Showing papers by "Melbourne Business School published in 2019"
TL;DR: The authors combine behavioral agency and family business literature to analyze the role of dominant firm principals in constraining the managerial agent's (CEO's) response to equity-based pay, and find that dominant firms have a strong influence on the CEO's response to pay.
102 citations
TL;DR: For example, this article found that CEOs that score high on extraversion or openness and low on conscientiousness are less likely to decrease their firm's strategic risk taking as the value of their stock options increases.
Abstract: __Research Summary:__ We draw upon applied psychology literature to explore interagent 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 Chief Executive Officers (CEOs) of S&P 1,500 firms in manufacturing industries, indicate 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.
__Managerial Summary:__ We study the effect of CEO personality on their behavioral responses to stock option pay. Our findings reveal that CEOs that score high on extraversion or openness and low on conscientiousness are less likely to decrease their firm's strategic risk taking as the value of their stock options increases. That is, the tendency of CEOs to become more risk averse in their strategic choices as their option wealth increases (due to loss aversion) is weaker for highly extraverted and more open CEOs, but stronger for more conscientiousness CEOs. Overall, our findings suggest that board of directors need to consider personality traits of their CEOs when designing compensation packages with the intention to align incentives of CEOs with shareholder risk preferences.
71 citations
TL;DR: The authors discuss how a global value chain approach serves to usefully move internalization theory towards a better understanding of the increasingly important "middle ground" between markets and hierarchies in the contemporary highly globalized international business scene.
Abstract: In a research note in this issue, Strange and Humphrey discuss how a global value chain (GVC) approach serves to usefully move internalization theory towards a better understanding of the increasingly important ‘middle ground’ between markets and hierarchies in the contemporary highly globalized international business scene. After a brief recount of their main arguments, we argue that their discussion needs to the extended, as it does not adequately recognize important differences between internalization theory and the GVC approach. Specifically, the approaches differ on the notions of efficiency, opportunism, and level of analysis. We then argue that internalization theory can benefit from the systemic view implied in the GVC approach, and discuss the role of trust as a coordinating mechanism in international business. This leads to a more general discussion of internalization theory and the difficulty of encompassing dynamic considerations such as learning and foreign operation mode combinations and flexibility within value chain interdependencies. We conclude with a research agenda that flows from our discussion.
62 citations
TL;DR: In this article, the effects of different forms of team member diversity on different aspects of GVT effectiveness in a single sample were explored and compared in the context of Global Virtual Team (GVT) member diversity.
49 citations
TL;DR: This paper explored the impact of religious norms on the relationship between corporate social responsibility (CSR) and firm value and found that strong local religious norms in the area surrounding firms' headquarters attenuate the positive effect of CSR on firm value.
Abstract: We explore the impact of religious norms on the relationship between corporate social responsibility (CSR) and firm value. Employing a longitudinal sample of publicly listed U.S. firms, we document that strong local religious norms in the area surrounding firms’ headquarters attenuate the positive effect of CSR on firm value. In cross-sectional analyses, we find that the attenuating effect of strong local religious norms is amplified for firms with heightened litigation risk. We also find that the positive effect of CSR on firm value is amplified for firms headquartered in areas where prevailing religious norms are more tolerant of risk-taking. Further, we find that strong religious norms attenuated the positive association between CSR and abnormal stock returns during the 2008–2009 financial crisis. Taken together, our findings cast local religious norms as an important contextual factor that influences the insurance value of CSR—the protection that CSR affords against stakeholder reactions to negative events.
49 citations
TL;DR: In this article, the antecedents and consequences of an organization-level inclusion climate are investigated, showing that identity-conscious programs (programs that target specific identity groups) generate an inclusion climate and that individual employees perceive the organization as fulfilling its diversity management obligations and respond with higher levels of affective commitment.
Abstract: This study investigates the antecedents and consequences of organization-level inclusion climate. A national sample of human resource decision-makers from 100 organizations described their firms' formal diversity management programs; 3,229 employees reported their perceptions of, and reactions to, their employers' diversity management. Multilevel analyses demonstrate that identity-conscious programs (programs that target specific identity groups) generate an inclusion climate. Moreover, the analyses provide evidence of multilevel mediation: In organizations with an inclusion climate, individual employees perceive the organization as fulfilling its diversity management obligations and respond with higher levels of affective commitment. This study represents an important step toward understanding how a shared perception of organizational inclusiveness develops and how inclusion climate facilitates the achievement of diversity management objectives. The findings also shed light on the important role of identity-conscious programs in promoting organizational commitment within a diverse workforce.
