Subject: Management · Type: Research Proposal · Level: Master’s · ~3624 words · Harvard referencing
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Abstract
The rapid normalisation of remote working since 2020 has transformed how organisations structure knowledge work, yet the consequences for employee productivity and wellbeing remain contested. Early evidence points in competing directions: some studies report gains in output and autonomy, while others document intensified working hours, blurred work–home boundaries and professional isolation. This proposal outlines a study designed to examine how remote working arrangements influence both the productivity and the psychological wellbeing of employees in UK knowledge-based organisations, and how these two outcomes interact. Adopting a pragmatist philosophy and a mixed-methods sequential explanatory design, the research will combine a cross-sectional online survey of approximately 250 employees with 15 to 20 follow-up semi-structured interviews. Quantitative data will be analysed using descriptive and inferential statistics, while qualitative data will be examined through reflexive thematic analysis. The study is framed by the Job Demands–Resources model and Self-Determination Theory, providing a lens through which the relationship between working arrangements, motivation and strain can be interpreted. The proposal details the aim, objectives and research questions, situates the study within existing literature, and sets out the methodology, ethical safeguards, an illustrative timeline and the anticipated theoretical and practical contribution. The intended outcome is a nuanced account that moves beyond the simplistic question of whether remote working “works” towards understanding the conditions under which it supports, or undermines, sustainable performance.
Background and Rationale
Remote working shifted from a marginal arrangement to a mainstream mode of organising within a matter of weeks during the COVID-19 pandemic. Barrero, Bloom and Davis (2021) argue that this shift will endure well beyond the public health emergency, with hybrid patterns becoming a durable feature of professional and managerial work, sustained by revised employee expectations and investment in collaboration technology. Employers now face strategic decisions about office footprints, performance management and the design of work itself, and they are making these decisions with a limited and often conflicting evidence base.
Two outcomes dominate the practitioner and academic conversation: productivity and wellbeing. On productivity, the influential field experiment by Bloom et al. (2015) found that home-based call-centre staff in a Chinese travel agency were significantly more productive than their office-based counterparts, largely because of fewer interruptions and reduced sickness absence. However, the generalisability of that finding to complex, collaborative knowledge work is uncertain, and the same participants later expressed a preference to return to the office, citing loneliness. The task in that study was discrete, individually measurable and routine, which is precisely the kind of work least representative of the interdependent, judgement-intensive activity that characterises most professional roles. On wellbeing, Felstead and Henseke (2017) report that remote workers experience greater autonomy and job satisfaction but also work more intensively and struggle to switch off, a pattern often described as the “autonomy paradox”: the very freedom that makes remote working attractive can encourage employees to extend rather than contain their effort.
The rationale for this study rests on three observations. First, productivity and wellbeing are frequently studied in isolation, whereas managers must weigh them together; a gain in short-term output achieved through work intensification may be unsustainable if it erodes wellbeing over time, ultimately surfacing as burnout, absence or turnover. Second, much of the pandemic-era evidence was gathered under crisis conditions, with employees managing home schooling, caring responsibilities and health anxiety, which limits its relevance to voluntary, well-resourced hybrid arrangements (Wang et al., 2021). Kniffin et al. (2021) similarly caution that pandemic findings conflate the effect of remote working itself with that of an acute, involuntary and isolating context. Third, UK-specific, post-pandemic evidence remains comparatively thin, with much of the strongest work drawn from the United States or from single-firm settings. Addressing these gaps is timely, because organisational policy is currently being written and will shape working lives for years. This study therefore seeks to generate practically relevant, theoretically grounded insight for a decision that most UK knowledge-based organisations are actively confronting.
Aim, Objectives and Research Questions
The aim of this research is to critically examine the impact of remote working arrangements on employee productivity and wellbeing within UK knowledge-based organisations, and to understand how these two outcomes relate to one another.
The objectives are:
1. To evaluate how employees perceive the effect of remote working on their individual productivity. 2. To assess the influence of remote working on dimensions of employee wellbeing, including work–life balance, autonomy and professional isolation. 3. To investigate the mechanisms, such as flexibility, communication and boundary management, that shape these outcomes. 4. To develop evidence-based recommendations for the design of sustainable remote and hybrid working policies.
The central research question is: How does remote working influence employee productivity and wellbeing in UK knowledge-based organisations?
