Content area
The intersection of digital technology and the knowledge economy has led to the rapid development of the online knowledge payment (OKP) industry, which has attracted increasing attention from scholars across various disciplines. However, research in this field remains relatively fragmented and lacks a coherent framework for understanding the evolutionary trajectory and mechanisms of OKP platforms. This study addresses this gap by conducting a bibliometric review of 226 core papers retrieved from Scopus and Web of Science databases and applying the Elaboration Likelihood Model (ELM) to interpret the underlying business logic of OKP models. Through ELM-guided classification, this paper distinguishes between central route mechanisms (such as knowledge quality and credibility in paid Q&A) and peripheral route mechanisms (such as emotional appeal and interactivity in live broadcast formats). The bibliometric analysis reveals emerging research trends focused on hybrid platform strategies, artificial intelligence-driven personalization, and blockchain-enabled trust systems, indicating a shift from static content monetization to dynamic, user-centered knowledge experiences. By integrating the ELM with quantitative mapping of the OKP research landscape, this study constructs a dual-perspective framework that links user cognitive processing with evolving platform affordances. The findings theoretically illustrate how persuasion and participation coexist in knowledge-driven digital environments, offering practical guidance for platform designers, knowledge creators, and policymakers seeking to promote innovation and user engagement in the OKP field.
Introduction
Online Knowledge Payment (OKP) defines a digital business model in which individuals monetize their knowledge by offering paid services such as Q&A consulting, live teaching, or pre-recorded lectures. OKP is the offspring of the knowledge economy and the digital platform economy, and offers a new value creation pattern for general knowledge creators. While OKP shares some commonalities with Massive Open Online Courses (MOOCs) and EdTech platforms (Voudoukis and Pagiatakis 2022), it is distinct in its grassroots and entrepreneurial orientation and focus on an individual-led monetization model rather than institutional education (Qu et al. 2022). For example, MOOCs such as Coursera provide structured, typically free academic content backed by universities (Ślósarz 2024). In contrast, OKP platforms such as (Guo et al. 2023), Udemy, and even WeChat prioritize real-time interaction, expertise, and business incentives. Statista (2023) forecasts that the global online education market will be valued at $203.8 billion by 2025, with a compound annual growth rate of 8.2%. As one of the fast-growing segments in this market, OKP has been further boosted by the New Crown Epidemic, which has reshaped professional learning and knowledge consumption habits (Imran et al., 2022).
With the development of OKP platforms like Zhihu, Udemy, and Ximalaya, features such as real-time interaction, content modularization, and dynamic pricing have been integrated. These innovations have sparked growing interest in academia across multiple dimensions. For example, Russo et al. (2023) and Wi et al. (2022) studied how platform design affects user behavior; Goyanes and Vara-Miguel (2017) focused on content monetization; while (Kou and Sun 2024) and Tu and Zhao et al. (2018) studied perceived value and trust.
While OKP practices and platforms have garnered growing interest, the literature remains fragmented and under-theorized. Just one review paper (Qi et al. 2019) has attempted to offer a general synthesis of the OKP domain, which is mainly descriptive and lacks formal theoretical underpinnings. As Paul and Rosado-Serrano (2019) point out, research domains with fewer than 40 systematic reviews are generally considered emerging and need positioning research.
Compared to the well-studied MOOC field, OKP as a persuasive and commercial mechanism remains underexplored, especially regarding comprehensive theoretical explanations (Cisel and Pontalier 2021). To this end, this study combines bibliometric analysis with theory-driven business model research, which draws upon the Elaboration Likelihood Model (ELM) (Petty and Cacioppo 1986). The ELM refers to a dual-path, center and peripheral, model that describes how audiences process persuasive information and is therefore highly appropriate for examining how OKP platforms design content and experiences to influence knowledge purchase behavior. This model allows us to go beyond description and account for the persuasive effects of OKP through theory-driven strategic insight. In so doing, this study also adheres to best practice in bibliometric studies, verifying the methodological foundations of Zupic and Čater (2015), while also referring to recent contributions highlighting advances in bibliometric techniques, including Naeini et al. (2022), Zamani et al. (2022), and Zamani et al. (2025) which emphasize the integration of network analysis, topic evolution, and science mapping in emerging interdisciplinary fields.
Therefore, this study integrates available research evidence to understand the OKP industry development systematically. The following two research questions are explored:
Research Question 1: Who are the prominent authors, journals, and countries that have made major contributions to OKP research development?
Research Question 2: What are the main gaps, limitations, and future research areas of OKP?
Answering Research Question 1 will help reveal the knowledge structure and changes in hot topics in OKP research. Research Question 2 can expand areas that need further research, such as cross-cultural research, platform innovation, and theoretical expansion. This integrated approach provides a reference for academic theorization and strategic development of stakeholders in the field of digital knowledge economy.
The remainder of this paper is organized as follows. We begin by discussing the “Theoretical Foundations and Business Models of Online Knowledge Payment.” The subsequent section details the “RESEARCH METHODOLOGY”. Next, the “ANALYSIS” and “Discussion and Theoretical Implications” sections provide a bibliometric and business model analysis. We then present the “Practical Implications, Limitations, and Future Research,” which covers the study's findings, implications, limitations, and future research directions. The paper concludes with a summary of its main contributions in the “Conclusion”.
Theoretical Foundations and Business Models of Online Knowledge Payment
Theoretical background of the knowledge economy and OKP
The core of the knowledge economy lies in commoditizing knowledge production, dissemination, and consumption (Powell and Snellman 2004). This context has driven the rapid development of the online knowledge payment (OKP) industry. OKP is a typical knowledge economy model that relies on consumers’ perceived value and willingness to pay for knowledge content (Ying et al. 2023). The OKP platform is an intermediary to help knowledge creators structure and deliver their expertise through standardized products (e.g., paid quizzes and recorded and live courses). This structured approach extends knowledge accessibility to broader consumers, including professionals, learners, and amateurs across fields (Zhang et al. 2022). In addition, the rapid development of digital technologies such as artificial intelligence, blockchain, and big data has changed how users access knowledge (P. Gao et al. 2021). Features such as personalized recommendation algorithms, real-time interactive tools, and social media integration are increasingly important in shaping consumer behavior in the OKP field (Daradkeh et al. 2022; Hu and Noor 2024; Li 2024).
