The Experts below are selected from a list of 243 Experts worldwide ranked by ideXlab platform
Byoung G. Park - One of the best experts on this subject based on the ideXlab platform.
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The Comprehensive Effects of Sales Force Management: A Dynamic Structural Analysis of Selection, Compensation, and Training
Management Science, 2021Co-Authors: Doug J. Chung, Byungyeon Kim, Byoung G. ParkAbstract:This study provides a comprehensive model of an agent’s behavior in response to multiple sales management instruments, including compensation, recruiting/termination, and training. The model takes into account many of the key elements that constitute a realistic sales force setting: allocation of effort, forward-looking behavior, present bias, training effectiveness, and employee selection and attrition. By understanding how these elements jointly affect agents’ behavior, the study provides guidance on the optimal design of sales management policies. A field validation, by comparing counterfactual and actual outcomes under a new policy, attests to the accuracy of the model. The results demonstrate a tradeoff between adjusting fixed and variable pay; how sales training serves as an alternative to compensation; a potential drawback of hiring high-performing, experienced salespeople; and how utilizing a leave package leads to sales force restructuring. In addition, the study offers a key methodological contribution by providing formal identification conditions for hyperbolic time preference. The key to identification is that under a multiperiod nonlinear incentive system, an agent’s proximity to a goal affects only future payoffs in nonpecuniary benefit periods, providing exclusion restrictions on the current payoff. This paper was accepted by Matthew Shum, marketing.
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the comprehensive effects of sales force management a Dynamic Structural Analysis of selection compensation and training
Management Science, 2021Co-Authors: Doug J. Chung, Byungyeon Kim, Byoung G. ParkAbstract:This study provides a comprehensive model of an agent’s behavior in response to multiple sales management instruments, including compensation, recruiting/termination, and training. The model takes ...
Doug J. Chung - One of the best experts on this subject based on the ideXlab platform.
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The Comprehensive Effects of Sales Force Management: A Dynamic Structural Analysis of Selection, Compensation, and Training
Management Science, 2021Co-Authors: Doug J. Chung, Byungyeon Kim, Byoung G. ParkAbstract:This study provides a comprehensive model of an agent’s behavior in response to multiple sales management instruments, including compensation, recruiting/termination, and training. The model takes into account many of the key elements that constitute a realistic sales force setting: allocation of effort, forward-looking behavior, present bias, training effectiveness, and employee selection and attrition. By understanding how these elements jointly affect agents’ behavior, the study provides guidance on the optimal design of sales management policies. A field validation, by comparing counterfactual and actual outcomes under a new policy, attests to the accuracy of the model. The results demonstrate a tradeoff between adjusting fixed and variable pay; how sales training serves as an alternative to compensation; a potential drawback of hiring high-performing, experienced salespeople; and how utilizing a leave package leads to sales force restructuring. In addition, the study offers a key methodological contribution by providing formal identification conditions for hyperbolic time preference. The key to identification is that under a multiperiod nonlinear incentive system, an agent’s proximity to a goal affects only future payoffs in nonpecuniary benefit periods, providing exclusion restrictions on the current payoff. This paper was accepted by Matthew Shum, marketing.
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the comprehensive effects of sales force management a Dynamic Structural Analysis of selection compensation and training
Management Science, 2021Co-Authors: Doug J. Chung, Byungyeon Kim, Byoung G. ParkAbstract:This study provides a comprehensive model of an agent’s behavior in response to multiple sales management instruments, including compensation, recruiting/termination, and training. The model takes ...
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Do Bonuses Enhance Sales Productivity? A Dynamic Structural Analysis of Bonus-Based Compensation Plans
Marketing Science, 2014Co-Authors: Doug J. Chung, Thomas J. Steenburgh, K. SudhirAbstract:We estimate a Dynamic Structural model of sales force response to a bonus-based compensation plan. This paper provides substantive insight into how different elements of the compensation plan enhance productivity. We find evidence that 1 bonuses enhance productivity across all segments; 2 overachievement commissions help sustain the high productivity of the best performers, even after attaining quotas; and 3 quarterly bonuses help improve performance of the weak performers by serving as pacers to keep the sales force on track in achieving its annual sales quotas. The paper also introduces two main methodological innovations to the marketing literature: First, we implement empirically the method proposed by Arcidiacono and Miller [Arcidiacono P, Miller RA 2011 Conditional choice probability estimation of Dynamic discrete choice models with unobserved heterogeneity. Econometrica 796:1823--1867] to accommodate unobserved latent-class heterogeneity using a computationally light two-step estimator. Second, we illustrate how discount factors can be estimated in a Dynamic Structural model using field data through a combination of 1 an exclusion restriction separating current and future payoff and 2 a finite-horizon model in which there is no forward-looking behavior in the last period.
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Do Bonuses Enhance Sales Productivity? A Dynamic Structural Analysis of Bonus-Based Compensation Plans
SSRN Electronic Journal, 2010Co-Authors: Doug J. Chung, Thomas J. Steenburgh, K. SudhirAbstract:We estimate a Dynamic Structural model of sales force response to a bonus based compensation plan. Substantively, the paper sheds insights on how different elements of the compensation plan enhance productivity. We find evidence that: (1) bonuses enhance productivity across all segments; (2) overachievement commissions help sustain the high productivity of the best performers even after attaining quotas; and (3) quarterly bonuses help improve performance of the weak performers by serving as pacers to keep the sales force on track to achieve their annual sales quotas. The paper also introduces two main methodological innovations to the marketing literature: First, we implement empirically the method proposed by Arcidiacono and Miller (2011) to accommodate unobserved latent class heterogeneity using a computationally light two-step estimator. Second, we illustrate how discount factors can be estimated in a Dynamic Structural model using field data through a combination of (1) an exclusion restriction separating current and future payoff and (2) a finite horizon model in which there is no forward looking behavior in the last period.
