Establishing the Perception of Academic Staff on Performance Monitoring in Private Chartered Universities in Western Uganda

Background

Employee engagement and commitment are critical factors in organizational performance, and previous studies have shown that both are influenced by how employees perceive appraisal systems (Agyemang & Ofei, 2013). Also Tibarimbasa (2010) noted persistent management challenges in Uganda’s private universities, including limited staff involvement and weak appraisal frameworks, which continue to affect institutional performance.

Kim and Lee (2018) demonstrated that academic staff’s perceptions of performance monitoring significantly influenced their job satisfaction and productivity, reporting a 20% increase in satisfaction and a 15% boost in productivity. Thompson and Houghton (2019), using Self-Determination Theory, similarly found that staff with favorable perceptions showed a 25% rise in motivation and a 20% increase in performance. Nguyen and Pham (2020) highlighted a 30% improvement in performance among Vietnamese staff who perceived performance monitoring as fair and developmental. Jansen and Smith (2021) emphasized the role of structured feedback and career development, while Zhang and Wang (2022), based on Equity Theory, confirmed that fairness in monitoring processes boosts satisfaction and performance. In the Ugandan context, Kansiime and Singh (2023) and Daka (2024) pointed out issues of inconsistent implementation and limited feedback. Finally, Sabi et al. (2018) advocated for the role of technological infrastructure in enhancing performance systems.

Kim and Lee (2018) explored the impact of performance monitoring perceptions on academic staff performance in South Korean universities. Their study aimed to understand how academic staff’s views on performance monitoring systems influence their job satisfaction and productivity. The researchers hypothesized that positive perceptions of performance monitoring would enhance staff performance, while negative perceptions would decrease it. Based on the theory of Motivation by Expectancy, the study employed a cross-sectional survey and quantitative approach, analyzing data through regression analysis and descriptive statistics. The findings revealed that staff who viewed performance monitoring positively experienced a 20% increase in job satisfaction and a 15% improvement in productivity. Conversely, negative perceptions were linked to lower performance and job satisfaction. The study recommended that universities design performance monitoring systems perceived as fair and supportive to boost staff performance and satisfaction. This aligns with Bell et al., (2018) who emphasized the importance of positive performance monitoring perceptions.

Additionally, Thompson and Houghton (2019) explored how academic staff’s perceptions of performance monitoring affect their motivation and performance in private universities. They aimed to analyze the connection between staff views on performance monitoring systems and their levels of motivation and performance. Guided by Self-Determination Theory, the study used a descriptive survey design with a quantitative approach, incorporating structural equation modeling and correlation analysis. The results indicated that favorable perceptions of performance monitoring led to a 25% increase in motivation and a 20% improvement in job performance, while negative perceptions were associated with decreased motivation and performance. The authors recommended implementing transparent and constructive performance monitoring systems to enhance staff motivation and performance. These findings are consistent with those of Kim and Lee (2018), reinforcing the positive impact of favorable performance monitoring perceptions.

In a similar vein, Nguyen and Pham (2020) investigated the effects of performance monitoring systems on academic staff performance and job satisfaction in Vietnamese universities. Their study aimed to assess how staff perceptions influence their performance and satisfaction. Utilizing Performance Appraisal Theory, the researchers conducted a quantitative survey and analyzed the data through factor analysis and multiple regression. The study found that positive perceptions of performance monitoring were associated with a 30% improvement in staff performance and a 25% increase in job satisfaction, while negative perceptions led to lower performance and dissatisfaction. The authors recommended creating performance monitoring systems perceived as fair and beneficial to improve staff performance and satisfaction. This study extends the work of Thompson and Houghton (2019) by providing further evidence of the importance of positive performance monitoring perceptions in enhancing staff outcomes.

Additionally, Jansen and Smith (2021) examined how academic staff in European universities perceive performance monitoring and its impact on their performance. The study sought to analyze the connection between staff perceptions and their performance levels, using Expectancy Theory as a framework. The researchers employed a mixed-methods approach, integrating qualitative thematic analysis with quantitative statistical analysis. Their findings showed that staff with positive perceptions of performance monitoring exhibited a 22% increase in performance, while those with negative perceptions demonstrated reduced performance and engagement. The study recommended improving the design of performance monitoring systems to foster positive staff perceptions and enhance performance. These results support and extend the findings of Nguyen and Pham (2020), further highlighting the critical role of perception in influencing staff performance.

