Capstone Project & Research Methodology
Common Course for BBA (Honours) & BCA (Honours) — NEP-2020
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| Parameter | Details |
| Course Code | SEC701 (BBA) / SEC701 (BCA) |
| Course Title | Capstone Project & Research Methodology |
| Course Type | Skill Enhancement Course (SEC) — Common to BBA & BCA |
| Credits | 8 (across two semesters) |
| L-T-P | 2 – 0 – 12 |
| Contact Hours | 14 hours per week (2 Lectures + 12 Lab/Project Work) |
| Total Hours (Year) | 210 hours (30 weeks across Semesters VII & VIII) |
| Semester Offered | Semester VII & VIII (Fourth Year — Honours) |
| Prerequisites | Completion of BBA/BCA Degree (Semesters I–VI) |
| Assessment | Internal (50 marks) + External (50 marks) = 100 marks per semester |
| Target Students | BBA (Honours) and BCA (Honours) — dual cohort |
1. Course Description
The Capstone Project & Research Methodology course is an intensive, year-long culminating experience designed for both BBA (Honours) and BCA (Honours) students in their fourth year under the NEP-2020 framework. It serves a dual purpose: (a) to equip students with rigorous research methodology skills applicable across business and computing disciplines, and (b) to provide a structured environment for executing an independent, original research or development project under faculty supervision.
Unlike conventional research methodology courses confined to a single discipline, this course is intentionally cross-disciplinary. It recognizes that modern research — whether investigating consumer behaviour, financial markets, supply chain optimization, software architecture, machine learning models, or human-computer interaction — shares a common methodological core. The course bridges the epistemological traditions of social science research (dominant in business) and design science/experimental research (dominant in computing) within a unified framework.
Students progress through the complete research lifecycle: identifying a viable problem, reviewing literature, formulating research questions or hypotheses, designing a methodology, collecting and analysing data, interpreting findings, and communicating results through a formal dissertation and oral defence. BBA students apply these skills to business problems (marketing, finance, HR, strategy, entrepreneurship), while BCA students apply them to computing problems (software systems, algorithms, data analytics, AI/ML, cybersecurity, HCI). The lecture component delivers common methodological knowledge; the lab/project component is discipline-specific, supervised by domain-expert faculty.
2. Course Objectives
Upon completion of this course, students will be able to:
- Articulate the philosophical foundations of research — positivist, interpretivist, pragmatist, and design-science paradigms — and select the appropriate paradigm for their specific research problem, whether business or computing in nature.
- Conduct a systematic literature review using academic databases, identify research gaps, and position their work within the existing body of knowledge in their discipline.
- Design a methodologically sound research study — selecting from quantitative, qualitative, mixed-methods, experimental, design science, case study, and action research approaches — with appropriate sampling, measurement, and validity considerations.
- Apply discipline-appropriate data collection techniques: surveys, interviews, focus groups, and secondary data for business research; system logs, benchmarks, A/B tests, user analytics, and prototype instrumentation for computing research.
- Analyse data using appropriate tools: statistical software (SPSS, R, Python) for quantitative analysis; thematic coding and content analysis for qualitative data; performance metrics, accuracy measures, and computational evaluation for CS artefacts.
- Produce a formal dissertation that meets academic standards of structure, argumentation, citation, and clarity, and defend it before an examination panel.
- Execute a self-managed project spanning problem definition, planning, iterative execution, risk management, and delivery — demonstrating the professional competencies expected of Honours graduates.
3. Course Outcomes (COs)
Upon successful completion of this course, students will be able to:
| CO | Description | Bloom’s Level |
| CO1 | Formulate a research problem by identifying gaps in the literature and articulating clear, answerable research questions or testable hypotheses appropriate to the discipline (business or computing). | Creating |
| CO2 | Design and justify a research methodology — including choice of paradigm, approach, data collection instruments, sampling strategy, and analysis plan — with explicit attention to validity, reliability, and ethical considerations. | Evaluating |
| CO3 | Execute data collection and analysis using discipline-appropriate tools and techniques, interpreting results in the context of the research questions and acknowledging limitations. | Applying |
| CO4 | Produce a scholarly dissertation and deliver an oral defence that communicates research rationale, method, findings, and implications with clarity, rigour, and intellectual integrity. | Creating |
| CO5 | Demonstrate self-directed project management — including milestone planning, risk mitigation, supervisor communication, and iterative revision — over a sustained, year-long research engagement. | Applying |
CO-PO Mapping
| CO | PO1 | PO2 | PO3 | PO4 | PO5 | PO6 |
| CO1 | 3 | 3 | 2 | 2 | — | — |
| CO2 | 3 | 3 | 3 | 2 | 3 | — |
| CO3 | 2 | 3 | 3 | 3 | 3 | 2 |
| CO4 | 2 | 2 | — | 3 | 2 | 3 |
| CO5 | 2 | 2 | 3 | 2 | 3 | 1 |
3 = Strongly Mapped | 2 = Moderately Mapped | 1 = Slightly Mapped | — = Not Mapped
4. Course Content
Unit 1: Foundations of Research (Lectures 1–8)
Contact Hours: 8 Lectures + 12 Lab
Unit Objective: To establish a strong epistemological and practical foundation in research — covering paradigms, problem formulation, literature review, and research ethics — applicable equally to business and computing disciplines.
