Innovation and Technology Management: Curriculum

What do you need to know about the degree program?

The most important facts about the master’s degree program “Innovation and Technology Management” can be summarized as follows:

  • Start: mid-September
  • Duration: 4 semesters
  • Format: extra-occupational
  • ECTS: 120
  • Language of instruction: German and sometimes English
  • Admission requirements: relevant Bachelor’s degree + English to a B2 level
  • Hours of teaching: Thursday and Friday afternoon and evening as well as Saturday morning
  • Semester abroad: possible in 2nd or 3rd semester
  • Final degree: Master of Science in Engineering (MSc)
  • Costs per semester: EUR 363.36 per semester, +EUR 75 contribution for additional services, +EUR 19.70 ÖH contribution + expenses for literature or online access
  • Semester times:
    -Winter semester: mid-September to end of January
    -Summer semester: mid-February to end of June

What can I expect in terms of courses and curriculum?

Here you can find the courses of the current curriculum:

1. Semester

Name ECTS
SWS
Creativity and Problem Solving Techniques (MMTM1G)
German / iMod
4.00
-
Creativity and Problem Solving Techniques (KREA)
German / UE
4.00
2.00
Elective 1) Programming 2) IT- and Data Protection-Law (MMTM1F)
German / iMod
4.00
-
IT and Data Protection Law (ITRE)
German / ILV
4.00
3.00

Course description

This course provides students with basic knowledge on data protection law according to the General Data Protection Regulation. Optionally, they can receive an additional qualification as certified data protection officer ("Datenschutzbeauftragter") according to Austrian Standards.

Methodology

Lecture, case studies and e-learning

Learning outcomes

After passing this course successfully students are able to ...

  • advise companies and organisations with regard to data protection
  • supervise and coordinate the implementation of data protection laws
  • assume the responsibilities of a data protection officer ("Datenschutzbeauftragter") according to Art 39 GDPR

Course contents

  • development and scope of the General Data Protection Regulation
  • principles relating to the processing of personal data (Lawfulness, special categories of personal data)
  • obligations of controller and processor
  • rights of the data subject
  • ensuring of of data protection and data security (especially according to ISO 27001)
  • remedies, liabilty and sanctions
  • basic principles of the Austrian Data Protection Act

Prerequisites

Basic kwnoledge on private and public law

Literature

  • Forgó, Grundriss Datenschutzrecht (2018)
  • Marzi/Pallwein-Prettner, Datenschutzrecht (2018)
  • Knyrim, Datenschutz-Grundverordnung - Das neue Datenschutzrecht in Österreich und der EU (2016)

Assessment methods

  • written exam, oral participation and e-learning

Anmerkungen

Further informations will be provided via Moodle

Programming (PROG)
German / ILV
4.00
3.00

Course description

In this course, students who have no programming knowledge, acquire basic knowledge and skills in the fields of computer science and programming languages. This course requires a RaspberryPi and a sensor/cable set for each project group.

Methodology

Self-study, e-learning, lecture, practical exercises, discussion, project work

Learning outcomes

After passing this course successfully students are able to ...

  • understand general basics of computer science
  • differentiate between various types of programming languages ​​and their application areas
  • apply the basics of programming in different programming languages
  • create a simple computer game using the Python programming language
  • control the GPIO pins of a RaspberryPi with Python

Course contents

  • Computer Science (e.g., hardware, software, binary system, etc.)
  • Types of programming languages ​​and pseudocode
  • Variables, data types and operators
  • If Else Conditions
  • Functions
  • Loops
  • Arrays
  • Python
  • RaspberryPi
  • CPIO control of RaspberryPi with Python
  • Data analysis of measured values ​from sensors
  • Linux Terminal Commands
  • Python libraries

Prerequisites

We recommend students without IT or programming skills to visit the Warming Up course "Computer Science".

