Organizational Research Methods

Qualitative researchers have developed and employed a variety of phenomenological methodologies to examine individuals’ experiences. However, there is little guidance to help researchers choose between these variations to meet the specific needs of their studies. The purpose of this article is to illuminate the scope and value of phenomenology by developing a typology that classifies and contrasts five popular phenomenological methodologies. By explicating each methodology’s differing assumptions, aims, and analytical steps, the article generates a series of guidelines to inform researchers’ selections. Subsequent sections distinguish the family of phenomenological methodologies from other qualitative methodologies, such as narrative analysis and autoethnography. The article then identifies institutional work and organizational identity as topical bodies of research with particular research needs that phenomenology could address.

The ubiquity of surveys in organizational research means that their quality is of paramount importance. Commonly this has been addressed through the use of sophisticated statistical approaches with scant attention paid to item comprehension. Linguistic theory suggests that while everyone may understand an item, they may comprehend it in different ways. We explore this in two studies in which we administered three published scales and asked respondents to indicate what they believed the items meant, and a third study that replicated the results with an additional scale. These demonstrate three forms of miscomprehension: instructional (where instructions are not followed), sentential (where the syntax of a sentence is enriched or depleted as it is interpreted), and lexical (where different meanings of words are deployed). These differences in comprehension are not appreciable using conventional statistical analyses yet can produce significantly different results and cause respondents to tap into different concepts. These results suggest that item interpretation is a significant source of error, which has been hitherto neglected in the organizational literature. We suggest remedies and directions for future research.

Partial least squares path modeling (PLS) has been increasing in popularity as a form of or an alternative to structural equation modeling (SEM) and has currently considerable momentum in some management disciplines. Despite recent criticism toward the method, most existing studies analyzing the performance of PLS have reached positive conclusions. This article shows that most of the evidence for the usefulness of the method has been a misinterpretation. The analysis presented shows that PLS amplifies the effects of chance correlations in a unique way and this effect explains prior simulations results better than the previous interpretations. It is unlikely that a researcher would willingly amplify error, and therefore the results show that the usefulness of the PLS method is a fallacy. There are much better ways to compensate for the attenuation effect caused by using latent variable scores to estimate SEM models than creating a bias into the opposite direction.

This article addresses Rönkkö and Evermann’s criticisms of the partial least squares (PLS) approach to structural equation modeling. We contend that the alleged shortcomings of PLS are not due to problems with the technique, but instead to three problems with Rönkkö and Evermann’s study: (a) the adherence to the common factor model, (b) a very limited simulation designs, and (c) overstretched generalizations of their findings. Whereas Rönkkö and Evermann claim to be dispelling myths about PLS, they have in reality created new myths that we, in turn, debunk. By examining their claims, our article contributes to reestablishing a constructive discussion of the PLS method and its properties. We show that PLS does offer advantages for exploratory research and that it is a viable estimator for composite factor models. This can pose an interesting alternative if the common factor model does not hold. Therefore, we can conclude that PLS should continue to be used as an important statistical tool for management and organizational research, as well as other social science disciplines.

The purpose of the present article is to take stock of a recent exchange in Organizational Research Methods between critics and proponents of partial least squares path modeling (PLS-PM). The two target articles were centered around six principal issues, namely whether PLS-PM: (a) can be truly characterized as a technique for structural equation modeling (SEM), (b) is able to correct for measurement error, (c) can be used to validate measurement models, (d) accommodates small sample sizes, (e) is able to provide null hypothesis tests for path coefficients, and (f) can be employed in an exploratory, model-building fashion. We summarize and elaborate further on the key arguments underlying the exchange, drawing from the broader methodological and statistical literature to offer additional thoughts concerning the utility of PLS-PM and ways in which the technique might be improved. We conclude with recommendations as to whether and how PLS-PM serves as a viable contender to SEM approaches for estimating and evaluating theoretical models.