B_MOP Technical thesis methodology

Institute of Technology and Business in České Budějovice
winter 2026
Extent and Intensity
2/2/0. 5 credit(s). Type of Completion: zk (examination).
Teacher(s)
Ing. Miluše Balková, Ph.D. (seminar tutor)
Ing. Monika Burghauserová (seminar tutor)
Ing. Tereza Matasová (seminar tutor)
prof. Ing. Marek Vochozka, MBA, Ph.D., dr. h.c. (seminar tutor)
Guaranteed by
prof. Ing. Marek Vochozka, MBA, Ph.D., dr. h.c.
Department of Corporate Finance and Economics – School of Expertness and Valuation – Rector – Institute of Technology and Business in České Budějovice
Supplier department: Department of Corporate Finance and Economics – School of Expertness and Valuation – Rector – Institute of Technology and Business in České Budějovice
Timetable of Seminar Groups
B_MOP/K04: Sat 10. 10. 14:50–16:20 D515, Sun 1. 11. 9:40–14:10 D515, Sat 21. 11. 13:05–17:35 D515, Sat 12. 12. 9:40–11:10 D515, M. Burghauserová
B_MOP/P01: Thu 8:00–9:30 I317A, M. Vochozka
B_MOP/S01: Fri 18:10–19:40 D316, M. Burghauserová
B_MOP/S02: Wed 13:05–14:35 D316, M. Balková
B_MOP/S03: Wed 18:10–19:40 D315, M. Burghauserová
Prerequisites
OBOR(RLZp) || OBOR(RLZk)

The course has no subject prerequisites. No prior knowledge of statistics is assumed; quantitative methods are practised in MS Excel from the ground up. Enrolment is governed by the course enrolment restrictions.

Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives supported by learning outcomes

The aim of the course is to acquire the knowledge and practical skills of scientific research methods and of the preparation, writing, presentation and defence of academic texts.


The course treats an academic text as a research report written in real time: over the semester the student works on a single complex, unstructured, open-ended task and writes about it continuously. Students learn to choose a method proportionate to the research problem, apply it correctly to real data, interpret the results and record the whole procedure so that it is verifiable and repeatable. The outcome is a text publishable at the level expected of a bachelor student.


Part of the aim is that students learn to use artificial-intelligence tools responsibly and transparently: they recognise where such a tool supports their own work and where it replaces it, and they declare its use properly.

Learning outcomes

Upon successful completion of the course the student:

  1. selects a suitable topic and defines the aim of the work;
  2. identifies a research gap and verifies the availability of data and methods to fill it;
  3. formulates research questions and hypotheses matching the stated aim;
  4. carries out a sound literature review including a critical assessment of source quality;
  5. chooses a research design (quantitative, qualitative or mixed) proportionate to the research question and defends that choice;
  6. distinguishes primary and secondary data, designs their collection and assembles a sample;
  7. applies basic qualitative methods (structured interview, content analysis, case study);
  8. applies basic quantitative methods (descriptive statistics, hypothesis testing, correlation and regression analysis) using MS Excel;
  9. assesses the validity and reliability of their procedure and states the limitations of their research;
  10. writes the methodology both in bullet form and as continuous text;
  11. presents and interprets results appropriately, including tables and figures;
  12. discusses the results and confronts them with the state of knowledge;
  13. formulates the contribution of the results for a named addressee — who specifically may use the result, for what decision, how the research gap is narrowed and under what conditions the contribution holds — and writes it as a separate numbered chapter Contribution of the Results;
  14. evaluates the results and formulates the conclusion;
  15. applies the rules of formal presentation and the ČSN ISO 690 citation standard;
  16. observes the principles of research ethics and avoids plagiarism;
  17. uses artificial-intelligence tools in line with the rules of the Institute, recognises the boundary between permissible support and impermissible substitution of their own work, and declares AI use transparently;
  18. duly presents and defends their work.
Syllabus

Lectures:

