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Article

From Algorithms to Altruism: Mapping the Human-Tech Synergy for Sustainable Workplaces Through Artificial Intelligence (AI), Innovative Work Behavior, Leader-Member Exchange, Organizational Citizenship Behavior and Role Clarity

by
Muhammad Asif Zaheer
1,2,
Temoor Anjum
1,2,
Azadeh Amoozegar
2 and
Petra Heidler
3,*
1
University Institute of Management Sciences, PMAS-Arid Agriculture University Rawalpindi, Rawalpindi 44000, Pakistan
2
Faculty of Education and Liberal Arts, INTI International University, Nilai 71800, Negeri Sembilan, Malaysia
3
Institute International Trade and Sustainable Economy, IMC University of Applied Sciences, 3500 Krems, Austria
*
Author to whom correspondence should be addressed.
Adm. Sci. 2025, 15(9), 339; https://doi.org/10.3390/admsci15090339
Submission received: 9 May 2025 / Revised: 29 July 2025 / Accepted: 11 August 2025 / Published: 29 August 2025
(This article belongs to the Special Issue HRM and Employee Performance Improvement: Strategies for Employee Development in a Constantly Changing Workplace)
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Versions Notes

Abstract

Corporate team unity and role clarity are crucial for organizational success and human resources. This study examines how job clarity affects employee performance and innovative work behavior (IWB) via organizational citizenship behavior (OCB). Additionally, to determine how artificial intelligence (AI) information and leader-member exchange (LMX) moderate the relationship between job clarity, IWB, and employee performance. This research focused on Pakistan’s Federal Capital Territory (FCT) Islamabad, and Punjab province’s IT sectors. The self-administered questionnaire received data from 555 IT professionals. The suggested model was tested using Smart PLS structural equation modeling. Results showed that job clarity and OCB significantly improve IWB and employee performance. Role clarity, IWB, and employee performance are partly mediated by OCB. In addition, LMX adversely moderates the relationship between job clarity and IWB and employee performance, but not AI information. Emphasis is primarily placed on elucidating the respective roles of the employees in order to ensure that they are aware of the expectations placed upon them. Consequently, they are able to demonstrate task performances that are not stipulated in their job descriptions but directly relate to their performance improvement. The current study reveals that human resources (HR) and management should prioritize job clarity and OCB to boost individual performance and IWB.

1. Introduction

In the current era, companies are adopting more efficient and effective methods to achieve high performance by setting clear goals and objectives (Hamouche & Parent-Lamarche, 2023; Nayebpour & Sehhat, 2024). Nowadays, competition is increasing to gain a competitive edge, and organizations and human resources management must clarify roles and engage the employees to be active in the accomplishment of tasks successfully (Caracuzzo et al., 2024). Leadership must be transformative to promote organizational citizenship behavior (OCB) and role clarity for improved performance (Putri & Muhdiyanto, 2018; Yen et al., 2024). With clear roles, empowerment, artificial intelligence (AI) information, and commitment, employees voluntarily engage in OCB to enhance organizational success (Henry & Julius, 2011; Krajcsák & Bakacsi, 2024; Sa’adah & Rijanti, 2022). Organizations and human resources can maintain long-term performance by effectively detecting and addressing employee problems to achieve the benchmark (Antony et al., 2024; Kiu & Chan, 2024). If problems are not addressed properly, it can lead to burnout, which promotes turnover and cutbacks, worsens staffing shortages, and unclarity increases the workload of the remaining staff (Coco et al., 2023). Consequently, it is essential to understand the significance of role clarity, leader-member exchange (LMX), OCB, artificial intelligence (AI) information, and innovative work behavior (IWB) incentivizing personnel to optimize operations to meet targets.
In the current era, the world has become more dynamic and globalized, and new advanced technical working procedures can dominate the market (Fios et al., 2024; Gouda, 2022). Supervisors’ feedback and role clarity help employees in demonstrating OCB, which benefits the organization in the long run, and OCB prevails automatically with individuals’ clear roles that are directed towards IWB and performance enhancement (Peng & Chiu, 2010; Shin et al., 2017; Taamneh et al., 2018). Due to unclear roles, employees experience role conflict, dissatisfaction, stress, emotional exhaustion, and burnout which ultimately reduces productivity (Ekmekcioglu et al., 2024; Coco et al., 2023; Ghorpade et al., 2011). Role clarity enhanced the employee’s perceived service quality and effectiveness, which is linked with individual performance (Lang et al., 2007; Sniffen et al., 2019). Literature indicates that research on role clarity, AI information, LMX, and OCB in relation to enhancing IWB and individual performance is limited. A literature gap exists in the perspective of LMX and AI information with different variables and other cultural settings to enhance outcomes, whether in the form of creative behavior or performance (Ali et al., 2024; Arshad et al., 2024). Role theory believes that people’s behaviors in social interactions are crucial in defining the parameters of social exchange and its potential future developments. Moreover, role theory states that the adoption of suitable roles by each participant is an ongoing process that facilitates role fulfillment and serves as a stimulus for further engagement (Broderick, 1998; Burns, 1992). The current study contributes to the literature and has several theoretical and practical implications by addressing the gap and advancing the existing knowledge, and further details are given in the last section.
The current study investigates the concepts of role clarity, OCB, LMX, and AI information regarding enhancing performance and IWB from the perspective of the information technology (IT) sector. Therefore, this research explores the elements that can increase the performance and generate innovative working behavior in IT firms operating in the Federal Capital Territory (FCT) Islamabad and the Punjab province of Pakistan. Specifically, our research addresses the following research questions:
RQ1. 
What is the impact of role clarity on OCB, IWB and employee performance in the IT sector?
RQ2. 
How does OCB mediate the association of role clarity with IWB and employee performance in IT firms of FCT Islamabad and Punjab province of Pakistan?
RQ3. 
How does AI information and LMX moderate the relationship of role clarity with IWB and employee performance in IT firms?
In addition, we offer a technically solid integrated model for the IT sector in particular by adding several aspects to the current scenario for long-term efficacy in emerging nations. The structure of the subsequent sections is as follows: The second section analyzes the pertinent background literature on role clarity, OCB, AI information, LMX, IWB, and employee performance with a theoretical framework and development of hypotheses based on a wide-ranging literature review. The third section covered the methodology, and the fourth section presents the results with a discussion. Conclusion and implications were provided in the final section.