To gain insight into how knowledge products are effectively marketed and consumed, a powerful theoretical perspective is provided by the Elaboration Likelihood Model (ELM) proposed by Petty and Cacioppo (1986). The ELM consists of two main persuasion pathways: the central and peripheral. In the context of OKP, the central pathway refers to consumer engagement based on rational cognitive assessments of content quality, expertise, and provider credibility, which are typically observed when consumers carefully select professional courses or participate in purposeful paid Q&A sessions (Zhang et al. 2019). In contrast, the peripheral pathway works through affective or heuristic cues (e.g., speaker charisma, platform interactions, or aesthetic design) that can significantly enhance consumer engagement in real-time scenarios like live streaming (X. Gao et al. 2021). Most critically, in the OKP case, both paths to persuasion operate simultaneously and interdependently (Liu and Zheng 2024). At the same time, distinguishing between these two mechanisms through the Elaboration Likelihood Model (ELM) framework provides a deeper theoretical basis for understanding consumer behaviour in OKP and a practical analytical perspective for designing and improving OKP business models. This dual-path theoretical perspective will guide subsequent explorations of OKP’s defining characteristics and diverse business models.
The definition and key features of OKP
The term Online Knowledge Payment (OKP) is often confused with the concepts of knowledge exchange, transfer, and sharing, which has led to its ambiguous definition in the context of the knowledge economy (Qi et al. 2019). However, OKP is unique in that it directly monetizes expertise by individuals or organizations through digital platforms(Qi et al. 2019). In contrast, knowledge sharing refers to the informal process of exchanging information, skills, and experience within an organization or team to improve collaboration and performance (Nguyen et al. 2023; Yoshikawa et al. 2023). Similarly, Massive Open Online Courses (MOOCs) are typically accessible, free, scholarly courses offered by academic institutions that are designed to enable scalability rather than personalization or monetization of academic education (Ślósarz 2024; X. Wang et al. 2023).
To clearly distinguish OKP from these related concepts, we define OKP in this study as:
A digital business practice in which knowledge providers personalize and package their expertise through an interactive or modular (e.g., live streaming, paid Q&A, or micro courses) format, and consumers engage in a paid exchange.
This definition emphasizes OKP’s transactional, decentralized, and customizable nature. Whereas MOOCs prioritize formalized courses and mass accessibility, and knowledge sharing is usually non-commercial and for internal organizational use only, OKPs focus on delivering expertise in a personalized, real-time manner in a market-driven environment.
OKP products have different features that allow them to meet the specific needs of different knowledge consumers. First, the OKP product is highly modular: modularity represents flexibility and customization, and learners can choose their way to learn knowledge, such as video tutorials, Q&A sessions, or even live broadcasts. This makes OKP attractive to amateur and professional learners (Iman Vidya Kemal 2016). At the same time, this flexibility enhances autonomy and perceived value (Jin and Xu 2021). Besides, the products are digitally accessible. Information is given in various modes that fit all needs: video, audio, and interactive, across all devices and locations (Ziegler and Sloan 2017). If appropriately used, real-time technologies such as live streaming or interactive applications will significantly boost user engagement, building immediacy and interaction (Seddon et al. 2023). These design elements typically correspond to peripheral route factors under the ELM framework (Li and Kang 2022). OKP products often have some added value certificate upon completion, downloadable educational material, or even AI-curated content. These features enhance the central route appeal (i.e., content credibility) and user satisfaction (Zhang et al. 2019).
These features highlight how the OKP platform activates central and peripheral perception routes, consistent with the Elaboration Likelihood Model (ELM). Knowledge products’ modularity and knowledge content quality contribute to users’ rational evaluation, while real-time interactivity and design features affect user experience perception. These findings lay the foundation for classifying OKP business models in the next section.
OKP’s business model and development trends
The online knowledge payment (OKP) market can be roughly divided into five major business models, which are derived from existing typologies in platform practice and research literature (Cisel and Pontalier 2021; Qi et al. 2019).
Paid Q&A platforms (e.g., Zhihu) emphasize immediacy and pertinence, providing users with personalized expert responses (Zhang et al. 2019). Consumers rely on the content’s credibility and domain expertise, so they mainly activate central path processing under the Elaboration Likelihood Model (ELM).
Live courses are known for their interactivity and emotional resonance, making them suitable for complex content. They also stimulate marginal path participation through the charm of the anchor and platform design (Shi et al. 2020; S. Wang et al. 2023; Zun and Yasin 2024).
Paid audio (e.g., Get App): This model focuses on mobility and convenience, and is aimed at users seeking passive or background learning. Due to the lower energy investment, it tends to play on the peripheral path (Zhou and Xiu 2024).
Knowledge communities are centered around user-generated content, promote peer-to-peer interaction and co-learning, and often incorporate central and peripheral cues, depending on content structure and social signals (Liu et al. 2020).
Prerecorded courses: These courses emphasize structured, in-depth content and are often aligned with central path processing, especially when it comes to credibility, teaching quality, and long-term knowledge growth (X. Zhang et al. 2023).
As the ELM explains, this classification highlights the diversity of OKP formats and their differentiated cognitive appeal. It also provides a framework to analyze how platform design, delivery methods, and content complexity influence consumer decisions. Figure 1 intuitively shows OKP’s business process.
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Fig. 1
The ecosystem of online knowledge platforms (OKPs).
This figure illustrates the three core components of the OKP ecosystem: Content Generators (including Key Opinion Leaders, Professional Generated Content, and User Generated Content), Platform Operators (categorized into comprehensive, online education, Q&A, audio, and social platforms), and Customers. The dashed arrows indicate the flow of content creation and consumption.
As OKP platforms expand globally, addressing cross-cultural adaptability and platform localization strategies becomes critical. Research on globalized cross-border learning platforms emphasizes that knowledge products must adapt to local languages, cultural norms, and user expectations to enhance learning engagement and platform retention (Yi et al. 2017). Furthermore, the pricing model must be modified step by step based on the income levels of various regions to enhance market penetration and inclusiveness of users. Collaboration with local content providers and institutions can also lend credibility to the platform and build trust in a diversified market (Guo et al. 2024). These regional and cultural factors offer a reference background for the technological development of the OKP model.
