Interpretive Structural Modeling in the Adoption of IoT Services
KSII Transactions on Internet and Information Systems (2019) Vol. 13 (3) : 1184-1198
Impact Factor 1.5
Quartile Q3
Citations 7
Abstract
This study aims to use ISM to identify the enablers affecting the acceptance of IoT services. For this purpose, this study conducted an ISM analysis and a MICMAC analysis, extracted the enablers from Internet of Things-An Action Plan for Europe published by the EU for the research, and conducted interviews and surveys.
Research Overview
This study employs Interpretive Structural Modeling (ISM) to analyze the hierarchical relationships among enablers affecting IoT service adoption, providing a systematic framework for understanding complex interdependencies.
Research Motivation
IoT Adoption Challenges
- Complex ecosystem of technologies
- Multiple stakeholder requirements
- Interrelated adoption factors
- Need for systematic analysis
ISM Approach Benefits
- Reveals hierarchical relationships
- Identifies driving and dependent factors
- Provides actionable insights
- Enables strategic planning
Methodology
ISM (Interpretive Structural Modeling)
- Purpose: Identify hierarchical relationships among variables
- Process: Expert judgment-based structural analysis
- Output: Multi-level hierarchical model
MICMAC Analysis
- Classification: Driver, linkage, dependent, autonomous
- Driving Power: Influence on other variables
- Dependence: Influenced by other variables
Data Collection
- Source: EU IoT Action Plan enablers
- Method: Expert interviews and surveys
- Participants: IoT industry professionals
- Validation: Iterative refinement
Key Enablers Identified
Technology Factors
- Network infrastructure
- Sensor technology
- Data analytics capability
- Platform interoperability
Business Factors
- Business model innovation
- Value proposition clarity
- Ecosystem collaboration
- Market readiness
Policy Factors
- Regulatory framework
- Standards development
- Privacy protection
- Security requirements
Social Factors
- User acceptance
- Digital literacy
- Trust in technology
- Behavioral change
ISM Results
Level 1 (Top): Dependent Variables
- IoT service adoption
- User satisfaction
- Market growth
Level 2: Intermediate Variables
- Service quality
- User experience
- Trust and security
Level 3: Driving Variables
- Technology infrastructure
- Business models
- Policy support
Level 4 (Bottom): Fundamental Enablers
- Standards
- Regulations
- Investment
MICMAC Classification
Driver Variables (High Driving, Low Dependence)
- Policy and regulation
- Technology standards
- Investment support
Linkage Variables (High Driving, High Dependence)
- Platform development
- Security solutions
- Data management
Dependent Variables (Low Driving, High Dependence)
- User adoption
- Service quality
- Market outcomes
Autonomous Variables (Low Driving, Low Dependence)
- Limited direct influence
- Peripheral factors
Implications
For Policymakers
- Focus on fundamental enablers
- Develop comprehensive frameworks
- Coordinate stakeholder actions
- Address driver variables first
For Industry
- Prioritize key enablers
- Build ecosystem partnerships
- Invest in infrastructure
- Address user concerns
For Researchers
- Structured analysis approach
- Relationship mapping
- Priority identification
- Strategic planning support
Contributions
Methodological
- ISM application to IoT adoption
- Systematic enabler analysis
- Hierarchical structure revelation
Practical
- Priority setting guidance
- Strategic roadmap development
- Resource allocation optimization
Limitations
- Expert judgment dependency
- Static analysis snapshot
- Context-specific findings
- Binary relationship assumptions
Future Research
- Dynamic ISM models
- Cross-country comparisons
- Longitudinal studies
- Quantitative validation
Publication Details
Journal: KSII Transactions on Internet and Information Systems
Impact Factor: 1.5 (Q3)
Citations: 7
DOI: 10.3837/tiis.2019.03.007