WEEK 04 — DIGITAL TRANSFORMATION
Technology Acceptance Models & Emerging Paradigms
Understanding TAM, UTAUT, IoT, Cloud, Edge, and Fog Computing
CHAPTER 01
Technology Acceptance Model (TAM)
A theoretical framework that explains and predicts how users come to accept and use a technology.
THEORETICAL FRAMEWORK
What drives adoption?
Developed by Fred Davis in 1986, the Technology Acceptance Model (TAM) is widely used in information systems research to understand user behavior regarding technology adoption.
REFLECTION QUESTION
External variables such as System Features, User Characteristics, Social Influence, and Training influence Perceived Usefulness (PU) and Perceived Ease of Use (PEOU).
Perceived Usefulness (PU)
The degree to which a person believes that using a particular technology will enhance their job performance or productivity.
Perceived Ease of Use (PEOU)
The degree to which a person believes that using a technology will be free of effort.
Attitude Toward Use
Determined by perceived usefulness and perceived ease of use. A positive attitude can lead to higher technology adoption.
Behavioral Intention
The attitude toward use affects the behavioral intention to use the technology.
CHAPTER 02
Applications of TAM
Real-world case studies of applying TAM to analyze technology adoption in various industries.
CASE STUDY
E-Commerce & Healthcare
When BestBuy introduces a new AI-powered virtual shopping assistant, TAM helps analyze how external variables like personalized recommendations and online reviews influence perceived usefulness and ease of use.
Similarly, a hospital adopting an AI-powered chatbot for patient triage can evaluate acceptance. Patients appreciate the simple interface (ease of use), while staff value the reduction in waiting times and filtering of non-urgent cases (usefulness).
IKEA'S SELF-RETURN KIOSK
When forced to use a new technology in public, what types of apprehensions do you face? How can these apprehensions be alleviated?
Class Discussion
CHAPTER 03
The UTAUT Model
The Unified Theory of Acceptance and Use of Technology integrates elements from several existing models.
THEORETICAL FRAMEWORK
Beyond Usefulness & Ease
Introduced by Venkatesh et al. in 2003, UTAUT integrates elements from TAM, the Theory of Planned Behavior, and the Motivational Model to provide a comprehensive view of technology acceptance.
UTAUT identifies four primary constructs that significantly influence technology acceptance: Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions.
Performance Expectancy
How much the technology is perceived to enhance job performance.
Effort Expectancy
Reflects the ease of use associated with the technology.
Social Influence
How much individuals perceive that important others believe they should use the new technology.
Facilitating Conditions
The resources and support available to individuals, such as infrastructure and training.
MODERATING VARIABLES
Factors Influencing Adoption
Unlike simpler models, UTAUT introduces moderating variables—Age, Gender, Experience, and Voluntariness of Use—that affect how strongly the primary constructs influence acceptance.
KEY INSIGHT
CHAPTER 04
Emerging Computing Paradigms
Understanding the infrastructure that powers modern digital transformation: IoT, Cloud, Edge, and Fog Computing.
INTERNET OF THINGS
Connecting the Physical World
The Internet of Things (IoT) refers to a network of interconnected devices that collect, share, and act on data. From smart thermostats to connected cars, these devices constantly gather information.
The Industrial Internet of Things (IIoT) brings these concepts to manufacturing, improving efficiency. However, managing these devices introduces significant privacy and security challenges.
Cloud Computing
Delivers computing resources (storage, processing, software) over the internet. Offers scalability and centralized remote access (e.g., AWS, Google Drive).
Edge Computing
Processes data near its source (e.g., on IoT devices or local servers). Reduces latency and bandwidth usage, critical for real-time tasks like autonomous vehicles.
Fog Computing
Acts as a bridge between cloud and edge. Distributes processing tasks across local networks, analyzing data locally before sending summaries to the cloud.
ARCHITECTURE
Cloud Computing Overview
CONCLUSION
Summary
Technology adoption depends on perceived usefulness, ease of use, social influence, and facilitating conditions. Meanwhile, the infrastructure supporting these technologies has evolved from centralized cloud computing to distributed edge and fog architectures, enabling real-time IoT applications.
KEY TAKEAWAYS
- TAM relies on Perceived Usefulness and Perceived Ease of Use.
- UTAUT adds Social Influence, Facilitating Conditions, and moderating variables.
- IoT connects devices to collect and share data.
- Cloud provides centralized processing, while Edge and Fog bring computation closer to the data source.