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Sensemaking is often associated with processing large or complex amount of data obtained from diverse and distributed sources. Sensemaking enables leaders to have a better grasp of what the data represents and what insights they can get from it. Thus, sensemaking is considered extremely important in mature markets where the competition is fierce. To-date, the research base on sensemaking has not moved far from the conceptual realm, however. In response, this research provides a conceptual framework that explains the core processes of sensemaking - noticing, interpretation and action - and examines how emerging technologies such as Social Network Analysis (SNA) and Machine Learning (ML) techniques help to enhance the human sensemaking process in generating valuable insights during data analysis. Design Science Research (DSR) is adopted as a research methodology in the context of financial transactional data analysis, aiming to make sense of the data while exploring conceptions of customer value for a mainstream commercial bank alongside the perceived need for banking products. Three analytical models are introduced, examining Connected Customer Lifetime Value (CCLV), Network Relationship Equity (NRE) and product purchasing frequency based on customer 'personas'. The former models employ SNA techniques in providing novelty, the latter combines the outcomes of SNA with ML clustering algorithms to provide a base on which product holdings and purchase frequency analysis are overlaid - providing a novel form of recommendation. Ongoing evaluation of the developed models is used to explore the nuances of the sensemaking process and the ability of such models to support that process (in the given domain).

Web-based service providers have long been required to deliver high quality services in accordance with standards and customer requirements. Increasingly, however, providers are required to think beyond service quality and develop a deeper understanding of their customers’ Quality of Experience (QoE). Whilst models exist that assess the QoE of Web Application, significant challenges remain in defining QoE factors from a Web engineering perspective, as well as mapping between so called ‘objective’ and ‘subjective’ factors of relevance. Specifically, the following challenges are considered as general fundamental problems for assessing QoE: (1) Quantifying the relationship between QoE factors; (2) predicting QoE as well as dealing with the limited data available in relation to subjective factors; (3) optimising and controlling QoE; and (4) perceiving QoE. In response, this research presents a novel model, called QoEWA (and associated software instantiation) that integrates factors through Key Performance Indicators (KPIs) and Key Quality Indicators (KQIs). The mapping is incorporated into a correlation model that assesses QoE, in particular, that of Web Application, with a consideration of defining the factors in terms of quality requirements derived from web architecture. The data resulting from the mapping is used as input for the proposed model to develop artefacts that: quantify, predict, optimise and perceive QoE. The development of QoEWA is framed and guided by Design Science Research (DSR) approach, with the purpose of enabling providers to make more informed decisions regarding QoE and/or to optimise resources accordingly. The evaluation of the designed artefacts is based on a build-and-evaluate cycle that provides feedback and a better understanding of the utilised solutions. The key artefacts are developed and evaluated through four iterations: Iteration 1 utilises the Actual Versus-Target approach to quantify QoE, and applies statistical analysis to evaluate the outputs. Iteration 2: utilises a Machine Learning (ML) approach to predict QoE, and applies statistical tests to compare the performance of ML algorithms. Iteration 3 utilises the Multi-Objective Optimisation (MOO) approach to optimise QoE and control the balance between resources and user experience. Iteration 4 utilises the Agent-Based Modelling approach to perceive and gain insights into QoE. The design of iteration 4 is rigorously tested using verified and validated models.

As organizations strive to remain competitive despite economic pressures and the rapid pace of technology innovation, Enterprise Resource Planning (ERP) systems provide businesses with the functional best practices and customisable capabilities they need to adapt to these changes. However, the high rate of failure of these Enterprise Resource Planning (ERP) systems remain a challenge for the managers who are usually tasked with ensuring the huge investments achieve its purpose and yield a return on investment. In attempting to explain the reasons behind these Enterprise Resource Planning (ERP) implementation failures, previous research has identified user adoption as one of the main drivers of ERP implementation success. An abundance of technology adoption theories and change management models have been proposed as solutions to user adoption, by measuring different factors they consider are important to the adoption of these systems. However, ERP systems still fail to achieve their intended purpose because these theories have not been able to explicitly explain how these factors impact on adoption. To address this gap, this research takes a comprehensive approach to identifying the change management influences on user adoption by integrating change management and technology adoption constructs. The main contribution of this research is a common-sense perspective of the realities of user resistance. The study provides a causal model derived from Structural equation modelling, which explains the multiple influence relationships between the measured constructs. An adoption tool is also developed for managers to use as a yardstick to benchmark the effectiveness of an implementation strategy. Using a structural equation modelling approach, a theory was developed from a survey of 616 ERP users across 6 organizations. Six constructs were identified as the key influences of user adoption – Trust, Communication and Engagement, System Qualities, Training, Organizational Benefits and Resistance. The theory explains the multiple influence relationships between these constructs and Adoption, and assists in the pinpointing of failure points that need to be addressed by Enterprise Resource Planning project managers. The resulting theory developed indicates the multiple influence relationships between these constructs as determinants of Adoption of ERP systems. In addition, the theory assists in the pinpointing of failure points that need to be addressed by Enterprise Resource Planning (ERP) project managers.

