Despite the concern that has been expressed about potential method biases, and the pervasiveness of research settings with the potential to produce them, there is disagreement about whether they really are a problem for researchers in the behavioral sciences. Therefore, the purpose of this review is to explore the current state of knowledge about method biases. First, we explore the meaning of the terms “method” and “method bias” and then we examine whether method biases influence all measures equally. Next, we review the evidence of the effects that method biases have on individual measures and on the covariation between different constructs. Following this, we evaluate the procedural and statistical remedies that have been used to control method biases and provide recommendations for minimizing method bias.

Sources of Method Bias in Social Science Research and Recommendations on How to Control It

The Social Psychology of Organizations.

Purpose – Partial least squares (PLS) path modeling is a variance-based structural equation modeling (SEM) technique that is widely applied in business and social sciences. Its ability to model composites and factors makes it a formidable statistical tool for new technology research. Recent reviews, discussions, and developments have led to substantial changes in the understanding and use of PLS. The paper aims to discuss these issues. Design/methodology/approach – This paper aggregates new insights and offers a fresh look at PLS path modeling. It presents new developments, such as consistent PLS, confirmatory composite analysis, and the heterotrait-monotrait ratio of correlations. Findings – PLS path modeling is the method of choice if a SEM contains both factors and composites. Novel tests of exact fit make a confirmatory use of PLS path modeling possible. Originality/value – This paper provides updated guidelines of how to use PLS and how to report and interpret its results.

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Using PLS path modeling in new technology research: updated guidelines

This article addresses Ronkko 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 Ronkko 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 Ronkko 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.

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Common Beliefs and Reality About PLS: Comments on Rönkkö and Evermann (2013)

Social scientists often estimate models from correlational data, where the independent variable has not been exogenously manipulated; they also make implicit or explicit causal claims based on these models. When can these claims be made? We answer this question by first discussing design and estimation conditions under which model estimates can be interpreted, using the randomized experiment as the gold standard. We show how endogeneity – which includes omitted variables, omitted selection, simultaneity, common-method variance, and measurement error – renders estimates causally uninterpretable. Second, we present methods that allow researchers to test causal claims in situations where randomization is not possible or when causal interpretation could be confounded; these methods include fixed-effects panel, sample selection, instrumental variable, regression discontinuity, and difference-in-differences models. Third, we take stock of the methodological rigor with which causal claims are being made in a social sciences discipline by reviewing a representative sample of 110 articles on leadership published in the previous 10 years in top-tier journals. Our key finding is that researchers fail to address at least 66% and up to 90% of design and estimation conditions that make causal claims invalid. We conclude by offering 10 suggestions on how to improve non-experimental research.

On making causal claims : A review and recommendations

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On making causal claims: A review and recommendations

We take historical stock of charisma, tracing its origins and how it has been conceptualized in the sociological and organizational sciences literatures. Although charisma has been intensely studied, the concept is still not well understood and much of the research undertaken cannot inform policy. We show that the major obstacles to advancing our understanding of charisma have included issues with its definition, its confusion with transformational leadership, the use of questionnaire measures, and that it has not been studied using correctly specified causal models. To help spawn a new genre of research on charisma, we use signaling theory to provide a general definition of charisma, and make suggestions about how charisma should be conceptualized, operationalized, and modeled. We also describe trends and patterns in articles we reviewed, using cocitation as well as bibliometric analyses, and discuss the practical implications of our findings.

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Charisma: An Ill-Defined and Ill-Measured Gift

One stream of leadership theory suggests that leaders are evaluated via inferential ob- server processes that compare the fit of the target to a prototype of an ideal (charismatic) leader. Alternatively, attributional theories of leadership suggest that evaluations depend on knowledge of past organizational performance, which is attributed to the leader's skills. We develop a novel theory showing how inferential and attributional processes simultaneously explain top-level leader evaluation, and, ultimately, leader retention and selection. We argue that observers will mostly rely on attributional mechanisms when performance signals clearly indicate good or poor performance outcomes. However, under conditions of attributional ambiguity (i.e., when performance signals are unclear), observers will mostly rely on inferential processes. In Study 1, we tested our theory in an unconventional context--U.S. presidential elections--and found that the two processes, due to the leader's charisma and country's economic performance, interact in predicting whether a leader is selected. Using a business context and an experimental design, in Study 2, we showed that CEO charisma and firm performance interact in predicting leader retention, confirming the results we found in Study 1. Thus, our results suggest that this phenomenon is quite general and can apply to various performance domains

/pdf/when-does-charisma-matter-for-top-level-leaders-effect-of-5ari6n45pz.pdf

When Does Charisma Matter for Top-Level Leaders? Effect of Attributional Ambiguity

https://serval.unil.ch/resource/serval:BIB_985E8B5C56D4.P001/REF.pdf

Philippe Jacquart

Papers

On making causal claims : A review and recommendations

On making causal claims: A review and recommendations

Charisma: An Ill-Defined and Ill-Measured Gift

When Does Charisma Matter for Top-Level Leaders? Effect of Attributional Ambiguity

The far side of leadership: Rather difficult to face