This is supported by three subsidiary questions:
- RQ1: How do employees perceive the relationship between remote working and their productivity?
- RQ2: How does remote working affect employee wellbeing across autonomy, work–life balance and isolation?
- RQ3: What organisational and individual factors moderate the relationship between remote working, productivity and wellbeing?
Literature Context
The literature can be organised around three themes: productivity, wellbeing, and the theoretical mechanisms that link a working arrangement to its outcomes. Reviewing them together clarifies both what is known and where the present study can contribute.
Remote Working and Productivity
Beyond Bloom et al. (2015), the productivity evidence is genuinely mixed, and much of the apparent disagreement dissolves once the type of productivity in question is specified. Studies that measure individual, task-based output frequently report gains, attributing them to fewer distractions, the absence of a commute and longer uninterrupted periods of focused work. Studies that examine collaborative or innovative output tell a more cautious story. Yang et al. (2022), analysing the communication data of more than 60,000 Microsoft employees, found that firm-wide remote working made collaboration networks more static and siloed, reduced the bridging ties that connect otherwise separate groups, and shifted communication towards asynchronous channels. Such conditions may protect routine execution while quietly eroding the spontaneous knowledge sharing and informal learning on which longer-term innovation depends. The distinction between individual task productivity and collaborative or innovative productivity therefore appears critical, yet it is not always made explicit in either the academic or the practitioner debate. A further complication is measurement: self-rated productivity, on which much survey research relies, is vulnerable to social desirability and to employees’ own uncertainty about how to judge their output.
Remote Working and Wellbeing
Remote working can enhance perceived autonomy and eliminate commuting strain, both of which are consistently associated with higher job satisfaction (Felstead and Henseke, 2017). Yet the same flexibility carries risks. Golden, Veiga and Dino (2008) demonstrate that extensive remote working can heighten professional isolation, which in turn dampens job performance and increases turnover intentions, an effect that strengthens as the proportion of time spent away from colleagues rises. Eurofound (2020) similarly reports that teleworkers during the pandemic were markedly more likely to work in their free time and to experience difficulty disconnecting, raising the prospect of a longer-term erosion of recovery time. The Chartered Institute of Personnel and Development (CIPD, 2022) adds an important managerial dimension, noting that wellbeing outcomes depend heavily on line-manager capability, the quality of support provided and the clarity of expectations around availability. Taken together, this strand suggests that wellbeing is not a straightforward beneficiary of remote working; rather, it is contingent on whether autonomy is accompanied by adequate boundaries, connection and support.
Theoretical Mechanisms
The third theme concerns the mechanisms linking arrangement to outcome. The Job Demands–Resources model (Bakker and Demerouti, 2007) frames remote working as simultaneously altering job resources, such as autonomy, flexibility and reduced commuting, and job demands, such as availability expectations, technological friction and social isolation. In this model, resources drive a motivational pathway towards engagement and performance, while demands drive a health-impairment pathway towards strain and exhaustion; remote working feeds both pathways at once, which is why its net effect is indeterminate in the abstract. Self-Determination Theory (Deci and Ryan, 2000) complements this by explaining why particular resources matter: it identifies autonomy, competence and relatedness as basic psychological needs, and predicts that arrangements which satisfy autonomy while frustrating relatedness will produce mixed motivational and wellbeing outcomes, an account that fits the observed pattern of high autonomy alongside troubling isolation. Together these frameworks imply that remote working is neither inherently beneficial nor harmful; its effects are contingent on how it reshapes the balance of demands and resources and on whether it supports or undermines core psychological needs. This contingent, mechanism-focused view directly informs the study’s attention to mediating and moderating factors, and is summarised in the conceptual framework in Figure 1.
Figure 1: Conceptual framework linking remote working intensity, mediating mechanisms and outcomes.
Methodology
Research Philosophy
The study adopts a pragmatist philosophy. Pragmatism prioritises the research question over allegiance to a single paradigm and legitimises the combination of quantitative and qualitative approaches where doing so yields a fuller answer (Saunders, Lewis and Thornhill, 2019). Given that the phenomenon under investigation involves both measurable patterns, such as the statistical association between remote-working intensity and reported strain, and subjective meanings, such as how an individual experiences the loss of a shared office, a single-paradigm stance would be unnecessarily restrictive. Pragmatism instead treats the two forms of knowledge as complementary, allowing the strengths of one method to offset the limitations of the other.
