This study investigates user ideas about the role and value of tags in social media. An analysis of 45 interviews with heavy Web users reveals that user perceptions of tags differ from common assumptions held by researchers and designers of social tagging systems. Among beliefs held by participants were that tags were query suggestions or links to other pages, sites, or advertisements - although most identified tags as categories or keywords - and that tags were generated automatically by the computer system. Several participants believed that tags were intended for not only other users but also systems such as search engines. Our findings indicate that Web users, including those who are taggers themselves, experience a high level of uncertainty and confusion about the nature, purpose and value of tags.

Online question-answering services provide mechanisms for knowledge exchange by allowing users to ask and answer questions on a wide range of topics. A key question for designing such services is whether charging a price has an effect on answer quality. Two field experiments using one such service, Google Answers, offer conflicting answers to this question. To resolve this inconsistency, we re-analyze data from Harper et al. [5] and Chen et al. [2] to study the price effect in greater depth. Decomposing the price effect into two different levels yields results that reconcile those of the two field experiments. Specifically, we find that: (1) a higher price significantly increases the likelihood that a question receives an answer and (2) for questions that receive an answer, there is no significant price effect on answer quality. Additionally, we find that the rater background makes a difference in evaluating answer quality.

Abstract University libraries invest a massive amount of resources in digitizing information for the Web, yet there is growing concern that much of this information is being underutilized. The present study uses the technology acceptance model (TAM) to investigate university library website resources (ULWR) usage. We categorize users based on academic roles and then analyze them as subgroups in order to observe different adoption patterns across groups. A total of 299 usable responses was collected from four different universities and across three populations: undergraduate, master, and doctoral student/faculty groups. The findings show that different library users indeed access ULWR for different reasons, resulting in a need for tailored managerial efforts. Overall, the extended TAM explains undergraduate students' usage best; the explanatory power of the model is significantly lower for the doctoral student/faculty group. Some of the findings challenge results reported in TAM research in other fields. The unexpected findings may result from the application of the model to a different context. Detailed theoretical implications and managerial guidance are offered.

Abstract Instrument validation is a critical step that researchers should employ in order to ensure the generation of scientifically valid knowledge. Without it, the basis of research findings and the generalization of such are threatened. This is especially true in the social sciences, a discipline in which the majority of published articles utilize subjective instruments in the collection of data. Consequently, instrument validation has increasingly become common practice in the social sciences, yet implementation of this practice differs greatly among the social‐science disciplines. The assessment of instrument validation undertaken in this study attempts to provide guidance for reviewers, editors, authors, and readers, by offering various techniques of validity and analyzing the quality of a set of psychometric journal articles. In this research, six attributes of instrument validation areas are identified as validation protocol. The Journal of the American Society for Information Science and Technology ( JASIST ), which is widely recognized as a leading journal in the field of information science, was selected for examination to determine how well a set of research articles ranked in meeting instrument‐validation protocol. Findings show that while researchers are becoming increasingly attentive to certain validation issues, standards on validation processes and reporting might prove helpful. This paper identifies areas for improvement in the reporting of validity measures and offers ways for researchers to implement them.

Experience management refers to the capture, structuring, analysis, synthesis, and reuse of an organization's experience in the form of documents, plans, templates, processes, data, etc. The problem of managing experience effectively is not unique to software development, but the field of software engineering has had a high-level approach to this problem for some time. The Experience Factory is an organizational infrastructure whose goal is to produce, store, and reuse experiences gained in a software development organization. This paper describes The Q-Labs Experience Management System (Q-Labs EMS), which is based on the Experience Factory concept and was developed for use in a multinational software engineering consultancy. A critical aspect of the Q-Labs EMS project is its emphasis on empirical evaluation as a major driver of its development and evolution. The initial prototype requirements were grounded in the organizational needs and vision of Q-Labs, as were the goals and evaluation criteria later used to evaluate the prototype. However, the Q-Labs EMS architecture, data model, and user interface were designed to evolve, based on evolving user needs. This paper describes this approach, including the evaluation that was conducted of the initial prototype and its implications for the further development of systems to support software experience management.

In this paper, we propose a qualitative, inductive method for characterizing and evaluating software maintenance processes, thereby identifying their specific problems and needs. This method encompasses a set of procedures which attempt to determine causal links between maintenance problems and flaws in the maintenance organization and process. This allows for a set of concrete steps to be taken for maintenance quality and productivity improvement, based on a tangible understanding of the relevant maintenance issues in a particular maintenance environment. Moreover, this understanding provides a solid basis on which to define relevant software maintenance models and measures. A case study of the application of this method, called Q-MOPP, is presented to further illustrate its feasibility and benefits. © 1998 John Wiley & Sons, Ltd.

In order to improve software maintenance processes, we need to be able to first characterize and assess them. This task needs to be performed in depth and with objectivity since the problems are complex. One approach is to set up a measurement program specifically aimed at maintenance. However, establishing a measurement program requires that one understands the issues and is able to characterize the maintenance environment and processes in order to collect suitable and cost-effective data. Also, enacting such a program and getting usable data sets takes time. A short term substitute is needed. We propose a characterization process aimed specifically at maintenance and based on a general qualitative analysis methodology. This process is rigorously defined in order to be repeatable and usable by people who are not acquainted with such analysis procedures. A basic feature of our approach is that maintenance changes are analyzed in order to understand the flaws in the change process. Guidelines are provided and a case study is shown that demonstrates the usefulness of the approach.>

We have applied the discrimination analysis proposed by Norris, Pilsmorth, and Baldwin to the diagnosis of valvular heart diseases. They proposed the diagnosis method which uses concepts from fuzzy set theory. It consists of two independent parts: discrimination analysis and connectivity analysis. We performed the experiments in order to evaluate the effectiveness of the proposed discrimination analysis part of the method. Also, we extended the original method to handle partial manifestation of symptoms and severity of diseases by using fuzzy sets. In addition, we introduced the concept of prototypicalness of patients with a particular disease to improve the performance of the diagnosis. The results of the experiments are very promising. In the best case, we achieved a rate of true positive diagnosis of 81% while maintaining a rate of false positive diagnosis at the low level of 10%. We report the quantitative results of the experiments.>