Exploring The Role Of Omni Channel Retailing Technologies
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Exploring the Role of Omni-Channel Retailing Technologies
Technological advances allow the development of an omnichannel strategy to create a seamless experience for customers. This study has adopted a systematic literature review approach to examine, synthesize, and extend a body of literature in the area of omnichannel retailing and the role of technology, taking into account both retailers' and customers' perspectives. We review 499 research papers to highlight the evolution of omnichannel research with a special focus on technology usage. After extracting the key theoretical foundations underpinning technology-empowered omnichannel retailing, we synthesize the empirical findings and identify emerging topics from the customer perspective including customer value, customer experience, showrooming and web rooming, and customer privacy concerns as well as the key themes from the retailer perspective consisting of channel integration, personalization, and resource challenges. Based on the knowledge from the theoretical and empirical insights, we develop three important future research areas to inspire further studies in this domain.
Deep Learning Applications in Operations Research
The model-based approach for carrying out the classification and identification of tasks has led to progression of the machine learning paradigm in diversified fields of technology. Deep Learning Applications in Operations Research presents the varied applications of this model-based approach. Apart from the classification process, the machine learning (ML) model has become effective enough to predict future trends of any sort of phenomenon. Such fields as object classification, speech recognition, and face detection have sought extensive applications of artificial intelligence (AI) and machine learning as well. The application of AI and ML has also become increasingly common in the domains of agriculture, health sectors, and insurance. Operations research is the branch of mathematics used to perform many operational tasks in other allied domains, and the book explains how the implementation of automated strategies in optimization and parameter selection can be carried out by AI and ML. Operations research has many beneficial aspects to aid in decision making. Arriving at the proper decision depends on a number of factors; this book examines how AI and ML can be used to model equations and define constraints to solve problems more easily and discover proper and valid solutions. This book also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost. Case studies examine how to streamline operations and unearth data to make better business decisions. The concepts presented in this book can bring about and guide unique research directions to the future application of AI-enabled technologies.
Exploring Omnichannel Retailing
This book compiles the current state of knowledge on omnichannel retailing, a new concept in which all sales and interaction channels are considered together, and which aims to deliver a seamless customer experience regardless of the channel. It highlights case studies and examples related to each of the many barriers to an omnichannel approach, demonstrating not just success stories, but also failures. While omnichannel has already been recognized as an emerging retail trend, the articles in this book fill an important gap in research on the topic. Providing readers with essential insights on the omnichannel strategy and its implementation, the book will also stimulate academic discussion on this emerging trend.