Exploring the dimensions of place branding: an application of the ICON model to the branding of Toronto

Purpose: The purpose of this paper is to explore the place branding dimensions of a city undergoing a concerted effort to build a distinctive brand for itself. Design/methodology/approach: A qualitative, exploratory approach is adopted, applying the ICON model of place branding to the multistakeholder city branding strategy of Toronto. A combination of interviews, participant observation, content analysis and professional reflection inform the study. Findings: Toronto’s emergence as a creative city with global standing has been achieved, in part, through a holistic and collaborative approach that is integrated, contextualized, organic and new. Practical implications: Place and destination promoters are offered a practical application of the ICON model of place branding, informing future initiatives and offering insight into good practice. Originality/value: Viewed through the lens of the ICON model, the paper provides insights into the collaborative and innovate practices that characterize effective city branding

Exploring the dimensions of place branding: an application of the ICON model to the branding of Toronto

Purpose: The purpose of this paper is to explore the place branding dimensions of a city undergoing a concerted effort to build a distinctive brand for itself. Design/methodology/approach: A qualitative, exploratory approach is adopted, applying the ICON model of place branding to the multistakeholder city branding strategy of Toronto. A combination of interviews, participant observation, content analysis and professional reflection inform the study. Findings: Toronto’s emergence as a creative city with global standing has been achieved, in part, through a holistic and collaborative approach that is integrated, contextualized, organic and new. Practical implications: Place and destination promoters are offered a practical application of the ICON model of place branding, informing future initiatives and offering insight into good practice. Originality/value: Viewed through the lens of the ICON model, the paper provides insights into the collaborative and innovate practices that characterize effective city branding

"Destination Pine Ridge": a longitudinal case study of barriers to collaboration in culturally appropriate tourism initiatives

2013 Fall.Includes bibliographical references.According to Ross et al. (2011) there are many barriers to genuine collaboration and natural resource co-management between Indigenous groups and westernized government groups but do these barriers exist for partnerships with Indigenous groups in other realms? This thesis is a specific case study of a partnership between the Pine Ridge Area Chamber of Commerce, the National Park Service, and several other South Dakota entities involved with the region's tourism industry. This partnership, as a strategy to increase tourism to the Pine Ridge Indian Reservation in South Dakota through education, has had to tackle many of the same barriers as Ross et al. (2011) argues exist for natural resource co-management attempts, but have also made significant achievements. A participatory epistemology and Pierre Bourdieu's (2009[1977], 1991, 1986) concept of capitals elaborate the case study analysis. This partnership has a long way to go before it is truly and equally collaborative, and has to confront many barriers until Lakota knowledge is incorporated into NPS interpretation. It has, though, accomplished many important steps to facilitating a mutually beneficial partnership have been accomplished, as well as individual growth and understanding among the participants

Social and Semantic Contexts in Tourist Mobile Applications

The ongoing growth of the World Wide Web along with the increase possibility of access information through a variety of devices in mobility, has defi nitely changed the way users acquire, create, and personalize information, pushing innovative strategies for annotating and organizing it. In this scenario, Social Annotation Systems have quickly gained a huge popularity, introducing millions of metadata on di fferent Web resources following a bottom-up approach, generating free and democratic mechanisms of classi cation, namely folksonomies. Moving away from hierarchical classi cation schemas, folksonomies represent also a meaningful mean for identifying similarities among users, resources and tags. At any rate, they suff er from several limitations, such as the lack of specialized tools devoted to manage, modify, customize and visualize them as well as the lack of an explicit semantic, making di fficult for users to bene fit from them eff ectively. Despite appealing promises of Semantic Web technologies, which were intended to explicitly formalize the knowledge within a particular domain in a top-down manner, in order to perform intelligent integration and reasoning on it, they are still far from reach their objectives, due to di fficulties in knowledge acquisition and annotation bottleneck. The main contribution of this dissertation consists in modeling a novel conceptual framework that exploits both social and semantic contextual dimensions, focusing on the domain of tourism and cultural heritage. The primary aim of our assessment is to evaluate the overall user satisfaction and the perceived quality in use thanks to two concrete case studies. Firstly, we concentrate our attention on contextual information and navigation, and on authoring tool; secondly, we provide a semantic mapping of tags of the system folksonomy, contrasted and compared to the expert users' classi cation, allowing a bridge between social and semantic knowledge according to its constantly mutual growth. The performed user evaluations analyses results are promising, reporting a high level of agreement on the perceived quality in use of both the applications and of the speci c analyzed features, demonstrating that a social-semantic contextual model improves the general users' satisfactio

