Search results

The purpose of this paper is to argue for the reflective use of visual techniques in qualitative inter-viewing and suggests using visuals not only as projective techniques to elicit answers, but also as facilitation techniques throughout the interview process.

By reflecting on their own research projects in organization and management studies, the authors develop a practical approach to visual interviewing – making use of both projective and facilitation techniques. The paper concludes by discussing the limitations of visualization techniques, and suggesting directions for future research on visually enhanced interviewing.

The integration of projective and facilitation techniques enables the interviewer to build rapport with the respondent(s), and to elicit deeper answers by providing cognitive stimulation. In the course of the interview, such an integrative approach brings along further advantages, most notably focusing attention, maintaining interaction, and fostering the co-construction of knowledge between the interviewer and the interviewee(s).

This paper is reflective of what is currently occurring in the field of qualitative interviewing, and presents a practical approach for the integration of visual projection and facilitation in qualitative interviews.

Despite the pervasiveness of teen-targeted food advertising on social media, little is known about the persuasive elements (or power) found within those ads. This research study aims to engage with the concept of “visual style” to explore the range of visual techniques used in Instagram food marketing to teenagers.

A participatory study was conducted with 57 teenagers, who used a specially designed mobile app to capture images of the teen-targeted food marketing they encountered for seven days. A visual thematic analysis was used to assess and classify the advertisements that participants captured from Instagram and specifically tagged with “visual style”.

A total of 142 food advertisements from Instagram were tagged with visual style, and classified into five main styles: Bold Focus, Bespoke, Absurd, Everyday and Sensory.

This study contributes to an improved understanding about how the visual is used as a marketing technique to capture teenagers’ attention, contributing to the persuasive power of marketing messages.

Food marketing is a significant part of the young consumer’s marketplace, and this study provides new insight into the sophisticated nature of such marketing – revealing the visual styles used to capture the attention of its brand-aware audience.

The aim of this study is to investigate how the information needs satisfaction of visual art teachers affects their creativity. Visual art teachers’ information seeking behaviour and specifically the association of information needs satisfaction with creativity has been an understudied area, despite competent information seeking being considered essential for high quality practices of art teachers.

A questionnaire survey was developed addressing the information seeking behaviour of art teachers, informed by Wilson’s model (1981), including visual art teachers’ information needs, information resources, obstacles faced while seeking information and the perceived impact of information needs satisfaction on visual art teachers’ creativity.

The study included 298 visual art teachers in Greece. The results demonstrated that the key information needs of art teachers were mainly related to materials’ properties, techniques for creating artwork and artwork promotion methods. Online information sources were the preferred sources of art information, followed by colleagues, personal collections and visits to galleries and museums. Our study identified lack of time, lack of specialized libraries and copyright, as the main barriers to information seeking.

Information about art plays a substantial role in visual art education, while visual art teachers’ information needs satisfaction positively influences their creative endeavours. There is a need to further explore the digital information needs of visual art teachers.

Information makes an important contribution to the promotion of the creativity of visual artists. This work aims to explore relevant research through a systematic review of the literature and discuss the impact of information on visual artists' creativity.

A systematic literature review was conducted through Preferred Reporting Items for Systematic reviews and Meta-Analyses method. The authors searched and retrieved 1,320 papers from which, after evaluation, 41 papers have been analyzed.

Two thematic categories were identified for visual artists' information needs: (1) the need for professional development and (2) the need for creative techniques and materials. In terms of information sources visual artists employ, the authors have also identified seven broad categories: (1) conventional resources (galleries, museums, etc.), (2) professional scholar sources, (3) digital art websites, (4) informal information online and colleagues, (5) libraries, (6) personal collections and (7) professional scholar social networks. In addition, the study proceeded to classify the obstacles faced by visual artists in their search for visual information into two general categories: (1) environmental barriers and (2) digital literacy barriers.

Although the investigation of the information needs satisfaction of visual artists as well as the evaluation of their information behavior patterns and information literacy competences is essential, it is understudied. This paper summarizes the relevant literature in a concrete and systematic way providing evidences to be considered in a variety of situations, i.e. developing lifelong learning programs, managing visual art library collections, library services development for artists, etc.

Digital tools have been used to document cultural heritage with high-quality imaging and metadata. However, some of the historical assets are totally or partially unlabeled and some are physically damaged, which decreases their attractiveness and induces loss of value. This paper introduces a new framework that aims at tackling the cultural data enrichment challenge using machine learning.

This framework focuses on the automatic annotation and metadata completion through new deep learning classification and annotation methods. It also addresses issues related to physically damaged heritage objects through a new image reconstruction approach based on supervised and unsupervised learning.

The authors evaluate approaches on a data set of cultural objects collected from various cultural institutions around the world. For annotation and classification part of this study, the authors proposed and implemented a hierarchical multimodal classifier that improves the quality of annotation and increases the accuracy of the model, thanks to the introduction of multitask multimodal learning. Regarding cultural data visual reconstruction, the proposed clustering-based method, which combines supervised and unsupervised learning is found to yield better quality completion than existing inpainting frameworks.

This research work is original in sense that it proposes new approaches for the cultural data enrichment, and to the authors’ knowledge, none of the existing enrichment approaches focus on providing an integrated framework based on machine learning to solve current challenges in cultural heritage. These challenges, which are identified by the authors are related to metadata annotation and visual reconstruction.

The purpose of the paper is to study multiple viewpoints which are required to access the more informative similarity features among the tweets documents, which is useful for achieving the robust tweets data clustering results.

