Topic Editors

School of Management and Economics, University of Electronic Science and Technology of China, Chengdu 611731, China
Dr. Chao Wen
The James F. Dicke College of Business Administration, Ohio Northern University, Ada, OH 45810, USA
Dr. Benjamin George
Department of Information, Department of Operations, and Technology Management, John and Lillian Neff College of Business & Innovation, University of Toledo, 2801 W. Bancroft St., Toledo, OH 43606-3390, USA

Online User Behavior in the Context of Big Data

Abstract submission deadline
1 September 2024
Manuscript submission deadline
31 October 2024
Viewed by
17357

Topic Information

Dear Colleagues,

With the widespread adoption of the Internet and mobile devices, there has been an exponential increase in the amount of user behavior data that can be collected. The vast array of user data includes not only basic information such as browsing history, but also extends to user interactions such as purchasing patterns, commenting behavior, and social media sharing trends. Fortunately, with advancements in technology, we can utilize techniques such as machine learning, natural language processing, and data mining to sift through the massive amounts of data and uncover valuable insights about user behavior. Through user behavior analysis, we can gain a deep understanding of users, including their interests, needs, preferences, and behavior patterns. Armed with this knowledge, businesses can tailor their services to provide better user experiences, accurate identification of user portraits, and personalized recommendations that accurately meet user demands. For example, through text-mining analysis of linguistic data collected from streamers, we can identify the specific linguistic characteristics that are strongly correlated with sales performance in livestreaming e-commerce. This insight can be used to create tailored marketing strategies that aim to improve livestreaming performance (Liu et al., 2023). By using insights obtained through user behavior analysis, businesses can make informed decisions about critical areas of operation such as product design, marketing strategies, and more. Based on this background, this Special Issue mainly focuses on user behavior in the context of big data. Both qualitative and quantitative empirical studies are welcome. Examples of the topics of interest include but are not limited to:

  • Change and innovation management in the context of big data.
  • The intersection of big data, artificial intelligence, user behavior, and business value.
  • Analytics and challenges related to user behavior in the context of big data.
  • Marketing and management decision-making using big data analytics.
  • Analysis of user behavior in Africa and Latin America using big data techniques.
  • Mining user preferences and patterns through big data-driven user behavior.
  • Exploratory studies of user behavior analytics using big data techniques.
  • The emergence and evolution of user behavior analytics through big data.
  • Consumer psychology and decision-making in the context of human–AI interactions.
  • Livestreaming e-commerce and social e-commerce.
  • Digital transformation, digital operation, and enterprise-level big data applications.
  • Management and operation of online platforms and markets based on big data.
  • User online reviews and fake reviews identification.
  • Consumer privacy rights and protection in the context of big data analytics and AI applications.
  • Ethical considerations related to big data analytics and AI applications.
  • Generative AI security, data security and privacy, and corporate digital responsibility.

Dr. Jiaming Fang
Dr. Chao Wen
Dr. Benjamin George
Topic Editors

Keywords

  • big data
  • artificial intelligence
  • generative AI
  • machine learning
  • online user behavior
  • consumer psychology
  • online platform
  • human–AI interaction
  • livestreaming eCommerce
  • corporate digital responsibility

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Behavioral Sciences
behavsci
2.6 2.6 2011 21.5 Days CHF 2200 Submit
Businesses
businesses
- - 2021 23.8 Days CHF 1000 Submit
Journal of Theoretical and Applied Electronic Commerce Research
jtaer
5.6 9.5 2006 38.4 Days CHF 1000 Submit

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Published Papers (12 papers)