  1. Introduction. Science, research and the academic text. Copyright, plagiarism, research ethics; AI, authorship and responsibility; the three-level model of admissibility.
  2. Types of academic texts and their structure. Information sources, the Institute library, the National Technical Library, research databases. Criteria of relevance and quality.
  3. From topic to research problem: title, introduction, aim. The research gap and operationalisation.
  4. Literature review I — search strategy, keywords, Boolean operators, search protocol. AI in source searching and the verification of hallucinated citations.
  5. Literature review II — synthesis of findings, citation analysis and keyword analysis in Web of Science and Scopus.
  6. Research design — quantitative, qualitative and mixed; validity and reliability.
  7. Data and sampling — primary and secondary data, data sources, sampling techniques, sample size.
  8. Qualitative methods — interview, focus group, observation, case study, content analysis, coding.
  9. Quantitative methods I — surveys, scales, descriptive statistics, hypothesis testing. AI in data processing and the protection of personal data.
  10. Quantitative methods II — correlation, regression, time series, multivariate methods, an introduction to neural networks.
  11. Methods in business economics — financial analysis, comparison and benchmarking, modelling and simulation, scenarios, expert methods.
  12. Results and discussion — data presentation, interpretation, confrontation with the literature, limitations. Interpretation as the area where AI cannot replace the author.
  13. Conclusion, abstract, finalisation of the text. Presentation and defence. The AI use declaration and the citation of AI tools.

Seminars:


The seminar is run as the continuous writing of one academic text. Students work largely on their own and consult the text with the teacher as it grows; each part submitted is returned with comments. Topics change every semester and each must meet three conditions: a research gap exists, data are available, and methods exist to process them.

  1. Topic selection. Registration with the National Technical Library. Introduction to the Principles of the Use of Artificial Intelligence. Originality check of a short text.
  2. Analysis of a model academic text: the compulsory parts and their order, including the numbered chapter Contribution of the Results between the discussion and the conclusion. Searching in Web of Science and Scopus.
  3. Title and introduction; formulation of the aim and of the research gap.
  4. Literature review — phase one.
  5. Literature review — phase two; synthesis.
  6. Design of the student research; hypotheses or research questions.
  7. Assembling the data set; description of the sample.
  8. Practising a qualitative method on an assigned case.
  9. Descriptive statistics and a hypothesis test in MS Excel.
  10. A regression model on the student own data.
  11. Application of a field-specific method to the student topic; methodology in bullet form.
  12. Methodology as text; results and discussion; the chapter Contribution of the Results in three paragraphs (contribution to practice — contribution to knowledge — for whom it is intended and under what conditions it holds).
  13. Conclusion referring to the contribution of the results, abstract, finalisation. The AI use declaration as a compulsory part of submission. Trial presentation.
Literature
    required literature
  • VOCHOZKA, M. a Z. ROWLAND, 2026. Metody odborné práce: skripta. České Budějovice: Vysoká škola technická a ekonomická v Českých Budějovicích. ISBN 978-80-7468-218-6.
  • VOCHOZKA, M. a Z. ROWLAND, 2026. Metody odborné práce: cvičebnice. České Budějovice: Vysoká škola technická a ekonomická v Českých Budějovicích. ISBN 978-80-7468-220-9.
  • VOCHOZKA, M. a Z. ROWLAND, 2026. Metodika psaní odborné práce na VŠTE. České Budějovice: Vysoká škola technická a ekonomická v Českých Budějovicích.
  • VOCHOZKA, M. a Z. ROWLAND, 2026. Zásady používání umělé inteligence při zpracování odborných prací na VŠTE. České Budějovice: Vysoká škola technická a ekonomická v Českých Budějovicích.
    recommended literature
  • SAUNDERS, M. N. K., P. LEWIS a A. THORNHILL, 2023. Research Methods for Business Students. 9th ed. Harlow: Pearson. ISBN 978-1-292-40272-7.
  • BELL, E., B. HARLEY a A. BRYMAN, 2022. Business Research Methods. 6th ed. Oxford: Oxford University Press. ISBN 978-0-19-886944-3.
  • CRESWELL, J. W. a J. D. CRESWELL, 2023. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. 6th ed. Thousand Oaks: SAGE. ISBN 978-1-07-181794-0.
  • HENDL, J., 2023. Kvalitativní výzkum: základní teorie, metody a aplikace. 5. přeprac. vyd. Praha: Portál. ISBN 978-80-262-1968-2.
  • BAILEY, S., 2025. Academic Writing: A Handbook for International Students. 6th ed. Abingdon: Routledge. ISBN 978-1-032-83417-7.
  • ČSN ISO 690, 2022. Informace a dokumentace – Pravidla pro bibliografické odkazy a citace informačních zdrojů. Praha: Česká agentura pro standardizaci.
  • ŠANDEROVÁ, J., 2005. Jak číst a psát odborný text ve společenských vědách: několik zásad pro začátečníky. Praha: Sociologické nakladatelství. ISBN 80-86429-40-7.
Organizační formy výuky
Lecture
Seminar
Tutorial
Komplexní výukové metody
Frontal Teaching
Group Teaching - Cooperation
Critical Thinking
Individual Work– Individual or Individualized Activity
Teaching Supported by Multimedia Technologies
E-learning
Student Workload
ActivitiesNumber of Hours of Study Workload
Daily StudyCombined Study
Discussion of results66
Text formatting22
Hypotheses, research questions55
Literary research1414
Methodology1414
Title and introduction88
Preparation for the Mid-term Test55
Preparation for Seminars, Exercises, Tutorial 34
Preparation of presentation, defence55
Results1313
Conclusion66
Attendance on Lectures262
Attendance on Seminars/Exercises/Tutorial/Excursion2616
Total:130130
Metody hodnocení a jejich poměr
Test – mid-term 10%
Presentation 20%
Seminary Work 70%
Podmínky testu