Topics Covered:
1.1 What is Research? – Definition and purpose of research — contribution to knowledge – The research onion (Saunders et al.): philosophy → approach → strategy → choice → time horizon → techniques – Characteristics of good research: systematic, logical, empirical, replicable, objective – Research vs. development vs. problem-solving — the distinction between creating knowledge and building products – BBA Context: Business research for decision-making (market research, consumer insights, financial analysis, organizational diagnosis) – BCA Context: Computing research for innovation (algorithm design, system evaluation, empirical software engineering, human-computer interaction studies)
1.2 Research Paradigms and Philosophical Foundations – Positivism: objective reality, causal relationships, hypothesis testing, generalization — dominant in quantitative business research and computational experiments – Interpretivism: subjective meaning, social construction, context-dependence — dominant in qualitative business research (consumer culture, organizational behaviour) and HCI/user experience studies – Pragmatism: practical consequences as truth criterion, methodological pluralism — common in mixed-methods and applied research – Design Science: creating and evaluating artefacts as a form of knowledge contribution — dominant in computer science and information systems research (Hevner et al. 2004; Peffers et al. 2007) – Critical Realism: distinguishing the real, the actual, and the empirical — increasingly used in both business and computing for complex socio-technical systems – Paradigm selection criteria for BBA and BCA research problems
1.3 Problem Identification and Formulation – Sources of research problems: literature gaps, industry challenges, personal observation, supervisor expertise – From topic to problem to research question — narrowing the scope – Characteristics of a good research problem: significant, feasible, clear, ethical, original (the FINER criteria) – Formulating research questions (RQs) and hypotheses – Business: “What factors influence customer churn in Indian e-commerce platforms?” – Computing: “How does model compression affect inference latency and accuracy in mobile-deployed NLP models?” – Conceptual frameworks and theoretical grounding — the role of theory in research – Variables: independent, dependent, moderating, mediating, control – Dual-discipline illustration: The same conceptual structure (“What factors influence X?”) mapped to a marketing problem (X = brand loyalty) and a computing problem (X = software adoption)
1.4 Literature Review – Purpose: mapping the field, identifying gaps, avoiding reinvention, positioning one’s work – Types of literature review: narrative, systematic, scoping, meta-analysis – Systematic Literature Review (SLR) protocol: research questions → search strategy → inclusion/exclusion criteria → quality assessment → data extraction → synthesis – Academic databases and search techniques: – Business: Scopus, Web of Science, Google Scholar, EBSCO, ProQuest, JSTOR, SSRN, NBER – Computing: IEEE Xplore, ACM Digital Library, arXiv, DBLP, CiteSeerX, SpringerLink – Boolean operators, citation chaining (forward and backward), search string construction – Reference management tools: Zotero, Mendeley, EndNote, BibTeX – Critical reading and synthesis — moving from annotated bibliography to thematic synthesis – Writing the literature review: funnel structure, identifying the gap, theoretical framework – Common pitfalls: summarizing without synthesizing, missing key papers, outdated references, citation blindness
1.5 Research Ethics and Integrity – Ethical principles: respect for persons, beneficence, non-maleficence, justice (Belmont Report) – Informed consent, anonymity, confidentiality, data protection – Institutional Review Board (IRB) / Ethics Committee — purpose and process – Plagiarism: definition, types (direct, mosaic, self-plagiarism, accidental), consequences – Citation ethics and academic integrity – Research misconduct: fabrication, falsification, questionable research practices – Business-specific ethics: respondent privacy in surveys/interviews, anonymity in organizational research, insider research and power dynamics, competitive sensitivity – Computing-specific ethics: user data and privacy (GDPR, Indian DPDP Act 2023), algorithmic fairness and bias, responsible AI, ethical hacking and vulnerability disclosure, open-source licensing and attribution – Intellectual Property Rights (IPR): patents, copyright, trademarks, trade secrets — relevance to research – Research data management: storage, retention, sharing, reproducibility
Tutorial / Lab Activities (Unit 1):
- Paradigm mapping exercise: Given 10 research abstracts (5 business, 5 computing), students identify the paradigm, justify their classification, and discuss whether an alternative paradigm could work.