Let “N” be the number of tweets documents for the topics extraction. Unwanted texts, punctuations and other symbols are removed, tokenization and stemming operations are performed in the initial tweets pre-processing step. Bag-of-features are determined for the tweets; later tweets are modelled with the obtained bag-of-features during the process of topics extraction. Approximation of topics features are extracted for every tweet document. These set of topics features of N documents are treated as multi-viewpoints. The key idea of the proposed work is to use multi-viewpoints in the similarity features computation. The following figure illustrates multi-viewpoints based cosine similarity computation of the five tweets documents (here N = 5) and corresponding documents are defined in projected space with five viewpoints, say, v₁,v₂, v₃, v₄, and v₅. For example, similarity features between two documents (viewpoints v₁, and v₂) are computed concerning the other three multi-viewpoints (v₃, v₄, and v₅), unlike a single viewpoint in traditional cosine metric.

Healthcare problems with tweets data. Topic models play a crucial role in the classification of health-related tweets with finding topics (or health clusters) instead of finding term frequency and inverse document frequency (TF–IDF) for unlabelled tweets.

Topic models play a crucial role in the classification of health-related tweets with finding topics (or health clusters) instead of finding TF-IDF for unlabelled tweets.

This paper aims to explore the concept of information in records and archives management (RAM) from a fresh, visual perspective by using arts-informed methodology and the draw-and-write technique.

Students and practitioners of RAM in Australia were asked to answer the question, “what is information?” in a drawing and then to describe the drawing in words. This produced a data set of 255 drawings of information or “iSquares”, for short. Compositional interpretation and a framework of graphic representations by Engelhardt were applied to determine how participants envision information and what the renderings imply for RAM.

The images reveal an overwhelming recognition in RAM of the diversity of media formats of information and the hyperconnectivity of information in networked information systems; and illustrate the central place of human beings within these systems. These findings offer striking, accessible illustrations of major concepts in RAM and enable new understandings through the construction of stories.

There are both pedagogical applications and practical implications of this work for students, practitioners and knowledge workers. The graphical representations of information in this research deepen the understanding of textual definitions of information. The data set of iSquares provides opportunities to create new storyboards to explain information definitions, practices and phenomena in RAM disciplines, and, to explain related concepts such as data, information, knowledge and wisdom hierarchy.

This is the first study in RAM disciplines to provide visual illustrations of information using graphical image representations.

Visual analytics is increasingly becoming a prominent technology for organizations seeking to gain knowledge and actionable insights from heterogeneous and big data to support decision-making. Whilst a broad range of visual analytics platforms exists, limited research has been conducted to explore the specific factors that influence their adoption in organizations. The purpose of this paper is to develop a framework for visual analytics adoption that synthesizes the factors related to the specific nature and characteristics of visual analytics technology.

This study applies a directed content analysis approach to online evaluation reviews of visual analytics platforms to identify the salient determinants of visual analytics adoption in organizations from the standpoint of practitioners. The online reviews were gathered from Gartner.com, and included a sample of 1,320 reviews for six widely adopted visual analytics platforms.

Based on the content analysis of online reviews, 34 factors emerged as key predictors of visual analytics adoption in organizations. These factors were synthesized into a conceptual framework of visual analytics adoption based on the diffusion of innovations theory and technology–organization–environment framework. The findings of this study demonstrated that the decision to adopt visual analytics technologies is not merely based on the technological factors. Various organizational and environmental factors have also significant influences on visual analytics adoption in organizations.

This study extends the previous work on technology adoption by developing an adoption framework that is aligned with the specific nature and characteristics of visual analytics technology and the factors involved to increase the utilization and business value of visual analytics in organizations.

This study highlights several factors that organizations should consider to facilitate the broad adoption of visual analytics technologies among IT and business professionals.

This study is among the first to use the online evaluation reviews to systematically explore the main factors involved in the acceptance and adoption of visual analytics technologies in organizations. Thus, it has potential to provide theoretical foundations for further research in this important and emerging field. The development of an integrative model synthesizing the salient determinants of visual analytics adoption in enterprises should ultimately allow both information systems researchers and practitioners to better understand how and why users form perceptions to accept and engage in the adoption of visual analytics tools and applications.

In the community of visual tracking or object tracking, discriminatively learned correlation filter (DCF) has gained more importance. When it comes to speed, DCF gives the best performance. The purpose of this study is to anticipate the object visually. For tracking the object visually, the authors proposed a new model based on the convolutional regression technique. Features like HOG and Harris are used for the process of feature extraction. The authors’ proposed method will give the best results when compared with other existing methods.

The visual tracking of many real-world applications such as robotics, smart monitoring systems, independent driving and human-computer interactions are a major and current research problem in the field of computer vision. This refers to the automated trajectory prediction of an arbitrary target object, often given in the first frame in a bounding box while moving about in successive video frames. In the community of visual tracking or object tracking, DCF has gained more importance. Discriminative trackers strive to train a classifier that differentiates the target item from the background. The fundamental concept is to train a correlation filter that creates high responses around the target and low responses elsewhere. For tracking the object visually, the authors proposed a new model based on the convolutional regression technique. Features like HOG and Harris are used for the process of feature extraction. Through experimental analysis, the authors have evaluated several performance assessment metrics such as accuracy, precision, F-measure and specificity. The authors’ proposed method will give the best results when compared with other existing methods.

This process involved DCF which gained more importance. When it comes to speed, DCF gives the best performance. The main objective of this study is to anticipate the object visually. For tracking the object visually, the authors proposed a new model based on the convolutional regression technique for tracking the objects and these results will be used for identifying the action of the object.

The main theme exists in the process is to identify the tracking motion of the object by using convolution regression with varied features. This method proves that it will provide better results when compared to state of art methods.