1,958 research outputs found
Moderating Role of Perceived Corruption
No full text학위논문(석사)--아주대학교 일반대학원 :경영학과,2020. 81. Introduction 1
2. Literature Review 3
2.1 CSR and Firm Performance 3
2.2 Institutional Theory and CSR 6
2.3 Corruption and CSR 8
3. Hypotheses 10
3.1 CSR and Firm Performance 10
3.2 Corruption and Firm Performance 12
3.3 Moderating Role of Corruption 15
3.3.1 Institutional Quality/Law Enforcement (IQLE) 15
3.3.2 Internal Compliance/Ethical Management (ICEM) 17
4. Data and Methods 19
4.1 Sample 19
4.2 Independent Variables 20
4.3. Dependent Variable 23
4.4 Control Variables 23
4.5 Analysis 24
5. Results 25
5.1 Reliability and Validity tests for CSR, IQLE, and ICEM scale 25
5.2 Results of Correlation and Regression Analyses 26
6. Discussion and Future Research 30
References 36MasterDetermining a moderating role of perceived corruption between corporate social responsibility (CSR) and firm’s financial performance is an interesting and challenging research question for researchers. By applying the corruption framework suggested by Gaviria (2002), this dissertation examines whether perceived corruption measures such as Institutional Quality/Law Enforcement (IQLE) and Internal Compliance and Ethical Management (ICEM) moderate the relationship between CSR and firm performance. The results showed that there is a negative moderating impact of IQLE on a positive relationship between CSR and firm’s financial performance. This study also found that ICEM as a moderator contributed to strengthening the positive relationship between CSR and firm’s financial performance
Marzieh Abbas: Cook Prize 2025, Silver Medal Acceptance Speech
No full textAuthor Marzieh Abbas gives an acceptance speech for Yasmeen Lari, Green Architect: The True Story of Pakistan’s First Woman Architect (Clarion)https://educate.bankstreet.edu/cook/1014/thumbnail.jp
Effect of drilling parameters on hole quality of Ti-6Al-4V titanium alloy in dry drilling
No full textBinary Pattern for Nested Cardinality Constraints for Software Product Line of IoT-Based Feature Models
No full textSoftware product line (SPL) is extensively used for reusability of resources in family of products. Feature modeling is an important technique used to manage common and variable features of SPL in applications, such as Internet of Things (IoT). In order to adopt SPL for application development, organizations require information, such as cost, scope, complexity, number of features, total number of products, and combination of features for each product to start the application development. Application development of IoT is varied in different contexts, such as heat sensor indoor and outdoor environment. Variability management of IoT applications enables to find the cost, scope, and complexity. All possible combinations of features make it easy to find the cost of individual application. However, exact number of all possible products and features combination for each product is more valuable information for an organization to adopt product line. In this paper, we have proposed binary pattern for nested cardinality constraints (BPNCC), which is simple and effective approach to calculate the exact number of products with complex relationships between application's feature models. Furthermore, BPNCC approach identifies the feasible features combinations of each IoT application by tracing the constraint relationship from top-to-bottom. BPNCC is an open source and tool-independent approach that does not hide the internal information of selected and non-selected IoT features. The proposed method is validated by implementing it on small and large IoT application feature models with "n'' number of constraints, and it is found that the total number of products and all features combinations in each product without any constraint violation.This work was supported by the National Research Foundation of Korea through the Korean Government (MSIP) under Grant NRF-2016R1C1B2008624
Introduction to the Special Issue on Decision Analysis and Social Media
No full textPublished as:
Ali E. Abbas, Jay Simon, Chris Smith (2017) Introduction to the Special Issue on Decision Analysis and Social Media. Decision
Analysis 14(4):227-228. https://doi.org/10.1287/deca.2017.036
Methodology matters ⋯ but so does interpretation!