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16 pages, 320 KiB  
Article
Information Collection and Personalized Service Strategy of Monopoly under Consumer Misrepresentation
by Mingyue Zhong, Yan Cheng, Shu-e Mei and Weijun Zhong
J. Theor. Appl. Electron. Commer. Res. 2024, 19(2), 1321-1336; https://doi.org/10.3390/jtaer19020067 - 31 May 2024
Viewed by 183
Abstract
To decrease privacy risks, consumers may choose to misrepresent themselves when they are asked to offer personal information. Using a game theoretic model, this study examines the impact of consumer misrepresentation on both a monopolistic firm and consumers. The results show that consumer [...] Read more.
To decrease privacy risks, consumers may choose to misrepresent themselves when they are asked to offer personal information. Using a game theoretic model, this study examines the impact of consumer misrepresentation on both a monopolistic firm and consumers. The results show that consumer misrepresentation may benefit the firm, but hurt consumers under certain conditions. In addition, we find that when the unit cost of personalized service is low, consumer misrepresentation may encourage the firm to provide a higher personalized service level. Moreover, when consumers misrepresent themselves and the firm only covers part of the market, a greater unit value of consumer private information will reduce the firm’s profit, while a greater unit cost of personalized service will increase the firm’s profit. The analysis reported here provides important insights regarding the application of consumer information in online personalized marketing and consumer privacy protection. Full article
(This article belongs to the Topic Online User Behavior in the Context of Big Data)
22 pages, 1240 KiB  
Article
Role of Algorithm Awareness in Privacy Decision-Making Process: A Dual Calculus Lens
by Sujun Tian, Bin Zhang and Hongyang He
J. Theor. Appl. Electron. Commer. Res. 2024, 19(2), 899-920; https://doi.org/10.3390/jtaer19020047 - 20 Apr 2024
Viewed by 677
Abstract
In the context of AI, as algorithms rapidly penetrate e-commerce platforms, it is timely to investigate the role of algorithm awareness (AA) in privacy decisions because it can shape consumers’ information-disclosure behaviors. Focusing on the role of AA in the privacy decision-making process, [...] Read more.
In the context of AI, as algorithms rapidly penetrate e-commerce platforms, it is timely to investigate the role of algorithm awareness (AA) in privacy decisions because it can shape consumers’ information-disclosure behaviors. Focusing on the role of AA in the privacy decision-making process, this study investigated consumers’ personal information disclosures when using an e-commerce platform with personalized algorithms. By integrating the dual calculus model and the theory of planned behavior (TPB), we constructed a privacy decision-making model for consumers. Sample data from 581 online-shopping consumers were collected by a questionnaire survey, and SmartPLS 4.0 software was used to conduct a structural equation path analysis and a mediating effects test on the sample data. The findings suggest that AA is a potential antecedent to the privacy decision-making process through which consumers seek to evaluate privacy risks and make self-disclosure decisions. The privacy decision process goes through two interrelated trade-offs—that threat appraisals and coping appraisals weigh each other to determine the (net) perceived risk and, then, the (net) perceived risk and the perceived benefit weigh each other to decide privacy attitudes. By applying the TPB to the model, the findings further show that privacy attitudes and subjective norms jointly affect information-disclosure intention whereas perceived behavioral control has no significant impact on information-disclosure intention. The results of this study give actionable insights into how to utilize the privacy decision-making process to promote algorithm adoption and decisions regarding information disclosure, serving as a point of reference for the development of a human-centered algorithm based on AA in reference to FEAT. Full article
(This article belongs to the Topic Online User Behavior in the Context of Big Data)
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19 pages, 758 KiB  
Article
eWOM Information Richness and Online User Review Behavior: Evidence from TripAdvisor
by Xueyu Liu, Jie Lin, Xiaoyan Jiang, Tingzhen Chang and Haowen Lin
J. Theor. Appl. Electron. Commer. Res. 2024, 19(2), 880-898; https://doi.org/10.3390/jtaer19020046 - 18 Apr 2024
Cited by 1 | Viewed by 720
Abstract
The growing number of online users commenting on review platforms has fueled the development of electronic word–of–mouth (eWOM). At the same time, merchants have improved their requirements for the length and frequency of online reviews. However, few studies have examined the updating mechanism [...] Read more.
