Globalized markets, product complexity, and increased requirements on quality lead to growing complexity of business and manufacturing processes. Game-Based learning environments and business simulation games offer great potential to prepare employees the increasing complexity. As it is unclear who profits most from these learning environments, we did a study with 66 participants on a game for conveying Production Planning and Control and Quality Management. In our research model we combined personality attributes and two common technology acceptance models to determine factors projecting performance in the game and projected later use of business simulation games in general. We found that main drivers for usage are performance expectancy and transfer of skill, i.e., the perceived applicability of the learned knowledge and skills for the later work. The attained performance is unrelated to the projected use. The article concludes with guidelines to increase the likelihood for the later use of business simulation games and for increasing their overall efficacy.

1. Projecting Efficacy and Use of Business Simulation
Games in the Production Domain using Technology
Acceptance Models
Philipp Brauner
Ralf Philipsen
Martina Ziefle
Human-Computer Interaction Center
RWTH Aachen University, Germany
Philipp Brauner , Ralf Philipsen, Martina Ziefle, Projecting Efficacy and Use of
Business Simulation Games in the Production Domain Using Technology Acceptance
Models, Advances in Ergonomics of Manufacturing: Managing the Enterprise of the
Future, Volume 490 of the series Advances in Intelligent Systems and Computing pp
607-620, Springer International Publishing (2016)

2. RWTH Aachen University, Human-Computer Interaction Center
 Context of the Work & Motivation
 What determines performance in production
environments?
 Study on the usage intention of business simulation
games in the production domain.
– Determinants of
 interaction
 performance
 acceptance
 Discussion and Outlook
Agenda
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2016-07-31 P. Brauner et al. – "Projecting Efficacy and Use of Business Simulation Games in the Production
Domain using Technology Acceptance Models" - AHFE 2016

3. RWTH Aachen University, Human-Computer Interaction Center
2016-07-31
page 3
 Human-Human & Human-Technology communication
 Design, development, and evaluation of interactive systems
– Requirements analysis
– Usability & User Experience
– Technology Acceptance and risk perception
– Human Factors, User Diversity & Aging population
 Interdisciplinary team, 20 researchers
Human-Computer Interaction Center at
RWTH Aachen University, Germany
P. Brauner et al. – "Projecting Efficacy and Use of Business Simulation Games in the Production
Domain using Technology Acceptance Models" - AHFE 2016

4. RWTH Aachen University, Human-Computer Interaction Center
Context: Part of the Cluster of Excellence
“Integrative Production Technology for High-Wage Countries”
 Goal: Strengthen competitiveness of high wage countries
 Engineering of future production systems
– New materials and processes
– Improved and smarter machinery
– Optimize assembly cells, shop floor, cross-company cooperation
 > 25 Institutes, > 100 researchers, > 60M€
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2016-07-31 P. Brauner et al. – "Projecting Efficacy and Use of Business Simulation Games in the Production
Domain using Technology Acceptance Models" - AHFE 2016

5. RWTH Aachen University, Human-Computer Interaction Center
Our research objective:
Optimize cross-company cooperation
 Optimize cross-company supply chains (SC)
– Technical factors influencing performance of SCs
– Human Factors influencing performance of SCs
– Interface Factors on SCs performance
– Interrelationship of technical, interface, and human factors
 Why are humans considered?
– Humans make final decision
– Overview over not explicitly modelled relationships
(e.g., closed-world assumption)
– Complexity and uncertainty increases,
less time for making decisions
Information flow
flow of goods
Supply Chain
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2016-07-31 P. Brauner et al. – "Projecting Efficacy and Use of Business Simulation Games in the Production
Domain using Technology Acceptance Models" - AHFE 2016

6. RWTH Aachen University, Human-Computer Interaction Center
Our research objective:
Optimize cross-company cooperation
Information flow
flow of goods
Supply Chain
page 6
2016-07-31
Goal 1: Understand system and user factors that
influence efficiency, effectivity, and user satisfaction in
Enterprise Resource Planning Systems, Supply Chains
and Quality Management.
P. Brauner et al. – "Projecting Efficacy and Use of Business Simulation Games in the Production
Domain using Technology Acceptance Models" - AHFE 2016
Goal 2: Supporting decision makers in production
networks by providing appropriate user interfaces,
decision support systems and training tools.

