Knowledge that Transforms

This paper addresses a thus-far neglected dimension in human-artificial intelligence (AI) augmentation: machine-induced reflections. By establishing a grounded theoretical-informed model of machine-induced reflection, we contribute to the ongoing discussion in information systems (IS) regarding AI and research on reflection theories. In our multistage study, physicians used a machine learning-based (ML) clinical decision support system (CDSS) to see if and how this interaction can stimulate reflective practice in the context of an X-ray diagnosis task. By analyzing verbal protocols, performance metrics, and survey data, we developed an integrative theoretical foundation to explain how ML-based systems can help stimulate reflective practice. Individuals engage in more critical or shallower modes depending on whether they perceive a conflict or agreement with these CDSS systems, which in turn leads to different levels of reflection depth. By uncovering the process of machine-induced reflections, we offer IS research a different perspective on how such AI-based systems can help individuals become more reflective, and consequently more effective, professionals. This perspective stands in stark contrast to the traditional, efficiency-focused view of ML-based decision support systems and also enriches theories on human-AI augmentation.

Online markets for mental health care (OMMH) allow clients to connect remotely with counselors to receive psychological therapy. Rooted in signaling theory and in the specific context of an OMMH, we theorize relative credibility of signals as the boundary condition that determines whether the demonstration signal of responsiveness to client questions substitutes or complements the two description signals of professional qualifications and counseling style in predicting market demand for counselors from new clients in an OMMH. Based on a panel dataset of 823 observations from 309 counselors participating on YiXinLi, a leading OMMH in China, we tested our hypotheses using linguistic and sentiment analysis methods and zero-inflated negative binomial models. We found broad support for nine out of ten hypotheses. Findings are robust with respect to different measures of variables, potential endogeneity from the simultaneity of responsiveness and counselor demand, and potential selection bias from both observable and endogenous covariates. Our study extends the literature on signaling in online markets in the unique context of OMMH by showing that: (1) relative credibility of signals is the boundary condition that determines when a demonstration signal will complement and when it will substitute for a description signal, (2) previous clients’ feedback on counselors’ empathy and warmth was deemed not credible by new clients in the context of online counseling, and (3) responsiveness to client questions is the most influential predictor of market demand from new clients in an OMMH.

Online business-to-business auctions for used IT products have emerged as a viable market for finding a second life for these products, rather than having them end up in landfills as e-waste. As part of the growing “secondary market” landscape, these online B2B auctions are significantly affected by adverse selection since uncertainty about product quality from their first life remains in place. We study how these adverse selection costs may be identified and reduced in online B2B auctions for mobile phones using a proprietary data set for pallets of iPhone devices. We focus on the differences between carrier-locked and unlocked iPhones, and the degree to which the jailbreaking of devices may lead to adverse selection costs. We first show that uncertainty with respect to the possibility of jailbreaking-to-unlock induces significant adverse selection costs in this market. We identify a clear method that these adverse selection costs may be reduced through policies implemented in the primary market. We find that adverse selection costs exist with respect to jailbreaking-to-unlock, by comparing prices obtained for locked and unlocked devices, as well as pallets where this information is not disclosed. However, when some of the uncertainty surrounding jailbreaking-to-unlock is removed by virtue of an exogenous policy change implemented by Verizon in the primary market, i.e., to sell all iPhones as factory unlocked, adverse selection costs are significantly reduced. Our work has significant implications for enhancing the efficiency of secondary markets for IT products, by virtue of highlighting the connections between primary and secondary markets. Managerial and theoretical implications that emerge from this work are discussed in the paper.

Design artifacts in online innovation communities are increasingly becoming a primary source of innovation for organizations. A distinguishing feature of such communities is that they are organized around design artifacts, not around people. The search for novel innovations thus equates to a search for novel designs. This is not a trivial problem since the novelty of a design is a function of its relationship to other designs, and this relationship changes as each design is added. These relations between artifacts affect both consumption and production. Moreover, these relations form a landscape whose structure affects the emergence of novelty. We find evidence for our theorizing using an analysis of over 35,000 Thingiverse design artifacts. This work identifies the differential effects of different forms of novelty, visual and verbal, on subsequent innovation, and identifies the differential effects of different degrees of structure in the landscape on novelty.

In many instances, online contest platforms rely on contestants to ensure submission quality. This scalable evaluation mechanism offers a collective benefit. However, contestants may also leverage it to achieve personal, competitive benefits. Our study examines this tension from a status-theoretic perspective, suggesting that the conflict between competitive and collective benefits, and the net implication for evaluation efficacy, is influenced by contestants’ status. On the one hand, contestants of lower status may be viewed as less skilled and hence more likely to make mistakes. Therefore, low-status contestants may attract more evaluations if said evaluations are driven predominantly by an interest in collective benefits. On the other hand, if evaluations are driven largely by an interest in personal, competitive benefits, a low-status contestant makes for a less attractive target and hence may attract fewer evaluations. We empirically test these competing possibilities using a dataset of coding contests from Codeforces. The platform allows contestants to assess others’ submissions and improve evaluations (a collective benefit) by devising test cases (hacks) in addition to those defined by the contest organizer. If a submission is successfully hacked, the hacker earns additional points, and the target submission is eliminated from the contest (a competitive benefit). We begin by providing qualitative evidence based on semi-structured interviews conducted with contestants spanning the status spectrum at Codeforces. Next, we present quantitative evidence exploiting a structural change at Codeforces wherein many contestants experienced an arbitrary status reduction unrelated to their performance because of sudden changes to the platform’s color-coding system around contestant ratings. We show that status-loser contestants received systematically more evaluations from other contestants, absent changes in their short-run submission quality. Finally, we show that the excess evaluations allocated toward affected contestants were less effective, indicating status-driven evaluations as potentially less efficacious. We discuss the implications of our findings for managing evaluation processes in online contests.

