Influence Of Information Research Articles

Сигнатура мятежа: как теория СОК позволяет обнаруживать признаки протестной мобилизации в Сети

The purpose of this study is to detect signs of political mobilization in social networks during the period of intensification of internal political struggle (including street actions of the opposition) at the beginning of 2021 in Russia. This study suggests a new quantitative indicator to reveal online communities (networks) characterized by strong user engagement and high level of political mobilization. VK-communities were mapped and analyzed in order to demonstrate the capability of this tool and determine levels of mobilization. The hypothesis of the study was that mobilized communities are able to turn virtual grievances into street protests. The object of study is a set (cluster) of related protest VK-communities (groups/hubs) through which messages from the most influential information donors are brought to public notice on the Internet. The proposed indicator for the calculation of mobilization level is based on the theory of self-organized criticality (SOC). The theory of SOC was initially created to describe a broad class of natural science phenomena, yet it has recently made its way into humanities and social sciences. Timeline of work: from January 10 to April 30, 2021. It was found that some groups (about 10%) in the considered cluster had a high mobilization index during this period. Many of the mobilized groups were very influential: they were both large-scale information donors and highly specialized protest communities. The practical significance of this study lies in improving the methods of monitoring network activity to identify groups that can influence the behavior of their members (both online and on the streets). This study also has theoretical significance related to the accumulation of knowledge about SOC effects in social systems.

Social Media Marketing Strategies and Their Effects on Consumer Engagement and Buying Behavior in the Fashion Industry

In this literature review, we look at how fashion brands are using social media to influence customer involvement and purchase decisions. Fashion companies are capitalizing on the growing importance of digital platforms by connecting with customers through social media in order to increase sales, brand loyalty, and brand awareness. This paper takes a look at some of the most important social media sites, like Instagram, Facebook, TikTok, and Pinterest, and it showcases the tactics and methods that top fashion firms have used to succeed on these platforms. Quality content, influencer partnerships, and interactive campaigns are some of the elements that increase engagement, which is measured by metrics like likes, shares, and comments. In addition, the study delves into the psychological elements that influence purchasing decisions, like social proof and FOMO (Fear of Missing Out), showing how these elements transform interest into plans to buy. Algorithm updates, content fatigue, and ethical worries about data security and privacy are among the typical problems that the assessment touches on. This study offers fashion marketers practical suggestions for improving their social media strategy by combining actual data with theoretical concepts. The results show that social media has a major impact on consumer behavior and point to places where more study is needed to fill in the gaps.

The power of Facebook friends: An investigation of young adolescents’ processing of social advertising on social networking sites

Abstract This study investigates the underlying mechanisms of how young adolescents process social advertising (i. e., advertising on social networking sites which shows how many and which of the user’s friends have ‘liked’ the brand’s page). Particularly, two experiments examined the role of brand trust in adolescents’ attitude formation and how brand trust is predicted by theories of social proof and persuasion knowledge. In addition, the moderating role of brand familiarity and brand value is investigated. The first experiment (N = 142) showed that higher brand trust was induced for social advertising for unfamiliar but not for familiar brands through the principle of social proof. This means that friends’ likes may reduce uncertainty and increase trust in unfamiliar brands through social advertising, something which enhances brand attitudes. Persuasion knowledge could not explain the effects of social advertising. The second experiment (N = 72) showed that social advertisements are more effective for brands that are symbolic (versus non-symbolic). Managerial and policy implications regarding social advertising targeting adolescents are discussed.

A Dual Process Model of the Influence of Recommender Systems on Purchase Intentions in Online Shopping Environments

Whereas much research has looked at how recommendation systems influence online purchase intentions, this article illustrates the dual process model by which they occur. Using two studies, we fill the research void in interactive marketing by demonstrating how the dual processes of social proof and risk avoidance mediate the impact of recommendation labels on consumer decision-making contingent upon their level of involvement. Study 1 (n = 73), used a mixed-subjects design with a college student sample to demonstrate that both types of recommendation labels will lead to higher purchase intentions in an online setting. Most importantly, it provides evidence for the main effect of our theoretical model across different product categories. Study 2 (n = 160) provides support for our remaining four hypotheses by demonstrating the underlying process through which recommendation labels have a two-fold effect on purchase intentions. Specifically, the recommendation label increased the risk avoidance effect for high-involvement consumers and enhanced the social proof effect for low-involvement consumers. In both cases, the recommendation labels increased purchase intentions. Implications of our findings for theoretical and practical contributions and future directions are also explored.

