The initial idea for this research is based on the gap that I found in my company performance and specifically in the process of value offering and the relationship with customers. The company has recently faced the challenges of a loss of market share and competitive advantage. The aim of this study is to provide a validated value offering constructs for the company. That is, knowing the customers’ needs, value and preferences better; identifying what kind of value the customers are looking for and improving the value offerings of the company in order to raise customers´ satisfaction. Understanding the priority of value offering elements from customers’ perspective, the nature of customer satisfaction and the relationship between the elements of value offering and customers´ satisfaction are the main objectives. The relevant literature and research related to value offering including its elements, value, perceived value and customer satisfaction are reviewed comprehensively. The methodology used is a case study. Both quantitative and qualitative methods are used for collecting and analysing the data. In qualitative phase, 16 customers were selected for in-depth interviews and for quantitative phase 268 customers who all are dentists responded a survey made up of 24 scales. The result showed that the highest significant element is Price value and also it was revealed in which important parts related to value offering and customer satisfaction the company is performing poorly.

This study sought to determine customer satisfaction levels of faculty and staff with their technology support office in a large university in Florida. The focus of research was to determine if there were any differences in customer satisfaction based on four demographics: gender, faculty versus staff, educational level and age. An anonymous customer satisfaction survey included 26 Likert-type scale questions measuring 16 service quality dimensions was administered to the population. The 16 service quality dimensions included 10 dimensions from Zeithaml et al. (1990), five dimensions from Besterfield et al. (1995, 2003), and one dimension, overall satisfaction. Findings showed there was a statistically significant difference in two demographics, gender and faculty versus staff. Regarding gender, there were no differences in 14 of 16 dimensions examined. The two dimensions with differences were tangibles and understanding the customer, with males having lower customer satisfaction than females. Regarding faculty versus staff differences, there were no differences in all the dimensions other than the courtesy dimension for which faculty had a lower level of customer satisfaction level than staff. Regarding educational level and age, there were no differences in any of the 16 dimensions.

The objective of this research is to determine and develop a model that is capable of accurately measuring customer satisfaction for different industries and in particularly for the Printed Wiring Boards (PWB) Manufacturers. The new model will incorporate data not being collected or utilized by the survey method of determining customer satisfaction. The method used is a weighted average of satisfaction among several researched categories with percentages that accurately represent the relative importance of multiple facets of the PWB manufacturers customer satisfaction. A very common term in quality assurance is that "What is not measured accurately can not be evaluated or managed correctly," thus customer satisfaction is a very important aspect of any business, industry, or government. A satisfied customer will do more business and recommend it to other potential customers. Thus the business will grow and more revenues result. On the other hand, an unsatisfied customer will abandon the business and encourage more customers not to get involved with the same business so the business may decline and lose its market share and profitability. The categories that contribute to PWB customer satisfaction will be determined by conducting surveys among the leaders and best in the business of the PWB industry in addition to discovery of related articles that define the categories of the customer satisfaction for the PWB manufacturers. Once the categories are determined, the research concentrates on the weighting of the categories that most contribute to the PWB customer's satisfaction and a measure of satisfaction is derived. The model is easily applied to any other kind of PWB business or service industry. The model is based on empirical methods that will give an accurate measurement for the PWB customer's satisfaction. This in turn allows organizations the opportunity for improving customer satisfaction and increasing market share. The algorithm is based on characteristics deemed important by customers. Thus the customer satisfaction index can be computed and monitored on a regular basis without costly surveys. The major difference between this new model and the standard methods of determining customer satisfaction using the surveys is that this model will utilize data available with the proposals, sales, shipping, receiving, quality, engineering, manufacturing, and purchasing departments. The developed method to measure customer satisfaction utilizing internal data can be more cost effective, more accurate, can provide individual customer satisfaction scores, can measure whether or not these individual scores are statistically lower than the majority, and can provide satisfaction measures in real time none of which can be supplied by the survey method.

