When employer brand image aids employee satisfaction and engagement

Purpose – The purpose of this paper is to test whether employee characteristics (age, gender, role and experience) influence the effects of employer brand image, for warmth and competence, on employee satisfaction and engagement. Design/methodology/approach – Members of the public were surveyed as to their satisfaction and engagement with their employer and their view of their employer brand image. Half were asked to evaluate their employer’s “warmth” and half its “competence”. The influence of employee characteristics was tested on a “base model” linking employer image to satisfaction and engagement using a mediated moderation model. Findings – The base model proved valid; satisfaction partially mediates the influence of employer brand image on engagement. Age, experience gender, and whether the role involved customer contact moderate both the influence of the employer brand image and of satisfaction on engagement. Practical implications – Engagement varies with employee characteristics, and both segmenting employees and promoting the employer brand image differentially to specific groups are ways to counter this effect. Originality/value – The contexts in which employer brand image can influence employees in general and specific groups of employees in particular are not well understood. This is the first empirical study of the influence of employer brand image on employee engagement and one of few that considers the application of employee segmentation

Large Margin Image Set Representation and Classification

In this paper, we propose a novel image set representation and classification method by maximizing the margin of image sets. The margin of an image set is defined as the difference of the distance to its nearest image set from different classes and the distance to its nearest image set of the same class. By modeling the image sets by using both their image samples and their affine hull models, and maximizing the margins of the images sets, the image set representation parameter learning problem is formulated as an minimization problem, which is further optimized by an expectation -maximization (EM) strategy with accelerated proximal gradient (APG) optimization in an iterative algorithm. To classify a given test image set, we assign it to the class which could provide the largest margin. Experiments on two applications of video-sequence-based face recognition demonstrate that the proposed method significantly outperforms state-of-the-art image set classification methods in terms of both effectiveness and efficiency

An Image Based Feature Space and Mapping for Linking Regions and Words

We propose an image based feature space and define a mapping of both image regions and textual labels into that space. We believe the embedding of both image regions and labels into the same space in this way is novel, and makes object recognition more straightforward. Each dimension of the space corresponds to an image from the database. The coordinates of an image segment(region) are calculated based on its distance to the closest segment within each of the images, while the coordinates of a label are generated based on their association with the images. As a result, similar image segments associated with the same objects are clustered together in this feature space, and should also be close to the labels representing the object. The link between image regions and words can be discovered from their separation in the feature space. The algorithm is applied to an image collection and preliminary results are encouraging