Project – Algorithmic Employee Monitoring and Its Effects on Worker Autonomy, Motivation and Organisational Commitment. A Study of Selected telecommunications companies in Lagos State.

CHAPTER ONE

INTRODUCTION

1.1 Background to the Study

The advancement of digital technologies has significantly transformed the organisation, supervision, and evaluation of employees in contemporary workplaces. Organisations increasingly employ digital systems to coordinate tasks, assess productivity, monitor employee activities, and support managerial decision-making. One important development in this transformation is algorithmic management, which involves the use of computer-based algorithms and data-driven systems to perform or support managerial functions traditionally undertaken by human supervisors. These functions may include work allocation, performance assessment, scheduling, target setting, feedback, and the identification of deviations from established performance standards. Kellogg, Valentine, and Christin (2020) explain that algorithmic systems are changing the nature of workplace control by creating new mechanisms through which organisations direct and evaluate workers. Similarly, Parent-Rocheleau and Parker (2022) demonstrate that algorithms can influence job design by changing employees’ work demands, resources, and discretion. Although these technologies can improve operational coordination and provide managers with timely information, they also raise important questions about employees’ ability to exercise independent judgement, remain motivated, and maintain commitment to their organisations. Consequently, algorithmic employee monitoring has become an important subject for organisational and human resource management research.

Algorithmic employee monitoring refers to the use of digital technologies, automated analytical systems, and algorithmic tools to collect, process, evaluate, or interpret information about employees’ work activities and performance. Such monitoring may involve tracking call-handling time, customer-service ratings, sales performance, attendance, response times, task completion, and compliance with organisational procedures. Unlike traditional supervision, which often depends on periodic observation and direct interaction between supervisors and employees, algorithmic monitoring may operate continuously and generate performance indicators in real time. These systems can help organisations identify operational problems, compare performance across teams, and provide evidence for managerial decisions. Parent-Rocheleau and Parker (2022) identify monitoring, goal setting, performance management, scheduling, compensation, and job termination as management functions that algorithms can perform or influence. Parent-Rocheleau and colleagues (2024), through the development of an algorithmic management questionnaire, also identify monitoring and performance rating as important dimensions of workers’ exposure to algorithmic management. However, the usefulness of these systems depends on how monitoring information is collected, interpreted, and applied. Where employees perceive monitoring as excessive, unexplained, or primarily punitive, it may generate concerns about privacy, fairness, and managerial control. The implications of algorithmic monitoring therefore extend beyond technological efficiency to the quality of employees’ working experiences.

One major concern associated with algorithmic employee monitoring is worker autonomy. Worker autonomy refers to the degree of discretion employees possess over the methods, timing, sequencing, and decisions involved in performing their jobs. Autonomy allows employees to apply professional judgement, organise their activities, solve problems, and adapt their approaches to changing circumstances. Hackman and Oldham (1976), in their job characteristics model, identify autonomy as an important job characteristic because it gives employees a sense of responsibility for work outcomes and can influence internal work motivation. In workplaces where algorithms prescribe detailed procedures, impose rigid targets, or continuously assess employees against predetermined indicators, workers may have fewer opportunities to determine how their tasks should be performed. This may be particularly relevant where employees are expected to follow system-generated instructions even when their experience suggests that another approach would be more appropriate. Parent-Rocheleau and Parker (2022) argue that algorithmic management can reshape job autonomy and other work-design characteristics by changing how tasks are assigned, evaluated, and controlled. Nevertheless, algorithmic monitoring does not necessarily eliminate autonomy in every situation. Performance information may also help employees identify areas for improvement and organise their work more effectively when they retain meaningful discretion. The central issue is therefore how the design and use of monitoring systems influence employees’ perceived freedom to exercise judgement in their daily work.

Employee motivation represents another important dimension of the relationship between algorithmic monitoring and workplace behaviour. Motivation concerns the processes that initiate, direct, and sustain an individual’s effort towards achieving work-related goals. Employees may be motivated by internal factors, such as personal interest, professional growth, and satisfaction from accomplishing meaningful tasks, as well as external factors, such as remuneration, recognition, promotion, and performance incentives. According to Deci and Ryan (2000), self-determination theory emphasises the importance of autonomy, competence, and relatedness in supporting high-quality motivation and psychological well-being. When algorithmic monitoring provides constructive feedback, clarifies expectations, and helps employees recognise improvements in their performance, it may strengthen perceptions of competence and encourage goal-directed effort. Conversely, when employees experience constant surveillance, inflexible performance targets, or threats of penalties based on automated evaluations, monitoring may be perceived as controlling and may weaken their sense of self-direction. Siegel, König, and Lazar (2022), in a meta-analysis of electronic monitoring research, examined its relationships with job satisfaction, stress, performance, and counterproductive work behaviour, demonstrating the importance of considering multiple employee outcomes rather than assuming that monitoring has uniformly beneficial effects. The influence of algorithmic monitoring on motivation may therefore depend on whether employees perceive it as supportive performance feedback or as a mechanism of excessive control.

