No abstract

Most important, in an age of rapidly proliferating knowledge, the central domain is a social network that absorbs, creates, transforms, buys, sells, and communicates knowledge. Its stronghold is the knowledge embedded in a dense web of social, economic, contractual, and administrative relationships" (Badaracco 1991, pp. 13-14).

A B S T R A C TThis study examines the role of neutralization and deterrence in discouraging employees from using Shadow IT: tools, services and systems used in an organization but not authorized by the IT department.Our study provides a unique contribution to the IT security literature by studying effects of neutralization on both intentions (self-reported) and actual behavior, as well as examining the role of shame as a mediator. We surveyed employees from four organizations and found that the "metaphor of the ledger" neutralization technique predicts Shadow IT intention and actual Shadow IT usage. We also find that neutralization and deterrence effects influence shame.

Purpose – The purpose of this paper is to identify the technological risks in the context of open source software (OSS) and suggest an integrative OSS risk taxonomy. Design/methodology/approach – The authors conducted an extensive literature review followed by expert interviews and applied the method for taxonomy development. Findings – This research has identified an integrative OSS risk taxonomy composed of 8 categories with 51 risk items. Originality/value – This taxonomy is a very useful tool for practitioners during the decision-making process when evaluating, assessing and calculating risks related to OSS adoption. Moreover, researchers can use it as a starting point for future studies to better understand the OSS phenomenon.

This survey deals with the problem of evaluating the submissions to crowdsourcing websites on which data is increasing rapidly in both volume and complexity. Usually expert committees are installed to rate submissions, select winners and adjust monetary rewards. Thus, with an increasing number of submissions, this process is getting more complex, time-consuming and hence expensive. In this paper we suggest following text mining methodology, foremost similarity measurements and clustering algorithms, to evaluate the quality of submissions to crowdsourcing contests semi-automatically. We evaluate our approach by comparing text mining based measurement of more than 40'000 submissions with the real-world decisions made by expert committees using Precision and Recall together with F 1 -score.