The Experts below are selected from a list of 20997 Experts worldwide ranked by ideXlab platform
Jorn H Block - One of the best experts on this subject based on the ideXlab platform.
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how to pay nonfamily managers in large family firms a Principal Agent Model
Family Business Review, 2011Co-Authors: Jorn H BlockAbstract:A large number of family firms employ nonfamily managers. This article analyzes the optimal compensation contracts of nonfamily managers employed by family firms using Principal—Agent analysis. The Model shows that the contracts should have low incentive levels in terms of short-term performance measures. This finding is moderated by nonfamily managers’ responsiveness to incentives, their level of risk aversion, and measurement errors of effort related to short-term performance. The Model allows a comparison between the contracts of family and nonfamily managers. This comparison shows that the contracts of family managers should include relatively greater incentives in terms of short-term performance measures. A number of propositions regarding the compensation of nonfamily managers employed by family firms are formulated. The implications of the Model for family business research and practice are discussed.
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how to pay non family managers in large family firms a Principal Agent Model
Social Science Research Network, 2010Co-Authors: Jorn H BlockAbstract:A large number of family firms employ non-family managers. This paper analyzes the optimal compensation contracts of non-family managers employed by family firms using Principal-Agent analysis. The Model shows that the contracts should have low incentive levels in terms of short-term performance measures. This finding is moderated by non-family managers’ responsiveness to incentives, their level of risk aversion, and measurement errors of effort related to short-term performance. The Model allows a comparison between the contracts of family and non-family managers. This comparison shows that the contracts of family managers should include relatively greater incentives in terms of short-term performance measures. A number of propositions regarding the compensation of non-family managers employed by family firms are formulated. The implications of the Model for family business research and practice are discussed.
Jitesh H Panchal - One of the best experts on this subject based on the ideXlab platform.
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toward a theory of systems engineering processes a Principal Agent Model of a one shot shallow process
IEEE Systems Journal, 2020Co-Authors: Salar Safarkhani, Ilias Bilionis, Jitesh H PanchalAbstract:Systems engineering processes (SEPs) coordinate the effort of different individuals to generate a product satisfying certain requirements. As the involved engineers are self-interested Agents, the goals at different levels of the systems engineering hierarchy may deviate from the system-level goals, which may cause budget and schedule overruns. Therefore, there is a need of a systems engineering theory that accounts for the human behavior in systems design. As experience in the physical sciences shows, a lot of knowledge can be generated by studying simple hypothetical scenarios, which nevertheless retain some aspects of the original problem. To this end, the objective of this article is to study the simplest conceivable SEP, a PrincipalAgent Model of a one-shot, shallow SEP. We assume that the systems engineer (SE) maximizes the expected utility of the system, while the subsystem engineers (sSE) seek to maximize their expected utilities. Furthermore, the SE is unable to monitor the effort of the sSE and may not have complete information about their types. However, the SE can incentivize the sSE by proposing specific contracts. To obtain an optimal incentive, we pose and solve numerically a bilevel optimization problem. Through extensive simulations, we study the optimal incentives arising from different system-level value functions under various combinations of effort costs, problem-solving skills, and task complexities. Our numerical examples show that, the passed-down requirements to the Agents increase as the task complexity and uncertainty grow and they decrease with increasing the Agents' costs.
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towards a theory of systems engineering processes a Principal Agent Model of a one shot shallow process
arXiv: Multiagent Systems, 2019Co-Authors: Salar Safarkhani, Ilias Bilionis, Jitesh H PanchalAbstract:Systems engineering processes coordinate the effort of different individuals to generate a product satisfying certain requirements. As the involved engineers are self-interested Agents, the goals at different levels of the systems engineering hierarchy may deviate from the system-level goals which may cause budget and schedule overruns. Therefore, there is a need of a systems engineering theory that accounts for the human behavior in systems design. To this end, the objective of this paper is to develop and analyze a Principal-Agent Model of a one-shot (single iteration), shallow (one level of hierarchy) systems engineering process. We assume that the systems engineer maximizes the expected utility of the system, while the subsystem engineers seek to maximize their expected utilities. Furthermore, the systems engineer is unable to monitor the effort of the subsystem engineer and may not have a complete information about their types or the complexity of the design task. However, the systems engineer can incentivize the subsystem engineers by proposing specific contracts. To obtain an optimal incentive, we pose and solve numerically a bi-level optimization problem. Through extensive simulations, we study the optimal incentives arising from different system-level value functions under various combinations of effort costs, problem-solving skills, and task complexities.
