2018
DOI: 10.1155/2018/6561417
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Complexity in the Acceptance of Sustainable Search Engines on the Internet: An Analysis of Unobserved Heterogeneity with FIMIX‐PLS

Abstract: This paper analyses the complexity of user behaviour when facing the challenge of using sustainable applications, such as Internet search engines. This paper analyses an acceptance model using extended TAM (Technology Acceptance Model) with Trust as an added external variable. It was suggested that Trust indirectly influences the final Intention to Use with the perceptions of Utility and Ease of Use. To test the proposed model, a survey was carried out with users from different geographical areas of Spain (n=4… Show more

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Cited by 36 publications
(35 citation statements)
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“…Likewise, trying to boost sales of products such as computers and accessories does not generate enthusiasm among users and is not perceived as an effective tactic by customers [65][66][67]. This fact must be carefully considered by company executives willing to participate in temporary events based on offers and discounts published on the Internet [68][69][70].…”
Section: Discussionmentioning
confidence: 99%
“…Likewise, trying to boost sales of products such as computers and accessories does not generate enthusiasm among users and is not perceived as an effective tactic by customers [65][66][67]. This fact must be carefully considered by company executives willing to participate in temporary events based on offers and discounts published on the Internet [68][69][70].…”
Section: Discussionmentioning
confidence: 99%
“…Several studies have been carried out with machine learning models to analyze social networks, users' opinions, and to identify the key factors that influence different cases. Supervised methods using the classification and categorization of key factors, such as Maximum Entropy (MaxEnt) and Support Vector Machines (SVMs), have been used to perform social network analysis with machine learning using technological research methods to identify the important factors in different areas of research [14,29]. However, there are other types of approaches based on sentiment analysis with UGC, such as Naïve Bayes, Linear Regression, or Deep Learning [3].…”
Section: Sentiment Analysis With Social Network Analysismentioning
confidence: 99%
“…In Figure 7b, the resolution was increased to understand the proximity of the communities of nodes, so they appeared with a greater contraction and a smaller distance among them. In this way, we can identify which topic is more related to another, which provides a better understanding of how communities work and interact with each other [48,50,59]. Therefore, Figure 6a-d shows the size distribution; the y-axis shows the size in number of nodes in a network, while the x-axis shows the modularity class.…”
Section: Results Of Methodsmentioning
confidence: 99%
“…Finally, as indicated by Saura and Bennett [3], the results of both processes (i.e., the sentiment of each identified topic (positive, negative, and neutral) as a result of the LDA and the SA) can be analyzed with DTM techniques to obtain insights. In this case, textual analysis software or languages such as Python should be used in order to establish relationships between the most repeated words and their links between databases classified into sentiments and topics [59,60].…”
Section: Methods 2: a Three-stage Methods For Data Text Miningmentioning
confidence: 99%