Program

Program overview

Here you can find an overview of our program, spread over the four semesters. You can also see which courses you can follow at each university, and details about the internship and thesis which leads to the fulfilment of the program.

Semester 1
Fall term - Year 1
AIX-Marseille University- Management and International Business

Compulsory courses: 

  •  
  • Elective courses:
    • Introduction to International Business
    • International Trade
    • Project Management
    • Creativity builds Business
    • Digital Business Models
    • Business and Law
    • Personal Leadership (or how to work better with others and take control of your life)
    • Building High Performance Teams
    • Intercultural Management
Semester 2
Spring term - Year 1
University of Turku - IT Management

Courses:

Electives Digital Business:

Electives Business Analytics:

Semester 3
Fall term - Year 2
Tilburg University - Business and IT integration

Compulsory courses:

Elective courses:

Semester 4
Spring term - Year 2
Internship & MSc. Thesis

The fourth semester of the IMMIT program includes:

    • An internship in an internationally operating company. This internship could be anywhere in the world and gives you fast track career opportunities.
    • A MSc. thesis (30 ECTS in total).

Meet the program staff

When you pursue your IMMIT degree you will be taught by a team of lecturers with different, but excellent academic backgrounds from all over the world. These lecturers from different universities will guide you in becoming ‘hybrid’ managers at the intersection of Information Systems and business functions. You can see a few of our staff members below. You can press every staff member’s picture to access their personal page.

IAE Aix-Marseille Graduate School of Management

Dean Antonin Ricard
Dr. Jocelyn Husser
Prof. Dr. Patrick Rosseau

Dean Antonin Ricard                        Prof. Dr. Jocelyn Husser               Prof. Dr. Patrick Rousseau

University of Turku

Prof. Dr. Hannu Salmela
Dr. Eija Koskivaara
Dr. Timo Leino
Dr. Farhan Ahmad

Prof. Dr. Hannu Salmela    Dr. Eija Koskivaara            Dr. Timo Leino                    Dr. Farhan Ahmad

Tilburg University

Prof. Dr. Anne-Francoise Rutkowski
Dr. Emiel Caron
Dr. Hans Weigand

Prof. Dr. Anne-Francoise                   Dr. Emiel Caron                                Dr. Hans Weigand                      Rutkowski   

Supporting staff

Vesna Nedimović - Managing Coordinator
bc. Hülya Koçak - Education Coordinator
Fréderic Tonton - Eductation Coordinator

Vesna Nedimović –                            bc. Hülya Koçak –                             Fréderic Tonton –                Managing Coordinator                     Education Coordinator                    Education Coordinator

Business Information Management

The goal of the course is to provide the students with a solid business-
side perspective on Information Systems. The course is composed of three parts:
Part I: IS/IT strategy – Concepts and Principles
Part II: E-Business – Internal and External Integration
Part III: An Economic approach

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Python for Data Science

The course is composed of 4 main parts; an introduction to python programming environment, data preprocessing, linear regression, and a part on data visualization.

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Digitalisation

To define a digital marketing plan recommendation to address a business problem. Use different digital marketing media and to explain their respective interest in the current communication context. To select and choose the best ones to build a digital marketing plan in order to achieve a specific company goal.

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Business Simulation

To train participants to manage a firm in an international setting, competing and co-operating, in order to create value for shareholders, with a number of firms in a B-to-B industrial global world located in different countries.

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Financial Accounting

Understanding the economic and financial reasoning of accounting,
Understanding the process of financial analysis,

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Corporate Finance

The course aims to give an understanding and a complementary overview of the tools that are used to tackle with financial decision faced by individual and firms. It allows to be able to analyze and decide the opportunity of an investment project in an uncertain environment.

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Multicutural environment

Intercultural Management is about learning how to lead global organizations with a diversity of profiles. Today’s business environment is increasingly diverse. Managers in multinational companies work across continents and time zones on global projects, connecting with colleagues spread out across the world. Working remotely is not an option anymore.

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Marketing

1. Understand marketing issues and its contribution to the company
2. Understand the marketing approach and the main concepts of marketing
strategy and operational marketing
3. Understand how to drive a marketing plan and take marketing decisions

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Statistics and Econometrics

With the development of new technologies, statistical analysis has become an essential aspect of our societies and businesses. The objective of this course is to give students the fundamentals in this field and an introduction to a statistical analysis software: Stata. On successful completion of this course the student will:
– Compute relevant statistics from a raw database. `
– Produce relevant graphical analyses (data visualization)
– Conduct hypothesis testing
– Perform basic econometric modelling
– Interpret the results of an econometric analysis

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Project Management 

It is frequently said that, “change is the only constant in the world of business”.  Successful organisations in all industry sectors are increasingly turning to programmes and projects to predict change outcomes, and to plan and manage the delivery of complex change initiatives, in response to the socio-economic, political and technical demands of the environments in which they operate.  This course introduces the concepts, and offers insights into best practice for project initiation, planning and delivery in the context of business critical change.