Literature

  • various Lynda Courses (www.lynda.com)

Assessment methods

  • Exam (30%), Project (70%)

Anmerkungen

Further information and teaching materials will be provided via a Moodle course.

Empirical Market Research (MAFO)
English / iMod
4.00
-
Empirical Market Research (MAFO)
English / ILV
4.00
2.00

Course description

In this course, students acquire basic knowledge and skills in the field of empirical marketing research.

Methodology

Self-study, lecture, discussion, exercises, field work in groups (interviews, survey)

Learning outcomes

After passing this course successfully students are able to ...

  • • outline objects of cognition and functions of marketing research
  • • plan and manage a marketing research project
  • • distinguish between in-house research and third-party research
  • • distinguish between primary and secondary research; big data, social media data
  • • decide whether to use quantitative or qualitative research techniques
  • • explain measurement concepts and design questionnaires or online-surveys
  • • draw a sample and distinguish sample from census
  • • conduct field work, i.e. run a survey and assure data quality
  • • analyse data, interpret and present marketing research results for decision making

Course contents

  • • marketing research process, functions and uses
  • • defining the research problem, formulating research objectives, research proposal
  • • research design and application: exploratory, descriptive or causal research
  • • in-house research versus third-party research
  • • primary versus secondary data; big data, social media data
  • • research techniques: quantitative versus qualitative; marketing research online-communities
  • • basic modes and types for gathering survey data: personal interviews, telephone interviews, online surveys, focus groups
  • • data measurement (nominal, ordnial, scale measures) and questionnaire development
  • • basic concepts involved with sampling and axioms about sample size
  • • field work and data quality issues
  • • data analysis: qualitative (analysis of topics, grounded theory) and quantitative (descriptive statistics, inference analysis) methods
  • • marketing research report, visuals, oral presentation and discussion of results

Prerequisites

Basic knowledge in scientific work

Literature

  • • Burns/Veek/Bush: Marketing Research, 8th Global Edition, Pearson Education Ltd. 2017
  • • Pecher: Marketing Research - Script on Approaches, Research Concepts, Quantitative and Qualitative Methods as well as Analysis Techniques, V01 of August 2018

Assessment methods

  • written test (30%) + elaboration of a marketing research study (70%)

Anmerkungen

Further information and course materials are made available through the accompagnying Moodle course.

Innovation Management (MMTM1B)
English / iMod
5.00
-
Innovation Management (INNO)
English / ILV
5.00
3.00

Course description

In this course students acquire knowledge and develop vompetences in the field of Innovation Management.

Methodology

self-study, lecture, discussion, group work, case studies, term paper, wiki

Learning outcomes

After passing this course successfully students are able to ...

  • distinguish between different forms of innovation
  • know about different innovation strategies
  • to establish an innovation-friendly corporate culture
  • to apply various project selection methods
  • to know about a systematic innovation process from idea finding to market entry

Course contents

  • Innovation
  • Motivation and Relevance of Innovation Management
  • Sources of Innovation
  • Open Innovation
  • Types of Innovation
  • Innovation Diffusion
  • Innovation Strategies
  • Innovation process incl. Stage Gate Process
  • Management of the NPD process
  • R&D project selection
  • Management of teams in the NPD process
  • Industrial property rights (in particular patents, utility models, designs)

Prerequisites

key concepts in business administration

Literature

  • Schilling, M. A. (2017). Strategic Management of Technological Innovation. 5th ed., New York: McGraw-Hill Education.
  • additional papers will be provided

Assessment methods

  • written test (70%) + wiki case study (25%) + short term paper (5%)

Anmerkungen

Further information and material regarding this course are provided via LMS moodle.

Innovative Information And Communication Technologies (MMTM1E)
English / iMod
4.00
-
Innovative Information And Communication Technologies (KOMM)
English / ILV
4.00
3.00

Course description

In this lecture students acquire knowledge and develop Competences in the field of information- and communication-technology

Methodology

self-study, lecture, Discussion, E-Learning

Learning outcomes

After passing this course successfully students are able to ...

