yazdan shirmohammadi; Arash Bostan manesh
Abstract
Using artificial intelligence technology, smart stores transfer a lot of customer and product information (big data) including facial recognition, smart sensors, smart shelves, automatic payment and interactive displays at high speed based on the fifth generation (5G) internet. Since the spread of the ...
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Using artificial intelligence technology, smart stores transfer a lot of customer and product information (big data) including facial recognition, smart sensors, smart shelves, automatic payment and interactive displays at high speed based on the fifth generation (5G) internet. Since the spread of the corona virus has changed the way of life and business today, that's why marketers have used new strategies based on artificial intelligence to advance. This research analyzed the hedonic factors of customers' purchases based on the Hedonic Information Systems Acceptance Model (HISAM). The sampling method of this research was simple random and its number was 404 people. The measurement tool in this research is a questionnaire. Statistical analysis was done using structural equation method and using SPSS and Amos software. To determine the causal relationship between the variables using the structural equation model method and significance levels in order to test the hypotheses, a p_value smaller than 0. 05 was considered. The results of this research showed that the perceived ease of use, perceived benefit and perceived enjoyment have a positive and significant effect on the purchase intention due to the technology readiness of customers. Also, the results of the research indicated that the mediating variable of technology readiness was effective from optimism, innovation, discomfort and insecurity, and perceived ease of use, perceived enjoyment, and perceived benefit had a positive effect on customers' purchase intentions from smart stores in the era of Corona.
Mohammad Ehsan Basiri; Shirin Habibi; Shahla Nemati
Abstract
With the spread of Covid-19 disease, quarantine, and social isolation, people are increasingly posting their opinions about the coronavirus on social networks such as Twitter. However, no study has yet been reported to analyze online opinions of individuals in order to understand their feelings about ...
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With the spread of Covid-19 disease, quarantine, and social isolation, people are increasingly posting their opinions about the coronavirus on social networks such as Twitter. However, no study has yet been reported to analyze online opinions of individuals in order to understand their feelings about the Covid-19 epidemic in Iran. This study analyzes the emotions in the opinions of the Iranian people on the social network Twitter during the Corona crisis. For this purpose, a deep neural network model is presented. As there is no labeled dataset of Covid-19 tweets, the proposed model is first trained on the Stanford University Sentiment140 dataset, which contains 1.6 million tweets, and then used to classify the two classes of emotions contained in the collected corona-related tweets in Iran. The results show that the percentage of tweets with negative emotions is significantly higher than positive tweets. Also, the change in negative emotions of people in different months is proportional to the change in patient statistics.