- RQ formulation workshop: Students bring a broad topic of interest and iteratively narrow it to a well-formed research question with instructor and peer feedback.
- SLR scoping exercise: Each student defines a preliminary search string, runs it on at least two databases, documents hits, and refines based on results.
- Ethics case analysis: Analyse real cases (e.g., Facebook emotional contagion study, Cambridge Analytica, Volkswagen emissions research, autonomous vehicle testing ethics) using Belmont principles.
- Plagiarism detection exercise: Students submit a short annotated bibliography; Turnitin/Urkund reports are discussed in class to illustrate proper paraphrasing and citation.
Unit 2: Research Design and Methodology (Lectures 9–16)
Contact Hours: 8 Lectures + 12 Lab
Unit Objective: To develop competence in selecting, designing, and justifying research methodologies — spanning the full spectrum from quantitative experiments to qualitative fieldwork to design-science artefact creation.
Topics Covered:
2.1 Quantitative Research Design – Survey research: – Cross-sectional vs. longitudinal designs – Questionnaire design: question types (open, closed, Likert, semantic differential), wording principles, ordering, pilot testing – Sampling: probability (simple random, stratified, cluster, systematic) and non-probability (convenience, purposive, quota, snowball) – Sample size determination: power analysis, confidence intervals, rules of thumb – Reliability and validity: internal consistency (Cronbach’s α), test-retest, content validity, construct validity, criterion validity – Experimental and quasi-experimental design: – True experiments: random assignment, manipulation, control groups – Pre-experimental, quasi-experimental, and factorial designs – Internal vs. external validity threats – BBA application: A/B testing in marketing, pricing experiments, behavioural economics interventions – BCA application: controlled software engineering experiments, algorithm benchmarking, usability testing with randomized tasks – Secondary data analysis: – Sources: government databases (RBI, NSSO, MOSPI), industry reports, financial statements, open datasets (Kaggle, UCI, Google Dataset Search) – Advantages and limitations of secondary data – Panel data, time-series, cross-sectional data — distinctions
2.2 Qualitative Research Design – Phenomenology: understanding lived experience (e.g., what is it like to be a first-time entrepreneur?) – Ethnography: immersion in a culture or community (e.g., how do agile teams actually work day-to-day?) – Grounded theory: building theory from data through systematic coding (Glaser & Strauss; Strauss & Corbin) – Case study research (Yin): single vs. multiple case designs, holistic vs. embedded units of analysis – Narrative inquiry: analysing stories and accounts – Sampling in qualitative research: purposive, theoretical, maximum variation, snowball — the logic of information-rich cases – Saturation and sample adequacy in qualitative research – BBA application: case studies of organizations, consumer ethnography, leadership narrative analysis – BCA application: case studies of software projects, ethnographic studies of developer teams, user experience narratives
2.3 Mixed-Methods Research – Rationale for mixing methods: complementarity, triangulation, development, initiation, expansion – Design typologies (Creswell & Plano Clark): – Convergent parallel design – Explanatory sequential design (QUAN → qual) – Exploratory sequential design (qual → QUAN) – Embedded design – Integration at interpretation — when and how to mix – Visual diagrams for mixed-methods procedures (notation system)
2.4 Design Science Research (DSR) — Core for BCA, Relevant for BBA – What is design science? Creating and evaluating IT artefacts as a research contribution – Artefact types: constructs, models, methods, instantiations, design theories – The DSR process model (Peffers et al. 2007): 1. Problem identification and motivation 2. Definition of objectives for a solution 3. Design and development 4. Demonstration 5. Evaluation 6. Communication – Evaluation methods in DSR: experimental, observational, analytical, descriptive, testing-based – Design principles and design theories as research outputs – BCA examples: designing a novel algorithm, building a prototype system, developing a security protocol, creating a new software architecture pattern – BBA relevance: designing a new business model, creating a management framework, developing a decision support tool, designing an assessment instrument
2.5 Action Research and Participatory Approaches – Action research cycle: diagnose → plan → act → evaluate → reflect – The researcher as change agent — insider vs. outsider stance – Participatory action research (PAR) — community-based, emancipatory – BBA applications: organizational change interventions, process improvement studies – BCA applications: participatory design of systems, action research in software process improvement
2.6 Validity, Reliability, and Rigour – Quantitative validity: internal, external, statistical conclusion, construct (Cook & Campbell) – Qualitative trustworthiness (Lincoln & Guba): credibility, transferability, dependability, confirmability – Design science evaluation: validity of artefact, utility, efficacy, quality – Triangulation: data, investigator, theory, methodological triangulation – Threats to validity in business research vs. computing research — a comparative table – Common methodological errors in undergraduate dissertations
Tutorial / Lab Activities (Unit 2):
- Methodology matching exercise: Given 8 research questions (4 business, 4 computing), students select and defend the most appropriate research design.
