17.06.2017

Marketing Science Conference 2017

39th Annual ISMS Marketing Science Conference (June 7 – 10, 2017) in Los Angeles

Florian Stahl, Daniela Schmitt, Anreas Lanz and Verena Schoenmueller presented six recent research projects at the 39th Annual ISMS Marketing Science Conference (June 7 – 10, 2017) in Los Angeles:

  1. The Pattern of Online Reviews
    • Authors: Verena Schoenmueller, Oded Netzer, Florian Stahl, University of Mannheim, Mannheim, Germany; Columbia University, New York
    • Abstract: Past research has highlighted that online reviews follow a J-shaped distribution implying that reviews are skewed to the positive end of the rating scale, with a few reviews in the mid-range and some reviews at the negative end of the scale. But so far the prevalence and reasons behind the J-shaped distribution of product reviews have been largely neglected in previous research. In this research we aim at 1) investigating the robustness of the J-shaped distribution, 2) understanding its drivers and 3) analyzing its impact on consumer behavior. Building on a large set of online reviews - over 78 million reviews from 16 major online platforms, we identify a robust J-shaped distribution on many but not all of the platforms and product categories. Using secondary datasets, survey data and lab experiments, we demonstrate that the J-shaped distribution is primarily driven by self-selection mechanisms while we rule out multiple alternative explanations of the J-shaped distribution such as review fraud and scale truncation mechanisms. Finally, we show how the J-shaped distribution can affect the informativeness of the reviews as well as the key measures of online reviews (the average rating and the number of reviews).
  2. From Zero to Hero - How to Balance Lost Returns and Wasted Investments Using Predictions of Rare Events
    • Authors: Andreas Lanz, Jacob Goldenberg, Daniel Shapira, Florian Stahl
    • Abstract; This paper addresses decision problems associated with rare events, which arise in the context of resource allocation within a set of products, for example, music artists who recently signed up on a user-generated content network. Among these artists only few will eventually become successful in the long-term as success is a rare event and thus per se not likely to occur. Hence, for record labels investments in all artists are associated with an extreme waste of resources, whereas selecting too few artists comes at the cost of lost returns. The above-described trade-off concerns not only record labels but also multinational companies with broad and deep product pipelines or venture capital funds investing in start-ups. We propose a novel managerial framework how to maximize profits when deciding on how many as well as which recently launched products, start-ups, or upcoming music artists to select in an early stage of the life cycle, namely by solving the trade-off between wasted investments and lost returns. The profit maximization draws on the available information, the respective return on investment as well as the chosen prediction model, which makes it a suitable tool for managers. We apply our framework in the context of SoundCloud, the world’s leading user-generated content network in the music domain, and make recommendations in which artists to invest shortly after sign-up.
  3. Climb or Jump - Status-based Seeding in User-generated Content Networks
    • Authors: Andreas Lanz, Jacob Goldenberg, Daniel Shapira, Florian Stahl, University of Mannheim, Mannheim, Germany; Interdisciplinary Center (IDC) Herzliya, Herzliya, Israel; Guilford Glazer School of Business & Management, Beer Sheva, Israel
    • Abstract: This paper addresses optimal seeding policies in user-generated content networks by challenging the role of influencers. Using data from SoundCloud, the world’s leading user- generated content network in the music domain, we study creators of music who seek to build and increase their follower base by directing promotional actions to other users of the networking platform. Focusing on the network status of both creator and seeding targets, we find that, in particular, unknown creators of music do not benefit from seeding high-status users. In fact, it appears that unknown creators should ignore predominant seeding policies and slowly “climb” across status levels of seeding targets rather than attempt to “jump” towards those with the highest status. Our research extends the existing seeding literature by introducing the concept of risk to dissemination dynamics in online communications. We show evidence that unknown creators of music do not seed specific status levels but rather choose a portfolio of seeding targets while solving risk versus return trade-offs. We discuss managerial implications for information dissemination and optimal seeding in user-generated content networks.
  4.  Short- and Long-term Effects of Price Promotions on Consumption
    • Authors: Daniela Schmitt, Florian Stahl, Raghuram Iyengar, University of Mannheim, Mannheim, Germany; University of Pennsylvania, Philadelphia, PA
