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In recent years, researchers have shown an increased interest in monitoring and quantifying social influence in practical decision-making processes. Many of them focus on influence identification and maximisation in social networks with much reliance on social networking data. However, the common features in decision making, that is, the presence of uncertainties and multiple criteria, have not been considered in previous studies when investigating socially influenced decision making. Given the lack of a framework that jointly captures social influence in decision making, uncertainty and multiple criteria, this thesis tackles this gap in a two-stage process. Firstly, a belief-based model is proposed to quantitatively characterise the effects of social influence when individuals make decisions under uncertainty. Belief functions are used in order to represent individuals' uncertainties toward the decision. Meanwhile, social influence is regarded as a source of decision information with uncertainty; hence, the decision-making process is characterised as a series of evidential reasoning by individuals after they receive any influence from social neighbours. By doing so, the evolving process of individuals' decisions under uncertainty in the context of influence spreading in social networks is captured in a quantitative way. Secondly, a model that considers the effects of social influence on individuals' decisions with multiple criteria is proposed. Specifically, this thesis investigates how social influence can be characterised in the context of multiple criteria decision analysis (MCDA), where voluntary vaccination decision making is selected as a case study. An integrated model is proposed, in which an individual's subjective judgements on the decision criteria of vaccine uptake are formulated in the framework of MCDA, while the spread of social influence is further incorporated in the decision-making process by using the evidential reasoning approach. Finally, this thesis further explores the possibility of considering social influence from a macro/global perspective in the specific context of vaccination in England; specifically, the interplay between internal migration and overall vaccination behaviours. As social networks play a central role in information dissemination and opinion formation, any change in local communities is likely to cause variations in group behaviours. Thus, considering that migration can explicitly lead to changes in population structure, and therefore the social networks of multiple individuals, a regression analysis is performed in order to identify the level of association that migration can have in the resulting vaccination coverage rate of different local authorities across England. The result indicates a significant association of internal migration with vaccination behaviours. The contributions of this thesis to existing studies are: 1) it takes into account the characteristics of the decision-making environments, including uncertainties and multiple criteria associated with decision problems when analysing the effects of social influence on decision making; 2) it provides new insights into socially influenced vaccination decision by formulating individuals' decisions in the context of MCDA framework and incorporating social influence into the framework; 3) it analyses socially influenced vaccination decision from macro/global behaviours and identifies the association between internal migration and vaccination behaviours. In the future work, this thesis can be linked with epidemic models in order to characterise the interplay between disease spreading and socially influenced vaccination behaviours in consideration that vaccination impedes disease transmission while disease severity is a significant criterion for vaccination decisions. Also, in terms of the proposed belief-based models, different evidence combination rules can be considered to explore their efficacy in characterising the uncertainties existing in the evaluation of both decision criteria and social influence.

This thesis focuses on combining data envelopment analysis (DEA) and multi-objective linear programming (MOLP) methods to set targets by referencing peers' performances and decision-makers' (DMs) preferences. A large number of past papers have proven the importance of a company having a target; however, obtaining a feasible but challenging target has always been a difficult topic for companies. Since DEA was proposed in 1978, it has become one of the most popular performance assessment tools. The performance possibility set and efficient frontier established by DEA provide solid and scientific reference information for managers to evaluate an individual's efficiency. Based on the successful experience of DEA in performance assessment, many scholars have mentioned that DEA can be used to set appropriate targets as well; however, traditional DEA models do not include DMs' preference information that is crucial to a target-setting process. Therefore, several MOLP methods have been introduced to include DMs' preferences in the target-setting process based on the DEA efficient frontier and performance possibility set. The trade-off-based method is one of the most popular interactive methods that have been incorporated with DEA. However, there are several gaps in the current research: (1) the trade-off-based method could take so many interactions that no DMs could finish the interactive process; (2) DMs might find it very difficult to provide the preference information required by MOLP models; and (3) DMs cannot have an intuitive view in terms of the efficient frontier. Regarding the gaps above, this thesis proposes three new trade-off-based interactive target-setting models based on the DEA performance possibility set and efficient frontier to improve DMs' experience when setting targets. The three models can work independently or can be combined during the decision-making process. The piecewise linear model uses a piecewise linear assumption to simulate DMs' real utility function. It gradually narrows down the region that could contain DMs' most-preferred solution (MPS) until it reaches an acceptable range. This model could help DMs who have limited time for interaction but want to have a global view of the entire efficient frontier. This model has also been proven very helpful when DMs are not sensitive to close efficient solutions. The prioritized trade-off model provides a new way for a DM to know about the efficient frontier, which allows the DM to explore the efficient frontier following the preferred direction with a series of trade-off tables and trade-off figures as visual aids. The stepwise trade-off model focuses on situations where the number of objectives (outputs/inputs for the DEA model) is quite large and DMs cannot provide all indifference trade-offs between all the objectives simultaneously. To release the DMs' burden, the stepwise model starts from two objectives and gradually includes new objectives in the decision-making process, with the assumption that the indifference trade-offs between previous objectives are fixed, until all objectives are included. All three models have been validated through numerical examples and case studies of a Chinese state-owned bank to help DMs to explore their MPS in the DEA production possibility set.

