For information and reference only – not to be submitted as your own work. This model extract shows one way to structure and reference a PSY201 ECA answer. Use it to understand the approach, then write your own response in your own words, following SUSS academic integrity and GenAI rules.

About this assessment

The PSY201 Organisational Psychology End-of-Course Assessment (July 2026) asks one question in three parts: the link between employee wellbeing and organisational outcomes (Q1a, 35 marks), practical wellbeing strategies and their challenges (Q1b, 30 marks), and the use of AI in managing wellbeing, including at least one ethical risk (Q1c, 30 marks). References in APA format carry 5 marks, and the word limit is 2,800 words with no allowance.

The full brief is on our PSY201 ECA July 2026 question page. The extract below covers the introduction and the core argument of each part; it is shorter than a full submission.

Model answer extract

Introduction

Employee wellbeing refers to the overall quality of an employee’s experience at work, covering psychological states such as engagement and positive affect as well as the absence of strain such as exhaustion and burnout. This answer argues that wellbeing is best understood through the Job Demands–Resources (JD-R) model (Bakker & Demerouti, 2017), which explains why wellbeing drives performance, motivation and retention, and why organisational strategies must change job conditions rather than only individual coping. The final section evaluates AI as a wellbeing tool and argues that its benefits depend on how privacy and surveillance risks are governed.

Q1a: Employee wellbeing and organisational outcomes

The JD-R model proposes that every job contains two broad groups of characteristics. Job demands, such as workload, time pressure and emotional labour, require sustained effort and, when chronic, lead to exhaustion through a health-impairment process. Job resources, such as autonomy, supervisor support and feedback, help employees meet demands and start a motivational process that builds engagement (Bakker & Demerouti, 2017). Schaufeli and Bakker (2004) supported this dual-process structure: job demands mainly predicted burnout, while job resources mainly predicted engagement, and burnout and engagement in turn related to health problems and turnover intention respectively.

Performance. Wright and Cropanzano (2000) found that psychological wellbeing predicted job performance, whereas job satisfaction did not. This matters for the argument: it is not simply “happy” employees who perform better, but employees whose broader psychological wellbeing is intact. At the level of business units, Harter et al. (2002) found in a meta-analysis that employee engagement and satisfaction were related to productivity, profit, customer satisfaction and turnover.

Motivation. Self-determination theory adds the “why” behind the JD-R motivational process. When work satisfies the basic needs for autonomy, competence and relatedness, employees show more autonomous motivation, better wellbeing and higher-quality performance (Deci et al., 2017). Job resources such as autonomy and supportive supervision can be read as need-satisfying conditions.

Retention. Burnout, characterised by exhaustion, cynicism and reduced efficacy, is consistently associated with withdrawal from the job, including absenteeism and the intention to leave (Maslach & Leiter, 2016). Consider an illustrative case of a Singapore hospital ward where nurses work long rotating shifts with little say over rosters. JD-R predicts rising exhaustion from the demands and weak engagement from the missing autonomy, which is the combination most likely to push experienced staff to leave.

Critical evaluation. The relationship is not one-directional. Good performance and recognition can raise wellbeing, so causality runs both ways. Much of the evidence, including Harter et al. (2002), is correlational at a single point in time, and outcomes such as profit are also shaped by markets and leadership. The JD-R model is also broad: almost any job feature can be labelled a demand or a resource, which makes it flexible but harder to falsify (Bakker & Demerouti, 2017). The defensible conclusion is that wellbeing is a meaningful driver of outcomes, strongest when measured as engagement and psychological wellbeing rather than general satisfaction.

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Q1b: Supporting employee wellbeing in practice

The JD-R model suggests two routes for organisations: reduce or manage demands, and increase resources. Two strategies follow from this.

Strategy 1: Job redesign and workload control. Organisations can review workloads, staffing levels and decision rights, for example by letting teams plan their own rosters or by removing low-value reporting tasks. Reducing chronic demands interrupts the health-impairment process, while added autonomy strengthens the motivational process (Bakker & Demerouti, 2017). The expected outcomes are lower exhaustion, steadier performance and fewer resignations. The main challenge is cost: redesign may require extra headcount, and line managers may resist losing control over scheduling.

Strategy 2: Supportive leadership and manager training. Supervisor support is one of the most consistent job resources. Training managers to hold regular check-ins, recognise effort and respond early to signs of strain helps satisfy employees’ needs for relatedness and competence (Deci et al., 2017), which supports autonomous motivation and commitment. The challenge is consistency: training effects fade if managers are not measured and rewarded for people outcomes.

Why individual-only programmes are not enough. Many employers rely on resilience workshops, mindfulness sessions or wellbeing apps. In a large UK study of 46,336 workers in 233 organisations, Fleming (2024) found that participants in such individual-level interventions were generally no better off on wellbeing measures than non-participants, and interpreted this through the JD-R lens: individual programmes do not change the job demands that cause strain. This supports prioritising organisational-level change, with individual programmes as a supplement. A further challenge is stigma: employees may avoid wellbeing services if they fear it signals weakness to their supervisor, so confidentiality and leadership role-modelling matter.

