13,011 research outputs found
Economia colaborativa
A importĂąncia de se proceder Ă anĂĄlise dos principais desafios jurĂdicos que a economia colaborativa coloca â pelas implicaçÔes que as mudanças de paradigma dos modelos de negĂłcios e dos sujeitos envolvidos suscitam â Ă© indiscutĂvel, correspondendo Ă necessidade de se fomentar a segurança jurĂdica destas prĂĄticas, potenciadoras de crescimento econĂłmico e bem-estar social.
O Centro de Investigação em Justiça e Governação (JusGov) constituiu uma equipa multidisciplinar que, alĂ©m de juristas, integra investigadores de outras ĂĄreas, como a economia e a gestĂŁo, dos vĂĄrios grupos do JusGov â embora com especial participação dos investigadores que integram o grupo E-TEC (Estado, Empresa e Tecnologia) â e de outras prestigiadas instituiçÔes nacionais e internacionais, para desenvolver um projeto neste domĂnio, com o objetivo de identificar os problemas jurĂdicos que a economia colaborativa suscita e avaliar se jĂĄ existem soluçÔes para aqueles, refletindo igualmente sobre a conveniĂȘncia de serem introduzidas alteraçÔes ou se serĂĄ mesmo necessĂĄrio criar nova regulamentação.
O resultado desta investigação Ă© apresentado nesta obra, com o que se pretende fomentar a continuação do debate sobre este tema.Esta obra Ă© financiada por fundos nacionais atravĂ©s da FCT â Fundação para a CiĂȘncia e a Tecnologia, I.P., no Ăąmbito do Financiamento UID/05749/202
Corporate Social Responsibility: the institutionalization of ESG
Understanding the impact of Corporate Social Responsibility (CSR) on firm performance as it relates to industries reliant on technological innovation is a complex and perpetually evolving challenge. To thoroughly investigate this topic, this dissertation will adopt an economics-based structure to address three primary hypotheses. This structure allows for each hypothesis to essentially be a standalone empirical paper, unified by an overall analysis of the nature of impact that ESG has on firm performance. The first hypothesis explores the evolution of CSR to the modern quantified iteration of ESG has led to the institutionalization and standardization of the CSR concept. The second hypothesis fills gaps in existing literature testing the relationship between firm performance and ESG by finding that the relationship is significantly positive in long-term, strategic metrics (ROA and ROIC) and that there is no correlation in short-term metrics (ROE and ROS). Finally, the third hypothesis states that if a firm has a long-term strategic ESG plan, as proxied by the publication of CSR reports, then it is more resilience to damage from controversies. This is supported by the finding that pro-ESG firms consistently fared better than their counterparts in both financial and ESG performance, even in the event of a controversy. However, firms with consistent reporting are also held to a higher standard than their nonreporting peers, suggesting a higher risk and higher reward dynamic. These findings support the theory of good management, in that long-term strategic planning is both immediately economically beneficial and serves as a means of risk management and social impact mitigation. Overall, this contributes to the literature by fillings gaps in the nature of impact that ESG has on firm performance, particularly from a management perspective
Regulating US Big Tech Companies beyond Local Markets: Building African Digital Markets: (a Theoretical Perspective)
This paper is the first â theory-setting â building block of a broader project on the challenges the transition to an algorithmic society poses in a developmental context. The aim of the overall project is to unearth the day-to-day enforcement hurdles algorithmic and data-driven business models create for developing countries and their economic regulators in particular. To that end, we focus on competition and sectoral regulators in Africa as the units for an upcoming empirical analysis. An initial inventory of existing literature shows that challenges exist at several levels in a spatial sense â the lack of experience with big tech at national level, the lack of coordination between budding Regional Competition Regimes (RCR) in Africa and the lack of a world competition regulator in the aftermath of the failed attempt to designate the WTO as such. Additionally, there is a temporal mismatch between the levels of advancement in enforcement against big tech, with the EU being far ahead from the US, and Africa still lagging even further behind. Given the above context, the current paper discusses several theoretical approaches for the study, contextualization and normative interpretation of the above and further developments that subsequent empirical work will reveal. The main problem we explore in this paper is the issue of divergence, which we then approach through several legal tools allowing for coherence. More specifically, we theoretically explore the opportunities and limitations for cohering by legal means through 1) legal emulation from the most experienced enforcer (the EU or US) or 2) through âhome-grownâ digital-competition-building exercise within the auspices of African RCRs
Coincidental Generation
Generative AI models are emerging as a versatile tool across diverse
industries with applications in synthetic data generation computational art
personalization of products and services and immersive entertainment Here we
introduce a new privacy concern in the adoption and use of generative AI models
that of coincidental generation Coincidental generation occurs when a models
output inadvertently bears a likeness to a realworld entity Consider for
example synthetic portrait generators which are today deployed in commercial
applications such as virtual modeling agencies and synthetic stock photography
We argue that the low intrinsic dimensionality of human face perception implies
that every synthetically generated face will coincidentally resemble an actual
person all but guaranteeing a privacy violation in the form of a
misappropriation of likeness
A Proposed Meta-Reality Immersive Development Pipeline: Generative AI Models and Extended Reality (XR) Content for the Metaverse
