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The vulnerability of corporate reputation : leadership for sustainable long-term value
This book explores the role that reputation plays in the success and failures of companies and their board members. It asserts that reputation should be acknowledged by boards to promote and establish good governance within existing legal and regulatory contexts, allowing organizations to create more 'sustainable' and meaningful results. This book focuses on the traditional topics of reputation risk management, the process of reputation, and reputational excellence and examines leaders whose reputation and foresight could benefit the organization they steer. It concludes that reputation is both vulnerable and intangible, but remains a valuable way to create good relationships with critical stakeholders and a useful tool for boards and top executives to set strategies and policies.
Book
Strategic Reputation Management
Strategic Reputation Management examines the ways in which organizations achieve “goodness” through reputation, reputation management, and reputation strategies. It presents a contemporary model of strategic reputation management, helping organizations and stakeholders to analyze the business environment as a communicative field of symbols and meanings in which the organization is built or destroyed. Authors Pekka Aula and Saku Mantere introduce the eight generic reputation strategies, through which organizations can organize their stakeholder relationships in various ways. They illustrate their arguments using real-world examples and studies, from the Finnish Ski Association to Philip Morris International.
This book will serve as required reading in advanced courses covering public-relations practice, advanced topics in PR, corporate communication, management, and marketing. Professionals working in PR, business, management, and marketing will also find much of interest in this volume.
eBook
Locality Preserving Matching
Seeking reliable correspondences between two feature sets is a fundamental and important task in computer vision. This paper attempts to remove mismatches from given putative image feature correspondences. To achieve the goal, an efficient approach, termed as locality preserving matching (LPM), is designed, the principle of which is to maintain the local neighborhood structures of those potential true matches. We formulate the problem into a mathematical model, and derive a closed-form solution with linearithmic time and linear space complexities. Our method can accomplish the mismatch removal from thousands of putative correspondences in only a few milliseconds. To demonstrate the generality of our strategy for handling image matching problems, extensive experiments on various real image pairs for general feature matching, as well as for point set registration, visual homing and near-duplicate image retrieval are conducted. Compared with other state-of-the-art alternatives, our LPM achieves better or favorably competitive performance in accuracy while intensively cutting time cost by more than two orders of magnitude.
Journal Article
Signaling family firm identity : family firm identification and its effects on job seekers' perceptions about a potential employer
Sandra Wolf develops a better understanding of the importance of clearly communicating family influence. She examines the efficacy of brand elements that signal family influence and that help external stakeholders to identify a family firm. An experiment with 543 students in Germany and Switzerland is carried out to empirically test the derived hypothesis. The results highlight two important findings. Firstly, the importance of a family firm tagline as well as the family name as brand elements are able to signal \"family firm\" and this helps potential employees to immediately categorize the potential employer. Secondly, a positive relationship between the identification of a family firm and applicant attraction was confirmed as to that the relationship is serially mediated by perceived brand authenticity and perceived benevolence.--Verso, cover.
Book
Weakly-supervised Semantic Guided Hashing for Social Image Retrieval
Hashing has been widely investigated for large-scale image retrieval due to its search effectiveness and computation efficiency. In this work, we propose a novel Semantic Guided Hashing method coupled with binary matrix factorization to perform more effective nearest neighbor image search by simultaneously exploring the weakly-supervised rich community-contributed information and the underlying data structures. To uncover the underlying semantic information from the weakly-supervised user-provided tags, the binary matrix factorization model is leveraged for learning the binary features of images while the problem of imperfect tags is well addressed. The uncovered semantic information enables to well guide the discrete hash code learning. The underlying data structures are discovered by adaptively learning a discriminative data graph, which makes the learned hash codes preserve the meaningful neighbors. To the best of our knowledge, the proposed method is the first work that incorporates the hash code learning, the semantic information mining and the data structure discovering into one unified framework. Besides, the proposed method is extended to one deep approach for the optimal compatibility of discriminative feature learning and hash code learning. Experiments are conducted on two widely-used social image datasets and the proposed method achieves encouraging performance compared with the state-of-the-art hashing methods.
