Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Measurement in Digital Innovation Units - An Information Asymmetry Perspective

Digital Innovation Units (DIU) are dedicated organizational units that companies increasingly set up to explore and develop new digital innovations. Despite the growing scholarly attention on DIUs, there is little evidence about their actual performance and, specifically, about the importance of information alignment between the DIU and the main company. To address this gap our study focuses on the concept of information asymmetries to investigate the factors that enable or hinder the reduction of information asymmetries between members of the DIU and the main company. Based on 21 exploratory interviews with both sides, we propose a framework showing three barriers (Exploratory Goal Setting, Outcome Uncertainty and Assessment Misfit) and subsequent enablers that can reduce information asymmetries between them. Our findings expand knowledge on the relationship between DIUs and their main company and the role that Performance Measurement Systems play to align and steer the digital innovation efforts of DIUs.
Imagine All the People: Characterizing Social Music Sharing on Reddit

Music shapes our individual and collective identities, and in turn is shaped by the social and cultural contexts it occurs in. Qualitative approaches to the study of music have uncovered rich connections between music and social context but are limited in scale, while computational approaches process large amounts of musical data but lack information on the social contexts music is embedded in. In this work, we develop a set of neural embedding methods to understand the social contexts of online music sharing, and apply them to a novel dataset containing 1.3M instances of music sharing in Reddit communities. We find that the patterns of how people share music in public are related to, but often differ from, how they listen to music in private. We cluster artists into social genres that are based entirely on aggregate sharing patterns and reflect where artists are invoked. We also characterize the social and cultural contexts music is shared in by measuring associations with social dimensions such as age and political affiliation. Finally, we observe that a significant amount of sharing is attributable to extra-musical factors—additional meanings that people have associated with songs. We develop two methods to quantify the extra-musicality of music sharing. Our methodology is widely applicable to the study of online social contexts, and our results reveal novel cultural associations that contribute to a better understanding of the online music ecosystem.
Misinformation Adoption or Rejection in the Era of COVID-19

The COVID-19 pandemic has led to a misinformation avalanche on social media, which produced confusion and insecurity in netizens. Learning how to automatically recognize adoption or rejection of misinformation about COVID-19 enables the understanding of the effects of exposure to misinformation and the threats it presents. By casting the problem of recognizing misinformation adoption or rejection as stance classification, we have designed a neural language processing system operating on micro-blogs which takes advantage of Graph Attention Networks relying on lexical, emotion, and semantic knowledge to discern the stance of each micro-blog with respect to COVID-19 misinformation. This enabled us not only to obtain promising results, but also allowed us to use a taxonomy of COVID-19 misinformation themes and concerns to characterize the misinformation adoption or rejection that can be best recognized automatically.
How-to Present News on Social Media: A Causal Analysis of Editing News Headlines for Boosting User Engagement

To reach a broader audience and optimize traffic toward news articles, media outlets commonly run social media accounts and share their content with a short text summary. Despite its importance of writing a compelling message in sharing articles, the research community does not own a sufficient understanding of what kinds of editing strategies effectively promote audience engagement. In this study, we aim to fill the gap by analyzing media outlets' current practices using a data-driven approach. We first build a parallel corpus of original news articles and their corresponding tweets that eight media outlets shared. Then, we explore how those media edited tweets against original headlines and the effects of such changes. To estimate the effects of editing news headlines for social media sharing in audience engagement, we present a systematic analysis that incorporates a causal inference technique with deep learning; using propensity score matching, it allows for estimating potential (dis-)advantages of an editing style compared to counterfactual cases where a similar news article is shared with a different style. According to the analyses of various editing styles, we report common and differing effects of the styles across the outlets. To understand the effects of various editing styles, media outlets could apply our easy-to-use tool by themselves.
Human Experts or Artificial Intelligence? Algorithm Aversion in Human Experts or Artificial Intelligence? Algorithm Aversion in Fake News Detection Fake News Detection

The sheer volume of social media posts is forcing social platforms to supplement human-based fake news detection with machine-learning algorithms. Hence, this study investigates algorithm aversion in the context of fake news detection. Based on an empirical study with 238 subjects using artificial fake social media posts, our results suggest that fake news flags have a weaker warning effect if the warning contains additional information on the method used to detect fake news using either human expert opinions or artificial intelligence (AI). Algorithm aversion was reflected in participants assuming that AI algorithms erroneously classify posts as fake more often than human experts do. Furthermore, results showed that flags based on human experts have a more substantial effect on user behavior than neutral flags. These insights can practically be used to assist social media platforms in how to inform their users about the underlying method used to detect fake news.
Challenges and Success potentials of Platform Cooperatives: Insights from a Multiple Case Study