2. Theoretical Framework and Hypotheses Development

Role theory states that when individuals are inconsistent with their work and ambiguity about their respective roles may lead to stress, dissatisfaction, and ineffectiveness, thus resulting in less productivity. Role theory prescribes the negative influence of role conflict and role ambiguity on the performance of employees, as well as work satisfaction (Schuler, 1975). Role theory argues that organizational context creates the perception for leaders or managers to make the requirements of the role clear. Moreover, personal factors are important influencers in determining managerial roles (Behavior et al., 2004). Role theory involves an important facet of life and states that individuals are members of social positions and they expect reactions for their own behavior as well as those of others. Moreover, role theory explains the performance of behavior that is a result of the given role (Biddle, 1986). Role theory includes that OCB is not only involved in the organizational context as it creates valued principles, but also the provision of interlocked roles and positions in the social system (Lamertz, 2006). Role theory posits that the behaviors of individuals in social interactions are essential in establishing the parameters of social exchange and its potential future developments. According to role theory, the adoption of appropriate roles by each participant is a continuous process that encourages role fulfillment and functions as a catalyst for additional engagement (Burns, 1992).

2.1. Role Clarity and Employee Performance

Clear task assignments reduce voluntary turnover, job strain, and poor performance (Alhajaj & Ahmad, 2024; Lyons, 1971). Role clarity has a noteworthy influence on performance (Lau, 2011). Large organizations have clear responsibilities and established feedback mechanisms; hence, their employee performance is better than SME’s (Thangavelu & Sudhahar, 2017). After making their roles or tasks clear to employees, proper feedback can improve their task and contextual performance because they know the expectations of them (Whitaker et al., 2007). Employee performance and interest in their jobs increase when they understand their roles, which can lead to job satisfaction and decreased turnover (Hassan, 2013). Even highly competent and motivated individuals cannot execute well without clear roles (Solomon et al., 1985). Henderson et al. (2016) state that trust and communication with role clarity are essential for engaging project members in the best possible way. Consequently, we are proposing the following hypothesis:
H1. 
Role clarity has a significant positive impact on employee performance.

2.2. Role Clarity and Innovative Work Behavior

Wang et al. (2022) describe the employees’ understanding of their roles as role clarity, which is essential to their innovative behavior as a contextual factor. Role clarity has a positive association with innovative work behavior (Kundu et al., 2020). Previous studies have asserted that workers with poor role clarity struggle to understand responsibilities and are stressed and anxious (Newman et al., 2015). Lack of role clarity may substantially hinder innovative roles (Hegazy et al., 2023; Tikas, 2024). Conversely, workers with high role clarity report reduced stress levels and high levels of psychological empowerment that improve their output at work (Fried et al., 2003; Gilboa et al., 2008; Hall, 2008). Further, employees frequently go far beyond expectations and engage in extra-role behaviors, including IWB (Adil et al., 2023; Alge et al., 2006; Janssen, 2000; Kundu et al., 2020). Consequently, we are proposing the following hypothesis:
H2. 
Role clarity has a significant positive influence on IWB.

2.3. Role Clarity and Organizational Citizenship Behavior

Supervisors’ feedback and role clarity enable employees to display OCB that benefits the organization in the long run, but knowing the job and expectations motivates people to work hard (Peng & Chiu, 2010; Yadav & Kumar, 2017; Yadav & Rangnekar, 2015). Role clarity and identification positively impact the OCB because employees feel empowered to take actions that benefit the company (Dávila & Finkelstein, 2010). A significant positive association is found between role clarity and OCB among Indian executives (Yadav & Rangnekar, 2016). When instructors know their roles, they naturally display school-impacting behaviors (Belogolovsky & Somech, 2010). When role expectations are explicit, OCB prevails, especially when leaders prioritize prevention (Shin et al., 2017). When employees understand their jobs, they become devoted to the company, forming OCB (Farooqui, 2012). Role clarity and OCB positively impact organizational success and make it easier (Yadav et al., 2022). Employee empowerment, role clarity, and organizational commitment affect OCB (Henry & Julius, 2011). Hence, we are proposing the following hypothesis:
H3. 
Role clarity has a significant positive influence on OCB.

