business intelligence and smart services · 2019-04-30 · foster provost & tom fawcett (2013)....
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Business Intelligence and Smart Services
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Business Intelligence and Smart ServicesCompulsory courses
Compulsory coursesSchool of Business and Economics
Service DesignFull course descriptionThe course on Service Design explores the role of innovation in services on the nexus of people,technology, organisations and information. You will learn the foundations of service science andservice provision systems and how to think in terms of service design. What is design thinking aboutservices? It is a method for successfully developing innovation from a perspective of clients. It aims toensure that the service is useful, usable and desirable from the client’s point of view andeffective, efficient and distinctive from the supplier’s point of view (Mager 2008).The approach helps build innovations by going beyond the consideration of technological andeconomical perspectives. (Service) Design Thinking takes a user-centric perspective enablingpeople to develop radically new products, smart services or entire business models. The designthinking methods will encourage you to think in diverse ways to develop a broad range of new ideas(diverging). Subsequently, it also helps to identify the best ideas and focuses on developing theirunique elements (converging). Therefore, service design can be seen as an iterative process tovisualise, formulate, and choreograph solutions to problems that do not necessarily exist today; thecourse helps you to observe and interpret requirements and behavioural patterns and transform theminto future smart services.Given the large variety of smart services, there is a growing need for professionals who can designthese experiences and performances. This course will provide you with the required background andexperience to design smart services.
Course objectivesAt the end of the course you will have developed in four different areas: knowledge and insights,academic attitude, global citizenship, and interpersonal competences. After successfully finishing thecourse you will be able to:
Demonstrate knowledge and understanding of the role service design, principles, processes andmethods play in developing smart services
Leverage / use service design as an approach to generate stakeholder insights needed for smartservices
Integrate academic knowledge on service design and business insights to develop new ideas andinnovative services
Demonstrate academic reasoning and critical thinking based on evidence and theoryCommunicate in a clear and effective mannerSuccessfully work together and manage tasks in interdisciplinary teams
PrerequisitesNo predefined prerequisites.
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Recommended readingBrown, T. (2008). “Design thinking”, Harvard business review, Vol. 86 No. 6, pp. 84-92.Dalia, D. (2017). Service Design at a speed and scale, Design at IBM.Jaakkola, E.,Helkkula, A., and Aarikka-Stenroos, L. (2015). "Service experience co-creation:
conceptualization, implications, and future research directions", Journal of Service Management, Vol.26 No. 2, pp.182-205.
Mahr, D., N. Kalogeras, G. Odekerken‐Schröder, (2013). "A service science approach for improvinghealthy food experiences", Journal of Service Management, Vol. 24 No.4, pp. 435-471.
Teixeira, J. G., Patrício, L., Huang, K.H., and Constantine, L. (2017). “The MINDS Method: IntegratingManagement and Interaction Design Perspectives for Service Design,” Journal of Service Research,Vol. 20 No. 3, pp. 240-258.
The Service Designshow (on youtube)https://www.youtube.com/channel/UCYpyoyI0DiujtiRuN-VgxWg Yu, E. and Sangiorgi, D. (2017),“Service Design as an Approach to Implement the Value Cocreation Perspective in New ServiceDevelopment,” Journal of Service Research, Vol. 00 No. 0, pp. 1-19.
EBC4219Period 13 Sep 201826 Oct 2018
Print course descriptionECTS credits: 5.0Instruction language: EnglishCoordinators:F.D. MahrE.C. BrüggenTeaching methods: PBL
School of Business and Economics
Business AnalyticsFull course descriptionThis course introduces data analytics methods which are often used to support business decisions,particularly data-intensive decision problems. Data science topics such as predictive modeling, datamining, different types of modeling problems, model evaluation and model deployment are discussed.Students also obtain hands-on experience in using the relevant tools and develop basicprogramming/scripting skills, using the R programming language for real data applications.This courserelates to several application areas where business problems are studied from a data perspective,business decision making is supported using systematic data analysis. Examples of applications areoperations, manufacturing, supply-chain management, customer behavior modeling, marketingcampaign performance, workflow procedures, and so on. Many decision problems in these applicationareas are characterized by large uncertainty in data. Uncertainty modeling techniques, discussed in
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this course, are designed to support data driven analysis under these circumstances.
Course objectivesThe course introduces conventional quantitative techniques for business decision making andobtaining hands-on experience in analyzing business processes using available data and quantitativetechniques.After successfully finishing this course, you will be able to:
List several data analytics methodsAnalyse data by using data science concepts.Understand and develop how data can be used to provide new insights into business and create
value for the business.Translate business problems into canonical data mining tasks and study business problems from a
data perspective.Interpret and communicate application results from data science concepts in a business context.
PrerequisitesNo predefined prerequisites. Knowledge of principles of business modeling, statistics and probabilitytheory are recommended. Having basic computer programming/scripting skills in programminglanguages such as R/S-plus, C++ or MATLAB is a plus.
Recommended readingLecture slides.Foster Provost & Tom Fawcett (2013). Data Science for Business: What you need to know about
data mining and data-analytic thinking. O'Reilly Media; 1st edition (August 19, 2013), ISBN-10:1449361323, ISBN-13: 978-1449361327.