The realization of an interoperable and scalable virtual platform, currently known as the âmetaverse,â is inevitable, but many technological challenges need to be overcome first. With the metaverse still in a nascent phase, research currently indicates that building a new 3D social environment capable of interoperable avatars and digital transactions will represent most of the initial investment in time and capital. The return on investment, however, is worth the financial risk for firms like Meta, Google, and Apple. While the current virtual space of the metaverse is worth 84.09 billion by the end of 2028. But the creation of an entire alternate virtual universe of 3D avatars, objects, and otherworldly cityscapes calls for a new development pipeline and workflow. Existing 3D modeling and digital twin processes, already well-established in industry and gaming, will be ported to support the need to architect and furnish this new digital world. The current development pipeline, however, is cumbersome, expensive and limited in output capacity. This paper proposes a new and innovative immersive development pipeline leveraging the recent advances in artificial intelligence (AI) for 3D model creation and optimization. The previous reliance on 3D modeling software to create assets and then import into a game engine can be replaced with nearly instantaneous content creation with AI. While AI art generators like DALL-E 2 and DeepAI have been used for 2D asset creation, when combined with game engine technology, such as Unreal Engine 5 and virtualized geometry systems like Nanite, a new process for creating nearly unlimited content for immersive reality is possible. New processes and workflows, such as those proposed here, will revolutionize content creation and pave the way for Web 3.0, the metaverse and a truly 3D social environment
Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review
In this paper, a critical bibliometric analysis study is conducted, coupled
with an extensive literature survey on recent developments and associated
applications in machine learning research with a perspective on Africa. The
presented bibliometric analysis study consists of 2761 machine learning-related
documents, of which 98% were articles with at least 482 citations published in
903 journals during the past 30 years. Furthermore, the collated documents were
retrieved from the Science Citation Index EXPANDED, comprising research
publications from 54 African countries between 1993 and 2021. The bibliometric
study shows the visualization of the current landscape and future trends in
machine learning research and its application to facilitate future
collaborative research and knowledge exchange among authors from different
research institutions scattered across the African continent
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Comparison of value perception of children in playing videogames and traditional games: Turkish and British samples
The present study examined the value perceptions of Turkish and British children according to various variables and determined to what extent traditional and videogame genres preferred by children predict their value perceptions, dominant case design, one of the mixed research designs, was used. While the quantitative approach was mainly used in the research, the qualitative approach was used as a supporter. The data collection process of the study lasted for two academic years. In the first year, the study was conducted with 243 primary school students studying in Nottingham (England). In the following year it was conducted with 267 primary school students studying in Ankara (Turkey). A total of 510 primary school students with ages ranging from 9-11 years were recruited for the study. Data were collected using the âPersonal Information Formâ and âMoral Dilemma Stories Inventory for Childrenâ. Both were developed in English and then adapted into Turkish. It was found that, in both sample, girlsâ value perception scores were significantly higher than boysâ scores. Videogames primarily produced for entertainment were the most preferred games by Turkish and British children while educational and serious videogames were the least preferred. Traditional games in sports (soccer, cricket, etc.) were preferred more by British children, while traditional action games (dodgeball, playing tag etc.) were preferred more by Turkish children. It was found that action-adventure and role-playing videogames predicted childrenâs value perception negatively, and simulation and puzzle videogame genres predicted childrenâs value perceptions positively. Furthermore, movement-based traditional games (sports, action) predicted childrenâs value perceptions positively, while traditional competitive (racing) games predicted childrenâs value perceptions negatively
DIGITAL PROCTORING IN HIGHER EDUCATION: A SYSTEMATIC LITERATURE REVIEW
To improve the academic integrity of online examination, digital proctoring systems have been implemented in higher education worldwide, particularly during the COVID-19 pandemic. In this paper, we conducted a literature review of the research on digital proctoring in higher education. We found 115 relevant publications in nine databases. We applied topic modeling methods to analyze the corpus which resulted in eight topics. The review shows that the previous studies focus largely on the systemsâ development, adoption of the systems, the effects of proctored online exams on studentsâ performance, and the legal, ethical, security, and privacy issues of digital proctoring. The annual topic trends indicate future research concerns, such as systemsâ development, online programs (MOOCs) and proctoring, along with various issues of using digital proctoring. The results of the review provide useful insights as well as implications for future research on digital proctoring, a crucial process for digitalizing higher education
Modelling and Solving the Single-Airport Slot Allocation Problem
