The links between active cooperation and eco-innovation orientation of firms: A multi-analysis study

The environmental orientation of companies is key for firms to gain a competitive advantage against peers. However, the high level of novelty and uncertainty involved with eco-innovations requires additional knowledge and capabilities that go beyond the firm and that can be achieved through cooperation. Thus, it is crucial to analyse how cooperation affects the elements that drive eco-innovation. This study tests the impact of cooperation on the environmental orientation of companies while innovating using structural equation modelling with partial least squares and multigroup analysis and a fuzzy-set qualitative comparative analysis for a sample of Spanish companies. Results suggest that companies that do not cooperate are less eco-innovation-oriented and show lower dependence on external information sources, although their impact on the orientation to product and process innovation is higher. This work leads to some theoretical conclusions and implications for researchers and practitioners.     

Using partial least squares in operations management research: A practical guideline and summary of past research

The partial least squares (PLS) approach to structural equation modeling (SEM) has been widely adopted in business research fields such as information systems, consumer behavior, and marketing. The use of PLS in the field of operations management is also growing. However, questions still exist among some operations management researchers regarding whether and how PLS should be used. To address these questions, our study provides a practical guideline for using PLS and uses examples from the operations management literature to demonstrate how the specific points in this guideline can be applied. In addition, our study reviews and summarizes the use of PLS in the recent operations management literature according to our guideline. The main contribution of this study is to present a practical guideline for evaluating and using PLS that is tailored to the operations management field.     

Some Decompositions of OLSEs and BLUEs Under a Partitioned Linear Model

We consider in this paper a partitioned linear model { y , X ₁ β ₁ + X ₂ β ₂ , σ ² σ } and two corresponding small models { y , X ₁ β ₁ , σ ² σ } and { y , X ₂ β ₂ , σ ² σ } . We derive necessary and sufficient conditions for (i) the ordinary least squares estimator under the full model to be the sum of the ordinary least squares estimators under the two small models; (ii) the best linear unbiased estimator under the full model to be the sum of the best linear unbiased estimators under the two small models; (iii) the best linear unbiased estimator under the full model to be the sum of the ordinary least squares estimators under the two small models. The proofs of the main results in this paper also demonstrate how to use the matrix rank method for characterizing various equalities of estimators under general linear models.