Managing customer relationship management projects:the case of a large French telecommunication company

Customer relationship management (CRM) implementation projects reflect a growing conceptual shift from the traditional engineering view of projects. Such projects are complex and risky because they call for both organisational and technological changes. This requires effective project management across various phases of the implementation process. However, few empirical researches have dealt with these project management issues. The aim of this research is to investigate how a “project team” manages CRM implementation projects successfully, across the different phases of the implementation process. We conducted an in-depth case study of the “Firm-Clients Branch” of a large telecommunications company in France. The findings show that, to manage CRM implementation projects successfully, an integrated and balanced approach is required. This involves appropriate system selection, effective process re-engineering and further development of organizational structures. We highlight the need for a “technochange approach” to achieve successful organisational transition and effective CRM implementation. The study reveals that the project team plays a central role throughout the implementation phases. Furthermore the effectiveness of technochange depends on project team performance, technology efficiency and close coordination with stakeholders.

Dynamic decision-making based on NFR for managing software variability and configuration selection

Due to dynamic variability, identifying the specific conditions under which non-functional requirements (NFRs) are satisfied may be only possible at runtime. Therefore, it is necessary to consider the dynamic treatment of relevant information during the requirements specifications. The associated data can be gathered by monitoring the execution of the application and its underlying environment to support reasoning about how the current application configuration is fulfilling the established requirements. This paper presents a dynamic decision-making infrastructure to support both NFRs representation and monitoring, and to reason about the degree of satisfaction of NFRs during runtime. The infrastructure is composed of: (i) an extended feature model aligned with a domain-specific language for representing NFRs to be monitored at runtime; (ii) a monitoring infrastructure to continuously assess NFRs at runtime; and (iii) a exible decision-making process to select the best available configuration based on the satisfaction degree of the NRFs. The evaluation of the approach has shown that it is able to choose application configurations that well fit user NFRs based on runtime information. The evaluation also revealed that the proposed infrastructure provided consistent indicators regarding the best application configurations that fit user NFRs. Finally, a benefit of our approach is that it allows us to quantify the level of satisfaction with respect to NFRs specification.

Modelling question selection behaviour in online communities

Value of online Question Answering (QandA) communities is driven by the question-answering behaviour of its members. Finding the questions that members are willing to answer is therefore vital to the effcient operation of such communities. In this paper, we aim to identify the parameters that cor- relate with such behaviours. We train different models and construct effective predictions using various user, question and thread feature sets. We show that answering behaviour can be predicted with a high level of success.