  • to explain the importance of Informationstechnologie for enterprises
  • to list the potential harms of cyber crime and discuss appropriate countermeasures
  • to list typical tasks of an IT-departmant in a company
  • to discuss areas of application for new information technologies (eg IoT, Big Data, Blockchain etc.)
  • to contribute to the development of the digitals strategy of a company

Course contents

  • Informationstechnologie n general
  • enterprise ressource planning
  • business intelligence
  • cloud computing
  • cyber crime
  • IT-management
  • E-Commerce
  • Big Data
  • Artificial Intelligence
  • Internet of Things
  • Virtual Reality
  • Blockchain
  • Digital office

Prerequisites

key concepts in IT and electronics

Literature

  • Turban et al, Information Technology for Management

Assessment methods

  • final written exam (80%), projekt work and studies (20 %)

Anmerkungen

Further information regarding this course is provided via the accompanying moodle-course.

Internationa Management and Entrepreneurship (MMTM1A)
German / iMod
5.00
-
Internationa Management and Entrepreneurship (INUF)
German / ILV
5.00
3.00

Course description

The aim of this module is to enable students to understand basic concepts in the area of international management as well as gaining the competence to apply them.

Methodology

Blended Learning - lectures, discussions, exercises, case studies and distance learning elements combined

Learning outcomes

After passing this course successfully students are able to ...

  • differentiate between various business objectives
  • define basic elements of business strategies
  • ponder the pros and cons of different organisational forms
  • differentiate between intrinsic and extrinsic motivation
  • outline the functions and instruments of an HR management
  • evaluate different, specific management styles
  • analyse the concept of entrepreneurship as business method
  • evaluate different management duties in an international environment
  • apply the learned content on practical examples.

Course contents

  • Management
  • Strategy
  • Culture and Organisation
  • Controlling
  • Leadership and Motivation
  • Human Resource Management
  • Entrepreneurship
  • International Management

Prerequisites

Basics of business administration

Literature

  • Wala, Thomas; Groblschegg, Sabine (2016): Kernelemente der Unternehmensführung, aktuelleAuflage, Linde-Verlag
  • Büter: Internationale Unternehmensführung. Entscheidungsorientierte Einführung

Assessment methods

  • E-Learning Modules (15%) + Case Study Group Work (15%) + Written Exam (70%)

Anmerkungen

This course is set up as a flipped-classroom concept. Content for self-studying sessions (i.e. Videos,..) is being provided via the Moodle platform.

Technology and Knowledge Management (MMTM1C)
German / iMod
4.00
-
Technology and Knowledge Management (TEWI)
German / SE
4.00
2.00

Course description

Students acquire basic knowledge and skills in technology and knowledge management during the course. The students also acquire the knowledge in self-study. This course focuses on the elaboration of a topic of one's choice and the presentation and discussion of the findings

Methodology

Self-study, seminar work in the field of technology and knowledge management (in a team), presentation and discussion, lecture on basic knowledge and methodical approach

Learning outcomes

After passing this course successfully students are able to ...

  • understand knowledge management as a concept
  • prepare an intellectual capital statement and initiate knowledge exchange in Communities of Pracitice
  • differentiate between different types of technologies
  • assess the performance of technologies
  • discuss the advantages and disadvantages of new technologies for one's own company/new and old markets
  • create a technology roadmap
  • know different ways of technology exploitation
  • be aware of intellectual property rights
  • see trend topics such as AI, recycling management etc. in the context of new technologies.

