The natural resource-based view of the firm (NRBV): constraints and opportunities for a green team in professional sport

Burgeoning expectations of the sport industry’s role in managing the environmental impact on its community are increasing. While previous research has focused on factors contributing to environmental involvement, little is known about the organization’s approach in dealing with these responsibilities. An exploratory case evaluation of a Major League Baseball team in evaluating the constraints, demands and opportunities of managing environment issues is undertaken. Specifically, the Natural-Resource-Based View of the firm (NRBV) is used to frame the assessment of the team’s capabilities and strategies with consideration of internal and external dynamics. Qualitative methods were which resulted in the identified key themes and implications

Knowledge management view of environmental sustainability in manufacturing SMEs in the Philippines

This study highlights the role of knowledge management (KM) in enabling small and medium enterprises (SMEs) in a manufacturing industry in a developing country to engage in environmentally sustainable business. Drawing on the knowledge-based view of the firm, it argues that resource-constrained SMEs rely on their relational capital to augment their capability to innovate in order to find better and environmentally sound ways of doing business. However, SMEs need to harness their KM orientation in order to leverage the knowledge-based resources emanating from their relational capital towards building their innovation capability. This capability is essential in integrating effective environmental management practices in business. The findings from our analysis of data from a survey of 241 manufacturing SMEs in the Philippines support these hypotheses and underscore the importance of developing an organisational capability to engage in KM in order to adopt sustainable business practices. The implications of the findings are also discussed.

Mixed-norm sparse representation for multi view face recognition

Face recognition with multiple views is a challenging research problem. Most of the existing works have focused on extracting shared information among multiple views to improve recognition. However, when the pose variation is too large or missing, 'shared information' may not be properly extracted, leading to poor recognition results. In this paper, we propose a novel method for face recognition with multiple view images to overcome the large pose variation and missing pose issue. By introducing a novel mixed norm, the proposed method automatically selects candidates from the gallery to best represent a group of highly correlated face images in a query set to improve classification accuracy. This mixed norm combines the advantages of both sparse representation based classification (SRC) and joint sparse representation based classification (JSRC). A trade off between the ℓ1-norm from SRC and ℓ2,1-norm from JSRC is introduced to achieve this goal. Due to this property, the proposed method decreases the influence when a face image is unseen and has large pose variation in the recognition process. And when some face images with a certain degree of unseen pose variation appear, this mixed norm will find an optimal representation for these query images based on the shared information induced from multiple views. Moreover, we also address an open problem in robust sparse representation and classification which is using ℓ1-norm on the loss function to achieve a robust solution. To solve this formulation, we derive a simple, yet provably convergent algorithm based on the powerful alternative directions method of multipliers (ADMM) framework. We provide extensive comparisons which demonstrate that our method outperforms other state-of-the-arts algorithms on CMU-PIE, Yale B and Multi-PIE databases for multi-view face recognition.

Multi-view subspace clustering for face images

In many real-world computer vision applications, such as multi-camera surveillance, the objects of interest are captured by visual sensors concurrently, resulting in multi-view data. These views usually provide complementary information to each other. One recent and powerful computer vision method for clustering is sparse subspace clustering (SSC); however, it was not designed for multi-view data, which break down its linear separability assumption. To integrate complementary information between views, multi-view clustering algorithms are required to improve the clustering performance. In this paper, we propose a novel multi-view subspace clustering by searching for an unified latent structure as a global affinity matrix in subspace clustering. Due to the integration of affinity matrices for each view, this global affinity matrix can best represent the relationship between clusters. This could help us achieve better performance on face clustering. We derive a provably convergent algorithm based on the alternating direction method of multipliers (ADMM) framework, which is computationally efficient, to solve the formulation. We demonstrate that this formulation outperforms other alternatives based on state-of-The-Arts on challenging multi-view face datasets.