2.4. Organizational Citizenship Behavior and Employee Performance

OCB enhances employee performance, organizational commitment, perception of fairness, well-being, and happiness (Cho & Johanson, 2008; Harwiki, 2016; Hidayah & Harnoto, 2018). Huei et al. (2014) found that OCB intervened in the extrinsic motivation and employee performance. When employees execute tasks beyond their job description, they tend to perform better to make the company successful, which enhances the worker performance and organizational productivity (Vipraprastha et al., 2018). OCB significantly affects employee performance and engagement, but employee involvement can intervene in the OCB and employee performance to boost organizational productivity (Hermawan et al., 2020). OCB has a considerable effect on employee performance (Cho & Johanson, 2008). Passionate employees who do task- and non-task-related activities well can boost employee performance with involvement of engagement (Qadeer et al., 2016). Therefore, we are proposing the following hypothesis:
H4. 
OCB has a significant positive impact on employee performance.

2.5. Organizational Citizenship Behavior and Innovative Work Behavior

OCB refers to voluntary employee actions that are not directly or explicitly compensated by the formal reward system, yet contribute to the general efficiency of the firm (Bies & Organ, 1989). OCB exhibited a significant positive effect on IWB (Al-Shami et al., 2023). OCB is about what managers want from their subordinates to accomplish established goals (M. A. Khan et al., 2020). Akturan and Çekmecelioğlu (2016) revealed that OCB can influence employee behavior in organizations in creative ways. This includes assisting new coworkers, streamlining the work process, putting in extra time, attending business functions, and contributing innovative ideas for improvement (Bambale, 2014). The literature frequently reports on OCB dimensions and confirms that these are typically crucial in formulating IWB for the welfare of the organization (Davison et al., 2020). Consequently, we are proposing the following hypothesis:
H5. 
OCB has a significant positive influence on IWB.

2.6. Organizational Citizenship Behavior as a Mediator

In the current age of globalization and technological innovation, job clarity fosters organizational support and enhances employee engagement, which has a substantial impact on employee performance, with OCB as a mediator (Utami, 2022). Employees voluntarily perform OCB to improve organizational effectiveness if they have clear roles (Sa’adah & Rijanti, 2022). When superiors communicate well with employees, it can assist in generating OCB and boost employee performance (Somech & Ron, 2007). The OCB promotes role clarity and employee performance in an organization due to work satisfaction, dedication, fairness, and leader support (Lepine et al., 2002). Role clarity, devotion, and empowerment predict OCB and enhance employee performance (Henry & Julius, 2011). If a firm has ethical leadership, then role clarity improves OCB (Ayu Putu Widani Sugianingrat et al., 2019). When all roles are clear to employees, then OCB mediates the association of work satisfaction with individual performance (Margahana, 2018). Clarifying staff roles improved OCB and employee performance (H. Khan et al., 2017). Work satisfaction, organizational commitment, and culture improve OCB, which boosts employee performance (Purnama, 2013). Unclear employees’ duties lower the OCB and employee performance; therefore, individual roles are also key to understanding organizational functioning and performance (Saha et al., 2019). Consequently, we are proposing the following hypotheses:
H6a. 
OCB mediates the relationship between role clarity and employee performance.
H6b. 
OCB mediates the relationship between role clarity and IWB.

2.7. LMX as a Moderator

Liden and Maslyn (1998) stated that LMX denotes the caliber of the interaction between leaders and their subordinates. S. Almazrouei and Bani-Melhem (2023) proposed that LMX theory serves as the groundwork for various endeavors aimed at investigating the association between different leadership styles and their resulting consequences. Within the domain of leadership, this particular focus lies in the interactions between workers and supervisors (H. S. Almazrouei et al., 2020). It is underpinned by theoretical frameworks concerning mutual exchanges, interpersonal roles, the notion of roles, and equity (Graen, 1976; Liden et al., 1997; Waxin et al., 2019). Strong LMX relationships promote the exchange of knowledge, cooperation, and transparent communication between individuals for creating new philosophies (Graen & Uhl-Bien, 1995). Employees are more inclined to exhibit innovative behaviors when they possess a high-quality association with their superiors and obtain a comprehensive comprehension of their roles and responsibilities. Such an environment fosters feelings of empowerment, support, and motivation (Malik et al., 2015; Selvarajan et al., 2018). LMX discusses the procedure where the leader possesses knowledge regarding the relationship between the employee and manager (Dulebohn et al., 2012). The prevailing consensus is that a high-caliber LMX benefits both employees and employers by positively influencing employee performance. A variety of critical organizational outcomes are impacted by the caliber of exchange relations that exist between managers and employees (Graen & Uhl-Bien, 1995). Therefore, we are proposing the following hypotheses:
H7a. 
LMX moderates the relationship between role clarity and employee performance.
H7b. 
LMX moderates the relationship between role clarity and IWB.

2.8. Artificial Intelligence Information as a Moderator

Literature on AI has stated that automating repetitive tasks that do not involve complexity and creativity will reduce manual processes, freeing up human resources to have creative thoughts and help them to innovate (Ågerfalk, 2020; Mikalef & Gupta, 2021). Besides this, studies have explored many other ways in which AI technology expands human abilities and enhances cognitive strengths (James Wilson & Daugherty, 2018). Role clarity is a main concern when it comes to inter-department coordination, which is the key enabler of creativity and innovation in the organization (Evanschitzky et al., 2012). Previous literature has demonstrated that AI helps to have better inter-departmental coordination (Mikalef & Gupta, 2021). Moreover, AI helps to generate multiple content-enhancing IWB of an employee (Fui-Hoon Nah et al., 2023). Clear role refers to the understanding of individuals regarding their roles, requirements, and how their work fits into the company’s objectives (Wang et al., 2022). AI amplifies the relationship by automating routine operations, accelerating decision-making, as well as providing immediate feedback, which helps employees to achieve organizational goals (Huang & Rust, 2018; Jenkins, 2003; Lin et al., 2024). Consequently, we proposed the following hypotheses:
H8a. 
AI information moderates the relationship between role clarity and employee performance.
H8b. 
AI information moderates the relationship between role clarity and IWB.
The relationship of various variables is shown in Figure 1.

