Online User Behavior in the Context of Big Data

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Behavioral Sciences behavsci	2.5	2.6	2011	27 Days	CHF 2200
Businesses businesses	-	-	2021	24.5 Days	CHF 1000
Journal of Theoretical and Applied Electronic Commerce Research jtaer	5.1	9.5	2006	32 Days	CHF 1000

29 pages, 13855 KiB

Open AccessArticle

Cart-State-Aware Discovery of E-Commerce Visitor Journeys with Process Mining

by Bilal Topaloglu, Basar Oztaysi and Onur Dogan

J. Theor. Appl. Electron. Commer. Res. 2024, 19(4), 2851-2879; https://doi.org/10.3390/jtaer19040138 - 17 Oct 2024

Viewed by 696

Abstract

Understanding customer journeys is key to e-commerce success. Many studies have been conducted to obtain journey maps of e-commerce visitors. To our knowledge, a complete, end-to-end and structured map of e-commerce journeys is still missing. In this research, we proposed a four-step methodology [...] Read more.

Understanding customer journeys is key to e-commerce success. Many studies have been conducted to obtain journey maps of e-commerce visitors. To our knowledge, a complete, end-to-end and structured map of e-commerce journeys is still missing. In this research, we proposed a four-step methodology to extract and understand e-commerce visitor journeys using process mining. In order to obtain more structured process diagrams, we used techniques such as activity type enrichment, start and end node identification, and Levenshtein distance-based clustering in this methodology. For the evaluation of the resulting diagrams, we developed a model utilizing expert knowledge. As a result of this empirical study, we identified the most significant factors for process structuredness and their relationships. Using a real-life big dataset which has over 20 million rows, we defined activity-, behavior-, and process-level e-commerce visitor journeys. Exploitation and exploration were the most common journeys, and it was revealed that journeys with exploration behavior had significantly lower conversion rates. At the process level, we mapped the backbones of eight journeys and tested their qualities with the empirical structuredness measure. By using cart statuses at the beginning and end of these journeys, we obtained a high-level end-to-end e-commerce journey that can be used to improve recommendation performance. Additionally, we proposed new metrics to evaluate online user journeys and to benchmark e-commerce journey design success. Full article

(This article belongs to the Topic Online User Behavior in the Context of Big Data)

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18 pages, 1222 KiB

Open AccessArticle

Encountering Product Information: How Flashes of Insight Improve Your Decisions on E-Commerce Platforms

by Lu Wang, Guangling Zhang and Dan Jiang

J. Theor. Appl. Electron. Commer. Res. 2024, 19(3), 2180-2197; https://doi.org/10.3390/jtaer19030106 - 30 Aug 2024

Viewed by 677

Abstract

Serendipity-oriented recommendation systems have been widely applied in major e-commerce and social platforms. Platform managers aim to enhance user satisfaction and increase platform sales by creating serendipitous encounters with information. Previous research has shown that the unexpectedness of encountering product information in serendipity-oriented [...] Read more.

Serendipity-oriented recommendation systems have been widely applied in major e-commerce and social platforms. Platform managers aim to enhance user satisfaction and increase platform sales by creating serendipitous encounters with information. Previous research has shown that the unexpectedness of encountering product information in serendipity-oriented recommendation systems can effectively stimulate positive emotions in customers, resulting in unplanned purchases, such as impulse buying. However, little research has focused on another critical aspect of encountering product information: perceived value. Our study suggests that encountering product information can positively affect the intention to purchase planned products (focal products) based on their perceived value. To explore this, we conducted three experiments and found that: (1) encountering product information positively influences planned product purchase intention (e.g., reduced decision-making time, improved focal product purchase intention), compared to the absence of encountering product information (precision-oriented recommendation systems); (2) this effect is mediated by customer inspiration; and (3) the characteristics of recommendation system strategies can moderate this effect. Specifically, when the strategy features exhibit a low level of explainability, the impact of encountering product information on customer inspiration and purchase intention is more significant than when a high level of explainability is presented. Full article

(This article belongs to the Topic Online User Behavior in the Context of Big Data)