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a Principal Agent Model of systems engineering processes with application to satellite design
arXiv: Multiagent Systems, 2019Co-Authors: Salar Safarkhani, Vikranth Reddy Kattakuri, Ilias Bilionis, Jitesh H PanchalAbstract:We present a Principal-Agent Model of a one-shot, shallow, systems engineering process. The process is one-shot in the sense that decisions are made during one time step and that they are final. The term shallow refers to a one-layer hierarchy of the process. Specifically, we assume that the systems engineer has already decomposed the problem in subsystems, and that each subsystem is assigned to a different subsystem engineer. Each subsystem engineer works independently to maximize their own expected payoff. The goal of the systems engineer is to maximize the system-level payoff by incentivizing the subsystem engineers. We restrict our attention to requirement-based system-level payoffs, i.e., the systems engineer makes a profit only if all the design requirements are met. We illustrate the Model using the design of an Earth-orbiting satellite system where the systems engineer determines the optimum incentive structures and requirements for two subsystems: the propulsion subsystem and the power subsystem. The Model enables the analysis of a systems engineer's decisions about optimal passed-down requirements and incentives for sub-system engineers under different levels of task difficulty and associated costs. Sample results, for the case of risk-neutral systems and subsystems engineers, show that it is not always in the best interest of the systems engineer to pass down the true requirements. As expected, the Model predicts that for small to moderate task uncertainties the optimal requirements are higher than the true ones, effectively eliminating the probability of failure for the systems engineer. In contrast, the Model predicts that for large task uncertainties the optimal requirements should be smaller than the true ones in order to lure the subsystem engineers into participation.
Rajiv D Banker - One of the best experts on this subject based on the ideXlab platform.
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the Principal Agent Model of organization control theory
Social Science Research Network, 2012Co-Authors: Rajiv D BankerAbstract:We propose an analytical Model that integrates two parallel streams of the literature, agency theory and organizational control theory that deal with related issues. In doing so, we provide new insights into agency theory by introducing the concept of a congruent Agent, and new insights into organizational control theory by using a contracting Model to analyze behavior and outcome forms of controls. We study outcome-based control, i.e. contracting on the outcome; behavior-based control, i.e. contracting on a signal of effort; behavior and outcome control, i.e. contracting on the outcome and a signal of effort, which is new to the organizational control literature; and clan control, i.e. employing Agents with preferences aligned with the Principal’s, which is new to the agency literature. To interpret clan control in the context of agency theory, we draw from recent theory and experimental evidence in behavioral economics by distinguishing between two types of Agents: a self-interested Agent, who behaves as Modeled in the traditional agency literature; and a congruent Agent, who cares about the Principal’s payoff, which can be viewed as a form of “social” preferences in behavioral economics and a form of clan control resulting from “socialization” in organization theory. Corresponding to Ouchi’s (1979) view of organizational control, we analytically investigate conditions under which outcome-based control, behavior-based control, and clan control are optimal. We highlight four key dimensions, outcome uncertainty, behavior uncertainty, task uncertainty, the degree of congruence, in determining the optimal control strategy. Furthermore, we expand to four types of control and perform a simulation analysis that extends our analytical results.
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performance evaluation metrics for information systems development a Principal Agent Model
2011Co-Authors: Rajiv D Banker, Chris F KemererAbstract:The information systems (IS) development activity in large organizations is a source of increasing cost and concern to management. IS development projects are often over-budget, late, costly to maintain, and not done to the satisfaction of the requesting user. These problems exist, in part, due to the organization of the IS development process, where information systems development is typically assigned by the user (Principal) to a systems developer (Agent). These two parties do not have perfectly congruent goals, and therefore a contract is developed to specify their relationship. An inability to directly monitor the Agent requires the use of performance measures, or metrics, to represent the Agent's actions to the Principal. The use of multiple measures is necessary given the multi-dimensional nature of successful systems development. In practice such contracts are difficult to develop satisfactorily, due in part to an inability to specify appropriate metrics. This paper develops a Principal-Agent Model...
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screening versus sorting in a Principal Agent Model with moral hazard and adverse selection
Social Science Research Network, 2010Co-Authors: Rajiv D Banker, Jose M PlehndujowichAbstract:This paper proposes a Principal-Agent Model of moral hazard and adverse selection that introduces the notion of screening, which is distinct from sorting; and distinguishes between ability that is privately known by the Agent versus general ability that is observable by the Principal and market. Sorting is the traditional process by which the adverse selection problem is resolved. Screening is the process we propose by which Agents that are deemed to be unsuitable are rejected. Used in conjunction with sorting, we consider ex-ante screening on the basis of the (observable) measure of general ability; and ex-post screening on the basis of the private measure of ability. We identify the benefits and costs incurred by the Principal associated with hiring an Agent with superior ability, and derive the manner in which the compensation mechanism should be adjusted to take into account the roles of screening. The Principal may prefer Agents with inferior qualifications, rejecting those who are “overqualified”; conversely, she may select an Agent with a distinguished pedigree, rejecting Agents who are “underqualified”. Screening alters the relationship between the compensation mechanism and the characteristics of the agency relationship, causing established results to change, such as the negative link between risk and incentives. The empirical ramifications of screening are important: not controlling for screening introduces bias and inconsistency in estimation.