7. RWTH Aachen University, Human-Computer Interaction Center
What determines performance in
Complex Cyber-Physical Production Systems?
2016-07-31
page 7
Domain expertise
Personality states & traits
Trust, Self-efficacy,
Motivation, …
Uncertainty, randomness
Non-linear interactions
Disruptions & Seasonal changes
Feedback loops, …
Interface Design
Visual complexity
Information visualization
Decision Support, …
Efficiency, Effectivity,
Profit, Quality, Satisfaction
of Workers and Customers
USERSYSTEM INTERFACE
PERFORMANCE

P. Brauner et al. – "Projecting Efficacy and Use of Business Simulation Games in the Production
Domain using Technology Acceptance Models" - AHFE 2016

8. RWTH Aachen University, Human-Computer Interaction Center
How can Human and Interface factors be investigated?
Business Simulation Games!
 Convergence between field and laboratory study
 Simplified & controllable (game-based) environments
 Experimentally manipulate complexity and interface
 Empirical methodology to quantify human performance
– Identify and measure influencing personality factors
– Identify and measure influencing interface factors
– Build a formal model that explains performance
 Side-effect:
Usable for game-based learning (GBL) in
education and professional trainings
Test in the field
(ecological validity)
Controlled
experiment in
laboratory
(internal validity)
We are here
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2016-07-31 P. Brauner et al. – "Projecting Efficacy and Use of Business Simulation Games in the Production
Domain using Technology Acceptance Models" - AHFE 2016

9. RWTH Aachen University, Human-Computer Interaction Center
Development of business simulation game
 Extends „Beer Distribution Game“
 Based on System Dynamics model
 Includes product quality
– Product intact or broken
– Supplier's quality varies
– Internal production quality varies
 Increased complexity (tradeoff 3 parallel tasks)
– Management of stock levels
– Investment in incoming goods inspection
– Investment in internal quality management
 Over 20 variables in the user interface
 Players must infer state of the production
Part of the game’s simulation model:
Stock level at a given time t:
S(t) = S(t-1) + O(t-1) – D(t)
Net profit:
P(t) = R(t) – cStock×S(t) – Iigi(t) –
Iipq(t) – C(t-1) – …
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2016-07-31 P. Brauner et al. – "Projecting Efficacy and Use of Business Simulation Games in the Production
Domain using Technology Acceptance Models" - AHFE 2016

10. RWTH Aachen University, Human-Computer Interaction Center
The Quality Management Game’s interface
 Web-based game environment
– Accessible across the world (scale)
– Laboratory studies (scope)
 Captures all metrics and interactions
 Controllable conditions
– Varying supplier’s quality, own production quality,
customer’s demand, user interface, …
 Map game performance and other metrics to
Human-Factors
– Identification of attitude, aptitude & motivation
– Evaluation of the game, interface, …
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2016-07-31 P. Brauner et al. – "Projecting Efficacy and Use of Business Simulation Games in the Production
Domain using Technology Acceptance Models" - AHFE 2016

11. RWTH Aachen University, Human-Computer Interaction Center

12. RWTH Aachen University, Human-Computer Interaction Center
Business Simulation Games as a
Research Lab for Understanding System, Interface, and User Factors
 Interactive Business simulations
– Forrester’s Beer Distribution Game, Goldratt’s Game
– Quality Management Game
 Several studies
– Do System, Interface, and Human factors influence performance?
 Questions addressed
– Replication of similar studies? ✓
– Raises awareness for Quality Management? ✓
– Do human factors exist that explain performance? ✓
– Which human factors influence performance? ❓
– Do interface aspects influence performance? ✓
– Which interface aspects influence performance? ✓
– How can users be supported to make better decisions? ❓
2016-07-31
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Domain using Technology Acceptance Models" - AHFE 2016

