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Communities in a network are groups of nodes that are more strongly connected to each other. This article proposes a novel method for community detection in dynamic networks, focusing on influential nodes and overlapping communities. The method, named community detection based on adaptive multi-centrality aggregation (CDAMA), tackles two key challenges identifying influential nodes and overlapping communities. CDAMA introduces the Adaptive multi-centrality aggregation (AMCA) approach to identify influential nodes. AMCA integrates multiple centrality measures. The adaptive overlap control and merging (AOC-CM) approach addresses overlapping communities. AOC-CM utilizes structural, temporal, and semantic factors to strategically merge communities while preserving those with minimal overlap. CDAMA consists of five phases: receiving network snapshots, selecting influential nodes, launching communities, checking overlap and merging communities, and updating communities. Evaluation on three benchmark datasets demonstrates that CDAMA outperforms existing state-of-th-art methods in terms of Newman modularity, Modularity with split penalty and density modularity and Execution time. This suggests CDAMA is a valuable tool for tasks like viral marketing, information diffusion analysis, and network resilience studies. [ABSTRACT FROM AUTHOR]

This research aims to gain insight on the perception that minors have of viral challenges as an entertainment format and the motivations behind their participation in this digital entertainment phenomenon. A qualitative study was performed by way of twelve focus groups with sixty-two minors aged between eleven and seventeen years from Spain. For minors, viral challenges represent a form of entertainment in an interactive context, perceived as innocuous, ephemeral content from which nothing more is required than for the user to have a good time. This appears to lead the minors interviewed to ignore the meaning and origin of the viral challenges they visualise and share, neither do they regard this to be necessary. It is also important to underline the relativisation of risk and danger in favour of spectacularisation and virality. [ABSTRACT FROM AUTHOR]

Online social networks are crucial in propagating information and exerting influence through word-of-mouth transmission. Influence maximization (IM) is the fundamental task in social network analysis to find the group of nodes that maximizes the influence in the social network. IM has different applications like viral marketing, campaigning, advertising, and so on. Literature has presented various algorithms based on different approaches to address the IM problem, including nature-inspired algorithms. Most of the work focuses on the static social network. The proposed work first employs nature-inspired Cuckoo Search Optimization to solve the IM problem in dynamic networks. The proposed algorithm applies the fuzzy-logic-based technique to optimize the nests. We also perform statistical tests to show the effectiveness of the proposed algorithm with the benchmark algorithms. The experimental results are performed on five datasets and compare the results with the state-of-the-art algorithms. The results show that the proposed algorithm gives better results than the nature-inspired state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

We investigate the participation and effectiveness of paid endorsers in sponsored tweet campaigns. We manipulate the financial pay rate offered to endorsers on a Chinese paid endorsement platform, where payouts are contingent on participation rather than engagement outcomes. Hence, our design can distinguish between variation in participation and variation in outcomes, even if people select to endorse only specific tweets. Also, the lack of compensation for effort allows one to attribute differences in outcomes to precontractual selection rather than postcontractual behavior. The main finding is that endorsers exhibited adverse selection. Several observed and unobserved endorser characteristics associated with a higher propensity to participate had a negative association with being an effective endorser given participation. This adverse selection results in a conundrum when trying to recruit a sizable number of high-quality endorsers. Only 9.5%–11.8% of the endorsers were above the median in both the propensity to participate and the propensity to be effective compared to a benchmark of 25% in the absence of any association. A simulation analysis of various targeting approaches that leverages our data of actual endorsements and outcomes shows that targeting candidate endorsers by scoring and ranking them using models taking into account adverse selection on observables improves campaign outcomes by 13%–55% compared to models ignoring adverse selection. This paper was accepted by Eric Anderson, marketing. Funding: This project was made possible by financial support extended to J. Peng through a Penn Lauder Center for International Business Education & Research PhD Grant and a Wharton Baker Retailing Center PhD Research Grant. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2019.01897. [ABSTRACT FROM AUTHOR]