Research Approach
An abductive approach will be used. Established theory, principally the Job Demands–Resources model and Self-Determination Theory, informs the survey design and the initial coding framework, while the qualitative phase remains open to unexpected explanations that may refine or extend that theory (Saunders, Lewis and Thornhill, 2019). Abduction avoids both the rigidity of pure deduction, which risks confirming only what the researcher already expects, and the atheoretical drift of pure induction, which risks producing description without explanation. The study therefore begins with theoretically derived expectations, tests them against survey data, and then uses interviews to interrogate and potentially reshape those expectations.
Research Design
A mixed-methods sequential explanatory design will be employed (Creswell and Plano Clark, 2018). The design proceeds in two connected phases. Phase one is a quantitative cross-sectional survey that establishes patterns and relationships across a relatively large sample. Phase two is a qualitative interview stage that explains and contextualises the survey findings, particularly any results that are surprising, counter-intuitive or ambiguous. The sequencing is deliberate: the qualitative sample and topic guide are informed directly by the quantitative results, so that interviews can probe precisely those relationships that the numbers leave unexplained. This tight coupling between phases is the principal advantage of the explanatory design over a simple parallel study, and it strengthens the integration of the two strands.
Sampling
The target population is employees in UK knowledge-based organisations, such as professional services, technology and higher education, who undertake at least part of their work remotely. For the survey, non-probability purposive and snowball sampling will be used, distributed through professional networks and relevant online communities. A target of approximately 250 completed responses is set to support meaningful inferential analysis, including multiple regression with several predictors and moderators. The reliance on non-probability sampling is acknowledged as a limitation on statistical generalisability, and the achieved sample will be characterised in detail so that readers can judge transferability. For the interviews, a purposive sub-sample of 15 to 20 respondents will be selected using maximum variation sampling to capture a spread of roles, remote-working intensity and reported outcomes, deliberately including cases that appear to run counter to the survey’s central tendencies. Volunteers will be identified via an opt-in question at the end of the survey, which will also collect contact details separately from their anonymous responses.
Data Collection
The survey will be administered online using Qualtrics and will incorporate validated scales wherever possible to enhance reliability. Wellbeing will be measured using the Warwick–Edinburgh Mental Wellbeing Scale (Tennant et al., 2007), autonomy and demands items will be drawn from established Job Demands–Resources instruments, and self-rated productivity will be assessed with items adapted from prior remote-working research. Demographic and work-pattern items, including remote-working intensity, role type, managerial responsibility and home-working conditions, will enable the subgroup and moderation analysis required by RQ3. The survey will be piloted with a small group of respondents to check item clarity, completion time and routing before full distribution. Semi-structured interviews, lasting approximately 45 minutes and conducted via Microsoft Teams, will follow a topic guide derived from the survey findings, allowing participants to elaborate on their lived experience of remote working, to explain apparent contradictions in their own responses and to introduce issues the survey did not anticipate. The correspondence between the study’s constructs and their operationalisation is summarised in Table 1.
Table 1. Constructs, indicative measures and linked research questions
| Construct | Indicative measure / source | Data source | Research question |
|---|---|---|---|
| Remote-working intensity | Days per week worked remotely; self-classified pattern | Survey | RQ1–RQ3 |
| Productivity | Self-rated output and effectiveness items adapted from prior remote-working research | Survey + interview | RQ1 |
| Wellbeing | Warwick–Edinburgh Mental Wellbeing Scale (Tennant et al., 2007) | Survey | RQ2 |
| Autonomy and job demands | Items from established Job Demands–Resources instruments | Survey | RQ2–RQ3 |
| Work–life boundaries | Boundary-management and “switching off” items; interview accounts | Survey + interview | RQ2 |
| Isolation and connectedness | Professional-isolation items; interview accounts | Survey + interview | RQ2 |
| Moderating factors | Managerial support, role type, home environment | Survey + interview | RQ3 |
Data Analysis
Quantitative data will be analysed in SPSS. Descriptive statistics will summarise the sample and the distribution of key variables, and inferential techniques, including correlation and multiple regression analysis, will test relationships between remote-working intensity, productivity and wellbeing. Moderation will be examined using interaction terms in the regression models to address RQ3, identifying, for example, whether managerial support buffers the relationship between remote-working intensity and isolation. Qualitative data will be transcribed and analysed using reflexive thematic analysis (Braun and Clarke, 2006), which offers a flexible yet rigorous method for identifying and interpreting patterns of meaning across the interview corpus, treating the researcher’s interpretive engagement as a resource rather than a source of bias to be eliminated. Integration will occur principally at the interpretation stage, where qualitative themes are used to explain, qualify and add depth to the quantitative results, consistent with the explanatory logic of the design. Where the two strands diverge, that divergence will itself be reported and examined, since contradictions between measured association and lived account are often analytically productive.
