Modeling and maximizing influence diffusion in social networks for viral marketing

Wang, Wenjun; Street, W. Nick

doi:10.1007/s41109-018-0062-7

Cited by 45 publications

(26 citation statements)

References 44 publications

(80 reference statements)

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“…Furthermore, in order to increase brand awareness, quality, and value, marketers need to use information that is interesting, useful, or informative in order to achieve the goal of a positive change in brand equity. Our findings are supported by the research results of Lindgreen, Dobele and Vanhamme (2013) and Street [16].…”

Section: Empirical Results Of the Lisrel Modelsupporting

confidence: 91%

“…This might be in part due to the fact that such content might be more memorable than the types of viral content. In General, our findings are supported by the research results of Eckler and Bolls [21], Lekhanya [14] and Wang and Street [16].…”

Section: H3 a -3 D : The Effect Of Viral Marketing On Consumer Purchasupporting

confidence: 89%

“…The result of the reception of such content might lead to such e-mails being considered spam by recipients. This finding supports those of Phelps, Lewis, Mobilo, Perry and Raman, [51] Huang, Su, Zhou and Liu [7] Lekhanya [14] and Wang and Street [16], who speculate that people don't necessarily open all chain e-mails. In fact, some people do not like seeing "[Fwd]" in the subject line of their e-mails.…”

Section: H1 E -1 H : the Effect Of Viral Marketing On Brand Loyaltysupporting

confidence: 84%

“…Additionally, viral marketing, like word-of-mouth, illustrates the concept of exponential growth (i.e. if a message is spread to three people initially, these three people might further spread the message to another three people that they know and so on) [7,14,15,16]. A third characteristic of viral marketing is that all messages contain some type of brand information.…”

Section: Viral Marketingmentioning

confidence: 99%

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Interrelationships between Viral Marketing and Purchase Intention via Customer-Based Brand Equity

Liu¹,

Yu-ning²

2019

JBMS

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The advent of the Internet has presented several challenges as well as opportunities for marketers. Marketers have found more innovative methods of communicating effectively with their target market, and viral marketing is one of these methods. This study tries to explore interrelationships between viral marketing and purchase intention via customer-based brand equity. The empirical results reveal that viral marketing is made up of interesting, useful and informative subjects; message interaction; message reception; and offensive content, in which each had differing effects on customer-based brand equity and purchase intention. Additionally, we also find that the interaction with viral marketing messages is extremely important in order to positively influence either brand equity or purchase intentions, because mere message reception have detrimental effects on brand equity and purchase intention. Furthermore, by including brand equity as a mediator, the negative effect of message reception on consumer purchase intentions can be transformed into a positive effect.

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Section: Empirical Results Of the Lisrel Modelsupporting

confidence: 91%

Section: H3 a -3 D : The Effect Of Viral Marketing On Consumer Purchasupporting

confidence: 89%

Section: H1 E -1 H : the Effect Of Viral Marketing On Brand Loyaltysupporting

confidence: 84%

Section: Viral Marketingmentioning

confidence: 99%

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Interrelationships between Viral Marketing and Purchase Intention via Customer-Based Brand Equity

Liu¹,

Yu-ning²

2019

JBMS

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“…In this context, that food delivery apps are a modern, convenient service that might interest others. Indeed, this pattern replicates viral marketing processes, that is, individuals who receive valuable information share it with others in their networks [126].…”

Section: Theoretical Implicationsmentioning

confidence: 71%

Mobile Apps Use and WOM in the Food Delivery Sector: The Role of Planned Behavior, Perceived Security and Customer Lifestyle Compatibility

2020

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This research examines the phenomenon of food delivery services from the mobile app user’s perspective and how consumers’ lifestyles are changing because of the convenience provided by the apps. By means of an online survey targeted at US food delivery app customers, our study analyzes the main motivations that lead them to use and recommend these technology-based services. The results of the study revealed that some of the theory of planned behavior model variables (i.e., attitude toward the behavior, subjective norms), influence customer use and word-of-mouth (WOM) intentions. Security influences intention to spread WOM, whereas customer lifestyle compatibility influences intention to use the food delivery apps. A post hoc analysis revealed that perceived control is only important for older customers, who need to perceive that they control the apps before they will recommend them to other customers. The findings of the study are discussed and contrasted with previous research in the field. The managerial implications derived from the findings provide practical guidance for food delivery app companies. Further research avenues are suggested to encourage scholars to continue investigating the challenge of the diffusion of mobile apps in the food delivery and related sectors.

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STAR: Spread of innovations on graph structures with the Susceptible‐Tattler‐Adopter‐Removed model

Parviero,

Hellton,

Canright

et al. 2024

Stat

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Adoptions of a new innovation such as a product, service or idea are typically driven both by peer‐to‐peer social interactions and by external influence. Social graphs are usually used to efficiently model the peer‐to‐peer interactions, where new adopters influence their peers to also adopt the innovation. However, the influence to adopt may also spread through individuals close to the adopters, known as tattlers, who only share information regarding the innovation. We extend an inhomogeneous Poisson process model accounting for both external and peer‐to‐peer influence to include an optional tattling stage, and we term the extension the Susceptible‐Tattler‐Adopter‐Removed (STAR) model. In an extensive simulation study, the proposed model is shown to be stable and identifiable and to accurately identify tattling when present. Further, using simulations, we show that both inference and prediction of the STAR model are quite robust against missing edges in the social graph, a common situation in real‐world data. Simulations and theoretical considerations demonstrate that, when edges are missing, the STAR model is able to accurately estimate the shares attributed to the external and internal sources of influence. Furthermore, the STAR model may be used to improve the inference of the external and viral parameters and subsequent predictions even when tattling is not part of the real data‐generating mechanism.

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Modeling and maximizing influence diffusion in social networks for viral marketing

Cited by 45 publications

References 44 publications

Interrelationships between Viral Marketing and Purchase Intention via Customer-Based Brand Equity

Interrelationships between Viral Marketing and Purchase Intention via Customer-Based Brand Equity

Mobile Apps Use and WOM in the Food Delivery Sector: The Role of Planned Behavior, Perceived Security and Customer Lifestyle Compatibility

STAR: Spread of innovations on graph structures with the Susceptible‐Tattler‐Adopter‐Removed model

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