This study aims to examine the impact of celebrity endorsers and viral marketing on purchase interest in Scarlett Whitening beauty products, focusing on the beauty community in Singkawang City. Using a quantitative approach, data was collected through questionnaires distributed to a purposive sample of 100 customers and consumers. The independent variables are celebrity endorsers and viral marketing, while the dependent variable is purchase interest. T-test results reveal that both celebrity endorsers and viral marketing significantly influence purchase interest individually. Additionally, F-test results confirm that these factors collectively have a significant simultaneous effect on purchase interest in Scarlett Whitening beauty products in Singkawang City. [ABSTRACT FROM AUTHOR]

The rise of digital technology and social media platforms has made traditional marketing approaches less effective in capturing the attention of the tech-savvy Generation Y. There has been an increased interest in guerrilla marketing tactics, which employ unconventional strategies to engage consumers. This study investigated the impact of guerrilla marketing, brand image, and brand awareness on the purchasing decisions of Generation Y consumers. The study adopted the Hierarchy-of-Effects Theory and conducted an empirical study the following constructs: viral marketing, ambush marketing, buzz marketing, street graphics marketing, brand awareness, brand image, and purchase decision. Data was collected from 442 Generation Y respondents in Thailand. Confirmatory factor analysis was used to analyse the model and construct's reliability. At the same time, the partial least squares structural equation modelling technique was adopted to explore the relationship of the study constructs. The results indicated that the purchase decision of Generation Y was significantly influenced by ambush marketing, buzz marketing, and street graphics marketing. The research revealed that brand image and brand awareness exerted a substantial impact on purchase decisions and served as key mediators in the relationship between guerrilla marketing and purchase decisions among Generation Y. The study emphasised the importance of guerrilla marketing techniques and leverage of the power of social media to influence purchase behaviour. Marketing managers should consider these techniques to attract Generation Y. [ABSTRACT FROM AUTHOR]

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In the digital age, viral marketing has emerged as a key driver in shaping brand equity, particularly in the highly competitive Egyptian home appliances sector. Traditional marketing strategies have proven insufficient in reaching consumers with the same efficacy as viral campaigns, which leverage digital platforms and consumer networks. This study examines the influence of viral marketing on brand equity dimensions – brand awareness, perceived quality, brand loyalty, and brand association – through the lens of both customer perception and legal frameworks Using a quantitative research design, data were collected from 270 respondents across 389 companies, with a response rate of approximately 70% of the total population in Egypt’s home appliances sector, with simple linear regression analysis employed to assess the relationships. The results indicate a strong positive effect of viral marketing on brand equity. Specifically, viral marketing accounts for 27.2% of the variance in brand awareness (R² = 0.272, p < 0.001), 28.1% in perceived quality (R² = 0.281, p < 0.01), 13.6% in brand loyalty (R² = 0.136, p < 0.05), and 40.9% in brand association (R² = 0.409, p < 0.001). Furthermore, the study finds that regulatory compliance plays a moderating role, ensuring that viral marketing campaigns remain within ethical and legal bounds, thereby enhancing consumer trust and the long-term impact of marketing efforts.These findings highlight the strategic value of integrating viral marketing with a firm understanding of legal frameworks to optimize brand equity in Egypt’s home appliances industry. This research provides actionable insights for brand managers and marketers looking to maximize the efficacy of their viral campaigns while maintaining brand integrity. AcknowledgmentThe authors are thankful to the Deanship of Graduate Studies and Scientific Research at University of Bisha for supporting this work through the Fast-Track Research Support Program.

Meme marketing taps into the potential of digital culture. Despite considerable practitioner interest, meme marketing is under-researched. The present study aims to fulfil two purposes. First, to conceptualise meme marketing, we draw on speech act theory and the theory of consumption values to define meme marketing. On this basis, we differentiate meme marketing from relevant digital marketing constructs, including e-WOM, digital content marketing, and viral marketing. Second, to validate the proposed definition, we empirically perform a qualitative content analysis to taxonomize meme marketing speech acts. Based on the content analysis of 699 meme marketing posts, we found that meme marketing serves as a rich communication source, sending intentions of the brand through single (i.e. assertive, directive, expressive) as well as combined (i.e. assertive entailing expressive, assertive entailing directive) speech acts that provide customers with epistemic, emotional, and social values. Important theoretical and managerial implications for academic researchers and marketers are also discussed. [ABSTRACT FROM AUTHOR]