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Leader and Behaviour Agility

This session will emphasize the importance of emotional intelligence and stress management in a change process and will allow each participant to know and work on his own communication profile and his/her personal vulnerabilities in situations of stress.

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IT governance and IT service management

The student knows what the typical IT services are and understands the challenges and requirements in providing and managing those services. She or he understands the influence of business strategy and the nature of the company on IT services. She or he understands the levels of IT management and the relationships between IT function and other business functions. She or he has basic knowledge on relevant models of this field presented both in the literature and in standards (e.g. ISO) and frameworks (e.g. ITIL).

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Research Methods

After the course, students know:

  • the essential parts of a master’s thesis
  • the difference between quantitative and qualitative study
  • how to write an introduction and research questions
  • how to choose relevant literature for their research question
  • how to select appropriate methodology for their research question
  • the basics of quantitative or qualitative data collection methods
  • how to collect data for their research question
  • how to analyse their data
  • the quality criteria for quantitative or qualitative methods
  • how to write the methodology chapter
  • to present and discuss their research topic
  • to consider ethical perspectives in their research
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Enterprise Architecture

After completing the course the students know and can apply the most common enterprise architecture design principles for developing, maintaining and changing ICT-asset and business services portfolio in companies and public services. Students alsounderstand the role of governance in transparent management of related activities.

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Digital Economy and Business Models

After having completed the course the student:

  • has an advanced-level understanding of selected contemporary key topics such as artificial intelligence and blockchain in the field of digital economy
  • is equipped with a set of theoretical and conceptual tools to analyze, evaluate information and future scenarios related to digital economy
  • has an advanced-level understanding of the role(s) of platforms and intermediaries in digital economy
  • is able to study and analyze managerial problems faced by digital economy companies by employing different theoretical and analytical frameworks
  • is able to document and develop business models for digital businesses
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Business analytics is concerned with mathematical and statistical models and data visualization tools that transform data into business insights. This fact makes business analytics well-suited to the present-day era of digitalization and big data.

In this course we focus on the dominant Python programming language in the field of business analytics. Python has the inbuilt tools for statistical data analysis, data engineering, machine learning and visualization, widely used in companies. Additionally, Python offers a powerful but easy-to-learn set of tools that can assist in finding data insights.

Learning objectives:

  • Understand business analytics concepts, techniques, issues, applications
  • Solve the most common business data analysis tasks with tools of Python programming language
  • Identify and create appropriate data visualization models to understand different business problems
  • Perform basic supervised (regression and classification) and unsupervised (clustering) machine learning
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The data management course aims at giving the student the theoretical knowledge and practical skills for managing large amounts of data, both structured and unstructured, for the purpose of data analytics. The course uses the relational model as its starting point, and teaches the storage and access structures relevant to relational databases. Special attention is given to techniques for accessing both structured and unstructured data sources, and to joining data when the data sources do not share unique identifiers for data. The approaches of data integration and data federation are discussed in detail and compared against each other.

Learning objectives:

  • use the relational model to define storage structures
  • use the structured query language (SQL) both for retrieval of data and for managing data structures and content
  • understand the basics of data normalization and how it is utilized in distributed computing
  • learn to apprise when data integration, data warehousing, or data federation is the best solution for managing multiple incompatible data sources.
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The course covers the second language academic writing process and all key skills related to second language thesis writing. Practical guidance is given on style, organization, and grammatical competence

Learning outcomes:

By the end of the course the students should

  • be familiar with academic writing in a second language and be able to identify the main features of an academic text
  • have explored the social, cultural and cross cultural challenges and expectations of adademic writing in a second language
  • have knowledge of the different types of academic texts and research papers – argumentative – analytical – defining – interpretive and have produced specific examples
  • be able to structure a text at the macro and micro levels and understand how cohesion and coherence is produced
  • have developed their critical thinking skills through the process of argumentation e.g. synthesizing and evaluation skills
  • have practiced writing the draft of an academic text, and also revised and proofread the text for submission
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Languages and communication studies

By the end of the course the students should

  • be familiar with academic writing in a second language and be able to identify the main features of an academic text
  • have explored the social, cultural and cross cultural challenges and expectations of adademic writing in a second language
  • have knowledge of the different types of academic texts and research papers – argumentative – analytical – defining – interpretive and have produced specific examples
  • be able to structure a text at the macro and micro levels and understand how cohesion and coherence is produced
  • have developed their critical thinking skills through the process of argumentation e.g. synthesizing and evaluation skills
  • have practiced writing the draft of an academic text, and also revised and proofread the text for submission
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Business Process Integration

The objective of this course is to focus on how enterprises transform their structure and processes, and how they can integrate data from various sources in order to collaborate with each other by integrating their business processes.

