Online customer experience in e-retailing: an empirical model of antecedents and outcomes

The theoretical understanding of online shopping behavior has received much attention. Less focus has been given to the formation of the customer experience (CE) that results from online shopper interactions with e-retailers. This study develops and empirically tests a model of the relationship between antecedents and outcomes of online customer experience (OCE) within Internet shopping websites using an international sample. The study identifies and provides operational measures of these variables plus the cognitive and affective components of OCE. The paper makes contributions towards new knowledge and understanding of how e-

Customer service 140 characters at a time: the users’ perspective

Electronic word of mouth (eWoM) has been adopted by Internet users as a way of communicating their consumption preferences and experiences. Consumers are able to reach out to others, unknown to them, and have online conversations that can influence their behaviour. Organisations need to understand how to respond to these brand-related conversations conducted via social media. By looking through the lens of social capital, this paper contributes to social media and social capital research by studying the perceptions that 44 social media users have of companies that interact with them online. The users value social networks and support as part of their online relationships. However, several new value categories are identified when compared to previous research. Further research is required to investigate possible segmentation approaches and alternative methodological choices.

Scaling up data mining techniques to large datasets using parallel and distributed processing

Advances in hardware and software technology enable us to collect, store and distribute large quantities of data on a very large scale. Automatically discovering and extracting hidden knowledge in the form of patterns from these large data volumes is known as data mining. Data mining technology is not only a part of business intelligence, but is also used in many other application areas such as research, marketing and financial analytics. For example medical scientists can use patterns extracted from historic patient data in order to determine if a new patient is likely to respond positively to a particular treatment or not; marketing analysts can use extracted patterns from customer data for future advertisement campaigns; finance experts have an interest in patterns that forecast the development of certain stock market shares for investment recommendations. However, extracting knowledge in the form of patterns from massive data volumes imposes a number of computational challenges in terms of processing time, memory, bandwidth and power consumption. These challenges have led to the development of parallel and distributed data analysis approaches and the utilisation of Grid and Cloud computing. This chapter gives an overview of parallel and distributed computing approaches and how they can be used to scale up data mining to large datasets.

The future of video analytics for surveillance and its ethical implications

The current state of the art and direction of research in computer vision aimed at automating the analysis of CCTV images is presented. This includes low level identification of objects within the field of view of cameras, following those objects over time and between cameras, and the interpretation of those objects’ appearance and movements with respect to models of behaviour (and therefore intentions inferred). The potential ethical problems (and some potential opportunities) such developments may pose if and when deployed in the real world are presented, and suggestions made as to the necessary new regulations which will be needed if such systems are not to further enhance the power of the surveillers against the surveilled.

Adapted customer relationship management implementation framework:facilitating value creation in nursing homes

This paper proposes a framework to support Customer Relationship Management (CRM) implementation in nursing homes. The work extends research by Cheng et al. (2005) who conducted in-depth questionnaires to identify critical features (termed value-characteristics), which are areas identified as adding the most value if implemented. Although Cheng et al. did proposed an implementation framework, summary of, and inconsistent inclusion of value-characteristics, limits the practical use of this contribution during implementation. In this paper we adapt the original framework to correct perceived deficiencies. We link the value characteristics to operational, analytical, strategic and/or collaborative CRM solution types, to allow consideration in context of practical implementation solutions. The outcome of this paper shows that, practically, a 'one solution meets all characteristic' approach to CRM implementation within nursing homes is inappropriate. Our framework, however, supports implementers in identifying how value can be gained when implementing a specific CRM solution within nursing homes; which subsequently support project management and expectation management.

The tenant as a customer: can good service improve real estate performance?

Customer Relationship Management (CRM) theory suggests that good customer service results in satisfied customers, who in turn are more likely to remain loyal and recommend the service provider to others. Proponents of good customer service for tenants claim that landlords should see a return on any investment in their customer service, in the form of enhanced real estate performance. This paper begins by reviewing research on customer service returns in other industries. Through consideration of the characteristics of real estate markets, the paper explains how factors such as (inter alia) lease terms, property market cycles, and property type, might determine the relationship between customer service and real estate performance. The paper concludes that further research is needed to isolate specific aspects of customer service that are most appreciated by customers. It suggests that the financial returns which accrue to landlords adopting a customer-focused approach might indeed be quantified, and suggests an appropriate method for such future research.

Towards developing a customer relationship management (CRM) strategy for supporting pre-implementation activities in business

Effectively preparing and planning for Customer Relationship Management (CRM) strategy is critical to CRM implementation success. A lack of a common and systematic way to implement CRM means that focus must be placed on the pre-implementation stage to ensure chance of success. Although existing CRM implementation approaches evidence the need to concentrate mostly on the pre-implementation stage, they fail to address some key issues, which raises the need for a generic framework that address CRM strategy analysis. This paper proposes a framework to support effective CRM pre-implementation strategy development.