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Estimating Passenger Car Equivalent Factors for Heterogeneous Traffic Using Occupancy-Density Linear Regression Model
No full textA variety of methods have been proposed in the existing literature for the estimation of passenger car equivalent (PCE) factors. These methods are based on the comparison of selected attributes of different vehicles. This research, for the first time, utilizes the basic notion of the linear relationship between road area occupancy and density for the estimation of PCE factors for different vehicle types in heterogeneous traffic. Aerial photographs obtained from an unmanned aerial vehicle (UAV) were analyzed to estimate the road area occupancy and the number of vehicles classified in seven selected groups. A linear least-squares regression model was developed between road area occupancy and classified vehicle count. The coefficients of the occupancy-density linear regression model were used to estimate PCE and motorcycle equivalent (MCE) factors. The comparison of the estimated set of PCE values with the values reported in the literature shows that PCE factors estimated using the proposed method are reasonable and produce a better occupancy-density relationship than the other studies. In comparison with the existing methods that rely on lane-based measurements, the proposed method is well suited for traffic with weak/no lane discipline, as it considers the entire road width and the dynamics of lateral movement of different types of vehicles. The proposed method does not need extensive traffic data of speeds, headways, flow rates, and so forth, and is applicable on aerial photographs obtained from other sources, such as satellites.Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported with funding from Exascale Open Data Analytics Lab, National Center for Big Data and Cloud Computing (NCBC) and the Higher Education Commission of Pakistan.
Acknowledgments
The authors are thankful to research students Syed Hassan Ali, Haseeb Ahmed, Zohaib Ahmed, Aqib Abbasi, Asad Rehan, Mirza Ali Haider, Syed Abbas Hasan Zaidi, and Omema for their help in this research
Multi-Objective Optimum Solutions for IoT-Based Feature Models of Software Product Line
No full textA software product line is used for the development of a family of products utilizing the reusability of existing resources with low costs and time to market. Feature Model (FM) is used extensively to manage the common and variable features of a family of products, such as Internet of Things (IoT) applications. In the literature, the binary pattern for nested cardinality constraints (BPNCC) approach has been proposed to compute all possible combinations of development features for IoT applications without violating any relationship constraints. Relationship constraints are a predefined set of rules for the selection of features from an FM. Due to high probability of relationship constraints violations, obtaining optimum features combinations from large IoT-based FMs are a challenging task. Therefore, in order to obtain optimum solutions, in this paper, we have proposed multi-objective optimum-BPNCC that consists of three independent paths (first, second, and third). Furthermore, we applied heuristics on these paths and found that the first path is infeasible due to space and execution time complexity. The second path reduces the space complexity; however, time complexity increases due to the increasing group of features. Among these paths, the performance of the third path is best as it removes optional features that are not required for optimization. In experiments, we calculated the outcomes of all three paths that show the significant improvement of optimum solution without constraint violation occurrence. We theoretically prove that this paper is better than previously proposed optimization algorithms, such as a non-dominated sorting genetic algorithm and an indicator-based evolutionary algorithm.This work was supported by the National Research Foundation of Korea through the Korean government (MSIP) under Grant NRF-2016R1C1B2008624
AI DRIVEN NET ZERO ENERGY SMART GRID 2.0 REVOLUTIONIZES WITH 90 MVA TRANSFORMERSANDRENEWABLES
No full textSynthesis of a Nickel Single-Atom Catalyst Based on Ni-N4-xCx Active Sites for Highly Efficient CO2 Reduction Utilizing a Gas Diffusion Electrode
No full textA Ni single-atom catalyst with N-N4-xCx active sites is prepared in a single pyrolysis step in which the Ni single atom is incorporated in the carbon framework through nitrogen and carbon coordination utilizing the ionothermal synthesis method. In comparison to the complicated synthesis procedures of single-atom catalysts, this method provides a general and facile method to obtain single-atom catalysts with an opportunity to synthesize catalysts at a large scale. The precursors used in this method such as adenine, fructose, and glucose are derived from the biomass which is the essential requirement for a green process. The synthetic procedure developed here enables tunable properties of the catalysts, such as the density of active sites and characteristics of the carbon framework. In this study, the catalytic properties of our materials are investigated for an electrochemical CO2 reduction reaction. The overall catalytic activity of the catalyst depends on the density of active sites, but the properties of the carbon framework also affect the intrinsic activity of the catalyst. From the commercial prospect, a Ni single-atom catalyst with a high density of N -N4-xCx active sites can achieve a current density of -300 mA cm(-2) with a CO faradaic efficiency of 99.4% at an overpotential of 235 mV in a gas diffusion electrode cell system.
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