After completing this course, you will be able to:

  • Explain the use of and evaluate alternative business models for developing business-to-business solutions;
  • Suggest and evaluate alternatives to business-to-business solutions;
  • Explain and appraise the use of the Supply Chain Operations Reference Model for business-to-business solutions;
  • Explain and justify alternative business integration patterns for formulating enterprise integration solutions;
  • Compare data and process interoperability techniques and implement business-to-business solutions.
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Advanced Data Management

Data Management is about the design and execution of plans that enable an organization to control and enhance the value of data throughout its lifecycle. Data Management is increasingly becoming an enterprise responsibility (rather than an IT task), towards customers, employees, government, and other stakeholders. Motivations for organizations to implement Data Management include both regulatory compliance and economic benefits.
The objective of this course is to provide an in-depth coverage of Data Management and its related concepts from a practical as well as academic perspective. Through the lens of Data Management, we study capabilities within organizations that enable value creation from information while ensuring integrity and security of the data. We will also experience applying tools and frameworks such as data dictionaries and ontologies.
Throughout the course, data management frameworks and processes will be presented, and their strategic applications and impact on businesses and organization through value creation will be discussed. Students work on cases in tutorials and have to write a research paper.
In summary, after completing this course students should be able to:

  • Discuss how data is captured, organized and managed, and how to interpret knowledge from data;
  • Analyze the value of information within organizations and the relationship between business and IT management;
  • Apply and analyze data management tools and frameworks for interpreting organizational data;
  • Evaluate data designs for value creation within organizations;
  • Formulate the knowledge about data management and data governance from academic papers, discussion sessions and practice.
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Cybersecurity Risk Management

Enterprises are more and more dependent on Information Technology. This dependency creates risks: business continuity risks, data quality risks, compliance risks, and strategic risks. Increasingly, the security of one enterprise depends on other enterprises in a network, and can no longer be dealt on its own. Moreover, the impact of a breach of security now extends into physical reality, potentially affecting powerplants, traffic control systems and the lives of millions of people. The complex domain of interdependent physical and information security is called cybersecurity.

Most managers regard security as a technical issue. This attitude is limited. Instead, managers need to view cybersecurity from a business perspective, focusing on the potential impact of security threats to the organization. Risk management is about assessing the likelihood and impact of a risk, and making trade-offs in taking measures to prevent, detect, respond to and recover from incidents. The purpose is to make organizations resilient to risks.

This course will give an overview of common cybersecurity and risk management approaches, and explain which approach is suitable for which kind of risk. The course covers security engineering (Anderson, 2008),  cyber risk management (Refsdal, Solhaug, & Stolen, 2015), and practical approaches to cyber resilience (PAS 555: 2013), contrasted with a scientific perspective on the role of security in society (Helbing, 2013; Power, 2007). In particular, the ethical and legal aspects will be discussed (privacy, data protection, GDPR). At the end of the course, students are able to

  • Describe common frameworks for cyber security and resilience (ISO 27001/2, PAS 555),
  • Distinguish various types of security risks (confidentiality, integrity, availability)
  • Analyze a system and perform a systematic risk assessment (likelihood and impact)
  • Explain the use of common types of security measures, including cryptography, to address these risks
  • Discuss ethical and legal aspects of security, including privacy and data protection (GDPR)
  • Appreciate the multi-disciplinary nature of security risks and their impact on society.
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Business AI and Emerging Trends
With the digitalization of enterprise information systems, the proliferation of internet and the widespread use of mobile devices and sensor technology, the amount of data stored in systems has grown enormously. Simultaneously, high speed CPU and fast massive memory enable new business analytics, like process mining, text mining and social network analysis. After completing this course, you will be able to:
  • Explain important trends in Artificial Intelligence and related digital technology;
  • Assess digital technology from a business value perspective;
  • Make use of LLM prompt engineering and process mining tools for business AI on a basic level;
  • Apply ethics in design (Responsible AI).
    The course consists of lectures, lab sessions, guest lectures and a group paper assignment. Topics covered in the lectures include:
    • Knowledge graphs
    • Smart auditing
    • Process mining
    • Blockchain applications
    • Managing AI
    • Social network analysis;
    • Responsible AI
    The choice of topics can change over the years, following new trends.
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IT Governance and Strategic Sourcing

