The interaction effects of online reviews and free samples on consumers’ downloads: An empirical analysis

Li, Shengli; Li, Fan

doi:10.1016/j.ipm.2019.102071

Cited by 14 publications

(8 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Google Play Store provides rating and review features those users can share their app user experience. This feature is probably the fastest way for a potential start-up app user to look for references and recommendations regarding performance, reliability, economy, or security [23], [24].…”

Section: Start-up Mobile Apps Downloadmentioning

confidence: 99%

See 1 more Smart Citation

Influences of Social Learning on Customer’s Intention to Download an Application from a Start-up Company

Justitia¹,

Ambarrina²,

Raharjana³

et al. 2022

TEM Journal

View full text Add to dashboard Cite

We examine the power of social learning on customers' intention to download an application (app) from a start-up company, usually a small business. This method is often referred to as social learning. In this case, the intention to download is influenced by the information learned from online groups and forums, product reviews, as well as wordof-mouth. In this study, the model is approached to examine customer behavior involving three social learning platforms; and how this can influence the intention to download an app from start-up companies.

show abstract

Section: Start-up Mobile Apps Downloadmentioning

confidence: 99%

“…Developers also benefit from no advanced payment when testing an app [57]. If an app is free but offers paid services, some services can be offered for free, and the rest is paid [59]; or make the service free for a while [23]. This will increase the number of downloads.…”

Section: Research Limitations and Future Workmentioning

confidence: 99%

Influences of Social Learning on Customer’s Intention to Download an Application from a Start-up Company

Justitia¹,

Ambarrina²,

Raharjana³

et al. 2022

TEM Journal

View full text Add to dashboard Cite

show abstract

“…On the internet, various types of UGC are generated, and customer reviews are considered to be the most influential UGC, which establishes a communication channel among different customers, as well as between service providers and customers (Casaló et al, 2015). For customers, online customer reviews are revealed to be an essential information source in decision-making (Shengli and Fan, 2019), a statistic indicates that almost 95% of travelers read online reviews before purchasing a travel product (Ady and Quadri-Felitti, 2015). For service providers, customer reviews reflect customers' needs and expectations, enabling novel approaches to increase customer satisfaction (Phillips et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Exploring Sources of Satisfaction and Dissatisfaction in Airbnb Accommodation Using Unsupervised and Supervised Topic Modeling

Ding

Choo

et al. 2021

Front. Psychol.

View full text Add to dashboard Cite

This study aims to examine key attributes affecting Airbnb users' satisfaction and dissatisfaction through the analysis of online reviews. A corpus that comprises 59,766 Airbnb reviews form 27,980 listings located in 12 different cities is analyzed by using both Latent Dirichlet Allocation (LDA) and supervised LDA (sLDA) approach. Unlike previous LDA based Airbnb studies, this study examines positive and negative Airbnb reviews separately, and results reveal the heterogeneity of satisfaction and dissatisfaction attributes in Airbnb accommodation. In particular, the emergence of the topic “guest conflicts” in this study leads to a new direction in future sharing economy accommodation research, which is to study the interactions of different guests in a highly shared environment. The results of topic distribution analysis show that in different types of Airbnb properties, Airbnb users attach different importance to the same service attributes. The topic correlation analysis reveals that home like experience and help from the host are associated with Airbnb users' revisit intention. We determine attributes that have the strongest predictive power to Airbnb users' satisfaction and dissatisfaction through the sLDA analysis, which provides valuable managerial insights into priority setting when developing strategies to increase Airbnb users' satisfaction. Methodologically, this study contributes by illustrating how to employ novel approaches to transform social media data into useful knowledge about customer satisfaction, and the findings can provide valuable managerial implications for Airbnb practitioners.

show abstract

“…Preferences, prices and other criteria are still very relevant; however, the weight of the opinion of other users is certainly growing [1][2][3]. Some studies show that a large majority of people reads public reviews on the Web before buying any object [4,5]. Reviews are very important to know the quality of the products and services offered by a company; for example, a restaurant may advertise that it offers a free WiFi service, but the reviews may warn us that the connection quality is very low.…”

Section: Introductionmentioning

confidence: 99%

“…output : A list of pairs L = {(α 1 , p 1 ), ..., (α l , p l )}, where α k is an aspect and p k its corresponding polarity. 1 L ←− Empty; 2 Split rev to a set of normalised sentences and store them in Sents; 3 for x ∈ Sents do4 LetÂ be the set of aspects extracted from x using Equation3;Let p be the polarity obtained with Equation (4);7Add the pair (α, p) to L;…”

mentioning

confidence: 99%

Introducing Sentiment Analysis of Textual Reviews in a Multi-Criteria Decision Aid System

et al. 2020

View full text Add to dashboard Cite

Nowadays, most decision processes rely not only on the preferences of the decision maker but also on the public opinions about the possible alternatives. The user preferences have been heavily taken into account in the multi-criteria decision making field. On the other hand, sentiment analysis is the field of natural language processing devoted to the development of systems that are capable of analysing reviews to obtain their polarity. However, there have not been many works up to now that integrate the results of this process with the analysis of the alternatives in a decision support system. SentiRank is a novel system that takes into account both the preferences of the decision maker and the public online reviews about the alternatives to be ranked. A new mechanism to integrate both aspects into the ranking process is proposed in this paper. The sentiments of the reviews with respect to different aspects are added to the decision support system as a set of additional criteria, and the ELECTRE methodology is used to rank the alternatives. The system has been implemented and tested with a restaurant data set. The experimental results confirm the appeal of adding the sentiment information from the reviews to the ranking process.

show abstract

The interaction effects of online reviews and free samples on consumers’ downloads: An empirical analysis

Cited by 14 publications

References 36 publications

Influences of Social Learning on Customer’s Intention to Download an Application from a Start-up Company

Influences of Social Learning on Customer’s Intention to Download an Application from a Start-up Company

Exploring Sources of Satisfaction and Dissatisfaction in Airbnb Accommodation Using Unsupervised and Supervised Topic Modeling

Introducing Sentiment Analysis of Textual Reviews in a Multi-Criteria Decision Aid System

Contact Info

Product

Resources

About