- Questionnaire design and peer review: Each student designs a 10-item questionnaire, which is peer-reviewed using a checklist of best practices.
- Qualitative coding practice: Students are given a short interview transcript and practice open coding, axial coding, and theme development.
- Design science case analysis: Analyse a published DSR paper (from IS or CS literature) and map it to the Peffers process model.
- Mixed-methods design task: Given a real research problem, draft a mixed-methods design with a procedural diagram and justification of the mixing strategy.
- Methodology chapter outline: Each student produces a draft outline of their dissertation’s methodology chapter with justification for each choice.
Unit 3: Data Collection and Analysis (Lectures 17–24)
Contact Hours: 8 Lectures + 12 Lab
Unit Objective: To develop hands-on competence in collecting and analysing data — both quantitative (statistical, computational) and qualitative (thematic, content) — using disciplinary-standard tools and reporting conventions.
Topics Covered:
3.1 Quantitative Data Analysis — Statistical Foundations – Descriptive statistics: measures of central tendency (mean, median, mode), dispersion (range, variance, standard deviation, IQR), distribution shape (skew, kurtosis) – Data visualization: histograms, box plots, scatter plots, bar charts, heatmaps — choosing the right chart – Inferential statistics: – Sampling distributions and the Central Limit Theorem – Estimation: point estimates, confidence intervals – Hypothesis testing: null and alternative hypotheses, Type I and Type II errors, p-values, significance (α), power (1-β), effect size – Parametric tests: t-tests (one-sample, independent, paired), ANOVA (one-way, factorial), Pearson correlation – Non-parametric tests: Mann-Whitney U, Wilcoxon signed-rank, Kruskal-Wallis, Spearman’s ρ, Chi-square – Regression analysis: – Simple and multiple linear regression – Logistic regression (binary, multinomial) – Model diagnostics: R², adjusted R², residuals, multicollinearity (VIF), heteroscedasticity – Mediation and moderation analysis (conceptual introduction) – Factor analysis: exploratory (EFA) and confirmatory (CFA) — conceptual introduction – Tools: SPSS, R, Python (pandas, statsmodels, scipy.stats), JASP, jamovi
3.2 Quantitative Data Analysis — Computational Evaluation (BCA Focus) – Performance metrics: – Classification: accuracy, precision, recall, F1-score, ROC-AUC, confusion matrix – Regression: MAE, MSE, RMSE, R² – Clustering: silhouette score, Davies-Bouldin index – Information retrieval: precision@k, recall@k, MAP, NDCG – Experimental design for CS: – Benchmarking protocols: datasets, baselines, ablation studies – Cross-validation: k-fold, stratified, leave-one-out, time-series split – Statistical tests for model comparison: McNemar’s test, paired t-test, Wilcoxon – Reproducibility: random seeds, hyperparameter reporting, environment specification – System evaluation: – Throughput, latency, scalability, resource utilization – Load testing, stress testing, profiling – A/B testing for software features — statistical considerations – User studies and usability evaluation: – System Usability Scale (SUS), NASA-TLX, UEQ – Task completion rate, time-on-task, error rate – Think-aloud protocol and retrospective probing
3.3 Qualitative Data Analysis – Preparing qualitative data: transcription, translation, anonymization, data management – Thematic analysis (Braun & Clarke): familiarization → initial coding → theme generation → theme review → definition → writing – Content analysis: deductive and inductive approaches, coding frames, inter-coder reliability – Template analysis, framework analysis (Ritchie & Spencer), matrix displays – Coding: open, axial, selective (grounded theory tradition); descriptive, in vivo, process, emotion coding – Computer-Assisted Qualitative Data Analysis Software (CAQDAS): NVivo, ATLAS.ti, MAXQDA, Dedoose – Writing up qualitative findings: thematic maps, respondent quotations, narrative weaving – BBA examples: analysing interview data on consumer motivations, coding annual report narratives, thematic analysis of leadership stories – BCA examples: analysing developer interview data, coding user feedback themes, qualitative analysis of bug report descriptions
3.4 Mixed Data Integration and Interpretation – Joint displays: side-by-side comparison, statistics-by-themes – Merging quantitative and qualitative findings – Addressing discordant findings — when numbers and narratives disagree – Writing integrated findings in a dissertation
3.5 Research Software and Tools — Hands-On Orientation – Statistical: SPSS, R (RStudio), Python (Jupyter Notebook), JASP – CAQDAS: NVivo, ATLAS.ti – Reference management: Zotero (open source), Mendeley, EndNote – Survey platforms: Google Forms, Microsoft Forms, Qualtrics, KoboToolbox – Project management: Trello, Notion, GitHub Projects – Writing: LaTeX (Overleaf) for CS dissertations; MS Word / Google Docs for business dissertations – Plagiarism checking: Turnitin, Urkund, iThenticate – Data repositories: Zenodo, Figshare, GitHub, OSF
Tutorial / Lab Activities (Unit 3):
- SPSS/R/Python lab — Descriptive and inferential statistics: Students are given a dataset and must compute descriptive statistics, run t-tests/ANOVA, correlation, and regression, interpreting output.