Measuring influence of one person on another has applications in advertising and marketing and across the sciences. Most approaches involve inferring influence based on speech and social media. In contrast, this paper takes existing spending data and attributes influence on to the spenders and those likely to have caused their spending. The resulting metric, Social Value, is expressed in units of behavior over time. While a person's total influence on others is called their Social Value, a person's behavior caused by someone else is called their Following Value. These metrics can also be used across an entire community, customer base, or audience, allowing an objective measure of how much spending or other behavior is social versus nonsocial. These measures in turn open up the potential to test interventions and campaigns to measure viral spread as well as overall shifts in social influence. This article presents a computational model for estimating Social Value, as well as validation of the estimation approach in a study involving players of an online game. A noncommercial open-source implementation of the computational model accompanies this paper. [ABSTRACT FROM AUTHOR]

Social Networks have been in continuous growing during the last decades. The huge amount of information and applications has led to an increase in the interest of scientists and practitioners in the study of problems related to the influence in Social Networks. Some of the wide variety of real-world applications in this area are viral marketing, disease analysis, rumor detection, public opinion, among others. In this paper, the Adaptive Multi Round Influence Maximization problem is studied, in which the influence of a set of selected users (seed set) is propagated in multiple rounds independently, with the possibility of selecting different seed sets in each round. Therefore, seed sets can be adaptively selected based on the propagation results in the previous rounds. Since each node is activated with a certain probability, the total number of activated nodes must be calculated through an Influence Diffusion Model (IDM), which results in a rather computationally demanding method. In this research, the Independent Cascade Model is considered, which is one of the most extended IDMs, and also the one used in the best previous method. Practitioners highlight the relevance of designing an algorithm capable of efficiently solving the problem. In this research, the problem is addressed by considering the Variable Neighborhood Search methodology, proposing a novel constructive method that relies on independent probability based on events, and an intelligent local search method. Our best algorithm is compared with the state-of-the-art method, named AdaIMM, to analyze the performance of the proposal. The obtained results show the superiority of the proposal in both quality (influence spread) and computing time, obtaining the best solution in all the 40 instances considered requiring half of the computing time than the best previous approach (28 s vs. 53 s). Additionally, the best previous method presents an average deviation of 24.23%. These results are further confirmed by conducting non-parametric statistic tests. [ABSTRACT FROM AUTHOR]

The article talks about the African viral dance (AVD) trends. Topics include the importance of AVD videos as a medium for Africans to assert themselves on the Internet, its popularity than the official song it originally meant to promote such as the "Gwara gwara" by DJ Bongz that yields dance clips with million views than the official release, a background on the rise of Afrobeats music, the tactics of digital embodiment in AVD, and a social media controversy surrounding AVDs in 2010.

Copyright of Al-Bahith Al-Aalami is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the rapid development of the mobile Internet, online social networks are playing an increasingly vital role in the dissemination of information. Accurately predicting the size of information cascades in advance has become a crucial issue, particularly in the realms of viral marketing, risk management and resource allocation. There are numerous studies that have tackled this prediction task, but the outcomes are unsatisfactory. In this paper, we explore the predictability of information cascade size through the lens of percolation theory. Our investigation reveals that the accuracy of cascade size prediction is notably diminished in the proximity of the threshold, evident in both artificial and empirical networks. Moreover, we observe a degradation and an user-level difference in prediction performance as social media platforms undergo evolution. Our findings underscore the necessity for additional factors to enhance prediction accuracy. [ABSTRACT FROM AUTHOR]

