SelectedWorks @ Melbourne Business School (The University of Melbourne)
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Ch 16 Kulik TrainingChapter 2019-05-23 FINAL.pdf
No full textThe story by now is familiar: Women are reluctant to initiate negotiations in the workplace. When women do negotiate, they ask for too little, they are too willing to accept early offers, andthey are too quick to accommodate. As a result, women are repeatedly disadvantaged in salary, developmental opportunities, and other resources that they need for successful careers. In this chapter, we consider whether women-focused negotiation training might offer a gendered solution to the gendered problems that women face in workplace negotiations. Historically, negotiation training has focused on best practices that are treated as gender-blind. In contrast, women-focused negotiation training assumes that gender matters a great deal. Guided by the same principles that underlie recommendations for women-focused leadership development programs, we investigate how gender colorsand influences each stage of a successful negotiation: the pre-negotiation preparation and groundwork, the during-negotiation behaviorsand dialogue, and the post-negotiation persistence. The chapter delivers a framework that outlines the “what” (content) and the “how” (delivery) that might constitute a women-focused negotiation training course. We expand the portfolio of trained behaviorto include skills, strategies and tactics that might be particularly – and even uniquely – relevant to female negotiators
Collective Action Problems During Market Formation: The Role of Resource Allocation
No full textCollective action is critical for successful market formation. However, relatively little is known about how and under what conditions actors overcome collective action problems to successfully form new markets. Using the benefits of simulation methods, we uncover how collective action problems result from actor resource allocation decisions interacting with each other and how the severity of these problems depends on central market- and actor-related characteristics. Specifically, we show that collective action problems occur when actors undervalue the benefits of market-oriented resource allocation and when actors contribute resources that are imperfectly substitutable. Further, we show that collective action problems occur when actors are embedded in networks with others sharing a similar role in market formation. Collectively, our findings contribute new insights to organization theory on collective action and market formation and to strategy on value creation and strategic decision-making regarding resource allocation
Action at a Distance: Client Relations as a Conduit for External Institutional Influence
No full textPrior empirical research on institutional change shows that the actions of institutional actors in one locale can impact organizational behavior in another. In this paper, we explore how extrajurisdictional regulative and normative pressures faced by a set of organizations affect the strategic decision making of their suppliers. We utilize a novel dataset that tracks the adoption of a green building practice by a panel of 226 architecture studios in Australia from 2008–2015. In line with our theoretical predictions, firms respond to regulation, social norms and competitive pressures from cities where they do not operate but where their prospective clients do. We find that a focal studio’s position in the market for the new practice shapes its response to extrajurisdictional regulation but not its response to extra-jurisdictional norms. The study advances our understanding of how network mechanisms shape firms’ conceptions of the institutional field that governs exchange. We discuss resultant effects on organizational behavior which may be crucial to market formation
1 Behavioral Agency and Affect.docx
No full textWe advance behavioral agency theory by exploring the influence of mood or “affect” on the behavioral consequences of equity incentives. Drawing on insights from psychology and behavioral decision theory, we describe how affect influences agent risk behavior. We argue that positive affect amplifies both the extent to which executives reduce strategic risk taking in response to risk bearing and engage in strategic risk taking in response to incentives for further enrichment. Building again on the psychology literature, we describe how CEO accountability attenuates the influence of affect on CEO risk behavior in response to equity incentives. We test our expectations in a longitudinal dataset of CEO equity incentives and strategic risk taking by U.S. firms for the period 1994—2013
CEO EQUITY RISK BEARING AND STRATEGIC RISK TAKING: THE MODERATING EFFECT OF CEO PERSONALITY
No full textWe draw upon applied psychology literature to explore inter-agent differences in perceived risk to their equity when making strategic risk decisions. Our theory suggests behavioral agency’s predicted negative relationship between equity risk bearing and strategic risk taking is contingent upon four personality traits. Our empirical analyses, based on personality profiles of 158 CEOs of S&P 1,500 firms in manufacturing industries, indicates the relationship between executive risk bearing and strategic risk taking crosses from negative to positive for high extraversion, greater openness and low conscientiousness. These findings demonstrate that agency based predictions of CEO risk taking in response to compensation – and board attempts at creating incentive alignment using compensation – are enhanced by integrating insights from personality trait literature
Diverse Effects of Diversity: Disaggregating Effects of Diversity in Global Virtual Team
No full textGlobal Virtual Team (GVT) member diversity provides many advantages but also poses many challenges. Diversity comes in different forms that each have different effects on GVT dynamics and performance. Past research typically explored the effect of only one type of diversity at a time. Using multi-source, multi-wave data from 5,728 individuals working in 804 consulting project GVTs, the present study is unique in that it explores and compares the effects of different forms of team member diversity on different aspects of GVT effectiveness in a single sample. It proposes a refined theoretical model that differentiates between the effects of personal versus contextual diversity and articulates how these distinct forms of diversity affect different aspects of GVT effectiveness (i.e., task outcomes versus psychological outcomes). The results reveal that (1) team member diversity in general has a substantial effect on GVT effectiveness; (2) contextual diversity has a positive effect on task outcomes; and (3) personal diversity has a negative effect on psychological outcomes. Implications for practice and future research are discussed
