NEW ARTIFACTS FOR THE KNOWLEDGE DISCOVERY VIA DATA ANALYTICS (KDDA) PROCESS

Recently, the interest in the business application of analytics and data science has increased significantly. The popularity of data analytics and data science comes from the clear articulation of business problem solving as an end goal. To address limitations in existing literature, this dissertation provides four novel design artifacts for Knowledge Discovery via Data Analytics (KDDA). The first artifact is a Snail Shell KDDA process model that extends existing knowledge discovery process models, but addresses many existing limitations. At the top level, the KDDA Process model highlights the iterative nature of KDDA projects and adds two new phases, namely Problem Formulation and Maintenance. At the second level, generic tasks of the KDDA process model are presented in a comparative manner, highlighting the differences between the new KDDA process model and the traditional knowledge discovery process models. Two case studies are used to demonstrate how to use KDDA process model to guide real world KDDA projects. The second artifact, a methodology for theory building based on quantitative data is a novel application of KDDA process model. The methodology is evaluated using a theory building case from the public health domain. It is not only an instantiation of the Snail Shell KDDA process model, but also makes theoretical contributions to theory building. It demonstrates how analytical techniques can be used as quantitative gauges to assess important construct relationships during the formative phase of theory building. The third artifact is a data mining ontology, the DM3 ontology, to bridge the semantic gap between business users and KDDA expert and facilitate analytical model maintenance and reuse. The DM3 ontology is evaluated using both criteria-based approach and task-based approach. The fourth artifact is a decision support framework for MCDA software selection. The framework enables users choose relevant MCDA software based on a specific decision making situation (DMS). A DMS modeling framework is developed to structure the DMS based on the decision problem and the users\u27 decision preferences and. The framework is implemented into a decision support system and evaluated using application examples from the real-estate domain

Lighting in multi-user office environments:improving employee wellbeing through personal control

Personal control over lighting in offices improves satisfaction of user

Adaptive algorithms for real-world transactional data mining.

The accurate identification of the right customer to target with the right product at the right time, through the right channel, to satisfy the customer’s evolving needs, is a key performance driver and enhancer for businesses. Data mining is an analytic process designed to explore usually large amounts of data (typically business or market related) in search of consistent patterns and/or systematic relationships between variables for the purpose of generating explanatory/predictive data models from the detected patterns. It provides an effective and established mechanism for accurate identification and classification of customers. Data models derived from the data mining process can aid in effectively recognizing the status and preference of customers - individually and as a group. Such data models can be incorporated into the business market segmentation, customer targeting and channelling decisions with the goal of maximizing the total customer lifetime profit. However, due to costs, privacy and/or data protection reasons, the customer data available for data mining is often restricted to verified and validated data,(in most cases,only the business owned transactional data is available). Transactional data is a valuable resource for generating such data models. Transactional data can be electronically collected and readily made available for data mining in large quantity at minimum extra cost. Transactional data is however, inherently sparse and skewed. These inherent characteristics of transactional data give rise to the poor performance of data models built using customer data based on transactional data. Data models for identifying, describing, and classifying customers, constructed using evolving transactional data thus need to effectively handle the inherent sparseness and skewness of evolving transactional data in order to be efficient and accurate. Using real-world transactional data, this thesis presents the findings and results from the investigation of data mining algorithms for analysing, describing, identifying and classifying customers with evolving needs. In particular, methods for handling the issues of scalability, uncertainty and adaptation whilst mining evolving transactional data are analysed and presented. A novel application of a new framework for integrating transactional data binning and classification techniques is presented alongside an effective prototype selection algorithm for efficient transactional data model building. A new change mining architecture for monitoring, detecting and visualizing the change in customer behaviour using transactional data is proposed and discussed as an effective means for analysing and understanding the change in customer buying behaviour over time. Finally, the challenging problem of discerning between the change in the customer profile (which may necessitate the effective change of the customer’s label) and the change in performance of the model(s) (which may necessitate changing or adapting the model(s)) is introduced and discussed by way of a novel flexible and efficient architecture for classifier model adaptation and customer profiles class relabeling

Benefits of the application of web-mining methods and techniques for the field of analytical customer relationship management of the marketing function in a knowledge management perspective