3. Materials and Methods

This study is quantitative in nature, and a self-administered questionnaire was used to gather the data. We used a deductive research approach to test the proposed model. The data is collected from employees of Information Technology organizations operational in FCT Islamabad and the Punjab province of Pakistan. We performed convenience sampling, and the questionnaire was based on items developed by former authors. The convenience sampling technique involves selecting individuals who are both readily accessible and inexpensive, in contrast to other sampling approaches. The total population of IT professionals is more than 150,000 in Punjab province and FCT Islamabad. In 1970, Krejcie & Morgan devised a method for calculating sample size for a certain population, and we required 384 responses. A final sample of 555 employees was used for data analysis to test the suggested model through SmartPLS. The suggested model underwent testing using structural equation modeling (SEM) with SmartPLS. When researchers use structural equation models, PLS-SEM (partial least squares structural equation modeling) has many advantages. In practical applications, PLS-SEM offers a more acceptable methodology due to its unique methodological properties (Hair et al., 2014). Moreover, the gathered data were from people with diverse cultures and backgrounds; thus, it was suitable in the current research context to reduce generalizability and possible sampling bias to enhance the sustainability. Furthermore, Table 1 specifies the demographic characteristics of the gathered data.
Furthermore, Table 2 presents the descriptive statistics of the variables. The overall mean score of RC was 3.4435 (SD = 1.07455), and IWB was 3.5045 (SD = 1.14403). Similarly, AII and OCB revealed overall mean scores of 3.8444 (SD = 0.8151) and 3.5277 (SD = 0.94035), respectively. Likewise, LMX and EP exhibited overall mean scores of 3.496 (SD = 1.1228) and 3.5795 (SD = 1.05336), respectively. This shows the positive perceptions of all these variables among employees. Finally, this quantitative nature of data exhibited normal distribution as skewness and kurtosis values are within an acceptable range.

Measurement of Scales

Various scales created by different scholars were employed to measure the outcomes. To measure IWB, we adapted six items of (Scott & Bruce, 1994). We used six items of role clarity developed by Rizzo et al. (1970). To measure the OCB, we used seven items developed by Van Dyne and LePine (1998). Seven items of LMX were adapted from (Scandura & Graen, 1984). Artificial intelligence information (AII) was measured by adapting six items of (Wixom & Todd, 2005). Finally, eighteen items of employee performance were adapted from (Koopmans et al., 2014). Details of all items are given in the Appendix A.

4. Results

Convergent validity includes factor loading, AVE, and CR were measured through the PLS-SEM Algorithm. Composite reliability and Cronbach’s alpha (must exceed 0.7) were used to assess the reliability of indicators and the dependability of internal consistency (Ringle et al., 2023). Factor loading shows how the indicator affects each component. AVE also sums the squares of the standardized factor loadings to illustrate how much of each item’s variation the latent concept explains. The average proportion of a construct’s variance accounted for by its measurement items is AVE. AVE must be 0.50 to be normal (Osman et al., 2024). Convergent validity analysis initially determines if all indications load a component considerably. Similarly, Harman’s single-factor test was performed to assess the extent of common method bias, and after extraction of the single factor, which is 43.45% of total variance, since it is far less than 50%, therefore, we conclude that there is no threat of common method bias. Moreover, we ensured respondent anonymity. OCB2, LMX3, EP9, and EP18 have factor loadings below 0.7. These items were excluded from further analysis due to poor factor loading, and Table 3 presents the details of all items.
In 2015, Henseler et al. gave the criterion for discriminant validity, examined through the heterotrait-monotrait (HTMT) ratio and Fornell and Larcker (Henseler et al., 2015). Table 4 presents the results that support the discriminant validity of the research variables.
All variables in Table 5 exhibit HTMT ratios below 0.85, which is necessary for evaluating discriminant validity by this method.