Selection of scientific papers.
EBC4220Period 13 Sep 201826 Oct 2018
Print course descriptionECTS credits: 5.0Instruction language: EnglishCoordinator:R.J. De Almeida e Santos NogueiraTeaching methods: Lecture(s), PBLAssessment methods: Participation, Written exam
School of Business and Economics
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Business Intelligence for Smart ServicesFull course descriptionBusiness Intelligence for Smart Services covers a set of theories and methodologies that handle largeamounts of data and information to assist decision-makers with automated processes,measurements, and analysis of business performance. This course will combine theoreticalframeworks and practical approaches to explain the structural utilisation of business intelligence forsmart services within organisations. The course explores the underlying technologies facilitating theintegration of business intelligence and business analytics by focusing on relevant digital platformsand data systems.This course is based on practical case studies on Business Intelligence applications and it provides themeans to apply information tools used to assist decision-makers.
Course objectivesAfter following this course, you will have gained the following competences:
Understand the main concepts of Business Intelligence and its role in organizational decision-making and smart service innovation. You will acquire knowledge of data warehouse models and toolsfor visually reporting and analysing data.
Knowledge application: You will learn to use and your knowledge on realistic cases and datasets.Critical Thinking: You will be provided with literature. The material also includes academic papers in
which the research methodology to measure the impact of decision-making in the context of smartservices is discussed.
Research Skills: You will apply business intelligence techniques, which directly contribute to yourresearch skills. Moreover, you will gain experience with reporting data and data mining by using twointuitive Business Intelligence tools.
Communication and professional attitude: to realize the above learning objectives, interaction,feedback, and teamwork will be key. As a result, you will also sharpen your communication skills andimprove your professional attitude.PrerequisitesThis is a mandatory course for the MSc Business Intelligence and Smart Services Program. There areno specific pre-requisites, although an understanding of database technology is helpful.
Recommended readingMandatory:
The 3rd edition of Ramesh Sandra, Dursun Delen, Efraim Turban, Business Intelligence: AManagerial Perspective on Analytics.
“Information Technology Implementers’ responses to user resistance: Nature and Effects” bySuzanne Rivard and Liette Lapointehttp://www.misq.org/skin/frontend/default/misq/pdf/appendices/2012/V36I3_Appendices/RivardLapointeAppendices.pdf
“Shackled to the Status Quo: The inhibiting Effects of Incumbent System Habit, Switching Costs, andInertia on a new System Acceptance”, Great Polites and Elena Karahannahttps://pdfs.semanticscholar.org/1618/7ce4cc7163bb8a0b4937ad4346d79a26f949.pdf
User Cynisism at ETI as described in Cynicism as user resistance in IT implementation, LisenSelander and Ola Henfridsson
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http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2575.2011.00386.x/full
Recommended:
Four Strategies for the age of Smart Services:https://hbr.org/2005/10/four-strategies-for-the-age-of-smart-services
Datawarehouse Overview available athttps://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/sigrecord.pdf
Healthcatalysthttps://www.healthcatalyst.com/news/three-health-catalyst-clients-recognized-for-data-driven-quality-success-hfma/
“Why do employees resist knowledge management systems? An empirical study from the statusquo bias and inertia perspectives” by Jia Li, Minghui Liu, Xuan Liuhttps://www.researchgate.net/profile/Xuan_Liu38/publication/309609207_CHB-online_version/links/581982bf08ae1f34d24acff6.pdf
A study on Big Data and Datawarehousehttp://www.ijcttjournal.org/Volume9/number-4/IJCTT-V9P137.pdf
Read Ambient Intelligence and Smart Environments: The state of the Art. This article can be foundhere https://pdfs.semanticscholar.org/f585/8c4ddd20fcf3854508d67bcd8d12d44007e1.pdf
Read Autonomous Systems. Social, Legal and Ethical Issues. This article can be found here:http://www.raeng.org.uk/publications/reports/autonomous-systems-report
Read Homes and their Users: A systematic analysis and key challengeshttps://core.ac.uk/download/pdf/29109288.pdf
EBC4221Period 229 Oct 201821 Dec 2018
Print course descriptionECTS credits: 5.0Instruction language: EnglishCoordinator:V. UroviTeaching methods: PBLAssessment methods: Participation, Written exam
School of Business and Economics
Study coaching trajectoryFull course description‘You get the best effort from others not by lighting a fire beneath them, but by building a fire withinthem’ – Bob Nelson.
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The aim of the Study Coaching Trajectory is to support students in their development of theiracademic, professional and personal competencies as described in the profile of the programme. Thecontext of the competence development is the ideal job after graduation.
The activities in the Study Coaching Trajectory follow a returning cycle of
goal-setting: based on a self-assessment the student indicates certain competencies or skills1.that need to be developedplanning of activities: the students plans very specific and concrete actions to work on these2.skills or competencies and takes into account possible resourcesreflection: the students reflects on activities and describes the influence of the actions on the3.development of the competencies (this may also include feedback from other).
