Developing a Robotics-Based Advocacy Framework for Electronics Engineering: Integrating KANO, IPA, and QFD to Inspire Future Engineers

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Electronics Engineering (ECE) continues to play a vital role in technological innovation, industrial automation, intelligent systems, and the development of Industry 4.0. Despite its strategic importance, many secondary school students perceive ECE as highly technical, mathematically demanding, and difficult to understand. This perception often discourages students from considering Electronics Engineering as a future academic or professional pathway. The article reviewed here proposes an innovative solution by employing educational robotics not merely as a teaching tool but as an advocacy strategy for promoting Electronics Engineering among prospective students.

Rather than relying on conventional promotional activities, the researchers designed a theory-informed Robotics-Based Advocacy Framework grounded in student perceptions and institutional priorities. The framework was developed using an integrated decision-making methodology that combines the KANO Model, Importance–Performance Analysis (IPA), and Quality Function Deployment (QFD). This multi-stage approach enables student preferences to be translated systematically into institutional strategies capable of enhancing awareness, confidence, and interest in Electronics Engineering.

Unlike many educational robotics studies that primarily evaluate learning outcomes, this research positions robotics as a strategic mechanism for academic advocacy. The study further strengthens its contribution by integrating motivational theory, career development theory, and constructionist learning principles to explain why robotics experiences can influence students' educational aspirations. Consequently, the proposed framework provides valuable insights for engineering educators, curriculum designers, policymakers, and institutions seeking evidence-based approaches to attract future engineering students.

Bibliographic Information

Item Information
Article Title Developing a robotics-based advocacy framework for electronics engineering using an integrated KANO–IPA–QFD approach
Authors Helen Grace B. Gonzales, Alenogines L. San Diego, and Consorcio Namoco
Journal Innovation in Engineering
Volume & Issue Volume 3, Issue 1
Publication Year 2026
Pages 24–54
DOI https://doi.org/10.58712/ie.v3i1.47
Publisher RLS Society
ISSN 3047-5473
License Creative Commons Attribution 4.0 International (CC BY 4.0)
Keywords electronics engineering; robotics-based advocacy; educational robotics; industry 4.0; importance performance analysis

1. Research Background

  • Electronics Engineering remains essential for national technological development. The discipline underpins embedded systems, telecommunications, robotics, automation, intelligent devices, and digital manufacturing. As Industry 4.0 expands, demand for electronics engineers continues to increase.
  • Student interest in Electronics Engineering remains relatively low. Many prospective students perceive Electronics Engineering as mathematically difficult, abstract, and disconnected from everyday experiences. Such perceptions reduce motivation to pursue engineering education despite increasing industrial demand.
  • Educational robotics offers a promising solution. Robotics provides students with opportunities to design, assemble, program, test, and troubleshoot real engineering systems. These hands-on experiences make abstract engineering concepts more concrete and engaging.
  • Previous educational robotics studies have concentrated mainly on STEM learning outcomes. Existing research demonstrates improvements in computational thinking, collaboration, creativity, and problem-solving. However, relatively few studies investigate robotics as an advocacy strategy specifically designed to promote Electronics Engineering.
  • Institutions lack structured frameworks for robotics-based engineering advocacy. Schools frequently organize robotics demonstrations or competitions, yet these activities are often implemented without evidence-based planning regarding which robotics attributes most effectively encourage students to pursue Electronics Engineering.
  • This study addresses an important methodological gap. Instead of evaluating robotics activities descriptively, the researchers integrate KANO analysis, Importance–Performance Analysis (IPA), and Quality Function Deployment (QFD) to transform student preferences into institutional design priorities for advocacy.
  • The framework is supported by established educational theories. Expectancy–Value Theory (EVT), Social Cognitive Career Theory (SCCT), and Constructionism collectively explain how robotics experiences can increase perceived competence, strengthen career interest, and promote meaningful learning.
  • The research contributes to engineering education beyond curriculum development. Rather than improving classroom instruction alone, the study demonstrates how robotics can become an institutional strategy for attracting future Electronics Engineering students through learner-centered advocacy.

