Impact of Leadership Style, Employee Motivation, and Organisational Culture on Employee Productivity in the Technology UK

H1: The leadership style positively and significantly affects workplace productivity.

(Alshehri, 2024) showed that transformational leadership, marked by vision, inspiration, and individualised consideration, greatly boosts employee productivity. (Alshehri, 2024) further documented how transformational leaders inspire employees by articulating a compelling vision and building trust, collaboration, and empowerment, which spur improved performance levels.

H2: Employee motivation has a positive but insignificant impact on employee productivity.

(Ahmad, 2021) highlighted that worker motivation is a crucial productivity driver, demonstrating that employees with motivation exert more effort, commitment, and persistence. (Ahmad, 2021) further revealed that intrinsic and extrinsic motivation drivers, including personal satisfaction and rewards, explicitly affect the amount of energy invested in work by employees, hence enhancing performance.

H3: Organisational culture contributes positively and significantly to employee productivity.

(Page et al., 2019) explained that a healthy organisational culture often makes employees more productive with the help of trust, respect and a place to belong. According to the study, encouraging work cultures with open talk, joint efforts and ongoing learning increases employees’ engagement and the amount of effort they freely give.

3. Methods

The study utilised quantitative methods in the study to investigate the impact of leadership style, employee motivation and organisational culture on employee productivity in the UK tech sector. The rationale for opting quantitative research it its ability in delivering objective measurements and rigorous statistical analysis of how these factors affect each other (Taguchi, 2018). As the focus of the study is understanding how organisational culture, employee motivation and style of leadership affect employees, using quantitative data methods makes it possible to study and generalise the information for the whole population. The study by (Jones et al., 2019), argued that, quantitative methods assist in reducing any subjectivity and ensures the testing of hypotheses using appripriate statistical methods.

The data was collected through a structured questionnaire, meticulously designed to obtain in-depth and quantifiable information about employees’ impressions and experiences of leadership, motivation, organisational culture, and productivity in the UK technology industry. questionnaire wa developed on five-point Likert scale from “Strongly Agree” to “Strongly Disagree” to score the degree of agreement or disagreement. The scale gave the respondents a convenient means of expressing their views and enabling the researcher to measure differences in attitudes and behaviours precisely. The questionnaire comprises four sections: the independent variables, such as organisational culture, employee motivation, and leadership style and the dependent variable, such as employee productivity, enabling a specific and systematic data collection. The sample size in the study was 337 respondents, and the most frequent statistical formula employed in quantitative research was employed to calculate the sample size. The formula accounts for factors like the number of employees in the UK technology sector to calculate that the sample would accurately represent the greater population.

In addition, the process included selecting a confidence level of 95% which represents high confidence in the outcome, with a 5% margin of error which is allowable deviation from the true population parameteres. Due to this, the researcher determine the number of people needed for reliable and general findings. With this sample size, the findings are considered statistically appropriate for applying to the whole study population. The study utilised purposive and convenient sampling techniques to recruite participants employed in the UK technology sector and receive proper exposure to company culture, leaders and motivation Purposive sampling guarantees that proper respondents only are ensured to have fulfilled defend inclusion criteria, i.e., job title, length of experience, and organisational process participation are selected, thereby enhancing data appropriateness and quality (Mweshi & Sakyi, 2020). Convenience sampling method is used to help with data collection and to make sure there is diversity among the participants (Zickar & Keith, 2023).

Partial Least Squares Structural Equation Modelling (PLS-SEM) is the method used for data analysis suitable for appropriate analysis examining relationships between latent factors in social science research. The first step in analysis is performing Confirmatory Factor Analysis (CFA) to check the model’s reliability, and to assess the effectiveness among items. To ensure construct validity, analysed critical indicators included item factor loadings, composite reliabiltiy measures and average variance extracted (AVE). After the measurement model is confirmed, the structural model is examined to understand whether employee motivation, organisational culture, leadership style and employee productivity have the expected relationships shown by the hypotheses. At this stage, path coefficient analysis helps identify the effects’ strength and the direction they take. Additionally, a reliable and valid sample was achieved because the study used careful data screening methods. An analysis of the response was performed to help ensure more people took part and less biased data was collected. When the data was collected, any missing values were discovered and handled through suitable imputations so that the analysis would not be affected. By using a trustworthy and clean sample, we were able to show more clearly that relationships between motivation, leadership, culture and productivity had an impact on each other.