An important issue for healthcare service providers is to achieve high levels of patient satisfaction. Collecting patient feedback about their experience in hospital enables providers to analyse their performance in terms of the levels of satisfaction and to identify the strengths and limitations of their service delivery. A common method of collecting patient feedback is via online portals and the forums of the service provider, where the patients can rate and comment about the service received. A challenge in analysing patient experience collected via online portals is that the amount of data can be huge and hence, prohibitive to analyse manually. In this thesis, an automated approach to patient experience analysis via Sentiment Analysis, Topic Modelling, and Dependency Parsing methods is presented. The patient experience data collected from the National Health Service (NHS) online portal in the United Kingdom is analysed in the study to understand this experience. The study was carried out in three iterations: (1) In the first, the Sentiment Analysis method was applied, which identified whether a given patient feedback item was positive or negative. (2) The second iteration involved applying Topic Modelling methods to identify automatically themes and topics from the patient feedback. Further, the outcomes of the Sentiment Analysis study from the first iteration were utilised to identify the patient sentiment regarding the topic being discussed in a given comment. (3) In the third iteration of the study, Dependency Parsing methods were employed for each patient feedback item and the topics identified. A method was devised to summarise the reason for a particular sentiment about each of the identified topics. The outcomes of the study demonstrate that text-mining methods can be effectively utilised to identify patients’ sentiment in their feedback as well as to identify the themes and topics discussed in it. The approach presented in the study was proven capable of effectively automatically analysing the NHS patient feedback database. Specifically, it can provide an overview of the positive and negative sentiment rate, identify the frequently discussed topics and summarise individual patient feedback items. Moreover, an API visualisation tool is introduced to make the outcomes more accessible to the health care providers.

The disruptive nature of innovations and technological advancements present potentially huge benefits, however, it is critical to take caution because they also come with challenges. This author holds fast to the school of thought which suggests that every organisation or society should properly evaluate innovations and their attendant challenges from a strategic perspective, before adopting them, or else could get blindsided by the after effects. Big Data is one of such innovations, currently trending within industry and academia. The instinctive nature of Organizations compels them to constantly find new ways to stay ahead of the competition. It is for this reason, that some incoherencies exist in the field of big data. While on the one hand, we have some Organizations rushing into implementing Big Data Projects, we also have in possibly equal measure, many other organisations that remain sceptical and uncertain of the benefits of "Big Data" in general and are also concerned with the implementation costs. What this has done is, create a huge focus on the area of Big Data Implementation. Literature reveals a good number of challenges around Big Data project implementations. For example, most Big Data projects are either abandoned or do not hit their expected target. Unfortunately, most IS literature has focused on implementation methodologies that are primarily focused on the data, resources, Big Data infrastructures, algorithms etc. Rather than leaving the incoherent space that exists to remain, this research seeks to collapse the space and open opportunities to harness and expand knowledge. Consequently, the research takes a slightly different standpoint by approaching Big Data implementation from a Strategic Perspective. The author emphasises the fact that focus should be shifted from going straight into implementing Big Data projects to first implementing a Big Data Strategy for the Organization. Before implementation, this strategy step will create the value proposition and identify deliverables to justify the project. To this end, the researcher combines an Alignment theory, with Digital Business Strategy theory to create a Big Data Strategy Framework that Organisations could use to align their business strategy with the Big Data project. The Framework was tested in two case studies, and the study resulted in the generation of the strategic Big Data Goals for both case studies. This Big Data Strategy framework aided the organisation in identifying the potential value that could be obtained from their Big Data project. These Strategic Big Data Goals can now be implemented in Big data Projects.

This study presents a data-driven simulation framework in order to understand customer behaviour and therefore improve customer retention. The overarching system design methodology used for this study is aligned with the design science paradigm. The Social Media Domain Analysis (SoMeDoA) approach is adopted and evaluated to build a model on the determinants of customer satisfaction in the mobile services industry. Furthermore, the most popular machine learning algorithms for analysing customer churn are applied to analyse customer retention based on the derived determinants. Finally, a data-driven approach for agent-based modelling is proposed to investigate the social effect of customer retention. The key contribution of this study is the customer agent decision trees (CADET) approach and a data-driven approach for Agent-Based Modelling (ABM). The CADET approach is applied to a dataset provided by a UK mobile services company. One of the major findings of using the CADET approach to investigate customer retention is that social influence, specifically word of mouth has an impact on customer retention. The second contribution of this study is the method used to uncover customer satisfaction determinants. The SoMeDoA framework was applied to uncover determinants of customer satisfaction in the mobile services industry. Customer service, coverage quality and price are found to be key determinants of customer satisfaction in the mobile services industry. The third contribution of this study is the approach used to build customer churn prediction models. The most popular machine learning techniques are used to build customer churn prediction models based on identified customer satisfaction determinants. Overall, for the identified determinants, decision trees have the highest accuracy scores for building customer churn prediction models.