The objective of this course is to provide an in-depth coverage of IT governance, digital transformation and strategic sourcing topics from an integrative perspective of business and IT. Specifically, IT governance can be defined as the effective framework of leadership, organizational structures and processes that ensure that the organization’s IT sustains and extends the organization’s strategies and objectives. The motivations for organizations to implement effective IT governance include both regulatory compliance and economic benefits. Thus, it is critical to integrate the business, IT management, digital transformation, and strategic sourcing in the governance process, covering both internal and external parties as well as inter-organization or units.

The predominant approach for this course is “Learning by example”, i.e. demonstrating the IT governance and strategic sourcing frameworks, process, and discussing their strategic application and impact on businesses and organization. However, by extrapolating examples, students are prompted to “think through” and develop their own analytical and creative skills. This is emphasized by group discussion and case presentation during the course.
In summary, after completing this course students should be able to:

  • Design IT governance frameworks by integrating business, information technology, and strategic sourcing;
  • Apply, analyze, evaluate, design IT governance frameworks for digital transformation and strategic sourcing;
  • Synthesize the knowledge about IT governance and strategic sourcing from academic papers, discussion sessions and practice;
  • Evaluate a business problem and then apply it at the organizational level and design solutions.
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Service Oriented Enterprise

The objective of this course is to present the principles and fundamental underpinnings of Web Services and Service Oriented Architectures. Special emphasis will be given to service definitions, service assembly, modeling, web services programming and design aspects of real-life applications in manufacturing. The course will examine the use of process and data integration techniques to develop SOA-based applications and help students illustrate and justify the use of analysis/design principles and guidelines in SOA application development solutions. Prior knowledge of databases and XML-programming is required.

After completing this course, you will be able to:

  • Distinguish between the types, properties of services and best practises necessary for developing SOA-based applications and justify potential solutions;
  • Explain the use of process and data integration techniques to develop SOA-based applications;
  • Explain and justify the use of analysis/design principles and guidelines in SOA application development solutions;
  • Design and appraise software services in terms of operational requirements and quality criteria for enterprises.
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Enterprise Architecture as a Business Strategy

After completing this course, you will be able to:

  • Assess the Enterprise Architecture of a firm (by specifying the organizational architecture, the applications architecture, the process architecture, and the technology architecture of the firm);
  • Evaluate the impact of Enterprise Architecture on strategic alignment between business and technology in the firm;
  • Advise on how Enterprise Architecture will enhance business performance and value;
  • Advise on how Enterprise Architecture analysis can be used to strengthen IT governance;
  • Advise on standardization of data and process in order to enhance business and technology performance;
  • Analyze and develop an Enterprise Architecture by using (i) Application Portfolio and Process Analysis (Picture Approach; Groot et al, 2006), (ii) EA Analysis (Ross and Weill), 2006), (iii) Archimate Analysis (BizzDesign);
  • Discuss the strategic impact of IT trends on Enterprise Architecture;
  • Learn by example by demonstrating trends, developments, and case analyses;
  • Extrapolate case observations into generic findings and hypotheses on value of EA.
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Business Intelligence & Business Analytics

After completing this course, students will be able to:

  • apply the concepts of database management and use the Structured Query Language (SQL) to query and analyze business databases;
  • comment on the development and business implications of data transformation and data warehouses;
  • design and analyze an OLAP data cube for descriptive business analytics;
  • comment on the fundamental issues of knowledge discovery in databases, i.e. (the) data mining (process), such as learning algorithms for classification and prediction. Discuss the business relevance of data mining models;
  • interpret the substance of regression, fitting and supervised learning, including: interpreting parameters, designing the model, making choices;
  • comment on the Nearest Neighbor algorithm. Perform classification on data tables with Naïve Bayes;
  • design a decision tree on data;
  • analyze data sets using frequent item sets/association rules;
  • design clusters on data sets with different clustering techniques;
  • evaluate performance issues, complexity issues, business relevance and implementation;
  • work with (basic) data mining tools.
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Smart Business Networks

The course introduces the multi-sided platform business model and the importance of platform orchestration and governance strategies. It provides insights on the best practices to successfully manage platforms, to optimize the symbiotic relationship on platforms and understand performance metrics, and finally discusses evolution of new technologies into potential platforms. Students will have an opportunity to apply concepts from platform economics and organization using case studies and conceptualizing how to build platforms and their value proposition. In addition, students should be able to apply concepts discussed in the class and think like managers to further optimize profitability and interactions on existing platforms. 

