What Makes Popular Culture Popular?
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ASRXXX10.1177/0003122417728662American Sociological ReviewAskin and Mauskapf 7286622017 American Sociological Review 1 –35 What Makes Popular Culture © American Sociological Association 2017 https://doi.org/10.1177/0003122417728662DOI: 10.1177/0003122417728662 Popular? Product Features journals.sagepub.com/home/asr and Optimal Differentiation in Music Noah Askina and Michael Mauskapf b Abstract In this article, we propose a new explanation for why certain cultural products outperform their peers to achieve widespread success. We argue that products’ position in feature space significantly predicts their popular success. Using tools from computer science, we construct a novel dataset allowing us to examine whether the musical features of nearly 27,000 songs from Billboard’s Hot 100 charts predict their levels of success in this cultural market. We find that, in addition to artist familiarity, genre affiliation, and institutional support, a song’s perceived proximity to its peers influences its position on the charts. Contrary to the claim that all popular music sounds the same, we find that songs sounding too much like previous and contemporaneous productions—those that are highly typical—are less likely to succeed. Songs exhibiting some degree of optimal differentiation are more likely to rise to the top of the charts. These findings offer a new perspective on success in cultural markets by specifying how content organizes product competition and audience consumption behavior. Keywords consumption, music, optimal differentiation, popular culture, product features, typicality What makes popular culture popular? Schol- 1997; Uzzi and Spiro 2005; Yogev 2009), peer ars across the humanities and social sciences preferences and related social influence have spilled considerable ink trying to answer dynamics (Lizardo 2006; Mark 1998; Sal- this question. However, our understanding of ganik, Dodds, and Watts 2006), and various why certain cultural products succeed over elements in the institutional environment others remains incomplete. Popular culture (Hirsch 1972; Peterson 1990). tends to reflect, or is intentionally aimed Each of these signals plays an important toward, the tastes of the public, yet there exists role in determining which products audiences wide variation in the relative popularity of these products (Rosen 1981; Storey 2006). Extant research in sociology and related disci- aINSEAD plines suggests that audiences seek and use a bColumbia Business School wide range of information as signals of the Corresponding Author: quality and value of new products (Keuschnigg Noah Askin, INSEAD, Boulevard de Constance, 2015). This includes the characteristics of and 77305 Fontainebleau, France relations between cultural producers (Peterson E-mail: [email protected] 2 American Sociological Review 00(0) select, evaluate, and recommend to others. labels may not provide adequate or accurate These choices and the preferences they express information to consumers, who must instead vary widely over time and across individuals. rely on products’ underlying features to draw Nevertheless, research suggests that the inher- comparisons and make decisions. ent quality of cultural products also affects We build on these insights to propose a how audiences classify and evaluate them new explanation for why certain cultural (Goldberg, Hannan, and Kovács 2016; Jones products outperform their competitors to et al. 2012; Lena 2006; Rubio 2012; Salganik achieve success. In the context of popular et al. 2006). Certain product features may inde- music, we argue that audiences use musical pendently signal quality and attract audience features as signals to draw latent associations attention (Hamlen 1991), however, it is more between songs. These associations exist in likely that these features matter most as an partial independence from traditional catego- ensemble. They work both by creating a multi- ries. As such, feature-associations help organ- dimensional representation of products and by ize the choice set from which audiences select positioning those products across the plane of and evaluate products, positioning certain possible feature combinations.1 Rather than songs more advantageously. existing in a vacuum, cultural products are We hypothesize that hit songs are able to perceived in relation to one another in feature manage a similarity–differentiation tradeoff. space, and these relationships shape how con- Successful songs invoke conventional feature sumers organize and discern the art worlds combinations associated with previous hits around them (Becker 1982). while at the same time displaying some One way to think about how product posi- degree of novelty distinguishing them from tion shapes performance outcomes is through their peers. This prediction speaks to the com- the lens of categories research. This work petitive benefits of optimal differentiation, a highlights how social classification systems finding that reoccurs across multiple studies organize audiences’ expectations and prefer- and areas in sociology and beyond (Goldberg ences (Hsu 2006; Zuckerman 1999), helping et al. 2016; Lounsbury and Glynn 2001; Uzzi them draw connections between products. We et al. 2013; Zuckerman 2016). agree that producer categories play a signifi- In this article, we test this prediction with cant role in structuring taste and consumption the aim of better understanding the relation- behavior (Bourdieu 1993). However, much of ship between product features and success in the work in this area makes the implicit music. To that end, we constructed a novel assumption that category labels remain tightly dataset consisting of nearly 27,000 songs that coupled with a set of underlying product fea- appear on the Billboard Hot 100 charts between tures. Recent research shows that product fea- 1958 and 2016. The data include algorithmi- tures need not necessarily cluster or align with cally derived features that describe a song’s prevailing classification schemes (Anderson sonic qualities. Sonic features range from rela- 1991; Kovacs and Hannan 2015; Pontikes and tively objective musical characteristics, such Hannan 2014).2 as “key,” “mode,” and “tempo,” to perceptual Category labels (e.g., “country” in the case features that quantify a song’s “acousticness,” of musical genres) work well when navigat- “energy,” and “danceability,” among others. ing stable product markets with clearly First, we establish the baseline validity of defined category boundaries. These labels, individual features in predicting a song’s peak however, do not always reflect how audiences position and longevity on the charts. We then actually make sense of the world in which use these features to construct a measure of they are embedded. This is especially the case sonic similarity or typicality and examine its in contexts where products are complex and effect on chart performance. Popular opinion tastes are idiosyncratic and dynamic (Lena suggests that songs are most likely to succeed 2015). In these domains, extant category when they adhere to a conventional and Askin and Mauskapf 3 reproducible template (Dhanaraj and Logan different modes of explanation, and they require 2005; Thompson 2014). However, we find audiences to consider distinct dimensions of that the most successful songs in our dataset evaluation that are often context specific. Thus, are optimally differentiated from their peers. our ability to explain what constitutes a hit ver- Our results provide strong evidence that, sus a flop remains limited. net of other factors such as artist familiarity and genre affiliation, product features matter, Producer Characteristics and particularly in the way they structure songs’ Professional Networks relationships to each other. Using new, micro- level feature data to specify how cultural Scholars interested in this question traditionally content organizes how audiences distinguish take one of several approaches to explain the products compels us to rethink some of the determinants of cultural preferences and prod- basic mechanisms behind consumption and uct performance. The first set of explanations taste formation. These findings, and the data focuses on the characteristics of cultural pro- and methods we use, make important contri- ducers. These include artist reputation butions to cultural and economic sociology (Bourdieu 1993), past performance outcomes by offering a new perspective on success in (Peterson 1997), and the structure of artistic cultural markets. professional networks (Godart, Shipilov, and Claes 2014; Yogev 2009). Indeed, just as cul- tural products are perceived by audiences in CULTURAL PREFERENCES relation to one another, they are also created by AND THE SIMILArity– producers who form collaborative relationships DIFFERENTIATION TRADEOFF and draw inspiration from each other’s work. In the context of Broadway musicals, Uzzi Predicting how well a new product will fare and Spiro (2005) find that when the network of in the marketplace for audience attention collaborations between artists and producers presents a difficult challenge. This is primar- displays small-world properties, cultural pro- ily due to the countless variables and contin- ductions are more likely to achieve critical and gencies that may influence performance commercial success. Phillips (2011, 2013) outcomes. This challenge is particularly pro- finds that the artists who are most likely to re- nounced in the realm of the cultural or “cre- record and release jazz standards come, sur- ative”