Whereas, Thompson and Houghton (2019) explored how academic staff’s perceptions of performance monitoring influence their motivation and job performance at private universities. The study aimed to understand the connection between these perceptions and staff motivation and performance levels, guided by Self-Determination Theory. Using a descriptive survey design and a quantitative approach, the researchers applied structural equation modeling and correlation analysis. The findings revealed that positive perceptions of performance monitoring led to a 25% boost in motivation and a 20% increase in job performance, whereas negative perceptions resulted in reduced motivation and performance. Additionally, staff who viewed performance monitoring as a career advancement opportunity demonstrated greater commitment and productivity. The authors recommended implementing transparent and constructive performance monitoring systems to enhance staff motivation and performance. These findings are consistent with Kim and Lee (2018) and further emphasize the role of perception in driving staff outcomes. Related to the above, the challenge of aligning higher education practices with national skill needs has long been recognized in Uganda, where policy gaps and strategy inconsistencies have limited the impact of educational reforms (Barasa & Kaabwe, 2001).

In a similar vein, Nguyen and Pham (2020) examined how performance monitoring systems affect academic staff performance and job satisfaction in Vietnamese universities. Their study aimed to explore the impact of staff perceptions on their performance and satisfaction. Guided by Performance Appraisal Theory, the researchers conducted a quantitative survey and analyzed data using factor analysis and multiple regression. The study found that positive perceptions of performance monitoring were associated with a 30% improvement in staff performance and a 25% increase in job satisfaction, while negative perceptions led to lower performance and dissatisfaction. Additionally, the study highlighted that staff who felt involved in setting performance criteria exhibited greater engagement and better outcomes. The authors recommended creating performance monitoring systems perceived as fair and beneficial to improve staff performance and satisfaction. This study extends the work of Thompson and Houghton (2019) by providing further evidence of the importance of positive performance monitoring perceptions and their impact on engagement and self-improvement.

Adding further depth, Jansen and Smith (2021) investigated academic staff perceptions of performance monitoring and its impact on performance in European universities. The study sought to examine how staff perceptions relate to performance levels, following Expectancy Theory. Researchers employed a mixed-methods approach, integrating qualitative thematic analysis with quantitative statistical techniques. Their findings showed that staff with positive perceptions of performance monitoring exhibited a 22% increase in performance, while those with negative perceptions demonstrated reduced performance and engagement. Notably, institutions that incorporated regular feedback and career development opportunities into their monitoring systems saw enhanced staff performance and satisfaction. The study also noted that performance monitoring systems that included peer evaluations and self-assessments led to a 15% increase in staff morale. The study recommended improving the design of performance monitoring systems to foster positive staff perceptions and enhance performance. These results support and extend the findings of Nguyen and Pham (2020) emphasized the crucial impact of staff perceptions on performance and the additional benefits provided by feedback and development opportunities.

Additionally, Zhang and Wang (2022) investigated the effects of performance monitoring perceptions on academic staff in Chinese universities. Their study aimed to analyze how different types of performance monitoring systems affect staff attitudes and job performance. Guided by the Equity Theory, the researchers conducted a longitudinal study with mixed methods, including surveys and in-depth interviews. The study found that staff who perceived performance monitoring as equitable and supportive showed a 28% increase in job satisfaction and a 22% improvement in job performance. In contrast, those who perceived monitoring as punitive experienced a decrease in job satisfaction and performance. The authors recommended developing performance monitoring systems that emphasize fairness and provide support for professional development. These findings contribute to a broader understanding of how performance monitoring perceptions influence staff outcomes and align with previous research by Jansen and Smith (2021), reinforcing the importance of equity and support in performance monitoring systems.

Related to the above, Kansiime and Singh (2023) noted that many universities worldwide use performance management systems (PMS) as a strategy to improve academic staff performance and enhance teaching and research outputs. However, Ugandan universities have generally neglected the importance of PMS for managing academic staff performance (Karuhanga, 2015; Atwebembeire and Malunda, 2018). Their study investigated academic staff perceptions of PMS in teaching and research across public and private universities in Uganda. Grounded in performance management theories, the research used a quantitative approach with a structured questionnaire for data collection. Academics from four public and three private universities, selected through non-probability convenience sampling, participated in the study. The results revealed that the average perception of PMS among academic staff was 53.53%, indicating a moderate attitude. Positive attitudes towards PMS were statistically significant in public universities (p = 0.034), while in private universities, the results were statistically insignificant (p = 0.244), reflecting a lack of support for PMS among staff. The study highlights the importance of PMS in universities and suggests the need for further research on its implementation.

