sustainable representative participation institutionalizes employee representation within ongoing digital governance frameworks (Cardoso et al., 2023; Shin et al., 2023). The central research question guiding this investigation is whether sustainable representative participation mediates the relationship between behavioural intention to use AI and job satisfaction among employees in Nigerian financial institutions.

The research context is particularly salient. Abuja, Nigeria's Federal Capital Territory, has emerged as a commercial hub where banks and insurance firms are rapidly implementing AI in algorithmic lending, biometric validation, and automated customer service. The Nigerian financial sector presents a distinctive environment characterized by high digital ambition alongside infrastructure constraints, variable digital skill levels, and a culturally diverse workforce. These conditions render the sector an ideal setting for examining the human dimensions of AI adoption in emerging economies.

The Technology-Organization-Environment (TOE) framework provides the theoretical architecture for this study. Within this framework, behavioural intention represents a technological characteristic, sustainable representative participation constitutes an organizational characteristic, and job satisfaction serves as the focal human outcome. The TOE framework posits that the impact of technological drivers on individual outcomes is seldom direct; rather, such effects are mediated through complementary organizational processes (Obeng et al., 2026). This perspective aligns with socio-technical systems theory, which emphasizes that technological change interacts with organizational structures and human behaviour to produce emergent outcomes (Raisch & Krakowski, 2021).

This research makes three primary contributions. First, it provides empirical evidence demonstrating that the AI productivity paradox operates at the individual employee level, extending classic insights from information technology research to workforce psychology. Second, it introduces and validates the construct of sustainable representative participation, offering a quantifiable and manageable mechanism for addressing the human dimensions of AI adoption. Third, it extends the TOE framework by demonstrating that participative social structures serve as essential complements to technological innovation in emerging economy contexts.

LITERATURE REVIEW AND THEORETICAL FRAMEWORK

1. Theoretical Foundations: The TOE Framework

The Technology-Organization-Environment framework, originally developed by Tornatzky and Fleischer, provides a comprehensive lens for understanding technology adoption and its consequences. The framework posits that three contextual dimensions influence how technological innovations are assimilated and their subsequent effects: the technological context (characteristics of the technology itself), the organizational context (firm characteristics including structure, culture, and resources), and the environmental context (industry conditions, competitive pressures, and regulatory frameworks) (Obeng et al., 2026).

While the TOE framework has traditionally been applied to firm-level adoption decisions and operational performance outcomes, this study adapts it to examine individual employee outcomes. In this adaptation, behavioural intention to use AI represents the technological context operationalized at the individual level. Sustainable representative participation embodies the organizational context, specifically the participative structures that govern how technology-related decisions are made. Employee satisfaction constitutes the focal outcome, representing the human dimension of technological change. The environmental context including competitive pressures, regulatory requirements, and industry norms in Nigeria's financial sector provides the boundary conditions within which these relationships unfold.

This adaptation is theoretically defensible because organizations are not monolithic entities; rather, they are comprised of individuals whose experiences and responses to technology collectively shape organizational outcomes. The TOE framework's emphasis on contextual interactions aligns with the recognition that technology adoption produces differential effects depending on the organizational mechanisms available to mediate its impacts (Vial, 2021).

2. Behavioural Intention to Use AI and Job Satisfaction

Behavioural intention to use AI captures an employee's willingness and readiness to employ AI-enabled tools in work processes. According to the Technology Acceptance Model, intention is influenced by perceived usefulness (the extent to which AI is believed to enhance job performance) and perceived ease of use (the degree to which AI is believed to be free from effort) (Rahman et al., 2023). While intention generally predicts subsequent technology use, the relationship between intention and job satisfaction is more complex.

The introduction of AI into work environments can generate disruption, complexity, and uncertainty, particularly during early implementation phases. Employees may experience steep learning curves as they master new systems, alterations to established task structures that disrupt workflow routines, and concerns about job displacement that activate psychological threat responses (Tarafdar et al., 2019). These experiences intersect with the negative dimensions of digital transformation, where technological change induces stress and diminishes well-being.

From a motivational perspective, Self-Determination Theory proposes that well-being is enhanced when three basic psychological needs are fulfilled: autonomy (the experience of volition and choice), competence (the experience of mastery and effectiveness), and relatedness (the experience of connection and belonging) (Raisch & Krakowski, 2021). AI implementation may threaten these needs during early stages by reducing perceived autonomy (as algorithms prescribe work processes), challenging existing competence (as skills become obsolete), and disrupting relatedness (as automated systems replace human interaction). Consequently, under conditions lacking supportive organizational structures, increased behavioural intention to use AI may paradoxically associate with decreased job satisfaction.

