Unlike most other computing devices that are known to isolate their users, Smart Voice Assistant Speaker (SVAS) appears to improve the perception of social cohesion (i.e., Group Harmony) among its co-users. We hypothesize that the social cues emanated from the continued, and habituated, use of SVAS develop the “illusion of intimacy” which, in turn, ripples through the entire group, and help fulfill the need for social integration. The data collected from 218 families support this hypothesis. We argue that just as a puppy dog contributes to a happy family, so does the SVAS contributes to the social dynamics by making the users unconsciously fulfill their psychological needs and by increasing actual conversations among its users. Incidentally, the study compared the relative influence of three factors (the beta weight of “Hedonic Motivation” being the highest, followed by “Compatibility,” and then “Perceived Security”) that, as a whole, explain over 60% of the variance in Satisfaction of post-adoption SVAS use.

Hey Alexa! A Magic Spell of Social Glue?: Sharing a Smart Voice Assistant Speaker and Its Impact on Users’ Perception of Group Harmony

Voice assistants (VA) like Siri, Alexa, Google Assistant, Cortana, and Bixby are increasingly becoming popular among the general mass. They are being used by a large group of people who use English as their primary mode of communication (native speakers) as well as those who are secondary English language speakers (non-native speakers). However, from an end-user perspective very little is known about the usability, acceptability, satisfaction, and the usage pattern of the VA's between these two different group of users. The current study aims to identify if there exist any differences between these two group of users with respect to the overall usability and the satisfaction received after using the VA's. To answer the research questions a mixed methodology approach is undertaken comprising of an online questionnaire survey in conjunction with a real-world testing of the VA's. The experiment is conducted in Thailand with 275 users for the questionnaire phase and 52 users for the testing phase from 7 different countries. Siri and Alexa are used as the VA representatives since they are the two most popular ones in Thailand. Results suggest no significant differences in usability between the two user groups, however the same being significant for the satisfaction levels.

User Experience with Smart Voice Assistants: The Accent Perspective

The market for voice assistants (VAs) and other allied voice-based smart-home products is gradually emerging. The initial growth has been slower than expected; therefore, an in-depth simultaneous intention and diffusion analysis is needed for identifying the relevant factors along with finding out the target consumers. This work uses technology acceptance model as the core for analyzing the adoption intention of the VA-based system and extends it with three additional factors: compatibility, perceived complementarity and privacy concerns. The diffusion analysis for the same is done using the multivariate probit model. Certain characteristics of the VA-based systems like the network effects between the products/services and the importance of protecting personal information are considered in this work, apart from various demographic variables like age, gender, income and education levels. Two separate surveys were conducted for the purpose of data collection from 315 and 1945 participants residing in Thailand for analyzing the adoption and diffusion scenario, respectively. The results show that usefulness, ease of use, compatibility and perceived complementarity have significant positive effects on the purchase intention. In terms of diffusion of the VA market, unlike other Information Communication Technology-based products/services, the results show that the senior consumers are more likely to purchase the VAs and other allied smart-home devices within a given time frame when compared to the younger consumers. Therefore, new strategies should be developed that promote the usage of VAs by the young population for increasing the market demand.

Analyzing the adoption and diffusion of voice-enabled smart-home systems: empirical evidence from Thailand

Based on the post-acceptance model of information system continuance (PAMISC), this study investigates the influence of the early-stage users' personal traits (specifically personal innovativeness and technology anxiety) and ex-post instrumentality perceptions (specifically price value, hedonic motivation, compatibility and perceived security) on social diffusion of smart technologies measured by the intention to recommend artificial intelligence-based voice assistant systems (AIVAS) to others.,Survey data from 400 US AIVAS users were collected and analyzed with Statistical Product and Service Solutions (SPSS) 18.0 and the partial least square technique using advanced analysis of composites (ADANCO) 2.1.,AIVAS technology is presently at the early stage of market penetration (about 25% of market penetration in the USA). A survey of AIVAS technology users reveals that personal innovativeness is directly and indirectly (through confirmation and continuance) associated with a stronger intention to recommend the use of the device to others. Confirmation is associated with all four ex-post instrumentality perceptions (hedonic motivation, compatibility, price value and perceived security). Among the four, however, only hedonic motivation and compatibility are significant predictors of satisfaction, which lead to use continuance and, eventually, intention to recommend. Finally, technology anxiety is found to be indirectly (but not directly) associated with a lower intention to recommend.,This is the first study conducted on the early-stage AIVAS users that evaluates the influence of both personal traits and ex-post instrumentality perceptions on users' intention for continuance and recommendation to others.

