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In the competitive hospitality sector, the adoption of Artificial Intelligence (AI) is essential for enhancing operational efficiency and improving customer experiences. This study explores how key entrepreneurial traits—Entrepreneurial Orientation (EO), Entrepreneurial Self-Efficacy (ESE), and Perceived Business Performance (PBP)—influence managers' attitudes toward adopting AI in small- and medium-sized enterprises (SMEs). Ts research utilizes data from 287 respondents, gathered through field research with a survey designed to measure the relationships among constructs, employing structural equation modeling (SEM) for analysis. Results reveal that PBP and certain ESE dimensions, such as Initiating Investor Relationships and Developing New Products, have only a modest positive effect on AI adoption. In contrast, EO—specifically Proactiveness and Innovativeness—exhibits a weak negative impact. Importantly, none of these factors directly affect managers' attitudes toward AI. Instead, this study highlights that managers' positive attitudes are the strongest predictors of AI adoption, aligning with the Technology Acceptance Model (TAM). The findings offer new insights into key entrepreneurial factors driving AI adoption and emphasize the need for targeted education and supportive policies to facilitate AI integration in hospitality SMEs. Fostering a positive perspective on AI is more important for adoption than overcoming skepticism, as negative attitudes do not influence AI adoption. [ABSTRACT FROM AUTHOR]

Occupational safety, reflecting the likelihood of work-related accidents, is crucial in work systems. A risk management model identifies, analyzes, and prioritizes risks, followed by the strategic application of resources to mitigate, monitor, and control the probability and impact of future events. Models integrating safety, ergonomics, and operational efficiency in risk management are non-existent, especially in the food retail sector. The proposed risk management model assigns the risk level to Safety using the Hazard Identification and Risk Assessment index (HIRA), an integral part of the Global Safety Index (GSI), both indices with five risk levels: 1 to 5 (acceptable to very critical). The organizational hierarchy of the evaluated company includes levels from microtask to insignia. The research aims to apply the HIRA index from the microtask to the area level. The HIRA application was conducted in a food retail company, starting with the identification and characterization of tasks in the "food" section and "fresh products" area (butchery, fishmonger, bakery, charcuterie/takeaway, and fruits and vegetables sections). The risk level of each microtask was assessed, then aggregated to higher organizational levels. Results showed that two new solutions reduced the safety risk in the mentioned sections proving the HIRA value as decision-making tool. [ABSTRACT FROM AUTHOR]

The increase in greenhouse gas emissions prompts the transport sector towards new technological perspectives on personal mobility. Addressing sustainable mobility through electric micromobility requires interdisciplinary design research methods and approaches. In the context of the LEONARDO project, funded under the Horizon 2020 framework, this paper addresses a critical literature review on the design thinking, design research models, tools, and mixed methods to be undertaken for driving product mobility innovation in a cross-disciplinary context. Following the "research through design" research strategy, the authors applied the Double-Diamond design thinking model to frame the design research process in four phases, aligning with three overarching objectives, four specific research objectives, and 24 research tasks, supported by a total of 71 mixed methods and tools. As a result, the transdisciplinary process provides a co-designed energy-efficient stand-alone microvehicle and a scalable interdisciplinary design model for urban transport product innovation. In conclusion, this case study suggests the value of the Double-Diamond design thinking model as a design research instrument capable of addressing sustainable mobility and guiding interdisciplinary design research, design practice, and education in the industrial engineering and design disciplinary sectors. [ABSTRACT FROM AUTHOR]