Structural Model

We utilized the Smart-PLS evaluation outer model to examine direct and specific indirect paths to assess the complete research model (Hair et al., 2014). Moreover, the Q2 values for endogenous components exceeded zero, hence confirming predictive significance. Further, Figure 2 illustrates the algorithmic analysis, encompassing outer loadings and R2.
Figure 3 illustrates the outcomes of the route analysis utilizing bootstrapping (n = 5000) to assess the structural model. Figure 3 depicts the p-values alongside β values and the outcomes of the bootstrapping tests utilized to evaluate statistical significance.
Direct, indirect, and moderation analysis with a detailed description of results are presented in Table 6. Role clarity has a constructive and noteworthy effect on employee performance (β = 0.212, p < 0.001), on IWB (β = 0.254, p < 0.001) and OCB (β = 0.637, p < 0.001), hence supporting Hypotheses 1, 2, and 3. Individual IWB and role clarity have been found to positively and directly correlate in several studies (Frare & Beuren, 2021; Sitepu et al., 2020). Similarly, OCB directly influenced the employee performance (β = 0.289, p < 0.001) and IWB (β = 0.182, p < 0.001), which supports Hypotheses 4 and 5; therefore, these are accepted. Researchers state that employees must practice OCB at work in order to build IWB (Abdul-Rashid et al., 2017; Akturan & Çekmecelioğlu, 2016; Zhang et al., 2021). Role clarity has an indirect influence on employee performance (β = 0.184, p < 0.001) and IWB (β = 0.116, p < 0.001) via OCB, which supports H6a and H6b, hence accepted. OCB boosts employee productivity by motivating them to complete duties outside of their job description (Triani et al., 2020). As the world adopts new technological techniques and new policies with procedures but organizations must clearly define the roles to employees so they can work with a fixed pattern in mind regarding the intervening role of OCB that can influence their performance. Counterproductive work behavior is adversely correlated with role clarity (Rodopman, 2009). LMX negatively moderated the link of role clarity with employee performance (β = −0.173, p < 0.001) and with (β = −0.154, p < 0.001), which supports the H7a and H7b, hence these Hypotheses were accepted. A low-quality LMX has adverse employee attitudes and poor performance (Nnaebue et al., 2023; Walumbwa et al., 2011). AII exhibited no moderation in the association of role clarity with employee performance and IWB, which didn’t support Hypotheses H8a and H8b; hence, these Hypotheses were rejected. According to Wu et al. (2021), AI and humans may collaborate by utilizing each other’s advantages, which sets them apart from other technologies.

5. Discussion

In this era of globalization and technological advancement, when role clarity is prevailing, perceived organizational support is automatically developed with a good level of worker engagement, which has a considerable effect on individual performance with OCB. According to our first three hypotheses, role clarity has a significant positive influence on employee performance, IWB, and OCB. Role clarity helps employees understand their expectations of superiors. Additionally, team members must grasp their jobs and roles to contribute to team and organizational goals by performing at their best. Role clarity assists in promoting creative and IWB (Kundu et al., 2020). Employee performance is driven by organizational goals, and to stay competitive, a business must encourage knowledge exchange, which boosts employee performance. The clarity of roles eliminates overlapping tasks, miscommunication, and anxiety at work, which enhances the performance of staff in an effective manner.
Inadequate role clarity among employees leads to determining work requirements rather than effectively performing job activities, and also exhibits less extra role behavior (Chu et al., 2006; Onyemah, 2008). Hypotheses 4 and 5 present that OCB also has a positive influence on employee performance and IWB. When workers are clear with their roles, they don’t need constant direction on day-to-day work. This extra time and mental energy can be used to develop and implement innovative thoughts (Ohly et al., 2006). If all group members have high OCB, they moderate the association between individual OCB and employee performance, increasing institutional productivity (Bommer et al., 2007). According to Naqshbandi et al. (2016), OCBs strengthen internal networks and collaborations within the company, which afterwards foster innovation.
In organizations without role definition or support, role stresses arise, lowering employee performance and OCB (Eatough et al., 2011). Hypotheses 6a and 6b revealed that OCB partially mediates the association of role clarity with employee performance and IWB. If an organization has OCB personnel, managers should create an employee-friendly environment by helping them understand their roles to improve performance. OCB and role clarity boost performance; therefore, leaders must adapt their style (Putri & Muhdiyanto, 2018). Managers have a higher degree of OCB than employees because they know their jobs and help them grasp their tasks, which boosts performance (Turnipseed & Rassuli, 2005). When employees have a good organizational climate, they know what is expected of them, which improves OCB and makes employee performance lucrative (Subramani et al., 2015).
Role clarity and LMX have a substantial impact on performance with OCB (Sa’adah & Rijanti, 2022). In the current research context, Hypotheses 7a and 7b demonstrate that the link between role clarity and employee performance, as well as IWB, is weakened. Employee role clarity defines work roles and scope. The major goal of companies is to boost employee performance so they may be more productive. Gerstner and Day (1997) conducted a meta-analysis on LMX quality across various contexts. They discovered that LMX quality is associated with subordinates’ satisfaction with their supervisors, encompassing overall satisfaction, performance (as rated by supervisors and measured objectively), role conflict, role clarity, and turnover intentions. LMX of high quality can improve role clarity by promoting transparent communication, constructive feedback, and a mutual comprehension of objectives. Leaders who prioritize the development of positive LMX are more inclined to offer clear guidance on roles and expectations (H. S. Almazrouei et al., 2020).
The rise of AI has begun to alter the working relationship between people and machines in different fields. Role clarity involves a clear understanding of responsibilities, work processes, and the assignment of tasks that have to be performed. Moreover, employees can successfully move towards their goals in the presence of these aspects. The leader’s provision of guidance and support cultivates an atmosphere favorable for innovation and venturesomeness, which ultimately enhances the team members’ propensity for inventive work (Saeed et al., 2019). Hypotheses 8a and 8b revealed that AII exhibited no moderation in the association of role clarity with employee performance and IWB. AI has been widely recognized for its evident benefits over humans, including precision and efficiency (Lin et al., 2024; Yam et al., 2022). In current research, AII has a direct influence on employee performance and IWB, but exhibits no influence as a moderator.
We illustrated the interaction terms that demonstrate that AII diminishes the association of role clarity with employee performance and IWB, respectively, exhibiting a negative moderating effect in Figure 4 and Figure 5.

