Call for Papers
This sub-theme welcomes all researchers with interest in studying culture and creativity in organizations and markets.
Cultural or creative output in the form of unstructured data (e.g., text, audio, video, and images) can provide a rich window
into the culture and creativity of individuals, teams, organizations, and communities. Computational methods (e.g., machine
learning) allow us to measure these reflections of culture and creativity with greater nuance. This reciprocal relationship
provides an opportunity to rigorously test existing theory while integrating new explanatory concepts that could genuinely
help advance our understanding of the nature of social phenomena (Leavitt et al., 2021).
In this sub-theme, we encourage the use of empirical observation and measurements to understand the role of influence relations on social phenomena (e.g., culture, creativity, innovation, novelty, performance, etc). Our focus, however, is not on simply identifying patterns in the data. Rather, we are interested in using existing or uncovering new explanatory concepts – which may be directly observable or inferred from the measured data- to better understand the complex flow of information, ideas, and practices in creative and knowledge-based industries. We are particularly interested in exploring how, why, and to what extent these explanatory concepts help advance our scholarly understanding of theories concerning evaluation and valuation in organizations and markets. Our goal is to bring into conversation scholars using computational methods to study three topics that are germane to theories of organizations and markets: (1) evolution of culture, (2) evaluation and valuation in the creative and knowledge-based industries, and (3) co-evolution of culture, creativity, and valuation.
As our computational toolkit expands, so too should our view of observation of culture and language. There is a growing view of organizational culture as accessible and encoded in language (Chen, 2013; Crémer et al., 2007; Doyle et al., 2017; Srivastava & Goldberg, 2017; Srivastava et al., 2018; Yeaton, 2022). Language is, of course, embedded in nearly all types of unstructured data, including written text (Adam & Roscigno, 2005), spoken text ( Harrison et al., 2020), audio (Askin & Mauskapf, 2017), video (Jiang et al., 2019), and images (Efthymiou et al., 2021).
Such unstructured data can give us a more granular understanding of culture as well as the actual creative output of numerous creative and knowledge-based industries. Computational methods in natural language processing, audio, image, and video processing allow us to measure at scale directly the phenomenological properties, i.e., holistic experience of the products (Banerjee & Kaplan, 2021; Meyer et al., 2013). These properties have a direct bearing on the audience’s evaluation of the products and the producers. As a result, computational tools provide a fruitful and novel window into advancing our understanding of culture and creativity across various settings.
For instance, online communities are an important setting for studying this topic, as the proliferation of unstructured data is centered around these communities, which are an enormous and growing organizational form encompassing 3.14 billion unique users worldwide in 2020. Additionally, this setting is growing in theoretical importance in its own right, with novel organizational opportunities and challenges. Other market settings include publicly available databases of scholarly publications or domain-specific collections of multimedia data, such as WikiArt.
In short, we are interested in the many diverse aspects of culture and creativity. While we appreciate the value of projects that focus on data mining or other exploratory data analysis, this sub-theme is focused on work that advances theory. We welcome methodologically diverse and theoretically-grounded submissions to address questions such as:
1) How do (novel) computational approaches to measurement aid in understanding the dynamics of culture and creativity, including how culture and creativity co-evolve?
How does the measurement of phenomenological properties inform our understanding of existing constructs such as distinctiveness, differentiation, atypicality, novelty, purity, innovativeness, etc.?
How can the data we observe in online communities enrich our understanding of culture and creativity in a wide array of online and offline organizational settings?
2) How does online speech and action affect users’ behavior both online and offline?
How and to what extent have online communities managed to engender rich cultures of creativity and innovation that have democratized both scientific and entrepreneurial output (e.g., artistic, craft)?
How does online speech and action affect and accelerate negative outcomes ranging from gatekeeping to radicalization, hate crimes, misinformation, inequality, etc.?
What are the implications of online communities for the evaluation and valuation of creative products or cultural entrepreneurs?
What is the impact on evaluation of the increasing role of the digital crowd (e.g., Amazon reviews, Yelp, etc.) in contrast to experts/specialists?
3) How are these phenomenological properties evaluated and valued in markets?
What is the impact of explicit categories (e.g., genres ) and implicit categories (established through, e.g., automatic analysis of visual content, audio, or text) on evaluation and performance? How has this evolved over time?
To what extent do category labels and sub-labels help us understand how and to what extent information is classified into groups? How can this help us identify variations in properties or meanings of those groups over time?
More broadly, what are the implications of new constructs for existing and new theories of differentiation and valuation of creative output in knowledge and creative markets?
- Adams, J., & Roscigno, V.J. (2005): “White supremacists, oppositional culture and the World Wide Web.” Social Forces, 84 (2), 759–778.
- Askin, N., & Mauskapf, M. (2017): “What makes popular culture popular? Product features and optimal differentiation in music.” American Sociological Review, 82 (5), 910–944.
- Banerjee, M., & Kaplan, D. (2021): The Golden Convergence: How Creative Producers Become More Famous by Being Less Differentiated. Working paper.
- Chen, M.K. (2013): “The effect of language on economic behavior: Evidence from savings rates, health behaviors, and retirement assets.” American Economic Review, 103 (2),690–731.
- Crémer, J., Garicano, L., & Prat, A. (2007): “Language and the theory of the firm.” The Quarterly Journal of Economics, 122(1), 373–407.
- Doyle, G., Goldberg, A., Srivastava, S., & Frank, M.C. (2017): “Alignment at Work: Using Language to Distinguish the Internalization and Self-Regulation Components of Cultural Fit in Organizations.” In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Vancouver: Association for Computational Linguistics, 603–612.
- Efthymiou, A., Rudinac, S., Kackovic, M., Worring, M., & Wijnberg, N. (2021): “Graph Neural Networks for Knowledge Enhanced Visual Representation of Paintings.” In: MM '21: Proceedings of the 29th ACM International Conference on Multimedia. October 20–24, 2021 (virtual event). Association for Computing Machinery, 3710–3719, https://dl.acm.org/doi/pdf/10.1145/3474085.3475586.
- Harrison, J.S., Thurgood, G.R., Boivie, S., & Pfarrer, M.D. (2020): “Perception is reality: How CEOs’ observed personality influences market perceptions of firm risk and shareholder returns.” Academy of Management Journal, 63 (4), 1166–1195.
- Jiang, L., Yin, D., & Liu, D. (2019): “Can joy buy you money? The impact of the strength, duration, and phases of an entrepreneur’s peak displayed joy on funding performance.” Academy of Management Journal, 62 (6), 1848–1871.
- Leavitt, K., Schabram, K., Hariharan, P., & Barnes, C.M. (2021): “Ghost in the machine: On organizational theory in the age of machine learning.” Academy of Management Review, 46 (4), 750–777.
- Meyer, R.E., Höllerer, M.A., Jancsary, D., & Van Leeuwen, T. (2013): “The visual dimension in organizing, organization, and organization research: Core ideas, current developments, and promising avenues.” Academy of Management Annals, 7 (1), 489–555.
- Srivastava, S.B., & Goldberg, A. (2017): “Language as a window into culture.” California Management Review, 60 (1), 56–69.
- Srivastava, S.B., Goldberg, A., Manian, V.G., & Potts, C. (2018): “Enculturation trajectories: Language, cultural adaptation, and individual outcomes in organizations.” Management Science, 64 (3), 1348–1364.
- Yeaton, M. (2022): The Effect of Communication Networks on Cultural Change in Organizations: Evidence from Alt-Right Echo Chambers. Working paper.