Modeling word-of-mouth usage

Martin, William C.; Lueg, Jason E.

doi:10.1016/j.jbusres.2011.06.004

Cited by 146 publications

(123 citation statements)

References 42 publications

Supporting

Mentioning

109

Contrasting

Unclassified

Order By: Relevance

“…Past research efforts have studied various word-of-mouth (WOM) issues from identifying antecedents of WOM ( Rossi, 2008;Mazzarol, Sweeney, & Soutar, 2007) and characteristics of WOM speakers and listeners (Lee & Youn, 2009;Martin & Lueg, 2013) to platforms that influence WOM effects (Lee & Youn, 2009;Martin & Lueg, 2013). In particular, researchers have called for more research in investigating the antecedents of WOM (Mazzarol et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Investigating the ripple effect in virtual communities: An example of Facebook Fan Pages

Hsu

Wang

Chih

et al. 2015

Computers in Human Behavior

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Investigating the ripple effect in virtual communities: An example of Facebook Fan Pages

Hsu

Wang

Chih

et al. 2015

Computers in Human Behavior

View full text Add to dashboard Cite

“…In particular, the positive and negative effects of WoM on the consumer purchase decisions seem consolidated in the literature (e.g., Maxham 2001), even if there is some debate about how and when the information individuals acquire from WoM is effectively employed (Sweeneyet al 2008). It seems also established in the literature the importance of the reliability of information sources -and thus social proximity and the strength of ties -both for the consumer's decision-making as to the spread of new products (De Matos andRossi 2008, Martin andLueg 2013). In addition, social networks are frequently considered a suitable environment for the study of WoM, in both the consumer decision process and the dynamics of social "contagion" (Doyle 2007, Rogers 2010).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…It seems evident that the task of deciding with which access provider to hire service is definitely not an easy one, despite quite common and important, and subject to elevated risk of error. Objectively measuring the direct effects of word-of-mouth in markets is a long time problem (Martin and Lueg 2013). On top of that, anecdotal evidence suggests that some particular cultural and institutional circumstances of countries/regions may strengthen the influence of WoM and the social network in the hiring decision.…”

Section: Figure 1 -Inverse Herfindahl-hirschman Index For Mobile Accementioning

confidence: 99%

Schumpeterian competition and consumer networks: understanding concentration in the internet access market

Pereira¹

2017

Blucher Engineering Proceedings

View full text Add to dashboard Cite

Markets undergoing fast-paced product evolution impose a significant informational demand on consumers. They may have to decide among relatively heterogeneous offers while lacking the knowledge required for a fully assessed choice of suppliers. In this case, one important information source is the social network wherein the consumer is immersed, by means of the word-of-mouth (WoM). Beyond the pure informational role, we propose that WoM is a possible cause for the significant market concentration observed in at least one prominent hi-tech market, the internet access service, in which the market organization in many countries does not seem entirely explained by the existing economic literature. Applying an innovative approach, based on agent-based simulation and synthetic graph generation, we propose a model that is able to explain the connections between the inter-firm competition and the WoMinfluenced decision process of consumers. The model robustly demonstrates the relevant effects of WoM on the organization of the market in most situations. The results suggest that the competitive configuration of many other real markets could also be significantly influenced by WoM.Keywords: market concentration, consumer behavior, social networks, word-of-mouth, internet access service, agent-based simulation. ResumoMercados em rápida evolução do produto impõem uma demanda informacional significativa sobre os consumidores. Eles podem ter que decidir entre ofertas relativamente heterogêneas, mesmo sem dispor dos conhecimentos necessários para uma escolha totalmente informada dos fornecedores. Neste caso, uma fonte de informação importante é a rede social em que o consumidor está imerso, por meio do "bocaa-boca" (BaB). Para além do papel puramente informativo, propomos que o BaB é uma possível causa da significativa concentração de mercado observada em pelo menos um mercado de alta tecnologia, o serviço de acesso à Internet, no qual a organização do mercado em muitos países não parece inteiramente explicada pelo literatura econômica existente. Aplicando uma abordagem inovadora, baseada na simulação baseada em agentes e na geração de grafos sintéticos, propomos um modelo capaz de explicar as conexões entre a competição entre empresas e o processo decisório dos consumidores, influenciado pelo BaB. O modelo demonstra de forma robusta os efeitos relevantes do BaB na organização do mercado na maioria das situações. Os resultados sugerem que a configuração competitiva de muitos outros mercados reais também pode ser significativamente influenciada pelo BaB.Palavras-chave: concentração de mercado, comportamento do consumidor, redes sociais, boca-a-boca, serviço de acesso à internet, simulação baseada em agentes. Área ABEIN: 1.2 Competição, preços e estruturas de mercado Classificação JEL: C63, D43, L15.

show abstract

“…For example, during their stay at the destination, before going to any restaurant, travelers seek comments and reviews from other consumers about that particular restaurant, which then decisively influences their choice (Sen & Lerman, 2007;Chu & Kamal, 2008;Lee, Park & Han, 2008;Doh & Hwang, 2009;Lee & Youn, 2009;Martin & Lueg, 2013).…”

Section: Introductionmentioning

confidence: 99%

Channels of social influence for decision making in restaurants: A case study

Romero-Charneco¹,

Casado-Molina²,

Alarcón-Urbistondo³

2018

DAMeJ

View full text Add to dashboard Cite

Consumers use the Internet to obtain information on tourism products and services. When evaluating the alternatives, they are faced with a large volume of information that makes their purchasing decision difficult. In this context, the generalized use of mobile instant messaging (MIM) has led to the implementation of chatbots in these channels, to help to plan the purchase. This research explores restaurant selection through a WhatsApp mobile instant messaging (MIM) chatbot. A study is made of the channels consulted by travellers on Web 2.0 as well as the search models and restaurant selection processes, and a case study is presented. The results allow the diagnosis of the main criteria of user behaviour in this type of conversational interface in the decision-making process related to gastronomic consumption.Keywords: User-generated content (UGC); decision-making; tourist platforms; chatbots; MIM. ResumoOs consumidores pesquisam na Internet à procura de informações sobre produtos e serviços turísticos. No momento de avaliar as alternativas disponíveis, encontram-se perante um grande volume de informação que gera dificuldades na decisão de compra. Perante este contexto, o uso generalizado de serviços de mensagens instantâneas (SMI) têm vindo a provocar a implementação de chatbots nestes canais, com o fim de ajudar no planeamento da compra. Nesta investigação, estuda-se o processo de escolha de um restaurante com recurso a um chatbot existente para o serviço de mensagens instantâneas disponibilizado pela aplicação WhatsApp. Será feito o estudo dos canais consultados pelos turistas na Web 2.0 e dos modelos e processos de pesquisa na eleição de um restaurante, assim como é apresentado um estudo de caso. Os resultados do estudo vão permitir detetar as principais variáveis comportamentais dos utilizadores deste tipo de interfaces conversacionais no momento da tomada de decisões associada ao consumo gastronómico. Palavras-chave:Conteúdo gerado pelo utilizador (CGU); tomada de decisão; plataforma de turismo; chatbots; SMI. M. Romero-Charneco • A.M. Casado-Molina •P. Alarcón-Urbistondo[55]

show abstract

Modeling word-of-mouth usage

Cited by 146 publications

References 42 publications

Investigating the ripple effect in virtual communities: An example of Facebook Fan Pages

Investigating the ripple effect in virtual communities: An example of Facebook Fan Pages

Schumpeterian competition and consumer networks: understanding concentration in the internet access market

Channels of social influence for decision making in restaurants: A case study

Contact Info

Product

Resources

About