The growing number of online users commenting on review platforms has fueled the development of electronic word–of–mouth (eWOM). At the same time, merchants have improved their requirements for the length and frequency of online reviews. However, few studies have examined the updating mechanism of online reviews length and frequency from the perspective of businesses. This study explores the relationship between online commenting platform users and eWOM and examines how eWOM information richness affects online user review behavior. We used media richness theory (MRT) to quantify the information richness of eWOM content (linguistic, textual, and photographical) to build an empirical framework. For the research data, we used advanced big data analytics to retrieve and analyze TripAdvisor data on restaurant services in nine major tourist destinations, the United States, Mexico, and mainland Europe (including UK, Spain, Netherlands, etc.), over a long period of time. Based on >10 million eWOM, this study used multiple regression to examine the impact of eWOM information richness on online user review behavior, considering the moderating effect of information ambiguity. Our research results show that content information richness positively affects online user review behavior, increasing their frequency and length. Information ambiguity play a moderating role that strengthens this relationship. This supports our theoretical hypothesis. Finally, for greater applicability and reliability, we conducted a comparative study on the degree of differences in the relationship between eWOM and users based on different cultural backgrounds across countries. Full article
(This article belongs to the Topic Online User Behavior in the Context of Big Data)
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31 pages, 7111 KiB  
Article
Exploring Tourists’ Behavioral Patterns in Bali’s Top-Rated Destinations: Perception and Mobility
by Dian Puteri Ramadhani, Andry Alamsyah, Mochamad Yudha Febrianta and Lusiana Zulfa Amelia Damayanti
J. Theor. Appl. Electron. Commer. Res. 2024, 19(2), 743-773; https://doi.org/10.3390/jtaer19020040 - 1 Apr 2024
Cited by 1 | Viewed by 1251
Abstract
The tourism sector plays a crucial role in the global economy, encompassing both physical infrastructure and cultural engagement. Indonesia has a wide range of attractions and has experienced remarkable growth, with Bali as a notable example of this. With the rapid advancements in [...] Read more.
The tourism sector plays a crucial role in the global economy, encompassing both physical infrastructure and cultural engagement. Indonesia has a wide range of attractions and has experienced remarkable growth, with Bali as a notable example of this. With the rapid advancements in technology, travelers now have the freedom to explore independently, while online travel agencies (OTAs) serve as important resources. Reviews from tourists significantly impact the service quality and perception of destinations, and text mining is a valuable tool for extracting insights from unstructured review data. This research integrates multiclass text classification and a network analysis to uncover tourists’ behavioral patterns through their perceptions and movement. This study innovates beyond conventional sentiment and cognitive image analysis to the tourists’ perceptions of cognitive dimensions and explores the sentiment correlation between different cognitive dimensions. We find that destinations generally receive positive feedback, with 80.36% positive reviews, with natural attractions being the most positive aspect while infrastructure is the least positive aspect. We highlight that qualitative experiences do not always align with quantitative cost-effectiveness evaluations. Through a network analysis, we identify patterns in tourist mobility, highlighting three clusters of attractions that cater to diverse preferences. This research underscores the need for tourism destinations to strategically adapt to tourists’ varied expectations, enhancing their appeal and aligning their services with preferences to elevate destination competitiveness and increase tourist satisfaction. Full article
(This article belongs to the Topic Online User Behavior in the Context of Big Data)
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13 pages, 505 KiB  
Article
Communicating Nutritional Knowledge to the Chinese Public: Examining Predictive Factors of User Engagement on TikTok in China
by Min Zhu and ShaoPeng Che
Behav. Sci. 2024, 14(3), 201; https://doi.org/10.3390/bs14030201 - 2 Mar 2024
Viewed by 1033
Abstract
Objective: This study aims to identify content variables that theoretical research suggests should be considered as strategic approaches to facilitate science communication with the public and to assess their practical effects on user engagement metrics. Methods: Data were collected from the official Chinese [...] Read more.
Objective: This study aims to identify content variables that theoretical research suggests should be considered as strategic approaches to facilitate science communication with the public and to assess their practical effects on user engagement metrics. Methods: Data were collected from the official Chinese TikTok account (Douyin) of the Nutrition Research Institute of China National Cereals, Oils and Foodstuffs Corporation, China’s largest state-owned food processing conglomerate. Dependent variables included likes, shares, comments, subscription increases. Independent variables encompassed explanation of jargon (metaphor, personification, science visualization), communication remarks (conclusion presence, recommendation presence), and content themes. Descriptive analysis and negative binomial regression were employed, with statistical significance set at 0.05. Results: First, subscription increases were positively associated with personification (p < 0.05, 0.024) and science visualization (p < 0.01, 0.000). Second, a positive relationship existed between comments and communicator recommendations (p < 0.01, 0.000), while presenting conclusions negatively correlated with shares (p < 0.05, 0.012). Conclusions: Different strategies yielded improvements in various engagement metrics. This can provide practical guidance for communicators, emphasizing the need to balance scholarly presentation with sustaining appealing statistics. Full article