To pass the course a student must obtain at least 70 of 100 points in total and must pass the continuous test.


Seminar paper — 70 points. The paper is assessed using the course assessment form. The form contains three binary gates and ten scored content criteria of 7 points each (introduction, aim, research questions, literature review, methodology, results, discussion, conclusion, citations, formal presentation). The binary gates are: the work states the aim and also fulfils it; the work cites at least 15 relevant sources; the work contains an introduction, a literature review, a methodology, results, a discussion and a conclusion. If any gate is marked NO, the work is graded F regardless of the points obtained.


Presentation and defence of the seminar paper — 20 points.


Continuous test — 10 points. The test is a learning device, not a restriction. Students may repeat it without limit during the semester and each attempt draws a different set of questions. The test is passed only on reaching 100 % of correct answers; passing it earns 10 points towards the overall assessment. A successful attempt is recorded automatically in the course notebook. The test is open to students from week 1 to week 13 of the semester and closes after week 13.


Overall classification (100–0 points): A 100–90, B 89.99–84, C 83.99–77, D 76.99–73, E 72.99–70, FX 69.99–30, F 29.99–0.

Language of instruction
Czech
Teacher's information

Study materials. From the academic year 2026/2027 the course has its own complete set of materials in Czech and English: the textbook Research Methods (13 chapters), the Workbook (13 chapters with worked examples and exercises with answers), an audiobook, lecture slides, seminar worksheets including data files, a test bank, the Methodology of Academic Writing at the Institute, the Principles of the Use of Artificial Intelligence in the Preparation of Academic Work, templates for seminar, bachelor and master theses, model works of all three types, and an asynchronous course in Moodle. The materials are stored in the study materials of the course in the Information System.

Link to qualification theses. The Methodology of Academic Writing, the templates and the model works apply to seminar papers, bachelor theses and master theses alike. This course is where students first learn to work with them. The supervisor and opponent assessment forms in the Information System remain unchanged; the Methodology only interprets them.

Citation style. The basic citation style of the course is ČSN ISO 690, author-date (Harvard) referencing. APA 7 is an admissible alternative if the student chooses it in agreement with the teacher and applies it consistently. Only one citation style may be used in a single work; the chosen style is stated in the methodology chapter. Mixing styles is a reason for returning the work.

Artificial intelligence. Students are required to familiarise themselves with the Principles of the Use of Artificial Intelligence in the Preparation of Academic Work. The use of AI tools is declared in the AI use declaration, which is a compulsory part of the submitted text.

Software and information support. MS Excel. Web of Science and Scopus (via the Institute VPN). The catalogue and electronic information resources of the Institute library. Registration with the National Technical Library is compulsory. The plagiarism detection tool in the Information System. Citace.com. The Moodle course. Thesis templates in DOCX.

Continuous test. The test is open in the Information System from week 1 to week 13 of the semester, may be repeated without limit, and each attempt contains a different set of questions. Only an attempt with 100 % correct answers counts as passed; a successful attempt is recorded in the course notebook. The test closes after week 13.

Attendance and continuous submission. Attendance is compulsory for full-time students (both lectures and seminars). Absence not exceeding 30 % need not be excused; excused absence exceeding 30 % will be accepted only for serious reasons. Part-time students are obliged to consult their work with the teacher during the semester and to demonstrate progress on its parts (by e-mail or by uploading to the relevant submission folder in the Information System): introduction including the aim and research questions in week 2, literature review phase I in week 4, literature review phase II in week 5, methodology in week 7, results in week 9, discussion in week 11, conclusion in week 12 and the final text in week 13. The presentation takes place in the examination period.

The course is also listed under the following terms winter 2025, summer 2026.
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