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This paper focuses on image-to-text manuscript processing through Handwritten Text Recognition (HTR), a Machine Learning (ML) approach enabled by Artificial Intelligence (AI). With HTR now achieving high levels of accuracy, we consider its potential impact on our near-future information environment and knowledge of the past.

In undertaking a more constructivist analysis, we identified gaps in the current literature through a Grounded Theory Method (GTM). This guided an iterative process of concept mapping through writing sprints in workshop settings. We identified, explored and confirmed themes through group discussion and a further interrogation of relevant literature, until reaching saturation.

Catalogued as part of our GTM, 120 published texts underpin this paper. We found that HTR facilitates accurate transcription and dataset cleaning, while facilitating access to a variety of historical material. HTR contributes to a virtuous cycle of dataset production and can inform the development of online cataloguing. However, current limitations include dependency on digitisation pipelines, potential archival history omission and entrenchment of bias. We also cite near-future HTR considerations. These include encouraging open access, integrating advanced AI processes and metadata extraction; legal and moral issues surrounding copyright and data ethics; crediting individuals’ transcription contributions and HTR’s environmental costs.

Our research produces a set of best practice recommendations for researchers, data providers and memory institutions, surrounding HTR use. This forms an initial, though not comprehensive, blueprint for directing future HTR research. In pursuing this, the narrative that HTR’s speed and efficiency will simply transform scholarship in archives is deconstructed.

The organizational decision-making perspective (ODM) has a legacy regarding its concern for budgeting as an essential organizational routine in decision-making. Budgeting has also become a direct concern to organizational institutional theory (OIT) because of its prominent role in institution building, where budgeting can build trust in inter-organizational relationships. This paper builds on these two perspectives to explore organizational budget processes' formation, disruption, and re-creation over time.

We conducted a comprehensive review and critical analysis of the ODM and OIT perspectives, focusing on a fundamental paradox between ODM's emphasis on stability through organizational routines and OIT's focus on organizational legitimacy through the decoupled expression of organizational values. We then expanded on these paradoxical concerns in the context of budgeting, formalizing them into specific research propositions for future studies.

Tensions around the stability, decay, and re-creation of budgets as organizational routines emerge as a pressing issue requiring further empirical investigation from the ODM perspective. A critical issue in the OIT perspective is the potential for organizational budgets to provide an opportunity to decouple from practice through routinized expressions of rationality and to facilitate loose coupling in practice. These findings offer a fresh perspective and open up new avenues for future research in this area.

This paper contributes to the accounting and organizational research literature by shedding light on how organizations respond to the potential decay of budget routines and the manifestation of organizational values in decoupling processes by further re-creating and elaborating budget processes.