Negative Effects of Incentivised Viral Campaigns for Activity in Social Networks

Viral campaigns are crucial methods for word-of-mouth marketing in social communities. The goal of these campaigns is to encourage people for activity. The problem of incentivised and non-incentivised campaigns is studied in the paper. Based on the data collected within the real social networking site both approaches were compared. The experimental results revealed that a highly motivated campaign not necessarily provides better results due to overlapping effect. Additional studies have shown that the behaviour of individual community members in the campaign based on their service profile can be predicted but the classification accuracy may be limited.Comment: In proceedings of the 2nd International Conference on Social Computing and its Applications, SCA 201

Half a billion simulations: evolutionary algorithms and distributed computing for calibrating the SimpopLocal geographical model

Multi-agent geographical models integrate very large numbers of spatial interactions. In order to validate those models large amount of computing is necessary for their simulation and calibration. Here a new data processing chain including an automated calibration procedure is experimented on a computational grid using evolutionary algorithms. This is applied for the first time to a geographical model designed to simulate the evolution of an early urban settlement system. The method enables us to reduce the computing time and provides robust results. Using this method, we identify several parameter settings that minimise three objective functions that quantify how closely the model results match a reference pattern. As the values of each parameter in different settings are very close, this estimation considerably reduces the initial possible domain of variation of the parameters. The model is thus a useful tool for further multiple applications on empirical historical situations

Search for the optimal strategy to spread a viral video: An agent-based model optimized with genetic algorithms

Agent-based computational papers on viral marketing have been so far focused on the study of the word-of-mouth knowledge diffusion that merges the decisions to adopt a product and to share information about it. This approach is not suitable for the analysis of the viral video sharing because it is shared with no regard whether the sender has adopted the advertised product or not. This paper presents a more realistic model of viral video diffusion in which every agent that viewed the video shares it once with a random subset of her neighbors. The optimal seeding strategy is then searched with genetic algorithms. The seeding strategy found by the genetic algorithm includes into the initial seed the agents with most connections and lowest clustering ratios; some agents are also selected randomly. However, this complex seeding strategy does not perform significantly better than a simple strategy of selecting agents with many connections.Články popisující multiagentové simulační modely virálního marketingu se dosud zaměřovaly na studium "word-of-mouth" šíření znalosti o produktu, které spojuje rozhodnutí koupit produkt a šířit o něm informace. Tento přístup není vhodný pro analýzu šíření virálního videa, protože to je šířeno bez ohledu na to, zda jeho šiřitel zakoupil propagovaný produkt, nebo ne. Příspěvek prezentuje realističtější model šíření virálního videa, ve kterém každý agent, který video shlédl, jej právě jednou rozešle náhodné podmnožině svých sousedů. Optimální strategie volby agentů, kterým marketer sám na začátku pošle video, je hledána pomocí genetických algoritmů. Optimální strategie nalezená pomocí genetického algoritmu na počátku oslovuje agenty, kteří mají nejvíce spojení a nejnižší clustering ratios; někteří agenti jsou také vybíráni náhodně. Nicméně, tato komplexní strategie nepodává výrazně lepší výsledky než jednoduchá strategie volby agentů s mnoha vazbami.Agent-based computational papers on viral marketing have been so far focused on the study of the word-of-mouth knowledge diffusion that merges the decisions to adopt a product and to share information about it. This approach is not suitable for the analysis of the viral video sharing because it is shared with no regard whether the sender has adopted the advertised product or not. This paper presents a more realistic model of viral video diffusion in which every agent that viewed the video shares it once with a random subset of her neighbors. The optimal seeding strategy is then searched with genetic algorithms. The seeding strategy found by the genetic algorithm includes into the initial seed the agents with most connections and lowest clustering ratios; some agents are also selected randomly. However, this complex seeding strategy does not perform significantly better than a simple strategy of selecting agents with many connections

Choice of optimization routine for multi-agent models: A case of viral video marketing campaign

Very few agent-base computational models are optimized because the usually used optimization routine, the genetic algorithm, is extremely time-consuming. This paper explores how much precision is lost if a simpler optimization routine, mutational hill climber, is used instead. It shows on the case of a viral-video marketing model that even though the standard genetic algorithm is slightly more precise, the mutation hill climbing could be used as an approximate optimization routine for robustness check and scenario analysis.Jen málo multiagentových modelů je optimalizováno, protože obvykle užívaná optimalizační rutina, genetické algoritmy, jsou extrémně výpočetně náročné. Tento článek zkoumá, jak velká část přesnosti se ztratí, pokud je použita jednodušší optimalizační rutina, mutational hill climber. Článek ukazuje na příkladu virální video-marketingové kampaně, že ačkoli je standardní genetický algoritmus poněkud přesnější, mutation hill climbing může být použit jako přibližná optimalizační rutina pro ověření robustnosti a analýzu scénářů.Very few agent-base computational models are optimized because the usually used optimization routine, the genetic algorithm, is extremely time-consuming. This paper explores how much precision is lost if a simpler optimization routine, mutational hill climber, is used instead. It shows on the case of a viral-video marketing model that even though the standard genetic algorithm is slightly more precise, the mutation hill climbing could be used as an approximate optimization routine for robustness check and scenario analysis

