Followers’ preference for consideration and initiating structure leadership styles: Evidence from the laboratory

Decades of leadership research have shown that the two classical leadership styles of consideration and initiating structure robustly predict positive work outcomes, such as satisfaction or performance. In contrast, relatively little is known about whether these leadership styles also predict the emergence of leadership, and which factors might moderate followers’ preference for a particular leadership style. Across three lab experiments (N = 567) where participants were confronted with written or videotaped descriptions of potential group leaders, we examined followers’ leadership preference and whether followers’ personality and motives, or followers’ or leaders’ gender determine such a preference. Results showed that, although consideration leaders are liked more and initiating structure leaders are seen as more qualified, the two leadership styles are overall equally preferred. Followers’ openness is related to a preference for consideration, whereas the achievement motive is related to a preference for initiating structure. Results showed no evidence for general gender-effects in leadership preferences that are predicted in the literature

Predicting Leadership Initiative and its Success: A New Perspective on Leadership Emergence

Research on leadership emergence seeks to identify the mechanisms and variables that determine who will become leader in a group. In this paper, we aim to provide a new perspective on leadership emergence by disentangling two leadership components: (a) leadership initiative and (b) its success in attracting followers. Furthermore, in contrast to previous research on leadership emergence that focused almost exclusively on perceived leadership (usually assessed by questionnaires), we employed behavioral measures of both leadership components. In two large-scale lab studies (overall N = 754), we used an extensive set of variables as predictors of both leadership components, including not only a large number of personality traits and general intelligence, but also physical and physiological traits, such as facial attractiveness, height, waist-to-hip ratio, and testosterone. Across both studies, intelligence was the only robust predictor of both leadership components. In addition, leadership initiative was robustly predicted by extraversion and subjective competence, whereas success of leadership initiative was robustly predicted by the accuracy of the proposed answers. Importantly, several variables that were previously found to be related to leadership emergence, such as dominance, self-esteem, self-monitoring, self-efficacy, narcissism, height, and attractiveness, failed to predict any of the two leadership components in our studies

Measuring Implicit Motives With the Picture Story Exercise (PSE): Databases of Expert-Coded German Stories, Pictures, and Updated Picture Norms

We present two openly accessible databases related to the assessment of implicit motives using Picture Story Exercises (PSEs): (a) A database of 183,415 German sentences, nested in 26,389 stories provided by 4,570 participants, which have been coded by experts using Winter’s coding system for the implicit affiliation/intimacy, achievement, and power motives, and (b) a database of 54 classic and new pictures which have been used as PSE stimuli. Updated picture norms are provided which can be used to select appropriate pictures for PSE applications. Based on an analysis of the relations between raw motive scores, word count, and sentence count, we give recommendations on how to control motive scores for story length, and validate the recommendation with a meta-analysis on gender differences in the implicit affiliation motive that replicates existing findings. We discuss to what extent the guiding principles of the story length correction can be generalized to other content coding systems for narrative material. Several potential applications of the databases are discussed, including (un)supervised machine learning of text content, psychometrics, and better reproducibility of PSE research

Measuring Implicit Motives with the Picture Story Exercise (PSE): Databases of Expert-Coded German Stories, Pictures, and Updated Picture Norms

We present two openly accessible databases related to the assessment of implicit motives using Picture Story Exercises (PSEs): (a) A database of 183,415 German sentences, nested in 26,389 stories provided by 4,570 participants, which have been coded by experts using Winter's coding system for the implicit affiliation/intimacy, achievement, and power motives, and (b) a database of 54 classic and new pictures which have been used as PSE stimuli. Updated picture norms are provided which can be used to select appropriate pictures for PSE applications. Based on an analysis of the relations between raw motive scores, word count, and sentence count, we give recommendations on how to control motive scores for story length, and validate the recommendation with a meta-analysis on gender differences in the implicit affiliation motive that replicates existing findings. We discuss to what extent the guiding principles of the story length correction can be generalized to other content coding systems for narrative material. Several potential applications of the databases are discussed, including (un)supervised machine learning of text content, psychometrics, and better reproducibility of PSE research