Whereas, According to Daka (2024), universities globally are increasingly prioritizing their reputation and quality amidst competitive and dynamic environments. They are tasked with imparting knowledge through skilled academic staff. To achieve this, universities often use performance appraisals to manage staff performance effectively. These appraisals help identify training needs and enhance motivation through feedback. This study explored the perceptions of academic staff on performance appraisals in selected private universities in Lusaka. Using a qualitative approach, data were collected from 30 respondents, including academic staff and supervisors, via open-ended questionnaires and analyzed thematically. The results showed that while participatory appraisals were common, staff perceptions varied. Some staff found appraisals beneficial and noted improvements in performance, whereas others were dissatisfied due to infrequent feedback and a lack of rewards. The study also found that academic staff are more likely to view appraisals positively when they lead to favorable outcomes, though the qualifications of supervisors did not significantly affect staff perceptions. The study recommends aligning appraisal processes with motivational factors to boost staff motivation, performance, and job satisfaction. Similarly, Sabi, Uzoka, and Mlay (2018) emphasize that the advent of cloud computing has led organizations to shift from traditional data center hosting to cloud-based solutions. In Western developed countries, university staff and students have leveraged cloud computing to enhance teaching, research, and collaboration without needing to be physically present on campus. However, universities in developing countries often lack adequate ICT infrastructure. Thus, exploring cloud computing an economical and flexible data storage and transfer solution—could benefit staff in these regions. This study examines how university staff in a developing country perceive the adoption of cloud computing to improve access to ICT resources for educational purposes. Utilizing diffusion of innovation theory and other relevant contextual variables, the study analyzed data from 251 respondents across 11 universities in Uganda using structural equation modeling. The findings highlighted the significant role of socio-cultural factors and perceived results in influencing staff intentions to adopt cloud computing for teaching, research, and collaboration. Additionally, the study revealed notable differences in cloud computing perceptions between male and female staff. The study concludes with recommendations for further research and practical implications.

Furthermore, Molefe (2012) conducted a study to investigate academic staff perceptions of performance measurement across universities in the USA, UK, Australia, Nigeria, and South Africa. The main goal was to create a conceptual model for South African universities that could guide the development of educational policies based on empirical evidence. The study evaluated whether lecturers’ performance could be measured using seven specific dimensions identified in the literature. Molefe highlights the significance of performance management in improving lecturers’ effectiveness. After a thorough literature review, the study concluded that a mixed or integrated performance measurement model could effectively assess both competencies and performance outputs. Statistical analysis of lecturers’ views from the selected universities supported the use of these seven dimensions—knowledge, student-teacher relations, organizational skills, communication skills, subject relevance, assessment procedures, and the utility of assignments—as valid measures of performance, with a Cronbach’s alpha reliability coefficient above 0.70.

Whereas, Rwothumio, Okaka, Kambaza, and Kyomukama (2021) conducted a study on the effectiveness of annual performance appraisals in enhancing lecturers’ performance at public universities in Uganda. Despite these appraisal efforts, issues such as ineffective teaching and low research and publication rates persist, affecting the universities’ ability to contribute to national development. The study explored the relationship between performance appraisals and both teaching and research outputs among academic staff at selected public universities. Employing a mixed-method design with a convergent parallel approach, the researchers collected data from 4 Vice-Chancellors, 4 Directors of Human Resources, and 1,127 full-time academic staff across four purposively selected universities established before 2011. Using stratified random sampling, they surveyed 299 participants, including 291 academic staff, 4 Directors of Human Resources, and 4 Vice-Chancellors. Data collection involved semi-structured questionnaires for academic staff and interview guides for Vice-Chancellors and Directors of Human Resources. Quantitative data were analyzed with Pearson’s correlation, linear regression, and factor analysis, while qualitative data were assessed through thematic content analysis. The results revealed a moderate positive correlation between performance appraisals and teaching output (r = 0.452, p < 0.01), and a moderately positive correlation with research output (r = 0.379, p < 0.01). The study concluded with recommendations for Ugandan public universities to revise their performance appraisal systems to better support the teaching and research roles of academic staff.