Control and involvement are well researched concepts in traditional marketing, while interactivity is a relatively new concept associated with Internet marketing and communication. From an Internet interaction perspective, the research examines the impact of interactivity, control and involvement on customer satisfaction and loyalty in an eBanking context. Using Flow Theory as the theoretical foundation, it is proposed that these three Internet communication constructs lead to eService dependency and eService encounter satisfaction, which, in turn, enhance the overall satisfaction and loyalty to the principal bank at corporate level. A mixed methodology using both qualitative and quantitative approaches of data collection is adopted. In-depth interviews with eBankers and focus group studies with eBanking customers support the importance of interactivity, control and involvement in the eService encounter. The in-depth interviews and focus groups facilitate the identification of eService dependency as a new construct in the model. Participants¿ views also help the operationalisation of constructs and development of questionnaire for quantitative data collection. ii Analysis of the quantitative data using structural equation modelling shows support for all three constructs¿ hypothesised positive relationships with eService dependency and eService encounter satisfaction i.e. satisfaction at the service encounter level. It is also confirmed that eService dependency and eService encounter satisfaction have a positive impact on overall satisfaction with the principal bank i.e. satisfaction at corporate level. However, the influence of eService dependency and eService encounter satisfaction on loyalty to principal bank is not supported. The research concludes with the theoretical contributions and managerial implications of the research. Strategies to enhance interactivity with, control of and involvement by eBanking customers are recommended to eBankers. Limitations of the research and directions for future research in Internet and eService are also suggested.

This thesis investigates a generalisable cross-cultural model for brand loyalty by integrating extant theories of personal cultural orientation (of individualism and collectivism), self-congruity (actual, ideal, social, and ideal social self-congurity), customer satisfaction, attitudinal brand loyalty, and behavioural brand loyalty. Creating brand loyalty is a key branding issue in modern marketing. Brands are faced with the challenge of building, maintaining, and increasing their capacity to drive customer loyalty across borders with consumers of different cultures. Notwithstanding the growth of culturally centered brand loyalty research, the focus of research today continues to be on cross-cultural differences, often overlooking the generalisable cross-cultural path to consumer brand loyalty. This study instead addresses this overlooked topic of cross-cultural generalisabilities across nations. To assess the cross-cultural generalisability of the conceptual model, survey data from a non-student sample were collected from middle-class, Generation Y individuals of the relevant nationality who have always lived in China, Singapore, or the United States. After performing data cleaning procedures, 541 usable responses from three countries were analysed with the use of the SEM model. The findings show that the personal cultural orientation of collectivism has a positive effect on behavioural brand loyalty through ideal social self-congruity, customer satisfaction, and attitudinal brand loyalty. These findings extend brand loyalty research by considering how an individual’s personal cultural orientation impacts brand loyalty. Moreover, the findings offer marketers increased insight into consumers’ brand loyalty formation process in cross-cultural contexts. The limitations of the study and suggestions for future research are also presented.

<p> Supply chain management has been one of the most important issues in manufacturing industries. In order to improve customer satisfaction, suppliers usually extend their inbound production operations to outbound logistics operations. To improve the overall operational performance, it is necessary to study scheduling models which consider inbound production and outbound deliveries simultaneously. This thesis deals with supply chain problems on the operational level using deterministic models. </p> <p> Meeting due dates is always one of the most important concerns in scheduling and supply chain management. In most supplying contracts, customers require that suppliers either meet contracted due dates or pay tardiness penalties. In order to save delivery costs, suppliers usually deliver jobs in batches. Therefore, we will study supply chain scheduling problems with delivery costs, where our goal is to minimize the sum of the weighted number of tardy jobs and the batch-delivery costs on a single machine. </p> <p> In traditional manufacturing system, due dates are not considered as given by exogenous decisions. In modern supply chains, however, due dates are determined by taking into account the system's ability to meet the assigned due dates, which can be quoted with certain costs. Therefore, we will study supply chain scheduling problems with delivery costs and due date assignment, where our goal is to minimize the sum of the weighted number of tardy jobs, the due-date-assignment costs and the batch-delivery costs on a single machine. </p> <p> As we know, most machine scheduling models are intractable in terms of computational complexity. Therefore, for our above problems, which are even harder, we first prove their computational complexity. Then we propose pseudo-polynomial algorithms for optimal solutions. For some problems, the pseudo-polynomial algorithms perform in polynomial time for some special cases. Finally, we develop efficient approximation algorithms or fully polynomial time approximation schemes, which can be implemented easily in practice. </p> / Thesis / Doctor of Philosophy (PhD)

Over the last decade, retailing has been shifted to online eras of selling. And in order toidentify the needs of customers companies are using several tools and AI is one of them.AI is used as most prominent and most reliable tool to find out the needs andrequirements of the customers.This thesis will also try to find out the gaps which are not specified or developed inprevious researches like how AI is being used in retailing effectively and how it is beingapplied in retailing for customer segmentation and customer satisfaction.This draft will try to find out the solutions mostly by using qualitative data. And will tryto find out the best possible way to use AI in retailing.This thesis will also try to find out the rolls which chatboat assistance is playing andwill also try to find out how chatboat assistance is working and how it is trying to findout the needs and requirements of the consumers.After reading this paper draft readers will come to know, how AI is changing theconcept of retail marketing with extended customer support and how customers arebeing segmented with the help of AI.This draft will be helpful for the new entities entering in Digital marketing. Afterreading this draft they will come to know the concept of AI in retailing with targetcustomer base work.