The AI productivity paradox, originally identified at the organizational level by Brynjolfsson (1993), describes the phenomenon whereby initial technology investments disrupt workflow and introduce complexity, leading to short-term performance declines before positive effects materialize. This study extends this concept to the individual employee level, hypothesizing that behavioural intention to use AI generates immediate adaptation costs that negatively impact job satisfaction. Thus:

Hypothesis 1 (H1): Employees' behavioural intention to use AI has a negative direct effect on employee satisfaction.

3. AI Intention and Sustainable Representative Participation

Despite its disruptive potential, behavioural intention to use AI may simultaneously stimulate organizational restructuring in beneficial directions. When employees signal willingness to engage with AI, this openness to change may motivate organizations to design inclusion and collaboration mechanisms that channel employee input into technology governance.

Sustainable representative participation is defined as the degree to which employees are continuously engaged in decisions regarding AI adoption, implementation, and governance. In contrast to one-off consultations or episodic feedback exercises, sustainable participation manifests as institutionalized structures including formal committees, systematic feedback mechanisms, and participatory decision-making processes that endure over time (Cardoso et al., 2023; Kane et al., 2021). Within the TOE framework, these structures constitute a critical element of the organizational context that shapes how technological change is introduced and assimilated.

There is growing empirical evidence that organizations characterized by high levels of participatory cultures navigate digital transformation more effectively because they can align technology initiatives with employee expectations, concerns, and capabilities (Shin et al., 2023; Vial, 2021). When employees perceive that their voice matters in technology decisions, they experience greater psychological safety and are more likely to constructively engage with change rather than resist it. Furthermore, organizations that invest in participatory structures signal commitment to procedural justice, which enhances trust and cooperation during disruptive transitions.

The relationship between behavioural intention and organizational response may be mutually reinforcing. As employees develop intention to use AI, their expressed readiness may prompt management to formalize input channels, establish AI governance committees, and increase communication about digital strategy. In turn, these participatory structures may further strengthen employee intention by reducing uncertainty and demonstrating organizational commitment to inclusive change management. This reciprocal dynamic suggests that behavioural intention to use AI can positively predict sustainable representative participation. Thus:

Hypothesis 2 (H2): Behavioural intention towards the use of AI has a positive contribution to sustainable representative participation among employees.

4. Sustainable Representative Participation and Employee Satisfaction

Sustainable representative participation is expected to play a central role in enhancing employee satisfaction, particularly during periods of technological disruption. Employee voice theory explains that opportunities to express opinions, raise concerns, and influence decisions enhance perceptions of fairness, trust, and psychological safety (Chatterjee et al., 2023). When employees can shape how AI is implemented in their work environment, uncertainty is reduced, and a sense of shared ownership over technological change is fostered.

Applying Self-Determination Theory to the participation-satisfaction relationship reveals multiple pathways through which participatory practices enhance well-being. First, autonomy is supported when employees are involved in decision-making processes that affect their work, as they experience a sense of agency and control over their circumstances. Second, competence is supported through engagement with new technologies in contexts where learning is encouraged and mistakes are treated as development opportunities. Third, relatedness is supported through collaborative processes that maintain human connection even as automated systems are introduced (Wamba et al., 2021; Shin et al., 2023).

Empirical research across diverse organizational contexts demonstrates that participatory cultures and digital leadership positively influence employee well-being and performance outcomes. In a study of South Korean organizations, Shin et al. (2023) found that digital leadership enhanced digital culture and employees' digital capabilities, with downstream effects on organizational sustainability. Similarly, Cardoso et al. (2023) reported that digital culture and knowledge commitment to digital transformation significantly impacted organizational competitiveness, mediated by employee engagement and participation.

Within the specific context of AI adoption, representative participation may serve a protective function. Employees who perceive that they have meaningful voice in AI decisions report lower anxiety about job displacement and greater confidence that technology will augment rather than replace their roles. This sense of procedural justice and influence over the terms of technological change constitutes a powerful determinant of job satisfaction. Thus:

Hypothesis 3 (H3): Sustainable representative participation has a positive effect on employee satisfaction.

5. The Mediating Role of Sustainable Representative Participation

The TOE framework suggests that the impact of technological factors on individual outcomes is likely indirect, operating through organizational processes. Applied to the present context, behavioural intention to use AI may simultaneously activate two competing pathways: a negative direct pathway reflecting disruption and adaptation costs, and a positive indirect pathway operating through the strengthening of participatory structures.

