Higher education institutions (HEIs) represent a hub of research, creativity, and innovation, and their research work plays a crucial role in nation-building and human capital development (Ellis & Miller, 2014). Innovation, research, and action are mainly attributed to universities' work, and their pivotal role in disseminating knowledge is essential for the development of any country (Wahab & Tyasari, 2020). The wave of globalization has changed all spheres of life. The new changes require products and services to be of high quality. Today, the services sector is mainly dominated by professionalism and expertise, and it has necessitated that there must be quality educational institutions. Such institutions must provide up-to-date and up to the mark services (Sutanto, 2017). Educational institutions, especially higher education (universities), can change the pace of globalization by committing to quality, innovation, and increasing learning capabilities. To overcome this massive challenge, universities require effective leadership, learning and research capacity, creativity, and especially innovative capability to get the desired results (Ali & ahmad, 2021; Fritsch & Slavtchev, 2007; Heaton et al., 2019). The role of universities is more dynamic and has an enormous position in the development of any nation. They produce graduates that meet the cries of the changing environment. Being an agent of change, universities are devoted to working effectively in the areas of entrepreneurial endeavors, imparting quality education, community services, and research and innovation (Thomas & Pugh, 2020). The higher expectations from universities require universities to produce and enhance their learning paradigms and adopt the latest approaches and strategies. Reorientation, restructuring, and revising strategies to meet the new challenges of organizational innovation and creativity are mandatory at the university level (Cheng et al., 2018).

Higher education is considered the backbone of economic development. Universities are the center of knowledge creation, knowledge sharing, and knowledge applications (skills development), thus promoting positive societal change (Rahman et al., 2018). Universities sustain the quality and meet the market demands by producing knowledgeable and skillful graduates. The educated youth is a symbol of the working inventory that highlights the availability, accessibility, and utilization of the resources. Universities are the platforms that increase entrepreneurial education and spirit that lead to the creation of employment, knowledge creation, and distribution, development of science and art, flourishing and promoting culture, and help refine the services sector of any nation. To meet this end, universities need to be updated professionally with sound management that is capable, creative, innovative, and enriched in entrepreneurial leadership (Sutanto, 2017). The massive growth of information and communication technology keeps increasing the flow of globalization around the Earth.

One of the critical elements of successful universities is the employees’ creativity. Creativity is a popular notion and is highly valued by organizations and universities. The problem-solving through artistic, scientific, or organizational forms can better lead to creativity (Wang et al., 2022). Creativity also explains the novel use of new tools for problem-solving. Helplessness is also discussed in the previous literature with a connection with creativity; without such conditions, there are rare chances of creative works (Hass et al., 2016). The outcomes of creative thinking and creativity are all new patterns, new perceptions, new ideas, and innovative products.

Learning, a key to organizational development, refers to acquiring new things, lessons, ideas, and patterns that further the agenda of organizational creativity and innovation. Organizational learning portrays a change, transformation, and modification of organizational knowledge (Schulz, 2017). Scholars pay attention to the fact that organizations progress in response to staff learning capabilities. It is said that such learning develops capabilities in employees, and thus, the targets of achieving creativity, innovation, and organizational performance can be better harnessed. The increasing attention of researchers and academicians toward learning capabilities is that universities and other organizations depend heavily on learning the organizational culture and new agenda of the changing environment (Liao et al., 2017). Employees' learning capabilities and the flow experience are integral parts of staff development and enhancing their performance. Organizations that prioritize the factors will experience positive outcomes in employee engagement, high productivity, and organizational effectiveness. Recently, Inthavong et al. (2023) analyzed the role of learning in an organization and checked its impact on sustainable organizational performance through the mediating role of organizational networking and moderation of organizational innovation. They found that organizational learning is a key to organizational success and performance.