The future development of OKP will be increasingly influenced by the convergence of big data analytics, artificial intelligence (AI), and intelligent recommendation systems (Wang & Zhao, 2022). The platforms can use AI to analyze user preferences and consumption behavior for knowledge-product recommendations. While big data will enable the platforms to identify trending topics, it also enables the creation of content that best fits market demand (Narimani and Barberà, 2024). Chatbots and virtual assistants provide instant support and guidance to users, increasing engagement and satisfaction (Goel et al. 2024).
At the same time, social media platforms such as TikTok, Instagram, and YouTube are growing in importance in the field of informal education and knowledge monetization (S. Wang et al. 2023). Although the full integration of OKP business models with these platforms remains underexplored in the literature, scholars have highlighted the potential for convergence, primarily through AI-driven recommendation algorithms and content monetization infrastructure (Mihai et al. 2024; Qazi et al. 2024). Rather than replacing traditional OKP platforms, social media ecosystems can serve as complementary channels, expanding coverage and allowing more knowledge creators to enter the industry at a lower cost. This trend provides possibilities for hybrid platform strategies, and exploring how to embed the OKP mechanism into such environments while maintaining the learning quality of users and the benefits of creators may also become a new research direction.
Research Methodology
Procedure of the review
This study adopts a hybrid systematic review method, combining bibliometric analysis with business model analysis guided by the Elaboration Likelihood Model (ELM) to obtain a comprehensive and theoretically grounded understanding of online knowledge payment (OKP). This dual approach can reveal the research landscape and trends at the academic level and apply them to the practical level, thereby enhancing the depth of analysis.
This systematic review follows the SPAR-4-SLR protocol proposed by Paul and Rosado-Serrano (2019), which divides the review process into three stages: (1) identification and acquisition of relevant studies, (2) collation, screening, and purification, and (3) evaluation and reporting. This process ensures the methods’ transparency, integrity, and completeness throughout the review process.
The bibliometric analysis was conducted using VOSviewer (Van Eck and Waltman 2010), which supports identifying co-author networks, keyword co-occurrence patterns, and citation structures. Visual analysis helps to explore the knowledge structure and development history of OKP-related research. At the same time, the business model analysis is guided by the Elaboration Likelihood Model (ELM) to classify OKP platforms according to central and peripheral persuasion mechanisms. This analysis method helps to deeply understand the platform strategies and user participation models of different OKP forms.
Assembling
During the construction phase of the SPAR-4-SLR scheme, we systematically collected literature from two major academic databases, Web of Science (WOS) and Scopus, to ensure high coverage and credibility. The search scope covered publications from January 1, 2012, to October 31, 2024, coinciding with the emergence and development of OKP as a research field. The starting point of 2012 was chosen because early commercial OKP models began to emerge in China and around the world (Zhang et al. 2019). Given the interdisciplinary nature of OKP (integrating knowledge economy, consumer behaviour and digital education), different scholars may use different keywords instead (Qi et al. 2019). We adopt a two-pronged keyword classification framework proposed by Khalek and Chakraborty (2023). The first category covers knowledge-related concepts, while the second category targets consumer and platform behavior. The full keyword strings used in the Boolean search are detailed in Table 1.
Table 1. Search keywords.
VARIABLES | KEYWORDS |
|---|---|
Knowledge exchange & knowledge payment (research context) | (“online Q&A platform” OR “knowledge community” OR “MOOC” OR “online education” OR “knowledge payment” OR “pay for knowledge” OR “online paid knowledge” OR “online paid courses” OR “knowledge marketplaces” OR “knowledge commerce” OR “pay for knowledge” OR “pay for content” OR “content payment” OR “knowledge consumption” OR “knowledge platforms” OR “knowledge communities”) |
and | |
Consumer behavior (research body) | (“knowledge consumers” OR “knowledge seekers” OR “knowledge demanders” OR “customer engagement” OR “purchase intention” OR “interact*” OR “satisfaction” OR “motiv*” OR “adopt*” OR “continue*” OR “sales” OR “characteristic” OR “behavior” OR “customer behavior”) |
To balance sensitivity and specificity, the keywords were pilot tested and iteratively refined through multiple trial queries. Only vague or overly broad terms (e.g., “behavior,” “satisfaction”) that had high co-occurrence with the core concepts were retained. Furthermore, the final Boolean logic structure adopts paired topic layers connected by “AND” logic, while synonyms in each layer are connected by “OR” logic. This strategy ensures both conceptual depth and limits irrelevant searches.
The topic keyword structure is broken down as follows: Level 1 - Knowledge exchange/transfer concepts: (“online paid knowledge” or “knowledge payment” or “online Q&A” or “knowledge market” or “MOOC” or “knowledge commerce”…)； Level 2 - Consumer behavior constructs: (“purchase intention” or “knowledge seeker” or “engagement” or “customer behavior” or “interaction*” or “motivation*” or “sales”…) This structured approach reflects both the interdisciplinary complexity of OKP and the necessity of replicable and conceptually solid query design in systematic reviews.
Arranging
In the curation phase of the SPAR-4-SLR process, we retrieved 4492 articles from the Web of Science (WOS) and Scopus as the initial search pool using the advanced Boolean search logic described in Section “Assembling”. Since articles may be indexed in both databases, we performed a two-step deduplication process to manage record duplication. First, EndNote X21 was used to identify identical DOIs and metadata from different sources. Second, manual screening based on Excel was performed to remove near-duplicates and inconsistent indexing entries. A total of 1667 unique records were obtained.
Next, we applied Paul and Criado’s systematic inclusion and exclusion criteria to ensure quality and conceptual consistency (Paul and Criado 2020): 1. Only peer-reviewed journal articles were retained. Books, book chapters, dissertations, and conference papers were excluded. 2. Non-English publications were excluded to ensure language treatment and interpretation consistency. 3. Articles must explicitly explore online knowledge payment (OKP) topics, including platform strategies, user behavior, business models, or relevant theoretical frameworks.
The research team manually conducted topic-based relevance screening, using abstracts and full texts when necessary. Each article was evaluated using a pre-coding checklist consistent with the research conceptual model. Use Excel and EndNote to merge Scopus.csv files and WOS. txt files to remove duplicate data. Then, according to the format requirements of VOSviewer, save the merged data in Excel as a UTF-8. txt file using Notepad. Use VOSviewer’s custom data import mode to import and perform co-occurrence and overlay mapping. Through this process, we finally obtained a dataset of 226 articles published between 2012 and 2024. To assess whether the number of literature is sufficient, we performed a saturation check by plotting the number of articles published per year in the following text (see Section “ANALYSIS”), indicating that conceptual saturation was achieved within the defined research scope.
