Figure 1. Different Combinations of Form...

Figure 1.

Different Combinations of Form and Content Dimensions of Style Configure Coherent and Incoherent Style Typologies

Figure 2.

Outline of the Computer-assisted Routine Used to Manipulate Photographic Material

Figure 3.

Map of Electronic Music Sub-genres, with Values Assigned to the Main Sub-genres in the Sample

Figure 4.

Distribution of Relevant Measures and Style Typologies in the Sample

Figure 5.

Decile-wise Ranking Distribution of Each Style Typology (Percentages Represent within-group Proportions)

Figure 1.

Hypothesized Impact of Aesthetic and Technological Code Complexity on Lay and Professional Evaluation

Figure 1.

US Search Rates for ‘Heels’, ‘Pumps’, ‘Slides’ and ‘Sneakers’ (2008–2018; Google shopping searches)

Figure 2.

US Search Rates for ‘Heels’ and ‘Fashion Show’ (2008–2018; Google Web Searches)

Figure 3.

Shoes Used (Sample), Studies 1 and 3

Figure 4.

Perceived Power and Perceived Status as a Function of Shoe Style, Study 1

Figure 5.

Choice Set, Study 2

Figure 6.

Action Orientation Score as a Function of Shoe Style and Social Visibility, Study 3

Figure 7.

Abstraction Score as a Function of Shoe Style and Social Visibility, Study 3

Figure 1.

Social Network Structure of the Group

Figure 2.

Semantic Content Shared with Integrator

Figure 3.

Semantic Content Shared with Critic and Integrator. Link width scaled according to frequency of association;complexity of the component reduced in the mainpicture, full component displayed in the lower right

Figure 1.

Topics Distribution and Narrative Conventionality: An Illustration of Two Cases

Figure 2.

Narrative Conventionality – Craft Items Sold Relationship. Marginal Effects Estimated by Keeping the Other Covariates at Their Means

Figure 1.

Predicted Probabilities of Consumer Evaluations for Movies During Opening Week of Release

Figure 2.

Predicted Probabilities of Consumer Evaluations for Movies after Opening Week of Release

Figure 1.

Imperfect Imitation in the Semi-periphery

Table 1.

Overview of the Regression Models Used to Test the Hypotheses (Method, Dependent Variable and Main Regressors)

Table 2.

Descriptive Statistics and Pearson Correlation Matrix (N = 100)

Table 3.

Odds of Being Visually Garish

Table 4.

Influence of Style Typologies on the Likelihood (Model 6 and 7) and Progressive Odds (Model 8) of Occupying a Higher Position in the Ranking

Table 5.

Quantile Regression Predicting the Likelihood of Occupying a Higher Position in the Ranking

Table 1.

Descriptive Statistics and Pearson Correlation Coefficients

Table 2.

Multilevel Mixed-Effects Logistic Regression on Public Awards (Evaluation by Laypersons)

Table 3.

Multilevel Mixed-Effects Logistic Regression on Professional Awards (Evaluation by Professionals)

Table 1.

Individual Intensity of Communication

Table 2.

Contribution to Semantic Content

Table 3.

Properties of Semantic Content with and without Contribution by One of the Leaders

Table 4.

Properties of Semantic Content with Two Leaders and without Leaders

Table 1.

Two Examples of Narrative-topics Distribution

Table 2.

Descriptive Statistics and Correlation Matrix

Table 3.

Negative Binomial Regression Models

Table 1.

Fashion Houses in Paris and Milan (Sponsoring at Least Five Catwalks from 1999 to 2007)

Table 2.

Summary Statistics of Independent Variables

Table 3.

Elements of the Style Genome: Continuous Variable Coding (By Garment Type)

Table 4.

Elements of the Style Genome: Discrete Variable Coding (For All Garments)

Table 5.

Average Style Distance as a Function of Year Span

Table 6.

Logistic Regression of Review Meanrating on Designer's (or Design Team) Change (N = 525)

Table 7.

Within Designer Regression of Review Meanrating on Individual Designer's Style Distance between Two Seasons

Table 8.

Across Designers Regression of Other Designers' Reviews on Individual Designer's Style Distance between Two Seasons

Table 1.

Movie Descriptive Statistics and Correlation Matrix for Selected Variables

Table 2.

Consumer Evaluations for Movies During Opening Week of Release

Table 3.

Consumer Evaluations for Movies During Opening Week of Release

Table 4.

Count of Consumer Evaluations for Movies

Table 5.

Consumer Evaluation for Movies During Opening Week of Release with Demand Residual

Table 6.

Consumer Evaluation for Movies after Opening Week of Release with Demand Residual

Table 1.

Sources and Signals of Legitimacy

Table 2.

