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Part 5: Big Data and Machine Learning; International audience; Big data has gained academic relevance over the last decade and is also of interest to other role-players such as governments, businesses and the general public. Based on our previous work on the Big Data Readiness Index (BDRI) we place the focus on one under-investigated aspect of big data: the linkage to happiness. The BDRI, applied on Africa, includes the topic of happiness within the digital wellbeing driver, but the link between the two topics requires further investigation. Thus, two underlying questions emerge: what is the relation between happiness and big data? And how does Africa perform in digital wellbeing? This paper includes a structured literature review highlighting five key clusters indicating this link. Furthermore, we present some first empirical insights using the BDRI focusing on Africa. Overall, the African continent performs best in the social inclusion cluster of happiness, with the most room for improvement in the job creation cluster.

Part 6: Social Media and Analytics; International audience; In times of increasing importance of social media services, we have to rethink information literacy. One of the key assumptions of existing information literacy constructs is “static” information, meaning that information does not change. But compared to traditional and mostly unidirectional media services such as printed newspapers or television, this does not reflect the reality of a social media context. Here, information can be characterized as “dynamic”, meaning that, for example, every user can easily modify information before sharing it (again). A measurement construct covering these novel aspects of information literacy is missing, so far. Thus, the main objective of this paper is to develop a rigor and updated construct to measure and quantify social media information literacy of an individual social media user. We selected a comprehensive construct development framework that guided us through the investigation, and which includes qualitative as well as quantitative analyses. The outcome is a theoretically grounded and empirically derived social media information literacy (SMIL) construct. The paper ends with a discussion of potential future research directions.

Electronic word-of-mouth (eWOM) has attracted a great deal of attention in both academia and practice in recent years. Our empirical study investigates the relation of negative comments and consumers’ trust in fashion presentations focusing on clothing. It is a product category characterized by a lot of online conversation and opinion sharing, but is surprisingly under-researched regarding eWOM. We calculate a multiple regression model moderated by consumers’ social media experience to identify the impact of a dichotomous eWOM stimulus. We find that negative comments referring to a fashion presentation cause a significant decrease of trust. However, the more experienced a user is in dealing with social media services such as Facebook or YouTube, the weaker this effect will get. These findings contribute to the existing academic discourse about the impact of negative eWOM.

In this paper, we examine cryptocurrencies as a potentially disruptive sort of payment method. Due to its relative importance, we focus in particular on Bitcoin. Through an inductive, exploratory interview approach with 13 individuals in three distinct groups, the determinants usability, usefulness, and subjective norm that could make Bitcoin a game-changer are explored. The results reveal that most stakeholders consider perceived ease of use still rather low, with perceived usefulness varying according to the user group. The notion of Bitcoin as having much future potential as a payment method is confirmed across all interviewees. Interestingly, the underlying concept of a blockchain is also seen as a potential revolutionary way to create a more just society based on open platforms and open data. However, the reasons of why Bitcoin is actually a disruption to existing solutions varies widely.

Part 3: Big and Open Data; International audience; Facebook, Twitter, Instagram, and other players in the social media world have been on the rise during the last couple of years. In contrast to their popularity, their underlying business models are vague and often only linked to advertising. In this explorative study we identify new revenue sources for social media service providers besides advertising. Based on three use cases with Facebook, Tencent, and LinkedIn, we identify three possibly fruitful ways to extend existing social media business models. Subsequently, a survey with 301 respondents changes perspectives on the user’s willingness to pay in order to identify usage-related differences evoked by cultural and external circumstances. Four derived hypotheses lead the way to avenues of further research especially in terms of Big Data analytics with new e-commerce trends like Facebook’s Buy Button.

With a shift from the purchase of a product to the delivery of a service, cloud computing has revolutionized the software industry. Its cost structure has changed with the introduction of Software as a Service (SaaS), resulting in decreasing variable costs and necessary amendments to the software vendors’ pricing models. In order to justify the gap between the software’s price and the incremental cost of adding a new customer, it is essential for the vendor to focus on the added value for the client. This shift from cost- to value-based pricing models has so far not been thoroughly studied. Through literature review and expert interviews, a conceptual model for customer-centric SaaS pricing, especially Business Intelligence & Business Analytics tools, has been developed. The model has then been initially validated by discussions with the top five software players in this realm and builds a strong basis for further theoretical inquiry and practical application.