Search results

This study aims to investigate the influence of the reviewed establishment’s price level and the user’s social network size and reputation status on consumers’ tendency to post more visual imagery content. Furthermore, it explores the moderating effects of user experiences and geographic distance on these dynamics.

This study adopts a multi-method approach to explore both the determinants behind the sharing of user-generated photos in online reviews and their internal mechanisms. Using a comprehensive secondary data set from Yelp.com, the authors focused on restaurant reviews from a prominent tourist destination to construct econometric models incorporating time-fixed effects. To enhance the robustness of the authors’ findings, the authors complemented the big data analysis with a series of controlled experiments.

The reviewed establishments price level and the users reputation status and social network size incite corresponding motivations conspicuous display “reputation seeking” and social approval motivating users to incorporate more images in reviews. “User experiences can amplify the influence of these factors on image sharing.” An increase in the users geographical distance lessens the impact of the price level on image sharing, but it heightens the influence of the users reputation and social network size on the number of shared images.

As a result of this study, high-end establishments can increase their online visibility by leveraging user-generated visual content. A structured rewards program could significantly boost engagement by incentivizing photo sharing, particularly among users with elite status and extensive social networks. Additionally, online review platforms can enhance users’ experiences and foster more dynamic interactions by developing personalized features that encourage visual content production.

This research, anchored in trait activation theory, offers an innovative examination of the determinants of photo-posting behavior in online reviews by enriching the understanding of how the intricate interplay between users’ characteristics and situational cues can shape online review practices.

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.

While exploring the information experience within multispecies families, the subjective nature of humans and non-human entities, living beings and non-living objects becomes evident. This paper aims to reveal the underlying significance of information within socio-physical living environments shared among humans, cats and dogs as companions.

Gaining inspiration from the information experience approach and posthumanism, this is a phenomenological paper. Empirical material related to lived experiences of participating families were gathered through multispecies ethnography methods, followed by phenomenological reflections. The paper has been written based on excerpt-commentary-units and the inclusion of videos and images as an approach to convey the richness of the lived experiences and multiple perspectives.

Findings are organised into three main sections, each capturing lived experiences of information and its utilization from various frames. The paper shows how living beings, both human and animal, use their physical, sensual and moving bodies to acquire and convey information to and from each other. Moving beyond the living beings, the study discusses how non-living objects in the physical environment of a multispecies family also shape information. Material objects, spatial locations and even plants became sources of information for the family members. Lastly, the paper delves into the social environment of the family, where all members, human and animal, are actively shaped by information within their social interactions and companionship.

Considering information distributed across species and material objects in a shared, more-than-human environment, the article suggests implications for an information experience approach. It emphasizes how information shapes the in-between humans, animals and their environment, highlighting their reliance on each other for understanding and living a good shared life. There is a need for future research to explore the information experience within the internal subjective minds of members of multispecies families, bridging the gap in the understanding of these external information and their internal information processes.

Users' voluntary forwarding behavior opens a new avenue for companies to promote their brands and products on social networking sites (SNS). However, research on voluntary information disseminators is limited. This paper aims to bring an in-depth understanding of voluntary disseminators by answering the following questions: (1) What is the underlying mechanism by which some users are more enthusiastic to voluntarily forward content of interest? (2) How to identify them? We propose a theoretical model based on the Elaboration-Likelihood Model (ELM) and examine three types of factors that moderate the effect of preference matching on individual forwarding behavior, including personal characteristics, tweet characteristics and sender–receiver relationships.

Via Twitter API, we randomly crawled 1967 Twitter users' data to validate the conceptual framework. Each user’s original tweets and retweeted tweets, profile data such as the number of followers and followees and verification status were obtained. The final corpus contains 163,554 data points composed of 1,634 valid twitterers' retweeting behavior. Tweets produced by these core users' followees were also crawled. These data points constitute an unbalanced panel data and we employ different models — fixed-effects, random-effects and pooled logit models — to test the moderation effects. The robustness test shows consistency among these different models.

Preference matching significantly affects users' forwarding behavior, implying that SNS users are more likely to share contents that align with their preferences. In addition, we find that popular users with lots of followers, heavy SNS users who author tweets or forward other-sourced tweets more frequently and users who tend to produce longer original contents are more enthusiastic to disseminate contents of interest. Furthermore, interaction strength has a positive moderating effect on the relationship between preference matching and individuals' forwarding decisions, suggesting that users are more likely to disseminate content of interest when it comes from strong ties. However, the moderating effect of perceived affinity is significantly negative, indicating that an online community of individuals with many common friends is not an ideal place to engage individuals in sharing information.

This work brings about a deep understanding of users' voluntary forwarding behavior of content of interest. To the best of our knowledge, the current study is the first to examine (1) the underlying mechanism by which some users are more likely to voluntarily forward content of interest; and (2) how to identify these potential voluntary disseminators. By extending the ELM, we examine the moderating effect of tweet characteristics, sender–receiver relationships as well as personal characteristics. Our research findings provide practical guidelines for enterprises and government institutions to choose voluntary endorsers when trying to engage individuals in information dissemination on SNS.