The course aims to provide participants with a comprehensive understanding of digital platforms and their core concepts. By the end of the course, participants will be able to:

  • Define and Explain Digital Platforms: Understand the concept of multi-sided digital platforms, different types of platforms, and their fundamental characteristics, including network effects and economies of scale.
  • Identify Platform Business Models: Analyze various platform business models, including “Free platforms,” “Device funded platforms,” hybrid platforms, and marketplace platforms. Evaluate ways to monetize platforms through advertisements, data, and sales.
  • Evaluate Platform Design and Governance: Assess the importance of platform openness and the opportunities and challenges associated with opening up a platform. Understand how to harness network effects while ensuring value creation and protecting platform interests.
  • Develop Platform Participation Strategies: Recognize the “chicken and egg problem” in platforms and explore different strategies used to overcome this challenge. Analyze real-life case studies to understand the successful implementation of these strategies.
  • Examine the Role of Data in Platforms: Understand the significance of data on a platform, identify various data types collected by platforms, and explore methods to monetize data. Learn about data-driven network effects and their impact on platform growth.
  • Analyze Platform Pricing Decisions: Explore different pricing instruments chosen by platforms and understand the factors influencing pricing decisions. Determine which side of the market should be charged and which side should be subsidized, considering the influence of network effects. Learn how data can be leveraged to improve pricing decisions.
  • Create a Digital Transformation Strategy: Evaluate the opportunities and challenges involved in digital transformation into a platform-based model. Study strategies for successful digital transformation, with real-world case studies as examples.
  • Evaluate Platform Performance: Assess the performance of typical digital platforms using key performance indicators and related metrics, including metrics from the user level, group level, and the platform level. Evaluate platform performance and analyze platform strategy monetization and governance strategy based on the assessment.
  • Social Interactions and Transactions in Digital Platforms: Understand social interactions on digital platforms by analyzing elements such as incentives, trust, and social perceptions in the digital context. Analyze social commerce platforms as case studies.
  • Policy and Regulations in Platforms: Evaluate the interactions and relationships between users, platform managers, and policy makers. Derive policy implications for platform managers on how to create legitimacy for the new digital market such as sharing economy.

 

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Digital Transformation and Innovation

The course aims to prepare future managers to handle the emergence of Information Technology (IT) Innovation at the workplace in a responsible way. Rather than building a technology stack and then exploring the potential applications, organizations must consider the business and human context first. Trends do not exist in isolation. IT leaders must decide what combination of the trends will drive the most innovation and strategy. Congruently, the implementation of new Information Technologies has to be carefully assessed, controlled and risks have to be mitigated. This preferably aligning business and IT functions. However, with the fast pace of new emerging technological trends, and rising competition companies often embrace new technologies without always considering its impact carefully. Indeed, technological innovations are creating momentum affecting the organizational agility and potentially transforming businesses’ ecosystem. By digital transformation we mean the combined effects of several digital innovations bringing about novel actors (and actor constellations), structures, practices, values, and beliefs that change, threaten, replace or complement existing rules of the game within organizations, ecosystems, industries or fields. Digital transformation is intrinsically linked to disruptive and sustained innovations (e.g., product, process, marketing and organizational). There is a critical need for novel theorizing on digital innovation management that deals more adequately with the rapidly changing nature of innovation processes in a digital world. The course will address to theories of innovation, digital innovation and transformation. It will focus on the existing theories, strategies, costs and benefits for organizations to foster technological innovations breakthrough. Additionally, the societal impacts will be considered and analyzed as constituents of the business decision-making process.

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Interactive Data Transformation

The students of this course will learn how to design and build infrastructures for retrieving, integrating, managing, and querying data. Emphasis will be given both on the theoretical principles (presented and discussed during the lectures) as well as the best practical mechanisms (illustrated and practiced during the lab sessions) that enable the smooth and optimal usage of data. At the end of the course, participants will know the models and methodologies that are currently most commonly used for managing data and be able to quickly learn how to use contemporary technologies and infrastructures for interactive data management.

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Augmented Data Management for Public Data Exploration

This course is about the exploration of public data sets, including web pages (web scraping), from raw data to analytical results. It will focus on the use of AI in the transformation and presentation of the analytical results, specifically the use of large language models.

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