33 citations
TL;DR: When errors occur in clinical settings, it is important that they are recognised without defensiveness so that prompt corrective action can be taken and learning can occur.
Abstract: When errors occur in clinical settings, it is important that they are recognised without defensiveness so that prompt corrective action can be taken and learning can occur. Cognitive dissonance - the uncomfortable tension we experience when we hold two or more inconsistent beliefs - can hinder our ability to respond optimally to error. The aim of this paper is to describe the effects of cognitive dissonance, a construct developed and tested in social psychology. We discuss the circumstances under which dissonance is most likely to occur, provide examples of how it may influence clinical practice, discuss potential remedies and suggest future research to test these remedies in the clinical context. We apply research on cognitive dissonance from social psychology to clinical settings. We examine the factors that make dissonance most likely to occur. We illustrate the power of cognitive dissonance through two medical examples: one from history and one that is ongoing. Finally, we explore moderators at various stages of the dissonance process to identify potential remedies. We show that there is great opportunity for cognitive dissonance to distort judgements, delay optimal responses and hinder learning in clinical settings. We present a model of the phases of cognitive dissonance, and suggestions for preventing dissonance, reducing the distortions that can arise from dissonance and inhibiting dissonance-induced escalation of commitment. Cognitive dissonance has been studied for decades in social psychology but has not had much influence on medical education research. We argue that the construct of cognitive dissonance is very relevant to the clinical context and to medical education. Dissonance has the potential to interfere with learning, to hinder the process of coping effectively with error, and to make the accepting of change difficult. Fortunately, there is the potential to reduce the negative impact of cognitive dissonance in clinical practice.
31 citations
TL;DR: In this article, the authors formally define emotional labor, briefly summarize research in organizational behavior and social psychology on the causes and consequences of emotional labour, and present a normative analysis of its moral limits focused on conditional rights and duties of employers and employees.
Abstract: Our understanding of emotional labor, while conceptually and empirically substantial, is normatively impoverished: very little has been said or written expressly about its ethical dimensions or ramifications. Emotional labor refers to efforts undertaken by employees to make their private feelings and/or public emotion displays consistent with job and organizational requirements. We formally define emotional labor, briefly summarize research in organizational behavior and social psychology on the causes and consequences of emotional labor, and present a normative analysis of its moral limits focused on conditional rights and duties of employers and employees. Our focus is on three points of conflict involving rights and duties as they apply to the performance of emotional labor: when employees’ and organizations’ rights conflict, when employees’ rights conflict with their duties, and when organizations’ rights conflict with their duties. We discuss implications for future inquiry as well as managerial practice.
28 citations
TL;DR: In this article, an auxiliary likelihood-based approach is proposed to approximate Bayesian computation (ABC) for inference in state space models, which avoids evaluation of an intractable likelihood by matching summary statistics computed from observed data with statistics from data simulated from the true process.
Abstract: A new approach to inference in state space models is proposed, using approximate Bayesian computation (ABC). ABC avoids evaluation of an intractable likelihood by matching summary statistics computed from observed data with statistics computed from data simulated from the true process, based on parameter draws from the prior. Draws that produce a 'match' between observed and simulated summaries are retained, and used to estimate the inaccessible posterior; exact inference being feasible only if the statistics are sufficient. With no reduction to sufficiency being possible in the state space setting, we pursue summaries via the maximization of an auxiliary likelihood function. We derive conditions under which this auxiliary likelihood-based approach achieves Bayesian consistency and show that, in the limit, results yielded by the auxiliary maximum likelihood estimator are replicated by the auxiliary score. In multivariate parameter settings a separate treatment of each parameter dimension, based on integrated likelihood techniques, is advocated as a way of avoiding the curse of dimensionality associated with ABC methods. Three stochastic volatility models for which exact inference is either challenging or infeasible, are used for illustration.
28 citations
TL;DR: The concept of positive empathy is introduced-the experience of happiness in response to a coworker's positive experience and the real or imagined happiness in the coworker-as an alternative response to envy and distinguish it from related concepts in the organizational literature.