In order to support this process, we work with a personalized e-portfolio which you can access viawww.e-pf.nl. In the portfolio, you collect your learning experiences, insights and reflections. Thecourse runs during the whole academic year parallel to other courses, and as such the e-portfolioshows your continuous personal development throughout the year.In order to take your reflections to the next level, you are supported by a coach. Each student has anindividual coach. Additionally you have the opportunity to select a buddy who supports you duringyour learning and development. This can be a friend, a family member, or a professional in the field. As a student you are expected to demonstrate a high-level of self-directed learning skills.
Course objectivesThe objective of the Study Coaching Trajectory is:
To support students in their development of the academic competencies as described in the1.program profile and selected by youTo foster students’ reflection on the development of their competencies2.
This is important since it (a) stimulates both professional and personal development and (b) helpsstudents to orient themselves on the labour market.The Study Coaching Trajectory (the e-portfolio, coach and buddy), aims to stimulate:
thinking about where you want to go and what you need to develop (self-regulation and self-management)
in-depth self-evaluationreflection skillsconfidence in own strengths and being knowledgeable about the weaknesses
PrerequisitesNo certain knowledge required in order to start the Study Coaching Trajectory. Not necessary to finisha certain course first.Necessary to follow this course parallel to the other courses in the program.
Recommended readingNo specific handbook.Specific articles when necessary.Suggestions for literature can be found in the course manual.Study materials on the Student Portal.
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EBC4299Year1 Sep 201831 Aug 2019
Print course descriptionECTS credits: 4.0Instruction language: EnglishCoordinator:S.M.H.E. Nievelstein
Specialisation courses
Specialisation Business AnalyticsSchool of Business and Economics
Descriptive and Predictive AnalyticsFull course descriptionDescriptive and predictive analytics tools are used in several application areas for explaining andforecasting data patterns such as purchasing patterns of customers, credit payments of individuals,planning of operations and inventory levels where data patterns are linked to potential causal factors,including time. The methods and techniques covered in this course are particularly relevant forbusiness applications where data are collected over time and/or the data represent choices frommultiple alternatives. In addition, when multiple cross-sectional instances of the same phenomena –e.g. from different individuals, customers, companies or inventory locations – are observed over time,panel data models covered in this course allow for characterizing individual patterns as well as datapatterns over time to improve data description and prediction. Such time-dependence and cross-sectional dependence in data are not accounted for in conventional data analysis methods, hence thecourse provides advanced knowledge in data analysis. This course specifically aims to provide hands-on experience in using these statistical models in business cases.
Course objectivesThe course aims to introduce advanced probabilistic models and statistical techniques for descriptiveand predictive analytics for business cases. Time series models, discrete choice models and paneldata models constitute the core of the probabilistic and statistical techniques introduced in thecourse.After successfully finishing this course, you will be able to:
Use several statistical and econometric models for time series data, discrete choice data and paneldata.
Evaluate the applicability of different econometric models for a given business problem.Translate business problems to canonical time series, discrete choice or panel data models.
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Understand and use fundamental concepts of hypothesis testing and model comparison in analyzingbusiness data.
Apply time series, discrete choice and panel data models for describing and summarizing businessdata and for evaluating the potential future outcomes in a business problem.
Interpret and communicate the numerical results of time series, discrete choice and panel datamodels in a business context.PrerequisitesBusiness Analytics (2017-100-EBC4220). Recommended background knowledge includes statistics,econometrics, probability theory and elementary programming skills.
Recommended readingInstructor's slides
Shumway, R. H., & Stoffer, D. S. (2010). Time series analysis and its applications: with R examples.2nd Edition. Springer New York. Chapters 1-3.
Train, K. E. (2009). Discrete choice methods with simulation. 2nd Edition. Cambridge UniversityPress. Chapters 2-4.
Croissant, Y. (2012). Estimation of multinomial logit models in R: The mlogit Packages. R packageversion 0.2-2. URL: http://cran. r-project. org/web/packages/mlogit/vignettes/mlogit.pdf.
Croissant, Y., & Millo, G. (2008). Panel data econometrics in R: The plm package. Journal ofStatistical Software, 27(2), 1-43.
Pfaff, B. (2008). VAR, SVAR and SVEC models: Implementation within R package vars. Journal ofStatistical Software, 27(4), 1-32.
Rossi, P., & McCulloch, R. (2010). Bayesm: Bayesian inference for marketing/micro-econometrics. Rpackage version, 2, 357-365.
EBC4222Period 44 Feb 20195 Apr 2019
Print course descriptionECTS credits: 5.0Instruction language: EnglishCoordinator:N. BastürkTeaching methods: PBL, Presentation(s), Lecture(s)Assessment methods: Participation, Written exam
School of Business and Economics
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Smart Decision Support SystemsFull course descriptionWith the increasing amount of data available within organizations, firms and managers are faced withthe task of creating insights from these new and increasing sources of data. To make these insightsaccessible to end-users, firms have developed and used decision support systems (DSS) that aim tounlock data-driven insights for the use in day-to-day decision making. In general, DSS are softwaresolutions that seek to combine data with analytical models in order to analyse these data and guidemanagerial decision making. This way, they create value for the firm. In this course we focus ondeveloping DSS by combining data available to modern firms (i.e. both classical data as well as newerdata sources such as online and text data) with analytical techniques to analyse these data. Inparticular the focus will lie on developing models appropriate for the data at hand, and interpretingthe results from these analyses in order to base decisions on. As such, this course builds on andextends courses such as Business Analytics and Descriptive and Predictive Analytics.