Flow experience refers to “the state in which people are so intensely involved in an activity that nothing else seems to matter; the experience itself is so enjoyable that people will do it even at great cost, for the sheer sake of doing it” (Csikszentmihalyi, 1990). Individuals' positive and nice feelings during working periods are crucial for organizational development. Flow experiences help provide immediate and correct feedback, reducing distractions and promoting a sense of enjoyment, autonomy, and control (Kim et al., 2019). Organizational learning, flow experience, and organizational performance are associated with leadership in any organization. The continuous learning and grooming atmosphere can be created only with the help of effective leadership. Such leadership helps translate expectations into realities and dreams into actual performance (Neufeld et al., 2008). Effective leadership also involves creating a positive work culture that encourages collaboration, creativity, and innovation. This includes recognizing and valuing the contributions of each team member, promoting diversity and inclusion, and providing opportunities for professional growth and development (Hill & Bartol, 2015).

The context of this study is unique in the sense that the study has selected the Khyber Pakhtunkhwa (KP) province of Pakistan to get the data. KP faces different socio-economic and educational issues, calling for a fresh examination of higher education performance. University administrations in KP face administrative and financial issues that can be better resolved by understanding the leadership role and their futuristic and sustainable policy management (Ali & Ahmad, 2021). This study will explore the creative endeavors that would provide the basis for formulating policies regarding faculty and staff development. This study also contributes to the understanding and creation of new ideas for gaining a competitive advantage. This study uniquely attempts to grasp the staff learning capabilities and consequently improve the overall performance of higher education institutions in KP Pakistan. The study provides a new mediation in the shape of flow experience that has rarely been investigated in the case of HEIs. The mediation of flow experience signifies the fact that employees have attachments to their work and working settings. The study also takes effective leadership as a moderator variable and argues that effective leadership can change the level of organizational performance in the presence of creativity and innovation as well as organizational learning. The leadership translates dreams into reality and molds creative ideas into practical solutions. Leadership effectiveness is a primary driver of better performance by creating an atmosphere where employees work happily without stress and strain. The study attempts to know the perceived organizational performance of higher education institutions in Khyber Pakhtunkhwa, Pakistan. Moreover, understanding organizational performance can help identify the grey areas that need immediate attention and solutions.

Organizational learning (OL) has been regarded as one of the strategic measures to achieve long-term organizational success. Developing a business environment, the proper response to various challenges, and identifying new and critical opportunities for growth are due to organizational learning (Liao & Wu, 2009). One of the traditional ways to measure learning has been to use so-called "learning curves" and "experience curves’ however, such curves are considered inadequate measuring tools (Argote & Argote, 2013). Organizational learning is a complex and multidimensional construct consisting of different sub-processes. Jerez-Gomez et al. (2005) took OL as a multifaceted perspective, including organizational commitment, a systems perspective, experimentation and openness, and management knowledge transfer. Considering the current environment of uncertainty, universities need to keep learning constantly. In addition, the development of OL in organizations relies on the development of well-structured knowledge. As a result, universities can provide OL capabilities that support individual learning. We also found that learning organizations incorporate organizational development and learning into their operations. An organization should develop the ability to learn independently or as a group to satisfy consumers' demands (Basheer et al., 2018). An organization with a culture of organizational learning is not simply a knowledge repository but a means of transforming that knowledge into something useful. The development of core competence should be based on feedback received from customers, channels, and competitors. An organization must have strong learning capabilities to innovate, generate, accept, and implement new ideas, processes, products, and services (Gomes et al., 2021).

Working on learning capability in universities, Akhtar et al. (2011) concluded that higher education institutions are the learning centers where future leadership is prepared; they must set their internal structure to be learning-oriented where the staff learning capabilities are continuously improving. Continuous learning develops a sense of ownership, autonomy, and control over things, leading to higher productivity, motivation, and job satisfaction (Egan et al., 2004). Organizational learning capabilities facilitate collaboration among different industries. The learning environment must learn from and use other organizations' technical and informational resources. Similarly, universities are more connected regarding knowledge creation, distribution, and exchange. Various government institutions, like public sector organizations, collaborate for learning and development, and they are imperative for the smooth functioning of the government. Organizations prioritizing learning and development often seek partnerships and collaborations with external entities to access new knowledge and expertise (Costa et al., 2018; Zahoor & Al-Tabbaa, 2020). It is also argued that breaking down communication barriers is mandatory for clarifying goals and targets. Previous research explains that an organization supports organizational learning to develop notable characteristics like participatory decision-making. In participative decision-making, all the employees are involved, and their opinions are valued (Chiva et al., 2007). It can be summarized that organizational learning capabilities lead to organizational performance. In the light of the above literature, this study posits that;