4. Results and Analysis

4.1. Demographics

According to Table 1, there are more males (62.3%) than females (37.7%) in the 337 participants. Women hold 35.6% of the positions and other gender identities together are under 2%. As per (Hing et al., 2023), the data suggests that males have higher probability to be hired in the UK tech sector as they form a greater part of the industry workforce. The proportionately lower percentage of females indicates persistent gender imbalances in the sector.

Table 1. Gender.

As per Table 2, the age breakdown of the 337 respondents demonstrates that the most predominant category is the 26-35 years category, which comprises 38.6% of the population. The age bracket 8-25 years category at 28.2%. as these two younger age categories collectively form an overwhelming majority (66.8%), underscoring the young age profile of the technology workforce. The 36-45 years cohort holds 20.8%, followed by older workers in the 46-55 and over 55 associates at 8.9% and 3.5%, respectively. As (Higginbotham, 2024) found, the UK tech sector is dominated by early and mid-career workers showing signs of long-term success.

Table 2. Age.

4.2. Measurement Model Using Confirmatory Factor Analysis

As per Table 3, the research utilised Confirmatory Factor Analysis (CFA) to estimate the reliability and convergent validity of the measurement model, which is an essential step in validating the constructs being researched. It has been argued that, CFA effectively helps confirm that observed variables meet the requirements of the underlying latent constructs (Cheung et al., 2024). Cronbach’s alpha and composite reliability were both used to measure the unity of constructs and a value of 0.7 or higher is considered acceptable according to guidelines (Haji-Othman & Yusuff, 2022). According to Table 3, it shows that all the constructs exceed the established threshold, ensuring the quality of the measurement model. Leadership Styles, Employee Motivation, Organisational Culture and Employee Productivity show Cronbach’s alpha values of 0.838, 0.831, 0.890 and 0.902 respectively and composite reliability of 0.842, 0.839, 0.892 and 0.902, respectively. The analyses high iternal consostency in all latent variables which increases the confidence in the validity of the measurement tools.

Table 3. Reliability and convergent validity testing.

Individual factor loadings for each indicator were looked at to determine the degree to which they contribute to their constructs. (Cheung et al., 2024) studied factor loadings greater than 0.6 as worthwhile and a good indication of high construct representation. Table 3 displays factor loadings ranging from 0.840 to 0.890 for Leadership Styles, 0.827 to 0.913 for Employee Motivation, 0.891 to 0.932 for Organisational Culture, and 0.904 to 0.931 for Employee Productivity, confirming that all items meaningfully contribute to their constructs. In addition, the Average Variance Extracted (AVE) measures for all the constructs are above the 0.7 benchmark, standing at 0.755 for Leadership Styles, 0.748 for Employee Motivation, 0.820 for Organisational Culture, and 0.737 for Employee Productivity. These AVE measures all denote a high degree of convergent validity, as they show that the latent constructs account for much of the variance in their respective observed indicators. Therefore, the findings validate that the measurement model is valid and reliable and is a good foundation for further structural analysis in this study.

As per Table 4, the study employed the Heterotrait-Monotrait (HTMT) ratio to examine the discriminant validity of the involved constructs rigorously, an essential step to establish that every latent variable captures a distinct factor of the research model (Ab Hamid et al., 2017). HTMT score less than 0.85 is the standard signalling valid discriminant validity, ensuring that constructs are not interrelated and multicollinearity is properly addressed (Cheung et al., 2024). The findings in Table 4 illustrate that all HTMT relatedness between employee motivation, employee productivity, leadership style, and organisational culture is below this critical value. For instance, the relatedness between employee motivation and productivity is 0.443, indicating a moderate yet distinctive relationship. The HTMT ratio between leadership style and employee productivity is 0.620, and its relatedness with employee motivation is 0.595. These values support the theoretical assumption that though these constructs interrelate, they maintain conceptual distinctness, thus affirming the structural validity of the measurement model.

Table 4. Discriminant validity.

