Diffusion of Innovation
Adriana Alvarado García
Pankaj Avhad
Xing Yu
Nomaan Ahgharian
What is it?
A theory that explains how/why/at what rate new ideas are communicated between individuals and spread in society
What are the ELEMENTS?
Innovation
Communication Channel
Time
Social system
Attributes of Innovations
- Relative Advantage
- Compatibility
- Complexity
- Trialability
- Observability
How improved an innovation is
RElative Advantage
Compatibility
How compatible it is the user's life and lifestyle .
2
COMplexity
how difficult it is for adopters to learn
trialability
how easily adopters can explore
Observability
Results or benefit of using an innovation are visible
Applications
- (a) The adoption rate of an innovation will increase if it has either more competitors or more collaborators
- (b) The adoption rate of an innovation increases with the proportion of its competitors or collaborators adopted by the user
- (c) The users with higher standards of selecting innovations are less likely to adopt an innovation
From Social Network to Innovation Network
Rong, Mei 2013
-
User-generated categorization of data
-
Real time updates
-
Channel support - Mobile Applications with Open API’s
-
Identity establishments
TWITTER HASHTAG ADOPTION
Chang(2009)
Criticism
Baises
-
Pro-innovation bias
-
Individual-blame bais
-
RECALL PROBLEM
-
ISSUE OF EQUALITY
1
PRO-INNOVATION BIAS
- Positive
- Should be adopted by all
INDIVIDUAL-BLAME BAIS
-
Researches side with the change agents rather than the adopters!
REcall Problem
ISSUE OF EQUALITY
Quick Recap
- Provides frameworks for understanding adoption process
- Helps conceptualizing factors that contribute to the adoption
Diffusion of Innovation Theory:
Research Gap
RESEARCH GAP
Why certain innovations have not been adopted?
It is least understood in Diffusion of Innovation theory
Research Questions
- What are the contributing factors to the non-adoption behavior?
- Are there any differences between the two different types of non-adoptions -- rejected at the beginning and rejected after a while of use.
- How do the contributing factors change along time?
Research Procedure
| Phase one | Phase Two |
|---|---|
| Exploration | Verification |
| Data mining Machine Learning |
Interview Survey User Experiment |
Data Collection
| Phase One |
|---|
| Crawlers |
| Posts and related comments regarding the innovation( social media, etc) RQ1: Entire RQ2: Non-adopters vs. Quitters RQ3: T1 vs. T2 |
Phase one
| Predictors | Dependent Variables |
|---|---|
| Topic Modeling | Sentiment Analysis |
| Topic Distribution | Positive, Negative |
| Collect Human Comments From Online Forums |
| Random Forest |
Data Collection
| Phase One | Phase Two |
|---|---|
| Crawlers | interview/Prototype |
| Posts and related comments regarding the innovation( social Media, etc.) | Participant's feedback from interview/user experiments |
Phase Two
| Interview | User Experiments |
| experienced users professional designers | Prototypes |
| Semi-structured Interview | Observation Survey |
| Contributing Factors From Phase One |
Limitations
- The factors that we plan to find could be bound by the types of innovations(social media vs. electronic products)
- The result could be biased depending on the data source in phase one.
- There could be mistakes when discerning non-adopters and quitters based on text mining.
Questions?
Icons are courtesy
1
Thank you.
Diffusion of Innovation
By Nomaan Ahgharian
Diffusion of Innovation
In-class presentation about Diffusion of Innovation theory and gaps
