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AI-Based Disruption, Innovations, and New Business Models in E-Commerce: Empirical Research, Case Studies and Current Trends

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A special issue of Journal of Theoretical and Applied Electronic Commerce Research (ISSN 0718-1876).

Deadline for manuscript submissions: closed (28 February 2026) | Viewed by 10583

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Special Issue Editors

Prof. Dr. Stephan Böhm

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Guest Editor

Telecommunications and Mobile Media, RheinMain University of Applied Sciences, CAEBUS Center of Advanced E-Business Studies, Wiesbaden, Germany
Interests: innovation management and marketing; media & AI, technology acceptance; up-front user research; prototyping; mobile HCI and UX
Special Issues, Collections and Topics in MDPI journals

Dr. Sid Suntrayuth

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Guest Editor

International College, National Institute of Development Administration, Bangkok 10224, Thailand
Interests: public administration; policy implementation
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Müge Klein

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Guest Editor

Faculty of Economic and Administrative Sciences, Business Administration, Turkish-German University, Istanbul, Turkey
Interests: digital transformation; digital leadership; remote working; AI-disruption in business; digital marketing; process management

Prof. Dr. Ela Sibel Bayrak Meydanoğlu

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Guest Editor

Faculty of Economic and Administrative Sciences, Business Administration, Turkish-German University, Istanbul, Turkey
Interests: digital marketing; AI and marketing; digital transformation; e-business; augmented reality applications in marketing; sustainable marketing

Special Issue Information

Dear Colleagues,

We are pleased to announce a call for papers for an upcoming Special Issue focusing on the transformative role of artificial intelligence (AI) in the evolving landscape of electronic and mobile commerce. As AI technologies continue to advance, they are fundamentally reshaping e-commerce by enabling personalized experiences, intelligent automation, and innovative business models.

This Special Issue welcomes original research and conceptual papers that explore the impact of AI on digital commerce, with an emphasis on cutting-edge applications and strategic implications. We particularly encourage submissions addressing, but not limited to, the following topics:

AI-driven personalization and recommendation systems;
Conversational AI and intelligent chatbots in customer engagement;
AI-based predictive analytics and consumer behavior insights;
AI-enabled dynamic pricing and revenue optimization;
Novel AI-based business models and digital innovations in e-commerce;
AI for sustainable and ethical e-commerce practices.

There are two ways to participate in this JTAER Special Issue:

Authors can directly submit through the MDPI submission platform for the regular fee.
Authors could also participate in the 9th International Workshop on Entrepreneurship, Electronic, and Mobile Business, taking place in Istanbul, Turkey (October 9–10, 2025) to qualify for a discount. Selected papers from the IWEMB workshop will be invited to submit to this JTAER Special Issue and receive a 50% discount when accepted by JTAER for publication. Please visit the IWEMB website for information on participation and workshop deadlines (https://iwemb.org/).

Prof. Dr. Stephan Böhm
Dr. Sid Suntrayuth
Prof. Dr. Müge Klein
Prof. Dr. Ela Sibel Bayrak Meydanoğlu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Theoretical and Applied Electronic Commerce Research is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

artificial intelligence
e-commerce
business models
digital disruption
innovation

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Published Papers (6 papers)

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Research

17 pages, 374 KB

Open AccessArticle

The Personalization Paradox in AI-Driven Tourism E-Commerce: Psychological Reactance, Threat-Substitution, and the Moderating Role of Privacy Concerns

by Hongmei Duan, Ahmad Yahya Dawod and Guochao Wan

J. Theor. Appl. Electron. Commer. Res. 2026, 21(4), 127; https://doi.org/10.3390/jtaer21040127 - 21 Apr 2026

Cited by 1 | Viewed by 833

Abstract

AI-driven personalization (AIP) has become a core mechanism of digital commerce platforms, yet its psychological consequences remain theoretically fragmented. Drawing on the Stimulus–Organism–Response (SOR) framework and Psychological Reactance Theory (PRT), this study proposes a Threat-Substitution Mechanism (TSM) to explain how AIP shapes continuance intention in high-involvement online travel decisions. Using survey data from 488 Generation Y and Z users of Chinese online travel agencies and analyzing the model via PLS-SEM, results show that AIP significantly increases usage intention (UI) and reduces psychological reactance. Psychological reactance partially mediates the relationship between AIP and UI, indicating the presence of underlying psychological friction alongside dominant utilitarian benefits. Furthermore, privacy concerns amplify the negative relationship between AIP and reactance, suggesting that privacy-sensitive users exhibit heightened appraisal sensitivity rather than uniform resistance to personalization. By reconceptualizing the personalization paradox as a context-contingent threat appraisal process, this study advances electronic commerce research beyond parallel dual-effect models and clarifies the boundary conditions under which AIP enhances or constrains user continuance. Practical implications highlight the importance of algorithmic precision and autonomy-supportive design in AI-enabled commerce platforms. Full article

