Estimating the prevalence of sensitive behaviour and cheating with a dual design for direct questioning and randomized response

Randomized response is a misclassification design to estimate the prevalence of sensitive behaviour. Respondents who do not follow the instructions of the design are considered to be cheating. A mixture model is proposed to estimate the prevalence of sensitive behaviour and cheating in the case of a dual sampling scheme with direct questioning and randomized response. The mixing weight is the probability of cheating, where cheating is modelled separately for direct questioning and randomized response. For Bayesian inference, Markov chain Monte Carlo sampling is applied to sample parameter values from the posterior. The model makes it possible to analyse dual sample scheme data in a unified way and to assess cheating for direct questions as well as for randomized response questions. The research is illustrated with randomized response data concerning violations of regulations for social benefit.     

License to fail? How leader group prototypicality moderates the effects of leader performance on perceptions of leadership effectiveness

Leadership often serves as an explanatory category for performance outcomes (i.e., failure and success). This process can strengthen or weaken leadership effectiveness, because contingent on their performance leaders may gain or lose follower endorsement — the basis of leadership. Drawing on the social identity analysis of leadership, we hypothesized that leader group prototypicality and performance information interact to predict followers' perceptions of leadership effectiveness. Because group prototypical leaders are more trusted by their followers, we hypothesized that group prototypical leaders are evaluated as more effective after failure information than non-prototypical leaders. In contrast, we predicted that both prototypical and non-prototypical leaders should receive similar evaluations of leadership effectiveness after success. We found support for our predictions in a scenario experiment, a cross-sectional field study, and a laboratory experiment.     

Position in formal structure, personal characteristics and choices of advisors in a law firm: A logistic regression model for dyadic network data

This paper presents a statistical model for the analysis of binary sociometric choice data, the p₂ model, which provides a flexible way for using explanatory variables to model network structure. It is applied to examine the influence of the formal structure of an organization on interactions among its members. It is shown to provide a general and precise method for addressing this substantive issue. We identify the respective effects of position in the formal structure (status, seniority, division of work and office membership) and selected personal characteristics of members of a corporate law firm on their choices of advisors. Flows of advice are shown to be consistently shaped by status games and the pecking order in the firm. Other dimensions help members in mitigating the effect of this strong rule. This approach ultimately provides more understanding of how members of such firms try to balance cooperation and competition in terms of access to and management of key resources.     

Strategic leadership and the chief executive

This article is based on research undertaken among South African chief executives occupying the most senior operational positions in public-quoted companies. In looking at distinctive characteristics in these individuals the research attempts to bring together the activities that shape the chief executive's job and explore possible trends in their background; career route and behaviour patterns, and from the compositie picture to provide guidelines for executive development.     

The impact of attitude strength on customer-oriented priority setting by decision-makers: An empirical investigation

A key question in marketing decision-making pertains to what makes decision-makers focus on various types of information in different ways. Particularly in relation to a key marketing and management variable such as customer satisfaction this is an important issue. Costly derived customer satisfaction measures need to provide customer-oriented guidance regarding where to prioritise. Traditional research on intelligence use has mainly focused on objective research attributes, such as research quality. In this article, however, we will adopt the idea that decision-makers weigh information differently based on their perception of its relevance. With respect to crucial customer satisfaction information this represents an exciting, but nevertheless unexplored field of research. The results of latent variable modelling show that the strength of decision-makers’ attitudes toward customer satisfaction leads to a differentiated usage of satisfaction intelligence. By taking this into consideration, management and intelligence providers will be able to more effectively disseminate customer satisfaction information and facilitate a more customer-oriented perspective within firms.     

Music Education and minority groups cultural and musical identities in the ‘newer’ South Africa: white Afrikaners and Indians

Music Education, as well as cultural and musical identities are all being renegotiated, post‐Apartheid, within the so‐called ‘newer’ rather than the commonly known ‘new’ South Africa. The developing situation with certain minority groups is particularly interesting. Education in general has undergone much change since the first democratic elections in 1994: music education specifically has been affected by such change in terms of content, delivery and assessment. Within the South African context, cultural and musical identities are often intertwined with language, racial and even tribal identities, and discussing one implies the others. We are particularly interested here in the role of formal Music Education in relation to white Afrikaners and Indians as they renegotiate their cultural development, including musical aspects.     

A deletion/substitution/addition algorithm for classification neural networks,with applications to biomedical data

Neural networks are a popular machine learning tool, particularly in applications such as protein structure prediction; however, overfitting can pose an obstacle to their effective use. Due to the large number of parameters in a typical neural network, one may obtain a network fit that perfectly predicts the learning data, yet fails to generalize to other data sets. One way of reducing the size of the parmeter space is to alter the network topology so that some edges are removed; however it is often not immediately apparent which edges should be eliminated. We propose a data-adaptive method of selecting an optimal network architecture using a deletion/substitution/addition algorithm. Results of this approach to classification are presented on simulated data and the breast cancer data of Wolberg and Mangasarian [1990. Multisurface method of pattern separation for medical diagnosis applied to breast cytology. Proc. Nat. Acad. Sci. 87, 9193–9196].     

A reverse sunk cost effect in risky decision making: Sometimes we have too much invested to gamble

The sunk cost effect refers to the empirical finding that people tend to let their decisions be influenced by costs made at an earlier time in such a way that they are more risk seeking than they would be had they not made these costs. This finding seems to be in conflict with economic theory which implies that only incremental costs and benefits should affect decisions. The effect is often explained in terms of prospect theory of (Kahneman, D., Tversky, A., 1979. Prospect theory: An analysis of decision under risk. Econometrica 47, 263–291), suggesting that sunk costs may induce a ‘loss frame,’ consequently causing risk seeking behavior. We argue that sunk costs may also result in risk aversion. In the present study we investigated the effect of time and effort investments (Behavioral Sunk Costs) on risky decision making in gain and loss situations. The results show that, in agreement with prospect theory, participants were more risk averse in gain situations than in loss situations. Moreover, incurring Behavioral Sunk Costs appeared to increase risk aversive choices, i.e., a reverse sunk cost effect. Furthermore, the results suggest that, in loss situations, Behavioral Sunk Costs mainly lead to risk aversive behavior if opting for the ‘safe’ alternative is not accompanied by an increased possibility to regret the decision.     

The age of exploration and exploitation: Younger-looking leaders endorsed for change and older-looking leaders endorsed for stability

The current contribution extends theorizing on leadership and the exploration–exploitation dilemma using an evolutionary perspective. A theoretical connection is made between the exploration–exploitation dilemma and age-biased leadership preferences for exploratory change versus stable exploitation. For the majority of human evolution our species was semi- or entirely nomadic and the trade-off between exploration versus exploitation had substantial physical- and experience-based requirements which align with leadership opportunities as moderated by age. Thus, given the consistency and importance of correctly assigning leadership for the exploration–exploitation dilemma, human evolution has likely selected for age-biased leadership endorsement. Across three experiments we find that younger-looking leaders are endorsed for times of exploratory change and older-looking leaders for stable exploitation. Further, our results indicated that older leaders are endorsed for leading conservative exploitation of nonrenewable resources and younger leaders for exploration of renewable alternatives (i.e., green leadership). The results introduce an age-biased leadership endorsement hypothesis.