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This paper aims to contribute to the ongoing methodological discussions surrounding the adoption of ethnographic approaches in accounting by undertaking a comparative analysis of ethnography in anthropology and ethnography in qualitative accounting research. By doing so, it abductively speculates on the factors influencing the distinct characteristics of ethnography in accounting and explores their implications.

This paper uses a comparative approach, organizing the comparison using Van Maanen’s (2011a, 2011b) framework of field-, head- and text-work phases in ethnography. Furthermore, it draws on the author’s experience as a qualitative researcher who has conducted ethnographic research for more than a decade across the disciplines of anthropology and accounting, as well as for non-academic organizations, to provide illustrative examples for the comparison.

This paper finds that ethnography in accounting, when compared to its counterpart in anthropology, demonstrates a stronger inclination towards scientific aspirations. This is evidenced by its prevalence of realist tales, a high emphasis on “methodological rigour”, a focus on high-level theorization and other similar characteristics. Furthermore, the scientific aspiration and hegemony of the positivist paradigm in accounting research, when leading to a change of the evaluation criteria of non-positivist research, generate an impoverishment of interpretive and ethnographic research in accounting.

This paper provides critical insights from a comparative perspective, highlighting the marginalized position of ethnography in accounting research. By understanding the mechanisms of marginalization, the paper commits to reflexivity and advocates for meaningful changes within the field.

In laser powder bed fusion (L-PBF) additive manufacturing, spatter particles are ejected from the melt pool and can be detrimental to material performance and powder recycling. Quantifying spatter generation with respect to processing conditions is a step toward mitigating spatter and better understanding the phenomenon. This paper reveals process insights of spatter phenomena by automatically annotating spatter particles in high-speed video observations using machine learning.

A high-speed camera was used to observe the L-PBF process while varying laser power, laser scan speed and scan strategy on a constant geometry on an EOSM290 using Ti-6Al-4V powder. Two separate convolutional neural networks were trained to segment and track spatter particles in captured high-speed videos for spatter characterization.

Spatter generation and ejection angle significantly differ between keyhole and conduction mode melting. High laser powers lead to large ejections at the beginning of scan lines. Slow and fast build rates produce more spatter than moderate build rates at constant energy density. Scan strategies with more scan vectors lead to more spatter. The presence of powder significantly increases the amount of spatter generated during the process.

With the ability to automatically annotate a large volume of high-speed video data sets with high accuracy, an experimental design of observed parameter changes reveals quantitively stark changes in spatter morphology that can aid process development to mitigate spatter occurrence and impacts on material performance.