Course contents

  • Knowledge management (knowledge goals, knowledge acquisition, knowledge transfer etc.)
  • Intellectual capital statements
  • Communities of Practice
  • Concept of technology and types of technology
  • Technology lifecycle
  • Technology strategy
  • Early technology detection
  • Technology analysis, evaluation and selection
  • Technology roadmapping
  • Technology management
  • Ways of internal and external technology exploitation (technology sales, licensing, etc.)
  • Intellectual property rights
  • Recent developments in research and technology policy
  • In-depth knowledge of a topic of your choice

Prerequisites

Basic Principles of Business Administration

Literature

  • Schuh, G., Klappert, S. (2011): Technologiemanagement, 2. Auflage, Heidelberg et al.: Springer
  • Gerpott, T. J. (2005): Strategisches Technologie- und Innovationsmanagement, 2. Auflage, Schäffer-Poeschel: Verlag Stuttgart

Assessment methods

  • Written seminar paper (80%) + presentation and defense of seminar paper (20%)

Anmerkungen

Information and teaching materials will be provided through the accompanying Moodle course.

2. Semester

Name ECTS
SWS
Agile Software-Development & Lean UX (SOFT)
English / iMod
4.00
-
Agile Software-Development & Lean UX (SOFT)
English / ILV
4.00
3.00
Changemanagement (CHAN)
English / iMod
4.00
-
Changemanagement (CHAN)
English / ILV
4.00
2.00

Course description

In the course of the module, students will acquire basic competences in the area of organizational change management.

Methodology

Presentation, self-study, exercises, exchange of experiences, discussion, case studies

Learning outcomes

After passing this course successfully students are able to ...

  • distinguish types of change
  • anticipate internal and external barriers to successful change
  • identify success factors for succesful change
  • plan change management processes
  • define the most important steps and measures for a concrete change project
  • understand reasons for resistance to change
  • learn from failed change efforts

Course contents

  • Types of change
  • Phases of change
  • Barriers to change
  • Success factors for change
  • Reasons of why change efforts fail
  • Change Management
  • Architecture and design of change processes
  • Selected methods for organization development

Prerequisites

Basics of Economics

Literature

  • Stouten, J., Rousseau, D. M., de Cremer, D. (2018) Successful organizational change: Integrating the management Practice and scholarly articles . Academy of Management Annals, 12 (2), 752-788.

Assessment methods

  • Written exam (70%) + Case Study Work (30%)

Anmerkungen

Further materials will be provided in the related Moodle course.

Cost Management and Corporate Finance (WIRT)
English / iMod
4.00
-
Cost Management and Corporate Finance (WIRT)
English / ILV
4.00
2.00

Course description

During this course, students acquire further knowledge in the fields of financial analysis, cost management, financing and company valuation.

Methodology

lecture, exercises, discussion, e-learning, self-study, flipped classroom, business game

Learning outcomes

After passing this course successfully students are able to ...

  • to assess the advantage of an investment project by means of static or dynamic investment calculation
  • outline the typical contents of an investment guideline
  • Identify and implement cost reduction measures
  • Identify and implement measures to variabilize fixed costs
  • Calculate key figures to analyze the asset, profitability and liquidity situation
  • distinguish between different types of financing
  • determine the value of a company using the discounted cash flow method

Course contents

  • Accounting
  • Statement analysis
  • Cost management
  • capital budgeting
  • corporate financing
  • business valuation

Prerequisites

Basics of business administration, accounting, cost accounting

Literature

  • Berk, Jonathan, and Peter DeMarzo. Corporate Finance. Actual Edition. Harlow: Pearson Education Limited
  • Brealey, Richard A., Stewart C. Myers, und Franklin Allen. Principles of Corporate Finance. New York: McGraw-Hill Higher Education
  • Charles T. Horngren; Srikant M. Datar; Madhav V. Rajan, Cost Accounting, Global Edition, Pearson Education Limited
  • Eisl/Hofer/Losbichler, Grundlagen der finanziellen Unternehmensführung. Band IV: Controlling
  • Losbichler, Grundlagen der finanziellen Unternehmensführung. Band III: Cashflow, Investition und Finanzierung

Assessment methods

  • written final examination (50%) + points for immanent performance business game (50%)

Anmerkungen

Further information on the course and the teaching materials used will be provided in the accompanying Moodle course.