Organisational commitment is equally important when assessing the potential consequences of algorithmic employee monitoring. Organisational commitment generally refers to an employee’s psychological attachment to, identification with, and willingness to remain associated with an organisation. Meyer and Allen (1991) distinguish among affective commitment, which reflects emotional attachment; continuance commitment, which concerns the perceived costs of leaving; and normative commitment, which relates to a sense of obligation to remain. These dimensions are useful for understanding how workplace practices may shape employees’ relationships with their employers. When monitoring systems are implemented transparently, applied consistently, and used to support employee development, workers may interpret them as part of a fair and accountable work environment. In contrast, monitoring practices perceived as intrusive, unfair, or excessively punitive may weaken employees’ confidence in management and their emotional attachment to the organisation. Alder and Tompkins (1997) explain that employee responses to electronic performance monitoring are influenced by organisational justice and the manner in which monitoring systems are implemented. Their analysis suggests that employee involvement, appropriate monitoring boundaries, and supportive communication can affect the consequences of electronic monitoring. Thus, examining organisational commitment alongside autonomy and motivation can provide a broader understanding of how employees respond to algorithmic control in the workplace.

The telecommunications industry provides a particularly relevant setting for examining algorithmic employee monitoring because its operations depend heavily on digital infrastructure, performance measurement, customer-service delivery, and the coordination of geographically dispersed activities. Telecommunications companies may use automated systems to monitor customer-care representatives, sales personnel, technical-support teams, network operations staff, and other employees whose work generates measurable digital records. For example, customer-care employees may be assessed through call duration, customer satisfaction ratings, response times, and the number of cases resolved, while sales personnel may be evaluated using customer acquisition figures, conversion rates, and target achievement. Such indicators can help managers identify service-delivery problems and assess operational performance. However, reliance on narrow performance metrics may fail to capture important aspects of work, including the complexity of customer problems, the quality of professional judgement, and the time required to resolve unusual cases. Kellogg et al. (2020) explain that algorithmic control can become contested when workers and managers disagree over how performance should be measured and how algorithmic decisions should be challenged. In telecommunications companies, where employees may face demanding service targets and continuous operational pressures, it is important to understand whether monitoring systems support effective performance while preserving reasonable autonomy, motivation, and organisational commitment.

In Lagos State, telecommunications companies operate within a commercially significant and technologically intensive environment in which service quality, customer retention, operational efficiency, and workforce productivity are important organisational concerns. The use of digital performance systems within such companies provides a relevant context for examining the relationship between technological monitoring and employees’ workplace experiences. Nevertheless, the presence of digital performance indicators alone does not establish that every telecommunications company uses advanced algorithmic monitoring or that employees experience these systems in the same way. The nature and intensity of monitoring may differ across companies, departments, job roles, and managerial practices. Existing international research provides important insights into algorithmic management, electronic monitoring, job design, motivation, and employee responses (Kellogg et al., 2020; Parent-Rocheleau & Parker, 2022; Siegel et al., 2022), but the extent to which these relationships apply to employees in selected telecommunications companies in Lagos State requires context-specific investigation. In particular, it is necessary to establish how employees perceive algorithmic monitoring, whether such monitoring is associated with their ability to exercise discretion, and how it relates to their motivation and organisational commitment. This study therefore examines algorithmic employee monitoring and its effects on worker autonomy, motivation, and organisational commitment in selected telecommunications companies in Lagos State.

1.2 Statement of the Problem

The increasing use of digital performance systems has created opportunities for organisations to improve supervision, monitor productivity, and make more evidence-based managerial decisions. In telecommunications companies, algorithmic monitoring can provide managers with timely information about employee activities, service delivery, sales performance, and compliance with operational standards. However, the growing capacity to collect and analyse employee performance data raises concerns about the balance between organisational control and workers’ discretion. When monitoring systems prescribe how employees should perform tasks, evaluate their activities continuously, or restrict opportunities to exercise independent judgement, employees may perceive a reduction in their autonomy. This concern is important because autonomy is a recognised characteristic of job design and can influence employees’ experiences of responsibility and motivation (Hackman & Oldham, 1976; Parent-Rocheleau & Parker, 2022). The problem is that although algorithmic monitoring may improve managerial visibility, its implications for worker autonomy in selected telecommunications companies in Lagos State cannot be assumed without examining employees’ actual experiences.

