The success of Amazon Mechanical Turk (MTurk) as an online research platform has come at a price: MTurk has suffered from slowing rates of population replenishment, and growing participant ...non-naivety. Recently, a number of alternative platforms have emerged, offering capabilities similar to MTurk but providing access to new and more naïve populations. After surveying several options, we empirically examined two such platforms, CrowdFlower (CF) and Prolific Academic (ProA). In two studies, we found that participants on both platforms were more naïve and less dishonest compared to MTurk participants. Across the three platforms, CF provided the best response rate, but CF participants failed more attention-check questions and did not reproduce known effects replicated on ProA and MTurk. Moreover, ProA participants produced data quality that was higher than CF's and comparable to MTurk's. ProA and CF participants were also much more diverse than participants from MTurk.
Privacy and human behavior in the age of information Acquisti, Alessandro; Brandimarte, Laura; Loewenstein, George
Science (American Association for the Advancement of Science),
01/2015, Letnik:
347, Številka:
6221
Journal Article
Recenzirano
This Review summarizes and draws connections between diverse streams of empirical research on privacy behavior. We use three themes to connect insights from social and behavioral sciences: people's ...uncertainty about the consequences of privacy-related behaviors and their own preferences over those consequences; the context-dependence of people's concern, or lack thereof, about privacy; and the degree to which privacy concerns are malleable—manipulable by commercial and governmental interests. Organizing our discussion by these themes, we offer observations concerning the role of public policy in the protection of privacy in the information age.
We review different streams of social science literature on privacy with the goal of understanding consumer privacy decision making and deriving implications for policy. We focus on psychological and ...economic factors influencing both consumers' desire and consumers' ability to protect their privacy, either through individual action or through the implementation of regulations applying to firms. Contrary to depictions of online sharing behaviors as careless, we show how consumers fundamentally care about online privacy, and present evidence of numerous actions they take to protect it. However, we also document how prohibitively difficult it is to attain desired, or even desirable, levels of privacy through individual action alone. The remaining instrument for privacy protection is policy intervention. However, again for both psychological and economic reasons, the collective impetus for adequate intervention is often countervailed by powerful interests that oppose it.
This article is part of a Research Dialogue:
Krishna (2020): https://doi.org/10.1002/jcpy.1186
Oyserman & Schwarz (2020): https://doi.org/10.1002/jcpy.1189
Mulligan et al. (2020): https://doi.org/10.1002/jcpy.1190
Jagadish (2020): https://doi.org/10.1002/jcpy.1188
Acquisti et al. (2020): https://doi.org/10.1002/jcpy.1187
How privacy's past may shape its future Acquisti, Alessandro; Brandimarte, Laura; Hancock, Jeff
Science (American Association for the Advancement of Science),
2022-Jan-21, Letnik:
375, Številka:
6578
Journal Article
Recenzirano
An account of privacy’s evolutionary roots may hold lessons for policies in the digital age.
Corporate digital responsibility Lobschat, Lara; Mueller, Benjamin; Eggers, Felix ...
Journal of business research,
January 2021, 2021-01-00, 20210101, Letnik:
122
Journal Article
Recenzirano
Odprti dostop
We propose that digital technologies and related data become increasingly prevalent and that, consequently, ethical concerns arise. Looking at four principal stakeholders, we propose corporate ...digital responsibility (CDR) as a novel concept. We define CDR as the set of shared values and norms guiding an organization's operations with respect to four main processes related to digital technology and data. These processes are the creation of technology and data capture, operation and decision making, inspection and impact assessment, and refinement of technology and data. We expand our discussion by highlighting how to managerially effectuate CDR compliant behavior based on an organizational culture perspective. Our conceptualization unlocks future research opportunities, especially regarding pertinent antecedents and consequences. Managerially, we shed first light on how an organization's shared values and norms regarding CDR can get translated into actionable guidelines for users. This provides grounds for future discussions related to CDR readiness, implementation, and success.
Misplaced Confidences Brandimarte, Laura; Acquisti, Alessandro; Loewenstein, George
Social psychological & personality science,
05/2013, Letnik:
4, Številka:
3
Journal Article
Recenzirano
We test the hypothesis that increasing individuals’ perceived control over the release and access of private information—even information that allows them to be personally identified––will increase ...their willingness to disclose sensitive information. If their willingness to divulge increases sufficiently, such an increase in control can, paradoxically, end up leaving them more vulnerable. Our findings highlight how, if people respond in a sufficiently offsetting fashion, technologies designed to protect them can end up exacerbating the risks they face.
The increasing societal concern for consumer information privacy has led to the enforcement of privacy regulations worldwide. In an effort to adhere to privacy regulations such as General Data ...Protection Regulation (GDPR), many companies’ privacy policies have become increasingly lengthy and complex. In this study, we adopted the computational design science paradigm to design a novel privacy policy evolution analytics framework to help identify how companies change and present their privacy policies based on privacy regulations. The framework includes a Self-Attentive Annotation System (SAAS) that automatically annotates paragraph-length segments in privacy policies to help stakeholders identify data practices of interest for further investigation. We rigorously evaluated SAAS against state-of-the-art Machine Learning (ML) and Deep Learning (DL)-based methods on a well-established privacy policy dataset, OPP-115. SAAS outperformed conventional ML and DL models in terms of F1-score by statistically significant margins. We demonstrate the proposed framework’s practical utility with an in-depth case study of GDPR’s impact on Amazon’s privacy policies. The case study results indicate that Amazon’s post-GDPR privacy policy potentially violates a fundamental principle of GDPR by causing consumers to exert more effort to find information about first-party data collection. Given the increasing importance of consumer information privacy, the proposed framework has important implications for regulators and companies. We discuss several design principles followed by the SAAS that can help guide future design science-based e-commerce, health, and privacy research.
Advancements in information technology often task users with complex and consequential privacy and security decisions. A growing body of research has investigated individuals' choices in the presence ...of privacy and information security tradeoffs, the decision-making hurdles affecting those choices, and ways to mitigate such hurdles. This article provides a multi-disciplinary assessment of the literature pertaining to privacy and security decision making. It focuses on research on assisting individuals' privacy and security choices with soft paternalistic interventions that nudge users toward more beneficial choices. The article discusses potential benefits of those interventions, highlights their shortcomings, and identifies key ethical, design, and research challenges.
This article is part of a Research Dialogue:
Krishna (2020): https://doi.org/10.1002/jcpy.1186
Acquisti et al. (2020): https://doi.org/10.1002/jcpy.1191
Oyserman & Schwarz (2020): ...https://doi.org/10.1002/jcpy.1189
Mulligan et al. (2020): https://doi.org/10.1002/jcpy.1190
Jagadish (2020): https://doi.org/10.1002/jcpy.1188
How does information about a person's past, accessed now, affect individuals' impressions of that person? In 2 survey experiments and 2 experiments with actual incentives, we compare whether, when ...evaluating a person, information about that person's past greedy or immoral behaviors is discounted similarly to information about her past generous or moral behaviors. We find that, no matter how far in the past a person behaved greedily or immorally, information about her negative behaviors is hardly discounted at all. In contrast, information about her past positive behaviors is discounted heavily: recent behaviors are much more influential than behaviors that occurred a long time ago. The lesser discounting of information about immoral and greedy behaviors is not caused by these behaviors being more influential, memorable, extreme, or attention-grabbing; rather, they are perceived as more diagnostic of a person's character than past moral or generous behaviors. The phenomenon of differential discounting of past information has particular relevance in the digital age, where information about people's past is easily retrieved. Our findings have significant implications for theories of impression formation and social information processing.
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