In recent years, an alternative concept to capitalist-oriented digital platforms has emerged: platform cooperatives. Platform cooperatives are jointly owned and democratically controlled platforms that promise a more social alternative to platform capitalism. However, platform cooperatives and their challenges and success potentials have received little attention in research. To this end, we conduct a multiple case study and shed more light on the challenges and success potentials that result from the characteristics of platform cooperatives. The results show that the greatest challenges include their complex governance and above all, their lack of funding. To leverage their success potential, platform cooperatives should extend regionality and further sharpen their cooperative identity and social profile. They should also pursue a differentiation strategy and find ways to keep democratic governance manageable. Ultimately, they would benefit from new financing instruments. Our results contribute to research on digital platforms and platform cooperatives.
A Dataset of Multidimensional and Multilingual Social Opinions for Malta's Annual Government Budget

This paper presents three high quality social opinion datasets in the socio-economic domain, specifically Malta's annual Government Budgets of 2018, 2019 and 2020. They contain over 6,000 online posts of user-generated content in English and/or Maltese, gathered from newswires and social networking services. These have been annotated for multiple opinion dimensions, namely subjectivity, sentiment polarity, emotion, sarcasm and irony, and in terms of negation, topic and language. These datasets are a valuable resource for developing Opinion Mining tools and Language Technologies, and can be used as a baseline for assessing the state-of-the-art and for developing new advanced analytical methods for Opinion Mining. Moreover, they can be used for policy formulation, policy-making, decision-making and decision-taking. This research can also support similar initiatives in other countries, studies in the socio-economic domain and applied in other areas, such as Politics, Finance, Marketing, Advertising, Sales and Education.
Home Sweet Home – The Effect of Regional Presence on Trust in Home Sweet Home – The Effect of Regional Presence on Trust in Electronic Commerce Electronic Commerce

Imagery represents an important tool for interface design in human-computer interaction. Pictorial cues are powerful to affect consumer trust and behavior. While social and nature cues have been subject to extensive IS research, the use of regional imagery has received much less attention. We address this gap by means of a multi-method approach comprising an online survey and the proposal for an eye-tracking experiment. We find that Perceived Regional Presence drives trust and purchase intentions on an energy provider web interface. This paper is among the first to evaluate the effects of regional imagery and contributes to human-computer interaction research by introducing a novel type of trust cue. From a larger societal perspective, using regional imagery in the design of information systems could drive more sustainable consumer choices as it supports their decision-making in favor of regional products and services.
Tight sampling and discarding bounds for scenario programs with an arbitrary number of removed samples

The so-called scenario approach offers an efﬁcient framework to address uncertain optimisation problems with uncertainty represented by means of scenarios. The sampling-and-discarding approach within the scenario approach literature allows the decision maker to trade feasibility to performance. We focus on a removal scheme composed by a cascade of scenario programs that removes at each stage a superset of the support set associated to the optimal solution of each of these programs. This particular removal scheme yields a scenario solution with tight guarantees on the probability of constraint violation; however, existing analysis restricts the number of discarded scenarios to be a multiple of the dimension of the optimisation problem. Motivated by this fact, this paper presents pathways to extend the theoretical analysis of this removal scheme. We ﬁrst provide an extension for a restricted class of scenarios programs for which tight bounds can be obtained, and then we provide a conservative bound on the probability of constraint violation that is valid for any scenario program and an arbitrary number of removed scenarios, which is, however, not tight.
Knowledge Management in the Era of Artificial Intelligence - Developing an Integrative Framework

Artificial intelligence (AI) and its rapid technological advancement will considerably affect the future of work and the way organizations manage their knowledge management (KM) processes. Typical KM initiatives include the development, transfer, storage, and evaluation of a firm’s knowledge throughout the knowledge lifecycle, but often neglect ongoing advances in the AI area. Thus, organizations struggle to integrate AI into working environments to leverage outcome efficiency. Starting with a descriptive literature review, we draw on two KM strategies–personalization and codification–and introduce an adaptive, AI-specific approach for organizational KM implementation. Our approach supports KM strategy and research, as it outlines how AI affects current working processes. This enables to understand which role AI can take in the human-AI interaction. Knowledge managers are provided with a tool to align organizational KM with the business strategy and current technological progress in AI context. We also outline future research to validate the construct.
COVID-19 Cases and the Built Environment: Initial Evidence from New York City