This study aims to investigate the naturalistic decision-making role of business intelligence (BI) in the oil and gas (O&G) industry. O&G organisations spend a lot of time, effort and resources in maximising their operations to gain a competitive advantage. With the introduction of technological solutions, BI provides organisations with the ability to collect, sort, analyse and transform data into timely intelligence. However, the industry is characteristically competitive, difficult to predict and continuously changing with decision-makers sometimes faced with naturalistic decisions necessitating quick decisions under pressure, strict timeframes and with incomplete data. Literature on the role BI in the O&G industry has been minimal, with the focus being on how BI is used to assist rational decision-making. This study relies on data collected from two O&G organisations operating in different streams of the industry. Using a dynamic model of situated cognition, this study employs an interpretive, qualitative approach to data analysis in an attempt to fill the gap in the literature and determine whether BI plays any role in facilitating the decision-making process in response to naturalistic decisions. A dynamic model of situated cognition has been employed because of its strong correlation with naturalistic decision-making (NDM). The findings of this study indicate that different naturalistic decisions exist in the two streams of the industry and these decisions vary in their levels of complexity and domains. Furthermore, the findings show that while BI plays a major role NDM, this role is mitigated by the cognitive capabilities of individual decision-makers and their areas of expertise.

Understanding and managing change is a strategic objective for many organisations to successfully compete in a market place; as a result, organisations are leveraging their data asset and implementing data warehouses to gain business intelligence necessary to improve their businesses. Data warehouses are expensive initiatives, one-half to two-thirds of most data warehousing efforts end in failure. In the absence of well-formalised design methodology in the industry and in the context of the debate on data architecture in data warehousing, this thesis examines why multidimensional and relational data models define the data architecture landscape in the industry. The study develops a number of propositions from the literature and empirical data to understand the factors impacting the choice of logical data model in data warehousing. Using a comparative case study method as the mean of collecting empirical data from the case organisations, the research proposes a conceptual model for logical data model adoption. The model provides a framework that guides decision making for adopting a logical data model for a data warehouse. The research conceptual model identifies the characteristics of business requirements and decision pathways for multidimensional and relational data warehouses. The conceptual model adds value by identifying the business requirements which a multidimensional and relational logical data model is empirically applicable.

Supplier networks today are seeing a complete redirection in their purpose from a decade ago. Supplier networks focused originally on transaction-oriented exchanges for sending purchase orders electronically. However, based on the current increased need to understand business risks, supplier networks are demonstrating a clear shift in emphasis from establishing “transaction-based focus” relationships towards the evolution of network platforms. The Aberdeen Group (2011) demonstrates that 76 per cent of supplier networks increasingly are being used to identify new suppliers and market opportunities. Moreover, with social-networking features similar to Twitter, LinkedIn and Facebook (which are very recent phenomena), supplier networks have become more important in their role of spending management based on the ability to help organisations identify new suppliers while sharing information with other buyer organizations. Therefore, analysing data from supplier networks today has become a necessary strategy for optimizing transaction-focused procurement, in addition to improving supplier relationships. With this in mind, the Social Media Domain Analysis (SoMeDoA) framework has been developed to facilitate the decision-making process for selecting flexible suppliers within the e-procurement-based marketplace and apply it to a real set of data gathered from two social-networking sites (Twitter and LinkedIn). The research contributes a rigorous method that analyses effectively domain concepts and relations between notions from social networks and builds the domain ontology. The effectiveness of the framework, in analysing domain and relations, is evaluated by its application to varying datasets gathered from social networks, including the pharmaceutical domain. This model extrapolates findings from stages in the research and marries elements from various papers and frameworks therein, in order to produce a guideline model for organisations seeking a suitable supplier with whom to work. The results of the evaluation are encouraging, and provide concrete outcomes in an area that is little researched.