Course objectivesAfter this course, students:
Are able to translate a managerial problem into a research plan that includes suitable data and1.analysis choicesAre able to interpret the results of the research, and can translate these into managerial2.recommendationsHave become familiar with a variety of commonly encountered data types3.Are able to perform advanced summative analysis on data encountered4.Can identify suitable methods to analyse common data types encountered in firms5.Are able to develop their own models based on the learned methods and the available data6.
PrerequisitesExperience in R, such as gained in the course Business Analytics. Prior experience in businessmodelling and statistics is highly recommended (e.g. obtained in courses such as Business Analyticsand/or Descriptive and Predictive Analytics)
Recommended readingA selection of articles/book chapters will be made available.
EBC4223Period 58 Apr 20197 Jun 2019
Print course descriptionECTS credits: 5.0Instruction language: English
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Coordinator:N. HoltropTeaching methods: Lecture(s), PBLAssessment methods: Participation, Written exam
Specialisation Business Intelligence SystemsSchool of Business and Economics
Advanced Data Systems for Smart Services
Recommended readingResearch articles
EBC4224Period 44 Feb 20195 Apr 2019
Print course descriptionECTS credits: 5.0Instruction language: EnglishCoordinator:V. UroviTeaching methods: Lecture(s), PBLAssessment methods: Participation, Written exam
School of Business and Economics
Data VisualisationFull course descriptionIn the last decade, big data became an integral part of our economic and social life. This trend washeavily influenced by the technologically capabilities to store and collect data (Computing power, IoT,Cloud Computing, broadband expansion) and the increasing digitilization of social interactions (e.g.Facebook, Twitter, Instagram). Improved technologies are making it possible to process the resultingdata sets efficiently and effectively as the potential revenues are in many cases higher than the costs
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(Olshannikova et al, 2015). This leads to an exponentially growth of the total amount of available datathat can be used within industry and business , while the ability to analyze these data increase atmuch lower rate (Keim et al 2008). The result is that the (proper) use and the ability to correctlyinterpret data is playing an increasingly important role (Russom, 2013). The improvement of thehuman ability to manage data, extract information and gain knowledge from it is of vital importancein this context (Olshannikova, 2015). Visualization is an effective way to enhance the humancapabilities to extract and interpret information as also to support human decision making.
In this course students will learn the fundamentals of data visualization. We will study differentvisualization methods and discuss how they can be used to visualize and explore quantitativedatasets effectively. We will evaluate several approaches and learn how human perception interpretsvisualized data in various different ways.
Course objectivesThis course is an introduction to the field of Data Visualization. Students will learn the fundamentals ofdata visualization. We will study different visualization methods and discuss how they can be used tovisualize and explore quantitative datasets effectively. We will evaluate several approaches and learnhow human perception interprets visualized data in various different ways.
PrerequisitesThere are no formal prerequisites.
Recommended readingCourse bookLecture slidesAcademic papers and readings
EBC4225Period 58 Apr 20197 Jun 2019
Print course descriptionECTS credits: 5.0Instruction language: EnglishCoordinator:B.P.J. FoubertTeaching methods: PBL, Lecture(s)Assessment methods: Final paper, Participation, Assignment
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Specialisation Service DesignSchool of Business and Economics
Value proposition and pricing of Smart ServicesFull course description“Value Proposition and Pricing of Smart Services” is the second course (next to Interaction Design) inthe Service Design specialization of the MSc Business Intelligence & Smart Services.
The course is based on the idea that customers buy value propositions, and not products, services orfeatures. The course has two main objectives, namely to teach students how to create an outstandingvalue proposition for smart services and how to capture the value through an optimal value-basedpricing strategy.
The first half of the course is dedicated to introducing students to different strategies and tacticsmarketers use to create value for (and with) customers of smart services. In the second part of thecourse we focus on how firms can ensure that they reap the (financial) benefits for their valuecreation efforts. In particular, we focus on pricing, which is generally the least taught of the 4Psdespite its tremendous implications for firms’ financial and strategic performance. In particular, weexamine the user/consumer psychology of pricing for smart services as well as its strategic grounds.
