Miracle-dz/UniFAR-FTUs huggingface dataset:
The socially responsible performance in family firms: moderators and consequences on tax aggressiveness and earnings management

Esta investigacion se centra en analizar los determinantes y las consecuencias del rendimiento socialmente responsable en la empresa familiar. Concretamente, se diferencian dos objetivos altamente relacionados. En primer lugar, examinar el comportamiento de la empresa familiar hacia el desempeno social y medioambiental, considerando factores de contingencia como moderadores, asociados a aspectos de gobernanza y del entorno. En segundo lugar, examinar como la responsabilidad social corporativa impacta en las practicas de evasion de impuestos y manipulacion contable y como, la propiedad y la gestion familiar podrian moderar estas relaciones. Esta investigacion hace uso de una muestra internacional para el periodo 2006-2014 y propone varios modelos de regresion para datos de panel. La evidencia obtenida es la siguiente. Primero, los resultados respaldan la mayor orientacion de la empresa familiar hacia el compromiso socialmente responsable con el objetivo de preservar su riqueza socioemocional. Ademas, el comportamiento socialmente responsable de la empresa familiar se ve reforzado: (i) cuando es mayor la presencia de miembros de la familia en el equipo directivo y en el consejo de administracion (factores de gobernanza) y (ii) en contextos munificentes (factor ambiental). Respecto al segundo objetivo, se documentan dos evidencias principales y opuestas. Por un lado, el desempeno social y medioambiental esta relacionado negativamente con la evasion fiscal y positivamente con practicas de manipulacion contable; las empresas socialmente mas responsables muestran una menor orientacion hacia practicas de ahorro fiscal, pero tambien un comportamiento discrecional al promover acciones que enmascaran el rendimiento financiero y economico real de la empresa. No obstante, la principal evidencia es el efecto moderador de empresa familiar. La relacion negativa y positiva entre responsabilidad social y practicas de evasion de impuestos y mayor comportamiento discrecional al promover acciones que enmascaran el rendimiento financiero y economico real de la empresa, respectivamente, es moderada por la propiedad y gestion familiar. La propiedad y gestion familiar esta asociada positivamente con practicas de evasion fiscal y negativamente con practicas de manipulacion contable.
An empirical investigation of supply chain operations reference model practices and supply chain performance

Purpose The purpose of this paper is to develop and validate a scale measurement of supply chain operations reference (SCOR)-related performance indicators and proposed constructs, SCOR-related performance indicators as practices within the Indian manufacturing sector. Design/methodology/approach A literature-based model on SCOR processes with five constructs and respective performance indicators was empirically validated by using a structured questionnaire. A total of 155 respondents among Indian manufacturing sector participated in this research, and the returned questionnaires were analyzed by using structural equation modeling. Findings The study established a relationship among the SCOR-related performance indicators and overall supply chain performance indicators (OSCPI). The moderation effect of demographic characteristics, namely, employee size, company age and type of company showed significant differences between SCOR-related performance indicators and overall supply chain indicators. Research limitations/implications The scope of the study is limited to specific Indian manufacturing firms. The survey could not represent whole population of manufacturing sector. Practical implications The findings assist managers/supply chain practitioners in improving the performance measures identified using the standard framework, i.e., SCOR processes, overall supply chain performance measures as standard practices for Indian manufacturing sector for a profitable and sustainable business growth in global environment. Originality/value This research holds a value for suggested practices under SCOR processes and the proposed model for OSCPI, a path finder/performance measurement tool for supply chain professionals in the Indian context.
Foreign Direct Investment, Information Technology and Economic Growth Dynamics in Sub-Saharan Africa

The research assesses how information and communication technology (ICT) modulates the effect of foreign direct investment (FDI) on economic growth dynamics in 25 countries in Sub-Saharan Africa for the period 1980-2014. The employed economic growth dynamics areGross Domestic Product (GDP) growth, real GDP and GDP per capita while ICT is measured by mobile phone penetration and internet penetration. The empirical evidence is based on the Generalised Method of Moments. The study finds that both internet penetration and mobile phone penetration overwhelmingly modulate FDI to induce overall positive net effects on all three economic growth dynamics. Moreover, the positive net effects are consistently more apparent in internet-centric regressions compared to ? mobile phone? -oriented specifications. In the light of negative interactive effects, net effects are decomposed to provide thresholds at which ICT policy variables should be complemented with other policy initiatives in order to engender favorable outcomes on economic growth dynamics. Practical and theoretical implications are discussed.
With a little help from my friends: the positive contribution of teamwork to safety behaviour in public hospitals