Research Participants

Figure 1.	Different Combinations of Form and Content Dimensions of Style Configure Coherent and Incoherent Style Typologies
Figure 2.	Outline of the Computer-assisted Routine Used to Manipulate Photographic Material
Figure 3.	Map of Electronic Music Sub-genres, with Values Assigned to the Main Sub-genres in the Sample
Figure 4.	Distribution of Relevant Measures and Style Typologies in the Sample
Figure 5.	Decile-wise Ranking Distribution of Each Style Typology (Percentages Represent within-group Proportions)
Figure 1.	Hypothesized Impact of Aesthetic and Technological Code Complexity on Lay and Professional Evaluation
Figure 1.	US Search Rates for ‘Heels’, ‘Pumps’, ‘Slides’ and ‘Sneakers’ (2008–2018; Google shopping searches)
Figure 2.	US Search Rates for ‘Heels’ and ‘Fashion Show’ (2008–2018; Google Web Searches)
Figure 3.	Shoes Used (Sample), Studies 1 and 3
Figure 4.	Perceived Power and Perceived Status as a Function of Shoe Style, Study 1
Figure 5.	Choice Set, Study 2
Figure 6.	Action Orientation Score as a Function of Shoe Style and Social Visibility, Study 3
Figure 7.	Abstraction Score as a Function of Shoe Style and Social Visibility, Study 3
Figure 1.	Social Network Structure of the Group
Figure 2.	Semantic Content Shared with Integrator
Figure 3.	Semantic Content Shared with Critic and Integrator. Link width scaled according to frequency of association;complexity of the component reduced in the mainpicture, full component displayed in the lower right
Figure 1.	Topics Distribution and Narrative Conventionality: An Illustration of Two Cases
Figure 2.	Narrative Conventionality – Craft Items Sold Relationship. Marginal Effects Estimated by Keeping the Other Covariates at Their Means
Figure 1.	Predicted Probabilities of Consumer Evaluations for Movies During Opening Week of Release
Figure 2.	Predicted Probabilities of Consumer Evaluations for Movies after Opening Week of Release
Figure 1.	Imperfect Imitation in the Semi-periphery
Table 1.	Overview of the Regression Models Used to Test the Hypotheses (Method, Dependent Variable and Main Regressors)
Table 2.	Descriptive Statistics and Pearson Correlation Matrix (N = 100)
Table 3.	Odds of Being Visually Garish
Table 4.	Influence of Style Typologies on the Likelihood (Model 6 and 7) and Progressive Odds (Model 8) of Occupying a Higher Position in the Ranking
Table 5.	Quantile Regression Predicting the Likelihood of Occupying a Higher Position in the Ranking
Table 1.	Descriptive Statistics and Pearson Correlation Coefficients
Table 2.	Multilevel Mixed-Effects Logistic Regression on Public Awards (Evaluation by Laypersons)
Table 3.	Multilevel Mixed-Effects Logistic Regression on Professional Awards (Evaluation by Professionals)
Table 1.	Individual Intensity of Communication
Table 2.	Contribution to Semantic Content
Table 3.	Properties of Semantic Content with and without Contribution by One of the Leaders
Table 4.	Properties of Semantic Content with Two Leaders and without Leaders
Table 1.	Two Examples of Narrative-topics Distribution
Table 2.	Descriptive Statistics and Correlation Matrix
Table 3.	Negative Binomial Regression Models
Table 1.	Fashion Houses in Paris and Milan (Sponsoring at Least Five Catwalks from 1999 to 2007)
Table 2.	Summary Statistics of Independent Variables
Table 3.	Elements of the Style Genome: Continuous Variable Coding (By Garment Type)
Table 4.	Elements of the Style Genome: Discrete Variable Coding (For All Garments)
Table 5.	Average Style Distance as a Function of Year Span
Table 6.	Logistic Regression of Review Meanrating on Designer's (or Design Team) Change (N = 525)
Table 7.	Within Designer Regression of Review Meanrating on Individual Designer's Style Distance between Two Seasons
Table 8.	Across Designers Regression of Other Designers' Reviews on Individual Designer's Style Distance between Two Seasons
Table 1.	Movie Descriptive Statistics and Correlation Matrix for Selected Variables
Table 2.	Consumer Evaluations for Movies During Opening Week of Release
Table 3.	Consumer Evaluations for Movies During Opening Week of Release
Table 4.	Count of Consumer Evaluations for Movies
Table 5.	Consumer Evaluation for Movies During Opening Week of Release with Demand Residual
Table 6.	Consumer Evaluation for Movies after Opening Week of Release with Demand Residual
Table 1.	Sources and Signals of Legitimacy
Table 2.	Research Participants

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