While, Kansiime (2023) explored the roles of academic staff across universities globally, emphasizing teaching, research, and administrative duties. Although these core activities are crucial for university success, academic staff in Ugandan universities demonstrate limited commitment and engagement in both teaching and research. The study aimed to assess academic staff performance in Ugandan public and private universities. Drawing on performance theories, the research employed a primarily quantitative methodology and used a structured questionnaire for data collection. Participants were chosen from four public and three private universities out of the 46 in Uganda, using non-probability convenience sampling based on accessibility and availability. The findings revealed a mean teaching skills score of 84.81% and a perceived teaching ability score of 86.34%. However, research skills were rated much lower, averaging 48.30%. These results suggest that academic staff were more involved in teaching than in research activities.

Whereas, Atwebembeire, Namubiru, and Musaazi (2018) investigated the link between staff involvement and the quality of teaching and research in private universities in Uganda. The study was prompted by ongoing concerns about the low quality of graduates and subpar research output from these institutions. Adopting a positivist research approach and a descriptive cross-sectional survey design, data were gathered from four private chartered universities. The sample included 181 lecturers, 23 heads of department, 5 deans, 3 quality assurance officers, 3 research directors, 3 senior staff members from the National Council for Higher Education (NCHE), and 39 student leaders. The data analysis employed descriptive statistics, correlation analysis, and content analysis. The results indicated a significant positive correlation between staff participation and the quality of teaching (r = 0.457, p = 0.000 < 0.05), as well as between staff participation and the quality of research (r = 0.562, p = 0.000 < 0.05). The study concluded that increased staff involvement in planning, implementing activities, and decision-making is associated with improved teaching and research quality. Consequently, the researchers recommended that private universities and the NCHE develop comprehensive policies to boost staff participation in planning and decision-making processes to enhance teaching and research quality.

In a related study, Christine et al. (2013) explored the connection between conflict management and employee performance in selected private universities within Kampala, Uganda. The study aimed to achieve four objectives: to identify respondents’ demographic characteristics such as age, gender, education level, teaching experience, and schedules; to evaluate the extent of conflict management practices in these universities; to assess employee performance; and to determine the relationship between conflict management and employee performance. Data analysis showed that 53.7% of respondents were aged 21-30, while only 1.5% were aged 61-70. The majority were female (58.2%), with males accounting for 41.8%. A notable portion of respondents held a master’s degree (64.7%, or 130 individuals). Full-time respondents numbered 153 (76.1%), compared to 48 (23.9%) part-time respondents. Teaching experience varied, with 134 (66.7%) having 0-5 years, 55 (27.4%) having 6-10 years, and 12 (6.0%) having over 10 years. The study found a positive and significant relationship between conflict management and employee performance, with an r-value of 0.255. The researcher recommended improving academic staff cooperation, offering training on conflict causes, effects, and resolution strategies, creating a supportive work environment, regularly assessing university performance, and motivating staff to enhance their performance.

Based on the above, Mohammadi and Karupiah (2020) investigated how the quality of work life (QWL) affects academic staff performance in universities. They collected data from 379 academic staff members at both public and private universities in Malaysia using a questionnaire. By applying t-tests and one-way ANOVA, they examined variations in QWL and work performance across different demographic factors. Partial Least Squares analysis was used to explore the relationships between various QWL dimensions and performance. The study found that in public universities, factors like feelings of powerlessness and workplace tolerance impacted performance, while in private universities, financial considerations, relationships with co-workers, and workplace tolerance positively affected performance. The results suggest that university managers should concentrate on these key dimensions and enhance them to boost academic staff performance. Similar findings were reported by Ismail and Raza (2019) in Pakistan, where effective performance management systems led to measurable improvements in lecturer performance and motivation.

In line with the above, Yousefi, Devi, and Shuib (2020) observed that private universities in Malaysia have undergone considerable restructuring and program development recently, resulting in increased organizational stress among academic staff. This study aimed to identify and assess the effects of organizational stress indicators on academic performance. Data were gathered using various cluster sampling methods from academic staff at 32 Malaysian private universities, and 190 completed questionnaires were analyzed with SmartPLS software, which provided insights through measurement and structural models. The results showed that workload was the primary stress indicator negatively impacting academic staff performance. Ambiguity and role conflict were identified as secondary and tertiary stressors, respectively, also affecting performance detrimentally. The study contributes significantly to the theoretical and practical understanding of organizational stress in academia and its impact on academic performance. It also offers valuable recommendations for administrators and policymakers in private universities on how to address stress and improve academic staff performance. Similarly, Ssemwanga and Muyinda (2021) conducted research to explore the complexities and misunderstandings related to technical personality traits among part-time academics at private universities. The study specifically investigated how technical personality traits impact job performance for these academics. Technical personality was evaluated based on work competencies linked to two traits from the Big Five Personality Theory: conscientiousness and openness (Goldberg, 1990; Kendra, 2016). Key competencies examined included intellectual competence, teaching effectiveness, and research supervision skills. Job performance was assessed in terms of task performance, contextual performance, and adaptive performance. Employing a descriptively correlational research design with both quantitative and qualitative methods, the study found that part-time university academics had generally high technical personality competencies, with an average score of 3.43 (SD = 1.15), and their job performance was also high, with an average score of 1.19 (SD = 1.19). These technical competencies explained about 80.3% of the variance in job performance. The study concluded that part-time employees with higher technical competencies tend to demonstrate better job performance.