ABSTRACT Against the backdrop of decreased organizational slack in public sector organizations, we investigate the direct and indirect effect of teams on safety behaviour in an Italian acute care hospital. Quantitative and qualitative findings confirm the positive contribution of teams to individuals’ safety behaviour. The study contributes to the limited empirical research on safety behaviour in high-reliability settings as part of a wider conversation about public sector professionals under crisis. The findings can assist managers of public sector hospitals to facilitate conditions for effective team communication in the interest of patient safety, and can, conceivably, be extended to other public sector high-reliability settings.
A deep learning based framework for detecting and visualizing online malicious advertisement

Online advertisement detection has attracted increasing attention in recent years in both academia and industry. The existing advertising blocking systems are vulnerable to the evolution of new attacks and can cause time latency issues by querying remote servers. In this thesis, we propose a lightweight detection system depending only on lexical-based features. The statistical analysis of different URL sources shows that lexical-based features can provide sufficient information for online advertisement representation. Deep learning algorithms are used for online advertising classification and visualization. After optimizing the architecture of the deep neural network, the deep learning based approach can achieve satisfactory detection results with false negative rate as low as 1.31%. In URL visualization, with the implementation of AutoEncoder for feature preprocessing and t-SNE for visualization, our approach outperforms other dimensionality reduction algorithms and can generate clear clusters for different URL families. Finally, a sequence-2-sequence model is built for fake URL generation, which can create distinguishing characteristics for fake malicious URLs.
Wage Theft in Silence: Why Migrant Workers Do Not Recover Their Unpaid Wages in Australia

Wage Theft in Silence presents findings from the National Temporary Migrant Work Survey -- the most comprehensive study of working conditions and access to justice among international students, backpackers and other temporary migrants in Australia. It draws on responses from 4,322 temporary migrants across 107 nationalities of every region in the world, working in a range of jobs throughout all Australian states and territories. The report reveals that although the majority of migrant workers were paid well below minimum wage in Australia, the overwhelming majority suffered in silence. Indeed, among international students and backpackers who acknowledged they had been underpaid, only 9% took any action to recover wages they were owed. The report considers the institutions from whom those 9% sought assistance, and whether they were able to recover their wages. These include the Fair Work Ombudsman, education providers and unions. <br><br>The report also presents data on the attitudes and experiences of the 91% of migrant workers who suffered wage theft in silence. Though it is often assumed that most underpaid migrant workers are not interested or willing to take action to get the wages they are owed, in fact well over half of survey participants indicated that they were open to trying to recover their wages. The report presents findings on the range of psychological, practical and other barriers that deterred them from doing so. It concludes that many of these barriers can be addressed through practical measures and policy reform, and it presents a range of recommendations to government, parliament, business and the international education sector to make reporting wage theft possible and rational for migrant workers. These include recommendations for a new or improved process for wage recovery, better resourced support services, and a firewall between the labour regulator and the immigration regulator that guarantees that migrants’ visas will not be jeopardised if they report wage theft.
Credit Constraints and Labor Supply: Evidence from Bank Branching Deregulation

This paper examines labor supply adjustment‐both at the intensive and extensive margins‐following financial market development. Specifically, we exploit the staggered passage of bank branching deregulation in the United State to study the impact of relaxing credit constraints on labor supply decisions. We find strong evidence that improvements in how credit markets function decrease weekly hours worked, and that the effect is most significant for the lower‐middle (marginal) income group. Furthermore, we observe heterogeneous responses across demo graphic groups (race and income). In contrast, we find little to no evidence that deregulation has a significant impact on the extensive margin of participation.
Computational and mathematical modeling of medical images : advanced methods and applications in translational myology and surgical planning

