Abstract
Integrating emerging technologies, such as AI, the Metaverse, and IoT, revolutionizes management and brand practices. Brands can create captivating virtual experiences within the metaverse, including virtual storefronts and interactive events. Scientific data on brand management in the metaverse must be improved due to the concept’s early-stage development. While virtual environments exist, they do not fully encompass the metaverse’s scope. So, this research bridges this gap by exploring the relationship between brand management and the metaverse, focusing on consumer perceptions and their contribution to brand equity in this virtual realm. Netnography with a data mining approach was the methodology followed in this paper. Data were extracted by a metaverse community on the Reddit platform and, in total, 696 posts and comments were analyzed from June 2022 until May 2023. The results highlighted a positive and favorable consumer perception of brand management in the metaverse reality. This research contributes to the emerging field of metaverse brand management, investigating the impact of consumer perceptions on brand equity. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Abstract
Organizations aim to achieve operational efficiency capable of responding to high market competitiveness. The implementation of automation systems in organizational processes is a key factor in improving operational efficiency. This paper intends to contribute for a better understanding of the adoption of automation systems in organizations and analyze their impact on employee performance, considering the conditions under which they were implemented. The methodology for this study was qualitative research, in which semi-structured exploratory interviews conducted with employees from the Accounts Receivable department of automotive sector companies were carried out. The main goal was to understand their perception of the use of automation systems in their work tasks. The results of this research led to the conclusion that automation systems, even when underutilized, are beneficial in reducing repetitive and manual tasks. Nevertheless, the way in which they are implemented has a direct impact on the motivation of employees to use them.

Abstract
Online microtask labor has increased its role in the last few years and has provided the possibility of people who were usually excluded from the labor market to work anytime and without geographical barriers. While this brings new opportunities for people to work remotely, it can also pose challenges regarding the difficulty of assigning tasks to workers according to their abilities. To this end, cognitive personalization can be used to assess the cognitive profile of each worker and subsequently match those workers to the most appropriate type of work that is available on the digital labor market. In this regard, we believe that the time is ripe for a review of the current state of research on cognitive personalization for digital labor. The present study was conducted by following the recommended guidelines for the software engineering domain through a systematic literature review that led to the analysis of 20 primary studies published from 2010 to 2020. The results report the application of several cognition theories derived from the field of psychology, which in turn revealed an apparent presence of studies indicating accurate levels of cognitive personalization in digital labor in addition to a potential increase in the worker's performance, most frequently investigated in crowdsourcing settings. In view of this, the present essay seeks to contribute to the identification of several gaps and opportunities for future research in order to enhance the personalization of online labor, which has the potential of increasing both worker motivation and the quality of digital work.

Abstract
We present new Very Large Telescope Interferometer (VLTI)/GRAVITY near-infrared interferometric measurements of the angular size of the innermost hot dust continuum for 14 type 1 active galactic nuclei (AGNs). The angular sizes are resolved on scales of similar to 0.7 mas and the inferred ring radii range from 0.028 to 1.33 pc, comparable to those reported previously and a factor of 10-20 smaller than the mid-infrared sizes in the literature. Combining our new data with previously published values, we compiled a sample of 25 AGNs with bolometric luminosity ranging from 10(42) to 10(47) erg s(-1), with which we studied the radius-luminosity (R - L) relation for the hot dust structure. Our interferometric measurements of radius are offset by a factor of 2 from the equivalent relation derived through reverberation mapping. Using a simple model to explore the dust structure's geometry, we conclude that this offset can be explained if the 2 mu m emitting surface has a concave shape. Our data show that the slope of the relation is in line with the canonical R proportional to L-0.5 when using an appropriately non-linear correction for bolometric luminosity. In contrast, using optical luminosity or applying a constant bolometric correction to it results in a significant deviation in the slope, suggesting a potential luminosity dependence on the spectral energy distribution. Over four orders of magnitude in luminosity, the intrinsic scatter around the R - L relation is 0.2 dex, suggesting a tight correlation between the innermost hot dust structure size and the AGN luminosity.

Abstract
Geophysical studies on the lunar surface have, in the past, used various methods that contribute not only towards the knowledge of the lunar subsurface but also contribute towards the design of future lunar missions, namely those that will, in the near future, take humans to the Moon’s surface. This work analyzes a specific set of ground penetrating radar (GPR) data, collected during the Chang’E-4 mission of the Chinese Space Agency, using theYutu-2 rover within the von Kármán crater, on the far-side of the Moon. From this dataset two electrical parameters were estimated. The approach uses the backscatter of the electromagnetic wavefield in order to obtain estimates of the real component of the complex relative permittivity as well as the electrical resistivity. © 2024 International Multidisciplinary Scientific Geoconference. All rights reserved.

Abstract
The classic literature about innovation conveys innovation strategy the leading and starting role to generate business growth due to technology development and more effective managerial practices. The advent of Artificial Intelligence (AI) however reverts this paradigm in the context of Industry 5.0. The focus is moving from how innovation fosters AI to how AI fosters innovation. Therefore, our research question can be stated as follows: What factors influence the effect of AI on Innovation Capacity in the context of Industry 5.0? To address this question we conduct a scoping review of a vast body of literature spanning engineering, human sciences, and management science. We conduct a keyword-based literature search completed by bibliographic analysis, then classify the resulting 333 works into 3 classes and 15 clusters which we critically analyze. We extract 3 hypotheses setting associations between 4 factors: company age, AI maturity, manufacturing strategy, and innovation capacity. The review uncovers several debates and research gaps left unsolved by the existing literature. In particular, it raises the debate whether the Industry5.0 promise can be achieved while Artificial General Intelligence (AGI) remains out of reach. It explores diverging possible futures driven toward social manufacturing or mass customization. Finally, it discusses alternative AI policies and their incidence on open and internal innovation. We conclude that the effect of AI on innovation capacity can be synergic, deceptive, or substitutive depending on the alignment of the uncovered factors. Moreover, we identify a set of 12 indicators enabling us to measure these factors to predict AI's effect on innovation capacity. These findings provide researchers with a new understanding of the interplay between artificial intelligence and human intelligence. They provide practitioners with decision metrics for a successful transition to Industry 5.0.