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To enhance the understanding of the moderating influence of different bottom of the pyramid (BOP) income segments on the antecedents of pro-poor innovation acceptance.

In this study, 320 BOP consumers with a range of low-to-moderate literacy and low-income levels were used as a convenience non-probability sample for undertaking quantitative analyses.

Only the influence of perceived usefulness on intention is moderated by income segments, such that the effect will be stronger for low-income BOP segment. Moreover, the influences of relative advantage, compatibility and observability on intention are moderated by income segments.

This empirical work has considerable private sector and public policy implications for companies and government designing/selling products for millions of poor people in developing and emerging economies.

This study contributes originally to knowledge in the subject area as there are very few studies that clearly and systematically analyse the key antecedents influencing the adoption intention of pro-poor technological innovations in the BOP market.

Material extrusion is one of the most commonly used approaches within the additive manufacturing processes available. Despite its popularity and related technical advancements, process reliability and quality assurance remain only partially solved. In particular, the surface roughness caused by this process is a key concern. To solve this constraint, experimental plans have been exploited to optimize surface roughness in recent years. However, the latter empirical trial and error process is extremely time- and resource consuming. Thus, this study aims to avoid using large experimental programs to optimize surface roughness in material extrusion.

This research provides an in-depth analysis of the effect of several printing parameters: layer height, printing temperature, printing speed and wall thickness. The proposed data-driven predictive modeling approach takes advantage of Machine Learning (ML) models to automatically predict surface roughness based on the data gathered from the literature and the experimental data generated for testing.

Using ten-fold cross-validation of data gathered from the literature, the proposed ML solution attains a 0.93 correlation with a mean absolute percentage error of 13%. When testing with our own data, the correlation diminishes to 0.79 and the mean absolute percentage error reduces to 8%. Thus, the solution for predicting surface roughness in extrusion-based printing offers competitive results regarding the variability of the analyzed factors.

There are limitations in obtaining large volumes of reliable data, and the variability of the material extrusion process is relatively high.

Although ML is not a novel methodology in additive manufacturing, the use of published data from multiple sources has barely been exploited to train predictive models. As available manufacturing data continue to increase on a daily basis, the ability to learn from these large volumes of data is critical in future manufacturing and science. Specifically, the power of ML helps model surface roughness with limited experimental tests.