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Merging library traffic from dispersed service points into a combined services desk is not new, and many reasons prompt this move. George Mason University, Virginia’s largest public research institution, combined a total of 10 service desks located in four libraries on three distributed campuses. To consolidate services and reduce costs, the Mason Libraries established a “one-stop” service point in each library. With the goal of “one-stop” service point in each facility, the Mason Libraries recrafted physical spaces, reviewed policies, procedures, and workflows as well as revised staff roles and responsibilities.

This chapter explores why institutions embark on redesigning the traditional library service desk; discusses how changing service needs impact desk space; and addresses the effect on public services personnel. Observations are based on highlights from the evolution of George Mason University Libraries’ goal of a “one-stop” service point in each library to provide more efficient and consistent user-focused interactions and services.

As a manager of one of the facilities, the author provides insights on achieving a “one-stop” service point.

This chapter considers library staff needs, in concert with internal effort to not only refine user services influencing changes, but also revisit policies, procedures, and workflows to align staff roles and responsibilities. Mason Libraries is one of a few university library systems trying to implement single service points in all libraries.

The increasing prevalence and availability of big data represent a potentially revolutionary development for human resource management (HRM) scholars. Despite this, the current literature provides eclectic and often contradictory guidance for scholars attempting to conceptualize big data and subsequently incorporate it into relevant theoretical frameworks. The authors attempt to bridge this gap by discussing key considerations relevant to understanding and integrating big data into the existing theoretical landscape. Building on a novel, integrative definition of big data, the authors propose a parsimonious theoretical framework utilizing the established dimensions of complexity and dynamism as meta-attributes to bring order to the various attributes that have been proposed as central to defining big data (e.g., volume, variety, velocity, and variability). Throughout, the authors highlight numerous theoretical and empirical opportunities and considerations that this perspective holds for future HRM scholarship.