Course objectivesAfter this course, students will be able to:
Understand different strategies and tactics to create value for customers of smart servicesKnow how to create powerful value propositions for smart services and how to avoid the pitfalls in
value proposition designKnow how to identify and set the optimal price for smart servicesApply an analytical framework to assess value proposition and pricing strategies of smart services
Recommended readingOsterwalder, A., Y. Pigneur, G. Bernarda, A. Smith, T. Papadakos (2014), Value Proposition Design:
How to Create Products and Services Customers Want (Strategyzer), New Jersey: WileyResearch articles
EBC4227Period 58 Apr 20197 Jun 2019
Print course descriptionECTS credits: 5.0
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Instruction language: EnglishCoordinator:M. EbrahimTeaching methods: Lecture(s), PBLAssessment methods: Participation, Written exam, Assignment
School of Business and Economics
Interaction DesignFull course descriptionBroadly speaking, the study of service interaction involves unique challenges, such as:
1) An increasingly multi-disciplinary design environment spanning domains such as interaction1.design, UEX, human-computer interaction, affective computing, behavioral science andpsychology, services, and computer science and engineering domains including data mining,machine learning, sensor fusion and robotics.2) New and rapidly evolving software and hardware landscapes, giving rise to constantly2.evolving platforms upon which to develop smarter services, as well as parallel rapid evolution innew analytical techniques, ranging from sophisticated multivariate A/B testing to social mediaanalytics, to sensor fusion in wearable computing and smart cities. This requires a reimaginingof the many ways in which customers can interact with services.3) A rapidly developing ethical and legal landscape with unique considerations around privacy3.trade-offs, data collection and keeping customers informed.
This course will introduce students to the primary theoretical aspects of each of these challenges andequip them with the tools to begin to create and manage smarter services. Students will learn aboutscientific developments at the intersection of different disciplines giving rise to the services of thefuture, and will experience a wide-ranging introduction to multiple options to develop such services.
Divided into nine distinct topics, this course begins with a multi-disciplinary introduction to thecreation of smarter services, guiding students through a variety of relevant domains where smarterservices are currently evolving, before finally concluding with critical ethical considerations, shapingthe way smart services are created and ensuring customer uptake and continued usage.
Course objectivesWhen you have successfully finished this course, you will:
Be able to apply multi-disciplinary reasoning in approaching smart service design including1.human-computer interaction perspectives.Be able to discuss the diverse spectrum of software and hardware platforms available for smart2.service development.Gain a better understanding of the broad range of legal and ethical issues surrounding smart3.services.
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PrerequisitesA rudimentary understanding of service design and smart services.
Recommended readingBattistella, C., Battistella, C., De Toni, A. F., De Toni, A. F., Pessot, E., & Pessot, E. (2017). Open
accelerators for start-ups success: a case study. European Journal of Innovation Management, 20(1),80-111.
Dale, R. (2016). The return of the chatbots. Natural Language Engineering, 22(5), 811-817.Dennis, L., Fisher, M., Slavkovik, M., & Webster, M. (2016). Formal verification of ethical choices in
autonomous systems. Robotics and Autonomous Systems, 77, 1-14.Eyssel, F. (2017). An experimental psychological perspective on social robotics. Robotics and
Autonomous Systems, 87, 363-371.Gravina, R., Alinia, P., Ghasemzadeh, H., & Fortino, G. (2017). Multi-sensor fusion in body sensor
networks: State-of-the-art and research challenges. Information Fusion, 35, 68-80.Rafaeli, A., Altman, D., Gremler, D. D., Huang, M. H., Grewal, D., Iyer, B., ... & de Ruyter, K. (2016).
The Future of Frontline Research: Invited Commentaries. Journal of Service Research,1094670516679275.
EBC4226Period 44 Feb 20195 Apr 2019
Print course descriptionECTS credits: 5.0Instruction language: EnglishCoordinator:M.P. GrausTeaching methods: PBL, Presentation(s)Assessment methods: Participation
No specialisationSchool of Business and Economics
Descriptive and Predictive AnalyticsFull course descriptionDescriptive and predictive analytics tools are used in several application areas for explaining andforecasting data patterns such as purchasing patterns of customers, credit payments of individuals,planning of operations and inventory levels where data patterns are linked to potential causal factors,
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including time. The methods and techniques covered in this course are particularly relevant forbusiness applications where data are collected over time and/or the data represent choices frommultiple alternatives. In addition, when multiple cross-sectional instances of the same phenomena –e.g. from different individuals, customers, companies or inventory locations – are observed over time,panel data models covered in this course allow for characterizing individual patterns as well as datapatterns over time to improve data description and prediction. Such time-dependence and cross-sectional dependence in data are not accounted for in conventional data analysis methods, hence thecourse provides advanced knowledge in data analysis. This course specifically aims to provide hands-on experience in using these statistical models in business cases.
Course objectivesThe course aims to introduce advanced probabilistic models and statistical techniques for descriptiveand predictive analytics for business cases. Time series models, discrete choice models and paneldata models constitute the core of the probabilistic and statistical techniques introduced in thecourse.After successfully finishing this course, you will be able to:
Use several statistical and econometric models for time series data, discrete choice data and paneldata.
Evaluate the applicability of different econometric models for a given business problem.Translate business problems to canonical time series, discrete choice or panel data models.Understand and use fundamental concepts of hypothesis testing and model comparison in analyzing
business data.Apply time series, discrete choice and panel data models for describing and summarizing business
data and for evaluating the potential future outcomes in a business problem.Interpret and communicate the numerical results of time series, discrete choice and panel data
models in a business context.PrerequisitesBusiness Analytics (2017-100-EBC4220). Recommended background knowledge includes statistics,econometrics, probability theory and elementary programming skills.
Recommended readingInstructor's slides
Shumway, R. H., & Stoffer, D. S. (2010). Time series analysis and its applications: with R examples.2nd Edition. Springer New York. Chapters 1-3.