13. RWTH Aachen University, Human-Computer Interaction Center
Business Simulation Games as
Tools for Game-based Learning (GBL) in Education and Professional Trainings
 Research Questions:
– What are crucial factors for the use of serious games for knowledge
dissemination in quality management, mechanical engineering, and
production engineering?
– What are determinants for interaction with the game, performance and
acceptance (behavioral intention BI)?
2016-07-31
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Domain using Technology Acceptance Models" - AHFE 2016
Study approach based on technology acceptance model research. Survey-
based study combined with playing the Quality Management Game.

14. RWTH Aachen University, Human-Computer Interaction Center
Experimental Setup
 7 Dimensions from UTAUT2 (Venkatesh et al. 2012)
– Performance expectancy, Effort expectancy, Social-influence,
Price-value, Hedonic Motivation, Facilitating conditions, and Habit
 4 Dimensions of SG-TAM (Yusoff et al. 2010)
– Reward, Learner Control, Transfer of Skills, and Situated Learning
 Time-Value
 Self-efficacy in interacting with technology (Beier 1999)
 Attitude Towards Serious Games
 Need for Achievement (Nicholls 1984)
2016-07-31
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Interaction
Performance
Acceptance
P. Brauner et al. – "Projecting Efficacy and Use of Business Simulation Games in the Production
Domain using Technology Acceptance Models" - AHFE 2016

15. RWTH Aachen University, Human-Computer Interaction Center
UTAUT2
2016-07-31
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Domain using Technology Acceptance Models" - AHFE 2016
 Predict Use of consumer technology
 Intention To Use and Use governed by
– Performance Expectancy (PE)
– Effort Expectancy (EE)
– Facilitating conditions (FC)
– Social Influence (SI)
– Hedonic Motivation (HM)
– Habit (H)
– Price-Value (PV)
 Mediated by
– Age
– Gender
– Experience

16. RWTH Aachen University, Human-Computer Interaction Center
Experimental Setup
 7 Dimensions from UTAUT2 (Venkatesh et al. 2012)
– Performance expectancy, Effort expectancy, Social-influence,
Price-value, Hedonic Motivation, Facilitating conditions, and Habit
 4 Dimensions of SG-TAM (Yusoff et al. 2010)
– Reward, Learner Control, Transfer of Skills, and Situated Learning
 Time-Value
 Self-efficacy in interacting with technology (Beier 1999)
 Attitude Towards Serious Games
 Need for Achievement (Nicholls 1984)
2016-07-31
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Interaction
Performance
Acceptance
P. Brauner et al. – "Projecting Efficacy and Use of Business Simulation Games in the Production
Domain using Technology Acceptance Models" - AHFE 2016

17. RWTH Aachen University, Human-Computer Interaction Center
Description of the Sample
 66 participants
– both experts and students in the field of quality management in
production
 Age
20 – 56 years
Median: 24 years
 Gender
8 female, 57 male, 1 unspecified
2016-07-31
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18. RWTH Aachen University, Human-Computer Interaction Center
Results
Qualitative findings
2016-07-31
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Domain using Technology Acceptance Models" - AHFE 2016

19. RWTH Aachen University, Human-Computer Interaction Center
Qualitative Feedback
 mostly positive feedback on the overall game:
“[…]is really good and useful for understanding the concepts of PPC.”
(Production planning and control)
“Game was great… need more such games with different concepts. It
encourages involvement and understanding real life scenarios.”
“[…] is one of the best way[s] to learn practical industrial problems. This
is [a] very nice Game.”
2016-07-31
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Domain using Technology Acceptance Models" - AHFE 2016

