“Distinctive from What? And for Whom?” Deep Learning-Based Product Distinctiveness, Social Structure, and Third-Party Certifications

• Published in: Academy of Management journal 2023-08, Vol.66 (4), p.1016-1041
• Main Authors: Banerjee, Mitali, Cole, Benjamin M., Ingram, Paul
• Format: Article
• Language: English
• Subjects: Artists; Autonomy; Competition; Competitors; Computer vision; Deep learning; Distinctiveness; Gravity; Hierarchies; Learning; Modern art; Neural networks; Rewards; Social networks; Social structure; Visual artists
• ISSN: 0001-4273; 1948-0989
• DOI: 10.5465/amj.2021.0175

How do producers' distinctiveness and social structure influence third-party certifications? We argue that producers compete against prior and current competitors, and against their past selves. In the context of 153 artists active during a key period of the emergence of modern art (1905–1916), we utilize a convolutional neural network used in computer vision to extract feature vectors of artworks, and measure quantitative distance of these artists' works from canonical reference points. We find that artists are rewarded for distinctiveness from prior and current competitors and their past selves (up to a point). However, artists' autonomy to differentiate themselves depends on their position in the social structure, which we divide into supply-side artist-to-artist networks, and demand-side artist-to-gallerist networks. Artists with high or low supply-side status receive higher rewards for distinctiveness from current competitors than do artists with middle supply-side status. Artists with higher demand-side status receive higher rewards for distinctiveness from their own past, but lower rewards for distinctiveness from current competitors. These results show that peers strive to constrain each other to conform to positions of gravity within product space, and that market audiences deploy either higher or lower constraints on a producer's identity depending on the reference point.