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The social relations model (SRM; Kenny, 1994) explicitly proposes that leadership simultaneously operates at three levels of analysis: group, dyad, and individual (perceiver and target). With this model, researchers can empirically determine the amount of variance at each level as well as those factors that explain variance at these different levels. This chapter shows how the SRM can be used to address many theoretically important questions in the study of leadership and can be used to advance both the theory of and research in leadership. First, based on analysis of leadership ratings from seven studies, we find that there is substantial agreement (i.e., target variance) about who in the group is the leader and little or no reciprocity in the perceptions of leadership. We then consider correlations of leadership perceptions. In one analysis, we examine the correlations between task-oriented and socioemotional leadership. In another analysis, we examine the effect of gender and gender composition on the perception of leadership. We also explore how self-ratings of leadership differ from member perceptions of leadership. Finally, we discuss how the model can be estimated using conventional software.

This paper proposes and tests a model to explain the outcomes of three different information presentation formats. Based on cognitive fit theory, information visualization formats that best fit task characteristics are expected to lead to improved decision-making outcomes. We apply the Judgment and Decision-Making framework (Bonner, 2008) to investigate how certain factors can impact decision quality.

This study tests whether certain production variance presentation formats (percentages, dollar amounts, and schematic faces), task complexity, understanding of the presentation format, motivation, and effort increase the accuracy of a supervisor’s bonus calculation. A total of 281 students and professionals participated in this experiment. Their responses were examined using regression analysis.

Our results indicate that individuals mostly prefer the percentages presentation format and that the use of the percentages presentation format, a lower level of task complexity, and a better understanding of the variance presentation format lead to more accurate calculations in the experimental task.

Our study provides a call for further research on factors that influence the choice of presentation format as a potentially fruitful area for management accounting researchers.

We exhort practicing management accountants to exert direct influence on employees’ decision making through the use of variance presentation formats that fit their tasks and promote understanding.

Our experiment introduced two major innovations: it uses an interactive data visualization approach allowing subjects to select their preferred presentation format; and it focuses on production variances, a topic that has received less attention in the academic managerial accounting literature, but is still very relevant to practitioners.

This commentary on Kenny and Livi (2009) expands on aspects of leadership categorization theory that are consistent with the application of the social relations model (SRM). It critically considers limits to the generalization of the variance components analysis results described in Kenny and Livi's first example, and briefly summarizes results of an additional study that supports their findings. Finally, this commentary suggests interpretational issues of interest to researchers who wish to continue to apply the SRM to multi-level issues in the study of leadership.

Hall, Lord, and Foster (2009) have commented on whether variance partitioning in the social relations model would be the same in long-term groups and when groups have formal leaders. This reply follows their lead and speculates on how the variance partitioning would change. It considers the design and analysis issues in natural workgroups as well as the estimation of group effects.

The variance targeting estimator (VTE) for generalized autoregressive conditionally heteroskedastic (GARCH) processes has been proposed as a computationally simpler and misspecification-robust alternative to the quasi-maximum likelihood estimator (QMLE). In this paper we investigate the asymptotic behavior of the VTE when the stationary distribution of the GARCH process has infinite fourth moment. Existing studies of historical asset returns indicate that this may be a case of empirical relevance. Under suitable technical conditions, we establish a stable limit theory for the VTE, with the rate of convergence determined by the tails of the stationary distribution. This rate is slower than that achieved by the QMLE. The limit distribution of the VTE is nondegenerate but singular. We investigate the use of subsampling techniques for inference, but find that finite sample performance is poor in empirically relevant scenarios.

This study examines the impact of different Balanced Scorecard (BSC) formats (table, graph without summary measure, graph with a summary measure) on various decision outcomes: performance ratings, perceived informativeness, and decision efficiency.

Using an original case developed by the researchers, a total of 135 individuals participated in the experiment and rated the performance of carwash managers in two different scenarios: one manager excelled financially but failed to meet targets for all other three BSC perspectives and the other manager had the opposite results.

The evaluators rated managerial performance significantly lower in the graph format compared to a table presentation of the BSC. Performance ratings were significantly higher for the scenario where the manager failed to meet only financial perspective targets but exceeded targets for all other nonfinancial BSC perspectives, contrary to the usual predictions based on the financial measure bias. The evaluators reported that informativeness of the BSC was highest in the table or graph without summary measure formats, and, surprisingly, adding a summary measure to the graph format significantly reduced perceived informativeness compared to the table format. Decision efficiency was better for the graph formats (with or without summary measure) than for the table format.

Ours is the first study to compare tables, graphs with and without a summary measure in the context of managerial performance evaluations and to examine their impact on ratings, informativeness, and efficiency. We developed an original case to test the boundaries of the financial measure bias.

We extend earlier work on the NoVaS transformation approach introduced by Politis (2003a, 2003b). The proposed approach is model-free and especially relevant when making forecasts in the context of model uncertainty and structural breaks. We introduce a new implied distribution in the context of NoVaS, a number of additional methods for implementing NoVaS, and we examine the relative forecasting performance of NoVaS for making volatility predictions using real and simulated time series. We pay particular attention to data-generating processes with varying coefficients and structural breaks. Our results clearly indicate that the NoVaS approach outperforms GARCH model forecasts in all cases we examined, except (as expected) when the data-generating process is itself a GARCH model.

The dependent variables for this analysis include three measures of arms embargo effectiveness, which were referred to as ‘levels of effectiveness’ in the Framework Chapter. These are the embargo's success in causing a targeted policy change (level I effectiveness), success in changing arms flow to the target (level II effectiveness), and a measure of effectiveness to capture the arms embargo initiators satisfaction with the operation of the embargo (level III effectiveness).

We develop and validate measurement instruments for the business partner, watchdog, and scorekeeper roles of controllers. This study addresses calls to enhance the quality of survey research in management accounting by devoting more attention to scale development and especially to construct validity. By focusing on the activity sets of the controllers’ roles, we provide a theoretically and empirically grounded picture of their current roles. The measurement instruments presented in this study enable systematic research progress on controller roles, their relationships, antecedents, and performance outcomes.

In this paper we develop a new semi-parametric model for conditional correlations, which combines parametric univariate Generalized Auto Regressive Conditional Heteroskedasticity specifications for the individual conditional volatilities with nonparametric kernel regression for the conditional correlations. This approach not only avoids the proliferation of parameters as the number of assets becomes large, which typically happens in conventional multivariate conditional volatility models, but also the rigid structure imposed by more parsimonious models, such as the dynamic conditional correlation model. An empirical application to the 30 Dow Jones stocks demonstrates that the model is able to capture interesting asymmetries in correlations and that it is competitive with standard parametric models in terms of constructing minimum variance portfolios and minimum tracking error portfolios.