2. Research Objective

  • To develop a Robotics-Based Advocacy Framework for Electronics Engineering using an integrated KANO–IPA–QFD methodology.
  • To identify robotics learning attributes that students value most when considering Electronics Engineering.
  • To prioritize robotics learning attributes according to institutional importance and perceived performance.
  • To translate prioritized learner preferences into actionable institutional design requirements through Quality Function Deployment.
  • To synthesize the analytical findings into a theory-informed, learner-centered advocacy framework capable of strengthening awareness, confidence, and career interest in Electronics Engineering.
  • To obtain preliminary implementation evidence through a pilot robotics demonstration involving senior high school students.

3. Why This Research Matters

  • Provides a systematic framework for promoting Electronics Engineering through experiential robotics activities rather than conventional recruitment campaigns.
  • Bridges educational robotics research with engineering recruitment and advocacy strategies.
  • Demonstrates how student perceptions can directly inform institutional planning using engineering decision-making tools.
  • Integrates motivational psychology, career development, and experiential learning theories into engineering education.
  • Supports universities facing declining engineering enrollment despite increasing industrial demand.
  • Offers an evidence-based model that educational institutions can adapt when designing STEM outreach and engineering promotion programs.

4. Research Methodology

  • Research Design

    The researchers employed a sequential mixed-method framework development design specifically intended to construct a Robotics-Based Advocacy Framework for Electronics Engineering. Rather than evaluating an existing intervention, the study followed a progressive analytical process in which the results of one stage informed the next. Quantitative analyses identified learner-valued robotics attributes and institutional priorities, while qualitative evidence was used to refine and interpret the resulting framework. This approach ensured that the final advocacy model was grounded in empirical evidence rather than intuition.

  • Research Framework

    The methodology consisted of two complementary phases. Phase 1 focused on framework development through the integration of the KANO Model, Importance–Performance Analysis (IPA), and Quality Function Deployment (QFD). Phase 2 served as a preliminary implementation stage that evaluated the practical relevance of the framework through a pilot robotics demonstration involving senior high school students. The pilot was intended to provide initial evidence of advocacy effectiveness rather than formal validation of the framework.

  • Theoretical Foundations

    The framework was conceptually supported by three complementary educational theories. Expectancy–Value Theory (EVT) explained how perceived competence and task value influence student motivation. Social Cognitive Career Theory (SCCT) described how self-efficacy, mentorship, and contextual support shape career interests. Constructionism provided the pedagogical basis for using robotics as an experiential learning environment in which abstract electronics concepts become tangible through active construction and experimentation.

  • Participants

    The study involved 124 senior high school students from partner secondary schools in Cagayan de Oro City, Philippines. Participants were selected through purposive sampling because they represented the intended audience for Electronics Engineering advocacy activities. Their educational stage was considered appropriate for investigating how robotics experiences could influence awareness of engineering careers before university enrollment.

  • Research Instruments

    Two researcher-developed instruments were employed. The first, the KANO-Based Robotics Advocacy Instrument, collected data regarding student perceptions of twenty-five robotics learning attributes and included paired KANO questions, importance ratings, performance ratings, and open-ended responses. The second, the Pilot Demonstration Pretest–Posttest Questionnaire, evaluated short-term changes in awareness of Electronics Engineering, robotics learning perception, self-efficacy, perceived value of robotics, and career interest following participation in robotics activities.

  • Instrument Validation

    Both instruments underwent expert content validation using the Content Validity Index (CVI). Five specialists in engineering education, robotics, research methodology, and instrument development reviewed every questionnaire item. The KANO instrument achieved an overall Scale-Level Content Validity Index (S-CVI/Ave) of 0.98, while the pilot questionnaire obtained an S-CVI/Ave of 0.96, indicating excellent content validity before data collection.