With the widespread availability and pervasiveness of artificial intelligence (AI) in many application areas across the globe, the role of crowdsourcing has seen an upsurge in terms of importance for scaling up data-driven algorithms in rapid cycles through a relatively low-cost distributed workforce or even on a volunteer basis. However, there is a lack of systematic and empirical examination of the interplay among the processes and activities combining crowd-machine hybrid interaction. To uncover the enduring aspects characterizing the human-centered AI design space when involving ensembles of crowds and algorithms and their symbiotic relations and requirements, a Computer-Supported Cooperative Work (CSCW) lens strongly rooted in the taxonomic tradition of conceptual scheme development is taken with the aim of aggregating and characterizing some of the main component entities in the burgeoning domain of hybrid crowd-AI centered systems. The goal of this article is thus to propose a theoretically grounded and empirically validated analytical framework for the study of crowd-machine interaction and its environment. Based on a scoping review and several cross-sectional analyses of research studies comprising hybrid forms of human interaction with AI systems and applications at a crowd scale, the available literature was distilled and incorporated into a unifying framework comprised of taxonomic units distributed across integration dimensions that range from the original time and space axes in which every collaborative activity take place to the main attributes that constitute a hybrid intelligence architecture. The upshot is that when turning to the challenges that are inherent in tasks requiring massive participation, novel properties can be obtained for a set of potential scenarios that go beyond the single experience of a human interacting with the technology to comprise a vast set of massive machine-crowd interactions. [ABSTRACT FROM AUTHOR]

Architectural design decisions are primarily made through an interaction between an architect and a client during the conceptual design phase. However, in larger-scale public architecture projects, the client is frequently represented by a community that embraces numerous stakeholders. The scale, social diversity, and political layers of such collective clients make their interaction with architects challenging. A solution to address this challenge is using new information technologies that automate design interactions on an urban scale through crowdsourcing and artificial intelligence technologies. However, since such technologies have not yet been applied and tested in field conditions, it remains unknown how communities interact with such systems and whether useful concept designs can be produced in this way. To fill this gap in the literature, this paper reports the results of a case study architecture project where a novel crowdsourcing system was used to automate interactions with a community. The results of both quantitative and qualitative analyses revealed the effectiveness of our approach, which resulted in high-level stakeholder satisfaction and yielded conceptual designs that better reflect stakeholders' preferences. Along with identifying opportunities for using advanced technologies to automate design interactions in the concept design phase, we also highlight the challenges of such technologies, thus warranting future research. [ABSTRACT FROM AUTHOR]

Social loafing, the act of withholding effort in teams, has been identified as an important problem in virtual teams. A lack of social control and the inability to observe or trust that others are fulfilling their commitments are often cited as major causes of social loafing in virtual teams where there is geographic dispersion and a reliance on electronic communications. Yet, more research is needed to better understand such claims. The goal of this study was to examine the impact of control and trust on social loafing in virtual teams. To accomplish this, we proposed and empirically tested a multi-level research model that explains the relationships among team controls, trust, social loafing, and team performance. We tested the model with 272 information technology employees in 39 virtual teams. Results indicate that control and trust reduce social loafing separately and also jointly. [ABSTRACT FROM AUTHOR]

One of the primary tasks for commercial recommender systems is to predict the probabilities of users clicking items, e.g., advertisements, music and products. This is because such predictions have a decisive impact on profitability. The classic recommendation algorithm, collaborative filtering (CF), still plays a vital role in many industrial recommender systems. However, although straight CF is good at capturing similar users' preferences for items based on their past interactions, it lacks regarding (1) modeling the influences of users' sequential patterns from their individual history interaction sequences and (2) the relevance of users' and items' attributes. In this work, we developed an attention-based latent information extraction network (ALIEN) for click-through rate prediction, to integrate (1) implicit user similarity in terms of click patterns (analogous to CF), and (2) modeling the low and high-order feature interactions and (3) historical sequence information. The new model is based on the deep learning, which goes beyond the capabilities of econometric approaches, such as matrix factorization (MF) and k-means. In addition, the approach provides explainability to the recommendation by interpreting the contributions of different features and historical interactions. We have conducted experiments on real-world datasets that demonstrate considerable improvements over strong baselines. [ABSTRACT FROM AUTHOR]