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relative weights on performance measures in a Principal Agent Model with moral hazard and adverse selection
Social Science Research Network, 2009Co-Authors: Rajiv D Banker, Jose M Plehndujowich, Chunwei XianAbstract:This paper examines the role of multiple measures of performance in a Principal-Agent Model incorporating both moral hazard and adverse selection. The outcome of interest to the Principal depends stochastically on the Agent’s unobservable ability and effort, while the Principal implements a contract contingent on two noisy measures of the outcome. There are three main findings. First, the weights assigned the performance measures are reduced in the presence of adverse selection because the informational rent paid to the Agent lowers the return to the Principal of hiring the Agent, but it does not affect how informative one signal is relative to another. Second, the weights assigned the signals are decreasing in the sensitivity of performance to ability. Third, a signal is assigned more weight if and only if it is more precise and sensitive to the Agent’s effort; thus, the Banker and Datar (1989) result is robust to the introduction of adverse selection. An empirical test of the Model is provided in the context of the CEO pay-for-performance sensitivity and the investment opportunities set (IOS) of the firms they manage. If high IOS firms are more ability-intensive, the Model predicts the weights on the performance measures are decreasing in IOS. We examine a sample of 12,221 firm-year observations for 1,411 firms spanning the period 1992-2006 obtained from ExecuComp, CRSP, and Compustat. In agreement with the Model, we find that CEO compensation is less sensitive to accounting and stock returns in high IOS firms.
Salar Safarkhani - One of the best experts on this subject based on the ideXlab platform.
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toward a theory of systems engineering processes a Principal Agent Model of a one shot shallow process
IEEE Systems Journal, 2020Co-Authors: Salar Safarkhani, Ilias Bilionis, Jitesh H PanchalAbstract:Systems engineering processes (SEPs) coordinate the effort of different individuals to generate a product satisfying certain requirements. As the involved engineers are self-interested Agents, the goals at different levels of the systems engineering hierarchy may deviate from the system-level goals, which may cause budget and schedule overruns. Therefore, there is a need of a systems engineering theory that accounts for the human behavior in systems design. As experience in the physical sciences shows, a lot of knowledge can be generated by studying simple hypothetical scenarios, which nevertheless retain some aspects of the original problem. To this end, the objective of this article is to study the simplest conceivable SEP, a PrincipalAgent Model of a one-shot, shallow SEP. We assume that the systems engineer (SE) maximizes the expected utility of the system, while the subsystem engineers (sSE) seek to maximize their expected utilities. Furthermore, the SE is unable to monitor the effort of the sSE and may not have complete information about their types. However, the SE can incentivize the sSE by proposing specific contracts. To obtain an optimal incentive, we pose and solve numerically a bilevel optimization problem. Through extensive simulations, we study the optimal incentives arising from different system-level value functions under various combinations of effort costs, problem-solving skills, and task complexities. Our numerical examples show that, the passed-down requirements to the Agents increase as the task complexity and uncertainty grow and they decrease with increasing the Agents' costs.
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towards a theory of systems engineering processes a Principal Agent Model of a one shot shallow process
arXiv: Multiagent Systems, 2019Co-Authors: Salar Safarkhani, Ilias Bilionis, Jitesh H PanchalAbstract:Systems engineering processes coordinate the effort of different individuals to generate a product satisfying certain requirements. As the involved engineers are self-interested Agents, the goals at different levels of the systems engineering hierarchy may deviate from the system-level goals which may cause budget and schedule overruns. Therefore, there is a need of a systems engineering theory that accounts for the human behavior in systems design. To this end, the objective of this paper is to develop and analyze a Principal-Agent Model of a one-shot (single iteration), shallow (one level of hierarchy) systems engineering process. We assume that the systems engineer maximizes the expected utility of the system, while the subsystem engineers seek to maximize their expected utilities. Furthermore, the systems engineer is unable to monitor the effort of the subsystem engineer and may not have a complete information about their types or the complexity of the design task. However, the systems engineer can incentivize the subsystem engineers by proposing specific contracts. To obtain an optimal incentive, we pose and solve numerically a bi-level optimization problem. Through extensive simulations, we study the optimal incentives arising from different system-level value functions under various combinations of effort costs, problem-solving skills, and task complexities.