Consumers make trade-offs in their purchase decision making between extending market knowledge from exploring a product market and maximizing purchase value based on exploiting their current knowledge. The value of these strategies can be enhanced opportunistically by taking advantage of promotions. In this research, a new and unique datamining model was developed to process store scanner data for quantifying the brand selection behaviour of an individual consumer in reaction to promotions. Selected consumers in each of Pittsfield’s salty snack, yogurt, and toilet tissue markets were then segmented into four behavioural segments using clustering analysis based on their Prevalence of Promotion and their Value of Information from Purchases. The behavioural segmentation was valid, as the four generated behavioural segments in each product market could be differentiated by using demographic variables. In a product market, the demographic profiles of behavioural segments can be generated andused for improving the performance in targeting consumers. The generated demographic profiles of a behavioural segment explain how consumers in the segment react to promotions,which can be used for predicting how consumers will react in the future. Complementing demographic profiles, dynamic behavioural evolvements in consumer purchase lifecycles can also help to predict the purchase behaviours of consumers in the future from the purchases that the consumers have made. The evolvements enable people to understand how consumers with a given amount of market experiences make their purchase decisions via making trade-offs between market knowledge extension and immediate purchase value maximization. Product markets differ in their available number of brands for selection. The findings generated in a product market, however, cannot be generalized to a different product market. Consumers have different demographic-related purchase behaviours across frequently purchased consumer goods markets. Based on the findings in the research, the dissertation discusses and provides suggestions forretail businesses to improve their performances for achieving a competitive edge.

This study investigates the influence of unscheduled maintenance activities on delays and disruptions during the execution of aircraft heavy maintenance services by developing a simulation model based on Systems Dynamics (SD) and supported by an Evidential Reasoning (ER) rule model. The SD model studies the complex interrelationship between scheduled and unscheduled tasks and its impact on delays during a maintenance service execution. It was found that the uncertain nature of the unscheduled maintenance tasks hinders the planning, control and allocation of resources, increasing the chances to miss deadlines and incur in cost overruns. Utilising causal loop diagrams and SD simulation the research explored the relevance that the resource allocation management, the precise estimation of the unscheduled tasks and their prompt identification have on the maintenance check duration. The influence that delays and attitudes in the decision-making process have on project performance was also investigated. The ER rule model investigates the uncertainty present during the execution of a maintenance check by providing a belief distribution of the expected unscheduled maintenance tasks. Through a non-parametric discretisation process, it was found that the size and array of distribution intervals play a key role in the model estimation accuracy. Additionally, a sensitivity analysis allowed the examination of the significance that the weight, reliability and dependence of the different pieces of evidence have on model performance. By analysing and combining historical data, the ER rule model provides a more realistic and accurate prediction to analyse variability and ambiguity. This research extends SD capabilities by incorporating the ER rule for analysing system uncertainty. By using the belief distributions provided by the ER model, the SD model can simulate the variability of the process given certain pieces of evidence. This study contributes to the existing knowledge in aircraft maintenance management by analysing, from a different perspective, the impact of uncertain unscheduled maintenance activities on delays and disruptions through an integrated approach using SD and the ER rule. Despite the fact that this research focuses on studying a particular problem in the airline industry, the findings and conclusions obtained could be used to understand and address problems embodying similar characteristics. Therefore, it can be argued that, due to the close similarities between the heavy maintenance process and complex projects, these contributions can be extended to the Project Management field.