H1: Staff creativity has a significant effect on organizational performance.

Staff creativity heavily impacts organizational performance and effectiveness. When universities or other organizations encourage their staff to be innovative and creative, it will lead to successfully achieving their goals. Creativity helps generate new perspectives and unique ideas that provide the basis for decision-making, problem identification and solving, and timely task completion. Creativity sparks diversity of skills, rejuvenates abilities, and increases knowledge, meaningful views, and greater experiences (Kremer et al., 2019). According to Lee and Kim (2021), creativity is an individual's ability to produce something unique, both in terms of outcomes that can be measured and ideas (actions that make something new and different). Creative employees can contribute to the long-term survival of an organization by generating new and potentially useful ideas for developing new products, services, processes, and routines or improving existing ones (Akgunduz et al., 2018). According to Ye et al. (2020), creative employees create ideas, products, or procedures that are new or original and have the potential to benefit the organization as a whole.

Creativity helps to perform well since creativity fosters a culture that helps organizations achieve greater efficiency, respond more appropriately and quickly to opportunities, and adjust to change, grow, and compete in the dynamic and competitive business environment (Israel-Fishelson & Hershkovitz, 2022). Students can think positively and differently when they are creative enough. Innovative solutions and plausible connectivity between different subjects and topics are among the key issues that learners face, and such problems can be tackled using creativity. Creativity also promotes critical thinking among students and employees. Those individuals with creative powers will be calm, serious, problem-solvers, initiators, and flexible in their learning and understanding ventures. Effective communication and the art of deep learning have a close relationship with the creative environment in organizations, and the targets can be materialized with the help of creative thinking (?ywio?ek et al., 2022). Based on the rationale, it can be argued that staff creativity promotes organizational performance, and this study proposes that;

H2: Staff learning capabilities have a significant effect on organizational performance.

Csikszentmihalyi (1990) pioneered the concept of flow experience, explaining it as a conscious state where anybody is so absorbed in an activity that they can easily do it without thinking. There is so much immersion and focus on the things that it needs little effort to consider. The condition is where organizational staff or incredibly engaged in activities enjoy being a part of them (Csikszentmihalyi, 1991). Employees feel an enhanced focus, a lot of strength, and total concentration while performing certain activities in their routine tasks, like watching movies or participating in games (Ettis, 2017). Flow experience has many applications in our lives and makes activities interesting, for example, sports activities, online shopping, surfing websites, and ICT use (Tuunanen & Govindji, 2015). Published research used the concept in game-based learning and concluded that engaging learners/students in learning activities enhances their focus. In such an absorbing situation, students may become automatic with their studies, remain unaware of time, and perform their activities pleasurably (Hsieh et al., 2013; Hwang et al., 2012; Khan et al., 2017). Sun et al. (2017) argued that learning activities are immersive when learners reach the flow state, and then they will overcome various challenges proactively leading to superior learning outcomes. Chang et al. (2017) established that flow experiences give learners intense concentration, enabling them to ignore unrelated thoughts, resulting in losing consciousness in work and thus giving higher satisfaction and increased performance.

The flow experience aligned with job satisfaction contributes to employees’ productivity and enhances organizational performance. Employees enjoy their work, have clarity of goals, balance their challenges and skills, and develop their attention and a sense of control. The flow experience is an internal or intrinsic motivation that leads to a better feeling about jobs. Contentment from a job, fulfillment, and happiness resulting from employee flow experience are the outcomes of the flow experience (Maeran & Cangiano, 2013). Considering the above explanation, it can be stated that flow experience can mediate between the staff's learning capabilities, staff creativity, and organizational performance. The following hypotheses are posited;

H3: Staff flow experience mediates between staff creativity and organizational performance.