As behavioural intention to use AI prompts organizations to enhance representative participation, employees gain a sense of control and influence over how technology is implemented. This participatory experience, in turn, enhances satisfaction by fulfilling psychological needs for autonomy, competence, and relatedness. The net effect of AI intention on satisfaction depends on the relative strength of these countervailing pathways.

This pattern of inconsistent mediation where direct and indirect effects have opposite signs has been observed in previous AI research. Obeng et al. (2026) found that AI intention had a negative direct effect on organizational efficiency but a positive serial indirect effect through technology readiness and digital culture. The present study analogously proposes that sustainable representative participation serves as the social mechanism that transforms the disruptive potential of AI intention into workforce benefit. If the indirect pathway is sufficiently strong, it may fully compensate for the negative direct effect, resulting in a non-significant total effect.

Hypothesis 4 (H4): The relationship between behavioural intention to use AI and employee satisfaction is positively and significantly mediated by sustainable representative participation.

METHODOLOGY

1. Research Design and Sampling

A quantitative, cross-sectional survey design was employed, consistent with positivist research assumptions. The target population comprised employees of financial institutions in Abuja, Nigeria, where AI adoption is proceeding rapidly. Commercial banks and insurance firms in this context have implemented intelligent credit assessment systems, fraud detection algorithms, and customer relationship management platforms, making them appropriate sites for examining the human impact of AI.

Multistage sampling was utilized. First, major banking and insurance districts within Abuja Central Business District, Wuse, Garki, and Maitama were purposively selected to ensure organizational diversity. Regional and branch managers were contacted to secure institutional approval. Second, online questionnaires were distributed by human resource officers within each participating branch, with participants selected on a convenience basis from employee rosters.

Computer-assisted web interviewing (CAWI) was employed to standardize delivery, enhance anonymity, and reduce social desirability bias. Four hundred questionnaires were distributed; following data cleaning, 347 valid responses were retained, exceeding the minimum requirement of ten times the number of indicators for the most complex construct (Legate et al., 2023). Ethical clearance was obtained from the institutional review board, and all participants provided informed consent prior to survey completion.

2. Sample Characteristics

The sample demonstrated balanced gender distribution, with 54.8% male and 45.2% female participants. The largest age group was 31–40 years (38.0%), followed by 41–50 years (30.8%). Regarding educational attainment, 64.6% held bachelor's or associate degrees, while 27.7% held master's or doctoral degrees. Middle-level managers constituted the largest occupational category (48.4%), indicating that the sample captured employees most directly involved in operationalizing AI directives.

3. Measurement Instruments

All constructs were measured using multi-item scales adapted from prior validated studies, anchored on five-point Likert scales ranging from strongly disagree (1) to strongly agree (5). Prior to full deployment, the questionnaire was pilot tested with 30 employees to assess clarity and comprehension, with minor wording adjustments made based on feedback.

Behavioural Intention to Use AI (AIINT): A three-item scale based on technology acceptance literature as employed by Rahman et al. (2023) was used. Sample items include "I intend to use AI-enabled technology for managing my banking tasks" and "I plan to use AI banking services rather than only human financial advisors." The scale demonstrated strong internal consistency in the present study (Cronbach's α = 0.903).

Sustainable Representative Participation (SRP): A five-item scale was developed for this study, drawing on research on digital culture and participative decision-making (Cardoso et al., 2023; Shin et al., 2023). Items assessed frequency of consultation, existence of formal input channels, perceived voice efficacy, management responsiveness, and sustainability of representation. Sample items include "Employees are regularly consulted about how AI tools should be used in our work" and "Employee representation in AI governance is sustained, not occasional." The scale showed high reliability (Cronbach's α = 0.929).

Employee Satisfaction (ESAT): A three-item global job satisfaction scale, validated in African work environments by Obeng et al. (2026), was adapted for the Nigerian financial services context. Sample items include "Overall, I am satisfied with my job" and "I find real enjoyment in my work." Reliability was acceptable (Cronbach's α = 0.804).

4. Data Analysis Procedures

Data analysis proceeded in two stages. First, data screening, descriptive statistics, and reliability estimation were conducted using SPSS 23. Second, partial least squares structural equation modeling (PLS-SEM) was performed using SmartPLS 4.1.1.2, which is appropriate for predictive modeling, complex indirect effects, and medium-sized samples (Legate et al., 2023; Hair et al., 2022).

