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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.

This study aims to explore the impact of store atmospherics on urban bottom-of-the-pyramid consumers’ behavioural intentions to purchase apparel in an emerging African market context. This study also considers purchase antecedents to attitude, perceived behavioural control and social norms as determinants of urban bottom-of-the-pyramid consumers’ apparel behavioural intentions.

Using non-probability sampling, specifically purposive and interlocking sampling, data collection was secured from 881 economically active Namibian urban bottom-of-the-pyramid respondents through interviewer-administered questionnaires. Covariance-based structural equation modelling assessed the significant relationships among all constructs in the conceptual model.

This study found that for favourable apparel behavioural intentions of urban bottom-of-the-pyramid consumers to occur, apparel retailers should emphasise trust, perceived awareness and self-identity through apparel assortment and groupings, easy-to-read visible signage, together with competent, friendly and respectful sales personnel in their store atmospherics.

The findings of this study may guide apparel retailers in other emerging African markets to develop regional integration, market-based solutions and inclusive economic growth focusing on “non-essential” products, such as apparel, among urban bottom-of-the-pyramid consumers.

This study expands the intellectual boundaries of urban bottom-of-the-pyramid consumers’ behavioural intentions towards “non-essential” products. The theoretical framework supports the integration of both the stimulus-organism-response model and the theory of planned behaviour into one single model for empirical investigation. Additionally, adopting a novel theoretical framework helped identify the impact of store atmospherics from a bottom-of-the-pyramid perspective in an emerging African market context, such as Namibia.