Comparative Analysis of International Retailing Strategies

School of Business

About this use case

Students compare a retail store operating in Malaysia with its counterpart in the retailer’s home country, such as Daiso in Malaysia and Japan or Lotus’s in Malaysia and Thailand. They investigate market entry, growth and competitive positioning strategies; examine cultural influences on marketing and consumer behaviour; and communicate their findings through a poster and video presentation. Together, these outputs contribute 30% of the total course grade.

Learning outcomes:

Assess the influence of environmental factors on foreign-market selection in an international context.
Investigate the effects of global trends and issues on marketing initiatives during the strategic planning process.

This assessment was designed in response to the increasing ease with which students could use AI tools to generate case study analyses.
By requiring students to investigate and compare actual retail stores in different countries, the task shifts the focus from reproducing case-based answers to collecting observations, evaluating real-world practices, and applying international marketing concepts in an authentic context.
To help students understand:
- international retailing strategies,
- examine how culture influences marketing and consumer behaviour, and
- develop systematic comparative-analysis skills across retail operations in different countries.
1. Guide students in framing standardised research questions for Perplexity AI or Gemini’s Deep Research function.
2. Demonstrate how generative AI may be used to for the assessment task
3. To help students understand
  - international retailing strategies,
  - examine how culture influences marketing and consumer behaviour, and
  - develop systematic comparative-analysis skills across retail operations in different countries.her preliminary insights during the early research stage.
4. Ask students to critique and refine AI-generated outputs.
- Improved understanding of international retail market-entry, growth and competitive-positioning strategies.
- Greater awareness of how culture influences marketing approaches and consumer responses.
- Improve comparative-analysis skills across retail operations in different countries.
- Improved ability to critique and refine AI-generated research outputs.
- Greater confidence in validating preliminary findings against authoritative and firsthand sources.
- Clear communication of comparative findings through poster and video formats.

Try it out

This use case requires a complex level of effort to implement.

- This assessment approach can be adapted across disciplines by asking students to compare real-world practices, organisations, products, services, or policies across different contexts and communicate their findings through visual and video-based outputs.
- Rather than relying on case studies, students engage with authentic environments, collect evidence, and apply disciplinary concepts to analyse real-world phenomena.
- Select an international retail brand that operates in both the local market and its home country, and require students to compare the two contexts using international marketing frameworks.
- Require students to explain their findings through a poster and video presentation.
- Permit generative AI only during the initial research stage for framing standardised questions and gathering preliminary insights.
- Require all subsequent validation, analysis and production work to draw on authoritative sources and firsthand research without generative AI.
- Clarify that generative AI is not permitted during data validation, analysis, or production of the poster and video.
- Show students how to compare AI-generated information with authoritative source data.
- Start with an authentic, real-world task that requires students to gather evidence, make observations, and apply course concepts rather than relying solely on secondary case materials.
- Clearly define the point at which generative AI use must stop.
- Guide students in developing consistent research questions, critiquing AI-generated outputs and checking preliminary findings against authoritative evidence.
- Reinforce that data validation, comparative analysis, and final assessment outputs must be completed without the use of generative AI. Student has to take full responsibility for the work submitted.
Direct observation
Student learning is made visible through direct observation of their engagement during field visits and the artefacts they produce from the experience. Students capture photographs of real-world examples during the field visit, demonstrating their ability to identify and document relevant concepts in an authentic context. They then analyse these observations and communicate their insights through a video explainer, which may be presented as an academic presentation or a vlog-style reflection. The video provides evidence of students' understanding, critical thinking, and ability to connect theoretical concepts with real-world observations. By observing the selected photographs, the depth of analysis, and the clarity with which students explain and justify their findings, instructors can directly assess student learning and the application of course concepts in practice.

Supporting resources

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Sidebar

Unit: MKW3444	Condition: Individual
Class size: 100–160	Typology: Artefact
AI approach: Using and applying AI tools effectively	AI statement: Guided use
Visibility of Learning: Direct observation

About the educator

Validated by

Eric Tan, Education Designer, Education Excellence