36.6 Creativity
Implications of social media
- Wisdom of the Crowds
- Advertising almost free
36.6.1 (Bayus 2013) Crowdsourcing New Product Ideas over Time
from dell’s IdeaStorm community, serial ideators are more likely to have 1 idea that the organization will implement, but they don’t repeat this success.
Negative effect of past success can be mitigated for idators with more diverse commenting activity
- Fixation effect = unconscious plagiarism (or cryptomnesia) (R. L. Marsh and Landau 1995) (R. L. Marsh, Ward, and Landau 1999)
Good
First paper to study crowdfunding of ideas
Good theory: fixation effect
Good descriptive analysis
Cons
- Model: not taken into account rare events.
36.6.2 (Toubia and Netzer 2017) Idea generation, creativity, prototypicality
Creativity = balance(novelty , familiarity)
Beauty in avergeness effect
Automate read ideas to identify promising ones
Research questions
- How novelty and familiarity defined in the idea generation context? From literature using Geneplore
“novelty is the association of word stems that do not appear frequently together in text related to the topic under consideration” (p. 3)
“familiarity is the association of word stems that appear frequently together” (p. 3)
- How should novelty and familiarity be measured? semantic network co-word analysis (by the combinations of word stem instead of the word itself)
- What is the optimal balance between novelty and familiarity? beauty in averageness effect
idea = “a document made of words that attempts to add value given a particular idea generation topic” p. 2
Automatically recommend words to improve idea
Baseline for semantic network:
Pre-test idea: consumers generate initial set of ideas on a topic
Google results: top search (might be biased to high-quality contents)
Used Jaccard index for edge weights
Control variables: (Barrat, Barthélemy, and Vespignani 2007)
Frequencies of nodes in the network: average edge weight, coefficient of variation of edge weights, minimum edge weight, maximum edge weight, average node frequency, coefficient of variation of node frequencies, minimum node frequency, maximum node frequency, and the number of nodes in the subnetwork, length of the idea using number of characters
Clustering coefficients of the nodes in the network; average node clustering coefficient, coefficient of variation of node clustering coefficients, minimum node clustering coefficient, and maximum node clustering coefficient.
Prototypical distribution of edge weights using mean of the prototypical distribution
Measure distance between two distributions - The Kolmogorov-Smirnov statistics (2 cdfs). Alternatively could use Kullback-Leibler divergence
Idea evaluation: manual with 4 dimension: creativity, purchase interest, predicted popularity, writing quality
Alternative measure to edge weight distributions: Info retrieval literature: vector space representation: each document as a vector with dimensionality equal to the number of word stems in our dictionary (i.e., number of nodes in our semantic network
Specification of the baseline semantic network is dangerous to the sub-network distribution.
Robust to synonyms
Strengths:
Good way to measure a complex and highly qualitative construct
Good connection between the theory and method
Robust
- Different measures, ideas, evaluators, baseline networks.
Cons
- With other representations, the results do not hold
36.6.3 (Y. “Max”. Wei, Hong, and Tellis 2021) Machine leaning creativity
Crowdfunding: for both finance and marketing (market reaction, advertise ideas)
Combinatorial theory:
measure novelty, overshooting and undershooting, measure styles of imitation
Research questions
How to measure the similarity between all the projects on crowdfunding sites in an objective and automated way?
The relationship between the similarity pattern and funding performance
Can previous successful projects that are similar product a new project’s success?
Do people value novelty?
whether to overshoot or undershoot the funds raised?
Do people value atypicality?
Recommendation from the similarity measure
Data: 98,058 Kickstarter projects from 2009 - 2017 (from 3 categories: Film & Video, Music and publishing. only English.
Techniques: Semantic Similarity
Word2vec: word-level similarity
Word Mover’s Distance (WMD): Document-level similarity \(w_{ij} = \delta^{|t_i - t_j|} \times L(\gamma_0 - \gamma_1 d_{ij})\) where \(0 < \delta \le 1\) is the decay factor, \(d_{ij}\) is the WMD between 2 projects and \(L\) is the logistic function and \(\gamma\) are chosen based
Similarity network where each node is a project,and the strength of a link
Increases with degree of similarity
decreases with the time lapse between 2 projects
Funding performance
Whether the funding is successful
How much money is raised
Findings
The average level of success by prior projects is a good predictor of the current project’s funding performance
High novelty means less similar to all previous projects, good projects are balanced of being novel and appearing familiar to investors
Goals should be set close to the number by prior similar projects
An inverted U-shaped relation between atypicality (borrow from another stream) and funding performance
Recommendations:
goals should be benched marked by other previous projects ( \(\pm 10\)% goal adjustment)
project should be similar to prior projects
Combinotorial
Geneplore framework
Generation process: retrieve prior info and recombine in a creative way
Exploration process: these recombinations will be elaborated
Results are robust against unweighted network whether link is present when it passes certain threshold.
Network-based metrics
Amount of prior similarity: degree of similarity
Prior success rate: weighted average of previous similar projects.
Prior success residual: reweigh the success rate with other control variables
Goal overshoot: difference between the focal project’ funding goal and the average of previous project funding goal (in log)
Atypicality: use unweighted network (using the cutoff of .5), atypicality = proportion of isolated in \(i\) subnetwork.
Control variables
- Project-related features
Log funding goal
log number of images
Dummy for video
Log length of the project depreciation text
Dummy for project category
Time trend and quarter dummies
- Creator-related features
Dummy for prior project
Average success rate of the creator’s prior projects
Models
Success: logistic
Fund raised: regression
Information weighting: \(I_i\equiv \log(1 + \sum_{j:T_j<t_i}w_{ij})\) choosing this specification because
- when there is no similarity between the focal project and prior projects, the information weight should be 0
- Under the Bayesian framework, there is a diminishing return of more signals.
Info weight is used for all metrics except similarity and atypicality
36.6.4 Can AI do ideation? 2022
Basic research question: How to screen ideas
Based on 3 models:
Word Colocation
Content Atypicality
Inspiration Redundancy
Prediciton mode
LASSO
Random Forest
RuleFit
36.6.5 (Berger and Packard 2018) Content Atypicality
- Ideas are better if they are different from other in the same contest.
36.6.6 (Stephen, Zubcsek, and Goldenberg 2016) The Effects of Network Structure on Redundancy of Ideas
- Ideators with more diverse background tend to have better idea.