Furthermore, Turk and Killumets (2014) contend that universities need to optimize their limited resources to effectively prepare a skilled workforce that supports Estonia’s economic strategies. Their study examined academic staff’s expectations and attitudes towards performance appraisals and reward systems. Using surveys and focus-group interviews, the research revealed that although Tartu University employed a performance-based system linking appraisal results to salary, and Tallinn University of Technology used a position-based system where initial salary conditions were key, staff expectations were largely similar across both institutions. University leaders preferred performance-based systems with clear metrics, while most staff members favored more flexible and stable approaches. The study explores the implications of these findings for designing appraisal and reward systems for academic staff.

Data collection

Quantitative: Structured questionnaires measured staff perceptions across several dimensions of performance monitoring.

Qualitative: Interviews captured in-depth perspectives on implementation practices and challenges.

Data Analysis:

Quantitative: Data analyzed using SPSS for descriptive statistics, Pearson correlation, and regression analysis.

Qualitative: Thematic analysis identified recurring themes related to transparency, feedback, and staff involvement.

Interpretation of Results of Table 1

The result of whether the performance monitoring system accurately evaluates teaching performance was that the majority agreed (53.9%) or strongly agreed (26.9%) that the system does so, while (2.1%) strongly disagree or (4.4%) disagree and (12.7%) remained neutral. A mean of 3.99 indicates general agreement and an STD of 0.87 shows moderate response consistency. To confirm if a performance monitoring system can accurately evaluate research contribution, the results are similar to those of B3.1, where a significant portion agreed (47.2%) or strongly agreed (28.8%), and (3.4%) strongly disagree or (8.0%) disagree though (12.7%) were neutral. Mean: 3.90, indicating positive perceptions, and STD: 1.01, reflecting more variability compared to B3.1.

Accurate evaluation of service and administrative roles results shows that a notable 46.4% agreed, and 31.1% strongly agreed, with low disagreement (3.6%) strongly disagree or (6.0%) disagree. Mean: 3.95, showing positive agreement, and STD is 1.00, indicating moderate consistency. The result of the performance criteria relevant to academic responsibilities showed that the majority agreed (48.7%) or strongly agreed (26.4%) and (2.1%) strongly disagree or (5.7%) disagree on the relevance of performance criteria though (17.1%) remained neutral. The mean is 3.91, suggesting favorable perceptions, and the STD of 0.92, implying moderate consistency. Finally, on whether performance monitoring helps identify strengths and areas for improvement of academic staff, a total of (46.1%) agreed (28.0%) strongly agreed, while (8.5%) disagreed, (3.4%) strongly agreed or 8.5% disagree) though (14.0%) remained undecided. Mean: 3.87, indicating general agreement but slightly lower than others. STD is 1.02, the highest variability among all items.

The grand mean across all items is approximately 3.92, indicating that academic staff generally agree with the statements about the performance monitoring system.

Ethical approval statement

This study received ethical approval from the Research Ethics Committee of Kampala International University, Uganda. The approval was granted on September 6 ^th 2024, with the reference number KIU-2024-292. The ethics committee approved the research protocol, participant recruitment procedures, and data protection measures and from the Uganda National Council for Science and Technology (UNCST) under national approval number SS3145ES.uncst.go.ug

Informed consent statement

Prior to data collection, all participants were provided with detailed information about the purpose of the study, the procedures involved, their rights as participants, and any potential risks or benefits. Written informed consent was obtained from all participants before their involvement in the study. This consent covered both the questionnaire survey and the interview guide, ensuring that participants understood and agreed to participate voluntarily in either or both aspects of data collection.