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The study aimed to test the validity and reliability of the Polish version of the identity leadership inventory (ILI) proposed by Steffens, Haslam, Reicher et al. (2014) and to confirm the relationship between identity leadership and various job-related outcomes (i.e., trust in leaders, job satisfaction, work engagement and turnover intentions) among employees from Poland-based organizations. Identity leadership appears to be a universal construct (van Dick, Ciampa, & Liang, 2018) but no one has studied it in Poland so far.

The sample consisted of 1078 employees collected in two independent subsamples from different organizations located in Northern and Central Poland. We evaluated the ILI’s factorial structure using confirmatory factor analysis.

The results confirm that the 15-item Polish version of the ILI has a four-dimensional structure with factors representing prototypicality, advancement, entrepreneurship and impresarioship. It showed satisfactory reliability. The identity leadership inventory-short form (four items) also showed a good fit with the data. As expected, the relationships between identity leadership and important work-related outcomes (general level of job satisfaction, work engagement, trust toward the leader and turnover intentions) were also significant.

Despite the cultural specifics of Polish organizations, the research results were generally very similar to those in other countries, confirming the universality of the ILI as shown in the Global Identity Leadership Development project (GILD, see van Dick, Ciampa, & Liang, 2018; van Dick et al., 2021).

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.