Abstract: Although individuals are capable of feeling happiness for others' positive experiences, management scholars have thus far considered envy to be the sole emotional reaction of employees in response to coworkers' positive outcomes. In this article, we introduce the concept of positive empathy-the experience of happiness in response to a coworker's positive experience and the real or imagined happiness in the coworker-as an alternative response to envy and distinguish it from related concepts in the organizational literature. We develop a theoretical framework to explain the psychological processes that underlie envy and positive empathy, and identify individual and contextual contingencies that might incline employees to experience these emotions. Lastly, we discuss individual and organizational outcomes of envy and positive empathy and explain implications for management research and practice. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
28 citations
TL;DR: Fake news is identified as a sinister form of mass persuasion as mentioned in this paper, and the history of the precursors of the construct and a contemporary definition of fake news is reviewed. And the relevance of the fake news for the marketing communications field is highlighted.
Abstract: In this essay, fake news is identified as a sinister form of mass persuasion. The paper reviews the history of the precursors of the construct and offers a contemporary definition. Research findings about how consumers process fake news information are discussed. The essay highlights the relevance of fake news for the marketing communications field and ends with a call to action to researchers for the development of effective interventions.
TL;DR: In this paper, a qualitative study of a service company, a European telecommunications joint venture in Afghanistan, seeks to shed light and build theory on the human resource management (HRM) dimension of managerial learning and knowledge acquisition in hostile environments.
Abstract: Multinational enterprises (MNEs) have increasingly entered markets in less developed regions of the world afflicted with weak institutions and political conflict. Some are characterised by ‘extreme’ cases of institutional voids and terrorism, creating a hostile environment for the organisation and its people. This in-depth qualitative study of a service company, a European telecommunications joint venture in Afghanistan, seeks to shed light and build theory on the human resource management (HRM) dimension of managerial learning and knowledge acquisition in hostile environments, as part of the MNE’s organisational learning process. Specifically, we investigate how knowledge gaps can be addressed through supportive HR practices, and how knowledge classified as ‘rare’ can be captured and leveraged through HR interventions such as debriefing. We stipulate that HR practices and interventions adapted to hostile environments, together with expatriate willingness to learn and share new knowledge, play a c...
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TL;DR: In this paper, implicit copula models for the original parameters, with a Gaussian or skew-normal copula function and flexible parametric margins, are proposed to improve the accuracy of variational inference in high dimensions at limited or no additional computational cost.
Abstract: Variational 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.
TL;DR: In this article, the authors present a research agenda on cultural norms in communication, negotiation, and conflict management, organized around five questions: How do culture and norms relate to an individual's propensity to negotiate? How do tightness-looseness norms explain negotiators' reactions to norm conformity and norm violation? And three questions on communication, for example: What individual and cultural factors lead negotiators to use miscommunication as an opportunity rather than an obstacle? Or are there cultural differences in whether and what forms of schmoozing are normative?
Abstract: This paper elaborates a research agenda on cultural norms in communication, negotiation, and conflict management. Our agenda is organized around five questions on negotiation and conflict management, for example: How do culture and norms relate to an individual's propensity to negotiate? Or How do tightness‐looseness norms explain negotiators’ reactions to norm conformity and norm violation? And three questions on communication, for example: What individual and cultural factors lead negotiators to use miscommunication as an opportunity rather than an obstacle? Or Are there cultural differences in whether and what forms of schmoozing are normative? The present paper is based on three pillars: (a) ideas provided by the think tank participants (full list on website), (b) state of the art research and (c) the authors’ perspectives. Our goal is to inspire young, as well as, established researchers to purse these research streams and increase our understanding about the influence of cultural norms.
TL;DR: Self-help books with career advice for women who aspire to leadership are popular as discussed by the authors, which is somewhat surprising, in that the advice appears to take us back to the "fix the women" approach.
Abstract: Self-help books with career advice for women who aspire to leadership are popular. This popularity is somewhat surprising, in that the advice appears to take us back to the “fix the women” approach...
TL;DR: A new variational Bayes (VB) estimator for high-dimensional copulas with discrete, or a combination of discrete and continuous, margins is proposed, based on a variational approximation to a tractable augmented posterior and is faster than previous likelihood-based approaches.
Abstract: We propose a new variational Bayes (VB) estimator for high-dimensional copulas with discrete, or a combination of discrete and continuous, margins. The method is based on a variational approximatio...
TL;DR: It is found that third-party audiences are often economically unattractive, except for higher-priced media placements, given the high extra costs of targeting solutions and the relative inaccuracy.
