| 授業名 | Disruption by Big Data/Artificial Intelligence |
|---|---|
| Course Title | Disruption by Big Data/Artificial Intelligence |
| 担当教員 Instructor Name | Giulio Toscani |
| コード Couse Code | NUC449_N24B |
| 授業形態 Class Type | 講義 Regular course |
| 授業形式 Class Format | On Campus |
| 単位 Credits | 2 |
| 言語 Language | EN |
| 学位 Degree | BBA |
| 開講情報 Terms / Location | 2024 UG Nisshin Term4 |
授業の概要 Course Overview
Misson Statementとの関係性 / Connection to our Mission Statement
This course aims to equip aspiring and established leaders from various industries with the knowledge and skills to leverage Big Data and AI effectively, gaining a competitive advantage over their competitors. Participants will learn how to harness their data and employ cutting-edge technologies to establish stronger connections with their customers. By applying the principles of Disruptive innovation methodology, they will not only analyze data and utilize technology but also comprehend and expand their capabilities, seamlessly integrating them into their organization's strategic framework. Ultimately, this course empowers participants to become adept leaders who not only understand data and technology but also leverage them strategically to drive their organizations towards success.
授業の目的（意義） / Importance of this course
This course is instrumental because it will prepare leaders to harness the power of disruptive innovation before their competitors do. Participants will gain a deeper understanding of what disruptive innovation is and will learn how to spot potential threats and opportunities in their own business.
到達目標 / Achievement Goal
By studying this course, students will be able to:
Understand the forces of disruption that could impact their business and see how to spot them early
Uncover a new approach for customer centricity to spot and shape new opportunities by applying the jobs to be done framework
Learn, through practical examples, how to leverage new business models and potential application for their companies
Get hands-on experience using the disruptive innovation toolkit
Understand the forces of disruption that could impact their business and see how to spot them early
Uncover a new approach for customer centricity to spot and shape new opportunities by applying the jobs to be done framework
Learn, through practical examples, how to leverage new business models and potential application for their companies
Get hands-on experience using the disruptive innovation toolkit
本授業の該当ラーニングゴール Learning Goals
*本学の教育ミッションを具現化する形で設定されています。
LG1 Critical Thinking
LG4 Effective Communication
LG5 Business Perspectives (BSc)
LG6 Managerial Perspectives (BBA)
LG7 International Perspectives (BA)
LG4 Effective Communication
LG5 Business Perspectives (BSc)
LG6 Managerial Perspectives (BBA)
LG7 International Perspectives (BA)
受講後得られる具体的スキルや知識 Learning Outcomes
Understand the Role of Big Data and AI: Participants will gain a comprehensive understanding of how Big Data and AI technologies can be harnessed strategically to gain a significant competitive edge over competitors.
Explore Cutting-Edge Technologies: Participants will be exposed to the latest advancements in technology, enabling them to identify and leverage innovative tools that enhance customer connections and overall business performance.
Enhance Customer Understanding: By applying Disruptive innovation methodology, learners will be able to develop a deeper understanding of customer expectations, leading to more tailored products and services.
Become Dynamic and Strategic Leaders: Upon completion of the course, participants will have the skills and knowledge to go beyond traditional data analysis and technology utilization, positioning themselves as forward-thinking leaders who can drive their organizations towards success in a rapidly evolving business landscape.
Explore Cutting-Edge Technologies: Participants will be exposed to the latest advancements in technology, enabling them to identify and leverage innovative tools that enhance customer connections and overall business performance.
Enhance Customer Understanding: By applying Disruptive innovation methodology, learners will be able to develop a deeper understanding of customer expectations, leading to more tailored products and services.
Become Dynamic and Strategic Leaders: Upon completion of the course, participants will have the skills and knowledge to go beyond traditional data analysis and technology utilization, positioning themselves as forward-thinking leaders who can drive their organizations towards success in a rapidly evolving business landscape.