- ML evaluation lab (BCA): Students train two classifiers on a common dataset, compute precision/recall/F1/ROC-AUC, and perform a statistical comparison test.
- Thematic analysis workshop: Students code a provided interview transcript, develop themes, and produce a thematic map.
- NVivo/ATLAS.ti orientation: Hands-on session with CAQDAS — importing data, coding, querying, visualizing.
- Tool selection decision exercise: Given a research scenario, students justify their choice of data collection and analysis tools.
- Data chapter draft: Each student drafts the data analysis section of their dissertation with actual or mock data.
Unit 4: Capstone Project Execution and Communication (Lectures 25–30)
Contact Hours: 6 Lectures + Continuous Project Work
Unit Objective: To guide students through the complete capstone project lifecycle — from proposal through execution to dissertation writing and oral defence — while developing project management, academic writing, and presentation skills.
Topics Covered:
4.1 The Capstone Project Lifecycle – Phase 1 — Proposal and Planning (Semester VII, Weeks 1–8): – Selecting a supervisor and negotiating the research topic – Writing a research proposal: problem statement, RQs, literature review, proposed methodology, timeline, expected contributions – Proposal presentation and approval by a review panel – Ethical clearance submission (if required) – Phase 2 — Execution (Semester VII, Weeks 9–15 + Semester VIII, Weeks 1–8): – Literature review refinement – Data collection / artefact development – Data analysis / artefact evaluation – Iterative writing and supervisor feedback cycles – Progress seminar presentations – Phase 3 — Completion and Defence (Semester VIII, Weeks 9–15): – Finalizing the dissertation – Plagiarism check and supervisor approval – Submission and viva-voce examination
4.2 Research Proposal Writing – Structure of a research proposal: title, abstract, introduction, problem statement, research questions, literature review (preliminary), methodology, timeline (Gantt chart), expected contributions, references – Proposal evaluation criteria: clarity, feasibility, originality, methodological soundness – Common reasons for proposal rejection and how to avoid them – BBA vs. BCA proposal examples — annotated
4.3 Project Management for Research – Work breakdown structure (WBS) for a capstone project – Timeline planning: Gantt charts, milestones, critical path – Risk management: identifying risks (data access, tool failure, participant recruitment, scope creep), mitigation strategies – Supervisor relationship management: setting expectations, communication frequency, receiving and acting on feedback – Version control for research: Git for code and documents, backup strategies – Managing setbacks: what to do when data collection fails, the experiment doesn’t work, or results are null – The iterative nature of research — embracing revision
4.4 Academic Writing – Dissertation structure: – Title page, declaration, certificate, acknowledgements – Abstract (structured: background, aim, method, findings, conclusion) – Chapter 1: Introduction – Chapter 2: Literature Review – Chapter 3: Research Methodology – Chapter 4: Data Analysis / Artefact Description & Evaluation – Chapter 5: Discussion and Conclusion – References, Appendices – Writing principles: clarity, coherence, conciseness, criticality – Argumentation: claim → evidence → warrant – Paragraph structure: topic sentence → evidence → analysis → transition – Referencing styles: – APA 7th Edition (predominant in Business) – IEEE (predominant in Computing) – Harvard style (common alternative for Business) – Tables, figures, and visual displays of evidence – Common writing problems: vagueness, over-generalization, unsupported claims, redundancy, poor transitions – The editing process: structural edit → line edit → proofread – Supervisor feedback: how to receive it and act on it productively
4.5 Presentation and Oral Defence – Preparing the defence presentation: 15–20 slides, structured narrative – Slide design principles: clarity, minimal text, effective visuals – Anticipating examiner questions — common lines of questioning: – “Why did you choose this method over alternatives?” – “How do you know your findings are valid?” – “What is your main contribution?” – “If you could do it again, what would you change?” – “How do your findings generalize beyond your sample/case/system?” – Mock defence and peer feedback – Handling nervousness and presenting with confidence – The viva voce: format, etiquette, responding to criticism
4.6 Beyond the Capstone – Converting the dissertation into a research paper – Conference and journal selection for undergraduates – Publishing ethics: authorship, simultaneous submission, responding to reviewers – The dissertation as a portfolio piece for job interviews and graduate school applications
Tutorial / Lab Activities (Unit 4):
- Proposal pitch: Each student delivers a 5-minute proposal pitch to a faculty panel and peer audience, receiving structured feedback.