How Frauds in Times of Crisis Target People

ABSTRACT While there are no new frauds, internet technology provides new opportunities for fraudsters by facilitating volumes of attacks that law enforcement then struggles to address. Moreover, since context can affect how potential victims respond to frauds, crisis context influences how fraudsters design frauds. This article assesses fraudsters’ fraud design strategies during two external crisis events that impacted Australia: The Black Summer Bushfires that occurred from September 2019 to March 2020 and the onset and first year of the COVID-19 pandemic that occurred from January 2020 through January 2021. Targets, during these crises, were more likely to be vulnerable according to Steinmetz’s model victim for social engineering framework. This study shows that, in both crises, fraudsters deployed the social engineering techniques of “authority” and “scarcity,” techniques that are more likely to be successful based solely on initial contact. Fraudsters designed their requests to be easily actioned and crafted their scams to reference very recent events as the external crisis events evolved. Thus, they targeted broad audiences with minimal personal involvement. Furthermore, this study shows that fraudsters, when disseminating their scams via social media outlets, attempted to build “social proof” to expand their potential victim pool to include the marks’ social circles.

The backfire effect of sustainable social cues. New evidence on social moral licensing

To encourage individuals to consume sustainably, managers and policymakers often highlight positive examples that others have set regarding sustainable consumer behavior (e.g., donations to a good cause). This study shows that under certain circumstances, this strategy provokes a paradoxical effect such that consumers deliberately deviate from these positive examples. Merging the concepts of social proof and social moral licensing, the paper proposes a conceptual framework that elucidates two opposite effects that underlie the responses to positively charged behaviors of others (sustainable social cues). Generally, positive group behavior can encourage individuals to conform to the behaviors of others, but social moral licensing can lead to norm deviations, especially for individuals who have consumed more sustainably in the past. This leads to the intriguing notion that it is particularly the more sustainably consuming individuals who are demotivated by sustainable social cues. Across three experimental studies, this paper shows the processes and conditions that explain when and how consumers follow or deviate from sustainable social cues. The effect is shown to occur in both lab conditions and a natural environment.

Sentiment mutation and negative emotion contagion dynamics in social media: A case study on the Chinese Sina Microblog

Negative emotional contagion along with sentiment mutation through information propagation on social media is critical for mitigating disinformation and directing public opinion for compliance with key public interventions, such as vaccine uptake during a pandemic. Here, we develop a dynamic multiple negative emotional susceptible-forwarding-immune (MNE-SFI) model to examine how negative emotion spreads on social media and how sentiment mutation impacts by fitting the model to real multiple temporal information in messages with sentiments obtained from the Chinese Sina microblog. Emotional choices, meaning that individuals attempting to spread information are not only influenced by the objective emotions embedded in the influential information spread by influencers but also by subjective emotional tendencies, is an essential human behavior for information propagation. Hence, we seek to link the negative emotional contagion in the network at the macroscopic level to the emotional choices of individuals, and model parameters are used at the microcosmic level to measure the “copying” and “mutation” probabilities of negative sentiments in an event. Our results illustrate the emotional choices of users play essential roles in methods for mitigating harmful emotion spread and promoting meaningful emotion diffusion.

Heterogeneous Demand Effects of Recommendation Strategies in a Mobile Application: Evidence from Econometric Models and Machine-Learning Instruments

In this paper, we examine the effectiveness of various recommendation strategies in the mobile channel and their impact on consumers’ utility and demand levels for individual products. We find significant differences in effectiveness among various recommendation strategies. Interestingly, recommendation strategies that directly embed social proofs for the recommended alternatives outperform other recommendations. In addition, recommendation strategies combining social proofs with higher levels of induced awareness due to the prescribed temporal diversity have an even stronger effect on the mobile channel. We also examine the heterogeneity of the demand effect across items, users, and contextual settings, further verifying empirically the aforementioned information and persuasion mechanisms and generating rich insights. We also facilitate the estimation of causal effects in the presence of endogeneity using machine-learning methods. Specifically, we develop novel econometric instruments that capture product differentiation (isolation) based on deeplearning models of user-generated reviews. Our empirical findings extend the current knowledge regarding the heterogeneous impact of recommender systems, reconcile contradictory prior results in the related literature, and have significant business implications.