A second problem concerns the possible consequences of algorithmic monitoring for employee motivation. Organisations may introduce monitoring systems to encourage employees to meet targets, improve service quality, and maintain consistent standards. However, monitoring that is perceived as supportive may produce different responses from monitoring that employees experience as excessive, intrusive, or punitive. Where performance assessments are heavily dependent on automated indicators, employees may feel pressured to prioritise measurable targets at the expense of professional judgement, collaboration, or the careful handling of complex customer needs. Self-determination theory suggests that the satisfaction or frustration of employees’ psychological needs, particularly autonomy and competence, is important for understanding the quality of motivation they experience (Deci & Ryan, 2000). Furthermore, the meta-analysis by Siegel et al. (2022) highlights the need to examine electronic monitoring in relation to several employee outcomes, including stress and job satisfaction. The unresolved issue is whether algorithmic employee monitoring in the selected telecommunications companies is associated with stronger work motivation or whether it contributes to experiences of pressure and reduced self-direction.

A third problem relates to organisational commitment. Telecommunications companies depend on employees who are willing to contribute to organisational objectives, maintain service standards, and sustain constructive relationships with their employers. However, employees’ commitment may be affected when workplace monitoring is perceived as unfair, excessively intrusive, or insufficiently transparent. Employees who believe that automated assessments do not adequately reflect their work circumstances may question the fairness of managerial decisions based on such information. Alder and Tompkins (1997) argue that organisational justice and the implementation of electronic performance monitoring are important in understanding employee responses to monitoring practices. Similarly, Meyer and Allen (1991) explain that organisational commitment involves different forms of attachment that may be influenced by workplace experiences. Despite the operational advantages of algorithmic monitoring, there is a need to determine whether employees in selected telecommunications companies in Lagos State perceive these systems as fair and supportive or as practices that may weaken their psychological attachment to their organisations.

The fourth problem is the limited context-specific understanding of how algorithmic employee monitoring relates simultaneously to worker autonomy, motivation, and organisational commitment in selected telecommunications companies in Lagos State. These outcomes are interconnected but distinct: an employee may meet performance targets while experiencing limited autonomy, or may comply with monitoring requirements without developing stronger motivation or emotional attachment to the organisation. Research on algorithmic management has identified the complex relationship between automated control, work design, and employee responses (Kellogg et al., 2020; Parent-Rocheleau & Parker, 2022). However, findings from other industries or countries cannot automatically be generalised to telecommunications employees in Lagos State because organisational practices, job roles, performance systems, and employee experiences may differ. Without context-specific evidence, managers may find it difficult to determine how to implement monitoring systems in ways that support productivity while preserving employee well-being and commitment. The problem addressed by this study is therefore the need to investigate the relationship between algorithmic employee monitoring and worker autonomy, motivation, and organisational commitment in selected telecommunications companies in Lagos State.

1.3 Aim and Objectives of the Study

The aim of this study is to examine algorithmic employee monitoring and its effects on worker autonomy, motivation, and organisational commitment in selected telecommunications companies in Lagos State.

The specific objectives are to:

1. Examine the extent and nature of algorithmic employee monitoring in selected telecommunications companies in Lagos State.
2. Determine the relationship between algorithmic employee monitoring and worker autonomy in selected telecommunications companies in Lagos State.
3. Assess the relationship between algorithmic employee monitoring and employee motivation in selected telecommunications companies in Lagos State.
4. Examine the relationship between algorithmic employee monitoring and organisational commitment in selected telecommunications companies in Lagos State.

1.4 Research Questions

The following research questions will guide the study:

1. What is the extent and nature of algorithmic employee monitoring in selected telecommunications companies in Lagos State?
2. What is the relationship between algorithmic employee monitoring and worker autonomy in selected telecommunications companies in Lagos State?
3. What is the relationship between algorithmic employee monitoring and employee motivation in selected telecommunications companies in Lagos State?
4. What is the relationship between algorithmic employee monitoring and organisational commitment in selected telecommunications companies in Lagos State?

1.5 Research Hypothesis

The study will test the following null hypothesis at the 0.05 level of significance:

H₀: Algorithmic employee monitoring has no statistically significant relationship with worker autonomy, employee motivation, and organisational commitment in selected telecommunications companies in Lagos State.

1.6 Significance of the Study

The study is expected to be significant to telecommunications companies, human resource managers, employees, policymakers, technology developers, and future researchers.

Telecommunications companies: The findings may help telecommunications companies understand how algorithmic monitoring relates to employees’ autonomy, motivation, and organisational commitment. This knowledge may inform decisions about the design, implementation, and evaluation of digital performance-monitoring systems.