Academic libraries operate in a fluid environment, where they must provide, and demonstrate that they provide, a high quality service that is focussed on customers’ needs. It is broadly accepted that the way to provide a high quality service responsive to customers’ needs is to have a culture of quality that underpins all the organisation’s efforts, i.e. TQM. The literature on how to improve the service quality of libraries in particular, and organisations in general, is extensive and varied. But it is not informative to practitioners who wish to know what to do to improve the quality culture of their library. The literature provides many examples of what a high quality organisation looks like, and, by inference, what a low quality organisation looks like. However, anyone who has worked in an organisation knows that quality culture is not binary but is instead a developmental process. This disconnection between the published research and known practice has led libraries to avoid attempts to measure, and therefore improve, their culture of quality. The purpose of this research is to facilitate engagement by directors of academic libraries with issues of quality culture. This is achieved by producing a new representation of the concept of quality culture, the Quality Maturity Model. The QMM enables library directors to assess their location on a roadmap to a culture of quality, guides them as to the next step forwards, enables them to measure their progress over time, and enables them to compare themselves to others and so learn from best practice. The characteristics of the research problem suggest the use of Design Science Research as the most appropriate research paradigm. This is a novel paradigm for library and information science research; one that has the potential to bridge the research-practice gap prevalent in this field. Design Science is iterative, creative and evaluative in the process of devising useful artefacts to attain specified goals. This research applies the Design Science Research Methodology (Peffers et al., 2008) as a framework and uses interpretive synthesis and grounded theory methods to create the Quality Maturity Model consistent with both theory and practice. Practice was identified via interviews with a cross-section of staff at ten academic library and information services in the UK. The QMM delineates 40 elements of quality culture, grouped into eight facets: Management of the organisation; environmental sensing; learning organisation attributes; attitude to change; attitude to quality; leadership; investment in staff; and alignment. The QMM has five maturity levels describing the progression from low quality maturity to high quality maturity for each of the elements. As a companion to the QMM, this research applied standard survey design methods to develop the Quality Culture Assessment Instrument. The QCAI enables library directors to self-assess the location of their library on the QMM using feedback from their library staff. The QMM rubric then enables library directors to identify what the next level of maturity looks like for each element. The evaluation of these artefacts demonstrates that they fulfil the aims of this research: changed the representation of quality culture and so promote engagement with such issues by academic library directors.

Enterprise Resource Planning Systems (ERP) are very large and complex software packages that run every aspect of an organization. Increasingly, ERP systems are used in higher education to teach business processes, essential knowledge for students competing in today’s business environment. Past research attempting to measure learning business processes with ERP has been inconclusive and lacking in rigor. This dissertation contains a comprehensive research study that uses a critical realist approach to measure business process learning from experiential ERP. Using a business simulation game as a proxy for understanding business processes, students from (1) a US undergraduate program in three separate classes, one using ERP experientially, and (2) two UK postgraduate programs, one experiencing ERP and one not, are assessed both quantitatively and qualitatively. The data analysis results in a causal mechanism for learning, complemented by a list that trigger or suppress that mechanism in particular cases. The results validate the efforts of those using ERP in the classroom, and reaffirm other educational business school endeavors, with educational implications as follows. First, before attempting to learn business processes, students must know about business in order to enable them to learn this complex topic. Second, experiencing ERP systems indeed helps students understand business processes, with a cohesive curriculum integrating ERP benefitting students the most and students at the postgraduate level learning more deeply. Third, students are using the knowledge gained in university classes to make business decisions. Fourth, students should be encouraged to use all information possible for making business decisions instead of relying on their personal understanding of today’s current market, relying on their own business intuition or work experience. Last, teaching methods may need to be adjusted for postgraduates, especially those coming into programs with significant work experience.

This thesis focuses on investigating inter-departmental Electronic Information Sharing (EIS) through Inter-organisational Systems (IOS) in the context of Local Government Authorities (LGAs) in England and Wales. There is an increasing demand to involve LGA departments in sharing information electronically in order to reduce the cost of storing and managing data, increase the level of data accuracy and timeliness and improve the accountability of the authority. During the last decades, several LGAs in the United Kingdom have started to employ IOSs to support information sharing and networked collaboration within their departments in order to meet a diverse range of citizen needs including housing services, social care services and education services. However, reaching a great level of crossdepartmental collaboration is not easy and requires additional time and effort. Normative literature proposed various models and frameworks that examine various issues and factors influencing the effort of EIS in the private and public domain. However, the applicability and validity of those models in the context of LGAs is arguable. Therefore, this research proposes and validates a novel conceptual framework that can be used as a tool for decision-making while sharing information electronically. The framework consists of four main levels: (a) investigation and presentation of factors influencing Electronic Information Sharing in LGAs based on external environment, organisational capacity, technology environment, EIS characteristics and inter-departmental environment, (b) investigation and presentation of the phases that departments adopt while participating in the EIS effort, (c) mapping of the influential factors onto the participation phases and (d) prioritisation of the factors influencing EIS in LGAs in relation to different phases. By validating the conceptual framework through using a qualitative, interpretive, multiple case study research strategy, this thesis attempts to contribute to the theoretical, methodological and practical aspects of inter-departmental EIS. Despite the results of the cases cannot be generalised, yetthey can allow others to relate their views with the onesreported in this thesis.