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32 pages, 610 KiB

Open AccessReview

Understanding Users’ Acceptance of Artificial Intelligence Applications: A Literature Review

by Pengtao Jiang, Wanshu Niu, Qiaoli Wang, Ruizhi Yuan and Keyu Chen

Behav. Sci. 2024, 14(8), 671; https://doi.org/10.3390/bs14080671 - 2 Aug 2024

Viewed by 1710

Abstract

In recent years, with the continuous expansion of artificial intelligence (AI) application forms and fields, users’ acceptance of AI applications has attracted increasing attention from scholars and business practitioners. Although extant studies have extensively explored user acceptance of different AI applications, there is [...] Read more.

In recent years, with the continuous expansion of artificial intelligence (AI) application forms and fields, users’ acceptance of AI applications has attracted increasing attention from scholars and business practitioners. Although extant studies have extensively explored user acceptance of different AI applications, there is still a lack of understanding of the roles played by different AI applications in human–AI interaction, which may limit the understanding of inconsistent findings about user acceptance of AI. This study addresses this issue by conducting a systematic literature review on AI acceptance research in leading journals of Information Systems and Marketing disciplines from 2020 to 2023. Based on a review of 80 papers, this study made contributions by (i) providing an overview of methodologies and theoretical frameworks utilized in AI acceptance research; (ii) summarizing the key factors, potential mechanisms, and theorization of users’ acceptance response to AI service providers and AI task substitutes, respectively; and (iii) proposing opinions on the limitations of extant research and providing guidance for future research. Full article

(This article belongs to the Topic Online User Behavior in the Context of Big Data)

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16 pages, 704 KiB

Open AccessArticle

Enhancing E-Business Communication with a Hybrid Rule-Based and Extractive-Based Chatbot

by Onur Dogan and Omer Faruk Gurcan

J. Theor. Appl. Electron. Commer. Res. 2024, 19(3), 1984-1999; https://doi.org/10.3390/jtaer19030097 - 1 Aug 2024

Viewed by 939

Abstract

E-businesses often face challenges related to customer service and communication, leading to increased dissatisfaction among customers and potential damage to the brand. To address these challenges, data-driven and AI-based approaches have emerged, including predictive analytics for optimizing customer interactions and chatbots powered by [...] Read more.

E-businesses often face challenges related to customer service and communication, leading to increased dissatisfaction among customers and potential damage to the brand. To address these challenges, data-driven and AI-based approaches have emerged, including predictive analytics for optimizing customer interactions and chatbots powered by AI and NLP technologies. This study focuses on developing a hybrid rule-based and extractive-based chatbot for e-business, which can handle both routine and complex inquiries, ensuring quick and accurate responses to improve communication problems. The rule-based QA method used in the chatbot demonstrated high precision and accuracy in providing answers to user queries. The rule-based approach achieved impressive 98% accuracy and 97% precision rates among 1684 queries. The extractive-based approach received positive feedback, with 91% of users rating it as “good” or “excellent” and an average user satisfaction score of 4.38. General user satisfaction was notably high, with an average Likert score of 4.29, and 54% of participants gave the highest score of 5. Communication time was significantly improved, as the chatbot reduced average response times to 41 s, compared to the previous 20-min average for inquiries. Full article

(This article belongs to the Topic Online User Behavior in the Context of Big Data)

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16 pages, 320 KiB

Open AccessArticle

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 611

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

Open AccessArticle

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 1300

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

Open AccessArticle

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 2 | Viewed by 1565

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

Open AccessArticle

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 5 | Viewed by 2644

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

Open AccessArticle

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 1407

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

Open AccessArticle

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 1764

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

Open AccessArticle

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 4 | Viewed by 1467

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

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20 pages, 8257 KiB

Open AccessArticle

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 3361

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

Open AccessArticle

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 1806

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

Open AccessArticle

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 1590

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, β_direct = 0.5960, 95% CI 0.2402 to 0.9518; β_indirect = 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), β_preventive = −0.0562, 95% CI −0.1435 to −0.0063; β_corrective = −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

Open AccessArticle

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 2495

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

Open AccessArticle

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 6 | Viewed by 3507

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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