(This article belongs to the Special Issue AI-Based Disruption, Innovations, and New Business Models in E-Commerce: Empirical Research, Case Studies and Current Trends)

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24 pages, 1218 KB

Open AccessArticle

How Technology Characteristics and Social Factors Shape Consumer Behavior in Artificial Intelligence-Powered Fashion Curation Platforms

by Dayun Jeong

J. Theor. Appl. Electron. Commer. Res. 2026, 21(3), 81; https://doi.org/10.3390/jtaer21030081 - 2 Mar 2026

Cited by 1 | Viewed by 1341

Abstract

The rapid evolution of technology characteristics has significantly influenced various sectors, including fashion, in which technology-enabled platforms have increasingly been utilized to enhance personalization and consumer engagement. This study investigates the effect of these characteristics on consumer behavior within fashion curation platforms. Integrating the task–technology fit and the unified theory of acceptance and use of technology models, this study examines key constructs using structural equation modeling. Data were collected via a week-long survey of 300 Korean consumers using fashion curation platforms. The findings reveal that technology characteristics exert a significant influence on task–technology fit and effort expectancy. Additionally, hedonic motivation, social influence, and facilitating conditions were pivotal in shaping behavioral intention. The novelty of this work lies in the fact that it extends the integrated model framework to a fashion curation context to offer a more nuanced understanding. Moreover, the findings provide practical insights for optimizing technology-enabled fashion platforms to boost user adoption and engagement. Full article

(This article belongs to the Special Issue AI-Based Disruption, Innovations, and New Business Models in E-Commerce: Empirical Research, Case Studies and Current Trends)

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17 pages, 857 KB

Open AccessArticle

Driving Service Stickiness in the AI Subscription Economy: The Roles of Algorithmic Curation, Technological Fluidity, and Cognitive Efficiency

by Bokyung Kim and Joonyong Park

J. Theor. Appl. Electron. Commer. Res. 2026, 21(1), 30; https://doi.org/10.3390/jtaer21010030 - 9 Jan 2026

Viewed by 1098

Abstract

This study examines the psychological mechanisms underlying service stickiness during the mature phase of the AI subscription economy, with particular attention to the paradox of subscription fatigue. To enhance conceptual clarity, AI-driven stimuli—specifically Algorithmic Curation and Technological Fluidity—are defined as perceived attributes at the individual level. Employing the Stimulus–Organism–Response (S-O-R) framework, the research explores how these perceived stimuli influence consumers’ internal states (Cognitive Efficiency and Serendipity) and subsequent behavioral responses (Service Stickiness). Empirical analysis using partial least squares structural equation modeling (PLS-SEM) on data from U.S. subscription service users yields several theoretical insights. Cognitive Efficiency is identified as the primary driver of stickiness, indicating that, in the context of subscription fatigue, the utilitarian benefit of reduced cognitive effort surpasses hedonic enjoyment. Additionally, the study identifies a “Frictionless Trap,” in which excessive Technological Fluidity negatively affects Serendipity (β = −0.195), suggesting that an entirely seamless experience may create a filter bubble that limits unexpected discovery. As a result, Serendipity does not significantly affect stickiness in the aggregate model. However, post hoc analysis demonstrates that Serendipity remains significant for high-income users, while Cognitive Efficiency is most influential in high-frequency utilitarian contexts, such as food services. These findings indicate that sustainable retention depends on reducing cognitive load while intentionally introducing friction to preserve opportunities for discovery. Full article

(This article belongs to the Special Issue AI-Based Disruption, Innovations, and New Business Models in E-Commerce: Empirical Research, Case Studies and Current Trends)

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29 pages, 522 KB

Open AccessArticle

Crowdfunding as an E-Commerce Mechanism: A Deep Learning Approach to Predicting Success Using Reduced Generative AI Embeddings