This study delves further into the use of viral marketing to boost customer loyalty, brand awareness, and brand trust in coffee shops. By looking at two case studies of coffee businesses that use influencer engagement and organic viral marketing tactics, a qualitative approach is used. By using two case studies with distinct approaches, we hope to shed light on how viral marketing can be successfully applied in a variety of situations. Eleven informants' interview data are evaluated through the use of coding and theming techniques. The study concludes that while the Story, Hype, Actionable, Relevant, and Emotional (SHARE) strategy is applied in both circumstances, the first case, which takes an organic approach, achieves higher levels of trust in the generated content than the influencer-involved case. Furthermore, the study reveals that the utilization of viral marketing tactics in conjunction with the SHARE concept has the potential to enhance consumer awareness, trust, and loyalty. This study makes a valuable contribution by providing in-depth understandings of the ways in which viral marketing can be utilized to improve consumer loyalty, brand awareness, and brand trust. It also closes a gap in the literature by explaining how Viral Marketing strategies can be strengthened through the application of the SHARE concept. [ABSTRACT FROM AUTHOR]

Despite the widespread significance of digital marketing in disseminating information about products and services across a vast customer base via diverse networks, a noteworthy proportion of businesses still struggle to comprehend the crucial factors underpinning the success of viral campaigns. This study aims not only to bridge this knowledge gap but also to introduce an innovative framework that underscores various factors that amplify the potency of social networks and emphasizes an often-overlooked element in customer engagement: the psychological state of customers. Empirical validation of the framework was conducted using a sample of 135 respondents, which was analyzed using the structured equation modeling technique. The study's findings show that the strength of social connections (strong ties) and the psychological disposition of customers significantly shape the generation and viral dissemination of marketing content across diverse networks. The importance of this research lies in its potential application by commercial companies for conducting promotional and marketing campaigns. By leveraging the proposed model, businesses can effectively promote their products and services, thus achieving their strategic objectives and gaining a competitive advantage in an environment characterized by intense competition and constant change. Doi: 10.28991/HIJ-2024-05-01-011 Full Text: PDF

This paper presents a bi-level game model for pricing in a supply chain where the manufacturer (He) is the leader, and the retailer (She) is the follower. The leader decides on the wholesale price, and the follower decides on the selling price and selects seed nodes. The main idea of the model is that the marketing strategy used for promoting the product is focused on giving free samples to potential customers. Hence, the importance of analyzing a social network becomes evident. To maximize her profit, the retailer decides based on three factors: first, the leader's decision about wholesale price; second, the social network structure, which is critical for selecting the seed nodes; and third, people's valuation of the product. Therefore, a bi-level Mixed-Integer Nonlinear Programming model is developed to consider the social binds between potential customers. To solve this model, we employ a meta-heuristic algorithm. Finally, the effect of the model's parameters on decision variables and the objective functions is discussed. Based on the analysis and discussions, the production cost has a prominent impact on the players’ decisions and profits. Furthermore, instead of spending all the marketing budget on increasing seed nodes, it is suggested that they be spent on market research and improving good publicity. Moreover, deciding whether the players want to maximize the profit or market penetration is required before diving into decision-making.

Viral marketing is one of the most important marketing trends of today as it offers companies the opportunity to reach large masses, low cost, increases brand awareness and has higher conversion rates. In addition, more trust in the content shared by friends or family members and more interaction due to interesting, entertaining or emotional shares are among the features that make viral marketing different. Although it has so many advantages and is used extensively in the field, the lack of a bibliometric analysis on viral marketing and the fact that it does not find enough space in the literature makes the study important. The aim of this study is to identify the leading journals, authors, publications and main research themes in this field using bibliometric and thematic content analyses, to provide an overview of current viral marketing research and to prepare a conceptual framework for future research. Another aim of the study is to identify the missing areas in the viral marketing literature and to create a framework for future research in this field. In this study where quantitative research method was used, bibliometric analysis method was used through VOSviewer 1.6.18 software programme. Between 2003 and 2023, 222 studies were analysed by bibliometric map analysis method with co-citation, author and entity and bibliographic matching analysis. As a result of the content analysis, the studies in the viral marketing literature were divided into three main categories and 10 trends that make up these categories.