High-dimensional copula variational approximation through transformation
No full textVariational methods are attractive for computing Bayesian inference for highly parametrized models and large datasets where exact inference is impractical. They approximate a target distribution - either the posterior or an augmented posterior - using a simpler distribution that is selected to balance accuracy with computational feasibility. Here we approximate an element-wise parametric transformation of the target distribution as multivariate Gaussian or skew-normal. Approximations of this kind are implicit copula models for the original parameters, with a Gaussian or skew-normal copula function and flexible parametric margins. A key observation is that their adoption can improve the accuracy of variational inference in high dimensions at limited or no additional computational cost. We consider the Yeo-Johnson and G&H transformations, along with sparse factor structures for the scale matrix of the Gaussian or skew-normal. We also show how to implement efficient reparametrization gradient methods for these copula-based approximations. The efficacy of the approach is illustrated by computing posterior inference for three different models using six real datasets. In each case, we show that our proposed copula model distributions are more accurate variational approximations than Gaussian or skew-normal distributions, but at only a minor or no increase in computational cost
Marginally-calibrated deep distributional regression
No full textDeep neural network (DNN) regression models are widely used in applications requiring state-of-the-art predictive accuracy. However, until recently there has been little work on accurate uncertainty quantification for predictions from such models. We add to this literature by outlining an approach to constructing predictive distributions that are `marginally calibrated\u27. This is where the long run average of the predictive distributions of the response variable matches the observed empirical margin. Our approach considers a DNN regression with a conditionally Gaussian prior for the final layer weights, from which an implicit copula process on the feature space is extracted. This copula process is combined with a non-parametrically estimated marginal distribution for the response. The end result is a scalable distributional DNN regression method with marginally calibrated predictions, and our work complements existing methods for probability calibration. The approach is first illustrated using two applications of dense layer feed-forward neural networks. However, our main motivating applications are in likelihood-free inference, where distributional deep regression is used to estimate marginal posterior distributions. In two complex ecological time series examples we employ the implicit copulas of convolutional networks, and show that marginal calibration results in improved uncertainty quantification. Our approach also avoids the need for manual specification of summary statistics, a requirement that is burdensome for users and typical of competing likelihood-free inference methods
Variational Bayes Estimation of Discrete-Margined Copula Models with Application to Time Series
No full textWe propose a new variational Bayes estimator for high-dimensional copulas with discrete, or a combination of discrete and continuous, margins. The method is based on a variational approximation to a tractable augmented posterior, and is faster than previous likelihood-based approaches. We use it to estimate drawable vine copulas for univariate and multivariate Markov ordinal and mixed time series. These have dimension , where is the number of observations and is the number of series, and are difficult to estimate using previous methods. The vine pair-copulas are carefully selected to allow for heteroskedasticity, which is a feature of most ordinal time series data. When combined with flexible margins, the resulting time series models also allow for other common features of ordinal data, such as zero inflation, multiple modes and under- or over-dispersion. Using six example series, we illustrate both the flexibility of the time series copula models, and the efficacy of the variational Bayes estimator for copulas of up to 792 dimensions and 60 parameters.This far exceeds the size and complexity of copula models for discrete data that can be estimated using previous methods. Matlab code implementing the method, and supplementary materials, are both available online
Bayesian Variable Selection for Non-Gaussian Responses: A Marginally Calibrated Copula Approach
No full textWe propose a new highly flexible and tractable Bayesian approach to undertake variable selection in non-Gaussian regression models. It uses a copula decomposition for the vector of observations on the dependent variable. This allows the marginal distribution of the dependent variable to be calibrated accurately using a nonparametric or other estimator. The family of copulas employed are `implicit copulas\u27 that are constructed from existing hierarchical Bayesian models used for variable selection, and we establish some of their properties. Even though the copulas are high-dimensional, they can be estimated efficiently and quickly using Monte Carlo methods. A simulation study shows that when the responses are non-Gaussian the approach selects variables more accurately than contemporary benchmarks. A marketing example illustrates that accounting for even mild deviations from normality can lead to a substantial improvement. To illustrate the full potential of our approach we extend it to spatial variable selection for fMRI data. It allows for voxel-specific marginal calibration of the magnetic resonance signal at over 6,000 voxels, leading to a considerable increase in the quality of the activation maps