6. Implications

6.1. Theoretical Implications

This research extends the role theory by incorporating role clarity, OCB, AII, LMX, IWB, and employee performance because this integrated model examines the complex associations among these variables in enhancing the field of organizational psychology and management from the perspective of role theory within the distinct context of the IT industry. The current study contributed to the field of role clarity, OCB, AII, LMX, IWB, and employee performance, as no study had examined their relationship together with the mediating role of OCB and the moderating role of AII and LMX to uncover these new perspectives. Nowadays, with the advancement of technology, organizations are facing new challenges to cope with the critical issues. Therefore, current research added to existing knowledge by exploring the mechanism of enhancing creative behavior with performance. Role theory offers a conceptual framework for understanding how individuals’ interactions, behaviors, and expectations are shaped, as well as how they perceive their positions within an organization (Biddle, 1986). When employees understand their duties, their alignment with the organization’s objectives enhances (Mukherjee & Malhotra, 2006). Role theory offers a paradigm for comprehending human behavior within social systems, including organizations, predicated on the expectations linked to their roles. Utilizing role theory, organizations may provide a regulated but flexible work environment that fosters citizenship behaviors, creativity, and overall success.

6.2. Practical Implications

In this age of globalization and technological advancement, organizations should create ways to engage employees with full role clarity, thus they are encouraged to exhibit exceptional job-related behaviors and contribute to organizational profitability and success. Organizations and human resources management must delineate employee roles that can motivate and involve their staff to get desired results. Institutions should cultivate an environment that improves employees’ understanding of their clear role. Human resources and recruiting managers are advised to talk clearly and openly about job roles during job interviews and when new job roles are taken up. When employees are uncertain about their roles, they exhibit diminished motivation and engagement. In the current dynamic business environment, personnel roles often fluctuate. To prevent uncertainty and ambiguity, employees must get explicit information promptly following any alteration in work responsibilities. To improve role clarity between staff, supervisors must provide explicit facts and consistently convey job objectives and expectations. Moreover, role clarity is the chief ingredient and foundation for the rest of the variables; therefore, the importance of clear roles is aligned with long-term sustainability. Furthermore, the immediate supervisor should provide regular feedback to the employees in order to enhance their innovative behavior and performance, because it all depends on the clarity of roles.

6.3. Limitations and Future Recommendations

Several limitations must be acknowledged when evaluating the outcomes of this study. This research was confined to FCT Islamabad and the Punjab province of Pakistan. As the research team is a multi-professional team from different countries, future work could include cultural comparisons within different cultures, e.g., Europe, such as Austria, Germany, Italy, and Sweden. Moreover, future researchers may concentrate on role clarity with varying styles of leadership, organizational cultures, psychological empowerment, employee creativity, the role of human resources, and perceived organizational support.

7. Conclusions

As the world is becoming technologically advanced along with diversification in organizations, it is much more important for managers and employees to be clear about their respective roles and the contribution they make towards organizational success. Role clarity has a significant positive influence on OCB. Contemporary companies and human resource management must implement preventive steps to maintain their performance relative to competitors. Clarity of roles and OCB can serve as a spark in a creative work environment, thereby increasing employee commitment to fully harness their potential and exert effort. Clarity of roles, OCB, LMX, AII, and IWB are essential in multinational firms operating in developing nations, as they bolster individual dedication and are fundamental for fostering an inventive atmosphere and ensuring the continuing viability of the organizations. It is determined that both role clarity and OCB affect employee performance and IWB. Consequently, mentally fulfilled and dedicated personnel may execute their responsibilities effectively to achieve the intended outcomes. Furthermore, OCB partially mediated the association of role clarity with IWB and employee performance. AII and LMX influence the IWB and employee performance directly, but LMX exhibited negative moderation, and AII didn’t demonstrate moderation.

Author Contributions

All authors contributed equally. All authors have read and agreed to the published version of the manuscript.

