Monday, 19 October 2026

A review of the phenomenological, the ethnographic and the hermeneutic approaches as a kind of research methodology: for housing studies

 A review of the phenomenological, the ethnographic and the hermeneutic approaches as a kind of research methodology

 

The phenomenological approach, the ethnographic approach and the hermeneuticapproach are considered as a kind of research methodology. Comment on each of them to what extent they are clearly formulated or relatively standardized in their practices, preferably with regard to the field of Housing Studies.

All three are qualitative, interpretive methodologies rather than tightly standardized “procedures.” In Housing Studies, ethnography is the most practice-oriented and comparatively codified; phenomenology is moderately standardized but highly dependent on the chosen philosophical variant; hermeneutics is the least standardized because it is primarily a theory of interpretation that must be operationalized by the researcher.

Overall assessment

Approach

Core aim

Degree of standardization

Typical Housing Studies application

Phenomenology

Describe the lived experience and essential meanings of a phenomenon

Moderate: recognizable procedures, but multiple competing schools

Residents’ lived experience of home, overcrowding, relocation, or housing insecurity

Ethnography

Understand social life, practices and meanings within a natural setting

Relatively high in practical conventions: fieldwork, participant observation, field notes, reflexivity

Estate life, informal housing, neighbourhood relations, homelessness, housing-policy implementation

Hermeneutics

Interpret texts, practices and experiences within their historical and cultural context

Low to moderate: no single canonical method; interpretation is central

Interpreting housing policy documents, residents’ narratives, media representations, or planning disputes

Phenomenological approach

Phenomenology asks how people experience a phenomenon from within their own lifeworld. In housing research, it is well suited to questions such as: What does “home” mean to tenants facing redevelopment? or How do older residents experience safety, privacy and belonging in public housing?

It is partly formulated but not fully standardized. Common elements include purposive sampling of people with direct experience, in-depth interviews, written experiential accounts, and analysis that moves from concrete descriptions to themes or essential structures of experience. However, there are important variants—descriptive phenomenology, interpretive/hermeneutic phenomenology, and lifeworld approaches—and they differ in whether the researcher should “bracket” prior assumptions or explicitly interpret experience through theory and context.

For a Housing Studies dissertation, this means the method can be made reasonably transparent if the student specifies:

·        The phenomenological school adopted, for example descriptive or hermeneutic phenomenology.

·        The phenomenon, such as “the experience of living in subdivided flats.”

·        Sampling criteria, usually participants with firsthand experience.

·        Data-generation methods, commonly in-depth interviews, diaries, photographs, or home-walkthrough accounts.

·        The analytic sequence, including familiarization, meaning-unit identification, thematic development, and formulation of the essence of the experience.

Its strength is depth: it can reveal dimensions of housing quality that technical indicators miss, including sensory experience, attachment, dignity, control and existential security. Its limitation is that findings are not designed for statistical generalization, and quality depends heavily on the researcher’s ability to conduct sensitive interviews and avoid imposing preconceived categories.

Ethnographic approach

Ethnography studies people in their everyday social settings through sustained engagement, participant observation, informal conversation, field notes, interviews and documents. In Housing Studies, it is especially valuable for understanding how housing is actually lived: household routines, neighbourly relations, estate management, informal economies, homelessness, resistance to redevelopment, and the local effects of housing policy.

It is the most clearly practice-based and relatively standardized of the three, although still flexible. Established conventions include entering a natural setting, spending extended time in the field, recording detailed field notes, observing as well as asking, maintaining reflexivity about the researcher’s role, and building an holistic account of the setting. Housing ethnographies may involve home visits, walking interviews, neighbourhood observation, attendance at residents’ meetings, document collection, and creative methods such as maps, photographs or object elicitation.

A housing ethnography is relatively clear when it specifies:

·        The field site: an estate, shelter, informal settlement, housing office, or neighbourhood.

·        The period and intensity of fieldwork.

·        The researcher’s role: observer, participant-observer, tenant, volunteer, or co-researcher.

·        Data sources: observations, conversations, interviews, photographs, policy papers and field notes.

·        Ethical safeguards, particularly because housing fieldwork often concerns vulnerable people and private domestic space.