SDGsとの関連性 Relevance to Sustainable Development Goals
Goal 4 質の高い教育をみんなに（Quality Education）
教育手法 Teaching Method
| 教育手法 Teaching Method | % of Course Time | |
|---|---|---|
| インプット型 Traditional | 50 % | |
| 参加者中心型 Participant-Centered Learning | ケースメソッド Case Method | 30 % |
| フィールドメソッド Field Method | 20 % | 合計 Total | 100 % |
事前学修と事後学修の内容、レポート、課題に対するフィードバック方法 Pre- and Post-Course Learning, Report, Feedback methods
Required amount of preparation: there will be several cases and a final individual report. Each case requires at least 2 hours each.The final report is approximately 10 hours
No prerequisite Knowledge/Experience
No prerequisite Knowledge/Experience
授業スケジュール Course Schedule
第1日（Day1）
Introduction to Disruption and Big Data AIIn this session, we will explore the concept of disruption and its impact on various industries. We will delve into the fundamentals of Big Data AI and how it is revolutionizing traditional business models. You will gain insights into the potential of data-driven innovation and understand the key components of a disruptive ecosystem.
Disruptive Innovation Workshop
●使用するケース
Case 1 Navozyme第2日（Day2）
Understanding Big Data and AI TechnologiesThis session will provide an in-depth understanding of Big Data and AI technologies. We will cover the principles of data collection, storage, and processing at scale, as well as the core concepts and algorithms of artificial intelligence. You will learn about the interplay between Big Data and AI and how they enable disruptive applications and solutions.
●使用するケース
Case 2 Meetup第3日（Day3）
Leveraging Big Data for Disruption In this session, we will explore how organizations can leverage Big Data to drive disruption in their respective industries. We will discuss real-world examples of companies that have successfully utilized Big Data to transform their business models and gain a competitive edge. You will also discover various data-driven strategies and techniques for identifying disruptive opportunities.●使用するケース
Case 3 Mentimeter第4日（Day4）
Unleashing the Power of AI for DisruptionBuilding upon the previous session, we will focus on the transformative potential of AI in driving disruption. We will examine the role of machine learning, deep learning, and other AI techniques in enabling groundbreaking applications. Through case studies, you will understand how AI-powered solutions are reshaping industries and creating new market dynamics.
●使用するケース
Case 4 Quatranittu第5日（Day5）
Overcoming Challenges in Disruptive Data-driven Innovation Disruption through Big Data AI is not without its challenges. In this session, we will discuss the obstacles and risks associated with data-driven innovation. We will address issues such as data privacy, ethical considerations, and regulatory frameworks. You will gain insights into best practices for mitigating risks and navigating the complex landscape of disruptive technologies.●使用するケース
Case 5 Replika第6日（Day6）
Design Thinking and Disruption Design thinking plays a crucial role in the process of disruption. In this session, we will explore the principles of design thinking and its application in driving data-driven innovation. You will learn how to adopt a user-centric approach to identify unmet needs, prototype disruptive solutions, and create meaningful experiences for customers in the digital era.●使用するケース
Case 6 Revolut第7日（Day7）
Embracing Disruption: Strategies for SuccessIn the final session, we will examine strategies for embracing disruption and capitalizing on the opportunities presented by Big Data AI. We will explore organizational agility, talent acquisition, and cultural transformation as key drivers for success in disruptive environments. You will leave with actionable insights and a roadmap for harnessing the power of data-driven innovation to shape the future of your industry.
