JOURNAL OF TEACHER EDUCATION AND RESEARCH

Flexible Working Practices and Employee Retention in England: An Integrative Review and Conceptual Framework of Post-COVID-19 Evidence

Ben Bander Abudawood — 2026-09-14

Aim – This research aims to explore the link between flexible working practices and staff turnover in England after the onset of COVID-19. It will take a particular interest in how working flexibly might impact the likelihood of employees staying within an organisation and the conditions that influence this.
Design/methodology/approach – The study employs an integrative literature review approach to compile the evidence related to the English employment area after the COVID-19 pandemic. The main synthesis included eight empirical studies that focused on areas related to healthcare, education, policing, and social work. The analysis is based on Job Demands-Resources theory and Social Exchange theory to explain the relationship between flexible working, employee experience and retention.
Findings: The findings suggest that flexible working alone is not enough to boost staff retention. It may be more important to see if they can make good use of the flexibility that is offered. This experience is affected by managerial support, workload, organisational culture and the practical demands of various occupations. In the evidence that was reviewed, three conditions were especially significant, namely: meaningful employee control, organisational support and occupational feasibility. In these situations, flexible working could benefit work-life compatibility, autonomy and perceptions of organisation support thus facilitating continued work.
Originality/value – The paper separates formal opportunities for flex from employees’ experience of flex. It suggests that experience of flexibility is a more relevant consideration of why people stay in the job than any policy availability and outlines a framework that links flexible working with employee retention through both the job and the employee/organisation relationship.

Privacy-Preserving Machine Learning for Secure Business Intelligence

Umida Otaxanova — 2026-09-14

Business intelligence (BI) increasingly relies on machine-learning models trained on customer, employee and transaction data, and these models can leak the data they were trained on through their parameters and outputs. Privacy-preserving machine learning offers formal protection, principally through differential privacy (DP), which bounds what any output reveals about any individual record, and through federated learning, which keeps data at its source. Both impose a utility cost. This paper examines the privacy–utility trade-off for a representative BI regression model in a simulation of 4,000 records. Without protection the model attains a hold-out R² of 0.69. Centralised training with output-perturbation DP retains R² of 0.68 at a privacy budget of ε = 0.5 and 0.64 at ε = 0.25, falling to 0.38 at ε = 0.1; federated training with per-client DP across five sites loses utility faster, retaining 0.62 at ε = 0.5 and 0.42 at ε = 0.25. The paper explains why local protection costs more than central protection, maps the trade-off to BI use cases by sensitivity and precision requirements, and proposes an architecture and governance for privacy-preserving BI in which the privacy budget is a managed resource. The contribution is a practical account of how organisations can obtain analytical value from sensitive data under formal privacy guarantees.

Smart Contracts for Automating Corporate Governance and Contractual Compliance

Rayxan Saylaubekovna Razakova — 2026-09-14

Corporate governance and contractual compliance rest on obligations, such as approvals, disclosures, covenant tests, payments and reporting deadlines, that are specified in documents and fulfilled by people, with the result that obligations are missed, delayed and expensively audited. Smart contracts, programs on a distributed ledger that execute obligations automatically when their conditions are met, promise to remove this gap between specification and execution for obligations that can be codified. This paper develops a framework for deciding which governance and compliance obligations to automate and examines three enforcement regimes in a simulation of 2,000 obligations, of which 75% are codifiable: manual compliance, rule-based workflow automation and smart-contract enforcement with manual fallback for non-codifiable obligations. Smart-contract enforcement reduces missed obligations from 206 to 64, mean delay from 5.0 to 1.3 days and cost per obligation by 45%, and raises audit completeness from 70% to 99%, but introduces 29 rigidity incidents in which automatic execution was inappropriate given circumstances the contract could not observe. Rule-based workflow captures about half the improvement without rigidity. The paper argues that the automation decision turns on codifiability, on the cost of rigidity relative to the cost of omission, and on the availability of trusted inputs, and proposes governance arrangements for the smart contracts themselves.

Algorithmic Management and Employee Autonomy: Investigating the Trade-Off Between Efficiency and Well-Being

Nasiba Matnazarovna Eshchanova — 2026-09-14

Algorithmic management, the use of software to assign, direct, monitor and evaluate work, raises operational efficiency but has been associated with reduced employee autonomy and well-being, particularly in platform and logistics work. This paper models the trade-off and asks whether it is inherent or a matter of design. Efficiency rises with the intensity of algorithmic management at a diminishing rate; autonomy falls with intensity but less so where workers retain discretion over how to meet algorithmic targets; and well-being depends on autonomy and, secondarily, on efficiency. A simulation of 600 work settings shows that well-being falls with intensity, with a standardised indirect effect through autonomy of −0.65 and a small positive direct effect (+0.13), and that the well-being slope is halved by worker discretion (−0.20 at low discretion against −0.10 at high). Efficiency rises comparably in both cases (slopes 0.34 and 0.36). A combined efficiency–well-being index peaks at 82% intensity under low discretion and at full intensity under high discretion, so that discretion design shifts the frontier rather than merely trading along it. The paper interprets the findings through self-determination and job-design theory and proposes design principles, namely discretion over means, transparency of criteria, contestability of evaluations and limits on monitoring, that preserve well-being without sacrificing efficiency.