Response rates in organizational science, 1995-2008: A meta-analytic review and guidelines for survey researchers

This study expands upon existing knowledge of response rates by conducting a large-scale quantitative review of published response rates. This allowed a fine-grained comparison of response rates across respondent groups. Other unique features of this study are the analysis of response enhancing techniques across respondent groups and response rate trends over time. In order to aid researchers in designing surveys, we provide expected response rate percentiles for different survey modalities. We analyzed 2,037 surveys, covering 1,251,651 individual respondents, published in 12 journals in I/O Psychology, Management, and Marketing during the period 1995-2008. Expected response rate levels were summarized for different types of respondents and use of response enhancing techniques was coded for each study. First, differences in mean response rate were found across respondent types with the lowest response rates reported for executive respondents and the highest for non-working respondents and non-managerial employees. Second, moderator analyses suggested that the effectiveness of response enhancing techniques was dependent on type of respondents. Evidence for differential prediction across respondent type was found for incentives, salience, identification numbers, sponsorship, and administration mode. When controlling for increased use of response enhancing techniques, a small decline in response rates over time was found. Our findings suggest that existing guidelines for designing effective survey research may not always offer the most accurate information available. Survey researchers should be aware that they may obtain lower/higher response rates depending on the respondent type surveyed and that some response enhancing techniques may be less/more effective in specific samples. This study, analyzing the largest set of published response rates to date, offers the first evidence for different response rates and differential functioning of response enhancing techniques across respondent types

The Interactive Effect of a Leader’s Sense of Uniqueness and Sense of Belongingness on Followers’ Perceptions of Leader Authenticity

Researchers have emphasized the value of authenticity, but not much is known about what makes a person authentic in the eyes of others. Our research takes an interpersonal perspective to examine the determinants of followers’ perceptions of leader authenticity. Building on social identity theory, we propose that two fundamental self-identifications–a leader’s sense of uniqueness and sense of belongingness–interact to influence followers’ perceptions of a leader’s authenticity via perceptions of a leader’s self-concept consistency. In a field study conducted among leader–follower dyads and in a controlled laboratory experiment, we find that when a leader feels a low sense of belongingness, there is a positive relationship between a leader’s sense of uniqueness and perceptions of leader authenticity. When a leader feels a low sense of uniqueness, there is a positive relationship between a leader’s sense of belongingness and perceptions of leader authenticity. This is because followers perceive this leader as having high self-concept consistency

Towards Structural Systems Pharmacology to Study Complex Diseases and Personalized Medicine

Genome-Wide Association Studies (GWAS), whole genome sequencing, and high-throughput omics techniques have generated vast amounts of genotypic and molecular phenotypic data. However, these data have not yet been fully explored to improve the effectiveness and efficiency of drug discovery, which continues along a one-drug-one-target-one-disease paradigm. As a partial consequence, both the cost to launch a new drug and the attrition rate are increasing. Systems pharmacology and pharmacogenomics are emerging to exploit the available data and potentially reverse this trend, but, as we argue here, more is needed. To understand the impact of genetic, epigenetic, and environmental factors on drug action, we must study the structural energetics and dynamics of molecular interactions in the context of the whole human genome and interactome. Such an approach requires an integrative modeling framework for drug action that leverages advances in data-driven statistical modeling and mechanism-based multiscale modeling and transforms heterogeneous data from GWAS, high-throughput sequencing, structural genomics, functional genomics, and chemical genomics into unified knowledge. This is not a small task, but, as reviewed here, progress is being made towards the final goal of personalized medicines for the treatment of complex diseases