- Gantt chart and risk register: Students create a detailed project plan with timeline and risk register.
- Writing workshop — literature review: Students bring a draft literature review section for peer critique using a structured rubric.
- Writing workshop — methodology and findings: Structured peer review of the methodology chapter and preliminary findings.
- Mock defence: Each student presents their work-in-progress in a simulated viva environment with faculty posing typical examiner questions.
- Final dissertation submission: Formal submission after plagiarism check, supervisor sign-off, and formatting compliance.
5. Weekly Teaching Plan (30 Weeks across 2 Semesters)
Semester VII (15 Weeks)
Semester VIII (15 Weeks)
| Week | Unit | Lecture Topic (2 hrs) | Lab/Project Work (12 hrs) | Milestone |
| 16 | Unit 3 | Mixed Data Integration; Research Software & Tools — Comprehensive Orientation | Data collection/analysis execution; Supervisor consultations | |
| 17 | Unit 3 | Tool Selection for Different Research Scenarios — BBA and BCA comparison | Intensive data collection/artefact development | |
| 18 | Unit 4 | The Capstone Project Lifecycle; Research Proposal Writing (retrospective review) | Data analysis/artefact evaluation continues | |
| 19 | Unit 4 | Project Management for Research: WBS, Gantt Charts, Risk, Supervisor Relationship, Managing Setbacks | Progress review meetings; Draft chapters due to supervisor | |
| 20 | — | Progress Seminar — Students present work-in-progress (15 min + Q&A) | Faculty and peer feedback integration | Progress Seminar completed |
| 21 | Unit 4 | Academic Writing I: Dissertation Structure, Writing Principles, Argumentation, Referencing Styles (APA vs. IEEE) | Writing workshop — literature review chapter peer review | |
| 22 | Unit 4 | Academic Writing II: Tables & Figures, Common Problems, The Editing Process, Responding to Feedback | Writing workshop — methodology and findings peer review | |
| 23 | — | Intensive Writing Week — No lecture; full draft preparation | Supervisor feedback on full draft; Plagiarism check (first pass) | Full draft due |
| 24 | Unit 4 | Presentation & Oral Defence: Slide Design, Anticipating Questions, Viva Voce Etiquette | Mock defence sessions in small groups | |
| 25 | — | Mock Defence Week — Simulated viva with faculty panel | Individual mock defences with structured feedback | Mock defence completed |
| 26 | — | Revision Week — Addressing mock defence feedback; Final edits | Supervisor sign-off; Plagiarism check (final) | |
| 27 | Unit 4 | Beyond the Capstone: Publishing, Conference Selection, Portfolio Use | Final formatting; Submission preparation | |
| 28 | — | Dissertation Submission | Final submission with all documentation | Dissertation submitted |
| 29 | — | Viva Voce Examinations | Individual oral defence before examination panel | Viva Voce completed |
| 30 | — | Result processing; Grade finalization; Outstanding revisions if any | Minor corrections submission (if required by examiners) | Course completed |
6. BBA vs. BCA Research Differentiation Guide
This course is intentionally cross-disciplinary. The table below guides students and supervisors in selecting appropriate research approaches for each discipline.
| Research Dimension | BBA (Business) | BCA (Computing) |
| Typical research problems | Consumer behaviour, marketing effectiveness, financial performance, HR practices, strategy implementation, entrepreneurship, supply chain optimization, organizational culture | Algorithm design/optimization, software architecture, ML model development, cybersecurity solutions, HCI/user experience, data analytics systems, network protocols, IoT applications |
| Dominant paradigms | Positivist (quantitative surveys), Interpretivist (qualitative case studies), Pragmatist (mixed methods) | Design Science (artefact creation), Positivist (experiments/benchmarks), Interpretivist (user studies) |
| Common research designs | Survey research, case study, correlational, experimental (A/B testing), grounded theory | Design science, controlled experiment, benchmarking, simulation, action research, user study |
| Typical data sources | Surveys (consumers, employees, managers), interviews, financial reports, government databases (RBI, NSSO), company records | System logs, benchmark datasets, code repositories, sensor data, API outputs, user interaction logs, open datasets (Kaggle, UCI, GitHub) |
| Sample / data size | Survey: n=100–500+; Interviews: 10–30 participants; Cases: 1–5 organizations | Benchmarks: standard datasets; User studies: 10–30 participants; System evaluation: varied, performance-based |
| Primary analysis methods | Descriptive/inferential statistics, regression, factor analysis, thematic analysis, content analysis | Performance metrics (accuracy, precision, recall, F1, latency, throughput), statistical tests for model comparison, complexity analysis (Big-O), thematic analysis for qualitative CS studies |
