Knowledge that Transforms

In this article, the effect of IT knowledge on the overconfidence of venture capitalists (VCs) in their IT investments is examined. Our findings show that the effect of IT knowledge on overconfidence is nonlinear. VCs with moderate levels of IT knowledge are least overconfident. At the same time, VCs with moderate levels of IT knowledge are most resistant to the biasing effects of past successes. Past failures show a negative association with overconfidence independent of the level of the VC’s IT knowledge. Finally, the negative association between stakes and VC overconfidence is stronger with greater levels of IT knowledge. These results shed light on the highly disputed role of IT knowledge in the domain of IT investments.

The ease of Internet stock trading has lured relatively inexperienced investors into the financial markets. This paper is a study of the consequences of the influx of these uninformed traders with a dynamic equilibrium framework. The results show that these strategic, uninformed online traders who adopt feedback strategies cannot outperform those who do not follow feedback strategies and that feedback trading cannot affect market equilibrium. The results also show that an informed trader’s equilibrium strategy and expected profit remain unchanged with or without feedback trading. The presence of feedback trading in the market does not affect the speed at which information gets incorporated into prices. If uninformed traders aggregately adopt a more aggressive feedback trading strategy, they bear a higher risk. It is therefore important to manage and contain these uninformed traders’ risks. The implications for regulating and designing such Internet trading systems are also discussed.

While the impacts of health information technology (HIT) are widely studied, prior research presents mixed findings. In this study, a granular examination of the impact of HIT systems on how resources are allocated to healthcare tasks and processes was undertaken. A longitudinal field study that combined interview, archival, observation, and survey data was conducted. The effects of telemedicine on the input allocative efficiency of the healthcare process through the reallocation of organizational resources was evaluated and an assessment of whether gains in allocative efficiency resulted in improvements in organizational outcomes, such as lower hospitalization rates and lower uncertainty in patient wait time, was conducted. Applying the theory of swift and even flow, our findings suggest that the gains in allocative efficiency for some processes are associated with improved organizational outcomes.

Despite the importance of investing in information technology, research on business value of information technology (BVIT) shows contradictory results, raising questions about the reasons for divergence. Kohli and Devaraj (2003) provided valuable insights into this issue based on a meta-analysis of 66 BVIT studies. This paper extends Kohli and Devaraj by examining the influences on BVIT through a meta-analysis of 303 studies published between 1990 and 2013. We found that BVIT increases when the study does not consider IT investment, does not use profitability measure of value, and employs primary data sources, fewer IT-related antecedents, and larger sample size. Considerations of IT alignment, IT adoption and use, and interorganizational IT strengthen the relationship between IT investment on BVIT, whereas the focus on environmental theories dampens the same relationship. However, the use of productivity measures of value, the number of dependent variables, the economic region, the consideration of IT assets and IT infrastructure or capability, and the consideration of IT sophistication do not affect BVIT. Finally, BVIT increases over time with IT progress. Implications for future research and practice are discussed.

Achieving the promised business benefits of an IT system is intimately tied to the continued incorporation of the system into the work practices it is intended to support. While much is known about different social, cognitive, and technical factors that influence initial adoption and use, less is known about the role of emotional factors in users’ behaviors. Through an in-depth field study conducted in two North American universities, we examine the role of emotions in how specific IT use patterns emerge. We find that there are five different characteristics of an IT stimulus event (cues) that, when interacting in a reinforcing manner, elicit a single class of emotions (uniform affective responses) and, when interacting in an oppositional manner, elicit mixed emotions (ambivalent affective responses). While users respond to uniform emotions with clear adaptation strategies, they deal with ambivalent emotions by combining different adaptation behaviors, a vacillating strategy between emphasizing positive and negative aspects of the stimulus. Surprisingly, these ambivalent emotions and vacillating strategies can lead to active and positive user engagement, exhibited in task and tool adaptation behaviors and improvisational use patterns that, despite their nonconformity to terms of use, can have positive organizational implications.

This special issue acknowledges important innovations in the world of service and within this domain we are particularly interested in exploring the rise and influence of web-based crowd-sourcing and algorithmic rating and ranking mechanisms. We suggest that a useful way to make sense of these digital service innovations and their novel implications is to recognize that they are materialized in practice. We thus need effective conceptual and analytical tools that allow us to take materiality seriously in our studies of service innovation. To this end, we propose some theoretical ideas relating to a sociomaterial perspective, and then highlight empirically how this perspective helps us analyze the specific service materializations enacted through the algorithmic configuring of crowd-sourced data, and how these make a difference in practice to the outcomes produced.

This paper presents a mobile application usability conceptualization and survey instrument following the 10-step procedure recommended by MacKenzie et al. (2011). Specifically, we adapted Apple’s user experience guidelines to develop our conceptualization of mobile application usability that we then developed into 19 first-order constructs that formed 6 second-order constructs. To achieve our objective, we collected four datasets: content validity (n = 318), pretest (n = 440), validation (n = 408), and cross-validation (n = 412). The nomological validity of this instrument was established by examining its impact on two outcomes: continued intention to use and mobile application loyalty. We found that the constructs that represented our mobile application usability conceptualization were good predictors of both outcomes and compared favorably to an existing instrument based on Microsoft’s usability guidelines. In addition to being an exemplar of the recent procedure of MacKenzie et al. to validate an instrument, this work provides a rich conceptualization of an instrument for mobile application usability that can serve as a springboard for future work to understand the impacts of mobile application usability and can be used as a guide to design effective mobile applications.

This paper resumes the discussion in information systems research on the use of partial least squares (PLS) path modeling and shows that the inconsistency of PLS path coefficient estimates in the case of reflective measurement can have adverse consequences for hypothesis testing. To remedy this, the study introduces a vital extension of PLS: consistent PLS (PLSc). PLSc provides a correction for estimates when PLS is applied to reflective constructs: The path coefficients, inter-construct correlations, and indicator loadings become consistent. The outcome of a Monte Carlo simulation reveals that the bias of PLSc parameter estimates is comparable to that of covariance-based structural equation modeling. Moreover, the outcome shows that PLSc has advantages when using non-normally distributed data. We discuss the implications for IS research and provide guidelines for choosing among structural equation modeling techniques.