How to exploit social networks to make internet content spread rapidly and consistently is an interesting question in marketing management. Although epidemic models have been employed to comprehend the spread dynamics of internet content, such as viral videos, the effects of advertising and individual sharing on information dissemination are difficult to distinguish. This gap forbids us to evaluate the efficiency of marketing strategies. In this paper, we modify a classic mean-field SIR (susceptible–infected–recovered) model, incorporating the influences of sharing and advertising in viral videos. We mathematically analyze the global stability of the system and propose an agent-based modeling approach to evaluate the efficiency of sharing and advertising. We further provide a case study of music videos on YouTube to show the validity of our model. [ABSTRACT FROM AUTHOR]

Summary: The influential node in the network is the node that has a higher impact on network functioning compared to the other nodes. The influential node detection in the complex network is crucial for rumor containment, virus spreading, viral marketing, and so forth. The researchers designed several influential node detection methods; still, detecting community and influential node selection with minimal computational complexity by considering the relationship between the nodes is challenging. Hence, an optimal community detection along with the hybrid K‐shell decomposition method is introduced in this research. Initially, the optimal community from the complex network is identified to reduce the computation burden. For this, the Improved Dingo (IDingo) algorithm is introduced by hybridizing the hunting behavior of Dingo and the rough encircling behavior of Harris Hawk. After detecting the optimal community, the influential node identification is devised using the proposed hybrid K‐shell decomposition methods. The potential edge weights are considered while ranking the nodes. The performance of a proposed method is analyzed using six various datasets and accomplished the maximal cluster coefficient of 0.56578, 0.25674, 0.24022, 0.5968, 0.23419, and 0.10196 for Karate, Dolphins, C‐Elegance, Facebook, Gowalla, and Power Grid Dataset. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Scientific Development for Studies & Research (JSD) is the property of Journal of Scientific Development for Studies & Research (JSD) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Influence maximization (IM) has shown wide applicability in various fields over the past few decades, e.g., viral marketing, rumor control, and prevention of infectious diseases. Nevertheless, existing research on IM primarily focuses on ordinary networks with pairwise connections between nodes, which fall short in the representation of higher-order relations. Influence maximization on hypergraphs (HIM) has received limited research attention. A novel evaluation function, which aims to evaluate the spreading influence of selected nodes on hypergraphs, i.e., expected diffusion value on hypergraph (HEDV), is proposed in this work. Then, an advanced greedy-based algorithm, termed HEDV-greedy, is proposed to select seed nodes with maximum spreading influence on the hypergraph. We conduct extensive experiments on eight real-world hypergraph datasets, benchmarking HEDV-greedy against eight state-of-the-art methods for the HIM problem. Extensive experiments conducted on real-world datasets highlight the effectiveness and efficiency of our proposed methods. The HEDV-greedy algorithm demonstrates a marked reduction in time complexity by two orders of magnitude compared to the conventional greedy method. Moreover, HEDV-greedy outperforms other state-of-the-art algorithms across all datasets. Specifically, under conditions of lower propagation probability, HEDV-greedy exhibits an average improvement in solution accuracy of 25.76%. [ABSTRACT FROM AUTHOR]

Copyright of Gharee for Economics & Administration Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This research analyses how high emotional cause-related marketing claims influence attitudes and viral behaviors. With a SEM and an international sample of 1,232 respondents (Spain, England, and Ecuador) exposed to a video viral cause marketing stimulus, analysis shows, from a global international perspective (1) the influence of emotions on attitudes; (2) the attitudinal constructs consistency; (3) the simultaneous impact of the emotions also on viral behaviors; (4) the effect of attitudes variables on viral behaviors; and (5) the strong viral consequences connection. From a cross-cultural perspective, significant differences are identified. Recommendations in the context analysed: (1) convenience of including emotional claims, (2) adaption of international campaigns to local cultures to strength certain advantages; however, tailoring locally a global campaign cannot be affordable. [ABSTRACT FROM AUTHOR]