Ethnography is powerful because it captures context, practice and institutional dynamics that surveys or interviews alone may miss; comparative housing research increasingly values it for revealing how institutional contexts enable or constrain housing interventions. Its weaknesses are time, access, ethical complexity, researcher influence on the setting, and difficulty in replicating the study exactly.

Hermeneutic approach

Hermeneutics focuses on interpretation: understanding meaning by relating parts of a text, narrative or practice to its wider context. It is particularly useful in Housing Studies for analysing residents’ accounts of displacement, policy documents, planning reports, media narratives, historical records, or contested meanings of “affordable housing,” “home,” and “neighbourhood renewal.”

It is the least standardized as a stand-alone research method. Hermeneutics supplies an interpretive logic—the “hermeneutic circle,” in which the researcher moves between parts and whole, text and context, prior understanding and revised understanding—but it does not prescribe a fixed sampling plan, interview format, coding scheme, or sample size. In practice, researchers often combine it with semi-structured interviews, thematic analysis, document analysis, or hermeneutic phenomenology.

For housing research, a hermeneutic study can be made defensible by documenting:

·        The texts, narratives or practices to be interpreted.

·        The researcher’s pre-understanding, theoretical position and assumptions.

·        The historical, policy and cultural context of the housing issue.

·        The interpretive movement between individual accounts and the wider housing context.

·        How alternative interpretations were considered and how conclusions were justified.

For example, a study of urban renewal in Hong Kong could interpret residents’ interview narratives alongside government consultation documents, planning reports and media coverage. The aim would not be to count frequencies, but to understand how displacement, compensation, community and home are constructed and contested across different texts and social positions.

Practical judgment

·        Most standardized in operational practice: ethnography, because its fieldwork conventions—site selection, participant observation, field notes, reflexivity and triangulation—are comparatively well established.

·        Moderately standardized: phenomenology, especially when a named variant is adopted and its analytic steps are explicitly reported.

·        Least standardized: hermeneutics, because it is better understood as an interpretive paradigm that requires the researcher to design the concrete procedures.

For a Housing Studies dissertation, the best formulation is not simply “I will use a hermeneutic approach.” A stronger methodological statement would be: “This study adopts a hermeneutic interpretive stance and uses semi-structured interviews and policy-document analysis, with thematic analysis guided by the hermeneutic circle.” Similarly, “phenomenology” should name its variant, and “ethnography” should specify the field site, duration, participant role and observation methods. This transparency is what makes these flexible qualitative methodologies academically credible.

Some basic topics in philosophy for students learning the subject of research methods: Videos

Some basic topics in philosophy for students learning the subject of research methods: Videos


1. Axiology

2. Ontology

3. Epistemology

A note on using Bloom’s taxonomy of learning to foster learning: for advanced management accounting students

A note on using Bloom’s taxonomy of learning to foster learning: for advanced management accounting students

 

As a lecturer on advanced management accounting, I encourage my students to spare more effort to critically discuss their assignment topics, rather than simply describing the main ideas. Also, they are able to assess very useful and focused information from generative AI. In this regard, suggest how Bloom's taxonomy of learning could offer some concrete guidance to foster students' intellectual learning. It would be useful to illustrate the guidance with reference to the following assignment tasks: "Required: Using relevant academic and professional sources, critically analyse how digitalisation affects management accounting and evaluate its role in enhancing organisational performance and sustainable value creation".

 

Bloom’s taxonomy as a scaffold

The revised taxonomy frames learning as progressively more demanding cognitive activity: remember, understand, apply, analyse, evaluate, and create. Its real value for this assignment is not to make students “climb levels” mechanically, but to ensure that descriptive knowledge becomes an evidence-based argument and, ultimately, a defensible recommendation.

For this task, the threshold is analyse and evaluate: students must go beyond saying that AI, ERP, cloud systems, analytics, or automation affect management accounting. They need to explain how, under what conditions, with what trade-offs, and how convincingly these changes contribute to performance and sustainable value.

Turning the task into thinking

Bloom level

What students should do

Prompt for this assignment

Remember

Identify key concepts accurately.

What do digitalisation, management accounting, organisational performance, and sustainable value creation mean?

Understand

Explain mechanisms in their own words.

How could real-time data, process automation, or predictive analytics change budgeting, costing, performance measurement, and control?