Assessing
The evaluation phase included two complementary analytical approaches: bibliometric visualization and theory-guided business model evaluation. First, we analyzed and visualized the bibliographic data of 226 selected articles using VOSviewer version 1.6.20. The software was used for multiple analyses: Keyword co-occurrence mapping, using the complete count method; Threshold setting: minimum keyword occurrences set to 5; Normalization: association strength method; Clustering: modularity-based clustering, with a default resolution of 1.0; and Temporal overlay: mapping the evolution of keywords over time. This visual mapping enabled the identification of topic clusters, influential authors, institutional contributions, and evolving research hotspots in the OKP field.
Second, we conducted a business model content analysis using the refined likelihood model (ELM) as a conceptual perspective. Using keyword clusters, abstracts, and relevant full texts, we manually categorized OKP-related platforms and practices based on their association with the following factors: central path persuasion factors (e.g., knowledge quality, perceived value, instructor expertise) and peripheral path cues (e.g., interactivity, emotional tone, platform design). This ELM-guided categorization enables us to identify how different business models (e.g., live Q&A, streaming courses, micro-audio) engage consumers through different processing pathways. This, in turn, helps reveal underexplored psychological mechanisms and points to potential theoretical and methodological gaps in the current OKP literature. The overall process of literature identification, selection, and synthesis is illustrated in Fig. 2, following the structure of assembling, arranging, and assessing.
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Fig. 2
Flowchart of the systematic literature review process.
This figure outlines the three-stage process (Assembling, Arranging, and Assessing) used for identifying, screening, and selecting relevant articles for this study. The flowchart on the right details the number of articles included or excluded at each step of the purification process, resulting in a final selection of 236 articles for analysis.
Analysis
This section provides a comprehensive bibliometric analysis based on descriptive indicators and co-occurrence maps, providing a deeper understanding of the research topics. The data from both databases were thoroughly cleansed to focus on the main contributors regarding publications, journals, and countries. In addition, the study mapped the keywords that appeared together using network analysis. This will provide a comprehensive view that will enhance one’s understanding of the research environment, identify key journals for the discipline, and recognize major authors.
Meanwhile, this section also integrates the business model analysis based on the Elaboration Likelihood Model (ELM). By applying the ELM as a conceptual perspective, we divide OKP-related business strategies into central path persuasion mechanisms (e.g., knowledge quality, teaching credibility) and peripheral path cues (e.g., interactivity, visual appeal, emotional tone). This two-layer approach enables us to understand the patterns of different OKP platforms from the perspective of consumer decision paths, thus adding behavioral-level practical significance to the bibliometric research results.
Overview of the publications
Figure 3 compares the annual number of publications and citations related to OKP research in the Web of Science (WOS) and Scopus databases from 2012 to 2024. In the Scopus database, publication activity peaks in 2023, with 20 articles and 473 citations. In contrast, the WOS database records the highest number of publications in 2021, with 23 articles, and the number of citations peaks in 2022, with a total of 847. From 2021, the number of publications in the WOS database has declined moderately, and the number of citations has declined after 2022. However, the number of publications and citations in the Scopus database has been on an upward trend.
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Fig. 3
Annual publication and citation trends in OKP research (2012–2023).
The bar chart represents the annual number of publications retrieved from Web of Science (WOS) and Scopus (left y-axis), while the line chart illustrates the annual number of citations for these publications (right y-axis).
The significant growth in OKP-related publications and citations between 2020 and 2022 may be attributed to the global impact of the COVID-19 pandemic. During this period, widespread lockdowns and telework policies accelerated the popularity of online education and knowledge-sharing platforms, prompting scholars to explore new digital content delivery models (Bao, 2020; Huang et al. 2020). Although the number of publications has slightly declined after 2022, this does not necessarily mean a decline in research interest. On the contrary, Web 3.0, the rise of AI-driven educational platforms, and the demand for personalized online learning continue to expand the relevance and application of OKP across disciplines (Fan et al. 2024).
Most influential authors, journals, and countries (RQ1)
This section presents the bibliometric findings that address RQ1, aiming to identify the most influential authors, journals, and countries contributing to the OKP research domain. The analysis was conducted using VOSviewer (version 1.6.20), employing the full counting method and setting the minimum keyword occurrence threshold to 5. Association strength normalization and modularity-based clustering (resolution = 1.0) were applied to generate the keyword co-occurrence and co-authorship network visualizations. These parameter settings follow standard bibliometric practices, ensuring replicability and thematic clarity (Van Eck and Waltman 2010). We used Excel spreadsheets to unify the top 20 entries in the Web of Science (WOS) and Scopus databases and delete duplicate entries to improve comparability. The results are shown in Tables 2–4, which show the most cited authors, journals, and submission countries.
Table 2. Most-cited authors of WOS and Scopus.
author | documents | citations |
|---|---|---|
Hollenbeck, Linda d. | 2 | 1182 |
Majchrzak, Ann | 1 | 502 |
Srivastava, Rajendra k. | 1 | 499 |
Zhang, Zhonghua | 3 | 211 |
Fan, Weiguo | 4 | 199 |
Garcia Martinez M. | 2 | 192 |
Wang, g. Alan | 2 | 179 |
Jiao, Jian | 1 | 138 |
Haas, Martine r. | 1 | 135 |
Hwang e.g. | 2 | 134 |
Table 3. Most-cited journals of WOS and Scopus.
source | documents | ABDC | Cite Score | IF | citations |
|---|---|---|---|---|---|
journal of computer-mediated communication | 3 | A* | 9.6 | 5.4 | 624 |
journal of knowledge management | 14 | A | 13.7 | 6.6 | 410 |
decision support systems | 2 | A | 14.7 | 6.7 | 219 |
research policy | 2 | A* | 12.8 | 7.5 | 142 |
information processing & management | 4 | A | 17 | 7.4 | 119 |
Internet research | 2 | A | 10.1 | 5.9 | 117 |
online information review | 3 | A | 7.3 | 3.1 | 110 |
MIS Quarterly | 2 | A* | 13.1 | 7.0 | 106 |
journal of retailing and consumer services | 2 | A* | 20.4 | 11 | 105 |
journal of business research | 3 | A* | 20.3 | 10.5 | 101 |
Table 4. Most-cited countries of WOS and Scopus.