Funding

The APC was funded by INTI International University, Malaysia, Persiaran Perdana BBN Putra Nilai, 71800 Nilai, Negeri Sembilan, Malaysia.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the ARID Ethics Committee (protocol code PMAS-AAUR/D.FOSS/ 1229 and date of approval 10 October 2023).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Role Clarity (RC)
RC1 I feel certain about how much authority I have
RC2 Clear, planned goals and objectives for my job
RC3 I know that I have divided my time properly
RC4 I know what my responsibilities are
RC5 I know exactly what is expected of me
RC6 The explanation is clear of what has to be done
Organizational Citizenship Behavior (OCB)
OCB1 I volunteer to do things for my work group.
OCB2 I help orient new employees in my work group.
OCB3 I attend functions that help my work group, even though they are beyond the formal requirements of my job.
OCB4 I assist others in my work group with their work for the benefit of the group.
OCB5 I get involved in order to benefit my work group.
OCB6 I help others in my work group learn about the work.
OCB7 I help others in my work group with their work responsibilities.
Employee Performance (EP)
Task performance
EP1 I managed to plan my work so that it was done on time.
EP2 My planning was optimal.
EP3 I kept in mind the results that I had to achieve in my work.
EP4 I was able to separate the main issues from the side issues at work.
EP5 I was able to perform my work well with minimal time and effort.
Contextual performance
EP6 I took on extra responsibilities.
EP7 I started new tasks myself, when my old ones were finished.
EP8 I took on challenging work tasks, when available.
EP9 I worked at keeping my job knowledge up-to-date.
EP10 I worked at keeping my job skills up-to-date.
EP11 I came up with creative solutions to new problems.
EP12 I kept looking for new challenges in my job.
EP13 I actively participated in work meetings.
Counterproductive work behavior
EP14 I complained about unimportant matters at work.
EP15 I made problems greater than they were at work.
EP16 I focused on the negative aspects of a work situation, instead of on the positive aspects.
EP17 I spoke with colleagues about the negative aspects of my work.
EP18 I spoke with people from outside the organization about the negative aspects of my work.
Artificial Intelligence Information (AII)
AII1 AI tools produce correct information
AII2 There are a few errors in the information I obtain from AI tools
AII3 The information provided by AI tools is accurate
AII4 AI tools provide me with the most recent information
AII5 AI tools produce the most current information
AII6 The information from AI tools is always up to date
Innovative Work Behavior (IWB)
IWB1 Creating new ideas in critical situations
IWB2 Searching out advanced up to date technology with the latest reliable working methods, techniques, tools, etc.
IWB3 Analyze and generate reliable solutions for problems
IWB4 Motivating colleagues and making them enthusiastic for innovative ideas, according to the situation
IWB5 Converting proposed innovative ideas into useful applications
IWB6 Presenting innovative ideas systematically with respect to the situation
Leader-Member Exchange (LMX)
LMX1 Do you usually feel that you know where you stand, and do you usually know how satisfied your immediate supervisor is with what you do?
LMX2 How well do you feel that your immediate supervisor understands your problems and needs?
LMX3 How well do you feel that your immediate supervisor recognizes your potential?
LMX4 Regardless of how much formal authority your immediate supervisor has built into his or her position, what are the chances that he or she would be personally inclined to use power to help you solve problems m your work?
LMX5 Again, regardless of the amount of formal authority your immediate supervisor has, to what extent can you count on him or her to “bail you out” at his or her expense when you really need it?
LMX6 I have enough confidence in my immediate supervisor that I would defend and justify his or her decisions if he or she were not present to do so
LMX7 How would you characterize your working relationship with your immediate supervisor?