  • Data Collection

    Students first participated in structured robotics advocacy sessions that introduced Electronics Engineering concepts through robot assembly, programming, sensing, troubleshooting, and collaborative problem solving. Following the activities, participants completed the KANO survey and pilot questionnaire under supervised conditions. Additional qualitative reflections were collected to support interpretation of the quantitative findings.

  • KANO Analysis

    The KANO Model classified each robotics learning attribute into six categories: Must-Be, One-Dimensional, Attractive, Indifferent, Reverse, or Questionable. Better and Worse coefficients were also calculated to estimate the satisfaction gained when an attribute was present and the dissatisfaction generated when it was absent. This analysis enabled the researchers to distinguish essential instructional features from motivational elements that could strengthen advocacy effectiveness.

  • Importance–Performance Analysis (IPA)

    The Better coefficient derived from the KANO analysis represented attribute importance, while the mean functional rating represented perceived performance. Every robotics attribute was plotted within a four-quadrant IPA matrix consisting of Keep Up the Good Work, Concentrate Here, Low Priority, and Possible Overkill. This procedure identified which robotics features required the greatest institutional attention during framework development.

  • Quality Function Deployment (QFD)

    The prioritized learner needs identified through KANO and IPA became the customer requirements ("WHATs") within the House of Quality matrix. These requirements were translated into institutional design responses ("HOWs"), including experiential robotics ecosystems, structured curriculum frameworks, faculty development, mentorship and industry linkages, career pathway integration, resource sustainability, community engagement, and showcase platforms. Relationship weighting enabled the researchers to rank institutional priorities objectively.

  • Pilot Implementation Analysis

    Following development of the advocacy framework, students participated in a pilot robotics demonstration. Descriptive pretest–posttest analysis, paired statistical testing, effect size computation, and thematic analysis of student reflections were conducted to examine whether robotics experiences produced observable improvements in awareness, confidence, perceived value, and career interest related to Electronics Engineering.

5. Key Findings

The Integrated KANO–IPA–QFD Framework Successfully Identified Student Priorities

The integrated analytical framework effectively transformed student perceptions into structured institutional priorities. Rather than evaluating robotics activities descriptively, the methodology systematically progressed from identifying learner-valued attributes, prioritizing them according to strategic importance, and translating them into institutional design requirements. This sequential process produced a comprehensive Robotics-Based Advocacy Framework grounded directly in empirical student responses.

Hands-on Robotics Experiences Emerged as the Most Attractive Learning Attributes

KANO analysis identified three robotics features as Attractive attributes: hands-on robot building, sensor integration, and confidence-building activities. These attributes were not necessarily expected by students but substantially increased enthusiasm and satisfaction whenever they were incorporated into robotics advocacy programs. The findings reinforce the educational value of experiential engineering activities over purely theoretical instruction.

Affordable Robotics Kits Produced the Greatest Satisfaction Gains

Better–Worse coefficient analysis demonstrated that affordable robotics kits generated the highest satisfaction potential (56.19%), followed closely by sensor integration (55.56%) and hands-on robot building (55.14%). These results indicate that accessibility and practical engagement strongly influence students' perceptions of Electronics Engineering as an attainable field of study.

Clear Learning Modules Represented the Greatest Source of Dissatisfaction

Among all evaluated attributes, the absence of clear instructional modules and tutorials produced the highest dissatisfaction coefficient (-23.36%). This finding emphasizes that engaging robotics hardware alone is insufficient; structured instructional guidance remains essential for successful engineering advocacy.

Importance–Performance Analysis Highlighted Critical Institutional Priorities

IPA revealed that fourteen robotics attributes occupied the Keep Up the Good Work quadrant, indicating high importance and satisfactory implementation. Only one attribute—community-centered robotics activities—appeared within the Concentrate Here quadrant, identifying it as the most urgent institutional improvement area requiring additional strategic attention.

Quality Function Deployment Prioritized Institutional Design Strategies

The House of Quality analysis translated learner expectations into institutional planning priorities. The highest-ranked strategic response was the establishment of an Experiential Robotics Ecosystem, followed by a Structured Curriculum Framework, Faculty Development, and Mentorship and Industry Linkages. These priorities demonstrate that effective advocacy requires coordinated institutional support rather than isolated robotics events.