Currently, there are about 200 overly congested airports where airport capacity does not suffice to accommodate airline demand. These airports play a critical role in the global air transport system since they concern 40% of global passenger demand and act as a bottleneck for the entire air transport system. This imbalance between airport capacity and airline demand leads to excessive delays, as well as multi-billion economic, and huge environmental and societal costs. Concurrently, the implementation of airport capacity expansion projects requires time, space and is subject to significant resistance from local communities. As a short to medium-term response, Airport Slot Allocation (ASA) has been used as the main demand management mechanism. The main goal of this thesis is to improve ASA decision-making through the proposition of models and algorithms that provide enhanced ASA decision support. In doing so, this thesis is organised into three distinct chapters that shed light on the following questions (IâV), which remain untapped by the existing literature. In parentheses, we identify the chapters of this thesis that relate to each research question. I. How to improve the modelling of airline demand flexibility and the utility that each airline assigns to each available airport slot? (Chapters 2 and 4) II. How can one model the dynamic and endogenous adaptation of the airportâs landside and airside infrastructure to the characteristics of airline demand? (Chapter 2) III. How to consider operational delays in strategic ASA decision-making? (Chapter 3) IV. How to involve the pertinent stakeholders into the ASA decision-making process to select a commonly agreed schedule; and how can one reduce the inherent decision-complexity without compromising the quality and diversity of the schedules presented to the decision-makers? (Chapter 3) V. Given that the ASA process involves airlines (submitting requests for slots) and coordinators (assigning slots to requests based on a set of rules and priorities), how can one jointly consider the interactions between these two sides to improve ASA decision-making? (Chapter 4) With regards to research questions (I) and (II), the thesis proposes a Mixed Integer Programming (MIP) model that considers airlinesâ timing flexibility (research question I) and constraints that enable the dynamic and endogenous allocation of the airportâs resources (research question II). The proposed modelling variant addresses several additional problem characteristics and policy rules, and considers multiple efficiency objectives, while integrating all constraints that may affect airport slot scheduling decisions, including the asynchronous use of the different airport resources (runway, aprons, passenger terminal) and the endogenous consideration of the capabilities of the airportâs infrastructure to adapt to the airline demandâs characteristics and the aircraft/flight type associated with each request. The proposed model is integrated into a two-stage solution approach that considers all primary and several secondary policy rules of ASA. New combinatorial results and valid tightening inequalities that facilitate the solution of the problem are proposed and implemented. An extension of the above MIP model that considers the trade-offs among schedule displacement, maximum displacement, and the number of displaced requests, is integrated into a multi-objective solution framework. The proposed framework holistically considers the preferences of all ASA stakeholder groups (research question IV) concerning multiple performance metrics and models the operational delays associated with each airport schedule (research question III). The delays of each schedule/solution are macroscopically estimated, and a subtractive clustering algorithm and a parameter tuning routine reduce the inherent decision complexity by pruning non-dominated solutions without compromising the representativeness of the alternatives offered to the decision-makers (research question IV). Following the determination of the representative set, the expected delay estimates of each schedule are further refined by considering the whole airfieldâs operations, the landside, and the airside infrastructure. The representative schedules are ranked based on the preferences of all ASA stakeholder groups concerning each scheduleâs displacement-related and operational-delay performance. Finally, in considering the interactions between airlinesâ timing flexibility and utility, and the policy-based priorities assigned by the coordinator to each request (research question V), the thesis models the ASA problem as a two-sided matching game and provides guarantees on the stability of the proposed schedules. A Stable Airport Slot Allocation Model (SASAM) capitalises on the flexibility considerations introduced for addressing research question (I) through the exploitation of data submitted by the airlines during the ASA process and provides functions that proxy each requestâs value considering both the airlinesâ timing flexibility for each submitted request and the requestsâ prioritisation by the coordinators when considering the policy rules defining the ASA process. The thesis argues on the compliance of the proposed functions with the primary regulatory requirements of the ASA process and demonstrates their applicability for different types of slot requests. SASAM guarantees stability through sets of inequalities that prune allocations blocking the formation of stable schedules. A multi-objective Deferred-Acceptance (DA) algorithm guaranteeing the stability of each generated schedule is developed. The algorithm can generate all stable non-dominated points by considering the trade-off between the spilled airline and passenger demand and maximum displacement. The work conducted in this thesis addresses several problem characteristics and sheds light on their implications for ASA decision-making, hence having the potential to improve ASA decision-making. Our findings suggest that the consideration of airlinesâ timing flexibility (research question I) results in improved capacity utilisation and scheduling efficiency. The endogenous consideration of the ability of the airportâs infrastructure to adapt to the characteristics of airline demand (research question II) enables a more efficient representation of airport declared capacity that results in the scheduling of additional requests. The concurrent consideration of airlinesâ timing flexibility and the endogenous adaptation of airport resources to airline demand achieves an improved alignment between the airport infrastructure and the characteristics of airline demand, ergo proposing schedules of improved efficiency. The modelling and evaluation of the peak operational delays associated with the different airport schedules (research question III) provides allows the study of the implications of strategic ASA decision-making for operations and quantifies the impact of the airportâs