Data Analytics (DATE)
German / iMod
4.00
-
Data Analytics (DATE)
German / ILV
4.00
3.00

Course description

This course addresses statistical methods and programming skills to handle and analyze structured data and preprocess them for applying machine learning techniques. In this context, students learn to describe datasets using descriptive statistics, to apply analysis methods such as correlation and regression analysis, and to build and assess prediction models for classification and approximation problems.

Methodology

Besides lectures on subject matter, the primary focus of this course is set to practical exercises using selected tools (Excel, Tableau, Python and Jupyter Notebooks) as well as interactive discussions of data analysis methods. At the beginning of each lecture, short tests are conducted in order to examine the required knowledge.

Learning outcomes

After passing this course successfully students are able to ...

  • preprocess, visualize and analyze datasets using Excel and Tableau.
  • load a dataset in a Python Notebook, work with it, and explore it using descriptive statistics and selected statistical methods.
  • identify relationships between dependent and independent variables and create a prediction model using regression.
  • classify data points in a multi-dimensional feature space using simple supervised machine learning techniques, such as nearest neighbor algorithms or decision trees.
  • evaluate and improve the performance of classification and regression models.
  • cluster data points in a multi-dimensional feature space using the kMeans algorithm and determine the optimal number of clusters.
  • select and apply a machine learning model for a real-world dataset in order to create a prediction model.

Course contents

  • Data wrangling, visualization and analysis
  • Application of Python for Data Science activities
  • Regression analysis
  • Classification
  • Performance assessment
  • Cluster analysis
  • Predictive analytics (case study)

Prerequisites

Python programming, fundamentals of statistics

Literature

  • Slides
  • Jupyter Notebooks
  • Additional reading: McKinney, W. (2012). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O'Reilly Media.

Assessment methods

  • Written exam at the beginning of each lecture (30 points, 15 to 45 minutes, no repetition possible)
  • Computer-based test at the end of the course (70 points, open book, 90 minutes)

Anmerkungen

Further information and course materials are available on the Moodle course.

Elective 2: 1) Machine Learning 2) Smart Homes & Assistive Technologies (WP_2)
German / iMod
5.00
-
Machine Learning (MACH)
German / ILV
5.00
3.00

Course description

This course gives an overview of fundamentals as well as relevant formalisms and techniques of Machine Learning (ML). Thereby, traditional areas like logic or neural networks and younger streams like probabilistic models, methods of supervised and unsupervised learning, or Deep Learning approaches are addressed. The exercises focus on implementing ML techniques in Python in order to create intelligent software artefacts. Prerequisites include Python programming and statistics.

Methodology

The course consists of 5 topics, each one starting with a theoretical introduction and a practical session in which programming exercises are conducted using pre-given Python scripts (software package Anaconda as well as Jupyter Notebooks). Participants have to work on self-study units and exercises that end with a concluding test in the upcoming lecture. An integrative case study on a topical area has to be elaborated in a group project which is presented at the end of the semester.

Learning outcomes

After passing this course successfully students are able to ...

  • explain fundamentals of Data Mining and Machine Learning as well as select relevant formalisms and techniques for practical problems (Knowledge Engineering).
  • consider uncertainity through probabilistic modeling and use reasoning based on such models (Bayes theorem, Bayesian networks, Markov models)
  • apply supervised learning techniques (perceptron, nearest-neighbor, decision trees, naive bayes) for real-world data-sets
  • implement techniques for unsivervised learning (clustering, principal component analysis) or reinforcement learning (robotics, AlphaZero) for sample data-sets
  • detect objects and patterns in structured and unstructured data using single-, multi-layer artificial neural networks and Deep Learning architectures
  • plan and realize a Machine Learning project throughout all phases (data engineering, explorative data analysis, training and validation of ML models) for a specific application scenario

Course contents

  • Knowledge Engineering
  • Probabilistic Modeling
  • Supervised Learning
  • Unsupervised Learning
  • Neural Networks and Deep Learning
  • Application of ML techniques

Prerequisites

Python programming, statistics and data analysis

Literature

  • Slides, Jupyter Notebooks
  • Additional literature: Ertl, W. (2016). Grundkurs Künstliche Intelligenz: Eine praxisorientierte Einführung. 4. Auflage, Springer Verlag.