Train, K. E. (2009). Discrete choice methods with simulation. 2nd Edition. Cambridge UniversityPress. Chapters 2-4.
Croissant, Y. (2012). Estimation of multinomial logit models in R: The mlogit Packages. R packageversion 0.2-2. URL: http://cran. r-project. org/web/packages/mlogit/vignettes/mlogit.pdf.
Croissant, Y., & Millo, G. (2008). Panel data econometrics in R: The plm package. Journal ofStatistical Software, 27(2), 1-43.
Pfaff, B. (2008). VAR, SVAR and SVEC models: Implementation within R package vars. Journal ofStatistical Software, 27(4), 1-32.
Rossi, P., & McCulloch, R. (2010). Bayesm: Bayesian inference for marketing/micro-econometrics. Rpackage version, 2, 357-365.
EBC4222
Business Intelligence and Smart Services
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Period 44 Feb 20195 Apr 2019
Print course descriptionECTS credits: 5.0Instruction language: EnglishCoordinator:N. BastürkTeaching methods: PBL, Presentation(s), Lecture(s)Assessment methods: Participation, Written exam
School of Business and Economics
Smart Decision Support SystemsFull course descriptionWith the increasing amount of data available within organizations, firms and managers are faced withthe task of creating insights from these new and increasing sources of data. To make these insightsaccessible to end-users, firms have developed and used decision support systems (DSS) that aim tounlock data-driven insights for the use in day-to-day decision making. In general, DSS are softwaresolutions that seek to combine data with analytical models in order to analyse these data and guidemanagerial decision making. This way, they create value for the firm. In this course we focus ondeveloping DSS by combining data available to modern firms (i.e. both classical data as well as newerdata sources such as online and text data) with analytical techniques to analyse these data. Inparticular the focus will lie on developing models appropriate for the data at hand, and interpretingthe results from these analyses in order to base decisions on. As such, this course builds on andextends courses such as Business Analytics and Descriptive and Predictive Analytics.
Course objectivesAfter this course, students:
Are able to translate a managerial problem into a research plan that includes suitable data and1.analysis choicesAre able to interpret the results of the research, and can translate these into managerial2.recommendationsHave become familiar with a variety of commonly encountered data types3.Are able to perform advanced summative analysis on data encountered4.Can identify suitable methods to analyse common data types encountered in firms5.Are able to develop their own models based on the learned methods and the available data6.
PrerequisitesExperience in R, such as gained in the course Business Analytics. Prior experience in business
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modelling and statistics is highly recommended (e.g. obtained in courses such as Business Analyticsand/or Descriptive and Predictive Analytics)
Recommended readingA selection of articles/book chapters will be made available.
EBC4223Period 58 Apr 20197 Jun 2019
Print course descriptionECTS credits: 5.0Instruction language: EnglishCoordinator:N. HoltropTeaching methods: Lecture(s), PBLAssessment methods: Participation, Written exam
School of Business and Economics
Value proposition and pricing of Smart ServicesFull course description“Value Proposition and Pricing of Smart Services” is the second course (next to Interaction Design) inthe Service Design specialization of the MSc Business Intelligence & Smart Services.
The course is based on the idea that customers buy value propositions, and not products, services orfeatures. The course has two main objectives, namely to teach students how to create an outstandingvalue proposition for smart services and how to capture the value through an optimal value-basedpricing strategy.
The first half of the course is dedicated to introducing students to different strategies and tacticsmarketers use to create value for (and with) customers of smart services. In the second part of thecourse we focus on how firms can ensure that they reap the (financial) benefits for their valuecreation efforts. In particular, we focus on pricing, which is generally the least taught of the 4Psdespite its tremendous implications for firms’ financial and strategic performance. In particular, weexamine the user/consumer psychology of pricing for smart services as well as its strategic grounds.
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Course objectivesAfter this course, students will be able to:
Understand different strategies and tactics to create value for customers of smart servicesKnow how to create powerful value propositions for smart services and how to avoid the pitfalls in
value proposition designKnow how to identify and set the optimal price for smart servicesApply an analytical framework to assess value proposition and pricing strategies of smart services
Recommended readingOsterwalder, A., Y. Pigneur, G. Bernarda, A. Smith, T. Papadakos (2014), Value Proposition Design:
How to Create Products and Services Customers Want (Strategyzer), New Jersey: WileyResearch articles
EBC4227Period 58 Apr 20197 Jun 2019
Print course descriptionECTS credits: 5.0Instruction language: EnglishCoordinator:M. EbrahimTeaching methods: Lecture(s), PBLAssessment methods: Participation, Written exam, Assignment
School of Business and Economics
Advanced Data Systems for Smart Services
Recommended readingResearch articles
EBC4224Period 44 Feb 20195 Apr 2019
Print course description
Business Intelligence and Smart Services
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ECTS credits: 5.0Instruction language: EnglishCoordinator:V. UroviTeaching methods: Lecture(s), PBLAssessment methods: Participation, Written exam
School of Business and Economics
Data VisualisationFull course descriptionIn the last decade, big data became an integral part of our economic and social life. This trend washeavily influenced by the technologically capabilities to store and collect data (Computing power, IoT,Cloud Computing, broadband expansion) and the increasing digitilization of social interactions (e.g.Facebook, Twitter, Instagram). Improved technologies are making it possible to process the resultingdata sets efficiently and effectively as the potential revenues are in many cases higher than the costs(Olshannikova et al, 2015). This leads to an exponentially growth of the total amount of available datathat can be used within industry and business , while the ability to analyze these data increase atmuch lower rate (Keim et al 2008). The result is that the (proper) use and the ability to correctlyinterpret data is playing an increasingly important role (Russom, 2013). The improvement of thehuman ability to manage data, extract information and gain knowledge from it is of vital importancein this context (Olshannikova, 2015). Visualization is an effective way to enhance the humancapabilities to extract and interpret information as also to support human decision making.
