GRA 8283 Leading and Organizing Digitally
GRA 8283 Leading and Organizing Digitally
Rapid and unpredictable change, emergence of ubiquitous intelligent technology, and the consistent decline of routine work render conventional static organizational designs inadequate and provide a vastly expanded repertoire of available ways to organize work. Participants will learn about the demands and opportunities facing leaders and organizational designers in a digital work environment. These developments will have consequences for internal processes such as the ways we organize and the way leaders understand their roles. Participants will learn about how the traditional role of leadership needs to adapt to the changing nature of work in the digital age, human-AI collaboration, agile ways of organizing, how leadership relates to learning and innovation, and how digital technologies, as a value-creating resource, may affect leadership.
The candidates will acquire knowledge about leadership in digitally enabled organizations. They will learn about the changing nature of work and how that affects leadership and organizations, as well as the benefits and issues arising from the use of intelligent technologies in leadership and organizational processes such as decision-making. Candidates will also acquire knowledge about organizational learning, human-machine interaction, digital mindsets as well as agile and collaborative organizational forms.
- The candidates will be able to apply knowledge about human and machine cognition to leadership and organizational processes.
- The candidates will be able to design, develop, and manage organizations that make use of the dual-natured knowledge concerning humans and machines.
- The candidate will be able to identify the different digital mindsets represented in their organization or team and know how to both influence and leverage the digital mindsets of others
- The candidate will be able to evaluate the organizational context, and take steps to create the environment needed to support the learning and innovation that drive digital transformation The candidates will be able to design and lead agile and collaborative organizations
- The candidates will be able to critically assess risks and opportunities associated with human-AI collaboration.
- The candidates will be able to see their own strengths and limitations as a key to designing, developing, and managing teams and organizations that make effective use of technology.
- The candidate will be able to reflect on their own digital mindset beliefs and the implications their fundamental beliefs about new technology have on others around them
- The candidate will develop a broader competence for enabling organizational change and renewal as it relates to new technology and digital transformation
- The candidates will be able to critically assess the appropriateness of different organizational forms for different purposes and conditions.
- Digital work and leadership: Changing nature of work, automation, experimentation, and introduction to Artificial Intelligence in management
- Digital mindsets: Influencing and leveraging individual beliefs about personal and situational resources in the context of technological change.
- Hybrid work arrangements
- Agile organizing: Agile development methods, organizational design, and change.
- Digital augmentation and organizational intelligence: Digital support of human problem solving and decision-making. Bias and algorithmic accountability. Organizing intelligent human and digital actors.
- Digital work and leadership: Changing nature of work, automation, experimentation, and introduction to Artificial Intelligence in management
- Digital mindsets: Influencing and leveraging individual beliefs about personal and situational resources in the context of technological change.
- Hybrid work arrangements
- Agile organizing: Agile development methods, organizational design, and change.
- Digital augmentation and organizational intelligence: Artificial Intelligence and digital support of human problem solving and decision-making. Bias and algorithmic accountability. Organizing intelligent human and digital actors.
1 ECTS credit corresponds to a workload of 26-30 hours.
Attendance to all sessions in the course is compulsory. If you have to miss part(s) of the course you must ask in advance for leave of absence. More than 25% absence in a course will require retaking the entire course. It's the student's own responsibility to obtain any information provided in class that is not included on the course homepage/ It's learning or other course materials.
Candidates may be called in for an oral hearing as a verification/control of written assignments.
The course is a part of a full Executive MBA programme and examination in all courses must be passed in order to obtain a certificate.
In all BI Executive courses and programmes, there is a mutual requirement for the student and the course responsible regarding the involvement of the student's experience in the planning and implementation of courses, modules and programmes. This means that the student has the right and duty to get involved with their own knowledge and practice relevance, through the active sharing of their relevant experience and knowledge.
Granted admission to the EMBA programme. Please consult our student regulations.
Disclaimer
Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.
| Assessments |
|---|
Exam category: Submission Form of assessment: Submission PDF Weight: 70 Grouping: Individual Duration: 4 Week(s) Comment: Individual assignment, counts 70% of the final grade. Exam code: GRA 82832 Grading scale: ECTS Resit: Examination when next scheduled course |
Exam category: Submission Form of assessment: Submission PDF Weight: 30 Grouping: Group (2 - 8) Duration: 4 Week(s) Comment: Group assignment, counts 30% of the final grade. Exam code: GRA 82833 Grading scale: ECTS Resit: Examination when next scheduled course |
All exams must be passed to get a grade in this course.
| Activity | Duration | Comment |
|---|---|---|
Teaching | 32 Hour(s) | |
Prepare for teaching | 25 Hour(s) | |
Student's own work with learning resources | 63 Hour(s) | Self study, feedback activities/counselling and exam |
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 4 ECTS credit corresponds to a workload of at least 110 hours.