●使用するケース
Case 7 Eathwith成績評価方法 Evaluation Criteria
*成績は下記該当項目を基に決定されます。
*クラス貢献度合計はコールドコールと授業内での挙手発言の合算値です。
*クラス貢献度合計はコールドコールと授業内での挙手発言の合算値です。
| 講師用内規準拠 Method of Assessment | Weights |
|---|---|
| コールドコール Cold Call | 0 % |
| 授業内での挙手発言 Class Contribution | 40 % |
| クラス貢献度合計 Class Contribution Total | 40 % |
| 予習レポート Preparation Report | 0 % |
| 小テスト Quizzes / Tests | 0 % |
| シミュレーション成績 Simulation | 0 % |
| ケース試験 Case Exam | 0 % |
| 最終レポート Final Report | 60 % |
| 期末試験 Final Exam | 0 % |
| 参加者による相互評価 Peer Assessment | 0 % |
| 合計 Total | 100 % |
評価の留意事項 Notes on Evaluation Criteria
教科書 Textbook
- Williams, L.「Disrupt: Think the unthinkable to spark transformation in your business.」FT press（2015）
参考文献・資料 Additional Readings and Resource
How Incumbents Survive and Thrive By: Julian Birkinshaw
Persuade Your Company to Change Before It's Too Late By: Pontus M.A. Siren; Scott D. Anthony; Utsav Bhatt
Adapting to Digital Disruption By: Julian Birkinshaw; Thomas W. Malnight; Ivy Buche; Pontus M.A. Siren; Scott D. Anthony; Utsav Bhatt; Jonathan Knee; Alison Beard
Can Big Tech Be Disrupted? By: Jonathan Knee; Alison Beard
Persuade Your Company to Change Before It's Too Late By: Pontus M.A. Siren; Scott D. Anthony; Utsav Bhatt
Adapting to Digital Disruption By: Julian Birkinshaw; Thomas W. Malnight; Ivy Buche; Pontus M.A. Siren; Scott D. Anthony; Utsav Bhatt; Jonathan Knee; Alison Beard
Can Big Tech Be Disrupted? By: Jonathan Knee; Alison Beard
授業調査に対するコメント Comment on Course Evaluation
The final course grade for a student will be determined based on the following weight distribution, incorporating three evaluation objective criteria:
Class Participation: 40%
Objective Criteria 1: Engagement Level
This evaluation measures the extent of a student's active engagement in class discussions. Recognizing the crucial role of communication in managerial success, a substantial portion of the student's grade is attributed to effective oral communication. Written assignments are expected to demonstrate clarity, logical coherence, grammatical accuracy, spell-check verification, persuasiveness, supported by examples, and substantiated by citations for data, ideas, or other content. It should embody the student's utmost effort, incorporating and applying insights from course readings.
Class Participation Guidelines:
Participation entails attending all class sessions, completing assigned readings, and actively participating in exercises and discussions. Full attendance throughout on-campus sessions is mandatory, and tardiness or early departure may lead to removal from the class. Grading criteria for effective class participation encompass preparedness, substantive comments, relevance to discussions, peer interaction, and effective communication.
Individual Final Project: 60%
Objective Criteria 2: Analytical Depth
Serving as the culmination of course learnings, the Individual Final Project necessitates students to leverage readings and discussions to present a thorough analysis.
Project Guidelines:
To prevent inappropriate use of ChatGPT and fulfill the course criteria, students must structure their assignments according to the six specified sections provided below. This entails creating a Word document (with a minimum of 2,000 words) that examines and assesses the significant impact of the course subject on the contemporary business landscape. Failure to adhere to these criteria will result in an automatic course failure.
Project Sections:
First of all, think of a real company you want to analyze.
1. Introduction: Briefly outline the significance of the course subject in the contemporary business environment using the real company you choose as an example. Clearly articulate how the real company chosen fits with this course, providing this section with at least two course slide screenshots to explain the fit.
2. Literature Review: Find and mention at least four articles not part of this course (from books, essays, or papers) that talk about the same field or a similar situation as the real company chosen. Make sure to give credit to these sources at the end of your assignment in the “References” section.
3. Selection: Examine how the course concepts helped you select the real company chosen, and describe how this process occurred, providing this section with at least two course slide screenshots.
