SLIDE 1
The unprepared transformation Industrie 4.0 and SMEs
- A large amount of SMEs are
are not making progress
- Where to start?
- Technology infrastructure
- Cybersecurity
- Required Capabilities
- Lack of Talents
- Technological and social
Predicting Acceptance of Novel Technology from Social Network Data - - PowerPoint PPT Presentation
Predicting Acceptance of Novel Technology from Social Network Data - - PowerPoint PPT Presentation
Predicting Acceptance of Novel Technology from Social Network Data Andr Calero Valdez Martina Ziefle Human-Computer Interaction Center, RWTH-Aachen University The unprepared transformation Industrie 4.0 and SMEs A large amount of SMEs
SLIDE 2
SLIDE 3
The Industrie 4.0 worker and engineer A complex set of attributes
- Attitude towards digitization
- High level of skills with IT
- in cybersecurity
- in network and decentralized
thinking
- in technology frameworks and
dynamics
- Etiquette of ICT
- Culture of digitization
- Acceptance of novel technology
What drives acceptance? How can we predict acceptance?
Photo Credit: Alamy
SLIDE 4
Technology Acceptance Research Theories and models since the 60s
- Technology Acceptance Model - TAM (1-3)
- Unified Theory of acceptance and use of Technology - UTAUT (1-2)
- Theory of reasoned action, theory of planned behavior
- Theory of Diffusion of Innovations (Rogers 1962)
SLIDE 5
UTAUT vs. Diffusion of Innovations (DOI) Static vs. dynamic model
UTAUT Focus on individual acceptance DOI Focus on societal acceptance
SLIDE 6
Bring both models together for prediction Static traits vs. context dependent perceptions
- Some characteristics are relatively stable, others change with context
- Static traits can be measured once
- Context dependent perceptions need constant reevaluation
- Social Influence changes most drastically with context
- ”People that are important to me, also use the software/tool/etc.”
- Social structure of an organization can be inferred from proxy data
- Shared projects, shared offices, shared authorship
- Experiment:
- Can we measure static traits once and predict behavior using simulated context
dependent perceptions from proxy data?
- Can we predict whether people use a software from common co-authorships?
SLIDE 7
Agent-based modelling Modelling user behavior individually
SLIDE 8
Batch-Simulation 161,051 Randomized local start conditions
True mean of sample +1 standard deviation −1 standard deviation
2500 5000 7500 10000 1 2 3 4 5 6
Predicted behavioral intention Frequency Histogram of mean behavioral intention of all runs (n=161,051)
Average simulated mean is lower than true mean
Data is based on full range of path−coefficients [0;1]
SLIDE 9
True model parameters help Integrating model parameters from previous research
M +1 SD −1 SD
20 40 60 80 1 2 3 4 5 6
Predicted behavioral intention Frequency Histogram of mean behavioral intention (n=539)
True coefficients match better
SLIDE 10
Using larger scale artificial data Prediction of 500 employee company
25 50 75 100 25 50 75 100
Density of network Percentage of people reaching a BI > 3.5
Effect of Network Density on Diffusion
Network size=500 Simulations = 91000
No ground truth
SLIDE 11
Discussion Agent-based modelling as a means to predict acceptance
- Agent-based models help with individual differences in prediction
- Allow identification of gate-keepers for acceptance
- Data proxies can differ in quality w.r.t. true social relations
- Storing personal data has strong regulations (GDPR)
- Ethical and legal considerations
- Digital Human Modelling of individuals
- Anonymization of data
- Loss of competitiveness if ignored
SLIDE 12
Thank you for your attention Predicting Acceptance of Novel Technology from Social Network Data
- Technology Acceptance models suffer from
static limitations
- Agent-based models can combine static and
dynamic aspects
- Prediction quality is high
M +1 SD −1 SD
20 40 60 80 1 2 3 4 5 6
Predicted behavioral intention Frequency Histogram of mean behavioral intention (n=539)
True coefficients match better
- Dr. André Calero Valdez