20. RWTH Aachen University, Human-Computer Interaction Center
Results
Quantitative findings
2016-07-31
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Domain using Technology Acceptance Models" - AHFE 2016

21. RWTH Aachen University, Human-Computer Interaction Center
Determinants for Increased Game Interaction
 The number of changes to the controls in the game varied from 3 to 39 (mean
22.6±10.6, median 22).
 Self-efficacy in interacting with technology single explanatory variable influencing
the number of changes. People with higher SET did significantly more changes
[ρ=.367, p=.028<.05].
2016-07-31
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P. Brauner et al. – "Projecting Efficacy and Use of Business Simulation Games in the Production
Domain using Technology Acceptance Models" - AHFE 2016
Determinants for Performance
 Most independent variables did not relate to the achieved Performance in the game.
 Only the attitude towards serious games ASG [ρ=-.327, p=.051<.1] and the need
for achievement NA [ρ=-.329, p=.050<.1] had a negative influence of performance.

22. RWTH Aachen University, Human-Computer Interaction Center
Determinants for Acceptance
2016-07-31
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Domain using Technology Acceptance Models" - AHFE 2016
EE PE HE FC SI PV TV REW LC TOS SL BI
EE — .732** .780** .447** .466** .601** .693** .698** .684** .434* .603**
PE — .671** .502** .630** .516** .733** .372* .834** .790**
HE — .497** .419* .309 .782** .495** .720** .289 .619**
FC — .280 .357* .365* .498** .463** .548**
SI —
PV — .717** .417* .633** .317 .415*
TV — .594** .359*
REW — .417* .745** .595**
LC — .354* .432**
TOS — .747**
SL —
SET .588** .410* .574** .399* .419** .472** .486** .372* .534** .407* .401*
ASG .398* .477** .357* .309 .491** .682**
AM .319 .334* .349* .441* .480* .470** .405*
Spearman’s ρ-correlation coefficients for the user factors and the UTAUT2 / SGTAM model dimensions
(listed if p<.1, *<.05, **<.001).

23. RWTH Aachen University, Human-Computer Interaction Center
Determinants for Acceptance
 Strong positive relationship of most variables on the intention to use the
game (BI).
 The three strongest influencing factors:
– Performance Expectancy PE [ρ=.790]
– Transfer of Skills TOS [ρ=.747]
– Hedonic Motivation HE [ρ=.619]
 No influence on BI by:
– Facilitating Conditions FC
– Social Influence SI
– Learner Control LC
– Situated Learning SL
2016-07-31
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Domain using Technology Acceptance Models" - AHFE 2016

24. RWTH Aachen University, Human-Computer Interaction Center
Determinants for Acceptance
 Due to small sample size the application of multiple linear regressions
yielded only in a single significant model for the user factors and for the
models’ factors:
– User Factors: Attitude towards serious games (ASG) single
predictor for intention to use the game, explaining 61.7% of the
variance in BI (r2=.617, β=.792)
– Model Factors: Performance Expectancy (PE) explains 59.2%
(r2=.592, β=.778) of the variance in BI.
2016-07-31
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Domain using Technology Acceptance Models" - AHFE 2016

25. RWTH Aachen University, Human-Computer Interaction Center
Subsumption and Outlook
 Overall acceptance of serious games for conveying
knowledge of production planning and control and quality
management was high.
 Combination of the UTAUT2 and the SG-TAM model to
investigate the acceptance of the QM-Game and business
simulation games in general seems to be very promising:
– both models complement each other and may in combination
contribute more explained variance in BI and later USE than
each individual model.
 Further studies needed to research isolated influence of
interwoven factors
2016-07-31
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Domain using Technology Acceptance Models" - AHFE 2016

