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Network analysis is a well consolidated research area in several disciplines. Within management and organizational studies, network scholars consolidated a set of research practices that allowed ease of data collection, high inter case comparability, establishment of nomological laws and commitment to social capital motivation. This paper aims to elicit the criticism it has received and highlight the unsettled lacunae.

This paper sheds light on Network Analysis’s breakthroughs, while showing how its scholars innovated by responding to critics, and identifying outstanding debates.

The paper identifies and discusses three streams of criticism that are still outstanding: the role of human agency, the meaning of social ties and the treatment of temporality.

This paper brings to fore current debates within the Network Analysis community, highlighting areas where future studies might contribute.

Leadership cannot exist without followership. The phenomenon of direction and guidance, coaching and mentoring, has at least three components: the leader, leadership, and followers. With each component, the composition of purpose and goals, ethics and morals, rights and duties, and skills and talents is critically important. While the leader is the central and the most important part of the leadership phenomenon, followers are important and necessary factors in the leadership equation. Leaders and followers are engaged in a common enterprise: they are dependent upon each other; their fortunes rise and fall together. Relational qualities define the leadership–followership phenomenon. A major component of such a relationship is how the leaders create and communicate new meaning to followers, perceive themselves relative to followers, and how the followers, in turn, perceive their leader. This mutual perception has serious ethical and moral implications – how leader uses or abuses power, and how followers are augmented or diminished. This chapter features the essentials of ethical and moral, corporate executive leadership in two parts: (1) the Theory of Ethical and Moral Leadership and (2) the Art of Ethical and Moral Leadership. Several contemporary cases such as inspirational leadership of JRD Tata, Crisis of Leadership at Infosys, and Headhunting for CEOs will illustrate our discussions on the ethics and morals of corporate executive leadership.

In their paper titled “A Miracle of Measurement or Accidental Constructivism? How PLS Subverts the Realist Search for Truth,” Cadogan and Lee (2022) cast serious doubt on PLS’s suitability for scientific studies. The purpose of this commentary is to discuss the claims of Cadogan and Lee, correct some inaccuracies, and derive recommendations for researchers using structural equation models.

This paper uses scenario analysis to show which estimators are appropriate for reflective measurement models and composite models, and formulates the statistical model that underlies PLS Mode A. It also contrasts two different perspectives: PLS as an estimator for structural equation models vs. PLS-SEM as an overarching framework with a sui generis logic.

There are different variants of PLS, which include PLS, consistent PLS, PLSe1, PLSe2, proposed ordinal PLS and robust PLS, each of which serves a particular purpose. All of these are appropriate for scientific inquiry if applied properly. It is not PLS that subverts the realist search for truth, but some proponents of a framework called “PLS-SEM.” These proponents redefine the term “reflective measurement,” argue against the assessment of model fit and suggest that researchers could obtain “confirmation” for their model.

Researchers should be more conscious, open and respectful regarding different research paradigms.

Researchers should select a statistical model that adequately represents their theory, not necessarily a common factor model, and formulate their model explicitly. Particularly for instrumentalists, pragmatists and constructivists, the composite model appears promising. Researchers should be concerned about their estimator’s properties, not about whether it is called “PLS.” Further, researchers should critically evaluate their model, not seek confirmation or blindly believe in its value.

This paper critically appraises Cadogan and Lee (2022) and reminds researchers who wish to use structural equation modeling, particularly PLS, for their statistical analysis, of some important scientific principles.