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In recent years, the COVID-19 pandemic and other global crises have significantly affected the lives of older adults, impacting their healthcare, social connections, and daily routines. While the increasing digitization and automation of services offer benefits such as remote healthcare access and reduced isolation, these technologies also pose challenges in terms of unfamiliarity, learning curves, and privacy and security concerns. Addressing these issues requires a collaborative approach across various fields, including health informatics, gerontology, social psychology, human–computer interaction, and cybersecurity and privacy. Understanding the cognitive, emotional, and sociocultural factors influencing older adults’ use of technologies is crucial for creating inclusive and accessible digital tools. This multidisciplinary effort, as highlighted in the special issue of Work, Aging and Retirement, aims to enhance our understanding of aging and technology in today’s world, empowering older adults to remain connected and maintain their well-being.

 

Technological advancements continue to result in fundamental changes to the work itself and the workplace. Although these changes can create challenges for older workers, older workers can draw from individual and contextual resources to maintain and enhance their wellbeing, motivation, and capacities, and thus achieving successful aging at work. These articles in this special issue characterize the different psychological mechanisms underlying workers’ responses to technological changes in the workplace, such as automation, digitization, and use of information and communications technologies. Integrating the findings from these articles, along with the existing theoretical models of successful aging at work, we propose a socio-ecological approach to guide future research on older workers’ adaptation to technological changes.

 

A key challenge facing the use of machine learning (ML) in organizational selection settings (e.g., the processing of loan or job applications) is the potential bias against (racial and gender) minorities. To address this challenge, a rich literature of Fairness-Aware ML (FAML) algorithms has emerged, attempting to ameliorate biases while maintaining the predictive accuracy of ML algorithms. Almost all existing FAML algorithms define their optimization goals according to a selection task, meaning that ML outputs are assumed to be the final selection outcome. In practice, though, ML outputs are rarely used as-is. In personnel selection, for example, ML often serves a support role to human resource managers, allowing them to more easily exclude unqualified applicants. This effectively assigns to ML a screening rather than a selection task. It might be tempting to treat selection and screening as two variations of the same task that differ only quantitatively on the admission rate. This paper, however, reveals a qualitative difference between the two in terms of fairness. Specifically, we demonstrate through conceptual development and mathematical analysis that miscategorizing a screening task as a selection one could not only degrade final selection quality but also result in fairness problems such as selection biases within the minority group. After validating our findings with experimental studies on simulated and real-world data, we discuss several business and policy implications, highlighting the need for firms and policymakers to properly categorize the task assigned to ML in assessing and correcting algorithmic biases.

 

Over the past two decades, behavioral research in privacy has made considerable progress transitioning from acontextual studies to using contextualization as a powerful sensitizing device for illuminating the boundary conditions of privacy theories. Significant challenges and opportunities wait, however, on elevating and converging individually contextualized studies to a context-contingent theory that explicates the mechanisms through which contexts influence consumers’ privacy concerns and their behavioral reactions. This paper identifies the important barriers occasioned by this lack of context theorizing on the generalizability of privacy research findings and argues for accelerating the transition from the contextualization of individual research studies to an integrative understanding of context effects on privacy concerns. It also takes a first step toward this goal by providing a conceptual framework and the associated methodological instantiation for assessing how context-oriented nuances influence privacy concerns. Empirical evidence demonstrates the value of the framework as a diagnostic device guiding the selection of contextual contingencies in future research, so as to advance the pace of convergence toward context-contingent theories in information privacy. This paper was accepted by Anindya Ghose, information systems. 

Research and practical development of data-anonymization techniques have proliferated in recent years. Yet, limited attention has been paid to examine the potentially disparate impact of privacy protection on underprivileged subpopulations. This study is one of the first attempts to examine the extent to which data anonymization could mask the gross statistical disparities between subpopulations in the data. We first describe two common mechanisms of data anonymization and two prevalent types of statistical evidence for disparity. Then, we develop conceptual foundation and mathematical formalism demonstrating that the two data-anonymization mechanisms have distinctive impacts on the identifiability of disparity, which also varies based on its statistical operationalization. After validating our findings with empirical evidence, we discuss the business and policy implications, highlighting the need for firms and policy makers to balance between the protection of privacy and the recognition/rectification of disparate impact. This paper was accepted by Chris Forman, information systems. 

Too much of a good thing can be harmful. Choice overload, a compelling paradox in consumer psychology, exemplifies this notion with the idea that offering more product options could impede rather than improve consumer satisfaction, even when consumers are free to ignore any available option. After attracting intense interest in the past decades from multiple disciplines, research on choice overload has produced voluminous yet paradoxical findings that are widely perceived as inconsistent even at the meta-analytic level. This paper launches an interdisciplinary inquiry to resolve the inconsistencies on both the conceptual and empirical fronts. Specifically, we identified a surprising butrobust pattern among the existing empirical evidence for the choiceoverload effect and demonstrated through mathematical analysis and extensive simulation studies that the pattern would only likely emerge from one specific type of latent mechanism underlying the moderated choiceoverload effect. The paper discusses the research and practical implications of our findings—namely, the broad promise of analytical meta-analysis (an emerging area for the use of data analytics) and machine learning to address the widely recognized inconsistencies in social and behavioral sciences, and the unique and salient role of the information systems community in developing this new era of meta-analysis.