Human resource managers: The study may provide insights into employee responses to automated performance assessment and workplace surveillance. The findings may help human resource managers develop clearer monitoring policies, appropriate feedback procedures, and employee-engagement practices.

Employees: The study may contribute to a better understanding of how algorithmic monitoring can influence work discretion, motivation, and the relationship between employees and their organisations. It may also highlight the importance of transparency, fairness, and opportunities for employees to raise concerns about monitoring systems.

Management and supervisors: The findings may help supervisors distinguish between monitoring that supports employee development and monitoring that may be perceived as excessive control. This distinction may encourage the use of performance data alongside professional judgement and constructive communication.

Policymakers and labour stakeholders: The study may provide context-specific evidence relevant to discussions on workplace digitalisation, employee privacy, fair performance assessment, and responsible use of automated systems. It may also identify areas requiring further organisational guidance or research.

Researchers and students: The study may contribute to the literature on algorithmic management, job autonomy, work motivation, and organisational commitment. Its focus on telecommunications companies in Lagos State may provide a basis for comparison with other industries and geographical settings.

1.7 Scope of the Study

The geographical scope of the study is Lagos State, Nigeria. The organisational scope covers selected telecommunications companies operating in the state. The content scope focuses on algorithmic employee monitoring as the explanatory variable and worker autonomy, employee motivation, and organisational commitment as the principal outcome variables. Algorithmic employee monitoring will be examined through practices such as digital tracking, automated performance assessment, target monitoring, and system-generated feedback, where applicable to the selected organisations. Worker autonomy will concern employees’ perceived discretion in performing their tasks; employee motivation will concern their willingness and drive to exert effort towards work-related goals; and organisational commitment will concern their psychological attachment to their employers. The study will be limited to the selected companies, respondents, and period of data collection. The final company selection and sample will depend on access, eligibility criteria, and the approved research methodology.

1.8 Limitations of the Study

The study may encounter limitations relating to access to telecommunications employees and organisational information about internal monitoring systems. Some employees may be reluctant to discuss monitoring practices because they fear that their responses could affect their employment or relationship with management. In addition, respondents may not know the technical details of the algorithms used by their employers and may only be able to report their experiences and perceptions. Self-reported responses may also be influenced by memory, individual interpretation, or concerns about confidentiality. These limitations can be reduced by protecting respondents’ anonymity, explaining the academic purpose of the research, using clear questionnaire items, and avoiding the collection of unnecessary identifying information. Where the study uses a cross-sectional survey, its findings will primarily establish statistical relationships rather than conclusively prove cause and effect. The findings will also be interpreted within the limits of the selected companies and sampling procedures.

1.9 Operational Definition of Terms

Algorithmic employee monitoring: The use of digital systems and algorithmic processes to collect, analyse, assess, or report information about employees’ work activities and performance.

Worker autonomy: The degree to which employees perceive that they have discretion over how, when, and in what sequence they perform their work tasks.

Employee motivation: The internal and external factors that influence employees’ willingness to initiate, direct, and sustain effort towards organisational and job-related goals.

Organisational commitment: The degree of an employee’s psychological attachment to, identification with, and willingness to remain associated with an organisation.

Electronic performance monitoring: The collection and use of electronically generated information to observe, evaluate, or manage employees’ work behaviour and performance.

Algorithmic management: The use of algorithmic systems to perform or support managerial activities such as monitoring, task allocation, scheduling, target setting, performance evaluation, and feedback.

Telecommunications companies: Organisations that provide telecommunications services, including mobile communication, voice and data services, connectivity, and related network-based services.

Selected telecommunications companies: The telecommunications organisations operating in Lagos State that are included in the study according to the researcher’s stated selection and sampling criteria.

1.10 Organisation of the Study

The study is organised into five chapters. Chapter One presents the introduction, background to the study, statement of the problem, aim and objectives, research questions, hypothesis, significance, scope, limitations, and operational definitions. Chapter Two reviews the conceptual, theoretical, and empirical literature relating to algorithmic employee monitoring, worker autonomy, employee motivation, and organisational commitment. Chapter Three presents the research methodology, including the research design, population, sample size, sampling technique, research instrument, validity and reliability, data-collection procedure, and method of data analysis. Chapter Four presents the data, analyses the findings, and discusses the results in relation to the research questions and hypothesis. Chapter Five provides the summary of findings, conclusion, recommendations, and suggestions for further research.

Project – Algorithmic Employee Monitoring and Its Effects on Worker Autonomy, Motivation and Organisational Commitment. A Study of Selected telecommunications companies in Lagos State.