Conceptual model for decomposing the value for the customer

Value has been defined in different theoretical contexts as need, desire, interest, standard /criteria, beliefs, attitudes, and preferences. The creation of value is key to any business, and any business activity is about exchanging some tangible and/or intangible good or service and having its value accepted and rewarded by customers or clients, either inside the enterprise or collaborative network or outside. “Perhaps surprising then is that firms often do not know how to define value, or how to measure it” (Anderson and Narus, 1998 cited by [1]). Woodruff echoed that we need “richer customer value theory” for providing an “important tool for locking onto the critical things that managers need to know”. In addition, he emphasized, “we need customer value theory that delves deeply into customer’s world of product use in their situations” [2]. In this sense, we proposed and validated a novel “Conceptual Model for Decomposing the Value for the Customer”. To this end, we were aware that time has a direct impact on customer perceived value, and the suppliers’ and customers’ perceptions change from the pre-purchase to the post-purchase phases, causing some uncertainty and doubts.We wanted to break down value into all its components, as well as every built and used assets (both endogenous and/or exogenous perspectives). This component analysis was then transposed into a mathematical formulation using the Fuzzy Analytic Hierarchy Process (AHP), so that the uncertainty and vagueness of value perceptions could be embedded in this model that relates used and built assets in the tangible and intangible deliverable exchange among the involved parties, with their actual value perceptions.

E-Learning as a shared service in shared services centers

Book Subtitle International Conference, CENTERIS 2010, Viana do Castelo, Portugal, October 20-22, 2010, Proceedings, Part II

A feedback-based decentralised coordination model for distributed open real-time systems

Moving towards autonomous operation and management of increasingly complex open distributed real-time systems poses very significant challenges. This is particularly true when reaction to events must be done in a timely and predictable manner while guaranteeing Quality of Service (QoS) constraints imposed by users, the environment, or applications. In these scenarios, the system should be able to maintain a global feasible QoS level while allowing individual nodes to autonomously adapt under different constraints of resource availability and input quality. This paper shows how decentralised coordination of a group of autonomous interdependent nodes can emerge with little communication, based on the robust self-organising principles of feedback. Positive feedback is used to reinforce the selection of the new desired global service solution, while negative feedback discourages nodes to act in a greedy fashion as this adversely impacts on the provided service levels at neighbouring nodes. The proposed protocol is general enough to be used in a wide range of scenarios characterised by a high degree of openness and dynamism where coordination tasks need to be time dependent. As the reported results demonstrate, it requires less messages to be exchanged and it is faster to achieve a globally acceptable near-optimal solution than other available approaches.

Service offloading in adaptive real-time systems

Smartphones and other internet enabled devices are now common on our everyday life, thus unsurprisingly a current trend is to adapt desktop PC applications to execute on them. However, since most of these applications have quality of service (QoS) requirements, their execution on resource-constrained mobile devices presents several challenges. One solution to support more stringent applications is to offload some of the applications’ services to surrogate devices nearby. Therefore, in this paper, we propose an adaptable offloading mechanism which takes into account the QoS requirements of the application being executed (particularly its real-time requirements), whilst allowing offloading services to several surrogate nodes. We also present how the proposed computing model can be implemented in an Android environment