Byungyeon Kim - One of the best experts on this subject based on the ideXlab platform.
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The Comprehensive Effects of Sales Force Management: A Dynamic Structural Analysis of Selection, Compensation, and Training
Management Science, 2021Co-Authors: Doug J. Chung, Byungyeon Kim, Byoung G. ParkAbstract:This study provides a comprehensive model of an agent’s behavior in response to multiple sales management instruments, including compensation, recruiting/termination, and training. The model takes into account many of the key elements that constitute a realistic sales force setting: allocation of effort, forward-looking behavior, present bias, training effectiveness, and employee selection and attrition. By understanding how these elements jointly affect agents’ behavior, the study provides guidance on the optimal design of sales management policies. A field validation, by comparing counterfactual and actual outcomes under a new policy, attests to the accuracy of the model. The results demonstrate a tradeoff between adjusting fixed and variable pay; how sales training serves as an alternative to compensation; a potential drawback of hiring high-performing, experienced salespeople; and how utilizing a leave package leads to sales force restructuring. In addition, the study offers a key methodological contribution by providing formal identification conditions for hyperbolic time preference. The key to identification is that under a multiperiod nonlinear incentive system, an agent’s proximity to a goal affects only future payoffs in nonpecuniary benefit periods, providing exclusion restrictions on the current payoff. This paper was accepted by Matthew Shum, marketing.
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the comprehensive effects of sales force management a Dynamic Structural Analysis of selection compensation and training
Management Science, 2021Co-Authors: Doug J. Chung, Byungyeon Kim, Byoung G. ParkAbstract:This study provides a comprehensive model of an agent’s behavior in response to multiple sales management instruments, including compensation, recruiting/termination, and training. The model takes ...
Yuan-sen Yang - One of the best experts on this subject based on the ideXlab platform.
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Direct‐Iterative Hybrid Solution in Nonlinear Dynamic Structural Analysis
Computer-Aided Civil and Infrastructure Engineering, 2017Co-Authors: Yuan-sen Yang, Weichung Wang, Jia-zhang LinAbstract:Although many advanced sparse direct solvers are widely used in Structural Analysis, these often require longer computing times than iterative solvers for well-conditioned Structural systems. However, iterative solvers cannot efficiently solve an ill-conditioned system when a structure becomes highly nonlinear. This work proposes a hybrid solution integrating a direct solver and an iterative solver to reduce overall computing time in solving a series of linear equations arising from nonlinear Dynamic Structural Analysis. The hybrid solution selects the iterative solver, which reuses the factorized matrices for preconditioning, and switches to the direct solver in the initial stage or when factorized matrices need updating. The performance of the hybrid solution is tested on the OpenSees platform. The results show that the hybrid solution outperforms the direct solver, even when the structure becomes highly nonlinear during Analysis.
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GPU parallelization of an object-oriented nonlinear Dynamic Structural Analysis platform
Simulation Modelling Practice and Theory, 2014Co-Authors: Yuan-sen Yang, Chung-ming Yang, Tung-ju HsiehAbstract:Abstract This work parallelized a widely used Structural Analysis platform called OpenSees using graphical processing units (GPU). This paper presents task decomposition diagrams with data flow and the sequential and parallel flowcharts for element matrix/vector calculations. It introduces a Bulk Model to ease the parallelization of the element matrix/vector calculations. An implementation of this model for shell elements is presented. Three versions of the Bulk Model—sequential, OpenMP multi-threaded, and CUDA GPU parallelized—were implemented in this work. Nonlinear Dynamic analyses of two building models subjected to a tri-axial earthquake were tested. The results demonstrate speedups higher than four on a 4-core system, while the GPU parallelism achieves speedups higher than 7.6 on a single GPU device in comparison to the original sequential implementation.
Jia-zhang Lin - One of the best experts on this subject based on the ideXlab platform.
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Direct‐Iterative Hybrid Solution in Nonlinear Dynamic Structural Analysis
Computer-Aided Civil and Infrastructure Engineering, 2017Co-Authors: Yuan-sen Yang, Weichung Wang, Jia-zhang LinAbstract:Although many advanced sparse direct solvers are widely used in Structural Analysis, these often require longer computing times than iterative solvers for well-conditioned Structural systems. However, iterative solvers cannot efficiently solve an ill-conditioned system when a structure becomes highly nonlinear. This work proposes a hybrid solution integrating a direct solver and an iterative solver to reduce overall computing time in solving a series of linear equations arising from nonlinear Dynamic Structural Analysis. The hybrid solution selects the iterative solver, which reuses the factorized matrices for preconditioning, and switches to the direct solver in the initial stage or when factorized matrices need updating. The performance of the hybrid solution is tested on the OpenSees platform. The results show that the hybrid solution outperforms the direct solver, even when the structure becomes highly nonlinear during Analysis.
