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A common element of market structure analysis is the spatial representation of firms’ competitive positions on maps. Such maps typically capture static snapshots in time. Yet, competitive positions tend to change. Embedded in such changes are firms’ trajectories, that is, the series of changes in firms’ positions over time relative to all other firms in a market. Identifying these trajectories contributes to market structure analysis by providing a forward-looking perspective on competition, revealing firms’ (re)positioning strategies and indicating strategy effectiveness. To unlock these insights, we propose EvoMap, a novel dynamic mapping framework that identifies firms’ trajectories from high-frequency and potentially noisy data. We validate EvoMap via extensive simulations and apply it empirically to study the trajectories of more than 1,000 publicly listed firms over 20 years. We find substantial changes in several firms’ positioning strategies, including Apple, Walmart, and Capital One. Because EvoMap accommodates a wide range of mapping methods, analysts can easily apply it in other empirical settings and to data from various sources.
Regulators worldwide have been implementing different privacy laws. They vary in their impact on the value for advertisers, publishers and users, but not much is known about these differences. This article focuses on three important privacy laws (i.e., General Data Protection Regulation [GDPR], California Consumer Privacy Act [CCPA] and Personal Information Protection Law [PIPL]) and compares their impact on the value for the three primary actors of the online advertising market, namely, advertisers, publishers and users. This article first compares these three privacy laws by developing a legal strictness score. It then uses the existing literature to derive the effects of the legal strictness of each privacy law on each actor’s value. Finally, it quantifies the three privacy laws’ impact on each actor’s value. The results show that GDPR and PIPL are similar and stricter than CCPA. Stricter privacy laws bring larger negative changes to the value for actors. As a result, both GDPR and PIPL decrease the actors’ value more substantially than CCPA. These value declines are the largest for publishers and are rather similar for users and advertisers. Scholars and practitioners can use our findings to explore ways to create value for multiple actors under various privacy laws.
For many services, consumers can choose among a range of optional tariffs that differ in their access and usage prices. Recent studies indicate that tariff-specific preferences may lead consumers to choose a tariff that does not minimize their expected billing rate. This study analyzes how tariff-specific preferences influence the responsiveness of consumers’ usage and tariff choice to changes in price. We show that consumer heterogeneity in tariff-specific preferences leads to heterogeneity in their sensitivity to price changes. Specifically, consumers with tariff-specific preferences are less sensitive to price increases of their preferred tariff than other consumers. Our results provide an additional reason why firms should offer multiple tariffs rather than a uniform nonlinear pricing plan to extract maximum consumer surplus.
Generative AI is a game changer – also in the financial sector. Institutions and their IT service providers need to consider carefully: Which AI approach will enable them to implement optimal solutions for themselves and their customers in this highly regulated environment? How did Finanz Informatik, as the savings banks’ digitalization partner, proceed here?
The significance of data and Artificial Intelligence (AI) has a profound impact on all industries, presenting both challenges and opportunities. Given its power and relevance, AI has not gone unnoticed in the public affairs sector. The upcoming German federal election in 2025 brings discussions about AI to the forefront, raising questions about the extent to which data will drive the public affairs field and how it will be handled.
Customer loyalty is a critical measure for success, showing if a firm's product is received well by its customers. To understand its development over time, two fundamental questions must be answered: (I) How will current customers' loyalty develop, and (II) will new customers' loyalty differ from current customers' loyalty? The authors empirically answer these questions based on a data set including ~500 B2B web technologies with jointly ~325 million customers spanning over 24 years. They show that loyalty hardly develops and, if so, it rather decreases than increases. The loyalty of current customers rarely changes and, if so, rather increases than decreases. New customers are most likely less loyal than current customers. These results show that by failing to account for these underlying developments, stakeholders, in most cases, draw the wrong conclusions about product value measured via customer lifetime value.
Existing table retrieval approaches estimate each table’s relevance for a particular information need and return a ranking of the most relevant tables. This approach is not ideal since the returned tables often include irrelevant data and the required information may be scattered across multiple tables. To address these issues, we propose the idea of fine-grained structured table retrieval and present our vision of R2D2, a system which slices tables into small tiles that are later composed into a structured result that is tailored to the user-provided information need. An initial evaluation of our approach demonstrates how our idea can improve table retrieval and relevant downstream tasks such as table question answering.
SAFE Update June 2024
(2024)
We use a structural VAR model to study the German natural gas market and investigate the impact of the 2022 Russian supply stop on the German economy. Combining conventional and narrative sign restrictions, we find that gas supply and demand shocks have large and persistent price effects, while output effects tend to be moderate. The 2022 natural gas price spike was driven by adverse supply
shocks and positive storage demand shocks, as Germany filled its inventories before the winter. Counterfactual simulations of an embargo on natural gas imports from Russia indicate similar positive price and negative output effects compared to what we observe in the data.