Journal Article
Employer brand management : practical lessons from the world's leading employers
\"A practical guide to the key global trends and practices that are transforming HR, talent acquisition and management.Building on the success of The Employer Brand, a conceptual introduction to what has now become a well-established concept; this is a practical guide to implementation, drawing on a much wider range of cases and examples. This book draws on the significant advances in employer brand practice among leading companies to give managers hands on advice for implementing successful employer brand planning, employer brand definition, employer brand implementation and specific applications. It will demonstrate how employer brand thinking can strengthen organisational HR strategy and reinforce HR's value to the business. Offers practical help in improving existing programmes of recruitment and talent management Demonstrates the importance of people in delivering the desired brand experience Gives the reader a personal grasp of a new approach to people management \"-- Provided by publisher.
Book
Human–computer collaboration for skin cancer recognition
The rapid increase in telemedicine coupled with recent advances in diagnostic artificial intelligence (AI) create the imperative to consider the opportunities and risks of inserting AI-based support into new paradigms of care. Here we build on recent achievements in the accuracy of image-based AI for skin cancer diagnosis to address the effects of varied representations of AI-based support across different levels of clinical expertise and multiple clinical workflows. We find that good quality AI-based support of clinical decision-making improves diagnostic accuracy over that of either AI or physicians alone, and that the least experienced clinicians gain the most from AI-based support. We further find that AI-based multiclass probabilities outperformed content-based image retrieval (CBIR) representations of AI in the mobile technology environment, and AI-based support had utility in simulations of second opinions and of telemedicine triage. In addition to demonstrating the potential benefits associated with good quality AI in the hands of non-expert clinicians, we find that faulty AI can mislead the entire spectrum of clinicians, including experts. Lastly, we show that insights derived from AI class-activation maps can inform improvements in human diagnosis. Together, our approach and findings offer a framework for future studies across the spectrum of image-based diagnostics to improve human–computer collaboration in clinical practice.
A systematic evaluation of the value of AI-based decision support in skin tumor diagnosis demonstrates the superiority of human–computer collaboration over each individual approach and supports the potential of automated approaches in diagnostic medicine.
Journal Article
The true value of CSR : corporate identity and stakeholder perceptions
\"By considering the importance of Corporate Social Responsibility (CSR) as a business paradigm but also as a growing scepticism about it's outcomes, The True Value of CSR answers questions about true value behind this concept, motivations of firms embedding CSR in their core strategies and a capacity of CSR to make a real difference on the market.The presented papers and essays discuss why CSR fails by not preventing organizations from the risk of fraud or wrongdoing or why it is often accused of being an instrument of organizational PR policies. The book puts forward theoretical, empirical and practical contributions from authors coming from various fields such as economics, philosophy, management or law dealing with questions including but not limited to CSR capacity to build organizational identity, CSR perceptions and behaviours it can generate or it's role in market settings. The authors, while presenting various approaches, empirical, theoretical or practice based reflections build a well balanced picture of CSR - a biased concept grounded in semantic emotionality of its 'social' component, which legitimacy and effectiveness depends on the institutional setting of relations between market and state\"-- Provided by publisher.
Book
End-to-End Learning of Deep Visual Representations for Image Retrieval
While deep learning has become a key ingredient in the top performing methods for many computer vision tasks, it has failed so far to bring similar improvements to instance-level image retrieval. In this article, we argue that reasons for the underwhelming results of deep methods on image retrieval are threefold: (1) noisy training data, (2) inappropriate deep architecture, and (3) suboptimal training procedure. We address all three issues. First, we leverage a large-scale but noisy landmark dataset and develop an automatic cleaning method that produces a suitable training set for deep retrieval. Second, we build on the recent R-MAC descriptor, show that it can be interpreted as a deep and differentiable architecture, and present improvements to enhance it. Last, we train this network with a siamese architecture that combines three streams with a triplet loss. At the end of the training process, the proposed architecture produces a global image representation in a single forward pass that is well suited for image retrieval. Extensive experiments show that our approach significantly outperforms previous retrieval approaches, including state-of-the-art methods based on costly local descriptor indexing and spatial verification. On Oxford 5k, Paris 6k and Holidays, we respectively report 94.7, 96.6, and 94.8 mean average precision. Our representations can also be heavily compressed using product quantization with little loss in accuracy.
Journal Article