country | documents | citations |
|---|---|---|
USA | 42 | 1685 |
China | 49 | 874 |
Australia | 13 | 746 |
India | 7 | 643 |
Spain | 3 | 582 |
New Zealand | 6 | 567 |
Lebanon | 3 | 563 |
Canada | 6 | 531 |
England | 8 | 473 |
Singapore | 4 | 254 |
The selected articles were published by 227 institutions across 42 countries, appeared in 87 journals, and cited 3,329 references from 196 sources. Instead of ranking authors and journals by publication count alone, we relied on citation impact to identify core academic influencers. This approach aligns with Bradford’s Law, which allows the identification of research concentration zones through citation density (Zhu et al., 2015). While citation counts were used for primary ranking, we acknowledge potential bias from co-authorship and self-citation effects, and findings are interpreted with appropriate caution.
Among the top contributors, Linda D. Hollebeek stands out with two highly cited papers that form a foundational framework for applying Uses and Gratifications (U&G) theory to digital content marketing and engagement (Hollebeek & Macky, 2019). Dr. Hollebeek’s work bridges customer engagement theory and platform strategies central to OKP contexts. Srivastava, a co-author of one of Hollebeek’s papers, ranks third. Zhang and Fan, though not dedicated OKP researchers, have contributed high-impact articles from educational technology and computer science perspectives, reinforcing the interdisciplinary nature of OKP research (Li et al., 2024). Some of their work explores how digital tools shape consumer decision-making, a core aspect of the central route in our ELM-based interpretation.
The top 10 journals were identified using a composite evaluation combining citation counts, publication frequency, and the ABDC journal quality ranking system (Jaafar et al., 2021). These journals span high-impact fields such as Management Information Systems, Policy and Innovation, and Decision Sciences, highlighting the dual practical and theoretical relevance of OKP scholarship. All ten are ranked A* or A in the ABDC list. While the ABDC ranking was prioritized for consistency across disciplines, we also considered supplementary indicators such as Cite Score and Impact Factor to verify journal influence.
As for country-level contributions, the United States ranks first in total citations (1,685 across 42 papers), followed by China with 49 publications and a lower citation count. While these numbers may suggest broader academic recognition for U.S.-based publications, further research would be needed to control for factors such as self-citation, journal scope, and regional language biases. Overall, the geographic distribution of research confirms the global relevance of OKP, with increasing activity from Asia-Pacific and Europe.
Keywords co-occurrence analysis
Keywords represent the essence of a paper, and keyword co-occurrence analysis can reveal research hotspots in a scientific field (Radhakrishnan et al., 2017). This study used a VOS-viewer to generate a keyword co-occurrence network for 226 papers. The size of the round nodes indicates the frequency of keyword occurrence, with larger nodes representing prominent topics within the field. The lines connecting the nodes show the strength of associations, with thicker lines signifying a higher frequency of co-occurrence in the same paper. The results are shown in Fig. 4, where 86 keywords with a frequency of five or more were selected for visualization. This threshold was determined based on common practice in co-occurrence studies using VOSviewer, balancing network density with thematic clarity (Van Eck & Waltman, 2010). Including lower-frequency keywords would have resulted in excessive fragmentation and visual noise.
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Fig. 4
Keyword co-occurrence network map of OKP research.
Generated by VOSviewer, this map visualizes the main research themes in the OKP field. Each node represents a keyword, and its size is proportional to the keyword's frequency of occurrence. The lines between nodes indicate co-occurrence relationships, and the different colors represent distinct research clusters.
The purple cluster contains foundational technology keywords such as “computer science,” “engineering,” and “information technology.” These keywords indicate that many OKP studies are about computer facilities and system platform operations. This cluster highlights the trend of cross-disciplinary integration with deep technical roots in this field. As the OKP platform develops, more and more artificial intelligence, big data, and scalable system design may be incorporated into the platform architecture and functions. These trends indicate that the development of intelligent technology in the future will make the OKP platform content delivery system more adaptable, personalized, and efficient.
The green cluster contains high-frequency keywords, including “social media”, “knowledge sharing”, and “behavior”. These keywords frequently appear in studies exploring the relationship between user interaction and digital platforms, reflecting the growing importance of social platforms as a channel for disseminating OKP knowledge. Scholars are increasingly studying how user behavior, engagement, and homepage updates affect the OKP effect on platforms such as TikTok, YouTube, or Zhihu, emphasizing the importance of optimizing sharing behavior and interactive participation when expanding the social platforms where the OKP business model is located.
The yellow cluster contains key commercial and evaluative keywords, such as “quality,” “performance,” “brand,” and “price.” These terms frequently appear in studies evaluating product value, creator reputation, and transaction dynamics in OKP environments. This cluster highlights the growing interest in entrepreneurship and branding in OKPs. Researchers are exploring how knowledge providers position themselves, differentiate their products, and influence consumer perceptions through pricing and quality signals. This trend suggests a growing tendency to understand knowledge monetization as a platform design rather than a personalized microenterprise. Creator reputation, value-added services, and strategic branding are key to success.
The fact that “computer science” appears in 30 papers and has 179 citations shows how important technological innovation is in OKP research. It reflects that advancements in digital tools like AI and data systems are the backbone of this field. The keyword “business,” found in 27 papers with 141 citations, highlights how OKP is becoming more closely tied to broader business systems. This shows a growing emphasis on transforming knowledge sharing into a profitable and scalable model. Meanwhile, “Information Science” (25 papers, 188 citations) reveals the efforts made by researchers to organize and share knowledge on digital platforms to make it easier and more convenient for users. (Table 5).
