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 of: https://thenounproject.com

1

Thank you.

 
Made with Slides.com