4. Challenges and opportunities: Identify and discuss challenges and opportunities associated to the business of the real company chosen.
5. Conclusions and Learning Reflections: Summarize key findings and insights, providing concluding remarks on the overall impact of the course subject on your learning, supported by a specific example and by at least three course slide screenshots.
6. References: Ensure proper citation using a recognized citation style (e.g., APA, MLA, Chicago).
Submission Guidelines:
The assessment of the assignment will consider the six sections outlined above, each assessing: the clarity of explanation, writing style, formatting, quality and quantity of visual content, and the feasibility of the project. Do not forget to put your name both on the file name and at the heading of the document
Contextualization Requirement:
Objective Criteria 3: Integration of Course Material
Note: Successful completion of the assignment hinges on contextualization, serving as the primary method to deter improper utilization of ChatGPT and meet course requirements. This entails integrating course-specific components such as personal notes, referenced quotes from lectures or peers, instances from course materials, and visual aids from presentation slides. Failure to contextualize your assignment within the course framework, as per the six-sections criteria, will lead to a failing grade. Previous unsuccessful assignments include those lacking the prescribed six-section structure and those discussing your project without integrating course material.
Class Participation: 40%
Objective Criteria 1: Engagement Level
This evaluation measures the extent of a student's active engagement in class discussions. Recognizing the crucial role of communication in managerial success, a substantial portion of the student's grade is attributed to effective oral communication. Written assignments are expected to demonstrate clarity, logical coherence, grammatical accuracy, spell-check verification, persuasiveness, supported by examples, and substantiated by citations for data, ideas, or other content. It should embody the student's utmost effort, incorporating and applying insights from course readings.
Class Participation Guidelines:
Participation entails attending all class sessions, completing assigned readings, and actively participating in exercises and discussions. Full attendance throughout on-campus sessions is mandatory, and tardiness or early departure may lead to removal from the class. Grading criteria for effective class participation encompass preparedness, substantive comments, relevance to discussions, peer interaction, and effective communication.
Individual Final Project: 60%
Objective Criteria 2: Analytical Depth
Serving as the culmination of course learnings, the Individual Final Project necessitates students to leverage readings and discussions to present a thorough analysis.
Project Guidelines:
To prevent inappropriate use of ChatGPT and fulfill the course criteria, students must structure their assignments according to the six specified sections provided below. This entails creating a Word document (with a minimum of 2,000 words) that examines and assesses the significant impact of the course subject on the contemporary business landscape. Failure to adhere to these criteria will result in an automatic course failure.
Project Sections:
First of all, think of a real company you want to analyze.
1. Introduction: Briefly outline the significance of the course subject in the contemporary business environment using the real company you choose as an example. Clearly articulate how the real company chosen fits with this course, providing this section with at least two course slide screenshots to explain the fit.
2. Literature Review: Find and mention at least four articles not part of this course (from books, essays, or papers) that talk about the same field or a similar situation as the real company chosen. Make sure to give credit to these sources at the end of your assignment in the “References” section.
3. Selection: Examine how the course concepts helped you select the real company chosen, and describe how this process occurred, providing this section with at least two course slide screenshots.
4. Challenges and opportunities: Identify and discuss challenges and opportunities associated to the business of the real company chosen.
5. Conclusions and Learning Reflections: Summarize key findings and insights, providing concluding remarks on the overall impact of the course subject on your learning, supported by a specific example and by at least three course slide screenshots.
6. References: Ensure proper citation using a recognized citation style (e.g., APA, MLA, Chicago).
Submission Guidelines:
The assessment of the assignment will consider the six sections outlined above, each assessing: the clarity of explanation, writing style, formatting, quality and quantity of visual content, and the feasibility of the project. Do not forget to put your name both on the file name and at the heading of the document
Contextualization Requirement:
Objective Criteria 3: Integration of Course Material
Note: Successful completion of the assignment hinges on contextualization, serving as the primary method to deter improper utilization of ChatGPT and meet course requirements. This entails integrating course-specific components such as personal notes, referenced quotes from lectures or peers, instances from course materials, and visual aids from presentation slides. Failure to contextualize your assignment within the course framework, as per the six-sections criteria, will lead to a failing grade. Previous unsuccessful assignments include those lacking the prescribed six-section structure and those discussing your project without integrating course material.
