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To understand how organizations, public or private, must increase their productivity perception (PP), independently of the sector. This article aims to analyze PP in the digital transformation (DT) process to determine how it is affected by technostress (TS) and work engagement (WE), two concepts that seem to be forces opposing PP.

The authors use data from a questionnaire addressed to personnel in two organizations (public and private). The analysis applies partial least squares technique to the 505 valid responses obtained from these organizations. This analysis is based not on representativeness but on uniqueness.

The results suggest a positive, significant relationship between DT and PP. This article integrates DT and its effects on aspects of people's health, PP and WE. The model thus includes interactions of technology with human elements. In both business and administrative environments, PP is key to optimizing resources and survival of organizations.

DT processes are different and complex because every organization is different. The authors recommend expanding this study to other sectors in both spheres, public and private. Aligning the objectives of the institutions for aid with DT is also quite complicated.

This study contributes to improving participating organizations. It also provides government institutions with a clear foundation from which to encourage actions that promote the health and WE of their workforce without reducing productivity. In addition, this study adds novelty to the research line.

The authors have deepened this line of research by developing fuller knowledge of the relationships among novel and necessary variables in organizations. The authors provide complementary, different and inspiring value in addressing this line of research.

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.