For decades, participant carelessness has been considered a problem in collecting data using surveys. Although participant carelessness cannot be disputed to exist, the impact it has on data quality or the level of influence or bias it produces in results is questionable. The main purpose of this paper is to determine whether participant carelessness is a substantial problem that significantly influences or biases the results of statistical analyses. This is accomplished by analyzing established management relationships through a comparison of the full, careful, and careless samples to determine the impact participant carelessness has on data results regarding correlations, t‑tests, and simple linear regressions. Four detection approaches were used to identify careless participants individually, in pairs, and in three method combinations. The second purpose of this paper is to use the resampled individual reliability (RIR) approach to detect careless participants and compare it to the individual reliability approach to determine whether the two approaches are fundamentally similar. Data were collected using Mechanical Turk (N = 678). Based on the findings, participant carelessness does not appear to be a substantial problem or demonstrate levels of bias in the results in this study. There are two significant differences between the full and careful samples with the t‑tests and the regression comparisons of fit statistics demonstrate the careful samples to have a weak improvement over the full sample; however, none indicate bias. The findings also suggest that the individual reliability and the RIR approaches are not entirely fundamentally similar. 

Many introductory research methods textbooks still divide the research paradigms into two broad approaches – positivist/quantitative/deductive and interpretive/qualitative/inductive. However, this bifurcation of research orientation does not do justice to the philosophical and methodological pluralism present within business research. This paper offers a third way by reflecting on a retroductive analysis in a critical realist case study. The philosophy of critical realism employs retroductive analysis to search for mechanisms underpinning the empirically observed events. Mechanism‑based theorising is a suggested way in the business research to develop middle‑range ‘sometimes true’ theories. This paper demonstrates the process of retroduction for the identification of mechanisms through an illustration of the data collection, coding and analysis process in a multilevel critical realist case study. In the process, it outlines the challenges faced and offers suggestions to overcome those challenges. This paper does not claim to provide a set of best practices for multilevel retroductive analysis. However, it is hoped that it sensitises business researchers to explore the critical realist perspective and to employ retroduction for mechanism‑based theorising. 

The purpose of this article is to demonstrate the utility of a mixed methods approach in the examination of one of the best‑known success factors of small and medium‑sized enterprises (SMEs): their structural flexibility (SF), and how this is related to their organisational life cycle (OLC). Previous research has proposed five factors to explain SF in large and medium‑sized organisations. By adopting an explanatory sequential design, this study demonstrates why a mono‑method approach is insufficient to explain this model when put into operation in SMEs. It also highlights a key aspect of mixed methods research: the integration challenge, which is illustrated with joint displays and using a weaving approach. In the first quantitative phase, data from 257 SMEs were collected, classified according to their OLC stage, and examined with exploratory factor analysis (EFA) and a two‑step cluster analysis. The EFA revealed five factors, one of which, called “decision‑making”, presented unexpected statistical behaviour and an unclear explanation, indicating a contrasting approach was necessary for better results explanation. It was not until the qualitative phase that we realised this aspect would be better named “centralisation in decision‑making”. This term is associated with growing and declining SMEs, and it may constrain their flexibility and limit their growth. Additionally, a new theme emerged in this phase: “innovation”, which had not been associated with the SF before. This paper provides evidence that the combined use of quantitative and qualitative approaches offers the possibility of exploring new dimensions and can lead to a more comprehensive understanding of the phenomenon in a way quantitative data alone may not allow.