We undertook an evidence-based study of 117 carbon offset providers from eight countries. From this study we contribute a conceptualization of venture performance and an agenda for research in this important domain. Our findings show how additionality (i.e., project feasibility without external funding), certification, standards, prices, and transparency distinguish the best carbon offset providers. We lay a foundation for understanding venture strategy formulation, market entry, and competition in these unregulated and volatile organizational environments. As sustainability and environmental issues continue to influence activities in the business world, understanding the issues we delineate in this paper becomes more important for management scholars and practitioners.

Anyone who has had to deal with a customer service representative (CSR) on the phone knows how crucial they are to the quality (or lack thereof) of the service received and to customer satisfaction. But imagine being at the other end of the line--in the CSR’s shoes. How do you stay motivated to deal with client after client after client? Now, take the perspective of a service unit manager-- what can you do to keep CSRs motivated and on task? Perhaps you should start by motivating yourself.

Publication year: 2012
Source: Journal of Business Venturing, Available online 12 January 2012

Zhi Tang, Jintong Tang

Although stakeholder power theory has been at the forefront of environmental studies, extant research has focused on stakeholders' power while firms' countering power has not been systematically examined. Furthermore, different stakeholders may prioritize social goals differently. In this paper, we propose that stakeholder–firm power difference determines firms' environmental performance and stakeholders' CSR orientation (i.e., the degree to which a stakeholder holds firms' engagement in CSR as important) moderates this relationship. Utilizing a sample of 144 Chinese small- and medium-sized enterprises (SMEs), we found that governments-, competitors-, and the media-firm power difference indeed significantly affect Chinese SMEs' environmental performance. Besides, governments' and the media's CSR orientation moderate the relationship between stakeholder–firm power difference and firms' environmental performance. Research and practical implications are discussed.

Publication year: 2011
Source: Applied Soft Computing, Available online 20 November 2011

Chih-Fong Tsai, Chihli Hung

Recommender systems, which recommend items of information that are likely to be of interest to the users, and filter out less favored data items, have been developed. Collaborative filtering is a widely used recommendation technique. It is based on the assumption that people who share the same preferences on some items tend to share the same preferences on other items. Clustering techniques are commonly used for collaborative filtering recommendation. While cluster ensembles have been shown to outperform many single clustering techniques in the literature, the performance of cluster ensembles for recommendation has not been fully examined. Thus, the aim of this paper is to assess the applicability of cluster ensembles to collaborative filtering recommendation. In particular, two well-known clustering techniques (self-organizing maps (SOM) andk-means), and three ensemble methods (the cluster-based similarity partitioning algorithm (CSPA), hypergraph partitioning algorithm (HGPA), and majority voting) are used. The experimental results based on the Movielens dataset show that cluster ensembles can provide better recommendation performance than single clustering techniques in terms of recommendation accuracy and precision. In addition, there are no statistically significant differences between either the three SOM ensembles or the threek-means ensembles. Either the SOM ork-means ensembles could be considered in the future as the baseline collaborative filtering technique.

Highlights

► This paper is the first study to examine the performance of clustering ensembles for collaborative filtering. ► In this paper, clustering ensembles by cluster-based similarity partitioning algorithm (CSPA), hypergraph partitioning algorithm (HGPA), and majority voting are compared with two single clustering techniques (i.e.k-means and SOM) in terms of accuracy, precision, and recall. ► The experimental results show that clustering ensembles outperform single clustering techniques. This allows future studies proposing novel clustering ensemble techniques to not only consider single clustering techniques as the baselines, but also compare either SOM ork-means ensembles for further validation in order to reach a more reliable conclusion.