EDB110 Introduction to E-Commerce Individual Assignment Sample

For information and reference only – not to be submitted as your own work.

This extract is a model answer for an individual e-commerce report on online research and communication tools, the kind set in EDB 110 Introduction to E-Commerce (20% weighting, CLO 2). No question page exists yet for this brief, so browse the recently solved Malaysian assessment questions for similar briefs. For a step-by-step planning guide, read the companion blog, how to write the EDB110 e-commerce report.

What the assignment asks

The report must explain how online research and communication tools support online business activities. It has four tasks, each with its own demand.

Model-answer extract

The business used in this extract is hypothetical: “Batik Kita”, a small Malaysian online store selling batik clothing and home textiles. All figures are illustrative and invented for demonstration.

1. Online research and surveys

Definition. Online research is the systematic collection, recording and analysis of information about customers, competitors and markets using Internet-based methods, including web surveys, analysis of site behaviour and monitoring of online conversations. It matters in e-commerce because the same medium that delivers the product also produces data about how people find, browse and buy it.

Type 1: web-page (self-administered) surveys. The questionnaire sits on a web page or is reached through a link, and respondents complete it at their own pace. Couper (2000) describes the strengths of this format as low cost per response, speed and the ability to use interactive features such as skip logic. Batik Kita could place a short post-purchase survey on its order confirmation page and learn why customers chose the shop over a marketplace seller.

Type 2: e-mail surveys. A questionnaire is delivered to a known list, either embedded in the message or through a link. This gives the researcher control over who is invited, which a public web-page survey does not. For Batik Kita, an e-mail survey to existing customers about a planned Raya collection would reach people already familiar with the brand. Evans and Mathur (2005) note that online surveys are fast and inexpensive but that their value depends on how well they are designed and targeted.

One quality issue: sampling and coverage bias. A survey is only as good as the people who answer it. Couper (2000) identifies coverage error and nonresponse as central concerns for web surveys, because people without Internet access, or who simply ignore the invitation, are missing from the results. Wright (2005) adds that online samples are often self-selected, so findings cannot automatically be generalised.

For Batik Kita, a link posted on social media would mostly attract existing fans. Their enthusiasm would overstate satisfaction and understate the reasons non-buyers leave. A sound response is to compare respondent characteristics with known customer data, send reminders, keep the questionnaire short, and state the limitation openly when reporting results.

2. Finding the right audience

Panel versus log file measurement. Both methods estimate who uses a site, but from opposite directions.

Batik Kita is too small to appear in most panel reports, so log or analytics data would be its main source. It would turn to panel-style data only to compare itself against larger fashion sites.

Technique 1: demographic and behavioural patterns. This groups visitors by age, location, device and behaviour. Suppose the (illustrative) data show that 62% of visitors are in Klang Valley and 70% browse on mobile. The firm would then design mobile-first product pages and favour delivery promotions for that region.

Technique 2: source and loss analysis. Source analysis identifies where visitors come from, such as search, social media or direct entry. Loss analysis finds the stage at which they leave: product page, basket, delivery options or payment. If illustrative data show that 40% of baskets are abandoned at the shipping-cost screen, the firm has a clear reason to test free-shipping thresholds. Reach and frequency, the other technique named in the brief, would suit paid advertising, where the question is how many different people see an advertisement and how often.

3. Monitoring congestion and delay

Source 1: network congestion. When traffic on a link or router exceeds its capacity, packets queue or are dropped and must be re-sent. The user experiences slow loading even if the shop’s own server is healthy. Congestion is highest at peak hours and during promotions such as 11.11 sales, when many shoppers use the network at once.

Source 2: server and page load. A web server handling too many simultaneous requests, or serving heavy pages with large images and many scripts, responds slowly. This delay sits inside the firm’s control. Compressing images, caching content and using a content delivery network reduce it.

Google’s web.dev guidance treats a Largest Contentful Paint of 2.5 seconds or less as “good” for loading, and an Interaction to Next Paint of 200 milliseconds or less as “good” for responsiveness. These give Batik Kita measurable targets for monitoring.

Managing perception, technique 1: progress indicators. Nielsen (1993) explains that delays under about one second keep the user’s flow of thought, while delays approaching ten seconds need feedback because attention drifts. Percent-done indicators are recommended for longer operations. Myers (1985) found in a controlled experiment that people preferred systems with such indicators. On a checkout page, a visible “Processing your payment” step with a progress bar reassures the buyer not to press the button twice.

Managing perception, technique 2: progressive loading. Showing the page structure, text and low-resolution placeholders first, then filling in large images afterwards, gives users something useful immediately. The wait still exists, but the user is engaged rather than staring at a blank screen.

4. Application to Batik Kita

• Online surveys as a communication tool. A two-question post-purchase survey, followed by an e-mail survey to repeat buyers, lets the shop ask about fabric quality and delivery. It also tells customers that their views are acted on, which supports the relationship as well as the research.
• Audience tracking and marketing decisions. Source and loss analysis shows which channels bring buyers rather than browsers. The shop can shift budget from low-converting channels and fix the checkout step where most baskets are lost.
• Delay monitoring and satisfaction. Slow pages are a common reason shoppers abandon a purchase, and a slow checkout suggests an unreliable business. Monitoring load times each week, and especially before sales, protects both conversion and reputation.

Need feedback on your own EDB 110 draft? Our assignment review service checks structure, referencing and rubric fit before you submit.

How this extract earns marks

About the expert

Questions like this are covered by Aina R., who holds a PhD in Marketing and Business Strategy from the University of Malaya. Her listed specialisms include marketing case studies, SWOT and PESTEL analysis, and consumer psychology and market research.

If your report needs a stronger argument or tighter structure, our management assignment helper service offers guidance and model answers from subject specialists.

References

Couper, M. P. (2000). Web surveys: A review of issues and approaches. Public Opinion Quarterly, 64(4), 464–494. https://doi.org/10.1086/318641

Evans, J. R., & Mathur, A. (2005). The value of online surveys. Internet Research, 15(2), 195–219. https://doi.org/10.1108/10662240510590360

Google. (n.d.). Web Vitals. web.dev. https://web.dev/articles/vitals

Myers, B. A. (1985). The importance of percent-done progress indicators for computer-human interfaces. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’85) (pp. 11–17). ACM.

Nielsen, J. (1993). Response times: The 3 important limits. Nielsen Norman Group. https://www.nngroup.com/articles/response-times-3-important-limits/

Wright, K. B. (2005). Researching Internet-based populations: Advantages and disadvantages of online survey research, online questionnaire authoring software packages, and web survey services. Journal of Computer-Mediated Communication, 10(3). https://doi.org/10.1111/j.1083-6101.2005.tb00259.x