1. User Assistance Systems
Prof. Dr. Alexander Mädche, University of Mannheim
SAP Series, Rethinking Business and IT
Walldorf, June 19th 2015

2. Motivation: Technological vs. Human Evolution
2
Cognitive abilities of humans did grow
slowly over the last 7 million years.
Exponential growth of computing
power observed in the last century.
Source: http://www.bio.utexas.edu/faculty/sjasper/Bio301M/humanevol.htmlSource: http://www.extremetech.com/extreme/203490-moores-law-is-dead-long-live-moores-law

3. Motivation: Software-intensive Systems in Enterprises
3
Technical & non-technical
approaches exist to address
these challenges:
• Training
• Help & Communities
• Decision support with
explanation
• Adaptive technologies
• …
Users of software-intensive systems are
faced with many challenges:
• Failure
• High learning efforts
• Lacking adoption
• Inefficient and uneffective use
• Suboptimal decisions
• …
 The need for systematically assisting users when using software-
intensive systems in enterprises is growing.

4. Agenda
4
Agenda
1 Motivation
2 What are User Assistance Systems?
3 Selected Research Examples
4 Outlook & Summary
4

5. Analogy: (Advanced) Driver Assistance Systems (ADAS)
5
http://www.caricos.com/cars/a/audi/2012_audi_a6/1024x768/152.html
Advanced Driver Assistance Systems, or ADAS,
are systems to help the driver in the driving process.
AssistanceSensorKnowledge
Environmental
Knowledge
Positioning Data
Enriched
Technology
User
Example: Car Navigation Technology

6. Analogy: Historical Evolution of ADAS Research
6
(Bengler, et. al., 2014)

7. Analogy: Further Assistance Systems
7
Health
Private Life
Manufacturing
Logistics

8. Definition: (Advanced) User Assistance Systems
8
(Advanced) user assistance systems provide
intelligence and automation capabilities for users
of software-intensive systems leveraging different
types of data sources and user interaction.

9. Core Assistance Capability-Oriented Classification
9
Degree of
Intelligence
Degree of
Automation
low
high
medium
low medium high
Intelligent
User Assistance
System
Autonomous,
Cooperative
User Assistance
Systems
Help & Feedback
User Assistance
Systems
Operational
User Assistance
System

10. Two Further Dimensions of User Assistance Systems
10
Types of Data
Sources
internal external
Types of User
Interaction
passive (pro-)active
Core
Assistance
Capabilities
intelligence automation
Degree of
Intelligence
Degree of
Automation
low
high
medium
low medium high
Intelligent
User Assistance
System
Autonomous,
Cooperative
User Assistance
Systems
Help & Feedback
User Assistance
Systems
Operational
User Assistance
System

11. Some Examples
11
Degree of
Intelligence
Degree of
Automation
low
high
medium
low medium high
Intelligent
User Assistance System
Autonomous, Cooperative
User Assistance Systems
Help & Feedback
User Assistance Systems
Operational
User Assistance System
Help
Community
Help
Guidance
Recommender
Prediction Intelligent
Agents
Business
Rules
Updates
Feedback
Systems
Cooperative
Cognitive
Systems
Language
Translation

12. Generic Reference Architecture of (Advanced) User
Assistance Systems
12
Existing
Core
System(s)
External Data
(sensor, user-
generated, …)
UI
User Assistance System
Intelligence
Meta-
data
Core Data
Automation
Types of Data
Sources
Types of User
Interaction
Degree of
Intelligence
& Automation
User Assistance Systems enrich existing software-intensive systems by internal / pre-
defined data and metadata or external data provided by sensors and generated by
users. Intelligence and automation capabilities leverage this data and augment it. User
interaction provides passive and pro-active ways to influence the user.

13. Agenda
13
Agenda
1 Motivation
2 What are User Assistance Systems?
3 Selected Research Examples
4 Outlook & Summary
13

14. Overview: Contemporary Help Assistance Systems
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Embedded Help
Generic Help
Community Help

15. Our Research: Contextualized Process Guidance for
Enterprise Software – Lab Experiment
15
(Morana et al., 2014)
• Lab Experiment Setup: 110
Students; 3 Groups: Extended
Guidance, Basic Guidance, No
Guidance
• Selected Results:
• Process Model Understanding:
Significant Increase with
Extended and Basic Guidance.
• Process Execution
Effectiveness: Significant for
Extended Guidance
• Process Execution Efficiency:
No significant differences.
3 Design Configurations:
Understand the potential of contextualized process guidance in enterprise software.

16. Our Research: Contextualized Process Guidance for
Enterprise Software – Field Experiment
16
(Morana et al., 2015)
• Field Experiment Setup:
Provide Process Guidance
Application to 270 IT
employees. Collect survey-
based data before and after
implementation as well as
usage data.
• Selected Results:
Process knowledge positively
impacts process execution
efficiency, effectiveness and
compliance.
Deliver contextualized process guidance in IT service ticket processing.

17. Innovation Prototype: Towards Wearable Process Guidance
Systems - The GoMobile Project
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https://vimeo.com/106369083
Prototype wearable process guidance systems following an augmented reality
paradigm.

