This post explains how AI-enabled qualitative research methods can accelerate insights from household e-waste studies, using the Kombolcha City mixed-methods study as an example. The primary keyword "qualitative analysis of e-waste practices" guides practical steps for researchers and NGOs who must synthesize surveys, focus group discussions, and key informant interviews rapidly. According to the Scientific Reports article by Ebrahim et al. (published 26 July 2026), the Kombolcha study combined a 423-household survey with six FGDs and 30 KIIs to measure knowledge, attitudes, and practices in 2025.
Key Takeaways
According to Scientific Reports, the 26 July 2026 mixed-methods study found that 57.9% of households in Kombolcha City had poor e-waste management practices. The study reports 60% of participants had poor knowledge and 58.2% had unfavorable attitudes in 2025. The study concludes that "Strengthening community awareness and promoting safe e-waste handling and disposal practices are essential to protect public health and the environment, " according to Ebrahim et al., Scientific Reports (2026).
- 423 households were surveyed in Kombolcha City in 2025, according to Ebrahim et al., Scientific Reports (2026).
- In July 2026 the authors reported 60% poor knowledge, 58.2% unfavorable attitudes, and 57.9% poor e-waste practices among respondents.
- Higher education increased the odds of good practice (AOR = 8.23, 95% CI: 1.45–46.79) and favorable attitudes increased the odds (AOR = 5.71, 95% CI: 2.96–11.05), according to the study.
- The authors recommend community awareness campaigns and stronger policy implementation to reduce public health risks, as stated in Scientific Reports (2026).
What Happened and how the study measured it
The study measured household e-waste knowledge, attitudes, and practices with a 2025 community-based cross-sectional survey and qualitative interviews, according to Ebrahim et al., Scientific Reports (published 26 July 2026).
According to the Scientific Reports article, the quantitative component used a standardized questionnaire on 423 households, analyzed with SPSS version 26, while the qualitative component used six focus group discussions and 30 key informant interviews analyzed thematically.
According to the authors, variables with p < 0.25 in bivariable analysis were entered into multivariable logistic regression and statistical significance was declared at p < 0.05, which produced adjusted odds ratios for education and attitude factors.
Findings Snapshot
| Date | Metric | Value | Implication |
|---|---|---|---|
| 2025 | Household sample size | 423 households | Sufficient sample for community-level KAP estimates, per Ebrahim et al., Scientific Reports (2026) |
| 2025 | Poor knowledge | 60.0% | Points to large awareness gaps that awareness campaigns must address, according to the study |
| 2025 | Unfavorable attitudes | 58.2% | Suggests attitudinal barriers to safe e-waste handling, per Ebrahim et al., Scientific Reports (2026) |
| 2025 | Poor e-waste management practices | 57.9% | Indicates majority of households practice unsafe disposal, increasing health and environmental risk |
| 2025 | Education effect (AOR) | 8.23 (95% CI: 1.45–46.79) | Higher education strongly associated with better e-waste practices, per multivariable analysis |
| 2025 | Favorable attitude effect (AOR) | 5.71 (95% CI: 2.96–11.05) | Positive attitudes associated with higher odds of good practice, per the study |
Implications for researchers and NGOs studying household e-waste
Researchers should use mixed-methods to capture both prevalence and reasoning, because Ebrahim et al., Scientific Reports (2026) combined quantitative KAP measures with six FGDs and 30 KIIs to explain why 57.9% of households had poor practices.
Program designers should prioritize education and attitude change, because the Kombolcha study found education (AOR = 8.23) and favorable attitudes (AOR = 5.71) were the strongest predictors of safer e-waste practices in 2025.
Policy teams should treat household behavior as a modifiable factor, because the authors conclude that "Strengthening community awareness and promoting safe e-waste handling and disposal practices are essential to protect public health and the environment" (Ebrahim et al., Scientific Reports, 2026).
