Rank aggregation aims at combining rankings of a set of items assigned by a sample of rankers to generate a consensus ranking. A typical solution is to adopt a distance-based approach to minimize the sum of the distances to the observed rankings. However, this simple sum may not be appropriate when the quality of rankers varies. This happens when rankers with different backgrounds may have different cognitive levels of examining the items. In this paper, we develop a new distance-based model by allowing different weights for different rankers. Under this model, the weight associated with a ranker is used to measure his/her cognitive level of ranking of the items, and these weights are unobserved and exponentially distributed. Maximum likelihood method is used for model estimation. Extensions to the cases of incomplete rankings and mixture modeling are also discussed. Empirical applications demonstrate that the proposed model produces better rank aggregation than those generated by Borda and the unweighted distance-based models.
Despite the burgeoning literature on stakeholder green pressure, research is scarce on how it influences eco-product innovation and new product performance. This article examines stakeholder green pressures as antecedents of eco-product innovation and new product performance in firms operating in resource-constrained countries. Using data gathered from surveys in Vietnam (N = 183) and Ghana (N = 217), we find that the positive effects of stakeholder green pressures on new product performance are serially mediated by environmental sustainability orientation and eco-product innovation. Our findings contribute to ongoing efforts to clarify the mechanisms channelling stakeholder pressures into new product performance in resource-constrained environments. 相似文献