An integrated assessment model for cross-country pipelines

The cross-country petroleum pipelines are environmentally sensitive because they traverse through varied terrain covering crop fields, forests, rivers, populated areas, desert, hills and offshore. Any malfunction of these pipelines may cause devastating effect on the environment. Hence, the pipeline operators plan and design pipelines projects with sufficient consideration of environment and social aspects along with the technological alternatives. Traditionally, in project appraisal, optimum technical alternative is selected using financial analysis. Impact assessments (IA) are then carried out to justify the selection and subsequent statutory approval. However, the IAs often suggest alternative sites and/or alternate technology and implementation methodology, resulting in revision of entire technical and financial analysis. This study addresses the above issues by developing an integrated framework for project feasibility analysis with the application of analytic hierarchy process (AHP), a multiple attribute decision-making technique. The model considers technical analysis (TA), socioeconomic IA (SEIA) and environmental IA (EIA) in an integrated framework to select the best project from a few alternative feasible projects. Subsequent financial analysis then justifies the selection. The entire methodology has been explained here through a case application on cross-country petroleum pipeline project in India.

A survey on the adoption and usage of broadband internet

In spite of the increasing significance of broadband Internet, there are not many research papers explicitly addressing issues pertaining to its adoption and postadoption. Previous research on broadband has mainly focused on the supply side aspect at the national level, ignoring the importance of the demand side which may involve looking more deeply into the use, as well as factors impacting organizational and individual uptake. In an attempt to fill this gap, the current study empirically verifies an integrated theoretical model comprising the theory of planned behavior and the IS continuance model to examine factors influencing broadband Internet adoption and postadoption behavior of some 1,500 organizations in Singapore. Overall, strong support for the integrated model has been manifested by our results, providing insight into influential factors. At the adoption stage, perceived behavioral control has the greatest impact on behavioral intention. Our findings also suggest that, as compared to attitude, subjective norms and perceived behavioral control more significantly affect the broadband Internet adoption decision. At the postadoption stage, intention is no longer the only determinant of broadband Internet continuance; rather, initial usage was found to significantly affect broadband Internet continuance.

Examining the strategy development process through the lens of complex adaptive systems theory

The development of strategy remains a debate for academics and a concern for practitioners. Published research has focused on producing models for strategy development and on studying how strategy is developed in organisations. The Operational Research literature has highlighted the importance of considering complexity within strategic decision making; but little has been done to link strategy development with complexity theories, despite organisations and organisational environments becoming increasingly more complex. We review the dominant streams of strategy development and complexity theories. Our theoretical investigation results in the first conceptual framework which links an established Strategic Operational Research model, the Strategy Development Process model, with complexity via Complex Adaptive Systems theory. We present preliminary findings from the use of this conceptual framework applied to a longitudinal, in-depth case study, to demonstrate the advantages of using this integrated conceptual model. Our research shows that the conceptual model proposed provides rich data and allows for a more holistic examination of the strategy development process. © 2012 Operational Research Society Ltd. All rights reserved.

Electronic commerce adoption:a study of business-to-business practice in Saudi Arabia

Electronic commerce (e-commerce) has become an increasingly important initiative among organisations. The factors affecting adoption decisions have been well-documented, but there is a paucity of empirical studies that examine the adoption of e-commerce in developing economies in the Arab world. The aim of this study is to provide insights into the salient e-commerce adoption issues by focusing on Saudi Arabian businesses. Based on the Technology-Organisational-Environmental framework, an integrated research model was developed that explains the relative influence of 19 known determinants. A measurement scale was developed from prior empirical studies and revised based on feedback from the pilot study. Non-interactive adoption, interactive adoption and stabilisation of e-commerce adoption were empirically investigated using survey data collected from Saudi manufacturing and service companies. Multiple discriminant function analysis (MDFA) was used to analyse the data and research hypotheses. The analysis demonstrates that (1) regarding the non-interactive adoption of e-commerce, IT readiness, management team support, learning orientation, strategic orientation, pressure from business partner, regulatory and legal environment, technology consultants‘ participation and economic downturn are the most important factors, (2) when e-commerce interactive adoption is investigated, IT readiness, management team support, regulatory environment and technology consultants‘ participation emerge as the strongest drivers, (3) pressure from customers may not have much effect on the non-interactive adoption of e-commerce by companies, but does significantly influence the stabilisation of e-commerce use by firms, and (4) Saudi Arabia has a strong ICT infrastructure for supporting e-commerce practices. Taken together, these findings on the multi-dimensionality of e-commerce adoption show that non-interactive adoption, interactive adoption and stabilisation of e-commerce are not only different measures of e-commerce adoption, but also have different determinants. Findings from this study may be valuable for both policy and practice as it can offer a substantial understanding of the factors that enhance the widespread use of B2B e-commerce. Also, the integrated model provides a more comprehensive explanation of e-commerce adoption in organisations and could serve as a foundation for future research on information systems.

