THESIS DEFENSE POSSIBLE QUESTIONS

What is your title?

Beyond the Green Label: Evaluating Student Eco-Literacy, Awareness, and Perceived Detection of Digital Greenwashing in Online Shopping Platforms

What is your study about?

It examines how student eco-literacy and awareness of greenwashing are related to students’ perceived ability to identify potentially misleading environmental claims on online shopping platforms.

Why did you choose this topic?

Online shoppers encounter environmental claims in product descriptions, labels, and images. Some claims may be unsupported or misleading, so it is relevant to examine how students understand these claims and assess their own ability to recognize them.

What is the main finding?

Both eco-literacy and awareness of greenwashing have significant positive associations with perceived detection. Awareness has the stronger standardized association in the combined model.

What is the most important qualification to your findings?

Perceived detection is self-reported. The study did not objectively test whether respondents could correctly identify genuine and misleading claims.

What problem does your study address?

Consumers may encounter vague, exaggerated, or unsupported environmental claims online and may find it difficult to evaluate whether those claims are genuine.

What is the research gap?

Prior work often focuses on environmental awareness, green purchasing, skepticism, or advertising content. This study focuses on the relationship between eco-literacy and greenwashing awareness with students’ perceived detection in online shopping contexts.

Why focus on online shopping platforms?

They present product claims through descriptions, labels, images, and seller-provided information, which consumers may not be able to verify immediately.

How is your study different from studies about green purchase intention?

Purchase intention concerns willingness or plans to buy. Our dependent variable is perceived ability to recognize potentially misleading environmental claims.

Why study students?

The study focuses on student consumers who participate in online shopping and encounter environmental claims. The findings should be generalized only with appropriate caution.

What theories or frameworks support your study?

The Environmental Literacy Framework, advertising literacy, and the Persuasion Knowledge Model.

How does the Environmental Literacy Framework relate to your study?

It provides the foundation for understanding students’ environmental knowledge and competencies, which relate to the eco-literacy variable.

How does the Persuasion Knowledge Model relate?

It explains how consumers use knowledge of persuasive tactics to recognize and respond to marketing attempts, which is relevant to awareness of greenwashing.

Why are there two independent variables?

The study examines two related but distinct factors: understanding environmental information and familiarity with misleading environmental marketing tactics.

Does your framework show causation?

No. The arrows represent the relationships examined, not causal effects. The research design is correlational.

What are your independent and dependent variables?

The independent variables are student eco-literacy and awareness of greenwashing. The dependent variable is perceived detection of digital greenwashing.

Why did you formulate null hypotheses?

The null hypotheses provide statements that can be tested statistically regarding whether each relationship and the combined predictive model are significant.

What happened to your null hypotheses?

The reported analyses show p-values below .001 for both individual predictors and the combined model, so the three null hypotheses are rejected based on the reported results.

What does significant mean in your study?

It means the observed statistical relationship is unlikely under the relevant null model at the chosen significance threshold. It does not prove causation or practical importance by itself.

Why did you use a quantitative approach?

The study measures responses numerically and uses statistical methods to describe the variables and examine their relationships.

Why correlational design?

The purpose is to examine associations among existing respondent characteristics and self-reported scores, without manipulating the variables.

Why 400 respondents?

The manuscript reports 400 participants. The group should explain the actual basis used to set or achieve this sample size, using the sampling or planning procedure documented by the researchers. Do not claim a calculation that was not performed.

Why voluntary response sampling?

Participants joined based on their willingness to answer the survey. It was a practical way to recruit student consumers, although it may introduce self-selection bias.

Who qualified as a respondent?

Student consumers who engage in online shopping. Be prepared to state the exact screening criteria used in the actual Google Form, since the manuscript does not fully detail them

Why use a questionnaire?

It allowed the researchers to collect standardized responses from 400 participants and measure the three study variables consistently.

Why five statements per variable?

The instrument used five statements for each of the three main variables, resulting in 15 scale items. The items cover aspects of each construct described in the manuscript.

Why use a five-point Likert scale?

It provides ordered response options from Strongly Disagree to Strongly Agree and allows respondents to indicate their level of agreement.

Why Google Forms and QR codes?

They provided online access to the survey and a way for participants to open it through printed QR codes.

What do your Cronbach’s alpha values mean?

They indicate internal consistency among items within each scale. They do not, by themselves, prove that the instrument measures the constructs validly.

How did you protect respondents?

The manuscript reports informed consent, voluntary participation, confidentiality, optional name provision, secure data storage, limited access, and academic use of the responses.

Why did you use mean scores?

To summarize the respondents’ average level of agreement with the statements for each variable.

Why simple linear regression?

To examine the relationship between each independent variable and perceived detection separately.

Why multiple linear regression?

To examine the two predictors together and assess their individual associations with perceived detection while accounting for the other predictor.

What does B = 0.8830 mean in Table 4?

In the fitted simple regression, a one-unit increase in the eco-literacy score is associated with an estimated 0.8830-unit increase in the perceived-detection score.

What does R² = .543 mean?

The two predictors in the combined model explain 54.3% of the variation in perceived detection scores in this sample and fitted model.

What does the remaining 45.7% mean?

It is variation not explained by the two predictors included in this model. It may relate to other measured or unmeasured factors and random variation.

Which variable is stronger?

Awareness of greenwashing, because its standardized coefficient in

Why compare standardized beta rather than B?

Standardized coefficients put predictors on a common standard-deviation scale, making their relative associations easier to compare. B coefficients remain in the original units.

Does a high mean prove students are knowledgeable?

No. The means reflect self-reported agreement, not performance on an objective knowledge test.

Does a significant regression prove that eco-literacy causes detection?

No. The correlational design supports statistical association and prediction, not cause and effect.

What is the practical implication for schools?

Schools may include activities about greenwashing tactics, environmental labels, unsupported claims, and evaluating evidence in environmental or consumer education.

What is the implication for online sellers?

Sellers should provide clear, accurate, and verifiable information about environmental benefits rather than relying on vague or unsupported claims.

What is the biggest limitation?

The dependent variable measures perceived ability, not actual detection accuracy. Self-reported confidence may not match performance.