Specificity of CEO human capital and compensation

Much research on top management compensation has focused on the relationship between pay and firm performance. Firms, however, may compensate executives for inputs such as skills, as well as for outputs such as firm performance. This study refocuses attention on the links between managerial abilities and compensation by examining pay differences between types of CEO successors who have differential skills—namely, internal and external successors. © 1997 John Wiley & Sons, Ltd.     

A psychometric assessment of the Lennox and Wolfe self-monitoring scale in the sales force

In today's turbulent business environment, salespeople and sales managers must constantly adapt to changes. Self-monitoring, i.e., the cognitive ability to adapt one's own behavior in response to the behavior of others, may be a meta-KSA (knowledge, skill, or ability) that influences adaptive selling behavior. To assist researchers and practitioners seeking to identify and improve the self-monitoring skills of salespeople, this study investigates Lennox and Wolfe's (1984) self-monitoring scale with a sample of salespeople and one with sales managers. Although a hypothesized relationship between self-monitoring and job performance was supported partially, our results highlight potential problems with the measure. Further investigations are needed to determine whether or not the problems lie with the scale or the concept of self-monitoring in a selling context.     

Evaluating the performance of a predictive modeling approach to identifying members at high-risk of hospitalization

Abstract

Aims: To evaluate the risk-of-hospitalization (ROH) models developed at Blue Cross Blue Shield of Louisiana (BCBSLA) and compare this approach to the DxCG risk-score algorithms utilized by many health plans.

Materials and Methods: Time zero for this study was December 31, 2016. BCBSLA members were eligible for study inclusion if they were fully insured; aged 80?years or younger; and had continuous enrollment starting on or before June 1, 2016, through time zero. Up to 2?years of historical claims data from time zero per patient was included for model development. Members were excluded if they had cancer, renal failure, or were admitted for hospice. The Blue Cross ROH models were developed using (1) regularized logistic regression and (2) random decision forests (a tree ensemble learning classification method). All models were generated using Scikit-learn: Machine Learning in Python. Prognostic capabilities of DxCG risk-score algorithms were compared to those of the Blue Cross models.

Results: When stratifying by the top 0.1% of members with the highest ROH, the Blue Cross logistic regression model had the highest area under the receiving operator characteristics curve (0.862) based on the result of 10-fold cross-validation. The Blue Cross random decision forests model had the highest positive predictive value (49.0%) and positive likelihood ratio (61.4), but sensitivity, specificity, negative predictive values, and negative likelihood ratios were similar across all four models.

Limitations: The Blue Cross ROH models were developed and evaluated using BCBSLA data, and predictive power may fluctuate if applied to other databases.

Conclusions: The predictability of the Blue Cross models show how member-specific, regional data can be used to accurately identify patients with a high ROH, which may allow healthcare workers to intervene earlier and subsequently reduce the healthcare burden for patients and providers.     

The Association Between Vertical Equity and Presidential Voting Behavior and Taxpayers’ Compliance

Journal of Business Ethics - Since taking office, the President of the United States has consistently refused to make his tax returns available for public scrutiny. In so doing, he has broken with...     

Process and context in choice models

We develop a general framework that extends choice models by including an explicit representation of the process and context of decision making. Process refers to the steps involved in decision making. Context refers to factors affecting the process, focusing in this paper on social networks. The extended choice framework includes more behavioral richness through the explicit representation of the planning process preceding an action and its dynamics and the effects of context (family, friends, and market) on the process leading to a choice, as well as the inclusion of new types of subjective data in choice models. We discuss the key issues involved in applying the extended framework, focusing on richer data requirements, theories, and models, and present three partial demonstrations of the proposed framework. Future research challenges include the development of more comprehensive empirical tests of the extended modeling framework.     

How Attitudes Toward Product Categories Drive Individual Brand Attitudes and Choice

Three experiments examine the role of attitudes toward the category to which a brand belongs in consumers’ attitudes toward individual brands. The core findings indicate that what consumers think generally about a category affects their evaluations of singular brands belonging to the category. Study 1 demonstrates that both consumers’ attitudes toward a category as well as their relative attitudes toward a brand versus intracategory competitors drive overall attitudes toward individual brands. Study 2 shows that manipulating attitudes toward a product category affects attitudes toward, and purchase intention of, individual brands belonging to that category. Study 3 demonstrates that more versus less favorably evaluated categories are more likely to exhibit brand positivity effects in judgments of singular brands. The results suggest the practical importance of measuring attitudes toward product categories, as well as the utility of marketing interventions aimed at the category level.     

Understanding sales manager effectiveness: Linking attributes to sales force values

Although sales managers influence a variety of sales force outcomes critically important in the dynamic business environment of the 21st Century, research examining sales manager effectiveness is limited. As such, an investigation of the attributes of effective sales managers, as well as the impact of these attributes on the sales force, is warranted. To explore these issues, the authors employed value laddering, an in-depth interview technique that facilitates the identification of key attributes and the subsequent linkages of those attributes to consequences and underlying values or goals held by respondents. Data collected from sales professionals resulted in 308 ladders that were subsequently aggregated across subjects and used to generate a hierarchical value map (HVM) reflecting the associations among constructs. The HVM highlights the attributes of effective sales managers, as identified by respondents, and reflects three major sales management roles: communicator, motivator and coach. Each of these roles is explored and managerial and research implications are discussed.