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The spread of fake news on Instagram is still a problem that needs to be solved. Teenagers are a generation that is vulnerable to fake news, for example, high school students. Students need media literacy to help them protect against fake news. The media literacy skills possessed by students influence the behavior of spreading fake news that they do. This study aims to examine the effect of student media literacy on the behavior of spreading fake news on Instagram among students at public high schools in Surabaya.

This study used an online survey to100 students at five public high school in Surabaya to get the data on their ability to respond to the fake news on social media Instagram.

It was found that there is a media literacy that has a significant effect on the behavior of spreading fake news on Instagram. Based on these findings, it can be concluded that media literacy influences the behavior of spreading fake news on Instagram, and other factors influence the rest. There are seven media literacy skills, and the high category are grouping, deduction, synthesis and abstraction abilities. Meanwhile, the abilities included in the medium category are analysis and evaluation abilities.

This paper will provide insight of the media literacy levels on teenagers in metropolitan city. This result can be used as guide to add the media literacy subject at high schools and can be used to strengthen the media literacy skills among teenagers.

Social media rumor combating is a global concern in academia and industry. Existing studies lack a clear definition and overall conceptual framework of users' rumor-combating behaviors. Therefore, this study attempts to empirically derive a typology of rumor-combating behaviors of social media users.

A three-phase typology development approach is adopted, including content analysis, multidimensional scaling (MDS), interpreting and labeling. Qualitative and quantitative data collection and analysis methods are employed.

The elicited 40 rumor-combating behaviors vary along two dimensions: high versus low difficulty of realization, and low versus high cognitive load. Based on the two dimensions, the 40 behaviors are further divided into four categories: rumor-questioning behavior, rumor-debunking behavior, proactive-appealing behavior, and literacy enhancement behavior.

This typology will serve as reference for social media platforms and governments to further explore the interventions to encourage social media users to counter rumor spreading based on various situations and different characteristics of rumor-combating behaviors.

This study provides a typology of rumor-combating behaviors from a novel perspective of user participation. The typology delves into the conceptual connotations and basic forms of rumor combating, allowing for a comprehensive understanding of the complete spectrum of users' rumor-combating behaviors. Furthermore, the typology identifies the similarities and the differences between various rumor-combating behaviors, thus providing implications and directions for future research on rumor-combating behaviors.

This study aims to investigate the mechanism of the misinformation spread based on the elaboration likelihood model and the effects of four factors – emotion, topic, authority and richness – on the depth, breadth and structural virality of misinformation spread.

The authors collected 2,514 misinformation microblogs and 142,006 reposts from Weibo, used deep learning methods to identify the emotions and topics of misinformation and extracted the structural characteristics of the spreading network using the network analysis method.

Results show that misinformation has a smaller spread size and breadth than true news but has a similar spread depth and structural virality. The differential influence of emotions on the structural characteristics of misinformation propagation was found: sadness can promote the breadth of misinformation spread, anger can promote depth and disgust can promote depth and structural virality. In addition, the international topic, the number of followers, images and videos can significantly and positively influence the misinformation's spread size, depth, breadth and structural virality.

The influencing factors of the structural characteristics of misinformation propagation are clarified, which is helpful for the detection and management of misinformation.

Social enterprises (SEs) face tensions when combining financial and social missions, and this is particularly evident in the scaling process. Although extant research mainly focuses on SEs that integrate their social and financial missions, this study aims to unpack social impact scaling strategies in differentiated hybrid organizations (DHOs) through the case of African SEs.

The study entails an inductive multiple case study approach based on four case SEs: work integration social enterprises (WISEs) and fair trade producer social enterprises (FTPSEs) in Uganda and Kenya. A total of 24 semi-structured interviews were collected together with multiple secondary data sources and then coded and analyzed through the rigorous Gioia et al. (2013) methodology to build a theoretical model.

The results indicate that SEs, as differentiated hybrids, implement four types of social impact scaling strategies toward beneficiaries and benefits (penetration, bundling, spreading and diversification) and unveil different dual mission tensions generated by each scaling strategy. The study also shows mutually reinforcing mechanisms named cross-bracing actions, which are paradoxical actions connected to one another for navigating tensions and ensuring dual mission during scaling.

This study provides evidence of four strategies for scaling social impact, with associated challenges and response mechanisms based on the cross-bracing effect between social and financial missions. Thus, the research provides a clear framework (social impact scaling matrix) for investigating differentiation in hybridity at scaling and provides new directions on how SEs scale their impact, with implications for social entrepreneurship and dual mission management literature.

The model offers a practical tool for decision-makers in SEs, such as managers and social entrepreneurs, providing insights into what scaling pathways to implement (one or multiples) and, more importantly, the implications and possible solutions. Response mechanisms are also useful for tackling specific tensions, thereby contributing to addressing the challenges of vulnerable, marginalized and low-income individuals. The study also offers implications for policymakers, governments and other ecosystem actors such as nongovernmental organizations (NGOs) and social investors.

Despite the growing body of literature on scaling social impact, only a few studies have focused on differentiated hybrids, and no evidence has been provided on how they scale only the social impact (without considering commercial scaling). This study brings a new perspective to paradox theory and hybridity, showing paradoxes come into view at scaling, and documenting how from a differentiation approach to hybridity, DHOs also implemented cross-bracing actions, which are reinforcement mechanisms, thus suggesting connections and synergies among the actions in social and financial mission, where such knowledge is required to better comprehend how SEs can achieve a virtuous cycle of profits and reinvestments in social impact.

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.

Particles bed binding by selective cement activation (SCA) method is a computer-aided manufacturing (CAM) technique used to produce cementitious elements. A computer-aided design file is sliced to generate G-codes before printing. This paper aims to study the effect of key input parameters for slicer software on the final properties of printed products.

