Why Do Digital Native News Media Fail? An Investigation of Failure in the Early Start-Up Phase

Digital native news media have great potential for improving journalism. Theoretically, they can be the sites where new products, novel revenue streams and alternative ways of organizing digital journalism are discovered, tested, and advanced. In practice, however, the situation appears to be more complicated. Besides the normal pressures facing new businesses, entrepreneurs in digital news are faced with specific challenges. Against the background of general and journalism specific entrepreneurship literature, and in light of a practice–theoretical approach, this qualitative case study research on 15 German digital native news media outlets empirically investigates what barriers curb their innovative capacity in the early start-up phase. In the new media organizations under study here, there are—among other problems—a high degree of homogeneity within founding teams, tensions between journalistic and economic practices, insufficient user orientation, as well as a tendency for organizations to be underfinanced. The patterns of failure investigated in this study can raise awareness, help news start-ups avoid common mistakes before actually entering the market, and help industry experts and investors to realistically estimate the potential of new ventures within the digital news industry

Library Link Resolvers and Analytics: Using Analytics Tools to Identify Usage Trends and Access Problems with Electronic Resources in Libraries

Analytics have become vital to exploring the use of websites in the library world. They allow us to discover who is using our sites, when they are using them, and how they are using them. I believe this data will only become more important as libraries continue to prove their worth in the digital world. However, there are some challenges with using analytics. One major issue is that some of the essential sites relating to the delivery of library materials do not actually belong to the library. One example of this is the link resolver, which attempts to connect patrons to electronic materials in various databases. This paper will detail the steps I took to add Google Analytics to our link resolver, 360 Link. I will provide examples of how I used Google Analytics to explore usage of online tools, to identify sources of citations, and to study errors

Aggregate Query Prediction under Dynamic Workloads

Large organizations have seamlessly incorporated data-driven decision making in their operations. However, as data volumes increase, expensive big data infrastructures are called to rescue. In this setting, analytics tasks become very costly in terms of query response time, resource consumption, and money in cloud deployments, especially when base data are stored across geographically distributed data centers. Therefore, we introduce an adaptive Machine Learning mechanism which is light-weight, stored client-side, can estimate the answers of a variety of aggregate queries and can avoid the big data backend. The estimations are performed in milliseconds and are inexepensive as the mechanism learns from past analytical-query patterns. However, as analytic queries are ad-hoc and analysts’ interests change over time we develop solutions that can swiftly and accurately detect such changes and adapt to new query patterns. The capabilities of our approach are demonstrated using extensive evaluation with real and synthetic datasets

Establishing an Extendable Benchmarking Framework for E-Fulfillment

The growth in attended home deliveries motivates research in prescriptive analytics for e-fulfillment. Introducing new analytics solutions, for instance, for vehicle routing or revenue management, requires simulation-based benchmarking and analyses on relevant problem scenarios. Unfortunately, creating the required systems induces high overhead for analytics researchers. This paper introduces the simulation-based benchmarking framework SiLFul, which aims to support scientific rigor and practical relevance of research by reducing this overhead. It provides a toolbox of approaches, a modular and extendable architecture, and a comprehensive, application-related data model. Thereby, it facilitates controllable analyses and transparent and replicable research. Moreover, we propose a research process that leverages the framework for evaluating analytics and allows continuous development of the framework as a community effort