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Alongside structural equation modeling (SEM), the complementary technique of partial least squares (PLS) path modeling helps researchers understand relations among sets of observed variables. Like SEM, PLS began with an assumption of homogeneity – one population and one model – but has developed techniques for modeling data from heterogeneous populations, consistent with a marketing emphasis on segmentation. Heterogeneity can be expressed through interactions and nonlinear terms. Additionally, researchers can use multiple group analysis and latent class methods. This chapter reviews these techniques for modeling heterogeneous data in PLS, and illustrates key developments in finite mixture modeling in PLS using the SmartPLS 2.0 package.

Identification in a regression discontinuity (RD) design hinges on the discontinuity in the probability of treatment when a covariate (assignment variable) exceeds a known threshold. If the assignment variable is measured with error, however, the discontinuity in the relationship between the probability of treatment and the observed mismeasured assignment variable may disappear. Therefore, the presence of measurement error in the assignment variable poses a challenge to treatment effect identification. This chapter provides sufficient conditions to identify the RD treatment effect using the mismeasured assignment variable, the treatment status and the outcome variable. We prove identification separately for discrete and continuous assignment variables and study the properties of various estimation procedures. We illustrate the proposed methods in an empirical application, where we estimate Medicaid takeup and its crowdout effect on private health insurance coverage.

The objective of this chapter is to provide strategy researchers with a general resource for applying structural equation modeling (SEM) in their research. This objective is important for strategy researchers because of their increased use of SEM, the availability of advanced SEM approaches relevant for their substantive interests, and the fact that important technical work on SEM techniques often appear in outlets that may not be not readily accessible. This chapter begins with a presentation of the basics of SEM techniques, followed by a review of recent applications of SEM in strategic management research. We next provide an overview of five types of advanced applications of structural equation modeling and describe how they can be applied to strategic management topics. In a fourth section we discuss technical developments related to model evaluation, mediation, and data requirements. Finally, a summary of recommendations for strategic management researchers using SEM is also provided.

Purpose – The purpose is to present item response theory (IRT) as a promising approach to deal with theoretical and methodological challenges which may not be adequately addressed with linear factor analysis (LFA) techniques.

Approach – We address this limitations and present IRT's approach to counteracting these difficulties. We further present two IRT models in greater detail and make a theoretical comparison between these models and LFA techniques. Then, we illustrate how IRT is applied in practice by analyzing a scale of trust by an IRT and an LFA approach and comparing their results.

Practical implications – Scale properties may vary among countries because some items may function differently in different contexts. Moreover, a scale's reliability depends on the value of the latent variable being assessed, which implies that the scale may discriminate better among certain ranges of the latent trait than among others. The theory we hereby present offers a more adequate approach to dealing with these issues than extant LFA methods.

Originality/value – This chapter presents a methodology of recognized value in other fields of knowledge into the field of strategy and international business, thereby advancing the methodologies available for doing research in these domains.

Changes are inevitable and immanent elements of the contemporary world. The study in this subject matter was carried out in 30 cities all over the world. It has been discovered that the pace of life is 10% faster now than it was in the early 1990s. In addition, the ‘pace of life’ has a cultural value today. Speed means both progress and success. Deceleration means failure and loss.¹ Organisation’s ability to adopt to changes as well as stay agile may be perceived as the source of relatively sustainable competitive advantage. Based on this ability, four kinds of organisations (adaptive, visionary, opportunistic and passive) as well as three levels of companies’ ability to compete were indicated. Companies of the highest level are ready to compete by its broader competences on market knowledge. Business metrics and market measurement systems are the key elements of building market knowledge and creating sustainable competitive advantage. Here the reader can find the presentations of marketing audit, benchmarking, activity-based costing, Balanced Scorecard, performance pyramid, EFQM excellence model, marketing ROI, performance prism along with the key tips and hints for selecting business metrics and building measurement systems. Development of business measurement systems is a sophisticated process, more chess then checkers. For every organisation which is ready to make informed decisions and increase its ability to compete with a long-term perspective, development of an efficient measurement system is a starting point.

The essential investments in new product development (NPD) made by industrial companies entail effective management of NPD activities. In this context, performance measurement is one of the means that can be employed in the pursuit of effectiveness.

Purpose – Despite the increasing use of formative measurement models in literature, little is known about potential consequences for substantive theory testing. Against this background, the aims of this chapter are (1) to highlight some problems that may arise when formative instead of reflective measures are used to test even simple theoretical models with covarianced-based methodologies, (2) to illustrate some approaches that might help overcome these problems, (3) to pinpoint potential interpretation difficulties of the results involving re-specified measurement models, and (4) to stimulate discussion on the implications for theory development when models are tested with formative measures.

Methodology/approach – Potential consequences of formative measurement models for theory testing are highlighted using an empirical study on consumer animosity as an illustrative example and applying covarianced-based structural equations modeling procedures for estimation purposes.

Findings – The empirical study shows (a) that some scaling options for the (composite) latent variable result in non-convergence problems, (b) that, assuming convergence, parameter estimates, standard errors, and significance levels vary depending on the scaling method used, and (c) that goodness-of-fit statistics cannot be used as diagnostic measures for the appropriateness of divergent results.

Originality/value of paper – The contribution of this chapter is two-fold: First, it shows that to enable estimation, it is often necessary to modify (i.e., expand) the original theoretical model in a conceptually reasonable manner and to do so before data collection. Second, it demonstrates that alternative scaling options for composite latent variables may result in inconsistent substantive conclusions. Consequently, the impact of formative measurement on theory testing is a critical topic and needs to receive further attention in future literature.