Table 5. The high-frequency keywords of the keywords co-occurrence analysis.
keyword | occurrences | Total link strength | keyword | occurrences | Total linkstrength |
|---|---|---|---|---|---|
computer science | 30 | 179 | impact | 13 | 99 |
business & economics | 27 | 141 | knowledge payment | 13 | 31 |
information science & library science | 25 | 188 | online knowledge sharing | 13 | 79 |
knowledge sharing | 25 | 144 | social media | 13 | 45 |
model | 19 | 116 | education & educational research | 12 | 46 |
communication | 17 | 67 | engineering | 12 | 50 |
virtual communities | 17 | 127 | trust | 12 | 88 |
behavior | 16 | 120 | information | 11 | 92 |
knowledge | 16 | 60 | psychology | 11 | 40 |
knowledge management | 16 | 65 | social networking (online) | 11 | 40 |
Figure 5 shows a keyword co-occurrence overlay visualization that captures the temporal evolution of research hotspots in the OKP field. The color of the nodes in the figure represents the average publication year associated with each keyword. Older topics are in darker colors (e.g., purple and blue), while more recent and emerging topics are in yellow. The size of the nodes reflects the frequency of the keywords, and the distance between nodes indicates how close they co-occur. This visualization method can map the changes in research focus over the past decade in real time.
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Fig. 5
Overlay visualization of the temporal evolution of OKP research topics.
This map, created with VOSviewer, shows the evolution of research hotspots over time. The color of each keyword node indicates its average publication year, with colors ranging from blue (older topics, around 2016) to yellow (more recent topics, around 2021), illustrating the shifting focus of research in the field.
From 2017 to 2019, scholars’ research focused on computer science and network technologies, focusing on infrastructure development such as systems and platforms. These technological foundations laid the foundation for the OKP ecosystem. After 2019, research shifted to an application-driven direction, including payment systems, monetization strategies, and consumer engagement mechanisms. This shift reflects the increasing application of business theories and behavioral models (e.g., motivation and user generation) in understanding and optimizing knowledge monetization. The following visualization charts the progress of OKP research and points out the paths ahead. It underlines the need for interdisciplinary research, combining technological innovation with consumer-oriented needs and forays into uncharted territory concerning global applicability, unique cultures, and monetization of knowledge.
The ELM analysis of business models
In this section, we utilized the Elaboration Likelihood Model (ELM) as an explanatory framework to analyze how OKP’s different business models fit with users’ cognitive processing pathways (i.e., central and peripheral pathways of persuasion). This approach builds on previous bibliometric findings and keyword clustering, providing a theoretically supported perspective to help us understand the persuasive mechanisms inherent in each model. The motivation for choosing ELM is that the academic community is increasingly concerned about information quality, emotional appeal, and platform interactivity in knowledge consumption, and the dual-path structure of ELM captures these dimensions well and explains user behavior.
Rather than simply applying ELM descriptively, this analysis aims to map the dynamics of OKP’s five main business models (paid Q&A, live courses, audio subscriptions, recorded courses, and knowledge communities) onto the ELM framework. In this way, this study explores how information processing (central pathway) or emotional/contextual cues (peripheral pathway) are primarily used to influence user behavior in each model context. Table 6 synthesizes the above classifications in combination with empirical keyword trends and existing theoretical literature, thereby enriching the interpretation of OKP’s monetization logic from the perspective of persuasive communication.
Table 6. Mapping OKP Business Models to the Elaboration Likelihood Model (ELM).
Business Model | Central Route | Peripheral Route | Theory | Method | Keywords | Examples |
|---|---|---|---|---|---|---|
Paid Q&A (29) | Trust, knowledge quality, and purchase behavior | Visual design, user interactivity | signal theory (9) customer value perception (2) Social Influence Theory (5) dual-process theory (1) social identity theory (5) social capital theory (7) | Regression (10) SEM (15) ML (1) ANN (1) | Users’ payment (21) Perceived uncertainty (4) Price (14) Knowledge quality (16) Purchase behavior (24) Trust (8) | (Xi Zhang et al. 2024),(Xing Zhang et al. 2024), (Liu et al. 2024),(Guo et al. 2024),(Ying et al. 2023),(Sun et al. 2022),(Jiang et al. 2023) |
Live Streaming (10) | Consumer experience, perceived value | Entertainment, linguistic disfluency | processing fluency theory (1) signaling theory (3) classic vicarious learning (VL) (2) UTAUT theory (4) | fixed-effect regression (4) panel vector autoregression (1) PLS-SEM (5) | Consumer satisfaction (6) Linguistic disfluency (1) Knowledge payment products (8) Consumer experience (3) Perceived value (5) | (J. Zhang et al. 2023), (Shi et al. 2020), (Zhang et al. 2020), (Yu et al. 2021), (S. Wang et al. 2023),(Jia et al. 2024), (Li et al. 2025) |
Video Course (12) | Knowledge relevance, perceived learning effectiveness | Presentation style, accessibility | feelings-as-information theory (1) cue utilization theory (2) cognition-affect-behavior technological acceptance model (3) expectancy motivation theory (4) collaborative learning theory (2) | PLS-SEM (2) SEM (10) | Consumers perceive (10) Knowledge purchase (12) Social media (1) Sustainable development (3) Quality (5) Higher education (9) TAM (2) | (Xi Zhang et al. 2023),(Ma et al. 2023),(Xu et al. 2021),(Sharma and Vidani 2023) |
Community Service (16) | Social trust, community governance | Platform engagement, gamification | perceived value theory (1) S-O-R framework (12) motivation theory (1) RFM model (1) expectation inconsistency theory (1) | regression analysis (3) ANCONA (2) structural-equation model (8) fsQCA (2) Bibliometric Analysis (1) | Social media (2) Knowledge Management (5) Knowledge sharing (10) Blockchain (1) User participation (6) Community governance (3) | (X. Wang et al. 2021), (Yu et al. 2021), (Wu et al. 2022), (N. Wang et al. 2021) |
Paid Audio (2) | Content uniqueness, consumer satisfaction | Brand reputation, auditory appeal | sensations-familiarity theory (1) temporal distance frameworks (1) | Bibliometrics (1) topic model (1) | Knowledge differentiation (1) Consumers’ decision (2) | (Zhou and Xiu 2024), (Rejeb et al. 2023) |
Source: author’s elaboration.
The data are based on the authors’ calculations of 69 key works during 2020–2024.
The comprehensive table categorizes OKP business models within the ELM model framework based on literature published between 2020 and 2024, highlighting their reliance on central or peripheral processing paths. During this period, there has significantly shifted in OKP research, from infrastructure and education delivery to persuasion mechanisms, monetization strategies, and user experience design. Early research (before 2020) tends to focus on platform building and general knowledge sharing, which is less consistent with the dual-route logic of the ELM.