The continuation and recommendation intention of artificial intelligence-based voice assistant systems (AIVAS): the influence of personal traits

Education field is affected by the COVID-19 pandemic which also affects how universities, schools, companies and communities function. One area that has been significantly affected is education at all levels, including both undergraduate and graduate. COVID-19 pandemic emphasis the psychological status of the students since they changed their learning environment. E-learning process focuses on electronic means of communication and online support communities, however social networking sites help students manage their emotional and social needs during pandemic period which allow them to express their opinions without controls. The paper will propose a Sentiment Analysis Model that will analyze the sentiments of students in the learning process with in their pandemic using Word2vec technique and Machine Learning techniques.The sentiment analysis model will start with the processing process on the student's sentiment and selects the features through word embedding then uses three Machine Learning classifies which are Naive Bayes, SVM and Decision Tree. Results including precision, recall and accuracy of all these classifiers are described in this paper. The paper helps understand the Egyptian student's opinion on learning process during COVID-19 pandemic.

Egyptian Student Sentiment Analysis Using Word2vec During the Coronavirus (Covid-19) Pandemic

Artificial intelligence technologies are beginning to be actively used in human life, this is facilitated by the appearance and wide dissemination of the Internet of Things (IoT). Autonomous devices are becoming smarter in their way to interact with both a human and themselves. New capacities lead to creation of various systems for integration of smart things into Social Networks of the Internet of Things. One of the relevant trends in artificial intelligence is the technology of recognizing the natural language of a human. New insights in this topic can lead to new means of natural human-machine interaction, in which the machine would learn how to understand human's language, adjusting and interacting in it. One of such tools is voice assistant, which can be integrated into many other intelligent systems. In this paper, the principles of the functioning of voice assistants are described, its main shortcomings and limitations are given. The method of creating a local voice assistant without using cloud services is described, which allows to significantly expand the applicability of such devices in the future.

Investigation and development of the intelligent voice assistant for the Internet of Things using machine learning

Introduction: Sentiment analysis is a complex problem whose solution essentially depends on the context, field of study andamount of text data. Analysis of publications shows that the authors often do not use the full range of possible data transformationsand their combinations. Only a part of the transformations is used, limiting the ways to develop high-quality classification models.Purpose: Developing and exploring a generalized approach to building a model, which consists in sequentially passing throughthe stages of exploratory data analysis, obtaining a basic solution, vectorization, preprocessing, hyperparameter optimization, andmodeling. Results: Comparative experiments conducted using a generalized approach for classical machine learning and deeplearning algorithms in order to solve the problem of sentiment analysis of short text messages in natural language processinghave demonstrated that the classification quality grows from one stage to another. For classical algorithms, such an increasein quality was insignificant, but for deep learning, it was 8% on average at each stage. Additional studies have shown that theuse of automatic machine learning which uses classical classification algorithms is comparable in quality to manual modeldevelopment; however, it takes much longer. The use of transfer learning has a small but positive effect on the classificationquality. Practical relevance: The proposed sequential approach can significantly improve the quality of models under developmentin natural language processing problems.

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Generalized approach to sentiment analysis of short text messages in natural language processing

Determining the tonality of the text is a difficult task, the solution of which essentially depends on the context, the field of study and the amount of text data. The analysis shows that the authors in their works do not jointly use the full range of possible transformations on the data and their combinations. The article explores a generalized approach, which consists in sequentially passing through the stages of intelligence analysis, obtaining a basic solution, vectorization, preprocessing, tuning hyperparameters and modeling. The experiments carried out by iterative application of these stages give a positive increase in quality for classical machine learning algorithms and a significant increase for deep learning.

Sergey Viktorovich Polyakov

Papers

Investigation and development of the intelligent voice assistant for the Internet of Things using machine learning

Generalized approach to sentiment analysis of short text messages in natural language processing

Research of heuristic approaches for determining the tonality of text messages in natural language processing problems