Validity, Reliability and Rigour
Reliability of quantitative scales will be checked using Cronbach’s alpha, and the use of validated instruments supports construct validity. The cross-sectional design nonetheless limits causal inference, since it captures a single point in time and cannot establish temporal order; this limitation will be acknowledged transparently, and causal language will be avoided in favour of the language of association. Common-method variance, a recognised risk when predictors and outcomes are gathered from the same self-report survey, will be mitigated through careful item wording, assured anonymity and the triangulation afforded by the interview strand. For the qualitative strand, trustworthiness will be pursued through an audit trail documenting analytical decisions, reflexive journaling to surface the researcher’s assumptions, and thick description to allow readers to assess transferability (Saunders, Lewis and Thornhill, 2019).
Ethical Considerations
The research will be conducted in accordance with the university’s research ethics policy, and ethical approval will be secured before any data collection begins. Several safeguards are central. Informed consent will be obtained via a participant information sheet and consent form explaining the purpose, procedures, the voluntary nature of participation and the right to withdraw without penalty up to the point of data anonymisation, after which withdrawal of a specific response may no longer be technically possible. Because the study touches on wellbeing, and could prompt participants to reflect on distress, isolation or dissatisfaction, questions will be framed sensitively and the interview protocol will allow participants to pause, skip items or stop entirely at any time. Signposting to sources of support, such as employer employee-assistance programmes and national mental-health charities, will be provided in the debrief so that no participant is left without a route to help.
Confidentiality and data protection will be maintained in line with the UK General Data Protection Regulation and the Data Protection Act 2018. Survey responses will be collected anonymously where possible, with contact details for interview volunteers stored separately from their survey data so that the two cannot be linked. Interview recordings will be stored on encrypted university systems, transcripts will be pseudonymised, and identifying details, including employer names, distinctive job titles and project references, will be removed from any reported extract. Data will be retained only for the period specified by institutional policy and then securely destroyed. A particular ethical sensitivity arises from the potential for participants to disclose dissatisfaction with, or criticism of, their employer. Care will therefore be taken to ensure that no data can be traced back to an individual in a way that could affect their employment, that no employer or client organisation is identifiable in the final report, and that recruitment does not proceed through channels, such as a single named employer, that would make participants indirectly identifiable.
Timeline (Illustrative)
The study is planned over a twelve-month period. The Gantt chart in Figure 2 sets out the eight phases and their indicative scheduling, and the accompanying table lists the same phases in text.
Figure 2: Illustrative twelve-month Gantt schedule (final phase highlighted).
| Phase | Activity | Indicative months |
|---|---|---|
| 1 | Literature review and finalisation of conceptual framework | Months 1–2 |
| 2 | Ethics application and approval | Months 2–3 |
| 3 | Survey design, piloting and refinement | Months 3–4 |
| 4 | Survey distribution and data collection | Months 4–6 |
| 5 | Quantitative analysis | Months 6–7 |
| 6 | Interview recruitment and data collection | Months 7–8 |
| 7 | Qualitative analysis and integration | Months 8–10 |
| 8 | Writing, revision and submission | Months 10–12 |
The schedule includes deliberate slack around recruitment, which is the stage most vulnerable to delay, and treats the two data-collection phases sequentially so that survey findings can shape the interview guide. A modest overlap between quantitative analysis and interview recruitment allows early results to inform the topic guide without stalling the project, and the writing phase is given a full quarter to accommodate the iterative integration of the two strands.
Expected Contribution
The study is expected to make both theoretical and practical contributions. Theoretically, by applying the Job Demands–Resources model and Self-Determination Theory jointly to a post-pandemic UK context, the research will clarify how remote working simultaneously reshapes job resources and job demands, and how this balance conditions the productivity–wellbeing relationship. Examining the two outcomes together, rather than separately, responds directly to a gap in a literature that has often treated them in isolation, and the explicit modelling of moderators offers a more precise account of the conditions under which remote working helps or harms than the prevailing “does it work?” framing allows.
