Roger Blake

An investigation of the intention to share media files over peer-to-peer networks

File-sharing over peer-to-peer (P2P) networks once consisted largely of music files, which, when shared, were infringements of copyrights. For this reason, studies of the behavioural intentions (BIs) to share files over P2P networks have often focused on the piracy of music files. However, with improved technology and increased bandwidth, large files such as videos are routinely shared. As industry-led efforts may have had some success stemming illegal file-sharing, and as new and legitimate applications of P2P file-sharing are emerging, it is important to include media files of all types and consider file-sharing that is both legitimate and that which constitutes piracy within the scope of our study. To study the intention to share media files over P2P networks, we evaluate two alternative models. The theory of planned behaviour (TPB) is the underlying theory for both models, one of which is based on the original TPB and one on the decomposed TPB. We test both models using previously validated instruments and find that both models can explain a significant portion of the variance in the intentions to share media files over P2P networks. However, the model based on the decomposed TPB can explain more of the variance. A second advantage of this model is that it can be more readily translated to managerial actions, which are also explored.

From Content to Context: The Evolution and Growth of Data Quality Research

Research in data and information quality has made significant strides over the last 20 years. It has become a unified body of knowledge incorporating techniques, methods, and applications from a variety of disciplines including information systems, computer science, operations management, organizational behavior, psychology, and statistics. With organizations viewing “Big Data”, social media data, data-driven decision-making, and analytics as critical, data quality has never been more important. We believe that data quality research is reaching the threshold of significant growth and a metamorphosis from focusing on measuring and assessing data quality—content—toward a focus on usage and context. At this stage, it is vital to understand the identity of this research area in order to recognize its current state and to effectively identify an increasing number of research opportunities within. Using Latent Semantic Analysis (LSA) to analyze the abstracts of 972 peer-reviewed journal and conference articles published over the past 20 years, this article contributes by identifying the core topics and themes that define the identity of data quality research. It further explores their trends over time, pointing to the data quality dimensions that have—and have not—been well-studied, and offering insights into topics that may provide significant opportunities in this area.

Discovering Data and Information Quality Research Insights Gained through Latent Semantic Analysis

In the recent decade, the field of data and information quality (DQ) has grown into a research area that spans multiple disciplines. The motivation here is to help understand the core topics and themes that constitute this area and to determine how those topics and themes from DQ relate to business intelligence (BI). To do so, the authors present the results of a study which mines the abstracts of articles in DQ published over the last decade. Using Latent Semantic Analysis (LSA) six core themes of DQ research are identified, as well as twelve dominant topics comprising them. Five of these topics--decision support, database design and data mining, data querying and cleansing, data integration, and DQ for analytics--all relate to BI, emphasizing the importance of research that combines DQ with BI. The DQ topics from these results are profiled with BI, and used to suggest several opportunities for researchers.

Using Business Intelligence for Operational Decision-Making in Call Centers

This paper proposes an operational business intelligence system for call centers. Using data collected from a large U.S. insurance company, the authors demonstrate a decision tree based solution to help the company achieve excellence through improved service levels. The initial results from this study provide insight into the factors affecting this firms call center service levels, and the solution developed in this paper provides two distinct advantages to managers. First, it enables them to identify key factors and the role they play in determining service levels. Second, a sliding window approach is proposed which allows managers to see the effects of resource reallocation on service levels on an on-going basis.

An Agenda for Case-Based Research

Let us face it. As individuals, only a few issues influence our research agendas. We tend to focus on one or two research streams in which we have had previous publishing success, we explore new topics based on what is “hot,” and perhaps we respond to calls from co-authors or other acquaintances. In addition, for case-based research, opportunity trumps all other factors. Since it is never easy to gain full access to processes or people in an organization, a perceptive researcher jumps at the chance to take advantage of conducting a case study at an organization to explore in depth into a research domain of interest. While this approach makes sense for individuals, it behooves us as a research community to set priorities that are not wholly opportunistic or personal. But how should such a community set its research priorities? One way is to express and perhaps debate them in outlets such as the Journal of Information Technology Case and Application Research (JITCAR) where case-based information technology (IT) researchers are likely to read. If one accepts the proposition that large-sample and case study data provide complementary insights about any specific research question, there is a need to balance the relative volume of research conducted by these two methods. Although not every research question is amenable to both case and large-sample analysis, most are. On grouping research questions into broad topics and further into subdisciplines of the information systems (IS) discipline, the possibility of providing the requisite balance becomes greater and the need to do so more compelling. This