Competing for narrative authority in capital markets: Activist short sellers vs. financial analysts

Activist short sellers (AShSs) and financial analysts are information intermediaries who analyze firm disclosures as well as produce and disseminate influential investment narratives. This study aims to better understand narrative challenges surrounding the legitimate expertise of financial analysts. Specifically, we examine how AShSs challenge sell-side financial analysts' narrative authority (i.e., the perception that they produce expert knowledge) in interpreting firms' performance and future prospects. We investigate how analysts respond (or do not respond) to this challenge. We use 442 AShS reports, 12 interviews with AShSs and analysts, and analysts' stock recommendations and target prices. In their criticisms of analysts (found in one-third of reports), AShSs frequently frame analysts as lacking market expertise and critical thinking – two core dimensions of analysts' narrative authority. Sixty-six percent of analysts, although explicitly criticized in AShS reports, do not engage in written responses in their equity research reports because they reportedly either adopt a renunciation attitude to the challenge or they engage in off-the-record discussions with certain market participants. However, 34% of analysts respond overtly by counter-framing AShSs as lacking market expertise and objectivity. After the dissemination of AShS reports, analysts, on average, do not revise their highly visible stock recommendations but they revise target prices downward. Theoretically, this study extends our understanding of the construction of narrative authority in capital markets as we examine a challenge to the expertise of influential information intermediaries.

Employing political persuasion to manage rapport: qualitative analysis of campaign leaflets in Sheffield Central constituency

Abstract Rapport management is argued to be a critical antecedent of voter support in election campaigns, as it can motivate cooperation. However, little research has been conducted exploring how rapport is managed in such practice. In the present study, the campaign leaflets of four political parties in Sheffield Central constituency for the 2017 UK general election were analysed in relation to rapport-management strategies. Three domains of discourse were identified and classified in terms of one or more of the six principles of persuasion: Reciprocity, Consistency and Commitment, Social Proof, Authority, Liking, and Scarcity. The findings showed that Reciprocity and Liking were the commonly employed principles in the rapport-management strategies. These two principles were established in the leaflets by justifying and explaining credentials. Authority played a major role in influencing the functioning of the other principles in the rapport-management strategies. Specifically, the more authority a party had, the stronger the commitment made to act as rapport-management strategies. In contrast, the less authority a party had, the greater social proof and scarcity were involved in the strategies.

Failed but validated? The effect of market validation on persistence and performance after a crowdfunding failure

We investigate the impact of market validation on persistence and subsequent performance following a specific type of failure (crowdfunding failure). We leverage a mixed methods design, employing a controlled lab experiment with entrepreneurs (Study 1) and a four-year lagged longitudinal field study which combines two archival databases (Study 2). In our experiment, we find that market validation encourages entrepreneur persistence through affective activation and cognition-based action intentions (specifically search and knowledge integration). We also find that another form of validation, expert validation, strengthens this relationship. In our field study, market validation is shown to be a stronger predictor of performance after a crowdfunding failure in comparison to expert validation. We draw from the social proof and wisdom of the crowd perspectives to develop our theoretical model and explain the implications of our findings.

The application of the theory of social influence on the recruitment of Division I male basketball players

ABSTRACT The theory of social influence reveals how people may be influenced by others. The theory categorizes two general mechanisms: (a) informational social influence and (b) normative social influence. Friends and family members represent normative social influences, while informational influences may be gleaned from the broadcast and print media, Internet, or brochures. The results represented a departure from other studies as informational social aspects exerted more weight on the choice of schools by male basketball players than normative social items in this study. The authors posit the combination of information readily accessed on phones and computers, as well as the recruit’s technological savviness from a young age, require recruiters to devote more time to providing relevant and recent information about their program and school on the Internet and other informational outlets to be successful.

Toward Children's Empathy Ability Analysis: Joint Facial Expression Recognition and Intensity Estimation Using Label Distribution Learning

