Knowledge that Transforms

In this paper, we draw on control theory to understand the conditions under which the use of agile practices is most effective in improving software project quality. Although agile development methodologies offer the potential of improving software development outcomes, limited research has examined how project managers can structure the software development environment to maximize the benefits of agile methodology use during a project. As a result, project managers have little guidance on how to manage teams who are using agile methodologies. Arguing that the most effective control modes are those that provide teams with autonomy in determining the methods for achieving project objectives, we propose hypotheses related to the interaction between control modes, agile methodology use, and requirements change. We test the model in a field study of 862 software developers in 110 teams. The model explains substantial variance in four objective measures of project quality—bug severity, component complexity, coordinative complexity, and dynamic complexity. Results largely support our hypotheses, highlighting the interplay between project control, agile methodology use, and requirements change. The findings contribute to extant literature by integrating control theory into the growing literature on agile methodology use and by identifying specific contingencies affecting the efficacy of different control modes. We discuss the theoretical and practical implications of our results.

With the proliferation of e-commerce, there is growing evidence that online impulse buying is occurring, yet relatively few researchers have studied this phenomenon. This paper reports on two studies that examine how variations in a website influence online impulse buying. The results reveal some relevant insights about this phenomenon. Specifically, although many participants had the urge to buy impulsively, regardless of website quality, this behavior's likelihood and magnitude was directly influenced by varying the quality of task-relevant and mood-relevant cues. Task-relevant cues include characteristics, such as navigability, that help in the attainment of the online consumer's shopping goal. Conversely, mood-relevant cues refer to the characteristics, such as visual appeal, that affect the degree to which a user enjoys browsing a website but that do not directly support a particular shopping goal. The implications of the results for both future research and the design of human-computer interfaces are discussed.

Intentional insider misuse of information systems resources (i.e., IS misuse) represents a significant threat to organizations. For example, industry statistics suggest that between 50%–75% of security incidents originate from within an organization. Because of the large number of misuse incidents, it has become important to understand how to reduce such behavior. General deterrence theory suggests that certain controls can serve as deterrent mechanisms by increasing the perceived threat of punishment for IS misuse. This paper presents an extended deterrence theory model that combines work from criminology, social psychology, and information systems. The model posits that user awareness of security countermeasures directly influences the perceived certainty and severity of organizational sanctions associated with IS misuse, which leads to reduced IS misuse intention. The model is then tested on 269 computer users from eight different companies. The results suggest that three practices deter IS misuse: user awareness of security policies; security education, training, and awareness (SETA) programs; and computer monitoring. The results also suggest that perceived severity of sanctions is more effective in reducing IS misuse than certainty of sanctions. Further, there is evidence that the impact of sanction perceptions vary based on one's level of morality. Implications for the research and practice of IS security are discussed.

Prior research on technology and team performance concludes that the fit of the technology to tasks influences team performance. It also suggests that the way teams appropriate technology influences performance. This research examines how fit and appropriation (from the Fit Appropriation Model) influence performance over time. Initially, the results show that fit better predicted performance; teams using poor-fitting technology performed worse than teams with better fitting technology. However, over a short time period (two days in this study), this initial fit no longer predicted performance; performance of teams using better fitting technology remained constant while teams using poor-fitting technology innovated and adapted, improving performance. There are two key findings from this study. First, fit can predict team performance soon after technology adoption, but initial assessments of fit are temporary as teams innovate and adapt; thus, our current theoretical models of fitting technology to a task likely will not be useful beyond the first use. Second, teams should understand how to better adapt existing technology and work structures. Because our current theories of task-technology fit failed to predict performance beyond the first use of technology, we believe that this calls for a reconsideration of what fit means for teams using technology.

Given the increasingly important role of the Internet in education, healthcare, and other essential services, it is important that we develop an understanding of the “digital divide.” Despite the widespread diffusion of the Web and related technologies, pockets remain where the Internet is used sparingly, if at all. There are large geographic variations, as well as variations across ethnic and racial lines. Prior research suggests that individual, household, and regional differences are responsible for this disparity. We argue for an alternative explanation: Individual choice is subject to social influence (“peer effects”) that emanates from geographic proximity; this influence is the cause of the excess variation. We test this assertion with empirical analysis of a data set compiled from a number of sources. We find, first, that widespread Internet use among people who live in proximity has a direct effect on an individual's propensity to go online. Using data on residential segregation, we test the proposition that the Internet usage patterns of people who live in more ethnically isolated regions will more closely resemble usage patterns of their ethnic group. Finally, we examine the moderating impact of housing density and directly measured social interactions on the relationship between Internet use and peer effects. Results are consistent across analyses and provide strong evidence of peer effects, suggesting that individual Internet use is influenced by local patterns of usage. Implications for public policy and the diffusion of the Internet are discussed.