Moreover, the table’s findings indicate that organisational culture strongly relates to employee productivity (0.719) and leadership style (0.714). However, these are still below the threshold, reaffirming that these highly interconnected constructs measure distinct phenomena. Organisational culture and employee motivation have a more moderate relatedness of 0.509, asserting construct distinctiveness. This interrelatedness pattern indicates an interdependence of variables with no sacrifice in discriminant validity. The findings confirm that each construct accounts uniquely for employee productivity, leadership relationships, motivation, and influence within the organisational context. Therefore, HTMT provides strong evidence for the measurement model’s validity and reliability, reinforcing the research design’s strength and allowing for certain interpretations of follow-up path analyses. This validation is essential to ensure that the hypothesised relationships assessed through the structural model rest upon empirically discriminant and theoretically coherent constructs.

4.3. Path Analysis

Table 5 shows the structural model testing conducted to test the expected relationships between the independent variables (employee motivation, leadership style, and organisational culture) and the dependent variable (employee productivity). Bootstrapping methods were used in the analysis to assess the strength and significance of these path coefficients, which were in line with methodological prescriptions (Kock, 2018). As shown in Table 5, the estimated path coefficient from employee motivation to employee productivity was 0.071, with a t-statistic of 1.125 and an associated p-value of 0.261. These findings show that the direct effect of employee motivation on employee productivity is positive but statistically insignificant in the sample studied, implying that motivation may not be a strong predictor of productivity in this situation. The asterisks beside the coefficients represent statistical significance at the 0.01 level (p < 0.01), indicating strong evidence that the relationship is not coincidental. In Table 5, Leadership Style and Organisational Culture both have strongly significant positive impacts on Employee Productivity, but Employee Motivation is not significant and contains no asterisks. This convention enables easy spotting of which paths in the model have significant impacts.

Table 5. Structural model.

Conversely, the path coefficient for Leadership Style concerning Employee Productivity is significantly stronger at 0.205, with the support of a t-statistic of 3.015 and an extremely significant p-value of 0.003. The statistically significant relationship substantiates that Leadership Style has a significant and positive influence on Employee Productivity, highlighting the key position that sound leadership has in maximising workforce performance. In addition, Organisational Culture has the highest significant impact on Employee Productivity with a path coefficient of 0.487, a remarkable t-statistic of 7.755, and a p-value below 0.001. This indicates how critical it is to develop a good organisational culture as a strategic catalyst for productivity within the workplace. The findings highlight that although employee motivation might have a weak direct influence on employees individually, leadership style and organisational culture play major roles in driving employee productivity. The large coefficients and high statistical significance for the latter two factors highlight their pivotal role as levers to improve organisational performance.

4.4. Model Explanatory Power

As per Table 6, the R-square value reflects the percentage of variance in the dependent variable explained by the independent variables in a regression equation. In this research, the value of R-square as 0.452 measures that about 45.2% of the variance in employee productivity is explained by the combined impact of leadership style, employee motivation, and organisational culture. It has been argued that an R-square value greater than 0.3 is acceptable in social science studies, reflecting that the model is useful in explaining the phenomenon studied (Ozili, 2023). Although the model accounts for almost half of the variance in employees’ productivity, the other 54.8% can be explained by factors outside the model.

Table 6. Predictive relevance and quality assessment.

5. Discussion

The study’s primary aim was to examine the impact of leadership style, employee motivation, and organisational culture on employee productivity in the UK technology industry. The research findings demonstrated that organisational culture positively affected productivity, followed by leadership style. In contrast, employee motivation had a positive but statistically non-significant direct effect. The findings were achieved through rigorous quantitative data analysis that leadership style and organisational culture greatly improve employee productivity, although employee motivation had only a small, insignificant effect. These findings highlight these variables’ intricate interdependency and varied inputs into workplace performance. In this case, the third hypothesis and recent study validated that organisational culture’s key role confirms earlier research highlighting culture as a main driver for productivity (Nzuva & Mwende-Kimanzi, 2022; Page et al., 2019). (Pougajendy et al., 2024) discovered that a positive organisational culture promotes respect, trust, and safety, facilitating employees to perform at their best, initiate actions, and go above minimum expectations. The strong association between productivity and culture indicates that fostering productive, innovative, and friendly workplaces is essential to organisations that want to maintain a competitive edge, particularly in rapidly changing industries like technology.