Abstract: Data brokers often use online browsing records to create digital consumer profiles they sell to marketers as pre-defined audiences for ad targeting. However, this process is a `black box': Little is known about the reliability of the digital profiles that are created, or of the audience identification provided by buying platforms. In this paper, we investigate using three field tests the accuracy of a variety of demographic and audience-interest segments. We examine the accuracy of over 90 third-party audiences across 19 data brokers.
Audience segments vary greatly in quality and are often inaccurate across leading data brokers. In comparison to random audience selection, the use of black-box data profiles on average increased identification of a user with a desired attribute by 0-77%. Audience identification can be improved on average by 123% when combined with optimization software. However, given the high extra costs of targeting solutions and the relative inaccuracy, we find that third-party audiences are often economically unattractive, except for higher-priced media placements.
TL;DR: The dynamic pricing problem faced by a monopolistic retailer who sells a storable product to forward-looking consumers is studied and it is shown that, given linear storage costs, the retailer can compute an optimal preannounced pricing policy in polynomial time by solving a dynamic program.
Abstract: We study the dynamic pricing problem faced by a monopolistic retailer who sells a storable product to forward-looking consumers. In this framework, the two major pricing policies (or mechanisms) studied in the literature are the preannounced (commitment) pricing policy and the contingent (threat or history dependent) pricing policy. We analyse and compare these pricing policies in the setting where the good can be purchased along a finite time horizon in indivisible atomic quantities. First, we show that, given linear storage costs, the retailer can compute an optimal preannounced pricing policy in polynomial time by solving a dynamic program. Moreover, under such a policy, we show that consumers do not need to store units in order to anticipate price rises. Second, under the contingent pricing policy rather than the preannounced pricing mechanism, (i) prices could be lower, (ii) retailer revenues could be higher, and (iii) consumer surplus could be higher. This result is surprising, in that these three facts are in complete contrast to the case of a retailer selling divisible storable goods (Dudine et al. in Am Econ Rev 96(5):1706–1719, 2006). Third, we quantify exactly how much more profitable a contingent policy could be with respect to a preannounced policy. Specifically, for a market with N consumers, a contingent policy can produce a multiplicative factor of $$\Omega (\log N)$$
more revenues than a preannounced policy, and this bound is tight.
TL;DR: In this paper, a spatial panel quantile model with unobserved heterogeneity is proposed, which is capable of capturing high-dimensional cross-sectional dependence and allows heterogeneous regression coefficients.
Abstract: This paper introduces a spatial panel quantile model with unobserved heterogeneity. The proposed model is capable of capturing high-dimensional cross-sectional dependence and allows heterogeneous regression coefficients. For estimating model parameters, a new estimation procedure is proposed. When both the time and cross-sectional dimensions of the panel go to infinity, the uniform consistency and the asymptotic normality of the estimated parameters are established. In order to determine the dimension of the interactive fixed effects, we propose a new information criterion. It is shown that the criterion asymptotically selects the true dimension. Monte Carlo simulations document the satisfactory performance of the proposed method. Finally, the method is applied to study the quantile co-movement structure of the U.S. stock market by taking into account the input-output linkages as firms are connected through the input-output production network.
TL;DR: In this article, the authors argue that consumers often do not have complete information about the choices they face and therefore have to spend time and effort acquiring information, and that information acquisition is costly.
Abstract: Consumers often do not have complete information about the choices they face and, therefore, have to spend time and effort acquiring information. Because information acquisition is costly, consumer...
01 Dec 2019
TL;DR: In this paper, the role of customer-facing or frontline employees (FLEs) as sources of inimitable knowledge valuable for service innovation is discussed, and a taxonomy of network domains connecting customer-and internal-facing employees and resource flows is proposed to provide a framework for FLE roles in knowledge networks for service-innovation.
Abstract: Service organizations often view customer-facing or frontline employees (FLEs) as sources of inimitable knowledge valuable for innovation. This is due to the experiential nature of service and subtle qualities of engaging customer interactions. Yet, organizations face significant challenges while leveraging the knowledge of their FLEs to develop service innovations. Drawing upon the open innovation and social network literatures, we theorize the role of FLE networks, and the degree to which these networks enable the flow of distinct content for realizing effective service innovation. Specifically, we conceptualize a taxonomy of network domains—connecting customer- and internal-facing employees, and resource flows—new knowledge and self-governance activities, to provide a framework for FLE roles in knowledge networks for service-innovation. Our taxonomy expands opportunities for theorizing the mechanisms of frontline knowledge networks in service innovation as well as identifying a “dark side” that undermines potential innovation gains if left unchecked. Future directions and implications for theory and practice are discussed.