For context on global risk and methods, see the UNEP Global E-waste Monitor for standardized metrics used in environmental health research.
How Evidano Helps
Evidano overview
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
Evidano supports transcription, thematic coding, cross-segment analysis, and AI chat over your documents, which maps directly to the Kombolcha study needs: clean transcripts from FGDs/KIIs, systematic codebooks for themes like knowledge and attitudes, and rapid cross-tabulation with survey segments.
Problem: Noisy interviews and slow transcription → Solution: automated, accurate transcription
The Kombolcha study used six FGDs and 30 KIIs, so research teams need reliable transcripts; Evidano's transcription feature can accelerate conversion of audio to text while preserving local terms and allowing PII redaction.
See Evidano transcription details at Evidano speech-to-text.
Problem: Manual coding delays analysis → Solution: AI-assisted thematic analysis and visualization
Because the Scientific Reports study required thematic analysis of qualitative data, Evidano's AI-tuned thematic extraction and hierarchical coding helps researchers move from raw text to prioritized themes quickly.
Evidano links thematic findings to frequency and cross-segment metrics so teams can show, for example, that education and attitudes predict practices as reported in Ebrahim et al., Scientific Reports (2026). See Evidano features for details.
Problem: Synthesis for policy briefings is time-critical → Solution: AI chat and extractable outputs
Qualitative evidence needs to be quotable; Evidano produces extractable, citable sentences and visualizations that teams can drop into presentations or policy briefs explaining the 60% knowledge gap and 57.9% poor practice rate reported in the Kombolcha study.
Evidano keeps data encrypted and separate from third-party model training, which supports research ethics and data governance in public health projects.
FAQ: qualitative analysis of e-waste practices
What were the key statistics reported in the Kombolcha e-waste study?
The key statistics were: 423 households surveyed in 2025, 60% poor knowledge, 58.2% unfavorable attitudes, and 57.9% poor e-waste management practices, as reported in Scientific Reports (Ebrahim et al., 26 July 2026).
The authors also reported multivariable associations: education (AOR = 8.23, 95% CI: 1.45–46.79) and favorable attitudes (AOR = 5.71, 95% CI: 2.96–11.05).
How can qualitative methods explain the high rate of poor practices?
Qualitative methods explain the why: the Kombolcha study used six FGDs and 30 KIIs in 2025 to explore cultural, economic, and informational barriers behind the 57.9% poor practice rate, according to Ebrahim et al., Scientific Reports (2026).
Researchers can use thematic coding to surface recurring barriers such as lack of disposal services or misinformation, then link those themes to survey segments for program design.
Can AI analyze FGDs and KIIs from the Kombolcha study reliably?
Yes, AI can accelerate coding and synthesis, provided transcripts are accurate and local terminology is captured; the Kombolcha study's six FGDs and 30 KIIs are a typical workload where AI-assisted coding reduces time to insight.
Teams should validate AI-generated codes against human reviewers to maintain rigor and reproducibility, especially for public health implications reported in the Scientific Reports article.
Is the Kombolcha dataset publicly available for reanalysis?
The Scientific Reports article is open access (published 26 July 2026), but the article does not publish the raw household dataset in the PDF; researchers should consult the article's data availability statement or contact the corresponding author Ahmed Mohammed Ebrahim via the published correspondence details.
When reusing human-subjects data, researchers must follow ethical rules and obtain permission as required by the original study.
Conclusion & Next Steps
The Kombolcha mixed-methods study (published 26 July 2026 in Scientific Reports) shows major gaps in household e-waste knowledge and practice that are driven by education and attitudes.
AI-enabled qualitative analysis shortens the path from FGDs and KIIs to actionable recommendations by automating transcription, extracting themes, and linking themes to survey segments, which helps programs target the 60% knowledge gap reported in the study.
If you run mixed-methods field studies or need faster synthesis for policy, Try Evidano for free to accelerate transcription, thematic coding, and evidence visualization.