Social networks are in continuous evolution, and its spreading has attracted the interest of both practitioners and the scientific community. In the last decades, several new interesting problems have aroused in the context of social networks, mainly due to an overabundance of information, usually named as infodemic. This problem emerges in several areas, such as viral marketing, disease prediction and prevention, and misinformation, among others. Then, it is interesting to identify the most influential users in a network to analyze the information transmitted, resulting in Social Influence Maximization (SIM) problems. In this research, the Budget Influence Maximization Problem (BIMP) is tackled. BIMP proposes a realistic scenario where the cost of selecting each node is different. This is modeled by having a budget that can be spent to select the users of a network, where each user has an associated cost. Since BIMP is a hard optimization problem, a metaheuristic algorithm based on Greedy Randomized Adaptive Search (GRASP) framework is proposed. [ABSTRACT FROM AUTHOR]

This study aimed to gather information from the literature regarding the role of social media in tobacco control among adolescents and young adults, both domestically and internationally. The study utilized a literature review from articles on Google Scholar and PubMed published between 2019 and 2023. Inclusion criteria encompassed open-access articles related to interventions using social media to control tobacco consumption among adolescents and young adults, excluding modeling studies. Intervention outcomes were assessed and extracted in narrative form. Fourteen articles met the inclusion criteria (11 original research articles and 3 articles with secondary data). Research designs in the original research articles included Randomized Control Trials (RCTs) and in-depth interviews. Studies utilizing WeChat and WhatsApp demonstrated favorable intervention outcomes in delivering tobacco control messages. Peer mentoring through Instagram was also considered easily accessible for adolescents. Other social media platforms facilitating global discussions, such as Twitter, can enhance public literacy on electronic cigarettes and facilitate the implementation of electronic cigarette policies. Social media interventions in tobacco control are more readily accepted by adolescents, easily accessible, cost-effective, and provide post-intervention social support. The presence of Key Opinion Leaders (KOLs) and message-writing techniques [ABSTRACT FROM AUTHOR]

We exist now in what can sometimes feel like an alternative universe, where fake news headlines go viral, algorithmic bias targets us with content that is popular and engaging but not necessarily trustworthy, chatbots are powered by artificial intelligence and deepfake video technology runs amok. The global rise of misinformation and the decline of consumer trust in both government and media have coincided with technological advances and tarnished demand for brand strategists' old tricks. One of these is storytelling. Now, if you are a brand that is being even a little disingenuous with your communications today, then to some you may as well be a bald-faced liar. Instead, regardless of the industry, whether B2B or consumer, brands today should be in the business of fact telling. At stake are a business' reputation, customer loyalty and the bottom line. Furthermore, it is up to us to help ensure that the information environment does not deteriorate further on our watch. The most exciting thing a brand can do today is to be audaciously authentic. But have we forgotten how to be? Moreover, can we tell the truth and still get consumers to add our products to their shopping carts, become brand advocates and loyalists and even learn to trust again? Within, we outline a framework for brands to meaningfully differentiate with truth telling. [ABSTRACT FROM AUTHOR]

The development of the internet shows that there is growth in more and more internet users worldwide, and Indonesia is one of them. However, in modern times this has spread widely to the economic and entrepreneurial fields, so the term E-commerce or online trading has emerged to meet daily needs such as Tiktok Shop. The success of Tiktok Shop in Indonesia is due to the way business actors change promotional strategies using viral marketing and celebrity endorsers to get public attention and viral news on social media. The purpose of this study is to ascertain how viral marketing and celebrity endorsements affect consumers' decisions to buy X NCT Dream skincare products via TikTok Shop. Data collecting for associative study is done by a quantitative technique utilizing a Google form to distribute surveys to up to 200 respondents online. After data processing, IBM SPSS 25 Version for Windows was used for analysis. Multiple linear analysis is the data analysis method employed. Every theory demonstrates relevance. The study concludes that buying decisions for X NCT Dream skincare products from TikTok Shop are significantly and favorably influenced by viral marketing and celebrity endorsers. [ABSTRACT FROM AUTHOR]