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Figure 1. Research Model.
Figure 1. Research Model.
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Figure 2. Structural Model (Algorithmic Analysis). Abbreviations: RC = Role Clarity, LMX = Leader Member Exchange, AII = Artificial Intelligence Information, OCB = Organizational Citizenship Behavior, EP = Employee Performance, IWB = Innovative Work Behavior.
Figure 2. Structural Model (Algorithmic Analysis). Abbreviations: RC = Role Clarity, LMX = Leader Member Exchange, AII = Artificial Intelligence Information, OCB = Organizational Citizenship Behavior, EP = Employee Performance, IWB = Innovative Work Behavior.
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Figure 3. Structural Model (Bootstrapping). Abbreviations: RC = Role Clarity, LMX = Leader Member Exchange, AII = Artificial Intelligence Information, OCB = Organizational Citizenship Behavior, EP = Employee Performance, IWB = Innovative Work Behavior.
Figure 3. Structural Model (Bootstrapping). Abbreviations: RC = Role Clarity, LMX = Leader Member Exchange, AII = Artificial Intelligence Information, OCB = Organizational Citizenship Behavior, EP = Employee Performance, IWB = Innovative Work Behavior.
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Figure 4. Results of the moderation graph. Abbreviations: RC = Role Clarity, LMX = Leader Member Exchange, EP = Employee Performance.
Figure 4. Results of the moderation graph. Abbreviations: RC = Role Clarity, LMX = Leader Member Exchange, EP = Employee Performance.
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Figure 5. Moderation Graph. Abbreviations: RC = Role Clarity, LMX = Leader Member Exchange, IWB= Innovative Work Behavior.
Figure 5. Moderation Graph. Abbreviations: RC = Role Clarity, LMX = Leader Member Exchange, IWB= Innovative Work Behavior.
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Table 1. Demographic Characteristics.
Table 1. Demographic Characteristics.
CharacteristicsFrequency(%)
Gender
Female18433.2
Male37166.8
Age Group
20 to 30 years 35063.1
31 to 40 years16429.5
41 to 50 years366.5
Above 50 years50.9
Qualification
Graduate34562.2
Postgraduate21037.8
Experience
Zero to 5 years 29252.6
6 to 10 years15427.7
11 to 20 years7914.2
Above 20 years305.4
Designation
Associate19635.3
Senior Associate26147.0
Assistant Manager7313.2
Manager254.5
Female respondents were 33.2% and male respondents were 66.8%. Similarly, 63.1% of employees were between the ages of 20 to 30 years, and 29.5% were between the ages of 31 to 40 years, but above 40 years of age, present below 8% which shows that IT organizations require young and energetic employees.
Table 2. Descriptive statistics.
Table 2. Descriptive statistics.
VariablesMinimumMaximumMeanStd. Deviation
RC1.001.003.44351.07455
IWB1.001.003.50451.14403
AII1.001.003.84440.8151
OCB1.001.003.52770.94035
LMX1.001.003.4961.1228
EP1.001.003.57951.05336
(N = 555), Abbreviations: RC = Role Clarity, LMX = Leader Member Exchange, AII = Artificial Intelligence Information, OCB = Organizational Citizenship Behavior, EP = Employee Performance, IWB = Innovative Work Behavior.
Table 3. Measurement Model (Estimates).
Table 3. Measurement Model (Estimates).
ConstructsItemsLoadingsCronbach’s AlphaComposite
Reliability (CR)		Average Variance Extracted (AVE)
AIIAII10.8290.8890.9150.643
AII20.798
AII30.800
AII40.772
AII50.806
AII60.805
RCRC10.8360.9360.9490.758
RC20.872
RC30.881
RC40.886
RC50.891
RC60.855
OCBOCB10.8290.9250.9410.727
OCB30.825
OCB40.862
OCB50.867
OCB60.861
OCB70.873
LMXLMX10.8610.9220.9390.721
LMX20.847
LMX40.844
LMX50.846
LMX60.866
LMX70.828
IWBIWB10.8770.9480.9590.794
IWB20.907
IWB30.902
IWB40.903
IWB50.889
IWB60.868
EPEP10.8250.9740.9760.721
EP20.874
EP30.878
EP40.874
EP50.861
EP60.861
EP70.858
EP80.862
EP100.858
EP110.846
EP120.859
EP130.862
EP140.870
EP150.810
EP160.794
EP170.787
Abbreviations: RC = Role Clarity, LMX = Leader Member Exchange, AII = Artificial Intelligence Information, OCB = Organizational Citizenship Behavior, EP = Employee Performance, IWB = Innovative Work Behavior.
Table 4. Fornell and Lacker’s criterion.
Table 4. Fornell and Lacker’s criterion.
AIIEPIWBLMXOCBRC
AII0.802
EP0.3800.849
IWB0.3780.5320.891
LMX0.3340.4940.4760.849
OCB0.3080.5650.4900.2900.853
RC0.3510.6480.6210.4990.6370.871
Abbreviations: RC = Role Clarity, LMX = Leader Member Exchange, AII = Artificial Intelligence Information, OCB = Organizational Citizenship Behavior, EP = Employee Performance, IWB = Innovative Work Behavior.
Table 5. HTMT Ratio.
Table 5. HTMT Ratio.
AIIEPIWBLMXOCBRC
AII
EP0.407
IWB0.4110.550
LMX0.3680.5200.506
OCB0.3390.5910.5230.313
RC0.3860.6740.6580.5350.684
Abbreviations: RC = Role Clarity, LMX = Leader Member Exchange, AII = Artificial Intelligence Information, OCB = Organizational Citizenship Behavior, EP = Employee Performance, IWB = Innovative Work Behavior.
Table 6. Direct, Mediation, and Moderation Effects.
Table 6. Direct, Mediation, and Moderation Effects.
HypothesesRelationshipOriginal Sample (O)Sample Mean (M)Standard Deviation (STDEV)T Statistics (|O/STDEV|)p Values
H1RC → EP0.2120.2120.0563.8150.000
H2RC → IWB0.2540.2540.0604.2610.000
H3RC → OCB0.6370.6380.03319.1820.000
H4OCB → EP0.2890.2880.0466.3470.000
H5OCB → IWB0.1820.1800.0454.0110.000
H6aRC → OCB → EP0.1840.1840.0306.0470.000
H6bRC → OCB → IWB0.1160.1150.0303.9070.000
H7aLMX × RC → EP−0.173−0.1730.0315.6020.000
H7bLMX × RC → IWB−0.154−0.1550.0324.8510.000
H8aAII × RC → EP−0.041−0.0430.0341.2160.224
H8bAII × RC → IWB−0.062−0.0630.0341.8000.072
Abbreviations: RC = Role Clarity, LMX = Leader Member Exchange, AII = Artificial Intelligence Information, OCB = Organizational Citizenship Behavior, EP = Employee Performance, IWB = Innovative Work Behavior.
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Zaheer, M.A.; Anjum, T.; Amoozegar, A.; Heidler, P. From Algorithms to Altruism: Mapping the Human-Tech Synergy for Sustainable Workplaces Through Artificial Intelligence (AI), Innovative Work Behavior, Leader-Member Exchange, Organizational Citizenship Behavior and Role Clarity. Adm. Sci. 2025, 15, 339. https://doi.org/10.3390/admsci15090339

AMA Style

Zaheer MA, Anjum T, Amoozegar A, Heidler P. From Algorithms to Altruism: Mapping the Human-Tech Synergy for Sustainable Workplaces Through Artificial Intelligence (AI), Innovative Work Behavior, Leader-Member Exchange, Organizational Citizenship Behavior and Role Clarity. Administrative Sciences. 2025; 15(9):339. https://doi.org/10.3390/admsci15090339

Chicago/Turabian Style

Zaheer, Muhammad Asif, Temoor Anjum, Azadeh Amoozegar, and Petra Heidler. 2025. "From Algorithms to Altruism: Mapping the Human-Tech Synergy for Sustainable Workplaces Through Artificial Intelligence (AI), Innovative Work Behavior, Leader-Member Exchange, Organizational Citizenship Behavior and Role Clarity" Administrative Sciences 15, no. 9: 339. https://doi.org/10.3390/admsci15090339

APA Style

Zaheer, M. A., Anjum, T., Amoozegar, A., & Heidler, P. (2025). From Algorithms to Altruism: Mapping the Human-Tech Synergy for Sustainable Workplaces Through Artificial Intelligence (AI), Innovative Work Behavior, Leader-Member Exchange, Organizational Citizenship Behavior and Role Clarity. Administrative Sciences, 15(9), 339. https://doi.org/10.3390/admsci15090339

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