Siri, Alexa, and Other Digital Assistants: A Study of Customer Satisfaction With Artificial Intelligence Applications

Siri, Alexa, and other digital assistants are rapidly becoming embraced by consumers and the adoption is projected to grow from 390 million to 1.8 billion for the period of 2015 to 2021. Digital assistants are offering benefits to consumers while also proving to be a disruptive technology for businesses. Coupling digital assistants with other artificial intelligence technologies offers the potential to transform companies by creating more efficient business processes, automating complex tasks, and improving the customer service experience. Businesses have begun integrating this technology into their operations with the expectation of achieving significant productivity gains. Customer satisfaction has been discussed extensively throughout marketing literature. Yet, there is little empirical evidence of customer satisfaction with digital assistants. This study used PLS-SEM to analyze 244 survey responses obtained from a cross-section of consumers. Using the Expectations Confirmation Theory as its foundation, the study identified that expectations and confirmation of expectations substantially explained customer satisfaction with digital assistants. For practice, the study provides guidance which allows firms to prioritize marketing and managerial activities. Firms should focus priorities on assisting digital assistant users to become aware of new skill capabilities while also providing relevant examples of how these skills can be used to meet user needs. In addition, priorities should be focused on assisting users with understanding how the average person can use digital assistants to perform more than just mundane tasks with relative ease. These priorities were identified as areas of high importance for customer satisfaction and require performance improvements

The inner and inter construct associations of the quality of data warehouse customer relationship data for problem enactment

This thesis was submitted for the degree of Doctor of Business Administration and awarded by Brunel University on behalf of Henley Management College.The literature identifies perceptions of data quality as a key factor influencing a wide range of attitudes and behaviors related to data in organizational settings (e.g. decision confidence). In particular, there is an overwhelming consensus that effective customer relationship management, CRM, depends on the quality of customer data. Data warehouses, if properly implemented, enable data integration which is a key attribute of data quality. The literature highlights the relevance of formulating problem statements because this will determine the course of action. CRM managers formulate problem statements through a cognitive process known as enactment. The literature on data quality is very fragmented. It posits that this construct is of a high order nature (it is dimensional), it is contextual and situational, and it is closely linked to a utilitarian value. This study addresses all these disperse views of the nature of data quality from a holistic perspective. Social cognitive theory, SCT, is the backbone for studying data quality in terms of information search behavior and enhancements in formulating problem statements. The main objective of this study is to explore the nature of a data warehouse's customer relationship data quality in situations where there is a need for understanding a customer relationship problem. The research question is What are the inner and inter construct associations of the quality of data warehouse customer relationship data for problem enactment? To reach this objective, a positivistic approach was adopted complemented with qualitative interventions along the research process. Observations were gathered with a survey. Scales were adjusted using a construct-based approach. Research findings confirm that data quality is a high order construct with a contextual dimension and a situational dimension. Problem sense making enhancements is a dependent variable of data quality in a confirmed positive association between both constructs. Problem sense making enhancements is also a high order construct with a mastering experience dimension and a self-efficacy dimension. Behavioral patterns for information search mode (scanning mode orientation vs. focus mode orientation) and for information search heuristic (template heuristic orientation vs. trial-and-error heuristic orientation) have been identified. Focus is the predominant information search mode orientation and template is the predominant information search heuristic orientation. Overall, the research findings support the associations advocated by SCT. The self-efficacy dimension in problem sense making enhancements is a discriminant for information search mode orientation (focus mode orientation vs. scanning mode orientation). The contextual dimension in data quality (i.e. data task utility) is a discriminant for information search heuristic (template heuristic orientation vs. trial-and-error heuristic orientation). A data quality cognitive metamodel and a data quality for problem enactment model are suggested for research in the areas of data quality, information search behavior, and cognitive enhancements. iiiTeradata, NC

Understanding Online Customer Touchpoints:A Deep Learning Approach to Enhancing Customer Experience in Digital Retail

This study investigates the main touchpoints that customers value most when shopping online and their attitudes towards them, using Ocado's customer reviews as a case study. Employing machine learning and deep learning methods, such as word2vec, CNN-based sentiment models, and embedding-based topic models, the analysis identified seven critical touchpoints across pre-purchase and post-purchase stages. Recommendations were provided regarding promotional opportunities, technology utilization, and customer experience creation, highlighting the need for different strategies based on customer stages in their journey. The findings offer valuable insights for retail companies transitioning to digital platforms, emphasizing the importance of understanding customer needs and prioritizing touchpoints. Future research could explore additional retail companies with various channels and incorporate different types of customer views to provide a broader perspective on touchpoints