A review of the role of sensors in mobile context-aware recommendation systems

Recommendation systems are specialized in offering suggestions about specific items of different types (e.g., books, movies, restaurants, and hotels) that could be interesting for the user. They have attracted considerable research attention due to their benefits and also their commercial interest. Particularly, in recent years, the concept of context-aware recommendation system has appeared to emphasize the importance of considering the context of the situations in which the user is involved in order to provide more accurate recommendations. The detection of the context requires the use of sensors of different types, which measure different context variables. Despite the relevant role played by sensors in the development of context-aware recommendation systems, sensors and recommendation approaches are two fields usually studied independently. In this paper, we provide a survey on the use of sensors for recommendation systems. Our contribution can be seen from a double perspective. On the one hand, we overview existing techniques used to detect context factors that could be relevant for recommendation. On the other hand, we illustrate the interest of sensors by considering different recommendation use cases and scenarios

Comparing Context-Aware Recommender Systems in Terms of Accuracy and Diversity: Which Contextual Modeling, Pre-filtering and Post-Filtering Methods Perform the Best

Although the area of Context-Aware Recommender Systems (CARS) has made a significant progress over the last several years, the problem of comparing various contextual pre-filtering, post-filtering and contextual modeling methods remained fairly unexplored. In this paper, we address this problem and compare several contextual pre-filtering, post-filtering and contextual modeling methods in terms of the accuracy and diversity of their recommendations to determine which methods outperform the others and under which circumstances. To this end, we consider three major factors affecting performance of CARS methods, such as the type of the recommendation task, context granularity and the type of the recommendation data. We show that none of the considered CARS methods uniformly dominates the others across all of these factors and other experimental settings; but that a certain group of contextual modeling methods constitutes a reliable “best bet” when choosing a sound CARS approach since they provide a good balance of accuracy and diversity of contextual recommendations.Politecnico di Bari, Italy; NYU Stern School of Busines

Context-Aware Recommendation Systems in Mobile Environments

Nowadays, the huge amount of information available may easily overwhelm users when they need to take a decision that involves choosing among several options. As a solution to this problem, Recommendation Systems (RS) have emerged to offer relevant items to users. The main goal of these systems is to recommend certain items based on user preferences. Unfortunately, traditional recommendation systems do not consider the user’s context as an important dimension to ensure high-quality recommendations. Motivated by the need to incorporate contextual information during the recommendation process, Context-Aware Recommendation Systems (CARS) have emerged. However, these recent recommendation systems are not designed with mobile users in mind, where the context and the movements of the users and items may be important factors to consider when deciding which items should be recommended. Therefore, context-aware recommendation models should be able to effectively and efficiently exploit the dynamic context of the mobile user in order to offer her/him suitable recommendations and keep them up-to-date.The research area of this thesis belongs to the fields of context-aware recommendation systems and mobile computing. We focus on the following scientific problem: how could we facilitate the development of context-aware recommendation systems in mobile environments to provide users with relevant recommendations? This work is motivated by the lack of generic and flexible context-aware recommendation frameworks that consider aspects related to mobile users and mobile computing. In order to solve the identified problem, we pursue the following general goal: the design and implementation of a context-aware recommendation framework for mobile computing environments that facilitates the development of context-aware recommendation applications for mobile users. In the thesis, we contribute to bridge the gap not only between recommendation systems and context-aware computing, but also between CARS and mobile computing.<br /