The concept of visiting a restaurant is no longer related to the consumption of a food. The total process of visiting a restaurant became a pleasant experience to elevate mood and satisfaction and create a memorable experience. In this thesis, we aim to case study three restaurants of the same brand using quantitative and qualitative measurements such as questionnaire, V/P theory, and measurements. Moreover, the results of the assessments will be used to investigate how lighting can improve the dining experience in the restaurant while taking into consideration reducing energy consumption. The results of the quantitative and qualitative analysis indicated the lighting in each of these restaurants differs from each other, and they require a redesign to enhance customer satisfaction and to be more energy efficient. The thesis presented a lighting solution which considers all the relevant factors for customer satisfaction from literature and standards. The solution can improve the customer experience in the restaurant and advance its compliance with the sustainability goals.

The retail sector is experiencing transforming changes driven by service robots (SR). While SR promise benefits such as improved inventory management, increased efficiency, and enhanced customer satisfaction, their integration presents technical and societal challenges. Technical hurdles encompass image classification under varying conditions, data security, localization, and obstacle detection. On the societal front, concerns revolve around public perception, safety, and employee apprehension regarding job security. This paper provides a comprehensive overview of these challenges and opportunities, highlighting that while SR have considerable potential, their optimal utilization in retail may be best suited for non-human facing tasks, given the current limitations of AI in human interactions.

The purpose of this article is to evaluate the impact a supervised machine learning classification model can have on the process of internal customer support within a large digitized company. Chatbots are becoming a frequently used utility among digital services, though the true general impact is not always clear. The research is separated into the following two questions: (1) Which supervised machine learning algorithm of naïve Bayes, logistic regression, and neural networks can best predict the correct support a user needs and with what accuracy? And (2) What is the effect on the productivity and customer satisfaction of using machine learning to sort customer needs? The data was collected from the internal server database of a large digital company and was then trained on and tested with the three classification algorithms. Furthermore, a survey was collected with questions focused on understanding how the current system affects the involved employees. A first finding indicates that neural networks is the best suited model for the classification task. Though, when the scope and complexity was limited, naïve Bayes and logistic regression performed sufficiently. A second finding of the study is that the classification model potentially improves productivity given that the baseline is met. However, a difficulty exists in drawing conclusions on the exact effects on customer satisfaction since there are many aspects to take into account. Nevertheless, there is a good potential to achieve a positive net effect. / Syftet med artikeln är att utvärdera den påverkan som en klassificeringsmodell kan ha på den interna processen av kundtjänst inom ett stort digitaliserat företag. Chatbotar används allt mer frekvent bland digitala tjänster, även om den generella effekten inte alltid är tydlig. Studien är uppdelad i följande två frågeställningar: (1) Vilken klassificeringsalgoritm bland naive Bayes, logistisk regression, och neurala nätverk kan bäst förutspå den korrekta hjälpen en användare är i behov av och med vilken noggrannhet? Och (2) Vad är effekten på produktivitet och kundnöjdhet för användandet av maskininlärning för sortering av kundbehov? Data samlades från ett stort, digitalt företags interna databas och används sedan i träning och testning med de tre klassificeringsalgoritmerna. Vidare, en enkät skickades ut med fokus på att förstå hur det nuvarande systemet påverkar de berörda arbetarna. Ett första fynd indikerar att neurala nätverk är den mest lämpade modellen för klassificeringen. Däremot, när omfånget och komplexiteten var begränsat presenterade även naive Bayes och logistisk regression tillräckligt. Ett andra fynd av studien är att klassificeringen potentiellt förbättrar produktiviteten givet att baslinjen är mött. Däremot existerar en svårighet i att dra slutsatser om den exakta effekten på kundnöjdhet eftersom det finns många olika aspekter att ta hänsyn till. Likväl finns en god potential i att uppnå en positiv nettoeffekt.