Necessary condition analysis (NCA): review of research topics and guidelines for good practice

Necessary condition analysis (NCA) is an increasingly used or suggested method in many business and management disciplines including, for example, entrepreneurship, human resource management, international business, marketing, operations, public and nonprofit management, strategic management, and tourism. In the light of this development, our work delivers a review of the topics analyzed with NCA or in which NCA is proposed as a method. The review highlights the tremendous possibilities of using NCA, which hopefully encourages other researchers to try the method. To support researchers in future NCA studies, this article also provides detailed guidelines about how to best use NCA. These cover eight topics: theoretical justification, meaningful data, scatter plot, ceiling line, effect size, statistical test, bottleneck analysis, and further descriptions of NCA.

Team Performance: Nature and Antecedents of Nonnormal Distributions

Team research typically assumes that team performance is normally distributed: teams cluster around average performance, performance variability is not substantial, and few teams inhabit the upper range of the distribution. Ironically, although most team research and methodological practices rely on the normality assumption, many theories actually imply nonnormality (e.g., performance spirals, team composition, team learning, punctuated equilibrium). Accordingly, we investigated the nature and antecedents of team performance distributions by relying on 274 performance distributions including 200,825 teams (e.g., sports, politics, firefighters, information technology, customer service) and more than 500,000 workers. First, regarding their overall nature, only 11% of the distributions were normal, star teams are much more prevalent than predicted by normality, the power law with an exponential cutoff is the most dominant distribution among nonnormal distributions (i.e., 73%), and incremental differentiation (i.e., differential performance trajectories across teams) is the best explanation for the emergence of these distributions. Second, this conclusion remained unchanged after examining theory-based boundary conditions (i.e., tournament versus nontournament contexts, performance as aggregation of individual-level performance versus performance as a team-level construct, performance assessed with versus without a hard left-tail zero, and more versus less sample homogeneity). Third, we used the team learning curve literature as a conceptual framework to test hypotheses and found that authority differentiation and lower temporal stability are associated with distributions with larger performance variability (i.e., a greater proportion of star teams). We discuss implications for existing theory, future research directions, and methodological practices (e.g., need to check for nonnormality, Bayesian analysis, outlier management).

If the public can vaccinate, why not students? Review of a student nurse placement in a mass vaccination centre

Delivery of the COVID-19 vaccine has been made possible in part through the use of mass vaccination centres (MVCs). The primary legal framework underpinning the MVC programme is a national protocol enabling registered and non-registered healthcare workers to contribute to the safe and effective administration of the vaccine. The national protocol provided a vehicle for an innovative supervised student nurse placement within an MVC in south Wales. This placement, for undergraduate pre-registration student nurses, formed part of a service improvement project. Through student feedback prior to, and following, the short placement, the learning was unequivocal in terms of knowledge and skills acquisition related to safe and effective vaccine administration, with students providing clear feedback on the positive nature of the placement experience. A placement within an MVC offers a rich educational experience for student nurses, which as yet appears to be underutilised across the UK.

Inflection Points, Kinks, and Jumps: A Statistical Approach to Detecting Nonlinearities

Inflection points, kinks, and jumps identify places where the relationship between dependent and independent variables switches in some important way. Although these switch points are often mentioned in management research, their presence in the data is either ignored, or postulated ad hoc by testing arbitrarily specified functional forms (e.g., U or inverted U-shaped relationships). This is problematic if we want accurate tests for our theories. To address this issue, we provide an integrative framework for the identification of nonlinearities. Our approach constitutes a precursor step that researchers will want to conduct before deciding which estimation model may be most appropriate. We also provide instructions on how our approach can be implemented, and a replicable illustration of the procedure. Our illustrative example shows how the identification of endogenous switch points may lead to significantly different conclusions compared to those obtained when switch points are ignored or their existence is conjectured arbitrarily. This supports our claim that capturing empirically the presence of nonlinearity is important and should be included in our empirical investigations.

An overview of applied robust methods

Data in social sciences are typically non-normally distributed and characterized by heavy tails. However, most widely used methods in social sciences are still based on the analyses of sample means and sample covariances. While these conventional methods continue to be used to address new substantive issues, conclusions reached can be inaccurate or misleading. Although there is no 'best method' in practice, robust methods that consider the distribution of the data can perform substantially better than the conventional methods. This article gives an overview of robust procedures, emphasizing a few that have been repeatedly shown to work well for models that are widely used in social and behavioural sciences. Real data examples show how to use the robust methods for latent variable models and for moderated mediation analysis when a regression model contains categorical covariates and product terms. Results and logical analyses indicate that robust methods yield more efficient parameter estimates, more reliable model evaluation, more reliable model/data diagnostics, and more trustworthy conclusions when conducting replication studies. R and SAS programs are provided for routine applications of the recommended robust method.

A Robust Bootstrap Test for Mediation Analysis

Mediation analysis is central to theory building and testing in organizational sciences. Scholars often use linear regression analysis based on normal-theory maximum likelihood estimators to test mediation. However, these estimators are very sensitive to deviations from normality assumptions, such as outliers, heavy tails, or skewness of the observed distribution. This sensitivity seriously threatens the empirical testing of theory about mediation mechanisms. To overcome this threat, we develop a robust mediation method that yields reliable results even when the data deviate from normality assumptions. We demonstrate the mechanics of our proposed method in an illustrative case, while simulation studies show that our method is both superior in estimating the effect size and more reliable in assessing its significance than the existing methods. Furthermore, we provide freely available software in R and SPSS to enhance its accessibility and adoption by empirical researchers.