EditorialPartial-Least Squares

Long Range Planning 45 (2012) 309e311 http://www.elsevier.com/locate/lrp

It is a pleasure to present this double issue of Long Range Planning. It is the first of a series of twodouble issues on a common theme, the use of partial-least squares analytical approaches in researchon strategy and management. The papers have been assembled by a very strong editorial team com-posed of Christian Ringle, Marko Sarstedt, and Joe Hair. Professors Ringle, Sarstedt and Hair areinternationally known for their work as researchers, teachers of research methods, and developers ofanalytical software. Their collaborative efforts have made these two special issues possible.

This special issue emphasizes the technical side of PLS modeling; the second special issue will bedevoted to empirical research using PLS modeling. Both issues present a number of exemplary pa-pers that have used partial-least squares modeling to explore topics in strategy and management.Taken together, the two special issues provide one of the most comprehensive introductions tothe use of PLS in management research published to date. Although these issues will be particularlyvaluable to researchers who have relatively little familiarity with PLS modeling, many of the articlesalso will be valuable to people who are experienced users of PLS.

This publication of this special issue also may raise questions for some readers of Long RangePlanning. Although LRP has published special issues devoted to topics that lie on the border of the-ory and methods, such as business models, it is unusual for LRP to organize a special issue arounda methodological theme. This raises two questions: why is LRP publishing an issue of this type, andwhy focus on partial-least squares modeling?

Why LRP?The use of evidence to inform practice is a cornerstone of the field of strategic management. Thework we carry out as researchers has a profound influence on the way we think about strategy, theway we teach our students to analyze strategy, and the practices of managers. Long Range Planningis committed to presenting work that is valuable to academics in their roles as both researchers andteachers of strategic management. This includes substantive insights into the activities of firms andmethodological work that helps to advance the process of generating new empirical insights intostrategy.

We also recognize that there is a widening gap between scholarly research in strategy and thepractice of strategic management. In part, this is an inevitable consequence of the increasing tech-nical sophistication of strategy research and the growing body of knowledge in strategy. The periodsince LRP was founded in 1968 has seen research in strategy grow from a handful of articles tothousands of pages per year detailing the findings of hundreds of research projects. As we learnmore, our research becomes more detailed, narrower, and more difficult to convey directly toa non-academic audience.

The role of Long Range Planning has evolved with the field. We recognize the importance of pre-senting material that serves academics primarily in their activities as researchers as well as substan-tive insights that can be taken directly into the classroom. These two issues on PLS modeling aredirected primarily to academics in their roles as researchers working in the field of strategic man-agement. The special issues present approaches to the analysis of empirical data that have been

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underutilized in the field of strategic management. PLS has become a major approach to structuralmodeling in the field of marketing and it has seen widespread application in the natural sciences,but it has been used relatively little by researchers in strategy and management (Hair et al., 2012).The two special issues on PLS modeling are intended to spread awareness of PLS among strategyresearchers and help them do a better job of using PLS to support their empirical work.

Why PLS?Partial-least squares modeling is particularly suited to research in strategic management. PLS isa strong approach for work intended to develop and refine theories. In contrast to techniquesfor structural modeling such as Amos and Lisrel, PLS makes less severe assumptions about theoret-ical closure in models. Where Amos and Lisrel are strong approaches to testing the fit of fully de-veloped models, PLS is a superior approach for developing and refining theoretical models.

Strategy researchers operate in a less tidy universe than experimental scientists. Research in strat-egy and management rarely involves tightly closed deductive logic that can fully support thehypothesis falsification described by Popper ([1937] 1959). Theories often are fragmentary,coarse-grained ideas that lie in the broad ground between pure speculation and deductive precision.Empirical research typically serves to indicate whether a theoretical approach appears to be useful; itrarely allows unqualified support or clear rejection of a theory.