26. RWTH Aachen University, Human-Computer Interaction Center
Subsumption and Outlook
 Guidelines for Business Simulation Games:
2016-07-31
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P. Brauner et al. – "Projecting Efficacy and Use of Business Simulation Games in the Production
Domain using Technology Acceptance Models" - AHFE 2016
Priority Guideline
1
Provide clearly visible short-term benefits of using the
game. E.g., by making clear that the conveyed skills will be
beneficial for an upcoming exam. (Based on PE)
2
The player must perceive the presented environment as a
simulacrum of the reality. E.g., by portraying realistic
production processes. (Based on TOS)
3
Consider learner diversity, especially in regard to different
levels of inclination towards games. E.g., by augmenting the
game environment with traditional forms of knowledge
dissemination. (Based on ASG)

27. RWTH Aachen University, Human-Computer Interaction Center
Subsumption and Outlook
 Guidelines for Business Simulation Games:
2016-07-31
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P. Brauner et al. – "Projecting Efficacy and Use of Business Simulation Games in the Production
Domain using Technology Acceptance Models" - AHFE 2016
Priority Guideline
4
Create enjoyable learning environments. E.g., by including
potential players in the development to ensure target specific
aesthetics and playfulness. (Based on HE)
5
Avoid unnecessary complexity of the user interface and the
simulation model, and provide a focused learning experience.
E.g., by reducing the perceived effort for mastering the game
through guided tutorials, help functions etc. (Based on EE)
6
Provide adequate and immediate feedback on the learning
performance. E.g., by linking the learning objectives with the
company’s profit or by adding motivational incentives (badges,
leaderboards, …). (Based on REW)