H4: Staff flow experience mediates between staff learning capabilities and organizational performance.

Among the leading factors of organizational effectiveness, past research works examined and concluded that effective leadership is a fundamental driver of effectiveness. Chemers (2014) termed effective leadership as the process where one person influences others through assistance and due support. Leadership effectiveness includes leaders' ability to control their fellows to perform their roles diligently and achieve organizational goals (Dhar & Mishra, 2001; Ha et al., 2016). Leadership effectiveness can be judged by how leaders have influenced attaining desired objectives and producing organizational outcomes. The adaptability of leadership to changing circumstances is the essence of organizational success and has been considered the most effective way to achieve targets (Uhl-Bien & Arena, 2018). Armstrong (2006) elucidated that leaders are responsible for managing human resources functions, collaborating with other departments, and, importantly, providing effective leadership, setting and enhancing strategic standards. Various approaches were proposed to leadership, including analyzing what leaders like, what and how they do, how their followers have their influence, how the various leadership styles affect situational conditions, and explaining the way they make significant changes in their organizations (Yukl, 2008).

Khan et al. (2019) assessed the effectiveness of leadership. They informed that multiple outcomes have been reported, including the performance and growth of leaders and the performance of their teams, the ability to work under stress and confront various challenges. Similarly, followers’ perceptions about their leaders, commitment to the group objective, psychological and physiological well-being, and development of followers. Copeland (2015) nurtured and developed a multivariate model that aimed to predict leaders’ effectiveness. The model assesses how leaders can responsibly and ethically contribute to organizational commitment and growth. This study views that organizational performance can be enhanced through effective leadership. Effective leadership will help strengthen the bond of staff learning capabilities, creativity, and organizational performance. Therefore, this study hypothesizes that;

H5: Effective leadership moderates the relationship between staff creativity and organizational performance.

H6: Effective leadership moderates the relationship between staff learning capabilities and organizational performance.

The survey-based design was selected for the study, commonly used in business studies where the researchers are mainly interested in knowing the attitudes, perceptions, beliefs, reactions, behaviors, ideas and opinions of a different population group. This design is used for understanding any study's descriptive and inferential statistics (Holtom et al., 2022).

We relied on a single method (mon-method); therefore, only a survey-based questionnaire was decided to be used for data collection from the university employees. The data has been gathered on the predetermined patterns using the rationale of previous papers and the problem-solving ability of the approach (Gottfredson & Aguinis, 2016). A set of premises that have already been tested and are considered precise, clear, efficient, and scientifically inquired are all used in this approach. The empirical observation and the questionnaire-based survey of the university employees make the deductive approach a feasible option.

The current study's population is employees of all universities located in Khyber Pakhtunkhwa (KP) province, Pakistan. As per the Higher Education Commission Islamabad, 43 universities in KP are chartered by the provincial or federal government. The 32 universities are in the public sector, while the remaining 11 universities are working in the private sector. More than 14000 employees (faculty and administration employees) work in all the universities in KP. Out of the total universities in KP, the study takes the three public sector universities and three private sector universities as a target population in the southern region to select a reasonable sample.

To specify the target population and sample size calculations, this study relied on the employees working as full-time employees of the universities in the southern region of KP. In this regard, the universities in Kohat, Bannu, and Dera Ismael Khan Divisions were focused. Three government and public sector agencies and three universities from the private sector were targeted for data collection.

The three universities from public and three universities were taken on the rationale to justify that the data represents both the private and public sectors, including the ownership basis of the universities and their impact on performance. The software smart-PLS can be run on a small size as there is no restriction on a large sample size in the software. So, the sample size is suitable enough to cope with the analysis better and more reliably. Table 1 below gives details of the target population. This study used the Yamane (1967) sample size formula to determine the sample size. This formula is considered very appropriate in the case of a large population.Table 2 is evident specifying the procedure;

As already mentioned, our target population was the employees of southern area universities of KP. So, the researcher arranged it personally and used the online method to approach the potential respondents.