The paid Q&A model is mainly consistent with the central path of the elaboration likelihood model, in which users engage in intensive, systematic information processing. In this model, knowledge seekers actively evaluate the credibility, expertise, and depth of responses provided by content creators. The emphasis on knowledge quality, expertise cues, and perceived value reflects the cognitive elaboration characteristics of central processing. The application of signaling theory reinforces this view. Jiang et al. (2024) and Liu et al. (2025) found that users rely on reputation indicators (e.g., past answers, peer recognition) to evaluate knowledge quality, which is consistent with the argument-based central persuasion path of ELM. In addition, Xing Zhang et al. (2024) applied social identity theory to show how professional affiliation and verified qualifications enhance user trust and content acceptance. These mechanisms suggest that information needs drive paid Q&A consumers and are willing to invest cognitive effort, confirming that the central path dominates their decision-making process. However, some recent studies have also pointed out that platform design and interface cues (e.g., likes, popularity badges) may introduce peripheral elements that subtly impact perception (Sun et al. 2022). This suggests a hybrid processing model in which credibility is constructed through information content and environmental cues.
The live-streaming model mainly attracts users through the peripheral persuasion pathway, which is characterized by users’ reliance on situational or emotional cues. In this model, users are often attracted by charismatic hosts, real-time interactions, and platform aesthetics rather than the depth of content. These cues trigger heuristic responses such as liking, social identity, or emotional resonance, consistent with ELM’s peripheral processing pathway. The persuasiveness of interface design and interactivity is also supported by processing fluency theory, which posits that users are likelier to like content that is easy to process emotionally or linguistically (S. Wang et al. 2023). J. Zhang et al. (2023) showed how language fluency affects user trust and satisfaction in OKP live-streaming sessions. In addition, Shi et al. (2020) and Yu et al. (2021) extended the Unified Theory of Acceptance and Use of Technology (UTAUT) by incorporating social influence and performance expectations as key factors in real-time knowledge commerce, showing how peer feedback and real-time verification drive consumer action. While the peripheral route is dominant in this model, some live courses (particularly those in the medical or technical fields) introduce elements of central processing, such as structured explanations or professional recognition (Jia et al. 2024; Li et al. 2025; Zhang et al. 2020). This suggests the potential for route integration, where the shift from peripheral appeal to central elaboration may depend on the complexity of the content and user motivation.
The paid course model is often consistent with the central route of the ELM because it requires learners to have high cognitive engagement as they seek in-depth, specialized knowledge (X. Zhang et al. 2023). These courses are usually in video format and designed to impart finance, education, or coding expertise. Consumers in this segment are often goal-oriented and motivated by utility or career advancement, which is consistent with the ELM concept of high elaboration likelihood. In this case, the decision-making process is influenced mainly by content quality, course structure, and perceived instructor expertise—all of which belong to the central route cues. Xu et al. (2021) and Ma et al. (2023) demonstrated that learners evaluate paid video courses based on perceived learning effects and clarity of knowledge goals. Similarly, Sharma and Vidani (2023) showed that the credibility of instructors with institutional or industry backgrounds plays a decisive role in user purchase intention.
Knowledge community platforms represent a complex hybrid model within the ELM framework in which core and peripheral processing cues operate in concert. These communities often focus on specific domains (e.g., health, parenting, finance) and blend user-generated content, expert Q&A, and social interactions, making it difficult to isolate a single dominant processing path. Instead, user engagement is determined by the interplay between content quality, relevance, and the social dynamics of the platform (X. Wang et al., 2021). Studies by Yu et al. (2021) and Wu et al. (2022) show that users are influenced by the accuracy, completeness, and relevance of the knowledge shared by their peers. This reflects the high cognitive engagement process when users search for credible, actionable information in high-risk situations (e.g., medical advice). At the same time, peripheral cues such as post popularity, visual design, comment reactions, or contributor badges can drive quick judgments, especially when users are cognitively overloaded or browsing casually. The dual nature of the model challenges the strict center/periphery dichotomy of the ELM. Scholars such as N. Wang et al. (2021) have argued that user motivations fluctuate dynamically within a single session—from quick browsing (low elaboration) to deep reading (high elaboration) depending on need urgency and perceived credibility. This suggests a dynamic route model, or integration with theories such as the stimulus-organism-response (SOR) framework, to capture changing attentional and cognitive states. Furthermore, social mechanisms such as emotional support, identity formation, and peer connections suggest a broader persuasion landscape beyond traditional ELM components.
The audio subscription model represents an underexplored area in OKP, but it offers a unique configuration of peripheral and central cues that warrants deeper theoretical inquiry. Compared to visual or interactive platforms, audio-based content is consumed in passive, mobile, and multitasking environments. This suggests a more significant influence of peripheral cues such as voice tone, emotional intonation, rhythm, and narrative style. Zhou and Xiu (2024) suggest the persuasive role of paralinguistic features (the speaker’s sound rather than what they say) as critical for establishing credibility and maintaining attention. At the same time, content complexity and topic relevance may engage central routing processing, especially in long-form educational podcasts or structured knowledge series, suggesting a dual processing condition. Given the limitations of the audio medium, the one-sided connection that audio OKP users have with the speaker may affect trust and learning motivation. Therefore, the interplay between intimacy cues and information content provides a rich opportunity to combine ELM with theories of speech perception, affective computing, and digital empathy. The literature in this area is currently sparse. Future research should investigate how voice-based cues influence persuasion and purchase behavior in OKP environments.
Discussion and Theoretical Implications
RQ1: Knowledge structure and academic achievements
The results of RQ1 show the development and current status of the OKP research field. The co-occurrence of keywords such as “computer science,” “business,” and “information science” reveals the multidisciplinary nature of the field, and the research focus has also shifted from pure technology and business behavior to the integration of the two. In particular, influential authors’ research results in marketing and information systems have shaped a new starting point for knowledge monetization and platform design. The proportion of A-level and A*-level journals in the ABDC list also reflects the fit between the field and business and management disciplines, indicating that academic recognition is becoming more mature.
