An ethnographic study of the enactment of service level agreements in complex IT-intensive business-to-business services.

Service level agreements (SLAs) for complex IT-intensive business-to-business (CITI-B2B) services are high-level representations of services to be enacted, with predominantly quantifiable performance targets. Inevitably, there is a gap between this representation and the nuanced practices of enactment adapting to emergent conditions over time. Overarching terms in the master agreement anticipate this gap; however, the nature of the practices that manage that gap is not well understood. This study aims to develop a deeper understanding of these everyday practices to identify potential areas for improving value realisation in SLA enactment. We conducted a long-term ethnographic study of the enactment of an SLA by a global IT provider and global financial services company, framed by relational theory of contract. Our analysis showed the gap was bridged by a cycle of enactment in which emergent conditions triggered relational interactions among participants, culminating in decisions to adapt the terms of the SLA in pursuit of value realisation. Further, our analysis showed that this cycle is enabled by informal mechanisms of learning, negotiating, and adapting that we conceptualise as relational capability, which is amenable to representation, refinement, innovation, and capability development. Exploiting this capability and as well as the information produced during the cycle of enactment could inform SLA design and enable the transformation of SLAs as evolving learning instruments

Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well