Product Adoption
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Author(s):  
Zhu Zhang ◽  
Xuan Wei ◽  
Xiaolong Zheng ◽  
Qiudan Li ◽  
Daniel Dajun Zeng

Detecting product adoption intentions on social media could yield significant value in a wide range of applications, such as personalized recommendations and targeted marketing. In the literature, no study has explored the detection of product adoption intentions on social media, and only a few relevant studies have focused on purchase intention detection for products in one or several categories. Focusing on a product category rather than a specific product is too coarse-grained for precise advertising. Additionally, existing studies primarily focus on using one type of text representation in target social media posts, ignoring the major yet unexplored potential of fusing different text representations. In this paper, we first formulate the problem of product adoption intention mining and demonstrate the necessity of studying this problem and its practical value. To detect a product adoption intention for an individual product, we propose a novel and general multiview deep learning model that simultaneously taps into the capability of multiview learning in leveraging different representations and deep learning in learning latent data representations using a flexible nonlinear transformation. Specifically, the proposed model leverages three different text representations from a multiview perspective and takes advantage of local and long-term word relations by integrating convolutional neural network (CNN) and long short-term memory (LSTM) modules. Extensive experiments on three Twitter datasets demonstrate the effectiveness of the proposed multiview deep learning model compared with the existing benchmark methods. This study also significantly contributes research insights to the literature about intention mining and provides business value to relevant stakeholders such as product providers.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Giada Mainolfi ◽  
Donata Tania Vergura

PurposeThe study aims to contribute to the knowledge on the role of the fashion bloggers in the product adoption process in both advanced and emerging markets. Specifically, the study investigates the impact of credibility, engagement and homophily on intentions to buy fashion products recommended by the blogger.Design/methodology/approachThe empirical research builds on an online survey with a sample of 402 consumers (189 Italian and 213 Taiwanese). The proposed model was tested through structural equation modeling.FindingsResults showed that homophily and the fashion blogger credibility positively influenced the engagement within the blog. Moreover, perceived similarity with the other blog's followers (homophily) and a higher engagement with the blog both translated in a stronger intention to buy the sponsored products and to spread a positive word-of-mouth about the fashion blogger.Practical implicationsThe study has practical implications since it identifies strategic suggestions for both companies that create partnerships with famous fashion bloggers and bloggers who have turned their diary-style websites into a business.Originality/valueThe study contributes to a better understanding of the influence exerted by blog engagement on intentions to follow blogger's recommendations. The study also examines credibility and homophily as antecedents of engagement, which have not been extensively researched in the past with respect to blogs.


2021 ◽  
Vol 13 (9) ◽  
pp. 5084
Author(s):  
Lan-Lan Wan ◽  
Hong-Youl Ha

Marketing literature emphasizes the importance of green product adoption for environmental sustainability. However, consumers’ evaluations of the key factors (for adopting green products) differ in critical ways. Drawing on a consumer–marketing interface, this study uses a binary logit model to investigate how consumers adopt two different types of products (e.g., glass and electronic). The results show that the impacts of the twelve factors behind consumer adoption of green products vary widely between glass and electronic products. Specifically, the analysis identifies four factors (eco-labeling, peer groups, cultural values, and environmental awareness) that have no influence on consumer adoption intentions. It also shows that males are more likely to have positive adoption intentions than females for both glass and electronic products. The authors conclude this paper by discussing the implications of these important findings for research and practice.


2021 ◽  
Vol 3 (1) ◽  
pp. 60-68
Author(s):  
Sivaganesan D

The users largely contributing towards product adoption or information utilization in social networks are identified by the process of influence maximization. The exponential growth in social networks imposes several challenges in the analyses of these networks. Important has been given to modeling structural properties while the relationship between users and their social behavior has being ignored in the existing literature. With respect to the social behavior, the influence maximization task has been parallelized in this paper. In order to maximize the influence in social networks, an interest based algorithm with parallel social action has been proposed. This is algorithm enables identifying influential users in social network. The interactive behavior of the user is weighted dynamically as social actions along with the interests of the users. These two semantic metrics are used in the proposed algorithm. An optimal influential nodes set is computed by implementing the machines with CPU architecture with perfect parallelism through community structure. This helps in reducing the execution time and overcoming the real-word social network size challenges. When compared to the existing schemes, the proposed algorithm offers improved efficiency in the calculation speed on real world networks.


Author(s):  
Ted G. Lewis ◽  
Waleed I. Al Mannai

This article explores the ongoing COVID-19 pandemic, asking how long it might last. Focusing on Bahrain, which has a finite population of 1.7M, it aimed to predict the size and duration of the pandemic, which is key information for administering public health policy. We compare the predictions made by numerical solutions of variations of the Kermack-McKendrick SIR epidemic model and Tsallis-Tirnakli model with the curve-fitting solution of the Bass model of product adoption. The results reiterate the complex and difficult nature of estimating parameters, and how this can lead to initial predictions that are far from reality. The Tsallis-Tirnakli and Bass models yield more realistic results using data-driven approaches but greatly differ in their predictions. The study discusses possible sources for predictive inaccuracies, as identified during our predictions for Bahrain, the United States, and the world. We conclude that additional factors such as variations in social network structure, public health policy, and differences in population and population density are major sources of inaccuracies in estimating size and duration.


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