Practically, the findings will offer UK employers evidence to inform the design of sustainable hybrid and remote-working policies, moving the managerial conversation beyond a binary “office versus home” framing towards the specific conditions, such as boundary management, communication norms and managerial support, that determine whether remote working delivers durable value. The recommendations will be particularly relevant to human-resource and operations managers designing flexible-working frameworks, and to line managers whose day-to-day practice, the evidence suggests, does much to shape wellbeing outcomes. By foregrounding the interaction between output and wellbeing, the study aims to support decisions that protect performance without externalising costs onto employee health, and thereby to contribute to a model of remote working that is sustainable for organisations and individuals alike.
References
Bakker, A.B. and Demerouti, E. (2007) ‘The job demands-resources model: state of the art’, Journal of Managerial Psychology, 22(3), pp. 309–328.
Barrero, J.M., Bloom, N. and Davis, S.J. (2021) Why working from home will stick. NBER Working Paper No. 28731. Cambridge, MA: National Bureau of Economic Research.
Bloom, N., Liang, J., Roberts, J. and Ying, Z.J. (2015) ‘Does working from home work? Evidence from a Chinese experiment’, The Quarterly Journal of Economics, 130(1), pp. 165–218.
Braun, V. and Clarke, V. (2006) ‘Using thematic analysis in psychology’, Qualitative Research in Psychology, 3(2), pp. 77–101.
Chartered Institute of Personnel and Development (2022) Flexible working: lessons from the pandemic. London: CIPD.
Creswell, J.W. and Plano Clark, V.L. (2018) Designing and conducting mixed methods research. 3rd edn. Thousand Oaks, CA: Sage.
Deci, E.L. and Ryan, R.M. (2000) ‘The “what” and “why” of goal pursuits: human needs and the self-determination of behavior’, Psychological Inquiry, 11(4), pp. 227–268.
Eurofound (2020) Living, working and COVID-19. Luxembourg: Publications Office of the European Union.
Felstead, A. and Henseke, G. (2017) ‘Assessing the growth of remote working and its consequences for effort, well-being and work-life balance’, New Technology, Work and Employment, 32(3), pp. 195–212.
Golden, T.D., Veiga, J.F. and Dino, R.N. (2008) ‘The impact of professional isolation on teleworker job performance and turnover intentions’, Journal of Applied Psychology, 93(6), pp. 1412–1421.
Kniffin, K.M., Narayanan, J., Anseel, F., Antonakis, J., Ashford, S.P., Bakker, A.B., Bamberger, P., Bapuji, H., Bhave, D.P., Choi, V.K., Creary, S.J., Demerouti, E., Flynn, F.J., Gelfand, M.J., Greer, L.L., Johns, G., Kesebir, S., Klein, P.G., Lee, S.Y., Ozcelik, H., Petriglieri, J.L., Rothbard, N.P., Rudolph, C.W., Shaw, J.D., Sirola, N., Wanberg, C.R., Whillans, A., Wilmot, M.P. and Vugt, M. van (2021) ‘COVID-19 and the workplace: implications, issues, and insights for future research and action’, American Psychologist, 76(1), pp. 63–77.
Saunders, M., Lewis, P. and Thornhill, A. (2019) Research methods for business students. 8th edn. Harlow: Pearson Education.
Tennant, R., Hiller, L., Fishwick, R., Platt, S., Joseph, S., Weich, S., Parkinson, J., Secker, J. and Stewart-Brown, S. (2007) ‘The Warwick-Edinburgh Mental Well-being Scale (WEMWBS): development and UK validation’, Health and Quality of Life Outcomes, 5(63), pp. 1–13.
Wang, B., Liu, Y., Qian, J. and Parker, S.K. (2021) ‘Achieving effective remote working during the COVID-19 pandemic: a work design perspective’, Applied Psychology, 70(1), pp. 16–59.
Yang, L., Holtz, D., Jaffe, S., Suri, S., Sinha, S., Weston, J., Joyce, C., Shah, N., Sherman, K., Hecht, B. and Teevan, J. (2022) ‘The effects of remote work on collaboration among information workers’, Nature Human Behaviour, 6(1), pp. 43–54.
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