declared capacity on each scheduleâs operational performance. In considering the preferences of the relevant ASA stakeholders (airlines, coordinators, airport, and air traffic authorities) concerning multiple operational and strategic ASA efficiency metrics (research question IV) the thesis assesses the impact of alternative preference considerations and indicates a commonly preferred schedule that balances the stakeholdersâ preferences. The proposition of representative subsets of alternative schedules reduces decision-complexity without significantly compromising the quality of the alternatives offered to the decision-making process (research question IV). The modelling of the ASA as a two-sided matching game (research question V), results in stable schedules consisting of request-to-slot assignments that provide no incentive to airlines and coordinators to reject or alter the proposed timings. Furthermore, the proposition of stable schedules results in more intensive use of airport capacity, while simultaneously improving scheduling efficiency. The models and algorithms developed as part of this thesis are tested using airline requests and airport capacity data from coordinated airports. Computational results that are relevant to the context of the considered airport instances provide evidence on the potential improvements for the current ASA process and facilitate data-driven policy and decision-making. In particular, with regards to the alignment of airline demand with the capabilities of the airportâs infrastructure (questions I and II), computational results report improved slot allocation efficiency and airport capacity utilisation, which for the considered airport instance translate to improvements ranging between 5-24% for various schedule performance metrics. In reducing the difficulty associated with the assessment of multiple ASA solutions by the stakeholders (question IV), instance-specific results suggest reductions to the number of alternative schedules by 87%, while maintaining the quality of the solutions presented to the stakeholders above 70% (expressed in relation to the initially considered set of schedules). Meanwhile, computational results suggest that the concurrent consideration of ASA stakeholdersâ preferences (research question IV) with regards to both operational (research question III) and strategic performance metrics leads to alternative airport slot scheduling solutions that inform on the trade-offs between the schedulesâ operational and strategic performance and the stakeholdersâ preferences. Concerning research question (V), the application of SASAM and the DA algorithm suggest improvements to the number of unaccommodated flights and passengers (13 and 40% improvements) at the expense of requests concerning fewer passengers and days of operations (increasing the number of rejected requests by 1.2% in relation to the total number of submitted requests). The research conducted in this thesis aids in the identification of limitations that should be addressed by future studies to further improve ASA decision-making. First, the thesis focuses on exact solution approaches that consider the landside and airside infrastructure of the airport and generate multiple schedules. The proposition of pre-processing techniques that identify the bottleneck of the airportâs capacity, i.e., landside and/or airside, can be used to reduce the size of the proposed formulations and improve the required computational times. Meanwhile, the development of multi-objective heuristic algorithms that consider several problem characteristics and generate multiple efficient schedules in reasonable computational times, could extend the capabilities of the models propositioned in this thesis and provide decision support for some of the worldâs most congested airports. Furthermore, the thesis models and evaluates the operational implications of strategic airport slot scheduling decisions. The explicit consideration of operational delays as an objective in ASA optimisation models and algorithms is an issue that merits investigation since it may further improve the operational performance of the generated schedules. In accordance with current practice, the models proposed in this work have considered deterministic capacity parameters. Perhaps, future research could propose formulations that consider stochastic representations of airport declared capacity and improve strategic ASA decision-making through the anticipation of operational uncertainty and weather-induced capacity reductions. Finally, in modelling airlinesâ utility for each submitted request and available time slot the thesis proposes time-dependent functions that utilise available data to approximate airlinesâ scheduling preferences. Future studies wishing to improve the accuracy of the proposed functions could utilise commercial data sources that provide route-specific information; or in cases that such data is unavailable, employ data mining and machine learning methodologies to extract airlinesâ time-dependent utility and preferences
Organizations decentered: data objects, technology and knowledge
Data are no longer simply a component of administrative and managerial work but a pervasive resource and medium through which organizations come to know and act upon the contingencies they confront. We theorize how the ongoing technological developments reinforce the traditional functions of data as instruments of management and control but also reframe and extend their role. By rendering data as technical entities, digital technologies transform the process of knowing and the knowledge functions data fulfil in socioeconomic life. These functions are most of the times mediated by putting together disperse and steadily updatable data in more stable entities we refer to as data objects. Users, customers, products, and physical machines rendered as data objects become the technical and cognitive means through which organizational knowledge, patterns, and practices develop. Such conditions loosen the dependence of data from domain knowledge, reorder the relative significance of internal versus external references in organizations, and contribute to a paradigmatic contemporary development that we identify with the decentering of organizations of which digital platforms are an important specimen
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