"Relational therapy" for retail franchisers and franchisees : staging into the experience economy

Retail franchisers struggle to differentiate their businesses in an increasingly commoditizing environment. In order to differentiate, these retailers have to adjust their processes and offerings, but this may harm the franchiser’s unified concept and lead to more opportunistic behavior of the franchisees. We believe that staging experiences may solve these problems for certain retail franchisers. This paper has two main objectives. The first objective is to provide some “relational therapy” for franchisers and franchisees that are struggling with the paradox of differentiating their businesses in a highly commoditized environment in spite of the standardized franchise settings. The second aim is to solve the paradox of keeping the commitment of the franchisees high and controlling the risk of opportunistic behavior by punishing defective franchisees. As a result, franchisers and franchisees must find a balance between dealing with internal conflicts and creating customer value. By using a comprehensive research model that takes into account the “ménage à trois” between franchiser, franchisee and customer, instead of dual relationships, the second paradox is untangled. Finally, we address the limitations of the concept and future directions are outlined.

Likelihood Ratio-Based Detection of Facial Features

One of the first steps in face recognition, after image acquisition, is registration. A simple but effective technique of registration is to align facial features, such as eyes, nose and mouth, as well as possible to a standard face. This requires an accurate automatic estimate of the locations of those features. This contribution proposes a method for estimating the locations of facial features based on likelihood ratio-based detection. A post-processing step that evaluates the topology of the facial features is added to reduce the number of false detections. Although the individual detectors only have a reasonable performance (equal error rates range from 3.3% for the eyes to 1.0% for the nose), the positions of the facial features are estimated correctly in 95% of the face images

Exactly energy-conserving electromagnetic Particle-in-Cell method in curvilinear coordinates

In this paper, we introduce and discuss an exactly energy-conserving Particle-in-Cell method for arbitrary curvilinear coordinates. The flexibility provided by curvilinear coordinates enables the study of plasmas in complex-shaped domains by aligning the grid to the given geometry, or by focusing grid resolution on regions of interest without overresolving the surrounding, potentially uninteresting domain. We have achieved this through the introduction of the metric tensor, the Jacobian matrix, and contravariant operators combined with an energy-conserving fully implicit solver. We demonstrate the method's capabilities using a Python implementation to study several one- and two-dimensional test cases: the electrostatic two-stream instability, the electromagnetic Weibel instability, and the geomagnetic environment modeling (GEM) reconnection challenge. The test results confirm the capability of our new method to reproduce theoretical expectations (e.g. instability growth rates) and the corresponding results obtained with a Cartesian uniform grid when using curvilinear grids. Simultaneously, we show that the method conserves energy to machine precision in all cases.Comment: 14 pages, 5 figure