Le Web Mining (WM) reste une technologie relativement méconnue. Toutefois, si elle est utilisée adéquatement, elle s'avère être d'une grande utilité pour l'identification des profils et des comportements des clients prospects et existants, dans un contexte internet. Les avancées techniques du WM améliorent grandement le volet analytique de la Gestion de la Relation Client (GRC). Cette étude suit une approche exploratoire afin de déterminer si le WM atteint, à lui seul, tous les objectifs fondamentaux de la GRC, ou le cas échéant, devrait être utilisé de manière conjointe avec la recherche marketing traditionnelle et les méthodes classiques de la GRC analytique (GRCa) pour optimiser la GRC, et de fait le marketing, dans un contexte internet. La connaissance obtenue par le WM peut ensuite être administrée au sein de l'organisation dans un cadre de Gestion de la Connaissance (GC), afin d'optimiser les relations avec les clients nouveaux et/ou existants, améliorer leur expérience client et ultimement, leur fournir de la meilleure valeur. Dans un cadre de recherche exploratoire, des entrevues semi-structurés et en profondeur furent menées afin d'obtenir le point de vue de plusieurs experts en (web) data rnining. L'étude révéla que le WM est bien approprié pour segmenter les clients prospects et existants, pour comprendre les comportements transactionnels en ligne des clients existants et prospects, ainsi que pour déterminer le statut de loyauté (ou de défection) des clients existants. Il constitue, à ce titre, un outil d'une redoutable efficacité prédictive par le biais de la classification et de l'estimation, mais aussi descriptive par le biais de la segmentation et de l'association. En revanche, le WM est moins performant dans la compréhension des dimensions sous-jacentes, moins évidentes du comportement client. L'utilisation du WM est moins appropriée pour remplir des objectifs liés à la description de la manière dont les clients existants ou prospects développent loyauté, satisfaction, défection ou attachement envers une enseigne sur internet. Cet exercice est d'autant plus difficile que la communication multicanale dans laquelle évoluent les consommateurs a une forte influence sur les relations qu'ils développent avec une marque. Ainsi le comportement en ligne ne serait qu'une transposition ou tout du moins une extension du comportement du consommateur lorsqu'il n'est pas en ligne. Le WM est également un outil relativement incomplet pour identifier le développement de la défection vers et depuis les concurrents ainsi que le développement de la loyauté envers ces derniers. Le WM nécessite toujours d'être complété par la recherche marketing traditionnelle afin d'atteindre ces objectives plus difficiles mais essentiels de la GRCa. Finalement, les conclusions de cette recherche sont principalement dirigées à l'encontre des firmes et des gestionnaires plus que du côté des clients-internautes, car ces premiers plus que ces derniers possèdent les ressources et les processus pour mettre en œuvre les projets de recherche en WM décrits.\ud ______________________________________________________________________________ \ud MOTS-CLÉS DE L’AUTEUR : Web mining, Gestion de la connaissance, Gestion de la relation client, Données internet, Comportement du consommateur, Forage de données, Connaissance du consommateu

Model of eco-socially conscious consumer behaviour related to choice and use of personal cars: evidence from an emerging economy

This thesis focuses on developing a model of eco-socially conscious consumer behaviour related to choice and use of personal cars. It presents empirical evidence relating to the factors that must be considered when promoting environmentally friendly cars (noted as alternative fuel vehicles (AFVs) throughout the thesis), especially in an emerging economy such as Pakistan. The rationale and motivation behind this project is that there is an increasing rate of environmental problems such as air pollution and CO₂ in emerging economies and relatively lower competence in developing strategies aimed at improving climate change resilience. Together with changing the climate, anti-environmental anthropogenic activities make it more difficult for affected communities to prosper. To curb these environmental problems, studies reported in the academic literature have suggested taking measures to reduce the impact of human activities on the environment and regulating consumption of environmentally harmful products. In response to these emerging demands, marketers have invested heavily, regarding both product development and promotion of pro-environmental behaviours, in various domains of commercial interest. One such area is the use of personal cars, a sector that is proliferating and, given that CO₂ emissions from cars are one of the most significant sources of environmental problems (particularly global warming), there is a need to promote alternative fuel vehicles (AFVs) and eco-social behaviours in the use of personal cars. This thesis reports on two major studies to answer three underlying research questions. The first study focuses on two research questions. The first research question, RQ1, explores how automobile industry consumers (those in the personal cars segment) define eco-socially conscious behaviour (ESCCB) related to the choice and use of personal cars in Pakistan. The second research question, RQ2, attempts to identify the profiles of different customer segments based ESCCB defined in RQ1. The second study is focused on the theoretical explanation of factors that are suggested in the literature to affect ESCCB related to the choice and use of personal cars. The Theory of Planned Behaviour (TPB) and Value-Beliefs-Norms Theory (VBN) have been converged to provide a holistic explanation of ESCCB. Based on scientific methodologies recommended for new scale development, the results reported in this thesis suggest that ESCCB related to choice and use of personal cars is a latent construct manifested in three underlying dimensions: eco-social use, eco-social purchase and eco-social conservation. A market segmentation approach using cluster and discriminant analysis suggests that three consumer segments exist in the Pakistani automobile market based on response towards eco-social behaviour and inclination towards choosing AFVs. The first segment, the conservatives, are not concerned about the environmental issues, prefer conventional cars, and are least sensitive to the eco-social use of personal cars. The second segment, the indifferents, are unsure whether they should buy AFVs and whether this will positively affect the environment. The third segment, and the largest one (51%), the enthusiasts, are highly inclined towards purchasing AFVs and eco-social use of personal cars to reduce the impact of the use of personal cars on the environment. The findings of Study 1 hold significant implications for marketing practitioners and policymakers. Some conceptual and methodological limitations are highlighted. The results of Study 2 suggest that the Theory of Planned Behaviour (TPB), Value- Belief-Norm (VBN) Theory and the integrated model, were all found to be very strong in explaining not only ESCCB intentions but also actual behaviour, related to purchase of environmentally friendly cars and conservation of fuel. Results showed that the integrated model based on TPB and VBN was stronger in predicting ESCCB-conservation (49.7 per cent variance) than TPB (46.7 per cent variance) and VBN (26.7 per cent variance). A similar pattern of results was evident for ESCCB-purchases (integrated model: 14.8 per cent variance, TPB: 12.5 per cent variance, VBN: 10.8 per cent variance). However, the predictive power of the three models for actual eco-socially conscious consumer behaviour (ESCCB) had slightly different results. TPB was found stronger to predict actual ESCCB (33.4 per cent variance), followed by the integrated model based on TPB and VBN (31.9 per cent variance) and VBN (15.7 per cent variance). This study contributes to both theoretical and practical aspects linked with ecosocially conscious consumer behaviour related to choice and use of personal cars. These contributions extend the theoretical literature related to eco-social behaviours and provides policy measures for marketing practitioners and public policy makers. The study findings not only provide guidelines for automobile related behaviours but can also be generalised in other areas