We use high-resolution mobile phone data with geolocation information and propose a novel technical framework to study how social influence propagates within a phone communication network and affects the offline decision to attend a performance event. Our fine-grained data are based on the universe of phone calls made in a European country between January and July 2016. We isolate social influence from observed and latent homophily by taking advantage of the rich spatial-temporal information and the social interactions available from the longitudinal behavioral data. We find that influence stemming from phone communication is significant and persists up to four degrees of separation in the communication network. Building on this finding, we introduce a new "influence" centrality measure that captures the empirical pattern of influence decay over successive connections. A validation test shows that the average influence centrality of the adopters at the beginning of each observational period can strongly predict the number of eventual adopters and has a stronger predictive power than other prevailing centrality measures. Our centrality measure can be used to improve optimal seeding strategies in contexts with influence over phone calls, such as targeted or viral marketing campaigns. We use high-resolution mobile phone data with geolocation information and propose a novel technical framework to study how social influence propagates within a phone communication network and affects the offline decision to attend a performance event. Our fine-grained data are based on the universe of phone calls made in a European country between January and July 2016. We isolate social influence from observed and latent homophily by taking advantage of the rich spatial-temporal information and the social interactions available from the longitudinal behavioral data. We find that influence stemming from phone communication is significant and persists up to four degrees of separation in the communication network. Building on this finding, we introduce a new "influence" centrality measure that captures the empirical pattern of influence decay over successive connections. A validation test shows that the average influence centrality of the adopters at the beginning of each observational period can strongly predict the number of eventual adopters and has a stronger predictive power than other prevailing centrality measures such as the eigenvector centrality and state-of-the-art measures such as diffusion centrality. Our centrality measure can be used to improve optimal seeding strategies in contexts with influence over phone calls, such as targeted or viral marketing campaigns. Finally, we quantitatively demonstrate how raising the communication probability over each connection, as well as the number of initial seeds, can significantly amplify the expected adoption in the network and raise net revenue after taking into account the cost of these interventions. History: Sam Ransbotham, Senior Editor; Yan Huang, Associate Editor. Funding: Y. Leng acknowledges the support provided by the National Science Foundation [Grant IIS-2153468]. E. Moro acknowledges the support provided by the National Science Foundation [Grant 2218748]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/isre.2023.1231. [ABSTRACT FROM AUTHOR]

In a range of studies across platforms, researchers have shown that online ratings are characterized by distributions with disproportionately heavy tails. The authors of this study focus on understanding the underlying process that yields such "J-shaped" or "extreme" distributions. They propose a novel theoretical mechanism behind the emergence of J-shaped distributions: differential attrition, or the idea that potential reviewers with moderate experiences are more likely to leave the pool of active reviewers than potential reviewers with extreme experiences. The authors present an analytical model that integrates this mechanism with two extant mechanisms: differential utility and base rates. They show that although all three mechanisms can give rise to extreme distributions, only the utility-based and attrition-based mechanisms can explain the authors' empirical observation from a large-scale field experiment that an unincentivized solicitation email from an online travel platform reduces review extremity. Subsequent analyses provide clear empirical evidence for the existence of both differential attrition and differential utility. [ABSTRACT FROM AUTHOR]

We study the effect of peer influence on products that exhibit positive network externalities to nonadopters, i.e., products that benefit adopters' friends even if they do not adopt. In contrast to products that exhibit positive network externalities upon adoption, this structure of incentives likely results in negative peer influence: the more friends that adopt the product, the smaller the incentive to adopt. We measure this effect empirically by using observational data from a large mobile carrier serving 5.7 million users. We estimate the effect of peer influence across five different products of this type. A naive approach to do this results in a positive estimate for peer influence due to unobserved homophily. We follow two approaches to address this issue. First, we suggest using the number of friends that end up adopting a product as a proxy for unobserved user-fixed effects. Second, we control for homophily by applying a shuffle test, i.e., we compare the effect of peer influence from the original data with the effect obtained from comparable randomly generated data without peer influence. We obtain negative estimates from both approaches, which adds robustness to our findings. Finally, we show that even for these products, the effect of peer influence associated with the first friends that adopt the product is positive because they still convey useful information that reduces uncertainty. The negative effect of peer influence arises only for subsequent friends that adopt the product. While these friends are unlikely to convey new information about the product, they decrease the economic incentive to adopt, resulting in a negative aggregate effect of peer influence. [ABSTRACT FROM AUTHOR]