This paper presents findings from the analysis of data contained in reports and documents maintained by an MIS department of a major pharmaceutical and nutritional manufacturer. To guide data collection, the paper develops a conceptual framework consisting of a descriptive scheme and an explanation model. The descriptive scheme classifies maintenance activities into four categories: adaptive, corrective, perfective, and ongoing support. The explanation model identifies meta‐factors that affect maintenance activity. The meta‐factors are age, size, programming language, processing environment, structured programming, modularization, analysis and design methodologies, end‐user involvement, documentation generation, and maintenance management. The paper analyses three business functions with high, middle, and low average time per repair maintenance project (ATM) measured in person‐hours over a period of one year. Each function has several systems, and each system consists of modules. The analysis of available data for all functions maintained shows that maintenance (both repair and update) consumed 49% of data processing (DP) resources. For the three functions, the majority of these resources are devoted to perfective maintenance and corrective maintenance. Guided by the explanation model, several variables belonging to the meta‐factors are identified. The following variables have the most significant influence over maintenance: real‐time processing, database processing, end‐user ongoing support, module size, number of runs and runtime per module, and number of reports and number of copies per function.
This paper presents an approach that aims at personalizing Web services composition and provisioning using context. Composition addresses the situation of a user's request that cannot be satisfied by any available service, and thus requires the combination of several Web services. Provisioning focuses on the deployment of Web services according to users' preferences. A Web service is an accessible application that other applications and humans can discover and trigger. Context is the information that characterizes the interactions between humans, applications, and the surrounding environment. Web services are subject to personalization if there is a need of accommodating users' preferences during service performance and outcome delivery. To be able to track personalization in terms of what happened, what is happening, and what might happen three types of context are devised, and they are referred to as user-, Web service-, and resource-context.
In tourism, ICT provides new channels anywhere/anytime for tourism services that impacted how customers access and consume those services, hence the emergence of the concept of e-tourism. Internet can be used to attract customers, communicate with them, customize their required services, access international markets, and provide all types of touristic information through e-tourism platforms. The salient feature of this research is to identify the key technological factors that influence customers' acceptance and use of the services provided through e-tourism platforms. Other behavioral aspects related to the use of these services are treated as exogenous factors. The study constructed an e-tourism technology acceptance model (ETAM) concentrating only on technology-related factors. To assist in understanding how users will reflect in these four technological factors affecting the use of e-services, the model recognizes two moderating influential factors: trust and attitude. The model is augmented with exogenous factors as intervening factors related to customer's background.
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