(This article belongs to the Topic Online User Behavior in the Context of Big Data)
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19 pages, 3021 KiB  
Article
Public Perception of Online P2P Lending Applications
by Sahiba Khan, Ranjit Singh, H. Kent Baker and Gomtesh Jain
J. Theor. Appl. Electron. Commer. Res. 2024, 19(1), 507-525; https://doi.org/10.3390/jtaer19010027 - 1 Mar 2024
Viewed by 1050
Abstract
This study examines significant topics and customer sentiments conveyed in reviews of P2P lending applications (apps) in India by employing topic modeling and sentiment analysis. The apps considered are LenDenClub, Faircent, i2ifunding, India Money Mart, and Lendbox. Using Latent Dirichlet Allocation, we identified [...] Read more.
This study examines significant topics and customer sentiments conveyed in reviews of P2P lending applications (apps) in India by employing topic modeling and sentiment analysis. The apps considered are LenDenClub, Faircent, i2ifunding, India Money Mart, and Lendbox. Using Latent Dirichlet Allocation, we identified and labeled 11 topics: application, document, default, login, reject, service, CIBIL, OTP, returns, interface, and withdrawal. The sentiment analysis tool VADER revealed that most users have positive attitudes toward these apps. We also compared the five apps overall and on specific topics. Overall, LenDenClub had the highest proportion of positive reviews. We also compared the prediction abilities of six machine-learning models. Logistic Regression demonstrates high accuracy with all three feature extraction techniques: bag of words, term frequency-inverse document frequency, and hashing. The study assists borrowers and lenders in choosing the most appropriate application and supports P2P lending platforms in recognizing their strengths and weaknesses. Full article
(This article belongs to the Topic Online User Behavior in the Context of Big Data)
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25 pages, 3464 KiB  
Article
A Two-Stage Nonlinear User Satisfaction Decision Model Based on Online Review Mining: Considering Non-Compensatory and Compensatory Stages
by Shugang Li, Boyi Zhu, Yuqi Zhang, Fang Liu and Zhaoxu Yu
J. Theor. Appl. Electron. Commer. Res. 2024, 19(1), 272-296; https://doi.org/10.3390/jtaer19010015 - 1 Feb 2024
Cited by 2 | Viewed by 990
Abstract
Mining user satisfaction decision stages from online reviews is helpful for understanding user preferences and conducting user-centered product improvements. Therefore, this study develops a two-stage nonlinear user satisfaction decision model (USDM). First, we use word2vec technology and lexicon-based sentiment analysis to mine the [...] Read more.
Mining user satisfaction decision stages from online reviews is helpful for understanding user preferences and conducting user-centered product improvements. Therefore, this study develops a two-stage nonlinear user satisfaction decision model (USDM). First, we use word2vec technology and lexicon-based sentiment analysis to mine the sentiment polarity of each product attribute in the reviews. Then, we develop KANO mapping rules using utility functions to classify consumer preferences based on attribute importance. Based on this, a two-stage nonlinear USDM is developed to describe post-purchase evaluation behavior. In the first non-compensatory stage, consumers determine their initial satisfaction level based on the performance of basic attributes. If the performance of these attributes is poor, it is almost impossible for users to be satisfied. In the compensatory stage, the performance of the remaining attributes collectively affects final satisfaction through participation in user utility calculation. With the use of reviews from JD.com, we develop a genetic algorithm to determine feasible solutions for the USDM and verify its validity and robustness. The USDM is proven to be effective in predicting user satisfaction compared to other classic models and machine learning algorithms. This study provides a universal pattern for user satisfaction decisions and extends the study on preference analysis. Full article
(This article belongs to the Topic Online User Behavior in the Context of Big Data)
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20 pages, 8257 KiB  
Article
A Consumer Behavior Analysis Framework toward Improving Market Performance Indicators: Saudi’s Retail Sector as a Case Study
by Monerah Alawadh and Ahmed Barnawi
J. Theor. Appl. Electron. Commer. Res. 2024, 19(1), 152-171; https://doi.org/10.3390/jtaer19010009 - 17 Jan 2024
Viewed by 1878
Abstract
Studying customer behavior and anticipating future trends is a challenging task, as customer behavior is complex and constantly evolving. To effectively anticipate future trends, businesses need to analyze large amounts of data, use sophisticated analytical techniques, and stay up-to-date with the latest research [...] Read more.