| Primary tools | SPSS, R, Stata, NVivo, ATLAS.ti, Excel, Qualtrics | Python (NumPy, pandas, scikit-learn, TensorFlow, PyTorch), R, Jupyter, Git, Docker, LaTeX |
| Typical dissertation structure | Introduction → Literature Review → Methodology → Data Analysis → Discussion → Conclusion (standard social science structure) | Introduction → Literature Review → Methodology/Design → System/Artefact Description → Evaluation → Discussion → Conclusion (DSR structure) |
| Nature of contribution | Empirical findings, theoretical insights, managerial implications, policy recommendations | Novel artefact (algorithm, system, tool, framework), empirical evaluation of existing techniques, design principles, methodological contribution |
| Referencing style | APA 7th Edition (preferred), Harvard | IEEE (preferred), ACM |
| Common pitfalls | Weak sampling, common method bias, low response rates, over-generalization from single cases, poor questionnaire design | Unclear baseline comparisons, overfitting, reproducibility failures, building without evaluation, treating development as research without methodological framing |
7. Assessment Scheme
7.1 Semester VII — Internal (50 Marks) + External (50 Marks) = 100 Marks
7.2 Semester VIII — Internal (50 Marks) + External (50 Marks) = 100 Marks
| Component | Description | Marks (Internal) | Marks (External) |
| Progress Seminar | Mid-semester oral presentation of work-in-progress to faculty panel | 10 | — |
| Dissertation Report | Final written dissertation (typically 15,000–25,000 words for BBA; 12,000–20,000 words for BCA) | — | 30 |
| Viva Voce | Oral defence of the dissertation before an examination panel (internal + external examiner) | — | 20 |
| Continuous Supervisor Assessment | Holistic evaluation of the year-long research engagement | 20 | — |
| Research Dissemination | Submission of a condensed research paper (4–6 pages) suitable for a conference or journal | 10 | — |
| Reflections & Learning Portfolio | A 1000-word reflective essay on the research journey + evidence of skill development | 10 | — |
| Total | 50 | 50 |
7.3 Dissertation Evaluation Rubric
| Criterion | Weight | Excellent (80–100%) | Good (60–79%) | Adequate (40–59%) | Inadequate (<40%) |
| Problem definition & rationale | 15% | Clear gap; well-motivated; significant | Clear but narrow; adequately motivated | Vague; weak motivation | Absent or trivial |
| Literature review | 15% | Comprehensive; critical synthesis; identifies gap | Adequate coverage; some synthesis | Descriptive; limited sources | Minimal; no synthesis |
| Methodology | 20% | Appropriate; justified; rigorous; ethical | Appropriate; adequately justified | Some weaknesses; partially justified | Inappropriate; unjustified |
| Analysis / Artefact evaluation | 20% | Thorough; correct; well-interpreted | Adequate; minor errors; reasonable interpretation | Superficial; some errors; weak interpretation | Absent; fundamentally flawed |
| Discussion & contribution | 15% | Deep insight; clear contribution; limitations acknowledged | Reasonable insight; contribution stated | Surface-level; contribution unclear | No discussion of contribution |
| Writing & presentation | 15% | Clear; well-structured; error-free; correctly referenced | Clear; minor errors; adequately referenced | Some clarity issues; referencing errors | Poorly written; plagiarism concerns |
8. Textbooks and References
Primary Textbooks (Latest Editions):
- Saunders, M., Lewis, P., & Thornhill, A. — Research Methods for Business Students, Pearson. (Core for BBA; useful for BCA on qualitative/survey methods)
- Creswell, J. W., & Creswell, J. D. — Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, Sage Publications. (Core for both BBA and BCA)
- Wazlawick, R. S. — Object-Oriented Analysis and Design for Information Systems: Modeling with UML, OCL, and IFML, Elsevier. (BCA — design science artefacts)
Supplementary References:
- Zikmund, W. G., Babin, B. J., Carr, J. C., & Griffin, M. — Business Research Methods, Cengage.
- Bryman, A., & Bell, E. — Business Research Methods, Oxford University Press.
- Yin, R. K. — Case Study Research and Applications: Design and Methods, Sage Publications.
- Braun, V., & Clarke, V. — Thematic Analysis: A Practical Guide, Sage Publications.
- Field, A. — Discovering Statistics Using IBM SPSS Statistics, Sage Publications.
- Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). “Design Science in Information Systems Research.” MIS Quarterly, 28(1), 75–105.
- Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). “A Design Science Research Methodology for Information Systems Research.” Journal of Management Information Systems, 24(3), 45–77.
- Easterbrook, S., Singer, J., Storey, M. A., & Damian, D. (2008). “Selecting Empirical Methods for Software Engineering Research.” In Guide to Advanced Empirical Software Engineering, Springer.