Apply

Use concepts in a specific organisational setting.

How might a manufacturer, retailer, or service organisation use digital tools to improve cost visibility or carbon-related decisions?

Analyse

Break causal claims into parts and compare relationships.

Which technologies alter which accounting practices, through what mechanisms, and what organisational conditions enable or obstruct the effect?

Evaluate

Judge competing claims using explicit criteria and evidence.

Does digitalisation improve performance and sustainability in all cases, or only when data quality, skills, governance, and strategy are aligned?

Create

Formulate an integrated, qualified argument.

What balanced framework or set of recommendations should management adopt to turn digital accounting capability into sustainable value?

Digitalisation can affect not only accounting techniques but also business models, control practices, and the role of controllers; this makes it especially suitable for causal and contingency-based analysis rather than a technology-by-technology description.

A practical writing rule

Encourage students to make every major paragraph follow this pattern:

1.    Claim: State a focused proposition—for example, digital tools can improve forecasting and decision speed.

2.    Mechanism: Explain why this may occur, such as automation and broader access to timely data.

3.    Evidence: Use an academic study and, where appropriate, a credible professional source.

4.    Critical qualification: Identify limitations, alternative explanations, or boundary conditions—for example, poor data governance, implementation costs, cyber risk, algorithmic bias, staff resistance, or a lack of analytical capability.

5.    Judgement: State the implication for financial performance and sustainable value creation.

This structure trains students to move from “digitalisation improves management accounting” to a more rigorous conclusion: it may improve information quality and strategic decision support, but its value depends on organisational readiness, strategic alignment, governance, and the ability to integrate financial with non-financial information. Recent literature similarly identifies automation, analytics, changing accountant roles, skills gaps, security issues, and cultural adaptation as central issues.

Questions that force critique

Instead of asking, “What are the benefits of digitalisation?”, set questions such as:

  • Compared with what traditional accounting process or control?
  • What is the causal pathway from a digital capability to a performance outcome?
  • What evidence supports the claim, and how transferable is it across industries or firm sizes?
  • Whose performance improves—shareholders, managers, employees, customers, or wider stakeholders?
  • Does efficiency necessarily equal sustainable value, or could digitalisation increase energy use, surveillance, inequality, or data-security exposure?
  • What conditions must hold for the claimed benefit to materialise?

These prompts operationalise analysis as identifying relationships and evaluation as making judgements against criteria and standards.

Using generative AI critically

Students can use generative AI as a thinking partner, not as an authority. Ask them to use it to generate candidate mechanisms, counterarguments, keywords, contrasts between theories, or a provisional essay map; then require them to verify every substantive claim against original academic and professional sources.

A useful discipline is an AI claim-audit table with four columns: AI-generated claim; source located; evidence supporting or contradicting it; revised student judgement. This makes source evaluation visible and rewards intellectual work rather than polished but untested prose.

 

References

1.    Bloom, B.S., Engelhart, M.D., Furst, E.J., Hill, W.H. and Krathwohl, D.R. (1956) Taxonomy of educational objectives: The classification of educational goals. Handbook I: Cognitive domain. New York: David McKay.

2.    Anderson, L.W. and Krathwohl, D.R. (eds.) (2001) A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. New York: Longman.

Sunday, 18 October 2026

A note on IT-enhanced management accounting practices: examples

A note on IT-enhanced management accounting practices: examples

 

Highlight 2 examples of contemporary IT-enhanced/ IT-enabled management accounting practices (e.g. with process improvement, process enhancement and process transformation impacts) with regard to the following fourth industrial revolution technologies:

1.    cloud computing

2.    Internet of things

3.    generative AI

4.    agentic AI and robotics

5.    blockchain

6.    big data and analytics

 

Below are two contemporary IT-enhanced/IT-enabled management accounting practices for each fourth-industrial-revolution technology, with their process-improvement, enhancement, or transformation impacts.

1. Cloud computing

Practice

Management accounting use

Impact

Cloud ERP-based rolling forecasts and real-time dashboards

Finance and operations data are unified in a cloud ERP, enabling management accountants to update sales, cost, cash and profit forecasts continuously rather than only at month-end. Cloud ERP provides a single real-time data model from which management reports, P&L and cash-flow information can be generated.