The study managed the focus group before distributing the questionnaire to correct the content and face validity-related issues. The changes were incorporated into the questionnaire, and they were suggested by the experts. The focus group consisted of three professors and a few experts in the field to prove that the questionnaire is a valid instrument and covers all the related and relevant factors of the proposed concepts (Sim & Waterfield, 2019). The focus group provided valuable suggestions by exploiting their collective knowledge and experiences. They helped gain a deeper understanding of employees' preferences and experiences, proposed feedback, and discussed other topics requiring insights from this study.

The researchers also conducted pilot testing in the preliminary phase to evaluate and refine the instruments, distribution procedures, or interventions before distribution or carrying out the main large-scale study. The questionnaire was pilot-tested with initial small-scale distribution to the employees. The pilot testing revealed some redressed grey areas before going to a large-scale research design and data collection.

The data was analyzed through SmartPLS using structural equation modeling (SEM). Because of the various pluses of the SEM, this study opted for PLS-SEM. The SEM is a more robust analytical technique involving the measurement scale and structural results. The SmartPLS software resolving SEM has many advantages over the simple regression model. Its predictability power is more sound and reliable than other techniques. It can simultaneously work on the latent and observed variables to correctly assess their strengths and impact per the proposed model (Sarstedt & Cheah, 2019).

SEM is a powerful statistical technique used in management sciences, and it combines factor analysis and regression analysis that help researchers examine complex relationships among different variables. SmartPLS provides a user-friendly interface, and the software is mainly popular in various fields of management and social sciences research (Ramayah et al., 2017).

The questionnaire was sent to a few professors of Bannu University, Gomal University, and Kohat University, taking their views on suitability and clarity to assess the face validity, They recommended the instrument by suggesting a few changes that were duly incorporated before the final data collection. Furthermore, exploratory factor analysis (EFA) was executed to confirm that the instrument selected was able to support the data. The overall reliability of the instrument in terms of alpha coefficients was 0.78, showing a higher level than the cutoff level of 0.70, as endorsed by (Nunnally, 1978). The individual item-wise reliability of constructs was somehow ranging from 0.59 to 0.78.

Table 3 explains the demographic information of the employees that were participants in this study. The respondents were diversified in age, education, experience, location, university, and income level. The respondent profile revealed 52.7% male respondents and 47.3% female. The age-wise distribution of the respondents showed that the data is rich in age-wise groups. In terms of size, the larger group in our data set is 34.1 % of the respondents, which have between 2000 and 4000 students.

Regarding education, a higher response rate was found, with 33.6% of respondents having a bachelor’s degree and 32.1% having a master's degree. From the experience perspective, the highest responses were obtained from 44.5% of respondents with less than two years of online shopping experience. The employees were also diverse geographically, and in terms of the location of the universities as described, the data was collected from the southern districts. Hence, the majority (43%) of respondents were Kohat University and Preston University Kohat employees. The responses of 7 % of employees were from the universities in Dera Ismail Khan.

When the required data is gathered from a single source, such as the same level of participants for measuring the independent/dependent constructs, or when a single source of data collection is used, it might breed the issue of common method bias (Podsakoff et al., 2003). For common method bias, the most commonly used method is Harman’s single-factor test. While considering the study’s hypotheses, the test explains that if a single factor explains more than 50% of the total variance, bias is threatened (Podsakoff et al., 2003). The results of the study show that using EFA (taking all items in the research), no single factor caused the major portion of the variance. The results show the first factor accounts for only 31.0% of the variance. Therefore, the findings communicate that the common method bias did not exist in this serious, creating any issue.

