Electoral Accountability and Selection with Personalized Information Aggregation

We study a model of electoral accountability and selection (EAS) in which heterogeneous voters can aggregate the incumbent's performance data into personalized signals through paying limited attention. Extreme voters' signals exhibit an own-party bias, which hampers their abilities to discern good and bad performances. While this effect alone would undermine EAS, there is a countervailing effect stemming from partisan disagreements, which make the centrist voter pivotal and could potentially enhance EAS. Overall, increasing mass polarization and shrinking attention spans have ambiguous effects on EAS, whereas correlating voters' signals unambiguously improves EAS and voter welfare

Paying for News: Opportunities for a New Business Model through Personalized News Aggregators (PNAs)

News consumption has been evolving from offline newspapers to online news. However, while offline newspapers sales are decreasing, online news business models have never been entrenched. Meanwhile, the new technology of social recommender systems enable automated news aggregation. Personalized news aggregators (PNAs) rely on this technology, and provide personalized news in visually appealing ways that might deliver the potential for a new business model. However, there is no research on PNA configuration or users’ willingness to pay (WTP). An empirical investigation with 116 participants examined usage features influencing PNA users’ adoption and their WTP for a paid-based service. First, we showed that perceived usefulness, usage comfort, awareness, and (social) personalization significantly influence intention to use a PNA. Users are also considering price. Second, we found an optimal price point of 1.88€ and a price range up to 6.83€ for monthly use

On the Search for New Revenue Models: An Empirical Investigation of Personalized News Aggregators

News consumption is evolving from offline newspapers to online news. Nevertheless, no profitable business model exists for online news, and publishers are still reporting drops in revenue. Personalized news aggregators (PNAs), which rely on new information and communication technologies, provide a new way to aggregate content that might provide the basis for a revenue model in order to design a business model. Nonetheless, there is very little research about user willingness to pay (WTP) for a PNA service, in part because WTP strongly depends on the ideal configuration of a PNA. Based on an adaptive conjoint analysis (ACA) with 146 participants, this study explores the importance of different attributes in a user’s estimation of total utility and a user’s WTP for changing attribute levels. We show that price, contract duration, and revenue model are the most important attributes. €2.50 per month would be acceptable in combination with an advertising-based revenue model. Changing the contract duration from 12 months to one month shows the highest WTP. However, even if the importance of personalization functionalities is high, there is limited WTP for it

Democratizing algorithmic news recommenders: how to materialize voice in a technologically saturated media ecosystem

The deployment of various forms of AI, most notably of machine learning algorithms, radically transforms many domains of social life. In this paper we focus on the news industry, where different algorithms are used to customize news offerings to increasingly specific audience preferences. While this personalization of news enables media organizations to be more receptive to their audience, it can be questioned whether current deployments of algorithmic news recommenders (ANR) live up to their emancipatory promise. Like in various other domains, people have little knowledge of what personal data is used and how such algorithmic curation comes about, let alone that they have any concrete ways to influence these data-driven processes. Instead of going down the intricate avenue of trying to make ANR more transparent, we explore in this article ways to give people more influence over the information news recommendation algorithms provide by thinking about and enabling possibilities to express voice. After differentiating four ideal typical modalities of expressing voice (alternation, awareness, adjustment and obfuscation) which are illustrated with currently existing empirical examples, we present and argue for algorithmic recommender personae as a way for people to take more control over the algorithms that curate people's news provision

The Next Page

The Next Page is a semi-annual newsletter published by Bucknell University\u27s Library and Information Technology department. The publication serves the community by providing software, project, and service updates. Regular features include a letter from the CIO, new staff updates, and student or alumni profiles. This issue includes the following articles: From the CIO, Teaching with Technology: Video as a Textbook, Seeing is Believing: Library Stacks Used as Art, Empowering Users with Password Station, New Library and IT Staff, Form and Function: Enhancements at the Library, and Alumni Profile

The Dark Side of Mindfulness: Workplace Socialization, Neoliberalism and the Self

Purpose: The aim of our paper is to analyze the role of mindfulness in organizational socialization, particularly how these techniques are mobilized by corporations to reshape employees’ subjectivities. Design/Methodology/Approach: Mindfulness is a process of awareness to moment-to-moment experience, allowing subjects to deal with emotions, sensations and thoughts in a non-judgmental way (Kabat–Zinn, 1991). Mindfulness has been characterized as the new opiate of the masses (Dawson and Turnbull, 2006) and the flagship technology of the self of neoliberal capitalism (Zizek, 2005), adjusting individuals “to the very conditions that cause their problems” (Purser, 2019, p.5). Over the past decade, several mindfulness interventions, such as MBSR (Mindfulness-based-stress reduction), have been implemented in corporate settings, aiming to improve employees’ resilience, flexibility, well-being and self-control. Recognizing that neoliberal selfhood requires individuals to rely on self-regulation devices to enhance their health and happiness, mindfulness interventions are emblematic examples of organizational socialization, as workers should undergo a set of performances to control, manage and regulate their affective states, thus increasing their productivity. By the “dark side of mindfulness”, we refer to the ways in which these practices are promoted, disseminated and applied to reconfigure workers’ subjectivities, leading to new articulations of neoliberal governmentalities coupling technologies of the self, affect and efficiency. Mindfulness becomes a disciplinary tool of self-control that aims at maximizing productivity through the moment-to-moment management of affect. Our paper draws on a qualitative methodology, including the thematic analysis of 44 papers published in the Harvard Business Review, and the examination of a specific mindfulness program carried out by the big tech corporation Amazon, which generated controversy. Findings: Our empirical findings are organized around four main themes: corporate mindfulness as an expansion of neoliberal selfhood; mindfulness and the ability to turn inner work into a driver of productivity; corporate mindfulness as an epiphenomenon of late capitalism; mindfulness as a technofix. Research Limitations: Our paper relies on a relatively limited data set, and by extending our research into a wider range of journals it would have been possible to identify alternative themes. Moreover, our theoretical framework (stemming from the neoliberal critique) may overshadow relevant phenomenological and embodied aspects. Theoretical and practical implications: This paper contributes to scholarship within Social Studies of Mindfulness and Organization Studies, unpacking the contemporary articulations of mindfulness, neoliberalism, affect and governmentality

Benchmarking: A methodology for ensuring the relative quality of recommendation systems in software engineering

This chapter describes the concepts involved in the process of benchmarking of recommendation systems. Benchmarking of recommendation systems is used to ensure the quality of a research system or production system in comparison to other systems, whether algorithmically, infrastructurally, or according to any sought-after quality. Specifically, the chapter presents evaluation of recommendation systems according to recommendation accuracy, technical constraints, and business values in the context of a multi-dimensional benchmarking and evaluation model encompassing any number of qualities into a final comparable metric. The focus is put on quality measures related to recommendation accuracy, technical factors, and business values. The chapter first introduces concepts related to evaluation and benchmarking of recommendation systems, continues with an overview of the current state of the art, then presents the multi-dimensional approach in detail. The chapter concludes with a brief discussion of the introduced concepts and a summary