Landspitali Scientific Fund, PI: Paolo Gargiulo, “Bone Modeling in patients undergoing THA”, A-2014-072. Rannis Icelandic Research Fund (Rannsoknasjodur), PI: Paolo Gargiulo, “Clinical evaluation score for Total Hip Arthroplasty planning and postoperative assessment”, 152368-051. Rannis Innovation Fund (Nyskopunarsjodur), PI: Paolo Gargiulo, “Clinical assessment for patients undergoing total hip replacement”. The IRCCS Fondazione Ospedale San Camillo, Venezia, Italia. The European Regional Development Fund -Cross Border Cooperation Programme Slovakia –Austria 2007–2013 (Interreg-IVa), project Mobilitat im Alter, MOBIL, N_00033 (partners: Ludwig Boltzmann Institute of Electrical Stimulation and Physical Rehabilitation, Austria, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Austria, and Faculty of Physical Education and Sports, Comenius University in Bratislava, Slovakia). Austrian national co-financing of the Austrian Federal Ministry of Science and Research. Ludwig Boltzmann Society (Vienna, Austria) to CH, SL, HK. The EU Commission Shared Cost Project RISE (Contract n. QLG5-CT-2001-02191) and The Austrian Ministry of Science funds to Prof. Dr. H. Kern Vienna (Austria) covered the clinical costs, the production of customized devices and the international management of the project. The Italian MIUR funds to UC and FC, the Laboratoryof Translational Myology, and the Italian C.N. R. funds to the Institute of Neuroscience, University of Padova, Italy, supported light microscopy, morphometry, and costs of data analyses; Some of the research reported here in was additionally supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases of the National Institutes of Health,under award number NIH NIAMS 1R03AR053706-01A2 to ALP. It should be noted that the respective content presented here is solely the responsibility of us,the authors,and does not necessarily represent the official views of the National Institutes of Health.
Pengembangan Sistem Informasi Berbasis Web untuk Peningkatan Kinerja Unit Bursa Kerja Khusus SMK Negeri 1 Tanjung Raya

SMK Negeri 1 Tanjung Raya merupakan salah satu satuan pendidikan yang berada di Kabupaten Agam. SMK Negeri 1 Tanjung Raya memiliki sebuah unit Bursa Kerja Khusus (BKK) yang bertugas memberikan informasi kepada siswa praktik kerja industri (prakerin) mengenai tempat magang yang dapat mereka pilih. Unit BKK juga berfungsi untuk memberikan informasi kepada alumni terkait lowongan pekerjaan. Informasi yang diberikan kepada siswa dan alumni dilakukan menggunakan papan pengumuman di sekolah. Cara tersebut dirasa kurang efektif karena informasi tidak cepat sampai kepada siswa dan lulusan yang membutuhkan. Siswa dan alumni harus sering datang ke sekolah agar mendapat informasi yang mutakhir sedangkan sebagian siswa dan alumni tinggal jauh dari sekolah dalam kondisi transportasi yang kurang baik. Untuk mengatasi permasalahan tersebut, dikembangkan sistem informasi berbasis web untuk pengelolaan data di unit BKK. Sistem informasi dirancang menggunakan bahasa pemrograman PHP dan basis data MySQL. Sistem melibatkan lima user yakni admin, operator BKK, operator prakerin, siswa dan alumni. Kelima level user yang terdaftar memiliki akun private untuk dapat masuk ke dalam sistem yakni username dan password dengan enkripsi MD5. Halaman utama sistem menampilkan informasi berita, lowongan kerja, dan rekapitulasi data alumni. Sistem diharapkan dapat meningkatkan kinerja unit BKK dalam memberikan informasi tentang lowongan kerja kepada siswa prakerin dan alumni.
Climate Disaster Risks - Empirics and a Multi-Phase Dynamic Model

Recent research in financial economics has shown that rare large disasters have the potential to disrupt financial sectors via the destruction of capital stocks and jumps in risk premia. These disruptions often entail negative feedback effects on the macroeconomy. Research on disaster risks has also actively been pursued in the macroeconomic models of climate change. Our paper uses insights from the former work to study disaster risks in the macroeconomics of climate change and to spell out policy needs. Empirically, the link between carbon dioxide emission and the frequency of climate related disaster is investigated using a panel data approach. The modeling part then uses a multi-phase dynamic macro model to explore the effects of rare large disasters resulting in capital losses and rising risk premia. Our proposed multi-phase dynamic model, incorporating climate-related disaster shocks and their aftermath as a distressed phase, is suitable for studying mitigation and adaptation policies as well as recovery policies.
Are We There Yet?: Outreach and Engagement in the Consortium for Institutional Cooperation Promotion and Tenure Policies

More than 20 years since Scholarship Reconsidered and 15 years since The Disciplines Speak raised awareness about multiple ways of defining, conducting, and rewarding engaged scholarship, faculty members continue to cite institutional barriers to outreach and engagement scholarship. This qualitative study analyzed promotion and tenure policies from 15 Consortium for Institutional Cooperation (CIC) institutions. Thematic and content analysis focused on documents, including policies, instructions, forms, and templates, and followed a two-stage coding process guided by both extant theory and emergent discovery. The study revealed unexpectedly wide variations in language used to describe faculty work; types of examples included in the documents; the role of outreach and engagement in the promotion and tenure process; and criteria for assessing quality and excellence. No policy stood out as an exemplar, though many incorporated exemplary elements. Implications for policy and practice and directions for future research are included in the conclusion.
Exploiting Apache Spark platform for CMS computing analytics