28. RWTH Aachen University, Human-Computer Interaction Center
 Brauner P, Runge S, Groten M, et al (2013) Human Factors in Supply Chain Management – Decision making in complex logistic scenarios. In: Yamamoto
S (ed) Proceedings of the 15th HCI International 2013, Part III, LNCS 8018. Springer-Verlag Berlin Heidelberg, Las Vegas, Nevada, USA, pp 423–432
 Brauner P (2014) Understanding Human Factors in Supply Chains and Quality Management by Using Business Simulations. In: Brecher C, Wesch-
Potente C (eds) Proceedings of the Conference of the Cluster Of Excellence “Integrative Production Technology For High Wage Countries” 2014/1, 1st
edn. Apprimus Verlag, Aachen, Germany, Aachen, Germany, pp 387–396
 Hering N, Meißner J, Runge S, Brauner P (2014) Exzellenzcluster: Was bestimmt die Performance meiner Supply-Chain? – Eine Untersuchung
technischer und menschlicher Einflussfaktoren im Hinblick auf die Effizienz von Lieferketten. Unternehmen der Zukunft - Zeitschrift für Betriebsorganisation
und Unternehmensentwicklung 27–28.
 Philipsen R, Brauner P, Stiller S, et al (2014a) The role of Human Factors in Production Networks and Quality Management. – How can modern ERP
system support decision makers? First International Conference, HCIB 2014, Held as Part of HCI International 2014, Heraklion, Crete, Greece, June 22-27,
2014. Proceedings, LNCS 8527. Springer Berlin Heidelberg, pp 80–91
 Philipsen R, Brauner P, Stiller S, et al (2014b) Understanding and Supporting Decision Makers in Quality Management of Production Networks. Advances
in the Ergonomics in Manufacturing. Managing the Enterprise of the Future 2014 : Proceedings of the 5th International Conference on Applied Human
Factors and Ergonomics, AHFE 2014. CRC Press, Boca Raton, pp 94–105
 Stiller S, Falk B, Philipsen R, et al (2014) A Game-based Approach to Understand Human Factors in Supply Chains and Quality Management. Procedia
CIRP 20:67–73. doi: 10.1016/j.procir.2014.05.033
 Brauner P, Ziefle M (2015) Human Factors in Production Systems – Motives, Methods and Beyond. In: Brecher C (ed) Advances in Production
Technology. Springer International Publishing, pp 187–199
 Mittelstädt V, Brauner P, Blum M, Ziefle M (2015) On the visual design of ERP systems – The role of information complexity, presentation and human
factors. 6th International Conference on Applied Human Factors and Ergonomics and the Affiliated Conferences, AHFE 2015. pp 270–277
 Calero Valdez A, Brauner P, Schaar AK, et al (2015) Reducing Complexity with simplicity - Usability Methods for Industry 4.0. 19thTriennial Congress of
the International Ergonomics Association (IEA 2015).
 Ziefle M, Brauner P, Speicher F (2015) Effects of data presentation and perceptual speed on speed and accuracy in table reading for inventory control.
Occupational Ergonomics 12:119–129. doi: 10.3233/OER-150229
 Brauner, P., Philipsen, R., Fels, A., Fuhrmann, M., Ngo, H., Stiller, S., Schmitt, R., Ziefle, M.: A Game-Based Approach to Meet the Challenges of Decision
Processes in Ramp-Up Management. Quality Management Journal. 23, 55–69 (2016).
 Calero Valdez, A., Brauner, P., Ziefle, M.: Preparing Production Systems for the Internet of Things The Potential of Socio-Technical Approaches in Dealing
with Complexity. In: Dimitrov, D. and Oosthuizen, T. (eds.) Proceedings of the 6th International Conference on Competitive Manufacturing 2016 (COMA
’16). pp. 483–487. CIRP, Stellenbosch, South Africa (2016).
 Brauner P, Ziefle M. How to train employees, identify task-relevant human factors, and improve software systems with Business Simulation Games.
Procedings of the International Conference on Competitive Manufacturing 2016, COMA ’16. Stellenbosch, SA; 2016. p. 541–6.
 Brauner, P., Calero Valdez, A., Philipsen, R., Ziefle, M.: Defective Still Deflective – How Correctness of Decision Support Systems Influences User’s
Performance in Production Environments. Proceedings of the Human-Computer Interaction International 2016. (in press)
 Brauner, P., Philipsen, R., Ziefle, M.: Projecting Efficacy and Use of Business Simulation Games in the Production Domain using Technology Acceptance
Models. Proceedings of the Applied Human Factors and Ergonomics Conference (AHFE 2016). (in press)
 Brauner, P., Philipsen, R., Calero Valdez, A., Ziefle, M.: On Studying Human Factors in Complex Cyber-Physical Systems, Workshop HFIDSS 2016,
Mensch &Computer 2016 (in press)
Publications
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Domain using Technology Acceptance Models" - AHFE 2016

29. RWTH Aachen University, Human-Computer Interaction Center
Thank you four your attention!
 Questions?
Dipl.-Inform. Philipp Brauner
Human-Computer Interaction Center
Chair for Communication Science
RWTH Aachen University, Germany
eMail: brauner@comm.rwth-aachen.de
page 29
2016-07-31
Funded by the German Research Foundation (DFG) within
the Cluster of Excellence “Integrated Production Technology
for High Wage Countries” (EXC 128).
P. Brauner et al. – "Projecting Efficacy and Use of Business Simulation Games in the Production
Domain using Technology Acceptance Models" - AHFE 2016

30. RWTH Aachen University, Human-Computer Interaction Center
Appendix
Additional slides
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Domain using Technology Acceptance Models" - AHFE 2016

31. RWTH Aachen University, Human-Computer Interaction Center
Evaluation of user interfaces:
 Research question:
– Do interfaces influence player’s performance?
 Interface optimizations based on user feedback
– Better spatial layout
– Key Performance Indicators (e.g., stock level)
 Method
– Study (N=40) with old vs. new interface, surveys
 Results
– Users preferred revised user interface
– Higher profits and higher product quality w. new interface
 Conclusion:
– Good interfaces crucial for performance
(V = 0.263, F1, 38 = 13.548, p = .001 < .05*)
revised interface
first interface
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Domain using Technology Acceptance Models" - AHFE 2016