RQ2 – Theoretical gaps and future research directions
The ELM-based business model analysis divides the persuasion mechanisms in OKP into central and peripheral path models to address RQ2. While this classification provides clarity, it also reveals some theoretical blind spots. First, some business models (such as knowledge communities and live education) defy simple classification and operate in a hybrid persuasion environment. Second, theoretical gaps exist when integrating emotional, social, and situational cues into existing frameworks. Current models (such as ELM, UTAUT, and TAM) can explain some behaviors but cannot fully grasp the fluidity, interactivity, and multisensory nature of OKP. Future research can try hybrid theoretical models that combine ELM with cue utilization theory, parasocial interaction, or affective computing to enhance explanatory power. In addition, the lack of regionally based research and culture-specific persuasion models limits the generalizability of the current findings, suggesting the need for a localized research agenda across geographic and cultural contexts.
Rethinking ELM: applicability, boundaries, and theoretical integration
Although this study adopts ELM to analyze consumer decisions under different OKP business models, empirical research in this field involves many disciplines, and the traditional “central processing” and “peripheral processing” models may not fully explain the complexity of user behavior in digital knowledge environments.
For some modes (such as paid Q&A and recorded courses), the central path of ELM is highly consistent with user motivations. These platforms prioritize content quality, perceived expertise, and knowledge credibility, which requires a high degree of cognitive refinement and is consistent with the logic-based processing of the central path. However, in live broadcast or audio modes, users’ decisions are often influenced by more peripheral factors, such as the host’s charm, emotional state, interaction points, or background beauty. These modes emphasize social presence and emotional engagement, which is consistent with the processing of the peripheral path.
Central and peripheral processing coexist in more complex models like knowledge communities. Users may alternate between browsing popular posts and deeply engaging in interactions depending on the context and mood. At this point, user behavior in OKP is not a choice between elaboration and heuristics but a continuous or repetitive process. Therefore, ELM may be integrated with other frameworks, such as the stimulus-organism-response (SOR) model or the two-stage cognitive theory, to better explain real-time transformation, emotional triggers, and situational adaptation.
In addition, ELM does not explain the impact of technology or platform-related factors on user behavior. TAM (Technology Acceptance Model) and UTAUT (Unified Theory of Acceptance and Use of Technology) provide complementary perspectives. Therefore, theoretical development in this field may require a hybrid framework that connects persuasive psychology and technology interaction.
Practical Implications, Limitations, and Future Research
Practical implications
This study has practical implications for digital entrepreneurs and platform developers involved in the online knowledge payment (OKP) ecosystem. Five major OKP business models are classified through the Elaboration Likelihood Model (ELM) to guide practitioners in improving user engagement and optimizing platform performance.
This study helps guide decisions on content design and delivery strategies by distinguishing between central and peripheral business models. For example, platforms such as paid Q&A and recorded courses should emphasize professionalism and clarity, while live broadcast and audio models focus more on interactivity and emotional tone. For grassroots creators, understanding this balance can improve user engagement even without a strong brand effect. In addition, integrating OKP into social platforms such as Douyin or YouTube provides new opportunities to transform social interactions into knowledge monetization.
Limitations of the current study
This study has some limitations. First, we retrieved Web of Science and Scopus data to ensure coverage. However, the analysis was limited to English journal articles, which may have excluded relevant studies published in other languages. Given that the OKP phenomenon is more prosperous in East Asia, this may have affected the sample’s representativeness. Second, the business model classification based on the Elaboration Likelihood Model (ELM) is theoretical, while the empirical test is based on user behavior tracking. Although the ELM approach draws on established theories and recent literature, the persuasion mechanism within the platform may be more complex than that of the ELM. Third, this study does not clearly distinguish between platform types, cultural backgrounds, or user demographic characteristics, which may significantly affect user behavior. Finally, the bibliometric analysis focuses more on research trends and content structure than user experience, interface design, or technological affordances. These may affect consumer decision-making in the OKP environment.
Future research directions
Future research on OKP should focus on theoretical improvement and technological advancement, and find empirical gaps through bibliometric analysis and model classification.
First, although this study applied ELM to interpret user behavior, future research should test the effectiveness of central/peripheral cue effects in different OKP forms through experimental research. Second, as shown in the keyword co-occurrence map, technologies such as artificial intelligence, blockchain, augmented reality/virtual reality, and sentiment analysis are rarely mentioned in the current OKP literature.
However, scholars have used these technologies in other fields. Future research can explore how these tools affect user decision-making in a hybrid model that integrates learning and social interaction. Third, cultural and regional differences in OKP behavior remain a key blind spot. Most existing studies focus on platforms in China or the United States and rarely consider multilingual, emerging markets, or subcultural contexts. This is also a direction for future research on cross-cultural and cross-regional OKP.
Fourth, despite the growing popularity of audio-based learning, audio models in OKP are seriously understudied. Researchers can study the role of intonation, fluency, and parasocial interaction in shaping trust and cognitive load. Theories of media psychology, affective computing, or auditory cognition may help expand traditional ELM applications to new modes.
Finally, cross-theoretical integration is important. As platform interactions become more immersive and multimodal, models such as TAM, UTAUT, and SOR can be combined with ELM to build a more persuasive knowledge and consumption framework.
Conclusion
This study combines bibliometric analysis with a dual-path business model framework based on the Elaboration Likelihood Model (ELM) to provide a structured understanding of online knowledge payment (OKP). Reviewing 226 articles and categorizing five main OKP models, we identify how different persuasive cues (covering knowledge quality, interactivity, and platform design) shape different forms of consumer behavior. Our findings contribute to the theoretical application of ELM in emerging digital environments and provide practical guidance for knowledge entrepreneurs and platform designers. Future OKP research should further test, refine, and extend these insights to support more inclusive and effective knowledge ecosystems as they evolve with new technologies and cultural changes.
Author contributions
The manuscript was written and conceptualized by the first author, J.G. The corresponding author, S.H.H., provided feedback throughout the process. The research objectives were developed collaboratively, forming the foundation for future research. Upon completion, the manuscript was reviewed by S.H.H. before submission.
Funding
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Data availability
The bibliographic data supporting the findings of this study were extracted from the Web of Science and Scopus databases, with search queries provided in the methodology section. The final dataset of included articles is available from the corresponding author upon reasonable request.
Competing interests
The authors declare no competing interests.
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors. Therefore, ethical approval was not required.
Informed consent
No informed consent was required as this study did not involve human participants or individual-level data.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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