The measurement model was assessed using indicator loadings (>0.70 threshold), composite reliability (CR >0.70), average variance extracted (AVE >0.50), and the heterotrait-monotrait ratio of correlations (HTMT <0.85). The structural model was evaluated using path coefficients (β) with significance determined through 5,000 bootstrap resamples, coefficients of determination (R²), and effect sizes (f²). Mediation was tested by examining the specific indirect effect and its bias-corrected 95% confidence interval.

Common method bias was assessed using Harman's single-factor test and marker variable analysis following procedures established by Obeng et al. (2026). As Farinloye (2021) emphasizes, rigorous quantitative analysis requires attention to both measurement validity and statistical assumptions; these were systematically evaluated prior to hypothesis testing.

RESULTS

1. Measurement Model Assessment

All indicator loadings exceeded the 0.70 threshold, ranging from 0.79 to 0.94, confirming indicator reliability. Composite reliability values ranged from 0.882 to 0.938, exceeding the recommended threshold, while average variance extracted values exceeded 0.60 for all constructs, establishing convergent validity. The Fornell-Larcker criterion and HTMT ratios supported discriminant validity, with all HTMT values below the conservative threshold of 0.85.

Harman's single-factor test revealed that a single factor accounted for only 34.2% of total variance, below the 50% threshold indicating substantial common method bias. Marker variable analysis yielded non-significant relations, further suggesting that common method bias did not seriously threaten the validity of findings.

2. Structural Model and Hypothesis Testing

The structural model demonstrated good explanatory power. The R² for sustainable representative participation was 0.187, while the R² for employee satisfaction was 0.521, indicating that the model explains more than half the variance in the focal outcome. Stone-Geisser Q² values obtained through blindfolding were 0.140 for SRP and 0.350 for ESAT, both substantially above zero, confirming predictive relevance. Variance inflation factor (VIF) values were below 1.4, ruling out multicollinearity concerns.

Hypothesis 1 predicted a negative direct effect of behavioural intention to use AI on employee satisfaction. The path coefficient was negative and significant (β = -0.38, p = 0.003), supporting H1. This finding confirms that, in the absence of mediating mechanisms, intention to adopt AI is associated with lower job satisfaction, reflecting the disruption that early AI experiences create.

Hypothesis 2 proposed a positive effect of AI intention on sustainable representative participation. The result was positive and significant (β = 0.43, p < 0.001), supporting H2. This indicates that when employees demonstrate readiness to employ AI, organizations tend to strengthen participatory governance structures.

Hypothesis 3 posited that sustainable representative participation positively relates to satisfaction. The path coefficient was strong and significant (β = 0.64, p < 0.001), supporting H3. The large effect size (f² = 0.40) underscores the critical importance of representation as a driver of employee well-being during digital transformation.

Hypothesis 4 involved the mediation pathway. The indirect effect of AI intention on satisfaction through sustainable representative participation was positive and significant (β = 0.28, p < 0.001), with a bias-corrected 95% confidence interval [0.20, 0.35] excluding zero, confirming mediation. Notably, the total effect of AI intention on satisfaction was non-significant (β = -0.11, p = 0.25), indicating that the positive indirect effect fully compensates the negative direct effect. This pattern of inconsistent mediation demonstrates that the overall relationship neutralizes through the intervention of sustainable representative participation.

DISCUSSION

This study investigated the relationship between employees' behavioural intention to use AI and job satisfaction in Nigerian financial institutions, with sustainable representative participation as a mediating mechanism. The findings reveal a nuanced pattern consistent with the AI productivity paradox extended to the individual level.

The negative direct effect of AI intention on satisfaction (H1) aligns with emerging evidence that early AI encounters can be disruptive at the individual level. Employees struggle with adapted workflows, learning new systems, and uncertainty introduced by automation (Jöhnk et al., 2021; Tarafdar et al., 2019). The moderate effect size (f² = 0.09) suggests that disruption is a tangible phenomenon requiring managerial attention, though not overwhelming in magnitude.

The positive effect of AI intention on sustainable representative participation (H2) demonstrates that behavioural intention does not operate in isolation but may stimulate organizational change. As employees signal readiness to work with AI, management often responds by formalizing input channels, establishing AI committees, and enhancing communication (Shin et al., 2023; Kane et al., 2021). In the Nigerian context, where upward voice has historically faced constraints, the push toward AI appears to be creating pathways toward more inclusive governance.

The strong positive relationship between sustainable representative participation and employee satisfaction (H3) reaffirms the central importance of voice in employee well-being. When workers perceive consistent, structured, sustained input into AI decisions, their needs for autonomy and procedural justice are satisfied, and fears of job displacement are reduced (Wamba et al., 2021; Cardoso et al., 2023). The large effect size positions participation as a central driver of maintaining satisfaction amid digital transformation.

