There is an app for that? The impact of community knowledge workers in Uganda

Rapidly expanding mobile network coverage in developing countries offers new ways to reach poor farmers in isolated places. This article explores the potential for Information and Communication Technology to provide agricultural information and extension services to smallholder farmers, and links this to empirical insights of an intervention in Uganda, where community knowledge workers rely on mobile devices to deliver context- and time-sensitive data to farmers. Consistent with theoretical insights related to information inefficiencies, we find that the intervention leads farmers to move away from low-risk low-return crops toward more commercially oriented commodities. Our analysis also suggests that, for the case of maize, the intervention causes farmers to sell less on the market, but at significantly higher prices.     

Caring is not enough: the importance of Internet skills for online privacy protection

This article explains Internet users’ self-help activities in protecting their privacy online using structural equation modeling. Based on a representative survey of Swiss Internet users, it reveals past experiences with privacy breaches as a strong predictor of current protective behavior. Further, in line with the ‘privacy paradox’ argument, caring about privacy (privacy attitudes) alone does not necessarily result in substantial self-protection. Most strikingly, however, general Internet skills are key in explaining users’ privacy behavior. These skills enable users to reduce risks of privacy loss while obtaining the benefits from online activities that increasingly depend on the revelation of personal data. Consequently, Internet skills are an essential starting point for public policies regarding users’ self-help in privacy protection.     

Beyond Google: Bibliographic Transformation to Connect Users to Good Content

ABSTRACT

At the 2017 North Carolina Serials Conference, keynote speaker Regina Romano Reynolds—Director, U.S. ISSN Center and Head, ISSN Section at the Library of Congress—explored how bibliographic transformation can connect users to good-quality content. Offering examples of current and potential serials management projects, Reynolds shared her vision for breaking down information silos, experimenting in the resource management space, and the need for visionary leadership.     

When ERM Met Alma: The Intricacies of Content Management in a Shared Consortia Landscape

ABSTRACT

In 2013, after nearly two decades of operating in a distributed legacy Integrated Library System (ILS) environment on local servers, the Orbis Cascade Alliance, a consortium of public and private academic libraries in Washington, Oregon, and Idaho, began a two-year-long process to migrate its 37 members to a shared implementation of Ex Libris's cloud-based Alma library management system (LMS) and Primo discovery interface. Although much has been written on electronic resource management (ERM) functionality at an institution level, little has been written on serials and ERM functionality and workflows within a shared consortial environment. This article discusses the challenges and opportunities of implementing a consortial-based LMS, with particular emphasis on serials and ERM functionality. Key migration issues related to serials control, acquisitions, licensing, administration, cataloging, statistics, and interoperability are examined at the institutional and large-scale networked levels. Benefits and limitations of using a shared consortial cloud-based LMS are explored, and the overall capabilities of the Alma LMS for electronic resource management are reviewed.     

Evaluating modified generalized information criterion in presence of multicollinearity

When there are many explanatory variables in the regression model, there is a chance that some of these are intercorrelated. This is where the problem of multicollinearity creeps in due to which precision and accuracy of the coefficients is marred, and the quest to find the best model becomes tedious. To tackle such a situation, Model selection criteria are applied for selecting the best model that fits the data. Current study focuses on the evaluation of the four unmodified and four modified versions of generalized information criteria—Akaike Information Criterion, Schwarz's Bayes Information Criteria, Hannan-Quinn Information Criterion, and Akaike Information Criterion corrected for small samples. A simulation study using SAS software was carried out in order to compare the unmodified and modified versions of the generalized information criteria and to discover the best version amongst the four modified model selection criteria, for identifying the best model, when the collinearity assumption is violated. For the proposed simulation, two samples of size 50 and 100, for three explanatory variables X₁, X₂, and X₃, are drawn from Normal distribution. Two situations of collinearity violations between X₁ and X₂ are looked into, first when ρ = 0.6 and second when ρ = 0.8. The outcomes of the simulations are displayed in the tables along with visual representations. The results revealed that modified versions of the generalized information criteria are more sensitive in identifying models marred with high multicollinearity as compared to the unmodified generalized information criteria.     

The exponentiated reduced Kies distribution: Properties and applications

Here we consider an exponentiated version of the reduced Kies distribution and discuss some of its properties. The parameters of the distribution are estimated by the method of maximum likelihood and illustrated with the help of certain real-life data sets. Asymptotic behavior of the maximum likelihood estimators of the parameters of the distribution is also studied by using certain simulated data sets.     

The performance of model averaged tail area confidence intervals

We investigate the exact coverage and expected length properties of the model averaged tail area (MATA) confidence interval proposed by Turek and Fletcher, CSDA, 2012, in the context of two nested, normal linear regression models. The simpler model is obtained by applying a single linear constraint on the regression parameter vector of the full model. For given length of response vector and nominal coverage of the MATA confidence interval, we consider all possible models of this type and all possible true parameter values, together with a wide class of design matrices and parameters of interest. Our results show that, while not ideal, MATA confidence intervals perform surprisingly well in our regression scenario, provided that we use the minimum weight within the class of weights that we consider on the simpler model.     

Goodness-of-fit tests based on Verma Kullback–Leibler information

In this article, a new consistent estimator of Veram’s entropy is introduced. We establish the entropy test based on the new information namely Verma Kullback–Leibler discrimination methodology. The results are used to introduce goodness-of-fit tests for normal and exponential distributions. The root of mean square errors, critical values, and powers for some alternatives are obtained by simulation. The proposed test is compared with other tests.     

Interval estimation: An information theoretic approach

ABSTRACT

We develop here an alternative information theoretic method of inference of problems in which all of the observed information is in terms of intervals. We focus on the unconditional case in which the observed information is in terms the minimal and maximal values at each period. Given interval data, we infer the joint and marginal distributions of the interval variable and its range. Our inferential procedure is based on entropy maximization subject to multidimensional moment conditions and normalization in which the entropy is defined over discretized intervals. The discretization is based on theory or empirically observed quantities. The number of estimated parameters is independent of the discretization so the level of discretization does not change the fundamental level of complexity of our model. As an example, we apply our method to study the weather pattern for Los Angeles and New York City across the last century.     

Rank and response: A field experiment on peer information and water use behavior

Perception of peer rank, or how we perform relative to our peers, can be a powerful motivator. While research exists on the effect of social information on decision making, there is less work on how ranked comparisons with our peers influence our behavior. This paper outlines a field experiment conducted with 3896 households in Castro Valley, California, which uses household mailers with various forms of social information and peer rank messaging to motivate water conservation. The experiment tests the effect of a visible peer rank on water use, and how the competitive framing of rank information influences behavioral response. The results show that households with relatively low or high water use in the pre-treatment period responded differently to how rank information was framed. I find that a neutrally-framed peer rank caused a small “boomerang effect” (i.e., an increase in average water use) for low water use households, but this effect was eliminated by competitive framing. At the same time, a competitively-framed peer rank demotivated high water use households, increasing their average water use over the full period of the experiment. This result is supported by evidence that the competitive frame on rank information increased water use for households who ranked “last” in the peer group – a detrimental “last place effect” from competitively-framed rankings.