The Experts below are selected from a list of 234 Experts worldwide ranked by ideXlab platform
Gad Yair - One of the best experts on this subject based on the ideXlab platform.
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the Social accident a theoretical model and a research agenda for studying the influence of Social and cultural characteristics on motor vehicle accidents
Accident Analysis & Prevention, 2007Co-Authors: Roni Factor, David Mahalel, Gad YairAbstract:The paper develops a sociological model to explain collisions between two drivers or more. The "Social Accident" model presented here integrates empirical findings from prior studies and extant sociological theories. Sociological theory posits that Social groups have unique cultural characteristics, which include a distinctive world view and ways of operating that influence its members. These cultural characteristics may cause drivers in different groups to interpret a given situation differently; therefore, they will make conflicting decisions that may possibly lead to road accidents. The proposed model may contribute to an understanding of the Social Mechanism related to interactions and communication among drivers by presenting new directions for understanding accidents and collisions. The paper concludes with suggestions for future research that will employ the model to assess its predictive and practical utility.
Marko Kohtamäki - One of the best experts on this subject based on the ideXlab platform.
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Relationship governance and learning in partnerships
The Learning Organization, 2010Co-Authors: Marko KohtamäkiAbstract:Purpose: Relationship learning is a topic of considerable importance for industrial networks, yet a lack of empirical research on the impact of relationship governance structures on relationship learning remains. The purpose of this paper is to analyze the impact of relationship governance structures on learning in partnerships. Design/methodology/approach: This paper contributes to the closure of the research gap by examining sample data drawn from 42 interviews on the subject of 199 customer-supplier relationships within the Finnish metal and electronics industries. As a method, the paper applies cluster analysis and analysis of variance mean-comparison. Findings: The results of this paper show that balanced hybrid governance structures explain learning in partnerships, which suggests that certain combinations of relationship governance Mechanisms (price, hierarchical, and Social Mechanism) produce the best learning outcomes in partnerships. Results suggest that managers should use hybrid relationship governance structures when governing their supplier partnerships. Research limitations/implications: The paper has some limitations such as limited sample size, cross-sectional data, and difficulties due to measuring Social phenomenon such as learning. Owing to the interview method being applied, research is bound to apply a sample data drawn from companies that operate in the west coast in Finland. These limitations need to be considered when applying the results. Practical implications: The results encourage managers to use different governance Mechanisms simultaneously when managing their company's supply chain partnerships. The result emphasizes the role of active relationship management. Originality/value: The paper is one of the first to empirically show that relationship learning is best facilitated by using various relationship governance Mechanisms simultaneously. Trust needs to be complemented by hierarchical and possibly by price Mechanism. © Emerald Group Publishing Limited.
Jeroen Maesschalck - One of the best experts on this subject based on the ideXlab platform.
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Toward a theoretical framework for ethical decision making of street-level bureaucracy: Existing models reconsidered
Administration and Society, 2010Co-Authors: Kim Loyens, Jeroen MaesschalckAbstract:Much research has been done on the way in which individuals in organizations deal with their discretion. This article focuses on the literature on street-level bureaucracy and the literature on ethical decision making. Despite their shared attempt to explain individual behavior and decision making, these research traditions have been developed quite independently. Moreover, although they both list relevant influencing factors, they do not succeed entirely in clarifying how and under which circumstances these factors have an impact on individual behavior and decision making. This article attempts to substantiate how the concept of Social Mechanism could help to open the black box of causation.
Roni Factor - One of the best experts on this subject based on the ideXlab platform.
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the Social accident a theoretical model and a research agenda for studying the influence of Social and cultural characteristics on motor vehicle accidents
Accident Analysis & Prevention, 2007Co-Authors: Roni Factor, David Mahalel, Gad YairAbstract:The paper develops a sociological model to explain collisions between two drivers or more. The "Social Accident" model presented here integrates empirical findings from prior studies and extant sociological theories. Sociological theory posits that Social groups have unique cultural characteristics, which include a distinctive world view and ways of operating that influence its members. These cultural characteristics may cause drivers in different groups to interpret a given situation differently; therefore, they will make conflicting decisions that may possibly lead to road accidents. The proposed model may contribute to an understanding of the Social Mechanism related to interactions and communication among drivers by presenting new directions for understanding accidents and collisions. The paper concludes with suggestions for future research that will employ the model to assess its predictive and practical utility.
Munindar P. Singh - One of the best experts on this subject based on the ideXlab platform.
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Detecting deception in reputation management
2004Co-Authors: Bin Yu, Munindar P. SinghAbstract:We previously developed a Social Mechanism for distributed reputation management, in which an agent combines testimonies from several witnesses to determine its ratings of another agent. However, that approach does not fully protect against spurious ratings generated by malicious agents. This paper focuses on the problem of deception in testimony propagation and aggregation. We introduce some models of deception and study how to efficiently detect deceptive agents following those models. Our approach involves a novel application of the well-known weighted majority technique to belief function and their aggregation. We describe simulation experiments to study the number of apparently accurate witnesses found in different settings, the number of witnesses on prediction accuracy, and the evolution of trust networks.
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a Social Mechanism of reputation management in electronic communities
Cooperative Information Agents, 2000Co-Authors: Bin Yu, Munindar P. SinghAbstract:Trust is important wherever agents must interact. We consider the important case of interactions in electronic communities, where the agents assist and represent principal entities, such as people and businesses. We propose a Social Mechanism of reputation management, which aims at avoiding interaction with undesirable participants. Social Mechanisms complement hard security techniques (such as passwords and digital certificates), which only guarantee that a party is authenticated and authorized, but do not ensure that it exercises its authorization in a way that is desirable to others. Social Mechanisms are even more important when trusted third parties are not available. Our specific approach to reputation management leads to a decentralized society in which agents help each other weed out undesirable players.
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CIA - A Social Mechanism of Reputation Management in Electronic Communities
Cooperative Information Agents IV - The Future of Information Agents in Cyberspace, 2000Co-Authors: Bin Yu, Munindar P. SinghAbstract:Trust is important wherever agents must interact. We consider the important case of interactions in electronic communities, where the agents assist and represent principal entities, such as people and businesses. We propose a Social Mechanism of reputation management, which aims at avoiding interaction with undesirable participants. Social Mechanisms complement hard security techniques (such as passwords and digital certificates), which only guarantee that a party is authenticated and authorized, but do not ensure that it exercises its authorization in a way that is desirable to others. Social Mechanisms are even more important when trusted third parties are not available. Our specific approach to reputation management leads to a decentralized society in which agents help each other weed out undesirable players.