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Identity regulation is part of a management control package. Organizations regulate employees’ self-identity to influence their behaviors. The success of this regulation depends on its trade-off with employees’ work identities and personalities. Organizational discourse nurtures this dynamic and interactive process. We focus on the regulation of an (undesired) organizational identity that is born at the intersection of race/ethnicity, gender, sex and migrant discrimination in accounting-related positions. We aim to analyze how Latina accountants who migrate to Spain perceive that their triple status as Latina, women and migrants affects their careers as accountants and interpret whether this triple intersectional discrimination aims to create a Latina accountant’s self-identity.

This critical study follows a phenomenological approach to analyze the experiences of women born in Latin America who migrated to Spain to occupy accounting-related positions. A thematic analysis of their semi-structured interviews allowed us to examine the challenges faced by Latina accountants in their accounting careers in Spain.

Our interviewees' narratives display an internalization of, even resignation to, a self-identity that we label “Latina accountant identity.” This identity is based on explicit discrimination discourses that cause them to suffer from the intersection of racism, sexism and migrant conditions and is nurtured by the discourses of their senior managers, co-workers and subordinates.

To the best of our knowledge, this is the first study to frame the regulation of an intersectional discriminatory identity that is used to control Latina accountants from the inside, acting on the triple condition of Latinas, women and foreigners, influencing their self-perceptions regarding work and personal lives.

In the wine industry, maintaining superior quality standards is crucial to meet the expectations of both producers and consumers. Traditional approaches to assessing wine quality involve labor-intensive processes and rely on the expertise of connoisseurs proficient in identifying taste profiles and key quality factors. In this research, we introduce an innovative and efficient approach centered on the analysis of volatile organic compounds (VOCs) signals using an electronic nose, thereby empowering nonexperts to accurately assess wine quality.

To devise an optimal algorithm for this purpose, we conducted four computational experiments, culminating in the development of a specialized deep learning network. This network seamlessly integrates 1D-convolutional and long-short-term memory layers, tailor-made for the intricate task at hand. Rigorous validation ensued, employing a leave-one-out cross-validation methodology to scrutinize the efficacy of our design.

The outcomes of these e-demonstrates were subjected to meticulous evaluation and analysis, which unequivocally demonstrate that our proposed architecture consistently attains promising recognition accuracies, ranging impressively from 87.8% to an astonishing 99.41%. All this is achieved within a remarkably brief timeframe of a mere 4 seconds. These compelling findings have far-reaching implications, promising to revolutionize the assessment and tracking of wine quality, ultimately affording substantial benefits to the wine industry and all its stakeholders, with a particular focus on the critical aspect of VOCs signal analysis.

This research has not been published anywhere else.