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The paper presents a research model and a measurement instrument for a research-in-progress study on the antecedents of success in IS offshoring projects. In this empirical-confirmatory study, we intend to analyse the impact of the constructs “offshoring expertise”, “trust in offshore service provider”, “project suitability”, “knowledge transfer”, and “liaison quality” on offshore project success. Constructs and indicators are derived from an extensive literature review. We plan to formulate a structural equation model and to test it using partial least squares (PLS) as an analysis technique. Our research model addresses the paucity of research that quantitatively examines offshoring success.
This paper empirically examines the current state of the IS offshoring phenomenon in Germany regarding project characteristics and success patterns. Relying on a sample of 304 projects conducted at various industry sectors and companies, results show that IS offshoring primarily occurs in sectors Telecommunications and IT at large corporations. Cost reduction is the main reason for going offshore and offshore projects are executed as part of a larger program at companies. Noticeably, most projects are delivered from India. Additionally, neither captive offshoring nor offshore outsourcing dominates as a de-livery option. Comparing different project subgroups regarding project success, the results reveal that projects delivered by an internal or partially owned service provider are more successful. Other project characteristics such as a project’s embedded-ness in a larger offshoring program, a project’s size, or a project’s offshoring degree in terms of relatively offshored labor hours show few significant differences. The paper addresses the paucity of empirical research on the current state of the IS offshoring phenomenon in Germany.
Identification of bats is most practically done by exploiting the characteristic features of their echolocation calls. This usually involves expert knowledge, expensive equipment and time-consuming post processing of previously recorded calls. Automated solutions exist, but are usually not as accurate as human experts. We present an automated solution for the processing of bat calls and identification of bat species with extremely high classification accuracy that can be used during live recording or in an automated post-processing software. Our algorithm is the first application of a Deep Convolutional Neural Network to classify bat species based on sound spectrogram images of their echolocation calls. We tested several deep CNN architectures including a modified Google Inception and a ResNet50 architecture. The nets were trained on a very large call database consisting of images of snippets of call spectrograms. All our software was developed in the Python programming language and an executable of the software is available on request.