Strategic supplier performance evaluation:a case-based action research of a UK manufacturing organisation

The main aim of this research is to demonstrate strategic supplier performance evaluation of a UK-based manufacturing organisation using an integrated analytical framework. Developing long term relationship with strategic suppliers is common in today's industry. However, monitoring suppliers' performance all through the contractual period is important in order to ensure overall supply chain performance. Therefore, client organisations need to measure suppliers' performance dynamically and inform them on improvement measures. Although there are many studies introducing innovative supplier performance evaluation frameworks and empirical researches on identifying criteria for supplier evaluation, little has been reported on detailed application of strategic supplier performance evaluation and its implication on overall performance of organisation. Additionally, majority of the prior studies emphasise on lagging factors (quality, delivery schedule and value/cost) for supplier selection and evaluation. This research proposes both leading (organisational practices, risk management, environmental and social practices) and lagging factors for supplier evaluation and demonstrates a systematic method for identifying those factors with the involvement of relevant stakeholders and process mapping. The contribution of this article is a real-life case-based action research utilising an integrated analytical model that combines quality function deployment and the analytic hierarchy process method for suppliers' performance evaluation. The effectiveness of the method has been demonstrated through number of validations (e.g. focus group, business results, and statistical analysis). Additionally, the study reveals that enhanced supplier performance results positive impact on operational and business performance of client organisation.

Design concept evaluation based on rough number and information entropy theory

Concept evaluation at the early phase of product development plays a crucial role in new product development. It determines the direction of the subsequent design activities. However, the evaluation information at this stage mainly comes from experts' judgments, which is subjective and imprecise. How to manage the subjectivity to reduce the evaluation bias is a big challenge in design concept evaluation. This paper proposes a comprehensive evaluation method which combines information entropy theory and rough number. Rough number is first presented to aggregate individual judgments and priorities and to manipulate the vagueness under a group decision-making environment. A rough number based information entropy method is proposed to determine the relative weights of evaluation criteria. The composite performance values based on rough number are then calculated to rank the candidate design concepts. The results from a practical case study on the concept evaluation of an industrial robot design show that the integrated evaluation model can effectively strengthen the objectivity across the decision-making processes.

miniDVMS v1.8 : A user manual(The data visualization and modeling system)

Today, the data available to tackle many scientific challenges is vast in quantity and diverse in nature. The exploration of heterogeneous information spaces requires suitable mining algorithms as well as effective visual interfaces. miniDVMS v1.8 provides a flexible visual data mining framework which combines advanced projection algorithms developed in the machine learning domain and visual techniques developed in the information visualisation domain. The advantage of this interface is that the user is directly involved in the data mining process. Principled projection methods, such as generative topographic mapping (GTM) and hierarchical GTM (HGTM), are integrated with powerful visual techniques, such as magnification factors, directional curvatures, parallel coordinates, and user interaction facilities, to provide this integrated visual data mining framework. The software also supports conventional visualisation techniques such as principal component analysis (PCA), Neuroscale, and PhiVis. This user manual gives an overview of the purpose of the software tool, highlights some of the issues to be taken care while creating a new model, and provides information about how to install and use the tool. The user manual does not require the readers to have familiarity with the algorithms it implements. Basic computing skills are enough to operate the software.

Web based expert elicitation of uncertainties in environmental model inputs

When constructing and using environmental models, it is typical that many of the inputs to the models will not be known perfectly. In some cases, it will be possible to make observations, or occasionally physics-based uncertainty propagation, to ascertain the uncertainty on these inputs. However, such observations are often either not available or even possible, and another approach to characterising the uncertainty on the inputs must be sought. Even when observations are available, if the analysis is being carried out within a Bayesian framework then prior distributions will have to be specified. One option for gathering or at least estimating this information is to employ expert elicitation. Expert elicitation is well studied within statistics and psychology and involves the assessment of the beliefs of a group of experts about an uncertain quantity, (for example an input / parameter within a model), typically in terms of obtaining a probability distribution. One of the challenges in expert elicitation is to minimise the biases that might enter into the judgements made by the individual experts, and then to come to a consensus decision within the group of experts. Effort is made in the elicitation exercise to prevent biases clouding the judgements through well-devised questioning schemes. It is also important that, when reaching a consensus, the experts are exposed to the knowledge of the others in the group. Within the FP7 UncertWeb project (http://www.uncertweb.org/), there is a requirement to build a Webbased tool for expert elicitation. In this paper, we discuss some of the issues of building a Web-based elicitation system - both the technological aspects and the statistical and scientific issues. In particular, we demonstrate two tools: a Web-based system for the elicitation of continuous random variables and a system designed to elicit uncertainty about categorical random variables in the setting of landcover classification uncertainty. The first of these examples is a generic tool developed to elicit uncertainty about univariate continuous random variables. It is designed to be used within an application context and extends the existing SHELF method, adding a web interface and access to metadata. The tool is developed so that it can be readily integrated with environmental models exposed as web services. The second example was developed for the TREES-3 initiative which monitors tropical landcover change through ground-truthing at confluence points. It allows experts to validate the accuracy of automated landcover classifications using site-specific imagery and local knowledge. Experts may provide uncertainty information at various levels: from a general rating of their confidence in a site validation to a numerical ranking of the possible landcover types within a segment. A key challenge in the web based setting is the design of the user interface and the method of interacting between the problem owner and the problem experts. We show the workflow of the elicitation tool, and show how we can represent the final elicited distributions and confusion matrices using UncertML, ready for integration into uncertainty enabled workflows.We also show how the metadata associated with the elicitation exercise is captured and can be referenced from the elicited result, providing crucial lineage information and thus traceability in the decision making process.