Empathy ability is one of the most important social communication skills in early childhood development. To analyze the children's empathy ability, facial expression analysis (FEA) is an effective way due to its ability to understand children's emotional states. Previous works mainly focus on recognizing the facial expression categories yet fail to estimate expression intensity, the latter of which is more important for fine-grained emotion analysis. To this end, this article first proposes to analyze children's empathy ability with both the categories and the intensities of facial expressions. A novel FEA method based on intensity label distribution learning is presented, which aims to recognize expression categories and estimate their intensity levels in an end-to-end framework. First, the intensity label distribution is generated for each frame in the expression sequence using a linear interpolation estimation and a Gaussian function to address the lack of reasonable annotations for expression intensity. Then, the extended intensity label distribution is presented to automatically encode the expression intensity in a multidimensional expression space, which aims to integrate the expression recognition and intensity estimation into a unified framework as well as boost the expression recognition performance by suppressing the variations in appearance caused by intensity and by emphasizing those variations among weak expressions. Finally, a Siamese-like convolutional neural network is presented to learn the expression model from a pair of frames that includes an expressive frame and its corresponding neutral frame using the extended intensity label distribution as the supervised information, thus effectively eliminating the expression-unrelated information's influence on FEA. Numerous experiments validate that the proposed method is promising in analysis of the differences in empathy ability between typically developing children and children with autism spectrum disorder.

Exploring the role of social proof of credibility and usability for internet banking adoption through media networks

Present study has offered two theoretical models which may helpful to understand the importance of social proof during internet banking (IB) adoption. The existing technology adoption model such as TAM has ignored the importance of social proof of credibility such as risk, security, and privacy. People are actively involved to take recommendations from close sources, experts, customers, and crowd opinion using social media platforms (SMPs). The purpose to gather information is to save from risk, security, and privacy issues especially when customers must share their personal and financial information during IB. It has found that conventional banks have positive word of mouth, recommendations, and reviews therefore the number of IB customers, profitability, and growth is high compared to Islamic banks. Conversely, SMPs have more negative word of mouth and stories which creates social proof regarding the uncertainty and risk in IB adoption. Findings highlights that people have social trust, confidence, and believe in their close sources and conventional banks.

Impact of gender and age on susceptibility to persuasion principles in advertisement

This investigation has two aims: to study the susceptibility of young consumers to persuasion principles of Cialdini and to identify which principles achieve a powerful effect of memory through advertising in consumers of different genders and ages. The instrument used is the adapted and modified STPS questionnaire, developed by Kaptein el at., as well as the subjective judgments of respondents about the degree to which they remember advertisements based on persuasion principles. The results of the regression analysis show that social proof is the only principle of persuasion whose persuasive power does not correspond to that of three principles: principle of reciprocity, principle of commitment and consistency and principle of unity. Practical application of principle of commitment and consistency and principle of reciprocity is more persuasive in uniform advertising when gender and age characteristics of the target groups are not taken into account. The causal link between the principle of social proof and memorization of advertising is established, where persuasion is done in a peripheral route, although consumers are susceptible to other principles. Ranking of the principles has established that the principle of commitment and consistency achieves greatest effectiveness in both genders, while the principle of reciprocity is most effective for women and the principle of authority - for men. Regarding the age groups, the following principles are observed: 18 to 20 - principle of social proof; 21 to 24 - principle of unity, and 25 to 35 - principle of commitment and consistency. Different consumer susceptibility to persuasion can be achieved in the studied age groups on a peripheral route with higher efficiency in women. This study is useful for organizations that offer different products and services through advertising.

DEGREE OF SUSCEPTIBILITY TO PERSUASION PRINCIPLES IN ADVERTISING: MEASURING THE PERSUASION PRINCIPLES AS PART OF ADVERTISING INFLUENCE

The present investigation has two aims: 1) to investigate the susceptibility of consumers to persuasion principles of Cialdidni and 2) to identify which of the principles achieves a high effect of advertising impact on different consumers. The instrument used was the adapted and modified STPS questionnaire developed by Kaptein el at., as well as the respondents' subjective judgments of the extent to which the advertisements affected based on the persuasion principles. The results of t-tests indicate that the principle of liking affects persuasion statistically significantly less than the application of all other principles for the whole sample, and through correlation analysis it is found that the principle of social proof is susceptible to the presence of influence from others three principles. Susceptibility to persuasion, achieved through the following principles: reciprocity, commitment and consistency, liking and unity, is statistically significantly higher in women due to its high sensitivity than in men, which confirms the results of other researchers.

Panic-gogy in the COVID-19 pandemic: developing online teaching self-efficacy by management faculty

The COVID-19 pandemic forced educational institutes worldwide to shift to online education in an emergency situation. The faculty used panic-gogy as a pedagogy tool to shift their courses to synchronous or asynchronous online mode. Focus group discussions were conducted to gather the panic-gogy experiences of management faculty. Bandura's self-efficacy building information sources were used as a framework to analyse management faculty's experiences. Vicarious experiences and social persuasions emerged as the most influential information sources of self-efficacy building. The analysis led to implications for management education administrators and human resources personnel related to training management faculty in online teaching and facilitating the process of building online teaching self-efficacy in management faculty.