The idea that closer things are more related than distant things, known as 'Tobler's first law of geography', is fundamental to understanding many spatial processes. If this concept applies to volunteered geographic information (VGI), it could help to efficiently allocate tasks in citizen science campaigns and help to improve the overall quality of collected data. In this paper, we use classifications of satellite imagery by volunteers from around the world to test whether local familiarity with landscapes helps their performance. Our results show that volunteers identify cropland slightly better within their home country, and do slightly worse as a function of linear distance between their home and the location represented in an image. Volunteers with a professional background in remote sensing or land cover did no better than the general population at this task, but they did not show the decline with distance that was seen among other participants. Even in a landscape where pasture is easily confused for cropland, regional residents demonstrated no advantage. Where we did find evidence for local knowledge aiding classification performance, the realized impact of this effect was tiny. Rather, the inherent difficulty of a task is a much more important predictor of volunteer performance. These findings suggest that, at least for simple tasks, the geographical origin of VGI volunteers has little impact on their ability to complete image classifications. [ABSTRACT FROM AUTHOR]

This study investigates how collective intelligence emerges in crowdsourcing for architectural design. Previous studies have revealed that collective intelligence emerges from collaboration and can outperform individual intelligence. As design is a highly collaborative practice, collective intelligence plays a vital role in the design process. In this study, we compare the structure of two architectural design crowdsourcing systems using several methods. The results of the analysis suggest that design crowdsourcing systems can give rise to the following three types of collective intelligence: (1) discussive, which emerges from an conversation between designers and clients; (2) synthetic, which emerges from a parallel and sequential design development; and (3) evaluative, which is based on the wisdom of the crowd in evaluating and selecting designs. The article concludes with recommendations for collaborative design method. [ABSTRACT FROM AUTHOR]

Thanks to recent technological advances, a new generation of low-cost, small, unmanned aerial vehicles (UAVs) is available. Small UAVs, often called drones, are enabling unprecedented applications but, at the same time, new threats are arising linked to their possible misuse (e.g., drug smuggling, terrorist attacks, espionage). In this paper, the main challenges related to the problem of drone identification are discussed, which include detection, possible verification, and classification. An overview of the most relevant technologies is provided, which in modern surveillance systems are composed into a network of spatially-distributed sensors to ensure full coverage of the monitored area. More specifically, the main focus is on the frequency modulated continuous wave (FMCW) radar sensor, which is a key technology also due to its low cost and capability to work at relatively long distances, as well as strong robustness to illumination and weather conditions. This paper provides a review of the existing literature on the most promising approaches adopted in the different phases of the identification process, i.e., detection of the possible presence of drones, target verification, and classification. [ABSTRACT FROM AUTHOR]

The utilization of crowdsourcing to acquire distant knowledge is increasing. In the new product development process, sustainable crowdsourcing is an effective way to exploit both external and internal resources to boost enterprise innovation quality and the efficiency of the competitive edge of macro tasks in a relatively long cycle. The challenge of sustainable crowdsourcing is how to design a proper incentive mechanism to achieve the maximum initiator profit and, at the same time, satisfy the solver's motivation so that they can continuously participate in the innovation process. In two situations, including a single motivation and multiple motivations of the solver, this paper analyzed the impact of a few factors on the initiator's profit and the incentive coefficient for the solver based on the Principal–Agent Model. From the model and simulation results, the solver's incentive coefficient is positively correlated to the solver's work quality and negatively correlated to the uncertainty of the enterprise operation, the solver's Effort Cost, the solver's degree of risk aversion, etc. If the initiator is more sensitive to the benefits of the solver's intrinsic motivation, the monetary incentive will be higher. The research results provide a theoretical basis to quantify the initiator's expected profit and design a proper incentive plan for the solver. Finally, the conclusions offer practical guidance for enterprise to execute incentive plans for sustainable crowdsourcing from the perspective of the solver's motivation. [ABSTRACT FROM AUTHOR]