The one factor at a time (OFAT) methodology is used to investigate the impact of selected parameters on the final properties of printed specimens, and the causes for the variations in outcomes of each variable are discussed.

Finer aggregates can generate a more compact layer, resulting in a denser product with higher strength. Fluid pressure is directly determined by voxel rate (r_V); however, high pressures enable better fluid penetration control for fortified products; for extreme r_Vs, residual voids in the interfaces between successive layers and single-line primitives impair mechanical strength. It was understood that printhead movement along the orientation of the parts in the powder bed improved the mechanical properties.

The design of experiment (DOE) method assesses the influence of process parameters on various input printing variables at the same time. As the resources are limited, a fractional factorial plan is carried out on a subset of a full factorial design; hence, providing physical interpretation behind changes in each factor is difficult. OFAT aids in analyzing the effect of a change in one factor on output while all other parameters are kept constant. The results assist engineers in properly considering the influence of variable variations for future DOE designs.

Industry 4.0 has an inimitable potential to create competitive advantages for the apparel industry by enhancing productivity, production, profitability, efficiency and effectiveness. This study, thus, aims to assess the digitalisation level of the Tanzanian apparel industry through the Industry 4.0 perspectives.

A mixed-methods-based approach was deployed. This study deployed semi-structured interviews, document review and observation methods for the qualitative approach. For the quantitative approach, closed-ended questionnaires were used to ascertain the digitalisation levels and maturity level of the textiles and apparel (T&A) factories and small and medium-sized textile enterprises in Tanzania. The sample size was 110, with participants engaged through the purposive sampling technique.

Industry 4.0 frameworks evolved into practices mainly since 2011 in several service and manufacturing industries globally. For Tanzania, the findings indicate that the overall maturity level of the T&A industries is 2.5 out of 5.0, demonstrating a medium level of adoption. Thus, the apparel industries are not operating under the industry 4.0 framework; they are operating within the third industrial revolution – Industry 3.0 – framework. For such industries to operate within the fourth industrial revolution – Industry 4.0 – that is only possible if there is significantly well-developed industrial infrastructure, availability of engineering talent, stable commercial partnerships, demand from the marketplace and transactional relationship with customers.

This study’s limitations include: firstly, Industry 4.0 is an emerging area; this resulted in limited theoretical underpinnings in the Tanzanian perspectives. Secondly, the studied industries may not suffice the need to generalise the findings for the entire country, thus needing another study.

Although Industry 4.0 conceptual frameworks have been on trial in several industries since 2011, this is amongst the first empirical research on Industry 4.0 in the Tanzanian apparel industry that assesses the digitalisation levels.

This study aims to examine how the theory of planned behaviour and technology acceptance theory can be used to understand the adoption of non-pharmaceutical interventions (NPIs). The relationships between attitudes, behavioural intentions towards using NPIs, actual use of NPIs and word-of-mouth (WOM) were examined and compared between early and late adopters.

A survey was conducted to test the hypotheses with partial least squares structural equation modelling (n = 278).

The results indicate that relationships between attitudes, intentions and behavioural intentions were positive and significant in the whole data set – and that there were differences between the early and late adopters. WOM had no substantial relationship with actual usage and early adopters’ behavioural intentions.

This research gives a better sense of how WOM impacts attitudes, behavioural intentions and actual usage among early and late adopters of NPIs and highlights the effectiveness of WOM, especially among late adopters of NPIs. Furthermore, using the TAM allows us to make specific recommendations regarding encouraging the use of NPIs. A new three-stage communications model is introduced that uses early adopters as influencers to reduce the NPI adoption time by late adopters.

This paper explores whether the Euro-area sovereign credit default swap market is prone to contagion effects. It investigates whether the sharp increase in sovereign CDS spread of a given country is due to a deterioration of the macroeconomic variables or some form of contagion.

For this purpose, the authors use an innovative approach, i.e. spatial econometrics. Although modeling spatial dependence is an attractive challenge, its application in the field of finance remains limited.

The empirical findings show strong evidence of spatial dependence highlighting the presence of pure contagion. Furthermore, evidence of wake-up call contagion-increased sensitivity of investors to fundamentals of neighboring countries and shift contagion-increased sensitivity to common factors are well recorded.

This study aims to study a crucial financial issue that gained increased research interest, i.e. financial contagion. A methodological contribution is made by extending the standard spatial Durbin model (SDM) to analyze and differentiate between several forms of contagion. The results can be used to understand how shocks are spreading through countries.

The adoption of social procurement, the emerging practice of using a firm's spending power to generate social value, requires buying firms to navigate conflicts of institutional logics. Adopting an institutional work perspective, this study aims to investigate how buying firms change their existing procurement institutions to adopt and advance social procurement.

The authors conducted an in-depth case study of a social procurement initiative in the UK. This case study comprised of 16 buying firms that were actively participating in the social procurement initiative at the time of data collection (2020–2021). The data were largely captured through a set of 41 semi-structured interviews.

Four types of institutional work were observed: reducing institutional conflicts, crossing institutional boundaries, legitimising institutional change and spreading the new institutional logic. These different types of institutional work appeared in a sequential way.

This study contributes to various strands of literature investigating the role of procurement in generating value and benefits within societies, adopting an institutional lens to investigate the buying firms' purposeful actions to change procurement institutions. Secondly, this study complements the existing literature investigating the conflicts of institutional logics by illustrating the ways firms address such institutional conflicts when adopting and advancing social procurement. Finally, this work contributes to the recently emerging research on institutional work that examines the creation and establishment of new institutions by considering the existing procurement institutions in the examination of institutional work.