How do technology resources such as computer ownership help create opportunity equality and facilitate income mobility? This is an important question because opportunity constraint, or the lack of opportunity equality, often manifests in low income mobility in many countries. This study addresses this question by examining the spillover effects of computer ownership of households in terms of household income mobility considering spatial proximity and social proximity. Drawing on knowledge spillovers theory, we hypothesize that households experience upward income mobility due to increased computer ownership among households in spatial proximity. Further, drawing on social capital theory, we hypothesize that households experience higher upward mobility due to increased computer ownership among households in the region that are socially proximate or in the same caste. Our empirical analysis of data from over 32,000 households across all states in India from two waves (2005 and 2011) of the Indian Human Development Survey supports our hypotheses. Our exploratory analyses suggest that households in regions with high social harmony experience higher upward income mobility from increased computer ownership among households in the region. We also found that increased computer ownership among households in the region helps households belonging to disadvantaged castes overcome low income mobility. Overall, the study shows how the returns to computer ownership in terms of household upward income mobility go beyond private returns, and spill over to other households in spatial proximity, particularly to those belonging to the same caste. A key implication for policymakers is to use twin levers of social harmony and technology resources to create opportunity equality to facilitate income mobility, instead of focusing solely on technology resources.
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.
Data-related harm and injustice are commonly viewed through instrumental, procedural, distributional, or representational theories of social justice. These theories do not account for the social injustice that occurs through the lack of recognition of individuals when data are first conceptualized. We explore the recognition of individuals in data conceptualization by drawing from information systems (IS) literature on data artifacts that acknowledges the fact that data are comprised of semantics and formats. Guided by recognition theory, we studied a project to expand sexual orientation and gender identity (SOGI) data collection at a public, U.S.-based LGBTQ+ welcoming university. We found that while the actors involved worked towards recognizing in SOGI data the fact that identities are layered, non-binary, plural, and fluid, the data themselves still misrecognized individuals due to data warping. We argue that data warping occurs because of recognition concessions between social recognition through data semantics and systems recognition through data formats. Such concessions are both necessary for some recognition but accessory to misrecognition. Our findings have implications for recognition theory, data justice, and information systems research, as well as for personal data in practice.
The rapid development of e-learning has drawn increasing attention to the issue of how learners’ learning activities can be better structured using technologies. This study focuses on how to improve e-learning performance by optimizing the structuring of learning sessions from the perspective of interleaving (i.e., mixing different topics in a learning session). Following the design science paradigm, this study chooses cognitive load theory as the kernel theory and proposes a new interleaving design—related-interleaving —that populates an interleaved session with related topics as a way of reducing cognitive load during an interleaved session. Drawing on the theoretical predictions, we design and instantiate a personalized learning system with the related-interleaving strategy by fusing educational strategies and machine learning techniques. The results from a two-month field experiment confirm that related-interleaving outperforms non-interleaving and unrelated-interleaving. Our findings also reveal that compared with unrelated-interleaving, related-interleaving benefits weak learners more and thus helps reduce learning performance disparities. This study demonstrates how personalized e-learning systems can be further improved from the perspective of interleaving.
People have conflicting responses for support from algorithms or humans in decision-making. On the one hand, they fail to benefit from algorithms due to algorithm aversion, as they reject decisions provided by algorithms more frequently than those made by humans. On the other hand, many prefer algorithmic over human advice, an effect of algorithm appreciation. However, currently, we lack a shared understanding of these constructs’ meaning and measurements, resulting in a lack of theoretical integration of empirical findings. Thus, in this research note, we conceptualize algorithm aversion as the preference for humans over algorithms in decision-making and analyze approaches in current research to measure this preference. First, we outline the implications of focusing on a specific understanding of algorithms as computational procedures or as embedded in material or nonmaterial objects. Then, we classify four decision configurations that distinguish individuals’ evaluations of algorithms, human advisors, their own judgments, or combinations of these. Consequently, we develop a classification scheme that provides guidance for future research to develop more specific hypotheses on the direction of preferences (aversion vs. appreciation) and the effect of moderators.