Characteristics of development and dissemination of environmental management systems in the area of production of medicines in Ukraine

The article presents the results of a study based on an expert survey of senior management and leading experts of enterprises for the production of medicines in Ukraine and typical problematic aspects of implementing environmental management systems (EMS). It was found that among the enterprises whose respondents took part in the survey, only 25% implemented EMS and passed the certification procedure following the requirements of ISO 14001. A significant problem identified is the lack of influential information tools and methodological developments for the implementation, operation, and benefits of EMS in pharmaceutical production, especially to ensure processes regarding the readiness of enterprises to respond to emergencies. Insufficient support was noted, particularly in regulatory and financial incentives, and no benefits could encourage more efficient greening of pharmaceutical production. The negative consequence is the lack of interest of small and medium enterprises in investing in new technologies and sustainable business practices to reduce environmental pressures. EMS should be implemented as a part of the overall management system of the enterprise to manage environmental aspects, meet mandatory legal requirements and voluntary commitments in accordance with the objectives and environmental policy of the enterprise, as well as to address risks and opportunities. It is important to note the urgency of developing theoretical and methodological principles of design, implementation, and further development of EMS to increase the environmental efficiency of the enterprise, greening in medicines production, and achieving sustainable development goals.

Trust Management in the World of Cloud Computing. Past Trends and Some New Directions

Over the past years, Cloud computing has become one of the most influential information technologies to combat computing needs because of its unprecedented advantages. In spite of all the social and economic benefits it provides, it has its own fair share of issues. These include privacy, security, virtualization, storage, and trust. The underlying issues of privacy, security, and trust are the major barriers to the adoption of cloud by individuals and organizations as a whole. Trust has been the least looked into since it includes both subjective and objective characteristics. There is a lack of review on trust models in this research domain. This paper focuses on getting insight into the nomenclature of trust, its classifications, trust dimensions and throws an insight into various trust models that exist in the current knowledge stack. Also, various trust evaluation measures are highlighted in this work. We also draw a comparative analysis of various trust evaluation models and metrics to better understand the notion of trust in cloud environments. Furthermore, this work brings into light some of the gaps and areas that need to be tackled toward solving the trust issues in cloud environments so as to provide a trustworthy cloud ecosystem. Lastly, we proposed a Machine Learning backed Rich model based solution for trust verification in Cloud Computing. We proposed an approach for verifying whether the right software is running for the correct services in a trusted manner by analyzing features generated from the output cloud processed data. The proposed scheme can be utilized for verifying the cloud trust in delivering services as expected that can be perceived as an initiative towards trust evaluation in cloud services employing Machine learning techniques. The experimental results prove that the proposed method verifies the service utilized with an accuracy of 99%.

An Interaction-Based Convolutional Neural Network (ICNN) Toward a Better Understanding of COVID-19 X-ray Images

The field of explainable artificial intelligence (XAI) aims to build explainable and interpretable machine learning (or deep learning) methods without sacrificing prediction performance. Convolutional neural networks (CNNs) have been successful in making predictions, especially in image classification. These popular and well-documented successes use extremely deep CNNs such as VGG16, DenseNet121, and Xception. However, these well-known deep learning models use tens of millions of parameters based on a large number of pretrained filters that have been repurposed from previous data sets. Among these identified filters, a large portion contain no information yet remain as input features. Thus far, there is no effective method to omit these noisy features from a data set, and their existence negatively impacts prediction performance. In this paper, a novel interaction-based convolutional neural network (ICNN) is introduced that does not make assumptions about the relevance of local information. Instead, a model-free influence score (I-score) is proposed to directly extract the influential information from images to form important variable modules. This innovative technique replaces all pretrained filters found by trial-and-error with explainable, influential, and predictive variable sets (modules) determined by the I-score. In other words, future researchers need not rely on pretrained filters; the suggested algorithm identifies only the variables or pixels with high I-score values that are extremely predictive and important. The proposed method and algorithm were tested on real-world data set and a state-of-the-art prediction performance of 99.8% was achieved without sacrificing the explanatory power of the model. This proposed design can efficiently screen patients infected by COVID-19 before human diagnosis and can be a benchmark for addressing future XAI problems in large-scale data sets.