Process enhancement: faster forecasting, reduced manual consolidation, and more timely variance analysis.

Cloud-based shared-service reporting and multi-entity consolidation

A group can centralise ledgers, budgets, project costs and KPIs on a cloud platform, allowing remote teams and subsidiaries to access consistent financial and operational data. Cloud ERP supports automated AP/AR, general-ledger management, forecasting and real-time financial reporting.

Process transformation: shifts reporting from decentralised spreadsheet preparation to continuous, enterprise-wide performance management.

2. Internet of Things

Practice

Management accounting use

Impact

IoT-enabled activity-based costing and standard-cost variance analysis

Sensors capture machine run-times, labour activity, product routing and process deviations, providing more accurate and timely cost-driver data for absorption costing, ABC and variance analysis.

Process improvement/enhancement: automates labour-intensive cost-data collection and improves the precision of cost allocation and variance investigation.

IoT asset lifecycle and maintenance-cost management

Connected assets report location, usage, fuel consumption, vibration, temperature and maintenance needs; this supports preventive maintenance, asset-utilisation analysis and lifecycle costing.

Process enhancement: reduces downtime, avoids unnecessary repair spend, and gives management accountants better data for capital-budgeting and replacement decisions.

3. Generative AI

Practice

Management accounting use

Impact

AI-generated variance commentary and management reports

Generative AI compares actual results with plans, explains variances, drafts narrative commentary and produces tailored reports for business partners. IBM identifies variance explanation, scenario generation and report creation among high-value finance use cases.

Process enhancement: turns descriptive reports into decision-oriented narratives and shortens reporting cycles.

AI-assisted month-end close and reconciliation support

Generative AI can reconcile inconsistent journal entries, prepare closing checklists, draft reclassification memos and generate initial financial reports.

Process improvement: reduces manual close preparation while retaining human review for judgement, estimates and control.

4. Agentic AI and robotics

Practice

Management accounting use

Impact

Autonomous procure-to-pay and invoice-matching agents

AI agents extract invoice data, retrieve contracts, match invoices to purchase orders and goods receipts, identify exceptions, draft supplier queries and update ERP records.

Process transformation: moves accounts payable from task-based automation to orchestrated end-to-end workflow execution, with human oversight for exceptions.

Agentic continuous close and treasury forecasting

Agents collect, match and reconcile data overnight; flag control breaches; prepare audit evidence; and consolidate cash balances to forecast inflows, outflows, surpluses and shortfalls.

Process transformation: supports a continuous rather than periodic close and enables proactive cash and working-capital management.

 

5. Blockchain

Practice

Management accounting use

Impact

Blockchain-based supply-chain cost and provenance tracking

Each movement of goods, transfer of ownership and transaction is recorded on an immutable shared ledger, enabling management accountants to verify supply-chain costs, inventory movements and product provenance.

Process enhancement: improves data integrity, traceability and auditability while reducing information-update costs.

Smart-contract-enabled supplier payments and trade finance

Smart contracts can trigger automatic payments when agreed delivery, inspection or documentation conditions are met, supporting working-capital management and supplier financing.

Process transformation: links operational events directly to settlement and accounting recognition, reducing reconciliation and payment-processing delays.

 

6. Big data and analytics

Practice

Management accounting use

Impact

Predictive budgeting, forecasting and cost estimation

Management accountants use internal and external historical data, machine-learning models and predictive analytics to improve cost estimates, budgets, cash-flow forecasts and risk assessments.

Process enhancement: improves forecast accuracy and shifts budgeting from static annual estimates toward dynamic, evidence-based planning.

Integrated financial and non-financial performance dashboards

Analytics platforms combine cost, operational, customer, sustainability and market data into real-time dashboards for variance analysis, balanced scorecards and resource-allocation decisions.

Process transformation: management accounting becomes a continuous decision-support function, covering descriptive, diagnostic, predictive and prescriptive analysis.

 

Overall implication

These technologies change management accounting from a mainly periodic reporting and control role into a real-time, predictive and decision-enabling role. The strongest transformation occurs where technologies are integrated—for example, IoT provides operational data, cloud ERP consolidates it, big-data analytics interprets it, and generative or agentic AI translates the results into actions, reports and automated workflows.