In this course students will learn the fundamentals of data visualization. We will study differentvisualization methods and discuss how they can be used to visualize and explore quantitativedatasets effectively. We will evaluate several approaches and learn how human perception interpretsvisualized data in various different ways.
Course objectivesThis course is an introduction to the field of Data Visualization. Students will learn the fundamentals ofdata visualization. We will study different visualization methods and discuss how they can be used tovisualize and explore quantitative datasets effectively. We will evaluate several approaches and learnhow human perception interprets visualized data in various different ways.
PrerequisitesThere are no formal prerequisites.
Recommended readingCourse book
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Lecture slidesAcademic papers and readings
EBC4225Period 58 Apr 20197 Jun 2019
Print course descriptionECTS credits: 5.0Instruction language: EnglishCoordinator:B.P.J. FoubertTeaching methods: PBL, Lecture(s)Assessment methods: Final paper, Participation, Assignment
School of Business and Economics
Interaction DesignFull course descriptionBroadly speaking, the study of service interaction involves unique challenges, such as:
1) An increasingly multi-disciplinary design environment spanning domains such as interaction1.design, UEX, human-computer interaction, affective computing, behavioral science andpsychology, services, and computer science and engineering domains including data mining,machine learning, sensor fusion and robotics.2) New and rapidly evolving software and hardware landscapes, giving rise to constantly2.evolving platforms upon which to develop smarter services, as well as parallel rapid evolution innew analytical techniques, ranging from sophisticated multivariate A/B testing to social mediaanalytics, to sensor fusion in wearable computing and smart cities. This requires a reimaginingof the many ways in which customers can interact with services.3) A rapidly developing ethical and legal landscape with unique considerations around privacy3.trade-offs, data collection and keeping customers informed.
This course will introduce students to the primary theoretical aspects of each of these challenges andequip them with the tools to begin to create and manage smarter services. Students will learn aboutscientific developments at the intersection of different disciplines giving rise to the services of thefuture, and will experience a wide-ranging introduction to multiple options to develop such services.
Divided into nine distinct topics, this course begins with a multi-disciplinary introduction to thecreation of smarter services, guiding students through a variety of relevant domains where smarterservices are currently evolving, before finally concluding with critical ethical considerations, shapingthe way smart services are created and ensuring customer uptake and continued usage.
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Course objectivesWhen you have successfully finished this course, you will:
Be able to apply multi-disciplinary reasoning in approaching smart service design including1.human-computer interaction perspectives.Be able to discuss the diverse spectrum of software and hardware platforms available for smart2.service development.Gain a better understanding of the broad range of legal and ethical issues surrounding smart3.services.
PrerequisitesA rudimentary understanding of service design and smart services.
Recommended readingBattistella, C., Battistella, C., De Toni, A. F., De Toni, A. F., Pessot, E., & Pessot, E. (2017). Open
accelerators for start-ups success: a case study. European Journal of Innovation Management, 20(1),80-111.
Dale, R. (2016). The return of the chatbots. Natural Language Engineering, 22(5), 811-817.Dennis, L., Fisher, M., Slavkovik, M., & Webster, M. (2016). Formal verification of ethical choices in
autonomous systems. Robotics and Autonomous Systems, 77, 1-14.Eyssel, F. (2017). An experimental psychological perspective on social robotics. Robotics and
Autonomous Systems, 87, 363-371.Gravina, R., Alinia, P., Ghasemzadeh, H., & Fortino, G. (2017). Multi-sensor fusion in body sensor
networks: State-of-the-art and research challenges. Information Fusion, 35, 68-80.Rafaeli, A., Altman, D., Gremler, D. D., Huang, M. H., Grewal, D., Iyer, B., ... & de Ruyter, K. (2016).
The Future of Frontline Research: Invited Commentaries. Journal of Service Research,1094670516679275.
EBC4226Period 44 Feb 20195 Apr 2019
Print course descriptionECTS credits: 5.0Instruction language: EnglishCoordinator:M.P. GrausTeaching methods: PBL, Presentation(s)Assessment methods: Participation
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Projects
Smart Service Innovation ProjectsSchool of Business and Economics
Smart Service Innovation Project 2Full course descriptionThe Smart Service Innovation Projects (SSIPs) aim to give students a challenging opportunity to applyand extend the knowledge they acquire in the mandatory and elective courses of the BISS masterprogram. Students will tackle a real business problem, in order to develop a smart service; that is, aservice that exploits today’s data- and technology-rich environment to support managerial decision-making or facilitate end-consumers’ daily lives. There are two SSIPs each year, one in the firstsemester and one in the second semester. The goal is to develop implement implement the smartservice.