The CERN IT provides a set of Hadoop clusters featuring more than 5 PBytes of raw storage with different open-source, user-level tools available for analytical purposes. The CMS experiment started collecting a large set of computing meta-data, e.g. dataset, file access logs, since 2015. These records represent a valuable, yet scarcely investigated, set of information that needs to be cleaned, categorized and analyzed. CMS can use this information to discover useful patterns and enhance the overall efficiency of the distributed data, improve CPU and site utilization as well as tasks completion time. Here we present evaluation of Apache Spark platform for CMS needs. We discuss two main use-cases CMS analytics and ML studies where efficient process billions of records stored on HDFS plays an important role. We demonstrate that both Scala and Python (PySpark) APIs can be successfully used to execute extremely I/O intensive queries and provide valuable data insight from collected meta-data.
Building E-Commerce Satisfaction and Boosting Sales: The Role of Social Commerce Trust and Its Antecedents

ABSTRACT Consumers are relying increasingly on social commerce for making their purchase decisions, and e-vendors have great interests in applying social commerce features in the traditional e-commerce sites to increase sales. Although the importance of trust has been well recognized in the literature, the previous studies have mainly focused on trust in e-commerce sites and failed to incorporate its multidimensional nature to study consumer behavior. To gain further insights into consumer decision-making, this study aims to develop a social commerce trust-based consumer decision-making framework. Based on the social-technical theory, we conceptualize social commerce trust in a multidimensional view including trust in social media, trust in e-commerce sites, trust in social commerce features, and trust in consumers. Data were collected from an online survey taken by U.S. Amazon consumers. Our results strongly support our new conceptualization of social commerce trust and demonstrate its importance by examining its effects on e-commerce outcomes. Further, trust in consumers and trust in social commerce features have stronger effects than trust in e-commerce sites and trust in social media in the formation of social commerce trust. Our study contributes to the theory by introducing the new conceptualization of social commerce trust and advancing our understanding of how to enhance social commerce trust. Practitioners can gain insights into the implementation of social commerce for building consumer trust and increasing sales.
Yuragi-based Approach with Delay Profile for Virtual Network Embedding in Software Defined Infrastructure

SDI (Software Defined Infrastructure) provides virtualized infrastructures to customers by slicing computing resources and network resources. One of the important problems for deploying SDI framework is to control the assignment of physical resources to a virtual network against changes of traffic demand and service demand. For this problem, VNE (Virtual Network Embedding) problem that maps a virtual network to physical resources has been addressed, but a centralized calculation was assumed. It is difficult to adopt the centralized approaches as a size of infrastructure increases and a number of VN requests increases. This is because the identification of current situation becomes more complicated. In this paper, we present a VNE method that works with a little information for the large, complicated, and uncertain SDI frameworks. To achieve this, the proposed method applies biological “Yuragi” principle. Yuragi, which is a Japanese word, represents a small perturbation to the system. Yuragi is a mechanism of adaptability of organisms and is often expressed as attractor selection model. This paper develops Yuragi-based VNE method that deals with node attribute and has a generality of a performance objective and runs in multi-slice environments. Simulation results show that the Yuragi-based method decreases VN migrations by about 45% than a heuristic method to adapt the fluctuations in resource requirements.
Bayesian and Frequentist Approach to Time Series Forecasting with Application to Kenya’s GDP per Capita

Real GDP per capita is an important indicator of a country’s or regional economic activity and is often used by decision makers in the development of economic policies. Expectations about future GDP per capita can be a primary determinant of investments, employment, wages, profits and stock market activities. This study employed both the frequentist and the Bayesian approaches to Kenya’s GDP per capita time series data for the period between 1980-2017 as obtained from the World Bank data portal. The autoregressive integrated moving average (ARIMA) and the state space models were fitted. The results of the study showed that the local linear trend model and the ARIMA(1,2,1) model are appropriate for forecasting the GDP per capita but the former outperforms the latter. The local linear trend model was used to perform a 3-step ahead forecast and the forecasted value was found to be U.S $ 1717.694, U.S $ 1844.446 and U.S $ 1971.198 for 2018, 2019 and 2020 respectively. The findings of this study showed that the state space models, which utilize the Bayesian approach, outperform the ARIMA models which use the frequentist approach in time series forecasting.