Social distancing (e.g., maintaining two-meter distance between people, restricting group gatherings) is recommended to reduce the risk of COVID-19 transmission. Social distancing guidelines might not broadly consider the varying built environment contexts in which people live, however. Controlling for key covariates, we examined the association of several built environment characteristics with COVID-19 cases in New York City. This cross-sectional ecological study used ZIP code tabulation area (ZCTA) data from New York City. We found positive, significant adjusted associations between the COVID-19 case rate and average people per household, percentage of Black or African American population, percentage of Hispanic population, and percentage of the population over sixty-five years old. Population per square kilometer and percentage taking public transit to work were both negatively associated with case rates. Percentage of sidewalks was negatively associated with case rates, but parks were not significant. ZCTAs with higher percentage area of sidewalks were associated with lower case rates, controlling for potential confounders. Consideration of the local characteristics of the built environment could inform context-specific COVID-19 prevention guidelines, such as increasing street space for pedestrian use in high-density neighborhoods.
Shock Index-C: An Updated and Simple Risk-Stratifying Tool in ST-Segment Elevation Myocardial Infarction

Background: Shock index (heart rate/systolic blood pressure, SI) is a simple scale with prognostic value in patients with ST-segment elevation myocardial infarction (STEMI) undergoing percutaneous coronary intervention (PCI). The present study introduces an updated version of SI that includes renal function. Methods: A total of 1,851 consecutive patients with STEMI undergoing PCI were retrospectively included at Cardiac Care Unit in Guangdong Provincial People's Hospital and divided into two groups according to their admission time: derivation database (from January 2010 to December 2013, n = 1,145) and validation database (from January 2014 to April 2016, n = 706). Shock Index-C (SIC) was calculated as (SI × 100)–estimated CCr. Calibration was evaluated using the Hosmer-Lemeshow statistic. The predictive power of SIC was evaluated using receiver operating characteristic (ROC) curve analysis. Results: The predictive value and calibration of SIC for in-hospital death was excellent in derivation [area under the curve (AUC) = 0.877, p < 0.001; Hosmer-Lemeshow chi-square = 3.95, p = 0.861] and validation cohort (AUC = 0.868, p < 0.001; Hosmer-Lemeshow chi-square = 5.01, p = 0.756). SIC exhibited better predictive power for in-hospital events than SI (AUC: 0.874 vs. 0.759 for death; 0.837 vs. 0.651 for major adverse clinical events [MACEs]; 0.707 vs. 0.577 for contrast-induced acute kidney injury [CI-AKI]; and 0.732 vs. 0.590 for bleeding, all p < 0.001). Cumulative 1-year mortality was significantly higher in the upper SIC tertile (log-rank = 131.89, p < 0.001). Conclusion: SIC was an effective predictor of poor prognosis and may have potential as a novel and simple risk stratification tool for patients with STEMI undergoing PCI.
The Effect of Adjunctive Mangosteen Pericarp on Cognition in People With Schizophrenia: Secondary Analysis of a Randomized Controlled Trial

Background: Cognitive impairment is prevalent and often highly burdensome in people with schizophrenia. The aim of this study was to investigate if mangosteen (Garcinia mangostana Linn.) pericarp extract may be an effective intervention to improve cognitive performance in this population. Methods: This was a secondary analysis of a larger randomized placebo-controlled trial that investigated a 24-weeks intervention of mangosteen pericarp extract supplementation in people diagnosed with schizophrenia. A subset of n = 114 participants with completed cognitive outcomes at follow up were included in this analysis. Using the Cogstate Brief Battery, the following cognitive outcomes were assessed: psychomotor function, attention, visual learning and memory (visual and working). Subgroup analyses investigated whether baseline clinical parameters (baseline cognitive functioning, illness severity and duration, depressive symptoms) moderated the relationship between mangosteen pericarp extract intervention and change in cognitive outcomes. Results: There were no significant between-group changes in any cognitive outcomes assessed. Subgroup analysis based on baseline cognition and clinical characteristics did not reveal any significant between-group difference in change. Conclusions: Mangosteen pericarp extract did not affect cognitive outcomes in people with schizophrenia. Further investigation regarding optimal dosing strategies for mangosteen interventions and the testing of additional cognitive domains may be warranted. Trial Registration: ANZCTR.org. au identifier: ACTRN12616000859482, registered 30 June 3 2016.
Iron Oxide Nanoparticles in Bioimaging – An Immune Perspective