- Wohlin, C., Runeson, P., Höst, M., Ohlsson, M. C., Regnell, B., & Wesslén, A. — Experimentation in Software Engineering, Springer.
- Booth, W. C., Colomb, G. G., Williams, J. M., Bizup, J., & FitzGerald, W. T. — The Craft of Research, University of Chicago Press.
- Montgomery, D. C. — Design and Analysis of Experiments, Wiley.
- Miles, M. B., Huberman, A. M., & Saldaña, J. — Qualitative Data Analysis: A Methods Sourcebook, Sage Publications.
Indian Context Readings:
- Sachdeva, J. K. — Business Research Methodology, Himalaya Publishing House.
- Kothari, C. R., & Garg, G. — Research Methodology: Methods and Techniques, New Age International. (Widely used across Indian universities — covers both business and technical research)
- Chawla, D., & Sondhi, N. — Research Methodology: Concepts and Cases, Vikas Publishing.
- Panneerselvam, R. — Research Methodology, PHI Learning. (Strong on quantitative methods and operations research)
Research Papers on Research Methodology:
- Saunders, M., Lewis, P., & Thornhill, A. (2019). “Understanding Research Philosophy and Approaches to Theory Development.” Chapter 4 in Research Methods for Business Students.
- Gregor, S., & Hevner, A. R. (2013). “Positioning and Presenting Design Science Research for Maximum Impact.” MIS Quarterly, 37(2), 337–355.
- Kitchenham, B., & Charters, S. (2007). “Guidelines for Performing Systematic Literature Reviews in Software Engineering.” EBSE Technical Report, Keele University/Durham University.
- Palvia, P., Leary, D., Mao, E., Midha, V., Pinjani, P., & Salam, A. F. (2004). “Research Methodologies in MIS: An Update.” Communications of the AIS, 14, 526–542.
- Venable, J., Pries-Heje, J., & Baskerville, R. (2016). “FEDS: A Framework for Evaluation in Design Science Research.” European Journal of Information Systems, 25(1), 77–89.
Online Resources:
- Google Scholar — https://scholar.google.com
- ResearchGate — https://www.researchgate.net
- Purdue OWL (APA Formatting) — https://owl.purdue.edu/owl/research_and_citation/apa_style
- IEEE Reference Guide — https://ieeeauthorcenter.ieee.org
- Overleaf (LaTeX for CS dissertations) — https://www.overleaf.com
- Swayam/NPTEL courses on Research Methodology
- Coursera: “Understanding Research Methods” (University of London)
- CSRankings.org — for identifying top CS research venues and papers
9. Mapping of Course Outcomes to Assessment
| Course Outcome | Assessment Method(s) |
| CO1: Formulate a research problem | Proposal (Sem VII), Progress Report, Dissertation, Viva Voce |
| CO2: Design and justify a methodology | Proposal, Methodology chapter, Term Paper, Viva Voce |
| CO3: Execute data collection and analysis | Progress Report, Supervisor Evaluation, Dissertation, Viva Voce |
| CO4: Produce a scholarly dissertation and oral defence | Dissertation Report, Viva Voce, Progress Seminar |
| CO5: Demonstrate self-directed project management | Supervisor Evaluation, Progress Report, Reflections & Learning Portfolio |
10. Integration with BBA and BCA Honours Programs
| Program Component | Integration |
| BBA — CC404 Business Research Methodology | This capstone builds directly on the foundational research methodology course taken in Semester IV. |
| BBA — SEC501/502 (Internship/Major Project) | The capstone represents a significant escalation in rigour and independence compared to the Year 3 project. |
| BCA — Programming and System Development Courses | The capstone provides the research framing for system-building projects, distinguishing research from pure development. |
| BBA — DSE Specialization | Students apply domain-specific knowledge (finance, marketing, HR, analytics) to their research problem. |
| BCA — DSE/Technical Electives | Students apply specialization knowledge (AI/ML, cybersecurity, data science, networks) to their research problem. |
| Both — NEP-2020 Research Orientation | This course fulfils the NEP-2020 mandate for research orientation in the Honours year, preparing students for postgraduate research or industry R&D roles. |
| Both — Employability | The project management, critical thinking, data analysis, and communication skills developed are directly transferable to professional roles in consulting, analytics, product management, software engineering, and research. |
Note: This syllabus is aligned with the AICTE Model Curriculum for UG Degree in BBA and BCA (NEP-2020). It is designed as a common syllabus that admitting universities may adapt for their specific program needs. The disciplinary differentiation is embedded within the common methodological framework — the lecture content is shared; the lab/project supervision is discipline-specific. Universities offering only one of the two programs may use this syllabus by de-emphasizing the cross-disciplinary examples while retaining the core methodological content.
Prepared for BBA (Honours) and BCA (Honours) — Fourth Year Capstone Experience, NEP-2020
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