Relevance & Research Question: In 1981, Sherwin Rosen theorized that some

markets, that are showing extreme inequalities in their distributions of income, do this

due to technologies allowing joint consumption and due to poor substitutabilities of

high-quality services by low-quality services. In 2021, artificial intelligence allows us to

investigate if this theory also applies to online-communication and if the reasons for

inequality are the ones described by Rosen. The research question of the present

article is therefore twofold: In a first part we will check if the superstar-phenomen is

also present digitally. In a second step we will see if the reaons for the existence of

the superstar-effect Rosen has given can be confirmed.

Methods & Data: Using a big data-technology called „Social Listening“, roughly 30

million text fragments regarding more than 5,000 german companies were collected

online. Using artificial intelligence, this data was categorized into different event types

and different tonalities (negative, neutral, positive). Gini coefficients as measures of

concentration were then used to get an overview of the inequalites of online-

communication. After that regression analysis was conducted to find evidence to

support or disprove Rosen’s theory.

Results: The data quite clearly show that online-communication is characterized by

quite strong inequalities. This statement is valid for the total number of fragments, all

five topics that are discussed and all tonalities (corrected Gini-coefficient > 0,9). Also,

there is limited evidence supporting Rosen’s theory of superstars (in particular his

explanation for the reasons of superstardom) in the world of online-communication.

Added Value: The results are important for market researchers and marketing

managers as they show the strength of superstardom in online communication. They

also somewhat show the validity and to some degree the limitations of Rosen’s theory.

In addition to that, the study can serve as an example of how big data can be used to

empirically verify the validity of theoretical work. It also hints at the fact that the debate

about the meaning of extreme inequalities of online-communication still needs to be

made.

Relevance & Research Question: Before making changes to a product portfolio or pricing strategy, the brightest minds in any business put effort in assessing the expected impact of such changes on profit or market share. One of the methods in assessing these changes is conjoint. The resulting simulation tool can identify the most optimal product / pricing scenario which promises to maximize value / volume. However, due to certain limitations of the methodology, conjoint gives directional information about the market share only but struggles to consider certain ‘real-life’ circumstances. On the other hand, time series forecasting can be used to predict market share using past ‘real-life’ data such as sales, distribution, and promotion. However, due to its dependency on history, this technique also has its shortcomings: it cannot predict any changes in the market or to a product that never happened before. The problem of using each method in isolation is that one cannot rely only on stated preferences or only on historical data to make an accurate prediction on sales. Can the insights be elevated when combining both data in one model?

Methods & Data: We show an approach to perform a data fusion between the key results of conjoint analysis and time series forecasting. We built one model that is fed with the switching matrix and price elasticities from a conjoint and complemented by time series data of sales, price and distribution. Through parallel optimization a period-based market simulator engine was built.

Results: We can show that this ‘new’ simulator is more suitable for planning yearly pricing strategies since its predictions are more accurate than looking at conjoint or time-series data in isolation. By adding historical data, the impact of promotions and seasonality become visible and lead to more accurate outcomes and insights.

Added Value: A model that takes the best of both worlds – conjoint results and time-series data – provides companies with the possibility to play with all relevant factors in one tool while having a more stable model. In consequence business decisions can be made with greater certainty and decrease the risk of making a wrong decision.

Relevance & Research Question: Within the last decade, research on natural language processing has seen great progress, mainly through the introduction and extension of the word embedding framework. Recently the introduction of models like BERT have led to even greater improvements in the field (Devlin et al. 2018). However, these advancements come at a cost: Word embedding models store the biases present within the training corpora (cf. Bender et al. 2021, Bolukbasi et al. 2016). Other researchers have shown that these biases can also be harnessed to generate valuable e.g., sociological, or psychological insights (e.g., Garg et al. 2018, Kozlowski et al. 2019, Charlesworth et al. 2021). I argue that there are even greater benefits if the contextualization of word embeddings is grounded in triangulation with other data types. I use word embedding models, contextualized with semi-structured interviews, to analyze how street renaming initiatives are perceived as a form of historic reappraisal of Germanys colonial past.

Methods & Data: For this project, two Skip-gram models were trained. The first one on 8.5 years of German national weekly newspaper articles (about 60,000 articles from about 450 issues), the second one was trained on approximately 730 million German tweets posted between October 8th, 2018 and August 15th, 2020. Additionally, semi-structured interviews were conducted and used during the method triangulation. The method developed by Kozlowski et al. (2019) was used to project terms within Skip-gram models onto socially constructed dimensions.

Results: Similar definitions of how colonialism is understood within the research field can be found in both data types. Most interview participants saw value in street renaming initiatives as tool to initiate a public discourse about Germany’s colonial past, to collectively process and reflect on Germany’s colonial heritage. The analysis of the text corpora in conjunction with word embeddings has shown that such a discourse is continuous, but not very prevalent and mostly negatively connotated.

Added Value: Triangulation of Skip-gram model analysis with semi-structured interviews offers additional insights that one of these methods alone would not. If both data types are interpreted with a coherent methodology, this enables new research perspectives and insights.