    • Abstract; Sales promotions form an important element of the marketing mix. Whereas a large stream of literature has investigated the impact of promotions on customer behavior for the grocery category, relatively less work has considered the effect of such promotions on the access to and consumption of services. The objective of this research is to investigate the short- and long-term effect of price promotions on customer retention and service consumption. We do so in the context of a large digital news publisher that offered temporarily lowered prices for digital subscriptions. Specifically, we analyze the effect of temporarily lowered prices of subscriptions on customer retention and consumption for both new and existing customers. The results show a significant increase in the retention and consumption for newly acquired customers in the short run as well as in the long run. The net impact of the promotion is, however, less positive than expected as existing customers both consume less, and churn more after missing the promotion. The latter showcases the tension that services providers may face when they offer promotions to acquire new customers. We validate our results using a dataset with another price promotion. We propose that current customers are frustrated with missing the promotion. We use data from a field experiment to validate our proposed mechanism. Existing customers were randomly exposed to an invitation to switch to a promoted, lower price. We find that exposure to the opportunity to switch can lower the drop in the consumption and churn of existing customers.
  5. Driving Demand By Managing Network Structure And Network Communication
    • Authors: Juliana Huppertz, Mark Heitmann, Florian Stahl, University of Hamburg, Hamburg, Germany; University of Mannheim, Mannheim, Germany
    • Abstract: This paper evaluates how network communication impacts the dynamic evolution of network structures and subsequently online success within an online social network. Current research has already investigated how different structural elements of a social network impact online success. This study aims to extend these findings by investigating the interplay between the content of network communication, network structure and network success. Our data covers 441 personal networks of music artists with dynamic information on music popularity, communication activity as well as network structure. We study the ego-network of music fans connected to the artist by obtaining measures of degree centrality, ego-network density and ego betweenness. We further differentiate four types of networking activities- friendship requests, song uploads and sending comments. Additionally, we coded the communication content within the network along four different categories, i.e. factual information, relationship building, self-revelation and appeal. For statistical inference, we apply quasi maximum likelihood estimation to model the dynamic impact of communication and networking activities on online success. Our results provide substantive insights into the relationship between communication, network structure and network success. First, we show that the communication content can directly impact and sustain online success. Therefore, we extend recent research findings by emphasizing the importance of communication content over and above the volume of communication. In particular, firms should especially align their communication to relationship building, self-revelation and appeals. Second, artists are able to achieve long-term impact on success by shaping their network structure as the network structure in turn drives demand. Specifically, self-revelation and relationship building contribute to network growth as well as to network density, whereas sending information can negatively impact network size evolution.
  6. Tell Me Who Your Brands Are And I Will Tell You Who You Vote For
    • Authors: Verena Schoenmueller, Oded Netzer, Florian Stahl, Columbia Business School, New York, NY, University of Mannheim, Mannheim, Germany.
    • Abstract; Past research increasingly highlights opportunities to learn about consumers’ attitudes and behavior from publicaly available social media data sources (e.g., Netzer et al. 2012; Culotta and Cutler 2016). In line with this evolving research stream, we show how information regarding the followers of brands and politicians on Twitter can be used to learn about the associations between politicians and brands. More specifically, we investigate which brands the followers of major politicians (e.g., Donald Trump, Hilary Clinton, Bernie Sanders) and political parties (e.g., Democratic Party and GOP) follow in order to identify the political position of each brand. Building on this we then show how this information relates to predictions of people’s voting behavior based on their brand preferences and contrast with conventional demographic predictors. Finally, we relate the brand-politician associations to brand image data using Y&R Brand Asset Valuator and show how the proximity of brands and politicians based on social networking data relates to the characteristics of the brands.

       

       

       



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