The Pilot Demonstration Produced Positive Educational Outcomes

Students demonstrated positive improvements across all five measured domains after participating in the robotics advocacy sessions. The overall mean increased from 3.02 during the pretest to 3.44 following the robotics demonstration. The findings provide preliminary evidence that robotics experiences can strengthen awareness of Electronics Engineering, improve self-confidence, increase perceived educational value, and encourage career interest.

The Resulting Framework is Theory-Informed, Learner-Centered, and Data-Driven

The final Robotics-Based Advocacy Framework integrates empirical findings with Expectancy–Value Theory, Social Cognitive Career Theory, and Constructionism. By combining educational theory with engineering decision-making techniques, the framework provides a structured approach for designing advocacy programs capable of promoting Electronics Engineering through meaningful robotics experiences.

6. Scientific Contribution

  • Introduces one of the first structured Robotics-Based Advocacy Frameworks specifically designed for Electronics Engineering, extending educational robotics beyond classroom instruction into institutional recruitment and engineering promotion.
  • Integrates three complementary engineering decision-making tools—KANO, Importance–Performance Analysis, and Quality Function Deployment—within a single framework development methodology.
  • Transforms student perceptions into institutional planning priorities, demonstrating how learner preferences can guide evidence-based engineering education strategies.
  • Strengthens theoretical integration by combining Expectancy–Value Theory, Social Cognitive Career Theory, and Constructionism to explain how robotics influences engineering motivation and career aspirations.
  • Provides empirical evidence supporting robotics as an engineering advocacy mechanism rather than solely an instructional technology.
  • Develops a replicable framework that other universities, engineering schools, and STEM outreach programs can adapt to strengthen student recruitment into engineering disciplines.

7. Industrial Implications

  • Engineering universities can employ robotics-based advocacy programs to improve recruitment into Electronics Engineering and related STEM disciplines.
  • Educational institutions can prioritize investments in experiential robotics laboratories, affordable robotics kits, and structured learning resources to maximize student engagement.
  • Engineering faculties can strengthen partnerships with industry by integrating mentorship programs and authentic engineering experiences into outreach activities.
  • Curriculum developers can use learner-centered evidence generated through KANO, IPA, and QFD to improve engineering education planning.
  • Government agencies responsible for STEM education can incorporate robotics advocacy into national engineering workforce development strategies supporting Industry 4.0.
  • The proposed framework offers a transferable planning model for engineering schools seeking sustainable approaches to increasing student awareness, motivation, and enrollment in technical disciplines.

8. Research Limitations

  • The framework was developed using respondents from a single geographical setting. All participants were senior high school students from partner secondary schools in Cagayan de Oro City, Philippines. Although this population appropriately represented the intended audience for Electronics Engineering advocacy, the findings may not fully represent students from other educational systems, cultural backgrounds, or national contexts.
  • The pilot implementation served only as preliminary support. The robotics demonstration was designed to examine the practical relevance of the proposed advocacy framework rather than to establish its long-term educational effectiveness. Consequently, the study does not claim that the framework has been comprehensively validated through large-scale implementation.
  • The research measured short-term changes in student perceptions. Improvements in awareness, perceived value, self-efficacy, and career interest were evaluated immediately after participation in the robotics activities. The study therefore does not determine whether these positive perceptions persist over time or translate into actual enrollment in Electronics Engineering programs.
  • The framework focuses specifically on Electronics Engineering advocacy. Although many components of the proposed framework may be transferable to other engineering disciplines, the research was intentionally designed around the characteristics, learning attributes, and career pathways associated with Electronics Engineering. Adaptation to other engineering fields would require additional investigation.
  • The analytical framework depends on student perceptions. KANO analysis, Importance–Performance Analysis, and Quality Function Deployment collectively provide a rigorous decision-making process, yet the resulting institutional priorities remain influenced by respondents' experiences, expectations, and perceptions of robotics learning activities.
  • The study did not evaluate long-term institutional implementation. While the framework identifies strategic priorities such as faculty development, mentorship, curriculum organization, and experiential robotics ecosystems, the effectiveness of implementing these recommendations across different educational institutions remains an opportunity for future research.