Studying customer behavior and anticipating future trends is a challenging task, as customer behavior is complex and constantly evolving. To effectively anticipate future trends, businesses need to analyze large amounts of data, use sophisticated analytical techniques, and stay up-to-date with the latest research and industry trends. In this paper, we propose a comprehensive framework to identify trends in consumer behavior using multiple layers of processing, including clustering, classification, and association rule learning. The aim is to help a major retailer in Saudi Arabia better understand customer behavior by utilizing the power of big data analysis. The proposed framework is presented as being generalized to gain insight into the generated big data and enable data-driven decision-making in other relevant domains. We developed this framework in collaboration with a large supermarket chain in Saudi Arabia, which provided us with over 1,000,000 sales transaction records belonging to around 30,000 of their loyal customers. In this study, we apply our proposed framework to those data as a case study and present our initial results of consumer clustering and association rules for each cluster. Moreover, we analyze our findings to figure out how we can further utilize intelligence to predict customer behavior in clustered groups. Full article
(This article belongs to the Topic Online User Behavior in the Context of Big Data)
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17 pages, 761 KiB  
Article
It’s Not Always about Wide and Deep Models: Click-Through Rate Prediction with a Customer Behavior-Embedding Representation
by Miguel Alves Gomes, Richard Meyes, Philipp Meisen and Tobias Meisen
J. Theor. Appl. Electron. Commer. Res. 2024, 19(1), 135-151; https://doi.org/10.3390/jtaer19010008 - 12 Jan 2024
Viewed by 1152
Abstract
Alongside natural language processing and computer vision, large learning models have found their way into e-commerce. Especially, for recommender systems and click-through rate prediction, these models have shown great predictive power. In this work, we aim to predict the probability that a customer [...] Read more.
Alongside natural language processing and computer vision, large learning models have found their way into e-commerce. Especially, for recommender systems and click-through rate prediction, these models have shown great predictive power. In this work, we aim to predict the probability that a customer will click on a given recommendation, given only its current session. Therefore, we propose a two-stage approach consisting of a customer behavior-embedding representation and a recurrent neural network. In the first stage, we train a self-supervised skip-gram embedding on customer activity data. The resulting embedding representation is used in the second stage to encode the customer sequences which are then used as input to the learning model. Our proposed approach diverges from the prevailing trend of utilizing extensive end-to-end models for click-through rate prediction. The experiments, which incorporate a real-world industrial use case and a widely used as well as openly available benchmark dataset, demonstrate that our approach outperforms the current state-of-the-art models. Our approach predicts customers’ click intention with an average F1 accuracy of 94% for the industrial use case which is one percentage point higher than the state-of-the-art baseline and an average F1 accuracy of 79% for the benchmark dataset, which outperforms the best tested state-of-the-art baseline by more than seven percentage points. The results show that, contrary to current trends in that field, large end-to-end models are not always needed. The analysis of our experiments suggests that the reason for the performance of our approach is the self-supervised pre-trained embedding of customer behavior that we use as the customer representation. Full article
(This article belongs to the Topic Online User Behavior in the Context of Big Data)
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19 pages, 1639 KiB  
Article
The Impact of Shared Information Presentation Time on Users’ Privacy-Regulation Behavior in the Context of Vertical Privacy: A Moderated Mediation Model
by Lei Zhuang, Rui Sun, Lijun Chen and Wenlong Tang
Behav. Sci. 2023, 13(9), 706; https://doi.org/10.3390/bs13090706 - 25 Aug 2023
Cited by 1 | Viewed by 1252
Abstract
Combining data-sharing models and algorithm technologies has led to new data flow structures and usage patterns. In this context, the presentation time of shared low-sensitivity information across platforms has become a crucial factor that affects user perception and privacy-regulation behavior. However, previous studies [...] Read more.
Combining data-sharing models and algorithm technologies has led to new data flow structures and usage patterns. In this context, the presentation time of shared low-sensitivity information across platforms has become a crucial factor that affects user perception and privacy-regulation behavior. However, previous studies have not conducted an in-depth exploration of this issue. Based on privacy process theory, this study discusses the impact and potential mechanism of the presentation time (immediate or delayed) of shared low-sensitivity information across platforms on privacy-regulation behavior. Through a pre-study and two online survey experimental studies, which included 379 participants in total, we verified that the immediate information presentation time has a significantly higher impact on online vigilance and privacy-regulation behavior than the delayed condition, βdirect = 0.5960, 95% CI 0.2402 to 0.9518; βindirect = 0.1765, 95% CI 0.0326 to 0.3397, and users’ perceived control as the moderating role influences online vigilance and privacy-regulation behaviors (preventive or corrective), βpreventive = −0.0562, 95% CI −0.1435 to −0.0063; βcorrective = −0.0581, 95% CI −0.1402 to −0.0065. Based on these results, we suggest that the presentation time of using shared low-sensitivity information across platforms should be concerned by companies’ recommendation algorithms to reduce users’ negative perceptions and privacy behaviors and improve user experience. Full article