This study evaluated the measurement and structural model, including hypotheses testing using SEM through partial least squares (PLS) estimation. As suggested by Hair et al. (1998), this study attempted to analyze the factor loadings, alpha coefficient, composite reliability (CR), and average variance extracted (AVE) to know the model's structural measures.

As shown in Table 4, the factor loadings of all items were above the threshold level, i.e., greater than 0.60, except SC5-SC12, DIA1, FL1, LE5-LE9, and OP5 and OP6, which were then eliminated on the ground of lower factor loadings (De Souzabido & Da Silva, 2019). Fornell and Larcker (1981) suggested a threshold level for factor loadings; the value of factor loadings should be above 0.60. The recommended cutoff levels for the alpha coefficient, CR, and AVE are 0.70, 0.70, and 0.50 (Fornell & Larcker, 1981; Hair, 2010; Hair et al., 1998; Nunally & Bernstein, 1978). In the case of this study, the values of the alpha coefficient and CR are greater than 0.70, and the AVE for all variables is above 0.50. Thus, the findings advocate a good convergent validity.

To evaluate the discriminant validity, the study used the approach suggested by (Gefen & Straub, 2005). The researcher followed Fornell and Larcker (1981) by analyzing the discriminant validity and comparing the correlations between factors with the square root of the AVE of all variables. Results in Table 4.17 reveal that the study’s results have acceptable discriminant validity because the square root of the AVE is above the correlation between components. This study assessed the items in the item loadings and cross-loadings to build the correlations. The findings show that all related variables' item loadings are higher than their cross-loadings for all other latent constructs. As a result, the results showed good discriminant validity. For descriptive statistics and correlation among variables, SPSS version 26 was used. Table 19 determines the results of descriptive statistics and correlations, showing that most of the variables are positively correlated with each other.

This study validated the structural model based on the proposed hypotheses after getting an acceptable measurement model and finding the content, convergent, and discriminant validity. The results evidence that standardized paths were used to analyze the proposed hypotheses and the relationships among variables. Using suggestions from previous research by Henseler et al. (2009), the path significance level was computed through the resampling method (Chin, 1998) with 2000 iterations. As shown in Figure 3, the staff creativity (? = 0.251, p<0.01) and SLC (? = 0.380, p<0.01) were positively and significantly influencing organizational performance, thus supporting hypotheses H1 and H2. Figure 3 also shows that the model explains 51.5% of the variance in flow experience, i.e., the explanatory power of the model for flow experience is 51.5%, indicating a good predictive power. Moreover, the results signify that staff creativity (? = 0.494, p<0.01) and SLC (? = 0.296, p<0.01) both have a positive and significant effect on flow experience, confirming hypotheses H3 and 4. Flow experience (? = 0.105, p<0.05) positively influences organizational performance. Therefore, the study approves hypothesis (H5). The model shows that the predictors bring a 60.8% change in organizational (university) performance. The results also show that staff creativity (indirect effect= 0.057, p < 0.05) and SLC (indirect effect= 0.034, p < 0.05) have positive and significant indirect effects on organizational performance through flow experience. Thus, mediation results indicate that hypotheses H6 and H7 were supported.

This study used Smart-PLS software to test the moderating effects of leadership effectiveness on flow organizational performance. Hypotheses H6 and H7 posit that leadership effectiveness has a moderating impact on organizational performance. Results of the study, as shown in Table 20, indicate that the interaction term of (leadership effectiveness × staff creativity) (? = 0.060, p<0.05) has a significant influence on organizational performance, thus supporting hypothesis H6. Furthermore, the study’s results show that the interaction term of (leadership effectiveness × SLC) (? = 0.054, p<0.05) too significantly impacts organizational performance, confirming hypothesis H7.

The study hypothesizes that staff flow experience mediates between staff creativity and organizational performance and between staff learning capabilities and university performance. The results of the study clarify that SC -> FE -> OP (? = 0.057, p<0.05) and SLC -> FE -> OP (? = 0.034, p<0.05) both have a significant indirect effect on university performance. The results in Table 6 confirm the mediation of the flow experience.