Digital native and digital immigrant user types characterise the differences between those who grew up in a world of ubiquitous information systems and those who pre-date it. The rise in computer-mediated communication (CMC) technologies such as emails, text messages, and social media creates more opportunities for interruptions. Researchers have explored the impact of growing up in a world of technology. However, little research has explored the potential differences between digital natives and digital immigrants handling technology interruptions. This paper uses a qualitative approach to examine the extent to which individuals handle CMC interruptions differently based on the role of technology and its level of pervasiveness in the environment in which they grew up. Thirty-three interviews were conducted with individuals from different population groups based on age and gender to understand how technology interruptions are handled in work and social life. The findings support existing pre- and post-technology interruption frameworks and explain how and why generational differences exist in the process of CMC interruption handling. The theoretical and practical implications are discussed.

Stream analytics focuses on analysis of signals generated simultaneously and over time. The specific patterns in the signals can indicate some of the outcomes such as failure of a device, etc. Therefore, novel ways to find specific patterns in the signals generated by many sources are required. In this paper, we extend the CRISP-DM process to include data preparation approaches for sequence mining. We present progression analysis, an approach for converting streams of records to be able to detect useful signals for analysis. To illustrate the process, we present a healthcare example where patients diagnosed with Tobacco Use Disorder develop multiple other diseases over multiple hospital visits. The common sequences of the diseases diagnosed in the TUD patients over multiple hospital visits are presented and discussed. Finally, the generalizability of the progression analysis is discussed.

Item-level Radio Frequency Identification (RFID) adoption is on the agenda for many retailers. However, before adopting this technology, managers need to be able to identify its direct and indirect benefits. These benefits are expected to be significant and have begun to be studied empirically and analytically in narrow, isolated segments (e.g., supply chain management). In this paper, we focus on applications of RFID in the retail sector, specifically in store operations. We have adapted the balanced scorecard (BSC) model as a decision-making framework to build a holistic model of RFID-enabled changes throughout retail store operations including marketing, merchandising, and supply chain management. After reviewing the existing literature, we conducted a Delphi study in which 10 consultants and senior managers from leading U.S. retailers were interviewed. The results indicate that benefits in the areas of merchandising and marketing may not be realized as directly as those in the supply chain, but their effects should not be underestimated. The proposed BSC model can also indicate potential opportunities for item-level RFID use in retailing and serve as a guideline for further studies.

With knowledge management systems (KMS) containing large repositories, a major issue is content organization. The ease of finding relevant information depends on the effectiveness of knowledge organization. Ontology, thesauri, and taxonomy are some of the key words that relate to knowledge organization. In this article we propose a schema for organizing knowledge that represents lessons learned from prior experience. Such knowledge from lessons learned has distinct characteristics so that it can be organized in specific ways for ease of discovery, retrieval, and also possible incorporation in formal learning. The proposed taxonomy includes concepts from domain related hierarchy, sources of lessons learned, formal learning, and collaborative inputs (Web 2.0). We describe the proposed taxonomy for organizing the lessons learned knowledge (also termed as knowledge nuggets) and provide details of a specific implementation of this taxonomy in a military organization. Such approaches to knowledge organization have the potential to be useful in many other knowledge management (KM) projects.