担当教員のプロフィール About the Instructor
An expert on the subjects of Leadership and Disruptive Innovation by Big Data and Artificial Intelligence, Prof. Toscani caters to the board of companies, academic and not-for-profit institutions, like CCL (Belgium), SDA Bocconi (Italy), Telefonica (Spain, Argentina, Brazil, Guatemala), Deppon Logistics (China), Navozyme (Singapore), Nike and Megafon (Russia), Parexcelence (India).
Prof. Toscani started his career as a patent attorney for chemical plants in Lugano, Switzerland. He also has an MBA, and a PhD in Management from the Royal Institute of Technology, Stockholm, and is involved in research projects on Machine Learning and CEO mindset with IESE Business School. He has also published in leading publications like Marketing Intelligence and Planning, Journal of Non-Profit and Public Sector Marketing etc.
In addition to holding the extensive management and teaching experience, Prof. Toscani also presided over the Global Management Programme at Universitas Telefonica, co-managed and taught with IESE Business School. He is currently the Academic Director and professor for the Programme in Artificial Intelligence for Business Strategy in ESADE Business School, Barcelona and Madrid, Spain.
His vast experience on Big Data/AI springs from his work on location-system behavioural analysis and time perception project at Telefonica. He has also participated in the programme on AI for business at the Massachusetts Institute of Technology, and is currently setting a start-up delivering Artificial Intelligence services.
He plays the side flute in a classical music orchestra, is a yoga teacher, a vipassana meditator and an ultra-trail runner with personal record of 120 km. Based out of Barcelona, he has worked, visited or lived in over 100 countries, most recently having cycled solo halfway across the Black Sea and Caucasus, published in his youtube channel.
（実務経験 Work experience）
Professor and Advisor
• ESADE Business and Law School, Barcelona, Spain. Adjunct Professor
Law School degree: Digital Technologies Impact
MIBA (Master in Business Analytics): Human + Machine strategy
Executive education: Remote teams; Digital Entrepreneurship; Leadership in AI.
• Pacifico Business School, Lima, Perú. Adjunct Professor
• University of Bath, Bath, UK. Visiting Professor. MBA Programme: Contemporary issues at the time of Big Data/ Artificial Intelligence
• NUCB Nagoya University of Commerce and Business, Nagoya, Japan. Visiting Professor. MBA programme: Disruption by Big Data|Artificial Intelligence
• Ranepa Business School, Moscow, Russia. Visiting Professor. Global MBA: Digital Entrepreneurship
• Politecnico, Milan, Italy. Visiting Professor. Master in Strategic Design: Design Thinking in AI
• Navozyme, Singapore. Advisory Board.
• Programme Director Universitas Telefónica. Barcelona, Spain.
Direction and Teaching of the Programmes for Telefonica Global Executives
Refereed Articles
- (2023) The effects of the COVID-19 pandemic for AI practitioners: the decrease in tacit knowledge sharing. Journal of Knowledge Management
- (2023) Cómo el trabajo en remoto está reduciendo el conocimiento implícito. Harvard Deusto Business Review
- (2022) Los mundos virtuales, un nuevo reto para la propiedad industrial de las marcas. Harvard Deusto Business Review
- (2022) The role of reciprocity and reputation in service relationships with Arts organisations. Journal of Services Marketing
- (2018) Arts Sponsorship Versus Sports Sponsorship: Which Is Better for Marketing Strategy?. Journal of Nonprofit & Public Sector Marketing