9. Future Research Opportunities

  • Conduct large-scale validation studies involving secondary schools, colleges, and universities across different regions and countries to evaluate the generalizability of the Robotics-Based Advocacy Framework.
  • Investigate whether increased awareness and career interest generated through robotics advocacy ultimately influence students' decisions to enroll in Electronics Engineering or other engineering degree programs.
  • Compare the effectiveness of robotics-based advocacy with other experiential STEM outreach strategies such as maker spaces, virtual laboratories, engineering design competitions, and augmented reality learning environments.
  • Integrate emerging Industry 4.0 technologies—including Artificial Intelligence, Digital Twins, Internet of Things (IoT), autonomous systems, and collaborative robotics—into future advocacy frameworks to examine their influence on engineering career motivation.
  • Evaluate how demographic characteristics, socioeconomic background, prior STEM exposure, and gender influence student responses to robotics-based engineering advocacy.
  • Develop quantitative structural models that examine the causal relationships among motivation, self-efficacy, perceived value, engineering identity, and career intention within robotics-based learning environments.
  • Assess the long-term institutional impact of implementing the proposed framework by measuring student recruitment, enrollment growth, retention, and graduation outcomes within Electronics Engineering programs.

10. Potential for Public Policy Citation

This research offers practical evidence that can support educational policy development aimed at strengthening the future engineering workforce. By demonstrating how robotics can function as an evidence-based advocacy mechanism rather than merely an instructional technology, the study provides valuable insights for government agencies responsible for science, technology, engineering, and mathematics (STEM) education. The framework aligns closely with current priorities related to Industry 4.0, digital transformation, workforce development, and engineering capacity building.

Education ministries may utilize the proposed framework when designing national STEM promotion initiatives that encourage students to pursue engineering careers through experiential learning. Rather than relying exclusively on promotional campaigns or career fairs, policymakers can incorporate structured robotics activities into secondary education outreach programs to improve awareness of Electronics Engineering and related technical disciplines.

Universities and higher education regulators may also adopt the framework when establishing engineering recruitment strategies, curriculum enhancement programs, and institutional outreach activities. The integration of learner-centered decision-making tools with established educational theories provides a transparent methodology for planning advocacy initiatives that reflect student preferences while supporting national workforce objectives.

In addition, the study contributes to broader discussions surrounding engineering education reform by emphasizing the importance of experiential learning, faculty development, mentorship, industry collaboration, and curriculum organization. These institutional priorities can inform future policies that seek to strengthen engineering education ecosystems capable of supporting sustainable technological development.

11. Who Should Read This Paper?

  • Engineering education researchers investigating student motivation, STEM recruitment, and robotics-based learning environments.
  • Electronics Engineering faculty members seeking evidence-based approaches for increasing student enrollment and improving outreach programs.
  • Curriculum developers responsible for designing experiential STEM education and Industry 4.0 learning initiatives.
  • Educational policymakers developing national strategies for engineering workforce development and STEM promotion.
  • Secondary school teachers interested in integrating robotics into engineering career exploration activities.
  • University administrators responsible for student recruitment, academic promotion, and institutional strategic planning.
  • Researchers working in educational robotics, engineering education, technology-enhanced learning, and multidisciplinary STEM research.
  • Industry partners involved in engineering outreach, workforce development, mentorship, and university collaboration programs.

12. Final Thoughts

This study demonstrates that robotics can play a substantially broader role than supporting technical instruction alone. By integrating learner preferences, engineering decision-making methodologies, and established educational theories, the researchers developed a comprehensive Robotics-Based Advocacy Framework capable of promoting Electronics Engineering through meaningful experiential learning. The systematic combination of the KANO Model, Importance–Performance Analysis, and Quality Function Deployment represents a significant methodological contribution because it converts student perceptions into concrete institutional planning priorities.

