Driven by changes in working practices and technology trends, organizations are increasingly reliant on mobile workers and the data they capture. However, while significant work has been carried out on increasing the usability of mobile devices and applications, little attention has been paid to the quality of data captured by mobile workers. If this data is inaccurate or untrustworthy, serious consequences can ensue. In this paper we study a system targeted at mobile workers in the highways sector that is deliberately designed to increase the accuracy and trustworthiness of the data collected. The resulting Inspections application has been very positively received by workers and we present lessons that we believe can be applied to other applications of this type.

This study examines productivity change in the Mid‐Atlantic surfclam and ocean quahog fishery, which has been managed since 1990 using Individual Transferable Quotas (ITQ). Productivity change is estimated through a Malmquist index from 1981–2008, capturing change before and after implementation of the quota system. We then decompose the index to examine changes in technical efficiency, scale efficiency, and technical change. Our findings indicate that the ITQ system has not sustained gains in vessel productivity. These results are thought to be driven by spatial changes in biomass and the inability to access more productive fishing grounds.

As in all collaborative work, trust is a vital ingredient of successful computer supported cooperative work, yet there is little in the way of design principles to help practitioners develop systems that foster trust. To address this gap, we present a set of design patterns, based on our experience designing systems with the explicit intention of increasing trust between stakeholders. We contextualize these patterns by describing our own learning process, from the development, testing and refinement of a trust model, to our realization that the insights we gained along the way were most usefully expressed through design patterns. In addition to a set of patterns for trust, this paper seeks to demonstrate of the value of patterns as a means of communicating the nuances revealed through ethnographic investigation.

Technical efficiency, which measures how well a firm transforms inputs into outputs, gives fishery managers important information concerning the economic status of the fishing fleet, and how regulations may be impacting vessel profitability. Data envelopment analysis (DEA), and the stochastic production frontier (SPF) have emerged as preferred methods to estimate efficiency in fisheries. Although each of the approaches has strengths and weaknesses, DEA has often been criticized because it is "deterministic" and fails to account for noise in the data. This paper presents a method for examining the underlying statistical structure of DEA models using bootstrap methods, and readily available software. The approach is then applied to a case study of the U.S. mid-Atlantic sea scallop dredge fleet. Results show that the 95% confidence interval for technically efficient output is well above the maximum sustained yield (MSY) level of output.Short Title: Measuring Vessel Efficiency

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