Irina Heimbach

Bio: Irina Heimbach is an academic researcher from Technische Universität Darmstadt. The author has contributed to research in topics: Product (category theory) & Recommender system. The author has an hindex of 8, co-authored 18 publications receiving 232 citations. Previous affiliations of Irina Heimbach include WHU - Otto Beisheim School of Management.

The Impact of Sharing Mechanism Design on Content Sharing in Online Social Networks

Abstract: Research on online content diffusion is vast but has rarely examined contextual factors, including the influence of online sharing mechanisms, such as social plugins e.g., Facebook's "Like" button,...

Content Virality on Online Social Networks: Empirical Evidence from Twitter, Facebook and Google+

Abstract: The virality of content describes its likelihood to be shared with peers. In this work, we investigate how content characteristics impact the sharing likelihood of news articles on Twitter, Facebook, and Google+. We examine a random sample of 4,278 articles from the most popular news websites in Germany categorized by human classifiers and text mining tools. Our analysis reveals commonalities and subtle differences between the three networks indicating different sharing patterns of their users.

The value of user’s Facebook profile data for product recommendation generation

Abstract: Most online shops apply recommender systems, i.e. software agents that elicit the users’ preferences and interests with the purpose to make product recommendations. Many of these systems suffer from the new user cold start problem which occurs when no transaction history is available for the particular new prospective buyer. External data from social networking sites, like Facebook, seem promising to overcome this problem. In this paper, we evaluate the value of Facebook profile data to create meaningful product recommendations. We find based on the outcomes of a user experiment that already simple approaches and plain profile data matching yield significant better recommendations than a pure random draw from the product data base. However, the most successful approaches use semantic categories like music/video, brands and product category information to match profile and product data. A second experiment indicates that recommendation quality seems to be stable for different profile sizes.

Software engineering economics

Abstract: This paper summarizes the current state of the art and recent trends in software engineering economics. It provides an overview of economic analysis techniques and their applicability to software engineering and management. It surveys the field of software cost estimation, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.

Improving consumer mindset metrics and shareholder value through social media: the different roles of owned and earned media

Abstract: Although research has examined the social media–shareholder value link, the role of consumer mindset metrics in this relationship remains unexplored. To this end, drawing on the elaboration likelihood model and accessibility/diagnosticity perspective, the authors hypothesize varying effects of owned and earned social media (OSM and ESM) on brand awareness, purchase intent, and customer satisfaction and link these consumer mindset metrics to shareholder value (abnormal returns and idiosyncratic risk). Analyzing daily data for 45 brands in 21 sectors using vector autoregression models, they find that brand fan following improves all three mindset metrics. ESM engagement volume affects brand awareness and purchase intent but not customer satisfaction, while ESM positive and negative valence have the largest effects on customer satisfaction. OSM increases brand awareness and customer satisfaction but not purchase intent, highlighting a nonlinear effect of OSM. Interestingly, OSM is more likely to incr...

Cutting through Content Clutter: How speech and image acts drive consumer sharing of social media brand messages

Abstract: Consumer-to-consumer brand message sharing is pivotal for effective social media marketing. Even as companies join social media conversations and generate millions of brand messages, it remains unclear what, how, and when brand messages stand out and prompt sharing by consumers. With a conceptual extension of speech act theory, this study offers a granular assessment of brands’ message intentions (i.e., assertive, expressive, or directive) and the effects on consumer sharing. A text mining study of more than two years of Facebook posts and Twitter tweets by well-known consumer brands empirically demonstrates the impacts of distinct message intentions on consumers’ message sharing. Specifically, the use of rhetorical styles (alliteration and repetitions) and cross-message compositions enhance consumer message sharing. As a further extension, an image-based study demonstrates that the presence of visuals, or so-called image acts, increases the ability to account for message sharing. The findings explicate brand message sharing by consumers and thus offer guidance to content managers for developing more effective conversational strategies in social media marketing.

The MAD Model of Moral Contagion: The Role of Motivation, Attention, and Design in the Spread of Moralized Content Online:

Abstract: With more than 3 billion users, online social networks represent an important venue for moral and political discourse and have been used to organize political revolutions, influence elections, and raise awareness of social issues. These examples rely on a common process to be effective: the ability to engage users and spread moralized content through online networks. Here, we review evidence that expressions of moral emotion play an important role in the spread of moralized content (a phenomenon we call moral contagion). Next, we propose a psychological model called the motivation, attention, and design (MAD) model to explain moral contagion. The MAD model posits that people have group-identity-based motivations to share moral-emotional content, that such content is especially likely to capture our attention, and that the design of social-media platforms amplifies our natural motivational and cognitive tendencies to spread such content. We review each component of the model (as well as interactions between components) and raise several novel, testable hypotheses that can spark progress on the scientific investigation of civic engagement and activism, political polarization, propaganda and disinformation, and other moralized behaviors in the digital age.