Impact of Online Product Reviews on Purchasing Decisions

Constantinides, Efthymios; Holleschovsky, Nina Isabel

doi:10.5220/0005861002710278

Cited by 40 publications

(32 citation statements)

References 33 publications

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“…Although implementing manual content analysis can be tedious and time consuming for the large amounts of text to be read, it proved to be successful at gaining better interpretation of Twitter content (He et al, 2017;He et al, 2013;Vidal et al, 2015). As an automatic alternative, text analysis based on machine learning algorithms have been used to extract meaningful information from the textual data, recording themes already established or commonly studied (Constantinides & Holleschovsky, 2016;Sengupta & Ghosh, 2020;van Zoonen & van der Meer, 2016). However, for the correct performance of these models, machine learning algorithms usually require a large external source of coded dataset to analyse the text units (Vidal, Ares, & Jaeger, 2018).…”

Section: Using Twitter As a Source For Gathering Consumers' Opinionmentioning

confidence: 99%

Co-occurrence networks of Twitter content after manual or automatic processing. A case-study on “gluten-free”

Puerta

Laguna

Vidal

et al. 2020

Food Quality and Preference

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Gathering information from social networks such as Twitter has emerged to obtain spontaneous and direct opinions of users about a topic. This study focuses on using co-occurrence networks to analyse Twitter information. The objectives were to study the impact of text pre-treatment (codification based in qualitative analysis or just pre-cleaning) and to apply co-occurrence networks for analysing what is said on Twitter about specific topics like "gluten-free". As such, 16,386 tweets in Spanish containing terms "sin-gluten" and "gluten-free" were collected. A subset of 3,000 tweets was used to make co-occurrence networks two ways: i) from the manually coded text and ii) from pre-cleaned text. Results indicate that the co-occurrence network from pre-cleaned text provides meaningful information showing structure and relevance for terms like the network from coded text. The whole set of tweets was used to explore Twitter information about gluten-free, showing users share information about products, occasions, social situations, and places but also to product characteristics, sensations, and diet or health issues related to the products. Five product categories, critical for the lack of gluten (bread, cake, cookie, beer, and pizza) occupied most tweets, and according to the related terms were intended to recommend how to get (buying or cooking) these gluten-free products and to exhibit what (how, when, and where) they prepare and eat. These aspects were different among products and separated co-occurrence networks allowed better identification.

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Section: Using Twitter As a Source For Gathering Consumers' Opinionmentioning

confidence: 99%

Co-occurrence networks of Twitter content after manual or automatic processing. A case-study on “gluten-free”

Puerta

Laguna

Vidal

et al. 2020

Food Quality and Preference

View full text Add to dashboard Cite

show abstract

“…Our beliefs and perceptions of reality and the choices we make are conditioned upon how others see and evaluate the world (Moro et al 2018b). For this reason, when we need to make a decision we often seek out the opinions of others (Constantinides and Holleschovsky 2016).…”

Section: Mining Online Reviewsmentioning

confidence: 99%

Unfolding the characteristics of incentivized online reviews

Costa

Guerreiro

Moro

et al. 2019

Journal of Retailing and Consumer Services

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“…Este cenário impõe em um grande desafio para negócios, pois os usuários não apenas criam e compartilham conteúdo pessoal em seus perfis, mas também, recomendações, opiniões, reclamações e impressões sobre produto e serviço (Alt & Reinhold, 2012). eWoM é visto como um forte determinante na decisão de compra, haja vista que cerca de dois terços dos consumidores verificam as avaliações de produto, serviços e marcas antes de decidirem adquiri-los (Ahmad & Laroche, 2017;Constantinides & Holleschovsky, 2016). A análise de dados relacionados ao eWoM tem o potencial de auxiliar na tomada de decisões por gestores, gerando respostas e melhorias significativas a partir da identificação de necessidades e problemas a serem resolvidos (Gavilanes, Flatten, & Brettel, 2018;Einwiller & Steilen, 2015).…”

Section: Introductionunclassified

Análise do setor de telecomunicação brasileiro: Uma visão sobre Reclamações

Sousa

Guimarães

Viana³

et al. 2020

risti

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Resumo: Mídias digitais estão cada vez mais presentes no cotidiano do ser humano. Este fato contribui para que o volume de conteúdo gerado por usuário aumente consideravelmente. De um ponto de vista prático, as análises desses dados requerem diferentes perspectivas e métodos para se obter resultados satisfatórios. Essas análises podem subsidiar a tomada de decisão por gestores por meio da identificação de necessidades e problemas, guiando o processo de melhoria continuada de produtos e serviços. Diante disso, este trabalho propõe uma análise de reclamações postadas em uma plataforma online de reclamações, a fim de identificar pontos que orientem a tomada de decisões das empresas e, consequentemente, melhorar o relacionamento com clientes. Os resultados obtidos permitem a identificação de uma cadeia de problemas relacionados. A principal contribuição deste estudo está na provisão de uma abordagem que auxilia no planejamento estratégico de corporações, levando em consideração situações reportadas pelos consumidores.

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Impact of Online Product Reviews on Purchasing Decisions

Cited by 40 publications

References 33 publications

Co-occurrence networks of Twitter content after manual or automatic processing. A case-study on “gluten-free”

Co-occurrence networks of Twitter content after manual or automatic processing. A case-study on “gluten-free”

Unfolding the characteristics of incentivized online reviews

Análise do setor de telecomunicação brasileiro: Uma visão sobre Reclamações

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