The mediation finding (H4) represents the study's most consequential theoretical contribution. The inconsistent mediation pattern negative direct effect, positive indirect effect, non-significant total effect parallels the dual-pathway model identified by Obeng et al. (2026) for organizational performance. Just as technology readiness and digital culture served as positive serial mediators in that study, sustainable representative participation plays an analogous role for employee satisfaction. The disruptive potential of AI intention meets an organizational capability that transforms this potential into workforce benefit. Without participation, AI intention would likely reduce employee satisfaction; with adequate participatory structures, the negative effect is fully buffered, and experiences become at least neutral.

The model's substantial explanatory power (R² = 0.521 for satisfaction) indicates that how organizations manage employee voice is a dominant factor determining whether AI adoption enhances or erodes workforce well-being. This finding aligns with socio-technical systems theory, which emphasizes that technological and social subsystems must be jointly optimized for positive outcomes to emerge (Raisch & Krakowski, 2021).

PRACTICAL IMPLICATIONS

For managers in Nigerian financial institutions and other emerging markets, the findings convey a clear imperative: investing in AI technology without corresponding investment in participatory structures risks employee satisfaction erosion. To harness AI's benefits while maintaining a satisfied workforce, organizations should implement several concrete practices.

First, standing AI advisory boards with elected employee representatives from diverse departments and hierarchical levels should be established. These bodies should meet regularly, maintain formal decision-making authority over aspects of AI implementation affecting employee work, and report transparently to the broader workforce (Julius et al., 2026). Second, semi-annual AI voice surveys should be conducted, with results publicly disclosed alongside explicit statements of how employee input has shaped digital strategy.

Third, participation metrics should be integrated into digital transformation performance scorecards. Success should be evaluated not only by uptime and accuracy but also by employee satisfaction, voice efficacy, and the quality of participatory processes. Fourth, middle managers who constituted nearly half the sample should be trained in facilitating inclusive AI discourse, ensuring frontline staff can express concerns and ideas without fear of reprisal.

Policymakers, including the Central Bank of Nigeria, can support these efforts by encouraging or requiring employee representation in digital governance committees. Such regulatory guidance would help institutionalize participation as a core element of responsible AI adoption in the financial sector, consistent with emerging frameworks for ethical AI governance (Farinloye et al., 2025; Vahedi et al., 2025).

LIMITATIONS AND FUTURE RESEARCH

Several limitations warrant acknowledgment. First, the cross-sectional design precludes causal inference. While the proposed sequence is theoretically grounded and mediation analysis provides support, longitudinal or experimental designs are needed to trace the development of participation structures in relation to AI intention and subsequent changes in satisfaction over time. Three-wave panel investigations would be particularly informative.

Second, all data were self-reported, raising potential concerns about common method variance. Although Harman's test and marker variable analysis suggested bias is not serious, future research should incorporate multi-source data, including supervisor evaluations of employee participation or objective measures such as AI committee meeting attendance and input documentation.

Third, the sample was limited to the financial sector in Abuja. While providing rich context-specific insights, generalization requires replication in other Nigerian cities (e.g., Lagos, Kano), other industries (manufacturing, telecommunications), and other Sub-Saharan African countries. Cross-contextual comparisons would help establish boundary conditions (Farinloye et al., 2025).

Fourth, the model included only a single mediator. In practice, technology readiness, digital culture, leadership quality, and individual digital fitness may operate as parallel or sequential mediators (Obeng et al., 2026). Incorporating these factors into more elaborate models would provide deeper understanding of AI's impact on employees.

Finally, although the sustainable representative participation scale was theory-based, further validation is needed. Future studies should develop additional items, include objective indicators, and test discriminant validity against related constructs such as psychological empowerment, perceived organizational support, and participative leadership (Vahedi et al., 2025).

CONCLUSION

This study has demonstrated that employees' behavioural intention to use AI is a double-edged phenomenon. On one hand, it generates immediate decreases in job satisfaction through adaptation strain. On the other hand, it triggers the development of sustainable representative participation, which powerfully enhances satisfaction. The finding that the negative direct path is completely offset by the positive indirect path indicates that employee voice is not merely beneficial but essential for transforming technological disruption into workforce well-being. For Nigerian financial institutions and other organizations navigating digital transformation, investing in meaningful, permanent, and deep-going participation structures is not an optional complement to AI adoption but a prerequisite for maintaining a satisfied, engaged, and future-ready workforce.

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