Knowledge that Transforms

As the use of information as a productive and tradeable asset becomes more pervasive, its—often unintended—side-effects start showing. This prompts us to systematically recognize these effects and,...

Organizations usually have difficulty adjusting to technology-enabled changes. Recent research has examined the interaction between technology and the key job outcomes of employees. But this research stream has done so using a linear lens even though this interplay has been recognized to be dynamic and complex. We challenge here this linearity assumption. We theorized that enterprise system (ES) use influences post-implementation job scope, and the change from pre- to post-implementation job scope perceptions will have a complex effect on job outcomes that are best captured by a polynomial model. Drawing on the anchoring-and-adjustment perspective in decision-making research, our polynomial model highlights the dynamic nature of employee reactions to changes in job scope brought about by an ES implementation that cannot be captured by traditional linear models. We found support for our model using data collected in a longitudinal field study from 2,794 employees at a telecommunications firm over a period of 12 months. Our findings highlight the key role an ES implementation can have in changing the nature of jobs and how those changes can, in turn, drive job performance and job satisfaction. This research also extends classical job characteristics research by arguing for a more complex relationship between the scope and outcomes of technology-supported jobs.

Some people contribute ideas for prosocial reasons in crowdsourcing; others do so for selfish reasons. Extending the theory of motivated information processing, the research posits that prosocial and proself individuals respond differently to reward structures in crowd idea generation. Two online experiments measured participants' prosocial versus proself orientation and manipulated whether participants received a competitive or cooperative reward structure. Study 2 also manipulated whether participants viewed an original or a common peer idea. Proselfs produced more ideas when receiving competitive rewards; the idea generation of prosocials was not affected by the reward structure. This interaction effect was mediated by task effort and moderated the impact of peer ideas. Proselfs generated the most ideas when viewing an original peer idea and receiving competitive rewards; this effect was not observed for prosocials. The study contributes to crowdsourcing research by demonstrating that participants' response to reward structures depends on their social value orientation. The implication is that crowdsourcing organizers should design tasks and rewards so they motivate participants with both prosocial and proself orientations.

Emerging literature has begun to investigate the role of technology in public health. Yet, a minimal amount is understood about whether, how, and why digital games, notably mobile games, might affect mental health, particularly depression. In this work, we examine the effect of location-based mobile gaming on local depression trends. We measure population-level depression using a well-established mechanism from the medical and public health literature, internet search of depression-related terms. We argue that the introduction of Pokémon Go, a mobile game that encourages outdoor physical activity, face-to-face socialization, and exposure to nature, may alleviate non-clinical forms of mild depression for users playing the game. To identify the effect, we employ a difference-in-differences approach to exploit the staggered release of Pokémon Go into 166 regions in 12 English-speaking countries. We empirically document a disproportionate decrease in depression-related search in those regions where users are able to play Pokémon Go. This finding lends credence to anecdotal claims that location-based mobile games may alleviate symptoms of depression of their users, underscoring the mental health opportunities of location-based mobile gaming and creating new opportunities for information systems research.

If managers assume a normal or near-normal distribution of Information Technology (IT) project cost overruns, as is common, and cost overruns can be shown to follow a power-law distribution, managers may be unwittingly exposing their organizations to extreme risk by severely underestimating the probability of large cost overruns. In this research, we collect and analyze a large sample comprised of 5,392 IT projects to empirically examine the probability distribution of IT project cost overruns. Further, we propose and examine a mechanism that can explain such a distribution. Our results reveal that IT projects are far riskier in terms of cost than normally assumed by decision makers and scholars. Specifically, we found that IT project cost overruns follow a power-law distribution in which there are a large number of projects with relatively small overruns and a fat tail that includes a smaller number of projects with extreme overruns. A possible generative mechanism for the identified power-law distribution is found in interdependencies among technological components in IT systems. We propose and demonstrate, through computer simulation, that a problem in a single technological component can lead to chain reactions in which other interdependent components are affected, causing substantial overruns. What the power law tells us is that extreme IT project cost overruns will occur and that the prevalence of these will be grossly underestimated if managers assume that overruns follow a normal or near-normal distribution. This underscores the importance of realistically assessing and mitigating the cost risk of new IT projects up front.

Markets in which similar goods of different qualities are sold suffer from information asymmetries and their negative consequences. Dealers have established themselves, and mediate these markets through their use of quality signals. While these signals help to mitigate information asymmetries, these markets still function well below their optimum: a large share of goods sold are overpriced, and most of the benefits are reaped by intermediaries. In this paper we build on prior research that proposes the use of blockchain as an enabler for trusted, decentralized asset documentation. Applying a socio-technical lens, we describe how blockchain-enabled multi-party certification affords dealers the action potential to send signals that are more closely correlated to the unobservable quality of the underlying good (i.e., signals with a higher fit) than the signals they send today. We then both theorize and experimentally explore the market effects of the two types of signals. Using data from a laboratory market experiment with 210 participants, we find empirical evidence that multi-party certification affords dealers the action potential to send signals of significantly higher fit than those sent by intermediaries alone, leading to a reduction in information asymmetries, a more efficient allocation of goods, and an increase in market fairness.

Artificial intelligence (AI) enables continuous monitoring of patients’ health, thus improving the quality of their health care. However, prior studies suggest that individuals resist such innovative technology. In contrast to prior studies that investigate individuals’ decisions for themselves, we focus on family members’ rejection of AI monitoring, as family members play a significant role in health care decisions. Our research investigates competing effects of emotions toward the rejection of AI monitoring for health care. Based on two scenario-based experiments, our study reveals that emotions play a decisive role in family members’ decision making on behalf of their parents. We find that anxiety about health care monitoring and anxiety about health outcomes reduce the rejection of AI monitoring, whereas surveillance anxiety and delegation anxiety increase rejection. We also find that for individual-level risks, perceived controllability moderates the relationship between surveillance anxiety and the rejection of AI monitoring. We contribute to the theory of Information System rejection by identifying the competing roles of emotions in AI monitoring decision making. We extend the literature on decision making for others by suggesting the influential role of anxiety. We also contribute to healthcare research in Information System by identifying the important role of controllability, a design factor, in AI monitoring rejection.

This study examines how the use of algorithmic control within gig economy platforms relates to the well-being and behavior of workers. Specifically, we explore how two different forms of algorithmic control—gatekeeping and guiding—correspond with (positive) challenge technostressors and (negative) threat technostressors experienced by Uber drivers. We also examine the moderating impact of algorithmic control transparency on these relationships, as well as the outcomes of technostressors in terms of continuance intentions and workaround use. Based on a survey of 621 U.S.-based Uber drivers, we find that gatekeeping and guiding algorithmic control positively relate to both challenge and threat technostressors. The study bridges the literature on control and technostress by conceptualizing algorithmic control as a condition that puts workers under stress. This stress is found to contribute to important behavioral consequences pertaining to both continuance intentions and workaround use. Findings from our work suggest that gig economy organizations can use algorithmic control to enhance challenge technostressors for their workers, thereby contributing to the cultivation of a more committed workforce. Furthermore, we find evidence disputing the assumption that algorithmic control transparency can mitigate the negative effects of threat technostressors.