EXPRESS: Variety Effects in Advertising

Users are often exposed to a sequence of short-lived marketing interventions (e.g., ads) within each usage session in mobile apps. This study examines how an increase in the variety of ads shown in a session affects a user's response to the next ad. The authors leverage the quasi-experimental variation in ad assignment in their data and propose an empirical framework that accounts for different types confounding to isolate the effects of a unit increase in variety. Across a series of models, the authors consistently show that an increase in ad variety in a session results in a higher response rate to the next ad: holding all else fixed, a unit increase in variety of the prior sequence of ads can increase the click-through rate on the next ad by approximately 13\%. The authors then explore the underlying mechanism and document empirical evidence for an attention-based account. The paper offers important managerial implications since it identifies a source of interdependence across ad exposures that is often ignored in the design of advertising auctions. Further, the attention-based mechanism suggests that platforms can incorporate real-time attention measures to help advertisers with targeting dynamics.

Determinants of Internationalization as Levers for Sustainability: A Study of the Portuguese Pharmaceutical Sector

The pharmaceutical industry is facing the pressure of a global economy, loss of value in local markets and the highly intense innovation that characterizes this sector. This has a heavy impact, particularly in smaller economies. With this investigation, we intend to identify the determinants of internationalization as levers for sustainability in the pharmaceutical export sector of a small economy. Data was collected from a sample representing 63% of the total universe, Portuguese pharmaceutical organizations with exporting activity. A contextualization of the sector and a bibliographic review were previously carried out, which laid the groundwork for the empirical framework. This study revealed a deeply internationalized sector conditioned by a few shortcomings, namely a certain lack of sustainable competitive advantages, relatively low investment in research and development (R&D), insufficient innovation in internationalization strategies as well as scarce institutional support. Our findings may help pave the way for a more complete understanding of the dynamics of internationalization in highly competitive sectors.

Determinants of Internationalization as Levers for Sustainability: A Study of the Portuguese Pharmaceutical Sector

The pharmaceutical industry is facing the pressure of a global economy, loss of value in local markets and the highly intense innovation that characterizes this sector. This has a heavy impact, particularly in smaller economies. With this investigation, we intend to identify the determinants of internationalization as levers for sustainability in the pharmaceutical export sector of a small economy. Data was collected from a sample representing 63% of the total universe, Portuguese pharmaceutical organizations with exporting activity. Contextualization of the sector and a bibliographic review were previously carried out, which laid the groundwork for the empirical framework. This study revealed a deeply internationalized sector conditioned by a few shortcomings, namely a certain lack of sustainable competitive advantages, relatively low investment in research and development (R&D), insufficient innovation in internationalization strategies as well as scarce institutional support. Our findings may help pave the way for a more complete understanding of the dynamics of internationalization in highly competitive sectors.

Non-empirical problems in fair machine learning

The problem of fair machine learning has drawn much attention over the last few years and the bulk of offered solutions are, in principle, empirical. However, algorithmic fairness also raises important conceptual issues that would fail to be addressed if one relies entirely on empirical considerations. Herein, I will argue that the current debate has developed an empirical framework that has brought important contributions to the development of algorithmic decision-making, such as new techniques to discover and prevent discrimination, additional assessment criteria, and analyses of the interaction between fairness and predictive accuracy. However, the same framework has also suggested higher-order issues regarding the translation of fairness into metrics and quantifiable trade-offs. Although the (empirical) tools which have been developed so far are essential to address discrimination encoded in data and algorithms, their integration into society elicits key (conceptual) questions such as: What kind of assumptions and decisions underlies the empirical framework? How do the results of the empirical approach penetrate public debate? What kind of reflection and deliberation should stakeholders have over available fairness metrics? I will outline the empirical approach to fair machine learning, i.e. how the problem is framed and addressed, and suggest that there are important non-empirical issues that should be tackled. While this work will focus on the problem of algorithmic fairness, the lesson can extend to other conceptual problems in the analysis of algorithmic decision-making such as privacy and explainability.

Identifying communicative functions in discourse with content types

Texts are not monolithic entities but rather coherent collections of micro illocutionary acts which help to convey a unitary message of content and purpose. Identifying such text segments is challenging because they require a fine-grained level of analysis even within a single sentence. At the same time, accessing them facilitates the analysis of the communicative functions of a text as well as the identification of relevant information. We propose an empirical framework for modelling micro illocutionary acts at clause level, that we call content types, grounded on linguistic theories of text types, in particular on the framework proposed by Werlich in 1976. We make available a newly annotated corpus of 279 documents (for a total of more than 180,000 tokens) belonging to different genres and temporal periods, based on a dedicated annotation scheme. We obtain an average Cohen’s kappa of 0.89 at token level. We achieve an average F1 score of 74.99% on the automatic classification of content types using a bi-LSTM model. Similar results are obtained on contemporary and historical documents, while performances on genres are more varied. This work promotes a discourse-oriented approach to information extraction and cross-fertilisation across disciplines through a computationally-aided linguistic analysis.

Learning in the Oil Futures Markets: Evidence and Macroeconomic Implications

Using oil futures, we examine expectation formation and how it alters the macroeconomic transmission of shocks. Our empirical framework, where investors learn about the persistence of oil-price movements, successfully replicates the fluctuations in oil-price futures since the late 1990s. By embedding this learning mechanism in an estimated model, we document that an increase in the persistence of TFP-driven fluctuations in oil demand largely accounts for investors’ perceptions that oil-price movements became increasingly permanent during the 2000s. Learning alters the macroeconomic impact of shocks, making the responses time-dependent and conditional on perceptions of shocks’ likely persistence.

Cognitive mapping as an approach to facilitate organ donation among African Americans

Making up 13.4% of the United States population, African Americans (AAs) account for 28.7% of candidates who are currently waiting for an organ donation. AAs are disproportionately affected by end-organ disease, particularly kidney disease, therefore, the need for transplantation among this population is high, and the high need is also observed for other solid organ transplantation. To this end, we worked with the AA community to derive an empirical framework of organ donation strategies that may facilitate AA decision-making. We used a cognitive mapping approach involving two distinct phases of primary data collection and a sequence of data analytic procedures to elicit and systematically organize strategies for facilitating organ donation. AA adults ( n = 89) sorted 27 strategies identified from nominal group technique meetings in phase 1 based on their perceived similarities. Sorting data were aggregated and analyzed using Multidimensional scaling and hierarchical cluster analyses. Among 89 AA participants, 68.2% were female, 65.5% obtained > high school education, 69.5% reported annual household income ≤ $50,000. The average age was 47.4 years (SD = 14.5). Derived empirical framework consisted of five distinct clusters: fundamental knowledge, psychosocial support, community awareness, community engagement, and system accountability; and two dimensions: Approach, Donor-related Information. The derived empirical framework reflects an organization scheme that may facilitate AA decision-making about organ donation and suggests that targeted dissemination of donor-related information at both the individual-donor and community levels may be critical for increasing donation rates among AAs.