Lecture note on replicability with an example for MBA students
Highlight 4 main ideas on the topic of replicability in
the context of doing an MBA dissertation project. Clarify your ideas a bit with
a few examples.
Replicability in an MBA dissertation means designing and
reporting your study so that another researcher could repeat it (or something
very close to it) and check whether similar results are obtained. In practice,
it’s about clarity, transparency, and methodological rigor rather than
expecting someone to literally rerun your project next year.
Below are four
main ideas, each with MBA‑relevant examples.
1. Clear, transparent research procedures
Replicability
starts with making your procedures fully explicit so another researcher
can follow the same steps. In social science and management research,
replication is defined as repeating a research procedure to verify the truth or
robustness of the findings.link.springer+1
What this means
for your dissertation
- Describe your sampling:
inclusion/exclusion criteria, where you recruited participants, time
frame.
- Specify your measures:
scales used, items, response formats, any translation or adaptation.
- Document your procedures:
how you distributed questionnaires, conducted interviews, handled non‑responses.
- Explain your analysis
steps: data cleaning, tests run, model choices, significance
thresholds.
Example
(survey-based dissertation)
You study the impact of perceived service quality on customer loyalty in Hong
Kong e-commerce:
- You state: “I surveyed
customers of my online outlet and two partner stores between March–April
2026; inclusion criteria: at least one purchase in last 6 months; target
sample: 300, final usable responses: 247.”
- You append the full
questionnaire, note that you used the 22-item SERVQUAL scale and a 5-item
behavioral loyalty scale, both on 7-point Likert scales.
- You detail that you tested
reliability (Cronbach’s alpha), did factor analysis, then ran multiple
regression with loyalty as the dependent variable and controlled for age,
gender, and frequency of purchase.
Because each step
is spelled out, another MBA student could replicate the survey with their own
outlet customers and see if the relationships hold.
2. Standardized, well-defined constructs and
measures
Replication is
easier when you use established constructs and measurement instruments,
rather than idiosyncratic or vaguely defined variables.openeconomics.zbw
What this means
for your dissertation
- Use validated scales from
prior literature for key variables (e.g., job satisfaction, organizational
commitment, perceived usefulness, purchase intention).
- Define each construct
conceptually and operationally (what it means and how you measure it).
- Justify any modifications to
existing scales (dropping items, changing wording, translating to
Chinese).
Example
(marketing/consumer behavior dissertation)
You examine how social media engagement influences purchase intention toward
K-pop merchandise:
- You define “social media
engagement” using a three-dimensional scale (consumption, contribution,
creation) from a well-cited paper.
- You use an established
“purchase intention” scale (e.g., three Likert items: likelihood to buy,
willingness to recommend, intention to continue buying).
- You report reliability and
validity evidence for both constructs.
A future
researcher could take the same scales, apply them to a different K-pop fandom
(e.g., another group) or another geography (e.g., Taiwan), and test whether the
engagement–intention relationship replicates.
3. Sufficient detail about data and analysis
for robustness checks
Replicability is
not only about repeating the study; it’s about checking the robustness
of your findings under similar or slightly varied conditions. Another
researcher might replicate your analysis with:link.springer+1
- Similar data from another
context.
- Different, but appropriate,
statistical methods.
- Slightly changed
operationalizations.
What this means
for your dissertation
- Report descriptive
statistics, correlations, model specifications, and assumptions checks
(e.g., normality, multicollinearity).
- Make clear what decisions
you took: how you handled missing data, outliers, non-response bias.
- If feasible, archive or at
least structure your data and analysis scripts so they could be
shared (even if you don’t actually publish them).
Example
(finance/investment dissertation)
You analyze the relationship between dividend yield and share price stability
among Hong Kong REITs:
- You specify the time window
(2016–2025), the list of REITs, data sources (HKEX, Bloomberg/Refinitiv,
etc.), and formulas used for volatility and dividend yield.
- You explain that you tested
both linear regression and panel data models, and why you chose the final
specification.
- You report effect sizes and
confidence intervals, not just p-values.
A future
researcher could replicate the study using Singapore REITs or extend the time
window and see if the “higher dividend yield → lower volatility” pattern holds,
making your findings more generalizable and robust.
4. Distinguishing context-specific insights
from generalizable claims
Replication is
central to deciding how far your conclusions can be generalized beyond
your specific dissertation setting.link.springer+1
What this means
for your dissertation
- Be explicit about boundary
conditions: sector, country (Hong Kong), time period, sample type
(e.g., MBA students, online shoppers).
- When you claim implications
for “businesses” or “managers,” make clear whether these are:
- Likely
generalizable (because constructs and relationships are theoretically
universal), or
- Mainly
context-specific to your sample and setting.
- Discuss how future replication
studies in different contexts could confirm, refine, or challenge your
findings.
Example
(management/HRM dissertation)
You test whether flexible working arrangements improve job satisfaction among
part-time workers in Hong Kong SMEs:
- You find a positive effect
and conclude that offering flexible hours is associated with higher
satisfaction.
- In your discussion, you note
that your sample is limited to Hong Kong SME employees, and local labor
laws and cultural attitudes may influence the effect.
- You suggest that replication
in large corporations, other Asian cities, or full-time workers could test
whether the relationship is stable or context-bound.
This shows you
understand that your dissertation is one piece of evidence; its real scientific
value grows when others can replicate or extend it.
Quick recap in dissertation terms
1.
Transparency of
methods – Write your
methodology so clearly that another MBA student could repeat your procedures
step-by-step.
2.
Use of established
constructs and scales – Build on
validated measures to make replication easier and your results comparable.
3.
Detailed data and
analysis reporting – Provide enough
information for others to rerun your analysis or test your findings with
similar data.
4.
Awareness of
generalizability and context – Frame your
conclusions with their limits and suggest where replication would strengthen or
challenge them.
** A collection of lecture notes on the subject of research methods for MBA students, 2026 June
A collection of blog notes on using chatgpt for research purpose.
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