Course objectivesAfter successfully finishing this course, you will be able to:
apply knowledge of frameworks, approaches, perspectives, and methodologies acquired throughthe BISS master courses for the development of smart services and the analysis of business data.
translate a general problem into a specific (research) question, and collect and analyze data toobtain an answer.
communicate the outcomes of your work and analysis in general and the value proposition of asmart service in particular.
develop your own innovative ideas in the context of an actual business case.
Recommended readingThe exact literature depends on the specific project of the group and it is to be decided by thestudents. Coordinators will provide relevant literature when students are confronted with new tools ormethods that have not been addressed in any of the courses. This content may take the form ofliterature, online learning materials (e.g., videos, moocs), or workshops.
EBP4002Semester 24 Feb 20195 Jul 2019
Print course descriptionECTS credits: 7.0Instruction language: EnglishCoordinator:C.P.M. van Hoesel
Business Intelligence and Smart Services
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Teaching methods: Lecture(s), PBL, Coaching, Skills, Work in subgroupsAssessment methods: Final paper, PresentationDays: Wednesday
School of Business and Economics
Smart Service Innovation Project 1Full course descriptionThe Smart Service Innovation Projects (SSIPs) aim to give students a challenging opportunity to applyand extend the knowledge they acquire in the mandatory and elective courses of the BISS masterprogram. Students will tackle a real business problem, in order to develop a smart service; that is, aservice that exploits today’s data- and technology-rich environment to support managerial decision-making or facilitate end-consumers’ daily lives. There are two SSIPs each year, one in the firstsemester and one in the second semester. The goal is to develop implement implement the smartservice.
Course objectivesAfter successfully finishing this course, you will be able to:
apply knowledge of frameworks, approaches, perspectives, and methodologies acquired throughthe BISS master courses for the development of smart services and the analysis of business data.
translate a general problem into a specific (research) question, and collect and analyze data toobtain an answer.
communicate the outcomes of your work and analysis in general and the value proposition of asmart service in particular.
develop your own innovative ideas in the context of an actual business case.
Recommended readingThe exact literature depends on the specific project of the group and it is to be decided by thestudents. Coordinators will provide relevant literature when students are confronted with new tools ormethods that have not been addressed in any of the courses. This content may take the form ofliterature, online learning materials (e.g., videos, moocs), or workshops.
EBP4001Semester 13 Sep 20181 Feb 2019
Print course descriptionECTS credits: 7.0
Business Intelligence and Smart Services
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Instruction language: EnglishCoordinators:S.T. JongenR.J. De Almeida e Santos NogueiraTeaching methods: PBL, Lecture(s), Coaching, Skills, Work in subgroupsAssessment methods: Final paper, PresentationDays: Wednesday
Thesis
Master thesisSchool of Business and Economics
Writing a Master Thesis Proposal BISSFull course descriptionThis skills training consists of three key elements: (1) Developing a research proposal, (2) Attendinglectures, (3) Attending research seminars in BISS.
Course objectivesThe objective of the skills training is to guide you such that you successfully start working on yourmaster thesis and plan your master thesis process.After having completed the skills training you should have a clear idea on:
The research topic of your master thesis;1.The research question that you will investigate;2.The research approaches that you can use to collect/analyse data and/or to develop a decision3.making tool;The specific quantitative methods required to realize the research project.4.
Recommended readingInstructor's slides and selected papers.
EBS4033Period 37 Jan 20191 Feb 2019
Print course description
Business Intelligence and Smart Services
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ECTS credits: 4.0Instruction language: EnglishCoordinator:N. BastürkTeaching methods: Lecture(s), PBL
School of Business and Economics
Master's Thesis (BISS, 13 ECTS)Full course descriptionAt the end of the master studies BISS, each student has to write a thesis. The thesis is written andpresented individually, and it is supervised by a staff member. General procedure and requirementsof the thesis follows standard SBE Master thesis requirements and these requirements are introducedduring the prerequisite course Writing a Master Thesis BISS (2017-300-EBS4033).
Course objectivesThe purpose of the BISS master thesis is to develop deeper knowledge, insights, capabilities andacademic attitude in the context of the BISS master program. The thesis demonstrates the students'capability to understand and apply the knowledge acquired during the whole program and tospecialize in a subject matter of choice in a self-directed manner. The BISS master thesis placesspecial emphasis on the technical/scientific/artistic aspects of the subject matter as well ascommunicating the findings in a clear and effective manner.
PrerequisitesWriting a master thesis proposal BISS (2017-300-EBS4033)
Recommended readingLiterature depends on the master thesis topic. The student decides the exact literature, with guidancefrom the thesis supervisor.
EMTH0006Semester 24 Feb 20195 Jul 2019
Print course descriptionECTS credits: 13.0Instruction language:
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EnglishCoordinator:N. Bastürk