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Innovation management

01TXISM

A.A. 2023/24

Course Language

Inglese

Course degree

Master of science-level of the Bologna process in Data Science And Engineering - Torino

Course structure
Teaching Hours
Teachers
Teacher Status SSD h.Les h.Ex h.Lab h.Tut Years teaching
Teaching assistant
Espandi

Context
SSD CFU Activities Area context
ING-IND/16
ING-IND/35
3
3
C - Affini o integrative
C - Affini o integrative
Attività formative affini o integrative
Attività formative affini o integrative
2022/23
Contemporary digital technologies allow organizations to collect, store and analyze massive amounts of information coming from production processes, from the installed base of physical products, and from users accessing services. The impact of these technologies is highly significant, and ranges from the continuous improvement in internal efficiency and the quality of firms' offering, all the way to the development of radically new and disruptive business models involving multiple firms organized in complex ecosystems. The course has the objective of providing students with an academically-grounded and managerially-oriented understanding of the way with which innovation processes (and, specifically, those based on digital technology and data science) can impact industries and can be proactively managed. The first part of the course will be dedicated to a general discussion on the economics and management of innovation, which will be followed by an in-depth discussion on digitally-enabled strategies and business models. Lectures will be accompanied by multiple examples and mini-case studies, aimed at helping students understand the application of theoretical concepts to business practice. Moreover, students will be organized in groups and required to carry out a project work aimed at studying real organizations and outlining potential digitalization strategies based on data science.
Contemporary digital technologies allow organizations to collect, store and analyze massive amounts of information coming from production processes, from the installed base of physical products, and from users accessing services. The impact of these technologies is highly significant, and ranges from the continuous improvement in internal efficiency and the quality of firms' offering, all the way to the development of radically new and disruptive business models involving multiple firms organized in complex ecosystems. The course has the objective of providing students with an academically-grounded and managerially-oriented understanding of the way with which innovation processes (and, specifically, those based on digital technology and data science) can impact industries and can be proactively managed. The first part of the course will be dedicated to a general discussion on the economics and management of innovation, which will be followed by an in-depth discussion on digitally-enabled strategies and business models. Lectures will be accompanied by multiple examples and mini-case studies, aimed at helping students understand the application of theoretical concepts to business practice. Moreover, students will be organized in groups and required to carry out a project work aimed at studying real organizations and outlining potential digitalization strategies based on data science.
At the end of the course, students will have acquired a concrete ability to analyze and manage business decisions related to big data in both strategic and operational terms.
At the end of the course, students will have acquired a basic competence in analyzing and managing business decisions related to digitalization and data science in both strategic and operational terms.
For an easier acquisition of the course contents, it might be useful for students to know the fundamentals of Economics and Business Organization, as well as the basics of Business Strategy.
For an easier acquisition of the course contents, it might be useful for students to know the fundamentals of Economics and Business Organization, as well as the basics of Business Strategy.
1. Technological Innovation, companies and sectors: Determinants, taxonomies and dynamics of innovation, dominant design and standard. Innovation in business models. Technological forecasting. 2. Big Data as a strategic resource of competition: the use in incremental vs. radical innovation; big data and business model innovation; big data and its relation with a firm’s core competencies 3. Big data and industry-level changes: how big data and the related digital technologies require new core competencies and new alliances between firms 4. Big Data and new organizational architecture: what type of organizational changes and configurations are needed to deploy big data and data scientists effectively 5. Big Data and AI and their role in the decision-making processes at the individual and organizational level.
1. Fundamentals of the theory of the firm 2. Fundamentals of the economics of innovation: the linear model of innovation, the actors involved in the innovation process, technological trajectories and paradigms, taxonomies of innovation and their impact on industries. 3. Dynamics of innovation, dominant designs and standards 4. Business models and business modeling. 5. Innovation strategy and Open Innovation 6. Digitally enabled business models and strategies
The course consists of lectures, with extensive use of case studies drawn from experience and empirical research, and practices.
The course consists of lectures, with extensive use of examples and case studies drawn from experience Moreover, students will be required to carry out a group-based project work on a real case.
Texts, readings, handouts and other learning resources
Cantamessa M., Montagna F. (2016) Management of Innovation and Product Development, Springer, London. Tidd J., Bessant J. (2018) Managing Innovation: Integrating Technological, Market and Organizational Change, 6th Edition, Wiley, NY Presentation and other reading materials will be uploaded during the course
Modalità di esame: Prova scritta (in aula); Elaborato progettuale in gruppo;
Exam: Written test; Group project;
Gli studenti e le studentesse con disabilità o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unità Special Needs, al fine di permettere al/la docente la declinazione più idonea in riferimento alla specifica tipologia di esame.
Exam: Written test; Group project;
The written exam will evaluate the degree to which students have learned and understood theoretical concepts. It will be based on a mixture of closed questions, open questions, and a short commentary to a text. The written exam will lead to a score of 20/30 The group-based project work will allow to evaluate the degree to which students are able to apply theoretical concepts in practice. It will lead to a score of 10/30, based on the following evaluation dimensions: theoretical soundness, internal coherence, business acumen, and presentation quality.
In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.
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