Assessment methods

  • Practical group project (submission and presentation at the end of the semester): 30%
  • Distance/self study units with test at the beginning of the upcoming lecture: 30%
  • Concluding computerbased test (60 minutes): 40%

Anmerkungen

Further readings and materials are provided in the Moodle course.

Smart Homes & Assistive Technologies (SMART)
German / ILV
5.00
3.00
Online-Marketing and E-Commerce (MARK)
English / iMod
4.00
-
Online-Marketing and E-Commerce (MARK)
English / ILV
4.00
2.00
Robotics (ROBO)
German / iMod
5.00
-
Robotics (ROBO)
German / ILV
5.00
3.00

Course description

Deeper Fundamentals of Robotics in Theory as well as online/offline programming of industrial robots

Methodology

selfstudy, lecutre, dicussion, exercise, practical lab

Learning outcomes

After passing this course successfully students are able to ...

  • control and programming of ABB IRB 120 six-axis-buckling arm robot (online and offline).
  • define industrial, mobile and service robots and their components, advantage & disadvantages, usability and limits.
  • analyse a given task in the field of robotics, define a solution path and solve the problem using given tools.

Course contents

  • State of the art in robotics and development trends
  • Different types of robots (industrial, mobile, service)
  • componentes of robots
  • robot programming
  • social, technological, economic and safety related aspects of robots
  • practical exercise using industrial robots

Prerequisites

Fundamentals in STEM, Fundamentals in programming

Literature

  • Hesse/Malisa (Hrsg.): Taschenbuch Robotik - Montage - Handhabung, aktuelle Auflage.

Assessment methods

  • Robotstudio project (30%) - Online-Robot programming (70%)

Anmerkungen

Furhter Details and learning materials will be provided in the associated moodle course

3. Semester

Name ECTS
SWS
Business Modelling (GEMO)
English / iMod
4.00
-
Business Modelling (GEMO)
English / ILV
4.00
2.00
Digital Leadership & New World Of Work (LEAD)
English / iMod
4.00
-
Digital Leadership & New World Of Work (LEAD)
English / UE
4.00
2.00
Innovation and Technology Project (PROJ)
German / iMod
4.00
-
Innovation and Technology Project (PROJ)
German / PRJ
4.00
2.00
International Intellectual Property Law (PATR)
German / iMod
4.00
-
International Intellectual Property Law (PATR)
German / ILV
4.00
2.00
Master´s Thesis Proposal and Scientific Writing (DISPO)
German / iMod
5.00
-
Master`s Thesis Proposal and Scientific Writing (DISPO)
German / UE
5.00
2.00
Smart Manufacturing (MANU)
German / iMod
5.00
-
Smart Manufacturing (MANU)
German / ILV
5.00
3.00
Technical Sociology and Technology Assessment (SOZIO)
German / iMod
4.00
-
Technical Sociology and Technology Assessment (SOZIO)
English / SE
4.00
2.00

Course description

In this course, concepts of sociology of technology and technology assessment are introduced, critically discussed and applied to selected areas of practice. Selected areas of practice will be evaluated in detail under the aspects of technology assessments (e.g. societal, economic, ethical, legal aspects). The areas of practice include Smart Care (Care 4.0) and mediatized or eFitness. This course further deals with presentation and discussion of various approaches, methods and intensities of presuming and end-user involvement.

Methodology

lecture; presentations; various discussion formats in small groups and in plenary; group work

Learning outcomes

After passing this course successfully students are able to ...

