Knowledge that Transforms

Prior usability assessment research has paid little attention to how product and feature ratings are influenced by the evaluation context. However, the evaluability hypothesis, which guides this research, suggests that the evaluation context is a vital factor in shaping user’s assessments and perceptions about technology features. pecifically, the evaluability hypothesis proposes that technology feature perceptions, and ultimately technology choices, will change when evaluating a single technology in isolation versus when simultaneously comparing more than one. To demonstrate the evaluability hypothesis effect in the context of consumer technology product evaluations, two experiments were conducted. Both studies support the evaluability hypothesis effect, showing that when two IT products are compared, hard-to-evaluate but easy-to-compare features are perceived to be more important and therefore have a larger influence on product preferences. Alternatively, when evaluating a single product in isolation, easy-to-evaluate features are perceived to be more important and therefore have a larger influence on product preferences. Consequently, different product preferences emerge (i.e., preference reversals) in different evaluation contexts. The results demonstrate that this theoretical lens is robust to the technology evaluation context, providing important theoretical and practical insights for technology design, usability assessments, and, ultimately, product acceptance.

The quality of an organization’s decisions depends on the quality of the data in its information systems. When technology records employees’ work automatically, information quality is ensured by algorithms that produce electronic representations of work. What happens when employees report their own work? We show that the quality of this self-reported information depends on how managers direct their employees to report their work in their organization’s information systems. We refer to constructing this representation as transfiguration work. We draw on a 15-month ethnography to specify the process of transfiguration work along with the characteristics of the system of transfiguration that supports it. Our specification of transfiguration work supplements the model of representation implicit in research on management information systems. We show how managers can take over information technology in enforcing the practices that employees follow to report their work. By doing so, we theorize a broader role for agency in the representation of work. We conclude that organizations can improve the quality of data in their information systems by shaping how managers enforce transfiguration work, rather than by changing the information systems where employees report their work.

Online reputation trading is a new phenomenon facilitated by the prosperity of e-commerce and social networks. Whether reputations will be reliable when people can purchase rather than build them originally is a natural concern and also a challenge to online marketplaces. In the present study, we examine a reputation market in an infinitely repeated game setting, where agents sell products and trade their online reputations. Agents exert effort to provide products, and their reputations are updated based on consumer feedback. High-type agents have a lower cost of effort than low types. In addition to reputation system, we consider products that are randomly audited, and agents do not receive payment for products that fail the audit. Our analysis depicts a separating equilibrium: high-type agents can be sorted out from low-type agents by their reputations, which contrasts with the results in Tadelis (2002). In a separating equilibrium, reputations become a perfect indicator of agents’ types, effort levels, and product quality. We demonstrate the key role of auditing in separating different types of agents, and reveal the substitution effect between auditing frequency and harshness of reputation systems. We also study the design of the reputation system and the audit mechanism in order to achieve different equilibria in the reputation market. By proposing online reputations as an asset, our paper generates implications for establishing reliable online environments and promoting effective online interactions.

We test the hypothesis that work practices complement IT investment, in part, by accelerating how rapidly employees acquire the skills needed to use new IT systems. We combine support request data from an EMR vendor with survey responses on work practices from 962 employees from 15 client nursing homes. Nursing homes using work practices that prior studies have shown to complement IT investment—those promoting discretion, teamwork, training, high staffing levels, and communication—experienced more rapid declines in requests for technical support. We then show that these benefits are due, in part, because the use of these work practices facilitates learning for workers in frontline occupations who otherwise may not have the freedom to experiment with and adapt the new technology systems. For many frontline workers, discretion was more important than training in explaining IT learning. Implications for the healthcare industry are discussed.

Information spillovers are benefits that a party obtains from the IT efforts of another party. Because these benefits arise from data and information sharing, they are best studied at a process level. Medical claims fraud detection is a prototypical data- and information-intensive process in insurance companies. Fraud detection efforts of one insurer can create spillover benefits through data and information sharing that occur from socialization between analysts and labor mobility between insurers. This paper theorizes three semi-collaborative networks formed between state-level subsidiaries of insurers (regulation-bound network), between subsidiaries of an insurer parent company (sibling network), and between insurers and hospitals (risk-sharing), and hypothesizes that these networks convey information spillovers. Because benefits realized by another party can lead to the reduction of IT investments by that party, the paper also examines the impact of semi-collaborative networks on future IT-related investments. The empirical analysis was conducted using 2011– 2013 data. A generalized linear model with a Tweedie distribution is used to correct for the finite mass of zeros for the dependent variables. The results reveal that the sibling network conveyed most of the spillover benefit, and the risk-sharing network did not contribute to fraud detection. The sibling network is also found to depress future spending on fraud detection.