Digital product innovation involves a meaning-making process. Designers of digital innovations often challenge established product meanings as they digitize physical products, such as cars, toothbrushes, and water bottles. A significant problem for product designers, however, is striking the right balance between the newness and comprehensibility of product meanings. Failure to do so may result in a digital product innovation that is too conventional or difficult to relate to or understand. Yet, the extant digital product innovation literature pays little, if any, attention to product meaning. To fill this void, this study examines a digital product innovation project in which product designers created a digital theater with product meanings beyond those of the traditional movie theater. Our theory, grounded in in-depth data collection and analysis, explains how product designers attribute meanings to their products in the process of digital innovation by enacting two meaning-making loops: a reinforcing loop that makes the product meaning comprehensible, and a differentiating loop that captures emerging product meanings. The two loops come together via meaning sedimentation, through which a new core product meaning is created. Our study contributes to the digital product innovation literature by shedding light on the essential role of meaning-making in innovation and offers an explanatory process theory.

Understanding information technology (IT) use is vital for the information systems (IS) discipline due to its substantial positive and negative consequences. In recent years, IT use for personal purposes has grown rapidly. Although personal use is voluntary and can often reflect fun, technostress is a common negative consequence of such use. When left unaddressed, technostress can cause serious harm to IT users. However, prior research has not explained how technostress forms over time or how its mitigation takes place in a personal—rather than organizational—environment. To address these research gaps, we conducted a qualitative study with narrative interviews of IT users who had experienced technostress. This study contributes to (1) the technostress literature by unpacking states in which technostress forms and can be mitigated and (2) the IT affordance literature by explaining the role of affordances and their actualizations in technostress as well as introducing the new concept of actualization cost. In terms of practice, our findings help individuals and societies identify the development of technostress, understand the activities required for its mitigation, and recognize mitigation barriers.

With today’s technological advancements, mobile phones and wearable devices have become extensions of an increasingly diffused and smart digital infrastructure. In this paper, we examine mobile health (mHealth) platforms and their health and economic impacts on the outcomes of chronic disease patients. To do so, we partnered with a major mHealth firm that provides one of the largest mobile health app platforms in Asia specializing in diabetes care. We designed and implemented a randomized field experiment based on detailed patient health activities (e.g., steps, exercises, sleep, food intake) and blood glucose values from 1,070 diabetes patients over several months. Our main findings show that the adoption of the mHealth app leads to an improvement in health behavior, which in turn leads to both short term metrics (such as reduction in patients’ blood glucose and glycated hemoglobin levels) and longer-term metrics (such as hospital visits and medical expenses). Patients who adopted the mHealth app undertook higher levels of exercise, consumed healthier food with lower calories, walked more steps and slept for longer times on a daily basis. They also were more likely to substitute offline visits with telehealth. A comparison of mobile versus PC-enabled versions of the same app demonstrates that the mobile version has a stronger effect than PC version in helping patients make these behavioral modifications with respect to diet, exercise, and lifestyle, which ultimately leads to an improvement in their healthcare outcomes. We also compared outcomes when the platform facilitates personalized health reminders to patients vis-à-vis generic (non-personalized) reminders. Surprisingly, we found that personalized mobile messages with patient-specific guidance can have an inadvertent (smaller) effect on patient app engagement and lifestyle changes, leading to a lower health improvement. However, they are more like to encourage a substitution of offline visits by telehealth. Overall, our findings indicate the massive potential of mHealth technologies and platform design in achieving better healthcare outcomes.

This paper explores the structure and design of digital strategic initiatives (DSI): identifiable competitive moves that depend on digital resources to create and appropriate economic value. We use the term digital deliberately, in line with the recent push for discerning the so-called IT “x” and Digital “x” phenomena. The paper contributes to basic science by precisely defining the digital strategic initiative concept and its essential elements: digital resources. It clarifies the difference between digital resources and established constructs such as IT resources and IT-enabled resources. We posit that the defining characteristics of digital resources are their modular design, encapsulation of value, and programmatic interface. This work also shows how the design and development of digital strategic initiatives thrive in an infrastructural, combinatorial, and servitized environment. Using illustrative cases, we demonstrate applications of the concepts by introducing two value creation pathways for DSI: (1) orchestration of digital resources and (2) creation of novel digital resources. The paper concludes by presenting open research questions and offering extensions for future inquiry.

Evidence shows that older users have lower performance levels for IT-enabled tasks than younger users. This is alarming at a time when the workforce is rapidly aging and organizational technologies are proliferating. Since the explanation for these lower performance levels remains unclear, managers are not sure how to help older users realize their full potential as contributors to organizational success. The research model presented here identifies the declining information-processing speed of older workers as the cause of their reduced capacity to perform IT-enabled tasks. According to the model, IT experience and IT self-efficacy reduce the negative impacts of this decline, whereas IT overload and the effort cost of IT use aggravate them. To test the model, data were collected using three complementary studies. The results supported the model and indicated five ways that organizations can help older users improve their capacity to perform IT-enabled tasks. Additional data collected in interviews with human resources directors confirmed the relevance of these solutions.