Social Sciences
Interest-based e-commerce survey data. The questionnaire consists of seven sections: The first part includes seven questions about respondents' basic information and platform usage behavior, aimed at determining if the respondents are suitable for this study. The second to seventh parts pertain to the measurement of research variables, covering user interactions, interactions with celebrities, visual appeal, perceived enjoyment, purchase intention, and self-indulgence, totaling 24 questions.
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This dataset focuses on the redevelopment and psychometric evaluation of the Adversity Response Profile for Indian Higher Education Institution (ARP-IHEI) students, emphasizing its importance in understanding how individuals respond to adversity. The data were gathered from a sample of 122 second year students at school of computing, MIT Art, Design and Technology University. The psychometric properties were rigorously examined using factor analysis.
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This dataset focuses on the redevelopment and psychometric evaluation of the Adversity Response Profile for Indian Higher Education Institution (ARP-IHEI) students, emphasizing its importance in understanding how individuals respond to adversity. The data were gathered from a sample of 122 second year students at school of computing, MIT Art, Design and Technology University.
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Simulation of the matrix-scaled consensus algorithm [1,2] on the network derived from the dataset socfb-Amherst41 [3,4].
References
[1] Trinh, M. H., Vu, D. V., Tran, Q. V., and Ahn, H.-S. "Matrix-scaled consensus." In Proc. of the 61st IEEE Conference on Decision and Control (CDC), pp. 346-351. IEEE, 2022. arXiv preprint arXiv:2204.10723 (2022)
[2] Trinh, M. H., Vu, H. H., Le-Phan, N.-M., and Nguyen, Q. N. "Matrix-Scaled Consensus over Undirected Networks." arXiv preprint arXiv:2303.14751 (2023).
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Privacy perception refers to the control individuals have over the use of their data, including determining who can access, share, and utilize it without interference or intrusion. In the context of the Internet of Things (IoT), particularly in Smart Home Data Monetization (SH-DM), users’ data is aggregated and made available to potential service providers to target end users with personalized advertisements.
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The purpose of this paper is to check whether exporting favours the outcomes of innovation, whether in itself or in interaction with efforts to innovate. Our results show that there is a favourable effect upon the results of innovation from exporting per se and in combination with the following actions aimed at introducing novel approaches: spending on research and development, training of personnel, cooperation, fundamental research, basic research, and technological developments.
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This study investigates whether the ingredients listed on restaurant menus can provide insights into a city's socioeconomic status. Using data from an online food delivery system, the study compares menu items with local education rates and rental prices. A machine learning model is developed to predict menu prices based on ingredients and socioeconomic factors. An efficiency metric is proposed to cluster restaurants to address autocorrelation, comparing ingredient averages to socioeconomic indicators.
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Data were collected through the Twitter API, focusing on specific vocabulary related to wildfires, hashtags commonly used during the Tubbs Fire, and terms and hashtags related to mental health, well-being, and physical symptoms associated with smoke and wildfire exposure. We focused exclusively on the period from October 8 to October 31, aligning precisely with the duration of the Tubbs Fire. The final dataset available for analysis consists of 90,759 tweets.
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The dataset explores the linguistic characteristics of Ukrainian online community members on "Lviv. Forum Ridne City" (https://misto.ridne.net/) based on gender (female/male). It includes vectors of male and female profiles, along with 36 control vectors for 18 women's profiles and 18 men's profiles. The dataset includes 48 linguistic characteristics of gender in online communication. The linguistic features analyzed encompass a wide range, including apology, modal designs, emotions, profanity, sports and politics references, and more.
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