by Hakan Gunduz, Muge Klein and Ela Sibel Bayrak Meydanoglu

J. Theor. Appl. Electron. Commer. Res. 2026, 21(1), 28; https://doi.org/10.3390/jtaer21010028 - 8 Jan 2026

Cited by 1 | Viewed by 1282

Abstract

Crowdfunding platforms like Kickstarter have reshaped early-stage financing by allowing entrepreneurs to connect directly with potential supporters. As a fast-expanding part of digital commerce, crowdfunding offers significant opportunities but also substantial risks for both entrepreneurs and platform operators, making predictive analytics an essential capability. Although crowdfunding shares some operational features with traditional e-commerce, its mix of financial uncertainty, emotionally charged storytelling, and fast-evolving social interactions makes it a distinct and more challenging forecasting problem. Accurately predicting campaign outcomes is especially difficult because of the high-dimensionality and diversity of the underlying textual and behavioral data. These factors highlight the need for scalable, intelligent data science methods that can jointly exploit structured and unstructured information. To address these issues, this study proposes a novel AI-based predictive framework that integrates a Convolutional Block Attention Module (CBAM)-enhanced symmetric autoencoder for compressing high-dimensional Generative AI (GenAI) BERT embeddings with meta-heuristic feature selection and advanced classification models. The framework systematically couples attention-driven feature compression with optimization techniques—Genetic Algorithm (GA), Jaya, and Artificial Rabbit Optimization (ARO)—and then applies Long Short-Term Memory (LSTM) and Gradient Boosting Machine (GBM) classifiers. Experiments on a large-scale Kickstarter dataset demonstrate that the proposed approach attains 77.8% accuracy while reducing feature dimensionality by more than 95%, surpassing standard baseline methods. In addition to its technical merits, the study yields practical insights for platform managers and campaign creators, enabling more informed choices in campaign design, promotional tactics, and backer targeting. Overall, this work illustrates how advanced AI methodologies can strengthen predictive analytics in digital commerce, thereby enhancing the strategic impact and long-term sustainability of crowdfunding ecosystems. Full article

(This article belongs to the Special Issue AI-Based Disruption, Innovations, and New Business Models in E-Commerce: Empirical Research, Case Studies and Current Trends)

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23 pages, 4784 KB

Open AccessArticle

Brand Image of Beijing’s Time-Honored Restaurants: An Analysis Through Large Language Model-Driven Review Mining

by Xiaohang Li, Aihua Zhou, Bin Meng and Ruize Wang

J. Theor. Appl. Electron. Commer. Res. 2025, 20(4), 300; https://doi.org/10.3390/jtaer20040300 - 2 Nov 2025

Viewed by 2490

Abstract

Understanding consumer perceptions of brand image is vital for the sustainable development of China Time-honored Brands, which combine cultural heritage with commercial value. This study aims to systematically analyze the brand image of Beijing’s time-honored restaurants by developing a large language model (LLM)-driven framework that advances beyond the limits of traditional text mining in semantic depth and adaptability. Using Dianping reviews from 2016 to 2022, we apply the Qwen3-32B model to map consumer feedback onto a Functional–Experiential–Symbolic (F–E–S) framework. Sentiment quantification and clustering analysis are employed to generate brand image profiles and identify common brand types, while topic modeling is used to uncover the specific consumer concerns shaping these perceptions. The results reveal a dual structure: the symbolic dimension, rooted in cultural heritage, is consistently high and stable, whereas the functional and experiential dimensions, associated with daily operations, are relatively low and highly volatile. Clustering further distinguishes two significantly different categories: comprehensive performers and heritage struggler brands. The key difference lies in whether brands can transform symbolic capital derived from historical legacy into positive consumer experiences through excellent operational performance. By integrating dynamic and structural perspectives, this study advances brand image research and provides data-driven insights to guide the targeted management and modernization of heritage brands. Full article

(This article belongs to the Special Issue AI-Based Disruption, Innovations, and New Business Models in E-Commerce: Empirical Research, Case Studies and Current Trends)

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24 pages, 1228 KB

Open AccessArticle

The Impact of Artificial Intelligence Service Competency Among Korean Citizens on Digital Utilization Outcomes in the Context of Digital Trade Expansion: The Mediating Role of E-Commerce

by Hyuk Kwon

J. Theor. Appl. Electron. Commer. Res. 2025, 20(4), 292; https://doi.org/10.3390/jtaer20040292 - 1 Nov 2025

Cited by 1 | Viewed by 1339

Abstract

This study empirically examines the impact of artificial intelligence (AI) service competency on digital economic activities, digital social participation, and daily life satisfaction. It also investigates the mediating role of e-commerce utilization in these relationships. The ultimate aim is to provide practical implications for enhancing citizens’ quality of life and promote their active participation in the digital society. This study employed data from 7001 respondents drawn from the 2023 Digital Information Gap Survey. The analysis was conducted using SPSS 29.0 and structural equation modeling (SEM) to examine the structural relationships between variables. The results revealed that perceived usefulness of AI technology had a significant positive effect on digital economic activities, while digital information competency positively influenced both digital economic activities and daily life satisfaction. In contrast, attitude toward AI technology did not have a significant effect on digital economic activities and even showed a negative association with digital social participation. Furthermore, digital information competency demonstrated a significant indirect effect on the overall digital outcomes through the mediating role of the level of e-commerce utilization. This study applies an integrated theoretical framework combining the Theory of Reasoned Action (TRA) and the Unified Theory of Acceptance and Use of Technology (UTAUT). It goes beyond theoretical interpretation by delivering practical implications that offer academic and policy value, particularly for developing policy strategies such as reducing the digital divide. Full article

(This article belongs to the Special Issue AI-Based Disruption, Innovations, and New Business Models in E-Commerce: Empirical Research, Case Studies and Current Trends)

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