Leadership style impacts impacts employees’ productivity, as shown by the high path coefficient and the first hypothesis, confirming that leadership is a primary determinant of employee performance (Ali & Yin, 2024; Alsheri, 2024). Transformational leadership, for instance, encourages employees by inspiring vision and personalised attention, building trust and collaboration that allow employees to perform at their best (Syarifuddin, 2023). This research’s evidence supports that effective leadership provides a favourable organisational climate that empowers employees, promotes open communication, and facilitates innovation (Ahn & Bessiere, 2022). However, the relatively lower effect in contrast to organisational culture implies that leadership effectiveness can be moderated by the cultural context in which it exists (Sunarni et al., 2023). The synergy between leadership and culture underscores leadership strategies tailoring to organisational values to achieve maximum productivity, an indication of the complexity attributed to the multifaceted influence of leadership on motivation and performance (Prayudi et al., 2024).

In the third hypothesis, although employee motivation had a positive but statistically non-significant direct influence on productivity, this does not deny its vital role. A study by (Uka & Prendi, 2021) validated this finding, as motivation affects the intensity and duration of employee effort, which are key for performance. A lack of statistical significance in this research could indicate the indirect effect motivation has through organisational culture and leadership mechanisms or the possibilities of nuances in measurement. Prior studies highlight that motivation is brought about by intrinsic and extrinsic motivators, including job satisfaction, feedback, and personal growth, which interplay with cultural and leadership factors to shape productivity (Ahmad, 2021; Ali & Anwar, 2021). Additionally, organisational practices that promote autonomy and opportunities for growth increase intrinsic motivation and indirectly contribute to productivity (Imran et al., 2025). Thus, motivation can be considered an integral, if mediated, variable that sustains employee productivity by reinforcing commitment and involvement in the organisational setting.

Table 7. Hypotheses table.

Conclusion

The study concluded that organisational culture and leadership style are critical in determining employee performance, with culture contributing most heavily. These results underline the need for organisations to foster positive, inclusive and creative cultures that keep employees motivated and help them work together. When organisations recognise how these factors work together, they can implement plans that improve their productivity and build strong, flexible places to work for long-term success.

Having a bigger sample of UK technology companies might have increased the strength and usefulness of the conclusions. The study did not include any mediating variables such as employee engagement and job satisfaction that maye have revealed in-depth insights on how leadership style, motivation and corporate culture influence productivity. By introducing mediators, indirect effects could have been better understood and the overall findings could have supported. Future research may take this study forward by incorporating mediating variables to clarify employee productivity dynamics in the technology industry. For instance, investigating the contribution of technological innovation adoption, work-life balance programs, or employee well-being may provide a better understanding of productivity drivers. Longitudinal analysis would be ideal to track the impact of changes in leadership patterns and organisational culture over time on productivity outcomes, especially considering how quickly the UK tech sector is changing. In addition, comparative studies between various geographic locations or industry sectors might uncover contextual variations, increasing the overall generalizability of findings. Qualitative approaches, including interviews or case studies, might be used in addition to quantitative findings to explore greater insight into employee experiences and perceptions of leadership and culture driving their productivity level. By following these studies, future academics will enhance the scholarly literature and offer practical knowledge to aid in the ongoing refinement of workforce productivity in dynamic technological environments.

It is recommended that UK tech companies emphasise developing a robust organisational culture and embracing transformational leadership approaches to increase worker productivity. Moreover, even though employee motivation exhibits less direct influence, including motivational practices in leadership and cultural models can indirectly increase workforce performance. A commitment to ongoing learning and shared communication will also sustain productivity in this rapidly evolving industry.

Author’s Contribution

K.S. contributed to the design and implementation of the study. K.S. contributed to the analysis of the results and the writing of the manuscript.

AVAILABILITY OF DATA AND MATERIALS

The data will be made available on reasonable request by contacting the corresponding author [K.S.].

Finding

The people who wrote this article did not receive any financial support from anyone.

Conflict of interest

The authors declare no conflicts of interest.

ACKNOWLEDGEMENTS

Declared None.

Appendices

Questionnaire

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