(This article belongs to the Topic Online User Behavior in the Context of Big Data)
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15 pages, 363 KiB  
Article
TEE: Real-Time Purchase Prediction Using Time Extended Embeddings for Representing Customer Behavior
by Miguel Alves Gomes, Mark Wönkhaus, Philipp Meisen and Tobias Meisen
J. Theor. Appl. Electron. Commer. Res. 2023, 18(3), 1404-1418; https://doi.org/10.3390/jtaer18030070 - 17 Aug 2023
Cited by 2 | Viewed by 1860
Abstract
Real-time customer purchase prediction tries to predict which products a customer will buy next. Depending on the approach used, this involves using data such as the customer’s past purchases, his or her search queries, the time spent on a product page, the customer’s [...] Read more.
Real-time customer purchase prediction tries to predict which products a customer will buy next. Depending on the approach used, this involves using data such as the customer’s past purchases, his or her search queries, the time spent on a product page, the customer’s age and gender, and other demographic information. These predictions are then used to generate personalized recommendations and offers for the customer. A variety of approaches already exist for real-time customer purchase prediction. However, these typically require expertise to create customer representations. Recently, embedding-based approaches have shown that customer representations can be effectively learned. In this regard, however, the current state-of-the-art does not consider activity time. In this work, we propose an extended embedding approach to represent the customer behavior of a session for both known and unknown customers by including the activity time. We train a long short-term memory with our representation. We show with empirical experiments on three different real-world datasets that encoding activity time into the embedding increases the performance of the prediction and outperforms the current approaches used. Full article
(This article belongs to the Topic Online User Behavior in the Context of Big Data)
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16 pages, 1672 KiB  
Article
The Impact of Artificial Intelligence Technology Stimuli on Sustainable Consumption Behavior: Evidence from Ant Forest Users in China
by Ping Cao and Shuailong Liu
Behav. Sci. 2023, 13(7), 604; https://doi.org/10.3390/bs13070604 - 20 Jul 2023
Cited by 3 | Viewed by 2492
Abstract
With the global economy and population growing rapidly, the problems of excessive resource consumption and environmental pollution have become increasingly serious. Thus, the need to promote sustainable development has become more urgent. Sustainable consumption behavior plays a crucial role in achieving sustainable development [...] Read more.
With the global economy and population growing rapidly, the problems of excessive resource consumption and environmental pollution have become increasingly serious. Thus, the need to promote sustainable development has become more urgent. Sustainable consumption behavior plays a crucial role in achieving sustainable development goals as it can significantly reduce both greenhouse gas emissions and resource consumption. Artificial intelligence technology has broken the limitations of time and space in environmental protection. For example, the Ant Forest leverages the design of “green energy” to inspire the public to engage in energy-saving and emission-reducing activities. To examine the impact mechanisms of customers’ sustainable consumption behavior, this study applies the stimulus-organism-response theory and the theory of planned behavior. The study conducts regression analysis and bootstrapping methods on a sample consisting of 280 Ant Forest users to explore the influence of artificial intelligence technology stimuli on sustainable consumption behavior and the mediating effects of customer-perceived value and customer stickiness. The results demonstrate a “linkage effect” between online green consumption habits and offline sustainable consumption behavior. Moreover, the study finds that passion and usability indirectly promote offline sustainable consumption behavior through customer-perceived value and customer stickiness. Specifically, the influence of customer-perceived emotional value (β = 0.121; β = 0.100) is stronger than that of customer-perceived social value (β = 0.043; β = 0.038). Due to the limitation of the sample size, future research should broaden its scope by incorporating additional variables, specifically customer-specific factors. Furthermore, more advanced research methods, such as big data analysis, should be employed to comprehensively explore the influencing factors of sustainable consumer behavior. Full article
(This article belongs to the Topic Online User Behavior in the Context of Big Data)
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