Mobile customer relationship management : An explorative investigation of the italian consumer market

Mobile customer relationship management (CRM) services seem to have all the characteristics commonly associated with successful mobile services and have accordingly been predicted to be among the most promising. However, real development of this sector has not been well explored so far, especially in relation to the actual supply of mobile CRM services to the public. The purpose of this paper is to reduce this gap by giving a first snapshot of the current development of the supply of mobile CRM services to consumers taken in the context of the Italian market. In order to do so, it firstly proposes a conceptual framework indicating the relevant aspects to investigate for assessing this kind of environments: the market, value propositions, actors and issues. Then it applies this framework to get an overview of the supply of mobile CRM services in Italy and provides some empirical insight about its current development obtained through an exhaustive survey of the current supply of 750 services from 353 firms

Analyzing the m-Business Landscape

The m-business landscape never stops to change and the impacts on the mobile market are constant as players reposition themselves on the market according to the new opportunities and threats brought by rapid technological developments. This paper provides a conceptual tool to better understand this player arena and its objective is threefold. The first one is to analyze the role of the key actors using ontology for defining and assessing their business models. The second objective is to analyze and visualize the interaction of actors with each other from a value system perspective. The final objective is to evaluate and represent the dependencies of the actors, their strategies and their convergence or divergence on different issues by using an approach borrowed from policy making

Environmental context significance in strategic decision support systems

Appraising the environmental context in which an organization deploys its activity is a necessity in order to make appropriate decisions and adapting strategies to a context in constant evolution, especially in a time where this context is increasingly complex, uncertain and disruptive. Decision makers therefore need more than ever better tools that aid them to analyze their environment, providing them the most pertinent information to take the most appropriate decisions. In this paper, we attempt to propose a set of reusable artifacts that would facilitate the development of decision support systems for assessing the organization's environment. In particular, we propose an ontology that defines the different elements that shall be taken into account in order to effectively and efficiently scan an environment. We then provide an overview of some analysis techniques and tools that could be useful to analyze, assess and visualize essential information about these elements. Finally, we present two decision support system prototypes that allow a partial analysis of the environment using appropriate interaction and visualization techniques

Leveraging mathematical models of disease dynamics and machine learning to improve development of novel malaria interventions

BACKGROUND: Substantial research is underway to develop next-generation interventions that address current malaria control challenges. As there is limited testing in their early development, it is difficult to predefine intervention properties such as efficacy that achieve target health goals, and therefore challenging to prioritize selection of novel candidate interventions. Here, we present a quantitative approach to guide intervention development using mathematical models of malaria dynamics coupled with machine learning. Our analysis identifies requirements of efficacy, coverage, and duration of effect for five novel malaria interventions to achieve targeted reductions in malaria prevalence. METHODS: A mathematical model of malaria transmission dynamics is used to simulate deployment and predict potential impact of new malaria interventions by considering operational, health-system, population, and disease characteristics. Our method relies on consultation with product development stakeholders to define the putative space of novel intervention specifications. We couple the disease model with machine learning to search this multi-dimensional space and efficiently identify optimal intervention properties that achieve specified health goals. RESULTS: We apply our approach to five malaria interventions under development. Aiming for malaria prevalence reduction, we identify and quantify key determinants of intervention impact along with their minimal properties required to achieve the desired health goals. While coverage is generally identified as the largest driver of impact, higher efficacy, longer protection duration or multiple deployments per year are needed to increase prevalence reduction. We show that interventions on multiple parasite or vector targets, as well as combinations the new interventions with drug treatment, lead to significant burden reductions and lower efficacy or duration requirements. CONCLUSIONS: Our approach uses disease dynamic models and machine learning to support decision-making and resource investment, facilitating development of new malaria interventions. By evaluating the intervention capabilities in relation to the targeted health goal, our analysis allows prioritization of interventions and of their specifications from an early stage in development, and subsequent investments to be channeled cost-effectively towards impact maximization. This study highlights the role of mathematical models to support intervention development. Although we focus on five malaria interventions, the analysis is generalizable to other new malaria interventions

Condition monitoring of marine and offshore machinery using evidential reasoning techniques

This paper first assesses the operational uncertainties of a particular piece of equipment in a marine and offshore system based on an oil analysis technique. Trend analysis, family analysis, environmental analysis, human reliability analysis and design analysis for each criterion are aggregated using evidential reasoning (ER) and analytical hierarchy process (AHP) algorithms. Data is collected from available statistics and supplemented by expert judgement from the related industry. The results provided in this study will be beneficial to the marine and offshore industries as indicators for monitoring and diagnosis of faults in machinery and thus assist practitioners in making better decisions in their maintenance management process. Furthermore, by changing the conditions that affect the operation of machinery, and through calculating a value for this operation, a benchmark for condition monitoring is constructed. The operational condition of machinery depends on many variables and their dependencies; thus, alteration of a criterion value will ultimately alter the operational conditions of the machinery. For any deviation to be corrected in a timely manner, the operational condition of the machinery has to be monitored properly and frequently

Conceptual models for designing information systems supporting the strategic analysis of technology environments

1 6 STRUCTURE OF THIS THESIS -Chapter I presents the motivations of this dissertation by illustrating two gaps in the current body of knowledge that are worth filling, describes the research problem addressed by this thesis and presents the research methodology used to achieve this goal. -Chapter 2 shows a review of the existing literature showing that environment analysis is a vital strategic task, that it shall be supported by adapted information systems, and that there is thus a need for developing a conceptual model of the environment that provides a reference framework for better integrating the various existing methods and a more formal definition of the various aspect to support the development of suitable tools. -Chapter 3 proposes a conceptual model that specifies the various enviromnental aspects that are relevant for strategic decision making, how they relate to each other, and ,defines them in a more formal way that is more suited for information systems development. -Chapter 4 is dedicated to the evaluation of the proposed model on the basis of its application to a concrete environment to evaluate its suitability to describe the current conditions and potential evolution of a real environment and get an idea of its usefulness. -Chapter 5 goes a step further by assembling a toolbox describing a set of methods that can be used to analyze the various environmental aspects put forward by the model and by providing more detailed specifications for a number of them to show how our model can be used to facilitate their implementation as software tools. -Chapter 6 describes a prototype of a strategic decision support tool that allow the analysis of some of the aspects of the environment that are not well supported by existing tools and namely to analyze the relationship between multiple actors and issues. The usefulness of this prototype is evaluated on the basis of its application to a concrete environment. -Chapter 7 finally concludes this thesis by making a summary of its various contributions and by proposing further interesting research directions

From Hype to Reality: a Case Study on the Evolution of the Swiss WISP Industry

The emerging use of WLAN technologies to provide WISP services in public locations has been a hot topic in the mobile industry as it threatened traditional mobile operator business models and their revenues. In contrast, some observers argued that this was simply an unjustified hype as WLAN would play a minor role or be integrated by mobile operators in their offerings. This paper studies the evolution of the WISP industry in Switzerland with two case studies. The first conducted in 2002 at the emergence of the industry to show how the hype surrounding WLAN resulted in the creation of a new industry attracting firms with various business models. The second conducted in 2006 after a period of consolidation to see how the industry evolved and whether the initial hype actually materialized