Social media sites contribute significantly to nature conservation in, that they enlighten and educate those members of the public who would ordinarily not be in a position, or would not be fortunate enough to visit the park and experience the various aspects first-hand. The purpose of this paper is to showcase social media pages related to a national game reserve in South Africa. This game reserve is the largest in the country and has a wide variety of conserved fauna and flora.

The theoretical framework used is the SCOPE framework that streamlines strategy development, content choice, refinement of online engagement, choice of social media platform and evaluations of social media campaigns.

The findings relate to the content found on these social media pages, as well as how members of the public interact with each other and officials from the game reserve in sharing experiences related to this wilderness area.

The research is related to the Kruger National Park in South Africa and is limited to three social media sites.

Through its social media presence, this South African game reserve is able to share experiences from what is effectively a living museum, as well as from its library and archives, with members of the public and allowing individual members to share their encounters with wildlife and their historical memories of this wilderness area.

This conceptual paper aims to explore government regulatory responses of social networking platforms (SNP) and tourism destination evangelism. This research draws on a two-phase data source review of government legislations that guarantee social media users and empirical papers related to social media platforms. The results revealed that Ghana has adopted specific legislations that manage and control SNP. To the best of the author’s knowledge, this study is the first of its kind that synthesized government legislation and empirical papers on social networking platforms in evangelising destinations which have been missing in extant literature.

In the context of integrated promotion, it is essential to promote destination images consistently across multiple digital channels. This study aims to examine the consistency of online destination images projected through the official tourism websites and the Instagram accounts of five main destinations in Southeast Asia.

Previous studies have used correspondence analyses to measure the relationship between categorical variables. In the present study, a Spearman’s rank-order correlation was performed after the correspondence analyses to cross-check the results.

Destinations in Southeast Asia tend to project images that are similar to each other. The correspondence analyses and Spearman’s correlation found that only one country in the area projected relatively consistent destination images. By contrast, the other destinations tend to promote inconsistent images through their official websites and Instagram accounts.

Previous studies have assessed the consistency of projected destination image by comparing communication channels managed by government/public organisations with channels of private sector organisations. This was achieved by comparing printed materials with digital channels. By contrast, this study highlights the importance of assessing a destination’s online projected image consistency across different digital platforms (official tourism websites and official Instagram accounts) within the perspective of integrated promotion.

Selfie is a popular self-expression platform to visually communicate and represent individual thoughts, beliefs, and creativity. However, not much has been investigated about selifie's pedagogical impact when used as an educational tool. Therefore, the authors seek to explore students' perceptions, emotions, and behaviour of using selfies for a classroom activity.

A triangulated qualitative approach using thematic, sentiment, and selfie visual analysis was used to investigate selfie perception, behaviour and creativity on 203 undergraduates. Sentiment analyses (SAs) were conducted using Azure Machine Learning and International Business Machines (IBM) Tone Analyzer (TA) to validate the thematic analysis outcomes, whilst the visual analysis reflected cues of behaviour and creativity portrayed.

Respondents indicated positive experiences and reflected selfies as an engaging, effortless, and practical activity that improves classroom dynamics. Emotions such as joy with analytical and confident tones were observed in their responses, further validating these outcomes. Subsequently, the visual cue analysis indicated overall positive emotions reflecting openness towards the experience, yet also reflected gender-based clique tendency with modest use of popular selfie gestures such as the “peace sign” and “chin shelf”. Furthermore, respondents also preferred to mainly manipulate text colours, frames, and colour blocks as a form of creative output.

The study's findings contribute to the limited studies of using selfies for teaching and learning by offering insights using thematic analysis, SA and visual cue analysis to reflect perception, emotions, and behaviour.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-11-2021-0608/

Drawing upon information processing theory (IPT) and natural resource-based view (NRBV), this study analyses the role of social media technologies (SMT) on internal and external environmental collaboration and green innovation (green product, process and managerial innovation).

This study took in-depth empirical research by developing a survey questionnaire to identify the relationship between SMTs, environmental collaboration and green innovation. The respondents of the questionnaire were supply chain professionals working in the manufacturing industry of Pakistan. The survey collected 475 responses, which were tested through PLS-SEM using Smart-PLS.

The study results indicate that SMTs positively influence both internal and external environmental collaboration. Furthermore, internal environmental collaboration (IEC) fosters green products and green managerial innovation. In contrast, external environmental collaboration (EEC) fosters green processes and green managerial innovation. This study has also tested the mediation of IEC and EEC, which shows that both IEC and EEC mediate all the relationships except green process and green product innovation. The results also revealed that innovation capabilities moderate the relationship between environmental collaboration and green innovation.

Though this study has various practical implications, it is not free of limitations. First, the data were collected from Pakistan, and the results may only be compared with other developing countries. Second, few social media platforms have been considered, but they are increasing in numbers and could be used in upcoming studies. Third, green innovation in the context of products, processes and management is considered, but the concept is evolving, and its other indicators can be taken in upcoming studies.

This study addresses the implication of SMTs, environmental collaboration, innovation capabilities and green innovation, which are helpful for managers and policymakers to design policies.

This study provides the seminal operationalization of SMTs in environmental collaboration and green innovation. This study emphasizes innovation capabilities that firms should adopt.