PLS modeling is a very valuable technique for research of this type. It requires less demandingassumptions about models and can produce unbiased estimates of parameters with small datasets that may not meet the requirements for modeling with Lisrel or Amos. However, PLS doesmore than just offer researchers flexibility in data analysis. It challenges researchers to think abouttheory in ways that many other approaches to data analysis do not. PLS is a predictive approach. Itfocuses attention on the degree to which a given set of independent phenomena can help us antic-ipate the behavior of a dependent phenomenon, without the assumption that the variables ina model provide a full account of the dependent phenomenon.

The use of PLS also calls attention to the fact that causality cannot be established by empiricalanalysis, regardless of the sophistication of the statistical technique. Causality ultimately is estab-lished by theory e all other standards (including temporal order) are potentially flawed. Re-searchers are compelled to clarify their theoretical precepts in setting up a PLS model.

PLS invites researchers to think about the context of their research. Unlike many other statisticalmodeling approaches, PLS readily accommodates formative indicators in measurement models.This opens new avenues for analysis, but it also demands that researchers think carefully aboutthe domain of constructs and the larger context in which research is carried out.

The special issue does an excellent job of highlighting these and other important features of PLS,and it helps to advance one of the central principles of Long Range Planning. LRP has publishedwork that brings together theory and empirical evidence to provide new insights into strategic man-agement for more than forty years. The special issue speaks compellingly to the value of PLS to re-search in strategic management.

The special issueThe papers in this first special issue cover a variety of topics and techniques. The first paper (Hairet al., 2012) provides an interesting analysis of the empirical methods used in strategic managementin recent decades. The authors document the spread and predominance of approaches based onlinear modeling, compare the field to sister disciplines such as marketing, and raise important ques-tions about expanding strategy research to take in a greater emphasis on structural modeling. Thisis the most important statement on the use of partial-least squares modeling in strategy researchsince Hulland’s (1999) pathbreaking article in SMJ more than a decade ago.

Rigdon’s (2012) article presents a provocative view of the use of PLS relative to other techniquesfor structural modeling. He challenges readers to approach questions of measurement and

310 Editorial

modeling in greater depth and argues for approaches to PLS modeling that would more clearly set itapart from many other techniques for structural modeling.

Becker et al. (2012) focus on one of the strengths of PLS that has been underexploited in strategyresearch, the ability to accommodate formative indicators in structural models. They develop a ty-pology of hierarchical latent variable models that include formative relationships and examine dif-ferent approaches to estimating the models. The different approaches are compared using bothsimulation methods and empirical research to provide new guidelines for the use of formative con-structs in PLS models.

The other papers in the issue present empirical analyses that also serve to illustrate different tech-nical aspects of PLS modeling. Furrer et al. (2012) examine response strategies in strategic alliancesand argue that PLS can be extended to provide a means of analyzing response strategies as circum-plex structures. Money et al. (2012) use the FIMIX-PLS segmentation methodology in SmartPLS toexplore reactions of stakeholders to organizational strategies for a sample of taxpayers dealing witha European revenue service. The approach allows them to identify important differences amongstakeholders that might otherwise be treated as homogenous. Gundergan et al. (2012) make inno-vative use of PLS modeling to explore new antecedents to alliance performance.

James Robins

References:

Becker, J.-M., Klein, K., Wetzels, M., 2012. Hierarchical latent variable models in PLS-SEM: guidelines for

using reflective-formative type models, Long Range Planning.Gunderan, S.P., Devinney, T., Richter, N.F., Ellis, R.S., 2012. Strategic implications for (non-equity) alliance

performance, Long Range Planning.Hair, J., Sarstedt, M., Pieper, T., Ringle, C., 2012. The use of partial least squares structural equation modeling

in strategic management research: a review of past practices and recommendations for future application,Long Range Planning.

Hulland, J., 1999. Use of partial least square (PLS) in strategic management research: a review of four recentstudies, Strategic Management Journal 20, 195e204.

Money, K., Hllenbrand, C., Henseler, J., da Camara, N., 2012. Exploring unanticipated consequences of strat-egy amongst stakeholder segments: the case of a European revenue service.

Popper, K., 1959. The Logic of Scientific Discovery. Routledge, London.Rigdon, E., 2012. Rethinking partial least squares modeling: in praise of simple methods, Long Range

Planning.

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