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Market structure analysis is a basic pillar of marketing research. Classic challenges in marketing such as pricing, campaign management, brand positioning, and new product development are rooted in an analysis of product substitutes and complements inferred from market structure. In this article, the authors present a method to support the analysis and visualization of market structure by automatically eliciting product attributes and brand's relative positions from online customer reviews. First, the method uncovers attributes and attribute dimensions using the \"voice of the consumer,\" as reflected in customer reviews, rather than that of manufacturers. Second, the approach runs automatically. Third, the process supports rather than supplants managerial judgment by reinforcing or augmenting attributes and dimensions found through traditional surveys and focus groups. The authors test the approach on six years of customer reviews for digital cameras during a period of rapid market evolution. They analyze and visualize results in several ways, including comparisons with expert buying guides, a laboratory survey, and correspondence analysis of automatically discovered product attributes. The authors evaluate managerial insights drawn from the analysis with respect to proprietary market research reports from the same period analyzing digital imaging products.

This paper complements a recent study by Ramos-Rodriguez and Ruiz-Navarro (2004) that investigated the intellectual structure of the strategic management field through co-citation analysis. By using authors as the units of analysis and incorporating all the citations that are included in the Science Citation Index and the Social Science Citation Index, we trace the evolution of the intellectual structure of the strategic management field during the period 1980-2000. Using a variety of data analytic techniques such as multidimensional scaling, factor analysis, and Pathfinder analysis, we (1) delineate the subfields that constitute the intellectual structure of strategic management; (2) determine the relationships between the subfields; (3) identify authors who play a pivotal role in bridging two or more conceptual domains of research; and (4) graphically map the intellectual structure in two-dimensional space in order to visualize spatial distances between intellectual themes. The analysis provides insights about the influence of individual authors as well as changes in their influence over time.

The authors propose a new, exploratory approach for analyzing market structures that leverages two recent methodological advances in natural language processing and machine learning. They customize a neural network language model to derive latent product attributes by analyzing the co-occurrences of products in shopping baskets. Applying dimensionality reduction to the latent attributes yields a two-dimensional product map. This method is well-suited to retailers because it relies on data that are readily available from their checkout systems and facilitates their analyses of cross-category product complementarity, in addition to within-category substitution. The approach has high usability because it is automated, is scalable and does not require a priori assumptions. Its results are easy to interpret and update as new market basket data are collected. The authors validate their approach both by conducting an extensive simulation study and by comparing their results with those of state-of-the-art, econometric methods for modeling product relationships. The application of this approach using data collected at a leading German grocery retailer underlines its usefulness and provides novel findings that are relevant to assortment-related decisions.

Traditionally, two approaches have been employed for structural equation modeling: covariance structure analysis and partial least squares. A third alternative, generalized structured component analysis, was introduced recently in the psychometric literature. The authors conduct a simulation study to evaluate the relative performance of these three approaches in terms of parameter recovery under different experimental conditions of sample size, data distribution, and model specification. In this study, model specification is the only meaningful condition in differentiating the performance of the three approaches in parameter recovery. Specifically, when the model is correctly specified, covariance structure analysis tends to recover parameters better than the other two approaches. Conversely, when the model is misspecified, generalized structured component analysis tends to recover parameters better. Finally, partial least squares exhibits inferior performance in parameter recovery compared with the other approaches. In particular, this tendency is salient when the model involves cross-loadings. Thus, generalized structured component analysis may be a good alternative to partial least squares for structural equation modeling and is recommended over covariance structure analysis unless correct model specification is ensured.

Studies have shown that online product reviews can indicate the position of a competitive brand. Even though reviews on different platforms may express different opinions, most studies are based on only one platform. This may lead to an inaccurate analysis of market structure. To solve this problem, we develop a novel market structure analysis based on multi-attribute group decision-making which can integrate reviews from different platforms. Multiple platforms more comprehensively reflect the market than single platforms do. To verify the effectiveness of the proposed method, we conduct a case study of mobile phone reviews across three top e-commerce platforms in China. In addition, we propose a process to generate priorities for product-attribute improvements using a cross-platform market structure analysis method. Our experiments demonstrate the effectiveness of the proposed method.

Structural equation modeling (SEM) is a powerful, yet complex, analytical technique. The use of SEM to examine strategic management phenomena has increased dramatically in recent years, suggesting that a critical evaluation of the technique's implementation is needed. We compared the use of SEM in 92 strategic management studies published in nine prominent journals from 1984 to 2002 to guidelines culled from methodological research. We found that the use and reporting of SEM often have been less than ideal, indicating that authors may be drawing erroneous conclusions about relationships among variables. Given these results, we offer suggestions for researchers on how to better deploy SEM within future inquiry.

A plural alliance structure involving multiple downstream partners has become increasingly popular, yet investigations of marketing alliances continue to address mainly dyadic structures. The authors present learning and dependence balancing as key mechanisms to understand the relative performance differences between plural and dyadic structures, as well as the determinants of effective collaboration in a plural structure. Two complementary studies test the performance of plural and dyadic structures in a wide range of high-tech industries. The analysis of both plural and dyadic structure alliances in an event study shows that plural structures outperform dyadic structures for the upstream firm when marketing alliances extend to product-related tasks, the upstream firm has more alliance experience, or the industry is growing fast; however, dyadic structures perform better when the upstream market is more competitive. A second study, focusing only on plural structure alliances, shows that horizontal relationship factors (i.e., market overlap and prior relationship between downstream partners) interact with the upstream firm's greater alliance experience and reputation to lead to better returns for the upstream firm.

The international marketing literature has produced divergent findings regarding the effect of a standardized marketing strategy on firm performance among large multinational corporations (MNCs). Prior research has typically considered external or environmental factors to reconcile different findings. This study employs multiple perspectives of strategic fit (moderation, mediation, profile deviation, and covariation) to examine the effect of the internal fit among strategy, structure, and processes of MNCs and explores their effects on firm performance. Empirical results based on 206 MNCs support the mediation, profile deviation, and covariation perspectives, but they fail to confirm the moderation perspective.

General morphological analysis (GMA) is a method for structuring and investigating the total set of relationships contained in multidimensional, usually non-quantifiable, problem complexes. Pioneered by Fritz Zwicky at the California Institute of Technology in the 1930s and 1940s, it relies on a constructed parameter space, linked by way of logical relationships, rather than on causal relationships and a hierarchal structure. During the past 10 years, GMA has been computerized and extended for structuring and analysing complex policy spaces, developing futures scenarios and modelling strategy alternatives. This article gives a historical and theoretical background to GMA as a problem structuring method, compares it with a number of other 'soft-OR' methods, and presents a recent application in structuring a complex policy issue. The issue involves the development of an extended producer responsibility (EPR) system in Sweden.

In his commentary on Varni et al.'s (Qual Life Res. doi:10.1007/s11136-013-0370-4, 2013) article, McIntosh (Qual Life Res. doi:10.1007/s11136-013-0465-y, 2013) has two main arguments. First, we should have paid more attention to statistical tests (i.e., χ2 values) instead of approximate fit indexes for our analysis, especially with the baseline model. Second, Bayesian methods are better than the frequentist methods we used in determining the model's invariance across age and gender groups. We believe that statistical tests do have a place in assessing model fit, but overemphasis on them, especially with larger sample sizes, can lead to errant decisions. Second, while we agree that Bayesian methods have the potential to contribute much to the field of assessing invariance, more development needs to be conducted before they can be widely utilized in assessing factorial invariance across groups.