One of the strongest aspects of the research lies in its interdisciplinary perspective. Educational psychology, career development, engineering education, quality management, and decision-support methodologies are combined into a coherent framework that addresses a practical challenge facing many engineering institutions: attracting future students to increasingly important technical disciplines. Rather than proposing isolated promotional activities, the study recommends coordinated institutional strategies involving curriculum design, faculty development, mentorship, experiential robotics ecosystems, and industry engagement.

The preliminary pilot implementation further strengthens the practical relevance of the framework by demonstrating positive improvements in students' awareness, confidence, perceived value, and career interest following participation in robotics activities. Although additional large-scale validation remains necessary, the findings suggest that learner-centered robotics experiences can become an effective mechanism for strengthening engineering identity among prospective students.

Overall, this article represents a valuable contribution to engineering education research by expanding the role of educational robotics from classroom instruction to strategic institutional advocacy. Universities, policymakers, educators, and engineering organizations seeking sustainable approaches to developing future engineering talent will find this framework both theoretically rigorous and practically applicable.



13. Suggested Citations

Teknomekanik (UNP)

Gonzales, H. G. B., San Diego, A. L., & Namoco, C. (2026). Developing a robotics-based advocacy framework for electronics engineering using an integrated KANO–IPA–QFD approach. Innovation in Engineering, 3(1), 24–54. https://doi.org/10.58712/ie.v3i1.47

APA (7th Edition)

Gonzales, H. G. B., San Diego, A. L., & Namoco, C. (2026). Developing a robotics-based advocacy framework for electronics engineering using an integrated KANO–IPA–QFD approach. Innovation in Engineering, 3(1), 24–54. https://doi.org/10.58712/ie.v3i1.47

IEEE Style

H. G. B. Gonzales, A. L. San Diego, and C. Namoco, "Developing a robotics-based advocacy framework for electronics engineering using an integrated KANO–IPA–QFD approach," Innovation in Engineering, vol. 3, no. 1, pp. 24–54, 2026, doi: 10.58712/ie.v3i1.47.

Harvard Style

Gonzales, H.G.B., San Diego, A.L. & Namoco, C., 2026. Developing a robotics-based advocacy framework for electronics engineering using an integrated KANO–IPA–QFD approach. Innovation in Engineering, 3(1), pp.24–54. Available at: https://doi.org/10.58712/ie.v3i1.47.

Vancouver Style

Gonzales HGB, San Diego AL, Namoco C. Developing a robotics-based advocacy framework for electronics engineering using an integrated KANO–IPA–QFD approach. Innovation in Engineering. 2026;3(1):24–54. doi:10.58712/ie.v3i1.47.

Chicago (Author–Date)

Gonzales, Helen Grace B., Alenogines L. San Diego, and Consorcio Namoco. 2026. "Developing a Robotics-Based Advocacy Framework for Electronics Engineering Using an Integrated KANO–IPA–QFD Approach." Innovation in Engineering 3 (1): 24–54. https://doi.org/10.58712/ie.v3i1.47.

MLA (9th Edition)

Gonzales, Helen Grace B., et al. "Developing a Robotics-Based Advocacy Framework for Electronics Engineering Using an Integrated KANO–IPA–QFD Approach." Innovation in Engineering, vol. 3, no. 1, 2026, pp. 24–54. https://doi.org/10.58712/ie.v3i1.47.

14. Editorial Note

This article review was prepared exclusively from the peer-reviewed publication Developing a robotics-based advocacy framework for electronics engineering using an integrated KANO–IPA–QFD approach published in Innovation in Engineering. All bibliographic information has been verified against the official journal webpage, while the scientific discussion is based solely on the contents of the published article. The review is intended to help readers quickly understand the research context, methodology, principal findings, scientific contributions, and potential applications without replacing the original publication. Readers interested in implementing or extending the proposed Robotics-Based Advocacy Framework are encouraged to consult the complete article for detailed methodological procedures, analytical results, figures, and supporting discussions.

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