Semantic and organization considerations in database conceptual modelling : the Semantic Conceptual Organizational Model (SECOM)

This thesis presents a new approach to designing large organizational databases. The approach emphasizes the need for a holistic approach to the design process. The development of the proposed approach was based on a comprehensive examination of the issues of relevance to the design and utilization of databases. Such issues include conceptual modelling, organization theory, and semantic theory. The conceptual modelling approach presented in this thesis is developed over three design stages, or model perspectives. In the semantic perspective, concept definitions were developed based on established semantic principles. Such definitions rely on meaning - provided by intension and extension - to determine intrinsic conceptual definitions. A tool, called meaning-based classification (MBC), is devised to classify concepts based on meaning. Concept classes are then integrated using concept definitions and a set of semantic relations which rely on concept content and form. In the application perspective, relationships are semantically defined according to the application environment. Relationship definitions include explicit relationship properties and constraints. The organization perspective introduces a new set of relations specifically developed to maintain conformity of conceptual abstractions with the nature of information abstractions implied by user requirements throughout the organization. Such relations are based on the stratification of work hierarchies, defined elsewhere in the thesis. Finally, an example of an application of the proposed approach is presented to illustrate the applicability and practicality of the modelling approach.

An integrated view of data quality in earth observation

Data quality is a difficult notion to define precisely, and different communities have different views and understandings of the subject. This causes confusion, a lack of harmonization of data across communities and omission of vital quality information. For some existing data infrastructures, data quality standards cannot address the problem adequately and cannot full all user needs or cover all concepts of data quality. In this paper we discuss some philosophical issues on data quality. We identify actual user needs on data quality, review existing standards and specification on data quality, and propose an integrated model for data quality in the eld of Earth observation. We also propose a practical mechanism for applying the integrated quality information model to large number of datasets through metadata inheritance. While our data quality management approach is in the domain of Earth observation, we believe the ideas and methodologies for data quality management can be applied to wider domains and disciplines to facilitate quality-enabled scientific research.

A four-factor user interaction model for content-based image retrieval

In order to bridge the “Semantic gap”, a number of relevance feedback (RF) mechanisms have been applied to content-based image retrieval (CBIR). However current RF techniques in most existing CBIR systems still lack satisfactory user interaction although some work has been done to improve the interaction as well as the search accuracy. In this paper, we propose a four-factor user interaction model and investigate its effects on CBIR by an empirical evaluation. Whilst the model was developed for our research purposes, we believe the model could be adapted to any content-based search system.

Nootropia : a user profiling model based on a self-organising term network

Artificial Immune Systems are well suited to the problem of using a profile representation of an individual’s or a group’s interests to evaluate documents. Nootropia is a user profiling model that exhibits similarities to models of the immune system that have been developed in the context of autopoietic theory. It uses a self-organising term network that can represent a user’s multiple interests and can adapt to both short-term variations and substantial changes in them. This allows Nootropia to drift, constantly following changes in the user’s multiple interests, and, thus, to become structurally coupled to the user.

A dynamic feature information model for integrated manufacturing planning and optimization

This paper presents a new, dynamic feature representation method for high value parts consisting of complex and intersecting features. The method first extracts features from the CAD model of a complex part. Then the dynamic status of each feature is established between various operations to be carried out during the whole manufacturing process. Each manufacturing and verification operation can be planned and optimized using the real conditions of a feature, thus enhancing accuracy, traceability and process control. The dynamic feature representation is complementary to the design models used as underlining basis in current CAD/CAM and decision support systems. © 2012 CIRP.

Integrating service user and practitioner expertise within a web-based system for collaborative mental-health risk and safety management

Objectives: To develop a decision support system (DSS), myGRaCE, that integrates service user (SU) and practitioner expertise about mental health and associated risks of suicide, self-harm, harm to others, self-neglect, and vulnerability. The intention is to help SUs assess and manage their own mental health collaboratively with practitioners. Methods: An iterative process involving interviews, focus groups, and agile software development with 115 SUs, to elicit and implement myGRaCE requirements. Results: Findings highlight shared understanding of mental health risk between SUs and practitioners that can be integrated within a single model. However, important differences were revealed in SUs' preferred process of assessing risks and safety, which are reflected in the distinctive interface, navigation, tool functionality and language developed for myGRaCE. A challenge was how to provide flexible access without overwhelming and confusing users. Conclusion: The methods show that practitioner expertise can be reformulated in a format that simultaneously captures SU expertise, to provide a tool highly valued by SUs. A stepped process adds necessary structure to the assessment, each step with its own feedback and guidance. Practice Implications: The GRiST web-based DSS (www.egrist.org) links and integrates myGRaCE self-assessments with GRiST practitioner assessments for supporting collaborative and self-managed healthcare.