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The author takes a comprehensive look at the accessibility of e-resources for all people, including those with disabilities, in the context of collection development (CD).

Employing a combination of research methodologies — policy analysis, content analysis, and phone survey—the author explores the extent to which the needs of people with disabilities are considered.

Several professional library organizations recommend accessibility-sensitive selection and procurement procedures. However, not all students enrolled in library school programs might learn about the issue. Few books on the subject cover the issue adequately. Nationwide, CD policies requiring conformance to accessibility standards are the exception; and when librarians meet to make decisions about the selection of specific e-resources, the needs of people with disabilities are rarely on their radar screens.

Researchers conducting similar surveys in the future might want to not only select a statistically more representative sample of academic libraries but also widen their focus and include both accessibility and usability in their investigations.

Textbook authors and course instructors in the area of CD need to address accessibility and usability. Librarians need to raise the issue with database and e-book vendors during license negotiations.

The acquisition of e-resources designed to be accessible and usable for all will enable people with disabilities to participate more fully in our information-driven society.

The data collected provide for a broad discussion of the extent to which the needs of people with disabilities are considered in connection with CD.

This chapter describes how the technologies of big data might apply to rural contexts. It considers the relative advantages and disadvantages of such ‘new’ innovations.

It uses two case studies, one of online community specialist groups linked to rural activities and a second from a policy shift relating to firearm legislation in the English context.

The chapter suggests that digital data in the forms discussed here can be both benign and underutilised in its potential. In relation to the management of datasets holding information on firearm owners, these need careful reflection regarding their establishment, access and general use.

The chapter provides insight into the rural context and makes a case that such locales are not immune from the influence of the dataverse. The appearance of ‘big data’ here is not without political implications. The case of UK firearm legislation reform demonstrates the implications of policy falling short of its potential and how a social science analysis can unpack the operation of power as well as position the debate more broadly.

Purpose: To outline recent developments in digital service delivery in order to encourage researchers to pursue collaborations with computer science, operations research, and data science colleagues and to show how such collaborations can expand the scope of research on emotion in service delivery.

Design/methodology/approach: Uses archived resources available at http://LivePerson.com to extract data based in genuine service conversations between agents and customers. We refer to these as “digital traces” and analyze them using computational science models.

Findings: Although we do not test significance or causality, the data presented in this chapter provide a unique lens into the dynamics of emotions in service; results that are not obtainable using traditional research methods.

Research limitations/implications: This is a descriptive study where findings unravel new dynamics that should be followed up with more research, both research using traditional experimental methods, and digital traces research that allows inferences of causality.

Practical implications: The digital data and newly developed tools for sentiment analyses allow exploration of emotions in large samples of genuine customer service interactions. The research provides objective, unobtrusive views of customer emotions that draw directly from customer expressions, with no self-report intervention and biases.

Originality/value: This is the first objective and detailed depiction of the actual emotional encounters that customers express, and the first to analyze in detail the nature and content of customer service work.