18. Our Research: Community-based Assistance System –
Contextualized User-to-User Support in Enterprise Software
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(Gass et al., 2015)
http://www.projectwechange.de/
Leverage social network site concepts to provide contexualized user-to-user support.

19. Our Ongoing Research: Gamified Community-User
Assistance Systems – Project Knowledge Management
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Benutzer der ProjectWorld
Projektphase als Gebäude
Kurzbeschreibung
Projektstatus (Beendet)
Direkte Verbindung mit der
Datenbasis
Nächste, erforderliche
Aufgaben im Projekt
Artefakte, welche von
anderen Nutzern bewertet
wurden
Details einer Projektphase
angezeigt mittels Hoover
Funktion
Leveraging gamification to further increase engagement of employees is a promising
concept. Gamification concepts provide an interesting extension of user assistance.
• Problem: Employees are not
enthusiastic to capture and
share knowledge about
previous projects
• Solution: Leverage and
extend the SAP JAM platform
to provide gamified
community assistance to
positively influence project
knowledge capturing, sharing
and reuse.
(Schacht et al., 2015)

20. Our Ongoing Research: Intelligent Feedback User
Assistance Systems - Energy consumption behavior change
Quantified Self is a trend. These systems can be
considered as feedback user assistance systems that
collect data and provide feedback to users.
20
See: http://quantifiedself.com/
• Problem: Currently energy
invoices are complex,
smart meter focuses only
on consumption data.
• Solution: Disaggregate
and map consumption to
household devices,
interlink devices with cost
information from SAP and
provide feedback and
prediction.
(Heckmann, 2015)

21. Our Ongoing Research: Intelligent User Assistance Systems
- Report Recommendation in Business Intelligence
21
(Kretzer et al. 2015)
• Problem: Many redundant
reports are created in
Business Intelligence
environments.
• Solution: Provide
intelligent recommendation
assistants that suggest
existing, related reports
during report creation
leveraging existing
metadata.
Leverage analytical techniques (e.g. recommender technologies) to proactively
influence users during reporting creation to reuse existing reports.

22. Agenda
22
Agenda
1 Motivation
2 What are User Assistance Systems?
3 Selected Research Examples
4 Outlook & Summary
22

23. Outlook: Countdown to Singularity?
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Source: http://www.singularity.com/images/charts/ExponentialGrowthofComputing.jpg
Scenario 1:
Technology takes
over.
Scenario 2:
Evolution of an
advanced human –
technology
cooperation
Ray Kurzweil predicts singularity, an intelligent explosion where technology is capable
of redesigning itself, expected to occur around 2045 …

24. Summary
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• Assisting users is an important paradigm in many
domains (automotive, health, …). Software-intensive
systems should also leverage this concept.1
• (Advanced) User Assistance Systems can be
classified along four dimensions: Data, Intelligence,
Automation, User Interaction2
• User assistance systems can have positive influence
on user behavior and outcomes.
• Further interdisciplinary research for designing
advanced human – technology cooperation required.
3

25. Thank you for your attention!
25
Prof. Dr. Alexander Mädche
University of Mannheim | Business School
Institute for Enterprise Systems (InES)
L 15, 1-6 | 4th floor | 68131 Mannheim | Germany
Phone +49 621 181-3606 | Fax +49 621 181-3627
maedche@es.uni-mannheim.de
http://eris.bwl.uni-mannheim.de
http://ines.uni-mannheim.de

26. References
Bengler, K., Dietmeyer, K., Färber, B., Maurer, M., Stiller, C., & Winner, H. (2014). Three Decades of Driver Assistance
Systems: Review and Future Perspectives. IEEE Intelligent Transportation Systems, October 2014,
doi:10.1109/MITS.2014.2336271
Gass, O., Öztürk, G., Schacht, S. and Maedche, A. (2015). “Designing an Enterprise Social Questions and Answers Site
to Enable Scalable User-to-User Support.” in: DESRIST 2015 Proceedings.
Kretzer, M., Kleinedler, M., Theilemann, C. and Maedche, A. (2015). Designing a Report Recommendation Assistant: A
First Design Cycle. To appear in: DESRIST 2015 Proceedings.
Morana, S., Schacht, S., Scherp, A., and Maedche, A. (2014). "Designing a Process Guidance System to Support User’s
Business Process Compliance," in ICIS 2014 Proceedings.
Morana,S., Gerards, T., and Maedche, A. (2015). “ITSM ProcessGuide – A Longitudinal and Multi-Method Field Study
for Real-World DSR Artifact Evaluation.” in: DESRIST 2015 Proceedings.
Heckmann, Carl. (2015). “SMARTICITY – A Feedback System for Energy Consumption and Costs”, in: Energy, Science,
Technology Conference, Karlsruhe.
Schacht, S., Reindl, A., Morana, S., and Maedche, A. (2015). „Projekterfahrungen spielend einfach mit der Pro-
jectWorld! – Ein gamifiziertes Projektwissensmanagementsystem“. Arbeitspapier, Institut für Enterprise Systems,
Universität Mannheim.
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27. Attribution
The Noun Project (http://thenounproject.com/)
 User from Wilson Joseph
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