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a Principal Agent Model of systems engineering processes with application to satellite design
arXiv: Multiagent Systems, 2019Co-Authors: Salar Safarkhani, Vikranth Reddy Kattakuri, Ilias Bilionis, Jitesh H PanchalAbstract:We present a Principal-Agent Model of a one-shot, shallow, systems engineering process. The process is one-shot in the sense that decisions are made during one time step and that they are final. The term shallow refers to a one-layer hierarchy of the process. Specifically, we assume that the systems engineer has already decomposed the problem in subsystems, and that each subsystem is assigned to a different subsystem engineer. Each subsystem engineer works independently to maximize their own expected payoff. The goal of the systems engineer is to maximize the system-level payoff by incentivizing the subsystem engineers. We restrict our attention to requirement-based system-level payoffs, i.e., the systems engineer makes a profit only if all the design requirements are met. We illustrate the Model using the design of an Earth-orbiting satellite system where the systems engineer determines the optimum incentive structures and requirements for two subsystems: the propulsion subsystem and the power subsystem. The Model enables the analysis of a systems engineer's decisions about optimal passed-down requirements and incentives for sub-system engineers under different levels of task difficulty and associated costs. Sample results, for the case of risk-neutral systems and subsystems engineers, show that it is not always in the best interest of the systems engineer to pass down the true requirements. As expected, the Model predicts that for small to moderate task uncertainties the optimal requirements are higher than the true ones, effectively eliminating the probability of failure for the systems engineer. In contrast, the Model predicts that for large task uncertainties the optimal requirements should be smaller than the true ones in order to lure the subsystem engineers into participation.
Jose M Plehndujowich - One of the best experts on this subject based on the ideXlab platform.
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screening versus sorting in a Principal Agent Model with moral hazard and adverse selection
Social Science Research Network, 2010Co-Authors: Rajiv D Banker, Jose M PlehndujowichAbstract:This paper proposes a Principal-Agent Model of moral hazard and adverse selection that introduces the notion of screening, which is distinct from sorting; and distinguishes between ability that is privately known by the Agent versus general ability that is observable by the Principal and market. Sorting is the traditional process by which the adverse selection problem is resolved. Screening is the process we propose by which Agents that are deemed to be unsuitable are rejected. Used in conjunction with sorting, we consider ex-ante screening on the basis of the (observable) measure of general ability; and ex-post screening on the basis of the private measure of ability. We identify the benefits and costs incurred by the Principal associated with hiring an Agent with superior ability, and derive the manner in which the compensation mechanism should be adjusted to take into account the roles of screening. The Principal may prefer Agents with inferior qualifications, rejecting those who are “overqualified”; conversely, she may select an Agent with a distinguished pedigree, rejecting Agents who are “underqualified”. Screening alters the relationship between the compensation mechanism and the characteristics of the agency relationship, causing established results to change, such as the negative link between risk and incentives. The empirical ramifications of screening are important: not controlling for screening introduces bias and inconsistency in estimation.
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relative weights on performance measures in a Principal Agent Model with moral hazard and adverse selection
Social Science Research Network, 2009Co-Authors: Rajiv D Banker, Jose M Plehndujowich, Chunwei XianAbstract:This paper examines the role of multiple measures of performance in a Principal-Agent Model incorporating both moral hazard and adverse selection. The outcome of interest to the Principal depends stochastically on the Agent’s unobservable ability and effort, while the Principal implements a contract contingent on two noisy measures of the outcome. There are three main findings. First, the weights assigned the performance measures are reduced in the presence of adverse selection because the informational rent paid to the Agent lowers the return to the Principal of hiring the Agent, but it does not affect how informative one signal is relative to another. Second, the weights assigned the signals are decreasing in the sensitivity of performance to ability. Third, a signal is assigned more weight if and only if it is more precise and sensitive to the Agent’s effort; thus, the Banker and Datar (1989) result is robust to the introduction of adverse selection. An empirical test of the Model is provided in the context of the CEO pay-for-performance sensitivity and the investment opportunities set (IOS) of the firms they manage. If high IOS firms are more ability-intensive, the Model predicts the weights on the performance measures are decreasing in IOS. We examine a sample of 12,221 firm-year observations for 1,411 firms spanning the period 1992-2006 obtained from ExecuComp, CRSP, and Compustat. In agreement with the Model, we find that CEO compensation is less sensitive to accounting and stock returns in high IOS firms.