Q1c: AI in managing employee wellbeing

Opportunities. AI tools can analyse patterns in workload, overtime and survey responses to flag teams at risk of burnout before resignations occur. Conversational agents can offer round-the-clock, private first-line support and signpost employees to professional help. Used well, these tools add a job resource and help managers act earlier, which in JD-R terms protects the motivational process and reduces the cost of absenteeism and turnover.

Key ethical concern: privacy and surveillance. Wellbeing analytics often depends on continuous data about employees’ behaviour, communication and even mood. Ball (2010) shows that workplace monitoring can damage trust and morale and can deepen power imbalances between employer and employee. In a scoping review of people analytics, Tursunbayeva et al. (2022) identified privacy, transparency and fair, proportionate use of data as central ethical risks. The irony is that a tool meant to improve wellbeing can itself become a job demand if employees feel watched, which would undo the benefit the organisation was seeking.

Addressing the risk. Organisations can apply principles that recur across AI ethics guidelines, such as transparency, fairness, responsibility and privacy (Jobin et al., 2019):

• Collect the minimum data needed, aggregate it at team level and keep individual mental health data out of performance decisions.
• Explain clearly what is collected and why, obtain consent and comply with Singapore’s Personal Data Protection Act 2012.
• Keep a human in the loop: AI flags, but trained managers or HR professionals decide and act.
• Involve employees and audit tools regularly for bias and unintended effects (Tursunbayeva et al., 2022).

Evaluated critically, AI is neither a cure nor a threat in itself. Its value for wellbeing depends on whether it adds resources without adding the demand of surveillance.

Conclusion

The JD-R model explains why wellbeing links to performance, motivation and retention, while the evidence shows that the link is real but bidirectional and context-dependent. Practical strategies work best when they change demands and resources rather than ask employees to cope harder. AI can extend those resources, but only under governance that protects privacy and trust.

Want expert feedback on your own PSY201 ECA? Our psychology specialist can review your structure, theory use and APA references. Read the step-by-step PSY201 ECA guide, or get PSY201 guidance. You can also check your reference list with our APA citation and referencing check.

This sample is a learning resource. It must not be submitted, in whole or in part, as your own work.

About the expert

Clarisse Ong Li Mei is a psychology expert on the My Assignment Help SG team, with subjects listed as psychology, counselling, mental health and research papers, and 16+ years of experience. Related samples: PSY367 work stress sample and HRM203 human behaviour in organisations sample.

References

Bakker, A. B., & Demerouti, E. (2017). Job demands–resources theory: Taking stock and looking forward. Journal of Occupational Health Psychology, 22(3), 273–285. https://doi.org/10.1037/ocp0000056

Ball, K. (2010). Workplace surveillance: An overview. Labor History, 51(1), 87–106. https://doi.org/10.1080/00236561003654776

Deci, E. L., Olafsen, A. H., & Ryan, R. M. (2017). Self-determination theory in work organizations: The state of a science. Annual Review of Organizational Psychology and Organizational Behavior, 4, 19–43. https://doi.org/10.1146/annurev-orgpsych-032516-113108

Fleming, W. J. (2024). Employee well-being outcomes from individual-level mental health interventions: Cross-sectional evidence from the United Kingdom. Industrial Relations Journal, 55(2), 162–182. https://doi.org/10.1111/irj.12418

Harter, J. K., Schmidt, F. L., & Hayes, T. L. (2002). Business-unit-level relationship between employee satisfaction, employee engagement, and business outcomes: A meta-analysis. Journal of Applied Psychology, 87(2), 268–279. https://doi.org/10.1037/0021-9010.87.2.268

Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2

Maslach, C., & Leiter, M. P. (2016). Understanding the burnout experience: Recent research and its implications for psychiatry. World Psychiatry, 15(2), 103–111. https://doi.org/10.1002/wps.20311

Schaufeli, W. B., & Bakker, A. B. (2004). Job demands, job resources, and their relationship with burnout and engagement: A multi-sample study. Journal of Organizational Behavior, 25(3), 293–315. https://doi.org/10.1002/job.248

Tursunbayeva, A., Pagliari, C., Di Lauro, S., & Antonelli, G. (2022). The ethics of people analytics: Risks, opportunities and recommendations. Personnel Review, 51(3), 900–921. https://doi.org/10.1108/PR-12-2019-0680

Wright, T. A., & Cropanzano, R. (2000). Psychological well-being and job satisfaction as predictors of job performance. Journal of Occupational Health Psychology, 5(1), 84–94. https://doi.org/10.1037/1076-8998.5.1.84