The Long-term Diffusion of Digital Platforms — An Agent-based Model

In recent years, many industries have experienced the rise of digital platforms (e.g., eBay, Uber, or Takeaway.com). A common characteristic of these concepts is that they focus on fragmented markets populated by many small firms, which often show a high fluctuation. However, established diffusion models based on Bass (1969) do not account for fluctuation in the market potential, although the exit of adopters and the entry of new firms could change the diffusion curve significantly. Thus, we propose an extension of the Bass Model to account for the exit and entry of (potential) adopters and empirically test this framework in a real-world setting. Using two decades of adopter data of leading digital platforms and information on the complete market potential, we employ agent-based models to analyze the effects of fluctuation on the platform diffusion. Initial results confirm the existence of high fluctuation and indicate relevant impacts on the diffusion curve

The Impact Of Viral Marketing On Corporate Brand Reputation

This paper reports on the impact of viral marketing on corporate brand reputation. The study aimed to analyse and evaluate the use of viral marketing and the impact it has on the reputation of corporate branding of South African companies. The study was conducted in four South African provinces. The sample consisted of 75 companies, selected using a stratified sampling method, with respondents completing a five-point Likert scale questionnaire with the assistance of an interviewer. The results revealed that the majority of respondents were either neutral or disagreed that people make positive comments about their companies via viral marketing. The paper will benefit company managers, marketing managers, company owners, and all affiliated stakeholders in emphasizing a new way to consider future viral marketing strategies, understanding its impact on corporate brand reputation, and how to manage negative comments pertaining to corporate brand reputation. Most work on viral marketing has concentrated on viral marketing campaigns, with little emphasis on the impact of viral marketing on corporate brand reputation. The findings are limited by the study’s exploratory, quantitative nature and small sample. Generalizing should be done with care and further research with larger samples and consideration of other provinces is therefore recommended

Viewer Behavior On Social Media: Viral Marketing of A Movie Trailer In Indonesia

A trailer is a brief description of a film and provides a 1 to 3 minute cinematic experience that displays images from the film to influence consumer behavior. This research was conducted to propose a conceptual model regarding affective, cognitive, and environmental responses to viral marketing, which are moderated by audience behavior, for the movie trailer of “Spiderman: Far from Home.” The film was released in July 2019 by Marvel Cinematic Universe (MCU). This study adopted the wheel of consumer analysis to bridge the research gap. An online survey was forwarded to 200 respondents using structured questionnaires through social media sites, such as Line, WhatsApp, Facebook, and e-mail. The data were then analyzed using structural equation modeling (SEM). The results showed that the audience’s affective, cognitive, and environmental responses significantly influenced viral marketing. The results further indicated that the audience’s behavior was not a moderating variable, as the significance level was less than 0.05. The results can contribute to determining social media marketing strategies for promoting film trailers that are beneficial for companies, especially in Indonesia. Therefore, the companies can grow and become more competitive in the film industry. Although this study discusses viral marketing in the film industry, the results can also contribute to other industries, in order to increase the popularity of their products

The Role of Viral Marketing in Social Media on Brand Recognition and Preference

Viral marketing is one of the most effective and imperative marketing strategies. The prominence of digital technology and social media has elevated the importance of viral marketing campaigns by increasing their cost efficiency and enabling them to reach targeted audiences rapidly. This study aimed to examine the influence of viral marketing strategies on brand recognition and brand preference by developing a framework for the effectiveness of viral marketing (7I’s) in social media contexts and testing the associations among the 7I’s, brand recognition and brand preference. A quantitative research method with a structured questionnaire as the research tool was employed to collect data from a total of 286 respondents in Thailand. Structural equation modelling (SEM) was utilized to test the proposed hypotheses. The results showed that effective viral marketing relates positively to brand recognition (b = 0.440) and preference (b = 0.298). The mediation analysis also revealed that brand recognition partially mediates the relationship between effective viral marketing and brand preference. In terms of the moderating effects, the results indicated a stronger influence for effective viral marketing on brand preference among younger respondents (b = 0.336) than among older respondents (b = 0.278). This research makes a significant contribution to the existing literature by validating a theory-driven framework based on the novel concept of the 7I’s and its potential effect on customers’ brand perceptions. Doi: 10.28991/esj-2021-01315 Full Text: PD

A framework for evaluating viral marketing effectiveness using social media

The dramatic employment of digital trend technologies, Internet and social media has fundamental touch on business and opened up opportunities in marketing field. Word-of-mouth communication can be helpful in social media for marketing and it is called as viral marketing. However, there are many online retailers, though managed to retain product or product reputation but unsuccessful to grow their business because do not know or do not understand viral marketing techniques. Therefore, this study had been discovered the techniques used in viral marketing on social media. In order to use the viral marketing techniques efficiently, the discovered techniques were evaluated. To evaluate the techniques, a framework to evaluate the techniques was designed and named as VMT Evaluation Framework. Viral marketing techniques had been applied by online retailers (SME) on three different sites of Facebook which are FB Profile, FB Page and FB Group. The result of using the viral marketing techniques on Facebook was based on engagement rate. The engagement rate showed the most effective VMT is Be Fun/Interesting for FB Profile, Web-linked Viral for FB Page, and Draw Motivation is the only one that had been used by the retailers for FB Group. At the end, the result performance of each viral marketing techniques were ranked out in the proposed enhancement model of digital marketing. Hence, the online retailers that using Facebook (social media) to market their product or services, they will able to focus and use more viral marketing techniques according its effectiveness