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Despite the growth of alternative means of electronic messaging, email continues to be a crucial business communication tool. In many organizations, it is common to find a large proportion of business-critical data in email form. However, as the volume of business-critical email continues to grow, conventional approaches for managing paper-based records of business value are increasingly unsuited to the digital realities. Yet effective approaches to automate the management of email are essential to ensure organizational efficiency, accountability and regulatory compliance. Our research aims to develop a model that characterizes information managers' context-specific email triage strategies for identifying emails of business value so that it can be applied to automatically classify email records. We conducted a pilot study of two information managers' email triage practices in two different contexts and develop machine-learning models of lexical and non-lexical features that are involved in the appraisal of business value. An experiment with a machine learning algorithm trained on about two hundred emails in two different business contexts indicates that the automation of email triage for identifying records of business value is highly context dependent and that automated classifiers must be trained to recognize "business value" specifically for that context. [ABSTRACT FROM AUTHOR]