Three dimensional modelling of customer satisfaction, retention and loyalty for measuring quality of service

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The aim of this thesis is to propose a model that explains the relationship between customer satisfaction, retention and loyalty based on service quality attributes. The three elements of satisfaction, retention and loyalty towards products represent ongoing challenges for the corporate financial performance. Customer behaviour analysis (known as business intelligence or customer relationship management or customer experience management) has become a major factor in the corporate decision making and strategic planning processes. Prevailing logic dictates that by improving service attributes one should expect better customer satisfaction levels. Consequently, improved satisfaction levels should increase the probability of customer retention and degree of loyalty. Substantial research work has been dedicated to explain the importance of customer behaviour measurement for industry. However, there is little evidence that there has been an overall integrating empirical research that relates the three elements of satisfaction, retention and loyalty with respect to service quality attributes. Empirical data collected from the UK mobile telecommunication for this research shows that such an objective model that is capable of capturing this three dimensional relationship will contribute towards more robust decision making and better strategic planning. The proposed thesis extracts the data about key service attributes from a combination of literature review, surveys, and interviews from the UK mobile telecommunication industry. Responses were analysed using multiple regression, regression analysis with dummy variables, logistic regression, logistic regression with dummy variables and structural equation modelling (SEM) to test variables and their interrelationships. This study makes a step forward and contributes to the body of knowledge as it: (a) highlights the role of service attribute performance towards customer satisfaction, consequently identifies attributes that affect satisfaction and dissatisfaction of customers, (b) maps the relationship between attribute importance and attribute performance, (c) optimise resource allocation process using importance-performance analysis (IPA), (d) classifies customers with respect to the role and length of relationship they have with the company (switching probability), and (e) describes the interrelationship between customer satisfaction, retention and loyalty. The novelty of the research lies in: (a) establishment of a framework that links service attribute performance to customer satisfaction and then to customer future intentions (customer retention and customer loyalty), and (b) provision of a model that could assist key decision makers in prudent usage of resources for maximum profitability. This dissertation presents a novel approach methodology and modelling construct for customer behaviour analysis. For proof of concept it presents a case study in the mobile telecommunication industry. It is worth noting that in this research work Customer Retention is interpreted as probability of switching between service providers. Customer Loyalty is interpreted as referral (word-of-mouth) activity by existing customers

Three dimensional modelling of customer satisfaction, retention and loyalty for measuring quality of service

The aim of this thesis is to propose a model that explains the relationship between customer satisfaction, retention and loyalty based on service quality attributes. The three elements of satisfaction, retention and loyalty towards products represent ongoing challenges for the corporate financial performance. Customer behaviour analysis (known as business intelligence or customer relationship management or customer experience management) has become a major factor in the corporate decision making and strategic planning processes. Prevailing logic dictates that by improving service attributes one should expect better customer satisfaction levels. Consequently, improved satisfaction levels should increase the probability of customer retention and degree of loyalty. Substantial research work has been dedicated to explain the importance of customer behaviour measurement for industry. However, there is little evidence that there has been an overall integrating empirical research that relates the three elements of satisfaction, retention and loyalty with respect to service quality attributes. Empirical data collected from the UK mobile telecommunication for this research shows that such an objective model that is capable of capturing this three dimensional relationship will contribute towards more robust decision making and better strategic planning. The proposed thesis extracts the data about key service attributes from a combination of literature review, surveys, and interviews from the UK mobile telecommunication industry. Responses were analysed using multiple regression, regression analysis with dummy variables, logistic regression, logistic regression with dummy variables and structural equation modelling (SEM) to test variables and their interrelationships. This study makes a step forward and contributes to the body of knowledge as it: (a) highlights the role of service attribute performance towards customer satisfaction, consequently identifies attributes that affect satisfaction and dissatisfaction of customers, (b) maps the relationship between attribute importance and attribute performance, (c) optimise resource allocation process using importance-performance analysis (IPA), (d) classifies customers with respect to the role and length of relationship they have with the company (switching probability), and (e) describes the interrelationship between customer satisfaction, retention and loyalty. The novelty of the research lies in: (a) establishment of a framework that links service attribute performance to customer satisfaction and then to customer future intentions (customer retention and customer loyalty), and (b) provision of a model that could assist key decision makers in prudent usage of resources for maximum profitability. This dissertation presents a novel approach methodology and modelling construct for customer behaviour analysis. For proof of concept it presents a case study in the mobile telecommunication industry. It is worth noting that in this research work Customer Retention is interpreted as probability of switching between service providers. Customer Loyalty is interpreted as referral (word-of-mouth) activity by existing customers.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Minimax Concave Penalty Regularized Adaptive System Identification

We develop a recursive least square (RLS) type algorithm with a minimax concave penalty (MCP) for adaptive identification of a sparse tap-weight vector that represents a communication channel. The proposed algorithm recursively yields its estimate of the tap-vector, from noisy streaming observations of a received signal, using expectation-maximization (EM) update. We prove the convergence of our algorithm to a local optimum and provide bounds for the steady state error. Using simulation studies of Rayleigh fading channel, Volterra system and multivariate time series model, we demonstrate that our algorithm outperforms, in the mean-squared error (MSE) sense, the standard RLS and the $\ell_1$-regularized RLS

Toward Data-Driven Radar STAP

Catalyzed by the recent emergence of site-specific, high-fidelity radio frequency (RF) modeling and simulation tools purposed for radar, data-driven formulations of classical methods in radar have rapidly grown in popularity over the past decade. Despite this surge, limited focus has been directed toward the theoretical foundations of these classical methods. In this regard, as part of our ongoing data-driven approach to radar space-time adaptive processing (STAP), we analyze the asymptotic performance guarantees of select subspace separation methods in the context of radar target localization, and augment this analysis through a proposed deep learning framework for target location estimation. In our approach, we generate comprehensive datasets by randomly placing targets of variable strengths in predetermined constrained areas using RFView, a site-specific RF modeling and simulation tool developed by ISL Inc. For each radar return signal from these constrained areas, we generate heatmap tensors in range, azimuth, and elevation of the normalized adaptive matched filter (NAMF) test statistic, and of the output power of a generalized sidelobe canceller (GSC). Using our deep learning framework, we estimate target locations from these heatmap tensors to demonstrate the feasibility of and significant improvements provided by our data-driven approach in matched and mismatched settings.Comment: 39 pages, 24 figures. Submitted to IEEE Transactions on Aerospace and Electronic Systems. This article supersedes arXiv:2201.1071

Do site and type of metastasis in breast cancer show a changing pattern with increased age? A cross comparison of clinicopathological characteristics between age groups

Abstract Background In here, we evaluated pattern of metastasis and cross-compared clinicopathological features between different age groups with breast cancer (BC). Methods This study was conducted in the Shiraz Breast Cancer Registry (largest BC registry in Iran). Patients were classified as  60 years old (group 3). The three age groups were compared regarding clinical and baseline characteristics. Results Overall, 564 individuals entered group 1, 4519 group 2, and 670 group 3. Group 1 had lower rates of tumor necrosis (p  60 years old had more lung metastasis (33 vs. 12.6 and 6.7% for groups 2 and 1, respectively) (p < 0.001). Younger groups had more < 5-year recurrence (16.3, 11.7, and 8.9%, for groups 1, 2, and 3, respectively) (p = 0.023). Conclusion Pattern and site of recurrence changes according to age in BC. This brings up the question whether age is an independent predictor of organ of metastasis or is site of metastasis the result of other clinicopathological determinants which differ between age groups