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This study aims to investigate whether the model containing five organisational determinants of corporate entrepreneurship developed by Kuratko, Hornsby and Covin is valid in Serbia.

The model was tested on a sample that included managers and employees from four banks in Serbia. The Corporate Entrepreneurship Assessment Instrument (CEAI) was used as the research instrument and factor analysis was used as the basic statistical method. This study examined whether the 48 items that compose the CEAI could be grouped in the context of the national culture of Serbia to provide the five determinants that were identified by Kuratko, Hornsby and Covin.

The results show that the factor analysis identified four determinants identical to those in the CEAI model. However, time availability failed the validity test in previous studies conducted in Serbia and other countries with collectivist cultures. It can be concluded that collectivism connected with high-power distance, uncertainty avoidance and the polychromatic concept of time caused the cultural limitation of time availability as a determinant of corporate entrepreneurship.

This study indicates that national culture could be a factor that determines the validity of organisational determinants of corporate entrepreneurship and that these factors cannot be taken for granted in cultures other than those in which the theory of corporate entrepreneurship arose. Finally, corporate entrepreneurship has been investigated in the banking industry, which is unusual because it is globally renowned for its conservatism.

Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.

The proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.

The comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.

This study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.

This study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.

In the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.

This study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.

This chapter explores how department stores came at the end of the 19th century to be at the origin of what is now called “fashion tourism.” Contributing to a new “geography of commerce,” it highlights the role of the space of the department store both as a place of conspicuous fashion consumption and tourism. Further, it demonstrates how Parisian department stores helped consolidate Paris's place as the capital of fashion and luxury. Far from being only places to buy the latest in fashion, the latter became indeed a symbol as quintessentially Parisian as the Eiffel Tower and as necessary to visit for the “Paris experience.”