  • critically discuss concepts of sociology of technology and technology assessment
  • reflect upon areas of practice such as care and fitness in the context of technology assessment
  • explain models to design and manage user-centered and user-triggered Innovation Processes (e.g. prosuming)

Course contents

  • Introduction to theoretical concepts of sociology of technology, and technology assessment
  • Development and effect of technologies at a macro and a micro level: Tension between technology and society
  • Discussion of selected areas of practice e.g. eSports / Mediatized Fitness, Smart Care
  • Key terms and definitions (e.g. end-user, stakeholder, prosumer, participatory design, inclusive design)
  • Presentation of various approaches and intensities of end-user involvement

Prerequisites

No specific requirements needed.

Literature

  • Bauchspies, W., Croissant, J., Restivo, S. (2005): Science, Technology and Society. A sociological approach. Wiley-Blackwell. (selected chapters)
  • Kaabi-Linke Timo. „Technik im Ausnahmezustand: Wenn Dinge widerspenstig werden.“ In: Zeitschrift für Erziehungswissenschaften, 2013. 16(2). 267-285
  • Pavitt, Keith. The process of innovation. Vol. 89. SPRU, 2003.
  • Friesacher, Heiner. „Pflege und Technik – eine kritische Analyse“. In: Pflege und Gesellschaft, 2010. 15(4). 293 - 313
  • Pfadenhauer, Michaela, and Tilo Grenz. "Mediatisierte Fitness? Über die Entstehung eines Geschäftsmodells." Mediatisierte Welten. VS Verlag für Sozialwissenschaften, 2012. 87-109.
  • Assistive Technologien Ethische Aspekte der Entwicklung und des Einsatzes Assistiver Technologien Stellungnahme der Bioethikkommission beim Bundeskanzleramt (2009). URL: https://www.bka.gv.at/DocView.axd?CobId=39411
  • Nedopil, Ch.; Schauber, C.; Glende, S. (2013). AAL Stakeholders and their Requirements. A collection of characteristics and requirements of primary, secondary, and tertiary users of AAL solutions, and a guideline for user-friendly AAL design. Luxembourg: Ambient Assisted Living Association. URL:http://www.aal-europe.eu/wp-content/uploads/2015/02/AALA_Knowledge-Base_YOUSE_online.pdf
  • Toolbox: Methods of User Integration for AAL Innovations. URL: http://www.aal-europe.eu/wp-content/uploads/2015/02/AALA_ToolboxA5_online.pdf
  • Blättel-Mink; B.; Hellmann, K. U. (Hrsg.) (2010). Prosumer Revisited. Zur Aktualität einer Debatte. VS. (S.13-48).
  • Neven, L. (2010). ‘But obviously not for me’: robots, laboratories and the defiant identity of elder test users. In: Sociology of Health & Illness, 32 (2), 335–347.

Assessment methods

  • Discussion paper (80%) + Presentation of discussion paper (20%)

Anmerkungen

Literature and further materials for the course will be uploaded on Moodle.

4. Semester

Name ECTS
SWS
Enterprise Simulation (SIMU)
English / iMod
4.00
-
Enterprise Simulation (SIMU)
English / UE
4.00
2.00
Innovation and Technology Policy and Subsidies (FTIP)
German / iMod
4.00
-
Innovation and Technology Policy and Subsidies (FTIP)
German / ILV
4.00
2.00
Master´s Thesis (MAST)
German / iMod
16.00
-
Master´s Thesis (MAST)
German / EXAM
16.00
1.00
Praxis Dialogue Innovation and Technology Management (DIALO)
German / iMod
2.00
-
Praxis Diallogue Innovation and Technology Management (DIALO)
German / VO
2.00
2.00
Start-up-Management & Corporate Venturing (STAR)
German / iMod
4.00
-
Start-up-Management & Corporate Venturing (STAR)
German / ILV
4.00
2.00