TL;DR: A new semiparametric distributional regression smoother that is based on a copula decomposition of the joint distribution of the vector of response values that produces distributional estimates that are locally adaptive with respect to the covariates and predictions that are more accurate than those from benchmark models.
Abstract: We propose a new semi-parametric distributional regression smoother that is based on a copula decomposition of the joint distribution of the vector of response values. The copula is high-dimensional and constructed by inversion of a pseudo regression, where the conditional mean and variance are semi-parametric functions of covariates modeled using regularized basis functions. By integrating out the basis coefficients, an implicit copula process on the covariate space is obtained, which we call a `regression copula'. We combine this with a non-parametric margin to define a copula model, where the entire distribution - including the mean and variance - of the response is a smooth semi-parametric function of the covariates. The copula is estimated using both Hamiltonian Monte Carlo and variational Bayes; the latter of which is scalable to high dimensions. Using real data examples and a simulation study we illustrate the efficacy of these estimators and the copula model. In a substantive example, we estimate the distribution of half-hourly electricity spot prices as a function of demand and two time covariates using radial bases and horseshoe regularization. The copula model produces distributional estimates that are locally adaptive with respect to the covariates, and predictions that are more accurate than those from benchmark models.
TL;DR: The 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, and combines with a non-parametrically estimated marginal distribution for the response.
Abstract: Deep 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'. 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.
TL;DR: In this paper, the Kullback-Leibler information criterion is used to select the regularization parameter of the penalized empirical likelihood estimator, which is derived as an estimator of the expected value of the kullback − Leibler Information criterion from an estimated model to the true data generating process.
TL;DR: In this paper, the authors employ a new approach to identify merger and acquisition (M&A) transactions financed by syndicated loans and provide evidence that acquirer announcement returns are higher in loan-financed M&A deals than in other deals.
Abstract: This paper employs a new approach to identify merger and acquisition (M&A) transactions financed by syndicated loans and provides evidence that acquirer announcement returns are higher in loan-financed M&A deals than in other deals. Utilizing an instrumental variable approach and a quasi-natural experiment, we provide evidence that lenders contribute to the higher acquirer announcement returns in loan-financed M&A deals. Lenders’ performance in M&A financing is persistent over time. Lenders’ participation in the M&A market can resolve uncertainty about the M&A deal quality, improve corporate governance by preventing value-destroying M&A transactions, and provide long-term monitoring benefits to acquirer shareholders.
TL;DR: This article investigated the impact of social media posts on the capital market when there is a change in political power and found that investors are more likely to react to tweets released by an influential source, such as Donald J. Trump, and firms that could help him in implementing or justifying his economic policy.
Abstract: We investigate President Donald J. Trump’s unprecedented use of social media, notably Twitter, to attack, pressure, and compliment specific firms. Our results show that Trump is more likely to tweet firms from his business network, and firms that could help him in implementing or justifying his economic policy. Our study offers a setting for studying the impact of social media posts on the capital market when there is a change in political power. The findings provide new evidence showing that investors are more likely to react to social media posts released by an influential source; for example, the market’s reactions to Trump’s posts were significantly and economically stronger after he won the 2016 presidential election. Our results suggest that investors are very sophisticated in processing the relevance for firm value of information transmitted on social media.
TL;DR: In this article, a hierarchical Bayesian approach is proposed for variable selection in non-Gaussian regression models. But their approach is not suitable for spatial variable selection, and it requires the dependence of the dependent variable to be calibrated using a nonparametric or other estimator.
Abstract: We 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 joint distribution 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' that are constructed from existing hierarchical Bayesian models widely 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 Markov chain Monte Carlo (MCMC). A simulation study shows that when the responses are non-Gaussian the approach selects variables more accurately than contemporary benchmarks. A real data example in the Web Appendix illustrates that accounting for even mild deviations from normality can lead to a substantial increase in accuracy. To illustrate the full potential of our approach we extend it to spatial variable selection for fMRI. Using real data, we show our method allows for voxel-specific marginal calibration of the magnetic resonance signal at over 6,000 voxels, leading to an increase in the quality of the activation maps.