Iron oxide nanoparticles (IONPs) bear big hopes in nanomedicine due to their (potential) applications in tumor therapy, drug delivery or bioimaging. However, as foreign entities, such particles may be recognized by the immune system and, thus, lead to inflammation, hypersensitivity or anaphylactic shock. In addition, an overload with iron is known to cause oxidative stress. In this short review, we summarize the biological effects of such particles with a major focus on IONP-formulations used for bioimaging purposes and their effects on the human immune system. We conclude that especially the characteristics of the particles (size, shape, surface charge, coating, etc.) as well as the presence of bystander substances, such as bacterial endotoxin are important factors determining the resulting biological and immunological effects of IONPs. Further studies are needed in order to establish clear structure-activity relationships.
Cytoreductive Nephrectomy Promoted Abscopal Effect of Camrelizumab Combined With Radiotherapy for Metastatic Renal Cell Carcinoma: A Case Report and Review of the Literature

There is little evidence around Camrelizumab combined with cytoreductive nephrectomy (CN) and radiotherapy (RT) as a treatment option for metastatic renal cell carcinoma (mRCC). The influence of CN on immune responses and the abscopal effect are not well understood. In this paper, we report a case of anti-programmed cell death-1 (PD-1) treated with combined RT once CN reduced the primary tumor burden (TB). This patient also encountered an increased response to targeted radiotherapy after immune resistance. We also observed a macrophage-to-lymphocyte ratio (MLR) peak, which may be correlated with subsequent pseudoprogression after thoracic radiotherapy. Consequently, even with the disease, this patient has remained stable. This peculiar instance suggests there is a need to investigate the underlying mechanisms of CN in promoting the abscopal effect during immunotherapy when combined with RT. It also suggests that there is a need for further investigation into the role of RT in overcoming immune resistance, and the value of MLR in predicting pseudoprogression. We hypothesize that a heavy tumor burden might suppress the abscopal effect, thereby ensuring that CN promotes it. However, radiotherapy may overcome immune resistance during oligoprogression.
Characterizing Promoter and Enhancer Sequences by a Deep Learning Method

Promoters and enhancers are well-known regulatory elements modulating gene expression. As confirmed by high-throughput sequencing technologies, these regulatory elements are bidirectionally transcribed. That is, promoters produce stable mRNA in the sense direction and unstable RNA in the antisense direction, while enhancers transcribe unstable RNA in both directions. Although it is thought that enhancers and promoters share a similar architecture of transcription start sites (TSSs), how the transcriptional machinery distinctly uses these genomic regions as promoters or enhancers remains unclear. To address this issue, we developed a deep learning (DL) method by utilizing a convolutional neural network (CNN) and the saliency algorithm. In comparison with other classifiers, our CNN presented higher predictive performance, suggesting the overarching importance of the high-order sequence features, captured by the CNN. Moreover, our method revealed that there are substantial sequence differences between the enhancers and promoters. Remarkably, the 20–120 bp downstream regions from the center of bidirectional TSSs seemed to contribute to the RNA stability. These regions in promoters tend to have a larger number of guanines and cytosines compared to those in enhancers, and this feature contributed to the classification of the regulatory elements. Our CNN-based method can capture the complex TSS architectures. We found that the genomic regions around TSSs for promoters and enhancers contribute to RNA stability and show GC-biased characteristics as a critical determinant for promoter TSSs.
Addressing Cancer Screening Inequities by Promoting Cancer Prevention Knowledge, Awareness, Self-Efficacy, and Screening Uptake Among Low-Income and Illiterate Immigrant Women in France

Objective: Cancer screening rates are suboptimal for disadvantaged populations in France, yet little evidence exists on their cancer-related knowledge and screening barriers. The main objective of this study was to examine cancer-related knowledge, awareness, self-efficacy, and perceptions of screening barriers among low-income, illiterate immigrant women in France following an 8-weeks cancer educational intervention. Methods: Semi-structured qualitative interviews were conducted with 164 female participants in the Ain department of France between January 2019 and March 2020. Adopting the Health Belief Model as an intervention and analytic framework, salient themes were identified using qualitative thematic analysis. Results: Increased levels of perceived susceptibility to and perceived severity of cancer contributed to higher motivation to get screened. Barriers to screening included low French proficiency, shame surrounding illiteracy, and constant worries due to precarious living conditions. Perceived benefits (e.g., valuing one’s health and health-promoting behaviors), cues to action from a trusted source, and greater self-efficacy (e.g., more autonomous in healthcare-seeking) outweighed perceived barriers, including cultural barriers. Conclusions: Implications include developing audience-responsive targeted cancer screening communication strategies and educational materials to increase screening rates and reduce cancer and cancer screening inequities.