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In banking services, how customers interact with their bank – whether it’s through the physical branch or online – plays a significant role in how they feel about the experience. It’s not just about performing the service; it’s also about building trust, keeping them committed and getting them engaged so that a long-lasting relationship is developed. While there’s abundant research about trust and commitment in banking, not many studies have looked at how customers see both the online and offline sides of banking, especially in credit unions. Credit unions emphasize proximity with members, so it’s important to understand how these different ways of interacting affect how much people trust the credit union and stay committed to it and how engaged they feel as members. This study aims to explore this issue.

A quantitative survey was conducted using partial least squares structural equation modeling (PLS-SEM). The survey was answered by 195 members of one of the most traditional credit unions in Brazil.

The results suggest that both face-to-face and digital channels’ dimensions impact trust, which in turn influences commitment. Engagement is influenced by both trust and commitment, providing inputs to the value co-creation process.

The research presents relevant contributions to academia by deepening the understanding of the role of different service channels in value co-creation and customer engagement. It also offers significant contributions to the cooperative, which can improve the member experience in interaction channels to consequently develop lasting relationships and stimulate the engagement of its members.

The purpose of this article is to analyze the state-of-the art in a systematic way, identifying the main research groups and their related topics. The types of studies found are fundamental for understanding the application of artificial neural networks (ANNs) in cemented soils and the potential for using the technique, as well as the feasibility of extrapolation to new geotechnical or civil and environmental engineering segments.

This work is characterized as being bibliometric and systematic research of an exploratory perspective of state-of-the-art. It also persuades the qualitative and quantitative data analysis of cemented soil improvement, biocemented or microbially induced calcite precipitation (MICP) soil improvement by prediction/modeling by ANN. This study sought to compile and study the state of the art of the topic which possibilities to have a critical view about the theme. To do so, two main databases were analyzed: Scopus and Web of Science. Systematic review techniques, as well as bibliometric indicators, were implemented.

This paper connected the network between the achievements of the researches and illustrated the main application of ANNs in soil improvement prediction, specifically on cemented-based soils and biocemented soils (e.g. MICP technique). Also, as a bibliometric and systematic review, this work could achieve the key points in the absence of researches involving soil-ANN, and it provided the understanding of the lack of exploratory studies to be approached in the near future.

Because of the research topic the article suggested other applications of ANNs in geotechnical engineering, such as other tests not related to geomechanical resistance such as unconfined compression test test and triaxial test.

This article systematically and critically presents some interesting points in the direction of future research, such as the non-approach to the use of ANNs in biocementation processes, such as MICP.

Regarding the social environment, the paper brings approaches on methods that somehow mitigate the computational use, or elements necessary for geotechnical improvement of the soil, thereby optimizing the same consequently.

Neural networks have been studied for a long time in engineering, but the current computational power has increased the implementation for several engineering applications. Besides that, soil cementation is a widespread technique and its prediction modes often require high computational strength, such parameters can be mitigated with the use of ANNs, because artificial intelligence seeks learning from the implementation of the data set, reducing computational cost and increasing accuracy.