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

Text Mining Approaches Oriented on Customer Care Efficiency

In the proposed work is performed a text classification for a chatbot application used by a company working in assistance services of automatic warehouses. industries. Specifically, text mining technique is adopted for the classification of questions and answers. Business Process Modeling Notation (BPMN) models describe the passage from “AS-IS” to “TO BE” in the context of the analyzed industry, by focusing the attention mainly on customer and technical support services where chatbot is adopted. A two-step process model is used to connect technological improvements and relationship marketing in chatbot assistance: the first step provides the hierarchical clustering able to classify questions and answers through Latent Dirichlet Allocation -LDA- algorithm, and the second one executes the Tag Cloud representing the visual representation of more frequent words contained in the experimental dataset. Tag cloud is used to show the critical issues that customers find in the usage of the proposed service. By considering an initial dataset, 24 hierarchical clusters are found representing the preliminary combination of the couple’s question-answer. The proposed approach is suitable to automatically construct a combination of chatbot questions and appropriate answers in intelligent systems

The inner and inter construct associations of the quality of data warehouse customer relationship data for problem enactment

The literature identifies perceptions of data quality as a key factor influencing a wide range of attitudes and behaviors related to data in organizational settings (e.g. decision confidence). In particular, there is an overwhelming consensus that effective customer relationship management, CRM, depends on the quality of customer data. Data warehouses, if properly implemented, enable data integration which is a key attribute of data quality. The literature highlights the relevance of formulating problem statements because this will determine the course of action. CRM managers formulate problem statements through a cognitive process known as enactment. The literature on data quality is very fragmented. It posits that this construct is of a high order nature (it is dimensional), it is contextual and situational, and it is closely linked to a utilitarian value. This study addresses all these disperse views of the nature of data quality from a holistic perspective. Social cognitive theory, SCT, is the backbone for studying data quality in terms of information search behavior and enhancements in formulating problem statements. The main objective of this study is to explore the nature of a data warehouse's customer relationship data quality in situations where there is a need for understanding a customer relationship problem. The research question is What are the inner and inter construct associations of the quality of data warehouse customer relationship data for problem enactment? To reach this objective, a positivistic approach was adopted complemented with qualitative interventions along the research process. Observations were gathered with a survey. Scales were adjusted using a construct-based approach. Research findings confirm that data quality is a high order construct with a contextual dimension and a situational dimension. Problem sense making enhancements is a dependent variable of data quality in a confirmed positive association between both constructs. Problem sense making enhancements is also a high order construct with a mastering experience dimension and a self-efficacy dimension. Behavioral patterns for information search mode (scanning mode orientation vs. focus mode orientation) and for information search heuristic (template heuristic orientation vs. trial-and-error heuristic orientation) have been identified. Focus is the predominant information search mode orientation and template is the predominant information search heuristic orientation. Overall, the research findings support the associations advocated by SCT. The self-efficacy dimension in problem sense making enhancements is a discriminant for information search mode orientation (focus mode orientation vs. scanning mode orientation). The contextual dimension in data quality (i.e. data task utility) is a discriminant for information search heuristic (template heuristic orientation vs. trial-and-error heuristic orientation). A data quality cognitive metamodel and a data quality for problem enactment model are suggested for research in the areas of data quality, information search behavior, and cognitive enhancements.EThOS - Electronic Theses Online ServiceTeradata, NCRGBUnited Kingdo

E-Commerce Digital Information Transparency and Satisfaction. Can We Have Too Much of a Good Thing?

Despite core product and service quality improvements and advances in shopping processes and technology, customers often report being unsatisfied with their online purchases. One plausible reason for lower customer satisfaction rates is too much or too little information that is shared with the customers about their orders. We show that when forming their perceptions about the purchases, customers form digital information satisfaction (DIS) levels as they evaluate supplementary informational services in addition to the core product being purchased. We believe that DIS is one of the dimensions of overall customer satisfaction. We also show that supplementary informational services are essential in meeting the increased informational needs of online shopping and, thus, can explain the decreased overall customer satisfaction level through the decreases in DIS. We develop and test the Digital Information Transparency and Satisfaction (DITS) model that shows how supplemental informational services influence digital information satisfaction (DIS_ in e-commerce. By doing so, this dissertation introduces a new dimension of satisfaction in the era of online shopping. This helps close the knowledge gap in the current research on overall customer satisfaction by showing that too much information transparency can harm the overall experience of the customers, thus leading to decreases in DIS. The study results provide a platform for future research on the influence of informational services provided during online shopping. Explaining the role of information shared with the customers in their perceptions of transparency and, consequently, DIS may help provide crucial practical business insights. Thus, by proposing the DITS model, this dissertation brings contributions to both theory and praxis by enhancing the understanding of DIS, which can serve as a robust foundation for future research on decreasing levels of overall customer satisfaction in a digital setting, as well as help companies improve their customer relationships

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse